print('\nWriting predicted eigenvec to OpenFOAM format...')
header_tensor = headerTensorOF(time, fieldname, len(mask))
fh = open(result_dir + fieldname, 'w')
fh.write(header_tensor)
np.savetxt(fh,
eigvec_pred_all,
fmt="(%.10f %.10f %.10f %.10f %.10f %.10f %.10f %.10f %.10f)")
fh.write(footer_tensor)
fh.close()
print('\nFinished writing predicted eigenvec to OpenFOAM format')
del eigvec_pred_all
"""
Calculate Barycentric Map
"""
t0 = t.time()
xy_bary, rgb_bary = getBarycentricMapData(eigval_test)
del eigval_test
xy_bary_pred, rgb_bary_pred = getBarycentricMapData(eigval_pred)
del eigvec_pred
t1 = t.time()
print('\nFinished getting Barycentric map data in {:.4f} s'.format(t1 - t0))
# Limit RGB values to max of 1
rgb_bary[rgb_bary > 1.] = 1.
rgb_bary_pred[rgb_bary_pred > 1.] = 1.
"""
Write Barycentric RGB back to OpenFOAM File
"""
print(
'\nAssigning true RGB values of confined domain to the whole domain, with out-of confinement RGB being (0, 0, 0)...'
)
# anomaly_idx_test4 = outliers4['anomaly_idx_test']
# anomaly_idx_train5 = outliers5['anomalyIdxTrains']
# anomaly_idx_test5 = outliers5['anomaly_idx_test']
"""
Postprocess Machine Learning Predictions
"""
t0 = t.time()
_, eigval_test, _ = processReynoldsStress(y_test,
make_anisotropic=False,
realization_iter=0)
# _, eigval_train, _ = processReynoldsStress(y_train, make_anisotropic=False, realization_iter=0)
t1 = t.time()
print('\nFinished processing Reynolds stress in {:.4f} s'.format(t1 - t0))
t0 = t.time()
xy_bary_test, rgb_bary_test = getBarycentricMapData(eigval_test)
# Limit RGB value to [0, 1]
rgb_bary_test[rgb_bary_test > 1.] = 1.
# xy_bary_train, rgb_bary_train = getBarycentricMapData(eigval_train)
rgb_bary_test_out = rgb_bary_test.copy()
rgb_bary_test_out[anomaly_idx_test] = gray
# rgb_bary_train_out = rgb_bary_train.copy()
# rgb_bary_train_out[anomaly_idx_train], rgb_bary_train_out[anomaly_idx_train2], rgb_bary_train_out[anomaly_idx_train3], \
# rgb_bary_train_out[anomaly_idx_train4], rgb_bary_train_out[anomaly_idx_train5] \
# = (0.4,)*3, (0.3,)*3, (0.2,)*3, (0.1,)*3, (0,)*3
t1 = t.time()
print('\nFinished getting Barycentric map data in {:.4f} s'.format(t1 -
t0))
t0 = t.time()
ccx_test_mesh, ccy_test_mesh, _, rgb_bary_test_mesh = interpolateGridData(
"""
Postprocess Machine Learning Predictions
"""
t0 = t.time()
# _, eigval_test, _ = processReynoldsStress(y_test, make_anisotropic=False, realization_iter=0)
# _, eigval_train, _ = processReynoldsStress(y_train, make_anisotropic=False, realization_iter=0)
y_pred_test3, eigval_pred_test, _ = processReynoldsStress(y_pred_test, make_anisotropic=False, realization_iter=realize_iter)
y_pred_train3, eigval_pred_train, _ = processReynoldsStress(y_pred_train, make_anisotropic=False, realization_iter=realize_iter)
t1 = t.time()
print('\nFinished processing Reynolds stress in {:.4f} s'.format(t1 - t0))
t0 = t.time()
# xy_bary_test, rgb_bary_test = getBarycentricMapData(eigval_test)
# xy_bary_train, rgb_bary_train = getBarycentricMapData(eigval_train)
xy_bary_pred_test, rgb_bary_pred_test = getBarycentricMapData(eigval_pred_test)
xy_bary_pred_train, rgb_bary_pred_train = getBarycentricMapData(eigval_pred_train)
t1 = t.time()
print('\nFinished getting Barycentric map data in {:.4f} s'.format(t1 - t0))
t0 = t.time()
