Use_Case_solution.ipynb

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    "![](./images/Title_ex_6.6.png)"
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    "# 1. Introduction\n",
    "\n",
    "In the previous exercises we explored different online data portals and analysis techniques to investigate vegetation and droughts. In this notebook we will put everything together in one use case and look at how vegetation production in a certain agricultural region is impacted by drought and how many people are likely to be directly impacted by that drought. We will be using the **Vegetation Health Index** from the [ASIS data portal](https://www.fao.org/giews/earthobservation/asis/index_1.jsp?lang=en) to look into a specific drought and its impact on vegetation.\n",
    "For more details on the methodology used to derive this index, have a look at [this paper](https://www.tandfonline.com/doi/full/10.1080/01431161.2015.1126378?scroll=top&needAccess=true).\n",
    "\n",
    "The **Vegetation Health Index (VHI)** illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index, combining the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI). VCI evaluates the current vegetation health in comparison to historical trends by relating the current dekadal NDVI to its long-term minimimum and maximum value. The TCI is calculated using a similar equation to the VCI, but relates the current temperature to the long-term maximum and minimum , as it is assumed that higher temperatures tend to cause a deterioration in vegetation conditions. A decrease in the VHI would, for example, indicate relatively poor vegetation conditions and warmer temperatures, signifying stressed vegetation conditions, and over a longer period would be indicative of drought.\n",
    "\n",
    "Within ASIS, VHI is available as a dekadal, monthly and annual metric. An interesting derived product is the **Mean Vegetation Health Index (MVHI)**, available per dekad and per season. For each pixel, this MVHI represents the temporally averaged VHI starting from the start of season up to the requested dekad and provides a good indication on drought conditions during a growing season of interest.\n",
    "\n",
    "In the scope of this use case, we will only focus on one (the first) growing season. During a typical growing season, a crop is most sensitive to a drought event during the first half of the season, when the crop is fully developing. Therefore, we will zoom in on the first half of the growing season and derive the MVHI for each pixel at the moment of the peak of the season (this moment could be different for different pixels!). For each pixel, we will be needing information on the timing of the peak of season, which we will get through the WaPOR phenology products.\n",
    "\n",
    "Based on the MVHI value, the growing season can be classified into different drought severity classes:\n",
    "\n",
    "- Class 1: MVHI < 0.25    : Extreme\n",
    "- Class 2: MVHI 0.25-0.35 : Severe\n",
    "- Class 3: MVHI 0.35-0.38 : Moderate \n",
    "- Class 4: MVHI 0.38-0.42 : Mild\n",
    "- Class 5: MVHI 0.42-1    : None\n",
    "- Class 6: MVHI > 1       : Insufficient data\n",
    "\n",
    "Once we derived these drought severity classes for our area of interest, we will be checking the impact of the drought on vegetation productivity in the most affected area by inspecting the biomass productivity metrics as provided through the WaPOR portal.\n",
    "\n",
    "Finally, we will also be looking at the number of people affected by drought. For this, we can make use of the WPOP database, providing numbers of population density throughout the world and also available through the FAO API.\n",
    "\n",
    "\n",
    "\n",
    "**Throughout the notebook, we ask a series of QUESTIONS. Reflect upon these, collect your answers in one WORD document and submit to the teaching staff. This part is mandatory in order to pass the course.**\n",
    "\n",
    "\n",
    "\n",
    "Notebook outline: \n",
    "- [1. Introduction](#1.-Introduction)\n",
    "- [2. Libraries](#2.-Import-the-necessary-libraries)\n",
    "- [3. Choose a drought event and location](#3.-Choose-a-drought-event-and-location)\n",
    "- [4. Extract land cover information](#4.-Land-cover-mask)\n",
    "- [5. Extract phenology](#5.-Extract-phenology)\n",
    "- [6. Get mean VHI](#6.-Get-mean-VHI)\n",
    "- [7. Check precipitation (and evapotranspiration) patterns](#7.-Check-precipitation-(and-evapotranspiration)-patterns)\n",
    "- [8. Impact of drought on vegetation](#8.-Impact-of-drought-on-vegetation)\n",
    "- [9. How many people are affected?](#9.-How-many-people-are-affected?)\n"
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   "source": [
    "# 2. Import the necessary libraries"
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      "/home/jeroendegerickx/miniconda3/envs/eoafrica/lib/python3.7/site-packages/pyproj/__init__.py:89: UserWarning: pyproj unable to set database path.\n",
      "  _pyproj_global_context_initialize()\n"
     ]
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   ],
   "source": [
    "import requests\n",
    "import json\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "import os \n",
    "import json\n",
    "from pathlib import Path\n",
    "import rioxarray\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "from osgeo import gdal\n",
    "import glob\n",
    "from shapely.geometry import Polygon"
   ]
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    "# 3. Choose a drought event and location\n",
    "\n",
    "First we need to choose a drought event of which we want to investigate the impact from. NASA's GRACE sensor provides coarse resolution information on Surface Soil Moisture, Root Zone Soil Moisture and Groundwater levels. This information can be accessed [HERE](https://nasagrace.unl.edu/Archive.aspx) to get a first impression on when and where dry conditions are occuring.\n",
    "\n",
    "Based on this information, we have already identified a potentially interesting drought event in the 2015 growing season of the Gadaref region in Sudan. Compared to 2015, 2018 has been a relatively wet year in the same region. Throughout this exercise we will compare both periods in this region and check whether we can see any impact of the drought on vegetation productivity.\n",
    "\n",
    "To delineate the region of interest to which we are going to clip our data we can make use of the FAO Global Administrative Unit Layer (GAUL) database which provides shapefiles for each departement within all countries."
   ]
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    "The GAUL dataset can be found in the folder ./data/GAUL. Different levels of detail exist: level 0 represents the country borders, level 1 represents the departement borders and level 2 (not provided here) goes up to province level. Feel free to inspect these shapefiles in QGIS to get familiar with the data.\n",
    "\n",
    "From the level 1 dataset, we will first isolate our country of interest (Sudan)."
   ]
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    "country = 'Sudan'\n",
    "gaul1_file = './data/GAUL/GAUL1_thinned.shp'\n",
    "\n",
    "# gaul1 = gpd.read_file(gaul1_file)\n",
    "# sudan = gaul1.loc[gaul1['ADM0_NAME'] == country]\n",
    "\n",
    "outdir = Path('./results')\n",
    "outdir.mkdir(exist_ok=True)\n",
    "\n",
    "outfile = str(outdir / 'Sudan.gpkg')\n",
    "# sudan.to_file(outfile, driver='GPKG')\n",
    "sudan = gpd.read_file(outfile)"
   ]
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   "source": [
    "Plot the country of interest and look at which departments are available:"
   ]
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       "      <td>Al Jazeera</td>\n",
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       "      <td>2757</td>\n",
       "      <td>Northern Darfur</td>\n",
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       "      <td>3000</td>\n",
       "      <td>Member State</td>\n",
       "      <td>NO</td>\n",
       "      <td>6</td>\n",
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       "      <td>27.515367</td>\n",
       "      <td>26.771089</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2761</td>\n",
       "      <td>Nile</td>\n",
       "      <td>1994</td>\n",
       "      <td>3000</td>\n",
       "      <td>Member State</td>\n",
       "      <td>NO</td>\n",
       "      <td>6</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>17.836519</td>\n",
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       "      <th>6</th>\n",
       "      <td>2762</td>\n",
       "      <td>Sennar</td>\n",
       "      <td>1994</td>\n",
       "      <td>3000</td>\n",
       "      <td>Member State</td>\n",
       "      <td>NO</td>\n",
       "      <td>6</td>\n",
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       "      <th>7</th>\n",
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       "      <td>Southern Darfur</td>\n",
       "      <td>1994</td>\n",
       "      <td>3000</td>\n",
       "      <td>Member State</td>\n",
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       "      <th>8</th>\n",
       "      <td>2769</td>\n",
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       "      <td>1994</td>\n",
       "      <td>3000</td>\n",
       "      <td>Member State</td>\n",
       "      <td>NO</td>\n",
       "      <td>6</td>\n",
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       "      <th>9</th>\n",
       "      <td>2770</td>\n",
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       "      <td>1994</td>\n",
       "      <td>3000</td>\n",
       "      <td>Member State</td>\n",
       "      <td>NO</td>\n",
       "      <td>6</td>\n",
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       "      <th>10</th>\n",
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       "      <td>3000</td>\n",
       "      <td>Member State</td>\n",
       "      <td>NO</td>\n",
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       "      <td>1994</td>\n",
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       "      <td>Member State</td>\n",
       "      <td>NO</td>\n",
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       "      <th>12</th>\n",
       "      <td>68783</td>\n",
       "      <td>Kassala</td>\n",
       "      <td>2006</td>\n",
       "      <td>3000</td>\n",
       "      <td>Member State</td>\n",
       "      <td>NO</td>\n",
       "      <td>6</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>11.581785</td>\n",
       "      <td>4.466513</td>\n",
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       "      <th>13</th>\n",
       "      <td>68784</td>\n",
       "      <td>Northern Kordofan</td>\n",
       "      <td>2006</td>\n",
       "      <td>3000</td>\n",
       "      <td>Member State</td>\n",
       "      <td>NO</td>\n",
       "      <td>6</td>\n",
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       "      <th>14</th>\n",
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       "      <td>Gadaref</td>\n",
       "      <td>2006</td>\n",
       "      <td>3000</td>\n",
       "      <td>Member State</td>\n",
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      "text/plain": [
       "    ADM1_CODE          ADM1_NAME  STR1_YEAR  EXP1_YEAR        STATUS  \\\n",
       "0        2745         Al Jazeera       1994       3000  Member State   \n",
       "1        2749          Blue Nile       1994       3000  Member State   \n",
       "2        2753           Khartoum       1000       3000  Member State   \n",
       "3        2755           Northern       1994       3000  Member State   \n",
       "4        2757    Northern Darfur       1994       3000  Member State   \n",
       "5        2761               Nile       1994       3000  Member State   \n",
       "6        2762             Sennar       1994       3000  Member State   \n",
       "7        2764    Southern Darfur       1994       3000  Member State   \n",
       "8        2769     Western Darfur       1994       3000  Member State   \n",
       "9        2770         White Nile       1994       3000  Member State   \n",
       "10       4150  Southern Kordofan       2011       3000  Member State   \n",
       "11      40772            Red Sea       1994       3000  Member State   \n",
       "12      68783            Kassala       2006       3000  Member State   \n",
       "13      68784  Northern Kordofan       2006       3000  Member State   \n",
       "14      68805            Gadaref       2006       3000  Member State   \n",
       "\n",
       "   DISP_AREA  ADM0_CODE ADM0_NAME  Shape_Leng  Shape_Area  \\\n",
       "0         NO          6     Sudan   10.195142    1.996427   \n",
       "1         NO          6     Sudan    8.866905    3.125260   \n",
       "2         NO          6     Sudan    7.145830    1.841496   \n",
       "3         NO          6     Sudan   29.929447   31.293111   \n",
       "4         NO          6     Sudan   27.515367   26.771089   \n",
       "5         NO          6     Sudan   17.836519   10.977293   \n",
       "6         NO          6     Sudan   11.869203    3.341615   \n",
