climate_data (3).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "climate data",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GocbHRYzTf99"
      },
      "source": [
        "The following notebook is based on the working Geospatial Data.\r\n",
        "\r\n",
        "The dataset can be downloaded from [here](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lsLKT9PDUcpf"
      },
      "source": [
        "The [CDS API](https://cds.climate.copernicus.eu/api-how-to) allows for programmatic access to this data store and is used to download the following data\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "*   Temperature of air at 2m above the surface - (2m Temperature - 2019 - December - First 7 days - 12:00 - Whole Available Region - NetCDF)\r\n",
        "\r\n",
        "*   Total precipitation - (Total Precipitation - 2019 - December - First 7 days -12:00 - Whole Available Region - NetCDF)\r\n",
        "\r\n",
        "\r\n",
        "*   Volumetric soil water layer 1 - (Volumetric soil water layer 1 - 2019 - December - First 7 days - 12:00 - Whole Available Region - NetCDF)\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YcrmA8dFafSz"
      },
      "source": [
        "## Creating '.cdsapirc' "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54whwymq7dKb",
        "outputId": "099711be-377d-469a-bf74-fa203eb21fc4"
      },
      "source": [
        "url = 'url: https://cds.climate.copernicus.eu/api/v2'\n",
        "key = 'key: 68734:ceaf88e4-e900-4691-91a0-a0fb14865ced'\n",
        "\n",
        "with open('/root/.cdsapirc', 'w') as f:\n",
        "    f.write('\\n'.join([url, key]))\n",
        "\n",
        "with open('/root/.cdsapirc') as f:\n",
        "    print(f.read())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "url: https://cds.climate.copernicus.eu/api/v2\n",
            "key: 68734:ceaf88e4-e900-4691-91a0-a0fb14865ced\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6GU4yiUJ53wD",
        "outputId": "1c41c212-0dcc-44a1-da0f-bd2f7af64b05"
      },
      "source": [
        "!pip install cdsapi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting cdsapi\n",
            "  Downloading https://files.pythonhosted.org/packages/d6/9e/952b99737b2dfc56229306abdd8f353b9114480db29e379ad2a621b12e6b/cdsapi-0.4.0.tar.gz\n",
            "Requirement already satisfied: requests>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from cdsapi) (2.23.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from cdsapi) (4.41.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.5.0->cdsapi) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.5.0->cdsapi) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.5.0->cdsapi) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.5.0->cdsapi) (2020.11.8)\n",
            "Building wheels for collected packages: cdsapi\n",
            "  Building wheel for cdsapi (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for cdsapi: filename=cdsapi-0.4.0-py2.py3-none-any.whl size=11693 sha256=d226930f8e61676d816a44e65261f43904b337802a7fea8e2f5f4d94331be919\n",
            "  Stored in directory: /root/.cache/pip/wheels/40/ee/c6/d596a9661e9408440cb33adc7881461cb1d69dbb455f808f78\n",
            "Successfully built cdsapi\n",
            "Installing collected packages: cdsapi\n",
            "Successfully installed cdsapi-0.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGUWUOiPakFy"
      },
      "source": [
        "## Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkFksyJk57yQ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import xarray as xr\n",
        "import matplotlib.pyplot as plt\n",
        "import cdsapi"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_oGvi9vAbKz1"
      },
      "source": [
        "# Downloading Dataset through cdsapi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMNrPjAoarsl"
      },
      "source": [
        "## Temperature of air at 2m above the surface"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXmq3Xei6m-7",
        "outputId": "6c74abce-7e7a-4e8c-da2d-72708b6515bb"
      },
      "source": [
        "c = cdsapi.Client()\n",
        "\n",
        "c.retrieve(\n",
        "    'reanalysis-era5-land',\n",
        "    {\n",
        "        'format': 'netcdf',\n",
        "        'variable': '2m_temperature',\n",
        "        'year': '2019',\n",
        "        'month': '12',\n",
        "        'day': [\n",
        "            '01', '02', '03',\n",
        "            '04', '05', '06',\n",
        "            '07',\n",
        "        ],\n",
        "        'time': '12:00',\n",
        "    },\n",
        "    'temp.nc')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-12-05 13:04:27,014 INFO Welcome to the CDS\n",
            "2020-12-05 13:04:27,015 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-land\n",
            "2020-12-05 13:04:27,173 INFO Request is completed\n",
            "2020-12-05 13:04:27,174 INFO Downloading http://136.156.132.153/cache-compute-0002/cache/data1/adaptor.mars.internal-1607148499.5357397-27875-7-129586ef-9939-48f7-8cb7-9d405f99cca4.nc to temp.nc (86.6M)\n",
            "2020-12-05 13:04:28,480 INFO Download rate 66.4M/s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Result(content_length=90793096,content_type=application/x-netcdf,location=http://136.156.132.153/cache-compute-0002/cache/data1/adaptor.mars.internal-1607148499.5357397-27875-7-129586ef-9939-48f7-8cb7-9d405f99cca4.nc)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvFusnuiawmC"
      },
      "source": [
        "## Total precipitation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tvok-AKpa2CD",
        "outputId": "3a2cdbb9-06d5-494a-99f1-457c9ba4707c"
      },
      "source": [
        "c1 = cdsapi.Client()\n",
        "\n",
        "c1.retrieve(\n",
        "    'reanalysis-era5-land',\n",
        "    {\n",
        "        'format': 'netcdf',\n",
        "        'variable': 'total_precipitation',\n",
        "        'year': '2019',\n",
        "        'month': '12',\n",
        "        'day': [\n",
        "            '01', '02', '03',\n",
        "            '04', '05', '06',\n",
        "            '07', \n",
        "        ],\n",
        "        'time': '12:00',\n",
        "    },\n",
        "    'precipitation1.nc')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-12-05 13:04:38,785 INFO Welcome to the CDS\n",
            "2020-12-05 13:04:38,785 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-land\n",
            "2020-12-05 13:04:40,423 INFO Request is completed\n",
            "2020-12-05 13:04:40,424 INFO Downloading http://136.156.132.210/cache-compute-0005/cache/data3/adaptor.mars.internal-1606910688.2140152-15575-5-df0a2539-f0cb-4ed2-8e0e-ca23d92bd7a2.nc to precipitation1.nc (86.6M)\n",
            "2020-12-05 13:04:42,056 INFO Download rate 53.1M/s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Result(content_length=90793088,content_type=application/x-netcdf,location=http://136.156.132.210/cache-compute-0005/cache/data3/adaptor.mars.internal-1606910688.2140152-15575-5-df0a2539-f0cb-4ed2-8e0e-ca23d92bd7a2.nc)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0C1oE-3a_4r"
      },
      "source": [
        "## Volumetric soil water layer 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_qrtNotbDXq",
        "outputId": "3cf759e3-0f09-455e-c2fb-5c50829a2e20"
      },
      "source": [
        "c2 = cdsapi.Client()\n",
        "\n",
        "c2.retrieve(\n",
        "    'reanalysis-era5-land',\n",
        "    {\n",
        "        'format': 'netcdf',\n",
        "        'variable': 'volumetric_soil_water_layer_1',\n",
        "        'year': '2019',\n",
        "        'month': '12',\n",
        "        'day': [\n",
        "            '01', '02', '03',\n",
        "            '04', '05', '06',\n",
        "            '07',\n",
        "        ],\n",
        "        'time': '12:00',\n",
        "    },\n",
        "    'volume_soil.nc')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-12-05 13:04:46,110 INFO Welcome to the CDS\n",
            "2020-12-05 13:04:46,111 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-land\n",
            "2020-12-05 13:04:46,174 INFO Request is completed\n",
            "2020-12-05 13:04:46,175 INFO Downloading http://136.156.132.201/cache-compute-0004/cache/data2/adaptor.mars.internal-1606830539.7203078-10169-1-ecc212be-ed44-4d9a-9c93-2bb986795e8e.nc to volume_soil.nc (86.6M)\n",
            "2020-12-05 13:04:47,453 INFO Download rate 67.9M/s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Result(content_length=90793112,content_type=application/x-netcdf,location=http://136.156.132.201/cache-compute-0004/cache/data2/adaptor.mars.internal-1606830539.7203078-10169-1-ecc212be-ed44-4d9a-9c93-2bb986795e8e.nc)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "58WLi8zEbVsz"
      },
      "source": [
        "# Opening dataset xarray"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8f_W6pB6ALj"
      },
      "source": [
        "ds = xr.open_dataset('temp.nc')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CQNNI1chbdHS"
      },
      "source": [
        "ds1 = xr.open_dataset('precipitation1.nc')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwoY2ZJ6bkJk"
      },
      "source": [
        "ds2 = xr.open_dataset('volume_soil.nc')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9V1-_njAbsw_"
      },
      "source": [
        "# Attributes\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yzLZ1HNtixRB"
      },
      "source": [
        "## 1. Temperature of air at 2m above the surface "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jnDpxCk7_rJ",
        "outputId": "70478238-1a70-4995-e60a-4a52e0f8b986"
      },
      "source": [
        "print(ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<xarray.Dataset>\n",
            "Dimensions:    (latitude: 1801, longitude: 3600, time: 7)\n",
            "Coordinates:\n",
            "  * longitude  (longitude) float32 0.0 0.1 0.2 0.3 ... 359.6 359.7 359.8 359.9\n",
            "  * latitude   (latitude) float32 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0\n",
            "  * time       (time) datetime64[ns] 2019-12-01T12:00:00 ... 2019-12-07T12:00:00\n",
            "Data variables:\n",
            "    t2m        (time, latitude, longitude) float32 ...\n",
            "Attributes:\n",
            "    Conventions:  CF-1.6\n",
            "    history:      2020-12-05 06:08:22 GMT by grib_to_netcdf-2.16.0: /opt/ecmw...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jv282qjS8UpV",
        "outputId": "b698ad06-30f8-44ec-885f-a0a970a1f194"
      },
      "source": [
        "print(ds['t2m'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<xarray.DataArray 't2m' (time: 7, latitude: 1801, longitude: 3600)>\n",
            "[45385200 values with dtype=float32]\n",
            "Coordinates:\n",
            "  * longitude  (longitude) float32 0.0 0.1 0.2 0.3 ... 359.6 359.7 359.8 359.9\n",
            "  * latitude   (latitude) float32 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0\n",
            "  * time       (time) datetime64[ns] 2019-12-01T12:00:00 ... 2019-12-07T12:00:00\n",
            "Attributes:\n",
            "    units:      K\n",
            "    long_name:  2 metre temperature\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49JrhyxKb4pM"
      },
      "source": [
        "## 2. Total precipitation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHu4M2dbb9s7",
        "outputId": "a47392df-65d7-444b-cb85-c0e66fb9df0c"
      },
      "source": [
        "print(ds1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<xarray.Dataset>\n",
            "Dimensions:    (latitude: 1801, longitude: 3600, time: 7)\n",
            "Coordinates:\n",
            "  * longitude  (longitude) float32 0.0 0.1 0.2 0.3 ... 359.6 359.7 359.8 359.9\n",
            "  * latitude   (latitude) float32 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0\n",
            "  * time       (time) datetime64[ns] 2019-12-01T12:00:00 ... 2019-12-07T12:00:00\n",
            "Data variables:\n",
            "    tp         (time, latitude, longitude) float32 ...\n",
            "Attributes:\n",
            "    Conventions:  CF-1.6\n",
            "    history:      2020-12-02 12:04:52 GMT by grib_to_netcdf-2.16.0: /opt/ecmw...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OAUTOxuScA3y",
        "outputId": "82947f3d-d0b2-40ce-fafd-fbd7cfa4bd19"
      },
      "source": [
        "print(ds1['tp'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<xarray.DataArray 'tp' (time: 7, latitude: 1801, longitude: 3600)>\n",
            "[45385200 values with dtype=float32]\n",
            "Coordinates:\n",
            "  * longitude  (longitude) float32 0.0 0.1 0.2 0.3 ... 359.6 359.7 359.8 359.9\n",
            "  * latitude   (latitude) float32 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0\n",
            "  * time       (time) datetime64[ns] 2019-12-01T12:00:00 ... 2019-12-07T12:00:00\n",
            "Attributes:\n",
            "    units:      m\n",
            "    long_name:  Total precipitation\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kH__oMYWcEhi"
      },
      "source": [
        "## 3. Volumetric soil water layer 1\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QCLH3tKcICp",
        "outputId": "886f1ecc-9c4a-4865-8c4f-46ae3224006d"
      },
      "source": [
        "print(ds2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<xarray.Dataset>\n",
            "Dimensions:    (latitude: 1801, longitude: 3600, time: 7)\n",
            "Coordinates:\n",
            "  * longitude  (longitude) float32 0.0 0.1 0.2 0.3 ... 359.6 359.7 359.8 359.9\n",
            "  * latitude   (latitude) float32 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0\n",
