def unwise_coadds(onegal,
galaxy=None,
radius_mosaic=30,
radius_mask=None,
pixscale=2.75,
ref_pixscale=0.262,
output_dir=None,
unwise_dir=None,
verbose=False,
log=None,
centrals=True):
'''Generate custom unWISE cutouts.
radius_mosaic and radius_mask in arcsec
pixscale: WISE pixel scale in arcsec/pixel; make this smaller than 2.75
to oversample.
'''
import fitsio
import matplotlib.pyplot as plt
from astrometry.util.util import Tan
from astrometry.util.fits import fits_table
from astrometry.libkd.spherematch import match_radec
from astrometry.util.resample import resample_with_wcs, ResampleError
from wise.forcedphot import unwise_tiles_touching_wcs
from wise.unwise import get_unwise_tractor_image
from tractor import Tractor, Image, NanoMaggies
from legacypipe.survey import imsave_jpeg
from legacypipe.catalog import read_fits_catalog
if galaxy is None:
galaxy = 'galaxy'
if output_dir is None:
output_dir = '.'
if unwise_dir is None:
unwise_dir = os.environ.get('UNWISE_COADDS_DIR')
if radius_mask is None:
radius_mask = radius_mosaic
radius_search = 5.0 # [arcsec]
else:
radius_search = radius_mask
# Initialize the WCS object.
W = H = np.ceil(2 * radius_mosaic / pixscale).astype('int') # [pixels]
targetwcs = Tan(onegal['RA'], onegal['DEC'], (W + 1) / 2.0, (H + 1) / 2.0,
-pixscale / 3600.0, 0.0, 0.0, pixscale / 3600.0, float(W),
float(H))
# Read the custom Tractor catalog.
tractorfile = os.path.join(output_dir, '{}-tractor.fits'.format(galaxy))
if not os.path.isfile(tractorfile):
print('Missing Tractor catalog {}'.format(tractorfile),
flush=True,
file=log)
return 0
primhdr = fitsio.read_header(tractorfile)
cat = fits_table(tractorfile)
print('Read {} sources from {}'.format(len(cat), tractorfile),
flush=True,
file=log)
keep = np.ones(len(cat)).astype(bool)
if centrals:
# Find the large central galaxy and mask out (ignore) all the models
# which are within its elliptical mask.
# This algorithm will have to change for mosaics not centered on large
# galaxies, e.g., in galaxy groups.
m1, m2, d12 = match_radec(cat.ra,
cat.dec,
onegal['RA'],
onegal['DEC'],
radius_search / 3600.0,
nearest=False)
if len(m1) == 0:
print('No central galaxies found at the central coordinates!',
flush=True,
file=log)
else:
pixfactor = ref_pixscale / pixscale # shift the optical Tractor positions
for mm in m1:
morphtype = cat.type[mm].strip()
if morphtype == 'EXP' or morphtype == 'COMP':
e1, e2, r50 = cat.shapeexp_e1[mm], cat.shapeexp_e2[
mm], cat.shapeexp_r[mm] # [arcsec]
elif morphtype == 'DEV' or morphtype == 'COMP':
e1, e2, r50 = cat.shapedev_e1[mm], cat.shapedev_e2[
mm], cat.shapedev_r[mm] # [arcsec]
else:
r50 = None
if r50:
majoraxis = r50 * 5 / pixscale # [pixels]
ba, phi = SGA.misc.convert_tractor_e1e2(e1, e2)
these = SGA.misc.ellipse_mask(W / 2, W / 2, majoraxis,
ba * majoraxis,
np.radians(phi),
cat.bx * pixfactor,
cat.by * pixfactor)
if np.sum(these) > 0:
#keep[these] = False
pass
print('Hack!')
keep[mm] = False
#srcs = read_fits_catalog(cat)
#_srcs = np.array(srcs)[~keep].tolist()
#mod = SGA.misc.srcs2image(_srcs, ConstantFitsWcs(targetwcs), psf_sigma=3.0)
#import matplotlib.pyplot as plt
##plt.imshow(mod, origin='lower') ; plt.savefig('junk.png')
#plt.imshow(np.log10(mod), origin='lower') ; plt.savefig('junk.png')
#pdb.set_trace()
srcs = read_fits_catalog(cat)
for src in srcs:
src.freezeAllBut('brightness')
#srcs_nocentral = np.array(srcs)[keep].tolist()
cat_nocentral = cat[keep]
## Find and remove all the objects within XX arcsec of the target
## coordinates.
#m1, m2, d12 = match_radec(T.ra, T.dec, onegal['RA'], onegal['DEC'], 5/3600.0, nearest=False)
#if len(d12) == 0:
# print('No matching galaxies found -- probably not what you wanted.')
# #raise ValueError
# nocentral = np.ones(len(T)).astype(bool)
#else:
# nocentral = ~np.isin(T.objid, T[m1].objid)
#T_nocentral = T[nocentral]
# Find and read the overlapping unWISE tiles. Assume the targetwcs is
# axis-aligned and that the edge midpoints yield the RA, Dec limits (true
# for TAN). Note: the way the roiradec box is used, the min/max order
# doesn't matter.
r, d = targetwcs.pixelxy2radec(np.array([1, W, W / 2, W / 2]),
np.array([H / 2, H / 2, 1, H]))
roiradec = [r[0], r[1], d[2], d[3]]
tiles = unwise_tiles_touching_wcs(targetwcs)
wbands = [1, 2, 3, 4]
wanyband = 'w'
vega_to_ab = dict(w1=2.699, w2=3.339, w3=5.174, w4=6.620)
# Convert the AB WISE fluxes in the Tractor catalog to Vega nanomaggies so
# they're consistent with the coadds, below.
for band in wbands:
f = cat.get('flux_w{}'.format(band))
e = cat.get('flux_ivar_w{}'.format(band))
print('Setting negative fluxes equal to zero!')
f[f < 0] = 0
#f[f/e < 3] = 0
f *= 10**(0.4 * vega_to_ab['w{}'.format(band)])
coimgs = [np.zeros((H, W), np.float32) for b in wbands]
comods = [np.zeros((H, W), np.float32) for b in wbands]
comods_nocentral = [np.zeros((H, W), np.float32) for b in wbands]
con = [np.zeros((H, W), np.uint8) for b in wbands]
for iband, band in enumerate(wbands):
for ii, src in enumerate(srcs):
src.setBrightness(
NanoMaggies(
**{wanyband: cat.get('flux_w{}'.format(band))[ii]}))
srcs_nocentral = np.array(srcs)[keep].tolist()
#srcs_nocentral = np.array(srcs)[nocentral].tolist()
# The tiles have some overlap, so for each source, keep the fit in the
# tile whose center is closest to the source.
for tile in tiles:
#print('Reading tile {}'.format(tile.coadd_id))
tim = get_unwise_tractor_image(unwise_dir,
tile.coadd_id,
band,
bandname=wanyband,
roiradecbox=roiradec)
if tim is None:
print('Actually, no overlap with tile {}'.format(
tile.coadd_id))
continue
print('Read image {} with shape {}'.format(tile.coadd_id,
tim.shape))
def _unwise_mod(tim, use_cat, use_srcs, margin=10):
# Select sources in play.
wisewcs = tim.wcs.wcs
timH, timW = tim.shape
ok, x, y = wisewcs.radec2pixelxy(use_cat.ra, use_cat.dec)
x = (x - 1.).astype(np.float32)
y = (y - 1.).astype(np.float32)
I = np.flatnonzero((x >= -margin) * (x < timW + margin) *
(y >= -margin) * (y < timH + margin))
#print('Found {} sources within the image + margin = {} pixels'.format(len(I), margin))
subcat = [use_srcs[i] for i in I]
tractor = Tractor([tim], subcat)
mod = tractor.getModelImage(0)
return mod
mod = _unwise_mod(tim, cat, srcs)
mod_nocentral = _unwise_mod(tim, cat_nocentral, srcs_nocentral)
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(targetwcs, tim.wcs.wcs)
except ResampleError:
continue
if len(Yo) == 0:
continue
# The models are already in AB nanomaggies, but the tiles / tims are
# in Vega nanomaggies, so convert them here.
coimgs[iband][Yo, Xo] += tim.getImage()[Yi, Xi]
comods[iband][Yo, Xo] += mod[Yi, Xi]
comods_nocentral[iband][Yo, Xo] += mod_nocentral[Yi, Xi]
con[iband][Yo, Xo] += 1
## Convert back to nanomaggies.
#vega2ab = vega_to_ab['w{}'.format(band)]
#coimgs[iband] *= 10**(-0.4 * vega2ab)
#comods[iband] *= 10**(-0.4 * vega2ab)
#comods_nocentral[iband] *= 10**(-0.4 * vega2ab)
for img, mod, mod_nocentral, n in zip(coimgs, comods, comods_nocentral,
con):
img /= np.maximum(n, 1)
mod /= np.maximum(n, 1)
mod_nocentral /= np.maximum(n, 1)
coresids = [img - mod for img, mod in list(zip(coimgs, comods))]
# Subtract the model image which excludes the central (comod_nocentral)
# from the data (coimg) to isolate the light of the central
# (coimg_central).
coimgs_central = [
img - mod for img, mod in list(zip(coimgs, comods_nocentral))
]
# Write out the final images with and without the central and converted into
# AB nanomaggies.
for coadd, imtype in zip((coimgs, comods, comods_nocentral),
('image', 'model', 'model-nocentral')):
for img, band in zip(coadd, wbands):
vega2ab = vega_to_ab['w{}'.format(band)]
fitsfile = os.path.join(
output_dir, '{}-{}-W{}.fits'.format(galaxy, imtype, band))
if verbose:
print('Writing {}'.format(fitsfile))
fitsio.write(fitsfile, img * 10**(-0.4 * vega2ab), clobber=True)
# Generate color WISE images.
kwa = dict(mn=-1, mx=100, arcsinh=0.5)
#kwa = dict(mn=-0.05, mx=1., arcsinh=0.5)
#kwa = dict(mn=-0.1, mx=2., arcsinh=None)
for imgs, imtype in zip(
(coimgs, comods, coresids, comods_nocentral, coimgs_central),
('image', 'model', 'resid', 'model-nocentral', 'image-central')):
rgb = _unwise_to_rgb(imgs[:2], **kwa) # W1, W2
jpgfile = os.path.join(output_dir,
'{}-{}-W1W2.jpg'.format(galaxy, imtype))
if verbose:
print('Writing {}'.format(jpgfile))
imsave_jpeg(jpgfile, rgb, origin='lower')
return 1
def wise_cutouts(ra, dec, radius, ps, pixscale=2.75, tractor_base=".", unwise_dir="unwise-coadds"):
"""
radius in arcsec.
pixscale: WISE pixel scale in arcsec/pixel;
make this smaller than 2.75 to oversample.
