pltname=D['outname'])
"""perform various analyses"""
msg = "sampling time: " + str(D['sampling_time']) + " seconds"
cc.WP(msg, D['wrt_file'])
msg = "model evidence: " + str(D['lnZ']) + \
" +/- " + str(D['dlnZ'])
cc.WP(msg, D['wrt_file'])
cp.plot_hist(flattrace,
D['pname'],
D['pname_plt'],
param_true=D['param_true'],
pltname=D['outname'])
cc.coef_summary(flattrace, D['pname'], D['outname'])
bounds = ((.25, 2.1), (-.8, 2.2), (0, .6))
cp.plot_cov(flattrace,
D['pname_plt'],
param_true=D['param_true'],
bounds=bounds,
figsize=[5.5, 5.5],
pltname=D['outname'])
cp.plot_prediction(flattrace,
D['name_list'],
D['Tt'],
D['At'],
D['It'],
feval,
cc.WP(msg, D['wrt_file'])
cp.plot_chains(D['rawtrace'], flattrace, D['nlinks'], D['pname'],
D['pname_plt'], pltname=D['outname'])
cp.plot_squiggles(D['rawtrace'], 0, 1, D['pname_plt'],
pltname=D['outname'])
"""perform various analyses"""
msg = "sampling time: " + str(D['sampling_time']) + " seconds"
WP(msg, D['wrt_file'])
msg = "model evidence: " + str(D['lnZ']) + \
" +/- " + str(D['dlnZ'])
cc.WP(msg, D['wrt_file'])
cc.coef_summary(flattrace, D['pname'], D['wrt_file'])
nxtprior = np.zeros((2, D['nparam']))
nxtprior[0, :] = np.mean(flattrace, 0) - 5*np.std(flattrace, 0)
nxtprior[1, :] = 10*np.std(flattrace, 0)
np.savetxt(D['outname'] + '_prior.csv', nxtprior)
cp.plot_hist(flattrace, D['pname'], D['pname_plt'], pltname=D['outname'])
cp.plot_cov(flattrace, D['pname_plt'], pltname=D['outname'],
tight_layout=False)
"""configure model prediction plots for Cp, H, S and G"""
name_list_l = [D['name_list_Cp'], D['name_list_H'], None, None]
Tt_l = [D['Tt_Cp'], D['Tt_H'], None, None]
At_l = [D['At_Cp'], D['At_H'], None, None]
