REST_API.ipynb

{
  "cells": [
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      "source": [
        "<a href=\"https://colab.research.google.com/github/trngbich/tutorial_WaPOR_API/blob/master/REST_API.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
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      "metadata": {
        "id": "b865c320"
      },
      "source": [
        "<div>\n",
        "<table style=\"width: 100%\">\n",
        "\t<tr>\n",
        "\t\t<td>\n",
        "\t\t<table style=\"width: 100%\">\n",
        "\t\t\t<tr>\n",
        "                <td><center><font size=\"6\"><b>Using WaPOR ReST API</b></font><center></td>\n",
        "\t\t\t</tr>\n",
        "\t\t</table>\n",
        "\t\t</td>\n",
        "\t\t<td><center><img src='https://github.com/MScWSDM49/topic4/blob/main/images/ihe-delft-institute_unesco_fc-lr.jpg?raw=1'></img></td>\n",
        "\t</tr>\n",
        "</table>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "48bad8ad",
      "metadata": {
        "id": "48bad8ad"
      },
      "source": [
        "# Table of contents\n",
        "1. [Learning objectives](#learningobs)\n",
        "2. [Introduction](#introduction)\n",
        "3. [Access WaPOR catalog and metadata](#catalog)\n",
        "4. [Get available data table](#datavail)\n",
        "5. [Get point timeseries](#point)\n",
        "6. [Get areal statistics timeseries](#area)\n",
        "7. [Get cropped raster](#raster)\n",
        "8. [Get crop water productivity](#wp)\n",
        "\n",
        "# 1. Learning objectives<a name=\"learningobs\"></a>\n",
        "\n",
        "- To define REST API\n",
        "- To access WaPOR REST API with Python script\n",
        "- To download WaPOR raster data through REST API\n",
        "- To visualise the downloaded timeseries and raster data\n",
        "\n",
        "\n",
        "# 2. Introduction<a name=\"introduction\"></a>\n",
        "\n",
        "## WaPOR\n",
        "\n",
        "FAO's web portal to monitor Water Productivity through Open-access of Remotely sensed derived data (WaPOR). This portal covers Africa and the Near East, with remotely sensed data, to monitor, in near real time, agricultural water and land productivity as well carbon dioxide uptake by vegetation.\n",
        "\n",
        "For information about the programme, please visit: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/\n",
        "\n",
        "\n",
        "## REST API\n",
        "REST (Representational State Transfer) API (Application Programming Interface) is an architectural style and set of conventions used for building web services. It defines a set of guidelines and principles for designing networked applications that can be accessed and interacted with over the internet.\n",
        "\n",
        "Watch [this video](https://www.youtube.com/watch?v=_mWDnkS0dDo) for more information.\n",
        "\n",
        "The ReST API documentation for accessing WaPOR database can be found at: https://io.apps.fao.org/gismgr/api/v1/swagger-ui.html\n",
        "\n",
        "\n",
        "## This tutorial\n",
        "In this tutorial, you will learn how to use FAO ReST API with Python scripting to access the WaPOR database.\n",
        "\n",
        "REST defines a set of standardized HTTP methods (GET, POST, PUT, DELETE, etc.) to perform actions on resources. Additionally, RESTful APIs often utilize common data formats like JSON (JavaScript Object Notation) for data representation.\n",
        "We're going to use the **requests** and **json** packages to connect to the database and we're going to use the **matplotlib** **pandas** and **rasterio** packages to visualize the downloaded daata. Make sure that these packages are installed in your conda environment.\n",
        "\n",
        "\n",
        "## API Token\n",
        "For some functions, you will need an API Token to authorize your access. Follow the steps below to get your API Token:\n",
        "\n",
        "### Step 1: Sign up/Log in an account on WaPOR portal\n",
        "\n",
        "Go to https://wapor.apps.fao.org/, click **Sign in**\n",
        "\n",
        "![](https://github.com/trngbich/tutorial_WaPOR_API/blob/master/img/1_signin.png?raw=1)\n",
        "\n",
        "If you don't have an account, create a new account, then log in.\n",
        "\n",
        "### Step 2:  Create API\n",
        "\n",
        "After logging in, go to **My WaPOR** > **My Profile**\n",
        "\n",
        "![](https://github.com/trngbich/tutorial_WaPOR_API/blob/master/img/2_mywapor.png?raw=1)\n",
        "\n",
        "![](https://github.com/trngbich/tutorial_WaPOR_API/blob/master/img/3_myprofile.png?raw=1)\n",
        "\n",
        "Under **API Token**, click **Generate API Token**\n",
        "\n",
        "![](https://github.com/trngbich/tutorial_WaPOR_API/blob/master/img/4_generatetoken.png?raw=1)\n",
        "\n",
        "The API Token is user's personal key, thus, should be stored and kept private. In case of losing API Token, revoke the old token and create a new one.\n",
        "\n",
        "![](https://github.com/trngbich/tutorial_WaPOR_API/blob/master/img/5_revoketoken.png?raw=1)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "835c9a9d",
      "metadata": {
        "id": "835c9a9d"
      },
      "source": [
        "# 3. Access WaPOR catalog and metadata <a name=\"catalog\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e958add9",
      "metadata": {
        "id": "e958add9"
      },
      "source": [
        "In this section, we will access WaPOR data catalog and access some metadata.\n",
        "\n",
        "First, import the libraries needed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "9a3b40c4",
      "metadata": {
        "id": "9a3b40c4"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "import json\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "09909b54",
      "metadata": {
        "id": "09909b54"
      },
      "source": [
        "We will start with accessing WaPOR data catalog using the following URL.\n",
        "\n",
        "Extra: Try changing the parameter overview and paged to True to see the difference in the response"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "5416d5e4",
      "metadata": {
        "id": "5416d5e4",
        "outputId": "d981ebaa-576b-4a5e-ea3c-da5208df599b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Request URL:  https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces?overview=False&paged=False\n"
          ]
        }
      ],
      "source": [
        "overview=False\n",
        "paged=False\n",
        "url = f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces?overview={overview}&paged={paged}'\n",
        "print('Request URL: ',url)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "35616997",
      "metadata": {
        "id": "35616997"
      },
      "source": [
        "Now, we will use the GET method in **requests** library to acquire responses from the server"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "34dfebf2",
      "metadata": {
        "id": "34dfebf2",
        "outputId": "5c0342bc-bf31-458f-ec7e-52d8e1734aa5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Response [200]>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "resp=requests.get(url) #Get response\n",
        "resp"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "30b7ff18",
      "metadata": {
        "id": "30b7ff18"
      },
      "source": [
        "The response is an object so we will convert it to a JSON format so we can see the content"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "4a7b623d",
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        "id": "4a7b623d",
        "outputId": "32308d34-693d-4424-d15c-225b42ec1c0c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Response:  {'requestId': 'eb177629-8ba7-4485-b5da-485c241ca4eb', 'timestamp': 1687808132513, 'runtime': 5, 'status': 200, 'message': 'OK', 'response': [{'code': 'AQUAMAPS', 'caption': 'Global spatial database on water and agriculture', 'description': 'Regional and global spatial datasets on water resources and water management considered as a standard information resource, produced by FAO or by external data providers.', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/AQUAMAPS'}]}, {'code': 'ASIS', 'caption': 'Agriculture Stress Index System', 'description': 'ASIS allows countries to fine-tune parameters of the system based on detailed land use maps and national crop statistics. At the country level, ASIS could be used in developing a remote sensing-based index for crop insurance.', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/ASIS'}]}, {'code': 'C2ATLAS', 'caption': 'Climate Change ATLAS', 'description': 'C2ATLAS (Climate Change ATLAS - CSIC).The Climate Change ATLAS provides open access to aggregated datasets of global and regional climate change projections for the 21st century produced by CMIP and CORDEX initiatives (including changes for essential climate variables  for both scenarios/periods and global warming levels).', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/C2ATLAS'}]}, {'code': 'C3S', 'caption': 'Copernicus Climate Change Service', 'description': 'The Copernicus Climate Change Service (C3S) will combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide.\\\\n\\\\nECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.\\\\n\\\\nC3S will provide key indicators on climate change drivers such as carbon dioxide and impacts, for example, reducing glaciers. The aim of these indicators will be to support European adaptation and mitigation policies in a number of sectors.', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/C3S'}]}, {'code': 'CHIRPS', 'caption': 'Climate Hazard group InfraRed Precipitation with Stations (CHIRPS)', 'description': 'Climate Hazard group InfraRed Precipitation with Stations (CHIRPS) is a quasi-global, 0.05° resolution, 1981 to near-present gridded precipitation time series.\\\\n\\\\nCHIRPS data are produced by scientists at the University of California, Santa Barbara (UCSB) Climate Hazard Group (CHG) and the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, in order to deliver complete, reliable, up-to-date data sets for a number of early warning objectives, like trend analysis and seasonal drought monitoring.', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/CHIRPS'}]}, {'code': 'CROPWATCH', 'caption': 'CropWatch', 'description': \"Cropwatch is China's leading agricultural monitoring system, using remote sensing and ground observation data to assess crop growth, yield and related information on national and global scales. The Cropwatch scientific team is affiliated to the Institute of Aerospace Information Innovation (AIR) of the Chinese Academy of Sciences.\", 'additionalInfo': {'creationDate': '1998-01-01', 'link': 'https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/a8bf6bbb-9af2-4c8e-8fe5-ddcbbf142835'}, 'tags': [{'name': 'CROP'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/CROPWATCH'}]}, {'code': 'CRTB', 'caption': 'Climate Risk Toolbox (CRTB)', 'description': 'Climate Risk Toolbox (CRTB)', 'tags': [{'name': 'CRTB'}, {'name': 'ASSESSMENT'}, {'name': 'RISK'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/CRTB'}]}, {'code': 'DLMF', 'caption': 'Desert Locust Monitoring and Forecasting', 'description': 'DLMF-Desert Locust monitoring and forecasting. Integrated with multi-source Earth Observation data, e.g. meteorological data, field data, and remote sensing data (such as GF series in China, MODIS and Landsat series in US, Sentinel series in EU), and self-developed models and algorithms for Desert Locust monitoring and forecasting, the research team constructed the ‘Vegetation pests and diseases monitoring and forecasting system’, which could regularly release thematical maps and reports on Desert Locust.', 'tags': [{'name': 'LOCUST'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/DLMF'}]}, {'code': 'FAOSTAT', 'caption': 'FAO Corporate Statistical Database', 'description': 'FAOSTAT provides free access to food and agriculture statistics (including crop, livestock, and forestry sub-sectors) for over 245 countries and territories and covers all FAO regional groupings from 1961 to the most recent year available.', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/FAOSTAT'}]}, {'code': 'FROM_GLC', 'caption': 'Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC)', 'description': 'Global land cover data are key sources of information for understanding the complex interactions between human activities and global change. FROM-GLC (Finer Resolution Observation and Monitoring of Global Land Cover) is the first 30 m resolution global land cover maps produced using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Our long-term goal in FROM-GLC is to develop a multiple stage approach to mapping global land cover so that the results can better meet the needs of land process modeling and can be easily cross-walked to existing global land cover classification schemes.', 'tags': [{'name': 'LCC'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/FROM_GLC'}]}, {'code': 'GAEZ_2015', 'caption': 'Global Agro-Ecological Zones (2015)', 'description': 'The Global Agro-Ecological Zones (GAEZ) database provides the agronomic backbone for various applications including the quantification of land productivity. Results are commonly aggregated for current major land use/cover patterns and by administrative units, land protection status, or broad classes reflecting infrastructure availability and market access conditions. With this large amount of data, a new system had to be created to make the data accessible to a variety of users. The result is the new GAEZ Data Portal, an interactive data access facility, which not only provides free access to data and information and allows visualization of data, but also provides the user with various analysis outputs and download options.', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/GAEZ_2015'}]}, {'code': 'GAEZ_V4', 'caption': 'Global Agro-Ecological Zones (2021)', 'description': 'The Global Agro-Ecological Zones (GAEZ) database provides the agronomic backbone for various applications including the quantification of land productivity. Results are commonly aggregated for current major land use/cover patterns and by administrative units, land protection status, or broad classes reflecting infrastructure availability and market access conditions. With this large amount of data, a new system had to be created to make the data accessible to a variety of users. The result is the new GAEZ Data Portal, an interactive data access facility, which not only provides free access to data and information and allows visualization of data, but also provides the user with various analysis outputs and download options.', 'additionalInfo': {'created': '2021 April 29', 'site': 'https://gaez-data-portal-hqfao.hub.arcgis.com/'}, 'tags': [{'name': 'GAEZ'}, {'name': 'AEZ'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/GAEZ_V4'}]}, {'code': 'GISMGR_TEST', 'caption': 'FAO GIS MANAGER (GISMGR) - Test and training workspace', 'description': 'FAO GISMGR Workspace for geospatial datasets', 'additionalInfo': {'created': '2021 Oct 15', 'wiki': 'https://en.wikipedia.org/wiki/Food_and_Agriculture_Organization'}, 'tags': [{'name': 'TEST'}, {'name': 'L1', 'index': 1, 'context': 'LEVELS'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/GISMGR_TEST'}]}, {'code': 'GLEAM3', 'caption': 'Global Livestock Environmental Assessment Model (GLEAM3)', 'description': 'Global Livestock Environmental Assessment Model', 'tags': [{'name': 'GLEAM'}, {'name': 'LIVESTOCK'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/GLEAM3'}]}, {'code': 'GLW', 'caption': 'Gridded Livestock of the World', 'description': 'Gridded Livestock of the World (GLW) is a peer-reviewed spatial dataset on the global distribution and abundance of livestock species. These data find applications in the fields of food and nutrition security, livelihoods and economic growth, human and animal health and welfare and natural resources and environment. The third version of the GLW has a reference year of 2010 and includes global distributions of cattle, buffaloes, sheep, goats, horses, pigs, chickens and ducks at a spatial resolution of 5 minutes of arc, approximately 10 km at the equator.', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/GLW'}]}, {'code': 