"""
npix = int(np.ceil(radius / pixscale))
print("Image size:", npix)
W = H = npix
pix = pixscale / 3600.0
wcs = Tan(ra, dec, (W + 1) / 2.0, (H + 1) / 2.0, -pix, 0.0, 0.0, pix, float(W), float(H))
# Find DECaLS bricks overlapping
decals = Decals()
B = bricks_touching_wcs(wcs, decals=decals)
print("Found", len(B), "bricks overlapping")
TT = []
for b in B.brickname:
fn = os.path.join(tractor_base, "tractor", b[:3], "tractor-%s.fits" % b)
T = fits_table(fn)
print("Read", len(T), "from", b)
primhdr = fitsio.read_header(fn)
TT.append(T)
T = merge_tables(TT)
print("Total of", len(T), "sources")
T.cut(T.brick_primary)
print(len(T), "primary")
margin = 20
ok, xx, yy = wcs.radec2pixelxy(T.ra, T.dec)
I = np.flatnonzero((xx > -margin) * (yy > -margin) * (xx < W + margin) * (yy < H + margin))
T.cut(I)
print(len(T), "within ROI")
# Pull out DECaLS coadds (image, model, resid).
dwcs = wcs.scale(2.0 * pixscale / 0.262)
dh, dw = dwcs.shape
print("DECaLS resampled shape:", dh, dw)
tags = ["image", "model", "resid"]
coimgs = [np.zeros((dh, dw, 3), np.uint8) for t in tags]
for b in B.brickname:
fn = os.path.join(tractor_base, "coadd", b[:3], b, "decals-%s-image-r.fits" % b)
bwcs = Tan(fn)
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(dwcs, bwcs)
except ResampleError:
continue
if len(Yo) == 0:
continue
print("Resampling", len(Yo), "pixels from", b)
xl, xh, yl, yh = Xi.min(), Xi.max(), Yi.min(), Yi.max()
print(
"python legacypipe/runbrick.py -b %s --zoom %i %i %i %i --outdir cluster --pixpsf --splinesky --pipe --no-early-coadds"
% (b, xl - 5, xh + 5, yl - 5, yh + 5)
+ " -P 'pickles/cluster-%(brick)s-%%(stage)s.pickle'"
)
for i, tag in enumerate(tags):
fn = os.path.join(tractor_base, "coadd", b[:3], b, "decals-%s-%s.jpg" % (b, tag))
img = plt.imread(fn)
img = np.flipud(img)
coimgs[i][Yo, Xo, :] = img[Yi, Xi, :]
tt = dict(image="Image", model="Model", resid="Resid")
for img, tag in zip(coimgs, tags):
plt.clf()
dimshow(img, ticks=False)
plt.title("DECaLS grz %s" % tt[tag])
ps.savefig()
# Find unWISE tiles overlapping
tiles = unwise_tiles_touching_wcs(wcs)
print("Cut to", len(tiles), "unWISE tiles")
# Here we assume the targetwcs is axis-aligned and that the
# edge midpoints yield the RA,Dec limits (true for TAN).
r, d = wcs.pixelxy2radec(np.array([1, W, W / 2, W / 2]), np.array([H / 2, H / 2, 1, H]))
# the way the roiradec box is used, the min/max order doesn't matter
roiradec = [r[0], r[1], d[2], d[3]]
ra, dec = T.ra, T.dec
T.shapeexp = np.vstack((T.shapeexp_r, T.shapeexp_e1, T.shapeexp_e2)).T
T.shapedev = np.vstack((T.shapedev_r, T.shapedev_e1, T.shapedev_e2)).T
srcs = read_fits_catalog(T, ellipseClass=EllipseE)
wbands = [1, 2]
wanyband = "w"
for band in wbands:
T.wise_flux[:, band - 1] *= 10.0 ** (primhdr["WISEAB%i" % band] / 2.5)
coimgs = [np.zeros((H, W), np.float32) for b in wbands]
comods = [np.zeros((H, W), np.float32) for b in wbands]
con = [np.zeros((H, W), np.uint8) for b in wbands]
for iband, band in enumerate(wbands):
print("Photometering WISE band", band)
wband = "w%i" % band
for i, src in enumerate(srcs):
# print('Source', src, 'brightness', src.getBrightness(), 'params', src.getBrightness().getParams())
# src.getBrightness().setParams([T.wise_flux[i, band-1]])
src.setBrightness(NanoMaggies(**{wanyband: T.wise_flux[i, band - 1]}))
# print('Set source brightness:', src.getBrightness())
# The tiles have some overlap, so for each source, keep the
# fit in the tile whose center is closest to the source.
for tile in tiles:
print("Reading tile", tile.coadd_id)
tim = get_unwise_tractor_image(unwise_dir, tile.coadd_id, band, bandname=wanyband, roiradecbox=roiradec)
if tim is None:
print("Actually, no overlap with tile", tile.coadd_id)
continue
print("Read image with shape", tim.shape)
# Select sources in play.
wisewcs = tim.wcs.wcs
H, W = tim.shape
ok, x, y = wisewcs.radec2pixelxy(ra, dec)
x = (x - 1.0).astype(np.float32)
y = (y - 1.0).astype(np.float32)
margin = 10.0
I = np.flatnonzero((x >= -margin) * (x < W + margin) * (y >= -margin) * (y < H + margin))
print(len(I), "within the image + margin")
subcat = [srcs[i] for i in I]
tractor = Tractor([tim], subcat)
mod = tractor.getModelImage(0)
# plt.clf()
# dimshow(tim.getImage(), ticks=False)
# plt.title('WISE %s %s' % (tile.coadd_id, wband))
# ps.savefig()
# plt.clf()
# dimshow(mod, ticks=False)
# plt.title('WISE %s %s' % (tile.coadd_id, wband))
# ps.savefig()
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(wcs, tim.wcs.wcs)
except ResampleError:
continue
if len(Yo) == 0:
continue
print("Resampling", len(Yo), "pixels from WISE", tile.coadd_id, band)
coimgs[iband][Yo, Xo] += tim.getImage()[Yi, Xi]
comods[iband][Yo, Xo] += mod[Yi, Xi]
con[iband][Yo, Xo] += 1
for img, mod, n in zip(coimgs, comods, con):
img /= np.maximum(n, 1)
mod /= np.maximum(n, 1)
for band, img, mod in zip(wbands, coimgs, comods):
lo, hi = np.percentile(img, [25, 99])
plt.clf()
dimshow(img, vmin=lo, vmax=hi, ticks=False)
plt.title("WISE W%i Data" % band)
ps.savefig()
plt.clf()
dimshow(mod, vmin=lo, vmax=hi, ticks=False)
plt.title("WISE W%i Model" % band)
ps.savefig()
resid = img - mod
mx = np.abs(resid).max()
plt.clf()
dimshow(resid, vmin=-mx, vmax=mx, ticks=False)
plt.title("WISE W%i Resid" % band)
ps.savefig()
# kwa = dict(mn=-0.1, mx=2., arcsinh = 1.)
kwa = dict(mn=-0.1, mx=2.0, arcsinh=None)
rgb = _unwise_to_rgb(coimgs, **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title("WISE W1/W2 Data")
ps.savefig()
rgb = _unwise_to_rgb(comods, **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title("WISE W1/W2 Model")
ps.savefig()
kwa = dict(mn=-1, mx=1, arcsinh=None)
rgb = _unwise_to_rgb([img - mod for img, mod in zip(coimgs, comods)], **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title("WISE W1/W2 Resid")
ps.savefig()
def main():
import argparse
parser = argparse.ArgumentParser(
description='This script creates small self-contained data sets that '
'are useful for test cases of the pipeline codes.')
parser.add_argument('ccds', help='CCDs table describing region to grab')
parser.add_argument('outdir', help='Output directory name')
parser.add_argument('brick', help='Brick containing these images')
parser.add_argument('--survey-dir', type=str, default=None)
parser.add_argument('--cache-dir', type=str, default=None,
help='Directory to search for cached files')
parser.add_argument('--wise', help='For WISE outputs, give the path to a WCS file describing the sub-brick region of interest, eg, a coadd image')
parser.add_argument('--wise-wcs-hdu', help='For WISE outputs, the HDU to read the WCS from in the file given by --wise.', type=int, default=0)
parser.add_argument('--fpack', action='store_true', default=False)
parser.add_argument('--gzip', action='store_true', default=False)
parser.add_argument('--pad', action='store_true', default=False,
help='Keep original image size, but zero out pixels outside ROI')
args = parser.parse_args()
v = 'SKY_TEMPLATE_DIR'
if v in os.environ:
del os.environ[v]
C = fits_table(args.ccds)
print(len(C), 'CCDs in', args.ccds)
C.camera = np.array([c.strip() for c in C.camera])
survey = LegacySurveyData(cache_dir=args.cache_dir, survey_dir=args.survey_dir)
if ',' in args.brick:
ra,dec = args.brick.split(',')
ra = float(ra)
dec = float(dec)
fakebricks = fits_table()
fakebricks.brickname = np.array([('custom-%06i%s%05i' %
(int(1000*ra), 'm' if dec < 0 else 'p',
int(1000*np.abs(dec))))])
fakebricks.ra = np.array([ra])
fakebricks.dec = np.array([dec])
bricks = fakebricks
outbricks = bricks
else:
bricks = survey.get_bricks_readonly()
outbricks = bricks[np.array([n == args.brick for n in bricks.brickname])]
assert(len(outbricks) == 1)
outsurvey = LegacySurveyData(survey_dir = args.outdir)
trymakedirs(args.outdir)
outbricks.writeto(os.path.join(args.outdir, 'survey-bricks.fits.gz'))
targetwcs = wcs_for_brick(outbricks[0])
H,W = targetwcs.shape
tycho2fn = survey.find_file('tycho2')
kd = tree_open(tycho2fn, 'stars')
radius = 1.
rc,dc = targetwcs.radec_center()
I = tree_search_radec(kd, rc, dc, radius)
print(len(I), 'Tycho-2 stars within', radius, 'deg of RA,Dec (%.3f, %.3f)' % (rc,dc))
# Read only the rows within range.
tycho = fits_table(tycho2fn, rows=I)
del kd
print('Read', len(tycho), 'Tycho-2 stars')
ok,tx,ty = targetwcs.radec2pixelxy(tycho.ra, tycho.dec)
#margin = 100
#tycho.cut(ok * (tx > -margin) * (tx < W+margin) *
# (ty > -margin) * (ty < H+margin))
print('Cut to', len(tycho), 'Tycho-2 stars within brick')
del ok,tx,ty
#tycho.writeto(os.path.join(args.outdir, 'tycho2.fits.gz'))
f,tfn = tempfile.mkstemp(suffix='.fits')
os.close(f)
tycho.writeto(tfn)
outfn = os.path.join(args.outdir, 'tycho2.kd.fits')
cmd = 'startree -i %s -o %s -P -k -n stars -T' % (tfn, outfn)
print(cmd)
rtn = os.system(cmd)
assert(rtn == 0)
os.unlink(tfn)
from legacypipe.gaiacat import GaiaCatalog
gcat = GaiaCatalog()
# from ps1cat.py:
wcs = targetwcs
step=100.
margin=10.
# Grid the CCD in pixel space
W,H = wcs.get_width(), wcs.get_height()
xx,yy = np.meshgrid(
np.linspace(1-margin, W+margin, 2+int((W+2*margin)/step)),
np.linspace(1-margin, H+margin, 2+int((H+2*margin)/step)))
# Convert to RA,Dec and then to unique healpixes
ra,dec = wcs.pixelxy2radec(xx.ravel(), yy.ravel())
healpixes = set()
for r,d in zip(ra,dec):
healpixes.add(gcat.healpix_for_radec(r, d))
for hp in healpixes:
hpcat = gcat.get_healpix_catalog(hp)
ok,xx,yy = wcs.radec2pixelxy(hpcat.ra, hpcat.dec)
onccd = np.flatnonzero((xx >= 1.-margin) * (xx <= W+margin) *
(yy >= 1.-margin) * (yy <= H+margin))
hpcat.cut(onccd)
if len(hpcat):
outfn = os.path.join(args.outdir, 'gaia', 'chunk-%05d.fits' % hp)
trymakedirs(os.path.join(args.outdir, 'gaia'))
hpcat.writeto(outfn)
outccds = C.copy()
cols = outccds.get_columns()
for c in ['ccd_x0', 'ccd_x1', 'ccd_y0', 'ccd_y1',
'brick_x0', 'brick_x1', 'brick_y0', 'brick_y1',
'skyver', 'wcsver', 'psfver', 'skyplver', 'wcsplver',
'psfplver' ]:
if c in cols:
outccds.delete_column(c)
outccds.image_hdu[:] = 1
# Convert to list to avoid truncating filenames
outccds.image_filename = [fn for fn in outccds.image_filename]
for iccd,ccd in enumerate(C):
decam = (ccd.camera.strip() == 'decam')
bok = (ccd.camera.strip() == '90prime')
im = survey.get_image_object(ccd)
print('Got', im)
if survey.cache_dir is not None:
im.check_for_cached_files(survey)
slc = (slice(ccd.ccd_y0, ccd.ccd_y1), slice(ccd.ccd_x0, ccd.ccd_x1))
psfkwargs = dict(pixPsf=True, gaussPsf=False, hybridPsf=False,
normalizePsf=False)
tim = im.get_tractor_image(slc, pixPsf=True,
subsky=False, nanomaggies=False,
no_remap_invvar=True, old_calibs_ok=True)
print('Tim:', tim.shape)
psfrow = psfhdr = None
if args.pad:
psf = im.read_psf_model(0, 0, w=im.width, h=im.height, **psfkwargs)
psfex = psf.psfex
else:
psf = tim.getPsf()
psfex = psf.psfex
# Did the PSF model come from a merged file?
for fn in [im.merged_psffn, im.psffn] + im.old_merged_psffns:
if not os.path.exists(fn):
continue
T = fits_table(fn)
I, = np.nonzero((T.expnum == im.expnum) *
np.array([c.strip() == im.ccdname for c in T.ccdname]))
if len(I) != 1:
continue
psfrow = T[I]
x0 = ccd.ccd_x0
y0 = ccd.ccd_y0
psfrow.polzero1[0] -= x0
psfrow.polzero2[0] -= y0
#psfhdr = fitsio.read_header(im.merged_psffn)
break
psfex.fwhm = tim.psf_fwhm
#### HACK
#psfrow = None
assert(psfrow is not None)
if psfrow is not None:
print('PSF row:', psfrow)
#else:
# print('PSF:', psf)
# print('PsfEx:', psfex)
skyrow = skyhdr = None
if args.pad:
primhdr = fitsio.read_header(im.imgfn)
imghdr = fitsio.read_header(im.imgfn, hdu=im.hdu)
sky = im.read_sky_model(splinesky=True, primhdr=primhdr, imghdr=imghdr)
#skyhdr = fitsio.read_header(im.splineskyfn)
#msky = im.read_merged_splinesky_model(slc=slc, old_calibs_ok=True)
else:
sky = tim.getSky()
