hestia-earth-distribution 0.3.1

Hestia's Distribution library

Hestia Data Utils

Utils library to manipulate distributions on the Hestia platform

Install

1. pip install hestia_earth.distribution
2. Optional: to generate distribution files, please install pymc 4.

Usage

By default, all output files will be stored under ./data folder. You can set the env variable DISTRIBUTION_DATA_FOLDER to store in a different folder.

To get univariate posterior distribution:

from hestia_earth.distribution.posterior_yield import get_post_ensemble, get_post

# get a single posterior distribution, run:
mu_ensemble, sd_ensemble = get_post_ensemble('GADM-GBR', 'wheatGrain')

# Or, if only instrested in the mean of the mu and sd values, run:
mu, sd = get_post('GADM-GBR', 'wheatGrain')

To calculate the probability of a set of values using bivariate distribution:

from hestia_earth.distribution.utils.MCMC_bivariate import calculate_fit_2d

# get the likelihood of a candidate based on the bivariate distribution between Grain
# yield and Nitrogen fertiliser usage of a given country and a given product, run:
yield_value = 8500
n_use = 200
likelihood = calculate_fit_2d([n_use, yield_value], 'GADM-GBR', 'wheatGrain')

Advance Usage

You can clone this repository to use the commands below.

Generate prior distribution

To generate yield prior file for all products:

python generate_prior_yield.py --overwrite

For more information, run python generate_prior_yield.py --help.

Generate likelihood data

In order to generate likelihood data (a spreadsheet of crop yield and fertiliser data) for a specific product and a specific country, run:

python generate_likelihood.py --product-id="wheatGrain" --country-id="GADM-GBR" --limit=1000

For more information, run python generate_likelihood.py --help.

Generate posterior distribution

• In order to generate posterior distribution (for Bayesian statistics) for a specific country, run:

python generate_posterior_yield.py --country-id="GADM-GBR"

or to generate the fertiliser usage:

python generate_posterior_fert.py --country-id="GADM-GBR"

or to generate the pesticide usage:

python generate_posterior_pest.py --country-id="GADM-GBR"

Note: all commands above will update the same CSV file so they must not be run at the same time.

Generate multivariate distribution

• In order to generate multivariate distribution (between yield and N, P, K fetiliser inputs) for a specific country and product, run:

python generate_MCMC_mv.py --country-id="GADM-GBR" --product-id="wheatGrain" --sample-size=10

Plotting

Prior Yield

To plot prior distribution by product by country:

python plot_prior_yield.py --country-id='GADM-GBR' --product-id='wheatGrain' --output-file='prior.png'

To plot FAO annual yield data, change --type parameter to one of the four options: fao_per_country, fao_per_product, fao_per_country_per_product, world_mu_signma. Example:

python plot_prior_yield.py --country-id='GADM-GBR' --output-file='fao-yield-gbr-allProducts.png' --type='fao_per_country'

For more information, run python plot_prior_yield.py --help.

Cycle Yield

To plot the bivariate distribution of yield data for Wheat, grain in United Kingdom:

python plot_cycle_yield.py --product-id=wheatGrain" --country-id="GADM-GBR" --limit=100

This will take a sample size of 100 and create a result.png file with the distribution.

For more information, run python plot_cycle_yield.py --help.

Posterior Yield

In order to plot the posterior distribution for a specific product and a specific country, run:

python plot_posterior_yield.py --country-id="GADM-GBR" --product-id="wheatGrain" --output-file="post.png"

For more information, run python plot_posterior_yield.py --help.

Plot bivariate distribution between Yield and fetiliser use

In order to plot the bivariate distribution between Yield and N, P or K fetiliser use for a specific product and a specific country, run:

python plot_MCMC_bivariate.py --y=8500 --n=200 --country-id='GADM-GBR' --product-id='wheatGrain' --output='gbr-yield-vs-nitrogen.png'

Or

python plot_MCMC_mv.py --y=8500 --n=200 --p=50 --k=50  --pest=5  --country-id='GADM-GBR' --product-id='wheatGrain' --output='gbr-yield-vs-nitrogen-on-demand.png'

Or

python plot_MCMC_mv.py --y=8500 --n=200 --p=50 --k=50  --country-id='GADM-GBR' --product-id='wheatGrain' --output='gbr-yield-vs-nitrogen-static.png'

For more information, run python plot_MCMC_bivariate.py --help.