1_Steps_to_Generate_SPI_Using_CHIRPS_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1618637-3744-4526-8b3a-3754809a43de",
   "metadata": {},
   "source": [
    "# Steps to Generate Standardized Precipitation Index Using CHIRPS Data\n",
    "\n",
    "To generate the **Standardized Precipitation Index** ([SPI](https://library.wmo.int/viewer/39629/download?file=wmo_1090_en.pdf&type=pdf&navigator=1)) - a proxy for meteorological drought, we will use monthly gridded **Satellite precipitation estimates** from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. (paper: [https://doi.org/10.1038/sdata.2015.66](https://doi.org/10.1038/sdata.2015.66)). \n",
    "\n",
    "The data it self are available for download from the [https://data.chc.ucsb.edu/products/CHIRPS-2.0/](https://data.chc.ucsb.edu/products/CHIRPS-2.0/)\n",
    "\n",
    "Let's assume we are working in the `python` or `conda` environment, with packages installed: `nco`, `cdo`, `gdal`, `numpy`, `xarray`, `climate-indices` and probably other necessary packages."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94eb36af-821c-45f0-a454-e60d2c50a1df",
   "metadata": {},
   "source": [
    "## 0. Working Directory\n",
    "\n",
    "For this exercise, I am working on these folder `/Temp/drought/met/` (applied to Mac/Linux machine) or `Z:/Temp/drought/met/` (applied to Windows machine) directory. I have some folder inside this directory:\n",
    "\n",
    "1. `00_downloads` # Place to put downloaded gridded precipitation data.\n",
    "2. `01_clipped` # As the downloaded files is come with global coverage, we'll need to clip it using bounding box, this folder is place to save the clip process.\n",
    "3. `02_aoi` # Place to put nc files from fill the null value near the coastline by interpolating from nearest grid process, matched the grid with the subset, and remove the value over the sea.\n",
    "4. `03_metadata_revision` # Revised nc file from metadata editing before the SPI calculation.\n",
    "5. `04_spi_intermediate` # Output from SPI calculation goes here.\n",
    "6. `05_spi` # Final SPI output that is CF-Compliant.\n",
    "7. `images` # In this folder I put some screenshot file as illustration, used in this notebook.\n",
    "\n",
    "Feel free to use your own preferences for this setting/folder arrangements.\n",
    "\n",
    "This step-by-step guide was tested using Windows 11 with WSL2 - Ubuntu 22 enabled running on Thinkpad T480 2019, i7-8650U 1.9GHz, 64 GB 2400 MHz DDR4."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199fad7d-c891-4f43-8a8d-ff39279c2ed6",
   "metadata": {},
   "source": [
    "## 1. Download the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c6e589-3ade-4ab5-a338-6441f9b143ea",
   "metadata": {},
   "source": [
    "Navigate to `00_downloads` folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f27fb996-d654-45b4-adce-c1d4d8e42d40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Volumes/Datenspeicherung/Temp/drought/met/00_downloads\n"
     ]
    }
   ],
   "source": [
    "%cd ./met/00_downloads"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "771e3798-fd58-490f-80b6-008eef7de6d8",
   "metadata": {},
   "source": [
    "Then execute below code to download the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "755d5c66-4e46-43f3-a612-6c527ff384bf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-12-01 15:51:50--  https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_monthly/netcdf/chirps-v2.0.monthly.nc\n",
      "Resolving data.chc.ucsb.edu (data.chc.ucsb.edu)... 128.111.100.31\n",
      "Connecting to data.chc.ucsb.edu (data.chc.ucsb.edu)|128.111.100.31|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 7224218840 (6.7G) [application/x-netcdf]\n",
      "Saving to: ‘chirps-v2.0.monthly.nc’\n",
      "\n",
      "chirps-v2.0.monthly 100%[===================>]   6.73G  3.68MB/s    in 14m 22s \n",
      "\n",
      "2023-12-01 16:06:13 (7.99 MB/s) - ‘chirps-v2.0.monthly.nc’ saved [7224218840/7224218840]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -c https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_monthly/netcdf/chirps-v2.0.monthly.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17a3b5f9-1d4f-403c-a00b-91795936f811",
   "metadata": {},
   "source": [
    "## 2. Pre process"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f3a5ec-b5c8-48b4-9253-c7bd338654ac",
   "metadata": {},
   "source": [
    "The first process will be clip the area of interest using bounding box. Example: Indonesia bounding box with format `lon1`,`lon2`,`lat1`,`lat2` is `90,145,-13,11`\n",
    "\n",
    "In this command:\n",
    "\n",
    "* `-selyear,1981/2022` selects the years from 1981 to 2022.\n",
    "* `sellonlatbox,90,145,-13,11` selects the spatial region with longitudes from 90 to 145 degrees and latitudes from -13 to 11 degrees.\n",
    "* The operations are applied in sequence: first, the time range is selected, and then the spatial clipping is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b5e0d547-19ae-4e48-bb0b-c89c966dfe72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mcdo(1) selyear: \u001b[0mProcess started\n",
      "\u001b[32mcdo(1) selyear: \u001b[0mProcessed 7257600000 values from 1 variable over 514 timesteps\n",
      "\u001b[32mcdo    sellonlatbox: \u001b[0mProcessed 7257600000 values from 1 variable over 504 timesteps [125.80s 1621MB]\n"
