def image_to_ndarray(filename, convert_grey=True, cmap=None, debug=False):
"""
Convert an image to a numpy array using pillow (matplotlib only supports the PNG format).
:param filename: absolute path of the image to open
:param convert_grey: if True and the number of layers is 3, it will be converted to a single layer of grey
:param cmap: colormap for the plots
:param debug: True to see plots
:return:
"""
from PIL import Image
if cmap is None:
cmap = gu.Colormap(bad_color='1.0').cmap
im = Image.open(filename)
array = np.asarray(im)
if array.ndim == 3 and convert_grey:
print('converting image to gray')
array = rgb2gray(array)
print(f'Image shape after conversion to ndarray: {array.shape}')
if debug:
gu.imshow_plot(array,
sum_axis=2,
plot_colorbar=True,
cmap=cmap,
reciprocal_space=False)
return array
#######################
# Initialize detector #
#######################
detector = exp.Detector(name=detector, binning=binning, roi=roi_detector)
nbz, nby, nbx = int(np.floor((detector.roi[3] - detector.roi[2]) / detector.binning[2])), \
int(np.floor((detector.roi[1] - detector.roi[0]) / detector.binning[1])), \
int(np.floor((detector.roi[3] - detector.roi[2]) / detector.binning[2]))
# for P10 data the rotation is around y vertical, hence gridded data range & binning in z and x are identical
###################
# define colormap #
###################
bad_color = '1.0' # white background
colormap = gu.Colormap(bad_color=bad_color)
my_cmap = colormap.cmap
plt.ion()
######################
# create the lattice #
######################
pivot, _, q_values, lattice, peaks = simu.lattice(
energy=energy,
sdd=sdd,
direct_beam=direct_beam,
detector=detector,
unitcell=unitcell,
unitcell_param=unitcell_param,
euler_angles=angles,
offset_indices=False)
def main(calc_self, user_comment):
"""
Protection for multiprocessing.
:param calc_self: if True, the cross-correlation will be calculated between same q-values
:param user_comment: comment to include in the filename when saving results
"""
##########################
# check input parameters #
##########################
global corr_count, current_point
assert len(
origin_qspace
) == 3, "origin_qspace should be a tuple of 3 integer pixel values"
assert type(calc_self) is bool, "unexpected type for calc_self"
assert len(q_range) > 1, "at least 2 values are needed for q_range"
print('the CCF map will be calculated for {:d} q values: '.format(
len(q_range)))
for idx in range(len(q_range)):
if calc_self:
print('q1 = {:.3f} q2 = {:.3f}'.format(q_range[idx],
q_range[idx]))
else:
print('q1 = {:.3f} q2 = {:.3f}'.format(q_range[0], q_range[idx]))
warnings.filterwarnings("ignore")
###################
# define colormap #
###################
bad_color = '1.0' # white background
colormap = gu.Colormap(bad_color=bad_color)
my_cmap = colormap.cmap
plt.ion()
###################################
# load experimental data and mask #
###################################
plt.ion()
root = tk.Tk()
root.withdraw()
file_path = filedialog.askopenfilename(
initialdir=datadir,
title="Select the 3D reciprocal space map",
filetypes=[("NPZ", "*.npz")])
data = np.load(file_path)['data']
file_path = filedialog.askopenfilename(initialdir=datadir,
title="Select the 3D mask",
filetypes=[("NPZ", "*.npz")])
mask = np.load(file_path)['mask']
print((data > hotpix_threshold).sum(), ' hotpixels masked')
mask[data > hotpix_threshold] = 1
data[np.nonzero(mask)] = np.nan
del mask
gc.collect()
file_path = filedialog.askopenfilename(initialdir=datadir,
title="Select q values",
filetypes=[("NPZ", "*.npz")])
qvalues = np.load(file_path)
qx = qvalues['qx']
qz = qvalues['qz']
qy = qvalues['qy']
del qvalues
gc.collect()
##############################################################
# calculate the angular average using mean and median values #
##############################################################
if plot_meandata:
q_axis, y_mean_masked, y_median_masked = xcca.angular_avg(
data=data,
q_values=(qx, qz, qy),
origin=origin_qspace,
nb_bins=250,
debugging=debug)
fig, ax = plt.subplots(1, 1)
ax.plot(q_axis, np.log10(y_mean_masked), 'r', label='mean')
ax.plot(q_axis, np.log10(y_median_masked), 'b', label='median')
ax.axvline(x=q_range[0],
ymin=0,
ymax=1,
color='g',
linestyle='--',
label='q_start')
ax.axvline(x=q_range[-1],
ymin=0,
ymax=1,
color='r',
linestyle=':',
label='q_stop')
ax.set_xlabel('q (1/nm)')
ax.set_ylabel('Angular average (A.U.)')
ax.legend()
plt.pause(0.1)
fig.savefig(savedir + '1D_average.png')
del q_axis, y_median_masked, y_mean_masked
##############################################################
# interpolate the data onto spheres at user-defined q values #
##############################################################
# calculate the matrix of distances from the origin of reciprocal space
distances = np.sqrt(
(qx[:, np.newaxis, np.newaxis] - qx[origin_qspace[0]])**2 +
(qz[np.newaxis, :, np.newaxis] - qz[origin_qspace[1]])**2 +
(qy[np.newaxis, np.newaxis, :] - qy[origin_qspace[2]])**2)
dq = min(qx[1] - qx[0], qz[1] - qz[0], qy[1] - qy[0])
q_int = dict() # create dictionnary
dict_fields = []
[dict_fields.append('q' + str(idx + 1))
for idx in range(len(q_range))] # ['q1', 'q2', 'q3', ...]
nb_points = []
for counter, q_value in enumerate(q_range):
indices = np.nonzero((np.logical_and((distances < q_value + dq),
(distances > q_value - dq))))
nb_voxels = indices[0].shape
print(
'\nNumber of voxels for the sphere of radius q ={:.3f} 1/nm:'.