       "7         NO          6     Sudan   19.548674   11.421107   \n",
       "8         NO          6     Sudan   15.196735    4.626362   \n",
       "9         NO          6     Sudan   12.787100    3.411092   \n",
       "10        NO          6     Sudan   16.877017   10.606348   \n",
       "11        NO          6     Sudan   27.625278   17.031892   \n",
       "12        NO          6     Sudan   11.581785    4.466513   \n",
       "13        NO          6     Sudan   22.019069   20.310855   \n",
       "14        NO          6     Sudan   12.649889    4.668343   \n",
       "\n",
       "                                             geometry  \n",
       "0   POLYGON ((33.64067 15.31577, 34.01519 15.18538...  \n",
       "1   POLYGON ((34.47623 12.55284, 34.65802 12.51429...  \n",
       "2   POLYGON ((32.11407 16.45235, 32.18403 16.39988...  \n",
       "3   POLYGON ((31.41221 22.00778, 32.41229 22.00094...  \n",
       "4   POLYGON ((26.20681 20.00610, 27.49695 20.00562...  \n",
       "5   POLYGON ((33.56318 21.72604, 33.25849 21.51465...  \n",
       "6   POLYGON ((33.82103 14.04371, 34.01282 13.98661...  \n",
       "7   POLYGON ((24.45512 13.10490, 24.75710 13.09588...  \n",
       "8   POLYGON ((22.84261 14.91736, 22.87306 14.88714...  \n",
       "9   POLYGON ((32.66610 15.24872, 32.77079 14.99412...  \n",
       "10  POLYGON ((29.97903 12.72309, 30.02043 12.71929...  \n",
       "11  MULTIPOLYGON (((38.58148 18.02542, 38.58324 18...  \n",
       "12  POLYGON ((36.00058 17.04983, 36.75530 17.00060...  \n",
       "13  POLYGON ((29.32831 16.60293, 31.08007 16.51394...  \n",
       "14  POLYGON ((34.13181 15.72588, 34.36509 15.59793...  "
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sudan.plot()\n",
    "sudan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4883808a-f222-49ae-a0c6-84b76cfd3362",
   "metadata": {},
   "source": [
    "Now create a new shapefile with just the departement of Gadaref. This will be our Region of Interest (ROI) for the remainder of this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1174cf55-1afe-419d-8751-cbe48d835f4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "department = 'Gadaref'\n",
    "\n",
    "roi = sudan.loc[sudan['ADM1_NAME'] == department]\n",
    "roifile = str(outdir / 'ROI.gpkg')\n",
    "roi.to_file(roifile, driver='GPKG')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "598db987-b4c4-40c3-b89e-8472008effee",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 4. Land cover mask\n",
    "\n",
    "In order to focus our analysis just on cropland and grassland, we need land cover information for our region of interest..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e934d2d7-3ad5-4f72-a2b6-d3f6d8d3aedf",
   "metadata": {},
   "source": [
    "## 4.1 Download data from WaPOR\n",
    "\n",
    "We will use the Copernicus Global Land Cover product available through the WaPOR data portal.\n",
    "\n",
    "Download the **100m version** of this product for our region of interest for both the wet (2018) and dry (2015) years.\n",
    "\n",
    "Make use of the **request_tif_files** function from exercise 6.2.3, but this time use the ROI shapefile as input for the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "335aa2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def request_tif_files(workspace, product_code, crs,\n",
    "                      outputfilename, dimensions,\n",
    "                      shapefile=None, labelcol=None,\n",
    "                      bbox=None, APIToken=None):\n",
    "    '''\n",
    "    Function to extract .tif files for one or multiple polygons from the FAO portal.\n",
    "    \n",
    "    Parameters:\n",
    "    - workspace: workspace in the FAO catalog where the product of interest is located.\n",
    "    - product_code: specific product you wish to download.\n",
    "   \n",
    "    - crs: EPSG code of projection system (int)\n",
    "    - dimensions: list of dimensions specifying the characteristics of the exact product\n",
    "      you wish to download.\n",
    "    \n",
    "    For spatial information, you can either provide a shapefile or a series of bounding boxes:\n",
    "    - shapefile: path to shapefile containing one or multiple polygons for which data\n",
    "      needs to be extracted.\n",
    "    - labelcol: this parameter is only required if \"shapefile\" is provided and represents\n",
    "      the name of the column in the shapefile which serves as a unique identifier for\n",
    "      the polygons.\n",
    "    - bbox: dictionary with keys representing label of the bbox\n",
    "        and values the actual bounding box in the CRS defined by the next parameter and\n",
    "        in the format [xmin, ymin, xmax, ymax].\n",
    "        \n",
    "    The function will return a list of download links.\n",
    "    '''\n",
    "    \n",
    "    # define necessary paths to the service\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",
    "    if APIToken is None:\n",
    "      # request API token\n",
    "      APIToken = input('Your API token: ')\n",
    "    # get access token\n",
    "    resp_signin = requests.post(path_sign_in,\n",
    "                              headers={'X-GISMGR-API-KEY': APIToken})\n",
    "    resp_signin = resp_signin.json()\n",
    "    AccessToken = resp_signin['response']['accessToken']\n",
    "    \n",
    "    # get datacube measure\n",
    "    cube_url = f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{product_code}/measures'\n",
    "    resp = requests.get(cube_url).json()\n",
    "    measure = resp['response']['items'][0]['code']\n",
    "    print('MEASURE: ', measure)\n",
    "    \n",
    "    # prepare spatial information, either from bbox or shapefile...\n",
    "    crs = f'EPSG:{crs}'\n",
    "    shapes = {}\n",
    "    if bbox is not None:\n",
    "        npoly = len(bbox.keys())\n",
    "        for name, box in bbox.items():\n",
    "            xmin, ymin, xmax, ymax = box[0], box[1], box[2], box[3]\n",
    "            shape = [\n",
    "                    [xmin,ymin],\n",
    "                    [xmin,ymax],\n",
    "                    [xmax,ymax],\n",
    "                    [xmax,ymin],\n",
    "                    [xmin,ymin]\n",
    "                    ]\n",
    "            shapes[name] = [shape]\n",
    "            \n",
    "    elif shapefile is not None:\n",
    "      polys = gpd.read_file(shapefile)\n",
    "      npoly = len(polys)\n",
    "      names = polys[labelcol].values\n",
    "      geometry = json.loads(polys.geometry.to_json())['features']\n",
    "      for k, name in enumerate(names):\n",
    "        shapes[name] = geometry[k]['geometry']['coordinates']\n",
    "\n",
    "    else:\n",
    "        raise ValueError('No spatial information provided!')\n",
    "    \n",
    "    print('Sending query for each polygon...')\n",
    "    output = []\n",
    "    i = 0    \n",
    "    for label, geom in shapes.items():\n",
    "      \n",
    "      print(f'Processing polygon {i+1} / {npoly}...')\n",
    "      \n",
    "      # build query\n",
    "      query = {\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\": product_code,\n",
    "          \"workspaceCode\": workspace,\n",
    "          \"language\": \"en\"\n",
    "        },\n",
    "        \"dimensions\": dimensions,\n",
    "        \"measures\": [\n",
    "          measure\n",
    "        ],\n",
    "        \"shape\": {\n",
    "          \"type\": \"Polygon\",\n",
    "          \"properties\": {\n",
    "              \"name\": crs},\n",
    "          \"coordinates\": geom}}}\n",
    "      \n",
    "      # Post query\n",
    "      resp_query = requests.post(path_query,\n",
    "                                 headers={'Authorization':'Bearer {0}'.format(AccessToken)},\n",
    "                                 json=query)\n",
    "      resp_query = resp_query.json()\n",
    "      job_url = resp_query['response']['links'][0]['href']\n",
    "      \n",
    "      # Get results\n",
    "      j = 0\n",
    "      print('RUNNING', end=\" \")\n",
    "      while j == 0:        \n",
    "          resp = requests.get(job_url)\n",
    "          resp = resp.json()\n",
    "          if resp['response']['status']=='RUNNING':\n",
    "              print('.', end =\" \")\n",
    "          if resp['response']['status']=='COMPLETED':\n",
    "              results = resp['response']['output']\n",
    "              out = results['downloadUrl']\n",
    "              j = 1\n",
    "          if resp['response']['status']=='COMPLETED WITH ERRORS':\n",
    "              print(resp['response']['log'])\n",
    "              out = None\n",
    "              j = 1\n",
    "      \n",
    "      output.append(out)\n",
    "\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b673945-db9b-4463-979a-6cf562698392",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEASURE:  LCC\n",
      "Sending query for each polygon...\n",
      "Processing polygon 1 / 1...\n",
      "RUNNING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ['https://io.apps.fao.org/gismgr/download/d9b73be8-480a-4edf-9915-4afc610da4e2/L2_LCC_dry_clipped.tif']\n",
      "MEASURE:  LCC\n",
      "Sending query for each polygon...\n",
      "Processing polygon 1 / 1...\n",
      "RUNNING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ['https://io.apps.fao.org/gismgr/download/bce70045-a9c9-487a-99fe-a577a0497190/L2_LCC_wet_clipped.tif']\n"
     ]
    }
   ],
   "source": [
    "product_code = 'L2_LCC_A'\n",
    "workspace = 'WAPOR_2'\n",
    "\n",
    "years = {'dry': 2015,\n",
    "         'wet': 2018}\n",
    "\n",
    "crs = 4326\n",
    "roifile = str(outdir / 'ROI.gpkg')\n",
    "labelcol = 'ADM1_NAME'\n",
    "\n",
    "for label, year in years.items():\n",
    "    outputfilename = f'L2_LCC_{label}_clipped.tif'\n",
    "    year_input = f'[{year}-01-01,{year+1}-01-01)'\n",
    "    dimensions = [{\"code\": \"YEAR\",\n",
    "                   \"values\": [year_input]}]\n",
    "    output = request_tif_files(workspace, product_code,\n",
    "                               crs, outputfilename,\n",
    "                               dimensions, shapefile=roifile,\n",
    "                               labelcol=labelcol)\n",
    "    print(output)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bec48d86-1524-49da-b9ec-03f311fbe485",
   "metadata": {},
   "source": [
    "QUESTION: does the land cover information available through WaPOR already reflect the difference in climatological conditions between both years? If yes, how?\n",
    "\n",
    "Hint: Open the downloaded products in QGIS and use the LC_style_CGLOPS.qml style file to properly visualize the land cover data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd524b4-01d5-4069-9a3a-3c4dd764619a",
   "metadata": {},
   "source": [
    "## 4.2 Create the mask\n",
    "\n",
    "We only want to focus our analysis on **grassland and cropland**. Create a mask based on both land cover products containing \"1\" for pixels of interest and NaN for other pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a9931ff-2677-4914-9ba1-0378c59d284f",
   "metadata": {},
   "outputs": [],
   "source": [
    "infiles = {'wet': './results/LC/L2_LCC_wet_clipped.tif',\n",
    "           'dry': './results/LC/L2_LCC_dry_clipped.tif'}\n",
    "\n",
    "for t, infile in infiles.items():\n",
    "    lcc = rioxarray.open_rasterio(infile)\n",
    "    lcc_masked = lcc.where(lcc.isin([30,41,42,43]))\n",
    "    lcc_binary = lcc_masked.where(np.isnan(lcc_masked), 1)\n",
    "    # export final mask to tiff file\n",
    "    outfile = f'./results/LC/L2_LCC_{t}_masked.tif'\n",
    "    lcc_binary.rio.to_raster(outfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7560a68-8421-4e41-b088-b3c420622d0f",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 5. Extract phenology"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33697d54-163c-4f8c-89be-e9c9ca0bb3a2",
   "metadata": {},
   "source": [
    "Phenology metrics will be extracted from the WaPOR database. \n",
    "\n",
    "For this exercise we are interested in the maximum of season (MOS), as this will define for each pixel up till when we need to compute the Mean Vegetation Health Index...\n",
    "\n",
    "Download this indicator for our region of interest and both the wet and dry year. Do this for both season 1 and 2 (S1 and S2).\n",
    "\n",
    "Make use of the **request_tif_files** function from exercise 6.2.3, but this time use the ROI shapefile as input for the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fbf93d5d-fc7c-40ff-af6a-22ce1e97e49b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEASURE:  PHE\n",
      "Sending query for each polygon...\n",
      "Processing polygon 1 / 1...\n",
      "RUNNING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ['https://io.apps.fao.org/gismgr/download/ddd55dfe-5fb3-4dd8-81a6-76f9e5aa0c4c/L2_PHE_dry_MOS_S1.tif']\n",
      "MEASURE:  PHE\n",
      "Sending query for each polygon...\n",
      "Processing polygon 1 / 1...\n",
      "RUNNING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ['https://io.apps.fao.org/gismgr/download/657e3cab-2995-451f-b1de-d04cd79662bb/L2_PHE_wet_MOS_S1.tif']\n",
