pltname=D['outname'] + '_Cp')
cp.plot_cov(flattrace[:, 3:],
D['pname_plt'][3:],
param_true=D['param_true'][3:],
figsize=[5.5, 5.5],
sciform=True,
pltname=D['outname'] + '_H')
cp.plot_prediction(flattrace,
D['name_list'],
D['Tt_Cp'],
D['At_Cp'],
D['It_Cp'],
feval_Cp,
D,
xlim=[1, 1000],
ylim=[-5, 45],
xlabel=r"$T \, (K)$",
ylabel=r"$C_p \, \left(J \, {mol}^{-1} K^{-1}\right)$",
param_true=D['param_true'],
pltname=D['outname'] + 'Cp')
cp.plot_prediction(flattrace,
D['name_list'],
D['Tt_Cp'],
D['At_Cp'],
D['It_Cp'],
feval_Cp,
D,
xlim=[1, 80],
ylim=[-2, 21],
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,
D,
colorL=['k', 'r'],
param_true=D['param_true'],
ylim=(1.1, 2.2),
pltname=D['outname'])
plt.show()
pltname=D['outname'])
cc.coef_summary(flattrace, D['pname'], D['outname'])
cp.plot_cov(flattrace,
D['pname_plt'],
param_true=D['param_true'],
figsize=[5.5, 5.5],
pltname=D['outname'],
sciform=True)
# rescale the data errors by the means of the hyperparameters
hyp_m = np.mean(flattrace[:, D['order'] + 1:], 0)
hyp_m_V = np.zeros(D['Et'].shape)
for ii in range(len(hyp_m)):
hyp_m_V[D['It'] == ii] = hyp_m[ii]
cp.plot_prediction(flattrace,
D['name_list'],
D['Tt'],
D['At'],
D['It'],
feval,
D,
yerr=D['Et'] / hyp_m_V,
colorL=sns.color_palette("Reds", 4)[1:],
param_true=D['param_true'],
ylim=(0, 2.5),
pltname=D['outname'])
plt.show()