geoprepare 0.6.105

A Python package to prepare (download, extract, process input data) for GEOCIF and related models

geoprepare

A Python package to prepare (download, extract, process input data) for GEOCIF and related models

• Free software: MIT license
• Documentation: https://ritviksahajpal.github.io/geoprepare

Installation

Note: The instructions below have only been tested on a Linux system

Install Anaconda

We recommend that you use the conda package manager to install the geoprepare library and all its dependencies. If you do not have it installed already, you can get it from the Anaconda distribution

Using the CDS API

If you intend to download AgERA5 data, you will need to install the CDS API. You can do this by following the instructions here

Create a new conda environment (optional but highly recommended)

geoprepare requires multiple Python GIS packages including gdal and rasterio. These packages are not always easy to install. To make the process easier, you can optionally create a new environment using the following commands, specify the python version you have on your machine (python >= 3.9 is recommended). we use the pygis library to install multiple Python GIS packages including gdal and rasterio.

conda create --name <name_of_environment> python=3.x
conda activate <name_of_environment>
conda install -c conda-forge mamba
mamba install -c conda-forge gdal
mamba install -c conda-forge rasterio
mamba install -c conda-forge xarray
mamba install -c conda-forge rioxarray
mamba install -c conda-forge pyresample
mamba install -c conda-forge cdsapi
mamba install -c conda-forge pygis
pip install wget
pip install pyl4c

Install the octvi package to download MODIS data

pip install git+https://github.com/ritviksahajpal/octvi.git

Downloading from the NASA distributed archives (DAACs) requires a personal app key. Users must configure the module using a new console script, octviconfig. After installation, run octviconfig in your command prompt to prompt the input of your personal app key. Information on obtaining app keys can be found here

Using PyPi (default)

pip install --upgrade geoprepare

Using Github repository (for development)

pip install --upgrade --no-deps --force-reinstall git+https://github.com/ritviksahajpal/geoprepare.git

Local installation

Navigate to the directory containing pyproject.toml and run the following command:

pip install .

For development (editable install):

pip install -e ".[dev]"

Pipeline

geoprepare follows a three-stage pipeline:

1. Download (geodownload) - Download and preprocess global EO datasets to dir_download and dir_intermed
2. Extract (geoextract) - Extract EO variable statistics per admin region to dir_output
3. Merge (geomerge) - Merge extracted EO files into per-country/crop CSV files for ML models and AgMet graphics

Additional utilities:

• Check (geocheck) - Validate that expected TIF files exist in dir_intermed after download
• Diagnostics (diagnostics) - Count and summarize files in the data directories

Usage

1. Download data (geodownload)

Downloads and preprocesses global EO datasets. Only requires geobase.txt. The [DATASETS] section controls which datasets are downloaded. Each dataset is processed to global 0.05° TIF files in dir_intermed.

from geoprepare import geodownload

geodownload.run([r"PATH_TO_geobase.txt"])

What it does:

• Iterates over datasets listed in [DATASETS] datasets
• Downloads raw data from each source to dir_download
• Processes to standardized global TIF files in dir_intermed
• Automatically skips files that already exist (re-downloads only the current year if before March 1st)

AEF dataset: When AEF is in the datasets list, geodownload reads countries from geoextract.txt (must be in the same directory as geobase.txt) to determine which geographic tiles to download.

2. Validate downloads (geocheck)

Checks that all expected TIF files exist in dir_intermed and are non-empty. Writes a timestamped report to dir_logs/check/.

from geoprepare import geocheck

# Basic check (file existence only)
geocheck.run([r"PATH_TO_geobase.txt"])

# With GDAL validation (slower, checks file readability)
geocheck.run([r"PATH_TO_geobase.txt"], gdal_check=True)

Supported datasets for checking: AEF, CHIRPS, NDVI, VIIRS, ESI, CPC, LST, NSIDC.

