def beamProfile(ask, folder=None, shape=(512, 512)):
stacks, folder = loadStacks(ask, folder)
n = len(stacks)
profile = np.zeros(shape)
norm = 0
for filename in stacks:
print(filename)
stack = Stack(filename=filename)
meanFrame = stack.imageData.mean(0)
# Beam identification
hist, edg = np.histogram(meanFrame, bins=100)
thres = edg[argrelextrema(hist, np.less)[0][0] + 1]
beamMask = np.zeros(shape=meanFrame.shape, dtype=bool)
beamMask[meanFrame < thres] = True
beamFrame = np.ma.masked_array(meanFrame, beamMask)
# Normalization
meanInt = beamFrame.mean()
profile += meanFrame / meanInt
norm += meanInt
stack.close()
norm /= n
hist, edg = np.histogram(profile, bins=100)
thres = edg[argrelextrema(hist, np.less)[0][0] + 2]
beam_mask = np.zeros(shape=profile.shape, dtype=bool)
beam_mask[profile < thres] = True
beamProfile = np.ma.masked_array(profile, beam_mask)
return beamProfile, norm, folder
