- Fits data to linear EOS model - Gets confidence intervals of fitting parameters for linear model - Plots distribution (histogram) of physical parameters ''' # Imports from eosfit import EOS, EOSmodel import numpy as np # Data V = np.array([8., 8.5, 9., 9.6, 10.2, 10.9, 11.6, 12.2, 13., 13.8, 14.5]) # [Ang^3] E = np.array([-4.65, -5.05, -5.3, -5.48, -5.57, -5.59, -5.575, -5.5, -5.4, -5.3, -5.18]) # [eV/atom] # Construct EOS model eos = EOS(V, E, ID='BM4', model=EOSmodel.BM4) V0_BM4, E0_BM4, B0_BM4 = eos.fit() # No need for initial guess (linear model) ci_BM4 = eos.get_phys_ci() # Done via simulation (need many samples for good statistical representation) print '''BM4 === V0 = {0} Ang^3 E0 = {1} eV/atom B0 = {2} GPa phys ci = {3} '''.format(V0_BM4, E0_BM4, B0_BM4*160.217, ci_BM4) # Plot distributions eos.plot_hist_V0('eosfit_example_BM4_V0_dist.png') eos.plot_hist_E0('eosfit_example_BM4_E0_dist.png') eos.plot_hist_B0('eosfit_example_BM4_B0_dist.png') eos.plot_hist_B0p('eosfit_example_BM4_B0p_dist.png')