# Did the sky model come from a merged file?
#msky = im.read_merged_splinesky_model(slc=slc, old_calibs_ok=True)
print('merged skyfn:', im.merged_skyfn)
print('single skyfn:', im.skyfn)
print('old merged skyfns:', im.old_merged_skyfns)
for fn in [im.merged_skyfn, im.skyfn] + im.old_merged_skyfns:
if not os.path.exists(fn):
continue
T = fits_table(fn)
I, = np.nonzero((T.expnum == im.expnum) *
np.array([c.strip() == im.ccdname for c in T.ccdname]))
skyrow = T[I]
skyrow.x0[0] = ccd.ccd_x0
skyrow.y0[0] = ccd.ccd_y0
# s_med = skyrow.sky_med[0]
# s_john = skyrow.sky_john[0]
# skyhdr = fitsio.read_header(fn)
assert(skyrow is not None)
### HACK
#skyrow = None
if skyrow is not None:
print('Sky row:', skyrow)
else:
print('Sky:', sky)
# Output filename format:
fn = ccd.image_filename.strip()
ccd.image_filename = os.path.join(os.path.dirname(fn),
'%s.%s.fits' % (os.path.basename(fn).split('.')[0], ccd.ccdname.strip()))
outim = outsurvey.get_image_object(ccd)
print('Output image:', outim)
print('Image filename:', outim.imgfn)
trymakedirs(outim.imgfn, dir=True)
imgdata = tim.getImage()
ivdata = tim.getInvvar()
# Since we remap DQ codes (always with Mosaic and Bok, sometimes with DECam),
# re-read from the FITS file rather than using tim.dq.
print('Reading data quality from', im.dqfn, 'hdu', im.hdu)
dqdata = im._read_fits(im.dqfn, im.hdu, slice=tim.slice)
print('Tim shape:', tim.shape, 'Slice', tim.slice)
print('image shape:', imgdata.shape, 'iv', ivdata.shape, 'DQ', dqdata.shape)
from collections import Counter
dqvals = Counter(dqdata.ravel())
print('DQ pixel counts:')
for k,n in dqvals.most_common():
print(' 0x%x' % k, ':', n)
if args.pad:
# Create zero image of full size, copy in data.
fullsize = np.zeros((ccd.height, ccd.width), imgdata.dtype)
fullsize[slc] = imgdata
imgdata = fullsize
fullsize = np.zeros((ccd.height, ccd.width), dqdata.dtype)
fullsize[slc] = dqdata
dqdata = fullsize
fullsize = np.zeros((ccd.height, ccd.width), ivdata.dtype)
fullsize[slc] = ivdata
ivdata = fullsize
else:
# Adjust the header WCS by x0,y0
crpix1 = tim.hdr['CRPIX1']
crpix2 = tim.hdr['CRPIX2']
tim.hdr['CRPIX1'] = crpix1 - ccd.ccd_x0
tim.hdr['CRPIX2'] = crpix2 - ccd.ccd_y0
# Add image extension to filename
# fitsio doesn't compress .fz by default, so drop .fz suffix
#outim.imgfn = outim.imgfn.replace('.fits', '-%s.fits' % im.ccdname)
if not args.fpack:
outim.imgfn = outim.imgfn.replace('.fits.fz', '.fits')
if args.gzip:
outim.imgfn = outim.imgfn.replace('.fits', '.fits.gz')
#outim.wtfn = outim.wtfn.replace('.fits', '-%s.fits' % im.ccdname)
if not args.fpack:
outim.wtfn = outim.wtfn.replace('.fits.fz', '.fits')
if args.gzip:
outim.wtfn = outim.wtfn.replace('.fits', '.fits.gz')
if outim.dqfn is not None:
#outim.dqfn = outim.dqfn.replace('.fits', '-%s.fits' % im.ccdname)
if not args.fpack:
outim.dqfn = outim.dqfn.replace('.fits.fz', '.fits')
if args.gzip:
outim.dqfn = outim.dqfn.replace('.fits', '.fits.gz')
if bok:
outim.psffn = outim.psffn.replace('.psf', '-%s.psf' % im.ccdname)
ccdfn = outim.imgfn
ccdfn = ccdfn.replace(outsurvey.get_image_dir(), '')
if ccdfn.startswith('/'):
ccdfn = ccdfn[1:]
outccds.image_filename[iccd] = ccdfn
print('Changed output filenames to:')
print(outim.imgfn)
print(outim.dqfn)
ofn = outim.imgfn
if args.fpack:
f,ofn = tempfile.mkstemp(suffix='.fits')
os.close(f)
fits = fitsio.FITS(ofn, 'rw', clobber=True)
fits.write(None, header=tim.primhdr)
fits.write(imgdata, header=tim.hdr, extname=ccd.ccdname)
fits.close()
if args.fpack:
cmd = 'fpack -qz 8 -S %s > %s && rm %s' % (ofn, outim.imgfn, ofn)
print('Running:', cmd)
rtn = os.system(cmd)
assert(rtn == 0)
h,w = tim.shape
if not args.pad:
outccds.width[iccd] = w
outccds.height[iccd] = h
outccds.crpix1[iccd] = crpix1 - ccd.ccd_x0
outccds.crpix2[iccd] = crpix2 - ccd.ccd_y0
wcs = Tan(*[float(x) for x in
[ccd.crval1, ccd.crval2, ccd.crpix1, ccd.crpix2,
ccd.cd1_1, ccd.cd1_2, ccd.cd2_1, ccd.cd2_2, ccd.width, ccd.height]])
if args.pad:
subwcs = wcs
else:
subwcs = wcs.get_subimage(ccd.ccd_x0, ccd.ccd_y0, w, h)
outccds.ra[iccd],outccds.dec[iccd] = subwcs.radec_center()
print('Weight filename:', outim.wtfn)
wfn = outim.wtfn
trymakedirs(wfn, dir=True)
ofn = wfn
if args.fpack:
f,ofn = tempfile.mkstemp(suffix='.fits')
os.close(f)
fits = fitsio.FITS(ofn, 'rw', clobber=True)
fits.write(None, header=tim.primhdr)
fits.write(ivdata, header=tim.hdr, extname=ccd.ccdname)
fits.close()
if args.fpack:
cmd = 'fpack -qz 8 -S %s > %s && rm %s' % (ofn, wfn, ofn)
print('Running:', cmd)
rtn = os.system(cmd)
assert(rtn == 0)
if outim.dqfn is not None:
print('DQ filename', outim.dqfn)
trymakedirs(outim.dqfn, dir=True)
ofn = outim.dqfn
if args.fpack:
f,ofn = tempfile.mkstemp(suffix='.fits')
os.close(f)
fits = fitsio.FITS(ofn, 'rw', clobber=True)
fits.write(None, header=tim.primhdr)
fits.write(dqdata, header=tim.hdr, extname=ccd.ccdname)
fits.close()
if args.fpack:
cmd = 'fpack -g -q 0 -S %s > %s && rm %s' % (ofn, outim.dqfn, ofn)
print('Running:', cmd)
rtn = os.system(cmd)
assert(rtn == 0)
psfout = outim.psffn
#if psfrow:
# psfout = outim.merged_psffn
print('PSF output filename:', psfout)
trymakedirs(psfout, dir=True)
if psfrow:
psfrow.writeto(psfout, primhdr=psfhdr)
else:
print('Writing PsfEx:', psfout)
psfex.writeto(psfout)
# update header
F = fitsio.FITS(psfout, 'rw')
F[0].write_keys([dict(name='EXPNUM', value=ccd.expnum),
dict(name='PLVER', value=psf.plver),
dict(name='PROCDATE', value=psf.procdate),
dict(name='PLPROCID', value=psf.plprocid),])
F.close()
skyout = outim.skyfn
#if skyrow:
# skyout = outim.merged_splineskyfn
print('Sky output filename:', skyout)
trymakedirs(skyout, dir=True)
if skyrow is not None:
skyrow.writeto(skyout, primhdr=skyhdr)
else:
primhdr = fitsio.FITSHDR()
primhdr['PLVER'] = sky.plver
primhdr['PLPROCID'] = sky.plprocid
primhdr['PROCDATE'] = sky.procdate
primhdr['EXPNUM'] = ccd.expnum
primhdr['IMGDSUM'] = sky.datasum
primhdr['S_MED'] = s_med
primhdr['S_JOHN'] = s_john
sky.write_fits(skyout, primhdr=primhdr)
# HACK -- check result immediately.
outccds.writeto(os.path.join(args.outdir, 'survey-ccds-1.fits.gz'))
outsurvey.ccds = None
outC = outsurvey.get_ccds_readonly()
occd = outC[iccd]
outim = outsurvey.get_image_object(occd)
print('Got output image:', outim)
otim = outim.get_tractor_image(pixPsf=True,
hybridPsf=True, old_calibs_ok=True)
print('Got output tim:', otim)
outccds.writeto(os.path.join(args.outdir, 'survey-ccds-1.fits.gz'))
# WISE
if args.wise is not None:
from wise.forcedphot import unwise_tiles_touching_wcs
from wise.unwise import (unwise_tile_wcs, unwise_tiles_touching_wcs,
get_unwise_tractor_image, get_unwise_tile_dir)
# Read WCS...
print('Reading TAN wcs header from', args.wise, 'HDU', args.wise_wcs_hdu)
targetwcs = Tan(args.wise, args.wise_wcs_hdu)
tiles = unwise_tiles_touching_wcs(targetwcs)
print('Cut to', len(tiles), 'unWISE tiles')
H,W = targetwcs.shape
r,d = targetwcs.pixelxy2radec(np.array([1, W, W/2, W/2]),
np.array([H/2, H/2, 1, H ]))
roiradec = [r[0], r[1], d[2], d[3]]
unwise_dir = os.environ['UNWISE_COADDS_DIR']
wise_out = os.path.join(args.outdir, 'images', 'unwise')
print('Will write WISE outputs to', wise_out)
unwise_tr_dir = os.environ['UNWISE_COADDS_TIMERESOLVED_DIR']
wise_tr_out = os.path.join(args.outdir, 'images', 'unwise-tr')
print('Will write WISE time-resolved outputs to', wise_tr_out)
trymakedirs(wise_tr_out)
W = fits_table(os.path.join(unwise_tr_dir, 'time_resolved_atlas.fits'))
print('Read', len(W), 'time-resolved WISE coadd tiles')
W.cut(np.array([t in tiles.coadd_id for t in W.coadd_id]))
print('Cut to', len(W), 'time-resolved vs', len(tiles), 'full-depth')
# Write the time-resolved index subset.
W.writeto(os.path.join(wise_tr_out, 'time_resolved_atlas.fits'))
# this ought to be enough for anyone =)
_,Nepochs = W.epoch_bitmask.shape
print('N epochs in time-resolved atlas:', Nepochs)
wisedata = []
# full depth
for band in [1,2,3,4]:
wisedata.append((unwise_dir, wise_out, tiles.coadd_id, band, True))
# time-resolved
for band in [1,2]:
# W1 is bit 0 (value 0x1), W2 is bit 1 (value 0x2)
bitmask = (1 << (band-1))
for e in range(Nepochs):
