def do_n_fits_register(nfits, xsh, ysh, imsize, usfac=10,
return_error=False, **kwargs):
"""
Test code
Parameters
----------
nfits : int
Number of times to perform fits
xsh : float
X shift from input to output image
ysh : float
Y shift from input to output image
imsize : int
Size of image (square)
"""
offsets = []
for ii in xrange(nfits):
im1,im2,temp = make_offset_images(xsh, ysh, imsize, **kwargs)
xoff,yoff,reg = register_images(
im1, im2,
return_registered=True, usfac=usfac)
chi2 = ((im1-reg)**2).sum() / im1.size
offsets.append([xoff,yoff,chi2])
return offsets
def compare_methods(im1,im2, ntests=100, noise=np.std,
usfac=201, **kwargs):
"""
Perform tests with noise added to determine the errors on the
'best-fit' offset
Parameters
----------
usfac : int
upsampling factor; governs accuracy of fit (1/usfac is best accuracy)
ntests : int
Number of tests to run
noise : func or real
Either a function to apply to im2 to determine the noise to use, or
a fixed noise level
"""
try:
noise = noise(im2)
except TypeError:
pass
try:
import progressbar
widgets = [progressbar.FormatLabel('Processed: %(value)d offsets in %(elapsed)s'), progressbar.Percentage()]
progress = progressbar.ProgressBar(widgets=widgets)
except ImportError:
def progress(x):
yield x
offsets = []
eoffsets = []
for test_number in progress(xrange(ntests)):
extra_noise = np.random.randn(*im2.shape) * noise
dxr, dyr, edxr, edyr = register_images(im1, im2+extra_noise, usfac=usfac,
return_error=True, **kwargs)
dxccs, dyccs, edxccs, edyccs = cross_correlation_shifts(im1,
im2+extra_noise,
errim2=im2*0+extra_noise.std(),
return_error=True, **kwargs)
# too slow!!!
dxccg, dyccg, edxccg, edyccg = 0,0,0,0
#dxccg, dyccg, edxccg, edyccg = cross_correlation_shifts(im1,
# im2+extra_noise, return_error=True, gaussfit=True,
# **kwargs)
dxchi, dychi, edxchi, edychi = chi2_shift(im1, im2+extra_noise,
err=im2*0+noise,
return_error=True, upsample_factor='auto', verbose=False, **kwargs)
offsets.append([dxr,dyr,dxccs,dyccs,dxccg,dyccg,dxchi,dychi])
eoffsets.append([edxr,edyr,edxccs,edyccs,edxccg,edyccg,edxchi,edychi])
return np.array(offsets),np.array(eoffsets)
def register_accuracy_test(im1,im2,usf_range=[1,100],**kwargs):
offsets = []
try:
import progressbar
widgets = [progressbar.FormatLabel('Processed: %(value)d offsets in %(elapsed)s)'), progressbar.Percentage()]
progress = progressbar.ProgressBar(widgets=widgets)
except ImportError:
def progress(x):
yield x
for usf in progress(xrange(*usf_range)):
dy,dx = register_images(im1,im2,usfac=usf,**kwargs)
offsets.append([dx,dy])
return np.array(offsets)