'GLW3', 'caption': 'Gridded Livestock of the World (GLW3, 2010)', 'description': 'This database contains peer-reviewed spatial datasets on livestock distributions for applications in development, environment and health. It consists of the most up to date version of the Gridded Livestock of the world (GLW 3). All datasets are licensed through a Creative Commons Attribution 4.0 International License. This dataset contains the global distribution of each species in 2010 expressed as the total number of animal per pixel (5 min of arc). Units: head/sqkm  for cattle, buffaloes, sheep, goats, horses, pigs; birds/ sqkm only for chicken and ducks', 'tags': [{'name': 'LIVESTOCK'}, {'name': 'GLW3'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/GLW3'}]}, {'code': 'GLW4', 'caption': 'Gridded Livestock of the World (GLW4, 2015)', 'description': 'This database contains peer-reviewed spatial datasets on livestock distributions for applications in development, environment and health. It consists of the most up to date version of the Gridded Livestock of the world (GLW 4). All datasets are licensed through a Creative Commons Attribution 4.0 International License. This dataset contains the global distribution of each species in 2015 expressed as the total number of animal per pixel (5 min of arc).', 'tags': [{'name': 'LIVESTOCK'}, {'name': 'GLW3'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/GLW4'}]}, {'code': 'GSOCSEQ', 'caption': 'Soil Organic Carbon', 'description': 'Soil Organic Carbon maps', 'additionalInfo': {'created': '2022 October 11', 'site': 'https://www.fao.org/documents/card/en/c/cb9002en'}, 'tags': [{'name': 'SOIL'}, {'name': 'CARBON'}, {'name': 'SOC'}, {'name': 'SLM'}, {'name': 'SSM'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/GSOCSEQ'}]}, {'code': 'IWMI', 'caption': 'International Water Management Institute (IWMI)', 'description': 'IWMI is a Research Center of CGIAR operating in more than 30 countries. IWMI’s Vision reflected in its Strategy 2019-2023, is ‘a water-secure world’. IWMI targets water and land management challenges faced by poor communities in developing countries, and through this contributes towards the achievement of the Sustainable Development Goals (SDGs) of reducing poverty and hunger and maintaining a sustainable environment.', 'additionalInfo': {'created': '2022 October 13', 'organization': 'https://data.apps.fao.org/catalog/organization/about/iwmi', 'site': 'https://www.iwmi.cgiar.org/'}, 'tags': [{'name': 'IWMI'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/IWMI'}]}, {'code': 'NASA', 'caption': 'National Aeronautics and Space Administration (NASA)', 'description': 'The National Aeronautics and Space Administration (NASA) is an independent agency of the United States Federal Government responsible for the civilian space program, as well as aeronautics and aerospace research.', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/NASA'}]}, {'code': 'NATURAL_EARTH', 'caption': 'Natural Earth', 'description': 'Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software. Natural Earth was built through a collaboration of many volunteers and is supported by NACIS (North American Cartographic Information Society), and is free for use in any type of project.', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/NATURAL_EARTH'}]}, {'code': 'NMME', 'caption': 'North American Multi-Model Ensemble (NMME)', 'description': 'The North American Multi-Model Ensemble (NMME) forecasts has been implemented to enable National Meteorological Services (NMSs) around the globe and the public to quickly access operational seasonal model forecasts for specific areas of interest. The NMME models include the CFSv2, two versions of the Canadian models CanCM4i (CMC1) and GEM-NEMO (CMC2), NOAA’s Geophysical Fluid Dynamic (GFDL), NOAA’s Geophysical Fluid Dynamic (GFDL-FLOR), National Aeronautics and Space Administration (NASA-GEOS5v2), the National Center for Atmospheric Research (NCAR-CCSM4), and the ensemble mean of all the models.', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/NMME'}]}, {'code': 'RDMS', 'caption': 'Regional Drought Monitoring System', 'description': 'Regional Drought Monitoring System for Middle East and North Africa', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/RDMS'}]}, {'code': 'RICCAR', 'caption': 'Regional Arab Climate Change Assessment Report', 'description': 'The Regional Initiative for the Assessment of Climate Change Impacts on Water Resources and Socio-Economic Vulnerability in the Arab Region (RICCAR) is a joint initiative of the United Nations and the League of Arab States launched in 2010.', 'additionalInfo': {'tagContexts': {'LEVELS_LIST': [{'name': 'L1', 'label': 'CLIMATE'}, {'name': 'L2', 'label': 'HYDROLOGY'}, {'name': 'L3', 'label': 'VULNERABILITY'}], 'CATEGORIES_LIST': [{'name': 'CDD', 'label': 'Maximum length of dry spell', 'level': 'L1'}, {'name': 'CWD', 'label': 'Maximum length of wet spell', 'level': 'L1'}, {'name': 'PR', 'label': 'Precipitation', 'level': 'L1'}, {'name': 'R10', 'label': 'Heavy precipitation days', 'level': 'L1'}, {'name': 'R20', 'label': 'Very heavy precipitation days', 'level': 'L1'}, {'name': 'SDII', 'label': 'Simple Daily Intensity Index', 'level': 'L1'}, {'name': 'SU', 'label': 'Number of summer days', 'level': 'L1'}, {'name': 'SU35', 'label': 'Number of hot days', 'level': 'L1'}, {'name': 'SU40', 'label': 'Number of very hot days', 'level': 'L1'}, {'name': 'TAS', 'label': 'Temperature', 'level': 'L1'}, {'name': 'TASMAX', 'label': 'Max Temperature', 'level': 'L1'}, {'name': 'TASMIN', 'label': 'Min Temperature', 'level': 'L1'}, {'name': 'TR', 'label': 'Number of tropical nights', 'level': 'L1'}, {'name': 'ET', 'label': 'EvapoTranspiration', 'level': 'L2'}, {'name': 'Q', 'label': 'Runoff', 'level': 'L2'}, {'name': 'Adaptive Capacity', 'label': 'Adaptive Capacity', 'level': 'L3'}, {'name': 'Exposure', 'label': 'Exposure', 'level': 'L3'}, {'name': 'Potential Impact', 'label': 'Potential Impact', 'level': 'L3'}, {'name': 'Sensitivity', 'label': 'Sensitivity', 'level': 'L3'}, {'name': 'Vulnerability', 'label': 'Vulnerability', 'level': 'L3'}]}}, 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/RICCAR'}]}, {'code': 'RICCAR_2', 'caption': 'Regional Arab Climate Change Assessment Report', 'description': 'The Regional Initiative for the Assessment of Climate Change Impacts on Water Resources and Socio-Economic Vulnerability in the Arab Region (RICCAR) is a joint initiative of the United Nations and the League of Arab States launched in 2010.', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/RICCAR_2'}]}, {'code': 'RSCROP', 'caption': 'Crop Pest and Disease Monitoring and Forecasting System', 'additionalInfo': {'site': 'http://en.rscrop.com/'}, 'tags': [{'name': 'CROP'}, {'name': 'PEST'}, {'name': 'DISEASE'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/RSCROP'}]}, {'code': 'RVF', 'caption': 'Rift Valley Fever', 'description': 'Rift Valley Fever', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/RVF'}]}, {'code': 'WAPOR_2', 'caption': 'FAO Water Productivity Open-access portal (WaPOR)', 'description': 'The FAO Water Productivity Open-access portal (WaPOR) uses remote sensing technologies to monitor and report on agricultural water productivity over Africa and the Near East.', 'additionalInfo': {'tagContexts': {'levels': [{'name': 'L1', 'label': 'CONTINENTAL (250m)', 'backgroundLayers': [{'label': 'Simple grey background', 'layer': 'NATURAL_EARTH:gray_hr_sr_w_3'}, {'label': 'NASA Bluemarble', 'layer': 'NASA:bluemarble_t_b'}, {'label': 'OpenStreet MAP', 'layer': 'OSM'}], 'decorationLayers': [{'label': 'Countries', 'layer': 'CIO:country_boundaries', 'selected': True}, {'label': 'Basins', 'layer': 'AQUASTAT:major_hydrobasins', 'selected': False}]}, {'name': 'L2', 'label': 'NATIONAL (100m)', 'backgroundLayers': [{'label': 'Simple grey background', 'layer': 'NATURAL_EARTH:gray_hr_sr_w_3'}, {'label': 'NASA Bluemarble', 'layer': 'NASA:bluemarble_t_b'}, {'label': 'OpenStreet MAP', 'layer': 'OSM'}], 'decorationLayers': [{'label': 'Countries', 'layer': 'CIO:country_boundaries', 'selected': True}, {'label': 'Basins', 'layer': 'AQUASTAT:major_hydrobasins', 'selected': False}]}, {'name': 'L3', 'label': 'SUB-NATIONAL (30m)', 'children': [{'name': 'AWA', 'label': 'Awash, Ethiopia', 'backgroundLayers': [{'label': 'OpenStreet MAP', 'layer': 'OSM'}, {'label': 'NASA Bluemarble', 'layer': 'NASA:bluemarble_t_b'}, {'label': 'Simple grey background', 'layer': 'NATURAL_EARTH:gray_hr_sr_w_3'}]}, {'name': 'BKA', 'label': 'Bekaa, Lebanon', 'backgroundLayers': [{'label': 'OpenStreet MAP', 'layer': 'OSM'}, {'label': 'NASA Bluemarble', 'layer': 'NASA:bluemarble_t_b'}, {'label': 'Simple grey background', 'layer': 'NATURAL_EARTH:gray_hr_sr_w_3'}]}, {'name': 'KOG', 'label': 'Koga, Ethiopia', 'backgroundLayers': [{'label': 'OpenStreet MAP', 'layer': 'OSM'}, {'label': 'NASA Bluemarble', 'layer': 'NASA:bluemarble_t_b'}, {'label': 'Simple grey background', 'layer': 'NATURAL_EARTH:gray_hr_sr_w_3'}]}, {'name': 'LAM', 'label': 'Lamego, Mozambique', 'backgroundLayers': [{'label': 'OpenStreet MAP', 'layer': 'OSM'}, {'label': 'NASA Bluemarble', 'layer': 'NASA:bluemarble_t_b'}, {'label': 'Simple grey background', 'layer': 'NATURAL_EARTH:gray_hr_sr_w_3'}]}, {'name': 'ODN', 'label': 'Office du Niger, Mali', 'backgroundLayers': [{'label': 'OpenStreet MAP', 'layer': 'OSM'}, {'label': 'NASA Bluemarble', 'layer': 'NASA:bluemarble_t_b'}, {'label': 'Simple grey background', 'layer': 'NATURAL_EARTH:gray_hr_sr_w_3'}]}, {'name': 'ZAN', 'label': 'Zankalon, Egypt', 'backgroundLayers': [{'label': 'OpenStreet MAP', 'layer': 'OSM'}, {'label': 'NASA Bluemarble', 'layer': 'NASA:bluemarble_t_b'}, {'label': 'Simple grey background', 'layer': 'NATURAL_EARTH:gray_hr_sr_w_3'}]}]}], 'categories': [{'name': 'C1', 'label': 'Water Productivity'}, {'name': 'C2', 'label': 'Water'}, {'name': 'C3', 'label': 'Land'}, {'name': 'C4', 'label': 'Climate'}, {'name': 'C5', 'label': 'Ancillary'}]}}, 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2'}]}, {'code': 'WATER', 'caption': 'Water Data', 'description': 'Water data, including water resources, agricultural water management, water accounting and productivity', 'tags': [{'name': 'WATER'}], 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WATER'}]}, {'code': 'WPOP', 'caption': 'WorldPop project', 'description': 'The WorldPop project was initiated in October 2013 to combine the AfriPop, AsiaPop and AmeriPop population mapping projects. It aims to provide an open access archive of spatial demographic datasets for Central and South America, Africa and Asia to support development, disaster response and health applications. The methods used are designed with full open access and operational application in mind, using transparent, fully documented and peer-reviewed methods to produce easily updatable maps with accompanying metadata and measures of uncertainty.', 'links': [{'rel': 'self', 'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WPOP'}]}]}\n"
          ]
        }
      ],
      "source": [
        "resp_dict=resp.json() #read response JSON string\n",
        "print('Response: ',resp_dict)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9c8a123b",
      "metadata": {
        "id": "9c8a123b"
      },
      "source": [
        "It's not easy to read this, so we will convert JSON to **pandas** DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "b04081f5",
      "metadata": {
        "id": "b04081f5",
        "outputId": "19d2134a-d262-456c-e374-93524d944243",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             code                                            caption  \\\n",
              "0        AQUAMAPS   Global spatial database on water and agriculture   \n",
              "1            ASIS                    Agriculture Stress Index System   \n",
              "2         C2ATLAS                               Climate Change ATLAS   \n",
              "3             C3S                  Copernicus Climate Change Service   \n",
              "4          CHIRPS  Climate Hazard group InfraRed Precipitation wi...   \n",
              "5       CROPWATCH                                          CropWatch   \n",
              "6            CRTB                        Climate Risk Toolbox (CRTB)   \n",
              "7            DLMF           Desert Locust Monitoring and Forecasting   \n",
              "8         FAOSTAT                 FAO Corporate Statistical Database   \n",
              "9        FROM_GLC  Finer Resolution Observation and Monitoring of...   \n",
              "10      GAEZ_2015                Global Agro-Ecological Zones (2015)   \n",
              "11        GAEZ_V4                Global Agro-Ecological Zones (2021)   \n",
              "12    GISMGR_TEST  FAO GIS MANAGER (GISMGR) - Test and training w...   \n",
              "13         GLEAM3  Global Livestock Environmental Assessment Mode...   \n",
              "14            GLW                     Gridded Livestock of the World   \n",
              "15           GLW3        Gridded Livestock of the World (GLW3, 2010)   \n",
              "16           GLW4        Gridded Livestock of the World (GLW4, 2015)   \n",
              "17        GSOCSEQ                                Soil Organic Carbon   \n",
              "18           IWMI    International Water Management Institute (IWMI)   \n",
              "19           NASA  National Aeronautics and Space Administration ...   \n",
              "20  NATURAL_EARTH                                      Natural Earth   \n",
              "21           NMME         North American Multi-Model Ensemble (NMME)   \n",
              "22           RDMS                 Regional Drought Monitoring System   \n",
              "23         RICCAR     Regional Arab Climate Change Assessment Report   \n",
              "24       RICCAR_2     Regional Arab Climate Change Assessment Report   \n",
              "25         RSCROP  Crop Pest and Disease Monitoring and Forecasti...   \n",
              "26            RVF                                  Rift Valley Fever   \n",
              "27        WAPOR_2  FAO Water Productivity Open-access portal (WaPOR)   \n",
              "28          WATER                                         Water Data   \n",
              "29           WPOP                                   WorldPop project   \n",
              "\n",
              "                                          description  \\\n",
              "0   Regional and global spatial datasets on water ...   \n",
              "1   ASIS allows countries to fine-tune parameters ...   \n",
              "2   C2ATLAS (Climate Change ATLAS - CSIC).The Clim...   \n",
              "3   The Copernicus Climate Change Service (C3S) wi...   \n",
              "4   Climate Hazard group InfraRed Precipitation wi...   \n",
              "5   Cropwatch is China's leading agricultural moni...   \n",
              "6                         Climate Risk Toolbox (CRTB)   \n",
              "7   DLMF-Desert Locust monitoring and forecasting....   \n",
              "8   FAOSTAT provides free access to food and agric...   \n",
              "9   Global land cover data are key sources of info...   \n",
              "10  The Global Agro-Ecological Zones (GAEZ) databa...   \n",
              "11  The Global Agro-Ecological Zones (GAEZ) databa...   \n",
              "12       FAO GISMGR Workspace for geospatial datasets   \n",
              "13    Global Livestock Environmental Assessment Model   \n",
              "14  Gridded Livestock of the World (GLW) is a peer...   \n",
              "15  This database contains peer-reviewed spatial d...   \n",
              "16  This database contains peer-reviewed spatial d...   \n",
              "17                           Soil Organic Carbon maps   \n",
              "18  IWMI is a Research Center of CGIAR operating i...   \n",
              "19  The National Aeronautics and Space Administrat...   \n",
              "20  Natural Earth is a public domain map dataset a...   \n",
              "21  The North American Multi-Model Ensemble (NMME)...   \n",
              "22  Regional Drought Monitoring System for Middle ...   \n",
              "23  The Regional Initiative for the Assessment of ...   \n",
              "24  The Regional Initiative for the Assessment of ...   \n",
              "25                                                NaN   \n",
