Skip to content

Commit

Permalink
Minor change
Browse files Browse the repository at this point in the history
  • Loading branch information
@Ronakshoghi
Ronakshoghi committed Oct 10, 2023
1 parent fe948af commit a8b46ac
Showing 1 changed file with 76 additions and 75 deletions.
151 changes: 76 additions & 75 deletions examples/data_6d/Data_6D.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -33,14 +33,14 @@ def rgb_to_hex(rgb):
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, gridsearch=False, plot=False)
# mat_ml.train_SVC(C=1, gamma=0.4, Fe=0.7, Ce=0.9, Nseq=2, gridsearch=False, plot=False)
print(f'Training successful.\nNumber of support vectors: {len(mat_ml.svm_yf.support_vectors_)}')

# Testing
sig_tot, epl_tot, yf_ref = CTD.Create_Test_Sig(Json="Data_Base_Updated_Final_Rotated_Test.json")
yf_ml = mat_ml.calc_yf(sig_tot, epl_tot, pred=False)
Results = CTD.training_score(yf_ref, yf_ml)

print(Results)
# Plot Hardening levels over a meshed space
# Plot initial and final hardening level of trained ML yield function together with data points
ngrid = 100
Expand All @@ -59,10 +59,9 @@ def rgb_to_hex(rgb):
Z3 = mat_ml.calc_yf(sig=Cart_hh_6D, epl=normalized_grad_hh * 0.01, pred=False)
Z4 = mat_ml.calc_yf(sig=Cart_hh_6D, epl=normalized_grad_hh * 0.015, pred=False)
Z5 = mat_ml.calc_yf(sig=Cart_hh_6D, epl=normalized_grad_hh * 0.02, pred=False)
Z6 = mat_ml.calc_yf(sig=Cart_hh_6D, epl=normalized_grad_hh * 0.03, pred=False)
#colors = colormaps("plasma", 6) # Create a color palette with 6 colors
#colors_hex = [rgb_to_hex(color) for color in colors]
colors_hex = ['#000033', '#000055', '#000077', '#000099', '#0000aa', '#0000ff']
Z6 = mat_ml.calc_yf(sig=Cart_hh_6D, epl=normalized_grad_hh * 0.025, pred=False)

colors_hex = ['#550000', '#990000', '#bb0000', '#cc3333', '#ee3333', '#ff5050']
fig = plt.figure(figsize=(4.2, 4.2))
ax = fig.add_subplot(111, projection='polar')
ax.grid(True)
Expand All @@ -78,14 +77,14 @@ def rgb_to_hex(rgb):
handle3 = Line2D([], [], color=colors_hex[2], label='Equivalent Plastic Strain : 1% ')
handle4 = Line2D([], [], color=colors_hex[3], label='Equivalent Plastic Strain : 1.5% ')
handle5 = Line2D([], [], color=colors_hex[4], label='Equivalent Plastic Strain : 2% ')
handle6 = Line2D([], [], color=colors_hex[5], label='Equivalent Plastic Strain : 3% ')
handle6 = Line2D([], [], color=colors_hex[5], label='Equivalent Plastic Strain : 2.5% ')
fig_leg = plt.figure(figsize=(4, 4))
ax_leg = fig_leg.add_subplot(111)
ax_leg.axis('off')
ax_leg.legend(handles=[handle1, handle2, handle3, handle4, handle5, handle6], loc="center")
fig_leg.savefig('Legend.png', dpi=300)
plt.show()
#

# Plot initial yield locus in pi-plane with the average yield strength from data
Z = mat_ml.calc_yf(sig=Cart_hh_6D, epl=normalized_grad_hh * 0, pred=False) # value of yield fct for every grid point
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5.6, 4.5))
Expand Down Expand Up @@ -142,9 +141,9 @@ def rgb_to_hex(rgb):

fig = plt.figure(figsize=(5.6, 4.7))

plt.scatter(Eq_Strains_Drawing, Eq_Stress_Drawing, label="Data", s=9, color='#9A1414')
plt.scatter(Strains_for_Response, Response, label="ML", s=10, color='black')
plt.axvline(x=0.002, color='black', linestyle='--', ymax=50.4/plt.ylim()[1])
plt.scatter(Eq_Strains_Drawing, Eq_Stress_Drawing, label="Data", s=8, color='black')
plt.scatter(Strains_for_Response, Response, label="ML", s=8, color='#c60000')
plt.axvline(x=0.002, color='black', linestyle='--', ymax=50.5/plt.ylim()[1])
text_x_position = 0.002 + 0.0005
text_y_position = 46
text_size = 12
Expand All @@ -158,15 +157,16 @@ def rgb_to_hex(rgb):
legend.legendHandles[0]._sizes = [50]
legend.legendHandles[1]._sizes = [50]
plt.tight_layout()
# fig.savefig('Reconstructed_Stress_Strain_Curve.png', dpi=300)
fig.savefig('Reconstructed_Stress_Strain_Curve.png', dpi=300)
plt.show()