      "MEASURE:  PHE\n",
      "Sending query for each polygon...\n",
      "Processing polygon 1 / 1...\n",
      "RUNNING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ['https://io.apps.fao.org/gismgr/download/6d99b363-561c-4046-a073-1d3bfc4d86f0/L2_PHE_dry_MOS_S2.tif']\n",
      "MEASURE:  PHE\n",
      "Sending query for each polygon...\n",
      "Processing polygon 1 / 1...\n",
      "RUNNING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ['https://io.apps.fao.org/gismgr/download/926f6d8d-7e85-4dee-8fed-6cd9fcad9ce4/L2_PHE_wet_MOS_S2.tif']\n"
     ]
    }
   ],
   "source": [
    "product_code = 'L2_PHE_S'\n",
    "workspace = 'WAPOR_2'\n",
    "\n",
    "years = {'dry': \"[2015-01-01,2016-01-01)\",\n",
    "         'wet': \"[2018-01-01,2019-01-01)\"}\n",
    "stages = ['MOS']\n",
    "seasons = [\"S1\", \"S2\"]\n",
    "\n",
    "crs = 4326\n",
    "\n",
    "for season in seasons:\n",
    "    for name, year in years.items():\n",
    "        for stage in stages:\n",
    "            outputfilename = f'L2_PHE_{name}_{stage}_{season}.tif'\n",
    "            dimensions = [{\"code\": \"SEASON\",\n",
    "                           \"values\": [season]},\n",
    "                          {\"code\": \"STAGE\",\n",
    "                           \"values\": [stage]},\n",
    "                          {\"code\": \"YEAR\",\n",
    "                           \"values\": [year]}]\n",
    "            output = request_tif_files(workspace, product_code,\n",
    "                                       crs, outputfilename,\n",
    "                                       dimensions, shapefile=roifile,\n",
    "                                       labelcol=labelcol)\n",
    "            print(output)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d418e68-63f8-4a16-947b-6c8603b29c4c",
   "metadata": {},
   "source": [
    "Upload the downloaded files into a designated folder *phenology* in your results directory (./results/phenology)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d41b6467",
   "metadata": {},
   "source": [
    "## 5.2 Check results visually and extract relevant information\n",
    "\n",
    "Now visualize MOS for the dry season, starting with S2.\n",
    "\n",
    "Note that MOS is expressed in dekad number, counting from the year before the year of interest, meaning:\n",
    "- dekad 1-36 is from the year before the year of interest \n",
    "- dekad 37-72 is from the year of interest \n",
    "- dekad 72-108 is from the year after the year of interest\n",
    "\n",
    "This also means that any number > 108 should be masked out!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "70d0f12c-1a9d-4c10-9402-97766bc3ba16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phe_dry_mos_img_s2 = rioxarray.open_rasterio(str(outdir / 'phenology' / 'L2_PHE_dry_MOS_S2.tif'))\n",
    "phe_dry_mos_img_s2 = phe_dry_mos_img_s2.where(phe_dry_mos_img_s2 <= 108)\n",
    "phe_dry_mos_img_s2.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b62bb14",
   "metadata": {},
   "source": [
    "As you can see in the result above, there are few pixels in the study area having a second growing season.\n",
    "In order to not make this exercise overly complex, we will ignore pixels with a second growing season.\n",
    "Mask these pixels in the S1 MOS product:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9fbe4ceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phe_dry_mos_img_s1 = rioxarray.open_rasterio(str(outdir / 'phenology' / 'L2_PHE_dry_MOS_S1.tif'))\n",
    "phe_dry_mos_img_s1 = phe_dry_mos_img_s1.where(phe_dry_mos_img_s1 <= 108)\n",
    "phe_dry_mos_img_s1 = phe_dry_mos_img_s1.where(np.isnan(phe_dry_mos_img_s2))\n",
    "phe_dry_mos_img_s1.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "724a45a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.0\n",
      "72.0\n"
     ]
    }
   ],
   "source": [
    "# check minimum and maximum values of MOS in the dry year:\n",
    "print(np.nanmin(phe_dry_mos_img_s1.values))\n",
    "print(np.nanmax(phe_dry_mos_img_s1.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a37c3311",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jeroendegerickx/miniconda3/envs/eoafrica/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: xarray.ufuncs is deprecated. Instead, use numpy ufuncs directly.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/home/jeroendegerickx/miniconda3/envs/eoafrica/lib/python3.7/site-packages/xarray/core/dataarray.py:3087: FutureWarning: xarray.ufuncs is deprecated. Instead, use numpy ufuncs directly.\n",
      "  if not reflexive\n",
      "/home/jeroendegerickx/miniconda3/envs/eoafrica/lib/python3.7/site-packages/xarray/core/variable.py:2429: FutureWarning: xarray.ufuncs is deprecated. Instead, use numpy ufuncs directly.\n",
      "  f(self_data, other_data) if not reflexive else f(other_data, self_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# note that some pixels have a peak of season which is outside of our year of interest (MOS < 36 or MOS > 72)\n",
    "# for safety, we will exclude these pixels and also exclude pixels with MOS very early in the year from our analysis\n",
    "phe_dry_mos_img_s1 = phe_dry_mos_img_s1.where(xr.ufuncs.logical_and(phe_dry_mos_img_s1 > 45, phe_dry_mos_img_s1 <= 72))\n",
    "phe_dry_mos_img_s1.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc3a48a",
   "metadata": {},
   "source": [
    "Now repeat these steps for the wet year as well...\n",
    "\n",
    "QUESTION: can you already see a difference between the wet and dry year in terms of timing of MOS, caused by the drought? Please describe!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b1e85c39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jeroendegerickx/miniconda3/envs/eoafrica/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: xarray.ufuncs is deprecated. Instead, use numpy ufuncs directly.\n",
      "  \n",
      "/home/jeroendegerickx/miniconda3/envs/eoafrica/lib/python3.7/site-packages/xarray/core/dataarray.py:3087: FutureWarning: xarray.ufuncs is deprecated. Instead, use numpy ufuncs directly.\n",
      "  if not reflexive\n",
      "/home/jeroendegerickx/miniconda3/envs/eoafrica/lib/python3.7/site-packages/xarray/core/variable.py:2429: FutureWarning: xarray.ufuncs is deprecated. Instead, use numpy ufuncs directly.\n",
      "  f(self_data, other_data) if not reflexive else f(other_data, self_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phe_wet_mos_img_s2 = rioxarray.open_rasterio(str(outdir / 'phenology' / 'L2_PHE_wet_MOS_S2.tif'))\n",
    "phe_wet_mos_img_s2 = phe_wet_mos_img_s2.where(phe_wet_mos_img_s2 <= 108)\n",
    "phe_wet_mos_img_s1 = rioxarray.open_rasterio(str(outdir / 'phenology' / 'L2_PHE_wet_MOS_S1.tif'))\n",
    "phe_wet_mos_img_s1 = phe_wet_mos_img_s1.where(phe_wet_mos_img_s1 <= 108)\n",
    "phe_wet_mos_img_s1 = phe_wet_mos_img_s1.where(np.isnan(phe_wet_mos_img_s2))\n",
    "phe_wet_mos_img_s1 = phe_wet_mos_img_s1.where(xr.ufuncs.logical_and(phe_wet_mos_img_s1 > 45, phe_wet_mos_img_s1 <= 72))\n",
    "phe_wet_mos_img_s1.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "182850b6-1a3a-4e21-8ba0-147ae41b7994",
   "metadata": {},
   "source": [
    "## 5.3 Update LC mask based on phenology\n",
    "\n",
    "Extend the LC mask with the pixels masked out in the previous step of the analysis (i.e. pixels having two growing seasons and/or no clear seasonality in our year of interest)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "db61f68e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lcc_mask_dry = rioxarray.open_rasterio(str(outdir / 'LC' / 'L2_LCC_dry_masked.tif'))\n",
    "lcc_mask_dry = lcc_mask_dry.where(~np.isnan(phe_dry_mos_img_s1.values))\n",
    "lcc_mask_dry.plot()\n",
    "plt.show()\n",
    "outfile = str(outdir / 'LC' / 'L2_LCC_dry_masked_pheno.tif')\n",
    "lcc_mask_dry.rio.to_raster(outfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "040cac11",
   "metadata": {},
   "source": [
    "Do the same for the wet year..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "14c8389b",
   "metadata": {},
   "outputs": [],
   "source": [
    "lcc_mask_wet = rioxarray.open_rasterio(str(outdir / 'LC' / 'L2_LCC_wet_masked.tif'))\n",
    "lcc_mask_wet = lcc_mask_wet.where(~np.isnan(phe_wet_mos_img_s1.values))\n",
    "outfile = str(outdir / 'LC' / 'L2_LCC_wet_masked_pheno.tif')\n",
    "lcc_mask_wet.rio.to_raster(outfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc14211-0b74-4257-aeaf-314e13322040",
   "metadata": {},
   "source": [
    "## 5.3 Apply the updated mask to all phenology layers\n",
    "\n",
    "Now we apply the extended land cover mask to the MOS data for season 1 in order to make sure pixels which we are not interested in, will not be processed in the future...\n",
    "\n",
    "Meanwhile, we also make sure the phenology values range from 1 - 36 as we only focus on our year of interest...\n",
    "You can make use of the function below to adjust the MOS of Season 1 for both the wet and dry years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c9c55fdd-7dd9-4704-98c6-b2407398a2ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mask_phenology(input_path, mask):\n",
    "    '''\n",
    "    Little function to execute a recurrent operation faster\n",
    "    '''\n",
    "    \n",
    "    src = rioxarray.open_rasterio(input_path)\n",
    "    src = src.where(~np.isnan(mask.values))\n",
    "    src = src - 36\n",
    "    output_path = input_path[:-4] + '_masked.tif'\n",
    "    src.rio.to_raster(output_path)\n",
    "    \n",
    "    return src"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b1eda9-502e-4000-8209-8fca66a16e7f",
   "metadata": {},
   "source": [
    "Now apply that function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b0f36463-854c-41af-8884-57cd5a6abc9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "infile = str(outdir / 'phenology' / 'L2_PHE_dry_MOS_S1.tif')\n",
    "phe_dry_mos = mask_phenology(infile, lcc_mask_dry)\n",
    "\n",
    "infile = str(outdir / 'phenology' / 'L2_PHE_wet_MOS_S1.tif')\n",
    "phe_wet_mos = mask_phenology(infile, lcc_mask_wet)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0837030-bfe9-4f9c-87ee-3dc7fffa8df1",
   "metadata": {},
   "source": [
    "# 6. Get mean VHI\n",
    "\n",
    "We will now extract the Mean VHI for both years for our region of interest through the FAO map catalog..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c8a9a39-66e6-458d-af04-3933420fde20",
   "metadata": {},
   "source": [
    "## 6.1 Extract the mean VHI dekadal from the FAO map catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3700f795-7b5a-4ff0-bf33-5fe4765cc036",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>caption</th>\n",
       "      <th>description</th>\n",
       "      <th>additionalInfo</th>\n",
       "      <th>workspaceCode</th>\n",
       "      <th>dataType</th>\n",
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       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PRO</td>\n",
       "      <td>Progress of Season</td>\n",
       "      <td>The indicator depicts the development of crops...</td>\n",
       "      <td>{'flags': [{'value': 250, 'description': 'off ...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VHI_D</td>\n",
       "      <td>Vegetation Health Index (Dekadal)</td>\n",
       "      <td>The Vegetation Health Index (VHI) illustrates ...</td>\n",
       "      <td>{'epsg': 4326, 'size': [40320, 14673], 'flags'...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VHI_M</td>\n",
       "      <td>Vegetation Health Index (Monthly)</td>\n",
       "      <td>The Vegetation Health Index (VHI) illustrates ...</td>\n",
       "      <td>{'epsg': 4326, 'size': [40320, 14673], 'flags'...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HDF</td>\n",
       "      <td>Historic Drought Frequency</td>\n",
       "      <td>Historic Agricultural Drought Frenquecy Maps d...</td>\n",
       "      <td>{'flags': [{'value': 251, 'description': 'inco...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</td>\n",
       "      <td>{'pixelTimeSeries': False, 'areaStats': False}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ASI_A</td>\n",
       "      <td>Agricultural Stress Index (Annual)</td>\n",
       "      <td>The Annual ASI depicts the percentage of arabl...</td>\n",
       "      <td>{'flags': [{'value': 251, 'description': 'off ...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ASI_D</td>\n",
       "      <td>Agricultural Stress Index (Near Real Time, Dek...</td>\n",
       "      <td>The Agricultural Stress Index (ASI) is a quick...</td>\n",
       "      <td>{'flags': [{'value': 251, 'description': 'off ...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>VCI_M</td>\n",
       "      <td>Vegetation Condition Index (Monthly)</td>\n",
       "      <td>The Vegetation Condition Index (VCI) evaluates...</td>\n",
       "      <td>{'epsg': 4326, 'size': [40320, 14673], 'flags'...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DI_A</td>\n",
       "      <td>Drought Intensity (Annual)</td>\n",
