geoprepare 0.3.6

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

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

Usage

from geoprepare import geoprepare, geoextract, geomerge

# Provide full path to the configuration files
# Download and preprocess data
geoprepare.run(['PATH_TO_geoprepare.txt'])

# Extract crop masks and EO variables
geoextract.run(['PATH_TO_geoprepare.txt', 'PATH_TO_geoextract.txt'])

# Merge EO files into one, this is needed to create AgMet graphics and to run the crop yield model
geomerge.run(['PATH_TO_geoprepare.txt', 'PATH_TO_geoextract.txt'])

Before running the code above, we need to specify the two configuration files. geoprepare.txt contains configuration settings for downloading and processing the input data. geoextract.txt contains configuration settings for extracting crop masks and EO variables.

Configuration files

geoprepare.txt

datasets: Specify which datasets need to be downloaded and processed dir_base: Path where to store the downloaded and processed files start_year, end_year: Specify time-period for which data should be downloaded and processed logfile: What directory name to use for the log files level: Which level to use for logging parallel_process: Whether to use multiple CPUs fraction_cpus: What fraction of available CPUs to use

[DATASETS]
datasets = ['CPC', 'SOIL-MOISTURE', 'LST', 'CPC', 'AVHRR', 'AGERA5', 'CHIRPS', 'CHIRPS-GEFS']

[PATHS]
dir_base = /home/servir/GEOCIF
dir_input = ${dir_base}/input
dir_log = ${dir_base}/log
dir_interim = ${dir_input}/interim
dir_download = ${dir_input}/download
dir_output = ${dir_base}/output
dir_global_datasets = ${dir_input}/global_datasets
dir_masks = ${dir_global_datasets}/masks
dir_regions = ${dir_global_datasets}/regions
dir_regions_shp = ${dir_regions}/shps
dir_crop_masks = ${dir_input}/crop_masks
dir_models = ${dir_input}/models

[AGERA5]
start_year = 2022

[AVHRR]
data_dir = https://www.ncei.noaa.gov/data/avhrr-land-normalized-difference-vegetation-index/access

[CHIRPS]
fill_value = -2147483648
prelim = /pub/org/chc/products/CHIRPS-2.0/prelim/global_daily/tifs/p05/
final = /pub/org/chc/products/CHIRPS-2.0/global_daily/tifs/p05/
start_year = 2022

[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//

[FLDAS]

[LST]
num_update_days = 7

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

[SOIL-MOISTURE]
data_dir = https://gimms.gsfc.nasa.gov/SMOS/SMAP/L03/

[LOGGING]
level = ERROR

[DEFAULT]
logfile = log
parallel_process = False
fraction_cpus = 0.5
start_year = 2022
end_year = 2022

geoextract.txt

countries: List of countries to process forecast_seasons: List of seasons to process mask: Name of file to use as a mask for cropland/croptype redo: Redo the processing for all days (True) or only days with new data (False) threshold: Use a threshold value (True) or a percentile (False) on the cropland/croptype mask floor: Value below which to set the mask to 0 ceil: Value above which to set the mask to 1 eo_model: List of datasets to extract from

[kenya]
category = EWCM
scale = ['admin1']  ; can be admin1 (state level) or admin2 (county level)
season = [1]  ; 1 is primary/long season, 2 is secondary/short season
crops = ['mz', 'sr', 'ml', 'rc', 'ww', 'tf']
use_cropland_mask = True

[ww]
mask = cropland_v9.tif

[mz]
mask = cropland_v9.tif

[sb]
mask = cropland_v9.tif

[rc]
mask = cropland_v9.tif

[tf]
mask = cropland_v9.tif

[sr]
mask = cropland_v9.tif

[ml]
mask = cropland_v9.tif

[DEFAULT]
redo = False
threshold = True
floor = 20
ceil = 90
scale = ['admin1']
season = [1]
countries = ['kenya']
forecast_seasons = [2022]
mask = cropland_v9.tif
shp_boundary = EWCM_Level_1.shp
eo_model = ['ndvi', 'cpc_tmax', 'cpc_tmin', 'cpc_precip', 'esi_4wk', 'soil_moisture_as1', 'soil_moisture_as2']

Credits

This package was created with Cookiecutter and the giswqs/pypackage project template.