def display_with_yield(disp = True):
"""Display the plot of the file with its yield point and returns the model and that point."""
"""Opens file browser and gets selection"""
model = getfile()
data = model.get_experimental_data()
yieldpoint = None
"""Gets radio button input as to which method to use to optimize"""
if yield_method.get() == 1:
yieldpoint = material_analytics.yield_stress_classic_unfitted(data)
elif yield_method.get() == 2:
yieldpoint = material_analytics.yield_stress_classic_fitted(data)
elif yield_method.get() == 3:
yieldpoint = material_analytics.yield_stress(data)
else:
elastic, plastic = material_analytics.kmeanssplit(data)
yieldpoint = plastic[0][None,]
if disp:
"""Displays the found yield stress"""
plot.plotmult2D(data, yieldpoint, title = 'File', marker1 = 'o', xtitle = 'Strain ($\epsilon$)', ytitle= 'Stress ($\sigma$)')
return model, yieldpoint
def constitutive_model():
"""Display the plot of the file fitted to a constitutive model"""
"""Displays the currently selected yield point"""
model, yieldpoint = display_with_yield(disp=False)
data = model.get_experimental_data()
"""Will need to be set to user-input guess"""
guess = [-150,1]
"""[0,1] is the first row, second column, which is the stress values"""
SS_stress = yieldpoint[0,1]
model_training_methods = ['Nelder-Mead','Powell','CG','Newton-CG','BFGS','L-BFGS-B','SLSQP','COBYLA','TNC','Basinhopping','Brute Force','Genetic Algorithm']
"""Because basinhopping, brute force, and GA are all in separate libraries, they are handled as separate cases."""
optmethod = optimization_method.get()-1
if optmethod<9:
model_params = optimization_suite.minimize_suite(model.mcfunc, methods=[model_training_methods[optmethod],], guess = guess ,SS_stress=SS_stress)
elif optmethod==9:
model_params = optimization_suite.minimize_suite(model.mcfunc, methods=[basinhopping,], guess = guess ,SS_stress=SS_stress)
elif optmethod==10:
model_params = optimization_suite.minimize_suite(model.mcfunc, methods=[brute,], guess = guess ,SS_stress=SS_stress)
else:
model_params = optimization_suite.GA_minimize(model.mcfunc, guess)
"""Plots the data versus the fitted irreversible model data"""
plot.plotmult2D(data, model.irreversible_model(model_params,SS_stress), title = 'Fitted Thermodynamics', xtitle = 'Strain ($\epsilon$)', ytitle= 'Stress ($\sigma$)')
def display_with_yield(disp=True):
"""Display the plot of the file with its yield point and returns the model and that point."""
"""Opens file browser and gets selection"""
model = getfile()
data = model.get_experimental_data()
yieldpoint = None
"""Gets radio button input as to which method to use to optimize"""
if yield_method.get() == 1:
yieldpoint = material_analytics.yield_stress_classic_unfitted(data)
elif yield_method.get() == 2:
yieldpoint = material_analytics.yield_stress_classic_fitted(data)
elif yield_method.get() == 3:
yieldpoint = material_analytics.yield_stress(data)
else:
elastic, plastic = material_analytics.kmeanssplit(data)
yieldpoint = plastic[0][None, ]
if disp:
"""Displays the found yield stress"""
plot.plotmult2D(data,
yieldpoint,
title='File',
marker1='o',
xtitle='Strain ($\epsilon$)',
ytitle='Stress ($\sigma$)')
return model, yieldpoint
def update():
"""Display the plot of the file with its yield point"""
"""Opens file browser and gets selection"""
name = tkFileDialog.askopenfilename()
"""Creates model for data"""
if 'xml' in name:
model = strainmodel(name, type='xml')
else:
model = strainmodel(name)
data = model.get_experimental_data()
"""Will need to be set to user-input guess"""
guess = [-150,1]
yieldpoint = None
"""Gets radio button input as to which method to use to optimize"""
if yield_method.get() == 1:
yieldpoint = material_analytics.yield_stress_classic_unfitted(data)
elif yield_method.get() == 2:
yieldpoint = material_analytics.yield_stress_classic_fitted(data)
elif yield_method.get() == 3:
yieldpoint = material_analytics.yield_stress(data)
else:
elastic, plastic = material_analytics.kmeanssplit(data)
yieldpoint = plastic[0][None,]
"""Displays the found yield stress"""
plot.plotmult2D(data, yieldpoint, title = 'File', xtitle = 'Strain ($\epsilon$)', ytitle= 'Stress ($\sigma$)')
"""[0,1] is the first row, second column, which is the stress values"""
SS_stress = yieldpoint[0,1]
model_training_methods = ['Nelder-Mead','Powell','CG','Newton-CG','BFGS','L-BFGS-B','SLSQP','COBYLA','TNC','Basinhopping','Brute Force','Genetic Algorithm']
"""Because basinhopping, brute force, and GA are all in separate libraries, they are handled as separate cases."""
