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]
plotflag = True
# MU4 model
eos1 = EOS(V, E, ID='MU4')
p0 = [12., -3., 1., 5.] # Order [V0, E0, B0, B0']
V0_MU4, E0_MU4, B0_MU4 = eos1.fit(p0) # Initial guess required (nonlinear model)
ci_MU4 = eos1.get_ci()
E_MU4 = eos1.eval()
R2_MU4 = eos1.get_rsquared()
if plotflag:
eos1.plot(filename='eosfit_example_MU4.png')
print '''MU4
===
V0 = {0} Ang^3
E0 = {1} eV/atom
B0 = {2} GPa
ci = {3}
E = {4} eV/atom
R^2 = {5}
'''.format(V0_MU4, E0_MU4, B0_MU4*160.217, ci_MU4, E_MU4, R2_MU4)
# BM5 model
eos2 = EOS(V, E, ID='BM5', model=EOSmodel.BM5)
V0_BM5, E0_BM5, B0_BM5 = eos2.fit() # No need for initial guess (linear model)