format(q_value), nb_voxels)
qx_voxels = qx[indices[0]] # qx downstream, axis 0
qz_voxels = qz[indices[1]] # qz vertical up, axis 1
qy_voxels = qy[indices[2]] # qy outboard, axis 2
int_voxels = data[indices]
if debug:
# calculate the stereographic projection
stereo_proj, uv_labels = fu.calc_stereoproj_facet(
projection_axis=1,
radius_mean=q_value,
stereo_center=0,
vectors=np.concatenate(
(qx_voxels[:, np.newaxis], qz_voxels[:, np.newaxis],
qy_voxels[:, np.newaxis]),
axis=1))
# plot the projection from the South pole
fig, _ = gu.scatter_stereographic(
euclidian_u=stereo_proj[:, 0],
euclidian_v=stereo_proj[:, 1],
color=int_voxels,
title='Projection from the South pole'
' at q={:.3f} (1/nm)'.format(q_value),
uv_labels=uv_labels,
cmap=my_cmap)
fig.savefig(savedir + 'South pole_q={:.3f}.png'.format(q_value))
plt.close(fig)
# plot the projection from the North pole
fig, _ = gu.scatter_stereographic(
euclidian_u=stereo_proj[:, 2],
euclidian_v=stereo_proj[:, 3],
color=int_voxels,
title='Projection from the North pole'
' at q={:.3f} (1/nm)'.format(q_value),
uv_labels=uv_labels,
cmap=my_cmap)
fig.savefig(savedir + 'North pole_q={:.3f}.png'.format(q_value))
plt.close(fig)
# look for nan values
nan_indices = np.argwhere(np.isnan(int_voxels))
# remove nan values before calculating the cross-correlation function
qx_voxels = np.delete(qx_voxels, nan_indices)
qz_voxels = np.delete(qz_voxels, nan_indices)
qy_voxels = np.delete(qy_voxels, nan_indices)
int_voxels = np.delete(int_voxels, nan_indices)
# normalize the intensity by the median value (remove the influence of the form factor)
print('q={:.3f}:'.format(q_value), ' normalizing by the median value',
np.median(int_voxels))
int_voxels = int_voxels / np.median(int_voxels)
q_int[dict_fields[counter]] = np.concatenate(
(qx_voxels[:, np.newaxis], qz_voxels[:, np.newaxis],
qy_voxels[:, np.newaxis], int_voxels[:, np.newaxis]),
axis=1)
# update the number of points without nan
nb_points.append(len(qx_voxels))
print('q={:.3f}:'.format(q_value), ' removing', nan_indices.size,
'nan values,', nb_points[counter], 'remain')
del qx_voxels, qz_voxels, qy_voxels, int_voxels, indices, nan_indices
gc.collect()
del qx, qy, qz, distances, data
gc.collect()
############################################
# calculate the cross-correlation function #
############################################
cross_corr = np.empty((len(q_range), int(180 / angular_resolution), 2))
angular_bins = np.linspace(start=0,
stop=np.pi,
num=corr_count.shape[0],
endpoint=False)
start = time.time()
print("\nNumber of processors: ", mp.cpu_count())
mp.freeze_support()
for ind_q in range(len(q_range)):
pool = mp.Pool(mp.cpu_count()) # use this number of processes
if calc_self:
key_q1 = 'q' + str(ind_q + 1)
key_q2 = key_q1
print('\n' + key_q2 +
': the CCF will be calculated over {:d} * {:d}'
' points and {:d} angular bins'.format(
nb_points[ind_q], nb_points[ind_q], corr_count.shape[0]))
for ind_point in range(nb_points[ind_q]):
pool.apply_async(xcca.calc_ccf_rect,
args=(ind_point, key_q1, key_q2, angular_bins,
q_int),
callback=collect_result,
error_callback=util.catch_error)
else:
key_q1 = 'q1'
key_q2 = 'q' + str(ind_q + 1)
print('\n' + key_q2 +
': the CCF will be calculated over {:d} * {:d}'
' points and {:d} angular bins'.format(
nb_points[0], nb_points[ind_q], corr_count.shape[0]))
for ind_point in range(nb_points[0]):
pool.apply_async(xcca.calc_ccf_rect,
args=(ind_point, key_q1, key_q2, angular_bins,
q_int),
callback=collect_result,
error_callback=util.catch_error)
# close the pool and let all the processes complete
pool.close()
pool.join(
) # postpones the execution of next line of code until all processes in the queue are done.
# normalize the cross-correlation by the counter
indices = np.nonzero(corr_count[:, 1])
corr_count[indices,
0] = corr_count[indices, 0] / corr_count[indices, 1]
cross_corr[ind_q, :, :] = corr_count
# initialize the globals for the next q value
corr_count = np.zeros(
(int(180 / angular_resolution),
2)) # corr_count is declared as a global, this should work
current_point = 0
end = time.time()
print('\nTime ellapsed for the calculation of the CCF map:',
str(datetime.timedelta(seconds=int(end - start))))
#######################################
# save the cross-correlation function #
#######################################
if calc_self:
user_comment = user_comment + '_self'
else:
user_comment = user_comment + '_cross'
filename = 'CCFmap_qstart={:.3f}_qstop={:.3f}'.format(q_range[0], q_range[-1]) +\
'_res{:.3f}'.format(angular_resolution) + user_comment
np.savez_compressed(savedir + filename + '.npz',
angles=180 * angular_bins / np.pi,
q_range=q_range,
ccf=cross_corr[:, :, 0],
points=cross_corr[:, :, 1])
#######################################
# plot the cross-correlation function #
#######################################
# find the y limit excluding the peaks at 0 and 180 degrees
indices = np.argwhere(
np.logical_and((angular_bins >= 20 * np.pi / 180),
(angular_bins <= 160 * np.pi / 180)))
vmax = 1.2 * cross_corr[:, indices, 0].max()
print('Discarding CCF values with a zero counter:',
(cross_corr[:, :, 1] == 0).sum(), 'points masked')
cross_corr[(cross_corr[:, :, 1] == 0),
0] = np.nan # discard these values of the CCF
dq = q_range[1] - q_range[0]
fig, ax = plt.subplots()
plt0 = ax.imshow(
cross_corr[:, :, 0],
cmap=my_cmap,
vmin=0,
vmax=vmax,
extent=[0, 180, q_range[-1] + dq / 2,
q_range[0] - dq / 2]) # extent (left, right, bottom, top)
ax.set_xlabel('Angle (deg)')
ax.set_ylabel('q (nm$^{-1}$)')
ax.set_xticks(np.arange(0, 181, 30))
ax.set_yticks(q_range)
ax.set_aspect('auto')
if calc_self:
ax.set_title('self CCF from q={:.3f} 1/nm to q={:.3f} 1/nm'.format(
q_range[0], q_range[-1]))
else:
ax.set_title('cross CCF from q={:.3f} 1/nm to q={:.3f} 1/nm'.format(
q_range[0], q_range[-1]))
gu.colorbar(plt0, scale='linear', numticks=5)
fig.savefig(savedir + filename + '.png')
plt.ioff()
plt.show()