# Which tiles have images for this epoch?
I = np.flatnonzero(W.epoch_bitmask[:,e] & bitmask)
if len(I) == 0:
continue
print('Epoch %i: %i tiles:' % (e, len(I)), W.coadd_id[I])
edir = os.path.join(unwise_tr_dir, 'e%03i' % e)
eoutdir = os.path.join(wise_tr_out, 'e%03i' % e)
wisedata.append((edir, eoutdir, tiles.coadd_id[I], band, False))
wrote_masks = set()
model_dir = os.environ.get('UNWISE_MODEL_SKY_DIR')
if model_dir is not None:
model_dir_out = os.path.join(args.outdir, 'images', 'unwise-mod')
trymakedirs(model_dir_out)
for indir, outdir, tiles, band, fulldepth in wisedata:
for tile in tiles:
wanyband = 'w'
tim = get_unwise_tractor_image(indir, tile, band,
bandname=wanyband, roiradecbox=roiradec)
print('Got unWISE tim', tim)
print(tim.shape)
if model_dir is not None and fulldepth and band in [1,2]:
print('ROI', tim.roi)
#0387p575.1.mod.fits
fn = '%s.%i.mod.fits' % (tile, band)
print('Filename', fn)
F = fitsio.FITS(os.path.join(model_dir, fn))
x0,x1,y0,y1 = tim.roi
slc = slice(y0,y1),slice(x0,x1)
phdr = F[0].read_header()
outfn = os.path.join(model_dir_out, fn)
for e,extname in [(1,'MODEL'), (2,'SKY')]:
pix = F[e][slc]
hdr = F[e].read_header()
crpix1 = hdr['CRPIX1']
crpix2 = hdr['CRPIX2']
hdr['CRPIX1'] -= x0
hdr['CRPIX2'] -= y0
#print('mod', mod)
#print('Model', mod.shape)
if e == 1:
fitsio.write(outfn, None, clobber=True, header=phdr)
fitsio.write(outfn, pix, header=hdr, extname=extname)
print('Wrote', outfn)
thisdir = get_unwise_tile_dir(outdir, tile)
print('Directory for this WISE tile:', thisdir)
base = os.path.join(thisdir, 'unwise-%s-w%i-' % (tile, band))
print('Base filename:', base)
masked = True
mu = 'm' if masked else 'u'
imfn = base + 'img-%s.fits' % mu
ivfn = base + 'invvar-%s.fits.gz' % mu
nifn = base + 'n-%s.fits.gz' % mu
nufn = base + 'n-u.fits.gz'
#print('WISE image header:', tim.hdr)
# Adjust the header WCS by x0,y0
wcs = tim.wcs.wcs
tim.hdr['CRPIX1'] = wcs.crpix[0]
tim.hdr['CRPIX2'] = wcs.crpix[1]
H,W = tim.shape
tim.hdr['IMAGEW'] = W
tim.hdr['IMAGEH'] = H
print('WCS:', wcs)
print('Header CRPIX', tim.hdr['CRPIX1'], tim.hdr['CRPIX2'])
trymakedirs(imfn, dir=True)
fitsio.write(imfn, tim.getImage(), header=tim.hdr, clobber=True)
print('Wrote', imfn)
fitsio.write(ivfn, tim.getInvvar(), header=tim.hdr, clobber=True)
print('Wrote', ivfn)
fitsio.write(nifn, tim.nims, header=tim.hdr, clobber=True)
print('Wrote', nifn)
fitsio.write(nufn, tim.nuims, header=tim.hdr, clobber=True)
print('Wrote', nufn)
if not (indir,tile) in wrote_masks:
print('Looking for mask file for', indir, tile)
# record that we tried this dir/tile combo
wrote_masks.add((indir,tile))
for idir in indir.split(':'):
tdir = get_unwise_tile_dir(idir, tile)
maskfn = 'unwise-%s-msk.fits.gz' % tile
fn = os.path.join(tdir, maskfn)
print('Mask file:', fn)
if os.path.exists(fn):
print('Reading', fn)
(x0,x1,y0,y1) = tim.roi
roislice = (slice(y0,y1), slice(x0,x1))
F = fitsio.FITS(fn)[0]
hdr = F.read_header()
M = F[roislice]
outfn = os.path.join(thisdir, maskfn)
fitsio.write(outfn, M, header=tim.hdr, clobber=True)
print('Wrote', outfn)
break
outC = outsurvey.get_ccds_readonly()
for iccd,ccd in enumerate(outC):
outim = outsurvey.get_image_object(ccd)
print('Got output image:', outim)
otim = outim.get_tractor_image(pixPsf=True,
hybridPsf=True, old_calibs_ok=True)
print('Got output tim:', otim)
def unwise_forcedphot(cat, tiles, band=1, roiradecbox=None,
use_ceres=True, ceres_block=8,
save_fits=False, get_models=False, ps=None,
psf_broadening=None,
pixelized_psf=False,
get_masks=None,
move_crpix=False,
modelsky_dir=None):
'''
Given a list of tractor sources *cat*
and a list of unWISE tiles *tiles* (a fits_table with RA,Dec,coadd_id)
runs forced photometry, returning a FITS table the same length as *cat*.
*get_masks*: the WCS to resample mask bits into.
'''
from tractor import NanoMaggies, PointSource, Tractor, ExpGalaxy, DevGalaxy, FixedCompositeGalaxy
if not pixelized_psf and psf_broadening is None:
# PSF broadening in post-reactivation data, by band.
# Newer version from Aaron's email to decam-chatter, 2018-06-14.
broadening = { 1: 1.0405, 2: 1.0346, 3: None, 4: None }
psf_broadening = broadening[band]
if False:
from astrometry.util.plotutils import PlotSequence
ps = PlotSequence('wise-forced-w%i' % band)
plots = (ps is not None)
if plots:
import pylab as plt
wantims = (plots or save_fits or get_models)
wanyband = 'w'
if get_models:
models = {}
wband = 'w%i' % band
fskeys = ['prochi2', 'pronpix', 'profracflux', 'proflux', 'npix',
'pronexp']
Nsrcs = len(cat)
phot = fits_table()
# Filled in based on unique tile overlap
phot.wise_coadd_id = np.array([' '] * Nsrcs)
phot.set(wband + '_psfdepth', np.zeros(len(phot), np.float32))
ra = np.array([src.getPosition().ra for src in cat])
dec = np.array([src.getPosition().dec for src in cat])
nexp = np.zeros(Nsrcs, np.int16)
mjd = np.zeros(Nsrcs, np.float64)
central_flux = np.zeros(Nsrcs, np.float32)
fitstats = {}
tims = []
if get_masks:
mh,mw = get_masks.shape
maskmap = np.zeros((mh,mw), np.uint32)
for tile in tiles:
print('Reading WISE tile', tile.coadd_id, 'band', band)
tim = get_unwise_tractor_image(tile.unwise_dir, tile.coadd_id, band,
bandname=wanyband, roiradecbox=roiradecbox)
if tim is None:
print('Actually, no overlap with tile', tile.coadd_id)
continue
if plots:
sig1 = tim.sig1
plt.clf()
plt.imshow(tim.getImage(), interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=10 * sig1)
plt.colorbar()
tag = '%s W%i' % (tile.coadd_id, band)
plt.title('%s: tim data' % tag)
ps.savefig()
plt.clf()
plt.hist((tim.getImage() * tim.inverr)[tim.inverr > 0].ravel(),
range=(-5,10), bins=100)
plt.xlabel('Per-pixel intensity (Sigma)')
plt.title(tag)
ps.savefig()
if move_crpix and band in [1, 2]:
realwcs = tim.wcs.wcs
x,y = realwcs.crpix
tile_crpix = tile.get('crpix_w%i' % band)
dx = tile_crpix[0] - 1024.5
dy = tile_crpix[1] - 1024.5
realwcs.set_crpix(x+dx, y+dy)
#print('CRPIX', x,y, 'shift by', dx,dy, 'to', realwcs.crpix)
if modelsky_dir and band in [1, 2]:
fn = os.path.join(modelsky_dir, '%s.%i.mod.fits' % (tile.coadd_id, band))
if not os.path.exists(fn):
raise RuntimeError('WARNING: does not exist:', fn)
x0,x1,y0,y1 = tim.roi
bg = fitsio.FITS(fn)[2][y0:y1, x0:x1]
#print('Read background map:', bg.shape, bg.dtype, 'vs image', tim.shape)
if plots:
plt.clf()
plt.subplot(1,2,1)
plt.imshow(tim.getImage(), interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=5 * sig1)
plt.subplot(1,2,2)
plt.imshow(bg, interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=5 * sig1)
tag = '%s W%i' % (tile.coadd_id, band)
plt.suptitle(tag)
ps.savefig()
plt.clf()
ha = dict(range=(-5,10), bins=100, histtype='step')
plt.hist((tim.getImage() * tim.inverr)[tim.inverr > 0].ravel(),
color='b', label='Original', **ha)
plt.hist(((tim.getImage()-bg) * tim.inverr)[tim.inverr > 0].ravel(),
color='g', label='Minus Background', **ha)
plt.axvline(0, color='k', alpha=0.5)
plt.xlabel('Per-pixel intensity (Sigma)')
plt.legend()
plt.title(tag + ': background')
ps.savefig()
# Actually subtract the background!
tim.data -= bg
# Floor the per-pixel variances
if band in [1,2]:
# in Vega nanomaggies per pixel
floor_sigma = {1: 0.5, 2: 2.0}
with np.errstate(divide='ignore'):
new_ie = 1. / np.hypot(1./tim.inverr, floor_sigma[band])
new_ie[tim.inverr == 0] = 0.
if plots:
plt.clf()
plt.plot((1. / tim.inverr[tim.inverr>0]).ravel(), (1./new_ie[tim.inverr>0]).ravel(), 'b.')
plt.title('unWISE per-pixel error: %s band %i' % (tile.coadd_id, band))
plt.xlabel('original')
plt.ylabel('floored')
ps.savefig()
tim.inverr = new_ie
# Read mask file?
if get_masks:
from astrometry.util.resample import resample_with_wcs, OverlapError
# unwise_dir can be a colon-separated list of paths
tilemask = None
for d in tile.unwise_dir.split(':'):
fn = os.path.join(d, tile.coadd_id[:3], tile.coadd_id,
'unwise-%s-msk.fits.gz' % tile.coadd_id)
if os.path.exists(fn):
print('Reading unWISE mask file', fn)
x0,x1,y0,y1 = tim.roi
tilemask = fitsio.FITS(fn)[0][y0:y1,x0:x1]
break
if tilemask is None:
print('unWISE mask file for tile', tile.coadd_id, 'does not exist')
else:
try:
tanwcs = tim.wcs.wcs
assert(tanwcs.shape == tilemask.shape)
Yo,Xo,Yi,Xi,_ = resample_with_wcs(get_masks, tanwcs, intType=np.int16)
# Only deal with mask pixels that are set.
I, = np.nonzero(tilemask[Yi,Xi] > 0)
# Trim to unique area for this tile
rr,dd = get_masks.pixelxy2radec(Yo[I]+1, Xo[I]+1)
good = radec_in_unique_area(rr, dd, tile.ra1, tile.ra2, tile.dec1, tile.dec2)
I = I[good]
maskmap[Yo[I],Xo[I]] = tilemask[Yi[I], Xi[I]]
except OverlapError:
# Shouldn't happen by this point
print('No overlap between WISE tile', tile.coadd_id, 'and brick')
# The tiles have some overlap, so zero out pixels outside the
# tile's unique area.
th,tw = tim.shape
xx,yy = np.meshgrid(np.arange(tw), np.arange(th))
rr,dd = tim.wcs.wcs.pixelxy2radec(xx+1, yy+1)
unique = radec_in_unique_area(rr, dd, tile.ra1, tile.ra2, tile.dec1, tile.dec2)
#print(np.sum(unique), 'of', (th*tw), 'pixels in this tile are unique')
tim.inverr[unique == False] = 0.
