def __init__(self,expnum=None,ccdname=None,ccdwcs=None):
"""Initialize the class with either the exposure number *and* CCD name, or
directly with the WCS of the CCD of interest.
"""
self.ps1dir = os.getenv('PS1CAT_DIR')
self.nside = 32
if ccdwcs is None:
from legacypipe.common import Decals, DecamImage
decals = Decals()
ccd = decals.find_ccds(expnum=expnum,ccdname=ccdname)[0]
im = DecamImage(decals,ccd)
self.ccdwcs = im.get_wcs()
else:
self.ccdwcs = ccdwcs
def __init__(self,expnum=None,ccdname=None,ccdwcs=None):
"""Initialize the class with either the exposure number *and* CCD name, or
directly with the WCS of the CCD of interest.
"""
self.ps1dir = os.getenv('PS1CAT_DIR')
if self.ps1dir is None:
raise ValueError('You must have the PS1CAT_DIR environment variable set to point to Pan-STARRS1 catalogs')
self.nside = 32
if ccdwcs is None:
from legacypipe.common import Decals
decals = Decals()
ccd = decals.find_ccds(expnum=expnum,ccdname=ccdname)[0]
im = decals.get_image_object(ccd)
self.ccdwcs = im.get_wcs()
else:
self.ccdwcs = ccdwcs
def __init__(self, expnum=None, ccdname=None, ccdwcs=None):
"""Initialize the class with either the exposure number *and* CCD name, or
directly with the WCS of the CCD of interest.
"""
self.ps1dir = os.getenv('PS1CAT_DIR')
if self.ps1dir is None:
raise ValueError(
'You must have the PS1CAT_DIR environment variable set to point to Pan-STARRS1 catalogs'
)
self.nside = 32
if ccdwcs is None:
from legacypipe.common import Decals
decals = Decals()
ccd = decals.find_ccds(expnum=expnum, ccdname=ccdname)[0]
im = decals.get_image_object(ccd)
self.ccdwcs = im.get_wcs()
else:
self.ccdwcs = ccdwcs
from astrometry.util.plotutils import *
from legacyanalysis.ps1cat import ps1cat
from legacypipe.common import Decals
from tractor import Image, PointSource, PixPos, NanoMaggies, Tractor
ps = PlotSequence('rewcs')
expnum, ccdname = 431109, 'N14'
cat = ps1cat(expnum=expnum, ccdname=ccdname)
stars = cat.get_stars()
print len(stars), 'stars'
decals = Decals()
ccd = decals.find_ccds(expnum=expnum,ccdname=ccdname)[0]
im = decals.get_image_object(ccd)
wcs = im.get_wcs()
tim = im.get_tractor_image(pixPsf=True, splinesky=True)
margin = 15
ok,stars.xx,stars.yy = wcs.radec2pixelxy(stars.ra, stars.dec)
stars.xx -= 1.
stars.yy -= 1.
W,H = wcs.get_width(), wcs.get_height()
stars.ix = np.round(stars.xx).astype(int)
stars.iy = np.round(stars.yy).astype(int)
stars.cut((stars.ix >= margin) * (stars.ix < (W-margin)) *
(stars.iy >= margin) * (stars.iy < (H-margin)))
plt.clf()
def psf_residuals(expnum,
ccdname,
stampsize=35,
nstar=30,
magrange=(13, 17),
verbose=0,
splinesky=False):
# Set the debugging level.
if verbose == 0:
lvl = logging.INFO
else:
lvl = logging.DEBUG
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
pngprefix = 'qapsf-{}-{}'.format(expnum, ccdname)
# Gather all the info we need about this CCD.
decals = Decals()
ccd = decals.find_ccds(expnum=expnum, ccdname=ccdname)[0]
band = ccd.filter
ps1band = dict(g=0, r=1, i=2, z=3, Y=4)
print('Band {}'.format(band))
#scales = dict(g=0.0066, r=0.01, z=0.025)
#vmin, vmax = np.arcsinh(-1), np.arcsinh(100)
#print(scales[band])
im = decals.get_image_object(ccd)
iminfo = im.get_image_info()
H, W = iminfo['dims']
wcs = im.get_wcs()
# Choose a uniformly selected subset of PS1 stars on this CCD.
ps1 = ps1cat(ccdwcs=wcs)
cat = ps1.get_stars(band=band, magrange=magrange)
rand = np.random.RandomState(seed=expnum * ccd.ccdnum)
these = rand.choice(len(cat) - 1, nstar, replace=False)
#these = rand.random_integers(0,len(cat)-1,nstar)
cat = cat[these]
cat = cat[np.argsort(cat.median[:, ps1band[band]])] # sort by magnitude
#print(cat.nmag_ok)
get_tim_kwargs = dict(const2psf=True, splinesky=splinesky)