            "  * time       (time) datetime64[ns] 2019-12-01T12:00:00 ... 2019-12-07T12:00:00\n",
            "Data variables:\n",
            "    swvl1      (time, latitude, longitude) float32 ...\n",
            "Attributes:\n",
            "    Conventions:  CF-1.6\n",
            "    history:      2020-12-01 13:49:03 GMT by grib_to_netcdf-2.16.0: /opt/ecmw...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "47yPpZxQcIHS",
        "outputId": "58e7fc01-b894-486a-d54d-557890112489"
      },
      "source": [
        "print(ds2['swvl1'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<xarray.DataArray 'swvl1' (time: 7, latitude: 1801, longitude: 3600)>\n",
            "[45385200 values with dtype=float32]\n",
            "Coordinates:\n",
            "  * longitude  (longitude) float32 0.0 0.1 0.2 0.3 ... 359.6 359.7 359.8 359.9\n",
            "  * latitude   (latitude) float32 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0\n",
            "  * time       (time) datetime64[ns] 2019-12-01T12:00:00 ... 2019-12-07T12:00:00\n",
            "Attributes:\n",
            "    units:      m**3 m**-3\n",
            "    long_name:  Volumetric soil water layer 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5z2UTPnHdCYo"
      },
      "source": [
        "# Statistics\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEi99n5ni2np"
      },
      "source": [
        "## 1. Temperature of air at 2m above the surface "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NibibQ88dl_"
      },
      "source": [
        "### Calculating mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3Jh4ReT8bWW",
        "outputId": "a9848c83-7fb3-4513-9643-630b5a0dd214"
      },
      "source": [
        "mean=ds.t2m.mean()\n",
        "print(mean.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "268.3318\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXXf3SXb8ts7"
      },
      "source": [
        "### Calculating median"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DxE_waH-8wBK",
        "outputId": "d0e77265-2357-4b54-c476-f258ff0847ea"
      },
      "source": [
        "median=ds.t2m.median()\n",
        "print(median.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "264.14520263671875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "22w4l-qU8_i8"
      },
      "source": [
        "### Calcualting standard deviation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9OF4XU1_81qB",
        "outputId": "8fd9da2a-0e77-425e-a503-de4c0b4dc2bd"
      },
      "source": [
        "standar_dev=ds.t2m.std()\n",
        "print(standar_dev.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "22.902767181396484\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BRunjfuJ9K2i"
      },
      "source": [
        "### Calculating variance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T1X8WGer9J9o",
        "outputId": "9d446ea1-9ae9-4021-8967-23b1c71c8d06"
      },
      "source": [
        "variance = ds.t2m.var()\n",
        "print(variance.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "524.5367431640625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQztSREf9V86"
      },
      "source": [
        "### Range"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WZsbbE0u9TEH",
        "outputId": "f71faf0f-7abc-482f-b0ae-aa23c2d3ed45"
      },
      "source": [
        "minimum = ds.t2m.min()\n",
        "maximum = ds.t2m.max()\n",
        "print('Range is',minimum.values,'to',maximum.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Range is 222.674560546875 to 315.6190185546875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OPqA4FyLaeFz"
      },
      "source": [
        "The mean and median of \"**Temperature of air at 2m above the surface**\" is same, which means that the distribution is maybe symmetric around **268.33**(which is the mean). The standard deviation is **22.90** is high implies that most of the observations spread far from the mean. The range is **222.675** to **315.756**  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gtkmPLgOZrlu"
      },
      "source": [
        "### Calculating Null"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8qaQKLE9g_A",
        "outputId": "97984457-c453-460d-fcd0-7fba1b044bcf"
      },
      "source": [
        "null=ds.t2m.isnull()\n",
        "print(null.sum().values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "29895159\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLAMRCXWetTk"
      },
      "source": [
        "The total missing values are **29895159**. The null observations can be filled with **mean** value."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "vD6LOVG98ZHR",
        "outputId": "cf39f713-2d59-4be4-db90-2ca8e8740841"
      },
      "source": [
        "ds.t2m[2].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x7faac789c048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QIaOCzvAZBOa"
      },
      "source": [
        "df = ds.to_dataframe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "6LQQoauXfnfx",
        "outputId": "c36be30e-540e-4d45-842a-90f1db849208"
      },
      "source": [
        "df.hist(bins=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f502fb15a20>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IaYbIJzigPz8"
      },
      "source": [
        "So on plotting the histogram of the observations are not symmetric even though the mean, median are same(almost). It is bimodal type of distribution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OCIBy-fyi_kH"
      },
      "source": [
        "## 2. Total precipitation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fArQPtqajS4-"
      },
      "source": [
        "### Calculating mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63ByKDpEjS4_",
        "outputId": "9971af24-7303-4b6d-a395-3fca74090ae6"
      },
      "source": [
        "mean=ds1['tp'].mean()\n",
        "print(mean.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.00069437234\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhYY7t5_jYhe"
      },
      "source": [
        "### Calculating median"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oubb3tSOjYhe",
        "outputId": "79683ef3-59a2-40be-8191-cfcb73bf0c0c"
      },
      "source": [
        "median=ds1['tp'].median()\n",
        "print(median.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0408461093902588e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "moD1Oy0hjYhe"
      },
      "source": [
        "### Calcualting standard deviation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W7xNWRFFjYhe",
        "outputId": "18fedaae-45da-4545-92df-1e5dd83aaeaa"
      },
      "source": [
        "standar_dev=ds1['tp'].std()\n",
        "print(standar_dev.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0025139511562883854\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cprYKrzkjYhe"
      },
      "source": [
        "### Calculating variance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ehs-_h16jYhe",
        "outputId": "8795320d-f2df-4334-9bf8-f13cdd609914"
      },
      "source": [
        "variance = ds1['tp'].var()\n",
        "print(variance.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6.319950898614479e-06\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7fWJ3oHwjYhe"
      },
      "source": [
        "### Range"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07NVRHlZjYhe",
        "outputId": "10ef9984-a3ec-4946-b8a4-50f8bf52fa7f"
      },
      "source": [
        "minimum = ds1['tp'].min()\n",
        "maximum = ds1['tp'].max()\n",
        "print('Range is',minimum.values,'to',maximum.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Range is 0.0 to 0.17055945098400116\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m8LEcCNrjYhe"
      },
      "source": [
        "For \"**Total precipitation**\" data mean is **0.000694** and median is **0.00001**. The standard deviation is **0.0025**, which is large as compared to mean indicating that data points are spread far from the mean. The range is **0** to **0.17**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6XmeJlCnjYhe"
      },
      "source": [
        "### Calculating Null"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMDcjz0LjYhe",
        "outputId": "0ec1ae96-2e47-4dc1-a2ab-70f93f76d151"
      },
      "source": [
        "null=ds1['tp'].isnull()\n",
        "print(null.sum().values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "29895159\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x4pA24BojYhf"
      },
      "source": [
        "The total missing values are **29895159**. The null observations can be filled with **mean** value or can be filled MICE or Multivariate Imputation by Chained Equation can be used, which is an imputation method which works by filling the missing data multiple times."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "ZmpYEhxjjYhf",
        "outputId": "cb8b3eeb-ea54-4245-de37-a0216e6da25b"
      },
      "source": [
        "ds1.tp[6].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x7f502f4e9358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zJ17+bmBnjtvzu1HP6wtdfD9QS0XajAG+bsxGHU+6RIhjVE/imBQ2Vh3ab38/RSjp8upCl5V43F64wESKeh7+3Dz+/JzpDO0SbiuVw89xHab6Gm5vJaq6JaLCQpxFwiJQp4UqqhYJhUXwmwdVWMDC39T+pl4X3Ol3mrkHHfOGsm6z6nngOhwo3dCXea0CPJVEy1JhWYnHA8Xr2bXhMspHj9UNZgMqPIQWbW9USyNtamTpXPiqC/+3GY8RGs87pVboNlW/IXPnrU8zs+NUvKiqnjP1PMStbX+oGZNka9d0kyzSe4mza/Pl9YVFLZrtPLvY2VYYpNu5pjih0Khet9TpiwR7PaQljz07423Zey5wNIk5Pomxup5hv5v4mgmMJcmuTW/Dn5hsWIQl9kVpovhS6G0VyarZCTtArOdSXDqQJClC4o5Psn8aNNO+lIl2LtWqvNQE8QDRlOBUv3Xjc8ROCC4HiulnZu4Gy9HovSx+7Y7xS/COTeCXynX3S2OkGbi0hq6tQUqDXH5BiDR90gU7Rc3twVLj2KSdXle9oiIdR5gtt5X7U4dA/12tB1/0OXpt+zdz8jmcfK71v1u3aLNtUffy4Lc3+ls06pTrbRdHzPswNNS0za2d66aNksx+kdkwBoidq96EPztnOqR6nWbVy1EzEK7aUynuPNEOXktmdSA07IyjYire7Mi/1jXrqZASlLlceHm7HKQXbXebarWoY0CcuiMMjHPPDz1x9s9ey4Hi9ewYuQhUIJczfv6eh87PL4p8D2aRcfasntDEtevWnY+4ZwexQbUyLLc8kg+uUS6h5VJbsRLO0DBAz/JKqbLsvKSW7K/ddca70SPH8Gdn7Yg/fr8gPbgzPLSQ3ttx0FIJ8XzzYEcEh7huhbon/B50tp5HELshbqVHVRAM6ORzaNmP5IOqUsvUERChN1QQYBjMOqqFRbWqp57QaSQEO4hR3wXNWHBlboZoBx7+naV+sOFB70ZT72P22oWVjoDkkOEhRBz8YtGkfcdDPXMdZ2QEb3pmIL3IovW+oUZdlipHjjB9fgu/s5ZwckZGUN83wrgFWvHm6oxAkdQC90RkJ/DXmM7j06r64artLwX+CngusFtV/8mufx4mu/dKzEjvj1W1YxlVl6TA2HXGu9GJE6YEaT2jXCTSeteGy0yH4StaLiPDQ+jsnO3UvYWOTH2kUECLRcCMlBAHiQbMwYKAsXpaVBa8mWybwnxQMW2q3WSpuEY4E6px7UVusjXuwaJ9utERRjzS2hVYjo2taaYCWzQj6841b0GGhgAQcVD1kXweKeTxJ6dQv7yQlsIiuTzO2Cje5Im+FhyNYiagdlGv6EyilVQri4SG+qYscILg135HAS8Fo7etNvpxTE69R4CbReQmVf1RZLeHgDcC76k6fAa4WFXvFZGTgVtFZL+qHm+7YTEsSYHBnB25uJFcS244jDWfHUFGhsPtgQAwx7lg7R2Bi6k4PkgeAN+eP8zsWjGDqRJQkZF/GBiYz6Gzs+Gx1fuH+f/r6Hejrq8VggHTkcGCYKpQWcVQfXzXO79q7yxL3bxKUdWf/Z3tdECSyxkhYYUFpTJs3QJPHjEzjnIJKRSQoUI4CJDnnIXe9d84+dyCfayPBUc1UcF60LvRBjgu3i/1srDqd03rGSfYqtP2t0NKRu8XAvep6v0AInID8FogFBiq+oDdVvGAqeo9kc+PicghYAPQEYHRx5a81tG5efA8/JmZivXOqpW4mzfirFuDs2IcfGXXprexc/1bjRrK88Cxt8RxzGwjYsQO1UrV1/N1keqpYgm2B7mq6pWgDbyqoOLY6PcKovWOI6P0ihF7VH0SYxzvi1xR0XaJE2uUDuxAFYLPGmebERa7Tnp7xfeda94CORcZGjJxALkccvImyquGIZdD5+aN/WlsFHI58xxtOQnnkUM4o6MLbYsa7pMaojtsTG9GFZM03U315yRtiLajkfNJJ9nmnp/a864IviZbgPUicktkeWvkVFuAhyPfH7HrmkJEXggUgB+387vqsSRnGPuOfZodYxcvrBAnTFamxyfQYjHs+KVQCEuR7hi+EL9YCl98v1isnD1U2Qia8kG3I3z1FYqlhvsGRWPAs/7ui7dVBNnZbYFvvDi5BT/3GPtGXA1v81+PhYf6i2ZWUYOs5HNhOpZW2fv4xyu+7zv26Yrvu858D4iQPzxtbFLq46xeha5djRyfNKorx2Xvk5+MPX+z7qn9QqORd1wSzWYEUq+DJLfnd4ezKpHPt30+pSmj9+FOZqsVkZOAa4FLVDv3UC1JgQEYY3eA1ZtqcXKho7cd+AErLLYXLghH9+pZnXh1LEOM+ijxC9+KYTkqZIibNbiVxu+oisoKikXBUYErq1Vb4YjxBvIj96WD1Oo0ahkno1XgJJeHbgi0chlKDnr4KHgezsYN+JvWoK7gTgh7H/mbuoenprqpR8KYmTTjEqKCe1DYMXaxeb47kp5f0qqH8ShwauT7KXZdslaIrAS+DPyuqv5HGg2qxWD99Zug+iUJvJ2qO8XA2BdNchaO0gNX2GicQHjCGnEP9WjnZQuEWbWBOzh1xEYTFXAVdoyoWicQPhF1Wq+ERbAtun3H8IXsGLvYqHxY+H0VXk0dYu9Df8Xeez9iZh7W3oUqzkwRXbMi8Xkqfm+HOtowjsXmYIpTiSUVXAdKNywMJBZfaNH3ILFjXyNSoRJOU4grJtI7ydKAm4GzROQMESkAu4GbkrTB7v8F4LOB51QnWbICAyIBWbWwHeSOsYvNC5DL4xQKJqAol18QFoHbZitEBUukQ15IqR7zokeXqk48mlEXcUyHlsuFM4tF7rZUdSqBoAgERL2gv7SoskkkIbAl6Pz8wozJTfdx3TF8Yf3tYxcbe1Mux/5bPoiUPPzx4brHVHPQ32M64bTvr0YSZkY6xFpsc85jm3t+TW8oMIOnWuq+6PNT8RwNwIyjohRAynh2ltFoqYeqloF3APuBO4HPq+odIvIhEXkNgIi8QEQeAc4D/l5E7rCH/wrwUuCNIvJ9uzwv9R9qWbIqqZBozETYmZqOOOjEdq681MRhWLdKVMGx01hrL3DyOTtaXyi01JRnkfUEErdy31B1VFNd5Vb+H+wbxIwA4jgwNITOz5sCM2F69iC4LL8gYKwKaqFZ2qHp+gItFYgC/JmZChuMlNM1lkbdaqNsL1yAMzaKs3YNzM2z974/B2DvnX/a2oU6badoMvBzm3v+or9JIEjqjsCrn1NxkOEhdq79VfYdvbKpJneL/VPXNBwYtIqqpJZLSlW/Anylat0HIp9vxqiqqo/7HPC5VBqRgKUvMKoNvNaoGs1fI2OjiG/tHDMzi704Yl7Imtls62HtDosiyKNupRH31+goLvycyxkBYQMMEUGLRZyRERM7cvQYQS4rcW07dcElmIitoxvCAlrTo1fcI/t/0MHvGLnIfE9ZPbXrtHfhHzmKMzKMPz0D0zNtG9jBqHoaqULEdY1jhghaXFCPdkrYRIPxDvp7GtawCAIsFzz9bMR2qYQ/OxcrhHqNcWIpxjtRpEQacRiDxJL/tc7IsFExBWqZQgEp5Nkxfgk7hi9k16a32cjuMt7EZOgZ1SlVTU1bQbWBnYhaKeJqS3UNdkdwxkbNeXM5nJGR8BhxBKdQWDin61Scrxt2i4DgJU3jZQ0Exc6Vl7Z9roBdp74TCnkQYd/EVRwoXp+KsAiIE5hOoYA7Po67YgXu2jU469birFmNu3Y17viYUY+2Wd8ESPQ8N/y7RI4PXcjLRlgE2/sp6+/2/G7zLkOlHS9FTAElN9GyVFgyM4x7br2/4nuY7iOXA1XUddFS2URk23UyOgq+4h89biK2uxjdXGtbRZ4ijPosyIOlHgigYgsleR7YmZEUCmGqhcq65DZlRtkHryoldZddOttxN63ucNOaXewYvwRxHNT32T91TSrnTELu5JNg5TjMFaFUMjPG4SGYm0dn58IYoo7MAKvubdJ63wsq2Krnp8rA3mv3WYi5bx141o3Re+kkFkzCkhEYEHnwI9NttXpvLZVDlRCejxTyaLGIXzRJ0LpGA1fcRbmhonEg4oAookHBJN+4gLquUU+5puML1QWeR/Byi+saT7AGUd/9QtQdOEkHtL1wAeK6TQmSbc55OEPD7OuioACTNG/umVsQVQrfu9+k3RcHKeRxVq8yGQhm57r7XEL8s1mdGLKei3nc+n6hGRf4Jlhu6c17LjBE5AHgBKZnK6vquSKyFrgR2Ao8APyKqh5Lcr7oKEmLxQU3QTt6l0KBIPGZHwqRPqDqgQ4T8UVw8rkFG4br4hQK+FM2uEzEpLUol2M7mkXBh9343bXyVHWAavVRUI53IeWFtzCKFoeD3o1dHwkH198/ey2v2PFnDD08AeGM0totyma2KI7QzRDKmjEW1X+3Gn/HRUGk/UZHZhiSzTB6xCtU9XDk+/uAr6rqh0Xkffb7bzdzwsBV1Z+fW3BhDZIGBlHcvaDegxt4A1W78QazJc9DxAHHQVatgOEhHMCbmDQj0mrPraTX7RLBrCGJgbSZ2UUcB4rXm2BMKlNe9FpdElx/6Lv34pdKOGvXkNuwDn/iBP78HH6pbMas0drW3aLVZyRQl3a7vXWIi0rvBP4ym2H06699LRDoCa4BXpf4yEh0c3SkLUND+HPz+MUiWi6lY0xMk6irbWDDqNIRV3gOzc3D5JTJgWR/rz833301RgMq6lM0ypxrCVw82+3c0zRap47vmzTfE5OQz+OMj4Wp8v1S2Xjs9TpNSxMc9Pf0bXurs+ymNWhQJSug1AMUOCAiCvy9ql4BbFLVx+32J4BNcQfaBF5vBRhm1J7N2imoin4ul43dIlKXQnL5ijQhvR6Fhzmjqlxuo5/V86BYNIbufM7YZoL9+mAWUU2rWXB7PRPoNPsmrjIeeq5rDN02s0BF6vs+/HvGvScH/T0m1sGu72wNiuaJtqPV2h5xKELZ77OBZ4fpB4HxElV9VEQ2AgdF5K7oRlVVK0wWYYXLFQArZa2G+lNxKn3G1ccvKRK8lGU/XL9wMr/nU+ok167ImBvUn4n87r7rZKoN7NZ+UI9+8+fvFHuf/CQ71/4qrBiHY8fD+JnYOin9QuT5qshA2ysVb0I6pZJKKZfUwFBXYIhIknwmR1X1ja02QFUftf8fEpEvYHLDPykiJ6nq4zYL46Gmzul5Jv7Ci+RS0pI1/EZGcDHHDST9JiRq0Y8CrcfsO3plmB3XGRvFn57BKbjppACPZjSu5/3UAotG7cvwb5u51S7mGcBb6mwXTKWolhCRMcBR1RP283bgQ5jEW5cAH7b/fzHxOQM1VLlcs35FRheocsc86O8x3koxhZIC+jFauCvMzJiULSXPqBmD4NF2CdLRxJW+jTt/E51+tQt7RYbePhQei9uXxlnTSw0yKDQSGL+rqt+st4OI/EEb198EfEFMTqQccL2q7hORm4HPi8ibgQcxCbYSERh9+9P8tryRQgFKZbbnd1d4LoVG7uUoLACdnQNb/lVn59I1HlfH8dh1tfZthrDzjajP+sVu0S3Squk9KNQVGKrasMpIkn3qHHs/8JMx648Ar2z1vHVpYfSTJAo2o4oYXbeMDIPMhxHV4T1NYNdYygSlgv2p6QVbWrkDI/S4gM1GQiQa6Bndx3ojwmCpctN8l1WhlBm9FyMiZwO/BZwePUZVf65D7WqNRg89xE/PExB90DIBkpzoiFPn5tH5+UWeKstZWACm58nnTdJBX+loweuEgXgVRJwqnPxCl6G+9rfrcofJAvdqswf4FHAlXSvfnh5OPleRiMwpFJCREfzpmTC3UqxhkMoOr/pzJjSSs2P8EiCwMXlUp5hf1qwYR48eT90w3TRSlWsMwtmFOMKB0g1h7q19k1d3r10t0o33M1NJxVNW1fgCxn1IdWceuvzZF3D/3HXsOvWdyOwsMjqK47qo+mYEHKSWTvCiZkKjPhXCQDVMrS45By0WM2Fh0RFTb14KBXRuPrKhy4bjqL2jav2BkvlbdTNBY78zSF5SIjLZaBfgcVU9u95Ojdxq19qPXxKRyzGlAMMnWlWPJmhrT1hUVzkcPVUGFu066714Dz5SWRAp5kWtFgzB+TOhUUlNIeCr8QQSyTqdKrSQQ07ZhD83F9MAACAASURBVDx2CLH5zVqqt9JBlq0HWwMGyEvqx6r6U/V2EJHvNTpJoxnGrRhBGojR34psU+DMRhfoF4IkdNHgvG3OeeTO3GrTiDRXJyATEoupO2Owlf5qVblb1jgOR56/inWeIrNzxq22n1jmTgk10YGyYfxyGvs08pI6A0BEhlV1LrpNRJorbtwFgtF+rZz8gStntdE1TMlBfyVQW0rIyDDesYleN6MvKY/lWXfrUU48Yw0rHj+EPzvb6yaFZGrD2ihQHpAZhvVIDRGRlVQ6MB2t3ieOpL/22wnX9Yyzn598slMxWpov4oyOpt+gfk3t0CEadSxqXUYzFpM/Mo0O5clPeb1LihnzvPZSWAzCDD6wYSRZ+gUR+TUReQK4HaNBuhW4JenxjWwYm4EtwIiI/BQLqqmVQAd62da559b7a84saqU6DtbvGLt4IcNtG7OL4ByL0pMvAaI2oUX2oQT0e66hXrLvB3/Mq172J+QnS2gPhGo/zqqrn7F+nen0kzBIyHuAZ1eVk0hMIxvGDuCNwCnAx1gQGJPA77RywU5x9vPP5OAt9YVFzQ6uDUER1N1Qz0NyecQ1eYCc4aG+Ui20S/TexQnmWi/29sIFfZdyvR/JPzGJPnFooUZ2F+k3YRGlnwVHmnEYIrIT+GtMqu1Pq+qHq7a/FPgr4LnAblX9p8i2S4Dfs1//SFXreZX8GJhptZ2NbBjXiMi1wBtUte+tlY1GvNXBdwH7564LK7Q1ozaRXN6U1RwbRYsl8Dy8qSmckRGc0VFT02AJd5bVHmL1itb04yi2n9h794fDyoBOodDzGVk/dcyQbnsO+nuw6YjaJo04DBFxMTn5tgGPADeLyE2q+qPIbg9hBu/vqTp2LfD7wLkYLdmt9thaFUrfD3xbRP6TSo/XX0/S1oaKdjVz5N9IcrJecs+txl7Tqg74QPH6moFksQ9rEFRVLuNPTCKbN4RlUxFB5+eXbAfZ6OWNE9zu6tXkNqzvVJOWBhW12HvfjkGnoliSOOH31OwjmpoN44XAfap6v6oWgRswReQWLqX6gKreDlSPaHcAB63R+hhwENhZ51p/D3wN+A8WbBi3Jv3JSQP3/k1E3oOpsz0drOy3OAynUKisJdAgcjuOeiPmaqI2D3niKWTNaptpVE1+oOmWZ359S9L7VzG7sIWq9j11ZUfbtiTocWctufxClcQlhDgSzuDSQoFynezLVawXkahx+QpbzweMnfjhyLZHgBclPG/csVvq7J9X1XcnPPcikgqM8+3/b4+s67s4DPW1rQei6WlvJHWDPz1D6QVnMXT4CBTyALirVuKfONH7EWOXqDVyU89j39FMWDQiqEPujI3iTXTfBbnvyhanSCfewSZtGIdV9dzUG9E8e22l0i/RQhB2IoERxGP0O1oumdFsB+wGizyDwgp9C9cefvAYnHISHJugfMZJuHc+gDM+3pOXv5vETfMll7cpJW6ocVRGHM7IMDo72/E8UnF2EuO44SyZ4MpuuOZqOkbvR4FTI99PseuSHvvyqmO/UWf/N9j/3x9Zl3jwnzRbbR54G/BSu+obmPrb/WXRFaeusKilbmpGDRVFfQ07RvU8/AcfxjnzdLytm5nZMsLKJ1ajx/tYWLTQKVV7SMXdL3HdZZ3FtB32TVwFwK7Nl+MdPd4xp4laI+5B/LslSVvSKaeLlJIP3gycJSJnYATAbuCChMfuB/5ERNbY79upFAYVtDv4T6qA+yTwfOATdnm+XddXiCNt64CbGpUEswwbc+GXynD0OO7xGcbvn4R8Du/48bba00nSVkFILo9TKGSzihTY+8QnOmNLCAzrtn54lH7zjEpKnLCofo87opJKyeitqmXgHZjO/07g86p6h4h8SEReAyAiLxCRR4DzgL8XkTvssUeBP8QInZuBD8Wpl0Tkpxv9niT7JLVhvEBVo4WOviYi/5Xw2K7RzENRz6+7KaHhVpXAHB7CHxvCefwI5SeaKkXeddIcvR7097Bj+MKeG22XEh1xrY2UzY2Wyx1UYdFbBC+50bsuqvoV4CtV6z4Q+XwzRt0Ud+xVwFUNLnG1iLwc6k6JPgPUTVCY9Nd6IvK04IuInMkA1sWIo109pz8zUxm/MV9E7n2I4tNPXrKpMGreM9dl/+y13W3MMiDNzlxy+dTOlWFsGEmWPmAVlW60cUvDEWTSGcZvAV8XkfsxEup04NLm27y0EdfFnzyBPzeP843bet2crrN/+rO9bsKSIppMMy0CG8U257wlOaCplR6oEwxSPQxV3ZrGeZJ6SX1VRM4Cnm5X3a2q8/WOWa5oqdzrJnScuBdy16nvZO/Df92D1ixN0ur4Gjl3RONmBlEttc05byE9j1Wxdi1xoRo7xnKiGQXc84FnA88DzheRizvTpMEljAOxI7euv4A9tB/o5ImeXXspkrawSOuc/YipZ9Mbh00fSbQsFZK61V4LPA34Pgu2CwXa0kGIyKn2HJvs+a5Q1b8WkQ8Cvwo8ZXf9HWsU6m9s/eOAYNTWvRFP51UMcZ3OjrGLM3VUn9GKcOjHBH+NSJRctEMoqcVhDAxJbRjnAs9UTX0CVgZ+U1VvE5EVmMRZB+22v1TVj6Z8vbZpVgB0XWj0gOWghusVraSSb/b8S/nZ7CyC5w+ewBCRLRg7dLSA0r8nOTapDuOHwObmm1YfVX1cVW+zn09gfJDr5UHpOQ1frhqj/EEatdVje373onWDGOyVUZtMgCRngLykABCRPwP+f0w69N+yy3vqHhQh6QxjPfAjEfkulflHXpO8qfURka0YH+D/BH4WeIe1k9yCmYUsStdrc6K8FWC4i/WcWhmVJYkmH6QXdXt+NzI0xP6peqn3M7pOC9H7cTUnBs0I3ot3R3UgVVKvA57eqtNS0hnGB+2F/gRTSClYUkFExoF/Bt6lqpOYKPKnYQzsj9e6lqpeoarnquq5eYbSak7qVD/MtVwl+/kFDdoW+PEfKN2QCYsu0igdS9rXyEjGoJVoBe4HWg7GSepW+81620XkO6r64lYaYPNU/TNwnar+i73ek5HtVwL/2sq5O0UqdokaHk39rFMW18Wf735FuAxDomejidlF9bmi9SMGaZbRy/dlAN1qZ4Dvi8hXaaGAUlKVVCOGWzlITNmrzwB3qupfRNafpKqP26+/iLGhLB3EGZiaA4PQYXSCVhNSdpokbUmSIyyuk93mnGcKDXk3LsRnJEjst1xRBD+l1CBd5Ca7tERaAqNVOfuzwEXAD0Tk+3bd7wBvEJHn2fM+APxa2y3sM4KEhXG0O8tIY5YS1zEtp6SCoV7fPZ/t+d0D9dvjnq242USwvsKGoX64btB+dy8YtAmGLbtdAM62q+5uJut4T8Wjqn5LVUVVn6uqz7PLV1T1IlV9jl3/mshsY+AR1zWzi5RjJsR1F6kUmqEbOvJBJBhdb3PPb7Bnd2mlTG4z59kxfGGWd6oROpBeUi8H7sXUEP8EcI+IvLTuQRHSEhj9c0e6RKv63SCNQaNjK+oRJ6DejCVN4txqlzoHSjeQW7+2182IJ8YW5hQKTaWujwvY84tF/Pm50EFjeyFpeYZlhiZc+oePAdtV9WWq+lJMTfC/THpwIoEhImMi5skUkbNF5DXWWB1wUTMtHnjEwRlqyWyDXyx2xH7RDT3zNvd83PXr2Ln2Vzt+rX7D74P67LFqpehM1QoPv1hEPY8dIxfFeuTVShmyzTkPyeUXtkeEkZZL4bn61SmjFwzaDANT0/vu4Iuq3kMTXlNJZxj/DgzbCMEDGAHxD5GLLi2jdCPUR8slthcuaGoWEMwu0i6BWasNrcyA6h6jPv7kCWSkNWE5yPSDC3Hc36ZiXZCaJvp/k2i5ZOqaBOdrQD/MOHupQlVNtvQRt4jIp0Xk5Xa5EhPrloikRm9R1RkReTPwCVX9SMRIvSwxxZqaKwminpf6w53W+RKp2MQBX/GeOpLKNTPSocLJIVIgKSzAFFVb1RECzT5LoWDpErWe0V7NeFSpKEI1ILwNeDsQuNH+fxhbRiISCwwReTFwIfBmuy7d+p4DSGgUTDCaWxKGZPVNHEZWVa/vqJtzKng2YxJjtsP+uevYMdJdbXS/JUjss9lDQ2yE91/YpWmSvvnvwhQW/4KtNXsm8PVWLriUCPS6ncwSG/diuOPjiQznTSFOspGa9dNvl0wP3hnqesrZ5zS49ztXXsrONW9p74Ju9wYPrXoAdpQBMXqLyOft/z8Qkdurl6TnaSbS+5siMmq/38/ClCajDmk/3MYOIok77cTpn6tSs8df3AQc7hi5KCvF2udUR2zH4c/NI4X2XGd1vnt11PpvkNF3Bu16vNP+/wvtnCSpl9SLReRHwF32+0+KSGK913Il9ZGQOKZIU8KXtOkXLMFMSX3FLxbbcrPsvxd/eeIMD0G5vdT0xpbXHZp1Ne8KAzLDiMSyXa6qD0YX4PKk50k6n/wrjL/uEXvx/wISB3ssN+Ie7B1j6RQoFNcF1+2IX7w7Pl7/2o6EgYftdhR99+IvYep1tGl47PVbUGPXSDFwT0R2isjdInKfiLwvZvuQiNxot/+nze6NiORF5BqrarpTRN7f4FLbYtbtSvBrgSZSg6jqwyb1U0j3hhYDwiIhMXIRUsjjTU3j5FvPwhKnVminBkUtNYU3NVX3OPU8EA0j1XeMX9KSu2kmLHpDtWFc/cYzyvA5serIuFQhyzrXVAoqKRFxMZHX24BHgJtF5CZV/VFktzcDx1T1J0RkN/BnwPnAecCQqj7Hmgx+JCL/qKoPVF3jbZiZxJlVNosVmPoYiUjaiz0sIv8DUBuw905MsaOMGHauvBRvegZ3fAwZGebgxFXpnFh91AN37ep0ztck4rocKN3ANvd8nEIBLfamjnJG++ROPgk9UXuAUD1rqI4cjyYlHJSsth0hHXXTC4H7rG0YEbkBeC0QFRivxZSZAPgn4O9s8lYFxkQkB4wARWAy5hrXA3uBPwWiM5gTqno0aUOTqqQuw/jubgEexdSpeHvSiywXdoxdzI7hC/FnZ8ltXI+sWQX59PLxuCtWmBTjdV70pDQMAqsi2oEEnUc39dcZ6XHQ34P35CG8qenYWIrQ80993NVmcKLlUhisGmzfMXIRzuhoz4VFT6+fjg1jC/Bw5PsjLK48Gu6jqmVgAliHER7TmLpBDwEfjRMAqjqhqg+o6hus3WLWtmxcRE5L+GuTCQxVPayqF6rqJlXdqKr/S1Wz6K0A610kuRzqK87oKPg+OjkFQfBUmxz09+DPzoV2hCRBU9UvkrhuxUixmYSD1eoI9ZXc2jVZjqFBJZJGBGDnqjexa9PbzCb7nIjrwpaNFYdpeWFW6c/PISkOiNqhJ0JDMSqpJAusF5FbIstbU2rFCzHmgZOBM4DftGEPsYjIq0XkXuC/gW9isoHvTXqxpF5SZ4vIV0Xkh/b7c0Xk95JeZMljvYt0fh73jNM4sf1Z+FPTyOYN7H3yk+ldplxCPQ8tlRMlG6y2U6jnxR7X6GXbXrgAZ2QkciKTGoWcOzB1PTIqOVC83vztxGF7fjfeiRP4E5Nsc84zg57xcZzxcfTeB2qfxAqdXZsTO9l0lLjnuNMZd5tIDXI4qA5qlysip3kUODXy/RS7jrh9rPppFcYJ6QJgn6qWVPUQxh5xbp0m/xHwM8A9qnoG8ErgP5L+3qQqqSsxgXslAFW9Heh9Epk+Q0ZGYGqasUdmmHjdT1LaUN/rqBXU89ByycwyWoiyre7gk4zMnLFR9k1eXdEGd9Uq/OOTkIs3gy1bz5kB4kDpBjOTKBRwRkdxTj4JMM+IPz2DNzGxkF6kCsnlzcChWMQ/PsGuk/pTQx2dEXUEX5It9bkZOEtEzrC1KnazuMjRTcAl9vPrga+pqmLUUD8HJkksRhjcVedaJasdckTEUdWvU1/AVJBUYIyq6ner1rXnwJ02IuYh7nTainrnt15kzt0PsfpLPyR/18O1922BwD1y+nUvQIaHcMbH6h8gTkXHfdDfg/radGcuq1YsOq83MYGMDOPPdq5kaxav0XkOFK9Hhkw8xqFXbsF9zjkgDu7Jm2oe4xQKqOeZWWdgx+oT1RTi1E/tLg5OoZDe5TTZUg9rk3gHsB/jTPR5m1HjQyLyGrvbZ4B1InIf8G4WDNcfx9gh7sAInqvtgL4Wx0VkHJNQ9joR+WuMDSQRSb2kDovI07DmGxF5PcbI0jeI6+JuWIeemMKbnulYug4TgxC/zTt+nNzms/AefwLJ5TkQGZWnyfj9k6Fwqkesu6P6OKOj7Bi+MJEP/q6z3otOTbNz1ZvwpqbDiPDcpo1oqVTzPqfhatlrY+pyYd/RK8PPL37Dxxj/oYf32JPxO4uDjI7CxCRaKoczVu/xJ9h10tvZ+/jHu9Hkmhz0bmTHyEVG9Rp5NgMhp56HX0pprJtiUJ6qfgX4StW6D0Q+z2FcaKuPm4pbX4fXYgzev4HJDbgK+IOkBycVGG8HrgDOEZFHMQaT7qaqbITvsffRvwUi6pAWUzzXwxkdRYslk4QvhvJd93bkulH235r47wuYFNTOyEioVtL5eaRQSJbi4/gk+GpmEvY3BcJg56o3ZZ36EmPV/rvgnJ/Au+u+2O3iuuB5OMNDxpZmZxjqeZSfPNQXtdD3z15bOYu2WgH3lJPxHnnMOKXEOZ42TWjQHiQ+oKq/DfjANQAi8mfAbyc5uKH+xgaVXK6qrwI2AOeo6kusa1bfcNZPnRF+PujdGPFcyodT1GovoWZxCgX82bmael1zkf7L5Opu2ohs3siuLf8bMB5Oaovs1GPXqe+EoQI4grtuDVDZCeybuKov6iFkpIcU8nh33hM/4BEHKeTxZ+dwxsfCZ6LWc9RLleJB78aFd199M5sulkx6nTTjhwYkNUiEzkZ6q6onIi+xnxPrunpNMArent9tI5RNRx56CQUde6OZQGS24AdT8FrH2GhYIDYitld4hw7jbtkMIuROPonyY4+jvoM4C78jGogFptaBs25tqOKLUzFlNoalx9SLz2T4i08t3iAO7tgo/rPOxLn7IYrPOJXCkydwfZ/y4aM134leBvVVZ0MINBAAIp9L5yL9JQxq0u1I7++JyE3AHiIGElX9l6QX6hVSKKCzs1UrE6iMAuOY1dkHKijJ5RcEUMw51Ne+czV1hofwDx2m/MJnkP+v+806q9Pdnt+N+mp0vzbVx86Vl5rf6LrGCyrFojsZ/c34d+6nXF21LxhcFfJIyePoq5/BugM/hrFRGBqq3Kf6WenDGXdqKEk8oPqFrkZ6D2N8fn8OeLVd2kqTm4RGCbmSsH/6swuBSIFPdlVBmWo1lTMyQu7kzQD45z4TGR4Kt4XqqOp04HZ2EaTP6Cf82Vn82VkKj00gm9abdfPGJnGgdEM4eyi/8Bx2nfLrNu11AX/dKiSfwx0fjw0UDITFcqzxvRTZXrgAVHGCErxVgsA7egzuvJ91X30A3bjWDCiKRdxVq8wxy0lYWNLwkuoSavNLvR04EVkQkbVJT5I00vvSmOVNLTQ6MZGEXLuAZwJvEJFnNnOOIApZCgXEEZwtm01K54oLLb4FWiqbCG3Xwb39PmSogOTy5Dasq3wpIp9DVVQbSQE7xYHSDeROt3FBUzM4hQLu6tU44+PsXHlpuN/Rc4ZhxZjxpFq3FufQUbRUxp+bDz1Ldm2+nJ0rL2V74YLw/ur8fMV5MgaPbc55zG97Hjo7h/zE6YAZSDnDQ5XeRqOjoIqUPPSJQ+h8EX96Jt692qYXWdIMjg0j6JhuxdTwvjWypFvTW0T+Jmb1BHCLqn4x6cWaJElCrroExWGcTRs59KotrL/++2ixiLiuUbkEEdqBzS6YceRz4FdWb3dXrcSfOIG7YgXeiRMV13HHx/Hn5vtSWIS4LszPw9w8zob1eKesZ+bkEVb8+4I3zOZ9j1LevJrcqaegk5Po1PSCzcdmp8Xzzaxj7Wq0XA7tGGn6tmekTxJbwtCB25BNG2F6LlRZ4qt5X+z/AXrosDF+j40C9d3Nl3Vywj5BVX/B/n9Go33r0YxK6nnAvXZ5LiZ8/c0i8lftNKAODRNyichbg9wsTz0VY6iz+IeeYui4FQ6+GvVRVZBf1JMKjOeHs2mjcR0s2jQYhTwyPkbuzK0V5/emZzofUdome+/7c+69/DTIubBiDGemSG7GZ+6nI89PqYQ7NUf5pNWmoxg1nQHqm6A/607sz83jHT3OvsNXmHsoDn6pzPb8bnaMX5LNNvqMQKjXc1II1LX+4SP4jzxuHDxcF1wHsbY89Ty0WDTeRkWTpsafnjHHFwq4KxYHeFa3YakxQCqpEBH5JRH5CxH5mIi8rpljkwqM5wKvUNW/VdW/BV4FnAP8IrC9ueamh6peEeRm2bBhw6Lt+6euYeeqN6HFEqu+fm/oTreoAJDt9MCkEvBnZ/EmJtFjx00ajnwORkZMpbtymcd+/uTwUHf1anIb1w/ECGrrl2fxT9kIZQ9EcOc8vr5/wf36qe2nIzPzOLfeic7OmXxYkToe4T0LcklZ3PGx0I0RVXBddm16G9sLF7BzVWPNZZZGpLM0eja3OefhrlppqimWyvjzc6Z+i+uGMwgA1MefncM7fhx/bj5c565aadS4TlV3UqWO2uact/QER/Lkg32BrZR6GfAD4IfAZSKSONoyqcBYA0QTI40Ba1XVAzpV1DdJQq6G7Ju4CnfdGrwJG6mjPn6xGLrHOoXCgooKFoJ8xsfA84yX1eb1MJTHWbECf2KSTZ/8T7Or60K5bHIqDQC5Ox6AH9wDc/PIU8co3PMYu57xfna8wAQCzm4QmJs3M7B8DsnnzIgS62abzxGUiXWGhsM01+p57FzzFnAdk1toaory4aPm/jgOuzZcxq4Nly1qT2ADWdYFeLpEw/KmQ4XQDucMDRu7X6FgZtilcphyI0yAGQ3YO3LE/K1P2YQzNNywLUtGaCgm/C3J0j/8HLBDVa9W1auBn7frEpFUYHwE+L6IXC0i/wB8D/hzm+zq35pscFKSJORKRtnD3bjedIKBx9TQUNj5oX4oQIIMnmDVUhvWIUcn4ehxKBg1lhOUMhXTQTaMlu4T9h2+wiRIHB6CfM4Iu7EhvNE8u7b+Bnd8+DcgZ4SnjAwjZ20FbJK6kRFj+LaGzP2z13LQ32OCo/J5dHY2vLfO+DjuqpU442OhwFHPqzCUw4KDwDb3fHYMX5haGduM5jjo76F82kbztysUTI4yEXRuHhxZKM2by+MMDZPbsB5nZNgIiUC96wjyxJH6Qa0RgtnGoAuPAVRJ3QdE61+catclIqmX1GeA/wH8H+ALwEtU9dOqOq2qv9VEYxNTKyFXK+fa+9SnoFDAWbnCPOBDQ0a9FBq9zWhJcnmzOGJqT+Ry5sWZmoZCgfLjTxj3wklr9FY/UZrxvkIEPXoMhobQ2Tn09ntwv3snWC+ovQ/8JVLIm233PhAKT39qKtbjRVzXbMvl8Gfn2D93HTo3j6wchw1rkTNPheEh/BMnwpFpdXT4Qe9G9s9dx8y253T852csZptzHs5td+IXi/jFIt7EJDIyDK5jbBXW4O0Xi2aGMWvqYLinnGwGWYW8SULoiPGqatKddqCFxuB4SQWsAO4UkW+IyNcxTkQrReQmG2tXl6ReUoLJm36mqn5IRE4TkRfGZLBNlbiEXC1jq9SZh9t0fIFnj0a8o5zRUbzjx63BzzVlLE87CX3gUZzR0dB90BkZwZ+b56DXx55RMQSJ5rb97B/hPPiwnS1U/oagTnc09uJA6YbYFztuduUXi/gPPYIzNIwzPkb5yBGcoWHcDeson7oe98FD7DrzPey9/6OmLc55OIUCo7kcO1deWpFKPaM7BG7TzvAQ/tw8OmNmjDo/H+aNMnEZebypKeNFVbJ2LPs++ZO2EmQLrrQD60nVX8IgCR9ovEttkg4FPgG8GHiD/X4CEyMxMOx96lPWiGu8QaKFVQLBISPDYPW4/vycmYV4PjIzjzc1bb4DTj63YPQbUOTmHxmjZrQwUhX7566rdBVO6PUijuCMjLB/9lpzz8QxzgRHjsJ3/gudnsF/8hA7xi4OEyMiDng++yavzgIBe4C7coXxcrIzZm9qKnQS8W2mhMCGAUbAOGOjxgaYz4Wzk6QqqTgGbaaRVB3VTyopVf1mvaXR8UkFxotU9e3AnL3oMWDgHO+1XAqLwkg+Z9QoNuOmeh4iDt7RY6FuVotFZM1q/MeeAAgTmaWWHrmHHChez/7pz+LPzrLrtHeFs4mgKFOs55J1r61nQN3mnh8mNwTYN3l16JUWdDzexIQJBrQR5UHW0+hsJRMa3cWbPGGq7s0vZCX2Iwkq1fNw160lt2XBQ5CVK9DnPh1ZucII/RQC9QbOYy6dAkodR0S+Zf8/ISKTkeWEiCT22kkqMEo28jqoh7GBfrP9JyAahObZ6NTchnXG2Dc6im8Nt86G9eQ2b8RduwYdGcI5yRST8UtlYxQcHjL75ZOm4upfnNFRdM0KnBUr2LX1N5CxEXaMX4KTzy0ySh709zQeBaq/yOspNkNw4I5ZLHKgeH3FTEat2i8zgneHg/6esKN3gtgbm0cqtO85gn98gmMvO31hVnp8EvfYFP7mtcjWUyvT77TCAKYSGZQZhqoGCWRXqOrKyLJCVVcmPU/Sv9DfYIzdG0Xkj4FvAX/SdKv7gUj+qNz6tSZ3zvgYzopxdN6mwFgxZjqt0REmn7UWPT5BbuN6a+Ar4KwYx928cSAf8Gr2TVyFHDqKqo9OnEBWrzI6aTvLCmo+ByO/qNCIerpUC5cgn9b2/O6aM7JAxVfN/tlr0ZkZU4Y2ExpdxRkZCWfSAWrdywHmVktoz9D5efzVY+ApUirjrl7dVvmAgUwlMmBGbxH5GRFZEfm+QkRelPT4pF5S1wHvxWQ6fBx4naoOnIXqoHfjQiR3uYR39JjxBjp5gwk4CzLUHjkW2i9Wfu1u/IlJdNrkYPKmZ/COHKX8aF8VHGyLvY9/nH2Hr2Df67KO0AAAIABJREFUsU+jhw6blXY2FgY5qm8ER42o4UBFVa2qiqZgqd7fj2QRrj7f/rnrzHYRXvmKP+UVO/6svR/ZRQbRXTT4u5WPHluYJUQGRP7sLH6pzEl77lnIxuy6OMemkGIZzedg1Qqj6m1nIDVIg7ABtGEAnwSmIt+n7bpE1NWpVGUxPAT8Y3RbM2lx+4VohLKzYgWydnWgZzNT6rERyOdxVq+CUgnJ5RArKHKnbcF/6FGT4jywgywx4jyUdoxfgj87Z9Kg244lrrJanF0jui44JrpOPS9cH8RoBOqpA6Ub2DF+CYU7H4ZViWfNPSeR6q5fUT90/Ijmh1LPQ1wX79gEQRZbLZVgdIjpM1cxdu9xiqeupTA1jczO1swr1YiBC+LsL2GQBFFdSJKnqr6IJO7IGonzaGbDp4B7MLmknrLrBo6KKfNJG5k/3QbmOVZnOztn8i35PoyPoWUT6ZrbchL+40/ijAwbTxD1kbWr2XXGu3v3Y7rE/qlrzIusfs2ZRRKq9w2/21FltS0juDaebwInM7qCmdnZriEy4ldfQwcRZ3jIRIOfmCE366PDOfJHptENawYvNqkdBkwlBdwvIr8uInm7vBO4P+nBdQWGqp6hqmdiorlfrarrVXUdphbGgbaa3SOiFfdkvsjQ45PoulVmVWDMy+VgqIAOWxdc14VSyaQGmZlZKHz/2JMmTgPYuf6t3f0hPSDwjgp85qsFQLUto1q4xI26D/p7OOjduDBzifGS2fvUpyq+D0Jyw0GMKQgi9ysM3nHBmrl8OCMnl6NwZBYt5MBT8E0sR4XxO6GaaRDv2QCqpC7DBGE/ikno+iIgceeVVGH4MzaIDgBV3WsvOrioj//EIfB8vHFTI0NWjONs2gjTM1AqI0cn0WmjZ/ePTy6kubBCJ4h6BaBYCl1SgdiCQ0uFJC92yyqZmA5qm3t+hdDYN3l1ZgzvEKHKNpKQM3QUCXJNjQzjz8zgnzgBs3Nw13/jTM7Bk08hJ6aRraeaINiCqSOzpFPfD9gMQ1UPqepuVd2oqptU9QJVPZT0+KQC4zER+T0R2WqX3wUea63JvSNIlhe8DOK6zJ2+htyTE+jMLDozQ/mBB/FPTIHn4U9M4qwcNym95+dMIFNVadbQcCti7B/B+mJx8HzKO0BUcASjznrCJNYOEqPX3j/92RRalxGL+sZlXH1mfulFobtskOY8cBDxi0X8w0fQYgl98BE4aQOUSnh33gMiOBs34K5bk8jzaRBnF2kavRtVFxWRIRG50W7/TxHZGtn2XBH5jojcISI/EJGaGSBF5GwR+aqI/DBy7O8l/clJBcYbgA0Y19p/sZ/fUPeIPiYYKannMXTzvTB5AjwPnZ3DHTfutTpvCi2VnzocHhfU9Y6bYu+buMrUhwB2nfpOck87o+9qe3eCJLUWAtKsGRKdzTV97PCFA6HW6gUH/T2hqskZGWHFgTuQQh5nbDTMbuBNLTjZBPml8Hx49BD+iamFNPfFIpQ9nM0be/VzOk8KM4yE1UXfDBxT1