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086a571
Add files via upload
Ronakshoghi Apr 24, 2023
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Merge branch 'AHartmaier:master' into Ronakshoghi-patch-1
Ronakshoghi Apr 24, 2023
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Delete data.py
Ronakshoghi Apr 24, 2023
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Ronakshoghi Apr 24, 2023
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189 changes: 122 additions & 67 deletions src/pylabfea/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
uses NumPy, SciPy, MatPlotLib, Pandas

Version: 4.0 (2021-11-27)
Last Update: (24-04-2023)
Authors: Ronak Shoghi, Alexander Hartmaier, ICAMS/Ruhr University Bochum, Germany
Email: alexander.hartmaier@rub.de
distributed under GNU General Public License (GPLv3)"""
Expand All @@ -17,19 +18,21 @@
import json
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate


class Data(object):
"""Define class for handling data from virtual mechanical tests in micromechanical
simulations and data from physical mechanical tests on materials with various
microstructures. Data is used to train machine learning flow rules in pyLabFEA.
"""

#I changed epl_crit from 2.e-3 to 1.e-3
def __init__(self, fname, mat_name="Simulanium", epl_crit=2.e-3, d_ep=5.e-4, epl_max=0.03, plot=False):
self.mat_data = dict()
self.mat_data['epc'] = epl_crit
self.mat_data['sdim'] = 6
self.mat_data['Name'] = mat_name
# CHANGES: This was True
self.mat_data['wh_data'] = True
self.mat_data['Ntext'] = 1
self.mat_data['tx_name'] = 'Random'
Expand Down Expand Up @@ -61,6 +64,7 @@ def read_data(self, Data_File):
Len_Sigma = len(Sigma[0])
seq_full = np.zeros(Len_Sigma)
peeq_full = np.zeros(Len_Sigma)
peeq_plastic = np.zeros(Len_Sigma)
Original_Stresses = np.zeros((Len_Sigma, 6))
Original_Plastic_Strains = np.zeros((Len_Sigma, 6))
Original_Total_Strains = np.zeros((Len_Sigma, 6))
Expand All @@ -71,16 +75,40 @@ def read_data(self, Data_File):

E_Plastic_6D = np.array([E_Plastic[0][i], E_Plastic[1][i], E_Plastic[2][i],
E_Plastic[3][i], E_Plastic[4][i], E_Plastic[5][i]])

peeq_plastic[i]=FE.eps_eq(E_Plastic_6D)
Original_Plastic_Strains[i, :] = E_Plastic_6D
peeq_full[i] = FE.eps_eq(E_Plastic_6D)


E_Total_6D = np.array([E_Total[0][i], E_Total[1][i], E_Total[2][i],
E_Total[3][i], E_Total[4][i], E_Total[5][i]])

peeq_full[i]=FE.eps_eq(E_Total_6D)
Original_Total_Strains[i] = E_Total_6D
Final_Data[key] = {"SEQ": seq_full, "PEEQ": peeq_full, "Load": Load,

Final_Data[key] = {"SEQ": seq_full, "PEEQ": peeq_plastic, "TEEQ": peeq_full, "Load": Load,
"Stress": Original_Stresses,
"Plastic_Strain": Original_Plastic_Strains, "Total_Strain": Original_Total_Strains}

SE_data={}
for key in Final_Data:
Stress=[Final_Data[key]["SEQ"]]
Strain_Total=[Final_Data[key]["TEEQ"]]
SE_data[key]={"Stress": Stress, "Strain": Strain_Total}
plt.scatter(SE_data[key]["Strain"], SE_data[key]["Stress"], s = 1)
plt.tick_params(axis = 'both', which = 'major', labelsize = 12)
plt.xlabel("Total Strain", fontsize = 14)
plt.ylabel("Stress", fontsize = 14)
plt.show()
SPE_data={}
for key in Final_Data:
Stress=[Final_Data[key]["SEQ"]]
Strain_Plastic=[Final_Data[key]["PEEQ"]]
SPE_data[key]={"Stress": Stress, "Strain": Strain_Plastic}
plt.scatter(SPE_data[key]["Strain"], SPE_data[key]["Stress"], s = 1)
plt.tick_params(axis = 'both', which = 'major', labelsize = 12)
plt.xlabel("Plastic Strain", fontsize = 14)
plt.ylabel("Stress", fontsize = 14)
plt.show()
return Final_Data

