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()