              "26                                  Rift Valley Fever   \n",
              "27  The FAO Water Productivity Open-access portal ...   \n",
              "28  Water data, including water resources, agricul...   \n",
              "29  The WorldPop project was initiated in October ...   \n",
              "\n",
              "                                                links  \\\n",
              "0   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "1   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "2   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "3   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "4   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "5   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "6   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "7   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "8   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "9   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "10  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "11  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "12  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "13  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "14  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "15  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "16  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "17  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "18  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "19  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "20  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "21  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "22  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "23  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "24  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "25  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "26  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "27  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "28  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "29  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
              "\n",
              "                                       additionalInfo  \\\n",
              "0                                                 NaN   \n",
              "1                                                 NaN   \n",
              "2                                                 NaN   \n",
              "3                                                 NaN   \n",
              "4                                                 NaN   \n",
              "5   {'creationDate': '1998-01-01', 'link': 'https:...   \n",
              "6                                                 NaN   \n",
              "7                                                 NaN   \n",
              "8                                                 NaN   \n",
              "9                                                 NaN   \n",
              "10                                                NaN   \n",
              "11  {'created': '2021 April 29', 'site': 'https://...   \n",
              "12  {'created': '2021 Oct 15', 'wiki': 'https://en...   \n",
              "13                                                NaN   \n",
              "14                                                NaN   \n",
              "15                                                NaN   \n",
              "16                                                NaN   \n",
              "17  {'created': '2022 October 11', 'site': 'https:...   \n",
              "18  {'created': '2022 October 13', 'organization':...   \n",
              "19                                                NaN   \n",
              "20                                                NaN   \n",
              "21                                                NaN   \n",
              "22                                                NaN   \n",
              "23  {'tagContexts': {'LEVELS_LIST': [{'name': 'L1'...   \n",
              "24                                                NaN   \n",
              "25                  {'site': 'http://en.rscrop.com/'}   \n",
              "26                                                NaN   \n",
              "27  {'tagContexts': {'levels': [{'name': 'L1', 'la...   \n",
              "28                                                NaN   \n",
              "29                                                NaN   \n",
              "\n",
              "                                                 tags  \n",
              "0                                                 NaN  \n",
              "1                                                 NaN  \n",
              "2                                                 NaN  \n",
              "3                                                 NaN  \n",
              "4                                                 NaN  \n",
              "5                                  [{'name': 'CROP'}]  \n",
              "6   [{'name': 'CRTB'}, {'name': 'ASSESSMENT'}, {'n...  \n",
              "7                                [{'name': 'LOCUST'}]  \n",
              "8                                                 NaN  \n",
              "9                                   [{'name': 'LCC'}]  \n",
              "10                                                NaN  \n",
              "11                [{'name': 'GAEZ'}, {'name': 'AEZ'}]  \n",
              "12  [{'name': 'TEST'}, {'name': 'L1', 'index': 1, ...  \n",
              "13         [{'name': 'GLEAM'}, {'name': 'LIVESTOCK'}]  \n",
              "14                                                NaN  \n",
              "15          [{'name': 'LIVESTOCK'}, {'name': 'GLW3'}]  \n",
              "16          [{'name': 'LIVESTOCK'}, {'name': 'GLW3'}]  \n",
              "17  [{'name': 'SOIL'}, {'name': 'CARBON'}, {'name'...  \n",
              "18                                 [{'name': 'IWMI'}]  \n",
              "19                                                NaN  \n",
              "20                                                NaN  \n",
              "21                                                NaN  \n",
              "22                                                NaN  \n",
              "23                                                NaN  \n",
              "24                                                NaN  \n",
              "25  [{'name': 'CROP'}, {'name': 'PEST'}, {'name': ...  \n",
              "26                                                NaN  \n",
              "27                                                NaN  \n",
              "28                                [{'name': 'WATER'}]  \n",
              "29                                                NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-769b23e6-d265-4c79-8224-f8814f492b4f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>code</th>\n",
              "      <th>caption</th>\n",
              "      <th>description</th>\n",
              "      <th>links</th>\n",
              "      <th>additionalInfo</th>\n",
              "      <th>tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AQUAMAPS</td>\n",
              "      <td>Global spatial database on water and agriculture</td>\n",
              "      <td>Regional and global spatial datasets on water ...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ASIS</td>\n",
              "      <td>Agriculture Stress Index System</td>\n",
              "      <td>ASIS allows countries to fine-tune parameters ...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C2ATLAS</td>\n",
              "      <td>Climate Change ATLAS</td>\n",
              "      <td>C2ATLAS (Climate Change ATLAS - CSIC).The Clim...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C3S</td>\n",
              "      <td>Copernicus Climate Change Service</td>\n",
              "      <td>The Copernicus Climate Change Service (C3S) wi...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHIRPS</td>\n",
              "      <td>Climate Hazard group InfraRed Precipitation wi...</td>\n",
              "      <td>Climate Hazard group InfraRed Precipitation wi...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>CROPWATCH</td>\n",
              "      <td>CropWatch</td>\n",
              "      <td>Cropwatch is China's leading agricultural moni...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>{'creationDate': '1998-01-01', 'link': 'https:...</td>\n",
              "      <td>[{'name': 'CROP'}]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>CRTB</td>\n",
              "      <td>Climate Risk Toolbox (CRTB)</td>\n",
              "      <td>Climate Risk Toolbox (CRTB)</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'name': 'CRTB'}, {'name': 'ASSESSMENT'}, {'n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DLMF</td>\n",
              "      <td>Desert Locust Monitoring and Forecasting</td>\n",
              "      <td>DLMF-Desert Locust monitoring and forecasting....</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'name': 'LOCUST'}]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>FAOSTAT</td>\n",
              "      <td>FAO Corporate Statistical Database</td>\n",
              "      <td>FAOSTAT provides free access to food and agric...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>FROM_GLC</td>\n",
              "      <td>Finer Resolution Observation and Monitoring of...</td>\n",
              "      <td>Global land cover data are key sources of info...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'name': 'LCC'}]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>GAEZ_2015</td>\n",
              "      <td>Global Agro-Ecological Zones (2015)</td>\n",
              "      <td>The Global Agro-Ecological Zones (GAEZ) databa...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>GAEZ_V4</td>\n",
              "      <td>Global Agro-Ecological Zones (2021)</td>\n",
              "      <td>The Global Agro-Ecological Zones (GAEZ) databa...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>{'created': '2021 April 29', 'site': 'https://...</td>\n",
              "      <td>[{'name': 'GAEZ'}, {'name': 'AEZ'}]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>GISMGR_TEST</td>\n",
              "      <td>FAO GIS MANAGER (GISMGR) - Test and training w...</td>\n",
              "      <td>FAO GISMGR Workspace for geospatial datasets</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>{'created': '2021 Oct 15', 'wiki': 'https://en...</td>\n",
              "      <td>[{'name': 'TEST'}, {'name': 'L1', 'index': 1, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>GLEAM3</td>\n",
              "      <td>Global Livestock Environmental Assessment Mode...</td>\n",
              "      <td>Global Livestock Environmental Assessment Model</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'name': 'GLEAM'}, {'name': 'LIVESTOCK'}]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>GLW</td>\n",
              "      <td>Gridded Livestock of the World</td>\n",
              "      <td>Gridded Livestock of the World (GLW) is a peer...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>GLW3</td>\n",
              "      <td>Gridded Livestock of the World (GLW3, 2010)</td>\n",
              "      <td>This database contains peer-reviewed spatial d...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'name': 'LIVESTOCK'}, {'name': 'GLW3'}]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>GLW4</td>\n",
              "      <td>Gridded Livestock of the World (GLW4, 2015)</td>\n",
              "      <td>This database contains peer-reviewed spatial d...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'name': 'LIVESTOCK'}, {'name': 'GLW3'}]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>GSOCSEQ</td>\n",
              "      <td>Soil Organic Carbon</td>\n",
              "      <td>Soil Organic Carbon maps</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>{'created': '2022 October 11', 'site': 'https:...</td>\n",
              "      <td>[{'name': 'SOIL'}, {'name': 'CARBON'}, {'name'...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>IWMI</td>\n",
              "      <td>International Water Management Institute (IWMI)</td>\n",
              "      <td>IWMI is a Research Center of CGIAR operating i...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>{'created': '2022 October 13', 'organization':...</td>\n",
              "      <td>[{'name': 'IWMI'}]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>NASA</td>\n",
              "      <td>National Aeronautics and Space Administration ...</td>\n",
              "      <td>The National Aeronautics and Space Administrat...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>NATURAL_EARTH</td>\n",
              "      <td>Natural Earth</td>\n",
              "      <td>Natural Earth is a public domain map dataset a...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NMME</td>\n",
              "      <td>North American Multi-Model Ensemble (NMME)</td>\n",
              "      <td>The North American Multi-Model Ensemble (NMME)...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>RDMS</td>\n",
              "      <td>Regional Drought Monitoring System</td>\n",
              "      <td>Regional Drought Monitoring System for Middle ...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>RICCAR</td>\n",
              "      <td>Regional Arab Climate Change Assessment Report</td>\n",
              "      <td>The Regional Initiative for the Assessment of ...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>{'tagContexts': {'LEVELS_LIST': [{'name': 'L1'...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>RICCAR_2</td>\n",
              "      <td>Regional Arab Climate Change Assessment Report</td>\n",
              "      <td>The Regional Initiative for the Assessment of ...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>RSCROP</td>\n",
              "      <td>Crop Pest and Disease Monitoring and Forecasti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>{'site': 'http://en.rscrop.com/'}</td>\n",
              "      <td>[{'name': 'CROP'}, {'name': 'PEST'}, {'name': ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>RVF</td>\n",
              "      <td>Rift Valley Fever</td>\n",
              "      <td>Rift Valley Fever</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>WAPOR_2</td>\n",
              "      <td>FAO Water Productivity Open-access portal (WaPOR)</td>\n",
              "      <td>The FAO Water Productivity Open-access portal ...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>{'tagContexts': {'levels': [{'name': 'L1', 'la...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>WATER</td>\n",
              "      <td>Water Data</td>\n",
              "      <td>Water data, including water resources, agricul...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'name': 'WATER'}]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>WPOP</td>\n",
              "      <td>WorldPop project</td>\n",
              "      <td>The WorldPop project was initiated in October ...</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "workspaces=pd.DataFrame.from_dict(resp_dict['response']) #convert response body to DataFrame object\n",
        "workspaces"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aaf4da76",
      "metadata": {
        "id": "aaf4da76"
      },
      "source": [
        "As you can see there are many workspaces that can be accessed through the FAO ReST API. In this tutorial, we want to show the example of WaPOR database. So we will use the workspace code of WaPOR: \"WaPOR_2\" (Number 27 in the list).\n",
        "\n",
        "For this we will use the workspace code \"WaPOR_2\" in our next request"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "475c2183",
      "metadata": {
        "id": "475c2183"
      },
      "outputs": [],
      "source": [
        "workspace='WAPOR_2'\n",
        "overview=False\n",
        "paged=False\n",
        "cubes_url= f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes?overview={overview}&paged={paged}'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "085574a6",
      "metadata": {
        "id": "085574a6"
      },
      "source": [
