qz = q_values["qz"]
qy = q_values["qy"]
print("Loaded: qx shape:", qx.shape, "qz shape:", qz.shape, "qy shape:", qy.shape)
if (
not (
*qx.shape,
*qz.shape,
*qy.shape,
)
== data.shape
):
raise ValueError("q values and data shape are incompatible")
# crop the q values to the region of interest used in plots
qx = util.crop_pad_1d(
array=qx, output_length=plot_range[0] + plot_range[1], crop_center=zcom
)
qz = util.crop_pad_1d(
array=qz, output_length=plot_range[2] + plot_range[3], crop_center=ycom
)
qy = util.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],
# crop q to accomodate a shape change of the original array
# (e.g. cropping to fit FFT shape requirement)
if qvalues_binned:
if len(qx) < pynx_shape[0]:
raise ValueError(
"qx declared binned, its length should be >= " "pynx_shape[0]"
)
if len(qy) < pynx_shape[2]:
raise ValueError(
"qy declared binned, its length should be >= " "pynx_shape[2]"
)
if len(qz) < pynx_shape[1]:
raise ValueError(
"qz declared binned, its length should be >= " "pynx_shape[1]"
)
qx = util.crop_pad_1d(qx, pynx_shape[0]) # qx along z
qy = util.crop_pad_1d(qy, pynx_shape[2]) # qy along x
qz = util.crop_pad_1d(qz, pynx_shape[1]) # qz along y
else:
if len(qx) < unbinned_shape[0]:
raise ValueError(
"qx declared unbinned, its length should be >= " "unbinned_shape[0]"
)
if len(qy) < unbinned_shape[2]:
raise ValueError(
"qy declared unbinned, its length should be >= " "unbinned_shape[2]"
)
if len(qz) < unbinned_shape[1]:
raise ValueError(
"qz declared unbinned, its length should be >= " "unbinned_shape[1]"
)
fig.savefig(datadir + "S" + str(scan) + "_maskpynx" + comment + ".png")
plt.close(fig)
del mask
gc.collect()
if load_qvalues:
file_path = filedialog.askopenfilename(
initialdir=datadir,
title="Select the file containing 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
qx = util.crop_pad_1d(qx, roi_size[0], crop_center=crop_center[0]) # qx along z
qy = util.crop_pad_1d(qy, roi_size[2], crop_center=crop_center[2]) # qy along x
qz = util.crop_pad_1d(qz, roi_size[1], crop_center=crop_center[1]) # qz along y
numz, numy, numx = len(qx), len(qz), len(qy)
qx = qx[: numz - (numz % binning[0]) : binning[0]] # along z downstream
qz = qz[: numy - (numy % binning[1]) : binning[1]] # along y vertical
qy = qy[: numx - (numx % binning[2]) : binning[2]] # along x outboard
np.savez_compressed(
datadir + "S" + str(scan) + "_qvalues_" + comment + ".npz", qx=qx, qz=qz, qy=qy
)
print("End of script")
plt.ioff()
plt.show()
("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, :] = util.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)