def data_error(self, X_data, ph_data, X_D, g_x_func, s, p):
"""
Calculate the error for a single data point (by summing each element
Sigma n-1 at a single phase equilibrium point).
(should be in loop:
for ph, ind in zip(p.m['Data phases'],
range(len(p.m['Data phases']))):
"""
from ncomp import phase_equilibrium_calculation as pec
from ncomp import dual_equal
import numpy
#Z_0 = sorted(X_D)[len(X_D) // 2]
# TODO: Estimate in middle of all equilibrium points in X_data
Z_0 = self.d_Z_0(X_data[0], X_data[1])
#dual_equal(s, p, g_x_func, Z_0)
X_eq, g_eq, phase_eq = pec(s, p, g_x_func, Z_0)
if len(X_eq) < len(X_data):
epsilon_x = len(X_data) # Sigma n - 1 elements of data points
elif len(X_eq) == len(X_data):
#Epsilon_x = []
epsilon_x = 0.0
data_ind = numpy.argsort(ph_data)
model_ind = numpy.argsort(phase_eq)
#X_data[data_ind]
for i in range(len(X_data)):
epsilon_x += abs(X_data[data_ind[i]] - X_eq[model_ind[i]])
#for X_sol, ph in zip(X_eq, phase_eq):
#for X_sol in X_eq:
#for ph in p.m['Data phases']:
# epsilon_x +=
#for ph_eq in phase_eq:
# for ph_dat in ph_data:
# if ph_eq == ph_dat:
# for i in range(p.m['n']):
# Epsilon_x.append(abs(X_sol[i] - X_data[i]))
#
# elif len(X_eq) > len(X_data):
# Epsilon_x = []
# for X in X_eq:
# for X_sol, ph in zip(X, phase_eq):
# if ph == ph_data:
# for i in range(p.m['n']):
# Epsilon_x.append(abs(X_sol[i] - X_data[i]))
# epsilon_x = min(Epsilon_x)
elif len(X_eq) > len(X_data):
epsilon_x = len(X_data)
return epsilon_x
def data_error(self, X_data, ph_data, X_D, g_x_func, s, p):
"""
Calculate the error for a single data point (by summing each element
Sigma n-1 at a single phase equilibrium point).
(should be in loop:
for ph, ind in zip(p.m['Data phases'],
range(len(p.m['Data phases']))):
"""
from ncomp import phase_equilibrium_calculation as pec
from ncomp import dual_equal
import numpy
#Z_0 = sorted(X_D)[len(X_D) // 2]
# TODO: Estimate in middle of all equilibrium points in X_data
Z_0 = self.d_Z_0(X_data[0], X_data[1])
#dual_equal(s, p, g_x_func, Z_0)
X_eq, g_eq, phase_eq = pec(s, p, g_x_func, Z_0)
if len(X_eq) < len(X_data):
epsilon_x = len(X_data) # Sigma n - 1 elements of data points
elif len(X_eq) == len(X_data):
#Epsilon_x = []
epsilon_x = 0.0
data_ind = numpy.argsort(ph_data)
model_ind = numpy.argsort(phase_eq)
#X_data[data_ind]
for i in range(len(X_data)):
epsilon_x += abs(X_data[data_ind[i]] - X_eq[model_ind[i]])
#for X_sol, ph in zip(X_eq, phase_eq):
#for X_sol in X_eq:
#for ph in p.m['Data phases']:
# epsilon_x +=
#for ph_eq in phase_eq:
# for ph_dat in ph_data:
# if ph_eq == ph_dat:
# for i in range(p.m['n']):
# Epsilon_x.append(abs(X_sol[i] - X_data[i]))
#
# elif len(X_eq) > len(X_data):
# Epsilon_x = []
# for X in X_eq:
# for X_sol, ph in zip(X, phase_eq):
# if ph == ph_data:
# for i in range(p.m['n']):
# Epsilon_x.append(abs(X_sol[i] - X_data[i]))
# epsilon_x = min(Epsilon_x)
elif len(X_eq) > len(X_data):
epsilon_x = len(X_data)
return epsilon_x
# Simulate specifications
if data.P is not None and data.T is not None and data.Z_0 is None:
ph_eq, mph_eq, mph_ph = \
psd(g_x_func, s, p, data.P, data.T, n=100,
#n_dual=1,
LLE_only=data.lle_only,
VLE_only=data.vle_only,
Plot_Results=True) # Tested/working
print('ph_eq = {}'.format(ph_eq))
print('mph_eq = {}'.format(mph_eq))
print('mph_ph = {}'.format(mph_ph))
if data.P is not None and data.T is not None and data.Z_0 is not None:
pec(s, p, g_x_func, data.Z_0, k=None, P=data.P, T=data.T,
tol=1e-9, Print_Results=True, Plot_Results=data.plot_gibbs)
# Not tested
if data.plot_gibbs: #TODO: No need for this if we plot in pec?
pass # Need to add tie lines
#options = plot.plot_options
#plot.plot_g_mix(s, p, options, figno=None)
#plot.plot_g_mix(s, p, g_x_func)#, figno=None)
if data.plot_isotherms is not None:
from ncomp import g_mix as g_x_func
from plot import IsoDetection
iso = IsoDetection(components=data.comps)
import time
start = time.time()
psd(g_x_func, s, p, data.P, data.T, n=100,
#n_dual=1,
LLE_only=data.lle_only,
VLE_only=data.vle_only,
Plot_Results=True) # Tested/working
print('ph_eq = {}'.format(ph_eq))
print('mph_eq = {}'.format(mph_eq))
print('mph_ph = {}'.format(mph_ph))
if data.P is not None and data.T is not None and data.Z_0 is not None:
pec(s,
p,
g_x_func,
data.Z_0,
k=None,
P=data.P,
T=data.T,
tol=1e-9,
Print_Results=True,
Plot_Results=data.plot_gibbs)
# Not tested
if data.plot_gibbs: #TODO: No need for this if we plot in pec?
pass # Need to add tie lines
#options = plot.plot_options
#plot.plot_g_mix(s, p, options, figno=None)
#plot.plot_g_mix(s, p, g_x_func)#, figno=None)
if data.plot_isotherms is not None:
from ncomp import g_mix as g_x_func
from plot import IsoDetection
