def finish(self, survey, brickname, version_header,
apradec=None, apertures=None):
# apradec = (ra,dec): aperture photometry locations
# apertures: RADII in PIXELS
if apradec is not None:
assert(apertures is not None)
(ra,dec) = apradec
ok,xx,yy = self.wcs.radec2pixelxy(ra, dec)
assert(np.all(ok))
del ok
apxy = np.vstack((xx - 1., yy - 1.)).T
ap_iphots = [np.zeros((len(ra), len(apertures)), np.float32)
for band in self.bands]
ap_dphots = [np.zeros((len(ra), len(apertures)), np.float32)
for band in self.bands]
ap_rphots = [np.zeros((len(ra), len(apertures)), np.float32)
for band in self.bands]
coimgs = []
comods = []
for iband,band in enumerate(self.bands):
coimg = self.co_images[band]
comod = self.co_models[band]
coiv = self.co_invvars[band]
con = self.co_nobs[band]
with np.errstate(divide='ignore', invalid='ignore'):
coimg /= coiv
comod /= coiv
coimg[coiv == 0] = 0.
comod[coiv == 0] = 0.
coimgs.append(coimg)
comods.append(comod)
hdr = copy_header_with_wcs(version_header, self.wcs)
self.add_to_header(hdr, band)
self.write_coadds(survey, brickname, hdr, band, coimg, comod, coiv, con)
if apradec is not None:
import photutils
mask = (coiv == 0)
with np.errstate(divide='ignore'):
imsigma = 1.0/np.sqrt(coiv)
imsigma[mask] = 0.
for irad,rad in enumerate(apertures):
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(coimg, aper, error=imsigma,
mask=mask)
ap_iphots[iband][:,irad] = p.field('aperture_sum')
ap_dphots[iband][:,irad] = p.field('aperture_sum_err')
p = photutils.aperture_photometry(coimg - comod, aper, mask=mask)
ap_rphots[iband][:,irad] = p.field('aperture_sum')
self.write_color_image(survey, brickname, coimgs, comods)
if apradec is not None:
return ap_iphots, ap_dphots, ap_rphots
def mask_outlier_pixels(survey,
tims,
bands,
targetwcs,
brickname,
version_header,
mp=None,
plots=False,
ps=None,
make_badcoadds=True,
refstars=None):
from legacypipe.bits import DQ_BITS
from scipy.ndimage.morphology import binary_dilation
H, W = targetwcs.shape
if make_badcoadds:
badcoadds_pos = []
badcoadds_neg = []
else:
badcoadds_pos = None
badcoadds_neg = None
star_veto = np.zeros(targetwcs.shape, np.bool)
if refstars:
gaia = refstars[refstars.isgaia]
# Not moving Gaia stars to epoch of individual images...
_, bx, by = targetwcs.radec2pixelxy(gaia.ra, gaia.dec)
bx -= 1.
by -= 1.
# Radius to mask around Gaia stars, in arcsec
radius = 1.0
pixrad = radius / targetwcs.pixel_scale()
for x, y in zip(bx, by):
xlo = int(np.clip(np.floor(x - pixrad), 0, W - 1))
xhi = int(np.clip(np.ceil(x + pixrad), 0, W - 1))
ylo = int(np.clip(np.floor(y - pixrad), 0, H - 1))
yhi = int(np.clip(np.ceil(y + pixrad), 0, H - 1))
if xlo == xhi or ylo == yhi:
continue
r2 = (((np.arange(ylo, yhi + 1) - y)**2)[:, np.newaxis] +
((np.arange(xlo, xhi + 1) - x)**2)[np.newaxis, :])
star_veto[ylo:yhi + 1, xlo:xhi + 1] |= (r2 < pixrad)
# if plots:
# import pylab as plt
# plt.clf()
# plt.imshow(star_veto, interpolation='nearest', origin='lower',
# vmin=0, vmax=1, cmap='hot')
# ax = plt.axis()
# plt.plot(bx, by, 'r.')
# plt.axis(ax)
# plt.title('Star vetos')
# ps.savefig()
with survey.write_output('outliers_mask', brick=brickname) as out:
# empty Primary HDU
out.fits.write(None, header=version_header)
for band in bands:
btims = [tim for tim in tims if tim.band == band]
if len(btims) == 0:
continue
debug(len(btims), 'images for band', band)
H, W = targetwcs.shape
# Build blurred reference image
sigs = np.array([tim.psf_sigma for tim in btims])
debug('PSF sigmas:', sigs)
targetsig = max(sigs) + 0.5
addsigs = np.sqrt(targetsig**2 - sigs**2)
debug('Target sigma:', targetsig)
debug('Blur sigmas:', addsigs)
coimg = np.zeros((H, W), np.float32)
cow = np.zeros((H, W), np.float32)
masks = np.zeros((H, W), np.int16)
R = mp.map(blur_resample_one,
[(tim, sig, targetwcs)
for tim, sig in zip(btims, addsigs)])
for tim, r in zip(btims, R):
if r is None:
continue
Yo, Xo, iacc, wacc, macc = r
coimg[Yo, Xo] += iacc
cow[Yo, Xo] += wacc
masks[Yo, Xo] |= macc
del Yo, Xo, iacc, wacc, macc
del r, R
#
veto = np.logical_or(
star_veto,
np.logical_or(
binary_dilation(masks & DQ_BITS['bleed'], iterations=3),
binary_dilation(masks & DQ_BITS['satur'], iterations=10)))
del masks
# if plots:
# plt.clf()
# plt.imshow(veto, interpolation='nearest', origin='lower', cmap='gray')
# plt.title('SATUR, BLEED veto (%s band)' % band)
# ps.savefig()
R = mp.map(compare_one, [(tim, sig, targetwcs, coimg, cow, veto,
make_badcoadds, plots, ps)
for tim, sig in zip(btims, addsigs)])
del coimg, cow, veto
masks = []
badcoadd_pos = None
badcoadd_neg = None
if make_badcoadds:
badcoadd_pos = np.zeros((H, W), np.float32)
badcon_pos = np.zeros((H, W), np.int16)
badcoadd_neg = np.zeros((H, W), np.float32)
badcon_neg = np.zeros((H, W), np.int16)
for r, tim in zip(R, btims):
if r is None:
# none masked
mask = np.zeros(tim.shape, np.uint8)
else:
mask, badco = r
if make_badcoadds:
badhot, badcold = badco
yo, xo, bimg = badhot
badcoadd_pos[yo, xo] += bimg
badcon_pos[yo, xo] += 1
yo, xo, bimg = badcold
badcoadd_neg[yo, xo] += bimg
badcon_neg[yo, xo] += 1
del yo, xo, bimg, badhot, badcold
del badco
# Apply the mask!
maskbits = get_bits_to_mask()
tim.inverr[(mask & maskbits) > 0] = 0.
tim.dq[(mask & maskbits) > 0] |= DQ_BITS['outlier']
# Write output!
from legacypipe.utils import copy_header_with_wcs
hdr = copy_header_with_wcs(None, tim.subwcs)
hdr.add_record(
dict(name='IMTYPE',
value='outlier_mask',
comment='LegacySurvey image type'))
hdr.add_record(dict(name='CAMERA', value=tim.imobj.camera))
hdr.add_record(dict(name='EXPNUM', value=tim.imobj.expnum))
hdr.add_record(dict(name='CCDNAME', value=tim.imobj.ccdname))
hdr.add_record(dict(name='X0', value=tim.x0))
hdr.add_record(dict(name='Y0', value=tim.y0))
# HCOMPRESS;: 943k
# GZIP_1: 4.4M
# GZIP: 4.4M
# RICE: 2.8M
extname = '%s-%s-%s' % (tim.imobj.camera, tim.imobj.expnum,
tim.imobj.ccdname)
out.fits.write(mask,
header=hdr,
extname=extname,
compress='HCOMPRESS')
del r, R
if make_badcoadds:
badcoadd_pos /= np.maximum(badcon_pos, 1)
badcoadd_neg /= np.maximum(badcon_neg, 1)
badcoadds_pos.append(badcoadd_pos)
badcoadds_neg.append(badcoadd_neg)
return badcoadds_pos, badcoadds_neg
def write_coadd_images(band,
survey, brickname, version_header, tims, targetwcs,
co_sky,
coadd_headers=None,
cowimg=None, cow=None, cowmod=None, cochi2=None,
cowblobmod=None,
psfdetiv=None, galdetiv=None, congood=None,
psfsize=None, **kwargs):
hdr = copy_header_with_wcs(version_header, targetwcs)
# Grab headers from input images...
get_coadd_headers(hdr, tims, band, coadd_headers)
imgs = [
('image', 'image', cowimg),
('invvar', 'wtmap', cow ),
]
if congood is not None:
imgs.append(('nexp', 'expmap', congood))
if psfdetiv is not None:
imgs.append(('depth', 'psfdepth', psfdetiv))
if galdetiv is not None:
imgs.append(('galdepth', 'galdepth', galdetiv))
if psfsize is not None:
imgs.append(('psfsize', 'psfsize', psfsize))
if cowmod is not None:
imgs.extend([
('model', 'model', cowmod),
('chi2', 'chi2', cochi2),
])
if cowblobmod is not None:
imgs.append(('blobmodel', 'blobmodel', cowblobmod))
for name,prodtype,img in imgs:
if img is None:
debug('Image type', prodtype, 'is None -- skipping')
continue
# Make a copy, because each image has different values for
# these headers...
hdr2 = fitsio.FITSHDR()
for r in hdr.records():
hdr2.add_record(r)
hdr2.add_record(dict(name='IMTYPE', value=name,
comment='LegacySurveys image type'))
hdr2.add_record(dict(name='PRODTYPE', value=prodtype,
comment='NOAO image type'))
if name in ['image', 'model', 'blobmodel']:
hdr2.add_record(dict(name='MAGZERO', value=22.5,
comment='Magnitude zeropoint'))
hdr2.add_record(dict(name='BUNIT', value='nanomaggy',
comment='AB mag = 22.5 - 2.5*log10(nanomaggy)'))
if name == 'image' and co_sky is not None:
hdr2.add_record(dict(name='COSKY_%s' % band.upper(), value=co_sky.get(band, 'None'),
comment='Sky level estimated (+subtracted) from coadd'))
if name in ['invvar', 'depth', 'galdepth']:
hdr2.add_record(dict(name='BUNIT', value='1/nanomaggy^2',
comment='Ivar of ABmag=22.5-2.5*log10(nmgy)'))
if name in ['psfsize']:
hdr2.add_record(dict(name='BUNIT', value='arcsec',
comment='Effective PSF size'))
with survey.write_output(name, brick=brickname, band=band,
shape=img.shape) as out:
out.fits.write(img, header=hdr2)
