From e180ef62cc7da1dae36bca655f1e3049da59f113 Mon Sep 17 00:00:00 2001 From: Ronakshoghi Date: Fri, 19 Jan 2024 11:07:42 +0100 Subject: [PATCH] Improve plotting. --- examples/Train-CPFEM/Data_6D.py | 64 ++++++++---------- examples/Train-CPFEM/Hardening_Levels.png | Bin 219466 -> 0 bytes examples/Train-CPFEM/Initial_Yield_Locus.png | Bin 123 -> 0 bytes examples/Train-CPFEM/Legend.png | Bin 68064 -> 0 bytes examples/Train-CPFEM/Legend2.png | Bin 32256 -> 0 bytes examples/Train-CPFEM/ML+ScatterData.png | Bin 247846 -> 0 bytes .../Reconstructed_Stress_Strain_Curve.png | Bin 123940 -> 0 bytes 7 files changed, 28 insertions(+), 36 deletions(-) delete mode 100644 examples/Train-CPFEM/Hardening_Levels.png delete mode 100644 examples/Train-CPFEM/Initial_Yield_Locus.png delete mode 100644 examples/Train-CPFEM/Legend.png delete mode 100644 examples/Train-CPFEM/Legend2.png delete mode 100644 examples/Train-CPFEM/ML+ScatterData.png delete mode 100644 examples/Train-CPFEM/Reconstructed_Stress_Strain_Curve.png diff --git a/examples/Train-CPFEM/Data_6D.py b/examples/Train-CPFEM/Data_6D.py index 45e8729..219f9e8 100644 --- a/examples/Train-CPFEM/Data_6D.py +++ b/examples/Train-CPFEM/Data_6D.py @@ -13,15 +13,15 @@ import pylabfea.training as CTD from matplotlib.lines import Line2D import matplotlib.lines as mlines +from matplotlib.gridspec import GridSpec +import random def rgb_to_hex(rgb): return '#{:02x}{:02x}{:02x}'.format(int(rgb[0]*255), int(rgb[1]*255), int(rgb[2]*255)) - -# Import Data -db = FE.Data("Data_Random_Texture.json", wh_data=True) #"Data_Base_Updated_Final_Rotated_Train.JSON" -#db = FE.Data("Data_Base_UpdatedE-07.json", Work_Hardening=False) -mat_ref = FE.Material(name="reference") # define reference material, J2 plasticity, linear w.h. +# Import data from micromechanical simulations +db = FE.Data("Data_Random_Texture.json", wh_data=True) +mat_ref = FE.Material(name="reference") # define reference material mat_ref.elasticity(E=db.mat_data['E_av'], nu=db.mat_data['nu_av']) mat_ref.plasticity(sy=db.mat_data['sy_av'], khard=4.5e3) mat_ref.calc_properties(verb=False, eps=0.02, sigeps=True) @@ -35,13 +35,14 @@ def rgb_to_hex(rgb): mat_ml.train_SVC(C=4, gamma=0.5, 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_Train.JSON") +# Testing +sig_tot, epl_tot, yf_ref = CTD.Create_Test_Sig(Json="Data_Random_Texture_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 +save_fig = True ngrid = 100 xx, yy = np.meshgrid(np.linspace(-1, 1, ngrid), np.linspace(0, 2, ngrid)) yy *= mat_ml.scale_seq @@ -59,31 +60,29 @@ def rgb_to_hex(rgb): 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 * 0.018, pred=False) 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)) +fig = plt.figure(figsize=(6.7, 4)) +fig.set_constrained_layout(True) ax = fig.add_subplot(111, projection='polar') -ax.grid(True) line = mat_ml.plot_data(Z, ax, xx, yy, c=colors_hex[0]) line2 = mat_ml.plot_data(Z2, ax, xx, yy, c=colors_hex[1]) line3 = mat_ml.plot_data(Z3, ax, xx, yy, c=colors_hex[2]) line4 = mat_ml.plot_data(Z4, ax, xx, yy, c=colors_hex[3]) line5 = mat_ml.plot_data(Z5, ax, xx, yy, c=colors_hex[4]) line6 = mat_ml.plot_data(Z6, ax, xx, yy, c=colors_hex[5]) -fig.savefig('Hardening_Levels.png', dpi=300) handle1 = Line2D([], [], color=colors_hex[0], label='Equivalent Plastic Strain : 0 ') handle2 = Line2D([], [], color=colors_hex[1], label='Equivalent Plastic Strain : 0.5% ') 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 : 1.8% ') 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], loc="center") -fig_leg.savefig('Legend.png', dpi=300) + +ax.legend(handles=[handle1, handle2, handle3, handle4, handle5, handle6], + loc='upper left', bbox_to_anchor=(1.05, 1)) +if save_fig: + fig.savefig('Hardening_Levels.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)) @@ -99,12 +98,13 @@ def rgb_to_hex(rgb): ax.tick_params(axis='both', which='major', labelsize=12) ax.legend(handles=legend_elements, loc='upper right', fontsize=12) plt.tight_layout() -fig.savefig('Initial_Yield_Locus.png', dpi=300) +if save_fig: + fig.savefig('Initial_Yield_Locus.png', dpi=300) plt.show() # Reconstruct Stress-Strain Curve Keys = list(db.Data_Visualization.keys()) -Key = "Us_A2B2C1D0E0F0_592bb_0abb1_Tx_Gs" # random.choice(Keys) # Us_A2B2C2D2E1F1_09ad0_0abb1_Tx_Gs +Key = random.choice(Keys) print("Selected Key is: {}".format(Key)) Stresses = db.Data_Visualization[Key]["Stress"] Eq_Stresses = db.Data_Visualization[Key]["Eq_Stress"] @@ -115,7 +115,6 @@ def rgb_to_hex(rgb): Strains_for_Response = [] Eq_Stress_Drawing = [] Eq_Strains_Drawing = [] - for counter, Eq_Strain in enumerate(Eq_Strains): if np.isnan(Eq_Shifted_Strains[counter]): continue @@ -139,7 +138,6 @@ def rgb_to_hex(rgb): Eq_Strains_Drawing.append(Eq_Strains[counter]) fig = plt.figure(figsize=(5.6, 4.7)) - 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]) @@ -156,13 +154,13 @@ 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) +if save_fig: + 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(np.logical_and(peeq_dat > 0.00019, peeq_dat < 0.00021))[0] sig_d0 = FE.s_cyl(db.mat_data['flow_stress'][ind0, :], mat_ml) ind1 = np.nonzero(np.logical_and(peeq_dat > 0.0149, peeq_dat < 0.0151))[0] #0.0248, 0.0252 @@ -177,28 +175,24 @@ def rgb_to_hex(rgb): 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 / FE.eps_eq(grad_hh)[:, None] # norm_6d Z0 = mat_ml.calc_yf(sig=Cart_hh_6D, epl=normalized_grad_hh * 0., pred=False) # value of yield function for every grid point Z1 = mat_ml.calc_yf(sig=Cart_hh_6D, epl=normalized_grad_hh * 0.015, pred=False) -fig = plt.figure(figsize=(4.2, 4.2)) + +fig = plt.figure(figsize=(6.7, 4)) +fig.set_constrained_layout(True) ax = fig.add_subplot(111, projection='polar') -ax.grid(True) 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="#550000", label='Equivalent Plastic Strain : 0 ') handle2 = mlines.Line2D([], [], color="#ff3333", label='Equivalent Plastic Strain : 1.5% ') -fig_leg = plt.figure(figsize=(4, 4)) -ax_leg = fig_leg.add_subplot(111) -ax_leg.axis('off') -ax_leg.legend(handles=[handle1, handle2], loc="center") -fig_leg.savefig('Legend2.png', dpi=300) +ax.legend(handles=[handle1, handle2],loc='upper left', bbox_to_anchor=(1.05, 1)) +if save_fig: + fig.savefig('ML+ScatterData.png', dpi=300) plt.show() # Plot initial Hardening level with scatter data points @@ -247,8 +241,6 @@ def rgb_to_hex(rgb): 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 = 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