def calculate_beam_uniformity (imagearray,roidim=100,neighborhood_size=400):
''' Function receives image array and optionally ROI dimension (#pixels). The optional neighborhood_size parameter determines the scale of the distances between local maxima.
Four ROIs are defined and for each ROI a mean, std and snr is calculated from the image array. The result is returned as a dictionary.
'''
thr = imagearray.min()/4.0
output = imagearray
x,y = findmax.find_max(imagearray,neighborhood_size, threshold=thr)
width = roidim
height = roidim
xdim ,ydim = np.shape(imagearray)
middle = [elem for elem in zip(x,y) if xdim/2 - neighborhood_size < elem[0] < xdim/2 + neighborhood_size and ydim/2 - neighborhood_size < elem[1] < ydim/2 + neighborhood_size]
x0 = int(middle[0][0]/2)
x1 = int(xdim + middle[0][0])/2
y0 = int(middle[0][1]/2)
y1 = int(ydim + middle[0][1])/2
widthx = int(xdim/10)
widthy = int(xdim/10)
deltax = int(xdim/10)
deltay = int(ydim/10)
roi1 = np.zeros(np.shape(imagearray))
roi1[x0 - widthx : x0 +widthx, y0 - widthy:y0 + widthy] = 1
roi1 = ma.make_mask(roi1)
roi2 = np.zeros(np.shape(imagearray))
roi2[x1 - widthx : x1 + widthx, y0 - widthy:y0 + widthy]=1
roi2 = ma.make_mask(roi2)
roi3 = np.zeros(np.shape(imagearray))
roi3[x0 - widthx : x0 + widthx, y1 - widthy:y1 + widthy]=1
roi3 = ma.make_mask(roi3)
roi4 = np.zeros(np.shape(imagearray))
roi4[x1 - widthx : x1 + widthx, y1 - widthy:y1 + widthy]=1
roi4 = ma.make_mask(roi4)
results = {}
tmp1 = ma.array(imagearray,mask=1-roi1)
print ma.count(tmp1)
results['roi1']={'mean':np.mean(tmp1),'std':np.std(tmp1)}
tmp2 = ma.array(imagearray,mask=1-roi2)
results['roi2']={'mean':np.mean(tmp2),'std':np.std(tmp2)}
tmp3 = ma.array(imagearray,mask=1-roi3)
results['roi3']={'mean':np.mean(tmp3),'std':np.std(tmp3)}
tmp4 = ma.array(imagearray,mask=1-roi4)
results['roi4']={'mean':np.mean(tmp4),'std':np.std(tmp4)}
avgmean = (results['roi1']['mean']+results['roi2']['mean']+results['roi3']['mean']+results['roi4']['mean'])/4.0
avgstd = (results['roi1']['std']+results['roi2']['std']+results['roi3']['std']+results['roi4']['std'])/4.0
if avgstd > 0:
tmpsnr = avgmean/avgstd
else:
tmpsnr = -999
results['avg']={'mean':avgmean,'std':avgstd,'snr':tmpsnr}
results['middle'] = middle
results['image'] = imagearray
return results
def findmax(self, frame_id, neighborhood_size):
x, y = findmax.find_max(self.pixelmap[frame_id], neighborhood_size)
return x, y