Z8A/hL4M3tsDvgccJmqPgt4OVDvRbsSeH+wj6rejknsmoikbrVHVfWdqvpTqvrTqvquQfSQAohmqDWus0ZQmEJJRfzZWROVPDWFPzOz6Fig4YjJP3Y8zF21lAmSCLaifmrXi6gd4bN/7rpQvZixGPU8o3Kam0e2bDaxGSvGaw+AxDFeVNZr0MxIcuD5lI8cofzQI11sfZdJRyUVVhdV1SIQVBeN8lrgGvv5n4BX2hx/24HbVfW/AFT1iC07UYvRmByAiVNX1BUYIvLBRidIsk9fEQkOCrw+Kjdb+0Qk53/FsVDXiLfNOc+8bMcn2gti6nO2Oectvj8xdErVoL62JXT2z12XYmuWKOpTvvs+ykeOgBtJ62+3RT9LIY8/afJP+cUi3tS0qdZnM0AjjqmzsYTeCaEpldT6oDqoXaKG5obVRaP72EzeE8A64GxARWS/iNwmIu9t0OzDIvI0FrJ2vB4TW5eIRv63b2mQZ0Qw05kPJr1gXxCZKWipbFIzh5sEcQu1k6jVmV1EDd3BLGYp0kxHHXhUbXPOM/Er5cV2oJZIcHyW+bY1quNInEIBPD8sxxqHPzOz6O9aPvQU7qpVJv094KxdTfmJxPbV/kdBkj/Gh1X13A60Ige8BHgBMAN8VURuVdWv1tj/7cAVwDki8ijw30BiD51GKqkrMfnTay3jdp/BIjrLiFQQk1zeTMebUFdUBKHZeuHR82YsCJhoidduoKVyRcGmtNi55i2pn7PfqH6u/aPHGqsB41xwh4yHlDMyDJ634DE1SBHd9UhHJZWkumi4j7VbrAKOYGYj/66qh1V1BlMO4qfjLmJtJZer6qswduhzVPUlqvpgkp8KDWYYqvoHSU800Ihj9LNNGqmTpsHIsKR4bxpFU+8Yuxh/fq4jKrF9xz4dGt0HpdpiKwT3WMslU9s+lzepzCMpXRpRfuqIKXdsVVHOpvX4Dz9W8SwMpIdUQDous2F1UYxg2A1Uj3RuAi4BvgO8HviaqqqI7AfeKyKjQBF4GcYovripqp6IBEkIp1tp6BIR8+3jrFmNWM+QJAz0Q94Gaaa8aPdc9YSGPzvbUf///bPXLmlhEVDxnKtvPKWanR24rikHPHEC5uZNJugVK8ht3DDwMRppuNXWqi4qIh8SkdfY3T4DrBOR+4B3A++zxx4D/gIjdL4P3KaqX65zue/Z6noXicgvBUvS37v0kiElocrbST3PlBUdGUaPTzQ8vJ6wcAoF46PeodHtUqPdSmu500+N3yDOkqhb0k+Eqc5dF/WT26G0WDSu6r4i80VT9XLrFtT3cfJtpEPvB1IKyourLqqqH4h8ngNiR0eq+jmMa20ShjGqrJ+LngITLtGQ5Skw1DcPvbfw3Z+ewc21eTtsjW9nfBiCmI0qqhPsDQodSaZnjaTNCI3thQsq7p336BOx+w1cErsBwhkfx5toPLACjFDxfGRoyHgV5XKwehUyMw/5HLp6vOEp+pbmjN59gaq2FYCUaG7ZbnRgvxBroLYR36iPlsvI+BhhmvMmpt5Rw653PD5R3vb87tgEe/1MR1N1t2DTUM9jm3t+6JE2SPdyqeBNnmhqf79YNNHg8/PG/nH0OMzMgir7fvDHHWpllxiw1CAicqaIfElEnhKRQyLyRWs7SUTSHrGt6MB+xbjQugu+4r4PxRJOPte6AbvOMUFRoUGhH1N0H/RuxBkeQj2Pnave1OvmZCRB/dBr0J+dM++WCBxLOEvpYwYw+eD1wOeBk4CTgT2YQMFEJBUYbUUHxiEify4id4nI7SLyBRFZbddvFZFZEfm+XT7V6FwtXT+SHkQKxvsjKHpPCsFFg26/6LawaOZ6+6c/i/rKvomrOtiijFq0VZpYfbyJSSgUTFboQWfAZhiYvvxaVS3b5XMYu0YikgqMtqIDa3AQeLaqPhdTZ+P9kW0/VtXn2eWyNq9TeVHbkUdrFev8PGIL9MjoCM6Gdbjr1rbuwbFUfMz7mMxG0TuaiVOKRX323v9R9j7yN+k0qFckFRb9JTD2isj77MD8dBsZ/hURWVtVMC+WpCI+Ljrwf7XeZlDVaD2N/8D4FneVMKeUr8w9ZwuF4xuQex5CGII1q5ByGY6Vm1JNOYUCPPusDrZ6aeIUCm17TGX0P0vp7yt2GTB+xf7/a1Xrd2NE25n1Dk6afPD+mOjAB5psaD3eBOyNfD9DRL4nIt8Ukf9Z6yAReWuQm+Wpp55q/qqBIFCf0Zvvxx9yOfHys6HsGf1qsZQ4MvWgv2fBlfOH9zbflmVO4ALbj3aTjHRYSsIiQPxkS79gi+LVWuoKC2hc0zu2/qhJkgiq+hcNjv83YHPMpt9V1S/afX4XYw8JssE9DpymqkdE5PnA/xGRZ6nqopxWqnoFZubDueee29bEr3zkCIUHRincU4JiCYasjjWSzqLRA5+pSdogi5DPGET6S93UcRqppFbY/5+OSW51k/3+aqDaCL4IOyupiYi8EVPu9ZVBYXJVnQfm7edbReTHmIyMtzS6XruUH3rU5LxRhanphfoXGRkZhhYTRy7F2QWw7ARGo5ref2DzSZ0C/LSq/qaq/ibwfOC0di4sIjuB9wKvsUmzgvUbbJIsRORM4CyaKFLeFurjz87hz862b9gbcGq+4JlBf3nT4kxwSaoaU6y4NygkNXpvwiS2Cijade3wd8AQcNCquP7DekS9FPiQiJQAH1NJKtViTXUT10XsGhmLcQqFbOa1jEiro89mGL1FRGIz2Aao6m1JzpNUYHwW+K6IfMF+fx0L1Z9awpYajFv/z8A/t3PujM6RpWzPyFignwzaDfhYnW1KZW6pmiQSGKr6xyKyFwg8li5V1e8lOXapsGRHSE2SZl3ujOXBUn53BkXdpKqvSOM8iQSGiJwGHAa+EF2nqg+l0Yhe0KieQkZGRkZd+i8oLxEi8mzgmUQivFX1s0mOTaqS+jILt2YEOAO4G3hW8mYOGBFvkKU8QqpHJlQzMhowYAJDRH4feDlGYHwF2AV8C2N2aEhSldRzqi7608DlzTR04FjmwiIkoRtl9D61I2SW/f1eYizlv6cwOCqpCK8HfhL4nqpeKiKbSF5Lo7V6GKp6m4i8qJVj+56q4krLmZ0r66fOr9UZZDOTDFjawiJk8ATGrKr6IlIWkZXAISrridclqQ0jGvHtYIqMP9ZUM/udaHxBgqju5YCpiJariEnpmDAQp61I+SwPVRdpMXhvyaEg/sBJjFtsZvArgVuBKUyd8EQknWGsiHwuY2waA+/6Gu38srQei9k/dY2pOVEqVwjRJB1zM4IlrbrO4d8yExydpUkV5VJm0FRSqhqYEj4lIvuAlba+USKSCowfqWrFEyAi52GKbww0y+XBbpVBqTlx0N/D9vxu3NWre92UJUOFwG/BlrUsGDCBISJfVdVXAgQJZKPrGpE0z8P7E67LyAg56O8Jl3r4xSL7566ru08S1PMoHz3GNvf8ts+VUUWmgoplUFKDiMiwrXexXkTWBPUvRGQrsCXpeeoKDBHZJSJ/C2wRkb+JLP9AmxX3MjLSpsJTyz0/ExwZnSelAkoislNE7haR+0TkfTHbh0TkRrv9P21HH91+mohMich7alzi1zA2i3OA2+znW4EvYtI0JaKRSuoxTJbY1/zf9s4/WK6iyuOf70xe8kKCIIRVVlACBK2giBARFSlLTUJwIbiETYCVaFHLKqC4KyqKRUX8seAvFhYQELIgAuHXgikWCGEJQrEghB+JCRgJEFcw/A6BIMnLe3P2j+55777J/Lgv786PN3M+Vbemp29P33N7ZvrcPt19Tqy8yBvAv6S9iONUI0szRtLXVceZR5zGktHoITpbvQCYCjwLPCRpoZk9nih2PLDOzPaUNAc4G0g+Ef2cwTGFBotqdi5wrqSvmNl/bK2sVRWGmS0Dlkm6ysx8ROFsNcXOu3QiPMtOfcY7T+x4L8PNohOVs8jMl9QBwGozexpA0gJgJpBUGDOBeTF9A3C+JJmZSTqCEAX1zRTXuljSVwlOXgHuBi42s1Q+f2qZpK6LyUclLS890lzAcZIkV1plvUT3tucvRDkNuo5TH5TPh6XQKeao2hqzdEeYO1iaOE5I1PIu4M+J98+y5bxCf5n48L4e2FHSeOBbwPdSSnwhITzFhYn0L9Lebi2T1Cnx9e/SVug4tVA+n/lejunjjsN6etybboPwdg4MwST1splNqYMI84BzzGxDMRJqOSSNiormw2b2wcSpuyQtS3uxWgGU1sbkiWb2p+RBu7sGceqHcuS33bZ2uZQc8rYvQl+fd2INpqNHFpB+wru2UnmOwbutd4l5ZctIGgVsB7wCfAT4saQ1wNeA70g6ucw1ihFS+yTtUcyMQepS/3HSLqudWiZvRtqLOE6SO3quhq5R2UXvG93VP3fR8SaSjHDXLulQId1Rg4eASZImShoNzGEgHHaRhcDcmJ4F3GWBT5jZbma2G/DvwI/MrNyqp+Lw41RgiaS7Jd0N3AV8Pe39VjVJSfoyYSSxe8mcxbbAfWkv4jil2FsbUU7YMAcFMyZ9k75X12UjlOMMkSwmvc2sN44KFgF5YL6ZrZR0JrDUzBYClwFXSloNvEpQKkNhp4SLp4vjdSCMLj4ELElTSa05jKsJS7X+DUiuDX4j67CpTmex6M1U3pSrMmP3U+ld86dUZd3XVLZ4WxLNTdnsyjOzWwnuxpN5ZyTSG4Gqwz4zm1fldB4Yz8BIo8goBrt+qkqtZbXrCbPxRwNI+htC0I3xksaP5ABKzshmxq6n0Pf8C/3va3Vg3sE59aAVdnGnZK2ZnTncSlIZkSUdJulJwlrf3wJrqLJJxAn4TuP60fvcX7CCZTcP4qTGlW+CjHZ6N4DKS6iGQNp/2w+AA4E/mtlE4NPAA1kIMFJJowwW913L1NxRPoFYBxYXric3ejS5rlHegWWMP+ikoxhAaST4kiL02cMmrcLYbGavADlJOTNbAgxrTbGkeZKek/RYPA5NnPt29JmyStL04VynXqR1h+6rdurDjJ2+BFbIxGmhM5hav21/AIqk3bSX0TzH8ETNZs45rXvz1+KOwnuAqyS9SLpt6LU4x8x+msyQNJmwAmBv4G+BOyXtZTbc9TROO3HbSxc1W4S2ZVrXHDSqC+tN5S2io8nINciIIe0IYybwFsHh4O3AU8BhdZJpJrDAzDaZ2TPAaoKvlY5hevexTB/7+WaL4XQwyqni/JCPmAcYQSapTEilMMzsTTPrM7NeM7vCzM6LJqrhcnL0SzVf0ttjXhq/Km1NoacHcpnMUTnO0FEOjR/X75criSuLBAYULN3RJtRyPviGpNfLHG9Ier1W5ZLulLSizDGT4PBqD2BfYC3ws6EKL+mEojOvl156aagfb1kWF67PZJ9Cu+KTsvXljp6roa+AxowZlO/KogwjZ5VUJtTahzEshz9m9pk05ST9Erglvk3jV6VY/yXAJQBTpkxpo6/FqYbHX68/ltJN/PTxwVuFcjmsUGDRhivqKVbL0U7mpjQ0bRG7pJ0Tbz8HrIjphcCcGGFqIjCJAcdZjuM0iUqro5TLQT7fccoCGDGrpLIi7SqpevBjSfsSBmxrCCEEiT5UriMED+kFTurkFVLFP6mbA5yGUvT8q1zVeN4dqSSKWOetkmqawjCzisuAzOyHwA8bKE7L4orCaQaLNl7FtNHHkItehYthb+vhkys5JzWSzI1h4177jB7S4H4VHMcpyx09VwOg7jHkxo5F+TzK55nWNYfp3cdmdp3FfdcGRVFlJNOyFFIebUIzTVKO47Q4xZ3008cdF8KyJvKyZiSOpjtthOEKw3Gcmvgy7zK02ZLZNLjCcBzH2SoMtdGmvDS4wnAcx9la3CTlOI7j1MSX1TqO4zip6bARhi+rdRwnM6Z3H1t1yW3bxdJwX1KO4zj1YSQuna2GL6t1HMdJyaARg3Ijaqf2sDGgLxuFIekQ4FwgD1xqZmeVnB8D/ArYH3gFmG1mayRNBc4CRgM9wDfM7K5MhCqDm6QcxxkWiwvXg3Lkuscwfdxx/R5s2x1hyNIdVeuR8sAFwAxgMnB0jDya5HhgnZntCZwDnB3zXwYOM7MPAHOBKzO8xS3wEYbjOEOmOLJQPs/08XMZNWEHrGczt6+7tMmSNZhsTFIHAKvN7GkASQsIkUcfT5SZCcyL6RuA8yXJzB5NlFkJjJU0xsw2ZSFYKT7C6ACm5o7aIujQ1PxsD0TkDIvcNtuEIEtm2MZNkO/A7iQb9+Zpooz2lzGzXmA9sGNJmSOBR+qlLMBHGHWndFVIvSb9St2gb+EW3QplV6iUeh+dmp9NrmsUuW1D7KzbXrpo2LI47cPU3FGgHPnttsM2b4a+sBHh9vXzmyxZEzCG4lhwgqSlifeXxABwmSBpb4KZalpWdZbDFUYdSdNB16P+WmUGoVwYaRQ9hSqHFQyzAvRsZnr3sVjBuKPn6sHltlK+JENth3q41nbSU/w+8+O2CaOJt3prxstod4awSuplM5tS4VyaKKPFMs9KGgVsR5j8RtIuwE3AcWb2VFqBtgZXGBmSdo15tXKLC9en6hirXkvRNDCEP7LyeaxgKCcACusHh2yf1jVncN1DrL8cQ1UAxbYppp3GUWz3ohmq8Ne/dryyAINCJvf/EDApRhh9DpgDHFNSZiFhUvt+YBZwl5mZpO2B/wZOM7P7shCmGq4whknWG5GK9U3Nzy67RLH/esk/q8rYjtP+ka3QP6oo/ayVc6yWcQdRqf0qKQRXFM2h2O7TuubQ17M5PFjE31293J23PEYmk95m1ivpZGARYVnt/Bh59ExgqZktBC4DrpS0GniVoFQATgb2BM6QdEbMm2ZmLw5bsDK4wqhBtQ6trrtWK8w5JM8DWyqLrenQo9IIyQp/gAY/SSbv3ZXE8Kn0AFLzc+V+gzIwoTGjwJSBdCOYjP4WZnYrcGtJ3hmJ9EZgiy/DzH4A/CAbKWrTNgrjyUee6be3Y4WBjq9OHV3LuDjI6v6S9bSYqcHnLobPUJRFmt+2RnX1T3iPJKaNPqbf7JoFvtN7hGJmFDb3lmSOvB90S9CC7TZU05VTR6yA9W7uH5VO7z62pcxSxZVcQH9Y2STW15fdxVxhjGASZhrl82DqH3EU81uxM3S2nuLKLVccoS36n56V64/JXS+sry8oj7peZWj0byiM7WB9fSgnNHp0yB/bjb21EXorVpEesxE5yhoOTVMYkq4F3hvfbg+8Zmb7StoNeAJYFc89YGZfSldpVAhWwPoY9OfZoozTHriyGDT6sj7i771vi+XSoUDl9hqKmTXTp/StpJq8/fIpR2FzbyIy3qZs//8+wmgMZta/zVjSzwg7F4s8ZWb7DrVO5YQVBpRD8kdTUjAhiCuPEUnR5JChPbrRTB8/l9zbt+e2P5+7VZ+v2GGW+00n8ipt8hxJpNlflKTYF2T+e3GF0VgkCfgH4FPDrWuLFT6JEYfTZiRWdo0Upo/9PNa7eWC/i3L0Pf/CkDdEFjv64Xb4WSmKRo3whiRv6e+juFQ8y4GRAR7Tu+F8AnjBzJ5M5E2U9CjwOvBdM7s3VU2lf7oR1qE46Wl1M9QhE04Y6Ex6e4MtfWw39OSgZ3O0rW/dyrRWGxG07GbKuj8oWsc9jNZVYUi6E3hnmVOnm9lvYvpo4JrEubXAu83sFUn7AzdL2tvMXi+tRNIJwAkA3Wyz5VU67MvsFFqlY5o+fi7q6oJ8Do0aBd1jYHMv9te3QIqTon1YoYD19EDBsL6+/hVGFfe8jFDq5Tet1RTkINwklR1m9plq56NPlL8nBAUpfmYTsCmmH5b0FLAXsLT089F51yUAb9MOnfXNdSitoCyKXn6VE9azOWRaIazMUy4ohbiCKMmgieIOeJgZ7sijpRUFxABK7f89Jmm2SeozwB/M7NlihqSdgFfNrE/S7sAk4OlmCei0FlNzRw3qmKt1vFkol+orcQi7nvvpA/oGL+V2hqQ4Wl5JlOIjjIYyh8HmKICDgTMlbSZsvP+Smb3acMmc5pLceDW6i9zYscFDqkWzzlsb+5dPV6IhnU9CMWQ6odqGjDhlUJNUsS7aiqYqDDP7Qpm8G4EbGy+N02oUd+mqqyu4W9/Ui/X0hKMF9gE4HY6RlbfaEUOzRxiOMxjlBnlCxcKEsUaNCkoiubfGzT5Os/ERhuM0EStghRzKFQZ8ABUnkYsonE9u0nTl4TQFVxiO02SsqAyqm53Czn4rvqlan+NkTpxP6yRcYTitSemEdokrkCHtYcgiZojjlKPN9tLUwhWGMzJIRAYs6w/IlYDTDNwk5TgtSjV/QMXJcvB9EE5jsMxieo8YXGE47UHShOU+xJxG4SMMxxnh+OjCaQg+6e04juOkwd2bO47jOKnpsNGsG3sdx3G2AiMssEhz1ELSIZJWSVot6bQy58dIujae/10MZV089+2Yv0rS9CzvsRRXGI7jOFuD2UBEz1pHFSTlgQuAGcBk4GhJk0uKHQ+sM7M9gXOAs+NnJxOcuO4NHAJcGOurC64wHMdxtpKMRhgHAKvN7Gkz6wEWADNLyswErojpG4BPx/DWM4EFZrbJzJ4BVsf66kLbzGG8wboNd9oNq5otRwkTgJebLUQZWlEulykdLlN6qsn1nuFW/gbrFt1ZuG5CyuLdkpJB4C6JAeAA3gX8OXHuWeAjJZ/vL2NmvZLWAzvG/AdKPvuulDINmbZRGMAqM5vSbCGSSFraajJBa8rlMqXDZUpPveUys0PqVXer4iYpx3Gc5vIcsGvi/S4xr2yZGNp6O+CVlJ/NDFcYjuM4zeUhYJKkiZJGEyaxF5aUWQjMjelZwF1mZjF/TlxFNZEQ0vrBegnaTiapS2oXaTitKBO0plwuUzpcpvS0qlyDiHMSJwOLgDww38xWSjoTWGpmC4HLgCslrQZeJSgVYrnrgMeBXuAks/oFC5Z1mC8Ux3EcZ+twk5TjOI6TClcYjuM4TiraQmHU2lbfQDnWSPq9pMeKa64l7SBpsaQn4+vb6yzDfEkvSlqRyCsrgwLnxXZbLmm/Bso0T9Jzsa0ek3Ro4lzdXR1I2lXSEkmPS1op6ZSY3+y2qiRX09pLUrekByUtizJ9L+ZPjG4qVke3FaNjfkU3Fg2Q6XJJzyTaad+Y35Dvr+0xsxF9ECaJngJ2B0YDy4DJTZJlDTChJO/HwGkxfRpwdp1lOBjYD1hRSwbgUOA2QMCBwO8aKNM84NQyZSfH73AMMDF+t/k6yLQzsF9Mbwv8MV672W1VSa6mtVe85/Ex3QX8LrbBdcCcmH8R8OWYPhG4KKbnANfWoZ0qyXQ5MKtM+YZ8f+1+tMMII822+maS3NJ/BXBEPS9mZvcQVlGkkWEm8CsLPABsL2nnBslUiYa4OjCztWb2SEy/ATxB2CHb7LaqJFcl6t5e8Z43xLdd8TDgUwQ3FbBlW5VzY9EImSrRkO+v3WkHhVFuW33dtsbXwIC4ch4NAAAHUElEQVQ7JD0s6YSY9w4zWxvTzwPvaIJclWRodtudHM0D8xOmuobLFE0mHyI8pbZMW5XIBU1sL0l5SY8BLwKLCSOZ18yst8x1B7mxAIpuLOoqk5kV2+mHsZ3OkTSmVKYy8jopaQeF0UocZGb7EbxOniTp4ORJC2Pjpq5jbgUZIr8A9gD2BdYCP2uGEJLGAzcCXzOz15PnmtlWZeRqanuZWZ+Z7UvYSXwA8L5GXr8cpTJJej/wbYJsHwZ2AL7VRBHbjnZQGA3dGl8NM3suvr4I3ET4Y71QHPrG1xebIFolGZrWdmb2QvzDF4BfMmBGaZhMkroInfJVZvZfMbvpbVVOrlZoryjHa8AS4KMEs05x82/yupXcWNRbpkOiSc/MbBPwnzSpndqVdlAYabbV1x1J4yRtW0wD04AVDN7SPxf4TaNlqyLDQuC4uILkQGB9whxTV0rsx58jtFVRprq7Oog29cuAJ8zs54lTTW2rSnI1s70k7SRp+5geC0wlzK0sIbipgC3bqpwbi3rL9IeEshdhTiXZTk35rbcVzZ51z+IgrID4I8GuenqTZNidsFplGbCyKAfBdvs/wJPAncAOdZbjGoLJYjPBTnt8JRkIK0YuiO32e2BKA2W6Ml5zOeHPvHOi/OlRplXAjDrJdBDB3LQceCweh7ZAW1WSq2ntBewDPBqvvQI4I/Gbf5Aw0X49MCbmd8f3q+P53Rso012xnVYAv2ZgJVVDvr92P9w1iOM4jpOKdjBJOY7jOA3AFYbjOI6TClcYjuM4TipcYTiO4zipcIXhOI7jpMIVhuM4jpMKVxgdhKQNtUsNuc7DFV3KSzpC0uStqONuSVOGWH6VpMPLnNtNCTfq7Y6k7yTSY6NL7x5JE5opl9OeuMJwhoWZLTSzs+LbIwjuthvBsRZiHdcNSfl61p8R/QrDzN6y4FvpL02Ux2ljXGF0INE9wk8krVAI+DQ75n8yPr3fIOkPkq4quqWWdGjMezgGorkl5n9B0vmSPgYcDvwkPuXukRw5SJogaU1Mj5W0QNITkm4CxiZkmybpfkmPSLo+OuGrdT/7KwTSWQaclMjPx/t8KHov/eeYn5N0YbyfxZJulTQrnlsj6WxJjwBHVZInXvO3sT0WJVxSfFUh+NFySQuqyDxOwevsg5IelTQz5u8m6d54vUdiuyJpZ0n3xLZdIekTks4CiqOKq1J9+Y4zHJq91dyPxh3Ahvh6JMFFdZ7gvvv/CIF7PklwRb0L4WHifoKrim6Ca+iJ8fPXALfE9BeA82P6chLBa4C7iS4YgAnAmpj+V2B+TO8D9AJTYpl7gHHx3LeILh9K7qO/3vh+OXBwTP+EGKgJOAH4bkyPAZYSggzNAm6N9/hOYF1RbkIQrG8mZN5CHkLshf8Fdor5sxP38xcGXGRsX+W7+BHwj8VyBNc244BtgO6YPwlYGtNfZ8DdTB7YNvmdltS9hpJAXn74kcVR9DTpdBYHAdeYWR/BO+tvCe6gXwceNLNnARRiDewGbACethCgB4LCOGGLWtNzMHAegJktl7Q85h9IMGndFwc2owlKqyLRAd32FoI0QfC5NCOmpwH7FEcPBK+pkwj3f70Fz6/PS1pSUu21NeR5L/B+YHHMzxN8ZUFQXldJuhm4uYro04DDJZ0a33cD7yYonPMVQov2AXvF8w8B8xU82d5sZo9VaxfHqQeuMJxSNiXSfQzvN9LLgNmzO0V5EQLhHD2Ma5bW9xUzWzQoMxEPuwJvVpNH0geAlWb20TKf/SxBIR4GnC7pAzYQZKhUtiPNbFVJ3fOAF4APEtpuI4SohQrxVT4LXC7p52b2qxr34TiZ4nMYncm9wOxo49+J0MFVc4m9CthdIQIcBBNMOd4gxKEusgbYP6ZnJfLvAY4BUAh6s0/MfwD4uKQ947lxkvaiChZiIbwm6aCYdWzi9CLgy/GpHEl7Kbievw84Ms5lvINgiitHJXlWATtJ+mjM75K0t6QcsKuZLSGYr7YDKs3BLAK+kpgj+lDM3w5YG0c/nyeMXpD0HuAFM/slcCkhRjrA5uL9OU69cYXRmdxEMJ0sI7iD/qaZPV+psJm9BZwI3C7pYYJiWF+m6ALgG3ESdw/gp4QO+1HCfECRXwDjJT0BnAk8HK/zEmFO5JpoprqfdJHdvghcEE1oydjRlwKPA48oLLW9mDBiupHgZv1xggvsR8rdTyV5LMSOnwWcHSfaHwM+Rujcfy3p9wTX2+dFhVaO7xPmQpZLWhnfA1wIzI31vo+B0c4ngWWxLWcD58b8S2IdPunt1B13b+6kQtJ4M9sQn4gvAJ40s3OaJMvdwKlmtnQYdRTvZ0fC6Orj1ZTmSCKuRptiZi83WxanvfARhpOWf4pP8CsJZpOLmyjLqwQ7/hYb94bALfF+7gW+3w7KIi5Xfowwcik0Wx6n/fARhuPUEUlfBE4pyb7PzE4qV95xWhlXGI7jOE4q3CTlOI7jpMIVhuM4jpMKVxiO4zhOKlxhOI7jOKn4f3FvKajk5wnTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ex6LIvM6jYhf"
      },
      "source": [
        "df1 = ds1.to_dataframe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZRiaEU0zqWX0",
        "outputId": "0fa5cb62-8afd-42ee-cca5-0b864f023a41"
      },
      "source": [
        "df1.skew()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tp    10.873617\n",
              "dtype: float32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "Cv-8g0zTjYhf",
        "outputId": "5570c7cc-290a-434a-93d5-f877a4efd1bf"
      },
      "source": [
        "df1.hist(bins=50, range=[0, 0.001])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f502f456550>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sB0NNbYhjYhf"
      },
      "source": [
        "From the plot it can be observed that the data points are right skewed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pkAxFsDawgco"
      },
      "source": [
        "## 3. Volumetric soil water layer 1\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4NbOjF60wttV"
      },
      "source": [
        "### Calculating mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmPFDsYOwttW",
        "outputId": "0dbe46be-6116-4887-f6d7-90294fa9c891"
      },
      "source": [
        "mean=ds2['swvl1'].mean()\n",
        "print(mean.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.26458362\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R3f5qtfXwttb"
      },
      "source": [
        "### Calculating median"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQ3Wzc-Ywttb",
        "outputId": "b2cdbbcb-59c1-45fd-a132-6537457c80df"
      },
      "source": [
        "median=ds2['swvl1'].median()\n",
        "print(median.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.2705504894256592\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTKbpWI2wttc"
      },
      "source": [
        "### Calcualting standard deviation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IBnujwtBwttc",
