def iterate(img, pos):
DD = 8
residual = 5000
ntry = 0
prev_centroid = np.array([5e9, 5e9])
new_pos = pos[:]
print pos
while (residual > 0.2) and (ntry < 10):
pos = map(np.round, new_pos)
# SA = filter(dat[pos[0] - DD:pos[0] + DD , pos[1]-DD : pos[1] +DD])
SA = dat[pos[0] - DD : pos[0] + DD, pos[1] - DD : pos[1] + DD]
new_centroid = C.wm(SA)
new_pos[1] = pos[1] + (new_centroid[0] - DD)
new_pos[0] = pos[0] + (new_centroid[1] - DD)
residual = np.sqrt(np.sum((new_centroid - prev_centroid) ** 2))
prev_centroid = new_centroid
print new_pos, new_centroid - DD, ntry, residual
ntry += 1
return np.array(new_pos), new_centroid, DD, SA
obj = FF[0].header['OBJECT']
if "Hg" not in obj:
print "skipping"
continue
dat = Bias.remove(FF)
DD = 8
cms = []
for i in range(len(positions)):
p = positions[i]
SA = dat[p[0]-DD:p[0]+DD , p[1]-DD:p[1]+DD]
SA -= np.median(SA)
centroid = C.wm(SA)
cms.append([p[1] - centroid[1] + DD, p[0] - centroid[0] + DD])
if len(files) == 1:
'''This is for debugging purposes'''
print "fig", p
pl.figure()
pl.imshow(SA)
pl.plot(centroid[0], centroid[1], 'o')
if False:
pl.figure()
pl.plot(np.mean(SA, axis=0), 'b', drawstyle='steps-mid')
pl.plot(np.mean(SA, axis=1), 'r', drawstyle='steps-mid')
pl.axvline(centroid[0],color='blue')
pl.axvline(centroid[1],color='red')
