def mcfunc(self, model_parameters): no_samples = 1 #experimental parameters T_service = 22. + 273. prec_stress = 0 SS_stress = 750 strain_stress, WTN = irreverisble.mechanics(prec_stress,SS_stress,T_service,model_parameters,no_samples) strain_stress = np.array(np.trim_zeros(strain_stress)).reshape(-1,2) #print strain_stress #---------------------------- cal_val = [] errors = [] #traverses experimental data points for iexp, data in enumerate(exp[:,0]): #finding nearest neighbors that surround the data points, and using them to determine the error for ical, data in enumerate(strain_stress[:,0]): ical = ical-1 # May or may not be advantageous to keep this instead of the range attribute for mem save left_stresspoint = strain_stress[ical,0] right_stresspoint = strain_stress[ical+1,0] exp_datapoint = exp[iexp,0] # finding the two nearest stress points and interpolating stress and strain if(exp_datapoint>left_stresspoint and exp_datapoint<right_stresspoint): # stores the differences between the successive approximations so we interpolate left_difference = exp_datapoint-left_stresspoint right_difference = right_stresspoint-exp_datapoint total_difference = left_difference+right_difference left_weight = left_difference/total_difference right_weight = right_difference/total_difference # interpolate strain based on stress interpolated_stress = left_weight*left_stresspoint + right_weight*right_stresspoint interpolated_strain = left_weight*strain_stress[ical,1] + right_weight*strain_stress[ical+1,1] strain_error = interpolated_strain - exp[iexp,1] #adds value, we want to find difference between these approximated data points and the real results cal_val.append([interpolated_stress,interpolated_strain]) errors.append(strain_error) break error_rms = self.error_evaluation_rms(errors) cal_val = np.asarray(cal_val) return error_rms
def irreversible_model(self, model_parameters, SS_stress): no_samples = 1 #experimental parameters T_service = 22. + 273. prec_stress = 0 strain_stress, WTN = irreverisble.mechanics(prec_stress,SS_stress,T_service,model_parameters,no_samples) return np.array(np.trim_zeros(strain_stress)).reshape(-1,2)
def irreversible_model(self, model_parameters, SS_stress):
no_samples = 1
#experimental parameters
T_service = 22. + 273.
prec_stress = 0
strain_stress, WTN = irreverisble.mechanics(prec_stress, SS_stress,
T_service,
model_parameters,
no_samples)
return np.array(np.trim_zeros(strain_stress)).reshape(-1, 2)
def mcfunc(self, model_parameters, SS_stress):
no_samples = 1
#experimental parameters
#SS_stress = 1009.384532 # determined by material_analytics.py
T_service = 22. + 273.
prec_stress = 0
strain_stress, WTN = irreverisble.mechanics(prec_stress, SS_stress,
T_service,
model_parameters,
no_samples)
strain_stress = np.array(np.trim_zeros(strain_stress)).reshape(-1, 2)
cal_val = []
errors = []
# this code, when uncommented, traverses all experimental data points and return their error
#traverses experimental data points
for iexp, data in enumerate(self.exp[:, 0]):
#finding nearest neighbors that surround the data points, and using them to determine the error
for ical, data in enumerate(strain_stress[:, 0]):
ical = ical - 1 # May or may not be advantageous to keep this instead of the range attribute for mem save
left_stresspoint = strain_stress[ical, 0]
right_stresspoint = strain_stress[ical + 1, 0]
exp_datapoint = self.exp[iexp, 0]
# finding the two nearest stress points and interpolating stress and strain
if (exp_datapoint > left_stresspoint
and exp_datapoint < right_stresspoint):
# stores the differences between the successive approximations so we interpolate
left_difference = exp_datapoint - left_stresspoint
right_difference = right_stresspoint - exp_datapoint
total_difference = left_difference + right_difference
left_weight = left_difference / total_difference
right_weight = right_difference / total_difference
# interpolate strain based on stress
interpolated_stress = left_weight * left_stresspoint + right_weight * right_stresspoint
interpolated_strain = left_weight * strain_stress[
ical, 1] + right_weight * strain_stress[ical + 1, 1]
strain_error = interpolated_strain - self.exp[iexp, 1]
#adds value, we want to find difference between these approximated data points and the real results
cal_val.append([interpolated_stress, interpolated_strain])
errors.append(strain_error)
break
error_rms = self.error_evaluation_rms(errors)
cal_val = np.asarray(cal_val)
return error_rms