# Make a QAplot of the positions of all the stars.
tim = im.get_tractor_image(**get_tim_kwargs)
img = tim.getImage()
#img = tim.getImage()/scales[band]
fig = plt.figure(figsize=(5, 10))
ax = fig.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)
#ax.imshow(np.arcsinh(img),cmap='gray',interpolation='nearest',
# origin='lower',vmin=vmax,vmax=vmax)
ax.imshow(img, **tim.ima)
ax.axis('off')
ax.set_title('{}: {}/{} AM={:.2f} Seeing={:.3f}"'.format(
band, expnum, ccdname, ccd.airmass, ccd.seeing))
for istar, ps1star in enumerate(cat):
ra, dec = (ps1star.ra, ps1star.dec)
ok, xpos, ypos = wcs.radec2pixelxy(ra, dec)
ax.text(xpos,
ypos,
'{:2d}'.format(istar + 1),
color='red',
horizontalalignment='left')
circ = plt.Circle((xpos, ypos), radius=30, color='g', fill=False, lw=1)
ax.add_patch(circ)
#radec = wcs.radec_bounds()
#ax.scatter(cat.ra,cat.dec)
#ax.set_xlim([radec[1],radec[0]])#*[1.0002,0.9998])
#ax.set_ylim([radec[2],radec[3]])#*[0.985,1.015])
#ax.set_xlabel('$RA\ (deg)$',fontsize=18)
#ax.set_ylabel('$Dec\ (deg)$',fontsize=18)
fig.savefig(pngprefix + '-ccd.png', bbox_inches='tight')
# Initialize the many-stamp QAplot
ncols = 3
nrows = np.ceil(nstar / ncols).astype('int')
inchperstamp = 2.0
fig = plt.figure(figsize=(inchperstamp * 3 * ncols, inchperstamp * nrows))
irow = 0
icol = 0
for istar, ps1star in enumerate(cat):
ra, dec = (ps1star.ra, ps1star.dec)
mag = ps1star.median[ps1band[band]] # r-band
ok, xpos, ypos = wcs.radec2pixelxy(ra, dec)
ix, iy = int(xpos), int(ypos)
# create a little tractor Image object around the star
slc = (slice(max(iy - stampsize, 0), min(iy + stampsize + 1, H)),
slice(max(ix - stampsize, 0), min(ix + stampsize + 1, W)))
# The PSF model 'const2Psf' is the one used in DR1: a 2-component
# Gaussian fit to PsfEx instantiated in the image center.
tim = im.get_tractor_image(slc=slc, **get_tim_kwargs)
stamp = tim.getImage()
ivarstamp = tim.getInvvar()
# Initialize a tractor PointSource from PS1 measurements
flux = NanoMaggies.magToNanomaggies(mag)
star = PointSource(RaDecPos(ra, dec), NanoMaggies(**{band: flux}))
# Fit just the source RA,Dec,flux.
tractor = Tractor([tim], [star])
tractor.freezeParam('images')
print('2-component MOG:', tim.psf)
tractor.printThawedParams()
for step in range(50):
dlnp, X, alpha = tractor.optimize()
if dlnp < 0.1:
break
print('Fit:', star)
model_mog = tractor.getModelImage(0)
chi2_mog = -2.0 * tractor.getLogLikelihood()
mag_mog = NanoMaggies.nanomaggiesToMag(star.brightness)[0]
# Now change the PSF model to a pixelized PSF model from PsfEx instantiated
# at this place in the image.
psf = PixelizedPsfEx(im.psffn)
tim.psf = psf.constantPsfAt(xpos, ypos)
#print('PSF model:', tim.psf)
#tractor.printThawedParams()
for step in range(50):
dlnp, X, alpha = tractor.optimize()
if dlnp < 0.1:
break
print('Fit:', star)
model_psfex = tractor.getModelImage(0)
chi2_psfex = -2.0 * tractor.getLogLikelihood()
mag_psfex = NanoMaggies.nanomaggiesToMag(star.brightness)[0]
#mn, mx = np.percentile((stamp-model_psfex)[ivarstamp>0],[1,95])
sig = np.std((stamp - model_psfex)[ivarstamp > 0])
mn, mx = [-2.0 * sig, 5 * sig]
