def compare_brick_to_ps1(brickname, ps, name='', basedir=''):
decals = Decals()
brick = decals.get_brick_by_name(brickname)
wcs = wcs_for_brick(brick)
magrange = (15,20)
ps1 = ps1cat(ccdwcs=wcs)
ps1 = ps1.get_stars(magrange=magrange)
print 'Got', len(ps1), 'PS1 stars'
T = fits_table(os.path.join(basedir, 'tractor', brickname[:3],
'tractor-%s.fits' % brickname))
I,J,d = match_radec(T.ra, T.dec, ps1.ra, ps1.dec, 1./3600.)
print 'Matched', len(I), 'stars to PS1'
T.cut(I)
ps1.cut(J)
bands = 'z'
ap = 5
allbands = 'ugrizY'
mags = np.arange(magrange[0], 1+magrange[1])
for band in bands:
iband = allbands.index(band)
piband = ps1cat.ps1band[band]
T.flux = T.decam_flux[:,iband]
T.mag = NanoMaggies.nanomaggiesToMag(T.flux)
print 'apflux shape', T.decam_apflux.shape
T.apflux = T.decam_apflux[:, iband, ap]
T.apmag = NanoMaggies.nanomaggiesToMag(T.apflux)
ps1mag = ps1.median[:,piband]
plt.clf()
for cc,mag,label in [('b', T.mag, 'Mag'), ('r', T.apmag, 'Aper mag')]:
plt.plot(ps1mag, mag - ps1mag, '.', color=cc, label=label, alpha=0.6)
mm,dd = [],[]
for mlo,mhi in zip(mags, mags[1:]):
I = np.flatnonzero((ps1mag > mlo) * (ps1mag <= mhi))
mm.append((mlo+mhi)/2.)
dd.append(np.median(mag[I] - ps1mag[I]))
plt.plot(mm, dd, 'o-', color=cc)
plt.xlabel('PS1 %s mag' % band)
plt.ylabel('Mag - PS1 (mag)')
plt.title('%sPS1 comparison: brick %s' % (name, brickname))
plt.ylim(-0.2, 0.2)
mlo,mhi = magrange
plt.xlim(mhi, mlo)
plt.axhline(0., color='k', alpha=0.1)
plt.legend()
ps.savefig()
def apphot_ps1stars(ccd, ps,
apertures,
decals,
sky_inner_r=40,
sky_outer_r=50):
im = decals.get_image_object(ccd)
tim = im.get_tractor_image(gaussPsf=True, splinesky=True)
img = tim.getImage()
wcs = tim.subwcs
magrange = (15,21)
ps1 = ps1cat(ccdwcs=wcs)
ps1 = ps1.get_stars(magrange=magrange)
print 'Got', len(ps1), 'PS1 stars'
band = ccd.filter
piband = ps1cat.ps1band[band]
print 'band:', band
ps1.cut(ps1.nmag_ok[:,piband] > 0)
print 'Keeping', len(ps1), 'stars with nmag_ok'
ok,x,y = wcs.radec2pixelxy(ps1.ra, ps1.dec)
apxy = np.vstack((x - 1., y - 1.)).T
ap = []
aperr = []
nmasked = []
with np.errstate(divide='ignore'):
ie = tim.getInvError()
imsigma = 1. / ie
imsigma[ie == 0] = 0
mask = (imsigma == 0)
for rad in apertures:
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(img, aper, error=imsigma, mask=mask)
aperr.append(p.field('aperture_sum_err'))
ap.append(p.field('aperture_sum'))
p = photutils.aperture_photometry((ie == 0), aper)
nmasked.append(p.field('aperture_sum'))
ap = np.vstack(ap).T
aperr = np.vstack(aperr).T
nmasked = np.vstack(nmasked).T
print 'Aperture fluxes:', ap[:5]
print 'Aperture flux errors:', aperr[:5]
print 'Nmasked:', nmasked[:5]
H,W = img.shape
sky = []
skysigma = []
skymed = []
skynmasked = []
for xi,yi in zip(x,y):
ix = int(np.round(xi))
iy = int(np.round(yi))
skyR = sky_outer_r
xlo = max(0, ix-skyR)
xhi = min(W, ix+skyR+1)
ylo = max(0, iy-skyR)
yhi = min(H, iy+skyR+1)
xx,yy = np.meshgrid(np.arange(xlo,xhi), np.arange(ylo,yhi))
r2 = (xx - xi)**2 + (yy - yi)**2
inannulus = ((r2 >= sky_inner_r**2) * (r2 < sky_outer_r**2))
unmasked = (ie[ylo:yhi, xlo:xhi] > 0)
#sky.append(np.median(img[ylo:yhi, xlo:xhi][inannulus * unmasked]))
skypix = img[ylo:yhi, xlo:xhi][inannulus * unmasked]