        "outputId": "5fc12e78-8708-4f57-810e-3294f3d4a4d6"
      },
      "source": [
        "standar_dev=ds2['swvl1'].std()\n",
        "print(standar_dev.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.12834720313549042\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TQo3sh5wttc"
      },
      "source": [
        "### Calculating variance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uyYLZojdwttc",
        "outputId": "0918e4ee-c7ba-4686-838d-1f2268e619e8"
      },
      "source": [
        "variance = ds2['swvl1'].var()\n",
        "print(variance.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.01647300273180008\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F0l9DQaowttd"
      },
      "source": [
        "### Range"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "irNHQ2lmwttd",
        "outputId": "310edb8e-e810-4b94-d10f-c69a50beca8e"
      },
      "source": [
        "minimum = ds2['swvl1'].min()\n",
        "maximum = ds2['swvl1'].max()\n",
        "print('Range is',minimum.values,'to',maximum.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Range is 0.0 to 0.7660064697265625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "am7C-y3awttd"
      },
      "source": [
        "For \"**Volumetric soil water layer 1**\" data mean is **0.26** and median is **0.27**, as the mean and median is nearly same the data points can be uniformly distributed. The standard deviation is **0.128**, which is large as compared to mean indicating that data points are spread far from the mean. The range is **0** to **0.766**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQ94o12iwttd"
      },
      "source": [
        "### Calculating Null"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfEtV85Qwtte",
        "outputId": "14cebf7b-baa9-41cb-bdfd-928c4cdddaa3"
      },
      "source": [
        "null=ds2['swvl1'].isnull()\n",
        "print(null.sum().values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "29895159\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A8RAUKuKwtte"
      },
      "source": [
        "The total missing values are **64061055**. The null observations can be filled with **mean** value or filling with MICE can be preferred."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "uaFMPk3gwtte",
        "outputId": "4dc591db-dd1e-4db5-d64c-02da2e7c7a8a"
      },
      "source": [
        "ds2['swvl1'][6].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x7f502f3346a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PkuqAsAnwttf"
      },
      "source": [
        "df2 = ds2.to_dataframe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "uSUdWCLJwttg",
        "outputId": "6c8c626b-b43f-4150-95fa-0b5b74762fa7"
      },
      "source": [
        "df2.hist(bins=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f502f2485c0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ProOrb0ZxRED"
      },
      "source": [
        "So from the plot the data points are unimodal type of distribution. As the standard deviation is large the data points spread far from the mean."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6GGsEqtEAX_W"
      },
      "source": [
        "# Preprocessing Geospatial Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPEcZMzzGB-w"
      },
      "source": [
        "## Interpolation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKLF9cp-Ak9k"
      },
      "source": [
        "# Create new latitude and longitude\n",
        "dx_new = 0.05\n",
        "newlon = np.arange(0, 360, dx_new)\n",
        "dx_new_ = -0.05\n",
        "newlat = np.arange(90, -90+dx_new_, dx_new_)\n",
        "#newlat = np.arange(-90, 90+dx_new, dx_new)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rlxAqTy-0QWy",
        "outputId": "d4c649a5-8dd6-4be9-e424-da0578e973fd"
      },
      "source": [
        "dx_new = -0.05\n",
        "test = np.arange(90, -90+dx_new, dx_new)\n",
        "print(test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 90.    89.95  89.9  ... -89.9  -89.95 -90.  ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YUs2oHJ7Ag4a"
      },
      "source": [
        "### 1. Temperature of air at 2m above the surface "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIHwAOkZBTjN",
        "outputId": "301d9246-9860-4e3e-fcf3-91878da2bce5"
      },
      "source": [
        "print(ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<xarray.Dataset>\n",
            "Dimensions:    (latitude: 1801, longitude: 3600, time: 7)\n",
            "Coordinates:\n",
            "  * longitude  (longitude) float32 0.0 0.1 0.2 0.3 ... 359.6 359.7 359.8 359.9\n",
            "  * latitude   (latitude) float32 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0\n",
            "  * time       (time) datetime64[ns] 2019-12-01T12:00:00 ... 2019-12-07T12:00:00\n",
            "Data variables:\n",
            "    t2m        (time, latitude, longitude) float32 ...\n",
            "Attributes:\n",
            "    Conventions:  CF-1.6\n",
            "    history:      2020-12-05 06:08:22 GMT by grib_to_netcdf-2.16.0: /opt/ecmw...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1dYSl5dAhjr"
      },
      "source": [
        "# Interpolate\n",
        "ds = ds.interp(latitude=newlat, longitude=newlon)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NB-vurOk_dIB",
        "outputId": "21fb4733-68f2-48dd-8c12-0e7981508cf5"
      },
      "source": [
        "print(ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<xarray.Dataset>\n",
            "Dimensions:    (latitude: 3601, longitude: 7200, time: 7)\n",
            "Coordinates:\n",
            "  * time       (time) datetime64[ns] 2019-12-01T12:00:00 ... 2019-12-07T12:00:00\n",
            "  * latitude   (latitude) float64 90.0 89.95 89.9 89.85 ... -89.9 -89.95 -90.0\n",
            "  * longitude  (longitude) float64 0.0 0.05 0.1 0.15 ... 359.8 359.9 359.9 360.0\n",
            "Data variables:\n",
            "    t2m        (time, latitude, longitude) float64 nan nan nan ... 243.1 nan nan\n",
            "Attributes:\n",
            "    Conventions:  CF-1.6\n",
            "    history:      2020-12-05 06:08:22 GMT by grib_to_netcdf-2.16.0: /opt/ecmw...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X-8UEsCDB3Tq"
      },
      "source": [
        "### 2. Total Precipiataiton "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbvPDtej_h-r",
        "outputId": "7b553525-37e3-4c8c-8e91-159fa67a3d17"
      },
      "source": [
        "print(ds1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<xarray.Dataset>\n",
            "Dimensions:    (latitude: 1801, longitude: 3600, time: 7)\n",
            "Coordinates:\n",
            "  * longitude  (longitude) float32 0.0 0.1 0.2 0.3 ... 359.6 359.7 359.8 359.9\n",
            "  * latitude   (latitude) float32 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0\n",
            "  * time       (time) datetime64[ns] 2019-12-01T12:00:00 ... 2019-12-07T12:00:00\n",
            "Data variables:\n",
            "    tp         (time, latitude, longitude) float32 ...\n",
            "Attributes:\n",
            "    Conventions:  CF-1.6\n",
            "    history:      2020-12-02 12:04:52 GMT by grib_to_netcdf-2.16.0: /opt/ecmw...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nrwc-74QB-2P"
      },
      "source": [
        "# Interpolate\n",
        "ds1 = ds1.interp(latitude=newlat, longitude=newlon)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4FcBdKfTEHWh",
        "outputId": "c76bd079-f2d9-4b75-c9f1-919578f4059f"
      },
      "source": [
        "print(ds1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<xarray.Dataset>\n",
            "Dimensions:    (latitude: 3601, longitude: 7200, time: 7)\n",
            "Coordinates:\n",
            "  * time       (time) datetime64[ns] 2019-12-01T12:00:00 ... 2019-12-07T12:00:00\n",
            "  * latitude   (latitude) float64 90.0 89.95 89.9 89.85 ... -89.9 -89.95 -90.0\n",
            "  * longitude  (longitude) float64 0.0 0.05 0.1 0.15 ... 359.8 359.9 359.9 360.0\n",
            "Data variables:\n",
            "    tp         (time, latitude, longitude) float64 nan nan nan ... 0.0 nan nan\n",
            "Attributes:\n",
            "    Conventions:  CF-1.6\n",
            "    history:      2020-12-02 12:04:52 GMT by grib_to_netcdf-2.16.0: /opt/ecmw...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1dwgmQXqCDyf"
      },
      "source": [
        "### 3. Volumteric soil water layer 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvd-IkEhlKhy",
        "outputId": "3e2c1330-6db3-477a-c82c-477b3598babd"
      },
      "source": [
        "print(ds2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<xarray.Dataset>\n",
            "Dimensions:    (latitude: 1801, longitude: 3600, time: 7)\n",
            "Coordinates:\n",
            "  * longitude  (longitude) float32 0.0 0.1 0.2 0.3 ... 359.6 359.7 359.8 359.9\n",
            "  * latitude   (latitude) float32 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0\n",
            "  * time       (time) datetime64[ns] 2019-12-01T12:00:00 ... 2019-12-07T12:00:00\n",
            "Data variables:\n",
            "    swvl1      (time, latitude, longitude) float32 ...\n",
            "Attributes:\n",
            "    Conventions:  CF-1.6\n",
            "    history:      2020-12-01 13:49:03 GMT by grib_to_netcdf-2.16.0: /opt/ecmw...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FPaafb2D_64B"
      },
      "source": [
        "# Interpolate\n",
        "ds2 = ds2.interp(latitude=newlat, longitude=newlon)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_hHrtv3AOmo",
        "outputId": "8f7f438d-4409-42a0-df66-5f1f0888075d"
      },
      "source": [
        "print(ds2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<xarray.Dataset>\n",
            "Dimensions:    (latitude: 3601, longitude: 7200, time: 7)\n",
            "Coordinates:\n",
            "  * time       (time) datetime64[ns] 2019-12-01T12:00:00 ... 2019-12-07T12:00:00\n",
            "  * latitude   (latitude) float64 90.0 89.95 89.9 89.85 ... -89.9 -89.95 -90.0\n",
            "  * longitude  (longitude) float64 0.0 0.05 0.1 0.15 ... 359.8 359.9 359.9 360.0\n",
            "Data variables:\n",
            "    swvl1      (time, latitude, longitude) float64 nan nan nan ... nan nan\n",
            "Attributes:\n",
            "    Conventions:  CF-1.6\n",
            "    history:      2020-12-01 13:49:03 GMT by grib_to_netcdf-2.16.0: /opt/ecmw...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ZqPLCTTrazB"
      },
      "source": [
        "## Taking care of skewness Total precipitation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PduuF9L-roB6"
      },
      "source": [
        "df1 = ds1.to_dataframe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "DxhKZAgTroB6",
        "outputId": "7dc27ab9-2ece-4dac-f27e-2c21c7c975c9"
      },
      "source": [
        "df1.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.073510e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.800715e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.524778e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.040842e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.387911e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.705581e-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 tp\n",
              "count  6.073510e+07\n",
              "mean   6.800715e-04\n",
              "std    2.524778e-03\n",
              "min    0.000000e+00\n",
              "25%    0.000000e+00\n",
              "50%    1.040842e-05\n",
              "75%    2.387911e-04\n",
              "max    1.705581e-01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4JgxJJkroB7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d272105-ebed-4db7-a87b-369f680c9070"
      },
      "source": [
        "df1.skew()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tp    10.873617\n",
              "dtype: float32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TkAglXVgHNmI"
      },
      "source": [
        "Totoal precipitation have a skewness of **10.873617**, which is highly skewed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "jBBEA7fOroB7",
        "outputId": "c40820af-9881-4fdc-fb9f-ca11c42dd6a5"
      },
      "source": [
        "df1.hist(bins=50, range=[0, 0.001])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8eb8f2c5c0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WNwu1XDHqeL"
      },
      "source": [
        "Taking square root of dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zH5lzXWrdfc"
      },
      "source": [
        "ds1_sqrt = np.sqrt(ds1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQYAzb-er379"
      },
      "source": [
        "df1_sqrt = ds1_sqrt.to_dataframe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "YE_vKzGWsA19",
        "outputId": "594bd71d-2eab-4d74-eb85-15a0a4e8cb41"
      },
      "source": [
        "df1_sqrt.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.073510e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.284089e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.269764e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.226209e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.545287e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.129868e-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 tp\n",
              "count  6.073510e+07\n",
              "mean   1.284089e-02\n",
              "std    2.269764e-02\n",
              "min    0.000000e+00\n",
              "25%    0.000000e+00\n",
              "50%    3.226209e-03\n",
              "75%    1.545287e-02\n",
              "max    4.129868e-01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B-d_y8AdsC7q",