# Generate a QAplot.
if (istar > 0) and (istar % (ncols) == 0):
irow = irow + 1
icol = 3 * istar - 3 * ncols * irow
#print(istar, irow, icol, icol+1, icol+2)
ax1 = plt.subplot2grid((nrows, 3 * ncols), (irow, icol),
aspect='equal')
ax1.axis('off')
#ax1.imshow(stamp, **tim.ima)
ax1.imshow(stamp,
cmap='gray',
interpolation='nearest',
origin='lower',
vmin=mn,
vmax=mx)
ax1.text(0.1,
0.9,
'{:2d}'.format(istar + 1),
color='white',
horizontalalignment='left',
verticalalignment='top',
transform=ax1.transAxes)
ax2 = plt.subplot2grid((nrows, 3 * ncols), (irow, icol + 1),
aspect='equal')
ax2.axis('off')
#ax2.imshow(stamp-model_mog, **tim.ima)
ax2.imshow(stamp - model_mog,
cmap='gray',
interpolation='nearest',
origin='lower',
vmin=mn,
vmax=mx)
ax2.text(0.1,
0.9,
'MoG',
color='white',
horizontalalignment='left',
verticalalignment='top',
transform=ax2.transAxes)
ax2.text(0.08,
0.08,
'{:.3f}'.format(mag_mog),
color='white',
horizontalalignment='left',
verticalalignment='bottom',
transform=ax2.transAxes)
#ax2.set_title('{:.3f}, {:.2f}'.format(mag_psfex,chi2_psfex),fontsize=14)
#ax2.set_title('{:.3f}, $\chi^{2}$={:.2f}'.format(mag_psfex,chi2_psfex))
ax3 = plt.subplot2grid((nrows, 3 * ncols), (irow, icol + 2),
aspect='equal')
ax3.axis('off')
#ax3.imshow(stamp-model_psfex, **tim.ima)
ax3.imshow(stamp - model_psfex,
cmap='gray',
interpolation='nearest',
origin='lower',
vmin=mn,
vmax=mx)
ax3.text(0.1,
0.9,
'PSFEx',
color='white',
horizontalalignment='left',
verticalalignment='top',
transform=ax3.transAxes)
ax3.text(0.08,
0.08,
'{:.3f}'.format(mag_psfex),
color='white',
horizontalalignment='left',
verticalalignment='bottom',
transform=ax3.transAxes)
if istar == (nstar - 1):
break
fig.savefig(pngprefix + '-stargrid.png', bbox_inches='tight')
def psf_residuals(expnum,ccdname,stampsize=35,nstar=30,
magrange=(13,17),verbose=0):
# Set the debugging level.
if verbose==0:
lvl = logging.INFO
else:
lvl = logging.DEBUG
logging.basicConfig(level=lvl,format='%(message)s',stream=sys.stdout)
pngprefix = 'qapsf-{}-{}'.format(expnum,ccdname)
# Gather all the info we need about this CCD.
decals = Decals()
ccd = decals.find_ccds(expnum=expnum,ccdname=ccdname)[0]
band = ccd.filter
ps1band = dict(g=0,r=1,i=2,z=3,Y=4)
print('Band {}'.format(band))
#scales = dict(g=0.0066, r=0.01, z=0.025)
#vmin, vmax = np.arcsinh(-1), np.arcsinh(100)
#print(scales[band])
im = DecamImage(decals,ccd)
iminfo = im.get_image_info()
H,W = iminfo['dims']
wcs = im.get_wcs()
# Choose a uniformly selected subset of PS1 stars on this CCD.
ps1 = ps1cat(ccdwcs=wcs)
cat = ps1.get_stars(band=band,magrange=magrange)
rand = np.random.RandomState(seed=expnum*ccd.ccdnum)
these = rand.choice(len(cat)-1,nstar,replace=False)
#these = rand.random_integers(0,len(cat)-1,nstar)
cat = cat[these]
cat = cat[np.argsort(cat.median[:,ps1band[band]])] # sort by magnitude
#print(cat.nmag_ok)
# Make a QAplot of the positions of all the stars.
tim = im.get_tractor_image(const2psf=True)
img = tim.getImage()
#img = tim.getImage()/scales[band]
fig = plt.figure(figsize=(5,10))
ax = fig.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)
#ax.imshow(np.arcsinh(img),cmap='gray',interpolation='nearest',
# origin='lower',vmin=vmax,vmax=vmax)
ax.imshow(img, **tim.ima)
ax.axis('off')
ax.set_title('{}: {}/{} AM={:.2f} Seeing={:.3f}"'.
format(band,expnum,ccdname,ccd.airmass,ccd.seeing))
for istar, ps1star in enumerate(cat):
ra, dec = (ps1star.ra, ps1star.dec)
ok, xpos, ypos = wcs.radec2pixelxy(ra, dec)
ax.text(xpos,ypos,'{:2d}'.format(istar+1),color='red',
horizontalalignment='left')
circ = plt.Circle((xpos,ypos),radius=30,color='g',fill=False,lw=1)
ax.add_patch(circ)
#radec = wcs.radec_bounds()
#ax.scatter(cat.ra,cat.dec)
#ax.set_xlim([radec[1],radec[0]])#*[1.0002,0.9998])
#ax.set_ylim([radec[2],radec[3]])#*[0.985,1.015])
#ax.set_xlabel('$RA\ (deg)$',fontsize=18)
#ax.set_ylabel('$Dec\ (deg)$',fontsize=18)
fig.savefig(pngprefix+'-ccd.png',bbox_inches='tight')
# Initialize the many-stamp QAplot
ncols = 3
nrows = np.ceil(nstar/ncols).astype('int')
inchperstamp = 2.0
fig = plt.figure(figsize=(inchperstamp*3*ncols,inchperstamp*nrows))
irow = 0
icol = 0
for istar, ps1star in enumerate(cat):
ra, dec = (ps1star.ra, ps1star.dec)
mag = ps1star.median[ps1band[band]] # r-band
ok, xpos, ypos = wcs.radec2pixelxy(ra, dec)
ix,iy = int(xpos), int(ypos)
# create a little tractor Image object around the star
slc = (slice(max(iy-stampsize, 0), min(iy+stampsize+1, H)),
slice(max(ix-stampsize, 0), min(ix+stampsize+1, W)))
