def galex_tractor_image(tile, band, galex_dir, radecbox, bandname):
from tractor import (NanoMaggies, Image, LinearPhotoCal,
ConstantFitsWcs, ConstantSky)
assert(band in ['n','f'])
#nicegbands = ['NUV', 'FUV']
#zps = dict(n=20.08, f=18.82)
#zp = zps[band]
imfn = os.path.join(galex_dir, tile.tilename.strip(),
'%s-%sd-intbgsub.fits.gz' % (tile.visitname.strip(), band))
gwcs = Tan(*[float(f) for f in
[tile.crval1, tile.crval2, tile.crpix1, tile.crpix2,
tile.cdelt1, 0., 0., tile.cdelt2, 3840., 3840.]])
(r0,r1,d0,d1) = radecbox
H,W = gwcs.shape
ok,xx,yy = gwcs.radec2pixelxy([r0,r0,r1,r1], [d0,d1,d1,d0])
#print('GALEX WCS pixel positions of RA,Dec box:', xx, yy)
if np.any(np.logical_not(ok)):
return None
x0 = np.clip(np.floor(xx-1).astype(int).min(), 0, W-1)
x1 = np.clip(np.ceil (xx-1).astype(int).max(), 0, W)
if x1-x0 <= 1:
return None
y0 = np.clip(np.floor(yy-1).astype(int).min(), 0, H-1)
y1 = np.clip(np.ceil (yy-1).astype(int).max(), 0, H)
if y1-y0 <= 1:
return None
debug('Reading GALEX subimage x0,y0', x0,y0, 'size', x1-x0, y1-y0)
gwcs = gwcs.get_subimage(x0, y0, x1 - x0, y1 - y0)
twcs = ConstantFitsWcs(gwcs)
roislice = (slice(y0, y1), slice(x0, x1))
fitsimg = fitsio.FITS(imfn)[0]
hdr = fitsimg.read_header()
img = fitsimg[roislice]
inverr = np.ones_like(img)
inverr[img == 0.] = 0.
zp = tile.get('%s_zpmag' % band)
photocal = LinearPhotoCal(NanoMaggies.zeropointToScale(zp), band=bandname)
tsky = ConstantSky(0.)
name = 'GALEX ' + hdr['OBJECT'] + ' ' + band
psfimg = galex_psf(band, galex_dir)
tpsf = PixelizedPSF(psfimg)
tim = Image(data=img, inverr=inverr, psf=tpsf, wcs=twcs,
sky=tsky, photocal=photocal, name=name)
tim.roi = [x0,x1,y0,y1]
return tim
def get_unwise_tractor_image(basedir,
tile,
band,
bandname=None,
masked=True,
**kwargs):
'''
masked: read "-m" images, or "-u"?
bandname: PhotoCal band name to use: default: "w%i" % band
'''
if bandname is None:
bandname = 'w%i' % band
mu = 'm' if masked else 'u'
# Allow multiple colon-separated unwise-coadd directories.
basedirs = basedir.split(':')
foundFiles = False
for basedir in basedirs:
thisdir = get_unwise_tile_dir(basedir, tile)
base = os.path.join(thisdir, 'unwise-%s-w%i-' % (tile, band))
imfn = base + 'img-%s.fits' % mu
ivfn = base + 'invvar-%s.fits.gz' % mu
# ppfn = base + 'std-%s.fits.gz' % mu
nifn = base + 'n-%s.fits.gz' % mu
nufn = base + 'n-u.fits.gz'
if not os.path.exists(imfn):
print('Does not exist:', imfn)
continue
print('Reading', imfn)
wcs = Tan(imfn)
twcs = ConstantFitsWcs(wcs)
F = fitsio.FITS(imfn)
img = F[0]
hdr = img.read_header()
H, W = img.get_info()['dims']
H, W = int(H), int(W)
roi = interpret_roi(twcs, (H, W), **kwargs)
if roi is None:
# No overlap with ROI
return None
# interpret_roi returns None or a tuple; drop the second element in the tuple.
roi, nil = roi
(x0, x1, y0, y1) = roi
wcs = wcs.get_subimage(x0, y0, x1 - x0, y1 - y0)
twcs = ConstantFitsWcs(wcs)
roislice = (slice(y0, y1), slice(x0, x1))
img = img[roislice]
if not os.path.exists(ivfn) and os.path.exists(
ivfn.replace('.fits.gz', '.fits')):
ivfn = ivfn.replace('.fits.gz', '.fits')
if not os.path.exists(nifn) and os.path.exists(
nifn.replace('.fits.gz', '.fits')):
nifn = nifn.replace('.fits.gz', '.fits')
if not os.path.exists(nufn) and os.path.exists(
nufn.replace('.fits.gz', '.fits')):
nufn = nufn.replace('.fits.gz', '.fits')
if not (os.path.exists(ivfn) and os.path.exists(nifn)
and os.path.exists(nufn)):
print('Files do not exist:', ivfn, nifn, nufn)
continue
foundFiles = True
break
if not foundFiles:
raise IOError('unWISE files not found in ' + str(basedirs) +
' for tile ' + tile)
print('Reading', ivfn)
invvar = fitsio.FITS(ivfn)[0][roislice]
if band == 4:
# due to upsampling, effective invvar is smaller (the pixels
# are correlated)
invvar *= 0.25
# print 'Reading', ppfn
#pp = fitsio.FITS(ppfn)[0][roislice]
print('Reading', nifn)
nims = fitsio.FITS(nifn)[0][roislice]
if nufn == nifn:
nuims = nims
else:
print('Reading', nufn)
nuims = fitsio.FITS(nufn)[0][roislice]
# print 'Median # ims:', np.median(nims)
good = (nims > 0)
invvar[np.logical_not(good)] = 0.
sig1 = 1. / np.sqrt(np.median(invvar[good]))
# Load the average PSF model (generated by wise_psf.py)
psffn = os.path.join(os.path.dirname(__file__), 'wise-psf-avg.fits')
print('Reading', psffn)
P = fits_table(psffn, hdu=band)
psf = GaussianMixturePSF(P.amp, P.mean, P.var)
sky = 0.
tsky = ConstantSky(sky)
# if opt.errfrac > 0:
# nz = (iv > 0)
# iv2 = np.zeros_like(invvar)
# iv2[nz] = 1./(1./invvar[nz] + (img[nz] * opt.errfrac)**2)
# print 'Increasing error estimate by', opt.errfrac, 'of image flux'
# invvar = iv2
tim = Image(data=img,
invvar=invvar,
psf=psf,
wcs=twcs,
sky=tsky,
photocal=LinearPhotoCal(1., band=bandname),
name='unWISE %s W%i' % (tile, band))
tim.sig1 = sig1
tim.roi = roi
tim.nims = nims
tim.nuims = nuims
tim.hdr = hdr
if 'MJDMIN' in hdr and 'MJDMAX' in hdr:
from tractor.tractortime import TAITime
tim.mjdmin = hdr['MJDMIN']
tim.mjdmax = hdr['MJDMAX']
tim.time = TAITime(None, mjd=(tim.mjdmin + tim.mjdmax) / 2.)
return tim