        "outputId": "f7a37a6d-58ed-4e54-bc5c-ab4c0a644646"
      },
      "source": [
        "df1_sqrt.skew()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tp    3.13238\n",
              "dtype: float32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jswWn9IyHxPd"
      },
      "source": [
        "As we can observe the  skewness is reduced to **3.13** from **10.56** which is still a notable differnce."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "Usu5bN7TsJE4",
        "outputId": "77cabad1-7004-4990-f529-39bd3b14b90d"
      },
      "source": [
        "df1_sqrt.hist(bins=50, range=[0, 0.1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f89d8b234e0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLDXP4Ijn8xl"
      },
      "source": [
        "The distribution is preety much the same but the range is smaller.\n",
        "Boxcox method can be used to take care of the skewed dataset but this dataset contains **0** values so it can't be used."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OA_YMfYqxudO"
      },
      "source": [
        "# Deep Learning for Geospatial Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UyWY0WFRDhVG"
      },
      "source": [
        "temp = (ds['t2m'].values).ravel()\n",
        "prep = (ds1_sqrt['tp'].values).ravel()\n",
        "soil = (ds2['swvl1'].values).ravel()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29l1FRMqN9Gy",
        "outputId": "238d3e2b-02b2-4ec5-9e14-381fa4dce0c1"
      },
      "source": [
        "print('temp is ',temp)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "temp is  [      nan       nan       nan ... 243.09929 243.09929 243.09929]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKWIQtttPSeM",
        "outputId": "c9a34d0a-f214-446b-f2ab-23c14b98b83f"
      },
      "source": [
        "print('prep is ',prep)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "prep is  [nan nan nan ...  0.  0.  0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DpXR7b86PTlS",
        "outputId": "a236d877-4ae3-4548-96ab-18710fd1372d"
      },
      "source": [
        "print('soil is ',soil)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "soil is  [      nan       nan       nan ... 0.2060981 0.2060981 0.2060981]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbIAfkHfRfES"
      },
      "source": [
        "dataset = pd.DataFrame({'temp':temp, 'soil':soil, 'prep':prep})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBMkgKypSR2J",
        "outputId": "e72e07e6-d93d-4810-9a16-279e066e2d56"
      },
      "source": [
        "print(dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                temp      soil  prep\n",
            "0                NaN       NaN   NaN\n",
            "1                NaN       NaN   NaN\n",
            "2                NaN       NaN   NaN\n",
            "3                NaN       NaN   NaN\n",
            "4                NaN       NaN   NaN\n",
            "...              ...       ...   ...\n",
            "45385195  243.099289  0.206098   0.0\n",
            "45385196  243.099289  0.206098   0.0\n",
            "45385197  243.099289  0.206098   0.0\n",
            "45385198  243.099289  0.206098   0.0\n",
            "45385199  243.099289  0.206098   0.0\n",
            "\n",
            "[45385200 rows x 3 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dpLy21gmW96Y"
      },
      "source": [
        "Using Mice to fill the missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lISgknNrQnTT",
        "outputId": "41b554f0-5c70-42ea-925a-8db283e4093e"
      },
      "source": [
        "!pip install impyute"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting impyute\n",
            "  Downloading https://files.pythonhosted.org/packages/37/28/86829f67c9affb847facaab94687761d3555539ec675f7577778c5b2680a/impyute-0.0.8-py2.py3-none-any.whl\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from impyute) (1.18.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from impyute) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from impyute) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->impyute) (0.17.0)\n",
            "Installing collected packages: impyute\n",
            "Successfully installed impyute-0.0.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjStaVfeQwuL"
      },
      "source": [
        "from impyute.imputation.cs import mice\n",
        "data = dataset.astype(float)\n",
        "dataset = mice(data.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I1dD-w6lW_IZ"
      },
      "source": [
        "Since using mice is consuming more memory as compared to alloted one and showing error, so I am filling missing values with mean."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFnsYKm7VFOI"
      },
      "source": [
        "mean = dataset['temp'].mean()\n",
        "dataset['temp'].fillna(mean, inplace=True) \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgmubIZZXiuX"
      },
      "source": [
        "mean = dataset['soil'].mean()\n",
        "dataset['soil'].fillna(mean, inplace=True) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ejnq4KRXs7o"
      },
      "source": [
        "mean = dataset['prep'].mean()\n",
        "dataset['prep'].fillna(mean, inplace=True) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCn6dnMBX5UQ",
        "outputId": "3e98a63e-f2de-4803-c03e-4781590982ea"
      },
      "source": [
        "dataset.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "temp    0\n",
              "soil    0\n",
              "prep    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vg2BOMorYhUY"
      },
      "source": [
        "Printing the dataset after filling in the missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bD9UsFX-YUTY",
        "outputId": "697a6b59-9d64-46b4-9529-e7574c2b1857"
      },
      "source": [
        "print(dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                temp      soil      prep\n",
            "0         260.213348  0.266105  0.012798\n",
            "1         260.213348  0.266105  0.012798\n",
            "2         260.213348  0.266105  0.012798\n",
            "3         260.213348  0.266105  0.012798\n",
            "4         260.213348  0.266105  0.012798\n",
            "...              ...       ...       ...\n",
            "45385195  243.099289  0.206098  0.000000\n",
            "45385196  243.099289  0.206098  0.000000\n",
            "45385197  243.099289  0.206098  0.000000\n",
            "45385198  243.099289  0.206098  0.000000\n",
            "45385199  243.099289  0.206098  0.000000\n",
            "\n",
            "[45385200 rows x 3 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1kAnJfMq9Tf4"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWIssg-3Ymxa"
      },
      "source": [
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VLxK0aNyYqY4",
        "outputId": "5ed3cfe5-e9ff-4eaa-d938-ea117b20cf27"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[2.6021335e+02 2.6610544e-01]\n",
            " [2.6021335e+02 2.6610544e-01]\n",
            " [2.6021335e+02 2.6610544e-01]\n",
            " ...\n",
            " [2.4309929e+02 2.0609809e-01]\n",
            " [2.4309929e+02 2.0609809e-01]\n",
            " [2.4309929e+02 2.0609809e-01]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJztjh_7YtAS",
        "outputId": "2ff85ba2-67de-47fa-9ef8-309539417fdc"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.01279823 0.01279823 0.01279823 ... 0.         0.         0.        ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WXFiXQ_rZ7rz"
      },
      "source": [
        "Splitting dataset into **Train / Validation /Test** in **80/ 10/ 10** i.e 80% data to training, 10% to validation and 10% to test, as we have a large data so it can be spilt in this fashion."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUmodUjh5IOz"
      },
      "source": [
        "# Importing library fot splitting\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDiiy_ycCBxP"
      },
      "source": [
        "train_ratio = 0.80\n",
        "validation_ratio = 0.1\n",
        "test_ratio = 0.1\n",
        "\n",
        "# train is now 98% of the entire data set\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=1 - train_ratio)\n",
        "\n",
        "# test is now 1% of the initial data set\n",
        "# validation is now 1% of the initial data set\n",
        "x_val, x_test, y_val, y_test = train_test_split(x_test, y_test, test_size=test_ratio/(test_ratio + validation_ratio)) \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Zg9UeclZ86J"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_val = sc.transform(x_val)\n",
        "x_test = sc.transform(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-LTNyt8-IYD"
      },
      "source": [
        "inputs = torch.from_numpy(x_train)\n",
        "targets = torch.from_numpy(y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LHAuQlDs-X9b",
        "outputId": "b5287cd0-88e4-4e05-d631-95466b7f4e4d"
      },
      "source": [
        "inputs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.2091,  0.0068],\n",
              "        [-1.0605,  0.6882],\n",
              "        [-0.2091,  0.0068],\n",
              "        ...,\n",
              "        [-0.2091,  0.0068],\n",
              "        [-0.2091,  0.0068],\n",
              "        [-0.2091,  0.0068]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "244KDZI4-aWg",
        "outputId": "984e695b-5fd4-4589-9405-c38b8287f226"
      },
      "source": [
        "targets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.0128, 0.0163, 0.0128,  ..., 0.0128, 0.0128, 0.0128])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17sCncri-cZR"
      },
      "source": [
        "from torch.utils.data import TensorDataset\n",
        "# Define dataset\n",
        "train_ds = TensorDataset(inputs, targets)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IAqL_gYO-m2f"
      },
      "source": [
        "from torch.utils.data import DataLoader"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0qwlaPD-pDR"
      },
      "source": [
        "# Define data loader\n",
        "batch_size = 1000\n",
        "train_dl = DataLoader(train_ds, batch_size, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkPNGD2HG0Bk"
      },
      "source": [
        "Observation in a batch of **1000** and printing first batch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-2ATO2P-_tG",
        "outputId": "37681217-4624-4927-c502-0c55231aff5a"
      },
      "source": [
        "for xb, yb in train_dl:\n",
        "    print(xb)\n",
        "    print(yb)\n",
        "    break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[-1.1914,  4.4760],\n",
            "        [ 1.2274,  2.2828],\n",
            "        [ 0.9368,  1.8149],\n",
            "        ...,\n",
            "        [ 2.0538, -2.5418],\n",
            "        [-0.2091,  0.0068],\n",
            "        [-0.2091,  0.0068]])\n",
            "tensor([0.0051, 0.0058, 0.0159, 0.0128, 0.0056, 0.0128, 0.0213, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0123, 0.0000, 0.0128, 0.0128,\n",
            "        0.0054, 0.0128, 0.0628, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0000, 0.0128, 0.0128, 0.0000, 0.0128, 0.0128,\n",
            "        0.0000, 0.0128, 0.0000, 0.0128, 0.0000, 0.0143, 0.0000, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0095, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0000, 0.0128, 0.0163, 0.0000, 0.0000, 0.0101, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0016, 0.0128, 0.0128, 0.0128, 0.0046,\n",
            "        0.0128, 0.0128, 0.0128, 0.0528, 0.0128, 0.0128, 0.0274, 0.0128, 0.0128,\n",
            "        0.0128, 0.0000, 0.0190, 0.0081, 0.0054, 0.0016, 0.0000, 0.0128, 0.0000,\n",
            "        0.0247, 0.0128, 0.0128, 0.0000, 0.0000, 0.0128, 0.0128, 0.0016, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0036, 0.0128, 0.0036, 0.0128, 0.0246, 0.0128,\n",
            "        0.0128, 0.0032, 0.0128, 0.0128, 0.0128, 0.0081, 0.0128, 0.0000, 0.0128,\n",
            "        0.0128, 0.0000, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0244, 0.0128, 0.0269, 0.0000, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0592, 0.0128, 0.0128, 0.0128, 0.0119, 0.0128, 0.0060,\n",
            "        0.0000, 0.0128, 0.0128, 0.0128, 0.0262, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0000, 0.0000, 0.0128, 0.0128, 0.0172,\n",
            "        0.0128, 0.0128, 0.0054, 0.0128, 0.0128, 0.0128, 0.0443, 0.0128, 0.0128,\n",
            "        0.0128, 0.0000, 0.0128, 0.0128, 0.0128, 0.0128, 0.0000, 0.0128, 0.0286,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0000, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0028, 0.0128, 0.0128, 0.0128, 0.0000,\n",
            "        0.0036, 0.0128, 0.0128, 0.0000, 0.0128, 0.0128, 0.0062, 0.0000, 0.0128,\n",
            "        0.0128, 0.0128, 0.0156, 0.0128, 0.0128, 0.0510, 0.0128, 0.0299, 0.0224,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0253, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128, 0.0062, 0.0387,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0000, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0028,\n",
            "        0.0128, 0.0070, 0.0128, 0.0128, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0000, 0.0128, 0.0036, 0.0128, 0.0000, 0.0459, 0.0000, 0.0128,\n",
            "        0.0128, 0.0181, 0.0128, 0.0128, 0.0077, 0.0128, 0.0128, 0.0128, 0.0000,\n",
            "        0.0000, 0.0000, 0.0128, 0.0128, 0.0128, 0.0091, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.1376, 0.0136, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0000, 0.0000, 0.0272, 0.0128, 0.0128, 0.0000,\n",
            "        0.0128, 0.0130, 0.0257, 0.0128, 0.0000, 0.0128, 0.0000, 0.0128, 0.0128,\n",