# The PSF model 'const2Psf' is the one used in DR1: a 2-component
# Gaussian fit to PsfEx instantiated in the image center.
tim = im.get_tractor_image(slc=slc, const2psf=True)
stamp = tim.getImage()
ivarstamp = tim.getInvvar()
# Initialize a tractor PointSource from PS1 measurements
flux = NanoMaggies.magToNanomaggies(mag)
star = PointSource(RaDecPos(ra,dec), NanoMaggies(**{band: flux}))
# Fit just the source RA,Dec,flux.
tractor = Tractor([tim], [star])
tractor.freezeParam('images')
print('2-component MOG:', tim.psf)
tractor.printThawedParams()
for step in range(50):
dlnp,X,alpha = tractor.optimize()
if dlnp < 0.1:
break
print('Fit:', star)
model_mog = tractor.getModelImage(0)
chi2_mog = -2.0*tractor.getLogLikelihood()
mag_mog = NanoMaggies.nanomaggiesToMag(star.brightness)[0]
# Now change the PSF model to a pixelized PSF model from PsfEx instantiated
# at this place in the image.
psf = PixelizedPsfEx(im.psffn)
tim.psf = psf.constantPsfAt(xpos, ypos)
#print('PSF model:', tim.psf)
#tractor.printThawedParams()
for step in range(50):
dlnp,X,alpha = tractor.optimize()
if dlnp < 0.1:
break
print('Fit:', star)
model_psfex = tractor.getModelImage(0)
chi2_psfex = -2.0*tractor.getLogLikelihood()
mag_psfex = NanoMaggies.nanomaggiesToMag(star.brightness)[0]
#mn, mx = np.percentile((stamp-model_psfex)[ivarstamp>0],[1,95])
sig = np.std((stamp-model_psfex)[ivarstamp>0])
mn, mx = [-2.0*sig,5*sig]
# Generate a QAplot.
if (istar>0) and (istar%(ncols)==0):
irow = irow+1
icol = 3*istar - 3*ncols*irow
#print(istar, irow, icol, icol+1, icol+2)
ax1 = plt.subplot2grid((nrows,3*ncols), (irow,icol), aspect='equal')
ax1.axis('off')
#ax1.imshow(stamp, **tim.ima)
ax1.imshow(stamp,cmap='gray',interpolation='nearest',
origin='lower',vmin=mn,vmax=mx)
ax1.text(0.1,0.9,'{:2d}'.format(istar+1),color='white',
horizontalalignment='left',verticalalignment='top',
transform=ax1.transAxes)
ax2 = plt.subplot2grid((nrows,3*ncols), (irow,icol+1), aspect='equal')
ax2.axis('off')
#ax2.imshow(stamp-model_mog, **tim.ima)
ax2.imshow(stamp-model_mog,cmap='gray',interpolation='nearest',
origin='lower',vmin=mn,vmax=mx)
ax2.text(0.1,0.9,'MoG',color='white',
horizontalalignment='left',verticalalignment='top',
transform=ax2.transAxes)
ax2.text(0.08,0.08,'{:.3f}'.format(mag_mog),color='white',
horizontalalignment='left',verticalalignment='bottom',
transform=ax2.transAxes)
#ax2.set_title('{:.3f}, {:.2f}'.format(mag_psfex,chi2_psfex),fontsize=14)
#ax2.set_title('{:.3f}, $\chi^{2}$={:.2f}'.format(mag_psfex,chi2_psfex))
ax3 = plt.subplot2grid((nrows,3*ncols), (irow,icol+2), aspect='equal')
ax3.axis('off')
#ax3.imshow(stamp-model_psfex, **tim.ima)
ax3.imshow(stamp-model_psfex,cmap='gray',interpolation='nearest',
origin='lower',vmin=mn,vmax=mx)
ax3.text(0.1,0.9,'PSFEx',color='white',
horizontalalignment='left',verticalalignment='top',
transform=ax3.transAxes)
ax3.text(0.08,0.08,'{:.3f}'.format(mag_psfex),color='white',
horizontalalignment='left',verticalalignment='bottom',
transform=ax3.transAxes)
if istar==(nstar-1):
break
fig.savefig(pngprefix+'-stargrid.png',bbox_inches='tight')
def main(decals=None, opt=None):
'''Driver function for forced photometry of individual DECam images.