       "      <td>Agricultural droughts are classified by their ...</td>\n",
       "      <td>{'epsg': 4326, 'size': [40320, 14673], 'flags'...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>VCI_D</td>\n",
       "      <td>Vegetation Condition Index (Dekadal)</td>\n",
       "      <td>The Vegetation Condition Index (VCI) evaluates...</td>\n",
       "      <td>{'epsg': 4326, 'size': [40320, 14673], 'flags'...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MVHI_A</td>\n",
       "      <td>Mean Vegetation Health Index (Annual)</td>\n",
       "      <td>The Mean Vegetation Health Index (Mean VHI) al...</td>\n",
       "      <td>{'epsg': 4326, 'size': [40320, 14673], 'flags'...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DI_D</td>\n",
       "      <td>Drought Intensity (Near Real Time, Dekadal)</td>\n",
       "      <td>Agricultural droughts are classified by their ...</td>\n",
       "      <td>{'epsg': 4326, 'size': [40320, 14673], 'flags'...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MVHI_D</td>\n",
       "      <td>Mean Vegetation Health Index (Dekadal)</td>\n",
       "      <td>The Mean Vegetation Health Index (Mean VHI) al...</td>\n",
       "      <td>{'epsg': 4326, 'size': [40320, 14673], 'flags'...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PHE</td>\n",
       "      <td>Crop-growing Season</td>\n",
       "      <td>The seasons’ progress is based on the long-ter...</td>\n",
       "      <td>{'unit': 'D', 'scale': 0, 'pixelMultiplier': 1...</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>2</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>[{'name': 'PHE'}, {'name': 'L3', 'index': 2, '...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      code                                            caption  \\\n",
       "0      PRO                                 Progress of Season   \n",
       "1    VHI_D                  Vegetation Health Index (Dekadal)   \n",
       "2    VHI_M                  Vegetation Health Index (Monthly)   \n",
       "3      HDF                         Historic Drought Frequency   \n",
       "4    ASI_A                 Agricultural Stress Index (Annual)   \n",
       "5    ASI_D  Agricultural Stress Index (Near Real Time, Dek...   \n",
       "6    VCI_M               Vegetation Condition Index (Monthly)   \n",
       "7     DI_A                         Drought Intensity (Annual)   \n",
       "8    VCI_D               Vegetation Condition Index (Dekadal)   \n",
       "9   MVHI_A              Mean Vegetation Health Index (Annual)   \n",
       "10    DI_D        Drought Intensity (Near Real Time, Dekadal)   \n",
       "11  MVHI_D             Mean Vegetation Health Index (Dekadal)   \n",
       "12     PHE                                Crop-growing Season   \n",
       "\n",
       "                                          description  \\\n",
       "0   The indicator depicts the development of crops...   \n",
       "1   The Vegetation Health Index (VHI) illustrates ...   \n",
       "2   The Vegetation Health Index (VHI) illustrates ...   \n",
       "3   Historic Agricultural Drought Frenquecy Maps d...   \n",
       "4   The Annual ASI depicts the percentage of arabl...   \n",
       "5   The Agricultural Stress Index (ASI) is a quick...   \n",
       "6   The Vegetation Condition Index (VCI) evaluates...   \n",
       "7   Agricultural droughts are classified by their ...   \n",
       "8   The Vegetation Condition Index (VCI) evaluates...   \n",
       "9   The Mean Vegetation Health Index (Mean VHI) al...   \n",
       "10  Agricultural droughts are classified by their ...   \n",
       "11  The Mean Vegetation Health Index (Mean VHI) al...   \n",
       "12  The seasons’ progress is based on the long-ter...   \n",
       "\n",
       "                                       additionalInfo workspaceCode dataType  \\\n",
       "0   {'flags': [{'value': 250, 'description': 'off ...          ASIS   RASTER   \n",
       "1   {'epsg': 4326, 'size': [40320, 14673], 'flags'...          ASIS   RASTER   \n",
       "2   {'epsg': 4326, 'size': [40320, 14673], 'flags'...          ASIS   RASTER   \n",
       "3   {'flags': [{'value': 251, 'description': 'inco...          ASIS   RASTER   \n",
       "4   {'flags': [{'value': 251, 'description': 'off ...          ASIS   RASTER   \n",
       "5   {'flags': [{'value': 251, 'description': 'off ...          ASIS   RASTER   \n",
       "6   {'epsg': 4326, 'size': [40320, 14673], 'flags'...          ASIS   RASTER   \n",
       "7   {'epsg': 4326, 'size': [40320, 14673], 'flags'...          ASIS   RASTER   \n",
       "8   {'epsg': 4326, 'size': [40320, 14673], 'flags'...          ASIS   RASTER   \n",
       "9   {'epsg': 4326, 'size': [40320, 14673], 'flags'...          ASIS   RASTER   \n",
       "10  {'epsg': 4326, 'size': [40320, 14673], 'flags'...          ASIS   RASTER   \n",
       "11  {'epsg': 4326, 'size': [40320, 14673], 'flags'...          ASIS   RASTER   \n",
       "12  {'unit': 'D', 'scale': 0, 'pixelMultiplier': 1...          ASIS   RASTER   \n",
       "\n",
       "    index                                       operation  hidden  \\\n",
       "0       1    {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "1       1    {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "2       1    {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "3       1  {'pixelTimeSeries': False, 'areaStats': False}   False   \n",
       "4       1    {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "5       1    {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "6       1    {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "7       1    {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "8       1    {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "9       1    {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "10      1    {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "11      1    {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "12      2    {'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",
       "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",
       "\n",
       "                                                 tags  \n",
       "0                                                 NaN  \n",
       "1                                                 NaN  \n",
       "2                                                 NaN  \n",
       "3                                                 NaN  \n",
       "4                                                 NaN  \n",
       "5                                                 NaN  \n",
       "6                                                 NaN  \n",
       "7                                                 NaN  \n",
       "8                                                 NaN  \n",
       "9                                                 NaN  \n",
       "10                                                NaN  \n",
       "11                                                NaN  \n",
       "12  [{'name': 'PHE'}, {'name': 'L3', 'index': 2, '...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workspace = 'ASIS'\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}'\n",
    "resp = requests.get(cubes_url).json()\n",
    "catalog = pd.DataFrame.from_dict(resp['response'])\n",
    "catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac40a121-8b75-4e2e-9eb0-95663b762b38",
   "metadata": {},
   "source": [
    "We are interested in the Mean Vegetation Health Index on dekadal scale. The mean VHI averages the VHI from start of the season 1 to the dekad given in the query payload. In order to download the right data we need to know the range of MOS in both the wet and dry years..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "70069417-1894-4dd3-a2e8-6a9a499450ef",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'phe_dry_mos' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_32765/674952429.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'MOS min dry: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphe_dry_mos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'MOS max dry: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphe_dry_mos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'MOS min wet: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphe_wet_mos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'MOS max wet: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphe_wet_mos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'phe_dry_mos' is not defined"
     ]
    }
   ],
   "source": [
    "print('MOS min dry: ', np.nanmin(phe_dry_mos))\n",
    "print('MOS max dry: ', np.nanmax(phe_dry_mos))\n",
    "print('MOS min wet: ', np.nanmin(phe_wet_mos))\n",
    "print('MOS max wet: ', np.nanmax(phe_wet_mos))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08dcc998-bd1a-4470-a1a8-c1f90c26505f",
   "metadata": {},
   "source": [
    "So basically we need to dowload data from dekad 10 onwards (month april) up till the end of the year...\n",
    "\n",
    "In order to link the dekad number with the files to download, we can prepare the following list, thereby making sure the dekad is presented in the format expected by the API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c373fc38-c238-4200-9a26-a0d26de79151",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dekad_no</th>\n",
       "      <th>dekad</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>[2015-04-01,2015-04-11)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>[2015-04-11,2015-04-21)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>[2015-04-21,2015-05-01)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>[2015-05-01,2015-05-11)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>[2015-05-11,2015-05-21)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>[2015-05-21,2015-06-01)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>[2015-06-01,2015-06-11)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17</td>\n",
       "      <td>[2015-06-11,2015-06-21)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18</td>\n",
       "      <td>[2015-06-21,2015-07-01)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "      <td>[2015-07-01,2015-07-11)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20</td>\n",
       "      <td>[2015-07-11,2015-07-21)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21</td>\n",
       "      <td>[2015-07-21,2015-08-01)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>22</td>\n",
       "      <td>[2015-08-01,2015-08-11)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23</td>\n",
       "      <td>[2015-08-11,2015-08-21)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24</td>\n",
       "      <td>[2015-08-21,2015-09-01)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>[2015-09-01,2015-09-11)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>26</td>\n",
       "      <td>[2015-09-11,2015-09-21)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>27</td>\n",
       "      <td>[2015-09-21,2015-10-01)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28</td>\n",
       "      <td>[2015-10-01,2015-10-11)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>29</td>\n",
       "      <td>[2015-10-11,2015-10-21)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>30</td>\n",
       "      <td>[2015-10-21,2015-11-01)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31</td>\n",
       "      <td>[2015-11-01,2015-11-11)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>32</td>\n",
       "      <td>[2015-11-11,2015-11-21)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>33</td>\n",
       "      <td>[2015-11-21,2015-12-01)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>34</td>\n",
       "      <td>[2015-12-01,2015-12-11)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>35</td>\n",
       "      <td>[2015-12-11,2015-12-21)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>36</td>\n",
       "      <td>[2015-12-21,2016-01-01)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10</td>\n",
       "      <td>[2018-04-01,2018-04-11)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11</td>\n",
       "      <td>[2018-04-11,2018-04-21)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>12</td>\n",
       "      <td>[2018-04-21,2018-05-01)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>13</td>\n",
       "      <td>[2018-05-01,2018-05-11)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>14</td>\n",
       "      <td>[2018-05-11,2018-05-21)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>15</td>\n",
       "      <td>[2018-05-21,2018-06-01)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>[2018-06-01,2018-06-11)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018-06-11,2018-06-21)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>18</td>\n",
       "      <td>[2018-06-21,2018-07-01)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>19</td>\n",
       "      <td>[2018-07-01,2018-07-11)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>20</td>\n",
       "      <td>[2018-07-11,2018-07-21)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>21</td>\n",