] # plot vertical dashed lines at these q values, leave [] otherwise
# position in pixels of the origin of the angular average in the array.
# if a nan value is used, the origin will be set at the middle of the array in the corresponding dimension.
threshold = 0 # data < threshold will be set to 0
debug = False # True to show more plots
xlim = None # limits used for the horizontal axis of the angular plot, leave None otherwise
ylim = None # limits used for the vertical axis of++ plots, leave None otherwise
save_txt = True # True to save q values and the average in .txt format
##########################
# end of user parameters #
##########################
###################
# define colormap #
###################
colormap = gu.Colormap()
my_cmap = colormap.cmap
##############################
# load reciprocal space data #
##############################
plt.ion()
root = tk.Tk()
root.withdraw()
file_path = filedialog.askopenfilename(initialdir=root_folder,
title="Select the diffraction pattern",
filetypes=[("NPZ", "*.npz")])
npzfile = np.load(file_path)
diff_pattern = pu.bin_data(npzfile[list(npzfile.files)[0]].astype(int),
(bin_factor, bin_factor, bin_factor),
debugging=False)
def main(user_comment):
"""
Protection for multiprocessing.
:param user_comment: comment to include in the filename when saving results
"""
##########################
# check input parameters #
##########################
global corr_count
if len(q_xcca) != 2:
raise ValueError("Two q values should be provided (it can be the same value)")
if len(origin_qspace) != 3:
raise ValueError("origin_qspace should be a tuple of 3 integer pixel values")
q_xcca.sort()
same_q = q_xcca[0] == q_xcca[1]
warnings.filterwarnings("ignore")
###################
# define colormap #
###################
bad_color = "1.0" # white background
colormap = gu.Colormap(bad_color=bad_color)
my_cmap = colormap.cmap
plt.ion()
###################################
# load experimental data and mask #
###################################
plt.ion()
root = tk.Tk()
root.withdraw()
file_path = filedialog.askopenfilename(
initialdir=datadir,
title="Select the 3D reciprocal space map",
filetypes=[("NPZ", "*.npz")],
)
data = np.load(file_path)["data"]
file_path = filedialog.askopenfilename(
initialdir=datadir, title="Select the 3D mask", filetypes=[("NPZ", "*.npz")]
)
mask = np.load(file_path)["mask"]
print((data > hotpix_threshold).sum(), " hotpixels masked")
mask[data > hotpix_threshold] = 1
data[np.nonzero(mask)] = np.nan
del mask
gc.collect()
file_path = filedialog.askopenfilename(
initialdir=datadir, title="Select q values", filetypes=[("NPZ", "*.npz")]
)
qvalues = np.load(file_path)
qx = qvalues["qx"]
qz = qvalues["qz"]
qy = qvalues["qy"]
del qvalues
gc.collect()
##############################################################
# calculate the angular average using mean and median values #
##############################################################
if plot_meandata:
q_axis, y_mean_masked, y_median_masked = xcca.angular_avg(
data=data,
q_values=(qx, qz, qy),
origin=origin_qspace,
nb_bins=250,
debugging=debug,
)
fig, ax = plt.subplots(1, 1)
ax.plot(q_axis, np.log10(y_mean_masked), "r", label="mean")
ax.plot(q_axis, np.log10(y_median_masked), "b", label="median")
ax.axvline(x=q_xcca[0], ymin=0, ymax=1, color="g", linestyle="--", label="q1")
ax.axvline(x=q_xcca[1], ymin=0, ymax=1, color="r", linestyle=":", label="q2")
ax.set_xlabel("q (1/nm)")
ax.set_ylabel("Angular average (A.U.)")
ax.legend()
plt.pause(0.1)
fig.savefig(savedir + "1D_average.png")
del q_axis, y_median_masked, y_mean_masked
##############################################################
# interpolate the data onto spheres at user-defined q values #
##############################################################
# calculate the matrix of distances from the origin of reciprocal space
distances = np.sqrt(
(qx[:, np.newaxis, np.newaxis] - qx[origin_qspace[0]]) ** 2
+ (qz[np.newaxis, :, np.newaxis] - qz[origin_qspace[1]]) ** 2
+ (qy[np.newaxis, np.newaxis, :] - qy[origin_qspace[2]]) ** 2
)
dq = min(qx[1] - qx[0], qz[1] - qz[0], qy[1] - qy[0])
q_int = {} # create dictionnary
dict_fields = ["q1", "q2"]
nb_points = []
for counter, q_value in enumerate(q_xcca):
if (counter == 0) or ((counter == 1) and not same_q):
indices = np.nonzero(
(np.logical_and((distances < q_value + dq), (distances > q_value - dq)))
)
nb_voxels = indices[0].shape
print(
"\nNumber of voxels for the sphere of radius q ={:.3f} 1/nm:".format(
q_value
),
nb_voxels,
)
qx_voxels = qx[indices[0]] # qx downstream, axis 0
qz_voxels = qz[indices[1]] # qz vertical up, axis 1
qy_voxels = qy[indices[2]] # qy outboard, axis 2
int_voxels = data[indices]
if debug:
# calculate the stereographic projection
stereo_proj, uv_labels = fu.calc_stereoproj_facet(
projection_axis=1,
radius_mean=q_value,
stereo_center=0,
vectors=np.concatenate(
(
qx_voxels[:, np.newaxis],
qz_voxels[:, np.newaxis],
qy_voxels[:, np.newaxis],
),
axis=1,
),
)
# plot the projection from the South pole
fig, _ = gu.scatter_stereographic(
euclidian_u=stereo_proj[:, 0],
euclidian_v=stereo_proj[:, 1],
color=int_voxels,
title="Projection from the South pole"