'''
if opt is None:
parser = get_parser()
opt = parser.parse_args()
Time.add_measurement(MemMeas)
t0 = Time()
if os.path.exists(opt.outfn):
print('Ouput file exists:', opt.outfn)
sys.exit(0)
if not opt.forced:
opt.apphot = True
zoomslice = None
if opt.zoom is not None:
(x0,x1,y0,y1) = opt.zoom
zoomslice = (slice(y0,y1), slice(x0,x1))
ps = None
if opt.plots is not None:
from astrometry.util.plotutils import PlotSequence
ps = PlotSequence(opt.plots)
# Try parsing filename as exposure number.
try:
expnum = int(opt.filename)
opt.filename = None
except:
# make this 'None' for decals.find_ccds()
expnum = None
# Try parsing HDU number
try:
opt.hdu = int(opt.hdu)
ccdname = None
except:
ccdname = opt.hdu
opt.hdu = -1
if decals is None:
decals = Decals()
if opt.filename is not None and opt.hdu >= 0:
# Read metadata from file
T = exposure_metadata([opt.filename], hdus=[opt.hdu])
print('Metadata:')
T.about()
else:
# Read metadata from decals-ccds.fits table
T = decals.find_ccds(expnum=expnum, ccdname=ccdname)
print(len(T), 'with expnum', expnum, 'and CCDname', ccdname)
if opt.hdu >= 0:
T.cut(T.image_hdu == opt.hdu)
print(len(T), 'with HDU', opt.hdu)
if opt.filename is not None:
T.cut(np.array([f.strip() == opt.filename for f in T.image_filename]))
print(len(T), 'with filename', opt.filename)
assert(len(T) == 1)
im = decals.get_image_object(T[0])
tim = im.get_tractor_image(slc=zoomslice, pixPsf=True, splinesky=True)
print('Got tim:', tim)
if opt.catfn in ['DR1', 'DR2']:
if opt.catalog_path is None:
opt.catalog_path = opt.catfn.lower()
margin = 20
TT = []
chipwcs = tim.subwcs
bricks = bricks_touching_wcs(chipwcs, decals=decals)
for b in bricks:
# there is some overlap with this brick... read the catalog.
fn = os.path.join(opt.catalog_path, 'tractor', b.brickname[:3],
'tractor-%s.fits' % b.brickname)
if not os.path.exists(fn):
print('WARNING: catalog', fn, 'does not exist. Skipping!')
continue
print('Reading', fn)
T = fits_table(fn)
ok,xx,yy = chipwcs.radec2pixelxy(T.ra, T.dec)
W,H = chipwcs.get_width(), chipwcs.get_height()
I = np.flatnonzero((xx >= -margin) * (xx <= (W+margin)) *
(yy >= -margin) * (yy <= (H+margin)))
T.cut(I)
print('Cut to', len(T), 'sources within image + margin')
# print('Brick_primary:', np.unique(T.brick_primary))
T.cut(T.brick_primary)
print('Cut to', len(T), 'on brick_primary')
T.cut((T.out_of_bounds == False) * (T.left_blob == False))
print('Cut to', len(T), 'on out_of_bounds and left_blob')
TT.append(T)
T = merge_tables(TT)
T._header = TT[0]._header
del TT
# Fix up various failure modes:
# FixedCompositeGalaxy(pos=RaDecPos[240.51147402832561, 10.385488075518923], brightness=NanoMaggies: g=(flux -2.87), r=(flux -5.26), z=(flux -7.65), fracDev=FracDev(0.60177207), shapeExp=re=3.78351e-44, e1=9.30367e-13, e2=1.24392e-16, shapeDev=re=inf, e1=-0, e2=-0)
# -> convert to EXP
I = np.flatnonzero(np.array([((t.type == 'COMP') and
(not np.isfinite(t.shapedev_r)))
for t in T]))
if len(I):
print('Converting', len(I), 'bogus COMP galaxies to EXP')
for i in I:
T.type[i] = 'EXP'
# Same thing with the exp component.
# -> convert to DEV
I = np.flatnonzero(np.array([((t.type == 'COMP') and
(not np.isfinite(t.shapeexp_r)))
for t in T]))
if len(I):
print('Converting', len(I), 'bogus COMP galaxies to DEV')
for i in I:
T.type[i] = 'DEV'
if opt.write_cat:
T.writeto(opt.write_cat)
print('Wrote catalog to', opt.write_cat)
else:
T = fits_table(opt.catfn)
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
cat = read_fits_catalog(T, ellipseClass=tractor.ellipses.EllipseE)
# print('Got cat:', cat)
print('Forced photom...')
