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')
import pylab as plt
import numpy as np
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)))
def read_forcedphot_ccds(ccds, survey):
ccds.mdiff = np.zeros(len(ccds))
ccds.mscatter = np.zeros(len(ccds))
Nap = 8
ccds.apdiff = np.zeros((len(ccds), Nap))
ccds.apscatter = np.zeros((len(ccds), Nap))
ccds.nforced = np.zeros(len(ccds), np.int16)
ccds.nunmasked = np.zeros(len(ccds), np.int16)
ccds.nmatched = np.zeros(len(ccds), np.int16)
ccds.nps1 = np.zeros(len(ccds), np.int16)
brickcache = {}
FF = []
for iccd, ccd in enumerate(ccds):
print('CCD', iccd, 'of', len(ccds))
F = fits_table(ccd.path)
print(len(F), 'sources in', ccd.path)
ccds.nforced[iccd] = len(F)
# arr, have to match with brick sources to get RA,Dec.
F.ra = np.zeros(len(F))
F.dec = np.zeros(len(F))
F.masked = np.zeros(len(F), bool)
maglo, maghi = 14., 21.
maxdmag = 1.
F.mag = -2.5 * (np.log10(F.flux) - 9)
F.cut((F.flux > 0) * (F.mag > maglo - maxdmag) *
(F.mag < maghi + maxdmag))
print(len(F), 'sources between', (maglo - maxdmag), 'and',
(maghi + maxdmag), 'mag')
im = survey.get_image_object(ccd)
print('Reading DQ image for', im)
dq = im.read_dq()
H, W = dq.shape
ix = np.clip(np.round(F.x), 0, W - 1).astype(int)
iy = np.clip(np.round(F.y), 0, H - 1).astype(int)
F.mask = dq[iy, ix]
print(np.sum(F.mask != 0), 'sources are masked')
for brickname in np.unique(F.brickname):
if not brickname in brickcache:
brickcache[brickname] = fits_table(
survey.find_file('tractor', brick=brickname))
T = brickcache[brickname]
idtoindex = np.zeros(T.objid.max() + 1, int) - 1
idtoindex[T.objid] = np.arange(len(T))
I = np.flatnonzero(F.brickname == brickname)
J = idtoindex[F.objid[I]]
assert (np.all(J >= 0))
F.ra[I] = T.ra[J]
F.dec[I] = T.dec[J]
F.masked[I] = (T.decam_anymask[J, :].max(axis=1) > 0)
#F.cut(F.masked == False)
#print(len(F), 'not masked')
print(np.sum(F.masked), 'masked in ANYMASK')
ccds.nunmasked[iccd] = len(F)
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
]
])
ps1 = ps1cat(ccdwcs=wcs)
stars = ps1.get_stars()
print(len(stars), 'PS1 sources')
ccds.nps1[iccd] = len(stars)
# Now cut to just *stars* with good colors
stars.gicolor = stars.median[:, 0] - stars.median[:, 2]
keep = (stars.gicolor > 0.4) * (stars.gicolor < 2.7)
stars.cut(keep)
print(len(stars), 'PS1 stars with good colors')
stars.cut(np.minimum(stars.stdev[:, 1], stars.stdev[:, 2]) < 0.05)
print(len(stars), 'PS1 stars with min stdev(r,i) < 0.05')
I, J, d = match_radec(F.ra, F.dec, stars.ra, stars.dec, 1. / 3600.)
print(len(I), 'matches')
band = ccd.filter
colorterm = ps1_to_decam(stars.median[J], band)
F.cut(I)
F.psmag = stars.median[J, ps1.ps1band[band]] + colorterm
K = np.flatnonzero((F.psmag > maglo) * (F.psmag < maghi))
print(len(K), 'with mag', maglo, 'to', maghi)
F.cut(K)
K = np.flatnonzero(np.abs(F.mag - F.psmag) < maxdmag)
print(len(K), 'with good mag matches (<', maxdmag, 'mag difference)')
ccds.nmatched[iccd] = len(K)
if len(K) == 0:
continue
F.cut(K)
ccds.mdiff[iccd] = np.median(F.mag - F.psmag)
ccds.mscatter[iccd] = (np.percentile(F.mag - F.psmag, 84) -
np.percentile(F.mag - F.psmag, 16)) / 2.
for i in range(Nap):
apmag = -2.5 * (np.log10(F.apflux[:, i]) - 9)
ccds.apdiff[iccd, i] = np.median(apmag - F.psmag)
ccds.apscatter[iccd, i] = (np.percentile(apmag - F.psmag, 84) -
np.percentile(apmag - F.psmag, 16)) / 2.