" at q={:.3f} (1/nm)".format(q_value),
uv_labels=uv_labels,
cmap=my_cmap,
)
fig.savefig(savedir + "South pole_q={:.3f}.png".format(q_value))
plt.close(fig)
# plot the projection from the North pole
fig, _ = gu.scatter_stereographic(
euclidian_u=stereo_proj[:, 2],
euclidian_v=stereo_proj[:, 3],
color=int_voxels,
title="Projection from the North pole"
" at q={:.3f} (1/nm)".format(q_value),
uv_labels=uv_labels,
cmap=my_cmap,
)
fig.savefig(savedir + "North pole_q={:.3f}.png".format(q_value))
plt.close(fig)
# look for nan values
nan_indices = np.argwhere(np.isnan(int_voxels))
# remove nan values before calculating the cross-correlation function
qx_voxels = np.delete(qx_voxels, nan_indices)
qz_voxels = np.delete(qz_voxels, nan_indices)
qy_voxels = np.delete(qy_voxels, nan_indices)
int_voxels = np.delete(int_voxels, nan_indices)
# normalize the intensity by the median value (remove the influence of
# the form factor)
print(
"q={:.3f}:".format(q_value),
" normalizing by the median value",
np.median(int_voxels),
)
int_voxels = int_voxels / np.median(int_voxels)
q_int[dict_fields[counter]] = np.concatenate(
(
qx_voxels[:, np.newaxis],
qz_voxels[:, np.newaxis],
qy_voxels[:, np.newaxis],
int_voxels[:, np.newaxis],
),
axis=1,
)
# update the number of points without nan
nb_points.append(len(qx_voxels))
print(
"q={:.3f}:".format(q_value),
" removing",
nan_indices.size,
"nan values,",
nb_points[counter],
"remain",
)
del qx_voxels, qz_voxels, qy_voxels, int_voxels, indices, nan_indices
gc.collect()
del qx, qy, qz, distances, data
gc.collect()
############################################
# calculate the cross-correlation function #
############################################
if same_q:
key_q2 = "q1"
print(
"\nThe CCF will be calculated over {:d} * {:d}"
" points and {:d} angular bins".format(
nb_points[0], nb_points[0], corr_count.shape[0]
)
)
else:
key_q2 = "q2"
print(
"\nThe CCF will be calculated over {:d} * {:d}"
" points and {:d} angular bins".format(
nb_points[0], nb_points[1], corr_count.shape[0]
)
)
angular_bins = np.linspace(
start=0, stop=np.pi, num=corr_count.shape[0], endpoint=False
)
start = time.time()
if single_proc:
for idx in range(nb_points[0]):
ccf_uniq_val, counter_val, counter_indices = xcca.calc_ccf_rect(
point=idx,
q1_name="q1",
q2_name=key_q2,
bin_values=angular_bins,
q_int=q_int,
)
collect_result_debug(ccf_uniq_val, counter_val, counter_indices)
else:
print("\nNumber of processors: ", mp.cpu_count())
mp.freeze_support()
pool = mp.Pool(mp.cpu_count()) # use this number of processes
for idx in range(nb_points[0]):
pool.apply_async(
xcca.calc_ccf_rect,
args=(idx, "q1", key_q2, angular_bins, q_int),
callback=collect_result,
error_callback=util.catch_error,
)
# close the pool and let all the processes complete
pool.close()
pool.join() # postpones the execution of next line of code until all
# processes in the queue are done.
end = time.time()
print(
"\nTime ellapsed for the calculation of the CCF:",
str(datetime.timedelta(seconds=int(end - start))),
)
# normalize the cross-correlation by the counter
indices = np.nonzero(corr_count[:, 1])
corr_count[indices, 0] = corr_count[indices, 0] / corr_count[indices, 1]
#######################################
# save the cross-correlation function #
#######################################
filename = (
"CCF_q1={:.3f}_q2={:.3f}".format(q_xcca[0], q_xcca[1])
+ "_points{:d}_res{:.3f}".format(nb_points[0], angular_resolution)
+ user_comment
)
np.savez_compressed(
savedir + filename + ".npz",
angles=180 * angular_bins / np.pi,
ccf=corr_count[:, 0],
points=corr_count[:, 1],
)
#######################################
# plot the cross-correlation function #
#######################################
# find the y limit excluding the peaks at 0 and 180 degrees
indices = np.argwhere(
np.logical_and(
(angular_bins >= 5 * np.pi / 180), (angular_bins <= 175 * np.pi / 180)
)
)
ymax = 1.2 * corr_count[indices, 0].max()
print(
"Discarding CCF values with a zero counter:",
(corr_count[:, 1] == 0).sum(),
"points masked",
)
corr_count[(corr_count[:, 1] == 0), 0] = np.nan # discard these values of the CCF
fig, ax = plt.subplots()
ax.plot(
180 * angular_bins / np.pi,
corr_count[:, 0],
color="red",
linestyle="-",
markerfacecolor="blue",
marker=".",
)
ax.set_xlim(0, 180)
ax.set_ylim(0, ymax)
ax.set_xlabel("Angle (deg)")
ax.set_ylabel("Cross-correlation")
ax.set_xticks(np.arange(0, 181, 30))
ax.set_title(
"CCF at q1={:.3f} 1/nm and q2={:.3f} 1/nm".format(q_xcca[0], q_xcca[1])
)
fig.savefig(savedir + filename + ".png")
_, ax = plt.subplots()
ax.plot(
180 * angular_bins / np.pi,
corr_count[:, 1],
linestyle="None",
markerfacecolor="blue",
marker=".",
)
ax.set_xlim(0, 180)
ax.set_xlabel("Angle (deg)")
ax.set_ylabel("Number of points")
ax.set_xticks(np.arange(0, 181, 30))
ax.set_title("Points per angular bin")
plt.ioff()
plt.show()
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")
def main(parameters):
"""
Protection for multiprocessing.
:param parameters: dictionnary containing input parameters
"""
def collect_result(result):
"""
Callback processing the result after asynchronous multiprocessing. Update the global arrays.
