def deconvolution_rl(image, psf=None, psf_shape=(10, 10, 10), iterations=20, debugging=False): """ Image deconvolution using Richardson-Lucy algorithm. The algorithm is based on a PSF (Point Spread Function), where PSF is described as the impulse response of the optical system. :param image: image to be deconvoluted :param psf: psf to be used as a first guess :param psf_shape: shape of the kernel used for deconvolution :param iterations: number of iterations for the Richardson-Lucy algorithm :param debugging: True to see plots :return: """ image = image.astype(np.float) ndim = image.ndim if psf is None: print('Initializing the psf using a', ndim, 'D multivariate normal window\n') print('sigma =', 0.3, ' mu =', 0.0) psf = pu.gaussian_window(window_shape=psf_shape, sigma=0.3, mu=0.0, debugging=False) psf = psf.astype(float) if debugging: gu.multislices_plot(array=psf, sum_frames=False, plot_colorbar=True, scale='linear', title='Gaussian window', reciprocal_space=False, is_orthogonal=True) im_deconv = np.abs( richardson_lucy(image=image, psf=psf, iterations=iterations, clip=False)) if debugging: image = abs(image) / abs(image).max() im_deconv = abs(im_deconv) / abs(im_deconv).max() gu.combined_plots(tuple_array=(image, im_deconv), tuple_sum_frames=False, tuple_colorbar=True, tuple_scale='linear', tuple_width_v=None, tuple_width_h=None, tuple_vmin=0, tuple_vmax=1, tuple_title=('Before RL', 'After ' + str(iterations) + ' iterations of RL (normalized)')) return im_deconv
def sum_roi(array, roi, debugging=False): """ Sum the array intensities in the defined region of interest. :param array: 2D or 3D array. If ndim=3, the region of interest is applied sequentially to each 2D frame, the iteration being peformed over the first axis. :param roi: [Vstart, Vstop, Hstart, Hstop] region of interest for the sum :param debugging: True to see plots :return: a number (if array.ndim=2) or a 1D array of length array.shape[0] (if array.ndim=3) of summed intensities """ ndim = array.ndim if ndim == 2: nby, nbx = array.shape elif ndim == 3: nbz, nby, nbx = array.shape else: raise ValueError('array should be 2D or 3D') if not 0 <= roi[0] < roi[1] <= nby: raise ValueError('0 <= roi[0] < roi[1] <= nby expected') if not 0 <= roi[2] < roi[3] <= nbx: raise ValueError('0 <= roi[2] < roi[3] <= nbx expected') if ndim == 2: sum_array = array[roi[0]:roi[1], roi[2]:roi[3]].sum() else: # ndim = 3 sum_array = np.zeros(nbz) for idx in range(nbz): sum_array[idx] = array[idx, roi[0]:roi[1], roi[2]:roi[3]].sum() array = array.sum(axis=0) if debugging: val = array.max() array[roi[0]:roi[1], roi[2]:roi[2] + 3] = val array[roi[0]:roi[1], roi[3] - 3:roi[3]] = val array[roi[0]:roi[0] + 3, roi[2]:roi[3]] = val array[roi[1] - 3:roi[1], roi[2]:roi[3]] = val gu.combined_plots(tuple_array=(array, sum_array), tuple_sum_frames=False, tuple_sum_axis=0, tuple_scale='log', tuple_title=('summed array', 'ROI integrated intensity'), tuple_colorbar=True) return sum_array
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() ########################################## # plot normalization by incident monitor # ########################################## nz, ny, nx = np.shape(data) print('Data shape:', nz, ny, nx) if normalize_flux: plt.ion() fig = gu.combined_plots(tuple_array=(monitor, data), tuple_sum_frames=(False, True), tuple_sum_axis=(0, 1), tuple_width_v=None, tuple_width_h=None, tuple_colorbar=(False, False), tuple_vmin=(np.nan, 0), tuple_vmax=(np.nan, np.nan), tuple_title=('monitor.min() / monitor', 'Data after normalization'), tuple_scale=('linear', 'log'), xlabel=('Frame number', 'Frame number'), ylabel=('Counts (a.u.)', 'Rocking dimension'), is_orthogonal=not use_rawdata, reciprocal_space=True) fig.savefig(savedir + 'monitor_S' + str(scans[scan_nb]) + '_' + str(nz) + '_' + str(ny) + '_' + str(nx) + '_' + str(binning[0]) + '_' + str(binning[1]) + '_' + str(binning[2]) + '.png') if flag_interact: cid = plt.connect('close_event', close_event) fig.waitforbuttonpress() plt.disconnect(cid) plt.close(fig) plt.ioff() comment = comment + '_norm' ########################
phased_fft[np.nonzero(mask)] = 0 # do not take mask voxels into account print("Max(retrieved amplitude) =", abs(phased_fft).max()) print( "COM of the retrieved diffraction pattern after masking: ", center_of_mass(abs(phased_fft)), ) del mask gc.collect() gu.combined_plots( tuple_array=(diff_pattern, phased_fft), tuple_sum_frames=False, tuple_sum_axis=(0, 0), tuple_width_v=None, tuple_width_h=None, tuple_colorbar=False, tuple_vmin=(-1, -1), tuple_vmax=np.nan, tuple_title=("measurement", "phased_fft"), tuple_scale="log", ) ########################################### # check alignment of diffraction patterns # ########################################### z1, y1, x1 = center_of_mass(diff_pattern) z1, y1, x1 = [int(z1), int(y1), int(x1)] print( "COM of retrieved pattern after masking: ", z1, y1,
] print(f"width for plotting: {width}") ############################################ # plot mask, monitor and concatenated data # ############################################ if save_mask: np.savez_compressed(setup.detector.savedir + "hotpixels.npz", mask=mask) gu.combined_plots( tuple_array=(monitor, mask), tuple_sum_frames=False, tuple_sum_axis=(0, 0), tuple_width_v=None, tuple_width_h=None, tuple_colorbar=(True, False), tuple_vmin=np.nan, tuple_vmax=np.nan, tuple_title=("monitor", "mask"), tuple_scale="linear", cmap=my_cmap, ylabel=("Counts (a.u.)", ""), ) max_y, max_x = np.unravel_index(abs(data).argmax(), data.shape) print( f"Max of the concatenated data along axis 0 at (y, x): ({max_y}, {max_x}) " f"Max = {int(data[max_y, max_x])}") # plot the region of interest centered on the peak # extent (left, right, bottom, top) fig, ax = plt.subplots(nrows=1, ncols=1)
################################################ # calculate the q matrix respective to the COM # ################################################ hxrd.Ang2Q.init_area('z-', 'y+', cch1=int(y0), cch2=int(x0), Nch1=numy, Nch2=numx, pwidth1=detector.pixelsize_y, pwidth2=detector.pixelsize_x, distance=setup.distance) # first two arguments in init_area are the direction of the detector if simulation: eta = bragg_angle_simu + tilt_simu * (np.arange(0, numz, 1) - int(z0)) qx, qy, qz = hxrd.Ang2Q.area(eta, 0, 0, inplane_simu, outofplane_simu, delta=(0, 0, 0, 0, 0)) else: qx, qz, qy, _ = pru.regrid(logfile=logfile, nb_frames=numz, scan_number=scan, detector=detector, setup=setup, hxrd=hxrd, follow_bragg=follow_bragg) if debug: gu.combined_plots(tuple_array=(qz, qy, qx), tuple_sum_frames=False, tuple_sum_axis=(0, 1, 2), tuple_width_v=None, tuple_width_h=None, tuple_colorbar=True, tuple_vmin=np.nan, tuple_vmax=np.nan, tuple_title=('qz', 'qy', 'qx'), tuple_scale='linear') qxCOM = qx[z0, y0, x0] qyCOM = qy[z0, y0, x0] qzCOM = qz[z0, y0, x0] print('COM[qx, qy, qz] = ', qxCOM, qyCOM, qzCOM) distances_q = np.sqrt((qx - qxCOM)**2 + (qy - qyCOM)**2 + (qz - qzCOM)**2) # if reconstructions are centered # and of the same shape q values will be identical del qx, qy, qz gc.collect() if distances_q.shape != diff_pattern.shape: print('\nThe shape of q values and the shape of the diffraction pattern are different: check binning parameters!') sys.exit()