# this is the default value...
nsigma = 4.
goodpix,lo,hi = sigmaclip(skypix, low=nsigma, high=nsigma)
# sigmaclip returns unclipped pixels, lo,hi, where lo,hi are
# mean(goodpix) +- nsigma * sigma
meansky = np.mean(goodpix)
sky.append(meansky)
skysigma.append((meansky - lo) / nsigma)
skymed.append(np.median(skypix))
skynmasked.append(np.sum(inannulus * np.logical_not(unmasked)))
sky = np.array(sky)
skysigma = np.array(skysigma)
skymed = np.array(skymed)
skynmasked = np.array(skynmasked)
print 'sky', sky[:5]
print 'median sky', skymed[:5]
print 'sky sigma', skysigma[:5]
psmag = ps1.median[:,piband]
ap2 = ap - sky[:,np.newaxis] * (np.pi * apertures**2)[np.newaxis,:]
if ps is not None:
plt.clf()
nstars,naps = ap.shape
for iap in range(naps):
plt.plot(psmag, ap[:,iap], 'b.')
#for iap in range(naps):
# plt.plot(psmag, ap2[:,iap], 'r.')
plt.yscale('symlog')
plt.xlabel('PS1 %s mag' % band)
plt.ylabel('DECam Aperture Flux')
#plt.plot(psmag, nmasked[:,-1], 'ro')
plt.plot(np.vstack((psmag,psmag)), np.vstack((np.zeros_like(psmag),nmasked[:,-1])), 'r-', alpha=0.5)
plt.ylim(0, 1e3)
ps.savefig()
plt.clf()
plt.plot(ap.T / np.max(ap, axis=1), '.')
plt.ylim(0, 1)
ps.savefig()
plt.clf()
dimshow(tim.getImage(), **tim.ima)
ax = plt.axis()
plt.plot(x, y, 'o', mec='r', mfc='none', ms=10)
plt.axis(ax)
ps.savefig()
color = ps1_to_decam(ps1.median, band)
print 'Color terms:', color
T = fits_table()
T.apflux = ap.astype(np.float32)
T.apfluxerr = aperr.astype(np.float32)
T.apnmasked = nmasked.astype(np.int16)
# Zero out the errors when pixels are masked
T.apfluxerr[T.apnmasked > 0] = 0.
#T.apflux2 = ap2.astype(np.float32)
T.sky = sky.astype(np.float32)
T.skysigma = skysigma.astype(np.float32)
T.expnum = np.array([ccd.expnum] * len(T))
T.ccdname = np.array([ccd.ccdname] * len(T)).astype('S3')
T.band = np.array([band] * len(T))
T.ps1_objid = ps1.obj_id
T.ps1_mag = psmag + color
T.ra = ps1.ra
T.dec = ps1.dec
T.tai = np.array([tim.time.toMjd()] * len(T)).astype(np.float32)
T.airmass = np.array([tim.primhdr['AIRMASS']] * len(T)).astype(np.float32)
T.x = (x + tim.x0).astype(np.float32)
T.y = (y + tim.y0).astype(np.float32)
if False:
plt.clf()
plt.plot(skymed, sky, 'b.')
