How to use BestiaPop¶

BestiaPop has three primary commands that you can pass in with the -a option:

generate-climate-file: this command will generate an input file for crop modelling software depending on the output type (-ot) being met or wth. When csv is selected, a file containing all years in the sequence, with all requested variables, will be produced for each lat/lon combination.
download-nc4-file: this command downloads NetCDF4 files from SILO or NASAPOWER
convert-nc4: currently not implemented, this command will allow you to convert NetCDF4 files to other formats like json or csv.

Examples¶

Generate Climate File¶

Generate MET output files using SILO cloud API, for Radiation, Min Temperature, Max Temperature and Daily Rain for years 2015 to 2016¶

NOTE:

the resulting MET files will be placed in the output directory specified by “-o”

the “-ot” parameter specifies the output type: met, dssat, csv or json.

python bestiapop.py -a generate-climate-file -s silo -y "2015-2016" -c "radiation max_temp min_temp daily_rain" -lat "-41.15 -41.05" -lon "145.5 145.6" -o C:\some\output\folder\ -ot met

Generate WTH (for DSSAT) output files using SILO cloud API, for Radiation, Min Temperature, Max Temperature and Daily Rain for years 2015 to 2016¶

python bestiapop.py -a generate-climate-file -s silo -y "2015-2016" -c "radiation max_temp min_temp daily_rain" -lat "-41.15 -41.05" -lon "145.5 145.6" -o C:\some\output\folder\ -ot wth

Generate MET output files using NASAPOWER cloud API, for Radiation, Min Temperature, Max Temperature and Daily Rain for years 2003 to 2016¶

python bestiapop.py -a generate-climate-file -s nasapower -y "2015-2016" -c "radiation max_temp min_temp daily_rain" -lat "-41.15 -41.05" -lon "145.5 145.6" -o C:\some\output\folder\ -ot met

Generate MET output files from Local Disk for Radiation, Min Temperature, Max Temperature and Daily Rain for years 1990 to 2010¶

NOTE:

all the required NetCDF files should be placed in a single directory which is then referenced with the –input parameter. The directory should have the following structure:

C:\input\folder:
    \__ 1990.daily_rain.nc
    \__ 1990.max_temp.nc
    \__ 1990.min_temp.nc
    \__ 1990.radiation.nc
    \__ 1991.daily_rain.nc
    \__ 1991.max_temp.nc
    \__ 1991.min_temp.nc
    \__ 1991.radiation.nc
    \__ ...
    \__ 2010.daily_rain.nc
    \__ 2010.max_temp.nc
    \__ 2010.min_temp.nc
    \__ 2010.radiation.nc

python bestiapop.py -a generate-climate-file -y "1990-2010" -c "radiation max_temp min_temp daily_rain" -lat "-41.15 -41.05" -lon "145.5 145.6" -i C:\some\input\folder\with\all\netcdf\files\ -o C:\some\output\folder\ -ot met

Download NetCDF4 File¶

Download SILO climate files for years 2010 to 1028 and the variables daily_rain and max_temp¶

This command will only download the file from AWS, it won’t perform any further processing.

NOTE: a year range must be separated by a dash, whereas multiple climate variables are separated by spaces

python bestiapop.py -a download-nc4-file --data-source silo -y "2010-2018" -c "daily_rain max_temp" -o C:\some\output\folder

PARALLEL COMPUTING¶

BestiaPop as of version 2.5 comes with parallel processing for multicore systems by leveraging python’s multiprocessing library. Not all actions have implemented this functionality yet but they will be added progressively. To enable multiprocessing just pass in the -m flag to the bestiapop.py command. By default it will leverage all your cores (whether physical or logical).

Parallelization is done based on the coordinate variable (whether latitude or longitude) that has the widest spread (highest data points count). This means, you will rip the benefits of multiple cores when your datasets reference wide ranges of lat or lon variables.

NOTE: When generating MET files from locally available NetCDF4 files based on SILO data, you might experience mixed results since SILO provides NetCDF4 files split into year-variable and MET files require multiple years in the same MET file. SILO has created the NetCDF4 files (as of 2020) to perform better when extracting spatial data points rather than time-based data points. This effectively means that it is slower to extract data for all days of the year from NetCDF4 files, for a single combination of lat/lon, than it is to extract data for all combinations of lat/lon for a single day. Since SILO NetCDF4 files are split into year-variable units you will always have to extract data from different files when using multiple years.

EXAMPLE: Generate MET output files from SILO Cloud API for Radiation, Min Temperature, Max Temperature and Daily Rain for years 2015 to 2016¶

python bestiapop.py -a generate-climate-file -s silo -y "2008-2016" -c "radiation max_temp min_temp daily_rain" -lat "-41.15 -41.05" -lon "145.5 145.6" -o C:\some\output\folder\ -m

Here, the -m at the end will engage multiple cores to process the tasks. If you have 8 available cores it will create 8 separate processes to download the data from the cloud and will then use 8 separate processes to generate the output files.