################################# if normalize_flux: data, monitor, monitor_title = pru.normalize_dataset( array=data, raw_monitor=monitor, frames_logical=frames_logical, norm_to_min=False, debugging=debug) plt.ion() fig = gu.combined_plots(tuple_array=(monitor, data), tuple_sum_frames=(False, True), tuple_sum_axis=(0, 1), tuple_width_v=(np.nan, np.nan), tuple_width_h=(np.nan, np.nan), tuple_colorbar=(False, False), tuple_vmin=(np.nan, 0), tuple_vmax=(np.nan, np.nan), tuple_title=('monitor.min() / monitor', 'Data after normalization'), tuple_scale=('linear', 'log'), xlabel=('Frame number', 'Frame number'), ylabel=('Counts (a.u.)', 'Rocking dimension')) fig.savefig(savedir + 'monitor_S' + str(scans[scan_nb]) + '.png') if flag_interact: fig.waitforbuttonpress() plt.close(fig) plt.ioff() comment = comment + '_norm' #############################################
######################## # phase offset removal # ######################## support = np.zeros(amp.shape) support[amp > isosurface_strain*amp.max()] = 1 zcom, ycom, xcom = center_of_mass(support) print("COM at (z, y, x): (", str('{:.2f}'.format(zcom)), ',', str('{:.2f}'.format(ycom)), ',', str('{:.2f}'.format(xcom)), ')') print("Phase offset at COM(amp) of:", str('{:.2f}'.format(phase[int(zcom), int(ycom), int(xcom)])), "rad") if debug: gu.combined_plots((phase[int(zcom), :, :], phase[:, int(ycom), :], phase[:, :, int(xcom)]), tuple_sum_frames=False, tuple_sum_axis=0, tuple_width_v=None, tuple_width_h=None, tuple_colorbar=True, tuple_vmin=np.nan, tuple_vmax=np.nan, tuple_title=('phase at COM in xy', 'phase at COM in xz', 'phase at COM in yz'), tuple_scale='linear', cmap=my_cmap, is_orthogonal=False, reciprocal_space=False) phase = phase - phase[int(zcom), int(ycom), int(xcom)] phase = pru.wrap(obj=phase, start_angle=-extent_phase/2, range_angle=extent_phase) if debug: gu.multislices_plot(phase, width_z=2*zrange, width_y=2*yrange, width_x=2*xrange, plot_colorbar=True, title='Phase after offset removal') print("Mean phase:", phase[support == 1].mean(), "rad") phase = phase - phase[support == 1].mean() + phase_offset del support, zcom, ycom, xcom gc.collect()
outofplane_simu, delta=(0, 0, 0, 0, 0)) else: qx, qz, qy, _ = setup.calc_qvalues_xrutils( hxrd=hxrd, nb_frames=numz, scan_number=scan, ) if debug: gu.combined_plots( tuple_array=(qz, qy, qx), tuple_sum_frames=False, tuple_sum_axis=(0, 1, 2), tuple_width_v=None, tuple_width_h=None, tuple_colorbar=True, tuple_vmin=np.nan, tuple_vmax=np.nan, tuple_title=("qz", "qy", "qx"), tuple_scale="linear", ) qxCOM = qx[z0, y0, x0] qyCOM = qy[z0, y0, x0] qzCOM = qz[z0, y0, x0] print(f"COM[qx, qz, qy] = {qxCOM:.2f}, {qzCOM:.2f}, {qyCOM:.2f}") distances_q = np.sqrt((qx - qxCOM)**2 + (qy - qyCOM)**2 + (qz - qzCOM)**2) # if reconstructions are centered # and of the same shape q values will be identical del qx, qy, qz gc.collect()
def beamstop_correction(data, setup, debugging=False): """ Correct absorption from the beamstops during P10 forward CDI experiment. :param data: the 3D stack of 2D CDI images, shape = (nbz, nby, nbx) or 2D image of shape (nby, nbx) :param setup: an instance of the class Setup :param debugging: set to True to see plots :return: the corrected data """ valid.valid_ndarray(arrays=data, ndim=(2, 3)) energy = setup.energy if not isinstance(energy, Real): raise TypeError( f"Energy should be a number in eV, not a {type(energy)}") print(f"Applying beamstop correction for the X-ray energy of {energy}eV") if energy not in [8200, 8700, 10000, 10235]: print("no beam stop information for the X-ray energy of {:d}eV," " defaulting to the correction for 8700 eV".format(int(energy))) energy = 8700 ndim = data.ndim if ndim == 3: pass elif ndim == 2: data = data[np.newaxis, :, :] else: raise ValueError("2D or 3D data expected") nbz, nby, nbx = data.shape directbeam_y = setup.direct_beam[0] - setup.detector.roi[0] # vertical directbeam_x = setup.direct_beam[1] - setup.detector.roi[2] # horizontal # at 8200eV, the transmission of 100um Si is 0.26273 # at 8700eV, the transmission of 100um Si is 0.32478 # at 10000eV, the transmission of 100um Si is 0.47337 # at 10235eV, the transmission of 100um Si is 0.51431 if energy == 8200: factor_large = 1 / 0.26273 # 5mm*5mm (100um thick) Si wafer factor_small = 1 / 0.26273 # 3mm*3mm (100um thick) Si wafer pixels_large = [-33, 35, -31, 36] # boundaries of the large wafer relative to the direct beam (V x H) pixels_small = [-14, 14, -11, 16] # boundaries of the small wafer relative to the direct beam (V x H) elif energy == 8700: factor_large = 1 / 0.32478 # 5mm*5mm (100um thick) Si wafer factor_small = 1 / 0.32478 # 3mm*3mm (100um thick) Si wafer pixels_large = [-33, 35, -31, 36] # boundaries of the large wafer relative to the direct beam (V x H) pixels_small = [-14, 14, -11, 16] # boundaries of the small wafer relative to the direct beam (V x H) elif energy == 10000: factor_large = 2.1 / 0.47337 # 5mm*5mm (200um thick) Si wafer factor_small = 4.5 / 0.47337 # 3mm*3mm (300um thick) Si wafer pixels_large = [-36, 34, -34, 35] # boundaries of the large wafer relative to the direct beam (V x H) pixels_small = [-21, 21, -21, 21] # boundaries of the small wafer relative to the direct beam (V x H) else: # energy = 10235 factor_large = 2.1 / 0.51431 # 5mm*5mm (200um thick) Si wafer factor_small = 4.5 / 0.51431 # 3mm*3mm (300um thick) Si wafer pixels_large = [-34, 35, -33, 36] # boundaries of the large wafer relative to the direct beam (V x H) pixels_small = [-20, 22, -20, 22] # boundaries of the small wafer relative to the direct beam (V x H) # define boolean arrays for the large and the small square beam stops large_square = np.zeros((nby, nbx)) large_square[directbeam_y + pixels_large[0]:directbeam_y + pixels_large[1], directbeam_x + pixels_large[2]:directbeam_x + pixels_large[3], ] = 1 small_square = np.zeros((nby, nbx)) small_square[directbeam_y + pixels_small[0]:directbeam_y + pixels_small[1], directbeam_x + pixels_small[2]:directbeam_x + pixels_small[3], ] = 1 # define the boolean array for the border of the large square wafer # (the border is 1 pixel wide) temp_array = np.zeros((nby, nbx)) temp_array[directbeam_y + pixels_large[0] + 1:directbeam_y + pixels_large[1] - 1, directbeam_x + pixels_large[2] + 1:directbeam_x + pixels_large[3] - 1, ] = 1 large_border = large_square - temp_array # define the boolean array for the border of the small square wafer # (the border is 1 pixel wide) temp_array = np.zeros((nby, nbx)) temp_array[directbeam_y + pixels_small[0] + 1:directbeam_y + pixels_small[1] - 1, directbeam_x + pixels_small[2] + 1:directbeam_x + pixels_small[3] - 1, ] = 1 small_border = small_square - temp_array if debugging: gu.imshow_plot( data, sum_frames=True, sum_axis=0, vmin=0, vmax=11, plot_colorbar=True, scale="log", title="data before absorption correction", is_orthogonal=False, reciprocal_space=True, ) gu.combined_plots( tuple_array=(large_square, small_square, large_border, small_border), tuple_sum_frames=(False, False, False, False), tuple_sum_axis=0, tuple_width_v=None, tuple_width_h=None, tuple_colorbar=False, tuple_vmin=0, tuple_vmax=11, is_orthogonal=False, reciprocal_space=True, tuple_title=( "large_square", "small_square", "larger border", "small border", ), tuple_scale=("linear", "linear", "linear", "linear"), ) # absorption correction for the large and small square beam stops for idx in range(nbz): tempdata = data[idx, :, :] tempdata[np.nonzero(large_square)] = ( tempdata[np.nonzero(large_square)] * factor_large) tempdata[np.nonzero(small_square)] = ( tempdata[np.nonzero(small_square)] * factor_small) data[idx, :, :] = tempdata if debugging: width = 40 _, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(12, 6)) ax0.plot( np.log10(data[:, directbeam_y, directbeam_x - width:directbeam_x + width].sum(axis=0))) ax0.set_title("horizontal cut after absorption correction") ax0.vlines( x=[ width + pixels_large[2], width + pixels_large[3], width + pixels_small[2], width + pixels_small[3], ], ymin=ax0.get_ylim()[0], ymax=ax0.get_ylim()[1], colors="b", linestyle="dashed", ) ax1.plot( np.log10(data[:, directbeam_y - width:directbeam_y + width, directbeam_x].sum(axis=0))) ax1.set_title("vertical cut after absorption correction") ax1.vlines( x=[ width + pixels_large[0], width + pixels_large[1], width + pixels_small[0], width + pixels_small[1], ], ymin=ax1.get_ylim()[0], ymax=ax1.get_ylim()[1], colors="b", linestyle="dashed", ) gu.imshow_plot( data, sum_frames=True, sum_axis=0, vmin=0, vmax=11, plot_colorbar=True, scale="log", title="data after absorption correction", is_orthogonal=False, reciprocal_space=True, ) # interpolation for the border of the large square wafer indices = np.argwhere(large_border == 1) data[np.nonzero(np.repeat(large_border[np.newaxis, :, :], nbz, axis=0))] = 0 # exclude border points for frame in range(nbz): # loop over 2D images in the detector plane tempdata = data[frame, :, :] for idx in range(indices.shape[0]): pixrow = indices[idx, 0] pixcol = indices[idx, 1] counter = (9 - large_border[pixrow - 1:pixrow + 2, pixcol - 1:pixcol + 2].sum() ) # number of pixels in a 3x3 window # which do not belong to the border tempdata[pixrow, pixcol] = ( tempdata[pixrow - 1:pixrow + 2, pixcol - 1:pixcol + 2].sum() / counter) data[frame, :, :] = tempdata # interpolation for the border of the small square wafer indices = np.argwhere(small_border == 1) data[np.nonzero(np.repeat(small_border[np.newaxis, :, :], nbz, axis=0))] = 0 # exclude border points for frame in range(nbz): # loop over 2D images in the detector plane tempdata = data[frame, :, :] for idx in range(indices.shape[0]): pixrow = indices[idx, 0] pixcol = indices[idx, 1] counter = (9 - small_border[pixrow - 1:pixrow + 2, pixcol - 1:pixcol + 2].sum() ) # number of pixels in a 3x3 window # which do not belong to the border tempdata[pixrow, pixcol] = ( tempdata[pixrow - 1:pixrow + 2, pixcol - 1:pixcol + 2].sum() / counter) data[frame, :, :] = tempdata if debugging: width = 40 _, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(12, 6)) ax0.plot( np.log10(data[:, directbeam_y, directbeam_x - width:directbeam_x + width].sum(axis=0))) ax0.set_title("horizontal cut after interpolating border") ax0.vlines( x=[ width + pixels_large[2], width + pixels_large[3], width + pixels_small[2], width + pixels_small[3], ], ymin=ax0.get_ylim()[0], ymax=ax0.get_ylim()[1], colors="b", linestyle="dashed", ) ax1.plot( np.log10(data[:, directbeam_y - width:directbeam_y + width, directbeam_x].sum(axis=0))) ax1.set_title("vertical cut after interpolating border") ax1.vlines( x=[ width + pixels_large[0], width + pixels_large[1], width + pixels_small[0], width + pixels_small[1], ], ymin=ax1.get_ylim()[0], ymax=ax1.get_ylim()[1], colors="b", linestyle="dashed", ) gu.imshow_plot( data, sum_frames=True, sum_axis=0, vmin=0, vmax=11, plot_colorbar=True, scale="log", title="data after interpolating the border of beam stops", is_orthogonal=False, reciprocal_space=True, ) return data
print('\nGridding the data in the orthonormal laboratory frame') data, mask, q_values, frames_logical = \ pru.grid_cdi(data=data, mask=mask, logfile=logfile, detector=detector, setup=setup, frames_logical=frames_logical, correct_curvature=correct_curvature, debugging=debug) # plot normalization by incident monitor for the gridded data if normalize_method != 'skip': plt.ion() tmp_data = np.copy(data) # do not modify the raw data before the interpolation tmp_data[tmp_data < 5] = 0 # threshold the background tmp_data[mask == 1] = 0 fig = gu.combined_plots(tuple_array=(monitor, tmp_data), tuple_sum_frames=(False, True), tuple_sum_axis=(0, 1), tuple_width_v=None, tuple_width_h=None, tuple_colorbar=(False, False), tuple_vmin=(np.nan, 0), tuple_vmax=(np.nan, np.nan), tuple_title=('monitor.min() / monitor', 'Gridded normed data (threshold 5)\n'), tuple_scale=('linear', 'log'), xlabel=('Frame number', "Q$_y$"), ylabel=('Counts (a.u.)', "Q$_x$"), position=(323, 122), is_orthogonal=not use_rawdata, reciprocal_space=True) fig.savefig(detector.savedir + f'monitor_gridded_S{scan_nb}_{nz}_{ny}_{nx}' + binning_comment + '.png') if flag_interact: fig.canvas.mpl_disconnect(fig.canvas.manager.key_press_handler_id) cid = plt.connect('close_event', close_event) fig.waitforbuttonpress() plt.disconnect(cid) plt.close(fig) plt.ioff() del tmp_data gc.collect()
def run(prm): """ Run the postprocessing. :param prm: the parsed parameters """ pretty = pprint.PrettyPrinter(indent=4) ################################ # assign often used parameters # ################################ bragg_peak = prm.get("bragg_peak") debug = prm.get("debug", False) comment = prm.get("comment", "") centering_method = prm.get("centering_method", "max_com") original_size = prm.get("original_size") phasing_binning = prm.get("phasing_binning", [1, 1, 1]) preprocessing_binning = prm.get("preprocessing_binning", [1, 1, 1]) ref_axis_q = prm.get("ref_axis_q", "y") fix_voxel = prm.get("fix_voxel") save = prm.get("save", True) tick_spacing = prm.get("tick_spacing", 50) tick_direction = prm.get("tick_direction", "inout") tick_length = prm.get("tick_length", 10) tick_width = prm.get("tick_width", 2) invert_phase = prm.get("invert_phase", True) correct_refraction = prm.get("correct_refraction", False) threshold_unwrap_refraction = prm.get("threshold_unwrap_refraction", 0.05) threshold_gradient = prm.get("threshold_gradient", 1.0) offset_method = prm.get("offset_method", "mean") phase_offset = prm.get("phase_offset", 0) offset_origin = prm.get("phase_offset_origin") sort_method = prm.get("sort_method", "variance/mean") correlation_threshold = prm.get("correlation_threshold", 0.90) roi_detector = create_roi(dic=prm) # parameters below must be provided try: detector_name = prm["detector"] beamline_name = prm["beamline"] rocking_angle = prm["rocking_angle"] isosurface_strain = prm["isosurface_strain"] output_size = prm["output_size"] save_frame = prm["save_frame"] data_frame = prm["data_frame"] scan = prm["scan"] sample_name = prm["sample_name"] root_folder = prm["root_folder"] except KeyError as ex: print("Required parameter not defined") raise ex prm["sample"] = (f"{sample_name}+{scan}",) ######################### # Check some parameters # ######################### if not prm.get("backend"): prm["backend"] = "Qt5Agg" matplotlib.use(prm["backend"]) if prm["simulation"]: invert_phase = False correct_refraction = 0 if invert_phase: phase_fieldname = "disp" else: phase_fieldname = "phase" if data_frame == "detector": is_orthogonal = False else: is_orthogonal = True if data_frame == "crystal" and save_frame != "crystal": print( "data already in the crystal frame before phase retrieval," " it is impossible to come back to the laboratory " "frame, parameter 'save_frame' defaulted to 'crystal'" ) save_frame = "crystal" axis_to_array_xyz = { "x": np.array([1, 0, 0]), "y": np.array([0, 1, 0]), "z": np.array([0, 0, 1]), } # in xyz order ############### # Set backend # ############### if prm.get("backend") is not None: try: plt.switch_backend(prm["backend"]) except ModuleNotFoundError: print(f"{prm['backend']} backend is not supported.") ################### # define colormap # ################### if prm.get("grey_background"): bad_color = "0.7" else: bad_color = "1.0" # white background colormap = gu.Colormap(bad_color=bad_color) my_cmap = colormap.cmap ####################### # Initialize detector # ####################### detector = create_detector( name=detector_name, template_imagefile=prm.get("template_imagefile"), roi=roi_detector, binning=phasing_binning, preprocessing_binning=preprocessing_binning, pixel_size=prm.get("pixel_size"), ) #################################### # define the experimental geometry # #################################### setup = Setup( beamline=beamline_name, detector=detector, energy=prm.get("energy"), outofplane_angle=prm.get("outofplane_angle"), inplane_angle=prm.get("inplane_angle"), tilt_angle=prm.get("tilt_angle"), rocking_angle=rocking_angle, distance=prm.get("sdd"), sample_offsets=prm.get("sample_offsets"), actuators=prm.get("actuators"), custom_scan=prm.get("custom_scan", False), custom_motors=prm.get("custom_motors"), dirbeam_detector_angles=prm.get("dirbeam_detector_angles"), direct_beam=prm.get("direct_beam"), is_series=prm.get("is_series", False), ) ######################################## # Initialize the paths and the logfile # ######################################## setup.init_paths( sample_name=sample_name, scan_number=scan, root_folder=root_folder, data_dir=prm.get("data_dir"), save_dir=prm.get("save_dir"), specfile_name=prm.get("specfile_name"), template_imagefile=prm.get("template_imagefile"), ) setup.create_logfile( scan_number=scan, root_folder=root_folder, filename=detector.specfile ) # load the goniometer positions needed in the calculation # of the transformation matrix setup.read_logfile(scan_number=scan) ################### # print instances # ################### print(f'{"#"*(5+len(str(scan)))}\nScan {scan}\n{"#"*(5+len(str(scan)))}') print("\n##############\nSetup instance\n##############") pretty.pprint(setup.params) print("\n#################\nDetector instance\n#################") pretty.pprint(detector.params) ################ # preload data # ################ if prm.get("reconstruction_file") is not None: file_path = (prm["reconstruction_file"],) else: root = tk.Tk() root.withdraw() file_path = filedialog.askopenfilenames( initialdir=detector.scandir if prm.get("data_dir") is None else detector.datadir, filetypes=[ ("NPZ", "*.npz"), ("NPY", "*.npy"), ("CXI", "*.cxi"), ("HDF5", "*.h5"), ], ) nbfiles = len(file_path) plt.ion() obj, extension = util.load_file(file_path[0]) if extension == ".h5": comment = comment + "_mode" print("\n###############\nProcessing data\n###############") nz, ny, nx = obj.shape print("Initial data size: (", nz, ",", ny, ",", nx, ")") if not original_size: original_size = obj.shape print("FFT size before accounting for phasing_binning", original_size) original_size = tuple( [ original_size[index] // phasing_binning[index] for index in range(len(phasing_binning)) ] ) print("Binning used during phasing:", detector.binning) print("Padding back to original FFT size", original_size) obj = util.crop_pad(array=obj, output_shape=original_size) ########################################################################### # define range for orthogonalization and plotting - speed up calculations # ########################################################################### zrange, yrange, xrange = pu.find_datarange( array=obj, amplitude_threshold=0.05, keep_size=prm.get("keep_size", False) ) numz = zrange * 2 numy = yrange * 2 numx = xrange * 2 print( f"Data shape used for orthogonalization and plotting: ({numz}, {numy}, {numx})" ) #################################################################################### # find the best reconstruction from the list, based on mean amplitude and variance # #################################################################################### if nbfiles > 1: print("\nTrying to find the best reconstruction\nSorting by ", sort_method) sorted_obj = pu.sort_reconstruction( file_path=file_path, amplitude_threshold=isosurface_strain, data_range=(zrange, yrange, xrange), sort_method=sort_method, ) else: sorted_obj = [0] ####################################### # load reconstructions and average it # ####################################### avg_obj = np.zeros((numz, numy, numx)) ref_obj = np.zeros((numz, numy, numx)) avg_counter = 1 print("\nAveraging using", nbfiles, "candidate reconstructions") for counter, value in enumerate(sorted_obj): obj, extension = util.load_file(file_path[value]) print("\nOpening ", file_path[value]) prm[f"from_file_{counter}"] = file_path[value] if prm.get("flip_reconstruction", False): obj = pu.flip_reconstruction(obj, debugging=True) if extension == ".h5": centering_method = "do_nothing" # do not center, data is already cropped # just on support for mode decomposition # correct a roll after the decomposition into modes in PyNX obj = np.roll(obj, prm.get("roll_modes", [0, 0, 0]), axis=(0, 1, 2)) fig, _, _ = gu.multislices_plot( abs(obj), sum_frames=True, plot_colorbar=True, title="1st mode after centering", ) # use the range of interest defined above obj = util.crop_pad(obj, [2 * zrange, 2 * yrange, 2 * xrange], debugging=False) # align