del xx,yy,rr,dd,unique
if plots:
sig1 = tim.sig1
plt.clf()
plt.imshow(tim.getImage() * (tim.inverr > 0),
interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=10 * sig1)
plt.colorbar()
tag = '%s W%i' % (tile.coadd_id, band)
plt.title('%s: tim data (unique)' % tag)
ps.savefig()
if pixelized_psf:
import unwise_psf
if (band == 1) or (band == 2):
# we only have updated PSFs for W1 and W2
psfimg = unwise_psf.get_unwise_psf(band, tile.coadd_id,
modelname='neo4_unwisecat')
else:
psfimg = unwise_psf.get_unwise_psf(band, tile.coadd_id)
if band == 4:
# oversample (the unwise_psf models are at native W4 5.5"/pix,
# while the unWISE coadds are made at 2.75"/pix.
ph,pw = psfimg.shape
subpsf = np.zeros((ph*2-1, pw*2-1), np.float32)
from astrometry.util.util import lanczos3_interpolate
xx,yy = np.meshgrid(np.arange(0., pw-0.51, 0.5, dtype=np.float32),
np.arange(0., ph-0.51, 0.5, dtype=np.float32))
xx = xx.ravel()
yy = yy.ravel()
ix = xx.astype(np.int32)
iy = yy.astype(np.int32)
dx = (xx - ix).astype(np.float32)
dy = (yy - iy).astype(np.float32)
psfimg = psfimg.astype(np.float32)
rtn = lanczos3_interpolate(ix, iy, dx, dy, [subpsf.flat], [psfimg])
if plots:
plt.clf()
plt.imshow(psfimg, interpolation='nearest', origin='lower')
plt.title('Original PSF model')
ps.savefig()
plt.clf()
plt.imshow(subpsf, interpolation='nearest', origin='lower')
plt.title('Subsampled PSF model')
ps.savefig()
psfimg = subpsf
del xx, yy, ix, iy, dx, dy
from tractor.psf import PixelizedPSF
psfimg /= psfimg.sum()
fluxrescales = {1: 1.04, 2: 1.005, 3: 1.0, 4: 1.0}
psfimg *= fluxrescales[band]
tim.psf = PixelizedPSF(psfimg)
if psf_broadening is not None and not pixelized_psf:
# psf_broadening is a factor by which the PSF FWHMs
# should be scaled; the PSF is a little wider
# post-reactivation.
psf = tim.getPsf()
from tractor import GaussianMixturePSF
if isinstance(psf, GaussianMixturePSF):
#
print('Broadening PSF: from', psf)
p0 = psf.getParams()
pnames = psf.getParamNames()
p1 = [p * psf_broadening**2 if 'var' in name else p
for (p, name) in zip(p0, pnames)]
psf.setParams(p1)
print('Broadened PSF:', psf)
else:
print('WARNING: cannot apply psf_broadening to WISE PSF of type', type(psf))
wcs = tim.wcs.wcs
ok,x,y = wcs.radec2pixelxy(ra, dec)
x = np.round(x - 1.).astype(int)
y = np.round(y - 1.).astype(int)
good = (x >= 0) * (x < tw) * (y >= 0) * (y < th)
# Which sources are in this brick's unique area?
usrc = radec_in_unique_area(ra, dec, tile.ra1, tile.ra2, tile.dec1, tile.dec2)
I, = np.nonzero(good * usrc)
nexp[I] = tim.nuims[y[I], x[I]]
if hasattr(tim, 'mjdmin') and hasattr(tim, 'mjdmax'):
mjd[I] = (tim.mjdmin + tim.mjdmax) / 2.
phot.wise_coadd_id[I] = tile.coadd_id
central_flux[I] = tim.getImage()[y[I], x[I]]
del x,y,good,usrc
# PSF norm for depth
psf = tim.getPsf()
h,w = tim.shape
patch = psf.getPointSourcePatch(h//2, w//2).patch
psfnorm = np.sqrt(np.sum(patch**2))
# To handle zero-depth, we return 1/nanomaggies^2 units rather than mags.
psfdepth = 1. / (tim.sig1 / psfnorm)**2
phot.get(wband + '_psfdepth')[I] = psfdepth
tim.tile = tile
tims.append(tim)
if plots:
plt.clf()
mn,mx = 0.1, 20000
plt.hist(np.log10(np.clip(central_flux, mn, mx)), bins=100,
range=(np.log10(mn), np.log10(mx)))
logt = np.arange(0, 5)
plt.xticks(logt, ['%i' % i for i in 10.**logt])
plt.title('Central fluxes (W%i)' % band)
plt.axvline(np.log10(20000), color='k')
plt.axvline(np.log10(1000), color='k')
ps.savefig()
# Eddie's non-secret recipe:
#- central pixel <= 1000: 19x19 pix box size
#- central pixel in 1000 - 20000: 59x59 box size
#- central pixel > 20000 or saturated: 149x149 box size
#- object near "bright star": 299x299 box size
nbig = nmedium = nsmall = 0
for src,cflux in zip(cat, central_flux):
if cflux > 20000:
R = 100
nbig += 1
elif cflux > 1000:
R = 30
nmedium += 1
else:
R = 15
nsmall += 1
if isinstance(src, PointSource):
src.fixedRadius = R
else:
### FIXME -- sizes for galaxies..... can we set PSF size separately?
galrad = 0
# RexGalaxy is a subclass of ExpGalaxy
if isinstance(src, (ExpGalaxy, DevGalaxy)):
galrad = src.shape.re
elif isinstance(src, FixedCompositeGalaxy):
galrad = max(src.shapeExp.re, src.shapeDev.re)
pixscale = 2.75
src.halfsize = int(np.hypot(R, galrad * 5 / pixscale))
#print('Set WISE source sizes:', nbig, 'big', nmedium, 'medium', nsmall, 'small')
minsb = 0.
fitsky = False
tractor = Tractor(tims, cat)
if use_ceres:
from tractor.ceres_optimizer import CeresOptimizer
tractor.optimizer = CeresOptimizer(BW=ceres_block, BH=ceres_block)
tractor.freezeParamsRecursive('*')
tractor.thawPathsTo(wanyband)
kwa = dict(fitstat_extras=[('pronexp', [tim.nims for tim in tims])])
t0 = Time()
R = tractor.optimize_forced_photometry(
minsb=minsb, mindlnp=1., sky=fitsky, fitstats=True,
variance=True, shared_params=False,
wantims=wantims, **kwa)
print('unWISE forced photometry took', Time() - t0)
if use_ceres:
term = R.ceres_status['termination']
# Running out of memory can cause failure to converge
# and term status = 2.
# Fail completely in this case.
if term != 0:
print('Ceres termination status:', term)
raise RuntimeError(
'Ceres terminated with status %i' % term)
if wantims:
ims1 = R.ims1
flux_invvars = R.IV
if R.fitstats is not None:
for k in fskeys:
x = getattr(R.fitstats, k)
fitstats[k] = np.array(x).astype(np.float32)
if save_fits:
for i,tim in enumerate(tims):
tile = tim.tile
(dat, mod, ie, chi, roi) = ims1[i]
wcshdr = fitsio.FITSHDR()
tim.wcs.wcs.add_to_header(wcshdr)
tag = 'fit-%s-w%i' % (tile.coadd_id, band)
fitsio.write('%s-data.fits' %
tag, dat, clobber=True, header=wcshdr)
fitsio.write('%s-mod.fits' % tag, mod,
clobber=True, header=wcshdr)
fitsio.write('%s-chi.fits' % tag, chi,
clobber=True, header=wcshdr)
if plots:
# Create models for just the brightest sources
bright_cat = [src for src in cat
if src.getBrightness().getBand(wanyband) > 1000]
print('Bright soures:', len(bright_cat))
btr = Tractor(tims, bright_cat)
for tim in tims:
mod = btr.getModelImage(tim)
tile = tim.tile
tag = '%s W%i' % (tile.coadd_id, band)
sig1 = tim.sig1
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=25 * sig1)
plt.colorbar()
plt.title('%s: bright-star models' % tag)
ps.savefig()
if get_models:
for i,tim in enumerate(tims):
tile = tim.tile
(dat, mod, ie, chi, roi) = ims1[i]
models[(tile.coadd_id, band)] = (mod, dat, ie, tim.roi, tim.wcs.wcs)
if plots:
for i,tim in enumerate(tims):
tile = tim.tile
tag = '%s W%i' % (tile.coadd_id, band)
(dat, mod, ie, chi, roi) = ims1[i]
sig1 = tim.sig1
plt.clf()
plt.imshow(dat, interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=25 * sig1)
plt.colorbar()
plt.title('%s: data' % tag)
ps.savefig()
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=25 * sig1)
plt.colorbar()
plt.title('%s: model' % tag)
ps.savefig()
plt.clf()
plt.imshow(chi, interpolation='nearest', origin='lower',
cmap='gray', vmin=-5, vmax=+5)
plt.colorbar()
plt.title('%s: chi' % tag)
ps.savefig()
nm = np.array([src.getBrightness().getBand(wanyband) for src in cat])
nm_ivar = flux_invvars
# Sources out of bounds, eg, never change from their default
# (1-sigma or whatever) initial fluxes. Zero them out instead.
nm[nm_ivar == 0] = 0.
phot.set(wband + '_nanomaggies', nm.astype(np.float32))
phot.set(wband + '_nanomaggies_ivar', nm_ivar.astype(np.float32))
dnm = np.zeros(len(nm_ivar), np.float32)
okiv = (nm_ivar > 0)
dnm[okiv] = (1. / np.sqrt(nm_ivar[okiv])).astype(np.float32)
okflux = (nm > 0)
mag = np.zeros(len(nm), np.float32)
mag[okflux] = (NanoMaggies.nanomaggiesToMag(nm[okflux])
).astype(np.float32)
dmag = np.zeros(len(nm), np.float32)
ok = (okiv * okflux)
dmag[ok] = (np.abs((-2.5 / np.log(10.)) * dnm[ok] / nm[ok])
).astype(np.float32)
mag[np.logical_not(okflux)] = np.nan
dmag[np.logical_not(ok)] = np.nan
phot.set(wband + '_mag', mag)
phot.set(wband + '_mag_err', dmag)
for k in fskeys:
phot.set(wband + '_' + k, fitstats[k])
phot.set(wband + '_nexp', nexp)
if not np.all(mjd == 0):
phot.set(wband + '_mjd', mjd)
rtn = wphotduck()
rtn.phot = phot
rtn.models = None
rtn.maskmap = None
if get_models:
rtn.models = models
if get_masks:
rtn.maskmap = maskmap
return rtn
def wise_cutouts(ra,
dec,
radius,
ps,
pixscale=2.75,
survey_dir=None,
unwise_dir=None):
'''
radius in arcsec.
pixscale: WISE pixel scale in arcsec/pixel;
make this smaller than 2.75 to oversample.
'''
if unwise_dir is None:
unwise_dir = os.environ.get('UNWISE_COADDS_DIR')
npix = int(np.ceil(radius / pixscale))
print('Image size:', npix)
W = H = npix
pix = pixscale / 3600.
wcs = Tan(ra, dec, (W + 1) / 2., (H + 1) / 2., -pix, 0., 0., pix, float(W),
float(H))
# Find DECaLS bricks overlapping
survey = LegacySurveyData(survey_dir=survey_dir)
B = bricks_touching_wcs(wcs, survey=survey)
print('Found', len(B), 'bricks overlapping')
TT = []
for b in B.brickname:
fn = survey.find_file('tractor', brick=b)
T = fits_table(fn)
print('Read', len(T), 'from', b)
primhdr = fitsio.read_header(fn)
TT.append(T)
T = merge_tables(TT)
print('Total of', len(T), 'sources')
T.cut(T.brick_primary)
print(len(T), 'primary')
margin = 20
ok, xx, yy = wcs.radec2pixelxy(T.ra, T.dec)
I = np.flatnonzero((xx > -margin) * (yy > -margin) * (xx < W + margin) *
(yy < H + margin))
T.cut(I)
print(len(T), 'within ROI')
#return wcs,T
# Pull out DECaLS coadds (image, model, resid).
dwcs = wcs.scale(2. * pixscale / 0.262)
dh, dw = dwcs.shape
print('DECaLS resampled shape:', dh, dw)
tags = ['image', 'model', 'resid']
coimgs = [np.zeros((dh, dw, 3), np.uint8) for t in tags]
for b in B.brickname:
fn = survey.find_file('image', brick=b, band='r')
bwcs = Tan(fn, 1) # ext 1: .fz
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(dwcs, bwcs)
except ResampleError:
continue
if len(Yo) == 0:
continue
print('Resampling', len(Yo), 'pixels from', b)
xl, xh, yl, yh = Xi.min(), Xi.max(), Yi.min(), Yi.max()
#print('python legacypipe/runbrick.py -b %s --zoom %i %i %i %i --outdir cluster --pixpsf --splinesky --pipe --no-early-coadds' %
# (b, xl-5, xh+5, yl-5, yh+5) + ' -P \'pickles/cluster-%(brick)s-%%(stage)s.pickle\'')
for i, tag in enumerate(tags):
fn = survey.find_file(tag + '-jpeg', brick=b)
img = plt.imread(fn)
img = np.flipud(img)
coimgs[i][Yo, Xo, :] = img[Yi, Xi, :]
tt = dict(image='Image', model='Model', resid='Resid')
for img, tag in zip(coimgs, tags):
plt.clf()
dimshow(img, ticks=False)
plt.title('DECaLS grz %s' % tt[tag])
ps.savefig()
# Find unWISE tiles overlapping
tiles = unwise_tiles_touching_wcs(wcs)
print('Cut to', len(tiles), 'unWISE tiles')
# Here we assume the targetwcs is axis-aligned and that the
# edge midpoints yield the RA,Dec limits (true for TAN).
r, d = wcs.pixelxy2radec(np.array([1, W, W / 2, W / 2]),
np.array([H / 2, H / 2, 1, H]))
# the way the roiradec box is used, the min/max order doesn't matter
roiradec = [r[0], r[1], d[2], d[3]]
ra, dec = T.ra, T.dec
srcs = read_fits_catalog(T)
wbands = [1, 2, 3, 4]
wanyband = 'w'
for band in wbands:
f = T.get('flux_w%i' % band)
f *= 10.**(primhdr['WISEAB%i' % band] / 2.5)
coimgs = [np.zeros((H, W), np.float32) for b in wbands]
comods = [np.zeros((H, W), np.float32) for b in wbands]
con = [np.zeros((H, W), np.uint8) for b in wbands]
for iband, band in enumerate(wbands):
print('Photometering WISE band', band)
wband = 'w%i' % band
for i, src in enumerate(srcs):
#print('Source', src, 'brightness', src.getBrightness(), 'params', src.getBrightness().getParams())
#src.getBrightness().setParams([T.wise_flux[i, band-1]])
src.setBrightness(
NanoMaggies(**{wanyband: T.get('flux_w%i' % band)[i]}))
# print('Set source brightness:', src.getBrightness())
# The tiles have some overlap, so for each source, keep the
# fit in the tile whose center is closest to the source.
for tile in tiles:
print('Reading tile', tile.coadd_id)
tim = get_unwise_tractor_image(unwise_dir,
tile.coadd_id,
band,
bandname=wanyband,
roiradecbox=roiradec)
if tim is None:
print('Actually, no overlap with tile', tile.coadd_id)
continue
print('Read image with shape', tim.shape)
# Select sources in play.
wisewcs = tim.wcs.wcs
H, W = tim.shape
ok, x, y = wisewcs.radec2pixelxy(ra, dec)
x = (x - 1.).astype(np.float32)
y = (y - 1.).astype(np.float32)
margin = 10.