        "Similar to the previous request, we can convert the response to DataFrame for readability"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "e8628656",
      "metadata": {
        "scrolled": false,
        "id": "e8628656",
        "outputId": "f4fa0483-ac8f-47ed-f797-b260d3bc6bb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 902
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    code                                            caption  \\\n",
              "0              L1_GBWP_A                   Gross Biomass Water Productivity   \n",
              "1              L1_NBWP_A                     Net Biomass Water Productivity   \n",
              "2              L1_AETI_A  Actual EvapoTranspiration and Interception (An...   \n",
              "3              L1_AETI_M  Actual EvapoTranspiration and Interception (Mo...   \n",
              "4              L1_AETI_D  Actual EvapoTranspiration and Interception (De...   \n",
              "..                   ...                                                ...   \n",
              "337  L3_GEZ_QUAL_NDVI_LT  Quality of Normalized Difference Vegetation In...   \n",
              "338  L3_KOG_QUAL_NDVI_LT  Quality of Normalized Difference Vegetation In...   \n",
              "339  L3_LAM_QUAL_NDVI_LT  Quality of Normalized Difference Vegetation In...   \n",
              "340  L3_ODN_QUAL_NDVI_LT  Quality of Normalized Difference Vegetation In...   \n",
              "341  L3_ZAN_QUAL_NDVI_LT  Quality of Normalized Difference Vegetation In...   \n",
              "\n",
              "                                           description  \\\n",
              "0    The annual Gross Biomass Water Productivity ex...   \n",
              "1    The annual Net Biomass Water Productivity expr...   \n",
              "2    The actual EvapoTranspiration and Interception...   \n",
              "3    The actual EvapoTranspiration and Interception...   \n",
              "4    The actual EvapoTranspiration and Interception...   \n",
              "..                                                 ...   \n",
              "337  The long-term NDVI quality layer is produced a...   \n",
              "338  The long-term NDVI quality layer is produced a...   \n",
              "339  The long-term NDVI quality layer is produced a...   \n",
              "340  The long-term NDVI quality layer is produced a...   \n",
              "341  The long-term NDVI quality layer is produced a...   \n",
              "\n",
              "                                        additionalInfo  \\\n",
              "0    {'format': 'Raster Dataset', 'unit': 'kg/m³ is...   \n",
              "1    {'format': 'Raster Dataset', 'unit': 'kg/m³ is...   \n",
              "2    {'format': 'Raster Dataset', 'unit': 'mm', 'da...   \n",
              "3    {'format': 'Raster Dataset', 'unit': 'mm', 'da...   \n",
              "4    {'format': 'Raster Dataset', 'unit': 'mm', 'da...   \n",
              "..                                                 ...   \n",
              "337  {'format': 'Raster Dataset', 'unit': '%', 'dat...   \n",
              "338  {'format': 'Raster Dataset', 'unit': '%', 'dat...   \n",
              "339  {'format': 'Raster Dataset', 'unit': '%', 'dat...   \n",
              "340  {'format': 'Raster Dataset', 'unit': '%', 'dat...   \n",
              "341  {'format': 'Raster Dataset', 'unit': '%', 'dat...   \n",
              "\n",
              "                                                  tags workspaceCode dataType  \\\n",
              "0    [{'name': 'L1', 'index': 1, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
              "1    [{'name': 'L1', 'index': 2, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
              "2    [{'name': 'L1', 'index': 3, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
              "3    [{'name': 'L1', 'index': 4, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
              "4    [{'name': 'L1', 'index': 5, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
              "..                                                 ...           ...      ...   \n",
              "337  [{'name': 'L3', 'index': 347, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
              "338  [{'name': 'L3', 'index': 349, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
              "339  [{'name': 'L3', 'index': 351, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
              "340  [{'name': 'L3', 'index': 357, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
              "341  [{'name': 'L3', 'index': 360, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
              "\n",
              "     index                                          operation  hidden  \\\n",
              "0      101  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
              "1      102  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
              "2      103  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
              "3      104  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
              "4      105  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
              "..     ...                                                ...     ...   \n",
              "337  30347       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
              "338  30349       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
              "339  30351       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
              "340  30357       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
              "341  30360       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
              "\n",
              "                                                 links  \n",
              "0    [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
              "1    [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
              "2    [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
              "3    [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
              "4    [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
              "..                                                 ...  \n",
              "337  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
              "338  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
              "339  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
              "340  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
              "341  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
              "\n",
              "[342 rows x 11 columns]"
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              "      <th>0</th>\n",
              "      <td>L1_GBWP_A</td>\n",
              "      <td>Gross Biomass Water Productivity</td>\n",
              "      <td>The annual Gross Biomass Water Productivity ex...</td>\n",
              "      <td>{'format': 'Raster Dataset', 'unit': 'kg/m³ is...</td>\n",
              "      <td>[{'name': 'L1', 'index': 1, 'context': 'LEVELS...</td>\n",
              "      <td>WAPOR_2</td>\n",
              "      <td>RASTER</td>\n",
              "      <td>101</td>\n",
              "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
              "      <td>False</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>L1_NBWP_A</td>\n",
              "      <td>Net Biomass Water Productivity</td>\n",
              "      <td>The annual Net Biomass Water Productivity expr...</td>\n",
              "      <td>{'format': 'Raster Dataset', 'unit': 'kg/m³ is...</td>\n",
              "      <td>[{'name': 'L1', 'index': 2, 'context': 'LEVELS...</td>\n",
              "      <td>WAPOR_2</td>\n",
              "      <td>RASTER</td>\n",
              "      <td>102</td>\n",
              "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
              "      <td>False</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>L1_AETI_A</td>\n",
              "      <td>Actual EvapoTranspiration and Interception (An...</td>\n",
              "      <td>The actual EvapoTranspiration and Interception...</td>\n",
              "      <td>{'format': 'Raster Dataset', 'unit': 'mm', 'da...</td>\n",
              "      <td>[{'name': 'L1', 'index': 3, 'context': 'LEVELS...</td>\n",
              "      <td>WAPOR_2</td>\n",
              "      <td>RASTER</td>\n",
              "      <td>103</td>\n",
              "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
              "      <td>False</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>L1_AETI_M</td>\n",
              "      <td>Actual EvapoTranspiration and Interception (Mo...</td>\n",
              "      <td>The actual EvapoTranspiration and Interception...</td>\n",
              "      <td>{'format': 'Raster Dataset', 'unit': 'mm', 'da...</td>\n",
              "      <td>[{'name': 'L1', 'index': 4, 'context': 'LEVELS...</td>\n",
              "      <td>WAPOR_2</td>\n",
              "      <td>RASTER</td>\n",
              "      <td>104</td>\n",
              "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
              "      <td>False</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>L1_AETI_D</td>\n",
              "      <td>Actual EvapoTranspiration and Interception (De...</td>\n",
              "      <td>The actual EvapoTranspiration and Interception...</td>\n",
              "      <td>{'format': 'Raster Dataset', 'unit': 'mm', 'da...</td>\n",
              "      <td>[{'name': 'L1', 'index': 5, 'context': 'LEVELS...</td>\n",
              "      <td>WAPOR_2</td>\n",
              "      <td>RASTER</td>\n",
              "      <td>105</td>\n",
              "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
              "      <td>False</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>337</th>\n",
              "      <td>L3_GEZ_QUAL_NDVI_LT</td>\n",
              "      <td>Quality of Normalized Difference Vegetation In...</td>\n",
              "      <td>The long-term NDVI quality layer is produced a...</td>\n",
              "      <td>{'format': 'Raster Dataset', 'unit': '%', 'dat...</td>\n",
              "      <td>[{'name': 'L3', 'index': 347, 'context': 'LEVE...</td>\n",
              "      <td>WAPOR_2</td>\n",
              "      <td>RASTER</td>\n",
              "      <td>30347</td>\n",
              "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
              "      <td>False</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>L3_KOG_QUAL_NDVI_LT</td>\n",
              "      <td>Quality of Normalized Difference Vegetation In...</td>\n",
              "      <td>The long-term NDVI quality layer is produced a...</td>\n",
              "      <td>{'format': 'Raster Dataset', 'unit': '%', 'dat...</td>\n",
              "      <td>[{'name': 'L3', 'index': 349, 'context': 'LEVE...</td>\n",
              "      <td>WAPOR_2</td>\n",
              "      <td>RASTER</td>\n",
              "      <td>30349</td>\n",
              "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
              "      <td>False</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>L3_LAM_QUAL_NDVI_LT</td>\n",
              "      <td>Quality of Normalized Difference Vegetation In...</td>\n",
              "      <td>The long-term NDVI quality layer is produced a...</td>\n",
              "      <td>{'format': 'Raster Dataset', 'unit': '%', 'dat...</td>\n",
              "      <td>[{'name': 'L3', 'index': 351, 'context': 'LEVE...</td>\n",
              "      <td>WAPOR_2</td>\n",
              "      <td>RASTER</td>\n",
              "      <td>30351</td>\n",
              "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
              "      <td>False</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>L3_ODN_QUAL_NDVI_LT</td>\n",
              "      <td>Quality of Normalized Difference Vegetation In...</td>\n",
              "      <td>The long-term NDVI quality layer is produced a...</td>\n",
              "      <td>{'format': 'Raster Dataset', 'unit': '%', 'dat...</td>\n",
              "      <td>[{'name': 'L3', 'index': 357, 'context': 'LEVE...</td>\n",
              "      <td>WAPOR_2</td>\n",
              "      <td>RASTER</td>\n",
              "      <td>30357</td>\n",
              "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
              "      <td>False</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>L3_ZAN_QUAL_NDVI_LT</td>\n",
              "      <td>Quality of Normalized Difference Vegetation In...</td>\n",
              "      <td>The long-term NDVI quality layer is produced a...</td>\n",
              "      <td>{'format': 'Raster Dataset', 'unit': '%', 'dat...</td>\n",
              "      <td>[{'name': 'L3', 'index': 360, 'context': 'LEVE...</td>\n",
              "      <td>WAPOR_2</td>\n",
              "      <td>RASTER</td>\n",
              "      <td>30360</td>\n",
              "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
              "      <td>False</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>342 rows × 11 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "resp=requests.get(cubes_url).json()\n",
        "catalog=pd.DataFrame.from_dict(resp['response'])\n",
        "catalog"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ace4f1ef",
      "metadata": {
        "id": "ace4f1ef"
      },
      "source": [
        "As you can see, there are 342 rows representing 342 data cubes in WaPOR database. To request details of a data cube, we will use the cube code in the following request"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "9b2b044a",
      "metadata": {
        "scrolled": true,
        "id": "9b2b044a",
        "outputId": "2a60b501-5ac9-42f9-d087-5afa8d9ed61b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'requestId': '56ec14be-e455-4552-ba60-6397feeacd69',\n",
              " 'timestamp': 1687808133002,\n",
              " 'runtime': 4,\n",
              " 'status': 200,\n",
              " 'message': 'OK',\n",
              " 'response': {'code': 'L2_AETI_A',\n",
              "  'caption': 'Actual EvapoTranspiration and Interception (Annual)',\n",
              "  'description': 'The actual EvapoTranspiration and Interception (ETIa) is the sum of the soil evaporation (E), canopy transpiration (T), and evaporation from rainfall intercepted by leaves (I). The value of each pixel represents the ETIa in a given year.',\n",
              "  'additionalInfo': {'format': 'Raster Dataset',\n",
              "   'unit': 'mm',\n",
              "   'dataType': 'Int32 (32bit Integer)',\n",
              "   'conversionFactor': 'the pixel value in the downloaded data must be multiplied by 0.1',\n",
              "   'noDataValue': -9999,\n",
              "   'spatialResolution': '100m (0.000992 degree)',\n",
              "   'spatialExtent': 'Africa and Near East',\n",
              "   'spatialReferenceSystem': 'EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)',\n",
              "   'temporalResolution': 'from January 2009 to present',\n",
              "   'temporalExtent': 'Annual',\n",
              "   'nearRealTime': 'New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later.',\n",
              "   'methodology': 'See ETIa by dekad for further information. The annual total is obtained by taking the ETIa in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year.\\n\\nUntil December 2019 the base input layers (NDVI, albedo, and fAPAR) for the Level 2 (100m) products were derived from the Proba-V satellite. Proba-V was decommissioned in June 2020. From January 2020 onwards the base input layers of NDVI, albedo and fAPAR for level 2 are derived from the Copernicus Sentinel-2 mission.'},\n",
              "  'tags': [{'name': 'L2', 'index': 3, 'context': 'LEVELS_LIST'},\n",
              "   {'name': 'WATER', 'index': 1, 'context': 'CATEGORIES_LIST'},\n",
              "   {'name': 'L2', 'index': 3, 'context': 'levels'},\n",
              "   {'name': 'C2', 'index': 1, 'context': 'categories'}],\n",
              "  'workspaceCode': 'WAPOR_2',\n",
              "  'dataType': 'RASTER',\n",
              "  'index': 203,\n",
              "  'operation': {'pixelTimeSeries': True, 'areaStatsTimeSeries': True},\n",
              "  'hidden': False,\n",
              "  'links': [{'rel': 'self',\n",
              "    'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/cubes/L2_AETI_A'},\n",