opti = None
if opt.ceres:
from tractor.ceres_optimizer import CeresOptimizer
B = 8
opti = CeresOptimizer(BW=B, BH=B)
tr = Tractor([tim], cat, optimizer=opti)
tr.freezeParam('images')
for src in cat:
src.freezeAllBut('brightness')
src.getBrightness().freezeAllBut(tim.band)
F = fits_table()
F.brickid = T.brickid
F.brickname = T.brickname
F.objid = T.objid
F.filter = np.array([tim.band] * len(T))
F.mjd = np.array([tim.primhdr['MJD-OBS']] * len(T))
F.exptime = np.array([tim.primhdr['EXPTIME']] * len(T))
ok,x,y = tim.sip_wcs.radec2pixelxy(T.ra, T.dec)
F.x = (x-1).astype(np.float32)
F.y = (y-1).astype(np.float32)
if opt.apphot:
import photutils
img = tim.getImage()
ie = tim.getInvError()
with np.errstate(divide='ignore'):
imsigma = 1. / ie
imsigma[ie == 0] = 0.
apimg = []
apimgerr = []
# Aperture photometry locations
xxyy = np.vstack([tim.wcs.positionToPixel(src.getPosition()) for src in cat]).T
apxy = xxyy - 1.
apertures = apertures_arcsec / tim.wcs.pixel_scale()
print('Apertures:', apertures, 'pixels')
for rad in apertures:
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(img, aper, error=imsigma)
apimg.append(p.field('aperture_sum'))
apimgerr.append(p.field('aperture_sum_err'))
ap = np.vstack(apimg).T
ap[np.logical_not(np.isfinite(ap))] = 0.
F.apflux = ap
ap = 1./(np.vstack(apimgerr).T)**2
ap[np.logical_not(np.isfinite(ap))] = 0.
F.apflux_ivar = ap
if opt.forced:
kwa = {}
if opt.plots is None:
kwa.update(wantims=False)
R = tr.optimize_forced_photometry(variance=True, fitstats=True,
shared_params=False, **kwa)
if opt.plots:
(data,mod,ie,chi,roi) = R.ims1[0]
ima = tim.ima
imchi = dict(interpolation='nearest', origin='lower', vmin=-5, vmax=5)
plt.clf()
plt.imshow(data, **ima)
plt.title('Data: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(mod, **ima)
plt.title('Model: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(chi, **imchi)
plt.title('Chi: %s' % tim.name)
ps.savefig()
F.flux = np.array([src.getBrightness().getFlux(tim.band)
for src in cat]).astype(np.float32)
F.flux_ivar = R.IV.astype(np.float32)
F.fracflux = R.fitstats.profracflux.astype(np.float32)
F.rchi2 = R.fitstats.prochi2 .astype(np.float32)
program_name = sys.argv[0]
version_hdr = get_version_header(program_name, decals.decals_dir)
# HACK -- print only two directory names + filename of CPFILE.
fname = os.path.basename(im.imgfn)
d = os.path.dirname(im.imgfn)
d1 = os.path.basename(d)
d = os.path.dirname(d)
d2 = os.path.basename(d)
fname = os.path.join(d2, d1, fname)
print('Trimmed filename to', fname)
#version_hdr.add_record(dict(name='CPFILE', value=im.imgfn, comment='DECam comm.pipeline file'))
version_hdr.add_record(dict(name='CPFILE', value=fname, comment='DECam comm.pipeline file'))
version_hdr.add_record(dict(name='CPHDU', value=im.hdu, comment='DECam comm.pipeline ext'))
version_hdr.add_record(dict(name='CAMERA', value='DECam', comment='Dark Energy Camera'))
version_hdr.add_record(dict(name='EXPNUM', value=im.expnum, comment='DECam exposure num'))
version_hdr.add_record(dict(name='CCDNAME', value=im.ccdname, comment='DECam CCD name'))
version_hdr.add_record(dict(name='FILTER', value=tim.band, comment='Bandpass of this image'))
version_hdr.add_record(dict(name='EXPOSURE', value='decam-%s-%s' % (im.expnum, im.ccdname), comment='Name of this image'))
keys = ['TELESCOP','OBSERVAT','OBS-LAT','OBS-LONG','OBS-ELEV',
'INSTRUME']
for key in keys:
if key in tim.primhdr:
version_hdr.add_record(dict(name=key, value=tim.primhdr[key]))
hdr = fitsio.FITSHDR()
units = {'mjd':'sec', 'exptime':'sec', 'flux':'nanomaggy',
'flux_ivar':'1/nanomaggy^2'}
columns = F.get_columns()
for i,col in enumerate(columns):
if col in units:
hdr.add_record(dict(name='TUNIT%i' % (i+1), value=units[col]))
outdir = os.path.dirname(opt.outfn)
if len(outdir):
trymakedirs(outdir)
fitsio.write(opt.outfn, None, header=version_hdr, clobber=True)
F.writeto(opt.outfn, header=hdr, append=True)
print('Wrote', opt.outfn)
print('Finished forced phot:', Time()-t0)
return 0
def main(decals=None, opt=None):
'''Driver function for forced photometry of individual DECam images.