plt.xlabel('sky median')
plt.ylabel('sigma-clipped sky')
ax = plt.axis()
lo,hi = min(ax),max(ax)
plt.plot([lo,hi],[lo,hi],'k-', alpha=0.25)
plt.axis(ax)
ps.savefig()
return T, tim.primhdr
def compare_to_ps1(ps, ccds):
decals = Decals()
allplots = []
for expnum,ccdname in ccds:
ccd = decals.find_ccds(expnum=expnum, ccdname=ccdname)
assert(len(ccd) == 1)
ccd = ccd[0]
im = decals.get_image_object(ccd)
print 'Reading', im
wcs = im.get_wcs()
magrange = (15,20)
ps1 = ps1cat(ccdwcs=wcs)
ps1 = ps1.get_stars(band=im.band, magrange=magrange)
print 'Got', len(ps1), 'PS1 stars'
# ps1.about()
F = fits_table('forced-%i-%s.fits' % (expnum, ccdname))
print 'Read', len(F), 'forced-phot results'
F.ra,F.dec = wcs.pixelxy2radec(F.x+1, F.y+1)
I,J,d = match_radec(F.ra, F.dec, ps1.ra, ps1.dec, 1./3600.)
print 'Matched', len(I), 'stars to PS1'
F.cut(I)
ps1.cut(J)
F.mag = NanoMaggies.nanomaggiesToMag(F.flux)
F.apmag = NanoMaggies.nanomaggiesToMag(F.apflux[:,5])
iband = ps1cat.ps1band[im.band]
ps1mag = ps1.median[:,iband]
mags = np.arange(magrange[0], 1+magrange[1])
psf = im.read_psf_model(0, 0, pixPsf=True)
pixscale = 0.262
apertures = apertures_arcsec / pixscale
h,w = ccd.height, ccd.width
psfimg = psf.getPointSourcePatch(w/2., h/2.).patch
ph,pw = psfimg.shape
cx,cy = pw/2, ph/2
apphot = []
for rad in apertures:
aper = photutils.CircularAperture((cx,cy), rad)
p = photutils.aperture_photometry(psfimg, aper)
apphot.append(p.field('aperture_sum'))
apphot = np.hstack(apphot)
print 'aperture photometry:', apphot
skyest = apphot[6] - apphot[5]
print 'Sky estimate:', skyest
skyest /= np.pi * (apertures[6]**2 - apertures[5]**2)
print 'Sky estimate per pixel:', skyest
fraction = apphot[5] - skyest * np.pi * apertures[5]**2
print 'Fraction of flux:', fraction
zp = 2.5 * np.log10(fraction)
print 'ZP adjustment:', zp
plt.clf()
for cc,mag,label in [('b', F.mag, 'Forced mag'), ('r', F.apmag, 'Aper mag')]:
plt.plot(ps1mag, mag - ps1mag, '.', color=cc, label=label, alpha=0.6)
mm,dd = [],[]
for mlo,mhi in zip(mags, mags[1:]):
I = np.flatnonzero((ps1mag > mlo) * (ps1mag <= mhi))
mm.append((mlo+mhi)/2.)