              "   {'rel': 'dimensions',\n",
              "    'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/cubes/L2_AETI_A/dimensions'},\n",
              "   {'rel': 'measures',\n",
              "    'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/cubes/L2_AETI_A/measures'},\n",
              "   {'rel': 'styles',\n",
              "    'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/cubes/L2_AETI_A/styles'},\n",
              "   {'rel': 'rasters',\n",
              "    'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/cubes/L2_AETI_A/rasters'}]}}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "cube_code='L2_AETI_A'\n",
        "workspace='WAPOR_2'\n",
        "cube_url=f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{cube_code}'\n",
        "resp=requests.get(cube_url).json()\n",
        "resp"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e1849cd0",
      "metadata": {
        "id": "e1849cd0"
      },
      "source": [
        "The metadata of this cube code is presentend in JSON format. For example, you can get the unit and converion factor as follows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "dc45816f",
      "metadata": {
        "id": "dc45816f",
        "outputId": "ffaaeec7-2479-4c0a-f74c-fd5f8162c634",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unit:  mm\n",
            "conversionFactor:  the pixel value in the downloaded data must be multiplied by 0.1\n"
          ]
        }
      ],
      "source": [
        "print('unit: ',resp['response']['additionalInfo']['unit'])\n",
        "print('conversionFactor: ',resp['response']['additionalInfo']['conversionFactor'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2616c387",
      "metadata": {
        "id": "2616c387"
      },
      "source": [
        "# 4. Get available data table<a name=\"datavail\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "331b552e",
      "metadata": {
        "id": "331b552e"
      },
      "source": [
        "In this section, we'll get a list of available raster data for a specific datacube given a time period using the *query* url"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "ee58c504",
      "metadata": {
        "id": "ee58c504"
      },
      "outputs": [],
      "source": [
        "path_query=r'https://io.apps.fao.org/gismgr/api/v1/query/'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cdbc7f48",
      "metadata": {
        "id": "cdbc7f48"
      },
      "source": [
        "We will need to write a query payload with these information:\n",
        "\n",
        "- Workspace code\n",
        "- Datacube code\n",
        "- Time range\n",
        "- Measure (quantity)\n",
        "- Dimension (time-step)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "39bfe242",
      "metadata": {
        "id": "39bfe242"
      },
      "outputs": [],
      "source": [
        "workspace='WAPOR_2'\n",
        "cube_code='L1_AETI_A'\n",
        "time_range=\"[2011-01-01,2014-01-01]\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f1e7cb51",
      "metadata": {
        "id": "f1e7cb51"
      },
      "source": [
        "To get *measure* (quantity) of the datacube, we will use this request"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "d94fb68f",
      "metadata": {
        "id": "d94fb68f",
        "outputId": "78dc440a-96b9-4ed4-cbfd-4d2d0b061ad7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'WATER_MM'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "#get datacube measure\n",
        "cube_url=f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{cube_code}/measures'\n",
        "resp=requests.get(cube_url).json()\n",
        "measure=resp['response']['items'][0]['code']\n",
        "measure"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ea61d121",
      "metadata": {
        "id": "ea61d121"
      },
      "source": [
        "To get *dimension* (quantity) of the datacube, we will use this request"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "a1797bab",
      "metadata": {
        "id": "a1797bab",
        "outputId": "d2cd3644-23a9-4237-96c3-866e26877c86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'YEAR'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "#get datacube time dimension\n",
        "cube_url=f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{cube_code}/dimensions'\n",
        "resp=requests.get(cube_url).json()\n",
        "items=pd.DataFrame.from_dict(resp['response']['items'])\n",
        "dimension=items[items.type=='TIME']['code'].values[0]\n",
        "dimension"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "91f894a2",
      "metadata": {
        "id": "91f894a2"
      },
      "source": [
        "Now, we will write all this information in a query payload as a python dictionary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "7015fc39",
      "metadata": {
        "id": "7015fc39"
      },
      "outputs": [],
      "source": [
        "query={\n",
        "  \"type\": \"MDAQuery_Table\", #type of query\n",
        "  \"params\": {\n",
        "    \"properties\": {\n",
        "      \"metadata\": True,\n",
        "      \"paged\": False\n",
        "    },\n",
        "    \"cube\": {\n",
        "      \"code\": cube_code,\n",
        "      \"workspaceCode\": workspace,\n",
        "      \"language\": \"en\"\n",
        "    },\n",
        "    \"dimensions\": [\n",
        "      {\n",
        "        \"code\": dimension,\n",
        "        \"range\": time_range\n",
        "      }\n",
        "    ],\n",
        "    \"measures\": [\n",
        "      measure\n",
        "    ],\n",
        "    \"projection\": {\n",
        "      \"columns\": [\n",
        "        \"MEASURES\"\n",
        "      ],\n",
        "      \"rows\": [\n",
        "        dimension\n",
        "      ]\n",
        "    }\n",
        "  }\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ff27b91d",
      "metadata": {
        "id": "ff27b91d"
      },
      "source": [
        "After that, we will use REST POST method to send this request"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "871e4a18",
      "metadata": {
        "id": "871e4a18",
        "outputId": "fc6014f4-704b-4291-ba67-b68be6ed452c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'requestId': 'b6413e66-8572-473d-b42a-370cb08376f4',\n",
              " 'timestamp': 1687808133267,\n",
              " 'runtime': 11,\n",
              " 'status': 200,\n",
              " 'message': 'OK',\n",
              " 'response': {'properties': {'flat': True,\n",
              "   'metadata': True,\n",
              "   'showEmptyRows': False,\n",
              "   'paged': False,\n",
              "   'pageSize': 20,\n",
              "   'page': 0},\n",
              "  'header': {'height': 1,\n",
              "   'rows': [[{'value': 'Year',\n",
              "      'type': 'ROW_COLUMN_HEADER',\n",
              "      'width': 1,\n",
              "      'height': 1},\n",
              "     {'value': 'Amount of Water',\n",
              "      'type': 'COLUMN_HEADER',\n",
              "      'width': 1,\n",
              "      'height': 1}]]},\n",
              "  'items': [[{'value': '2011', 'type': 'ROW_HEADER', 'width': 1, 'height': 1},\n",
              "    {'type': 'DATA_CELL',\n",
              "     'value': 1,\n",
              "     'metadata': {'raster': {'id': 'L1_AETI_11',\n",
              "       'bbox': [{'srid': 'EPSG:4326',\n",
              "         'value': [-30.0044643, -40.0044644, 65.0044644, 40.0044643]}]}}}],\n",
              "   [{'value': '2012', 'type': 'ROW_HEADER', 'width': 1, 'height': 1},\n",
              "    {'type': 'DATA_CELL',\n",
              "     'value': 1,\n",
              "     'metadata': {'raster': {'id': 'L1_AETI_12',\n",
              "       'bbox': [{'srid': 'EPSG:4326',\n",
              "         'value': [-30.0044643, -40.0044644, 65.0044644, 40.0044643]}]}}}],\n",
              "   [{'value': '2013', 'type': 'ROW_HEADER', 'width': 1, 'height': 1},\n",
              "    {'type': 'DATA_CELL',\n",
              "     'value': 1,\n",
              "     'metadata': {'raster': {'id': 'L1_AETI_13',\n",
              "       'bbox': [{'srid': 'EPSG:4326',\n",
              "         'value': [-30.0044643, -40.0044644, 65.0044644, 40.0044643]}]}}}],\n",
              "   [{'value': '2014', 'type': 'ROW_HEADER', 'width': 1, 'height': 1},\n",
              "    {'type': 'DATA_CELL',\n",
              "     'value': 1,\n",
              "     'metadata': {'raster': {'id': 'L1_AETI_14',\n",
              "       'bbox': [{'srid': 'EPSG:4326',\n",
              "         'value': [-30.0044643, -40.0044644, 65.0044644, 40.0044643]}]}}}]]}}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "resp=requests.post(path_query,json=query)\n",
        "resp_dict = resp.json() #read response JSON string\n",
        "resp_dict"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b7102f35",
      "metadata": {
        "id": "b7102f35"
      },
      "source": [
        "Again, the response is in JSON format. We will convert it to DataFrame for readability"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "7e1aac2c",
      "metadata": {
        "id": "7e1aac2c",
        "outputId": "ec384bef-ecee-4865-f0f1-27d95893d82b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   YEAR    rasterId                                               bbox\n",
              "0  2011  L1_AETI_11  [{'srid': 'EPSG:4326', 'value': [-30.0044643, ...\n",
              "1  2012  L1_AETI_12  [{'srid': 'EPSG:4326', 'value': [-30.0044643, ...\n",
              "2  2013  L1_AETI_13  [{'srid': 'EPSG:4326', 'value': [-30.0044643, ...\n",
              "3  2014  L1_AETI_14  [{'srid': 'EPSG:4326', 'value': [-30.0044643, ..."
            ],
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              "      <th></th>\n",
              "      <th>YEAR</th>\n",
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              "      <th>0</th>\n",
              "      <td>2011</td>\n",
              "      <td>L1_AETI_11</td>\n",
              "      <td>[{'srid': 'EPSG:4326', 'value': [-30.0044643, ...</td>\n",
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              "      <td>2012</td>\n",
              "      <td>L1_AETI_12</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013</td>\n",
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              "      <th>3</th>\n",
              "      <td>2014</td>\n",
              "      <td>L1_AETI_14</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "items=resp_dict['response']['items']\n",
        "items[0] #get first item\n",
        "values=[item[0]['value'] for item in items]\n",
        "rasterId=[item[1]['metadata']['raster']['id'] for item in items]\n",
        "bbox=[item[1]['metadata']['raster']['bbox'] for item in items]\n",
        "\n",
        "df=pd.DataFrame({dimension:values,\n",
        "                'rasterId':rasterId,\n",
        "                'bbox':bbox})\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "573eafb0",
      "metadata": {
        "id": "573eafb0"
      },
      "source": [
        "# 5. Get point timeseries<a name=\"point\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5dafc4f8",
      "metadata": {
        "id": "5dafc4f8"
      },
      "source": [
        "In this section, we will request an average time-series of a WaPOR data layer for a point (coordinates). We will use the same *query* url but different query type *PixelTimeSeries*.\n",
        "\n",
        "Similar to the previous section, we will write a query payload with information on:\n",
        "- Coordinates of the point\n",
        "- Coordinate reference system\n",
        "- Workspace code\n",
        "- Datacube code\n",
        "- Time period\n",
        "- Measure\n",
        "- Dimension"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "381c2bb0",
      "metadata": {
        "id": "381c2bb0",
        "outputId": "4d083181-d158-4e9c-c258-7412f38d6dac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MEASURE:  WATER_MM\n",
            "DIMENSION:  DAY\n"
          ]
        }
      ],
      "source": [
        "point= [37.95883206252312, 7.89534] #coordinates of a point\n",
        "crs=\"EPSG:4326\" #coordinate reference system\n",
        "cube_code=\"L1_PCP_E\" #Daily precipitation\n",
        "workspace='WAPOR_2'\n",
        "start_date=\"2009-01-01\"\n",
        "end_date=\"2019-01-01\"\n",
        "\n",
        "#get datacube measure\n",
        "cube_url=f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{cube_code}/measures'\n",
        "resp=requests.get(cube_url).json()\n",
        "measure=resp['response']['items'][0]['code']\n",
        "print('MEASURE: ',measure)\n",
        "\n",
        "#get datacube time dimension\n",
        "cube_url=f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{cube_code}/dimensions'\n",
        "resp=requests.get(cube_url).json()\n",
        "items=pd.DataFrame.from_dict(resp['response']['items'])\n",
        "dimension=items[items.type=='TIME']['code'].values[0]\n",
        "print('DIMENSION: ',dimension)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bc083c47",
      "metadata": {
        "id": "bc083c47"
      },
      "source": [
        "Now we will write these parameters in a query payload:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "0f07ed43",
      "metadata": {
        "id": "0f07ed43",
        "outputId": "45f7cbc0-0fcb-45ae-fa48-5dc8499137e1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'type': 'PixelTimeSeries',\n",
              " 'params': {'cube': {'code': 'L1_PCP_E',\n",
              "   'workspaceCode': 'WAPOR_2',\n",
              "   'language': 'en'},\n",
              "  'dimensions': [{'code': 'DAY', 'range': '[2009-01-01,2019-01-01)'}],\n",
              "  'measures': ['WATER_MM'],\n",
              "  'point': {'crs': 'EPSG:4326', 'x': 37.95883206252312, 'y': 7.89534}}}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "query_pixeltimeseries={\n",
        "  \"type\": \"PixelTimeSeries\",\n",
        "  \"params\": {\n",
        "    \"cube\": {\n",
        "      \"code\": cube_code,\n",
        "      \"workspaceCode\": workspace,\n",
        "      \"language\": \"en\"\n",
        "    },\n",
        "    \"dimensions\": [\n",
        "      {\n",
        "        \"code\": dimension,\n",
        "        \"range\": f\"[{start_date},{end_date})\"\n",
        "      }\n",
        "    ],\n",
        "    \"measures\": [\n",
        "      measure\n",
        "    ],\n",
        "    \"point\": {\n",
        "      \"crs\": crs, #latlon projection\n",
        "      \"x\":point[0],\n",
        "        \"y\":point[1]\n",
        "    }\n",
        "  }\n",
        "}\n",
        "\n",
        "query_pixeltimeseries"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b4e29e36",
      "metadata": {
        "id": "b4e29e36"
      },
      "source": [
        "Now, we can send this query payload using POST method. This step might take some time for the server to respond."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "5a68e65d",
      "metadata": {
        "id": "5a68e65d",
        "outputId": "6aa369a1-d5d4-49ea-9bf1-06810c5e4efa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'requestId': '6cb22b12-3226-48fa-9171-4615a937b088',\n",
              " 'timestamp': 1687808385181,\n",
              " 'runtime': 251644,\n",
              " 'status': 200,\n",
              " 'message': 'OK',\n",
              " 'response': {'header': ['day', 'value'],\n",
              "  'items': [['2009-01-01', 0.0],\n",
              "   ['2009-01-02', 0.0],\n",
              "   ['2009-01-03', 0.0],\n",
              "   ['2009-01-04', 0.0],\n",
              "   ['2009-01-05', 0.0],\n",
              "   ['2009-01-06', 0.0],\n",
              "   ['2009-01-07', 0.0],\n",
              "   ['2009-01-08', 0.0],\n",
              "   ['2009-01-09', 0.0],\n",
              "   ['2009-01-10', 0.0],\n",
              "   ['2009-01-11', 0.0],\n",
              "   ['2009-01-12', 0.0],\n",
              "   ['2009-01-13', 0.0],\n",
              "   ['2009-01-14', 0.0],\n",
              "   ['2009-01-15', 0.0],\n",
              "   ['2009-01-16', 0.0],\n",
              "   ['2009-01-17', 0.0],\n",
              "   ['2009-01-18', 0.0],\n",
              "   ['2009-01-19', 0.0],\n",