       "      <td>[2018-07-21,2018-08-01)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>22</td>\n",
       "      <td>[2018-08-01,2018-08-11)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>23</td>\n",
       "      <td>[2018-08-11,2018-08-21)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>24</td>\n",
       "      <td>[2018-08-21,2018-09-01)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>25</td>\n",
       "      <td>[2018-09-01,2018-09-11)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>26</td>\n",
       "      <td>[2018-09-11,2018-09-21)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>27</td>\n",
       "      <td>[2018-09-21,2018-10-01)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>28</td>\n",
       "      <td>[2018-10-01,2018-10-11)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>29</td>\n",
       "      <td>[2018-10-11,2018-10-21)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>30</td>\n",
       "      <td>[2018-10-21,2018-11-01)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>31</td>\n",
       "      <td>[2018-11-01,2018-11-11)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>32</td>\n",
       "      <td>[2018-11-11,2018-11-21)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>33</td>\n",
       "      <td>[2018-11-21,2018-12-01)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>34</td>\n",
       "      <td>[2018-12-01,2018-12-11)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>35</td>\n",
       "      <td>[2018-12-11,2018-12-21)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>36</td>\n",
       "      <td>[2018-12-21,2019-01-01)</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dekad_no                    dekad  year\n",
       "0         10  [2015-04-01,2015-04-11)  2015\n",
       "1         11  [2015-04-11,2015-04-21)  2015\n",
       "2         12  [2015-04-21,2015-05-01)  2015\n",
       "3         13  [2015-05-01,2015-05-11)  2015\n",
       "4         14  [2015-05-11,2015-05-21)  2015\n",
       "5         15  [2015-05-21,2015-06-01)  2015\n",
       "6         16  [2015-06-01,2015-06-11)  2015\n",
       "7         17  [2015-06-11,2015-06-21)  2015\n",
       "8         18  [2015-06-21,2015-07-01)  2015\n",
       "9         19  [2015-07-01,2015-07-11)  2015\n",
       "10        20  [2015-07-11,2015-07-21)  2015\n",
       "11        21  [2015-07-21,2015-08-01)  2015\n",
       "12        22  [2015-08-01,2015-08-11)  2015\n",
       "13        23  [2015-08-11,2015-08-21)  2015\n",
       "14        24  [2015-08-21,2015-09-01)  2015\n",
       "15        25  [2015-09-01,2015-09-11)  2015\n",
       "16        26  [2015-09-11,2015-09-21)  2015\n",
       "17        27  [2015-09-21,2015-10-01)  2015\n",
       "18        28  [2015-10-01,2015-10-11)  2015\n",
       "19        29  [2015-10-11,2015-10-21)  2015\n",
       "20        30  [2015-10-21,2015-11-01)  2015\n",
       "21        31  [2015-11-01,2015-11-11)  2015\n",
       "22        32  [2015-11-11,2015-11-21)  2015\n",
       "23        33  [2015-11-21,2015-12-01)  2015\n",
       "24        34  [2015-12-01,2015-12-11)  2015\n",
       "25        35  [2015-12-11,2015-12-21)  2015\n",
       "26        36  [2015-12-21,2016-01-01)  2015\n",
       "27        10  [2018-04-01,2018-04-11)  2018\n",
       "28        11  [2018-04-11,2018-04-21)  2018\n",
       "29        12  [2018-04-21,2018-05-01)  2018\n",
       "30        13  [2018-05-01,2018-05-11)  2018\n",
       "31        14  [2018-05-11,2018-05-21)  2018\n",
       "32        15  [2018-05-21,2018-06-01)  2018\n",
       "33        16  [2018-06-01,2018-06-11)  2018\n",
       "34        17  [2018-06-11,2018-06-21)  2018\n",
       "35        18  [2018-06-21,2018-07-01)  2018\n",
       "36        19  [2018-07-01,2018-07-11)  2018\n",
       "37        20  [2018-07-11,2018-07-21)  2018\n",
       "38        21  [2018-07-21,2018-08-01)  2018\n",
       "39        22  [2018-08-01,2018-08-11)  2018\n",
       "40        23  [2018-08-11,2018-08-21)  2018\n",
       "41        24  [2018-08-21,2018-09-01)  2018\n",
       "42        25  [2018-09-01,2018-09-11)  2018\n",
       "43        26  [2018-09-11,2018-09-21)  2018\n",
       "44        27  [2018-09-21,2018-10-01)  2018\n",
       "45        28  [2018-10-01,2018-10-11)  2018\n",
       "46        29  [2018-10-11,2018-10-21)  2018\n",
       "47        30  [2018-10-21,2018-11-01)  2018\n",
       "48        31  [2018-11-01,2018-11-11)  2018\n",
       "49        32  [2018-11-11,2018-11-21)  2018\n",
       "50        33  [2018-11-21,2018-12-01)  2018\n",
       "51        34  [2018-12-01,2018-12-11)  2018\n",
       "52        35  [2018-12-11,2018-12-21)  2018\n",
       "53        36  [2018-12-21,2019-01-01)  2018"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dekad numbers range between 10 and 36\n",
    "dekad_nr = list(range(10, 37))\n",
    "years = [2015, 2018]\n",
    "dekad_df = []\n",
    "\n",
    "for year in years:\n",
    "    dekad_list = []\n",
    "    for month in range(4, 13):\n",
    "        if month < 9:\n",
    "            dekad_list.append(f'[{year}-0{month}-01,{year}-0{month}-11)')\n",
    "            dekad_list.append(f'[{year}-0{month}-11,{year}-0{month}-21)')\n",
    "            dekad_list.append(f'[{year}-0{month}-21,{year}-0{month+1}-01)')\n",
    "        elif month == 9:\n",
    "            dekad_list.append(f'[{year}-0{month}-01,{year}-0{month}-11)')\n",
    "            dekad_list.append(f'[{year}-0{month}-11,{year}-0{month}-21)')\n",
    "            dekad_list.append(f'[{year}-0{month}-21,{year}-{month+1}-01)')\n",
    "        elif month == 12:\n",
    "            dekad_list.append(f'[{year}-{month}-01,{year}-{month}-11)')\n",
    "            dekad_list.append(f'[{year}-{month}-11,{year}-{month}-21)')\n",
    "            dekad_list.append(f'[{year}-{month}-21,{year+1}-0{month-11}-01)')\n",
    "        else:\n",
    "            dekad_list.append(f'[{year}-{month}-01,{year}-{month}-11)')\n",
    "            dekad_list.append(f'[{year}-{month}-11,{year}-{month}-21)')\n",
    "            dekad_list.append(f'[{year}-{month}-21,{year}-{month+1}-01)')\n",
    "            \n",
    "    intermediate_dict = {'dekad_no': dekad_nr, 'dekad': dekad_list,\n",
    "                         'year': [year] * len(dekad_nr)}\n",
    "    dekad_df.append(pd.DataFrame(intermediate_dict))\n",
    "\n",
    "dekad_df = pd.concat(dekad_df, axis=0, ignore_index=True)\n",
    "dekad_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c6a91a2-0164-4ea2-8b93-3f3fe03a9d7a",
   "metadata": {},
   "source": [
    "The following cell demonstrates how the download of the MVHI files was done.\n",
    "As it takes a long time to download all, we have prepared the data for you in the ./data/mvhi folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b94809fa-b9a9-4bc1-9c2d-551ecd64e446",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEASURE:  MVHI_D\n",
      "Sending query for each polygon...\n",
      "Processing polygon 1 / 1...\n",
      "RUNNING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ['https://io.apps.fao.org/gismgr/download/04b80618-c74f-49c8-9b84-1e4945252b90/MVHI_D_2015_10.tif']\n",
      "MEASURE:  MVHI_D\n",
      "Sending query for each polygon...\n",
      "Processing polygon 1 / 1...\n",
      "RUNNING . . . . . . . . . . . . . . . . . . . . ['https://io.apps.fao.org/gismgr/download/4b4e947e-b68c-4d1b-b4ac-38435964f65a/MVHI_D_2015_11.tif']\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_32765/3101698526.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     27\u001b[0m                       \u001b[0moutputfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdimensions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m                       \u001b[0mshapefile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mroifile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabelcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabelcol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m                       APIToken=api_token)\n\u001b[0m\u001b[1;32m     30\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_32765/3179998188.py\u001b[0m in \u001b[0;36mrequest_tif_files\u001b[0;34m(workspace, product_code, crs, outputfilename, dimensions, shapefile, labelcol, bbox, APIToken)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0;31m# get access token\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m     resp_signin = requests.post(path_sign_in,\n\u001b[0;32m---> 38\u001b[0;31m                               headers={'X-GISMGR-API-KEY': APIToken})\n\u001b[0m\u001b[1;32m     39\u001b[0m     \u001b[0mresp_signin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresp_signin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0mAccessToken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresp_signin\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'response'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accessToken'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/site-packages/requests/api.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m     \"\"\"\n\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"post\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/site-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    585\u001b[0m         }\n\u001b[1;32m    586\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 587\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    699\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    702\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    497\u001b[0m                     \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 499\u001b[0;31m                     \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    500\u001b[0m                 )\n\u001b[1;32m    501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    708\u001b[0m                 \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    709\u001b[0m                 \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 710\u001b[0;31m                 \u001b[0mchunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchunked\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    711\u001b[0m             )\n\u001b[1;32m    712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    447\u001b[0m                     \u001b[0;31m# Python 3 (including for exceptions like SystemExit).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m                     \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m                     \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/site-packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    442\u001b[0m                 \u001b[0;31m# Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m                     \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    445\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m                     \u001b[0;31m# Remove the TypeError from the exception chain in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1371\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1372\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1373\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1374\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1375\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    320\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 589\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    590\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1069\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1070\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1071\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1072\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1073\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/eoafrica/lib/python3.7/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m    927\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 929\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    930\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "product_code = 'MVHI_D'\n",
    "workspace = 'ASIS'\n",
    "\n",
    "season = \"S1\"\n",
    "land = \"C\"  \n",
    "\n",
    "crs = 4326\n",
    "\n",
    "api_token = '4c95eef5536df1786815b04ed92f8a9e7a59bb20976e7e832db2eeef8c4cafd53b0b74d222614556'\n",
    "\n",
    "for i, row in dekad_df.iterrows():\n",
    "    \n",
    "    year = row.year\n",
    "    dekad_no = row.dekad_no\n",
    "    dekad = row.dekad\n",
    "    \n",
    "    dimensions = [{\"code\": \"DEKAD\",\n",
    "                   \"values\": [dekad]},\n",
    "                  { \"code\": \"SEASON\",\n",
    "                   \"values\": [season]},\n",
    "                  {\"code\": \"LAND\",\n",
    "                   \"values\": [land]}]\n",
    "    \n",
    "    # indicate how you want to name your rasterfile\n",
    "    outputfilename = f'MVHI_D_{year}_{dekad_no}.tif' \n",
    "    \n",
    "    output = request_tif_files(workspace, product_code, crs,\n",
    "                      outputfilename, dimensions,\n",
    "                      shapefile=roifile, labelcol=labelcol,\n",
    "                      APIToken=api_token)\n",
