def decam_psf(filt, fwhm):
if filt not in 'ugrizY':
tpsf = psfmod.moffat_psf(fwhm, stampsz=511, deriv=False)
return psfmod.SimplePSF(tpsf)
fname = os.path.join(os.environ['DECAM_DIR'], 'data', 'psfs',
'psf_%s_deconv_mod.fits.gz' % filt[0])
normalizesz = 59
tpsf = fits.getdata(fname).T.copy()
tpsf /= numpy.sum(psfmod.central_stamp(tpsf, normalizesz))
# omitting central_stamp here places too much
# emphasis on the wings relative to the pipeline estimate.
tpsffwhm = psfmod.neff_fwhm(psfmod.central_stamp(tpsf))
from scipy.ndimage.filters import convolve
if tpsffwhm < fwhm:
convpsffwhm = numpy.sqrt(fwhm**2. - tpsffwhm**2.)
convpsf = psfmod.moffat_psf(convpsffwhm, stampsz=39, deriv=False)
tpsf = convolve(tpsf, convpsf, mode='constant', cval=0., origin=0)
else:
convpsffwhm = 0.
tpsf = psfmod.stamp2model(numpy.array([tpsf, tpsf, tpsf, tpsf]),
normalize=normalizesz)
nlinperpar = 3
pixsz = 9
extraparam = numpy.zeros(1,
dtype=[('convparam', 'f4', 3 * nlinperpar + 1),
('resparam', 'f4', (nlinperpar, pixsz,
pixsz))])
extraparam['convparam'][0, 0:4] = [convpsffwhm, 1., 0., 1.]
extraparam['resparam'][0, :, :, :] = 0.
tpsf.extraparam = extraparam
tpsf.fitfun = partial(psfmod.fit_linear_static_wing, filter=filt)
return tpsf
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)
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
default=numpy.inf,
help='pixel brightness limit for saturation')
args = parser.parse_args()
imagefn = args.imagefn[0]
ivarfn = args.ivarfn[0]
flagfn = args.flagfn[0]
if getattr(args, 'psffn', None):
# stamp = numpy.clip(fits.getdata(args.psffn), 1e-10, numpy.inf)
stamp = fits.getdata(args.psffn)
stamp[stamp < 0] = 0.
stamp = stamp / numpy.sum(stamp)
psf = psfmod.SimplePSF(stamp)
from functools import partial
psf.fitfun = partial(psfmod.wise_psf_fit, fname=args.psffn)
else:
print('using moffat')
psf = psfmod.SimplePSF(psfmod.moffat_psf(2.5, beta=2.5)[0])
res = process(imagefn,
ivarfn,
flagfn,
psf,
refit_psf=args.refit_psf,
verbose=args.verbose,
nx=4,
ny=4,
satlimit=args.satlimit)
outfn = args.outfn[0]
fits.writeto(outfn, res[0])
fits.append(outfn, res[1])
fits.append(outfn, res[2])
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)