# ccx_test_mesh, ccy_test_mesh, _, rgb_bary_test_mesh = interpolateGridData(ccx_test, ccy_test, rgb_bary_test, mesh_target=uniform_mesh_size, interp=interp_method, fill_val=0.3)
# ccx_train_mesh, ccy_train_mesh, _, rgb_bary_train_mesh = interpolateGridData(ccx_train, ccy_train, rgb_bary_train, mesh_target=uniform_mesh_size, interp=interp_method, fill_val=0.3)
ccx_test_mesh, ccy_test_mesh, _, rgb_bary_pred_test_mesh = interpolateGridData(ccx_test, ccy_test, rgb_bary_pred_test, mesh_target=uniform_mesh_size, interp=interp_method, fill_val=0.3)
ccx_train_mesh, ccy_train_mesh, _, rgb_bary_pred_train_mesh = interpolateGridData(ccx_train, ccy_train, rgb_bary_pred_train, mesh_target=uniform_mesh_size, interp=interp_method, fill_val=0.3)
t1 = t.time()
print('\nFinished interpolating mesh data for barycentric map in {:.4f} s'.format(t1 - t0))
t0 = t.time()
_, _, _, y_pred_test_mesh = interpolateGridData(ccx_test, ccy_test, y_pred_test, mesh_target=uniform_mesh_size, interp=interp_method, fill_val=0.3)
_, _, _, y_pred_train_mesh = interpolateGridData(ccx_train, ccy_train, y_pred_train, mesh_target=uniform_mesh_size, interp=interp_method, fill_val=0.3)
t0 = t.time()
_, eigval_test, _ = processReynoldsStress(y_test,
make_anisotropic=False,
realization_iter=0,
to_old_grid_shape=False)
# If filter was True, eigval_pred_test is a mesh grid
_, eigval_pred_test, _ = processReynoldsStress(y_pred_test,
make_anisotropic=False,
realization_iter=0,
to_old_grid_shape=False)
t1 = t.time()
print('\nFinished processing Reynolds stress in {:.4f} s'.format(t1 - t0))
t0 = t.time()
xy_bary_test, rgb_bary_test = getBarycentricMapData(eigval_test)
# If filter was True, both xy_bary_pred_test and rgb_bary_pred_test are mesh grids
xy_bary_pred_test, rgb_bary_pred_test = getBarycentricMapData(
eigval_pred_test, to_old_grid_shape=True)
t1 = t.time()
print('\nFinished getting Barycentric map data in {:.4f} s'.format(t1 -
t0))
t0 = t.time()
# Interpolate to desired uniform mesh
if 'bary' in plot_property or '*' in plot_property:
ccx_test_mesh, ccy_test_mesh, ccz_test_mesh, rgb_pred_test_mesh = case_slice.interpolateDecomposedSliceData_Fast(
ccx_test,
ccy_test,
ccz_test,
rgb_bary_pred_test,
"""
Process Uninterpolated Anisotropy Tensor
"""
# Retrieve Rij of this slice
rij = case.slices_val[slicename]
rij = expandSymmetricTensor(rij).reshape((-1, 3, 3))
# Rotate Rij if requested, rotateData only accept full matrix form
if rotate_data: rij = rotateData(rij, anglez=rot_z)
rij = contractSymmetricTensor(rij)
rij_tmp = rij.copy()
# Get bij from Rij and its corresponding eigenval and eigenvec
bij, eig_val, eig_vec = processReynoldsStress(rij_tmp, realization_iter=0, make_anisotropic=make_anisotropic, to_old_grid_shape=False)
# bij was (n_samples, 3, 3) contract it
bij = contractSymmetricTensor(bij)
# Get barycentric map coor and normalized RGB
xy_bary, rgb = getBarycentricMapData(eig_val, c_offset=c_offset, c_exp=c_exp)
"""
Interpolation
"""
# 1st coor is always x not matter vertical or horizontal slice
# 2nd coor is y if horizontal slice otherwise z, take appropriate confinebox limit
coor2_lim = confinebox[i][2:4] if case.slices_orient[slicename] == 'horizontal' else confinebox[i][4:]
# Selective interpolation upon request
if 'bary' in plot_property or '*' in plot_property:
x_mesh, y_mesh, z_mesh, rgb_mesh = case.interpolateDecomposedSliceData_Fast(case.slices_coor[slicename][:, 0], case.slices_coor[slicename][:, 1], case.slices_coor[slicename][:, 2], rgb,
slice_orient=case.slices_orient[slicename], target_meshsize=target_meshsize,
# No interpolation as it gives undefined colors in my barycentric map
interp_method='nearest', confinebox=confinebox[i])
if plot_type in ('3D', '*'): list_rgb.append(rgb_mesh)