    "    print(output)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d26b73a7-51ea-435f-a402-a72260b2578c",
   "metadata": {},
   "source": [
    "Before we can work properly with the MVHI maps, we need to warp them so their resolution matches with the LC mask."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d776f7d-b872-43bf-bb57-81ac1e00ae7f",
   "metadata": {},
   "source": [
    "## 6.2 Warp MVHI\n",
    "\n",
    "Again to optimize the workflow and automate the warping process a for loop is applied over all the files present in the inputfolder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c68f1283-5bf8-4aeb-afdf-a338c6f0fcfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define input_folder\n",
    "input_folder = Path(r'./data/mvhi' )\n",
    "# get list of tif files in the input_folder\n",
    "input_fhs = sorted(glob.glob(str(input_folder / '*.tif'))) \n",
    "\n",
    "# define the file of which we want the projection/resolution (destination info):\n",
    "dst_info = gdal.Info(gdal.Open(r'./results/phenology/L2_PHE_dry_MOS_S1_masked.tif'), format='json')\n",
    "\n",
    "# define output folder:\n",
    "output_folder = Path(r'./results/mvhi/warped')\n",
    "# create output_folder if it does not exist\n",
    "if not os.path.exists(output_folder): \n",
    "    os.makedirs(output_folder)\n",
    "    \n",
    "# loop over all files:\n",
    "for input_fh in input_fhs:\n",
    "    src_info = gdal.Info(gdal.Open(input_fh), format='json')\n",
    "    filename = os.path.basename(input_fh)\n",
    "    output_file = str(output_folder / f'{filename[:-4]}_warped.tif')\n",
    "    \n",
    "    output = gdal.Warp(output_file, input_fh, format='GTiff',\n",
    "              srcSRS=src_info['coordinateSystem']['wkt'],\n",
    "              dstSRS=dst_info['coordinateSystem']['wkt'],\n",
    "              width=dst_info['size'][0],\n",
    "              height=dst_info['size'][1],\n",
    "              outputBounds=(dst_info['cornerCoordinates']['lowerLeft'][0],\n",
    "                            dst_info['cornerCoordinates']['lowerLeft'][1],\n",
    "                            dst_info['cornerCoordinates']['upperRight'][0],\n",
    "                            dst_info['cornerCoordinates']['upperRight'][1]),\n",
    "              outputBoundsSRS=dst_info['coordinateSystem']['wkt'],\n",
    "              resampleAlg='near')\n",
    "    # (use the following line to make sure the output is written to disk correctly)\n",
    "    output = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46cb612b-660d-4490-bbd2-c62a345c15bb",
   "metadata": {},
   "source": [
    "## 6.3 Compute MVHI for the region of interest\n",
    "\n",
    "Now we want for each pixel the Mean VHI over the first half of the growing season. We simply need for each pixel the value of the MVHI which corresponds with the dekad at which the season peaks. This can be achieved by looping over the different dekads and assigning the right values to the right pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9491138-e738-4c8e-8b81-3b6229f2c499",
   "metadata": {},
   "outputs": [],
   "source": [
    "def produce_mvhi(mos_ds, mvhi_dir, year, outfile):\n",
    "    \n",
    "    # mos_ds = MOS information for your area of interest\n",
    "    \n",
    "    # define final result\n",
    "    output = xr.zeros_like(mos_ds)\n",
    "    \n",
    "    # check which unique MOS values occur, ignoring NaN\n",
    "    mos_val = list(np.unique(mos_ds.values[~np.isnan(mos_ds.values)]))\n",
    "    \n",
    "    for mos in mos_val:\n",
    "        mos = int(mos)\n",
    "        mvhi_mos = rioxarray.open_rasterio(str(mvhi_dir / f'MVHI_D_{year}_{mos}_warped.tif'))\n",
    "        # get MVHI values where phe_dry_mos = dekad\n",
    "        mask = np.where(mos_ds != mos, 0, mvhi_mos)\n",
    "        # add to final result\n",
    "        output = output + mask\n",
    "    \n",
    "    # make sure result contains NaN where MOS == np.NaN\n",
    "    output = output.where(~np.isnan(mos_ds))\n",
    "    # save to .tif file\n",
    "    output.rio.to_raster(outfile)\n",
    "    \n",
    "    return output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "158cfc92-78c2-43dc-a369-6e112c54effa",
   "metadata": {},
   "source": [
    "Apply function to wet and dry year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f9eeb1b-b6e8-4ca1-a3f1-146642cf23b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "outfile = str(output_folder / 'Final_mvhi_dry.tif')\n",
    "mvhi_dry = produce_mvhi(phe_dry_mos, output_folder, 2015, outfile)\n",
    "\n",
    "outfile = str(output_folder / 'Final_mvhi_wet.tif')\n",
    "mvhi_wet = produce_mvhi(phe_wet_mos, output_folder, 2018, outfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07e3b46b-9d7a-4b35-8ceb-3f9b93d1adb9",
   "metadata": {},
   "source": [
    "## 6.4 Classify MVHI values into drought severity classes\n",
    "\n",
    "Our map represents continuous values of the Mean VHI. To simplify and convert them to a drought intensity map, we can reclass the array. The classes are based on [this FAO website](https://www.fao.org/giews/earthobservation/asis/index_1.jsp?lang=en).\n",
    "\n",
    "- Class 1: MVHI < 0.25    : Extreme\n",
    "- Class 2: MVHI 0.25-0.35 : Severe\n",
    "- Class 3: MVHI 0.35-0.38 : Moderate \n",
    "- Class 4: MVHI 0.38-0.42 : Mild\n",
    "- Class 5: MVHI 0.42-1    : None\n",
    "- Class 6: MVHI > 1       : Insufficient data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4754b4c4-f8fc-4987-9e1c-b855695b0d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify_drought(mvhi, outfile):\n",
    "    \n",
    "    # define output\n",
    "    output = xr.zeros_like(mvhi)\n",
    "    class1 = np.where((mvhi < 0.25) & (mvhi > 0), 1, output)\n",
    "    class2 = np.where((mvhi < 0.35) & (mvhi >= 0.25), 2, output)\n",
    "    class3 = np.where((mvhi < 0.38) & (mvhi >= 0.35), 3, output) \n",
    "    class4 = np.where((mvhi < 0.42) & (mvhi >= 0.38), 4, output) \n",
    "    class5 = np.where((mvhi <= 1) & (mvhi >= 0.42), 5, output) \n",
    "    class6 = np.where(mvhi > 1, 6, output) \n",
    "    \n",
    "    output.values = class1 + class2 + class3 + class4 + class5 + class6\n",
    "    output = output.where(output != 0)\n",
    "\n",
    "    output.rio.to_raster(outfile)\n",
    "    \n",
    "    return output"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e63de163-19e3-43d9-b188-7e59515beb39",
   "metadata": {},
   "source": [
    "Apply this function for both wet and dry years and plot the result...\n",
    "\n",
    "QUESTION: Based on the figures you produce, where in the Gadaref province do you expect to see the highest impact of the drought on the vegetation? Please describe. Add the figures of MVHI for both the wet and dry years in your report!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e0270c17-da79-4560-871e-2cb3a407e5dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drought_dir = outdir / 'drought_severity'\n",
    "drought_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "outfile = str(drought_dir / 'drought_wet.tif')\n",
    "drought_wet = classify_drought(mvhi_wet, outfile)\n",
    "drought_wet.plot(cmap=\"RdBu\")\n",
    "plt.title(\"Drought Intenisty Classes Wet Year\")\n",
    "plt.ylabel(\"latitude\")\n",
    "plt.xlabel(\"longitude\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "outfile = str(drought_dir / 'drought_dry.tif')\n",
    "drought_dry = classify_drought(mvhi_dry, outfile)\n",
    "drought_dry.plot(cmap=\"RdBu\")\n",
    "plt.title(\"Drought Intenisty Classes Dry Year\")\n",
    "plt.ylabel(\"latitude\")\n",
    "plt.xlabel(\"longitude\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85aaedef-e860-430d-9a78-6ee825127599",
   "metadata": {},
   "source": [
    "Now you can clearly see where specifically in the Gadaref province the impact of the drought is expected to be highest..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc80cb82-b218-4120-a15f-1158e36c5a63",
   "metadata": {},
   "source": [
    "# 7. Check precipitation (and evapotranspiration) patterns\n",
    "\n",
    "QUESTION: Request precipitation and evapotranspiration data from the WaPOR data portal for a pixel clearly affected by the drought (based on the drought intensity maps you just produced). Compare the patterns between the dry and wet year. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3c99161a-2ac0-4e59-8fc5-670278d0b8e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f6f919ae410>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SPEI index\n",
    "\n",
    "# NOT PART OF THIS EXERCISE, BUT FYI...\n",
    "\n",
    "import cftime\n",
    "\n",
    "# Reading the one degree dataset...\n",
    "fn = './results/spei03_one_degree.nc' \n",
    "spei_ds = rioxarray.open_rasterio(fn)\n",
    "# # to shorten the list and delete recurring units\n",
    "spei_ds.attrs['units'] = 'Standardized values' \n",
    "\n",
    "# zoom in on one location\n",
    "array_pt = spei_ds[(spei_ds.time >= cftime.DatetimeProlepticGregorian(2015, 1, 1)) & \n",
    "                 (spei_ds.time <= cftime.DatetimeProlepticGregorian(2015, 12, 31)),\n",
    "                 (90-13), # latitude accounting for the pixel numbering in the array \n",
    "                 (180+35)] # longitude accounting for the pixel numbering in the array \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "array_pt.plot(ax=ax)\n",
    "plt.title(\"SPEI timeseries for (13.4N , 35.3E)\")\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('SPEI')\n",
    "plt.axhline(y=0)\n",
    "\n",
    "# zoom in on one location\n",
    "array_pt = spei_ds[(spei_ds.time >= cftime.DatetimeProlepticGregorian(2018, 1, 1)) & \n",
    "                 (spei_ds.time <= cftime.DatetimeProlepticGregorian(2018, 12, 31)),\n",
    "                 (90-13), # latitude accounting for the pixel numbering in the array \n",
    "                 (180+35)] # longitude accounting for the pixel numbering in the array \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "array_pt.plot(ax=ax)\n",
    "plt.title(\"SPEI timeseries for (13.4N , 35.3E)\")\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('SPEI')\n",
    "plt.axhline(y=0)\n",
    "\n",
    "# second part of the year 2015 is indeed drier compared to 2018..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3260718b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making some preparations...\n",
      "MEASURE:  WATER_MM\n",
      "DIMENSION:  DEKAD\n",
      "Sending query for each point...\n",
      "Processing point 1 / 1...\n",
      "Query successful!\n",
      "Making some preparations...\n",
      "MEASURE:  WATER_MM\n",
      "DIMENSION:  DEKAD\n",
      "Sending query for each point...\n",
      "Processing point 1 / 1...\n",
      "Query successful!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Dry year')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# precipitation vs evapotranspiration\n",
    "def get_pixel_timeseries(workspace, product_code,\n",
    "                         coordinates, crs,\n",
    "                         start_date, end_date):\n",
    "    '''\n",
    "    Function to extract one or multiple pixel time series from the FAO portal.\n",
    "    Parameters:\n",
    "    - workspace: workspace in the FAO catalog where the product of interest is located.\n",
    "    - product_code: specific product you wish to download.\n",
    "    - coordinates: dictionary with keys representing label of the point\n",
    "        and values the x,y coordinate pair in the CRS defined by the next parameter.\n",
    "        In case of Geographic CRS, the order should be [lat, lon].\n",
    "    - crs: EPSG code of projection system (int)\n",
    "    - start_date: start date of the time series.\n",
    "        Should be str in the following format: \"yyyy-mm-dd\".\n",
    "    - end_date: end date of the time series.\n",
    "        Should be str in the following format: \"yyyy-mm-dd\".\n",
    "        \n",
    "    The function will return a dataframe with time as index and each column\n",
    "    representing the query result for an individual point.\n",
    "    '''\n",
    "    \n",
    "    print('Making some preparations...')\n",
    "    \n",
    "    path_query = r'https://io.apps.fao.org/gismgr/api/v1/query/'\n",
    "    \n",
    "    crs = f'EPSG:{crs}'\n",
    "    \n",
    "    # get datacube measure\n",
    "    cube_url = f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{product_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/{product_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)\n",
    "    \n",
    "    print('Sending query for each point...')\n",
    "    npoints = len(coordinates.keys())\n",
    "    i = 0    \n",
    "    for label, coo in coordinates.items():\n",
    "        \n",
    "        print(f'Processing point {i+1} / {npoints}...')\n",
    "        query = {\n",
    "        \"type\": \"PixelTimeSeries\",\n",
    "        \"params\": {\n",
    "            \"cube\": {\n",
    "            \"code\": product_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,            \n",
    "            \"x\":coo[1],\n",
    "            \"y\":coo[0]\n",
    "            }\n",
