def cat_gaia_dr2(req, ver):
import json
from legacypipe.gaiacat import GaiaCatalog
import numpy as np
tag = 'gaia-dr2'
ralo = float(req.GET['ralo'])
rahi = float(req.GET['rahi'])
declo = float(req.GET['declo'])
dechi = float(req.GET['dechi'])
ver = int(ver)
if not ver in catversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
os.environ['GAIA_CAT_DIR'] = settings.GAIA_DR2_CAT_DIR
gaia = GaiaCatalog()
cat = gaia.get_catalog_radec_box(ralo, rahi, declo, dechi)
for c in [
'ra', 'dec', 'phot_g_mean_mag', 'phot_bp_mean_mag',
'phot_rp_mean_mag', 'pmra', 'pmdec', 'parallax', 'pmra_error',
'pmdec_error', 'parallax_error', 'astrometric_excess_noise'
]:
val = cat.get(c)
val[np.logical_not(np.isfinite(val))] = 0.
cat.set(c, val)
return HttpResponse(json.dumps(
dict(
rd=[(float(o.ra), float(o.dec)) for o in cat],
sourceid=[int(o.source_id) for o in cat],
gmag=[float(o.phot_g_mean_mag) for o in cat],
bpmag=[float(o.phot_bp_mean_mag) for o in cat],
rpmag=[float(o.phot_rp_mean_mag) for o in cat],
pmra=[float(o.pmra) for o in cat],
pmdec=[float(o.pmdec) for o in cat],
parallax=[float(o.parallax) for o in cat],
pmra_err=[float(o.pmra_error) for o in cat],
pmdec_err=[float(o.pmdec_error) for o in cat],
parallax_err=[float(o.parallax_error) for o in cat],
astrometric_excess_noise=[
float(o.astrometric_excess_noise) for o in cat
],
)),
content_type='application/json')
def cat_gaia_dr2(req, ver):
import json
from legacypipe.gaiacat import GaiaCatalog
import numpy as np
tag = 'gaia-dr2'
ralo = float(req.GET['ralo'])
rahi = float(req.GET['rahi'])
declo = float(req.GET['declo'])
dechi = float(req.GET['dechi'])
ver = int(ver)
if not ver in catversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
os.environ['GAIA_CAT_DIR'] = settings.GAIA_DR2_CAT_DIR
gaia = GaiaCatalog()
cat = gaia.get_catalog_radec_box(ralo, rahi, declo, dechi)
for c in ['ra','dec','phot_g_mean_mag','phot_bp_mean_mag', 'phot_rp_mean_mag',
'pmra','pmdec','parallax',
'pmra_error', 'pmdec_error', 'parallax_error',
'astrometric_excess_noise']:
val = cat.get(c)
val[np.logical_not(np.isfinite(val))] = 0.
cat.set(c, val)
return HttpResponse(json.dumps(dict(
rd=[(float(o.ra),float(o.dec)) for o in cat],
sourceid=[int(o.source_id) for o in cat],
gmag=[float(o.phot_g_mean_mag) for o in cat],
bpmag=[float(o.phot_bp_mean_mag) for o in cat],
rpmag=[float(o.phot_rp_mean_mag) for o in cat],
pmra=[float(o.pmra) for o in cat],
pmdec=[float(o.pmdec) for o in cat],
parallax=[float(o.parallax) for o in cat],
pmra_err=[float(o.pmra_error) for o in cat],
pmdec_err=[float(o.pmdec_error) for o in cat],
parallax_err=[float(o.parallax_error) for o in cat],
astrometric_excess_noise=[float(o.astrometric_excess_noise) for o in cat],
)),
content_type='application/json')
def cat_gaia_dr1(req, ver):
import json
from legacypipe.gaiacat import GaiaCatalog
tag = 'gaia-dr1'
ralo = float(req.GET['ralo'])
rahi = float(req.GET['rahi'])
declo = float(req.GET['declo'])
dechi = float(req.GET['dechi'])
ver = int(ver)
if not ver in catversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
os.environ['GAIA_CAT_DIR'] = settings.GAIA_DR1_CAT_DIR
gaia = GaiaCatalog()
cat = gaia.get_catalog_radec_box(ralo, rahi, declo, dechi)
return HttpResponse(json.dumps(dict(
rd=[(float(o.ra),float(o.dec)) for o in cat],
gmag=[float(o.phot_g_mean_mag) for o in cat],
)),
content_type='application/json')
def cat_gaia_dr1(req, ver):
import json
from legacypipe.gaiacat import GaiaCatalog
tag = 'gaia-dr1'
ralo = float(req.GET['ralo'])
rahi = float(req.GET['rahi'])
declo = float(req.GET['declo'])
dechi = float(req.GET['dechi'])
ver = int(ver)
if not ver in catversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
os.environ['GAIA_CAT_DIR'] = settings.GAIA_DR1_CAT_DIR
gaia = GaiaCatalog()
cat = gaia.get_catalog_radec_box(ralo, rahi, declo, dechi)
return HttpResponse(json.dumps(
dict(
rd=[(float(o.ra), float(o.dec)) for o in cat],
gmag=[float(o.phot_g_mean_mag) for o in cat],
)),
content_type='application/json')
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 read_gaia(targetwcs):
'''
*targetwcs* here should include margin
'''
from legacypipe.gaiacat import GaiaCatalog
gaia = GaiaCatalog().get_catalog_in_wcs(targetwcs)
debug('Got', len(gaia), 'Gaia stars nearby')