3. Extract crop masks and EO data (geoextract)

Extracts EO variable statistics (mean, median, etc.) for each admin region, crop, and growing season. Requires both geobase.txt and geoextract.txt.

from geoprepare import geoextract

geoextract.run([r"PATH_TO_geobase.txt", r"PATH_TO_geoextract.txt"])

What it does:

• For each country in [DEFAULT] countries:
  • Reads crop mask, admin boundary shapefile, and crop calendar
  • For each crop/scale/season combination, extracts EO statistics per admin region
  • Writes per-region CSV files to dir_output/{project_name}/crop_t{threshold}/{country}/{scale}/{crop}/{eo_var}/
• Generates EWCM region-assignment plots in dir_output/{project_name}/region_plots/

Key config values used:

• [DEFAULT] countries - which countries to process
• [PROJECT] project_name - output subdirectory name (e.g. FEWSNET)
• Per-country sections - category, scales, crops, growing_seasons, shp_boundary, calendar_file
• [EWCM] / [AMIS] - eo_model (list of EO variables to extract), calendar_file

4. Merge extracted data (geomerge)

Merges per-region/year EO CSV files into a single CSV per country-crop-season combination. Adds crop calendar info, harvest season assignment, and region-to-EWCM-region mapping.

from geoprepare import geomerge

geomerge.run([r"PATH_TO_geobase.txt", r"PATH_TO_geoextract.txt"])

What it does:

• For each country/scale/crop/season combination:
  • Merges all EO variable CSV files on (country, region, region_id, year, doy)
  • Adds datetime, crop calendar stages, harvest season assignment
  • Adds hemisphere/zone info and average temperature
  • Scales NDVI values: (ndvi - 50) / 200
  • Writes output to dir_output/{project_name}/crop_t{threshold}/{country}/{country}_{crop}_s{season}.csv
• Supports parallel execution via [PROJECT] parallel_merge = True

Configuration files

geoprepare uses INI-style configuration files parsed with Python's ConfigParser. When multiple files are passed, they are read in order and later files override earlier ones for duplicate keys.

Three configuration files are used:

• geobase.txt - Paths, dataset settings, logging (required by all stages)
• geoextract.txt - Per-country extraction settings, crop masks, calendars (required by extract/merge)
• geocif.txt - geocif-specific settings, ML configs (optional, only needed for geocif pipeline)

geobase.txt

Defines directory structure, dataset-specific settings, and global defaults.

NOTE: dir_base needs to be changed to your specific directory structure

[DATASETS]
datasets = ['CPC']
; Available: 'NDVI', 'AGERA5', 'CHIRPS', 'CPC', 'CHIRPS-GEFS', 'ESI', 'NSIDC',
;            'FLDAS', 'VHI', 'VIIRS', 'AVHRR', 'LST', 'SOIL-MOISTURE', 'FPAR'

[PATHS]
dir_base = /gpfs/data1/cmongp1/GEO

dir_inputs = ${dir_base}/inputs
dir_logs = ${dir_base}/logs
dir_download = ${dir_inputs}/download
dir_intermed = ${dir_inputs}/intermed
dir_metadata = ${dir_inputs}/metadata

dir_boundary_files = ${dir_metadata}/boundary_files
dir_crop_calendars = ${dir_metadata}/crop_calendars
dir_crop_masks = ${dir_metadata}/crop_masks
dir_images = ${dir_metadata}/images
dir_production_statistics = ${dir_metadata}/production_statistics

dir_output = ${dir_base}/outputs

[AEF]
; AlphaEarth Foundations satellite embeddings (2018-2024, 64 channels, 10m)
; Source: https://source.coop/tge-labs/aef  |  License: CC-BY 4.0
; Countries are read from geoextract.txt [DEFAULT] countries
buffer = 0.5
download_vrt = True
start_year = 2018
end_year = 2024

[AGERA5]
variables = ['Precipitation_Flux', 'Temperature_Air_2m_Max_24h', 'Temperature_Air_2m_Min_24h']

[CHIRPS]
fill_value = -2147483648
; CHIRPS version: 'v2' for CHIRPS-2.0 or 'v3' for CHIRPS-3.0
version = v3
; Disaggregation method for v3 only: 'sat' (IMERG) or 'rnl' (ERA5)
disagg = sat

[CHIRPS-GEFS]
fill_value = -2147483648
data_dir = /pub/org/chc/products/EWX/data/forecasts/CHIRPS-GEFS_precip_v12/15day/precip_mean/

[CPC]
data_dir = ftp://ftp.cdc.noaa.gov/Datasets

[ESI]
data_dir = https://gis1.servirglobal.net//data//esi//
list_products = ['4wk', '12wk']