    "        }\n",
    "        }\n",
    "        \n",
    "        resp_query = requests.post(path_query, json=query)\n",
    "        resp = resp_query.json()\n",
    "        results = resp['response']\n",
    "        df = pd.DataFrame(results['items'], columns=['Time', label])\n",
    "        df.index = pd.to_datetime(df['Time'], format='%Y-%m-%d')\n",
    "        df = df.drop(columns=['Time'])\n",
    "        if i == 0:\n",
    "            df_fin = df\n",
    "        else:\n",
    "            df_fin = pd.merge(df_fin, df, left_index=True, right_index=True)  \n",
    "        \n",
    "        i += 1\n",
    "        print('Query successful!')\n",
    "        \n",
    "    return df_fin\n",
    "\n",
    "crs = 4326 \n",
    "coordinates = {'POI': [13.4666, 35.3385]}\n",
    "\n",
    "product_code = \"L1_PCP_D\" \n",
    "workspace = 'WAPOR_2'\n",
    "\n",
    "start_date = \"2015-01-01\"\n",
    "end_date = \"2015-12-31\"\n",
    "\n",
    "pcp_dry = get_pixel_timeseries(workspace, product_code,\n",
    "                         coordinates, crs,\n",
    "                         start_date, end_date)\n",
    "pcp_dry = pcp_dry.rename(columns={'POI': 'pcp'})\n",
    "\n",
    "product_code = \"L1_AETI_D\" \n",
    "\n",
    "aeti_dry = get_pixel_timeseries(workspace, product_code,\n",
    "                         coordinates, crs,\n",
    "                         start_date, end_date)\n",
    "aeti_dry = aeti_dry.rename(columns={'POI': 'aeti'})\n",
    "\n",
    "data_dry = pd.merge(pcp_dry, aeti_dry, right_index=True, left_index=True)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "data_dry.plot(ax=ax)\n",
    "ax.set_title('Dry year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7ebf52e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making some preparations...\n",
      "MEASURE:  WATER_MM\n",
      "DIMENSION:  DEKAD\n",
      "Sending query for each point...\n",
      "Processing point 1 / 1...\n",
      "Query successful!\n",
      "Making some preparations...\n",
      "MEASURE:  WATER_MM\n",
      "DIMENSION:  DEKAD\n",
      "Sending query for each point...\n",
      "Processing point 1 / 1...\n",
      "Query successful!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Wet year')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "product_code = \"L1_PCP_D\" \n",
    "workspace = 'WAPOR_2'\n",
    "\n",
    "start_date = \"2018-01-01\"\n",
    "end_date = \"2018-12-31\"\n",
    "\n",
    "pcp_wet = get_pixel_timeseries(workspace, product_code,\n",
    "                         coordinates, crs,\n",
    "                         start_date, end_date)\n",
    "pcp_wet = pcp_wet.rename(columns={'POI': 'pcp'})\n",
    "\n",
    "product_code = \"L1_AETI_D\" \n",
    "\n",
    "aeti_wet = get_pixel_timeseries(workspace, product_code,\n",
    "                         coordinates, crs,\n",
    "                         start_date, end_date)\n",
    "aeti_wet = aeti_wet.rename(columns={'POI': 'aeti'})\n",
    "\n",
    "data_wet = pd.merge(pcp_wet, aeti_wet, right_index=True, left_index=True)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "data_wet.plot(ax=ax)\n",
    "ax.set_title('Wet year')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7214d68-9a35-47f3-900f-b65e88497734",
   "metadata": {},
   "source": [
    "# 8. Impact of drought on vegetation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e08c96-4f71-4d1b-9492-98fc5f8ad142",
   "metadata": {},
   "source": [
    "## 8.1 Net Primary Productivity throughout the season"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21bbd8aa-2be1-4e7e-b3fa-a8dc3bd9671c",
   "metadata": {},
   "source": [
    "Now let's zoom in on a specific location where the drought had highest impact according to our computed drought indicator...\n",
    "\n",
    "Retrieve the NPP timeseries from the WaPOR data portal for the given location, for both the wet and dry years.\n",
    "\n",
    "Location: [13.4712, 35.3531] (Lat, Lon)\n",
    "\n",
    "Make use of the **get_pixel_timeseries** function from exercise 6.2.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3b699089",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pixel_timeseries(workspace, product_code,\n",
    "                         coordinates, crs,\n",
    "                         start_date, end_date):\n",
    "    '''\n",
    "    Function to extract one or multiple pixel time series from the FAO portal.\n",
    "    Parameters:\n",
    "    - workspace: workspace in the FAO catalog where the product of interest is located.\n",
    "    - product_code: specific product you wish to download.\n",
    "    - coordinates: dictionary with keys representing label of the point\n",
    "        and values the x,y coordinate pair in the CRS defined by the next parameter.\n",
    "        In case of Geographic CRS, the order should be [lat, lon].\n",
    "    - crs: EPSG code of projection system (int)\n",
    "    - start_date: start date of the time series.\n",
    "        Should be str in the following format: \"yyyy-mm-dd\".\n",
    "    - end_date: end date of the time series.\n",
    "        Should be str in the following format: \"yyyy-mm-dd\".\n",
    "        \n",
    "    The function will return a dataframe with time as index and each column\n",
    "    representing the query result for an individual point.\n",
    "    '''\n",
    "    \n",
    "    print('Making some preparations...')\n",
    "    \n",
    "    path_query = r'https://io.apps.fao.org/gismgr/api/v1/query/'\n",
    "    \n",
    "    crs = f'EPSG:{crs}'\n",
    "    \n",
    "    # get datacube measure\n",
    "    cube_url = f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{product_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/{product_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)\n",
    "    \n",
    "    print('Sending query for each point...')\n",
    "    npoints = len(coordinates.keys())\n",
    "    i = 0    \n",
    "    for label, coo in coordinates.items():\n",
    "        \n",
    "        print(f'Processing point {i+1} / {npoints}...')\n",
    "        query = {\n",
    "        \"type\": \"PixelTimeSeries\",\n",
    "        \"params\": {\n",
    "            \"cube\": {\n",
    "            \"code\": product_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,            \n",
    "            \"x\":coo[1],\n",
    "            \"y\":coo[0]\n",
    "            }\n",
    "        }\n",
    "        }\n",
    "        \n",
    "        resp_query = requests.post(path_query, json=query)\n",
    "        resp = resp_query.json()\n",
    "        results = resp['response']\n",
    "        df = pd.DataFrame(results['items'], columns=['Time', label])\n",
    "        df.index = pd.to_datetime(df['Time'], format='%Y-%m-%d')\n",
    "        df = df.drop(columns=['Time'])\n",
    "        if i == 0:\n",
    "            df_fin = df\n",
    "        else:\n",
    "            df_fin = pd.merge(df_fin, df, left_index=True, right_index=True)  \n",
    "        \n",
    "        i += 1\n",
    "        print('Query successful!')\n",
    "        \n",
    "    return df_fin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0fadb90c-e573-4184-bf35-272cd6c6a950",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify parameters for API query\n",
    "point = {'test': [13.4666, 35.3385]} \n",
    "crs = 4326\n",
    "product_code = \"L1_NPP_D\"\n",
    "workspace = 'WAPOR_2'\n",
    "start_date_dry = \"2015-01-01\"\n",
    "end_date_dry = \"2015-12-31\"\n",
    "start_date_wet = \"2018-01-01\"\n",
    "end_date_wet = \"2018-12-31\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c499e066-a03e-4fea-8191-6f1ac6ffa95d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making some preparations...\n",
      "MEASURE:  NPP\n",
      "DIMENSION:  DEKAD\n",
      "Sending query for each point...\n",
      "Processing point 1 / 1...\n",
      "Query successful!\n",
      "Making some preparations...\n",
      "MEASURE:  NPP\n",
      "DIMENSION:  DEKAD\n",
      "Sending query for each point...\n",
      "Processing point 1 / 1...\n",
      "Query successful!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_dry</th>\n",
       "      <th>dekad</th>\n",
       "      <th>test_wet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.248</td>\n",
       "      <td>1</td>\n",
       "      <td>0.483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.272</td>\n",
       "      <td>2</td>\n",
       "      <td>0.474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.271</td>\n",
       "      <td>3</td>\n",
       "      <td>0.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.200</td>\n",
       "      <td>4</td>\n",
       "      <td>0.268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.209</td>\n",
       "      <td>5</td>\n",
       "      <td>0.198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_dry  dekad  test_wet\n",
       "0     0.248      1     0.483\n",
       "1     0.272      2     0.474\n",
       "2     0.271      3     0.346\n",
       "3     0.200      4     0.268\n",
       "4     0.209      5     0.198"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dry year\n",
    "df_dry = get_pixel_timeseries(workspace, product_code,\n",
    "                              point, crs,\n",
    "                              start_date_dry, end_date_dry)\n",
    "# add dekad number\n",
    "df_dry['dekad'] = range(1, len(df_dry)+1)\n",
    "\n",
    "# Wet year\n",
    "df_wet = get_pixel_timeseries(workspace, product_code,\n",
    "                              point, crs,\n",
    "                              start_date_wet, end_date_wet)\n",
    "\n",
    "# add dekad number\n",
    "df_wet['dekad'] = range(1, len(df_wet)+1)\n",
    "\n",
    "npp_df = pd.merge(df_dry, df_wet, left_on='dekad', right_on='dekad',\n",
    "                  suffixes=('_dry', '_wet'))\n",
    "npp_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c170a0db",
   "metadata": {},
   "source": [
    "Now plot both timeseries in one graph.\n",
    "\n",
    "QUESTION: Discuss how the drought had an impact on vegetation productivity for this specific location. Please include the plot in your answer to this question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a3ca3433-1a65-4add-a570-4ed960efce81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'NPP time series example for dry and wet year')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figs, ax = plt.subplots(1, figsize=(12, 6))\n",
    "\n",
    "npp_df['test_dry'].plot(ax=ax,label='Dry', marker='o')\n",
    "npp_df['test_wet'].plot(ax=ax,label='Wet', marker='o')\n",
    "\n",
    "ax.set_ylabel('NPP [gC/m2/day]')\n",
    "ax.legend(loc='upper right', ncol=5)\n",
    "ax.set_title('NPP time series example for dry and wet year')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7457749a-14d2-44c1-b0bd-03aa40f140f7",
   "metadata": {},
   "source": [
    "## 8.2 Total Biomassa Production during the season\n",
    "\n",
    "Let's now check the impact on total biomass production across the entire season, again for the location specified above.\n",
    "Retrieve the Total Biomass Production product from the WaPOR data portal.\n",
    "\n",
    "Since we now need a seasonal product, we need to adjust the **get_pixel_timeseries** function from exercise 6.2.3 a bit..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3b389733",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pixel_seasonal_value(workspace, product_code,\n",
    "                         coordinates, crs,\n",
    "                         dimensions):\n",
    "    '''\n",
    "    Function to extract one or multiple pixel time series from the FAO portal.\n",
    "    Parameters:\n",
    "    - workspace: workspace in the FAO catalog where the product of interest is located.\n",
    "    - product_code: specific product you wish to download.\n",
    "    - coordinates: dictionary with keys representing label of the point\n",
    "        and values the x,y coordinate pair in the CRS defined by the next parameter.\n",
    "        In case of Geographic CRS, the order should be [lat, lon].\n",
    "    - crs: EPSG code of projection system (int)\n",
    "    - dimensions: specifies for which year, season etc. the product needs to be downloaded\n",
    "        \n",
    "    The function will return a dataframe with time as index and each column\n",
    "    representing the query result for an individual point.\n",
    "    '''\n",
    "    \n",
    "    print('Making some preparations...')\n",
    "    \n",
    "    path_query = r'https://io.apps.fao.org/gismgr/api/v1/query/'\n",
    "    \n",
    "    crs = f'EPSG:{crs}'\n",
    "    \n",
    "    # get datacube measure\n",
    "    cube_url = f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{product_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/{product_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)\n",
    "    \n",
    "    print('Sending query for each point...')\n",
    "    npoints = len(coordinates.keys())\n",
    "    i = 0    \n",
    "    for label, coo in coordinates.items():\n",
    "        \n",
    "        print(f'Processing point {i+1} / {npoints}...')\n",