def parse_data(self, db, epl_crit, epl_max, d_ep):
Expand All @@ -106,8 +134,10 @@ def parse_data(self, db, epl_crit, epl_max, d_ep):
nu_av = 0.
sy_av = 0.
peeq_max = 0.
sy_list=[]
sig = []
epl = []
ept = []
ct = 0
for key, val in db.items():
# estimate yield point for load case
Expand All @@ -126,15 +156,25 @@ def parse_data(self, db, epl_crit, epl_max, d_ep):
e0 = val['PEEQ'][iel[-1]]
e1 = val['PEEQ'][ipl[0]]
sy = s0 + (epl_crit - e0) * (s1 - s0) / (e1 - e0)
sy_list.append(sy)
sy_av += sy / Nlc
eps = val['PEEQ'][ipl[-1]]
if eps > peeq_max:
peeq_max = eps
for i in ipl:
'''WARNING: select only values in intervals of d_epl for storing in data set !!!'''
sig.append(val['Stress'][i])
epl.append(val['Plastic_Strain'][i])

# for i in ipl[::]:
for i in ipl:
'''WARNING: select only values in intervals of d_epl for storing in data set !!!'''
# CHANGES: I satrted making these changes for shifting the data and make the plastic strain at 0.002 equal to zero
sig.append(val['Stress'][i])
temp_epl_1 = val['Plastic_Strain'][i]
temp_epl = (val['Plastic_Strain'][i])*(1 - (0.002 / (FE.eps_eq(val['Total_Strain'][i]))))
temp_eq_1 = FE.eps_eq(temp_epl_1)
temp_eq_2 = FE.eps_eq(temp_epl)
epl.append(temp_epl)
# epl.append(val['Plastic_Strain'][i])
# sig.append((val['Stress'][ipl[0]]))
# epl.append((val['Plastic_Strain'][ipl[0]]))
# estimate elastic constants from stresses in range [0.1,0.4]sy
''' WARNING: This needs to be improved !!!'''
ind = np.nonzero(np.logical_and(val['SEQ'] > 0.2 * sy, val['SEQ'] < 0.4 * sy))[0]
Expand All @@ -158,11 +198,13 @@ def parse_data(self, db, epl_crit, epl_max, d_ep):
self.mat_data['nu_av'] = nu_av
self.mat_data['sy_av'] = sy_av
self.mat_data['Nlc'] = Nlc
self.mat_data['sy_list']=sy_list
print(f'\n### Data set: {self.mat_data["Name"]} ###')
print(f'Type of microstructure: {Key_Translated["Texture_Type"]}')
print('Estimated elastic constants: E=%5.2f GPa, nu=%4.2f' % (E_av / 1000, nu_av))
print('Estimated yield strength: %5.2f MPa at PEEQ = %5.3f' % (sy_av, epl_crit))


def plot_set(self, db, mat_param):
fontsize = 18
cmap = plt.cm.get_cmap('viridis', 10)
Expand Down Expand Up @@ -204,72 +246,85 @@ def plot_set(self, db, mat_param):
plt.tick_params(axis="y", labelsize=fontsize - 4)
plt.show()

def plot_yield_locus(self, db, mat_param, active, scatter=False, data=None,
def plot_yield_locus(self, db, mat_data, active, scatter=False, data=None,
data_label=None, arrow=False, file=None, title=None,
fontsize=18):
fig, ax = plt.subplots(subplot_kw={'projection': 'polar'},
figsize=(15, 8))
cmap = plt.cm.get_cmap('viridis', 10)
# ms_max = np.amax(self.mat_param[active])
Ndat = len(mat_param[active])
v0 = mat_param[active][0]
scale = mat_param[active][-1] - v0
Ndat = len(mat_data[active])
v0 = mat_data[active][0]
scale = mat_data[active][-1] - v0
if np.any(np.abs(scale) < 1.e-3):
scale = 1.

sc = []
ppe = []
scy = []
for i in range(Ndat):
sc = []
for set_ind, key in enumerate(db.keys()):
val = mat_param[active][i]
hc = (val - v0) * 10
if active == 'work_hard':
sc.append(FE.s_cyl(mat_param['flow_stress'][set_ind, i, 0, :]))
label = 'PEEQ: ' + str(val.round(decimals=4))
color = cmap(hc)
elif active == 'texture':
sc = FE.s_cyl(mat_param['flow_stress'][i, 0, :, :])
ind = np.argsort(sc[:, 1]) # sort dta points w.r.t. polar angle
sc = sc[ind]
label = mat_param['ms_type']
color = (hc, 0, 1 - hc)
elif active == 'flow_stress':
sc = mat_param['flow_stress'][0, 0, :, :]
ax.plot(sc[:, 1], sc[:, 0], '.r', label='shear')
sc = mat_param['flow_stress'][0, 0, :, :]
label = 'Flow Stress'
color = 'b'
else:
raise ValueError('Undefined value for active field in "plot_yield_locus"')
Degree = []
Radius = []
for data in sc:
Degree.append(data[0])
Radius.append(data[1])
Radius, Degree = zip(*sorted(zip(Radius, Degree)))