I = np.flatnonzero((x >= -margin) * (x < W + margin) *
(y >= -margin) * (y < H + margin))
print(len(I), 'within the image + margin')
subcat = [srcs[i] for i in I]
tractor = Tractor([tim], subcat)
mod = tractor.getModelImage(0)
# plt.clf()
# dimshow(tim.getImage(), ticks=False)
# plt.title('WISE %s %s' % (tile.coadd_id, wband))
# ps.savefig()
# plt.clf()
# dimshow(mod, ticks=False)
# plt.title('WISE %s %s' % (tile.coadd_id, wband))
# ps.savefig()
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(wcs, tim.wcs.wcs)
except ResampleError:
continue
if len(Yo) == 0:
continue
print('Resampling', len(Yo), 'pixels from WISE', tile.coadd_id,
band)
coimgs[iband][Yo, Xo] += tim.getImage()[Yi, Xi]
comods[iband][Yo, Xo] += mod[Yi, Xi]
con[iband][Yo, Xo] += 1
for img, mod, n in zip(coimgs, comods, con):
img /= np.maximum(n, 1)
mod /= np.maximum(n, 1)
for band, img, mod in zip(wbands, coimgs, comods):
lo, hi = np.percentile(img, [25, 99])
plt.clf()
dimshow(img, vmin=lo, vmax=hi, ticks=False)
plt.title('WISE W%i Data' % band)
ps.savefig()
plt.clf()
dimshow(mod, vmin=lo, vmax=hi, ticks=False)
plt.title('WISE W%i Model' % band)
ps.savefig()
resid = img - mod
mx = np.abs(resid).max()
plt.clf()
dimshow(resid, vmin=-mx, vmax=mx, ticks=False)
plt.title('WISE W%i Resid' % band)
ps.savefig()
#kwa = dict(mn=-0.1, mx=2., arcsinh = 1.)
kwa = dict(mn=-0.1, mx=2., arcsinh=None)
rgb = _unwise_to_rgb(coimgs[:2], **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title('WISE W1/W2 Data')
ps.savefig()
rgb = _unwise_to_rgb(comods[:2], **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title('WISE W1/W2 Model')
ps.savefig()
kwa = dict(mn=-1, mx=1, arcsinh=None)
rgb = _unwise_to_rgb(
[img - mod for img, mod in list(zip(coimgs, comods))[:2]], **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title('WISE W1/W2 Resid')
ps.savefig()
return wcs, T
def unwise_forcedphot(cat,
tiles,
band=1,
roiradecbox=None,
use_ceres=True,
ceres_block=8,
save_fits=False,
get_models=False,
ps=None,
psf_broadening=None,
pixelized_psf=False,
get_masks=None,
move_crpix=False,
modelsky_dir=None,
tag=None):
'''
Given a list of tractor sources *cat*
and a list of unWISE tiles *tiles* (a fits_table with RA,Dec,coadd_id)
runs forced photometry, returning a FITS table the same length as *cat*.
*get_masks*: the WCS to resample mask bits into.
'''
from tractor import PointSource, Tractor, ExpGalaxy, DevGalaxy
from tractor.sersic import SersicGalaxy
if tag is None:
tag = ''
else:
tag = tag + ': '
if not pixelized_psf and psf_broadening is None:
# PSF broadening in post-reactivation data, by band.
# Newer version from Aaron's email to decam-chatter, 2018-06-14.
broadening = {1: 1.0405, 2: 1.0346, 3: None, 4: None}
psf_broadening = broadening[band]
if False:
from astrometry.util.plotutils import PlotSequence
ps = PlotSequence('wise-forced-w%i' % band)
plots = (ps is not None)
if plots:
import pylab as plt
wantims = (plots or save_fits or get_models)
wanyband = 'w'
if get_models:
models = []
wband = 'w%i' % band
Nsrcs = len(cat)
phot = fits_table()
# Filled in based on unique tile overlap
phot.wise_coadd_id = np.array([' '] * Nsrcs, dtype='U8')
phot.wise_x = np.zeros(Nsrcs, np.float32)
phot.wise_y = np.zeros(Nsrcs, np.float32)
phot.set('psfdepth_%s' % wband, np.zeros(Nsrcs, np.float32))
nexp = np.zeros(Nsrcs, np.int16)
mjd = np.zeros(Nsrcs, np.float64)
central_flux = np.zeros(Nsrcs, np.float32)
ra = np.array([src.getPosition().ra for src in cat])
dec = np.array([src.getPosition().dec for src in cat])
fskeys = ['prochi2', 'profracflux']
fitstats = {}
if get_masks:
mh, mw = get_masks.shape
maskmap = np.zeros((mh, mw), np.uint32)
tims = []
for tile in tiles:
info(tag + 'Reading WISE tile', tile.coadd_id, 'band', band)
tim = get_unwise_tractor_image(tile.unwise_dir,
tile.coadd_id,
band,
bandname=wanyband,
roiradecbox=roiradecbox)
if tim is None:
debug('Actually, no overlap with WISE coadd tile', tile.coadd_id)
continue
if plots:
sig1 = tim.sig1
plt.clf()
plt.imshow(tim.getImage(),
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=10 * sig1)
plt.colorbar()
tag = '%s W%i' % (tile.coadd_id, band)
plt.title('%s: tim data' % tag)
ps.savefig()
plt.clf()
plt.hist((tim.getImage() * tim.inverr)[tim.inverr > 0].ravel(),
range=(-5, 10),
bins=100)
plt.xlabel('Per-pixel intensity (Sigma)')
plt.title(tag)
ps.savefig()
if move_crpix and band in [1, 2]:
realwcs = tim.wcs.wcs
x, y = realwcs.crpix
tile_crpix = tile.get('crpix_w%i' % band)
dx = tile_crpix[0] - 1024.5
dy = tile_crpix[1] - 1024.5
realwcs.set_crpix(x + dx, y + dy)
debug('unWISE', tile.coadd_id, 'band', band, 'CRPIX', x, y,
'shift by', dx, dy, 'to', realwcs.crpix)
if modelsky_dir and band in [1, 2]:
fn = os.path.join(modelsky_dir,
'%s.%i.mod.fits' % (tile.coadd_id, band))
if not os.path.exists(fn):
raise RuntimeError('WARNING: does not exist:', fn)
x0, x1, y0, y1 = tim.roi
bg = fitsio.FITS(fn)[2][y0:y1, x0:x1]
assert (bg.shape == tim.shape)
if plots:
plt.clf()
plt.subplot(1, 2, 1)
plt.imshow(tim.getImage(),
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=5 * sig1)
plt.subplot(1, 2, 2)
plt.imshow(bg,
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=5 * sig1)
tag = '%s W%i' % (tile.coadd_id, band)
plt.suptitle(tag)
ps.savefig()
plt.clf()
ha = dict(range=(-5, 10), bins=100, histtype='step')
plt.hist((tim.getImage() * tim.inverr)[tim.inverr > 0].ravel(),
color='b',
label='Original',
**ha)
plt.hist(((tim.getImage() - bg) *
tim.inverr)[tim.inverr > 0].ravel(),
color='g',
label='Minus Background',
**ha)
plt.axvline(0, color='k', alpha=0.5)
plt.xlabel('Per-pixel intensity (Sigma)')
plt.legend()
plt.title(tag + ': background')
ps.savefig()
# Actually subtract the background!
tim.data -= bg
# Floor the per-pixel variances,
# and add Poisson contribution from sources
if band in [1, 2]:
# in Vega nanomaggies per pixel
floor_sigma = {1: 0.5, 2: 2.0}
poissons = {1: 0.15, 2: 0.3}
with np.errstate(divide='ignore'):
new_ie = 1. / np.sqrt(
(1. / tim.inverr)**2 + floor_sigma[band] +
poissons[band]**2 * np.maximum(0., tim.data))
new_ie[tim.inverr == 0] = 0.
if plots:
plt.clf()
plt.plot((1. / tim.inverr[tim.inverr > 0]).ravel(),
(1. / new_ie[tim.inverr > 0]).ravel(), 'b.')
plt.title('unWISE per-pixel error: %s band %i' %
(tile.coadd_id, band))
plt.xlabel('original')
plt.ylabel('floored')
ps.savefig()
assert (np.all(np.isfinite(new_ie)))
assert (np.all(new_ie >= 0.))
tim.inverr = new_ie
# Expand a 3-pixel radius around weight=0 (saturated) pixels
# from Eddie via crowdsource
# https://github.com/schlafly/crowdsource/blob/7069da3e7d9d3124be1cbbe1d21ffeb63fc36dcc/python/wise_proc.py#L74
## FIXME -- W3/W4 ??
satlimit = 85000
msat = ((tim.data > satlimit) | ((tim.nims == 0) &
(tim.nuims > 1)))
from scipy.ndimage.morphology import binary_dilation
xx, yy = np.mgrid[-3:3 + 1, -3:3 + 1]
dilate = xx**2 + yy**2 <= 3**2
msat = binary_dilation(msat, dilate)
nbefore = np.sum(tim.inverr == 0)
tim.inverr[msat] = 0
nafter = np.sum(tim.inverr == 0)
debug('Masking an additional', (nafter - nbefore),
'near-saturated pixels in unWISE', tile.coadd_id, 'band',
band)
# Read mask file?
if get_masks:
from astrometry.util.resample import resample_with_wcs, OverlapError
# unwise_dir can be a colon-separated list of paths
tilemask = None
for d in tile.unwise_dir.split(':'):
fn = os.path.join(d, tile.coadd_id[:3], tile.coadd_id,
'unwise-%s-msk.fits.gz' % tile.coadd_id)
if os.path.exists(fn):
debug('Reading unWISE mask file', fn)
x0, x1, y0, y1 = tim.roi
tilemask = fitsio.FITS(fn)[0][y0:y1, x0:x1]
break
if tilemask is None:
info('unWISE mask file for tile', tile.coadd_id,
'does not exist')
else:
try:
tanwcs = tim.wcs.wcs
assert (tanwcs.shape == tilemask.shape)
Yo, Xo, Yi, Xi, _ = resample_with_wcs(get_masks,
tanwcs,
intType=np.int16)
# Only deal with mask pixels that are set.
I, = np.nonzero(tilemask[Yi, Xi] > 0)
# Trim to unique area for this tile
rr, dd = get_masks.pixelxy2radec(Xo[I] + 1, Yo[I] + 1)
good = radec_in_unique_area(rr, dd, tile.ra1, tile.ra2,
tile.dec1, tile.dec2)
I = I[good]
maskmap[Yo[I], Xo[I]] = tilemask[Yi[I], Xi[I]]
except OverlapError:
# Shouldn't happen by this point
print('Warning: no overlap between WISE tile',
tile.coadd_id, 'and brick')
if plots:
plt.clf()
plt.imshow(tilemask, interpolation='nearest', origin='lower')
plt.title('Tile %s: mask' % tile.coadd_id)
ps.savefig()
plt.clf()
plt.imshow(maskmap, interpolation='nearest', origin='lower')
plt.title('Tile %s: accumulated maskmap' % tile.coadd_id)
ps.savefig()
# The tiles have some overlap, so zero out pixels outside the
# tile's unique area.
th, tw = tim.shape
xx, yy = np.meshgrid(np.arange(tw), np.arange(th))
rr, dd = tim.wcs.wcs.pixelxy2radec(xx + 1, yy + 1)
unique = radec_in_unique_area(rr, dd, tile.ra1, tile.ra2, tile.dec1,
tile.dec2)
debug('Tile', tile.coadd_id, '- total of', np.sum(unique),
'unique pixels out of', len(unique.flat), 'total pixels')
if get_models:
# Save the inverr before blanking out non-unique pixels, for making coadds with no gaps!
# (actually, slightly more subtly, expand unique area by 1 pixel)
from scipy.ndimage.morphology import binary_dilation
du = binary_dilation(unique)
tim.coadd_inverr = tim.inverr * du
tim.inverr[unique == False] = 0.