            "        0.0128, 0.0294, 0.0128, 0.0128, 0.0000, 0.0016, 0.0128, 0.0282, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0000, 0.0016, 0.0269, 0.0128,\n",
            "        0.0604, 0.0128, 0.0088, 0.0128, 0.0128, 0.0187, 0.0097, 0.0000, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0789, 0.0128, 0.0624, 0.0000,\n",
            "        0.0380, 0.0128, 0.0338, 0.0128, 0.0128, 0.0128, 0.0000, 0.0128, 0.0128,\n",
            "        0.0000, 0.0000, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0000, 0.0128,\n",
            "        0.0128, 0.0128, 0.0000, 0.0132, 0.0128, 0.0128, 0.0128, 0.0128, 0.0111,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0000, 0.0000, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0635, 0.0128, 0.0016, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0060, 0.0000, 0.0000, 0.0128, 0.0128, 0.0331, 0.0128, 0.0180, 0.0404,\n",
            "        0.0043, 0.0128, 0.0500, 0.0000, 0.0187, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0000, 0.0000, 0.0128, 0.0128, 0.0306, 0.0195, 0.0016, 0.0128, 0.0000,\n",
            "        0.0128, 0.0375, 0.0000, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0056,\n",
            "        0.0000, 0.0051, 0.0128, 0.0410, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0000, 0.0128, 0.1106, 0.0128, 0.0128, 0.0028, 0.0000, 0.0062, 0.0088,\n",
            "        0.0128, 0.0128, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128, 0.0243, 0.0128,\n",
            "        0.0000, 0.0000, 0.0000, 0.0128, 0.0128, 0.0128, 0.0128, 0.0000, 0.0023,\n",
            "        0.0128, 0.0128, 0.0128, 0.0032, 0.0062, 0.0048, 0.0072, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0869, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0219, 0.0128, 0.0128, 0.0550, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0291, 0.0128, 0.0162, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0000, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0000, 0.0174, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128, 0.0000,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0116, 0.0128, 0.0128, 0.0000, 0.0128, 0.0000, 0.0000, 0.0056, 0.0128,\n",
            "        0.0028, 0.0128, 0.0187, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0000, 0.0128, 0.0000, 0.0128, 0.0016, 0.0128, 0.0187, 0.0128,\n",
            "        0.0235, 0.0128, 0.0128, 0.0128, 0.0128, 0.0076, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0079, 0.0773, 0.0101, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0023, 0.0128, 0.0043, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0000, 0.0128, 0.0081, 0.0128, 0.0000, 0.0128, 0.0128, 0.0000, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0376, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0381, 0.0128, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0056, 0.0128, 0.0128, 0.0128, 0.0132, 0.0128, 0.0094, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0028, 0.0128, 0.0054, 0.0128, 0.0128, 0.0307, 0.0016,\n",
            "        0.0028, 0.0058, 0.0128, 0.0000, 0.0385, 0.0185, 0.0128, 0.0128, 0.0000,\n",
            "        0.0128, 0.0128, 0.0128, 0.0000, 0.0229, 0.0128, 0.0128, 0.0000, 0.0128,\n",
            "        0.0128, 0.0124, 0.0128, 0.0128, 0.0000, 0.0000, 0.0128, 0.0463, 0.0117,\n",
            "        0.0128, 0.0128, 0.0000, 0.0128, 0.0016, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0000, 0.0128, 0.0128, 0.0088, 0.0128, 0.0040, 0.0128, 0.0128, 0.0032,\n",
            "        0.0128, 0.0046, 0.0056, 0.0128, 0.0000, 0.0128, 0.0000, 0.0000, 0.0128,\n",
            "        0.0128, 0.0000, 0.0028, 0.0000, 0.0000, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0090, 0.0128, 0.0128, 0.0000, 0.0161, 0.0128, 0.0016, 0.0128,\n",
            "        0.0128, 0.0067, 0.0032, 0.0128, 0.0351, 0.0128, 0.0079, 0.0098, 0.0128,\n",
            "        0.0128, 0.0128, 0.0000, 0.1067, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0043, 0.0128, 0.0145, 0.0000, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0000, 0.0128,\n",
            "        0.0000, 0.0128, 0.0128, 0.0000, 0.0000, 0.0128, 0.0128, 0.0128, 0.0084,\n",
            "        0.0128, 0.0067, 0.0036, 0.0128, 0.0128, 0.0217, 0.0128, 0.0000, 0.0114,\n",
            "        0.0128, 0.0000, 0.0128, 0.0999, 0.0128, 0.0000, 0.0128, 0.0128, 0.0150,\n",
            "        0.0128, 0.0146, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128, 0.0032, 0.0546,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0444, 0.0000, 0.0128, 0.0128, 0.0128,\n",
            "        0.0338, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0000, 0.0128, 0.0157,\n",
            "        0.0128, 0.0128, 0.0128, 0.0016, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0067, 0.0000, 0.0094, 0.0128, 0.0128, 0.0036, 0.0128,\n",
            "        0.0128, 0.0255, 0.0128, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0956, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0076, 0.0599, 0.0000,\n",
            "        0.0228, 0.0056, 0.0128, 0.0128, 0.0215, 0.0494, 0.0077, 0.0470, 0.0128,\n",
            "        0.0128, 0.0000, 0.0128, 0.0048, 0.0000, 0.0299, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0101, 0.0191, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0106, 0.0128, 0.0128, 0.0128, 0.0023, 0.0170, 0.0128, 0.0128,\n",
            "        0.0028, 0.0128, 0.0128, 0.0129, 0.0128, 0.0023, 0.0128, 0.0128, 0.0233,\n",
            "        0.0128, 0.0000, 0.0128, 0.0128, 0.0054, 0.0000, 0.0128, 0.0000, 0.0000,\n",
            "        0.0000, 0.0128, 0.0128, 0.0128, 0.0614, 0.0000, 0.0000, 0.0128, 0.0023,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0269, 0.0128,\n",
            "        0.0128, 0.0128, 0.0000, 0.0000, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0128, 0.0023, 0.0128,\n",
            "        0.0128, 0.0124, 0.0128, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128, 0.0330,\n",
            "        0.0128, 0.0128, 0.0128, 0.0457, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128,\n",
            "        0.0128, 0.0128, 0.0128, 0.0094, 0.0128, 0.0128, 0.0128, 0.0016, 0.0128,\n",
            "        0.0128, 0.0000, 0.0295, 0.0128, 0.0000, 0.0128, 0.0128, 0.0016, 0.0000,\n",
            "        0.0277, 0.0128, 0.0000, 0.0128, 0.0128, 0.0128, 0.0046, 0.0128, 0.0128,\n",
            "        0.0128, 0.0051, 0.0330, 0.0128, 0.0128, 0.0000, 0.0000, 0.0128, 0.0128,\n",
            "        0.0128, 0.0000, 0.0000, 0.0128, 0.0081, 0.0128, 0.0000, 0.0128, 0.0128,\n",
            "        0.0000, 0.0000, 0.0128, 0.0128, 0.0128, 0.0000, 0.0128, 0.0000, 0.0128,\n",
            "        0.0128])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzDDqcg4_Jv5",
        "outputId": "8bc2ef72-e4e5-4059-fadb-112b601f73f0"
      },
      "source": [
        "# Define model\n",
        "model = nn.Linear(2, 1)\n",
        "print(model.weight)\n",
        "print(model.bias)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parameter containing:\n",
            "tensor([[ 0.1509, -0.5993]], requires_grad=True)\n",
            "Parameter containing:\n",
            "tensor([0.3903], requires_grad=True)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upuc4VKa_asd",
        "outputId": "03d7ba27-55b2-4253-b6a4-8cfe86296fe7"
      },
      "source": [
        "# Parameters\n",
        "list(model.parameters())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Parameter containing:\n",
              " tensor([[ 0.1509, -0.5993]], requires_grad=True), Parameter containing:\n",
              " tensor([0.3903], requires_grad=True)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vB_wlR4o_d_Y",
        "outputId": "ae8d899f-3142-40b9-d4ae-baaef767c41f"
      },
      "source": [
        "# Generate predictions\n",
        "preds = model(inputs)\n",
        "preds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.3547],\n",
              "        [-0.1821],\n",
              "        [ 0.3547],\n",
              "        ...,\n",
              "        [ 0.3547],\n",
              "        [ 0.3547],\n",
              "        [ 0.3547]], grad_fn=<AddmmBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkM39q6J_i3C"
      },
      "source": [
        "Loss Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yinalRA8_lZj"
      },
      "source": [
        "# Import nn.functional\n",
        "import torch.nn.functional as F\n",
        "# Define loss function\n",
        "loss_fn = F.mse_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6BKl4rOD8Qx",
        "outputId": "c4ce1ffd-64e5-4328-a6e5-3ce5a77b255e"
      },
      "source": [
        "targets.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([36308160])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MJG7MejEF-H"
      },
      "source": [
        "temp = targets.reshape(len(targets), 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94LvO4ftEMmB",
        "outputId": "70a4b86d-79c9-4671-a6e8-f3206105f9d9"
      },
      "source": [
        "temp.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([36308160, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zAMT6bAo_pYo",
        "outputId": "4ea88841-6135-4934-a36a-3c8fdb3ceb84"
      },
      "source": [
        "loss = loss_fn(preds, temp)\n",
        "print('The inital loss is', loss.item())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The inital loss is 0.000552099256310612\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_yyJV4tE9_n"
      },
      "source": [
        "## Optimize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fMF3QfeeE9Di"
      },
      "source": [
        "# Define optimizer\n",
        "opt = torch.optim.SGD(model.parameters(), lr=1e-5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sf1omxFfFCm5"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gd2L6L_EFEaw"
      },
      "source": [
        "# Utility function to train the model\n",
        "def fit(num_epochs, model, loss_fn, opt, train_dl):\n",
        "    \n",
        "    # Repeat for given number of epochs\n",
        "    for epoch in range(num_epochs):\n",
        "        \n",
        "        # Train with batches of data\n",
        "        for xb,yb in train_dl:\n",
        "            \n",
        "            # 1. Generate predictions\n",
        "            pred = model(xb)\n",
        "            \n",
        "            temp = yb.reshape(len(yb), 1)\n",
        "            \n",
        "            # 2. Calculate loss\n",
        "            loss = loss_fn(pred, temp)\n",
        "            \n",
        "            # 3. Compute gradients\n",
        "            loss.backward()\n",
        "            \n",
        "            # 4. Update parameters using gradients\n",
        "            opt.step()\n",
        "            \n",
        "            # 5. Reset the gradients to zero\n",
        "            opt.zero_grad()\n",
        "        \n",
        "        # Print the progress\n",
        "        print('Epoch [{}/{}], Loss: {:}'.format(epoch+1, num_epochs, loss.item()))\n",
        "        print(loss)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tfp7twAZFefu",
        "outputId": "ba4004e4-1194-4fce-c45f-6282d0615c2a"
      },
      "source": [
        "fit(5, model, loss_fn, opt, train_dl)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [1/5], Loss: 0.11125870794057846\n",
            "tensor(0.1113, grad_fn=<MseLossBackward>)\n",
            "Epoch [2/5], Loss: 0.02409730851650238\n",
            "tensor(0.0241, grad_fn=<MseLossBackward>)\n",
            "Epoch [3/5], Loss: 0.007241903804242611\n",
            "tensor(0.0072, grad_fn=<MseLossBackward>)\n",
            "Epoch [4/5], Loss: 0.0016955171013250947\n",
            "tensor(0.0017, grad_fn=<MseLossBackward>)\n",
            "Epoch [5/5], Loss: 0.00046997639583423734\n",
            "tensor(0.0005, grad_fn=<MseLossBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DaxPQxdqj--Z"
      },
      "source": [
        "On each epoch the model has improved significantly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W9Cgdyt0TspU",
        "outputId": "92924b63-46c2-4249-af6d-983ee27b4933"
      },
      "source": [
        "# Generate predictions\n",
        "preds = model(inputs)\n",
        "preds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0236],\n",
              "        [0.0188],\n",
              "        [0.0236],\n",
              "        ...,\n",
              "        [0.0236],\n",
              "        [0.0236],\n",
              "        [0.0236]], grad_fn=<AddmmBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3if0Y-MTxJS",
        "outputId": "f81aabfd-f2a7-480c-8a95-403c59ef83b1"
      },
      "source": [
        "# Compare with targets\n",
        "targets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.0128, 0.0163, 0.0128,  ..., 0.0128, 0.0128, 0.0128])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6eALJQeJNAiI",
        "outputId": "e2c37ddb-575e-4a4a-902d-4b2fda8613b0"
      },
      "source": [
        "temp = targets.reshape(len(targets), 1)\n",
        "            \n",
        "# 2. Calculate loss\n",
        "loss = loss_fn(preds, temp)\n",
        "print('mean squared error is',loss.item())\n",
        "print(np.sqrt(loss.item()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mean squared error is 0.000552099256310612\n",
            "0.023496792468560724\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_kJWLB2kmaK"
      },
      "source": [
        "The mean squared error of the train dataset is **0.00055**. On average each prediction differs from the actual target by about **0.0234**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmHEYK4Pg6_h"
      },
      "source": [
        "### Testing validation set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZyjsWFk4gEzr"
