def wise_psf_stamp(band):
# psf noise: ~roughly 0.1 count in outskirts of W1 and W2
if band >= 3:
raise ValueError('Need to stare at W3+ PSF more!')
if os.getenv('WISE_PSF_DIR', None) is None:
raise ValueError('WISE_PSF_DIR must be set.')
psfnoise = 0.1
stamp = fits.getdata(
os.path.join(os.getenv('WISE_PSF_DIR'),
'psf_model_w' + str(band) + '.fits'))
edges = numpy.concatenate(
[stamp[0, 1:-1], stamp[-1, 1:-1], stamp[1:-1, 0], stamp[1:-1, -1]])
medval = numpy.median(edges[edges != 0]) / 2
stamp[stamp == 0] = medval
stamp -= medval
from scipy import signal
stamp[stamp < 0] = 0.
# suppress spurious warnings in signal.wiener
olderr = numpy.seterr(invalid='ignore', divide='ignore')
stamp = signal.wiener(stamp, 11, psfnoise)
numpy.seterr(**olderr)
# taper linearly over outer 60 pixels?
stampszo2 = stamp.shape[0] // 2
xx, yy = numpy.mgrid[-stampszo2:stampszo2 + 1, -stampszo2:stampszo2 + 1]
edgedist = numpy.clip(stampszo2 - numpy.abs(xx), 0,
stampszo2 - numpy.abs(yy))
stamp = stamp * numpy.clip(edgedist / 60., stamp < 10, 1)
import psf
stamp = psf.center_psf(stamp, censize=19)
stamp = stamp / numpy.sum(stamp)
return stamp
def blend_psf(dstamp, mstamp, innerrad, outerrad):
stampsz = dstamp.shape[-1]
stampszo2 = stampsz // 2
xx = numpy.arange(stampsz, dtype='f4').reshape(1, -1) - stampszo2
yy = xx.copy().reshape(-1, 1)
rr = numpy.sqrt(xx**2 + yy**2)
weight = numpy.clip(1 - (rr - innerrad) / float(outerrad - innerrad), 0, 1)
dstamp = (dstamp + dstamp[::-1, :] + dstamp[:, ::-1] +
dstamp[::-1, ::-1]) / 4.
blended = dstamp * weight + mstamp * (1 - weight)
blended = psf.center_psf(blended)
return blended
def make_new_psfs(write=False, **kw):
path = os.path.join(os.environ['DECAM_DIR'], 'data', 'psfs', 'includeu')
res = {}
for f in filt:
tpsf = fits.getdata(os.path.join(path, 'psf_%s.fits.gz' % f))
tpsf = psf.center_psf(tpsf)
fitres = psf.fit_moffat(psf.central_stamp(tpsf, censize=19).copy())
fit = fitres[0]
deconvfac = 0.7
kernel = psf.moffat_psf(fit[1] * deconvfac,
yy=fit[4],
beta=fit[2],
xy=fit[3],
stampsz=69,
deriv=False)
psfde = restoration.richardson_lucy(tpsf, kernel, 20)
psfde = psf.center_psf(psfde)
res[f] = psfde
if write:
for f in filt:
fits.writeto(os.path.join(path, 'psf_%s_deconv.fits.gz' % f),
res[f], **kw)
return res
def wise_psf_stamp(band, nosmooth=False):
# psf noise: ~roughly 0.1 count in outskirts of W1 and W2
if band >= 3:
raise ValueError('Need to stare at W3+ PSF more!')
psfnoise = 0.1
stampfn = resource_filename('unwise_psf',
'data/psf_model_w'+str(band)+'.fits')
stamp = fits.getdata(stampfn)
edges = numpy.concatenate([stamp[0, 1:-1], stamp[-1, 1:-1],
stamp[1:-1, 0], stamp[1:-1, -1]])
medval = numpy.median(edges[edges != 0]) / 2
stamp[stamp == 0] = medval
stamp -= medval
from scipy import signal
stamp[stamp < 0] = 0.