del xx, yy, rr, dd, unique
if plots:
sig1 = tim.sig1
plt.clf()
plt.imshow(tim.getImage() * (tim.inverr > 0),
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=10 * sig1)
plt.colorbar()
tag = '%s W%i' % (tile.coadd_id, band)
plt.title('%s: tim data (unique)' % tag)
ps.savefig()
if pixelized_psf:
from unwise_psf import unwise_psf
if (band == 1) or (band == 2):
# we only have updated PSFs for W1 and W2
psfimg = unwise_psf.get_unwise_psf(band,
tile.coadd_id,
modelname='neo6_unwisecat')
else:
psfimg = unwise_psf.get_unwise_psf(band, tile.coadd_id)
if band == 4:
# oversample (the unwise_psf models are at native W4 5.5"/pix,
# while the unWISE coadds are made at 2.75"/pix.
ph, pw = psfimg.shape
subpsf = np.zeros((ph * 2 - 1, pw * 2 - 1), np.float32)
from astrometry.util.util import lanczos3_interpolate
xx, yy = np.meshgrid(
np.arange(0., pw - 0.51, 0.5, dtype=np.float32),
np.arange(0., ph - 0.51, 0.5, dtype=np.float32))
xx = xx.ravel()
yy = yy.ravel()
ix = xx.astype(np.int32)
iy = yy.astype(np.int32)
dx = (xx - ix).astype(np.float32)
dy = (yy - iy).astype(np.float32)
psfimg = psfimg.astype(np.float32)
rtn = lanczos3_interpolate(ix, iy, dx, dy, [subpsf.flat],
[psfimg])
if plots:
plt.clf()
plt.imshow(psfimg, interpolation='nearest', origin='lower')
plt.title('Original PSF model')
ps.savefig()
plt.clf()
plt.imshow(subpsf, interpolation='nearest', origin='lower')
plt.title('Subsampled PSF model')
ps.savefig()
psfimg = subpsf
del xx, yy, ix, iy, dx, dy
from tractor.psf import PixelizedPSF
psfimg /= psfimg.sum()
fluxrescales = {1: 1.04, 2: 1.005, 3: 1.0, 4: 1.0}
psfimg *= fluxrescales[band]
tim.psf = PixelizedPSF(psfimg)
if psf_broadening is not None and not pixelized_psf:
# psf_broadening is a factor by which the PSF FWHMs
# should be scaled; the PSF is a little wider
# post-reactivation.
psf = tim.getPsf()
from tractor import GaussianMixturePSF
if isinstance(psf, GaussianMixturePSF):
debug('Broadening PSF: from', psf)
p0 = psf.getParams()
pnames = psf.getParamNames()
p1 = [
p * psf_broadening**2 if 'var' in name else p
for (p, name) in zip(p0, pnames)
]
psf.setParams(p1)
debug('Broadened PSF:', psf)
else:
print(
'WARNING: cannot apply psf_broadening to WISE PSF of type',
type(psf))
wcs = tim.wcs.wcs
_, fx, fy = wcs.radec2pixelxy(ra, dec)
x = np.round(fx - 1.).astype(int)
y = np.round(fy - 1.).astype(int)
good = (x >= 0) * (x < tw) * (y >= 0) * (y < th)
# Which sources are in this brick's unique area?
usrc = radec_in_unique_area(ra, dec, tile.ra1, tile.ra2, tile.dec1,
tile.dec2)
I, = np.nonzero(good * usrc)
nexp[I] = tim.nuims[y[I], x[I]]
if hasattr(tim, 'mjdmin') and hasattr(tim, 'mjdmax'):
mjd[I] = (tim.mjdmin + tim.mjdmax) / 2.
phot.wise_coadd_id[I] = tile.coadd_id
phot.wise_x[I] = fx[I] - 1.
phot.wise_y[I] = fy[I] - 1.
central_flux[I] = tim.getImage()[y[I], x[I]]
del x, y, good, usrc
# PSF norm for depth
psf = tim.getPsf()
h, w = tim.shape
patch = psf.getPointSourcePatch(h // 2, w // 2).patch
psfnorm = np.sqrt(np.sum(patch**2))
# To handle zero-depth, we return 1/nanomaggies^2 units rather than mags.
# In the small empty patches of the sky (eg W4 in 0922p702), we get sig1 = NaN
if np.isfinite(tim.sig1):
phot.get('psfdepth_%s' % wband)[I] = 1. / (tim.sig1 / psfnorm)**2
tim.tile = tile
tims.append(tim)
if plots:
plt.clf()
mn, mx = 0.1, 20000
plt.hist(np.log10(np.clip(central_flux, mn, mx)),
bins=100,
range=(np.log10(mn), np.log10(mx)))
logt = np.arange(0, 5)
plt.xticks(logt, ['%i' % i for i in 10.**logt])
plt.title('Central fluxes (W%i)' % band)
plt.axvline(np.log10(20000), color='k')
plt.axvline(np.log10(1000), color='k')
ps.savefig()
# Eddie's non-secret recipe:
#- central pixel <= 1000: 19x19 pix box size
#- central pixel in 1000 - 20000: 59x59 box size
#- central pixel > 20000 or saturated: 149x149 box size
#- object near "bright star": 299x299 box size
nbig = nmedium = nsmall = 0
for src, cflux in zip(cat, central_flux):
if cflux > 20000:
R = 100
nbig += 1
elif cflux > 1000:
R = 30
nmedium += 1
else:
R = 15
nsmall += 1
if isinstance(src, PointSource):
src.fixedRadius = R
else:
### FIXME -- sizes for galaxies..... can we set PSF size separately?
galrad = 0
# RexGalaxy is a subclass of ExpGalaxy
if isinstance(src, (ExpGalaxy, DevGalaxy, SersicGalaxy)):
galrad = src.shape.re
pixscale = 2.75
src.halfsize = int(np.hypot(R, galrad * 5 / pixscale))
debug('Set WISE source sizes:', nbig, 'big', nmedium, 'medium', nsmall,
'small')
tractor = Tractor(tims, cat)
if use_ceres:
from tractor.ceres_optimizer import CeresOptimizer
tractor.optimizer = CeresOptimizer(BW=ceres_block, BH=ceres_block)
tractor.freezeParamsRecursive('*')
tractor.thawPathsTo(wanyband)
t0 = Time()
R = tractor.optimize_forced_photometry(fitstats=True,
variance=True,
shared_params=False,
wantims=wantims)
info(tag + 'unWISE forced photometry took', Time() - t0)
if use_ceres:
term = R.ceres_status['termination']
# Running out of memory can cause failure to converge and term
# status = 2. Fail completely in this case.
if term != 0:
info(tag + 'Ceres termination status:', term)
raise RuntimeError('Ceres terminated with status %i' % term)
if wantims:
ims1 = R.ims1
# can happen if empty source list (we still want to generate coadds)
if ims1 is None:
ims1 = R.ims0
flux_invvars = R.IV
if R.fitstats is not None:
for k in fskeys:
x = getattr(R.fitstats, k)
fitstats[k] = np.array(x).astype(np.float32)
if save_fits:
for i, tim in enumerate(tims):
tile = tim.tile
(dat, mod, _, chi, _) = ims1[i]
wcshdr = fitsio.FITSHDR()
tim.wcs.wcs.add_to_header(wcshdr)
tag = 'fit-%s-w%i' % (tile.coadd_id, band)
fitsio.write('%s-data.fits' % tag,
dat,
clobber=True,
header=wcshdr)
fitsio.write('%s-mod.fits' % tag, mod, clobber=True, header=wcshdr)
fitsio.write('%s-chi.fits' % tag, chi, clobber=True, header=wcshdr)
if plots:
# Create models for just the brightest sources
bright_cat = [
src for src in cat if src.getBrightness().getBand(wanyband) > 1000
]
debug('Bright soures:', len(bright_cat))
btr = Tractor(tims, bright_cat)
for tim in tims:
mod = btr.getModelImage(tim)
tile = tim.tile
tag = '%s W%i' % (tile.coadd_id, band)
sig1 = tim.sig1
plt.clf()
plt.imshow(mod,
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=25 * sig1)
plt.colorbar()
plt.title('%s: bright-star models' % tag)
ps.savefig()
if get_models:
for i, tim in enumerate(tims):
tile = tim.tile
(dat, mod, _, _, _) = ims1[i]
models.append(
(tile.coadd_id, band, tim.wcs.wcs, dat, mod, tim.coadd_inverr))
if plots:
for i, tim in enumerate(tims):
tile = tim.tile
tag = '%s W%i' % (tile.coadd_id, band)
(dat, mod, _, chi, _) = ims1[i]
sig1 = tim.sig1
plt.clf()
plt.imshow(dat,
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=25 * sig1)
plt.colorbar()
plt.title('%s: data' % tag)
ps.savefig()
plt.clf()
plt.imshow(mod,
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=25 * sig1)
plt.colorbar()
plt.title('%s: model' % tag)
ps.savefig()
plt.clf()
plt.imshow(chi,
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-5,
vmax=+5)
plt.colorbar()
plt.title('%s: chi' % tag)
ps.savefig()
nm = np.array([src.getBrightness().getBand(wanyband) for src in cat])
nm_ivar = flux_invvars
# Sources out of bounds, eg, never change from their initial
# fluxes. Zero them out instead.
nm[nm_ivar == 0] = 0.
phot.set('flux_%s' % wband, nm.astype(np.float32))
phot.set('flux_ivar_%s' % wband, nm_ivar.astype(np.float32))
for k in fskeys:
phot.set(k + '_' + wband,
fitstats.get(k, np.zeros(len(phot), np.float32)))
phot.set('nobs_%s' % wband, nexp)
phot.set('mjd_%s' % wband, mjd)
rtn = wphotduck()
rtn.phot = phot
rtn.models = None
rtn.maskmap = None
if get_models:
rtn.models = models
if get_masks:
rtn.maskmap = maskmap
return rtn
def main():
import argparse
parser = argparse.ArgumentParser(
description='This script creates small self-contained data sets that '
'are useful for test cases of the pipeline codes.')
