{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rpglcqUkiGQ-"
      },
      "source": [
        "# Download forecast precipitation dataset from CHIRPS-GEFS website using Python\n",
        "\n",
        "*Written by Men Vuthy, 2022*\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NNOwGDOtb9m2"
      },
      "source": [
        "### Objective\n",
        "\n",
        "\n",
        "\n",
        "*   The objective is to download forecast daily precipitation from CHIRPS-GEFS website by using Python, and save in local directory.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HsEEYpsBcRS4"
      },
      "source": [
        "Data folder:\n",
        "\n",
        "![img1](img-markdown/CHIRPS-datasets.png \"Img1\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twXmTpgEic0a"
      },
      "source": [
        "Import modules\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vSEATtIpRtNl"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "from urllib import request"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLLpkULJg5WJ"
      },
      "source": [
        "Parameter Setting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "lDlmABxCYHB1"
      },
      "outputs": [],
      "source": [
        "# Main website url\n",
        "url = 'https://data.chc.ucsb.edu/products/EWX/data/forecasts/CHIRPS-GEFS_precip_v12/05day/precip_mean/'\n",
        "\n",
        "# Directory for export\n",
        "export_dir = '/content/data_download/'\n",
        "\n",
        "# Start date range from i to j\n",
        "start_date_i = '2000-01-01'\n",
        "start_date_j = '2000-01-10'\n",
        "\n",
        "# End date range from i to j\n",
        "end_date_i = '2000-01-05'\n",
        "end_date_j = '2000-01-14'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BDM7twwag9IV"
      },
      "source": [
        "Start download"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "BDSbXEL4byaO"
      },
      "outputs": [],
      "source": [
        "# Create list of start date\n",
        "dateRange_pdcore = pd.date_range(start_date_i, start_date_j)\n",
        "dateRange = pd.DataFrame({'date':dateRange_pdcore })\n",
        "\n",
        "startDate = []\n",
        "for i in range(len(dateRange)):\n",
        "  ts = pd.to_datetime(str(dateRange.date.values[i])) \n",
        "  d = ts.strftime('%Y%m%d')\n",
        "  startDate.append(d)\n",
        "\n",
        "# Create list of end date\n",
        "dateRange_pdcore = pd.date_range(end_date_i, end_date_j)\n",
        "dateRange = pd.DataFrame({'date':dateRange_pdcore })\n",
        "\n",
        "endtDate = []\n",
        "for i in range(len(dateRange)):\n",
        "  ts = pd.to_datetime(str(dateRange.date.values[i])) \n",
        "  d = ts.strftime('%Y%m%d')\n",
        "  endtDate.append(d)\n",
        "\n",
        "# download_url, save_localFile = [], []\n",
        "for i in range(len(startDate)):\n",
        "  # Create file name\n",
        "  download_url = os.path.join(url + 'data-mean_'+ startDate[i]+ '_'+ endtDate[i]+ '.tif')\n",
        "\n",
        "  # Create file name for saving to local drive\n",
        "  save_localFile = export_dir + 'data-mean_'+ startDate[i]+ '_'+ endtDate[i]+ '.tif'\n",
        "\n",
        "  # Download remote and save locally\n",
        "  request.urlretrieve(download_url, save_localFile)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VFlafrwpi7Dc"
      },
      "source": [
        "After executing above code, the downloaded data will be located in the `export_dir`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s5e_hdkGdRTJ"
      },
      "source": [
        "Downloaded data:\n",
        "\n",
        "![img2](img-markdown/downloaded-data.png \"Img2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "download-CHIRPS-data.ipynb",
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    "kernelspec": {
      "display_name": "Python 3.7.10",
      "language": "python",
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