np.savez_compressed(savedir + 'S' + str(scans[scan_nb]) + '_pynx_previous' + comment, data=data)
np.savez_compressed(savedir + 'S' + str(scans[scan_nb]) + '_maskpynx_previous', mask=mask)
else: # new masking process
flatfield = pru.load_flatfield(flatfield_file)
hotpix_array = pru.load_hotpixels(hotpixels_file)
logfile = pru.create_logfile(setup=setup, detector=detector, scan_number=scans[scan_nb],
root_folder=root_folder, filename=specfile)
if use_rawdata:
q_values, data, _, mask, _, frames_logical, monitor = \
pru.gridmap(logfile=logfile, scan_number=scans[scan_nb], detector=detector, setup=setup,
flatfield=flatfield, hotpixels=hotpix_array, hxrd=None, follow_bragg=follow_bragg,
normalize=normalize_flux, debugging=debug, orthogonalize=False)
else:
q_values, rawdata, data, _, mask, frames_logical, monitor = \
pru.gridmap(logfile=logfile, scan_number=scans[scan_nb], detector=detector, setup=setup,
flatfield=flatfield, hotpixels=hotpix_array, hxrd=hxrd, follow_bragg=follow_bragg,
normalize=normalize_flux, debugging=debug, orthogonalize=True)
np.savez_compressed(savedir+'S'+str(scans[scan_nb])+'_data_before_masking_stack', data=rawdata)
if save_to_mat:
# save to .mat, the new order is x y z (outboard, vertical up, downstream)
savemat(savedir+'S'+str(scans[scan_nb])+'_data_before_masking_stack.mat',
{'data': np.moveaxis(rawdata, [0, 1, 2], [-1, -2, -3])})
del rawdata
gc.collect()
##############
# load files #
##############
flatfield = pru.load_flatfield(flatfield_file)
hotpix_array = pru.load_hotpixels(hotpixels_file)
logfile = pru.create_logfile(beamline=setup_pre.beamline,
detector=detector,
scan_number=scan,
root_folder=root_folder,
filename=specfile_name)
if filtered_data == 0:
_, data, _, mask, _, frames_logical, monitor = \
pru.gridmap(logfile=logfile, scan_number=scan, detector=detector, setup=setup_pre,
flatfield=flatfield, hotpixels=hotpix_array, hxrd=None, follow_bragg=False,
debugging=False, orthogonalize=False)
else:
root = tk.Tk()
root.withdraw()
file_path = filedialog.askopenfilename(initialdir=homedir + "pynxraw/",
title="Select 3D data",
filetypes=[("NPZ", "*.npz")])
data = np.load(file_path)['data']
data = data[detector.roi[0]:detector.roi[1],
detector.roi[2]:detector.roi[3]]
frames_logical = np.ones(
data.shape[0]) # use all frames from the filtered data
numz, numy, numx = data.shape
print("Shape of dataset: ", numz, numy, numx)