#F.about()
for c in [
'apflux_ivar', 'brickid', 'flux_ivar', 'mjd', 'objid',
'fracflux', 'rchi2', 'x', 'y'
]:
F.delete_column(c)
F.expnum = np.zeros(len(F), np.int32) + ccd.expnum
F.ccdname = np.array([ccd.ccdname] * len(F))
F.iforced = np.zeros(len(F), np.int32) + iccd
FF.append(F)
FF = merge_tables(FF)
return FF
bricks = survey.get_bricks_readonly()
bricks = bricks[(bricks.ra > ralo) * (bricks.ra < rahi) *
(bricks.dec > declo) * (bricks.dec < dechi)]
print(len(bricks), 'bricks')
I, = np.nonzero([
os.path.exists(survey.find_file('tractor', brick=b.brickname))
for b in bricks
])
print(len(I), 'bricks with catalogs')
bricks.cut(I)
for band in bands:
bricks.set('diff_%s' % band, np.zeros(len(bricks), np.float32))
bricks.set('psfsize_%s' % band, np.zeros(len(bricks), np.float32))
diffs = dict([(b, []) for b in bands])
for ibrick, b in enumerate(bricks):
fn = survey.find_file('tractor', brick=b.brickname)
T = fits_table(fn)
print(len(T), 'sources in', b.brickname)
brickwcs = wcs_for_brick(b)
ps1 = ps1cat(ccdwcs=brickwcs)
stars = ps1.get_stars()
print(len(stars), 'PS1 sources')
# Now cut to just *stars* with good colors
stars.gicolor = stars.median[:, 0] - stars.median[:, 2]
keep = (stars.gicolor > 0.4) * (stars.gicolor < 2.7)
stars.cut(keep)
print(len(stars), 'PS1 stars with good colors')
I, J, d = match_radec(T.ra, T.dec, stars.ra, stars.dec, 1. / 3600.)
print(len(I), 'matches')
for band in bands:
bricks.get('psfsize_%s' % band)[ibrick] = np.median(
T.decam_psfsize[:, survey.index_of_band(band)])
colorterm = ps1_to_decam(stars.median[J], band)
psmag = stars.median[J, ps1.ps1band[band]]
psmag += colorterm
decflux = T.decam_flux[I, survey.index_of_band(band)]
decmag = -2.5 * (np.log10(decflux) - 9)
#K = np.flatnonzero((psmag > 14) * (psmag < 24))
#print(len(K), 'with mag 14 to 24')
K = np.flatnonzero((psmag > 14) * (psmag < 21))
print(len(K), 'with mag 14 to 21')
decmag = decmag[K]
psmag = psmag[K]
K = np.flatnonzero(np.abs(decmag - psmag) < 1)
print(len(K), 'with good mag matches (< 1 mag difference)')
decmag = decmag[K]
psmag = psmag[K]
if False and ibrick == 0:
plt.clf()
#plt.plot(psmag, decmag, 'b.')
plt.plot(psmag, decmag - psmag, 'b.')
plt.xlabel('PS1 mag')
plt.xlabel('DECam - PS1 mag')
plt.title('PS1 matches for %s band, brick %s' %
(band, b.brickname))
ps.savefig()
mdiff = np.median(decmag - psmag)
diffs[band].append(mdiff)
print('Median difference:', mdiff)
bricks.get('diff_%s' % band)[ibrick] = mdiff
for band in bands:
d = diffs[band]
plt.clf()
plt.hist(d, bins=20, range=(-0.02, 0.02), histtype='step')
plt.xlabel('Median mag difference per brick')
plt.title('DR3 EDR PS1 vs DECaLS: %s band' % band)
ps.savefig()
print('Median differences in', band, 'band:', np.median(d))
if False:
plt.clf()
plt.hist(diffs['g'],
bins=20,
range=(-0.02, 0.02),
histtype='step',
color='g')
plt.hist(diffs['r'],
bins=20,
range=(-0.02, 0.02),
histtype='step',
color='r')
plt.hist(diffs['z'],
bins=20,
range=(-0.02, 0.02),
histtype='step',
color='m')
plt.xlabel('Median mag difference per brick')
plt.title('DR3 EDR PS1 vs DECaLS')
ps.savefig()
rr, dd = np.meshgrid(np.linspace(ralo, rahi, 400),
np.linspace(declo, dechi, 400))
I, J, d = match_radec(rr.ravel(),
dd.ravel(),
bricks.ra,
bricks.dec,
0.18,
nearest=True)
print(len(I), 'matches')
for band in bands:
plt.clf()
dmag = np.zeros_like(rr) - 1.