     ]
    }
   ],
   "source": [
    "!cdo -z zip_5 sellonlatbox,90,145,-13,11 -selyear,1981/2022 chirps-v2.0.monthly.nc ../01_clipped/idn_cli_chirps_precip_monthly.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29cb2c52-2f92-4a0b-969e-34adc75c6de8",
   "metadata": {},
   "source": [
    "Navigate to folder 01_clipped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7ba9596d-ff69-40f3-9031-cad8fde002c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Volumes/Datenspeicherung/Temp/drought/met/01_clipped\n"
     ]
    }
   ],
   "source": [
    "%cd ../01_clipped"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec50db83-06a1-4f69-b34c-8b7197675b4f",
   "metadata": {},
   "source": [
    "Next process will be fill the null value near the coastline by interpolating from nearest grid process, matched the grid with the subset, and remove the value over the sea.\n",
    "\n",
    "Below script combine the operations into a one-liner script for each file, and use in-memory processing for intermediate outputs. \n",
    "\n",
    "This script performs the following operations for each `.nc4` file:\n",
    "\n",
    "* `-remapbil,../subset/idn_subset_chirps.nc $fl` - Remaps the input file `$fl` to match the grid of your subset file.\n",
    "* `-fillmiss -` - Fills missing values in the remapped data. The `-` symbol is used to take the output of `remapbil` as input for `fillmiss`.\n",
    "* `ifthen ../subset/idn_subset_chirps.nc -` - Applies the ifthen operation using the subset file as the condition to keep data only on land. The `-` symbol takes the output of `fillmiss` as its input.\n",
    "* The final output for each file is saved in the `../02_aoi/` directory.\n",
    "\n",
    "This approach avoids writing intermediate files to disk by using in-memory streams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc63c898-6ba6-41d8-b2af-52ce444ad434",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mcdo(1) fillmiss: \u001b[0mProcess started\n",
      "\u001b[32mcdo(2) remapbil: \u001b[0mProcess started\n",
      "\u001b[32mcdo    ifthen: \u001b[0mFilling up stream1 >../../subset/idn_subset_chirps.nc< by copying the first timestep.\n",
      "\u001b[32mcdo(2) remapbil: \u001b[0mBilinear weights from lonlat (1100x480) to lonlat (923x341) grid, with source mask (111929)\n",
      "cdo(2) remapbil:                        1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 91\u001b[33mcdo    ifthen (Warning): \u001b[0mVariable land has a missing value of 0, this can lead to incorrect results with this operator!\n",
      "\u001b[32mcdo(2) remapbil: \u001b[0mProcessed 266112000 values from 1 variable over 504 timesteps\n",
      "\u001b[32mcdo(1) fillmiss: \u001b[0mProcessed 158630472 values from 1 variable over 504 timesteps\n",
      "\u001b[32mcdo    ifthen: \u001b[0mProcessed 158945215 values from 2 variables over 505 timesteps [72.69s 173MB]\n"
     ]
    }
   ],
   "source": [
    "!cdo -z zip_5 ifthen ../../subset/idn_subset_chirps.nc -fillmiss -remapbil,../../subset/idn_subset_chirps.nc idn_cli_chirps_precip_monthly.nc ../02_aoi/idn_cli_chirps_precip_1981_2022.nc\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6318136b-8d4f-4b74-b8ef-a407e24eb894",
   "metadata": {},
   "source": [
    "Navigate to folder `02_aoi`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bcf67e6b-c743-4d32-83af-66c3fac4219b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Volumes/Datenspeicherung/Temp/drought/met/02_aoi\n"
     ]
    }
   ],
   "source": [
    "%cd ../02_aoi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7530805-1a0a-43a6-ab53-885f818cb14e",
   "metadata": {},
   "source": [
    "Check the result from previous process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "77692a7e-8180-4c90-b532-27c8d11b456a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netcdf idn_cli_chirps_precip_1981_2022 {\n",
      "dimensions:\n",
      "\ttime = UNLIMITED ; // (504 currently)\n",
      "\tlon = 923 ;\n",
      "\tlat = 341 ;\n",
      "variables:\n",
      "\tfloat time(time) ;\n",
      "\t\ttime:standard_name = \"time\" ;\n",
      "\t\ttime:units = \"days since 1980-1-1 0:0:0\" ;\n",
      "\t\ttime:calendar = \"gregorian\" ;\n",
      "\t\ttime:axis = \"T\" ;\n",
      "\tdouble lon(lon) ;\n",
      "\t\tlon:standard_name = \"longitude\" ;\n",
      "\t\tlon:long_name = \"longitude coordinate\" ;\n",
      "\t\tlon:units = \"degrees_east\" ;\n",
      "\t\tlon:axis = \"X\" ;\n",
      "\tdouble lat(lat) ;\n",
      "\t\tlat:standard_name = \"latitude\" ;\n",
      "\t\tlat:long_name = \"latitude coordinate\" ;\n",
      "\t\tlat:units = \"degrees_north\" ;\n",
      "\t\tlat:axis = \"Y\" ;\n",
      "\tfloat precip(time, lat, lon) ;\n",
      "\t\tprecip:standard_name = \"convective precipitation rate\" ;\n",
      "\t\tprecip:long_name = \"Climate Hazards group InfraRed Precipitation with Stations\" ;\n",
      "\t\tprecip:units = \"mm/month\" ;\n",
      "\t\tprecip:_FillValue = -9999.f ;\n",
      "\t\tprecip:missing_value = -9999.f ;\n",
      "\t\tprecip:time_step = \"month\" ;\n",
      "\t\tprecip:geostatial_lat_min = -50.f ;\n",
      "\t\tprecip:geostatial_lat_max = 50.f ;\n",
      "\t\tprecip:geostatial_lon_min = -180.f ;\n",
      "\t\tprecip:geostatial_lon_max = 180.f ;\n",
      "\n",
      "// global attributes:\n",
      "\t\t:CDI = \"Climate Data Interface version 2.2.1 (https://mpimet.mpg.de/cdi)\" ;\n",
      "\t\t:Conventions = \"CF-1.6\" ;\n",
      "\t\t:institution = \"Climate Hazards Group.  University of California at Santa Barbara\" ;\n",
      "\t\t:title = \"CHIRPS Version 2.0\" ;\n",
      "\t\t:history = \"Fri Dec 01 16:33:19 2023: cdo -z zip_5 ifthen ../subset/idn_subset_chirps.nc -fillmiss -remapbil,../subset/idn_subset_chirps.nc idn_cli_chirps_precip_monthly.nc ../02_aoi/idn_cli_chirps_precip_1981_2022.nc\\nFri Dec 01 16:16:13 2023: cdo -z zip_5 sellonlatbox,90,145,-13,11 -selyear,1981/2022 chirps-v2.0.monthly.nc ../01_clipped/idn_cli_chirps_precip_monthly.nc\\ncreated by Climate Hazards Group\" ;\n",
      "\t\t:version = \"Version 2.0\" ;\n",
      "\t\t:date_created = \"2023-11-16\" ;\n",
      "\t\t:creator_name = \"Pete Peterson\" ;\n",
      "\t\t:creator_email = \"pete@geog.ucsb.edu\" ;\n",
      "\t\t:documentation = \"http://pubs.usgs.gov/ds/832/\" ;\n",
      "\t\t:reference = \"Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p., http://dx.doi.org/110.3133/ds832. \" ;\n",
      "\t\t:comments = \" time variable denotes the first day of the given month.\" ;\n",
      "\t\t:acknowledgements = \"The Climate Hazards Group InfraRed Precipitation with Stations development process was carried out through U.S. Geological Survey (USGS) cooperative agreement #G09AC000001 \\\"Monitoring and Forecasting Climate, Water and Land Use for Food Production in the Developing World\\\" with funding from: U.S. Agency for International Development Office of Food for Peace, award #AID-FFP-P-10-00002 for \\\"Famine Early Warning Systems Network Support,\\\" the National Aeronautics and Space Administration Applied Sciences Program, Decisions award #NN10AN26I for \\\"A Land Data Assimilation System for Famine Early Warning,\\\" SERVIR award #NNH12AU22I for \\\"A Long Time-Series Indicator of Agricultural Drought for the Greater Horn of Africa,\\\" The National Oceanic and Atmospheric Administration award NA11OAR4310151 for \\\"A Global Standardized Precipitation Index supporting the US Drought Portal and the Famine Early Warning System Network,\\\" and the USGS Land Change Science Program.\" ;\n",
      "\t\t:ftp_url = \"ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CHIRPS-latest/\" ;\n",
      "\t\t:website = \"http://chg.geog.ucsb.edu/data/chirps/index.html\" ;\n",
      "\t\t:faq = \"http://chg-wiki.geog.ucsb.edu/wiki/CHIRPS_FAQ\" ;\n",
      "\t\t:CDO = \"Climate Data Operators version 2.2.0 (https://mpimet.mpg.de/cdo)\" ;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "!ncdump -h idn_cli_chirps_precip_1981_2022.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3b1b0b-e1fa-491a-bc55-a2db615d833b",
   "metadata": {},
   "source": [
    "## 3. Modify the metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae55f5bd-e19d-426c-bccd-38aa2e2285fb",
   "metadata": {},
   "source": [
    "We need to modify/add some variables in order to prepared the data for the SPI calculation. Edit precipitation unit from `mm/month` to `mm`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d21ae02a-eca4-4252-8e14-1b23e94daaff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mcdo    setattribute: \u001b[0mProcessed 158630472 values from 1 variable over 504 timesteps [20.99s 145MB]\n"
     ]
    }
   ],
   "source": [
    "!cdo -z zip_5 -setattribute,precip@units=\"mm\" idn_cli_chirps_precip_1981_2022.nc ../03_metadata_revision/idn_cli_chirps_precip_1981_2022.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0371bfad-dce3-4eb4-95ce-086f603a57de",
   "metadata": {},
   "source": [
    "Navigate to folder `03_metadata_revision`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1fe5d381-6183-4e5c-8724-4353c151b4ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Volumes/Datenspeicherung/Temp/drought/met/03_metadata_revision\n"
     ]
    }
   ],
   "source": [
    "%cd ../03_metadata_revision"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c60dc82-ccbf-4265-9e6d-055572e2b257",
   "metadata": {},
   "source": [
    "Check the result from previous process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "249bd257-941a-44e1-9646-fb4e0133f30e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netcdf idn_cli_chirps_precip_1981_2022 {\n",
      "dimensions:\n",
      "\ttime = UNLIMITED ; // (504 currently)\n",
      "\tlon = 923 ;\n",
      "\tlat = 341 ;\n",
      "variables:\n",
      "\tfloat time(time) ;\n",
      "\t\ttime:standard_name = \"time\" ;\n",
      "\t\ttime:units = \"days since 1980-1-1 0:0:0\" ;\n",
      "\t\ttime:calendar = \"gregorian\" ;\n",
      "\t\ttime:axis = \"T\" ;\n",
      "\tdouble lon(lon) ;\n",
      "\t\tlon:standard_name = \"longitude\" ;\n",
      "\t\tlon:long_name = \"longitude coordinate\" ;\n",
      "\t\tlon:units = \"degrees_east\" ;\n",
      "\t\tlon:axis = \"X\" ;\n",
      "\tdouble lat(lat) ;\n",
      "\t\tlat:standard_name = \"latitude\" ;\n",
      "\t\tlat:long_name = \"latitude coordinate\" ;\n",
      "\t\tlat:units = \"degrees_north\" ;\n",
      "\t\tlat:axis = \"Y\" ;\n",
      "\tfloat precip(time, lat, lon) ;\n",
      "\t\tprecip:standard_name = \"convective precipitation rate\" ;\n",
      "\t\tprecip:long_name = \"Climate Hazards group InfraRed Precipitation with Stations\" ;\n",