# Plot initial and final hardening level of trained ML yield function together with data points
# get stress data
peeq_dat = FE.eps_eq(db.mat_data['plastic_strain'])
ind0 = np.nonzero(peeq_dat < 0.0002)[0]
# ind0 = np.nonzero(peeq_dat < 0.0002)[0]
ind0 =np.nonzero(np.logical_and(peeq_dat > 0.00018, peeq_dat < 0.00022))[0]
sig_d0 = FE.s_cyl(db.mat_data['flow_stress'][ind0, :], mat_ml)
ind1 = np.nonzero(np.logical_and(peeq_dat > 0.024, peeq_dat < 0.026))[0]
ind1 = np.nonzero(np.logical_and(peeq_dat > 0.0248, peeq_dat < 0.0252))[0]
sig_d1 = FE.s_cyl(db.mat_data['flow_stress'][ind1, :], mat_ml)
# calculate ML flow stresses
ngrid = 100
Expand All @@ -187,72 +187,73 @@ def rgb_to_hex(rgb):
fig = plt.figure(figsize=(4.2, 4.2))
ax = fig.add_subplot(111, projection='polar')
ax.grid(True)
line = mat_ml.plot_data(Z0, ax, xx, yy, c="blue")
line = mat_ml.plot_data(Z1, ax, xx, yy, c="red")
plt.scatter(sig_d0[:, 1], sig_d0[:, 0], s=6, c="black")
plt.scatter(sig_d1[:, 1], sig_d1[:, 0], s=6, c="black")
# fig.savefig('ML+ScatterData.png', dpi=300)
line = mat_ml.plot_data(Z0, ax, xx, yy, c="#600000")
line = mat_ml.plot_data(Z1, ax, xx, yy, c="#ff5050")
plt.scatter(sig_d0[:, 1], sig_d0[:, 0], s=5, c="black")
plt.scatter(sig_d1[:, 1], sig_d1[:, 0], s=5, c="black")
fig.savefig('ML+ScatterData.png', dpi=300)
plt.show()
handle1 = mlines.Line2D([], [], color="blue", label='Equivalent Plastic Strain : 0 ')
handle1 = mlines.Line2D([], [], color="#550000", label='Equivalent Plastic Strain : 0 ')
handle2 = mlines.Line2D([], [], color="#ff3333", label='Equivalent Plastic Strain : 2.5% ')
fig_leg = plt.figure(figsize=(4, 4))
ax_leg = fig_leg.add_subplot(111)
ax_leg.axis('off')
ax_leg.legend(handles=[handle1], loc="center")
ax_leg.legend(handles=[handle1, handle2], loc="center")
fig_leg.savefig('Legend2.png', dpi=300)
plt.show()