'''
if opt is None:
parser = get_parser()
opt = parser.parse_args()
Time.add_measurement(MemMeas)
t0 = Time()
if os.path.exists(opt.outfn):
print('Ouput file exists:', opt.outfn)
sys.exit(0)
if not opt.forced:
opt.apphot = True
zoomslice = None
if opt.zoom is not None:
(x0, x1, y0, y1) = opt.zoom
zoomslice = (slice(y0, y1), slice(x0, x1))
ps = None
if opt.plots is not None:
from astrometry.util.plotutils import PlotSequence
ps = PlotSequence(opt.plots)
# Try parsing filename as exposure number.
try:
expnum = int(opt.filename)
opt.filename = None
except:
# make this 'None' for decals.find_ccds()
expnum = None
# Try parsing HDU number
try:
opt.hdu = int(opt.hdu)
ccdname = None
except:
ccdname = opt.hdu
opt.hdu = -1
if decals is None:
decals = Decals()
if opt.filename is not None and opt.hdu >= 0:
# Read metadata from file
T = exposure_metadata([opt.filename], hdus=[opt.hdu])
print('Metadata:')
T.about()
else:
# Read metadata from decals-ccds.fits table
T = decals.find_ccds(expnum=expnum, ccdname=ccdname)
print(len(T), 'with expnum', expnum, 'and CCDname', ccdname)
if opt.hdu >= 0:
T.cut(T.image_hdu == opt.hdu)
print(len(T), 'with HDU', opt.hdu)
if opt.filename is not None:
T.cut(
np.array([f.strip() == opt.filename
for f in T.image_filename]))
print(len(T), 'with filename', opt.filename)
assert (len(T) == 1)
im = decals.get_image_object(T[0])
tim = im.get_tractor_image(slc=zoomslice, pixPsf=True, splinesky=True)
print('Got tim:', tim)
if opt.catfn in ['DR1', 'DR2']:
if opt.catalog_path is None:
opt.catalog_path = opt.catfn.lower()
margin = 20
TT = []
chipwcs = tim.subwcs
bricks = bricks_touching_wcs(chipwcs, decals=decals)
for b in bricks:
# there is some overlap with this brick... read the catalog.
fn = os.path.join(opt.catalog_path, 'tractor', b.brickname[:3],
'tractor-%s.fits' % b.brickname)
if not os.path.exists(fn):
print('WARNING: catalog', fn, 'does not exist. Skipping!')
continue
print('Reading', fn)
T = fits_table(fn)
ok, xx, yy = chipwcs.radec2pixelxy(T.ra, T.dec)
W, H = chipwcs.get_width(), chipwcs.get_height()
I = np.flatnonzero((xx >= -margin) * (xx <= (W + margin)) *
(yy >= -margin) * (yy <= (H + margin)))
T.cut(I)
print('Cut to', len(T), 'sources within image + margin')
# print('Brick_primary:', np.unique(T.brick_primary))
T.cut(T.brick_primary)
print('Cut to', len(T), 'on brick_primary')
T.cut((T.out_of_bounds == False) * (T.left_blob == False))
print('Cut to', len(T), 'on out_of_bounds and left_blob')
TT.append(T)
T = merge_tables(TT)
T._header = TT[0]._header
del TT
# Fix up various failure modes:
# FixedCompositeGalaxy(pos=RaDecPos[240.51147402832561, 10.385488075518923], brightness=NanoMaggies: g=(flux -2.87), r=(flux -5.26), z=(flux -7.65), fracDev=FracDev(0.60177207), shapeExp=re=3.78351e-44, e1=9.30367e-13, e2=1.24392e-16, shapeDev=re=inf, e1=-0, e2=-0)
# -> convert to EXP
I = np.flatnonzero(
np.array([((t.type == 'COMP') and (not np.isfinite(t.shapedev_r)))
for t in T]))
if len(I):
print('Converting', len(I), 'bogus COMP galaxies to EXP')
for i in I:
T.type[i] = 'EXP'
# Same thing with the exp component.
# -> convert to DEV
I = np.flatnonzero(
np.array([((t.type == 'COMP') and (not np.isfinite(t.shapeexp_r)))
for t in T]))
if len(I):
print('Converting', len(I), 'bogus COMP galaxies to DEV')
for i in I:
T.type[i] = 'DEV'
if opt.write_cat:
T.writeto(opt.write_cat)
print('Wrote catalog to', opt.write_cat)
else:
T = fits_table(opt.catfn)
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
cat = read_fits_catalog(T, ellipseClass=tractor.ellipses.EllipseE)
# print('Got cat:', cat)
print('Forced photom...')