      "\t\tprecip:units = \"mm\" ;\n",
      "\t\tprecip:_FillValue = -9999.f ;\n",
      "\t\tprecip:missing_value = -9999.f ;\n",
      "\t\tprecip:time_step = \"month\" ;\n",
      "\t\tprecip:geostatial_lat_min = -50.f ;\n",
      "\t\tprecip:geostatial_lat_max = 50.f ;\n",
      "\t\tprecip:geostatial_lon_min = -180.f ;\n",
      "\t\tprecip:geostatial_lon_max = 180.f ;\n",
      "\n",
      "// global attributes:\n",
      "\t\t:CDI = \"Climate Data Interface version 2.2.1 (https://mpimet.mpg.de/cdi)\" ;\n",
      "\t\t:Conventions = \"CF-1.6\" ;\n",
      "\t\t:institution = \"Climate Hazards Group.  University of California at Santa Barbara\" ;\n",
      "\t\t:title = \"CHIRPS Version 2.0\" ;\n",
      "\t\t:history = \"Fri Dec 01 17:32:24 2023: cdo -z zip_5 -setattribute,precip@units=mm idn_cli_chirps_precip_1981_2022.nc ../03_metadata_revision/idn_cli_chirps_precip_1981_2022.nc\\nFri Dec 01 16:33:19 2023: cdo -z zip_5 ifthen ../subset/idn_subset_chirps.nc -fillmiss -remapbil,../subset/idn_subset_chirps.nc idn_cli_chirps_precip_monthly.nc ../02_aoi/idn_cli_chirps_precip_1981_2022.nc\\nFri Dec 01 16:16:13 2023: cdo -z zip_5 sellonlatbox,90,145,-13,11 -selyear,1981/2022 chirps-v2.0.monthly.nc ../01_clipped/idn_cli_chirps_precip_monthly.nc\\ncreated by Climate Hazards Group\" ;\n",
      "\t\t:version = \"Version 2.0\" ;\n",
      "\t\t:date_created = \"2023-11-16\" ;\n",
      "\t\t:creator_name = \"Pete Peterson\" ;\n",
      "\t\t:creator_email = \"pete@geog.ucsb.edu\" ;\n",
      "\t\t:documentation = \"http://pubs.usgs.gov/ds/832/\" ;\n",
      "\t\t:reference = \"Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p., http://dx.doi.org/110.3133/ds832. \" ;\n",
      "\t\t:comments = \" time variable denotes the first day of the given month.\" ;\n",
      "\t\t:acknowledgements = \"The Climate Hazards Group InfraRed Precipitation with Stations development process was carried out through U.S. Geological Survey (USGS) cooperative agreement #G09AC000001 \\\"Monitoring and Forecasting Climate, Water and Land Use for Food Production in the Developing World\\\" with funding from: U.S. Agency for International Development Office of Food for Peace, award #AID-FFP-P-10-00002 for \\\"Famine Early Warning Systems Network Support,\\\" the National Aeronautics and Space Administration Applied Sciences Program, Decisions award #NN10AN26I for \\\"A Land Data Assimilation System for Famine Early Warning,\\\" SERVIR award #NNH12AU22I for \\\"A Long Time-Series Indicator of Agricultural Drought for the Greater Horn of Africa,\\\" The National Oceanic and Atmospheric Administration award NA11OAR4310151 for \\\"A Global Standardized Precipitation Index supporting the US Drought Portal and the Famine Early Warning System Network,\\\" and the USGS Land Change Science Program.\" ;\n",
      "\t\t:ftp_url = \"ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CHIRPS-latest/\" ;\n",
      "\t\t:website = \"http://chg.geog.ucsb.edu/data/chirps/index.html\" ;\n",
      "\t\t:faq = \"http://chg-wiki.geog.ucsb.edu/wiki/CHIRPS_FAQ\" ;\n",
      "\t\t:CDO = \"Climate Data Operators version 2.2.0 (https://mpimet.mpg.de/cdo)\" ;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "!ncdump -h idn_cli_chirps_precip_1981_2022.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98fed38-d7c5-4636-8db1-f291bbd3db4b",
   "metadata": {},
   "source": [
    "## 4. Standardized Precipitation Index calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "685e26aa-ddd4-4581-add1-a6a2657ccc4b",
   "metadata": {},
   "source": [
    "Let's read some paragraph below related to the python package that we will use to calculate the SPI.\n",
    "\n",
    "The [climate-indices](https://pypi.org/project/climate-indices/) python package enables the user to calculate SPI using any gridded netCDF dataset. However, there are certain requirements for input files that vary based on input type.\n",
    "\n",
    "* Precipitation unit must be written as `millimeters`, `milimeter`, `mm`, `inches`, `inch` or `in`.\n",
    "\n",
    "* Data dimension and order must be written as `lat`, `lon`, `time` (Windows machine required this order) or `time`, `lat`, `lon` (Works tested on Mac/Linux and Linux running on WSL). \n",
    "\n",
    "* If our study area are big, it's better to prepare all the data following this dimension order: `lat`, `lon`, `time` as all the data will force following this order during SPI calculation by NCO module. Let say you only prepare the data as is (leaving the order to `time`,`lat`, `lon`), it also acceptable but it will required lot of memory to use re-ordering the dimension, and usually NCO couldn't handle all the process and failed.\n",
    "\n",
    "Please make sure below points:\n",
    "\n",
    "- [x] You have installed `climate-indices` package to start working on SPI calculation. \n",
    "- [x] Variable name on precipitation `--var_name_precip`, usually terraclimate data use `ppt` as name while other precipitation data like CHIRPS using `precip` and IMERG using `precipitation` as a variable name. To make sure, check using command `ncdump -h file.nc` then adjust it in SPI script if needed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c5e04d1-8987-42d8-9432-342cf7ffb107",
   "metadata": {},
   "source": [