              "   ['2009-01-20', 0.0],\n",
              "   ['2009-01-21', 0.0],\n",
              "   ['2009-01-22', 0.0],\n",
              "   ['2009-01-23', 24.0],\n",
              "   ['2009-01-24', 12.0],\n",
              "   ['2009-01-25', 24.0],\n",
              "   ['2009-01-26', 0.0],\n",
              "   ['2009-01-27', 0.0],\n",
              "   ['2009-01-28', 0.0],\n",
              "   ['2009-01-29', 0.0],\n",
              "   ['2009-01-30', 0.0],\n",
              "   ['2009-01-31', 0.0],\n",
              "   ['2009-02-01', 0.0],\n",
              "   ['2009-02-02', 0.0],\n",
              "   ['2009-02-03', 0.0],\n",
              "   ['2009-02-04', 0.0],\n",
              "   ['2009-02-05', 0.0],\n",
              "   ['2009-02-06', 0.0],\n",
              "   ['2009-02-07', 5.1000000000000005],\n",
              "   ['2009-02-08', 0.0],\n",
              "   ['2009-02-09', 0.0],\n",
              "   ['2009-02-10', 0.0],\n",
              "   ['2009-02-11', 0.0],\n",
              "   ['2009-02-12', 6.800000000000001],\n",
              "   ['2009-02-13', 0.0],\n",
              "   ['2009-02-14', 0.0],\n",
              "   ['2009-02-15', 0.0],\n",
              "   ['2009-02-16', 8.6],\n",
              "   ['2009-02-17', 0.0],\n",
              "   ['2009-02-18', 0.0],\n",
              "   ['2009-02-19', 0.0],\n",
              "   ['2009-02-20', 0.0],\n",
              "   ['2009-02-21', 0.0],\n",
              "   ['2009-02-22', 0.0],\n",
              "   ['2009-02-23', 0.0],\n",
              "   ['2009-02-24', 0.0],\n",
              "   ['2009-02-25', 0.0],\n",
              "   ['2009-02-26', 0.0],\n",
              "   ['2009-02-27', 0.0],\n",
              "   ['2009-02-28', 0.0],\n",
              "   ['2009-03-01', 0.0],\n",
              "   ['2009-03-02', 0.0],\n",
              "   ['2009-03-03', 0.0],\n",
              "   ['2009-03-04', 0.0],\n",
              "   ['2009-03-05', 0.0],\n",
              "   ['2009-03-06', 0.0],\n",
              "   ['2009-03-07', 0.0],\n",
              "   ['2009-03-08', 0.0],\n",
              "   ['2009-03-09', 0.0],\n",
              "   ['2009-03-10', 0.0],\n",
              "   ['2009-03-11', 0.0],\n",
              "   ['2009-03-12', 0.0],\n",
              "   ['2009-03-13', 0.0],\n",
              "   ['2009-03-14', 0.0],\n",
              "   ['2009-03-15', 0.0],\n",
              "   ['2009-03-16', 0.0],\n",
              "   ['2009-03-17', 0.0],\n",
              "   ['2009-03-18', 0.0],\n",
              "   ['2009-03-19', 0.0],\n",
              "   ['2009-03-20', 0.0],\n",
              "   ['2009-03-21', 0.0],\n",
              "   ['2009-03-22', 0.0],\n",
              "   ['2009-03-23', 0.0],\n",
              "   ['2009-03-24', 0.0],\n",
              "   ['2009-03-25', 0.0],\n",
              "   ['2009-03-26', 0.0],\n",
              "   ['2009-03-27', 0.0],\n",
              "   ['2009-03-28', 42.0],\n",
              "   ['2009-03-29', 0.0],\n",
              "   ['2009-03-30', 42.0],\n",
              "   ['2009-03-31', 0.0],\n",
              "   ['2009-04-01', 0.0],\n",
              "   ['2009-04-02', 0.0],\n",
              "   ['2009-04-03', 0.0],\n",
              "   ['2009-04-04', 0.0],\n",
              "   ['2009-04-05', 0.0],\n",
              "   ['2009-04-06', 0.0],\n",
              "   ['2009-04-07', 0.0],\n",
              "   ['2009-04-08', 11.3],\n",
              "   ['2009-04-09', 22.5],\n",
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              "   ['2011-09-20', 0.0],\n",
              "   ['2011-09-21', 0.0],\n",
              "   ['2011-09-22', 0.0],\n",
              "   ['2011-09-23', 0.0],\n",
              "   ['2011-09-24', 16.400000000000002],\n",
              "   ['2011-09-25', 16.400000000000002],\n",
              "   ['2011-09-26', 0.0],\n",
              "   ['2011-09-27', 0.0],\n",
              "   ...],\n",
              "  'stats': {'count': 3652,\n",
              "   'min': 0.0,\n",
              "   'max': 80.5,\n",
              "   'range': 80.5,\n",
              "   'sum': 12425.000000000011,\n",
              "   'mean': 3.4022453450164325,\n",
              "   'std': 8.493821631711386}}}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "resp_query=requests.post(path_query,json=query_pixeltimeseries)\n",
        "resp=resp_query.json()\n",
        "resp"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2f982c1a",
      "metadata": {
        "id": "2f982c1a"
      },
      "source": [
        "We will convert the response to DataFrame format and visualize it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "d6ea0023",
      "metadata": {
        "id": "d6ea0023",
        "outputId": "f8251dda-4d2c-40fc-c591-1b89f963a62b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'L1_PCP_E')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "results=resp['response']\n",
        "df=pd.DataFrame(results['items'],columns=results['header'])\n",
        "df.index=pd.to_datetime(df.day,format='%Y-%m-%d') #conver 'day' column from string to datetime format\n",
        "ax=df.plot() #plot time-series\n",
        "ax.set_ylabel(measure) #y axis label\n",
        "ax.set_title(cube_code) #title"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ec6145e9",
      "metadata": {
        "id": "ec6145e9"
      },
      "source": [
        "To make the graph more meaningful, change the title to \"Daily Precipitation\" and y axis lable to \"Precipitation (mm/day)\"."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f06215f",
      "metadata": {
        "id": "3f06215f"
      },
      "source": [
        "**Optional:**\n",
        "\n",
        "For some applications, you might have multiple points. You can use a loop in python to request their timeseries in one go.\n",
        "\n",
        "First, define a list of points."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "287ce246",
      "metadata": {
        "id": "287ce246"
      },
      "outputs": [],
      "source": [
        "points=[\n",
        "        [37.95883206252312, 7.89534],\n",
        "       [37.45883206252312, 7.39534],\n",
        "       [37.45883206252312, 8.39534]\n",
        "        ] #list of point coordinates (X,Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6e4a38ee",
      "metadata": {
        "id": "6e4a38ee"
      },
      "source": [
        "Use a FOR loop to send requests for each point."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "e8fa0d37",
      "metadata": {
        "id": "e8fa0d37",
        "outputId": "20a0b1df-6621-43b9-d5a6-d33d3d7be86b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Date  Lat: 7.89534, Lon: 37.95883206252312  \\\n",
              "0     2009-01-01                                   0.0   \n",
              "1     2009-01-02                                   0.0   \n",
              "2     2009-01-03                                   0.0   \n",
              "3     2009-01-04                                   0.0   \n",
              "4     2009-01-05                                   0.0   \n",
              "...          ...                                   ...   \n",
              "3647  2018-12-27                                   3.8   \n",
              "3648  2018-12-28                                   0.0   \n",
              "3649  2018-12-29                                   0.0   \n",
              "3650  2018-12-30                                   0.0   \n",
              "3651  2018-12-31                                   0.0   \n",
              "\n",
              "      Lat: 7.39534, Lon: 37.45883206252312  \\\n",
              "0                                      0.0   \n",
              "1                                      0.0   \n",
              "2                                      0.0   \n",
              "3                                      0.0   \n",
              "4                                      0.0   \n",
              "...                                    ...   \n",
              "3647                                   0.0   \n",
              "3648                                   0.0   \n",
              "3649                                   0.0   \n",
              "3650                                   0.0   \n",
              "3651                                   0.0   \n",
              "\n",
              "      Lat: 8.39534, Lon: 37.45883206252312  \n",
              "0                                      0.0  \n",
              "1                                      0.0  \n",
              "2                                      0.0  \n",
              "3                                      0.0  \n",
              "4                                      0.0  \n",
              "...                                    ...  \n",
              "3647                                   2.6  \n",
              "3648                                   0.0  \n",
              "3649                                   0.0  \n",
              "3650                                   0.0  \n",
              "3651                                   0.0  \n",
              "\n",
              "[3652 rows x 4 columns]"
            ],
            "text/html": [
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              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Lat: 7.89534, Lon: 37.95883206252312</th>\n",
              "      <th>Lat: 7.39534, Lon: 37.45883206252312</th>\n",
              "      <th>Lat: 8.39534, Lon: 37.45883206252312</th>\n",
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              "      <th>0</th>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-03</td>\n",
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              "      <th>3647</th>\n",
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              "      <td>2.6</td>\n",
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              "    <tr>\n",
              "      <th>3648</th>\n",
              "      <td>2018-12-28</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>3650</th>\n",
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              "    <tr>\n",
              "      <th>3651</th>\n",
              "      <td>2018-12-31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "</table>\n",
              "<p>3652 rows × 4 columns</p>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-f8d244f5-8162-4355-9478-2a307a950750 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f8d244f5-8162-4355-9478-2a307a950750');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "for i,point in enumerate(points):\n",
        "    query_pixeltimeseries={\n",
        "      \"type\": \"PixelTimeSeries\",\n",
        "      \"params\": {\n",
        "        \"cube\": {\n",
        "          \"code\": cube_code,\n",
        "          \"workspaceCode\": workspace,\n",
        "          \"language\": \"en\"\n",
        "        },\n",
        "        \"dimensions\": [\n",
        "          {\n",
        "            \"code\": dimension,\n",
        "            \"range\": \"[2009-01-01,2019-01-01)\"\n",
        "          }\n",
        "        ],\n",
        "        \"measures\": [\n",
        "          measure\n",
        "        ],\n",
        "        \"point\": {\n",
        "          \"crs\": crs, #latlon projection\n",
        "          \"x\":point[0],\n",
        "            \"y\":point[1]\n",
        "        }\n",
        "      }\n",
        "    }\n",
        "\n",
        "    resp_query=requests.post(path_query,json=query_pixeltimeseries)\n",
        "    resp_vp=resp_query.json()\n",
        "    if resp_vp['message']=='OK':\n",
        "        results=resp_vp['response']\n",
        "        df=pd.DataFrame(results['items'],columns=['Date','Lat: {0}, Lon: {1}'.format(point[1],point[0])])\n",
        "        if i==0:\n",
        "            df_all=df\n",
        "        else:\n",
        "            df_all=pd.merge(df_all,df)\n",
        "    else:\n",
        "        print(resp_vp['message'])\n",
        "\n",
        "df_all"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b24cf61",
      "metadata": {
        "id": "5b24cf61"
      },
      "source": [
        "Now, we can plot the timeseries of multiple points at once"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "db6be6e8",
      "metadata": {
        "id": "db6be6e8",
        "outputId": "8018e7f1-5e8d-4c64-f68f-be09cdcf9fd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df_all.index=pd.to_datetime(df_all.Date,format='%Y-%m-%d') #conver 'day' column from string to datetime format\n",
        "df_all.plot() #plot time-series of all points"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a81653fb",
      "metadata": {
        "id": "a81653fb"
      },
      "source": [
        "# 6. Get areal statistics timeseries<a name=\"area\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ebbcb4a6",
      "metadata": {
        "id": "ebbcb4a6"
      },
      "source": [
        "We can also request timeseries of an area of interest (polygon) using the *query* url. For this type of query, you will need to authorize your access using the API token acquired before."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4db0914e",
      "metadata": {
        "id": "4db0914e"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "\n",
        "path_query=r'https://io.apps.fao.org/gismgr/api/v1/query/'\n",
        "path_sign_in=r'https://io.apps.fao.org/gismgr/api/v1/iam/sign-in/'\n",
        "\n",
        "APIToken=input('Your API token: ')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0813f0c0",
      "metadata": {
        "id": "0813f0c0"
      },
      "source": [
        "We will use our API token to authorize our access. After this step, we will have an access token, which is needed for our query payload."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "60ae3977",
      "metadata": {
        "id": "60ae3977",
        "outputId": "d73e95cb-0c17-4a10-d349-291dd05f9d85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'eyJhbGciOiJSUzI1NiIsImtpZCI6IjhkMDNhZTdmNDczZjJjNmIyNTI3NmMwNjM2MGViOTk4ODdlMjNhYTkiLCJ0eXAiOiJKV1QifQ.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.xoq_vMJRVmsN3j7jLulh1j8I9SvN6qHU5UUENym_vwJBXwPdvCjAhFz31oMXxKcyfEcNZ-fFHcDCE8QpDlKUY9PAOcdYR6SG3PMAQEWezTNB0chQwx3r7QlIyMIlHnSZbjuCbHbPlql8sN9j0r4WcySnTPX7DvDftE6IoXlyH7yVcZtRBuozIhDVnmQ-b6SSJt6EVTQNELyV1n3-3NuwyWDLhNVNN918m7zDwHNKXJ6TWQfpYqd3iG8yEA-Vucx2j6NDlAOTiXACec1N-7stAMj4AhjBgnRHNaBHzmcDMiH9Jif51ZPcKadYMn80zv2T5LlOSz97VW6AVlNgS4vRkQ'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "resp_signin=requests.post(path_sign_in,headers={'X-GISMGR-API-KEY':APIToken})\n",
        "resp_signin = resp_signin.json()\n",
        "AccessToken=resp_signin['response']['accessToken']\n",
        "AccessToken"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "31a1cd32",
      "metadata": {
        "id": "31a1cd32"
      },
      "source": [
        "Similar to previous sections, we will write a query payload with these information:\n",
        "- Workspace code\n",
        "- Datacube code\n",
        "- Time period\n",
        "- Measure\n",
        "- Dimention\n",
        "- Polygon coordinates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "4f9555e1",
      "metadata": {
        "id": "4f9555e1",
        "outputId": "00bf6cfc-6a04-46be-e9a2-a227b2ef8c4c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MEASURE:  WATER_MM\n",
            "DIMENSION:  DAY\n"
          ]
        }
      ],
      "source": [
        "crs=\"EPSG:4326\" #coordinate reference system\n",
        "cube_code=\"L1_PCP_E\"\n",
        "workspace='WAPOR_2'\n",
        "start_date=\"2009-01-01\"\n",
        "end_date=\"2019-01-01\"\n",
        "\n",
        "#get datacube measure\n",
        "cube_url=f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{cube_code}/measures'\n",
        "resp=requests.get(cube_url).json()\n",
        "measure=resp['response']['items'][0]['code']\n",
        "print('MEASURE: ',measure)\n",
        "\n",
        "#get datacube time dimension\n",
        "cube_url=f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{cube_code}/dimensions'\n",
        "resp=requests.get(cube_url).json()\n",
        "items=pd.DataFrame.from_dict(resp['response']['items'])\n",
        "dimension=items[items.type=='TIME']['code'].values[0]\n",