[FLDAS]
use_spear = False
data_types = ['forecast']
variables = ['SoilMoist_tavg', 'TotalPrecip_tavg', 'Tair_tavg', 'Evap_tavg', 'TWS_tavg']
leads = [0, 1, 2, 3, 4, 5]
compute_anomalies = False

[NDVI]
product = MOD09CMG
vi = ndvi
scale_glam = False
scale_mark = True
print_missing = False

[VIIRS]
product = VNP09CMG
vi = ndvi
scale_glam = False
scale_mark = True
print_missing = False

[LOGGING]
level = ERROR

[DEFAULT]
logfile = log
parallel_process = False
fraction_cpus = 0.35
start_year = 2001
end_year = 2026

geoextract.txt

Defines per-country extraction settings, crop masks, calendar files, and EO variables.

NOTE: For each country add a new section. Countries are organized into two categories: AMIS (global monitoring) and EWCM (early warning crop monitor).

Key settings:

• countries: List of countries to process (in [DEFAULT])
• category: AMIS or EWCM - determines calendar file and EO model
• scales: Admin level for extraction (admin_1 or admin_2)
• crops: List of crops to process (full names: maize, sorghum, millet, rice, winter_wheat, spring_wheat, teff, soybean)
• growing_seasons: List of seasons ([1] for primary, [1, 2] for primary + secondary)
• shp_boundary: Shapefile for admin boundaries
• calendar_file: Crop calendar Excel file
• mask: Cropland/crop-type mask filename
• threshold / floor / ceil: Mask thresholding settings
• eo_model: List of EO datasets to extract
• redo: Redo processing for all days (True) or only new data (False)

;;; AMIS countries ;;;
[argentina]
crops = ['soybean', 'winter_wheat', 'maize']

[brazil]
crops = ['maize', 'soybean', 'winter_wheat', 'rice']

[india]
crops = ['rice', 'maize', 'winter_wheat', 'soybean']

; ... (additional AMIS countries)

;;; EWCM countries ;;;
[kenya]
category = EWCM
scales = ['admin_1']
growing_seasons = [1, 2]
use_cropland_mask = True
shp_boundary = adm_shapefile.gpkg
calendar_file = EWCM_2025-04-21.xlsx
crops = ['maize']

[zambia]
category = EWCM
scales = ['admin_2']
growing_seasons = [1]
use_cropland_mask = True
shp_boundary = adm_shapefile.gpkg
calendar_file = EWCM_2025-04-21.xlsx
crops = ['maize']

; ... (additional EWCM countries)

;;; Crop masks ;;;
[maize]
mask = Percent_Maize.tif

[winter_wheat]
mask = Percent_Winter_Wheat.tif

[rice]
mask = Percent_Rice.tif

[soybean]
mask = Percent_Soybean.tif

; ... (additional crops)

;;; Calendar categories ;;;
[EWCM]
calendar_file = EWCM_2025-04-21.xlsx
eo_model = ['nsidc_surface', 'nsidc_rootzone', 'ndvi', 'cpc_tmax', 'cpc_tmin', 'chirps', 'chirps_gefs', 'esi_4wk']

[AMIS]
calendar_file = AMISCM_2025-04-21.xlsx

[DEFAULT]
method = JRC
redo = True
threshold = True
floor = 20
ceil = 90
countries = ["kenya"]
crops = ['maize']
category = AMIS
scales = ['admin_1']
growing_seasons = [1]
mask = cropland_v9.tif
use_cropland_mask = False
shp_boundary = gaul1_asap_v04.shp
statistics_file = statistics.csv
zone_file = countries.csv
eo_model = ['aef', 'nsidc_surface', 'nsidc_rootzone', 'ndvi', 'cpc_tmax', 'cpc_tmin', 'chirps', 'chirps_gefs', 'esi_4wk']

[PROJECT]
project_name = FEWSNET
parallel_extract = True
parallel_merge = False

geocif.txt (optional)

Only needed when running the geocif pipeline. Adds per-country geocif settings, ML model configuration, and overrides [DEFAULT] values like countries, method, and project_name.