    "Let's re-order the variables into `lat`,`lon`,`time` using `ncpdq` command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7d4a8577-4b79-4f01-902e-3c7fadacb510",
   "metadata": {},
   "outputs": [],
   "source": [
    "!ncpdq -a lat,lon,time idn_cli_chirps_precip_1981_2022.nc input0.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc20535-19b6-4e4f-ac0f-abf1ca452dd0",
   "metadata": {},
   "source": [
    "Check the header for the result `input0.nc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10fed628-286b-4990-b619-4de3fa54858d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netcdf input0 {\n",
      "dimensions:\n",
      "\ttime = 504 ;\n",
      "\tlon = 923 ;\n",
      "\tlat = UNLIMITED ; // (341 currently)\n",
      "variables:\n",
      "\tfloat time(time) ;\n",
      "\t\ttime:standard_name = \"time\" ;\n",
      "\t\ttime:units = \"days since 1980-1-1 0:0:0\" ;\n",
      "\t\ttime:calendar = \"gregorian\" ;\n",
      "\t\ttime:axis = \"T\" ;\n",
      "\tdouble lon(lon) ;\n",
      "\t\tlon:standard_name = \"longitude\" ;\n",
      "\t\tlon:long_name = \"longitude coordinate\" ;\n",
      "\t\tlon:units = \"degrees_east\" ;\n",
      "\t\tlon:axis = \"X\" ;\n",
      "\tdouble lat(lat) ;\n",
      "\t\tlat:standard_name = \"latitude\" ;\n",
      "\t\tlat:long_name = \"latitude coordinate\" ;\n",
      "\t\tlat:units = \"degrees_north\" ;\n",
      "\t\tlat:axis = \"Y\" ;\n",
      "\tfloat precip(lat, lon, time) ;\n",
      "\t\tprecip:standard_name = \"convective precipitation rate\" ;\n",
      "\t\tprecip:long_name = \"Climate Hazards group InfraRed Precipitation with Stations\" ;\n",
      "\t\tprecip:units = \"mm\" ;\n",
      "\t\tprecip:_FillValue = -9999.f ;\n",
      "\t\tprecip:missing_value = -9999.f ;\n",
      "\t\tprecip:time_step = \"month\" ;\n",
      "\t\tprecip:geostatial_lat_min = -50.f ;\n",
      "\t\tprecip:geostatial_lat_max = 50.f ;\n",
      "\t\tprecip:geostatial_lon_min = -180.f ;\n",
      "\t\tprecip:geostatial_lon_max = 180.f ;\n",
      "\n",
      "// global attributes:\n",
      "\t\t:CDI = \"Climate Data Interface version 2.2.1 (https://mpimet.mpg.de/cdi)\" ;\n",
      "\t\t:Conventions = \"CF-1.6\" ;\n",
      "\t\t:institution = \"Climate Hazards Group.  University of California at Santa Barbara\" ;\n",
      "\t\t:title = \"CHIRPS Version 2.0\" ;\n",
      "\t\t:history = \"Fri Dec  1 17:50:31 2023: ncpdq -a lat,lon,time idn_cli_chirps_precip_1981_2022.nc input0.nc\\nFri Dec 01 17:32:24 2023: cdo -z zip_5 -setattribute,precip@units=mm idn_cli_chirps_precip_1981_2022.nc ../03_metadata_revision/idn_cli_chirps_precip_1981_2022.nc\\nFri Dec 01 16:33:19 2023: cdo -z zip_5 ifthen ../subset/idn_subset_chirps.nc -fillmiss -remapbil,../subset/idn_subset_chirps.nc idn_cli_chirps_precip_monthly.nc ../02_aoi/idn_cli_chirps_precip_1981_2022.nc\\nFri Dec 01 16:16:13 2023: cdo -z zip_5 sellonlatbox,90,145,-13,11 -selyear,1981/2022 chirps-v2.0.monthly.nc ../01_clipped/idn_cli_chirps_precip_monthly.nc\\ncreated by Climate Hazards Group\" ;\n",
      "\t\t:version = \"Version 2.0\" ;\n",
      "\t\t:date_created = \"2023-11-16\" ;\n",
      "\t\t:creator_name = \"Pete Peterson\" ;\n",
      "\t\t:creator_email = \"pete@geog.ucsb.edu\" ;\n",
      "\t\t:documentation = \"http://pubs.usgs.gov/ds/832/\" ;\n",
      "\t\t:reference = \"Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p., http://dx.doi.org/110.3133/ds832. \" ;\n",
      "\t\t:comments = \" time variable denotes the first day of the given month.\" ;\n",
      "\t\t:acknowledgements = \"The Climate Hazards Group InfraRed Precipitation with Stations development process was carried out through U.S. Geological Survey (USGS) cooperative agreement #G09AC000001 \\\"Monitoring and Forecasting Climate, Water and Land Use for Food Production in the Developing World\\\" with funding from: U.S. Agency for International Development Office of Food for Peace, award #AID-FFP-P-10-00002 for \\\"Famine Early Warning Systems Network Support,\\\" the National Aeronautics and Space Administration Applied Sciences Program, Decisions award #NN10AN26I for \\\"A Land Data Assimilation System for Famine Early Warning,\\\" SERVIR award #NNH12AU22I for \\\"A Long Time-Series Indicator of Agricultural Drought for the Greater Horn of Africa,\\\" The National Oceanic and Atmospheric Administration award NA11OAR4310151 for \\\"A Global Standardized Precipitation Index supporting the US Drought Portal and the Famine Early Warning System Network,\\\" and the USGS Land Change Science Program.\" ;\n",
      "\t\t:ftp_url = \"ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CHIRPS-latest/\" ;\n",
      "\t\t:website = \"http://chg.geog.ucsb.edu/data/chirps/index.html\" ;\n",
      "\t\t:faq = \"http://chg-wiki.geog.ucsb.edu/wiki/CHIRPS_FAQ\" ;\n",
      "\t\t:CDO = \"Climate Data Operators version 2.2.0 (https://mpimet.mpg.de/cdo)\" ;\n",
      "\t\t:NCO = \"netCDF Operators version 5.1.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)\" ;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "!ncdump -h input0.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f889d10-2473-4487-a3a6-fc7a420046f6",
   "metadata": {},
   "source": [
    "After re-ordering the variables, sometimes user experience `lat` or `lon` dimension becomes `UNLIMITED` which is wrong. The `time` dimension should be the `UNLIMITED` dimension.\n",