      },
      "source": [
        "inputs_val = torch.from_numpy(x_val)\n",
        "targets_val = torch.from_numpy(y_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "881zeGaQhBtK"
      },
      "source": [
        "pred_val = model(inputs_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSQxG83VhNI2",
        "outputId": "2099e5aa-7e0d-403a-e41d-8960bddecc2c"
      },
      "source": [
        "pred_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.0245],\n",
              "        [ 0.0236],\n",
              "        [ 0.0236],\n",
              "        ...,\n",
              "        [ 0.0236],\n",
              "        [-0.0096],\n",
              "        [ 0.0236]], grad_fn=<AddmmBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mkQDdWihQDK",
        "outputId": "1b01a3df-5143-4ad7-c4dd-8e7571f6d9d2"
      },
      "source": [
        "targets_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.0032, 0.0128, 0.0128,  ..., 0.0128, 0.0541, 0.0128])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfCciTwkhUeC",
        "outputId": "2dd4bc9a-58cc-4576-b173-617fc6d2a0fe"
      },
      "source": [
        "temp1 = targets_val.reshape(len(targets_val), 1)\n",
        "            \n",
        "# 2. Calculate loss\n",
        "loss = loss_fn(pred_val, temp1)\n",
        "print(loss.item())\n",
        "print(np.sqrt(loss.item()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0005507125752046704\n",
            "0.02346726603600578\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fs_leHOtk1al"
      },
      "source": [
        "The mean squared error of the validation set is **0.0005507**, which is similar as compared to the trained model. The prediciton differs from the actual values by about **0.0235**(on average) which compared with the actual values it is not performing well."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mc3YTZmmtt6"
      },
      "source": [
        "# Model with a hidden layer "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pYMCd4qanLSx"
      },
      "source": [
        "The performance of the model can be improved with the inclusion of hidden layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0LX86KMzm16z"
      },
      "source": [
        "class Net(torch.nn.Module):\n",
        "    def __init__(self, n_feature, n_hidden, n_output):\n",
        "        super(Net, self).__init__()\n",
        "        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer\n",
        "        self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.hidden(x))      # activation function for hidden layer\n",
        "        x = self.predict(x)             # linear output\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5AQT1qopPQE"
      },
      "source": [
        "Here I have used rectified linear function as the activation function. As relu function is capable of reporting zero values which are negative. Hence provide a sparse representation and is desirable as it can accelerate learning and simplify the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qpFctOhnnfdQ"
      },
      "source": [
        "net = Net(n_feature=2, n_hidden=5, n_output=1) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvDP9BlTnuKO",
        "outputId": "fdba67a5-7584-45c5-acef-847dcb990562"
      },
      "source": [
        "fit(5, net, loss_fn, opt, train_dl)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [1/5], Loss: 0.6588041186332703\n",
            "tensor(0.6588, grad_fn=<MseLossBackward>)\n",
            "Epoch [2/5], Loss: 0.643675684928894\n",
            "tensor(0.6437, grad_fn=<MseLossBackward>)\n",
            "Epoch [3/5], Loss: 0.6625985503196716\n",
            "tensor(0.6626, grad_fn=<MseLossBackward>)\n",
            "Epoch [4/5], Loss: 0.7286423444747925\n",
            "tensor(0.7286, grad_fn=<MseLossBackward>)\n",
            "Epoch [5/5], Loss: 0.6853386163711548\n",
            "tensor(0.6853, grad_fn=<MseLossBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BeLvF-2zyTKE"
      },
      "source": [
        "So the model trained above is not trained well enough. The loss has decreased as well as increased. It is probably because I have use relu activation function.\n",
        "\n",
        "Relu function reports the negative values as zero, therefore makes its a sparse representation and would be switching off the hidden layer neurons while training it. Also I have scaled the dataset whose values is between -1 and 1, as result not much information reflected is by the neurons for the model to learn."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tBHmZPhE0KSy"
      },
      "source": [
        "## Training validation model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XljBYLs70NRr"
      },
      "source": [
        "inputs_val = torch.from_numpy(x_val)\n",
        "targets_val = torch.from_numpy(y_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oanJTnzl0NRr"
      },
      "source": [
        "prednew_val = net(inputs_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hF0VmB5H0NRs",
        "outputId": "114fcb54-f7d8-4876-ea8a-42582eb9d341"
      },
      "source": [
        "prednew_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-1.2961],\n",
              "        [-0.7494],\n",
              "        [-0.7494],\n",
              "        ...,\n",
              "        [-0.7494],\n",
              "        [-1.2137],\n",
              "        [-0.7494]], grad_fn=<AddmmBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4jwa_qh0NRv",
        "outputId": "a6cf47a1-d53f-4b87-b288-a987bcb5b5ec"
      },
      "source": [
        "targets_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.0032, 0.0128, 0.0128,  ..., 0.0128, 0.0541, 0.0128])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0N-RckB1JwcO",
        "outputId": "8d13674c-bf3a-4db4-e167-c36ca9418a7b"
      },
      "source": [
        "temp1 = targets_val.reshape(len(targets_val), 1)\n",
        "            \n",
        "# 2. Calculate loss\n",
        "loss = loss_fn(prednew_val, temp1)\n",
        "print(loss.item())\n",
        "print(np.sqrt(loss.item()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.67824786901474\n",
            "0.8235580544289152\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kAPhGwPu1rqH"
      },
      "source": [
        "The mean squared error of the model is **0.6782** and on average the predicted values differ from actual values by **0.8235** as a result the model has performed poorly. \n",
        "\n",
        "The predictions made by the model has negative values. As a result this model cannot be used for prediciton purposes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xAuZImc2D2N"
      },
      "source": [
        "In order to improve the above model scaling of the dataset can be done differently that allows the values between 0 and 1. One suggested scaling would be subtracting the minimum from the datapoint and dividing it by the maximum value of the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhFewPSvluVS"
      },
      "source": [
        "### Testing test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E357GMTol3B-"
      },
      "source": [
        "inputs_test = torch.from_numpy(x_test)\n",
        "targets_test = torch.from_numpy(y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QObuoSonl3B_"
      },
      "source": [
        "pred_test = model(inputs_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_82A0Kml3B_",
        "outputId": "07f7c9e1-7b76-42f3-eb27-39a89c7436df"
      },
      "source": [
        "pred_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0236],\n",
              "        [0.0236],\n",
              "        [0.0236],\n",
              "        ...,\n",
              "        [0.0236],\n",
              "        [0.0236],\n",
              "        [0.0236]], grad_fn=<AddmmBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rD2UtPh9l3CB",
        "outputId": "0d64561b-1db5-4f56-9336-3f44f662d3da"
      },
      "source": [
        "targets_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.0128, 0.0128, 0.0128,  ..., 0.0128, 0.0128, 0.0128])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i88CCM8ml3CB",
        "outputId": "f9234c0b-51a0-4e9d-dc09-35f264afda4a"
      },
      "source": [
        "temp2 = targets_test.reshape(len(targets_test), 1)\n",
        "            \n",
        "# 2. Calculate loss\n",
        "loss = loss_fn(pred_test, temp2)\n",
        "print(loss.item())\n",
        "print(np.sqrt(loss.item()))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0005542300059460104\n",
            "0.023542090092980493\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vul8D3sLmVD_"
      },
      "source": [
        "The mean squared error of the test set is **0.0005542** and on average the precited values and actual values differs by **0.02354**, which as compared to the actual values of precipitaion is not giving desirable results.\n",
        "\n",
        "With more epochs, hidden layer and different scaling techniques the model can be improved."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PIQcUPqyP9mc"
      },
      "source": [
        "## Saving model to checkpoint"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zhy_Jxe72wpi"
      },
      "source": [
        "Now as we have trained the model so it is important to save the trained model so that it can be used again and trained for better performance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JHQxHDI6c6_t"
      },
      "source": [
        "The following codes will be save the weights and biases of the trained model so that it can be used later. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_gNODwAOtUt",
        "outputId": "c87089a7-048e-4bdb-d6d3-7d75ecd4690c"
      },
      "source": [
        "print(\"Our model: \\n\\n\", model, '\\n')\n",
        "print(\"The state dict keys: \\n\\n\", model.state_dict().keys())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Our model: \n",
            "\n",
            " Linear(in_features=2, out_features=1, bias=True) \n",
            "\n",
            "The state dict keys: \n",
            "\n",
            " odict_keys(['weight', 'bias'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fl9ak73yPz6w"
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "WtVidrSfOwfj",
        "outputId": "33270295-c7f2-4a8c-b83c-04a7999179a0"
      },
      "source": [
        "torch.save(model.state_dict(), 'checkpoint.pth')\n",
        "\n",
        "# download checkpoint file\n",
        "files.download('checkpoint.pth')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_6d9edcd1-0f91-415c-8db5-80f6a946c98c\", \"checkpoint.pth\", 1077)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EFC8nmxm29-r"
      },
      "source": [
        "Now the model is saved with a file name **checkpoint.pth**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pC0rz7F_QKQb"
      },
      "source": [
        "## Loading model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P7PagquZQDRU",
        "outputId": "0ac0ab21-4260-47cc-f2fc-2c8216675a41"
      },
      "source": [
        "state_dict = torch.load('checkpoint.pth')\n",
        "print(state_dict.keys())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "odict_keys(['weight', 'bias'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DaD5_JsWQzdV"
      },
      "source": [
        "model1 = nn.Linear(2, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YLFQWuXxQQwb",
        "outputId": "6a5683c0-e5d9-4508-d4b0-54919ae7588f"
      },
      "source": [
        "model1.load_state_dict(state_dict)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vPddvWsGQ2T-",
        "outputId": "c1862f7e-c21e-4127-e2fd-aa6aaf2f30eb"
      },
      "source": [
        "model1.parameters()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<generator object Module.parameters at 0x7fb2f9b91f68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TX5WnXcq3Md3"
      },
      "source": [
        "Model is successfully loaded and can be used again to make predicitons."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VsMpoDV0RNSw",
        "outputId": "6b541ab4-e4bb-4086-f581-af5863270ec5"
      },
      "source": [
        "preds1 = model1(inputs)\n",
        "preds1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0236],\n",
              "        [0.0188],\n",
              "        [0.0236],\n",
              "        ...,\n",
              "        [0.0236],\n",
              "        [0.0236],\n",
              "        [0.0236]], grad_fn=<AddmmBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFxN6OMFRUIL"
      },
      "source": [
        "temp = targets.reshape(len(targets), 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQhhfB-ZRVK-",
        "outputId": "53b8ee59-ac15-4986-df5b-d92a9044c897"
      },
      "source": [
        "# 2. Calculate loss\n",
        "loss = loss_fn(preds1, temp)\n",
        "print(loss.item())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.000552099256310612\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjVW0JWWMDUY"
      },
      "source": [
        "# Concluding Remarks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUDHfwd7Dxod"
      },
      "source": [
        "Here I will list some of the problems that I have faced. After interpolating the data when it was used for the data preprocessing part of the deep learning, it was exhausting all the ram and the kernel was restarting again. So I have used the data without interpolation for training the deep learning model.\n",
        "Also the ram was getting exhausted when I was filling the missing values with the help of MICE, so I have filled the missing values with mean of the data."
      ]
    }
  ]
}