# suppress spurious warnings in signal.wiener
olderr = numpy.seterr(invalid='ignore', divide='ignore')
# update to scipy.signal means that Wiener filter uses an FFT
# to perform the various convolutions, which causes bad errors
# here unless we cast to f8. It's not that hard to do something
# a bit better than scipy.signal.wiener---morally we really want to do
# something like smooth in log space on radial lines---but I don't
# want to go further down that rabbit hole today.
stamp = signal.wiener(stamp.astype('f8'), 11, psfnoise)
stamp = stamp.astype('f4')
numpy.seterr(**olderr)
# taper linearly over outer 60 pixels?
stampszo2 = stamp.shape[0] // 2
xx, yy = numpy.mgrid[-stampszo2:stampszo2+1, -stampszo2:stampszo2+1]
edgedist = numpy.clip(stampszo2-numpy.abs(xx), 0,
stampszo2-numpy.abs(yy))
stamp = stamp * numpy.clip(edgedist / 60., stamp < 10, 1)
import psf
stamp = psf.center_psf(stamp, censize=19)
stamp = stamp / numpy.sum(stamp)
return stamp
def find_psf(xcen, shiftx, ycen, shifty, psfstack, weightstack,
imstack, stampsz=59, nkeep=100):
"""Find PSF from stamps."""
# let's just go ahead and correlate the noise
xr = numpy.round(shiftx)
yr = numpy.round(shifty)
psfqf = (numpy.sum(psfstack*(weightstack > 0), axis=(1, 2)) /
numpy.sum(psfstack, axis=(1, 2)))
totalflux = numpy.sum(psfstack, axis=(1, 2))
timflux = numpy.sum(imstack, axis=(1, 2))
toneflux = numpy.sum(psfstack, axis=(1, 2))
tmedflux = numpy.median(psfstack, axis=(1, 2))
tfracflux = toneflux / numpy.clip(timflux, 100, numpy.inf)
tfracflux2 = ((toneflux-tmedflux*psfstack.shape[1]*psfstack.shape[2]) /
numpy.clip(timflux, 100, numpy.inf))
okpsf = ((numpy.abs(psfqf - 1) < 0.03) &
(tfracflux > 0.5) & (tfracflux2 > 0.2))
if numpy.sum(okpsf) > 0:
shiftxm = numpy.median(shiftx[okpsf])
shiftym = numpy.median(shifty[okpsf])
okpsf = (okpsf &
(numpy.abs(shiftx-shiftxm) < 1.) &
(numpy.abs(shifty-shiftym) < 1.))
if numpy.sum(okpsf) <= 5:
print('Fewer than 5 stars accepted in image, keeping original PSF')
return None
if numpy.sum(okpsf) > nkeep:
okpsf = okpsf & (totalflux > -numpy.sort(-totalflux[okpsf])[nkeep-1])
psfstack = psfstack[okpsf, :, :]
weightstack = weightstack[okpsf, :, :]
totalflux = totalflux[okpsf]
xcen = xcen[okpsf]
ycen = ycen[okpsf]
shiftx = shiftx[okpsf]
shifty = shifty[okpsf]
for i in range(psfstack.shape[0]):
psfstack[i, :, :] = shift(psfstack[i, :, :], [-shiftx[i], -shifty[i]])
if (numpy.abs(xr[i]) > 0) or (numpy.abs(yr[i]) > 0):
weightstack[i, :, :] = shift(weightstack[i, :, :],
[-xr[i], -yr[i]],
mode='constant', cval=0.)
# our best guess as to the PSFs & their weights
# select some reasonable sample of the PSFs
totalflux = numpy.sum(psfstack, axis=(1, 2))
psfstack /= totalflux.reshape(-1, 1, 1)
weightstack *= totalflux.reshape(-1, 1, 1)
tpsf = numpy.median(psfstack, axis=0)
tpsf = psfmod.center_psf(tpsf)
if tpsf.shape == stampsz:
return tpsf
xc = numpy.arange(tpsf.shape[0]).reshape(-1, 1)-tpsf.shape[0]//2
yc = xc.reshape(1, -1)
rc = numpy.sqrt(xc**2.+yc**2.)
stampszo2 = psfstack[0].shape[0] // 2
wt = numpy.clip((stampszo2+1-rc)/4., 0., 1.)
overlap = (wt != 1) & (wt != 0)
def objective(par):
mod = psfmod.moffat_psf(par[0], beta=2.5, xy=par[2], yy=par[3],
deriv=False, stampsz=tpsf.shape[0])
mod /= numpy.sum(mod)
return ((tpsf-mod)[overlap]).reshape(-1)
from scipy.optimize import leastsq
par = leastsq(objective, [4., 3., 0., 1.])[0]
modpsf = psfmod.moffat_psf(par[0], beta=2.5, xy=par[2], yy=par[3],
deriv=False, stampsz=stampsz)
modpsf /= numpy.sum(psfmod.central_stamp(modpsf))
npsf = modpsf.copy()
npsfcen = psfmod.central_stamp(npsf, tpsf.shape[0])
npsfcen[:, :] = tpsf*wt+(1-wt)*npsfcen[:, :]
npsf /= numpy.sum(npsf)
return psfmod.SimplePSF(npsf, normalize=-1)
