point=psf_cen,
direction=(0, 0, 1),
voxel_size=voxel_size)
linecuts_dict = OrderedDict([('width_z', width_z), ('width_y', width_y),
('width_x', width_x), ('cut_z', cut_z),
('cut_y', cut_y), ('cut_x', cut_x)])
# define the maximum identical length that can share the linecuts (we need to concatenate them)
width_length = min(width_z.size, width_y.size, width_x.size)
linecuts = np.empty(
(6, width_length
)) # rows 0:3 for the widths, rows 3:6 for the corresponding cuts
idx = 0
for key in linecuts_dict.keys(): # order is maintained in OrderedDict
linecuts[idx, :] = pu.crop_pad_1d(
linecuts_dict[key],
output_length=width_length) # implicit crop from the center
idx += 1
# create nb_fit sets of parameters, one per data set
fit_params = Parameters()
for idx in range(
3): # 3 linecuts in orthogonal directions to be fitted by a gaussian
fit_params.add('amp_%i' % (idx + 1), value=1, min=0.1, max=100)
fit_params.add('cen_%i' % (idx + 1),
value=linecuts[idx, :].mean(),
min=linecuts[idx, :].min(),
max=linecuts[idx, :].max())
fit_params.add('sig_%i' % (idx + 1), value=5, min=0.1, max=100)
# run the global fit to all the data sets
################################
# optionally load the q values #
################################
if load_qvalues:
file_path = filedialog.askopenfilename(initialdir=datadir, title="Select the q values",
filetypes=[("NPZ", "*.npz")])
q_values = np.load(file_path)
qx = q_values['qx']
qz = q_values['qz']
qy = q_values['qy']
print('Loaded: qx shape:', qx.shape, 'qz shape:', qz.shape, 'qy shape:', qy.shape)
assert (*qx.shape, *qz.shape, *qy.shape) == data.shape, 'q values and data shape are incompatible'
# crop the q values to the region of interest used in plots
qx = pu.crop_pad_1d(array=qx, output_length=plot_range[0] + plot_range[1], crop_center=zcom)
qz = pu.crop_pad_1d(array=qz, output_length=plot_range[2] + plot_range[3], crop_center=ycom)
qy = pu.crop_pad_1d(array=qy, output_length=plot_range[4] + plot_range[5], crop_center=xcom)
print('Cropped: qx shape:', qx.shape, 'qz shape:', qz.shape, 'qy shape:', qy.shape)
q_range = (qx.min(), qx.max(), qz.min(), qz.max(), qy.min(), qy.max())
else:
# crop the q values to the region of interest used in plots
q_range = (0, plot_range[0] + plot_range[1], 0, plot_range[2] + plot_range[3], 0, plot_range[4] + plot_range[5])
print('q range:', [f'{val:.4f}' for val in q_range])
#############################################################
# define the positions of the axes ticks and colorbar ticks #
#############################################################
# use 5 ticks by default if tick_spacing is None for the axis
initialdir=root_folder,
title="Select original q values",
filetypes=[("NPZ", "*.npz")])
q_values = np.load(file_path)
qx = q_values['qx'] # 1D array
qy = q_values['qy'] # 1D array
qz = q_values['qz'] # 1D array
# crop q to accomodate a shape change of the original array (e.g. cropping to fit FFT shape requirement)
if qvalues_binned:
assert len(qx) >= pynx_shape[
0], 'qx declared binned, its length should be >= pynx_shape[0]'
assert len(qy) >= pynx_shape[
2], 'qy declared binned, its length should be >= pynx_shape[2]'
assert len(qz) >= pynx_shape[
1], 'qz declared binned, its length should be >= pynx_shape[1]'
qx = pu.crop_pad_1d(qx, pynx_shape[0]) # qx along z
qy = pu.crop_pad_1d(qy, pynx_shape[2]) # qy along x
qz = pu.crop_pad_1d(qz, pynx_shape[1]) # qz along y
else:
assert len(qx) >= unbinned_shape[
0], 'qx declared unbinned, its length should be >= unbinned_shape[0]'
assert len(qy) >= unbinned_shape[
2], 'qy declared unbinned, its length should be >= unbinned_shape[2]'
assert len(qz) >= unbinned_shape[
1], 'qz declared unbinned, its length should be >= unbinned_shape[1]'
qx = pu.crop_pad_1d(qx, unbinned_shape[0]) # qx along z
qy = pu.crop_pad_1d(qy, unbinned_shape[2]) # qy along x
qz = pu.crop_pad_1d(qz, unbinned_shape[1]) # qz along y
print('Length(q_original)=', len(qx), len(qz), len(qy), '(qx, qz, qy)')
voxelsize_z = 2 * np.pi / (qx.max() - qx.min()) # qx along z