optmethod = optimization_method.get()-1
if optmethod<9:
model_params = optimization_suite.minimize_suite(model.mcfunc, methods=[model_training_methods[optmethod],], guess = guess ,SS_stress=SS_stress)
elif optmethod==9:
model_params = optimization_suite.minimize_suite(model.mcfunc, methods=[basinhopping,], guess = guess ,SS_stress=SS_stress)
elif optmethod==10:
model_params = optimization_suite.minimize_suite(model.mcfunc, methods=[brute,], guess = guess ,SS_stress=SS_stress)
else:
model_params = optimization_suite.GA_minimize(model.mcfunc, guess)
"""Plots the data versus the fitted irreversible model data"""
plot.plotmult2D(data, model.irreversible_model(model_params,SS_stress), title = 'Fitted Thermodynamics', xtitle = 'Strain ($\epsilon$)', ytitle= 'Stress ($\sigma$)')
def constitutive_model():
"""Display the plot of the file fitted to a constitutive model"""
"""Displays the currently selected yield point"""
model, yieldpoint = display_with_yield(disp=False)
data = model.get_experimental_data()
"""Will need to be set to user-input guess"""
guess = [-150, 1]
"""[0,1] is the first row, second column, which is the stress values"""
SS_stress = yieldpoint[0, 1]
model_training_methods = [
'Nelder-Mead', 'Powell', 'CG', 'Newton-CG', 'BFGS', 'L-BFGS-B',
'SLSQP', 'COBYLA', 'TNC', 'Basinhopping', 'Brute Force',
'Genetic Algorithm'
]
"""Because basinhopping, brute force, and GA are all in separate libraries, they are handled as separate cases."""
optmethod = optimization_method.get() - 1
if optmethod < 9:
model_params = optimization_suite.minimize_suite(
model.mcfunc,
methods=[
model_training_methods[optmethod],
],
guess=guess,
SS_stress=SS_stress)
elif optmethod == 9:
model_params = optimization_suite.minimize_suite(model.mcfunc,
methods=[
basinhopping,
],
guess=guess,
SS_stress=SS_stress)
elif optmethod == 10:
model_params = optimization_suite.minimize_suite(model.mcfunc,
methods=[
brute,
],
guess=guess,
SS_stress=SS_stress)
else:
model_params = optimization_suite.GA_minimize(model.mcfunc, guess)
"""Plots the data versus the fitted irreversible model data"""
plot.plotmult2D(data,
model.irreversible_model(model_params, SS_stress),
title='Fitted Thermodynamics',
xtitle='Strain ($\epsilon$)',
ytitle='Stress ($\sigma$)')
def statistical_model():
"""Display the plot of the file fitted to a statistical model"""
"""Displays the currently selected yield point"""
model, yieldpoint = display_with_yield(disp=False)
data = model.get_experimental_data()
"""This is for the logarithmic model"""
if fit_method.get() == 1:
model = material_analytics.log_approx(data)
"""This is for the custom, slope-based model"""
if fit_method.get() == 2:
model = material_analytics.stress_model(data, yieldpoint)
"""Take some points from the model to plot them"""
fittedpts = material_analytics.samplepoints(model,[0.,max(data[:,0])],10000)
plot.plotmult2D(data,fittedpts,marker1 = 'o', marker2 = '-',title = 'Stress v Strain',xtitle ='Strain ($\epsilon$)',ytitle='Stress ($\sigma$)')
def statistical_model():
"""Display the plot of the file fitted to a statistical model"""
"""Displays the currently selected yield point"""
model, yieldpoint = display_with_yield(disp=False)
data = model.get_experimental_data()
"""This is for the logarithmic model"""
if fit_method.get() == 1:
model = material_analytics.log_approx(data)
"""This is for the custom, slope-based model"""
if fit_method.get() == 2:
model = material_analytics.stress_model(data, yieldpoint)
"""Take some points from the model to plot them"""
fittedpts = material_analytics.samplepoints(model,
[0., max(data[:, 0])], 10000)
plot.plotmult2D(data,
fittedpts,
marker1='o',
marker2='-',
title='Stress v Strain',
xtitle='Strain ($\epsilon$)',
ytitle='Stress ($\sigma$)')