def bowtie_polynom(modis_img,cs,folder):
print 'Determine overlap pattern... '
sw=10000/cs #stripwidth
overlaplist=[]#define list to store number of overlapped lines
#devide in parts with a width of 40 pixel
for i in sp.arange(0,modis_img.shape[1]-40,40):
part=modis_img[:,i:i+39]
#search in every scanning strip
samples=[]
for j in sp.arange(sw-2,part.shape[0]-sw,sw):
target=part[j-1:j+1,:] #cut out a target, which overlapped counter-part shall be found
searchwindow=part[j+2:j+sw+2] #,: cut out the window, where the overlapped counter part might be located
#start the search
c=[] #calculate correlation coefficients of every given offset from 3 to 11
for offset in sp.arange(3,sw/2+1):
imgpart=searchwindow[offset-3:offset-1] #,: cut out image, which has to be compared with the target
c.append(sp.corrcoef(imgpart.flatten(),target.flatten())[0,1])#calculate correlatoin coefficient
c=sp.array(c)
overl=sp.ndimage.measurements.maximum_position(c)[0]+3 #find the overlap with the highes correlation coefficient
samples.append([overl,c.max()]) #attach overlap and correlation coefficient to the sample list
samples=sp.array(samples)
#print i, samples[:,1].mean()
if samples[:,1].mean() > 0.9: #chek the mean correlation coefficient:
#print('Bowtie Correlation high - removing effect')
overlaplist.append([i+20,samples[:,0].mean()]) #save result, if correlation coefficient is high
#print(overlaplist)
o=sp.array(overlaplist)
X=o[:,0]
overlap=o[:,1]
#Calculate a second order Polynom to describe the overlap
p=sp.polyfit(X,overlap,2)
#print 'done, Overlap polynom: '+str(p)
else:
#print('low Bowtie correlation')
p = [1., 1., 1.]
#overlaplist.append([i+20,1])
#os.system('rm -r '+folder)
#print('scene deleted')
return p
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