dmag.ravel()[I] = bricks.get('diff_%s' % band)[J]
plt.imshow(dmag,
interpolation='nearest',
origin='lower',
vmin=-0.01,
vmax=0.01,
cmap='hot',
extent=(ralo, rahi, declo, dechi))
plt.colorbar()
plt.title('DR3 EDR PS1 vs DECaLS: %s band' % band)
plt.xlabel('RA (deg)')
plt.ylabel('Dec (deg)')
plt.axis([ralo, rahi, declo, dechi])
ps.savefig()
plt.clf()
# reuse 'dmag' map...
dmag = np.zeros_like(rr)
dmag.ravel()[I] = bricks.get('psfsize_%s' % band)[J]
plt.imshow(dmag,
interpolation='nearest',
origin='lower',
cmap='hot',
extent=(ralo, rahi, declo, dechi))
plt.colorbar()
plt.title('DR3 EDR: DECaLS PSF size: %s band' % band)
plt.xlabel('RA (deg)')
plt.ylabel('Dec (deg)')
plt.axis([ralo, rahi, declo, dechi])
ps.savefig()
if False:
for band in bands:
plt.clf()
plt.scatter(bricks.ra,
bricks.dec,
c=bricks.get('diff_%s' % band),
vmin=-0.01,
vmax=0.01,
edgecolors='face',
s=200)
plt.colorbar()
plt.title('DR3 EDR PS1 vs DECaLS: %s band' % band)
plt.xlabel('RA (deg)')
plt.ylabel('Dec (deg)')
plt.axis('scaled')
plt.axis([ralo, rahi, declo, dechi])
ps.savefig()
plt.clf()
plt.plot(bricks.psfsize_g, bricks.diff_g, 'g.')
plt.plot(bricks.psfsize_r, bricks.diff_r, 'r.')
plt.plot(bricks.psfsize_z, bricks.diff_z, 'm.')
plt.xlabel('PSF size (arcsec)')
plt.ylabel('DECaLS PSF - PS1 (mag)')
plt.title('DR3 EDR')
ps.savefig()
def star_profiles(ps):
# Run an example CCD, 292604-N4, with fairly large difference vs PS1.
# python -c "from astrometry.util.fits import *; T = merge_tables([fits_table('/project/projectdirs/desiproc/dr3/tractor/244/tractor-244%s.fits' % b) for b in ['2p065','4p065', '7p065']]); T.writeto('tst-cat.fits')"
# python legacypipe/forced_photom_decam.py --save-data tst-data.fits --save-model tst-model.fits 292604 N4 tst-cat.fits tst-phot.fits
# -> tst-{model,data,phot}.fits
datafn = 'tst-data.fits'
modfn = 'tst-model.fits'
photfn = 'tst-phot.fits'
catfn = 'tst-cat.fits'
img = fitsio.read(datafn)
mod = fitsio.read(modfn)
phot = fits_table(photfn)
cat = fits_table(catfn)
print(len(phot), 'forced-photometry results')
margin = 25
phot.cut((phot.x > 0 + margin) * (phot.x < 2046 - margin) *
(phot.y > 0 + margin) * (phot.y < 4096 - margin))
print(len(phot), 'in bounds')
cmap = dict([((b, o), i)
for i, (b, o) in enumerate(zip(cat.brickname, cat.objid))])
I = np.array(
[cmap.get((b, o), -1) for b, o in zip(phot.brickname, phot.objid)])
print(np.sum(I >= 0), 'forced-phot matched cat')
phot.type = cat.type[I]
wcs = Sip(datafn)
phot.ra, phot.dec = wcs.pixelxy2radec(phot.x + 1, phot.y + 1)
phot.cut(np.argsort(phot.flux))
phot.sn = phot.flux * np.sqrt(phot.flux_ivar)
phot.cut(phot.sn > 5)
print(len(phot), 'with S/N > 5')
ps1 = ps1cat(ccdwcs=wcs)
stars = ps1.get_stars()
print(len(stars), 'PS1 sources')
# Now cut to just *stars* with good colors
stars.gicolor = stars.median[:, 0] - stars.median[:, 2]
keep = (stars.gicolor > 0.4) * (stars.gicolor < 2.7)
stars.cut(keep)
print(len(stars), 'PS1 stars with good colors')
stars.cut(np.minimum(stars.stdev[:, 1], stars.stdev[:, 2]) < 0.05)
print(len(stars), 'PS1 stars with min stdev(r,i) < 0.05')
I, J, d = match_radec(phot.ra, phot.dec, stars.ra, stars.dec, 1. / 3600.)