dd.append(np.median(mag[I] - ps1mag[I]))
plt.plot(mm, dd, 'o-', color=cc)
mm = np.array(mm)
dd = np.array(dd)
plt.plot(mm, dd - zp, 'o--', lw=3, alpha=0.5, color=cc)
allplots.append((mm, dd, zp, cc, label))
plt.xlabel('PS1 %s mag' % im.band)
plt.ylabel('Mag - PS1 (mag)')
plt.title('PS1 - Single-epoch mag: %i-%s' % (expnum, ccdname))
plt.ylim(-0.2, 0.2)
mlo,mhi = magrange
plt.xlim(mhi, mlo)
plt.axhline(0., color='k', alpha=0.1)
plt.legend()
ps.savefig()
plt.clf()
# for mm,dd,zp,cc,label in allplots:
# plt.plot(mm, dd, 'o-', color=cc, label=label)
# plt.plot(mm, dd - zp, 'o--', lw=3, alpha=0.5, color=cc)
for sp,add in [(1,False),(2,True)]:
plt.subplot(2,1,sp)
for mm,dd,zp,cc,label in allplots:
if add:
plt.plot(mm, dd - zp, 'o--', lw=3, alpha=0.5, color=cc)
else:
plt.plot(mm, dd, 'o-', color=cc, label=label)
plt.ylabel('Mag - PS1 (mag)')
plt.ylim(-0.2, 0.05)
mlo,mhi = magrange
plt.xlim(mhi, mlo)
plt.axhline(0., color='k', alpha=0.1)
plt.axhline(-0.05, color='k', alpha=0.1)
plt.axhline(-0.1, color='k', alpha=0.1)
plt.xlabel('PS1 %s mag' % im.band)
plt.suptitle('PS1 - Single-epoch mags')
#plt.legend()
ps.savefig()
plt.clf()
for mm,dd,zp,cc,label in allplots:
plt.plot(mm, dd, 'o-', color=cc, label=label)
plt.plot(mm, dd - zp, 'o--', lw=3, alpha=0.5, color=cc)
plt.ylabel('Mag - PS1 (mag)')
plt.ylim(-0.2, 0.05)
mlo,mhi = magrange
plt.xlim(mhi, mlo)
plt.axhline(0., color='k', alpha=0.1)
plt.xlabel('PS1 %s mag' % im.band)
plt.suptitle('PS1 - Single-epoch mags')
ps.savefig()
def apphot_ps1stars(ccd,
ps,
apertures,
decals,
sky_inner_r=40,
sky_outer_r=50):
im = decals.get_image_object(ccd)
tim = im.get_tractor_image(gaussPsf=True, splinesky=True)
img = tim.getImage()
wcs = tim.subwcs
magrange = (15, 21)
ps1 = ps1cat(ccdwcs=wcs)
ps1 = ps1.get_stars(magrange=magrange)
print 'Got', len(ps1), 'PS1 stars'
band = ccd.filter
piband = ps1cat.ps1band[band]
print 'band:', band
ps1.cut(ps1.nmag_ok[:, piband] > 0)
print 'Keeping', len(ps1), 'stars with nmag_ok'
ok, x, y = wcs.radec2pixelxy(ps1.ra, ps1.dec)
apxy = np.vstack((x - 1., y - 1.)).T
ap = []
aperr = []
nmasked = []
with np.errstate(divide='ignore'):
ie = tim.getInvError()
imsigma = 1. / ie
imsigma[ie == 0] = 0
mask = (imsigma == 0)
for rad in apertures:
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(img, aper, error=imsigma, mask=mask)
aperr.append(p.field('aperture_sum_err'))
ap.append(p.field('aperture_sum'))
p = photutils.aperture_photometry((ie == 0), aper)
nmasked.append(p.field('aperture_sum'))
ap = np.vstack(ap).T
aperr = np.vstack(aperr).T
nmasked = np.vstack(nmasked).T
print 'Aperture fluxes:', ap[:5]
print 'Aperture flux errors:', aperr[:5]
print 'Nmasked:', nmasked[:5]
H, W = img.shape
sky = []
skysigma = []
skymed = []
skynmasked = []
for xi, yi in zip(x, y):
ix = int(np.round(xi))
iy = int(np.round(yi))
skyR = sky_outer_r
xlo = max(0, ix - skyR)
xhi = min(W, ix + skyR + 1)
ylo = max(0, iy - skyR)
yhi = min(H, iy + skyR + 1)
xx, yy = np.meshgrid(np.arange(xlo, xhi), np.arange(ylo, yhi))
r2 = (xx - xi)**2 + (yy - yi)**2
inannulus = ((r2 >= sky_inner_r**2) * (r2 < sky_outer_r**2))
unmasked = (ie[ylo:yhi, xlo:xhi] > 0)
#sky.append(np.median(img[ylo:yhi, xlo:xhi][inannulus * unmasked]))
skypix = img[ylo:yhi, xlo:xhi][inannulus * unmasked]
# this is the default value...
nsigma = 4.