opti = None
if opt.ceres:
from tractor.ceres_optimizer import CeresOptimizer
B = 8
opti = CeresOptimizer(BW=B, BH=B)
tr = Tractor([tim], cat, optimizer=opti)
tr.freezeParam('images')
for src in cat:
src.freezeAllBut('brightness')
src.getBrightness().freezeAllBut(tim.band)
F = fits_table()
F.brickid = T.brickid
F.brickname = T.brickname
F.objid = T.objid
F.filter = np.array([tim.band] * len(T))
F.mjd = np.array([tim.primhdr['MJD-OBS']] * len(T))
F.exptime = np.array([tim.primhdr['EXPTIME']] * len(T))
ok, x, y = tim.sip_wcs.radec2pixelxy(T.ra, T.dec)
F.x = (x - 1).astype(np.float32)
F.y = (y - 1).astype(np.float32)
if opt.apphot:
import photutils
img = tim.getImage()
ie = tim.getInvError()
with np.errstate(divide='ignore'):
imsigma = 1. / ie
imsigma[ie == 0] = 0.
apimg = []
apimgerr = []
# Aperture photometry locations
xxyy = np.vstack(
[tim.wcs.positionToPixel(src.getPosition()) for src in cat]).T
apxy = xxyy - 1.
apertures = apertures_arcsec / tim.wcs.pixel_scale()
print('Apertures:', apertures, 'pixels')
for rad in apertures:
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(img, aper, error=imsigma)
apimg.append(p.field('aperture_sum'))
apimgerr.append(p.field('aperture_sum_err'))
ap = np.vstack(apimg).T
ap[np.logical_not(np.isfinite(ap))] = 0.
F.apflux = ap
ap = 1. / (np.vstack(apimgerr).T)**2
ap[np.logical_not(np.isfinite(ap))] = 0.
F.apflux_ivar = ap
if opt.forced:
kwa = {}
if opt.plots is None:
kwa.update(wantims=False)
R = tr.optimize_forced_photometry(variance=True,
fitstats=True,
shared_params=False,
**kwa)
if opt.plots:
(data, mod, ie, chi, roi) = R.ims1[0]
ima = tim.ima
imchi = dict(interpolation='nearest',
origin='lower',
vmin=-5,
vmax=5)
plt.clf()
plt.imshow(data, **ima)
plt.title('Data: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(mod, **ima)
plt.title('Model: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(chi, **imchi)
plt.title('Chi: %s' % tim.name)
ps.savefig()
F.flux = np.array([
src.getBrightness().getFlux(tim.band) for src in cat
]).astype(np.float32)
F.flux_ivar = R.IV.astype(np.float32)
F.fracflux = R.fitstats.profracflux.astype(np.float32)
F.rchi2 = R.fitstats.prochi2.astype(np.float32)
program_name = sys.argv[0]
version_hdr = get_version_header(program_name, decals.decals_dir)
# HACK -- print only two directory names + filename of CPFILE.
fname = os.path.basename(im.imgfn)
d = os.path.dirname(im.imgfn)
d1 = os.path.basename(d)
d = os.path.dirname(d)
d2 = os.path.basename(d)
fname = os.path.join(d2, d1, fname)
print('Trimmed filename to', fname)
#version_hdr.add_record(dict(name='CPFILE', value=im.imgfn, comment='DECam comm.pipeline file'))
version_hdr.add_record(
dict(name='CPFILE', value=fname, comment='DECam comm.pipeline file'))
version_hdr.add_record(
dict(name='CPHDU', value=im.hdu, comment='DECam comm.pipeline ext'))
version_hdr.add_record(
dict(name='CAMERA', value='DECam', comment='Dark Energy Camera'))
version_hdr.add_record(
dict(name='EXPNUM', value=im.expnum, comment='DECam exposure num'))
version_hdr.add_record(
dict(name='CCDNAME', value=im.ccdname, comment='DECam CCD name'))
version_hdr.add_record(
dict(name='FILTER', value=tim.band, comment='Bandpass of this image'))
version_hdr.add_record(
dict(name='EXPOSURE',
value='decam-%s-%s' % (im.expnum, im.ccdname),
comment='Name of this image'))
keys = [
'TELESCOP', 'OBSERVAT', 'OBS-LAT', 'OBS-LONG', 'OBS-ELEV', 'INSTRUME'
]
for key in keys:
if key in tim.primhdr:
version_hdr.add_record(dict(name=key, value=tim.primhdr[key]))
hdr = fitsio.FITSHDR()
units = {
'mjd': 'sec',
'exptime': 'sec',
'flux': 'nanomaggy',
'flux_ivar': '1/nanomaggy^2'
}
columns = F.get_columns()
for i, col in enumerate(columns):
if col in units:
hdr.add_record(dict(name='TUNIT%i' % (i + 1), value=units[col]))
outdir = os.path.dirname(opt.outfn)
if len(outdir):
trymakedirs(outdir)
fitsio.write(opt.outfn, None, header=version_hdr, clobber=True)
F.writeto(opt.outfn, header=hdr, append=True)
print('Wrote', opt.outfn)
print('Finished forced phot:', Time() - t0)
return 0