# DJS, [decam-chatter 5486] Solved! GAIA separation of point sources
# from extended sources
# Updated for Gaia DR2 by Eisenstein,
# [decam-data 2770] Re: [desi-milkyway 639] GAIA in DECaLS DR7
# But shifted one mag to the right in G.
gaia.G = gaia.phot_g_mean_mag
gaia.pointsource = np.logical_or(
(gaia.G <= 19.) * (gaia.astrometric_excess_noise < 10.**0.5),
(gaia.G >= 19.) *
(gaia.astrometric_excess_noise < 10.**(0.5 + 0.2 * (gaia.G - 19.))))
# in our catalog files, this is in float32; in the Gaia data model it's
# a byte, with only values 3 and 31 in DR2.
gaia.astrometric_params_solved = gaia.astrometric_params_solved.astype(
np.uint8)
# Gaia version?
gaiaver = int(os.getenv('GAIA_CAT_VER', '1'))
#print('Assuming Gaia catalog Data Release', gaiaver)
gaia_release = 'G%i' % gaiaver
gaia.ref_cat = np.array([gaia_release] * len(gaia))
gaia.ref_id = gaia.source_id
gaia.pmra_ivar = 1. / gaia.pmra_error**2
gaia.pmdec_ivar = 1. / gaia.pmdec_error**2
gaia.parallax_ivar = 1. / gaia.parallax_error**2
# mas -> deg
gaia.ra_ivar = 1. / (gaia.ra_error / 1000. / 3600.)**2
gaia.dec_ivar = 1. / (gaia.dec_error / 1000. / 3600.)**2
for c in [
'ra_error', 'dec_error', 'parallax_error', 'pmra_error',
'pmdec_error'
]:
gaia.delete_column(c)
for c in [
'pmra', 'pmdec', 'parallax', 'pmra_ivar', 'pmdec_ivar',
'parallax_ivar'
]:
X = gaia.get(c)
X[np.logical_not(np.isfinite(X))] = 0.
# radius to consider affected by this star --
# FIXME -- want something more sophisticated here!
# (also see tycho.radius below)
# This is in degrees and the magic 0.262 (indeed the whole
# relation) is from eyeballing a radius-vs-mag plot that was in
# pixels; that is unrelated to the present targetwcs pixel scale.
gaia.radius = np.minimum(1800., 150. * 2.5**(
(11. - gaia.G) / 3.)) * 0.262 / 3600.