print(len(I), 'matches')
plt.clf()
ha = dict(histtype='step', bins=20, range=(0, 100), normed=True)
plt.hist(phot.flux, color='b', **ha)
plt.hist(phot.flux[I], color='r', **ha)
ps.savefig()
plt.clf()
plt.hist(phot.flux * np.sqrt(phot.flux_ivar), bins=100, range=(-10, 50))
plt.xlabel('Flux S/N')
ps.savefig()
K = np.argsort(phot.flux[I])
I = I[K]
J = J[K]
ix = np.round(phot.x).astype(int)
iy = np.round(phot.y).astype(int)
sz = 10
P = np.flatnonzero(phot.type == 'PSF ')
print(len(P), 'PSFs')
imed = len(P) / 2
i1 = int(len(P) * 0.75)
i2 = int(len(P) * 0.25)
N = 401
allmods = []
allimgs = []
for II, tt in [ #(I[:len(I)/2], 'faint matches to PS1'),
#(I[len(I)/2:], 'bright matches to PS1'),
#(P[i2: i2+N], '25th pct PSFs'),
#(P[imed: imed+N], 'median PSFs'),
#(P[i1: i1+N], '75th pct PSFs'),
#(P[-25:], 'brightest PSFs'),
(P[i2:imed], '2nd quartile of PSFs'),
(P[imed:i1], '3rd quartile of PSFs'),
#(P[:len(P)/2], 'faint half of PSFs'),
#(P[len(P)/2:], 'bright half of PSFs'),
]:
imgs = []
mods = []
shimgs = []
shmods = []
imgsum = modsum = 0
#plt.clf()
for i in II:
from astrometry.util.util import lanczos_shift_image
dy = phot.y[i] - iy[i]
dx = phot.x[i] - ix[i]
sub = img[iy[i] - sz:iy[i] + sz + 1, ix[i] - sz:ix[i] + sz + 1]
shimg = lanczos_shift_image(sub, -dx, -dy)
sub = mod[iy[i] - sz:iy[i] + sz + 1, ix[i] - sz:ix[i] + sz + 1]
shmod = lanczos_shift_image(sub, -dx, -dy)
iyslice = img[iy[i], ix[i] - sz:ix[i] + sz + 1]
myslice = mod[iy[i], ix[i] - sz:ix[i] + sz + 1]
ixslice = img[iy[i] - sz:iy[i] + sz + 1, ix[i]]
mxslice = mod[iy[i] - sz:iy[i] + sz + 1, ix[i]]
mx = iyslice.max()
# plt.plot(iyslice/mx, 'b-', alpha=0.1)
# plt.plot(myslice/mx, 'r-', alpha=0.1)
# plt.plot(ixslice/mx, 'b-', alpha=0.1)
# plt.plot(mxslice/mx, 'r-', alpha=0.1)
siyslice = shimg[sz, :]
sixslice = shimg[:, sz]
smyslice = shmod[sz, :]
smxslice = shmod[:, sz]
shimgs.append(siyslice / mx)
shimgs.append(sixslice / mx)
shmods.append(smyslice / mx)
shmods.append(smxslice / mx)
imgs.append(iyslice / mx)
imgs.append(ixslice / mx)
mods.append(myslice / mx)
mods.append(mxslice / mx)
imgsum = imgsum + ixslice + iyslice
modsum = modsum + mxslice + myslice
# plt.ylim(-0.1, 1.1)
# plt.title(tt)
# ps.savefig()
mimg = np.median(np.array(imgs), axis=0)
mmod = np.median(np.array(mods), axis=0)
mshim = np.median(np.array(shimgs), axis=0)
mshmo = np.median(np.array(shmods), axis=0)
allmods.append(mshmo)
allimgs.append(mshim)
plt.clf()
# plt.plot(mimg, 'b-')
# plt.plot(mmod, 'r-')
plt.plot(mshim, 'g-')
plt.plot(mshmo, 'm-')
plt.ylim(-0.1, 1.1)
plt.title(tt + ': median; sums %.3f/%.3f' %
(np.sum(mimg), np.sum(mmod)))
ps.savefig()
# plt.clf()
# mx = imgsum.max()
# plt.plot(imgsum/mx, 'b-')
# plt.plot(modsum/mx, 'r-')
# plt.ylim(-0.1, 1.1)
# plt.title(tt + ': sum')
# ps.savefig()
plt.clf()
plt.plot((mimg + 0.01) / (mmod + 0.01), 'k-')
plt.plot((imgsum / mx + 0.01) / (modsum / mx + 0.01), 'g-')
plt.plot((mshim + 0.01) / (mshmo + 0.01), 'm-')
plt.ylabel('(img + 0.01) / (mod + 0.01)')
plt.title(tt)
ps.savefig()
iq2, iq3 = allimgs
mq2, mq3 = allmods
plt.clf()
plt.plot(iq2, 'r-')
plt.plot(mq2, 'm-')
plt.plot(iq3, 'b-')
plt.plot(mq3, 'g-')
plt.title('Q2 vs Q3')
ps.savefig()
def star_profiles(ps):
# Run an example CCD, 292604-N4, with fairly large difference vs PS1.
# python -c "from astrometry.util.fits import *; T = merge_tables([fits_table('/project/projectdirs/desiproc/dr3/tractor/244/tractor-244%s.fits' % b) for b in ['2p065','4p065', '7p065']]); T.writeto('tst-cat.fits')"
# python legacypipe/forced_photom_decam.py --save-data tst-data.fits --save-model tst-model.fits 292604 N4 tst-cat.fits tst-phot.fits
# -> tst-{model,data,phot}.fits
datafn = 'tst-data.fits'
modfn = 'tst-model.fits'
photfn = 'tst-phot.fits'
catfn = 'tst-cat.fits'
img = fitsio.read(datafn)
mod = fitsio.read(modfn)
phot = fits_table(photfn)
cat = fits_table(catfn)
print(len(phot), 'forced-photometry results')
margin = 25
phot.cut((phot.x > 0+margin) * (phot.x < 2046-margin) *
(phot.y > 0+margin) * (phot.y < 4096-margin))
print(len(phot), 'in bounds')
cmap = dict([((b,o),i) for i,(b,o) in enumerate(zip(cat.brickname, cat.objid))])
I = np.array([cmap.get((b,o), -1) for b,o in zip(phot.brickname, phot.objid)])
print(np.sum(I >= 0), 'forced-phot matched cat')
phot.type = cat.type[I]
wcs = Sip(datafn)
phot.ra,phot.dec = wcs.pixelxy2radec(phot.x+1, phot.y+1)
phot.cut(np.argsort(phot.flux))
phot.sn = phot.flux * np.sqrt(phot.flux_ivar)
phot.cut(phot.sn > 5)
print(len(phot), 'with S/N > 5')
ps1 = ps1cat(ccdwcs=wcs)
stars = ps1.get_stars()
print(len(stars), 'PS1 sources')
# Now cut to just *stars* with good colors
stars.gicolor = stars.median[:,0] - stars.median[:,2]
keep = (stars.gicolor > 0.4) * (stars.gicolor < 2.7)
stars.cut(keep)
print(len(stars), 'PS1 stars with good colors')
stars.cut(np.minimum(stars.stdev[:,1], stars.stdev[:,2]) < 0.05)
print(len(stars), 'PS1 stars with min stdev(r,i) < 0.05')
I,J,d = match_radec(phot.ra, phot.dec, stars.ra, stars.dec, 1./3600.)
print(len(I), 'matches')
plt.clf()
ha=dict(histtype='step', bins=20, range=(0,100), normed=True)
plt.hist(phot.flux, color='b', **ha)
plt.hist(phot.flux[I], color='r', **ha)
ps.savefig()
plt.clf()
plt.hist(phot.flux * np.sqrt(phot.flux_ivar), bins=100,
range=(-10, 50))
plt.xlabel('Flux S/N')
ps.savefig()
K = np.argsort(phot.flux[I])
I = I[K]
J = J[K]
ix = np.round(phot.x).astype(int)
iy = np.round(phot.y).astype(int)
sz = 10
P = np.flatnonzero(phot.type == 'PSF ')
print(len(P), 'PSFs')
imed = len(P)/2
i1 = int(len(P) * 0.75)
i2 = int(len(P) * 0.25)
N = 401
allmods = []
allimgs = []
for II,tt in [#(I[:len(I)/2], 'faint matches to PS1'),
#(I[len(I)/2:], 'bright matches to PS1'),
#(P[i2: i2+N], '25th pct PSFs'),
#(P[imed: imed+N], 'median PSFs'),
#(P[i1: i1+N], '75th pct PSFs'),
#(P[-25:], 'brightest PSFs'),
(P[i2:imed], '2nd quartile of PSFs'),
(P[imed:i1], '3rd quartile of PSFs'),
#(P[:len(P)/2], 'faint half of PSFs'),
#(P[len(P)/2:], 'bright half of PSFs'),
]:
imgs = []
mods = []
shimgs = []
shmods = []
imgsum = modsum = 0
#plt.clf()
for i in II:
from astrometry.util.util import lanczos_shift_image
dy = phot.y[i] - iy[i]
dx = phot.x[i] - ix[i]
sub = img[iy[i]-sz : iy[i]+sz+1, ix[i]-sz : ix[i]+sz+1]
shimg = lanczos_shift_image(sub, -dx, -dy)
sub = mod[iy[i]-sz : iy[i]+sz+1, ix[i]-sz : ix[i]+sz+1]
shmod = lanczos_shift_image(sub, -dx, -dy)
iyslice = img[iy[i], ix[i]-sz : ix[i]+sz+1]
myslice = mod[iy[i], ix[i]-sz : ix[i]+sz+1]
ixslice = img[iy[i]-sz : iy[i]+sz+1, ix[i]]
mxslice = mod[iy[i]-sz : iy[i]+sz+1, ix[i]]
mx = iyslice.max()
# plt.plot(iyslice/mx, 'b-', alpha=0.1)
# plt.plot(myslice/mx, 'r-', alpha=0.1)
# plt.plot(ixslice/mx, 'b-', alpha=0.1)
# plt.plot(mxslice/mx, 'r-', alpha=0.1)
siyslice = shimg[sz, :]
sixslice = shimg[:, sz]
smyslice = shmod[sz, :]
smxslice = shmod[:, sz]
shimgs.append(siyslice/mx)
shimgs.append(sixslice/mx)
shmods.append(smyslice/mx)
shmods.append(smxslice/mx)
imgs.append(iyslice/mx)
imgs.append(ixslice/mx)
mods.append(myslice/mx)
mods.append(mxslice/mx)
imgsum = imgsum + ixslice + iyslice
modsum = modsum + mxslice + myslice
# plt.ylim(-0.1, 1.1)
# plt.title(tt)
# ps.savefig()
mimg = np.median(np.array(imgs), axis=0)
mmod = np.median(np.array(mods), axis=0)
mshim = np.median(np.array(shimgs), axis=0)
mshmo = np.median(np.array(shmods), axis=0)
allmods.append(mshmo)
allimgs.append(mshim)
plt.clf()
# plt.plot(mimg, 'b-')
# plt.plot(mmod, 'r-')
plt.plot(mshim, 'g-')
plt.plot(mshmo, 'm-')
plt.ylim(-0.1, 1.1)
plt.title(tt + ': median; sums %.3f/%.3f' % (np.sum(mimg), np.sum(mmod)))
ps.savefig()
# plt.clf()
# mx = imgsum.max()
# plt.plot(imgsum/mx, 'b-')
# plt.plot(modsum/mx, 'r-')
# plt.ylim(-0.1, 1.1)
# plt.title(tt + ': sum')
# ps.savefig()
plt.clf()
plt.plot((mimg + 0.01) / (mmod + 0.01), 'k-')
plt.plot((imgsum/mx + 0.01) / (modsum/mx + 0.01), 'g-')
plt.plot((mshim + 0.01) / (mshmo + 0.01), 'm-')
plt.ylabel('(img + 0.01) / (mod + 0.01)')
plt.title(tt)
ps.savefig()
iq2,iq3 = allimgs
mq2,mq3 = allmods
plt.clf()
plt.plot(iq2, 'r-')
plt.plot(mq2, 'm-')
plt.plot(iq3, 'b-')
plt.plot(mq3, 'g-')
plt.title('Q2 vs Q3')
ps.savefig()
def read_forcedphot_ccds(ccds, survey):
ccds.mdiff = np.zeros(len(ccds))
ccds.mscatter = np.zeros(len(ccds))
Nap = 8
ccds.apdiff = np.zeros((len(ccds), Nap))
ccds.apscatter = np.zeros((len(ccds), Nap))
ccds.nforced = np.zeros(len(ccds), np.int16)
ccds.nunmasked = np.zeros(len(ccds), np.int16)
ccds.nmatched = np.zeros(len(ccds), np.int16)
ccds.nps1 = np.zeros(len(ccds), np.int16)
brickcache = {}
FF = []
for iccd,ccd in enumerate(ccds):
print('CCD', iccd, 'of', len(ccds))
F = fits_table(ccd.path)
print(len(F), 'sources in', ccd.path)
ccds.nforced[iccd] = len(F)
# arr, have to match with brick sources to get RA,Dec.
F.ra = np.zeros(len(F))
F.dec = np.zeros(len(F))
F.masked = np.zeros(len(F), bool)
maglo,maghi = 14.,21.
maxdmag = 1.
F.mag = -2.5 * (np.log10(F.flux) - 9)
F.cut((F.flux > 0) * (F.mag > maglo-maxdmag) * (F.mag < maghi+maxdmag))
print(len(F), 'sources between', (maglo-maxdmag), 'and', (maghi+maxdmag), 'mag')
im = survey.get_image_object(ccd)
print('Reading DQ image for', im)
dq = im.read_dq()
H,W = dq.shape
ix = np.clip(np.round(F.x), 0, W-1).astype(int)
iy = np.clip(np.round(F.y), 0, H-1).astype(int)
F.mask = dq[iy,ix]
print(np.sum(F.mask != 0), 'sources are masked')
for brickname in np.unique(F.brickname):
if not brickname in brickcache:
brickcache[brickname] = fits_table(survey.find_file('tractor', brick=brickname))
T = brickcache[brickname]
idtoindex = np.zeros(T.objid.max()+1, int) - 1
idtoindex[T.objid] = np.arange(len(T))
I = np.flatnonzero(F.brickname == brickname)
J = idtoindex[F.objid[I]]
assert(np.all(J >= 0))
F.ra [I] = T.ra [J]
F.dec[I] = T.dec[J]
F.masked[I] = (T.decam_anymask[J,:].max(axis=1) > 0)
#F.cut(F.masked == False)
#print(len(F), 'not masked')
print(np.sum(F.masked), 'masked in ANYMASK')
ccds.nunmasked[iccd] = len(F)
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]])
ps1 = ps1cat(ccdwcs=wcs)
stars = ps1.get_stars()
print(len(stars), 'PS1 sources')
ccds.nps1[iccd] = len(stars)
# Now cut to just *stars* with good colors
stars.gicolor = stars.median[:,0] - stars.median[:,2]
keep = (stars.gicolor > 0.4) * (stars.gicolor < 2.7)
stars.cut(keep)
print(len(stars), 'PS1 stars with good colors')
stars.cut(np.minimum(stars.stdev[:,1], stars.stdev[:,2]) < 0.05)
print(len(stars), 'PS1 stars with min stdev(r,i) < 0.05')
I,J,d = match_radec(F.ra, F.dec, stars.ra, stars.dec, 1./3600.)
print(len(I), 'matches')
band = ccd.filter
colorterm = ps1_to_decam(stars.median[J], band)
F.cut(I)
F.psmag = stars.median[J, ps1.ps1band[band]] + colorterm
K = np.flatnonzero((F.psmag > maglo) * (F.psmag < maghi))
print(len(K), 'with mag', maglo, 'to', maghi)
F.cut(K)
K = np.flatnonzero(np.abs(F.mag - F.psmag) < maxdmag)
print(len(K), 'with good mag matches (<', maxdmag, 'mag difference)')
ccds.nmatched[iccd] = len(K)
if len(K) == 0:
continue
F.cut(K)
ccds.mdiff[iccd] = np.median(F.mag - F.psmag)
ccds.mscatter[iccd] = (np.percentile(F.mag - F.psmag, 84) -
np.percentile(F.mag - F.psmag, 16))/2.
for i in range(Nap):
apmag = -2.5 * (np.log10(F.apflux[:, i]) - 9)
ccds.apdiff[iccd,i] = np.median(apmag - F.psmag)
ccds.apscatter[iccd,i] = (np.percentile(apmag - F.psmag, 84) -
np.percentile(apmag - F.psmag, 16))/2.
#F.about()
for c in ['apflux_ivar', 'brickid', 'flux_ivar',
'mjd', 'objid', 'fracflux', 'rchi2', 'x','y']:
F.delete_column(c)
F.expnum = np.zeros(len(F), np.int32) + ccd.expnum
F.ccdname = np.array([ccd.ccdname] * len(F))
F.iforced = np.zeros(len(F), np.int32) + iccd
FF.append(F)
FF = merge_tables(FF)
return FF
bricks = survey.get_bricks_readonly()
bricks = bricks[(bricks.ra > ralo) * (bricks.ra < rahi) *
(bricks.dec > declo) * (bricks.dec < dechi)]
print(len(bricks), 'bricks')
I, = np.nonzero([os.path.exists(survey.find_file('tractor', brick=b.brickname))
for b in bricks])
print(len(I), 'bricks with catalogs')
bricks.cut(I)
for band in bands:
bricks.set('diff_%s' % band, np.zeros(len(bricks), np.float32))
bricks.set('psfsize_%s' % band, np.zeros(len(bricks), np.float32))
diffs = dict([(b,[]) for b in bands])
for ibrick,b in enumerate(bricks):
fn = survey.find_file('tractor', brick=b.brickname)
T = fits_table(fn)
print(len(T), 'sources in', b.brickname)
brickwcs = wcs_for_brick(b)
ps1 = ps1cat(ccdwcs=brickwcs)
stars = ps1.get_stars()
print(len(stars), 'PS1 sources')
# Now cut to just *stars* with good colors
stars.gicolor = stars.median[:,0] - stars.median[:,2]
keep = (stars.gicolor > 0.4) * (stars.gicolor < 2.7)
stars.cut(keep)
print(len(stars), 'PS1 stars with good colors')
I,J,d = match_radec(T.ra, T.dec, stars.ra, stars.dec, 1./3600.)
print(len(I), 'matches')
for band in bands:
bricks.get('psfsize_%s' % band)[ibrick] = np.median(
T.decam_psfsize[:, survey.index_of_band(band)])
colorterm = ps1_to_decam(stars.median[J], band)
psmag = stars.median[J, ps1.ps1band[band]]
psmag += colorterm
decflux = T.decam_flux[I, survey.index_of_band(band)]
decmag = -2.5 * (np.log10(decflux) - 9)
#K = np.flatnonzero((psmag > 14) * (psmag < 24))
#print(len(K), 'with mag 14 to 24')
K = np.flatnonzero((psmag > 14) * (psmag < 21))
print(len(K), 'with mag 14 to 21')
decmag = decmag[K]
psmag = psmag [K]
K = np.flatnonzero(np.abs(decmag - psmag) < 1)
print(len(K), 'with good mag matches (< 1 mag difference)')
decmag = decmag[K]
psmag = psmag [K]
if False and ibrick == 0:
plt.clf()
#plt.plot(psmag, decmag, 'b.')
plt.plot(psmag, decmag - psmag, 'b.')