# # Plot initial Hardening level with scatter data points
# Stress_Set = []
# Strain_Set = []
# for Key in list(db.Data_Visualization.keys()):
# Response_Stress = []
# Response_Strain = []
# Eq_Strains=list(db.Data_Visualization[Key]["Eq_Strain"])
# Stresses = db.Data_Visualization[Key]["Stress"]
# for counter, Eq_Strain in enumerate(Eq_Strains):
# if Eq_Strains[counter] < 0.0019 or Eq_Strains[counter] > 0.0021:
# continue
# else:
# Response_Strain.append(Eq_Strains[counter])
# Response_Stress.append(Stresses[counter])
# if len(Response_Strain) == 0:
# continue
# else:
# Stress_Set.append(Response_Stress[0])
# Strain_Set.append(Response_Strain[0])
# Stress_Set = np.array(Stress_Set)
# Stress_Set_Princ, Stress_Set_EV = FE.sprinc(Stress_Set)
# Stress_Set_Cyl = FE.s_cyl(Stress_Set_Princ)
# Seq_Cyl = []
# theta_Cyl = []
# for set in Stress_Set_Cyl:
# Seq_Cyl.append(set[0])
# theta_Cyl.append(set[1])
# Seq_Cyl = np.array(Seq_Cyl)
# theta_Cyl = np.array(theta_Cyl)
# ngrid = 100
# xx, yy = np.meshgrid(np.linspace(-1, 1, ngrid), np.linspace(0, 2, ngrid))
# yy *= mat_ml.scale_seq
# xx *= np.pi
# hh = np.c_[yy.ravel(), xx.ravel()]
# Cart_hh = FE.sp_cart(hh)
# zeros_array = np.zeros((10000, 3))
# Cart_hh_6D = np.hstack((Cart_hh, zeros_array))
# grad_hh = mat_ml.calc_fgrad(Cart_hh_6D)
# norm_6d = np.linalg.norm(grad_hh)
# normalized_grad_hh = grad_hh / norm_6d
# Z = mat_ml.calc_yf(sig=Cart_hh_6D, epl=normalized_grad_hh * 0, pred=False) # value of yield function for every grid point
# fig = plt.figure(figsize=(4.2, 4.2))
# ax = fig.add_subplot(111, projection='polar')
# ax.grid(True)
# line = mat_ml.plot_data(Z, ax, xx, yy, c = "black")
# plt.scatter(theta_Cyl, Seq_Cyl, s= 6, c = "red")
#
# fig.savefig('ML+ScatterData.png', dpi=300)
# handle1 = mlines.Line2D([], [], color = "blue", label='Equivalent Plastic Strain : 0 ')
# fig_leg = plt.figure(figsize=(4, 4))
# ax_leg = fig_leg.add_subplot(111)
# ax_leg.axis('off')
# ax_leg.legend(handles=[handle1], loc="center")
# plt.show()
# Plot initial Hardening level with scatter data points
Stress_Set = []
Strain_Set = []
for Key in list(db.Data_Visualization.keys()):
Response_Stress = []
Response_Strain = []
Eq_Strains=list(db.Data_Visualization[Key]["Eq_Strain"])
Stresses = db.Data_Visualization[Key]["Stress"]
for counter, Eq_Strain in enumerate(Eq_Strains):
if Eq_Strains[counter] < 0.0019 or Eq_Strains[counter] > 0.0021:
continue
else:
Response_Strain.append(Eq_Strains[counter])
Response_Stress.append(Stresses[counter])
if len(Response_Strain) == 0:
continue
else:
Stress_Set.append(Response_Stress[0])
Strain_Set.append(Response_Strain[0])
Stress_Set = np.array(Stress_Set)
Stress_Set_Princ, Stress_Set_EV = FE.sprinc(Stress_Set)
Stress_Set_Cyl = FE.s_cyl(Stress_Set_Princ)
Seq_Cyl = []
theta_Cyl = []
for set in Stress_Set_Cyl:
Seq_Cyl.append(set[0])
theta_Cyl.append(set[1])
Seq_Cyl = np.array(Seq_Cyl)
theta_Cyl = np.array(theta_Cyl)
ngrid = 100
xx, yy = np.meshgrid(np.linspace(-1, 1, ngrid), np.linspace(0, 2, ngrid))
yy *= mat_ml.scale_seq
xx *= np.pi
hh = np.c_[yy.ravel(), xx.ravel()]
Cart_hh = FE.sp_cart(hh)
zeros_array = np.zeros((10000, 3))
Cart_hh_6D = np.hstack((Cart_hh, zeros_array))
grad_hh = mat_ml.calc_fgrad(Cart_hh_6D)
norm_6d = np.linalg.norm(grad_hh)
normalized_grad_hh = grad_hh / norm_6d
Z = mat_ml.calc_yf(sig=Cart_hh_6D, epl=normalized_grad_hh * 0, pred=False) # value of yield function for every grid point
fig = plt.figure(figsize=(4.2, 4.2))
ax = fig.add_subplot(111, projection='polar')
ax.grid(True)
line = mat_ml.plot_data(Z, ax, xx, yy, c = "black")
plt.scatter(theta_Cyl, Seq_Cyl, s= 6, c = "red")

fig.savefig('ML+ScatterData.png', dpi=300)
handle1 = mlines.Line2D([], [], color = "blue", label='Equivalent Plastic Strain : 0 ')
fig_leg = plt.figure(figsize=(4, 4))
ax_leg = fig_leg.add_subplot(111)
ax_leg.axis('off')
ax_leg.legend(handles=[handle1], loc="center")
plt.show()

0 comments on commit a8b46ac

Please sign in to comment.