temp_array[np.nonzero(temp_mask)] = 1 # enough, numpy array is mutable hence mask will be modified
del temp_mask, dim, original_data, flag_pause
gc.collect()
data[mask == 1] = 0
flag_mask = False
#############################################
# mask or median filter isolated empty pixels
#############################################
if flag_medianfilter == 'mask_isolated' or flag_medianfilter == 'interp_isolated':
print("\nFiltering isolated pixels")
nb_pix = 0
for idx in range(pad_width[0], nz-pad_width[1]): # filter only frames whith data (not padded)
data[idx, :, :], numb_pix, mask[idx, :, :] = \
pru.mean_filter(data=data[idx, :, :], nb_neighbours=medfilt_order, mask=mask[idx, :, :],
interpolate=flag_medianfilter, min_count=3, debugging=debug)
nb_pix = nb_pix + numb_pix
print("Processed image nb: ", idx)
if flag_medianfilter == 'mask_isolated':
print("Total number of masked isolated pixels: ", nb_pix)
if flag_medianfilter == 'interp_isolated':
print("Total number of interpolated isolated pixels: ", nb_pix)
elif flag_medianfilter == 'median': # apply median filter
for idx in range(pad_width[0], nz-pad_width[1]): # filter only frames whith data (not padded)
data[idx, :, :] = scipy.signal.medfilt2d(data[idx, :, :], [3, 3])
print("Applying median filtering")
else:
print("Skipping median filtering")
#############################################
reciprocal_space=True)
#######################################################################
# interactive interface to check the PRTF along particular directions #
#######################################################################
if flag_interact:
if interpolate_nans: # filter out the nan values
linecut_prtf = np.copy(prtf_matrix)
print(
'\nInterpolating the 3D PRTF on nan values, it will take some time ...'
)
print(f'nb_nans before interpolation = {np.isnan(linecut_prtf).sum()}')
linecut_prtf, nb_filtered, _ = pru.mean_filter(
data=linecut_prtf,
nb_neighbours=1,
interpolate='interp_isolated',
min_count=0,
extent=1,
target_val=np.nan,
debugging=debug)
print(f'nb_nans after = {np.isnan(linecut_prtf).sum()}')
gu.multislices_plot(linecut_prtf,
sum_frames=False,
plot_colorbar=True,
cmap=my_cmap,
title='prtf_matrix after interpolation',
scale='linear',
vmin=0,
reciprocal_space=True)
np.savez_compressed(detector.savedir + 'linecut_prtf.npz',
data=linecut_prtf)
else:
