def residuals(p, im1, im2):
xsh, ysh = p['xsh'].value,p['ysh'].value
if use_fft:
shifted_img = shift.shiftnd(im2, (-ysh, -xsh))
else: # identical to skimage
shifted_img = scipy.ndimage.map_coordinates(im2, [yy+ysh,xx+xsh],
mode=mode)
if maxoff is not None:
xslice = slice(xcen-maxoff,xcen+maxoff,None)
yslice = slice(ycen-maxoff,ycen+maxoff,None)
# divide by sqrt(number of samples) = sqrt(maxoff**2)
residuals = np.abs(np.ravel((im1[yslice,xslice]-shifted_img[yslice,xslice])) / maxoff)
else:
if ignore_outside:
outsidex = min([(xlen-2*xsh)/2,xcen])
outsidey = min([(ylen-2*ysh)/2,xcen])
xslice = slice(xcen-outsidex,xcen+outsidex,None)
yslice = slice(ycen-outsidey,ycen+outsidey,None)
residuals = ( np.abs( np.ravel(
(im1[yslice,xslice]-shifted_img[yslice,xslice]))) /
(2*outsidex*2*outsidey)**0.5 )
else:
xslice = slice(None)
yslice = slice(None)
residuals = np.abs(np.ravel((im1-shifted_img))) / im1.size**0.5
if err is None:
return residuals
elif hasattr(err,'shape'):
if use_fft:
shifted_err = shift.shiftnd(err, (-ysh, -xsh))
else:
shifted_err = scipy.ndimage.map_coordinates(err, [yy+ysh,xx+xsh], mode=mode)
return residuals / shifted_err[yslice,xslice].flat
else:
return residuals / err
def test_shifts_1d(imsize,dx):
x = np.arange(imsize)
g = gaussian(x - (imsize-1)/2. )
sg = shift.shiftnd(g,(dx,))
assert np.all(np.abs(sg-gaussian(x-(imsize-1.)/2.-dx)) < 0.05)
def register(ref,
toshift,
method,
outdir=None,
center=None,
size=None,
overwrite=False):
'''Register and shift images'''
hdu_ref = fits.open(ref)
hdu_shift = fits.open(toshift)
if center is None:
# use center of image
center = [int(x / 2.) for x in hdu_ref[0].shape]
# resize
if size is not None:
refdat = Cutout2D(hdu_ref[0].data, position=center, size=size).data
shiftdat = Cutout2D(hdu_shift[0].data, position=center, size=size).data
else:
refdat = hdu_ref[0].data
shiftdat = hdu_shift[0].data
if method == 'point':
xoff, yoff = point_align(refdat, shiftdat)
elif method == 'extended':
xoff, yoff = extended_align(refdat, shiftdat)
else:
raise (NotImplementedError("method %s unknown" % method))
# shift
hdu_shift[0].data = shift.shiftnd(hdu_shift[0].data, (-yoff, -xoff))
hdu_shift[0].header.add_history(
'%s - %s' %
(os.path.basename(__file__), Time(Time.now(), format='fits')))
hdu_shift[0].header.add_history(
'%s - aligned to %s' %
(os.path.basename(__file__), os.path.basename(ref)))
hdu_shift[0].header.add_history('%s - shift_x: %.3f, shift_y: %.3f' %
(os.path.basename(__file__), -xoff, -yoff))
hdu_shift[0].header['ALIGNED'] = (True, 'Image aligned to reference')
hdu_shift[0].header['REFALGND'] = (os.path.basename(ref),
'Reference file for alignment')
hdu_shift[0].header['SXOFF'] = (-xoff, 'X shift')
hdu_shift[0].header['SYOFF'] = (-yoff, 'Y shift')
if outdir:
#write file and return filename
outfile = os.path.join(outdir, os.path.basename(toshift))
with warnings.catch_warnings():
warnings.simplefilter('ignore', VerifyWarning)
hdu_shift.writeto(outfile,
overwrite=overwrite,
output_verify='silentfix')
return outfile
else:
# if outdir is None, return hdu
return hdu_shift
def make_offset_extended(img, xsh, ysh, noise=1.0, mode='wrap',
noise_taper=False):
noise = np.random.randn(*img.shape)*noise
if noise_taper:
noise /= edge_weight(img.shape[0])
#newimage = scipy.ndimage.map_coordinates(img+noise, [yy,xx], mode=mode)
newimage = np.real(shift.shiftnd(img, (ysh, xsh))+noise)
return newimage
def chi2(im1, im2, dx, dy, err=None, upsample=1):
"""
Compute chi^2 between two images after shifting
"""
im1 = np.nan_to_num(im1)
im2 = np.nan_to_num(im2)
if err is None:
err = im2*0 + 1
if upsample > 1:
im1 = upsample_image(im1, upsample_factor=upsample, output_size=im1.shape, )
im2s = upsample_image(im2, upsample_factor=upsample, output_size=im2.shape, xshift=-dx*upsample, yshift=-dy*upsample)
err = upsample_image(err, upsample_factor=upsample, output_size=im2.shape, xshift=-dx*upsample, yshift=-dy*upsample)
#im2s = np.abs(shift(im2, -dx*upsample, -dy*upsample))
else:
