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()