:param result: the output of load_p10_file, containing the 2d data, 2d mask, counter for each frame, and the
file index
"""
nonlocal sumdata, mask, counter, nb_files, current_point
# result is a tuple: data, mask, counter, file_index
current_point += 1
sumdata = sumdata + result[0]
mask[np.nonzero(result[1])] = 1
counter.append(result[2])
sys.stdout.write('\rFile {:d} / {:d}'.format(current_point, nb_files))
sys.stdout.flush()
######################################
# load the dictionnary of parameters #
######################################
scan = parameters['scan']
samplename = parameters['sample_name']
rootdir = parameters['rootdir']
image_nb = parameters['file_list']
counterroi = parameters['counter_roi']
savedir = parameters['savedir']
load_scan = parameters['is_scan']
compare_end = parameters['compare_ends']
savemask = parameters['save_mask']
multiproc = parameters['multiprocessing']
threshold = parameters['threshold']
cb_min = parameters['cb_min']
cb_max = parameters['cb_max']
grey_bckg = parameters['grey_bckg']
###################
# define colormap #
###################
if grey_bckg:
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 = exp.Detector(name=parameters['detector'])
nb_pixel_y, nb_pixel_x = detector.nb_pixel_y, detector.nb_pixel_x
sumdata = np.zeros((nb_pixel_y, nb_pixel_x))
mask = np.zeros((nb_pixel_y, nb_pixel_x))
counter = []
####################
# Initialize paths #
####################
if type(image_nb) == int:
image_nb = [image_nb]
if len(counterroi) == 0:
counterroi = [0, nb_pixel_y, 0, nb_pixel_x]
assert (counterroi[0] >= 0
and counterroi[1] <= nb_pixel_y
and counterroi[2] >= 0
and counterroi[3] <= nb_pixel_x), 'counter_roi setting does not match the detector size'
nb_files = len(image_nb)
if nb_files == 1:
multiproc = False
if load_scan: # scan or time series
detector.datadir = rootdir + samplename + '_' + str('{:05d}'.format(scan)) + '/e4m/'
template_file = detector.datadir + samplename + '_' + str('{:05d}'.format(scan)) + "_data_"
else: # single image
detector.datadir = rootdir + samplename + '/e4m/'
template_file = detector.datadir + samplename + '_take_' + str('{:05d}'.format(scan)) + "_data_"
compare_end = False
detector.savedir = savedir or os.path.abspath(os.path.join(detector.datadir, os.pardir)) + '/'
print(f'datadir: {detector.datadir}')
print(f'savedir: {detector.savedir}')
#############
# Load data #
#############
plt.ion()
filenames = [template_file + '{:06d}.h5'.format(image_nb[idx]) for idx in range(nb_files)]
roi_counter = None
current_point = 0
start = time.time()
if multiproc:
print("\nNumber of processors used: ", min(mp.cpu_count(), len(filenames)))
mp.freeze_support()
pool = mp.Pool(processes=min(mp.cpu_count(), len(filenames))) # use this number of processes
for file in range(nb_files):
pool.apply_async(load_p10_file, args=(detector, filenames[file], file, counterroi, threshold),
callback=collect_result, error_callback=util.catch_error)
pool.close()
pool.join() # postpones the execution of next line of code until all processes in the queue are done.
# sort out counter values (we are using asynchronous multiprocessing, order is not preserved)
roi_counter = sorted(counter, key=lambda x: x[1])
else:
for idx in range(nb_files):
sys.stdout.write('\rLoading file {:d}'.format(idx + 1) + ' / {:d}'.format(nb_files))
sys.stdout.flush()
h5file = h5py.File(filenames[idx], 'r')
data = h5file['entry']['data']['data'][:]
data[data <= threshold] = 0
nbz, nby, nbx = data.shape
[counter.append(data[index, counterroi[0]:counterroi[1], counterroi[2]:counterroi[3]].sum())
for index in range(nbz)]
if compare_end and nb_files == 1:
data_start, _ = detector.mask_detector(data=data[0, :, :], mask=mask)
data_start = data_start.astype(float)
data_stop, _ = detector.mask_detector(data=data[-1, :, :], mask=mask)
data_stop = data_stop.astype(float)
fig, _, _ = gu.imshow_plot(data_stop - data_start, plot_colorbar=True, scale='log',
title='difference between the last frame and the first frame of the series')
nb_frames = data.shape[0] # collect the number of frames in the eventual series
data, mask = detector.mask_detector(data=data.sum(axis=0), mask=mask, nb_img=nb_frames)
sumdata = sumdata + data
roi_counter = [[counter, idx]]
end = time.time()
print('\nTime ellapsed for loading data:', str(datetime.timedelta(seconds=int(end - start))))
frame_per_series = int(len(counter) / nb_files)
print('')
if load_scan:
if nb_files > 1:
plot_title = 'masked data - sum of ' + str(nb_files)\
+ ' points with {:d} frames each'.format(frame_per_series)
else:
plot_title = 'masked data - sum of ' + str(frame_per_series) + ' frames'
filename = 'S' + str(scan) + '_scan.png'
else: # single image
plot_title = 'masked data'
filename = 'S' + str(scan) + '_image_' + str(image_nb[0]) + '.png'
if savemask:
fig, _, _ = gu.imshow_plot(mask, plot_colorbar=False, title='mask')
np.savez_compressed(detector.savedir+'hotpixels.npz', mask=mask)
fig.savefig(detector.savedir + 'mask.png')
y0, x0 = np.unravel_index(abs(sumdata).argmax(), sumdata.shape)
print("Max at (y, x): ", y0, x0, ' Max = ', int(sumdata[y0, x0]))
np.savez_compressed(detector.savedir + f'{sample_name}_{scan_nb:05d}_sumdata.npz', data=sumdata)
if save_to_mat:
savemat(detector.savedir + f'{sample_name}_{scan_nb:05d}_sumdata.mat', {'data': sumdata})
if len(roi_counter[0][0]) > 1: # roi_counter[0][0] is the list of counter intensities in a series
int_roi = []
[int_roi.append(val[0][idx]) for val in roi_counter for idx in range(frame_per_series)]
plt.figure()
plt.plot(np.asarray(int_roi))
plt.title('Integrated intensity in counter_roi')
plt.pause(0.1)
cb_min = cb_min or sumdata.min()
cb_max = cb_max or sumdata.max()
fig, _, _ = gu.imshow_plot(sumdata, plot_colorbar=True, title=plot_title, vmin=cb_min, vmax=cb_max, scale='log',
cmap=my_cmap)
np.savez_compressed(detector.savedir + 'hotpixels.npz', mask=mask)
fig.savefig(detector.savedir + filename)
plt.show()
def main(user_comment):
"""
Protection for multiprocessing.