parser.add_argument('ccds', help='CCDs table describing region to grab')
parser.add_argument('outdir', help='Output directory name')
parser.add_argument('brick', help='Brick containing these images')
parser.add_argument('--wise', help='For WISE outputs, give the path to a WCS file describing the sub-brick region of interest, eg, a coadd image')
parser.add_argument('--fpack', action='store_true', default=False)
parser.add_argument('--pad', action='store_true', default=False,
help='Keep original image size, but zero out pixels outside ROI')
args = parser.parse_args()
C = fits_table(args.ccds)
print(len(C), 'CCDs in', args.ccds)
C.camera = np.array([c.strip() for c in C.camera])
survey = LegacySurveyData()
bricks = survey.get_bricks_readonly()
outbricks = bricks[np.array([n == args.brick for n in bricks.brickname])]
assert(len(outbricks) == 1)
outsurvey = LegacySurveyData(survey_dir = args.outdir)
trymakedirs(args.outdir)
outbricks.writeto(os.path.join(args.outdir, 'survey-bricks.fits.gz'))
targetwcs = wcs_for_brick(outbricks[0])
H,W = targetwcs.shape
tycho = fits_table(os.path.join(survey.get_survey_dir(), 'tycho2.fits.gz'))
print('Read', len(tycho), 'Tycho-2 stars')
ok,tx,ty = targetwcs.radec2pixelxy(tycho.ra, tycho.dec)
margin = 100
tycho.cut(ok * (tx > -margin) * (tx < W+margin) *
(ty > -margin) * (ty < H+margin))
print('Cut to', len(tycho), 'Tycho-2 stars within brick')
del ok,tx,ty
tycho.writeto(os.path.join(args.outdir, 'tycho2.fits.gz'))
outccds = C.copy()
for c in ['ccd_x0', 'ccd_x1', 'ccd_y0', 'ccd_y1',
'brick_x0', 'brick_x1', 'brick_y0', 'brick_y1',
'plver', 'skyver', 'wcsver', 'psfver', 'skyplver', 'wcsplver',
'psfplver' ]:
outccds.delete_column(c)
outccds.image_hdu[:] = 1
# Convert to list to avoid truncating filenames
outccds.image_filename = [fn for fn in outccds.image_filename]
for iccd,ccd in enumerate(C):
decam = (ccd.camera.strip() == 'decam')
bok = (ccd.camera.strip() == '90prime')
im = survey.get_image_object(ccd)
print('Got', im)
slc = (slice(ccd.ccd_y0, ccd.ccd_y1), slice(ccd.ccd_x0, ccd.ccd_x1))
tim = im.get_tractor_image(slc, pixPsf=True, splinesky=True,
subsky=False, nanomaggies=False)
print('Tim:', tim.shape)
psf = tim.getPsf()
print('PSF:', psf)
psfex = psf.psfex
print('PsfEx:', psfex)
outim = outsurvey.get_image_object(ccd)
print('Output image:', outim)
print('Image filename:', outim.imgfn)
trymakedirs(outim.imgfn, dir=True)
imgdata = tim.getImage()
dqdata = tim.dq
if decam:
# DECam-specific code remaps the DQ codes... re-read raw
print('Reading data quality from', im.dqfn, 'hdu', im.hdu)
dqdata = im._read_fits(im.dqfn, im.hdu, slice=tim.slice)
ivdata = tim.getInvvar()
if args.pad:
# Create zero image of full size, copy in data.
fullsize = np.zeros((ccd.height, ccd.width), imgdata.dtype)
fullsize[slc] = imgdata
imgdata = fullsize
fullsize = np.zeros((ccd.height, ccd.width), dqdata.dtype)
fullsize[slc] = dqdata
dqdata = fullsize
fullsize = np.zeros((ccd.height, ccd.width), ivdata.dtype)
fullsize[slc] = ivdata
ivdata = fullsize
else:
# Adjust the header WCS by x0,y0
crpix1 = tim.hdr['CRPIX1']
crpix2 = tim.hdr['CRPIX2']
tim.hdr['CRPIX1'] = crpix1 - ccd.ccd_x0
tim.hdr['CRPIX2'] = crpix2 - ccd.ccd_y0
# Add image extension to filename
# fitsio doesn't compress .fz by default, so drop .fz suffix
outim.imgfn = outim.imgfn.replace('.fits', '-%s.fits' % im.ccdname)
if not args.fpack:
outim.imgfn = outim.imgfn.replace('.fits.fz', '.fits')
# if bok:
# outim.whtfn = outim.whtfn .replace('.wht.fits', '-%s.wht.fits' % im.ccdname)
# if not args.fpack:
# outim.whtfn = outim.whtfn .replace('.fits.fz', '.fits')
# else:
if True:
outim.wtfn = outim.wtfn .replace('.fits', '-%s.fits' % im.ccdname)
if not args.fpack:
outim.wtfn = outim.wtfn .replace('.fits.fz', '.fits')
if outim.dqfn is not None:
outim.dqfn = outim.dqfn .replace('.fits', '-%s.fits' % im.ccdname)
if not args.fpack:
outim.dqfn = outim.dqfn .replace('.fits.fz', '.fits')
if bok:
outim.psffn = outim.psffn.replace('.psf', '-%s.psf' % im.ccdname)
ccdfn = outim.imgfn
ccdfn = ccdfn.replace(outsurvey.get_image_dir(),'')
if ccdfn.startswith('/'):
ccdfn = ccdfn[1:]
outccds.image_filename[iccd] = ccdfn
print('Changed output filenames to:')
print(outim.imgfn)
print(outim.dqfn)
ofn = outim.imgfn
if args.fpack:
f,ofn = tempfile.mkstemp(suffix='.fits')
os.close(f)
fitsio.write(ofn, None, header=tim.primhdr, clobber=True)
fitsio.write(ofn, imgdata, header=tim.hdr, extname=ccd.ccdname)
if args.fpack:
cmd = 'fpack -qz 8 -S %s > %s && rm %s' % (ofn, outim.imgfn, ofn)
print('Running:', cmd)
rtn = os.system(cmd)
assert(rtn == 0)
h,w = tim.shape
if not args.pad:
outccds.width[iccd] = w
outccds.height[iccd] = h
outccds.crpix1[iccd] = crpix1 - ccd.ccd_x0
outccds.crpix2[iccd] = crpix2 - ccd.ccd_y0
wcs = Tan(*[float(x) for x in
[ccd.crval1, ccd.crval2, ccd.crpix1, ccd.crpix2,
ccd.cd1_1, ccd.cd1_2, ccd.cd2_1, ccd.cd2_2, ccd.width, ccd.height]])
if args.pad:
subwcs = wcs
else:
subwcs = wcs.get_subimage(ccd.ccd_x0, ccd.ccd_y0, w, h)
outccds.ra[iccd],outccds.dec[iccd] = subwcs.radec_center()
#if not bok:
if True:
print('Weight filename:', outim.wtfn)
wfn = outim.wtfn
# else:
# print('Weight filename:', outim.whtfn)
# wfn = outim.whtfn
trymakedirs(wfn, dir=True)
ofn = wfn
if args.fpack:
f,ofn = tempfile.mkstemp(suffix='.fits')
os.close(f)
fitsio.write(ofn, None, header=tim.primhdr, clobber=True)
fitsio.write(ofn, ivdata, header=tim.hdr, extname=ccd.ccdname)
if args.fpack:
cmd = 'fpack -qz 8 -S %s > %s && rm %s' % (ofn, wfn, ofn)
print('Running:', cmd)
rtn = os.system(cmd)
assert(rtn == 0)
if outim.dqfn is not None:
print('DQ filename', outim.dqfn)
trymakedirs(outim.dqfn, dir=True)
ofn = outim.dqfn
if args.fpack:
f,ofn = tempfile.mkstemp(suffix='.fits')
os.close(f)
fitsio.write(ofn, None, header=tim.primhdr, clobber=True)
fitsio.write(ofn, dqdata, header=tim.hdr, extname=ccd.ccdname)
if args.fpack:
cmd = 'fpack -g -q 0 -S %s > %s && rm %s' % (ofn, outim.dqfn, ofn)
print('Running:', cmd)
rtn = os.system(cmd)
assert(rtn == 0)
print('PSF filename:', outim.psffn)
trymakedirs(outim.psffn, dir=True)
psfex.writeto(outim.psffn)
if not bok:
print('Sky filename:', outim.splineskyfn)
sky = tim.getSky()
print('Sky:', sky)
trymakedirs(outim.splineskyfn, dir=True)
sky.write_fits(outim.splineskyfn)
outccds.writeto(os.path.join(args.outdir, 'survey-ccds-1.fits.gz'))
# WISE
if args.wise is not None:
from wise.forcedphot import unwise_tiles_touching_wcs
from wise.unwise import (unwise_tile_wcs, unwise_tiles_touching_wcs,
get_unwise_tractor_image, get_unwise_tile_dir)
# Read WCS...
print('Reading TAN wcs header from', args.wise)
targetwcs = Tan(args.wise)
tiles = unwise_tiles_touching_wcs(targetwcs)
print('Cut to', len(tiles), 'unWISE tiles')
H,W = targetwcs.shape
r,d = targetwcs.pixelxy2radec(np.array([1, W, W/2, W/2]),
np.array([H/2, H/2, 1, H ]))
roiradec = [r[0], r[1], d[2], d[3]]
unwise_dir = os.environ['UNWISE_COADDS_DIR']
wise_out = os.path.join(args.outdir, 'images', 'unwise')
print('Will write WISE outputs to', wise_out)
unwise_tr_dir = os.environ['UNWISE_COADDS_TIMERESOLVED_DIR']
wise_tr_out = os.path.join(args.outdir, 'images', 'unwise-tr')
print('Will write WISE time-resolved outputs to', wise_tr_out)
W = fits_table(os.path.join(unwise_tr_dir, 'time_resolved_neo1-atlas.fits'))
print('Read', len(W), 'time-resolved WISE coadd tiles')
W.cut(np.array([t in tiles.coadd_id for t in W.coadd_id]))
print('Cut to', len(W), 'time-resolved vs', len(tiles), 'full-depth')
# Write the time-resolved index subset.
W.writeto(os.path.join(wise_tr_out, 'time_resolved_neo1-atlas.fits'))
# this ought to be enough for anyone =)
Nepochs = 5
wisedata = []
# full depth
for band in [1,2,3,4]:
wisedata.append((unwise_dir, wise_out, tiles.coadd_id, band))
# time-resolved
for band in [1,2]:
# W1 is bit 0 (value 0x1), W2 is bit 1 (value 0x2)
bitmask = (1 << (band-1))
for e in range(Nepochs):
# Which tiles have images for this epoch?
I = np.flatnonzero(W.epoch_bitmask[:,e] & bitmask)
if len(I) == 0:
continue
print('Epoch %i: %i tiles:' % (e, len(I)), W.coadd_id[I])
edir = os.path.join(unwise_tr_dir, 'e%03i' % e)
eoutdir = os.path.join(wise_tr_out, 'e%03i' % e)
wisedata.append((edir, eoutdir, tiles.coadd_id[I], band))
wrote_masks = set()
for indir, outdir, tiles, band in wisedata:
for tile in tiles:
wanyband = 'w'
tim = get_unwise_tractor_image(indir, tile, band,
bandname=wanyband, roiradecbox=roiradec)
print('Got unWISE tim', tim)
print(tim.shape)
thisdir = get_unwise_tile_dir(outdir, tile)
print('Directory for this WISE tile:', thisdir)
base = os.path.join(thisdir, 'unwise-%s-w%i-' % (tile, band))
print('Base filename:', base)
masked = True
mu = 'm' if masked else 'u'
imfn = base + 'img-%s.fits' % mu
ivfn = base + 'invvar-%s.fits.gz' % mu
nifn = base + 'n-%s.fits.gz' % mu
nufn = base + 'n-u.fits.gz'
#print('WISE image header:', tim.hdr)
# Adjust the header WCS by x0,y0
wcs = tim.wcs.wcs
tim.hdr['CRPIX1'] = wcs.crpix[0]
tim.hdr['CRPIX2'] = wcs.crpix[1]
H,W = tim.shape
tim.hdr['IMAGEW'] = W
tim.hdr['IMAGEH'] = H
print('WCS:', wcs)
print('Header CRPIX', tim.hdr['CRPIX1'], tim.hdr['CRPIX2'])
trymakedirs(imfn, dir=True)
fitsio.write(imfn, tim.getImage(), header=tim.hdr, clobber=True)
print('Wrote', imfn)
fitsio.write(ivfn, tim.getInvvar(), header=tim.hdr, clobber=True)
print('Wrote', ivfn)
fitsio.write(nifn, tim.nims, header=tim.hdr, clobber=True)
print('Wrote', nifn)
fitsio.write(nufn, tim.nuims, header=tim.hdr, clobber=True)
print('Wrote', nufn)
if not (indir,tile) in wrote_masks:
print('Looking for mask file for', indir, tile)
# record that we tried this dir/tile combo
wrote_masks.add((indir,tile))
for idir in indir.split(':'):
tdir = get_unwise_tile_dir(idir, tile)
maskfn = 'unwise-%s-msk.fits.gz' % tile
fn = os.path.join(tdir, maskfn)
print('Mask file:', fn)
if os.path.exists(fn):
print('Reading', fn)
(x0,x1,y0,y1) = tim.roi
roislice = (slice(y0,y1), slice(x0,x1))
F = fitsio.FITS(fn)[0]
hdr = F.read_header()
M = F[roislice]
outfn = os.path.join(thisdir, maskfn)
fitsio.write(outfn, M, header=tim.hdr, clobber=True)
print('Wrote', outfn)
break
outC = outsurvey.get_ccds_readonly()
for iccd,ccd in enumerate(outC):
outim = outsurvey.get_image_object(ccd)
print('Got output image:', outim)
otim = outim.get_tractor_image(pixPsf=True, splinesky=True)
print('Got output tim:', otim)
