case = SliceProperties(time=time, casedir=casedir, casename=casename, rot_z=rot_z, result_folder=result_folder)
# Read slices and store them in case.slice_* dict
case.readSlices(properties=property, slicenames=slicenames, slicenames_sub=slicenames_sub)
# Initialize lists to store multiple slices coor and value data
list_x, list_y, list_z = [], [], []
list_rgb, list_bij, list_rij = [], [], []
# Go through specified slices
for i, slicename in enumerate(case.slicenames):
"""
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
# Remove NaN predictions
if bij_novelty == 'excl':
print(
"Since bij_novelty is 'excl', removing NaN and making y_pred_test realizable..."
)
nan_mask = np.isnan(y_pred_test).any(axis=1)
ccx_test = ccx_test[~nan_mask]
ccy_test = ccy_test[~nan_mask]
ccz_test = ccz_test[~nan_mask]
y_pred_test = y_pred_test[~nan_mask]
for _ in range(2):
y_pred_test = makeRealizable(y_pred_test)
# Rotate field
y_pred_test = expandSymmetricTensor(y_pred_test).reshape((-1, 3, 3))
y_pred_test = rotateData(y_pred_test, anglez=fieldrot)
y_pred_test = contractSymmetricTensor(y_pred_test)
t1 = t.time()
print('\nFinished bij prediction in {:.4f} s'.format(t1 - t0))
"""
Postprocess Machine Learning Predictions
"""
# Filter the result if requested.
# This requires:
# 1. Remove any component outside bound and set to NaN
# 2. Interpolate to 2D slice mesh grid with nearest method
# 3. Use 2D Gaussian filter to smooth the mesh grid while ignoring NaN, for every component
# 4. Make whatever between bound and limits realizable
if filter:
cc2_test = ccz_test if slicedir == 'vertical' else ccy_test
ccx_test_mesh, cc2_test_mesh, _, y_predtest_mesh = fieldSpatialSmoothing(
# Remove NaN predictions
if bij_novelty == 'excl':
print(
"Since bij_novelty is 'excl', removing NaN and making y_pred realizable..."
)
nan_mask = np.isnan(y_pred).any(axis=1)
ccx_test = ccx_test[~nan_mask]
ccy_test = ccy_test[~nan_mask]
ccz_test = ccz_test[~nan_mask]
y_pred = y_pred[~nan_mask]
for _ in range(2):
y_pred = makeRealizable(y_pred)
# Rotate field
y_pred = expandSymmetricTensor(y_pred).reshape((-1, 3, 3))
y_pred = rotateData(y_pred, anglez=fieldrot)
y_pred = contractSymmetricTensor(y_pred)
t1 = t.time()
print('\nFinished bij prediction in {:.4f} s'.format(t1 - t0))
"""
Postprocess Machine Learning Predictions
"""
# Filter the result if requested.
# This requires:
# 1. Remove any component outside bound and set to NaN
# 2. Interpolate to 2D slice mesh grid with nearest method
# 3. Use 2D Gaussian filter to smooth the mesh grid while ignoring NaN, for every component
# 4. Make whatever between bound and limits realizable
if filter:
cc2_test = ccz_test if slicedir == 'vertical' else ccy_test
ccx_test_mesh, cc2_test_mesh, _, y_pred_mesh = fieldSpatialSmoothing(