/
functions.py
136 lines (99 loc) · 3.53 KB
/
functions.py
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import numpy as np
import sys
import os
import xpcs
# parameters
distance = 4.0 # m
pix = 55e-6 # m
photon_energy = 10.0e3 #eV
h = 4.135667516e-15
c = 2.99792458e8
wavelength = h*c/photon_energy
center_x, center_y = 1069.5, 201.5 # tristan center
center = center_y, center_x
def calc_Iq(Q_map, I_map, mask, downsample, nbins, n_max, n_min, threshold):
# --- calculate I(q) based on a Q-map
# roi
smallx1 = np.int((center_x-n_max)//downsample)
smallx2 = np.int((center_x+n_max)//downsample)
smally1 = np.int((center_y-n_min)//downsample)
smally2 = np.int((center_y+n_max)//downsample)
# mask
if mask is None:
mask=np.ones(Q_map.shape) # unitary mask
Q_map = Q_map*mask
Q_map = Q_map[smally1:smally2,smallx1:smallx2]
I_map = I_map*mask
I_map = I_map[smally1:smally2,smallx1:smallx2]
# flatten 2D maps
Q_map_flat = Q_map.flatten()
I_map_flat = I_map.flatten()
# sort into bins with similar intensity
ind = np.argsort(Q_map_flat)
Q_map_flat = Q_map_flat[ind]
I_map_flat = I_map_flat[ind]
# Q-bins
edges = np.linspace(Q_map_flat[Q_map_flat>threshold].min(), Q_map_flat.max(), nbins+1)
#print(edges[0], edges[-1])
# group indices in different Q-bins
inds = np.digitize(Q_map_flat, edges)
Q_av = np.array([Q_map_flat[inds == i].mean() for i in range(0, nbins+1)])
I_av = np.array([I_map_flat[inds == i].mean() for i in range(0, nbins+1)])
return Q_av, I_av
def calc_q1d(Q_map, mask, downsample, nbins, n_max, n_min, threshold):
# --- calculate the 1D Q based on a 2D Q-map
# roi
smallx1 = np.int((center_x-n_max)/downsample)
smallx2 = np.int((center_x+n_max)/downsample)
smally1 = np.int((center_y-n_min)/downsample)
smally2 = np.int((center_y+n_max)/downsample)
# mask
if mask is None:
mask=np.ones(Q_map.shape) # unitary mask
Q_map = Q_map*mask
Q_map = Q_map[smally1:smally2,smallx1:smallx2]
# flatten 2D maps
Q_map_flat = Q_map.flatten()
# sort into bins with similar intensity
ind = np.argsort(Q_map_flat)
Q_map_flat = Q_map_flat[ind]
# Q-bins
edges = np.linspace(Q_map_flat[Q_map_flat>threshold].min(), Q_map_flat.max(), nbins+1)
print(edges[0], edges[-1])
# group indices in different Q-bins
inds = np.digitize(Q_map_flat, edges)
Q_av = np.array([Q_map_flat[inds == i].mean() for i in range(0, nbins+1)])
return Q_av
def make_rois_qmap(Q_map, mask, nbins, roi_min, roi_max, threshold, downsample):
# mask
if mask is None:
mask=np.ones(Q_map.shape) # unitary mask
Q_map = Q_map*mask
Q_map = Q_map
# flatten 2D maps
Q_map_flat = Q_map.flatten()
# sort into bins with similar intensity
ind = np.argsort(Q_map_flat)
Q_map_flat = Q_map_flat[ind]
# Q-bins
edges = np.linspace(Q_map_flat[Q_map_flat>threshold].min(), Q_map_flat.max(), nbins+1)
print(edges[0], edges[-1], edges.shape)
# group indices in different Q-bins
rois = np.digitize(Q_map, edges)
rois[(rois<roi_min) | (rois>roi_max)] = 0
inds = np.digitize(Q_map_flat, edges)
Q_av = np.array([Q_map_flat[inds == i].mean() for i in range(roi_min, roi_max+1)])
print(len(Q_av))
return rois, Q_av
def shift_image(X, dx, dy): # shift image and replace with zeros
X = np.roll(X, dy, axis=0)
X = np.roll(X, dx, axis=1)
if dy>0:
X[:dy, :] = 0
elif dy<0:
X[dy:, :] = 0
if dx>0:
X[:, :dx] = 0
elif dx<0:
X[:, dx:] = 0
return X