    "        query = {\n",
    "        \"type\": \"PixelTimeSeries\",\n",
    "        \"params\": {\n",
    "            \"cube\": {\n",
    "            \"code\": product_code,\n",
    "            \"workspaceCode\": workspace,\n",
    "            \"language\": \"en\"\n",
    "            },\n",
    "            \"dimensions\": dimensions,\n",
    "            \"measures\": [\n",
    "            measure\n",
    "            ],\n",
    "            \"point\": {\n",
    "            \"crs\": crs,            \n",
    "            \"x\":coo[1],\n",
    "            \"y\":coo[0]\n",
    "            }\n",
    "        }\n",
    "        }\n",
    "        \n",
    "        resp_query = requests.post(path_query, json=query)\n",
    "        resp = resp_query.json()\n",
    "        results = resp['response']\n",
    "        df = pd.DataFrame(results['items'], columns=['Time', label])\n",
    "        df.index = pd.to_datetime(df['Time'], format='%Y-%m-%d')\n",
    "        df = df.drop(columns=['Time'])\n",
    "        if i == 0:\n",
    "            df_fin = df\n",
    "        else:\n",
    "            df_fin = pd.merge(df_fin, df, left_index=True, right_index=True)  \n",
    "        \n",
    "        i += 1\n",
    "        print('Query successful!')\n",
    "        \n",
    "    return df_fin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f11d7757-a35f-46eb-80c4-e1db9bbb760e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify parameters for API query\n",
    "point = {'test': [13.4666, 35.3385]} \n",
    "crs = 4326\n",
    "product_code = \"L2_TBP_S\"\n",
    "workspace = 'WAPOR_2'\n",
    "year_dry = '[2015-01-01,2016-01-01)'\n",
    "year_wet = '[2018-01-01,2019-01-01)'\n",
    "season = \"S1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f463f228",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making some preparations...\n",
      "MEASURE:  LPR_S\n",
      "DIMENSION:  YEAR\n",
      "Sending query for each point...\n",
      "Processing point 1 / 1...\n",
      "Query successful!\n",
      "Making some preparations...\n",
      "MEASURE:  LPR_S\n",
      "DIMENSION:  YEAR\n",
      "Sending query for each point...\n",
      "Processing point 1 / 1...\n",
      "Query successful!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dry</th>\n",
       "      <td>3030.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wet</th>\n",
       "      <td>5510.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       test\n",
       "Dry  3030.0\n",
       "Wet  5510.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dry year\n",
    "dimensions = [{\"code\": \"YEAR\",\n",
    "               \"values\": [year_dry]},\n",
    "              {\"code\": \"SEASON\",\n",
    "               \"values\": [season]}]\n",
    "tbp_dry = get_pixel_seasonal_value(workspace, product_code,\n",
    "                              point, crs,\n",
    "                              dimensions)\n",
    "\n",
    "# Wet year\n",
    "dimensions = [{\"code\": \"YEAR\",\n",
    "               \"values\": [year_wet]},\n",
    "              {\"code\": \"SEASON\",\n",
    "               \"values\": [season]}]\n",
    "tbp_wet = get_pixel_seasonal_value(workspace, product_code,\n",
    "                              point, crs,\n",
    "                              dimensions)\n",
    "\n",
    "TBP_df = pd.concat([tbp_dry, tbp_wet], ignore_index=False)\n",
    "TBP_df.index = [\"Dry\", \"Wet\"]\n",
    "TBP_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1f07fce5",
   "metadata": {},
   "source": [
    "Now plot the values in one bar graph for quick comparison.\n",
    "\n",
    "QUESTION: Does this result confirm your earlier conclusion based on NPP? Doest this make sense, given how total biomass production in WaPOR is derived? Please include the plot in your answer to this question, as well as the product_code you used in the query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1e773957-4a69-4911-bcc3-17a46a8c2d90",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "figs, ax = plt.subplots(1, figsize=(12, 6))\n",
    "TBP_df['test'].plot.bar(ax=ax, label = 'TBP')\n",
    "ax.set_ylabel('TBP [kg/ha]')\n",
    "ax.legend(loc='upper right', ncol=5)\n",
    "ax.set_title('TBP druing season 1 in a dry and wet year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5d30834-eae3-4e6f-a164-f82a7db6dc1a",
   "metadata": {},
   "source": [
    "# 9. How many people are affected by the drought? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8183ca34-4b3b-46c4-9043-5beee28e7cd6",
   "metadata": {},
   "source": [
    "## 9.1 Get population count map\n",
    "\n",
    "From the FAO catalog, retrieve the population count for our region of interest and for the dry year only.\n",
    "\n",
    "Population count data is included in the \"WPOP\" dataset.\n",
    "\n",
    "Make use of the **request_tif_files** function from exercise 6.2.3, but this time use the ROI shapefile as input for the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "13bd7190-c8e0-4968-9380-dcef72e89873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>caption</th>\n",
       "      <th>description</th>\n",
       "      <th>additionalInfo</th>\n",
       "      <th>tags</th>\n",
       "      <th>workspaceCode</th>\n",
       "      <th>dataType</th>\n",
       "      <th>index</th>\n",
       "      <th>operation</th>\n",
       "      <th>hidden</th>\n",
       "      <th>links</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WPOP_T</td>\n",
       "      <td>Total Population</td>\n",
       "      <td>Estimated total number of people per grid-cell...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'ppl', 'd...</td>\n",
       "      <td>[{'name': 'L1', 'context': 'LEVELS_LIST'}, {'n...</td>\n",
       "      <td>WPOP</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>1</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>WPOP_D</td>\n",
       "      <td>Human Population Density</td>\n",
       "      <td>Estimated density of people per grid-cell. The...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'ppl/Km²'...</td>\n",
       "      <td>[{'name': 'L1', 'context': 'LEVELS_LIST'}, {'n...</td>\n",
       "      <td>WPOP</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>2</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     code                   caption  \\\n",
       "0  WPOP_T          Total Population   \n",
       "1  WPOP_D  Human Population Density   \n",
       "\n",
       "                                         description  \\\n",
       "0  Estimated total number of people per grid-cell...   \n",
       "1  Estimated density of people per grid-cell. The...   \n",
       "\n",
       "                                      additionalInfo  \\\n",
       "0  {'format': 'Raster Dataset', 'unit': 'ppl', 'd...   \n",
       "1  {'format': 'Raster Dataset', 'unit': 'ppl/Km²'...   \n",
       "\n",
       "                                                tags workspaceCode dataType  \\\n",
       "0  [{'name': 'L1', 'context': 'LEVELS_LIST'}, {'n...          WPOP   RASTER   \n",
       "1  [{'name': 'L1', 'context': 'LEVELS_LIST'}, {'n...          WPOP   RASTER   \n",
       "\n",
       "   index                                          operation  hidden  \\\n",
       "0      1  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "1      2  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "\n",
       "                                               links  \n",
       "0  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "1  [{'rel': 'self', 'href': 'https://io.apps.fao....  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workspace = 'WPOP'\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}'\n",
    "resp = requests.get(cubes_url).json()\n",
    "catalog = pd.DataFrame.from_dict(resp['response'])\n",
    "catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c9915884-6a40-4de3-920e-02399e1550cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEASURE:  PPL_T\n",
      "Sending query for each polygon...\n",
      "Processing polygon 1 / 1...\n",
      "RUNNING . . . . . . . . . . . . . . . . . . . . . ['https://io.apps.fao.org/gismgr/download/401178e3-73b6-4776-aecd-4f3c635e8a17/WPOP_count_dry.tif']\n"
     ]
    }
   ],
   "source": [
    "product_code = 'WPOP_T'\n",
    "\n",
    "# Specify years\n",
    "years = {'dry': '[2015-01-01,2016-01-01)'}\n",
    "\n",
    "# specify output directory\n",
    "wpop_dir = outdir / 'wpop'\n",
    "wpop_dir.mkdir(exist_ok=True, parents=True)\n",
    "\n",
    "for label, year in years.items():\n",
    "    dimensions = [{\"code\": \"YEAR\",\n",
    "               \"values\": [year]}]\n",
    "    outputfilename = f'WPOP_count_{label}.tif'\n",
    "    output = request_tif_files(workspace, product_code,\n",
    "                               crs, outputfilename,\n",
    "                               dimensions, shapefile=roifile,\n",
    "                               labelcol=labelcol)\n",
    "    print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79a84658-f64e-4d67-835e-1328b39f4ac2",
   "metadata": {},
   "source": [
    "Download the files and upload them into a wpop folder in your output directory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e2e5d95-3b78-4986-9978-d4263ba71f88",
   "metadata": {},
   "source": [
    "## 9.2 Warp the population data to the resolution of our drought analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3b68062a-3dbc-4a7a-884f-158016dd09dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the file of which we want the projection/resolution (destination info):\n",
    "dst_info = gdal.Info(gdal.Open(r'./results/mvhi/warped/Final_mvhi_dry.tif'), format='json')\n",
    "\n",
    "wpopfile = str(wpop_dir / 'WPOP_count_dry.tif')\n",
    "src_info = gdal.Info(gdal.Open(wpopfile), format='json')\n",
    "filename = os.path.basename(wpopfile)\n",
    "output_file = str(wpop_dir / (filename[:-4] + '_warped.tif'))\n",
    "    \n",
    "output = gdal.Warp(output_file, wpopfile, format='GTiff',\n",
    "          srcSRS=src_info['coordinateSystem']['wkt'],   \n",
    "          dstSRS=dst_info['coordinateSystem']['wkt'],  \n",
    "          srcNodata=src_info['bands'][0]['noDataValue'],\n",
    "          dstNodata=src_info['bands'][0]['noDataValue'],   \n",
    "          width=dst_info['size'][0],\n",
    "          height=dst_info['size'][1],\n",
    "          outputBounds=(dst_info['cornerCoordinates']['lowerLeft'][0],              \n",
    "                        dst_info['cornerCoordinates']['lowerLeft'][1],\n",
    "                        dst_info['cornerCoordinates']['upperRight'][0],\n",
    "                        dst_info['cornerCoordinates']['upperRight'][1]),\n",
    "          outputBoundsSRS=dst_info['coordinateSystem']['wkt'],\n",
    "          resampleAlg='near')\n",
    "output = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d4eb7fd-9aa3-4688-84ab-059c7c702a99",
   "metadata": {},
   "source": [
    "WPOP_T data represents a people count per pixel. When warping and adjusting the pixel size, this needs to be accounted for. You can quickly calculate a conversion factor based on the sizes of the arrays, as they both represent the exact same area:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fa5e436e-1f07-44f5-b573-eb545a15994e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "134280\n",
      "9418750\n",
      "70.14261245159368\n"
     ]
    }
   ],
   "source": [
    "WPOP_arr = rioxarray.open_rasterio(wpopfile)\n",
    "WPOP_pixcount = len(WPOP_arr['y']) * len(WPOP_arr['x'])\n",
    "print(WPOP_pixcount)\n",
    "\n",
    "drought_arr = rioxarray.open_rasterio('./results/drought_severity/drought_dry.tif')\n",
    "drought_pixcount = len(drought_arr['y']) * len(drought_arr['x'])\n",
    "print(drought_pixcount)\n",
    "\n",
    "conversion = drought_pixcount / WPOP_pixcount\n",
    "print(conversion)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a0a1d6f-eb9e-491a-914c-8e5c53b83d3e",
   "metadata": {},
   "source": [
    "Apply the conversion factor to the data and get the people count where the drought was extreme or severe.\n",
    "\n",
    "Make sure to first filter the WPOP data to get rid of the nodata value!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2482fab1-4f98-408a-8add-60f022e883ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In total 221250 people were directly affected by this drought, in a region occupied by a total of 1398764 inhabitants!\n"
     ]
    }
   ],
   "source": [
    "WPOP_tot = rioxarray.open_rasterio(output_file)\n",
    "nodata = -9999\n",
    "WPOP_tot = WPOP_tot.where(WPOP_tot != nodata)\n",
    "WPOP_tot = WPOP_tot / conversion\n",
    "\n",
    "WPOP_count_arr = np.where((drought_arr.values != 1) & (drought_arr.values != 2), 0, WPOP_tot.values)\n",
    "\n",
    "total_people = int(np.nansum(WPOP_tot))\n",
    "people_affected = int(np.nansum(WPOP_count_arr))\n",
    "print(f'In total {people_affected} people were directly affected by this drought, in a region occupied by a total of {total_people} inhabitants.')"
   ]
  }
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