gaia.delete_column('G')
return gaia
def read_gaia(wcs, bands):
'''
*wcs* here should include margin
'''
from legacypipe.gaiacat import GaiaCatalog
from legacypipe.survey import GaiaSource
gaia = GaiaCatalog().get_catalog_in_wcs(wcs)
debug('Got', len(gaia), 'Gaia stars nearby')
gaia.G = gaia.phot_g_mean_mag
# Sort by brightness (for reference-*.fits output table)
gaia.cut(np.argsort(gaia.G))
# Gaia to DECam color transformations for stars
color = gaia.phot_bp_mean_mag - gaia.phot_rp_mean_mag
# From Rongpu, 2020-04-12
# no BP-RP color: use average color
color[np.logical_not(np.isfinite(color))] = 1.4
# clip to reasonable range for the polynomial fit
color = np.clip(color, -0.6, 4.1)
for b, coeffs in [
('g', [
-0.1178631039, 0.3650113495, 0.5608615360, -0.2850687702,
-1.0243473939, 1.4378375491, 0.0679401731, -1.1713172509,
0.9107811975, -0.3374324004, 0.0683946390, -0.0073089582,
0.0003230170
]),
('r', [
0.1139078673, -0.2868955307, 0.0013196434, 0.1029151074,
0.1196710702, -0.3729031390, 0.1859874242, 0.1370162451,
-0.1808580848, 0.0803219195, -0.0180218196, 0.0020584707,
-0.0000953486
]),
('z', [
0.4811198057, -0.9990015041, 0.1403990019, 0.2150988888,
-0.2917655866, 0.1326831887, -0.0259205004, 0.0018548776
])
]:
mag = gaia.G.copy()
for order, c in enumerate(coeffs):
mag += c * color**order
gaia.set('decam_mag_%s' % b, mag)
del color
# For possible future use:
# BASS/MzLS:
# coeffs = dict(
# g = [-0.1299895823, 0.3120393968, 0.5989482686, 0.3125882487,
# -1.9401592247, 1.1011670449, 2.0741304659, -3.3930306403,
# 2.1857291197, -0.7674676232, 0.1542300648, -0.0167007725,
# 0.0007573720],
# r = [0.0901464643, -0.2463711147, 0.0094963025, -0.1187138789,
# 0.4131107392, -0.1832183301, -0.6015486252, 0.9802538471,
# -0.6613809948, 0.2426395251, -0.0505867727, 0.0056462458,
# -0.0002625789],
# z = [0.4862049092, -1.0278704657, 0.1220984456, 0.3000129189,
# -0.3770662617, 0.1696090596, -0.0331679127, 0.0023867628])
# force this source to remain a point source?
# Long history here, starting DJS, [decam-chatter 5486] Solved! GAIA separation
# of point sources from extended sources
# Updated for Gaia DR2 by Eisenstein,
# [decam-data 2770] Re: [desi-milkyway 639] GAIA in DECaLS DR7
# And made far more restrictive following BGS feedback.
gaia.pointsource = np.logical_or(
(gaia.G <= 18.) * (gaia.astrometric_excess_noise < 10.**0.5),
(gaia.G <= 13.))
# in our catalog files, this is in float32; in the Gaia data model it's
# a byte, with only values 3 and 31 in DR2.
gaia.astrometric_params_solved = gaia.astrometric_params_solved.astype(
np.uint8)
# Gaia version?
gaiaver = int(os.getenv('GAIA_CAT_VER', '1'))
gaia_release = 'G%i' % gaiaver
gaia.ref_cat = np.array([gaia_release] * len(gaia))
gaia.ref_id = gaia.source_id
gaia.pmra_ivar = 1. / gaia.pmra_error**2
gaia.pmdec_ivar = 1. / gaia.pmdec_error**2
gaia.parallax_ivar = 1. / gaia.parallax_error**2
# mas -> deg
gaia.ra_ivar = 1. / (gaia.ra_error / 1000. / 3600.)**2
gaia.dec_ivar = 1. / (gaia.dec_error / 1000. / 3600.)**2
for c in [
'ra_error', 'dec_error', 'parallax_error', 'pmra_error',
'pmdec_error'
]:
gaia.delete_column(c)
for c in [
'pmra', 'pmdec', 'parallax', 'pmra_ivar', 'pmdec_ivar',
'parallax_ivar'
]:
X = gaia.get(c)
X[np.logical_not(np.isfinite(X))] = 0.
# uniform name w/ Tycho-2
gaia.zguess = gaia.decam_mag_z
gaia.mag = gaia.G
# Take the brighter of G, z to expand masks around red stars.
gaia.mask_mag = np.minimum(gaia.G, gaia.zguess + 1.)
# radius to consider affected by this star, for MASKBITS
gaia.radius = mask_radius_for_mag(gaia.mask_mag)
# radius for keeping this source in the ref catalog
# (eg, for halo subtraction)
gaia.keep_radius = 4. * gaia.radius
gaia.delete_column('G')
gaia.isgaia = np.ones(len(gaia), bool)
gaia.istycho = np.zeros(len(gaia), bool)
gaia.isbright = (gaia.mask_mag < 13.)
gaia.ismedium = (gaia.mask_mag < 16.)
gaia.donotfit = np.zeros(len(gaia), bool)
# NOTE, must initialize gaia.sources array this way, or else numpy
# will try to be clever and create a 2-d array, because GaiaSource is
# iterable.
gaia.sources = np.empty(len(gaia), object)
if bands is not None:
for i, g in enumerate(gaia):
gaia.sources[i] = GaiaSource.from_catalog(g, bands)
return gaia