Merge remote-tracking branch 'origin/master'

examples/data_6d/Data_6D_new.py

@@ -25,7 +25,7 @@ def find_yloc(x, sunit, epl, mat):
 
 
 # Import Data
-db = FE.Data("Data_Base_Updated_Final_Rotated_Train.JSON", wh_data=True)
+db = FE.Data("Data_Base_Updated_Final_Rotated_Train.json", wh_data=True)
 # db = FE.Data("Data_Base_UpdatedE-07.json", Work_Hardening=False)
 # define reference material, J2 plasticity, linear w.h.
 mat_ref = FE.Material(name="reference")
@@ -37,12 +37,13 @@ def find_yloc(x, sunit, epl, mat):
 print(f'Successfully imported data for {db.mat_data["Nlc"]} load cases')
 mat_ml = FE.Material(db.mat_data['Name'], num=1)  # define material
 mat_ml.from_data(db.mat_data)  # data-based definition of material
-"""
+
 # Train SVC with data from all microstructures
-mat_ml.train_SVC(C=1, gamma=0.5, Fe=0.7, Ce=0.9, Nseq=2, 
+mat_ml.train_SVC(C=2, gamma=0.1, Fe=0.89, Ce=0.9, Nseq=1, 
                  gridsearch=False, plot=False)
 print('Training successful.\n' +
       f'Number of support vectors: {len(mat_ml.svm_yf.support_vectors_)}')
+mat_ml.export_MLparam(__file__, file='abq_data-C2-g01-Fe89-Ce9-N1', path='./')
 
 # Testing
 sig_tot, epl_tot, yf_ref = CTD.Create_Test_Sig(
@@ -168,8 +169,9 @@ def find_yloc(x, sunit, epl, mat):
 legend.legendHandles[1]._sizes = [50]
 plt.tight_layout()
 fig.savefig('Reconstructed_Stress_Strain_Curve.png', dpi=300)
-plt.show()"""
+plt.show()
 
+"""
 # Analysis of level of smoothness of the ML yield function using the gradient
 # of the yield function
 print('Repeating training for 4 combinations of hyperparameters.')
@@ -256,4 +258,6 @@ def find_yloc(x, sunit, epl, mat):
 ax2.legend(fontsize=14)
 ax2.tick_params(labelsize=14)
 plt.tight_layout()
-plt.show()
+plt.show()
+"""
+

examples/data_6d/Stress_Strain_Calculation.py

@@ -9,18 +9,25 @@
 import pylabfea as FE
 
 # Import Data
-db = FE.Data("Data_Base_Updated_Final_Rotated_Train.JSON", wh_data=True)
+db = FE.Data("Data_Base_Updated_Final_Rotated_Train.json", wh_data=True)
 
 # db.plot_yield_locus(db=db, mat_data=db.mat_data, active='flow_stress')
 print(f'Successfully imported data for {db.mat_data["Nlc"]} load cases')
 mat_ml = FE.Material(db.mat_data['Name'], num=1)  # define material
 mat_ml.from_data(db.mat_data)  # data-based definition of material
 
-# Train SVC with data and plot resulting stress-strain curves for 
-# simple load cases
-mat_ml.train_SVC(C=2, gamma=0.1, Fe=0.7, Ce=0.9, Nseq=1,
+# Train SVC with data and plot yield locus
+mat_ml.train_SVC(C=2, gamma=0.1, Fe=0.7, Ce=0.9, Nseq=2,
                  gridsearch=False, plot=False)
+print(f'Training completed. Generated {len(mat_ml.svm_yf.support_vectors_)}' +
+      ' support vectors.')
+sc = FE.sig_princ2cyl(mat_ml.msparam[0]['sig_ideal'])
+mat_ml.polar_plot_yl(data=sc, dname='training data', arrow=True)
+# export ML parameters for use in UMAT
+mat_ml.export_MLparam(__file__, file='abq_data-C2-g01-Fe7-Ce9-N2', path='./')
 
+# Calculate and plot resulting stress-strain curves for 
+# simple load cases
 print("\nCalculating stress-strain data. I'll be back!")
-mat_ml.calc_properties(eps=0.02, sigeps=True)
+mat_ml.calc_properties(eps=0.03, sigeps=True)
 mat_ml.plot_stress_strain()