:param user_comment: comment to include in the filename when saving results
"""
##########################
# check input parameters #
##########################
global corr_count
assert len(
q_xcca
) == 2, "Two q values should be provided (it can be the same value)"
assert len(
origin_qspace
) == 3, "origin_qspace should be a tuple of 3 integer pixel values"
q_xcca.sort()
if q_xcca[0] == q_xcca[1]:
same_q = True
else:
same_q = False
warnings.filterwarnings("ignore")
###################
# define colormap #
###################
bad_color = '1.0' # white background
colormap = gu.Colormap(bad_color=bad_color)
my_cmap = colormap.cmap
plt.ion()
###################################
# load experimental data and mask #
###################################
plt.ion()
root = tk.Tk()
root.withdraw()
file_path = filedialog.askopenfilename(
initialdir=datadir,
title="Select the 3D reciprocal space map",
filetypes=[("NPZ", "*.npz")])
data = np.load(file_path)['data']
file_path = filedialog.askopenfilename(initialdir=datadir,
title="Select the 3D mask",
filetypes=[("NPZ", "*.npz")])
mask = np.load(file_path)['mask']
print((data > hotpix_threshold).sum(), ' hotpixels masked')
mask[data > hotpix_threshold] = 1
data[np.nonzero(mask)] = np.nan
del mask
gc.collect()
file_path = filedialog.askopenfilename(initialdir=datadir,
title="Select q values",
filetypes=[("NPZ", "*.npz")])
qvalues = np.load(file_path)
qx = qvalues['qx']
qz = qvalues['qz']
qy = qvalues['qy']
del qvalues
gc.collect()
##############################################################
# calculate the angular average using mean and median values #
##############################################################
if plot_meandata:
q_axis, y_mean_masked, y_median_masked = xcca.angular_avg(
data=data,
q_values=(qx, qz, qy),
origin=origin_qspace,
nb_bins=250,
debugging=debug)
fig, ax = plt.subplots(1, 1)
ax.plot(q_axis, np.log10(y_mean_masked), 'r', label='mean')
ax.plot(q_axis, np.log10(y_median_masked), 'b', label='median')
ax.axvline(x=q_xcca[0],
ymin=0,
ymax=1,
color='g',
linestyle='--',
label='q1')
ax.axvline(x=q_xcca[1],
ymin=0,
ymax=1,
color='r',
linestyle=':',
label='q2')
ax.set_xlabel('q (1/nm)')
ax.set_ylabel('Angular average (A.U.)')
ax.legend()
plt.pause(0.1)
fig.savefig(savedir + '1D_average.png')
del q_axis, y_median_masked, y_mean_masked
##############################################################
# interpolate the data onto spheres at user-defined q values #
##############################################################
# calculate the matrix of distances from the origin of reciprocal space
distances = np.sqrt(
(qx[:, np.newaxis, np.newaxis] - qx[origin_qspace[0]])**2 +
(qz[np.newaxis, :, np.newaxis] - qz[origin_qspace[1]])**2 +
(qy[np.newaxis, np.newaxis, :] - qy[origin_qspace[2]])**2)
dq = min(qx[1] - qx[0], qz[1] - qz[0], qy[1] - qy[0])
theta_phi_int = dict() # create dictionnary
dict_fields = ['q1', 'q2']
nb_points = []
for counter, q_value in enumerate(q_xcca):
if (counter == 0) or ((counter == 1) and not same_q):
nb_pixels = (np.logical_and((distances < q_value + dq),
(distances > q_value - dq))).sum()
print(
'\nNumber of voxels for the sphere of radius q ={:.3f} 1/nm:'.
format(q_value), nb_pixels)
nb_pixels = int(nb_pixels / interp_factor)
print(
'Dividing the number of voxels by interp_factor: {:d} voxels remaining'
.format(nb_pixels))
indices = np.arange(0, nb_pixels, dtype=float) + 0.5
# angles for interpolation are chosen using the 'golden spiral method', so that the corresponding points
# are evenly distributed on the sphere
theta = np.arccos(
1 - 2 * indices / nb_pixels
) # theta is the polar angle of the spherical coordinates
phi = np.pi * (
1 + np.sqrt(5)
) * indices # phi is the azimuthal angle of the spherical coordinates
qx_sphere = q_value * np.cos(phi) * np.sin(theta)
qz_sphere = q_value * np.cos(theta)
qy_sphere = q_value * np.sin(phi) * np.sin(theta)
# interpolate the data onto the new points
rgi = RegularGridInterpolator((qx, qz, qy),
data,
method='linear',
bounds_error=False,
fill_value=np.nan)
sphere_int = rgi(
np.concatenate((qx_sphere.reshape(
(1, nb_pixels)), qz_sphere.reshape(
(1, nb_pixels)), qy_sphere.reshape(
(1, nb_pixels)))).transpose())
# look for nan values
nan_indices = np.argwhere(np.isnan(sphere_int))
if debug:
sphere_debug = np.copy(
sphere_int
) # create a copy to see also nans in the debugging plot
# remove nan values before calculating the cross-correlation function
theta = np.delete(theta, nan_indices)
phi = np.delete(phi, nan_indices)
sphere_int = np.delete(sphere_int, nan_indices)
# normalize the intensity by the median value (remove the influence of the form factor)
print('q={:.3f}:'.format(q_value),
' normalizing by the median value', np.median(sphere_int))
sphere_int = sphere_int / np.median(sphere_int)
theta_phi_int[dict_fields[counter]] = np.concatenate(
(theta[:, np.newaxis], phi[:, np.newaxis],