if scatter:
ax.plot(Radius, Degree, '.m', label=label)
if data is not None:
ax.plot(Radius, Degree, '.r', label=data_label)
ax.plot(Radius, Degree, label=label, color=color)

plt.legend(loc=(1.04, 0.7), fontsize=fontsize - 2)
plt.tick_params(axis="x", labelsize=fontsize - 4)
plt.tick_params(axis="y", labelsize=fontsize - 4)
if arrow:
dr = mat_param['sy_av']
drh = 0.08 * dr
plt.arrow(0, 0, 0, dr, head_width=0.05, width=0.004,
head_length=drh, color='r', length_includes_head=True)
plt.text(-0.12, dr * 0.87, r'$\sigma_1$', color='r', fontsize=22)
plt.arrow(2.0944, 0, 0, dr, head_width=0.05,
width=0.004, head_length=drh, color='r', length_includes_head=True)
plt.text(2.26, dr * 0.92, r'$\sigma_2$', color='r', fontsize=22)
plt.arrow(-2.0944, 0, 0, dr, head_width=0.05,
width=0.004, head_length=drh, color='r', length_includes_head=True)
plt.text(-2.04, dr * 0.95, r'$\sigma_3$', color='r', fontsize=22)
if title is not None:
plt.title(title)
if file is not None:
plt.savefig(file + '.pdf', format='pdf', dpi=300)
if active == 'flow_stress':
sc.append(FE.s_cyl(mat_data['flow_stress'][i]))
ppe.append(FE.eps_eq(mat_data['plastic_strain'][i]))
for j in range(len(ppe)):
if ppe[j] < 0.003:
scy.append(sc[j])
label='Flow Stress'
color='b'
scy=np.array(scy)
scy=np.array(scy)
scy=np.array(scy)
ax.scatter(scy[:, 1], scy[:, 0], marker = ".")
plt.show()



# for set_ind, key in enumerate(db.keys()):
# val = mat_param[active][i] 0.0020155
# hc = (val - v0) * 10
# if active == 'work_hard':
# sc.append(FE.s_cyl(mat_data['flow_stress'][set_ind, i, 0, :]))
# label = 'PEEQ: ' + str(val.round(decimals=4))
# color = cmap(hc)
# elif active == 'texture':
# sc = FE.s_cyl(mat_data['flow_stress'][i, 0, :, :])
# ind = np.argsort(sc[:, 1]) # sort dta points w.r.t. polar angle
# sc = sc[ind]
# label = mat_data['ms_type']
# color = (hc, 0, 1 - hc)
# elif active == 'flow_stress':
# sc = mat_data['flow_stress'][0, 0, :, :]
# ax.plot(sc[:, 1], sc[:, 0], '.r', label='shear')
# sc = mat_data['flow_stress'][0, 0, :, :]
# label = 'Flow Stress'
# color = 'b'
# # else:
# raise ValueError('Undefined value for active field in "plot_yield_locus"')


# Degree = []
# Radius = []
# for data in sc:
# Degree.append(data[0])
# Radius.append(data[1])
# Radius, Degree = zip(*sorted(zip(Radius, Degree)))
# if scatter:
# ax.plot(Radius, Degree, '.m', label=label)
# if data is not None:
# ax.plot(Radius, Degree, '.r', label=data_label)
# ax.plot(Radius, Degree, label=label, color=color)
# if arrow:
# plt.legend(loc=(1.04, 0.7), fontsize= 18 - 2)
# plt.tick_params(axis="x", labelsize= 18 - 4)
# plt.tick_params(axis="y", labelsize= 18 - 4)
# dr = mat_data["sv_av"]
# drh = 0.08 * dr
# plt.arrow(0, 0, 0, dr, head_width=0.05, width=0.004,
# head_length=drh, color='r', length_includes_head=True)
# plt.text(-0.12, dr * 0.87, r'$\sigma_1$', color='r', fontsize=22)
# plt.arrow(2.0944, 0, 0, dr, head_width=0.05,
# width=0.004, head_length=drh, color='r', length_includes_head=True)
# plt.text(2.26, dr * 0.92, r'$\sigma_2$', color='r', fontsize=22)
# plt.arrow(-2.0944, 0, 0, dr, head_width=0.05,
# width=0.004, head_length=drh, color='r', length_includes_head=True)
# plt.text(-2.04, dr * 0.95, r'$\sigma_3$', color='r', fontsize=22)
# plt.show()