Key overrides when geocif.txt is loaded:

• countries changes from geoextract.txt's list to geocif's list
• method changes from JRC to monthly_r
• project_name in [DEFAULT] becomes geocif (but [PROJECT] project_name stays FEWSNET)

[AGMET]
eo_plot = ['ndvi', 'cpc_tmax', 'cpc_tmin', 'chirps', 'esi_4wk', 'nsidc_surface', 'nsidc_rootzone']

;;; Per-country geocif settings ;;;
[zambia]
crops = ['maize']
admin_zone = admin_2
boundary_file = adm_shapefile.gpkg

[malawi]
crops = ['maize']
admin_zone = admin_2
boundary_file = adm_shapefile.gpkg

; ... (additional countries)

;;; ML model settings ;;;
[ML]
model_type = REGRESSION
target = Yield (tn per ha)
panel_model = True
lag_years = 3
; ... (see geocif.txt for full ML configuration)

[DEFAULT]
data_source = harvest
method = monthly_r
project_name = geocif
countries = ["zambia", "malawi", "south_africa", "madagascar", "mozambique", "angola", "zimbabwe"]
crops = ['maize']
admin_zone = admin_1
models = ['catboost']

Supported datasets

Dataset	Description	Source
AEF	AlphaEarth Foundations satellite embeddings (64-band, 10m)	source.coop
AGERA5	Agrometeorological indicators (precipitation, temperature)	CDS
CHIRPS	Rainfall estimates (v2 and v3)	CHC
CHIRPS-GEFS	15-day precipitation forecasts	CHC
CPC	Temperature (Tmax, Tmin)	NOAA CPC
ESI	Evaporative Stress Index (4-week, 12-week)	SERVIR
FLDAS	Land surface model outputs (soil moisture, precip, temp)	NASA
NDVI	Vegetation index from MODIS (MOD09CMG)	NASA
VIIRS	Vegetation index from VIIRS (VNP09CMG)	NASA
NSIDC	Soil moisture (surface, rootzone)	NSIDC
VHI	Vegetation Health Index	NOAA STAR
LST	Land Surface Temperature	NASA
AVHRR	Long-term NDVI	NOAA NCEI
FPAR	Fraction of Absorbed Photosynthetically Active Radiation	JRC
SOIL-MOISTURE	SMAP soil moisture	NASA

Upload package to PyPI

Navigate to the root of the geoprepare repository (the directory containing pyproject.toml):

cd /path/to/geoprepare

Step 1: Update version

Use bump2version to update the version in both pyproject.toml and geoprepare/__init__.py:

Using uv:

uvx bump2version patch --current-version X.X.X --new-version X.X.Y pyproject.toml geoprepare/__init__.py

Using pip:

pip install bump2version
bump2version patch --current-version X.X.X --new-version X.X.Y pyproject.toml geoprepare/__init__.py

Or manually edit the version in pyproject.toml and geoprepare/__init__.py.

Step 2: Clean old builds

Linux/macOS:

rm -rf dist/ build/ *.egg-info/

Windows (Command Prompt):

rmdir /s /q dist build geoprepare.egg-info

Windows (PowerShell):

Remove-Item -Recurse -Force dist/, build/, *.egg-info/ -ErrorAction SilentlyContinue

Step 3: Build and upload

Using uv (Linux/macOS):

uv build
uvx twine check dist/*
uvx twine upload dist/geoprepare-X.X.X*

Using uv (Windows):

uv build
uvx twine check dist\geoprepare-X.X.X.tar.gz dist\geoprepare-X.X.X-py3-none-any.whl
uvx twine upload dist\geoprepare-X.X.X.tar.gz dist\geoprepare-X.X.X-py3-none-any.whl

Using pip:

pip install build twine
python -m build
twine check dist/*
twine upload dist/geoprepare-X.X.X*

Replace X.X.X with your current version and X.X.Y with the new version.

Optional: Configure PyPI credentials

To avoid entering credentials each time, create a ~/.pypirc file (Linux/macOS) or %USERPROFILE%\.pypirc (Windows):

[pypi]
username = __token__
password = pypi-YOUR_API_TOKEN_HERE

Credits

This project was supported by was supported by NASA Applied Sciences Grant No. 80NSSC17K0625 through the NASA Harvest Consortium, and the NASA Acres Consortium under NASA Grant #80NSSC23M0034