with average reconstruction if counter == 0: # the fist array loaded will serve as reference object print("This reconstruction will be used as reference.") ref_obj = obj avg_obj, flag_avg = reg.average_arrays( avg_obj=avg_obj, ref_obj=ref_obj, obj=obj, support_threshold=0.25, correlation_threshold=correlation_threshold, aligning_option="dft", space=prm.get("averaging_space", "reciprocal_space"), reciprocal_space=False, is_orthogonal=is_orthogonal, debugging=debug, ) avg_counter = avg_counter + flag_avg avg_obj = avg_obj / avg_counter if avg_counter > 1: print("\nAverage performed over ", avg_counter, "reconstructions\n") del obj, ref_obj gc.collect() ################ # unwrap phase # ################ phase, extent_phase = pu.unwrap( avg_obj, support_threshold=threshold_unwrap_refraction, debugging=debug, reciprocal_space=False, is_orthogonal=is_orthogonal, ) print( "Extent of the phase over an extended support (ceil(phase range)) ~ ", int(extent_phase), "(rad)", ) phase = util.wrap(phase, start_angle=-extent_phase / 2, range_angle=extent_phase) if debug: gu.multislices_plot( phase, width_z=2 * zrange, width_y=2 * yrange, width_x=2 * xrange, plot_colorbar=True, title="Phase after unwrap + wrap", reciprocal_space=False, is_orthogonal=is_orthogonal, ) ############################################# # phase ramp removal before phase filtering # ############################################# amp, phase, rampz, rampy, rampx = pu.remove_ramp( amp=abs(avg_obj), phase=phase, initial_shape=original_size, method="gradient", amplitude_threshold=isosurface_strain, threshold_gradient=threshold_gradient, ) del avg_obj gc.collect() if debug: gu.multislices_plot( phase, width_z=2 * zrange, width_y=2 * yrange, width_x=2 * xrange, plot_colorbar=True, title="Phase after ramp removal", reciprocal_space=False, is_orthogonal=is_orthogonal, ) ######################## # phase offset removal # ######################## support = np.zeros(amp.shape) support[amp > isosurface_strain * amp.max()] = 1 phase = pu.remove_offset( array=phase, support=support, offset_method=offset_method, phase_offset=phase_offset, offset_origin=offset_origin, title="Phase", debugging=debug, ) del support gc.collect() phase = util.wrap( obj=phase, start_angle=-extent_phase / 2, range_angle=extent_phase ) ############################################################################## # average the phase over a window or apodize to reduce noise in strain plots # ############################################################################## half_width_avg_phase = prm.get("half_width_avg_phase", 0) if half_width_avg_phase != 0: bulk = pu.find_bulk( amp=amp, support_threshold=isosurface_strain, method="threshold" ) # the phase should be averaged only in the support defined by the isosurface phase = pu.mean_filter( array=phase, support=bulk, half_width=half_width_avg_phase ) del bulk gc.collect() if half_width_avg_phase != 0: comment = comment + "_avg" + str(2 * half_width_avg_phase + 1) gridz, gridy, gridx = np.meshgrid( np.arange(0, numz, 1), np.arange(0, numy, 1), np.arange(0, numx, 1), indexing="ij", ) phase = ( phase + gridz * rampz + gridy * rampy + gridx * rampx ) # put back the phase ramp otherwise the diffraction # pattern will be shifted and the prtf messed up if prm.get("apodize", False): amp, phase = pu.apodize( amp=amp, phase=phase, initial_shape=original_size, window_type=prm.get("apodization_window", "blackman"), sigma=prm.get("apodization_sigma", [0.30, 0.30, 0.30]), mu=prm.get("apodization_mu", [0.0, 0.0, 0.0]), alpha=prm.get("apodization_alpha", [1.0, 1.0, 1.0]), is_orthogonal=is_orthogonal, debugging=True, ) comment = comment + "_apodize_" + prm.get("apodization_window", "blackman") ################################################################ # save the phase with the ramp for PRTF calculations, # # otherwise the object will be misaligned with the measurement # ################################################################ np.savez_compressed( detector.savedir + "S" + str(scan) + "_avg_obj_prtf" + comment, obj=amp * np.exp(1j * phase), ) #################################################### # remove again phase ramp before orthogonalization # #################################################### phase = phase - gridz * rampz - gridy * rampy - gridx * rampx avg_obj = amp * np.exp(1j * phase) # here the phase is again wrapped in [-pi pi[ del amp, phase, gridz, gridy, gridx, rampz, rampy, rampx gc.collect() ###################### # centering of array # ###################### if centering_method == "max": avg_obj = pu.center_max(avg_obj) # shift based on max value, # required if it spans across the edge of the array before COM elif centering_method == "com": avg_obj = pu.center_com(avg_obj) elif centering_method == "max_com": avg_obj = pu.center_max(avg_obj) avg_obj = pu.center_com(avg_obj) ####################### # save support & vti # ####################### if prm.get("save_support", False): # to be used as starting support in phasing, hence still in the detector frame support = np.zeros((numz, numy, numx)) support[abs(avg_obj) / abs(avg_obj).max() > 0.01] = 1 # low threshold because support will be cropped by shrinkwrap during phasing np.savez_compressed( detector.savedir + "S" + str(scan) + "_support" + comment, obj=support ) del support gc.collect() if prm.get("save_rawdata", False): np.savez_compressed( detector.savedir + "S" + str(scan) + "_raw_amp-phase" + comment, amp=abs(avg_obj), phase=np.angle(avg_obj), ) # voxel sizes in the detector frame voxel_z, voxel_y, voxel_x = setup.voxel_sizes_detector( array_shape=original_size, tilt_angle=( prm.get("tilt_angle") * detector.preprocessing_binning[0] * detector.binning[0] ), pixel_x=detector.pixelsize_x, pixel_y=detector.pixelsize_y, verbose=True, ) # save raw amp & phase to VTK # in VTK, x is downstream, y vertical, z inboard, # thus need to flip the last axis gu.save_to_vti( filename=os.path.join( detector.savedir, "S" + str(scan) + "_raw_amp-phase" + comment + ".vti" ), voxel_size=(voxel_z, voxel_y, voxel_x), tuple_array=(abs(avg_obj), np.angle(avg_obj)), tuple_fieldnames=("amp", "phase"), amplitude_threshold=0.01, ) ######################################################### # calculate q of the Bragg peak in the laboratory frame # ######################################################### q_lab = ( setup.q_laboratory ) # (1/A), in the laboratory frame z downstream, y vertical, x outboard qnorm = np.linalg.norm(q_lab) q_lab = q_lab / qnorm angle = simu.angle_vectors( ref_vector=[q_lab[2], q_lab[1], q_lab[0]], test_vector=axis_to_array_xyz[ref_axis_q], ) print( f"\nNormalized diffusion vector in the laboratory frame (z*, y*, x*): " f"({q_lab[0]:.4f} 1/A, {q_lab[1]:.4f} 1/A, {q_lab[2]:.4f} 1/A)" ) planar_dist = 2 * np.pi / qnorm # qnorm should be in angstroms print(f"Wavevector transfer: {qnorm:.4f} 1/A") print(f"Atomic planar