        "print('DIMENSION: ',dimension)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9bd5f4c7",
      "metadata": {
        "id": "9bd5f4c7"
      },
      "source": [
        "Now, we will define our area of interest. You can do this either by defining the coordinates of a bounding box or reading a shapefile.\n",
        "\n",
        "If you want to use a bounding box, use the code below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "fe4695d5",
      "metadata": {
        "id": "fe4695d5",
        "outputId": "f81e3ea4-035c-4d41-da89-9358dcc8da40",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'type': 'AreaStatsTimeSeries',\n",
              " 'params': {'cube': {'code': 'L1_PCP_E',\n",
              "   'workspaceCode': 'WAPOR_2',\n",
              "   'language': 'en'},\n",
              "  'dimensions': [{'code': 'DAY', 'range': '[2009-01-01,2019-01-01)'}],\n",
              "  'measures': ['WATER_MM'],\n",
              "  'shape': {'type': 'Polygon',\n",
              "   'properties': {'name': 'EPSG:4326'},\n",
              "   'coordinates': [[[37.95883206252312, 7.89534],\n",
              "     [37.95883206252312, 12.3873979377346],\n",
              "     [43.32093, 12.3873979377346],\n",
              "     [43.32093, 7.89534],\n",
              "     [37.95883206252312, 7.89534]]]}}}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "bbox= [37.95883206252312, 7.89534, 43.32093, 12.3873979377346] #latlon\n",
        "xmin,ymin,xmax,ymax=bbox[0],bbox[1],bbox[2],bbox[3]\n",
        "Polygon=[\n",
        "          [xmin,ymin],\n",
        "          [xmin,ymax],\n",
        "          [xmax,ymax],\n",
        "          [xmax,ymin],\n",
        "          [xmin,ymin]\n",
        "        ]\n",
        "query_areatimeseries={\n",
        "  \"type\": \"AreaStatsTimeSeries\",\n",
        "  \"params\": {\n",
        "    \"cube\": {\n",
        "      \"code\": cube_code, #cube_code\n",
        "      \"workspaceCode\": workspace, #workspace code: use WAPOR for v1.0 and WAPOR_2 for v2.1\n",
        "      \"language\": \"en\"\n",
        "    },\n",
        "    \"dimensions\": [\n",
        "      {\n",
        "        \"code\": dimension, #use DAY DEKAD MONTH or YEAR\n",
        "        \"range\": f\"[{start_date},{end_date})\" #start date and endate\n",
        "      }\n",
        "    ],\n",
        "    \"measures\": [\n",
        "      measure\n",
        "    ],\n",
        "    \"shape\": {\n",
        "      \"type\": \"Polygon\",\n",
        "      \"properties\": {\n",
        "      \"name\": crs #coordinate reference system\n",
        "              },\n",
        "      \"coordinates\": [\n",
        "       Polygon\n",
        "      ]\n",
        "    }\n",
        "  }\n",
        "}\n",
        "\n",
        "query_areatimeseries"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9aa4f54c",
      "metadata": {
        "id": "9aa4f54c"
      },
      "source": [
        "Now, we will use POST method to request the timeseries for our area of interest."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "b3472277",
      "metadata": {
        "id": "b3472277",
        "outputId": "6e7eceb9-a842-46a4-dcd6-1fa6b155b3a7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'requestId': '69718885-1e11-43a8-ab6d-aa502c931d99',\n",
              " 'timestamp': 1687808612078,\n",
              " 'runtime': 17,\n",
              " 'status': 200,\n",
              " 'message': 'OK',\n",
              " 'response': {'workspaceCode': 'WAPOR_2',\n",
              "  'code': '69718885-1e11-43a8-ab6d-aa502c931d99',\n",
              "  'type': 'AREA STATS',\n",
              "  'priority': 20,\n",
              "  'additionalInfo': {'cube': {'code': 'L1_PCP_E',\n",
              "    'workspaceCode': 'WAPOR_2',\n",
              "    'language': 'en'},\n",
              "   'dimensions': [{'code': 'DAY', 'range': '[2009-01-01,2019-01-01)'}],\n",
              "   'measures': ['WATER_MM'],\n",
              "   'shape': {'type': 'Polygon',\n",
              "    'properties': {'name': 'EPSG:4326'},\n",
              "    'coordinates': [[[37.95883206252312, 7.89534],\n",
              "      [37.95883206252312, 12.3873979377346],\n",
              "      [43.32093, 12.3873979377346],\n",
              "      [43.32093, 7.89534],\n",
              "      [37.95883206252312, 7.89534]]]}},\n",
              "  'userId': 'R6NHnGyt4TOzu3mvOkmxM8RLE2u1',\n",
              "  'created': '2023-06-26 19:43:32',\n",
              "  'status': 'WAITING',\n",
              "  'links': [{'rel': 'self',\n",
              "    'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/jobs/69718885-1e11-43a8-ab6d-aa502c931d99'}]}}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "resp_query=requests.post(path_query,headers={'Authorization':'Bearer {0}'.format(AccessToken)},\n",
        "                                          json=query_areatimeseries)\n",
        "resp_query = resp_query.json()\n",
        "resp_query"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8817f818",
      "metadata": {
        "id": "8817f818"
      },
      "source": [
        "Unlike PointTimeSeries query, the AreaStatsTimeSeries query will not respond with timeseries data directly, but it will provide an url of the request (job)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "af53e444",
      "metadata": {
        "id": "af53e444",
        "outputId": "05c1f2d3-d60c-41fe-9f65-55d42d14c92d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/jobs/69718885-1e11-43a8-ab6d-aa502c931d99'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "job_url=resp_query['response']['links'][0]['href']\n",
        "\n",
        "job_url"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "db435780",
      "metadata": {
        "id": "db435780"
      },
      "source": [
        "We use this url in a GET request to get the results of the areal statistics computation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "42c03b07",
      "metadata": {
        "id": "42c03b07",
        "outputId": "a8ac1365-0bf1-4af0-90e1-87deaedd10f4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'requestId': 'ada30dd3-c228-44bc-a6a4-2c13e23530e5',\n",
              " 'timestamp': 1687808612163,\n",
              " 'runtime': 3,\n",
              " 'status': 200,\n",
              " 'message': 'OK',\n",
              " 'response': {'workspaceCode': 'WAPOR_2',\n",
              "  'code': '69718885-1e11-43a8-ab6d-aa502c931d99',\n",
              "  'type': 'AREA STATS',\n",
              "  'priority': 20,\n",
              "  'additionalInfo': {'cube': {'code': 'L1_PCP_E',\n",
              "    'workspaceCode': 'WAPOR_2',\n",
              "    'language': 'en'},\n",
              "   'dimensions': [{'code': 'DAY', 'range': '[2009-01-01,2019-01-01)'}],\n",
              "   'measures': ['WATER_MM'],\n",
              "   'shape': {'type': 'Polygon',\n",
              "    'properties': {'name': 'EPSG:4326'},\n",
              "    'coordinates': [[[37.95883206252312, 7.89534],\n",
              "      [37.95883206252312, 12.3873979377346],\n",
              "      [43.32093, 12.3873979377346],\n",
              "      [43.32093, 7.89534],\n",
              "      [37.95883206252312, 7.89534]]]}},\n",
              "  'userId': 'R6NHnGyt4TOzu3mvOkmxM8RLE2u1',\n",
              "  'created': '2023-06-26 19:43:32',\n",
              "  'status': 'WAITING',\n",
              "  'links': [{'rel': 'self',\n",
              "    'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/jobs/69718885-1e11-43a8-ab6d-aa502c931d99'}]}}"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "resp = requests.get(job_url)\n",
        "resp=resp.json()\n",
        "resp"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "64ec4b00",
      "metadata": {
        "id": "64ec4b00"
      },
      "source": [
        "It will take some time for the calculation to be finished. When it is finished, the response status will be changed from 'RUNNING' to 'COMPLETED' or 'COMPLETED WITH ERRORS'. If it is COMPLETED, the area time series results can be achieved from Response 'output'.\n",
        "\n",
        "We can use a WHILE loop to check when the *\"job\"* is done."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "576471fe",
      "metadata": {
        "id": "576471fe",
        "outputId": "2c6f45f5-e0e4-468b-91c5-d5c4f3594ded",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RUNNING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             day       avg  min  max  range\n",
              "0     2009-01-01  0.000157  0.0  0.3    0.3\n",
              "1     2009-01-02  0.000251  0.0  0.5    0.5\n",
              "2     2009-01-03  0.000292  0.0  0.6    0.6\n",
              "3     2009-01-04  0.000000  0.0  0.0    0.0\n",
              "4     2009-01-05  0.000616  0.0  0.3    0.3\n",
              "...          ...       ...  ...  ...    ...\n",
              "3647  2018-12-27  0.145076  0.0  6.5    6.5\n",
              "3648  2018-12-28  0.425117  0.0  7.2    7.2\n",
              "3649  2018-12-29  0.112115  0.0  6.4    6.4\n",
              "3650  2018-12-30  0.105399  0.0  6.4    6.4\n",
              "3651  2018-12-31  0.345734  0.0  9.6    9.6\n",
              "\n",
              "[3652 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ffe4f2ff-aad4-4029-8d5a-c287948aa499\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>day</th>\n",
              "      <th>avg</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-01</td>\n",
              "      <td>0.000157</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>0.000251</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-03</td>\n",
              "      <td>0.000292</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-05</td>\n",
              "      <td>0.000616</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3647</th>\n",
              "      <td>2018-12-27</td>\n",
              "      <td>0.145076</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.5</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3648</th>\n",
              "      <td>2018-12-28</td>\n",
              "      <td>0.425117</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.2</td>\n",
              "      <td>7.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3649</th>\n",
              "      <td>2018-12-29</td>\n",
              "      <td>0.112115</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.4</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3650</th>\n",
              "      <td>2018-12-30</td>\n",
              "      <td>0.105399</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.4</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3651</th>\n",
              "      <td>2018-12-31</td>\n",
              "      <td>0.345734</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.6</td>\n",
              "      <td>9.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3652 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ffe4f2ff-aad4-4029-8d5a-c287948aa499')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ffe4f2ff-aad4-4029-8d5a-c287948aa499 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ffe4f2ff-aad4-4029-8d5a-c287948aa499');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "i=0\n",
        "print('RUNNING',end=\" \")\n",
        "while i==0:\n",
        "    resp = requests.get(job_url)\n",
        "    resp=resp.json()\n",
        "\n",
        "    if resp['response']['status']=='RUNNING':\n",
        "        print('.',end =\" \")\n",
        "    if resp['response']['status']=='COMPLETED':\n",
        "        results=resp['response']['output']\n",
        "        df=pd.DataFrame(results['items'],columns=results['header'])\n",
        "        i=1\n",
        "    if resp['response']['status']=='COMPLETED WITH ERRORS':\n",
        "        print(resp['response']['log'])\n",
        "        i=1\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dcacfcf6",
      "metadata": {
        "id": "dcacfcf6"
      },
      "source": [
        "The output includes average, minimum, maximum, and range of all the pixels within the defined area."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "f15d5600",
      "metadata": {
        "id": "f15d5600",
        "outputId": "8826f627-92a2-4c72-a8c5-1f6e09710c06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='day'>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.index=pd.to_datetime(df.day,format='%Y-%m-%d')\n",
        "df.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "59f0f293",
      "metadata": {
        "id": "59f0f293"
      },
      "source": [
        "# 7. Get cropped raster<a name=\"raster\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4f2379e4",
      "metadata": {
        "id": "4f2379e4"
      },
      "source": [
        "With the REST API, we can also request the server to crop a raster layer to our area of interest. Similar to the previous type of query, we will also need to authorize our access and have an access token.\n",
        "\n",
        "If you have run [Section 6](#area), you should have an access token ready."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "955d01cf",
      "metadata": {
        "id": "955d01cf",
        "outputId": "bc561e98-0f6f-461a-e3fa-a5cf1db2027e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'eyJhbGciOiJSUzI1NiIsImtpZCI6IjhkMDNhZTdmNDczZjJjNmIyNTI3NmMwNjM2MGViOTk4ODdlMjNhYTkiLCJ0eXAiOiJKV1QifQ.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.xoq_vMJRVmsN3j7jLulh1j8I9SvN6qHU5UUENym_vwJBXwPdvCjAhFz31oMXxKcyfEcNZ-fFHcDCE8QpDlKUY9PAOcdYR6SG3PMAQEWezTNB0chQwx3r7QlIyMIlHnSZbjuCbHbPlql8sN9j0r4WcySnTPX7DvDftE6IoXlyH7yVcZtRBuozIhDVnmQ-b6SSJt6EVTQNELyV1n3-3NuwyWDLhNVNN918m7zDwHNKXJ6TWQfpYqd3iG8yEA-Vucx2j6NDlAOTiXACec1N-7stAMj4AhjBgnRHNaBHzmcDMiH9Jif51ZPcKadYMn80zv2T5LlOSz97VW6AVlNgS4vRkQ'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "AccessToken"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c03186c9",
      "metadata": {
        "id": "c03186c9"
      },
      "source": [
        "We will write a query payload with these information:\n",
        "- Workspace code\n",
        "- Datacube code\n",
        "- Measure\n",
        "- Dimensions\n",
        "- Polygon coordinates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "933eda3a",
      "metadata": {
        "id": "933eda3a",
        "outputId": "23eddfae-ff7e-476f-8954-8157def5e582",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MEASURE:  WATER_MM\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    code caption workspaceCode  hierarchical  type  index  \\\n",
              "0  MONTH   Month       WAPOR_2         False  TIME      1   \n",
              "\n",
              "                                               links  \n",
              "0  [{'rel': 'self', 'href': 'https://io.apps.fao....  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7e81cd35-2c95-4f86-ad84-68b5372cbc8b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>code</th>\n",
              "      <th>caption</th>\n",
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              "      <th>hierarchical</th>\n",
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              "      <th>links</th>\n",
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              "      <th>0</th>\n",
              "      <td>MONTH</td>\n",
              "      <td>Month</td>\n",
              "      <td>WAPOR_2</td>\n",
              "      <td>False</td>\n",
              "      <td>TIME</td>\n",
              "      <td>1</td>\n",
              "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e81cd35-2c95-4f86-ad84-68b5372cbc8b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7e81cd35-2c95-4f86-ad84-68b5372cbc8b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7e81cd35-2c95-4f86-ad84-68b5372cbc8b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "cube_code='L2_AETI_M'\n",
        "workspace='WAPOR_2'\n",
        "outputFileName='L2_AETI_M_201701_clipped.tif'\n",
        "\n",
        "#get datacube measure\n",
        "cube_url=f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{cube_code}/measures'\n",
        "resp=requests.get(cube_url).json()\n",
        "measure=resp['response']['items'][0]['code']\n",
        "print('MEASURE: ',measure)\n",
        "\n",
        "#get datacube time dimension\n",
        "cube_url=f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{cube_code}/dimensions'\n",
