def _determine_center_and_radius(data , manual=False, size=25):
sh = np.concatenate([data.scan_dimensions,data.frame_dimensions])
c = np.zeros((2,))
c[:] = (sh[-1] // 2, sh[-2] // 2)
c = cp.array(c)
radius = cp.ones((1,)) * sh[-1] // 2
inds = cp.array(data.indices[:size, :size].astype(cp.uint32))
cts = cp.array(data.counts[:size, :size].astype(cp.uint32))
dc_subset = sparse_to_dense_datacube_crop(inds,cts, (size,size), data.frame_dimensions, c, radius, bin=2)
dcs = cp.sum(dc_subset, (0, 1))
m1 = dcs.get()
m = (gaussian(m1.astype(cp.float32),2) > m1.max() * 3e-1).astype(cp.float)
r, y0, x0 = get_probe_size(m)
return 2 * np.array([y0,x0]), r*2
def define_bf_disk(dp, proc_dict):
#define bf disk
#get threshold values
if 'BF_thresh_low' in proc_dict:
bf_thresh_low = proc_dict['BF_thresh_low']
else:
bf_thresh_low = 0.01
if 'BF_thresh_high' in proc_dict:
bf_thresh_high = proc_dict['BF_thresh_high']
else:
bf_thresh_high = 0.05
#print(bf_thresh)
#bf= fs.get_bf_disk(dp, sub = 20, threshold = bf_thresh, plot_me = True)
#build PACBED from every 10th diff pattern
PACBED = np.average(dp.data[::10, ::10, :, :], axis=(0, 1))
r, x0, y0 = get_probe_size(PACBED,
thresh_lower=bf_thresh_low,
thresh_upper=bf_thresh_high)
bf = [r, x0, y0]
bf_exist = 1
return bf, bf_exist
power = 0.1 PACBED = np.average(dc.data4D[::10, ::10, :, :],axis=(0,1)) # Show fig,ax = plt.subplots(figsize=(8,8,)) ax.matshow(PACBED**power) plt.show() #%% thresh_lower = 0.01 thresh_upper =0.05#PACBED.mean() / PACBED.max() # 0.01 N = 100 r,x0,y0 = get_probe_size(PACBED, thresh_lower=thresh_lower, thresh_upper=thresh_upper, N=N) # Show fig,ax = plt.subplots(figsize=(8,8)) ax.matshow(PACBED**power) ax.scatter(y0,x0,color='r',s=10) circle = Circle((y0,x0),r,fill=False,edgecolor='r',linewidth=1) ax.add_patch(circle) plt.show() # Get mask