distance: {planar_dist:.4f} A") print(f"\nAngle between q_lab and {ref_axis_q} = {angle:.2f} deg") if debug: print( "Angle with y in zy plane = " f"{np.arctan(q_lab[0]/q_lab[1])*180/np.pi:.2f} deg" ) print( "Angle with y in xy plane = " f"{np.arctan(-q_lab[2]/q_lab[1])*180/np.pi:.2f} deg" ) print( "Angle with z in xz plane = " f"{180+np.arctan(q_lab[2]/q_lab[0])*180/np.pi:.2f} deg\n" ) planar_dist = planar_dist / 10 # switch to nm ####################### # orthogonalize data # ####################### print("\nShape before orthogonalization", avg_obj.shape, "\n") if data_frame == "detector": if debug: phase, _ = pu.unwrap( avg_obj, support_threshold=threshold_unwrap_refraction, debugging=True, reciprocal_space=False, is_orthogonal=False, ) gu.multislices_plot( phase, width_z=2 * zrange, width_y=2 * yrange, width_x=2 * xrange, sum_frames=False, plot_colorbar=True, reciprocal_space=False, is_orthogonal=False, title="unwrapped phase before orthogonalization", ) del phase gc.collect() if not prm.get("outofplane_angle") and not prm.get("inplane_angle"): print("Trying to correct detector angles using the direct beam") # corrected detector angles not provided if bragg_peak is None and detector.template_imagefile is not None: # Bragg peak position not provided, find it from the data data, _, _, _ = setup.diffractometer.load_check_dataset( scan_number=scan, detector=detector, setup=setup, frames_pattern=prm.get("frames_pattern"), bin_during_loading=False, flatfield=prm.get("flatfield"), hotpixels=prm.get("hotpix_array"), background=prm.get("background"), normalize=prm.get("normalize_flux", "skip"), ) bragg_peak = bu.find_bragg( data=data, peak_method="maxcom", roi=detector.roi, binning=None, ) roi_center = ( bragg_peak[0], bragg_peak[1] - detector.roi[0], # no binning as in bu.find_bragg bragg_peak[2] - detector.roi[2], # no binning as in bu.find_bragg ) bu.show_rocking_curve( data, roi_center=roi_center, tilt_values=setup.incident_angles, savedir=detector.savedir, ) setup.correct_detector_angles(bragg_peak_position=bragg_peak) prm["outofplane_angle"] = setup.outofplane_angle prm["inplane_angle"] = setup.inplane_angle obj_ortho, voxel_size, transfer_matrix = setup.ortho_directspace( arrays=avg_obj, q_com=np.array([q_lab[2], q_lab[1], q_lab[0]]), initial_shape=original_size, voxel_size=fix_voxel, reference_axis=axis_to_array_xyz[ref_axis_q], fill_value=0, debugging=True, title="amplitude", ) prm["transformation_matrix"] = transfer_matrix else: # data already orthogonalized using xrayutilities # or the linearized transformation matrix obj_ortho = avg_obj try: print("Select the file containing QxQzQy") file_path = filedialog.askopenfilename( title="Select the file containing QxQzQy", initialdir=detector.savedir, filetypes=[("NPZ", "*.npz")], ) npzfile = np.load(file_path) qx = npzfile["qx"] qy = npzfile["qy"] qz = npzfile["qz"] except FileNotFoundError: raise FileNotFoundError( "q values not provided, the voxel size cannot be calculated" ) dy_real = ( 2 * np.pi / abs(qz.max() - qz.min()) / 10 ) # in nm qz=y in nexus convention dx_real = ( 2 * np.pi / abs(qy.max() - qy.min()) / 10 ) # in nm qy=x in nexus convention dz_real = ( 2 * np.pi / abs(qx.max() - qx.min()) / 10 ) # in nm qx=z in nexus convention print( f"direct space voxel size from q values: ({dz_real:.2f} nm," f" {dy_real:.2f} nm, {dx_real:.2f} nm)" ) if fix_voxel: voxel_size = fix_voxel print(f"Direct space pixel size for the interpolation: {voxel_size} (nm)") print("Interpolating...\n") obj_ortho = pu.regrid( array=obj_ortho, old_voxelsize=(dz_real, dy_real, dx_real), new_voxelsize=voxel_size, ) else: # no need to interpolate voxel_size = dz_real, dy_real, dx_real # in nm if ( data_frame == "laboratory" ): # the object must be rotated into the crystal frame # before the strain calculation print("Rotating the object in the crystal frame for the strain calculation") amp, phase = util.rotate_crystal( arrays=(abs(obj_ortho), np.angle(obj_ortho)), is_orthogonal=True, reciprocal_space=False, voxel_size=voxel_size, debugging=(True, False), axis_to_align=q_lab[::-1], reference_axis=axis_to_array_xyz[ref_axis_q], title=("amp", "phase"), ) obj_ortho = amp * np.exp( 1j * phase ) # here the phase is again wrapped in [-pi pi[ del amp, phase del avg_obj gc.collect() ###################################################### # center the object (centering based on the modulus) # ###################################################### print("\nCentering the crystal") obj_ortho = pu.center_com(obj_ortho) #################### # Phase unwrapping # #################### print("\nPhase unwrapping") phase, extent_phase = pu.unwrap( obj_ortho, support_threshold=threshold_unwrap_refraction, debugging=True, reciprocal_space=False, is_orthogonal=True, ) amp = abs(obj_ortho) del obj_ortho gc.collect() ############################################# # invert phase: -1*phase = displacement * q # ############################################# if invert_phase: phase = -1 * phase ######################################## # refraction and absorption correction # ######################################## if correct_refraction: # or correct_absorption: bulk = pu.find_bulk( amp=amp, support_threshold=threshold_unwrap_refraction, method=prm.get("optical_path_method", "threshold"), debugging=debug, ) kin = setup.incident_wavevector kout = setup.exit_wavevector # kin and kout were calculated in the laboratory frame, # but after the geometric transformation of the crystal, this # latter is always in the crystal frame (for simpler strain calculation). # We need to transform kin and kout back # into the crystal frame (also, xrayutilities output is in crystal frame) kin = util.rotate_vector( vectors=[kin[2], kin[1], kin[0]], axis_to_align=axis_to_array_xyz[ref_axis_q], reference_axis=[q_lab[2], q_lab[1], q_lab[0]], ) kout = util.rotate_vector( vectors=[kout[2], kout[1], kout[0]], axis_to_align=axis_to_array_xyz[ref_axis_q], reference_axis=[q_lab[2], q_lab[1], q_lab[0]], ) # calculate the optical path of the incoming wavevector path_in = pu.get_opticalpath( support=bulk, direction="in", k=kin, debugging=debug ) # path_in already in nm # calculate the optical path of the outgoing wavevector path_out = pu.get_opticalpath( support=bulk, direction="out", k=kout, debugging=debug ) # path_our already in nm optical_path = path_in + path_out del path_in, path_out gc.collect() if correct_refraction: phase_correction = ( 2 * np.pi / (1e9 * setup.wavelength) * prm["dispersion"] * optical_path ) phase = phase + phase_correction gu.multislices_plot( np.multiply(phase_correction, bulk), width_z=2 * zrange, width_y=2 * yrange, width_x=2 * xrange, sum_frames=False, plot_colorbar=True, vmin=0, vmax=np.nan, title="Refraction correction on the support", is_orthogonal=True, reciprocal_space=False, ) correct_absorption = False if correct_absorption: amp_correction = np.exp( 2 * np.pi / (1e9 * setup.wavelength) * prm["absorption"] * optical_path ) amp = amp * amp_correction gu.multislices_plot( np.multiply(amp_correction, bulk), width_z=2 * zrange, width_y=2 * yrange, width_x=2 * xrange, sum_frames=False, plot_colorbar=True, vmin=1, vmax=1.1, title="Absorption correction on the support", is_orthogonal=True, reciprocal_space=False, ) del bulk, optical_path gc.collect() ############################################## # phase ramp and offset removal (mean value) # ############################################## print("\nPhase ramp removal") amp, phase, _, _, _ = pu.remove_ramp( amp=amp, phase=phase, initial_shape=original_size, method=prm.get("phase_ramp_removal", "gradient"), amplitude_threshold=isosurface_strain, threshold_gradient=threshold_gradient, debugging=debug, ) ######################## # phase offset removal # ######################## print("\nPhase offset removal") support = np.zeros(amp.shape) support[amp > isosurface_strain * amp.max()] = 1 phase = pu.remove_offset( array=phase, support=support, offset_method=offset_method, phase_offset=phase_offset, offset_origin=offset_origin, title="Orthogonal phase", debugging=debug, reciprocal_space=False, is_orthogonal=True, ) del support gc.collect() # Wrap the phase around 0 (no more offset) phase = util.wrap( obj=phase, start_angle=-extent_phase / 2, range_angle=extent_phase ) ################################################################ # calculate the strain depending on which axis q is aligned on # ################################################################ print(f"\nCalculation of the strain along {ref_axis_q}") strain = pu.get_strain( phase=phase, planar_distance=planar_dist, voxel_size=voxel_size, reference_axis=ref_axis_q, extent_phase=extent_phase, method=prm.get("strain_method", "default"), debugging=debug, ) ################################################ # optionally rotates back the crystal into the # # laboratory frame (for debugging purpose) # ################################################ q_final = None if save_frame in {"laboratory", "lab_flat_sample"}: comment = comment + "_labframe" print("\nRotating back the crystal in laboratory frame") amp, phase, strain = util.rotate_crystal( arrays=(amp, phase, strain), axis_to_align=axis_to_array_xyz[ref_axis_q], voxel_size=voxel_size, is_orthogonal=True, reciprocal_space=False, reference_axis=[q_lab[2], q_lab[1], q_lab[0]], debugging=(True, False, False), title=("amp", "phase", "strain"), ) # q_lab is already in the laboratory frame q_final = q_lab if save_frame == "lab_flat_sample": comment = comment + "_flat" print("\nSending sample stage circles to 0") (amp, phase, strain), q_final = setup.diffractometer.flatten_sample( arrays=(amp, phase, strain), voxel_size=voxel_size, q_com=q_lab[::-1], # q_com needs to be in xyz order is_orthogonal=True, reciprocal_space=False, rocking_angle=setup.rocking_angle, debugging=(True, False, False), title=("amp", "phase", "strain"), ) if save_frame == "crystal": # rotate also q_lab to have it along ref_axis_q, # as a cross-checkm, vectors needs to be in xyz order comment = comment + "_crystalframe" q_final = util.rotate_vector( vectors=q_lab[::-1], axis_to_align=axis_to_array_xyz[ref_axis_q], reference_axis=q_lab[::-1], ) ############################################### # rotates the crystal e.g. for easier slicing # # of the result along a particular direction # ############################################### # typically this is an inplane rotation, q should stay aligned with the axis # along which the strain was calculated if prm.get("align_axis", False): print("\nRotating arrays for visualization") amp, phase, strain = util.rotate_crystal( arrays=(amp, phase, strain), reference_axis=axis_to_array_xyz[prm["ref_axis"]], axis_to_align=prm["axis_to_align"], voxel_size=voxel_size, debugging=(True, False, False), is_orthogonal=True, reciprocal_space=False, title=("amp", "phase", "strain"), ) # rotate q accordingly, vectors needs to be in xyz order q_final = util.rotate_vector( vectors=q_final[::-1], axis_to_align=axis_to_array_xyz[prm["ref_axis"]], reference_axis=prm["axis_to_align"], ) q_final = q_final * qnorm print( f"\nq_final = ({q_final[0]:.4f} 1/A," f" {q_final[1]:.4f} 1/A, {q_final[2]:.4f} 1/A)" ) ############################################## # pad array to fit the output_size parameter # ############################################## if output_size is not None: amp = util.crop_pad(array=amp, output_shape=output_size) phase = util.crop_pad(array=phase, output_shape=output_size) strain = util.crop_pad(array=strain, output_shape=output_size) print(f"\nFinal data shape: {amp.shape}") ###################### # save result to vtk # ###################### print( f"\nVoxel size: ({voxel_size[0]:.2f} nm, {voxel_size[1]:.2f} nm," f" {voxel_size[2]:.2f} nm)" ) bulk = pu.find_bulk( amp=amp, support_threshold=isosurface_strain, method="threshold" ) if save: prm["comment"] = comment np.savez_compressed( f"{detector.savedir}S{scan}_amp{phase_fieldname}strain{comment}", amp=amp, phase=phase, bulk=bulk, strain=strain, q_com=q_final, voxel_sizes=voxel_size, detector=detector.params, setup=setup.params, params=prm, ) # save results in hdf5 file with h5py.File( f"{detector.savedir}S{scan}_amp{phase_fieldname}strain{comment}.h5", "w" ) as hf: out = hf.create_group("output") par = hf.create_group("params") out.create_dataset("amp", data=amp) out.create_dataset("bulk", data=bulk) out.create_dataset("phase", data=phase) out.create_dataset("strain", data=strain) out.create_dataset("q_com", data=q_final) out.create_dataset("voxel_sizes", data=voxel_size) par.create_dataset("detector", data=str(detector.params)) par.create_dataset("setup", data=str(setup.params)) par.create_dataset("parameters", data=str(prm)) # save amp & phase to VTK # in VTK, x is downstream, y vertical, z inboard, # thus need to flip the last axis gu.save_to_vti( filename=os.path.join( detector.savedir, "S" + str(scan) + "_amp-" + phase_fieldname + "-strain" + comment + ".vti", ), voxel_size=voxel_size, tuple_array=(amp, bulk, phase, strain), tuple_fieldnames=("amp", "bulk", phase_fieldname, "strain"), amplitude_threshold=0.01, ) ###################################### # estimate the volume of the crystal # ###################################### amp = amp / amp.max() temp_amp = np.copy(amp) temp_amp[amp < isosurface_strain] = 0 temp_amp[np.nonzero(temp_amp)] = 1 volume = temp_amp.sum() * reduce(lambda x, y: x * y, voxel_size) # in nm3 del temp_amp gc.collect() ############################## # plot slices of the results # ############################## pixel_spacing = [tick_spacing / vox for vox in voxel_size] print( "\nPhase extent without / with thresholding the modulus " f"(threshold={isosurface_strain}): {phase.max()-phase.min():.2f} rad, " f"{phase[np.nonzero(bulk)].max()-phase[np.nonzero(bulk)].min():.2f} rad" ) piz, piy, pix = np.unravel_index(phase.argmax(), phase.shape) print( f"phase.max() = {phase[np.nonzero(bulk)].max():.2f} " f"at voxel ({piz}, {piy}, {pix})" ) strain[bulk == 0] = np.nan phase[bulk == 0] = np.nan # plot the slice at the maximum phase gu.combined_plots( (phase[piz, :, :], phase[:, piy, :], phase[:, :, pix]), tuple_sum_frames=False, tuple_sum_axis=0, tuple_width_v=None, tuple_width_h=None, tuple_colorbar=True, tuple_vmin=np.nan, tuple_vmax=np.nan, tuple_title=("phase at max in xy", "phase at max in xz", "phase at max in yz"), tuple_scale="linear", cmap=my_cmap, is_orthogonal=True, reciprocal_space=False, ) # bulk support fig, _, _ = gu.multislices_plot( bulk, sum_frames=False, title="Orthogonal bulk", vmin=0, vmax=1, is_orthogonal=True, reciprocal_space=False, ) fig.text(0.60, 0.45, "Scan " + str(scan), size=20) fig.text( 0.60, 0.40, "Bulk - isosurface=" + str("{:.2f}".format(isosurface_strain)), size=20, ) plt.pause(0.1) if save: plt.savefig(detector.savedir + "S" + str(scan) + "_bulk" + comment + ".png") # amplitude fig, _, _ = gu.multislices_plot( amp, sum_frames=False, title="Normalized orthogonal amp", vmin=0, vmax=1, tick_direction=tick_direction, tick_width=tick_width, tick_length=tick_length, pixel_spacing=pixel_spacing, plot_colorbar=True, is_orthogonal=True, reciprocal_space=False, ) fig.text(0.60, 0.45, f"Scan {scan}", size=20) fig.text( 0.60, 0.40, f"Voxel size=({voxel_size[0]:.1f}, {voxel_size[1]:.1f}, " f"{voxel_size[2]:.1f}) (nm)", size=20, ) fig.text(0.60, 0.35, f"Ticks spacing={tick_spacing} nm", size=20) fig.text(0.60, 0.30, f"Volume={int(volume)} nm3", size=20) fig.text(0.60, 0.25, "Sorted by " + sort_method, size=20) fig.text(0.60, 0.20, f"correlation threshold={correlation_threshold}", size=20) fig.text(0.60, 0.15, f"average over {avg_counter} reconstruction(s)", size=20) fig.text(0.60, 0.10, f"Planar distance={planar_dist:.5f} nm", size=20) if prm.get("get_temperature", False): temperature = pu.bragg_temperature( spacing=planar_dist * 10, reflection=prm["reflection"], spacing_ref=prm.get("reference_spacing"), temperature_ref=prm.get("reference_temperature"), use_q=False, material="Pt", ) fig.text(0.60, 0.05, f"Estimated T={temperature} C", size=20) if save: plt.savefig(detector.savedir + f"S{scan}_amp" + comment + ".png") # amplitude histogram fig, ax = plt.subplots(1, 1) ax.hist(amp[amp > 0.05 * amp.max()].flatten(), bins=250) ax.set_ylim(bottom=1) ax.tick_params( labelbottom=True, labelleft=True, direction="out", length=tick_length, width=tick_width, ) ax.spines["right"].set_linewidth(1.5) ax.spines["left"].set_linewidth(1.5) ax.spines["top"].set_linewidth(1.5) ax.spines["bottom"].set_linewidth(1.5) fig.savefig(detector.savedir + f"S{scan}_histo_amp" + comment + ".png") # phase fig, _, _ = gu.multislices_plot( phase, sum_frames=False, title="Orthogonal displacement", vmin=-prm.get("phase_range", np.pi / 2), vmax=prm.get("phase_range", np.pi / 2), tick_direction=tick_direction, cmap=my_cmap, tick_width=tick_width, tick_length=tick_length, pixel_spacing=pixel_spacing, plot_colorbar=True, is_orthogonal=True, reciprocal_space=False, ) fig.text(0.60, 0.30, f"Scan {scan}", size=20) fig.text( 0.60, 0.25, f"Voxel size=({voxel_size[0]:.1f}, {voxel_size[1]:.1f}, " f"{voxel_size[2]:.1f}) (nm)", size=20, ) fig.text(0.60, 0.20, f"Ticks spacing={tick_spacing} nm", size=20) fig.text(0.60, 0.15, f"average over {avg_counter} reconstruction(s)", size=20) if half_width_avg_phase > 0: fig.text( 0.60, 0.10, f"Averaging over {2*half_width_avg_phase+1} pixels", size=20 ) else: fig.text(0.60, 0.10, "No phase averaging", size=20) if save: plt.savefig(detector.savedir + f"S{scan}_displacement" + comment + ".png") # strain fig, _, _ = gu.multislices_plot( strain, sum_frames=False, title="Orthogonal strain", vmin=-prm.get("strain_range", 0.002), vmax=prm.get("strain_range", 0.002), tick_direction=tick_direction, tick_width=tick_width, tick_length=tick_length, plot_colorbar=True, cmap=my_cmap, pixel_spacing=pixel_spacing, is_orthogonal=True, reciprocal_space=False, ) fig.text(0.60, 0.30, f"Scan {scan}", size=20) fig.text( 0.60, 0.25, f"Voxel size=({voxel_size[0]:.1f}, " f"{voxel_size[1]:.1f}, {voxel_size[2]:.1f}) (nm)", size=20, ) fig.text(0.60, 0.20, f"Ticks spacing={tick_spacing} nm", size=20) fig.text(0.60, 0.15, f"average over {avg_counter} reconstruction(s)", size=20) if half_width_avg_phase > 0: fig.text( 0.60, 0.10, f"Averaging over {2*half_width_avg_phase+1} pixels", size=20 ) else: fig.text(0.60, 0.10, "No phase averaging", size=20) if save: plt.savefig(detector.savedir + f"S{scan}_strain" + comment + ".png")
print('maximum at voxel:', piz, piy, pix, ' value=', result.max()) gu.multislices_plot(result, slice_position=(piz, piy, pix), sum_frames=False, plot_colorbar=True, vmin=0, vmax=1, reciprocal_space=False, is_orthogonal=True, title='result at max') fig = gu.combined_plots(tuple_array=(original_obj, original_obj, original_obj, result, result, result), tuple_sum_frames=False, tuple_sum_axis=(0, 1, 2, 0, 1, 2), tuple_colorbar=True, tuple_title=('Original', 'Original', 'Original', 'Result', 'Result', 'Result'), tuple_scale='linear', is_orthogonal=True, reciprocal_space=False, tuple_vmin=0, tuple_vmax=1, position=(321, 323, 325, 322, 324, 326)) if save: np.savez_compressed(savedir + filename + '.npz', obj=result) fig.savefig(savedir + filename + '.png') plt.ioff() plt.show()
###################################################################################### if width is None: width = [y0, numy-y0, x0, numx-x0] # plot the full range else: width = [min(width[0], y0, numy-y0), min(width[0], y0, numy-y0), min(width[1], x0, numx-x0), min(width[1], x0, numx-x0)] print(f'width for plotting: {width}') ############################################ # plot mask, monitor and concatenated data # ############################################ if save_mask: np.savez_compressed(detector.savedir + 'hotpixels.npz', mask=mask) gu.combined_plots(tuple_array=(monitor, mask), tuple_sum_frames=False, tuple_sum_axis=(0, 0), tuple_width_v=None, tuple_width_h=None, tuple_colorbar=(True, False), tuple_vmin=np.nan, tuple_vmax=np.nan, tuple_title=('monitor', 'mask'), tuple_scale='linear', cmap=my_cmap, ylabel=('Counts (a.u.)', '')) max_y, max_x = np.unravel_index(abs(data).argmax(), data.shape) print(f"Max of the concatenated data along axis 0 at (y, x): ({max_y}, {max_x}) Max = {int(data[max_y, max_x])}") # plot the region of interest centered on the peak # extent (left, right, bottom, top) fig, ax = plt.subplots(nrows=1, ncols=1) plot = ax.imshow(np.log10(data[y0-width[0]:y0+width[1], x0-width[2]:x0+width[3]]), vmin=vmin, vmax=vmax, cmap=my_cmap, extent=[x0-width[2]-0.5, x0+width[3]-0.5, y0+width[1]-0.5, y0-width[0]-0.5]) ax.set_title(f'{title} Peak at (y, x): ({y0},{x0}) Bragg peak value = {peak_int}') gu.colorbar(plot) fig.savefig(detector.savedir + f'sum_S{scan}.png') plt.show()