goodpix, lo, hi = sigmaclip(skypix, low=nsigma, high=nsigma)
# sigmaclip returns unclipped pixels, lo,hi, where lo,hi are
# mean(goodpix) +- nsigma * sigma
meansky = np.mean(goodpix)
sky.append(meansky)
skysigma.append((meansky - lo) / nsigma)
skymed.append(np.median(skypix))
skynmasked.append(np.sum(inannulus * np.logical_not(unmasked)))
sky = np.array(sky)
skysigma = np.array(skysigma)
skymed = np.array(skymed)
skynmasked = np.array(skynmasked)
print 'sky', sky[:5]
print 'median sky', skymed[:5]
print 'sky sigma', skysigma[:5]
psmag = ps1.median[:, piband]
ap2 = ap - sky[:, np.newaxis] * (np.pi * apertures**2)[np.newaxis, :]
if ps is not None:
plt.clf()
nstars, naps = ap.shape
for iap in range(naps):
plt.plot(psmag, ap[:, iap], 'b.')
#for iap in range(naps):
# plt.plot(psmag, ap2[:,iap], 'r.')
plt.yscale('symlog')
plt.xlabel('PS1 %s mag' % band)
plt.ylabel('DECam Aperture Flux')
#plt.plot(psmag, nmasked[:,-1], 'ro')
plt.plot(np.vstack((psmag, psmag)),
np.vstack((np.zeros_like(psmag), nmasked[:, -1])),
'r-',
alpha=0.5)
plt.ylim(0, 1e3)
ps.savefig()
plt.clf()
plt.plot(ap.T / np.max(ap, axis=1), '.')
plt.ylim(0, 1)
ps.savefig()
plt.clf()
dimshow(tim.getImage(), **tim.ima)
ax = plt.axis()
plt.plot(x, y, 'o', mec='r', mfc='none', ms=10)
plt.axis(ax)
ps.savefig()
color = ps1_to_decam(ps1.median, band)
print 'Color terms:', color
T = fits_table()
T.apflux = ap.astype(np.float32)
T.apfluxerr = aperr.astype(np.float32)
T.apnmasked = nmasked.astype(np.int16)
# Zero out the errors when pixels are masked
T.apfluxerr[T.apnmasked > 0] = 0.
#T.apflux2 = ap2.astype(np.float32)
T.sky = sky.astype(np.float32)
T.skysigma = skysigma.astype(np.float32)
T.expnum = np.array([ccd.expnum] * len(T))
T.ccdname = np.array([ccd.ccdname] * len(T)).astype('S3')
T.band = np.array([band] * len(T))
T.ps1_objid = ps1.obj_id
T.ps1_mag = psmag + color
T.ra = ps1.ra
T.dec = ps1.dec
T.tai = np.array([tim.time.toMjd()] * len(T)).astype(np.float32)
T.airmass = np.array([tim.primhdr['AIRMASS']] * len(T)).astype(np.float32)
T.x = (x + tim.x0).astype(np.float32)
T.y = (y + tim.y0).astype(np.float32)
if False:
plt.clf()
plt.plot(skymed, sky, 'b.')
plt.xlabel('sky median')
plt.ylabel('sigma-clipped sky')
ax = plt.axis()
lo, hi = min(ax), max(ax)
plt.plot([lo, hi], [lo, hi], 'k-', alpha=0.25)
plt.axis(ax)
ps.savefig()
return T, tim.primhdr