        "resp=requests.get(cube_url).json()\n",
        "items=pd.DataFrame.from_dict(resp['response']['items'])\n",
        "items"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "20d7b364",
      "metadata": {
        "id": "20d7b364"
      },
      "source": [
        "For the Actual Evapotranspiration and Interception layer, there is MONTH dimension. We will define which month we want to request raster. For example, we will get the raster data of January 2017."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "67fe9f56",
      "metadata": {
        "id": "67fe9f56"
      },
      "outputs": [],
      "source": [
        "month=\"[2017-01-01,2017-02-01)\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "03ef7e45",
      "metadata": {
        "id": "03ef7e45"
      },
      "source": [
        "For this example, we will the same area of interest as [Section 6](#area)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "e444485d",
      "metadata": {
        "id": "e444485d",
        "outputId": "fc6ec4cd-7dcf-4220-fea4-bf1d17d1b75c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[37.95883206252312, 7.89534],\n",
              " [37.95883206252312, 12.3873979377346],\n",
              " [43.32093, 12.3873979377346],\n",
              " [43.32093, 7.89534],\n",
              " [37.95883206252312, 7.89534]]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "Polygon"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2515cd4f",
      "metadata": {
        "id": "2515cd4f"
      },
      "source": [
        "Now, we will write these information in a payload."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "5884d567",
      "metadata": {
        "id": "5884d567",
        "outputId": "7e57a713-a24b-4a74-a686-72867e6bd787",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'type': 'CropRaster',\n",
              " 'params': {'properties': {'outputFileName': 'L2_AETI_M_201701_clipped.tif',\n",
              "   'cutline': True,\n",
              "   'tiled': True,\n",
              "   'compressed': True,\n",
              "   'overviews': True},\n",
              "  'cube': {'code': 'L2_AETI_M', 'workspaceCode': 'WAPOR_2', 'language': 'en'},\n",
              "  'dimensions': [{'code': 'MONTH', 'values': ['[2017-01-01,2017-02-01)']}],\n",
              "  'measures': ['WATER_MM'],\n",
              "  'shape': {'type': 'Polygon',\n",
              "   'coordinates': [[[37.95883206252312, 7.89534],\n",
              "     [37.95883206252312, 12.3873979377346],\n",
              "     [43.32093, 12.3873979377346],\n",
              "     [43.32093, 7.89534],\n",
              "     [37.95883206252312, 7.89534]]]}}}"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "query_payload={\n",
        "  \"type\": \"CropRaster\",\n",
        "  \"params\": {\n",
        "    \"properties\": {\n",
        "      \"outputFileName\": outputFileName,\n",
        "      \"cutline\": True,\n",
        "      \"tiled\": True,\n",
        "      \"compressed\": True,\n",
        "      \"overviews\": True\n",
        "    },\n",
        "    \"cube\": {\n",
        "      \"code\": cube_code,\n",
        "      \"workspaceCode\": workspace,\n",
        "      \"language\": \"en\"\n",
        "    },\n",
        "    \"dimensions\": [\n",
        "      {\n",
        "        \"code\": \"MONTH\",\n",
        "        \"values\": [\n",
        "          month\n",
        "        ]\n",
        "      }\n",
        "    ],\n",
        "    \"measures\": [\n",
        "      measure\n",
        "    ],\n",
        "    \"shape\": {\n",
        "      \"type\": \"Polygon\",\n",
        "      \"coordinates\": [Polygon]\n",
        "    }\n",
        "  }\n",
        "}\n",
        "\n",
        "query_payload"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3ac7c850",
      "metadata": {
        "id": "3ac7c850"
      },
      "source": [
        "Similar to Section 6, we will use POST method and obtain an url for our request."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "96011a40",
      "metadata": {
        "id": "96011a40",
        "outputId": "a367d88b-4ed3-40b0-ade3-da51ad0fbd6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/jobs/87a1759c-783f-42bc-8e6e-ab335ba714db'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "resp_query=requests.post(path_query,headers={'Authorization':'Bearer {0}'.format(AccessToken)},\n",
        "                                          json=query_payload)\n",
        "resp_query = resp_query.json()\n",
        "job_url=resp_query['response']['links'][0]['href']\n",
        "\n",
        "job_url"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6fb1a4f",
      "metadata": {
        "id": "a6fb1a4f"
      },
      "source": [
        "We have to wait the job to be completed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "91e2cb66",
      "metadata": {
        "id": "91e2cb66",
        "outputId": "2cfa7a0f-c173-46e5-b1e5-0c667401d4cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RUNNING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . {'downloadUrl': 'https://io.apps.fao.org/gismgr/download/6d1f74b9-b8c9-4deb-918c-2787a5e779a5/L2_AETI_M_201701_clipped.tif'}\n"
          ]
        }
      ],
      "source": [
        "i=0\n",
        "print('RUNNING',end=\" \")\n",
        "while i==0:\n",
        "    resp = requests.get(job_url)\n",
        "    resp=resp.json()\n",
        "\n",
        "    if resp['response']['status']=='RUNNING':\n",
        "        print('.',end =\" \")\n",
        "    if resp['response']['status']=='COMPLETED':\n",
        "        results=resp['response']['output']\n",
        "        print(resp['response']['output'])\n",
        "        i=1\n",
        "    if resp['response']['status']=='COMPLETED WITH ERRORS':\n",
        "        print(resp['response']['log'])\n",
        "        i=1"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aa8ef653",
      "metadata": {
        "id": "aa8ef653"
      },
      "source": [
        "After that, the response will contain an url to geoTIFF file in the output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "660a1000",
      "metadata": {
        "id": "660a1000",
        "outputId": "7e892826-867e-4541-abf4-747f021e97fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://io.apps.fao.org/gismgr/download/6d1f74b9-b8c9-4deb-918c-2787a5e779a5/L2_AETI_M_201701_clipped.tif'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "raster_url=resp['response']['output']['downloadUrl']\n",
        "raster_url"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "51b30585",
      "metadata": {
        "id": "51b30585"
      },
      "source": [
        "We can now use this url to download the cropped raster."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "8b67dd2a",
      "metadata": {
        "id": "8b67dd2a"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "if not os.path.exists('data'):\n",
        "    os.makedirs('data')\n",
        "\n",
        "response = requests.get(raster_url)\n",
        "file_path = os.path.join('./data',outputFileName)\n",
        "\n",
        "if response.status_code == 200:\n",
        "    with open(file_path, 'wb') as file:\n",
        "        file.write(response.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "11fb0801",
      "metadata": {
        "id": "11fb0801"
      },
      "source": [
        "Then, we can visualize the raster using rasterio."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rasterio"
      ],
      "metadata": {
        "id": "Pocz1eCcVIqV",
        "outputId": "3e2e2a19-b5ef-43b7-b411-1b6f49f496a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "Pocz1eCcVIqV",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting rasterio\n",
            "  Downloading rasterio-1.3.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (21.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m60.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting affine (from rasterio)\n",
            "  Downloading affine-2.4.0-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: attrs in /usr/local/lib/python3.10/dist-packages (from rasterio) (23.1.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from rasterio) (2023.5.7)\n",
            "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.10/dist-packages (from rasterio) (8.1.3)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.10/dist-packages (from rasterio) (0.7.2)\n",
            "Requirement already satisfied: numpy>=1.18 in /usr/local/lib/python3.10/dist-packages (from rasterio) (1.22.4)\n",
            "Collecting snuggs>=1.4.1 (from rasterio)\n",
            "  Downloading snuggs-1.4.7-py3-none-any.whl (5.4 kB)\n",
            "Requirement already satisfied: click-plugins in /usr/local/lib/python3.10/dist-packages (from rasterio) (1.1.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from rasterio) (67.7.2)\n",
            "Requirement already satisfied: pyparsing>=2.1.6 in /usr/local/lib/python3.10/dist-packages (from snuggs>=1.4.1->rasterio) (3.1.0)\n",
            "Installing collected packages: snuggs, affine, rasterio\n",
            "Successfully installed affine-2.4.0 rasterio-1.3.7 snuggs-1.4.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import rasterio\n",
        "from rasterio.plot import show\n",
        "\n",
        "# open the raster layer\n",
        "aeti = rasterio.open(file_path, driver='GTiff')\n",
        "\n",
        "# print the metadata\n",
        "print(aeti.meta)\n",
        "\n",
        "# plot the map\n",
        "show(aeti, title='AETI')"
      ],
      "metadata": {
        "id": "TZ-fGaMaU7m2",
        "outputId": "e1d38fb5-9c74-49e4-8943-bafc53d4ac9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        }
      },
      "id": "TZ-fGaMaU7m2",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'driver': 'GTiff', 'dtype': 'int16', 'nodata': -9999.0, 'width': 5405, 'height': 4528, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.000992063492, 0.0, 37.95882934644399,\n",
            "       0.0, -0.000992063492, 12.387400809704005)}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'AETI'}>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "688eef17",
      "metadata": {
        "id": "688eef17"
      },
      "source": [
        "# 8. Get crop water productivity<a name=\"wp\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fc322ff2",
      "metadata": {
        "id": "fc322ff2"
      },
      "source": [
        "WaPOR database provide Gross Biomass Water Productivity, which is the Total Biomass Production divided by Actual Evapotranspiration and Interception (AETI). In case of crop-specific water productivity, it is calculated as the crop yield (kg/ha) divided by AETI (m$^3$/ha) over the growing season. The crop yield is calculated by multiplying Total Biomass Production crop parameters:\n",
        "\n",
        "$Yield = TBP * cropMultiplier = TBP * LUE * HI * AoT / (1 - Θ)$\n",
        "\n",
        "Where\n",
        "1. Light Use Efficiency (LUE) is a coefficient for the efficiency by which vegetation converts energy\n",
        "into biomass. A fixed value of 2.7 is applied to cropland in WaPOR and, through this interface,\n",
        "the value is multiplied by 1.8 for C4 crops, which have higher LUE.\n",
        "2. The Harvest Index (HI) indicates how much of the biomass production contributes to the harvestable fraction of a crop (yield). It is expressed as the ratio of weight of dry grains over the above-ground dry matter.\n",
        "3. Above ground over Total biomass (AoT) ratio is influenced by several factors, including root development.\n",
        "4. The Moisture content ratio (θ) allows for converting fresh yield into dry yield by accounting for water content in harvested product.\n",
        "\n",
        "\n",
        "We can request this computation directly using WaPOR API when the crop parameters and growing season are known."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "waterProductivity=\"GBWP\" #\"GBWP\" or \"NBWP\"\n",
        "cropMultiplier=0.5\n",
        "startSeason=\"2015-01\" # \"YYYY-DK\" (Dekad)\n",
        "endSeason= \"2015-18\" # \"YYYY-DK\" (Dekad)\n",
        "resolution=\"100m\" #\"250m\" or \"100m\" , maybe \"30m\" works for some area"
      ],
      "metadata": {
        "id": "aynZSPjGKA3q"
      },
      "id": "aynZSPjGKA3q",
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will write these parameters into a query payload. The query type is CustomWaterProductivity.\n",
        "\n",
        "We will use the same Polygon in the previous example."
      ],
      "metadata": {
        "id": "sbdM5w9xOrSe"
      },
      "id": "sbdM5w9xOrSe"
    },
    {
      "cell_type": "code",
      "source": [
        "query_payload ={\n",
        "  \"type\": \"CustomWaterProductivity\",\n",
        "  \"params\": {\n",
        "    \"waterProductivity\": waterProductivity, #\"GBWP\" or \"NBWP\"\n",
        "    \"resolution\": resolution, #\"250m\" or \"100m\" , maybe \"30m\" works for some area\n",
        "    \"startSeason\": startSeason, # \"YYYY-DK\" (Dekad)\n",
        "    \"endSeason\": endSeason, # \"YYYY-DK\" (Dekad),\n",
        "    \"cropMultiplier\":cropMultiplier,\n",
        "    \"shape\": {\n",
        "      \"type\": \"Polygon\", #define coordinates of the area in geojson format\n",
        "      \"coordinates\": [Polygon]\n",
        "    }\n",
        "  }\n",
        "}"
      ],
      "metadata": {
        "id": "4KSTmy-8MwY0"
      },
      "id": "4KSTmy-8MwY0",
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For this query, we wil the same AccessToken as before. Similar to the previous section, we will also get job_url in the response of POST query."
      ],
      "metadata": {
        "id": "uWhxv7zpOzkG"
      },
      "id": "uWhxv7zpOzkG"
    },
    {
      "cell_type": "code",
      "source": [
        "resp_query=requests.post(path_query,headers={'Authorization':'Bearer {0}'.format(AccessToken)},\n",
        "                                          json=query_payload)\n",
        "resp_query = resp_query.json()\n",
        "job_url=resp_query['response']['links'][0]['href']\n",
        "\n",
        "job_url"
      ],
      "metadata": {
        "id": "YXzdOyV-NHhl",
        "outputId": "3ee0e8e8-cc6a-415b-ca1b-cf85699ae1c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "id": "YXzdOyV-NHhl",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/jobs/96639652-2a8a-4bdd-bfbb-838bb618d9ef'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After the computation is completed, the response will contain the url to download 3 rasters. The raster files for download are named as in the following example:\n",
        "\n",
        "- L2_AETI_1931_2009.tif = Total actual evapotranspiration and interception generated between dekad 31 in 2019 and dekad 09 in 2020 (1st of December 2019 – 31 March 2020) in m³/ha;\n",
        "- L2_TBP_1931_2009.tif = Yield generated in the same growing season, in kg/ha;\n",
        "- L2_GBWP_1931_2009.tif = Crop Water Productivity, with a scaling factor of 1000."
      ],
      "metadata": {
        "id": "zCK8VNYxPCf6"
      },
      "id": "zCK8VNYxPCf6"
    },
    {
      "cell_type": "code",
      "source": [
        "i=0\n",
        "print('RUNNING',end=\" \")\n",
        "while i==0:\n",
        "    resp = requests.get(job_url)\n",
        "    resp=resp.json()\n",
        "\n",
        "    if resp['response']['status']=='RUNNING':\n",
        "        print('.',end =\" \")\n",
        "    if resp['response']['status']=='COMPLETED':\n",
        "        results=resp['response']['output']\n",
        "        print('Link to download GBWP',results['bwpDownloadUrl'])\n",
        "        print('Link to download TBP',results['tbpDownloadUrl'])\n",
        "        print('Link to download AETI',results['wtrDownloadUrl'])\n",
        "        i=1\n",
        "    if resp['response']['status']=='COMPLETED WITH ERRORS':\n",
        "        print(resp['response']['log'])\n",
        "        i=1"
      ],
      "metadata": {
        "id": "JzmtqD4rNKIt",
        "outputId": "0f13099e-9870-4131-82f3-0a94f9a423eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "JzmtqD4rNKIt",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RUNNING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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