plot = MatPlotLib()
# plot datasets
plot.datasets()
# plot marginal prior distributions
plot.priors()
# plot error model distribution treating each dataset point as prediction
# and a random realization of parameters from prior distribution
from datasets import datasets
from error import h
for name, dataset in datasets.items():
dataset = dataset()
xs = dataset['x'].copy(deep=1)
for index in dataset.index:
xs.loc[index] = h.inverse(xs.loc[index])
dataset[r'$\xi$'] = xs
plot.errors(predictions=datasets)
# plot distributions for the initial model values
from initial import initial
plot.distributions(initial, suffix='-initial')
# report status
plot.status()
# generate report
from spux.report import generate
generate.report(authors=r'Jonas {\v S}ukys')
# plot marginal posterior distributions
plot.posteriors()
# plot pairwise joint posterior distributions
plot.posteriors2d()
# plot pairwise joint posterior distribution for selected parameter pairs
plot.posterior2d(r'$\sigma_\xi$', r'$\sigma_y$')
plot.posterior2d(r'$\tau$', 'K')
# plot posterior model predictions including observations
labels = ['x', 'y', r'$\xi$', 'S', r'$\Delta V$']
plot.predictions(labels=labels)
# plot quantile-quantile comparison of the error and residual distributions
plot.QQ()
# compute Nash-Sutcliffe efficiency for the model
plot.NSE('y')
# show metrics
plot.metrics()
# report status
plot.status()
# generate report
from spux.report import generate
authors = r'Jonas {\v S}ukys'
generate.report(authors)