def do_n_extended_fits(nfits, xsh, ysh, imsize, gaussfit=False,
maxoff=None, return_error=False, powerlaw=2.0, noise=1.0,
unsharp_mask=False, smoothfactor=5, zeropad=0,
shift_func=cross_correlation_shifts, sfkwargs={},
doplot=False,
**kwargs):
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
image = make_extended(imsize, powerlaw=powerlaw)
if zeropad > 0:
newsize = [s+zeropad for s in image.shape]
ylen,xlen = newsize
xcen = xlen/2-(1-xlen%2)
ycen = ylen/2-(1-ylen%2)
newim = np.zeros(newsize)
newim[ycen-image.shape[0]/2:ycen+image.shape[0]/2, xcen-image.shape[1]/2:xcen+image.shape[1]/2] = image
image = newim
if unsharp_mask:
from AG_fft_tools import smooth
offsets = []
for ii in progress(xrange(nfits)):
inim = image-smooth(image,smoothfactor)
offim = make_offset_extended(image, xsh, ysh, noise=noise, **kwargs)
offim -= smooth(offim,smoothfactor)
offsets.append( shift_func( inim, offim, return_error=return_error, **sfkwargs) )
else:
offsets = []
if doplot:
import pylab
pylab.figure(3); pylab.subplot(221); pylab.imshow(image-image.mean()); pylab.subplot(222); pylab.imshow(offim-offim.mean())
#subplot(223); pylab.imshow((abs(fft2(image-image.mean())*conj(fft2(offim-offim.mean())))))
pylab.subplot(223); pylab.imshow(abs(ifft2((fft2(image)*conj(fft2(offim))))))
pylab.subplot(224); pylab.imshow(abs(ifft2((fft2(image-image.mean())*conj(fft2(offim-offim.mean()))))))
draw()
for ii in progress(xrange(nfits)):
offim = make_offset_extended(image, xsh, ysh, noise=noise, **kwargs)
offsets.append( shift_func(
image,
offim,
return_error=return_error, **sfkwargs)
)
return offsets
def do_n_extended_fits(nfits, xsh, ysh, imsize, gaussfit=False,
maxoff=None, return_error=False, powerlaw=2.0, noise=1.0,
unsharp_mask=False, smoothfactor=5, zeropad=0,
shift_func=cross_correlation_shifts, sfkwargs={},
doplot=False,
**kwargs):
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
image = make_extended(imsize, powerlaw=powerlaw)
if zeropad > 0:
newsize = [s+zeropad for s in image.shape]
ylen,xlen = newsize
xcen = xlen/2-(1-xlen%2)
ycen = ylen/2-(1-ylen%2)
newim = np.zeros(newsize)
newim[ycen-image.shape[0]/2:ycen+image.shape[0]/2, xcen-image.shape[1]/2:xcen+image.shape[1]/2] = image
image = newim
if unsharp_mask:
from AG_fft_tools import smooth
offsets = []
for ii in progress(xrange(nfits)):
inim = image-smooth(image,smoothfactor)
offim = make_offset_extended(image, xsh, ysh, noise=noise, **kwargs)
offim -= smooth(offim,smoothfactor)
offsets.append( shift_func( inim, offim, return_error=return_error, **sfkwargs) )
else:
offsets = []
if doplot:
import pylab
pylab.figure(3); pylab.subplot(221); pylab.imshow(image-image.mean()); pylab.subplot(222); pylab.imshow(offim-offim.mean())
#subplot(223); pylab.imshow((abs(fft2(image-image.mean())*conj(fft2(offim-offim.mean())))))
pylab.subplot(223); pylab.imshow(abs(ifft2((fft2(image)*conj(fft2(offim))))))
pylab.subplot(224); pylab.imshow(abs(ifft2((fft2(image-image.mean())*conj(fft2(offim-offim.mean()))))))
draw()
for ii in progress(xrange(nfits)):
offim = make_offset_extended(image, xsh, ysh, noise=noise, **kwargs)
offsets.append( shift_func(
image,
offim,
return_error=return_error, **sfkwargs)
)
return offsets
def smooth_cube(cube,cubedim=0,parallel=True,numcores=None,**kwargs):
"""
parallel-map the smooth function
parallel - defaults True. Set to false if you want serial (for debug
purposes?)
numcores - pass to parallel_map (None = use all available)
"""
from AG_fft_tools import smooth
from contributed import parallel_map
if cubedim != 0:
cube = cube.swapaxes(0,cubedim)
cubelist = [cube[ii,:,:] for ii in xrange(cube.shape[0])]
Psmooth = lambda C: smooth(C,**kwargs)
if parallel:
smoothcube = array(parallel_map(Psmooth,cubelist,numcores=numcores))
else:
smoothcube = array(map(Psmooth,cubelist))
if cubedim != 0:
smoothcube = smoothcube.swapaxes(0,cubedim)
return smoothcube
def plane_smooth(cube,cubedim=0,parallel=True,numcores=None,**kwargs):
"""
parallel-map the smooth function
Parameters
----------
parallel: bool
defaults True. Set to false if you want serial (for debug purposes?)
numcores: int
pass to parallel_map (None = use all available)
"""
if not smoothOK:
return
if cubedim != 0:
cube = cube.swapaxes(0,cubedim)
cubelist = [cube[ii,:,:] for ii in xrange(cube.shape[0])]
Psmooth = lambda C: smooth(C,**kwargs)
if parallel:
smoothcube = array(parallel_map(Psmooth,cubelist,numcores=numcores))
else:
smoothcube = array(map(Psmooth,cubelist))
if cubedim != 0:
smoothcube = smoothcube.swapaxes(0,cubedim)
return smoothcube
def edge_weight(imsize, smoothsize=5, power=2):
img = np.ones([imsize,imsize])
smimg = smooth(img, smoothsize, ignore_edge_zeros=False)
return smimg**power
def edge_weight(imsize, smoothsize=5, power=2):
img = np.ones([imsize,imsize])
smimg = smooth(img, smoothsize, ignore_edge_zeros=False)
return smimg**power