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)