sphere_int[:, np.newaxis]),
axis=1)
# update the number of points without nan
nb_points.append(len(theta))
print('q={:.3f}:'.format(q_value), ' removing', nan_indices.size,
'nan values,', nb_points[counter], 'remain')
if debug:
# calculate the stereographic projection
stereo_proj, uv_labels = fu.calc_stereoproj_facet(
projection_axis=1,
radius_mean=q_value,
stereo_center=0,
vectors=np.concatenate(
(qx_sphere[:, np.newaxis], qz_sphere[:, np.newaxis],
qy_sphere[:, np.newaxis]),
axis=1))
# plot the projection from the South pole
fig, _ = gu.scatter_stereographic(
euclidian_u=stereo_proj[:, 0],
euclidian_v=stereo_proj[:, 1],
color=sphere_debug,
title='Projection from the South pole'
' at q={:.3f} (1/nm)'.format(q_value),
uv_labels=uv_labels,
cmap=my_cmap)
fig.savefig(savedir +
'South pole_q={:.3f}.png'.format(q_value))
plt.close(fig)
# plot the projection from the North pole
fig, _ = gu.scatter_stereographic(
euclidian_u=stereo_proj[:, 2],
euclidian_v=stereo_proj[:, 3],
color=sphere_debug,
title='Projection from the North pole'
' at q={:.3f} (1/nm)'.format(q_value),
uv_labels=uv_labels,
cmap=my_cmap)
fig.savefig(savedir +
'North pole_q={:.3f}.png'.format(q_value))
plt.close(fig)
del sphere_debug
del qx_sphere, qz_sphere, qy_sphere, theta, phi, sphere_int, indices, nan_indices
gc.collect()
del qx, qy, qz, distances, data
gc.collect()
############################################
# calculate the cross-correlation function #
############################################
if same_q:
key_q2 = 'q1'
print('\nThe CCF will be calculated over {:d} * {:d}'
' points and {:d} angular bins'.format(nb_points[0],
nb_points[0],
corr_count.shape[0]))
else:
key_q2 = 'q2'
print('\nThe CCF will be calculated over {:d} * {:d}'
' points and {:d} angular bins'.format(nb_points[0],
nb_points[1],
corr_count.shape[0]))
angular_bins = np.linspace(start=0,
stop=np.pi,
num=corr_count.shape[0],
endpoint=False)
start = time.time()
if single_proc:
for idx in range(nb_points[0]):
ccf_uniq_val, counter_val, counter_indices = \
xcca.calc_ccf_polar(point=idx, q1_name='q1', q2_name=key_q2, bin_values=angular_bins,
polar_azi_int=theta_phi_int)
collect_result_debug(ccf_uniq_val, counter_val, counter_indices)
else:
print("\nNumber of processors: ", mp.cpu_count())
mp.freeze_support()
pool = mp.Pool(mp.cpu_count()) # use this number of processes
for idx in range(nb_points[0]):
pool.apply_async(xcca.calc_ccf_polar,
args=(idx, 'q1', key_q2, angular_bins,
theta_phi_int),
callback=collect_result,
error_callback=util.catch_error)
# close the pool and let all the processes complete
pool.close()
pool.join(
) # postpones the execution of next line of code until all processes in the queue are done.
end = time.time()
print('\nTime ellapsed for the calculation of the CCF:',
str(datetime.timedelta(seconds=int(end - start))))
# normalize the cross-correlation by the counter
indices = np.nonzero(corr_count[:, 1])
corr_count[indices, 0] = corr_count[indices, 0] / corr_count[indices, 1]
#######################################
# save the cross-correlation function #
#######################################
filename = 'CCF_q1={:.3f}_q2={:.3f}'.format(q_xcca[0], q_xcca[1]) +\
'_points{:d}_interp{:d}_res{:.3f}'.format(nb_points[0], interp_factor, angular_resolution) + user_comment
np.savez_compressed(savedir + filename + '.npz',
angles=180 * angular_bins / np.pi,
ccf=corr_count[:, 0],
points=corr_count[:, 1])
#######################################
# plot the cross-correlation function #
#######################################
# find the y limit excluding the peaks at 0 and 180 degrees
indices = np.argwhere(
np.logical_and((angular_bins >= 5 * np.pi / 180),
(angular_bins <= 175 * np.pi / 180)))
ymax = 1.2 * corr_count[indices, 0].max()
print('Discarding CCF values with a zero counter:',
(corr_count[:, 1] == 0).sum(), 'points masked')
corr_count[(corr_count[:, 1] == 0),
0] = np.nan # discard these values of the CCF
fig, ax = plt.subplots()
ax.plot(180 * angular_bins / np.pi,
corr_count[:, 0],
color='red',
linestyle="-",
markerfacecolor='blue',
marker='.')
ax.set_xlim(0, 180)
ax.set_ylim(0, ymax)
ax.set_xlabel('Angle (deg)')
ax.set_ylabel('Cross-correlation')
ax.set_xticks(np.arange(0, 181, 30))
ax.set_title('CCF at q1={:.3f} 1/nm and q2={:.3f} 1/nm'.format(
q_xcca[0], q_xcca[1]))
fig.savefig(savedir + filename + '.png')
_, ax = plt.subplots()
ax.plot(180 * angular_bins / np.pi,
corr_count[:, 1],
linestyle="None",
markerfacecolor='blue',
marker='.')
ax.set_xlim(0, 180)
ax.set_xlabel('Angle (deg)')
ax.set_ylabel('Number of points')
ax.set_xticks(np.arange(0, 181, 30))
ax.set_title('Points per angular bin')
plt.ioff()
plt.show()
def main(parameters):
"""
Protection for multiprocessing.
:param parameters: dictionnary containing input parameters
"""
def collect_result(result):
"""
Callback processing the result after asynchronous multiprocessing.
Update the global arrays.
