Pop.loc[ipop, 'A22'] = A[1, 1]
Pop.loc[ipop, 'A12'] = A[0, 1]
Pop.loc[ipop, 'A66'] = A[2, 2]
Pop.loc[ipop, 'A16'] = A[0, 2]
Pop.loc[ipop, 'A26'] = A[1, 2]
B = B_from_lampam(lampam, mat, constraints.sym)
Pop.loc[ipop, 'B11'] = B[0, 0]
Pop.loc[ipop, 'B22'] = B[1, 1]
Pop.loc[ipop, 'B12'] = B[0, 1]
Pop.loc[ipop, 'B66'] = B[2, 2]
Pop.loc[ipop, 'B16'] = B[0, 2]
Pop.loc[ipop, 'B26'] = B[1, 2]
D = D_from_lampam(lampam, mat)
Pop.loc[ipop, 'D11'] = D[0, 0]
Pop.loc[ipop, 'D22'] = D[1, 1]
Pop.loc[ipop, 'D12'] = D[0, 1]
Pop.loc[ipop, 'D66'] = D[2, 2]
Pop.loc[ipop, 'D16'] = D[0, 2]
Pop.loc[ipop, 'D26'] = D[1, 2]
Pop.loc[ipop, 'N0'] = N0
Pop.loc[ipop, 'N45'] = N45
Pop.loc[ipop, 'N90'] = N90
Pop.loc[ipop, 'N-45'] = N135
ss_flatten = np.array(ss, dtype=str)
ss_flatten = ' '.join(ss_flatten)
popu.loc[ind, 'lampam1'] = lampam[0]
popu.loc[ind, 'lampam2'] = lampam[1]
popu.loc[ind, 'lampam3'] = lampam[2]
popu.loc[ind, 'lampam4'] = lampam[3]
popu.loc[ind, 'lampam5'] = lampam[4]
popu.loc[ind, 'lampam6'] = lampam[5]
popu.loc[ind, 'lampam7'] = lampam[6]
popu.loc[ind, 'lampam8'] = lampam[7]
popu.loc[ind, 'lampam9'] = lampam[8]
popu.loc[ind, 'lampam10'] = lampam[9]
popu.loc[ind, 'lampam11'] = lampam[10]
popu.loc[ind, 'lampam12'] = lampam[11]
A = A_from_lampam(lampam, mat_prop)
B = B_from_lampam(lampam, mat_prop, constraints.sym)
D = D_from_lampam(lampam, mat_prop)
# filter_ABD(A=A, B=B, D=D, sym=constraints.sym, ply_t=mat_prop.ply_t)
popu.loc[ind, 'A11'] = A[0, 0]
popu.loc[ind, 'A22'] = A[1, 1]
popu.loc[ind, 'A12'] = A[0, 1]
popu.loc[ind, 'A66'] = A[2, 2]
popu.loc[ind, 'A16'] = A[0, 2]
popu.loc[ind, 'A26'] = A[1, 2]
popu.loc[ind, 'B11'] = B[0, 0]
popu.loc[ind, 'B22'] = B[1, 1]
popu.loc[ind, 'B12'] = B[0, 1]
popu.loc[ind, 'B66'] = B[2, 2]
popu.loc[ind, 'B16'] = B[0, 2]
A11 = A[0, 0]
A22 = A[1, 1]
A12 = A[0, 1]
A66 = A[2, 2]
A16 = A[0, 2]
A26 = A[1, 2]
B = B_from_lampam(result.lampam, mat_prop)
B11 = B[0, 0]
B22 = B[1, 1]
B12 = B[0, 1]
B66 = B[2, 2]
B16 = B[0, 2]
B26 = B[1, 2]
D = D_from_lampam(result.lampam, mat_prop)
D11 = D[0, 0]
D22 = D[1, 1]
D12 = D[0, 1]
D66 = D[2, 2]
D16 = D[0, 2]
D26 = D[1, 2]
table_result.loc[i, 'diff A11 percentage'] \
= abs((A11 - A11_target)/A11_target)
table_result.loc[i, 'diff A22 percentage'] \
= abs((A22 - A22_target)/A22_target)
if abs(A12_target / A11_target) > 1e-8:
table_result.loc[i, 'diff A12 percentage'] \
= abs((A12 - A12_target)/A12_target)
n_values = 100
theta = np.linspace(-90, 90, n_values, endpoint=True)
lampam = np.zeros((n_values, 12), float)
D11 = np.zeros((n_values, ), float)
D22 = np.zeros((n_values, ), float)
D12 = np.zeros((n_values, ), float)
D66 = np.zeros((n_values, ), float)
buck = np.zeros((n_values, ), float)
for index in range(n_values):
ss = np.zeros((1, ), float)
ss[0] = theta[index]
lampam[index] = calc_lampam(ss)
D = D_from_lampam(lampam[index], mat)
D11[index] = D[0, 0]
D22[index] = D[1, 1]
D12[index] = D[0, 1]
D66[index] = D[2, 2]
buck[index] = buckling_factor(lampam[index],
mat,
n_plies=1,
N_x=10,
N_y=10,
length_x=0.1,
length_y=0.1)
fig, ax = plt.subplots(1, 1, sharex=True, figsize=(8, 5.8))
# sharex=True to align x-axes of the graphs
# figsize size of the combines sub-plots