im2s = np.abs(shift.shiftnd(im2, (-dy,-dx)))
err = np.abs(shift.shiftnd(err, (-dy,-dx)))
return ((im1-im2s)**2/err**2).sum()
def test_shifts_2d(imsize,dx,dy):
inds = np.indices([imsize]*2)
rr = ((inds[0] - (imsize-1)/2.)**2 + (inds[1] - (imsize-1)/2.)**2)**0.5
gg = gaussian(rr)
sg = shift.shiftnd(gg,(dy,dx))
rr2 = ((inds[0] - (imsize-1)/2. - dy)**2 + (inds[1] - (imsize-1)/2. - dx)**2)**0.5
assert np.all(np.abs(sg-gaussian(rr2) < 0.05))
def residuals(p, im1, im2):
xsh, ysh = p['xsh'].value, p['ysh'].value
if use_fft:
shifted_img = shift.shiftnd(im2, (-ysh, -xsh))
else: # identical to skimage
shifted_img = scipy.ndimage.map_coordinates(im2,
[yy + ysh, xx + xsh],
mode=mode)
if maxoff is not None:
xslice = slice(xcen - maxoff, xcen + maxoff, None)
yslice = slice(ycen - maxoff, ycen + maxoff, None)
# divide by sqrt(number of samples) = sqrt(maxoff**2)
residuals = np.abs(
np.ravel((im1[yslice, xslice] - shifted_img[yslice, xslice])) /
maxoff)
else:
if ignore_outside:
outsidex = min([(xlen - 2 * xsh) / 2, xcen])
outsidey = min([(ylen - 2 * ysh) / 2, xcen])
xslice = slice(xcen - outsidex, xcen + outsidex, None)
yslice = slice(ycen - outsidey, ycen + outsidey, None)
residuals = (np.abs(
np.ravel(
(im1[yslice, xslice] - shifted_img[yslice, xslice]))) /
(2 * outsidex * 2 * outsidey)**0.5)
else:
xslice = slice(None)
yslice = slice(None)
residuals = np.abs(np.ravel(
(im1 - shifted_img))) / im1.size**0.5
if err is None:
return residuals
elif hasattr(err, 'shape'):
if use_fft:
shifted_err = shift.shiftnd(err, (-ysh, -xsh))
else:
shifted_err = scipy.ndimage.map_coordinates(
err, [yy + ysh, xx + xsh], mode=mode)
return residuals / shifted_err[yslice, xslice].flat
else:
return residuals / err
def test_shifts_2d_asymm(imsize,dx,dy):
inds = np.indices([imsize,imsize*2])
rr = ((inds[0] - (imsize-1)/2.)**2 + (inds[1] - (imsize*2-1)/2.)**2)**0.5
gg = gaussian(rr)
sg = shift.shiftnd(gg,(dy,dx))
rr2 = ((inds[0] - (imsize-1)/2. - dy)**2 + (inds[1] - (imsize*2-1)/2. - dx)**2)**0.5
thr = gaussian(rr2)
assert np.all(np.abs(sg-thr < 0.05))
def register(ref,toshift,method,outdir=None,center=None,size=None,overwrite=False):
'''Register and shift images'''
hdu_ref = fits.open(ref)
hdu_shift = fits.open(toshift)
if center is None:
# use center of image
center = [int(x/2.) for x in hdu_ref[0].shape]
# resize
if size is not None:
refdat = Cutout2D(hdu_ref[0].data,position=center,size=size).data
shiftdat = Cutout2D(hdu_shift[0].data,position=center,size=size).data
else:
refdat = hdu_ref[0].data
shiftdat = hdu_shift[0].data
if method == 'point':
xoff,yoff = point_align(refdat,shiftdat)
elif method == 'extended':
xoff,yoff = extended_align(refdat,shiftdat)
else:
raise(NotImplementedError("method %s unknown"%method))
# shift
hdu_shift[0].data = shift.shiftnd(hdu_shift[0].data,(-yoff,-xoff))
hdu_shift[0].header.add_history('%s - %s' % (os.path.basename(__file__),Time(Time.now(),format='fits')))
hdu_shift[0].header.add_history('%s - aligned to %s' % (os.path.basename(__file__),os.path.basename(ref)))
hdu_shift[0].header.add_history('%s - shift_x: %.3f, shift_y: %.3f' % (os.path.basename(__file__),-xoff,-yoff))
hdu_shift[0].header['ALIGNED'] = (True,'Image aligned to reference')
hdu_shift[0].header['REFALGND'] = (os.path.basename(ref),'Reference file for alignment')
hdu_shift[0].header['SXOFF'] = (-xoff,'X shift')
hdu_shift[0].header['SYOFF'] = (-yoff,'Y shift')
if outdir:
#write file and return filename
outfile = os.path.join(outdir,os.path.basename(toshift))
with warnings.catch_warnings():
warnings.simplefilter('ignore',VerifyWarning)
hdu_shift.writeto(outfile,overwrite=overwrite,output_verify='silentfix')
return outfile
else:
# if outdir is None, return hdu
return hdu_shift
def chi2(location):
x = 0
template = glob.glob(location[:-4] + '/templates/*.fits')
images = glob.glob(location + '/*_a_.fits')
if len(template) == 1:
ref_data = fits.getdata(template[0])
ref_data = np.array(ref_data, dtype='float64')
print("\n-> Aligning images with chi2...")