    "\n",
    "Fortunately you can do this to fix the `lat` or `lon` dimension who becomes `UNLIMITED` using `ncks` command below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4ea5c8d-ea7f-4dcb-9ffe-05354103c87b",
   "metadata": {},
   "outputs": [],
   "source": [
    "!ncks --fix_rec_dmn lat input0.nc -o input1.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b45cc698-9885-4d13-bdcf-93dbaa41d5fe",
   "metadata": {},
   "source": [
    "And to make `UNLIMITED` the `time` dimension again using `ncks` command below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0da901d5-4e23-42bc-a1a2-dda4c8f75071",
   "metadata": {},
   "outputs": [],
   "source": [
    "!ncks --mk_rec_dmn time input1.nc -o input_spi.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e9fee6c-f7f9-4a33-9741-6f8ab2a3ac33",
   "metadata": {},
   "source": [
    "Check the header for the result `input_spi.nc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "099c9f7f-86cb-4ca6-9b50-2957fb16c6d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netcdf input_spi {\n",
      "dimensions:\n",
      "\tlat = 341 ;\n",
      "\tlon = 923 ;\n",
      "\ttime = UNLIMITED ; // (504 currently)\n",
      "variables:\n",
      "\tdouble lat(lat) ;\n",
      "\t\tlat:standard_name = \"latitude\" ;\n",
      "\t\tlat:long_name = \"latitude coordinate\" ;\n",
      "\t\tlat:units = \"degrees_north\" ;\n",
      "\t\tlat:axis = \"Y\" ;\n",
      "\tdouble lon(lon) ;\n",
      "\t\tlon:standard_name = \"longitude\" ;\n",
      "\t\tlon:long_name = \"longitude coordinate\" ;\n",
      "\t\tlon:units = \"degrees_east\" ;\n",
      "\t\tlon:axis = \"X\" ;\n",
      "\tfloat precip(lat, lon, time) ;\n",
      "\t\tprecip:standard_name = \"convective precipitation rate\" ;\n",
      "\t\tprecip:long_name = \"Climate Hazards group InfraRed Precipitation with Stations\" ;\n",
      "\t\tprecip:units = \"mm\" ;\n",
      "\t\tprecip:_FillValue = -9999.f ;\n",
      "\t\tprecip:missing_value = -9999.f ;\n",
      "\t\tprecip:time_step = \"month\" ;\n",
      "\t\tprecip:geostatial_lat_min = -50.f ;\n",
      "\t\tprecip:geostatial_lat_max = 50.f ;\n",
      "\t\tprecip:geostatial_lon_min = -180.f ;\n",
      "\t\tprecip:geostatial_lon_max = 180.f ;\n",
      "\tfloat time(time) ;\n",
      "\t\ttime:standard_name = \"time\" ;\n",
      "\t\ttime:units = \"days since 1980-1-1 0:0:0\" ;\n",
      "\t\ttime:calendar = \"gregorian\" ;\n",
      "\t\ttime:axis = \"T\" ;\n",
      "\n",
      "// global attributes:\n",
      "\t\t:CDI = \"Climate Data Interface version 2.2.1 (https://mpimet.mpg.de/cdi)\" ;\n",
      "\t\t:Conventions = \"CF-1.6\" ;\n",
      "\t\t:institution = \"Climate Hazards Group.  University of California at Santa Barbara\" ;\n",
      "\t\t:title = \"CHIRPS Version 2.0\" ;\n",
      "\t\t:history = \"Fri Dec  1 17:58:25 2023: ncks --mk_rec_dmn time input1.nc -o input_spi.nc\\nFri Dec  1 17:58:00 2023: ncks --fix_rec_dmn lat input0.nc -o input1.nc\\nFri Dec  1 17:50:31 2023: ncpdq -a lat,lon,time idn_cli_chirps_precip_1981_2022.nc input0.nc\\nFri Dec 01 17:32:24 2023: cdo -z zip_5 -setattribute,precip@units=mm idn_cli_chirps_precip_1981_2022.nc ../03_metadata_revision/idn_cli_chirps_precip_1981_2022.nc\\nFri Dec 01 16:33:19 2023: cdo -z zip_5 ifthen ../subset/idn_subset_chirps.nc -fillmiss -remapbil,../subset/idn_subset_chirps.nc idn_cli_chirps_precip_monthly.nc ../02_aoi/idn_cli_chirps_precip_1981_2022.nc\\nFri Dec 01 16:16:13 2023: cdo -z zip_5 sellonlatbox,90,145,-13,11 -selyear,1981/2022 chirps-v2.0.monthly.nc ../01_clipped/idn_cli_chirps_precip_monthly.nc\\ncreated by Climate Hazards Group\" ;\n",
      "\t\t:version = \"Version 2.0\" ;\n",
      "\t\t:date_created = \"2023-11-16\" ;\n",
      "\t\t:creator_name = \"Pete Peterson\" ;\n",
      "\t\t:creator_email = \"pete@geog.ucsb.edu\" ;\n",
      "\t\t:documentation = \"http://pubs.usgs.gov/ds/832/\" ;\n",
      "\t\t:reference = \"Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p., http://dx.doi.org/110.3133/ds832. \" ;\n",
      "\t\t:comments = \" time variable denotes the first day of the given month.\" ;\n",
      "\t\t:acknowledgements = \"The Climate Hazards Group InfraRed Precipitation with Stations development process was carried out through U.S. Geological Survey (USGS) cooperative agreement #G09AC000001 \\\"Monitoring and Forecasting Climate, Water and Land Use for Food Production in the Developing World\\\" with funding from: U.S. Agency for International Development Office of Food for Peace, award #AID-FFP-P-10-00002 for \\\"Famine Early Warning Systems Network Support,\\\" the National Aeronautics and Space Administration Applied Sciences Program, Decisions award #NN10AN26I for \\\"A Land Data Assimilation System for Famine Early Warning,\\\" SERVIR award #NNH12AU22I for \\\"A Long Time-Series Indicator of Agricultural Drought for the Greater Horn of Africa,\\\" The National Oceanic and Atmospheric Administration award NA11OAR4310151 for \\\"A Global Standardized Precipitation Index supporting the US Drought Portal and the Famine Early Warning System Network,\\\" and the USGS Land Change Science Program.\" ;\n",
      "\t\t:ftp_url = \"ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CHIRPS-latest/\" ;\n",
      "\t\t:website = \"http://chg.geog.ucsb.edu/data/chirps/index.html\" ;\n",