:param result: the output of load_p10_file, containing the 2d data, 2d mask,
counter for each frame, and the file index
"""
nonlocal sumdata, mask, counter, nb_files, current_point
# result is a tuple: data, mask, counter, file_index
current_point += 1
sumdata = sumdata + result[0]
mask[np.nonzero(result[1])] = 1
counter.append(result[2])
sys.stdout.write("\rFile {:d} / {:d}".format(current_point, nb_files))
sys.stdout.flush()
######################################
# load the dictionnary of parameters #
######################################
scan = parameters["scan"]
samplename = parameters["sample_name"]
rootdir = parameters["rootdir"]
image_nb = parameters["file_list"]
counterroi = parameters["counter_roi"]
savedir = parameters["savedir"]
load_scan = parameters["is_scan"]
compare_end = parameters["compare_ends"]
savemask = parameters["save_mask"]
multiproc = parameters["multiprocessing"]
threshold = parameters["threshold"]
cb_min = parameters["cb_min"]
cb_max = parameters["cb_max"]
grey_bckg = parameters["grey_bckg"]
###################
# define colormap #
###################
if grey_bckg:
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=parameters["detector"])
nb_pixel_y, nb_pixel_x = detector.nb_pixel_y, detector.nb_pixel_x
sumdata = np.zeros((nb_pixel_y, nb_pixel_x))
mask = np.zeros((nb_pixel_y, nb_pixel_x))
counter = []
####################
# Initialize paths #
####################
if isinstance(image_nb, int):
image_nb = [image_nb]
if len(counterroi) == 0:
counterroi = [0, nb_pixel_y, 0, nb_pixel_x]
if not (counterroi[0] >= 0 and counterroi[1] <= nb_pixel_y
and counterroi[2] >= 0 and counterroi[3] <= nb_pixel_x):
raise ValueError(
"counter_roi setting does not match the detector size")
nb_files = len(image_nb)
if nb_files == 1:
multiproc = False
if load_scan: # scan or time series
detector.datadir = (rootdir + samplename + "_" +
str("{:05d}".format(scan)) + "/e4m/")
template_file = (detector.datadir + samplename + "_" +
str("{:05d}".format(scan)) + "_data_")
else: # single image
detector.datadir = rootdir + samplename + "/e4m/"
template_file = (detector.datadir + samplename + "_take_" +
str("{:05d}".format(scan)) + "_data_")
compare_end = False
detector.savedir = (
savedir
or os.path.abspath(os.path.join(detector.datadir, os.pardir)) + "/")
print(f"datadir: {detector.datadir}")
print(f"savedir: {detector.savedir}")
#############
# Load data #
#############
plt.ion()
filenames = [
template_file + "{:06d}.h5".format(image_nb[idx])
for idx in range(nb_files)
]
roi_counter = None
current_point = 0
start = time.time()
if multiproc:
print("\nNumber of processors used: ",
min(mp.cpu_count(), len(filenames)))
mp.freeze_support()
pool = mp.Pool(processes=min(
mp.cpu_count(), len(filenames))) # use this number of processes
for file in range(nb_files):
pool.apply_async(
load_p10_file,
args=(detector, filenames[file], file, counterroi, threshold),
callback=collect_result,
error_callback=util.catch_error,
)
pool.close()
pool.join() # postpones the execution of next line of code
# until all processes in the queue are done.
# sort out counter values
# (we are using asynchronous multiprocessing, order is not preserved)
roi_counter = sorted(counter, key=lambda x: x[1])
else:
for idx in range(nb_files):
sys.stdout.write("\rLoading file {:d}".format(idx + 1) +
" / {:d}".format(nb_files))
sys.stdout.flush()
h5file = h5py.File(filenames[idx], "r")
data = h5file["entry"]["data"]["data"][:]
data[data <= threshold] = 0
nbz, nby, nbx = data.shape
for index in range(nbz):
counter.append(data[index, counterroi[0]:counterroi[1],
counterroi[2]:counterroi[3], ].sum())
if compare_end and nb_files == 1:
data_start, _ = detector.mask_detector(data=data[0, :, :],
mask=mask)
data_start = data_start.astype(float)
data_stop, _ = detector.mask_detector(data=data[-1, :, :],
mask=mask)
data_stop = data_stop.astype(float)
fig, _, _ = gu.imshow_plot(
data_stop - data_start,
plot_colorbar=True,
scale="log",
title="""difference between the last frame and
the first frame of the series""",
)
nb_frames = data.shape[
0] # collect the number of frames in the eventual series
data, mask = detector.mask_detector(data=data.sum(axis=0),
mask=mask,
nb_frames=nb_frames)
sumdata = sumdata + data
roi_counter = [[counter, idx]]
end = time.time()
print(
"\nTime ellapsed for loading data:",
str(datetime.timedelta(seconds=int(end - start))),
)
frame_per_series = int(len(counter) / nb_files)
print("")
if load_scan:
if nb_files > 1:
plot_title = (
"masked data - sum of " + str(nb_files) +
" points with {:d} frames each".format(frame_per_series))
else:
plot_title = "masked data - sum of " + str(
frame_per_series) + " frames"
filename = "S" + str(scan) + "_scan.png"
else: # single image
plot_title = "masked data"
filename = "S" + str(scan) + "_image_" + str(image_nb[0]) + ".png"
if savemask:
fig, _, _ = gu.imshow_plot(mask, plot_colorbar=False, title="mask")
np.savez_compressed(detector.savedir + "hotpixels.npz", mask=mask)
fig.savefig(detector.savedir + "mask.png")
y0, x0 = np.unravel_index(abs(sumdata).argmax(), sumdata.shape)
print("Max at (y, x): ", y0, x0, " Max = ", int(sumdata[y0, x0]))
np.savez_compressed(detector.savedir +
f"{sample_name}_{scan_nb:05d}_sumdata.npz",
data=sumdata)
if save_to_mat:
savemat(
detector.savedir + f"{sample_name}_{scan_nb:05d}_sumdata.mat",
{"data": sumdata},
)
if len(roi_counter[0][0]) > 1:
# roi_counter[0][0] is the list of counter intensities in a series
int_roi = [
val[0][idx] for val in roi_counter
for idx in range(frame_per_series)
]
plt.figure()
plt.plot(np.asarray(int_roi))
plt.title("Integrated intensity in counter_roi")
plt.pause(0.1)
cb_min = cb_min or sumdata.min()
cb_max = cb_max or sumdata.max()
fig, _, _ = gu.imshow_plot(
sumdata,
plot_colorbar=True,
title=plot_title,
vmin=cb_min,
vmax=cb_max,
scale="log",
cmap=my_cmap,
)
np.savez_compressed(detector.savedir + "hotpixels.npz", mask=mask)
fig.savefig(detector.savedir + filename)
plt.show()