for i in images:
data = fits.getdata(i)
data = np.array(data, dtype='float64')
dx, dy, edx, edy = chi2_shift(ref_data,
data,
upsample_factor='auto')
corrected_image = shift.shiftnd(data, (-dx, -dy))
hdu = fits.PrimaryHDU(corrected_image)
hdu.writeto(i[:-8] + '_A_.fits')
os.remove(i)
x += 1
print("-> %.1f%% aligned..." %
(float(x) / float(len(images)) * 100))
else:
print("-> Alignment failed: Template missing")
chi2shift = image_registration.chi2_shift(proj_7mmto3mm,
cutout_3mm.data,
err=errest,
upsample_factor=1000)
print(chi2shift)
print(chi2shift[:2] * cutout_3mm.wcs.wcs.cdelt * 3600)
print(chi2shift[:2] * cutout_3mm.wcs.wcs.cdelt)
print((((chi2shift[:2] * cutout_3mm.wcs.wcs.cdelt * 3600)**2).sum())**0.5)
"""
[-4.295500000000004, 3.9625000000000057, 0.0034999999999999892, 0.003500000000000003]
[0.0214775 0.0198125]
[5.96597222e-06 5.50347222e-06]
"""
ichi2shift = image_registration.chi2_shift_iterzoom(proj_7mmto3mm,
cutout_3mm.data,
err=errest,
upsample_factor=1000)
print(ichi2shift)
from image_registration.fft_tools import shift
xoff, yoff = chi2shift[:2]
corrected_7mm = shift.shiftnd(proj_7mmto3mm, (yoff, xoff))
import pylab as pl
pl.figure(1).clf()
pl.subplot(2, 2, 1).imshow(proj_7mmto3mm, origin='lower')
pl.subplot(2, 2, 2).imshow(cutout_3mm.data, origin='lower')
pl.subplot(2, 2, 3).imshow(proj_7mmto3mm * 3 - cutout_3mm.data, origin='lower')
pl.subplot(2, 2, 4).imshow(corrected_7mm * 3 - cutout_3mm.data, origin='lower')
"""
import glob
from astropy.io import fits
from image_registration import chi2_shift
from image_registration.fft_tools import shift
import numpy as np
image = '/home/andrew/sdi/targets/NGC6744/19:09:46.104_-63:51:27.00/rp/20.0/data/NORM_17:53:30.954_ref_A_.fits'
data = fits.getdata(image)
data = np.array(data, dtype='float64')
#image = '/home/andrew/sdi/targets/NGC6744/19:09:46.104_-63:51:27.00/rp/20.0/data/17:34:05.966_N_.fits'
#data = fits.getdata(image)
#Min = np.min(data)
#Max = np.max(data)
#new_min = 1
#new_max = -1
#data = (data-Min)*((new_max-new_min)/(Max-Min))
#data += new_min
#data -= np.mean(data)
#hdu = fits.PrimaryHDU(data)
#hdu.writeto('/home/andrew/sdi/targets/NGC6744/19:09:46.104_-63:51:27.00/rp/20.0/data/NORM3_17:34:05.966_N_.fits')
image2 = '/home/andrew/sdi/targets/NGC6744/19:09:46.104_-63:51:27.00/rp/20.0/data/NORM3_17:34:05.966_N_.fits'
data2 = fits.getdata(image2)
data2 = np.array(data2, dtype='float64')
dx, dy, edx, edy = chi2_shift(data, data2, upsample_factor='auto')
corrected_image = shift.shiftnd(data2, (-dy, -dx))
hdu2 = fits.PrimaryHDU(corrected_image)
hdu2.writeto(
'/home/andrew/sdi/targets/NGC6744/19:09:46.104_-63:51:27.00/rp/20.0/data/NORM5_align.fits'
)