      "\t\t:faq = \"http://chg-wiki.geog.ucsb.edu/wiki/CHIRPS_FAQ\" ;\n",
      "\t\t:CDO = \"Climate Data Operators version 2.2.0 (https://mpimet.mpg.de/cdo)\" ;\n",
      "\t\t:NCO = \"netCDF Operators version 5.1.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)\" ;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "!ncdump -h input_spi.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6210360-743e-4ed8-aab9-c8e85d2d5856",
   "metadata": {},
   "source": [
    "**Let's start the calculation!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1a7df68d-5ec7-4f2f-b18a-107aa715b4c8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-12-01  18:19:12 INFO Start time:    2023-12-01 18:19:12.794650\n",
      "2023-12-01  18:19:13 INFO Computing monthly SPI\n",
      "2023-12-01  18:19:32 INFO Computing 1-month SPI (Gamma)\n",
      "2023-12-01  18:20:11 INFO Computing 1-month SPI (Pearson)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "2023-12-01  18:21:27 INFO Computing 2-month SPI (Gamma)\n",
      "2023-12-01  18:22:04 INFO Computing 2-month SPI (Pearson)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "2023-12-01  18:23:16 INFO Computing 3-month SPI (Gamma)\n",
      "2023-12-01  18:23:54 INFO Computing 3-month SPI (Pearson)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "2023-12-01  18:25:08 INFO Computing 6-month SPI (Gamma)\n",
      "2023-12-01  18:25:45 INFO Computing 6-month SPI (Pearson)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "2023-12-01  18:27:02 INFO Computing 9-month SPI (Gamma)\n",
      "2023-12-01  18:27:41 INFO Computing 9-month SPI (Pearson)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "2023-12-01  18:29:00 INFO Computing 12-month SPI (Gamma)\n",
      "2023-12-01  18:29:41 INFO Computing 12-month SPI (Pearson)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "2023-12-01  18:30:59 INFO Computing 18-month SPI (Gamma)\n",
      "2023-12-01  18:31:39 INFO Computing 18-month SPI (Pearson)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "2023-12-01  18:32:55 INFO Computing 24-month SPI (Gamma)\n",
      "2023-12-01  18:33:35 INFO Computing 24-month SPI (Pearson)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "2023-12-01  18:34:54 INFO Computing 36-month SPI (Gamma)\n",
      "2023-12-01  18:35:34 INFO Computing 36-month SPI (Pearson)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "2023-12-01  18:36:53 INFO Computing 48-month SPI (Gamma)\n",
      "2023-12-01  18:37:34 INFO Computing 48-month SPI (Pearson)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "2023-12-01  18:38:53 INFO Computing 60-month SPI (Gamma)\n",
      "2023-12-01  18:39:34 INFO Computing 60-month SPI (Pearson)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "2023-12-01  18:40:54 INFO Computing 72-month SPI (Gamma)\n",
      "2023-12-01  18:41:33 INFO Computing 72-month SPI (Pearson)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:455: RuntimeWarning: divide by zero encountered in divide\n",
      "  alpha = 4.0 / (skew * skew)\n",
      "/opt/anaconda3/envs/wbg/lib/python3.10/site-packages/climate_indices/compute.py:459: RuntimeWarning: invalid value encountered in multiply\n",
      "  return loc - ((alpha * scale * skew) / 2.0)\n",
      "2023-12-01  18:42:52 INFO End time:      2023-12-01 18:42:52.876199\n",
      "2023-12-01  18:42:52 INFO Elapsed time:  0:23:40.081549\n"
     ]
    }
   ],
   "source": [
    "!spi --periodicity monthly --scales 1 2 3 6 9 12 18 24 36 48 60 72 --calibration_start_year 1991 --calibration_end_year 2020 --netcdf_precip input_spi.nc --var_name_precip precip --output_file_base ../04_spi_intermediate/idn_cli --multiprocessing all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad70c23-aa4e-41ea-bb08-d73fe49c5a83",
   "metadata": {},
   "source": [
    "We will use the gamma version, lets remove the pearson file. Don't forget to navigate to `04_spi_intermediate`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6c850bb-f844-43c3-8e06-c2ecfb7e5e43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Volumes/Datenspeicherung/Temp/drought/met/04_spi_intermediate\n",
      "rm: idn_cli_spi_pearson_*.nc: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "%cd ../04_spi_intermediate\n",
    "\n",
    "!bash -c 'rm idn_cli_spi_pearson_*.nc'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31397339-c2af-4218-be4a-bd14048a7c6a",
   "metadata": {},
   "source": [
    "Next, we need to re-rder the dimension back to `time`,`lat`,`lon` in order to follow the [CF Convention](https://cfconventions.org/conventions.html) and also for further processing using CDO, and CDO required the variable should be in `time`,`lat`,`lon`, while the output from SPI in `lat`,`lon`,`time`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "513bbe8c-b4a1-47ea-a4f6-358ebde6043a",
   "metadata": {},
   "source": [
    "Let's re-order the variables into `time`,`lat`,`lon` using `ncpdq` command from NCO and save the result to folder `05_spi`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5843a8b3-6c4e-41c6-bb85-07a2245329d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "!bash -c 'for fl in *.nc; do ncpdq -a time,lat,lon $fl ../05_spi/$fl; done'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7c6bbe-11c5-43fd-8029-742f44dba40b",
   "metadata": {},
   "source": [
    "Navigate to folder `05_spi`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c510de7b-56be-4f03-a290-359f3678bbea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Volumes/Datenspeicherung/Temp/drought/met/05_spi\n"
     ]
    }
   ],
   "source": [
    "%cd ../05_spi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "601d888a-d2d9-48b8-995f-b6cfeaaac5ab",
   "metadata": {},
   "source": [
    "Check the header for the result one of the output file `idn_cli_spi_gamma_12_month`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "78df2a06-b48e-4ff4-a045-97dc8b113c64",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netcdf idn_cli_spi_gamma_12_month {\n",
      "dimensions:\n",
      "\tlat = 341 ;\n",
      "\tlon = 923 ;\n",
      "\ttime = 504 ;\n",
      "variables:\n",
      "\tdouble lat(lat) ;\n",
      "\t\tlat:_FillValue = NaN ;\n",
      "\t\tlat:standard_name = \"latitude\" ;\n",
      "\t\tlat:long_name = \"latitude coordinate\" ;\n",
      "\t\tlat:units = \"degrees_north\" ;\n",
      "\t\tlat:axis = \"Y\" ;\n",
      "\tdouble lon(lon) ;\n",
      "\t\tlon:_FillValue = NaN ;\n",
      "\t\tlon:standard_name = \"longitude\" ;\n",
      "\t\tlon:long_name = \"longitude coordinate\" ;\n",
      "\t\tlon:units = \"degrees_east\" ;\n",
      "\t\tlon:axis = \"X\" ;\n",
      "\tfloat time(time) ;\n",
      "\t\ttime:_FillValue = NaNf ;\n",
      "\t\ttime:standard_name = \"time\" ;\n",
      "\t\ttime:axis = \"T\" ;\n",
      "\t\ttime:units = \"days since 1980-01-01\" ;\n",
      "\t\ttime:calendar = \"gregorian\" ;\n",
      "\tdouble spi_gamma_12_month(time, lat, lon) ;\n",
      "\t\tspi_gamma_12_month:_FillValue = NaN ;\n",
      "\t\tspi_gamma_12_month:long_name = \"Standardized Precipitation Index (Gamma), 12-month\" ;\n",
      "\t\tspi_gamma_12_month:valid_min = -3.09 ;\n",
      "\t\tspi_gamma_12_month:valid_max = 3.09 ;\n",
      "\n",
      "// global attributes:\n",
      "\t\t:description = \"SPI for 12-month scale computed from monthly precipitation input by the climate_indices package available from https://github.com/monocongo/climate_indices.\" ;\n",
      "\t\t:geospatial_lat_min = -11.0750009100884 ;\n",
      "\t\t:geospatial_lat_max = 5.92499934323132 ;\n",
      "\t\t:geospatial_lat_units = \"degrees_north\" ;\n",
      "\t\t:geospatial_lon_min = 94.9250040967017 ;\n",
      "\t\t:geospatial_lon_max = 141.025004783645 ;\n",
      "\t\t:geospatial_lon_units = \"degrees_east\" ;\n",
      "\t\t:history = \"Fri Dec  1 18:52:15 2023: ncpdq -a time,lat,lon idn_cli_spi_gamma_12_month.nc ../05_spi/idn_cli_spi_gamma_12_month.nc\" ;\n",
      "\t\t:NCO = \"netCDF Operators version 5.1.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)\" ;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "!ncdump -h idn_cli_spi_gamma_12_month.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8580597f-f4e2-4b43-a540-f6917d576579",
   "metadata": {},
   "source": [
    "Seems everything is correct from above result, congrats now we have the SPI data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d646fbc-22c5-4c24-91cc-f7a7c271b698",
   "metadata": {},
   "source": [
    "## 5. Visualising the SPI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "741ad90d-7dca-4a54-ada4-2ae1adbb2729",
   "metadata": {},
   "source": [
    "To see the result, let's visualise it for the year 2022, using SPI 12-month data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f3b872bf-e389-4540-9f5f-52914364a464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "ds = xr.open_dataset(\"idn_cli_spi_gamma_12_month.nc\")\n",
    "\n",
    "# Select data from Jan - Dec 2022\n",
    "start_date = \"2022-01-01\"\n",
    "end_date = \"2022-12-31\"\n",
    "data_2022 = ds[\"spi_gamma_12_month\"].sel(time=slice(start_date, end_date))\n",
    "\n",
    "# Plotting\n",
    "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(20, 10))\n",
    "fig.suptitle('Standardized Precipitation Index (Gamma), 12-month, 2022', fontsize=16, y=0.98)\n",
    "\n",
    "# Custom discrete color map\n",
    "cmap = plt.get_cmap('RdBu', 7)  # 7 discrete colors\n",
    "bounds = [-3, -2, -1, 0, 1, 2, 3]\n",
    "norm = colors.BoundaryNorm(bounds, cmap.N)\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    # Plot if the data for the month exists\n",
    "    if i < len(data_2022.time):\n",
    "        data = data_2022.isel(time=i)\n",
    "        pcm = ax.pcolormesh(data.lon, data.lat, data, cmap=cmap, norm=norm)\n",
    "        \n",
    "        # Set title with the corresponding month\n",
    "        time_val = pd.to_datetime(str(data.time.values))\n",
    "        ax.set_title(time_val.strftime('%B'))\n",
    "\n",
    "        # Set labels\n",
    "        ax.set_xlabel('Longitude')\n",
    "        ax.set_ylabel('Latitude')\n",
    "\n",
    "# Adjust vertical spacing\n",
    "plt.subplots_adjust(hspace=0.5)  # Adjust this value as needed for vertical spacing\n",
    "fig.colorbar(pcm, ax=axes.ravel().tolist(), shrink=0.7, ticks=np.arange(-3, 4), extend='both')\n",
    "\n",
    "# Save the map as a PNG\n",
    "plt.savefig('../images/idn_cli_chirps_spi12_2022.png', dpi=300)\n",
    "plt.show()\n"
   ]
  },
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   "source": [
    "End of notebook."
   ]
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