plt.xlabel('PS1 mag')
plt.xlabel('DECam - PS1 mag')
plt.title('PS1 matches for %s band, brick %s' % (band, b.brickname))
ps.savefig()
mdiff = np.median(decmag - psmag)
diffs[band].append(mdiff)
print('Median difference:', mdiff)
bricks.get('diff_%s' % band)[ibrick] = mdiff
for band in bands:
d = diffs[band]
plt.clf()
plt.hist(d, bins=20, range=(-0.02, 0.02), histtype='step')
plt.xlabel('Median mag difference per brick')
plt.title('DR3 EDR PS1 vs DECaLS: %s band' % band)
ps.savefig()
print('Median differences in', band, 'band:', np.median(d))
if False:
plt.clf()
plt.hist(diffs['g'], bins=20, range=(-0.02, 0.02), histtype='step', color='g')
plt.hist(diffs['r'], bins=20, range=(-0.02, 0.02), histtype='step', color='r')
plt.hist(diffs['z'], bins=20, range=(-0.02, 0.02), histtype='step', color='m')
plt.xlabel('Median mag difference per brick')
plt.title('DR3 EDR PS1 vs DECaLS')
ps.savefig()
rr,dd = np.meshgrid(np.linspace(ralo,rahi, 400), np.linspace(declo,dechi, 400))
I,J,d = match_radec(rr.ravel(), dd.ravel(), bricks.ra, bricks.dec, 0.18, nearest=True)
print(len(I), 'matches')
for band in bands:
plt.clf()
dmag = np.zeros_like(rr) - 1.
dmag.ravel()[I] = bricks.get('diff_%s' % band)[J]
plt.imshow(dmag, interpolation='nearest', origin='lower',
vmin=-0.01, vmax=0.01, cmap='hot',
extent=(ralo,rahi,declo,dechi))
plt.colorbar()
plt.title('DR3 EDR PS1 vs DECaLS: %s band' % band)
plt.xlabel('RA (deg)')
plt.ylabel('Dec (deg)')
plt.axis([ralo,rahi,declo,dechi])
ps.savefig()
plt.clf()
# reuse 'dmag' map...
dmag = np.zeros_like(rr)
dmag.ravel()[I] = bricks.get('psfsize_%s' % band)[J]
plt.imshow(dmag, interpolation='nearest', origin='lower',
cmap='hot', extent=(ralo,rahi,declo,dechi))
plt.colorbar()
plt.title('DR3 EDR: DECaLS PSF size: %s band' % band)
plt.xlabel('RA (deg)')
plt.ylabel('Dec (deg)')
plt.axis([ralo,rahi,declo,dechi])
ps.savefig()
if False:
for band in bands:
plt.clf()
plt.scatter(bricks.ra, bricks.dec, c=bricks.get('diff_%s' % band), vmin=-0.01, vmax=0.01,
edgecolors='face', s=200)
plt.colorbar()
plt.title('DR3 EDR PS1 vs DECaLS: %s band' % band)
plt.xlabel('RA (deg)')
plt.ylabel('Dec (deg)')
plt.axis('scaled')
plt.axis([ralo,rahi,declo,dechi])
ps.savefig()
plt.clf()
plt.plot(bricks.psfsize_g, bricks.diff_g, 'g.')
plt.plot(bricks.psfsize_r, bricks.diff_r, 'r.')
plt.plot(bricks.psfsize_z, bricks.diff_z, 'm.')
plt.xlabel('PSF size (arcsec)')
plt.ylabel('DECaLS PSF - PS1 (mag)')
plt.title('DR3 EDR')
ps.savefig()
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 run(self, ps=None, focus=False, momentsize=5, n_fwhm=100):
import pylab as plt
from astrometry.util.plotutils import dimshow, plothist
from legacyanalysis.ps1cat import ps1cat
import photutils
import tractor
fn = self.fn
ext = self.ext
pixsc = self.pixscale
F = fitsio.FITS(fn)
primhdr = F[0].read_header()
self.primhdr = primhdr
img, hdr = self.read_raw(F, ext)
self.hdr = hdr
# pre sky-sub
mn, mx = np.percentile(img.ravel(), [25, 98])
self.imgkwa = dict(vmin=mn, vmax=mx, cmap='gray')
if self.debug and ps is not None:
plt.clf()
dimshow(img, **self.imgkwa)
plt.title('Raw image')
ps.savefig()
M = 200
plt.clf()
plt.subplot(2, 2, 1)
dimshow(img[-M:, :M], ticks=False, **self.imgkwa)
plt.subplot(2, 2, 2)
dimshow(img[-M:, -M:], ticks=False, **self.imgkwa)
plt.subplot(2, 2, 3)
dimshow(img[:M, :M], ticks=False, **self.imgkwa)
plt.subplot(2, 2, 4)
dimshow(img[:M, -M:], ticks=False, **self.imgkwa)
plt.suptitle('Raw image corners')
ps.savefig()
img, trim_x0, trim_y0 = self.trim_edges(img)
fullH, fullW = img.shape
if self.debug and ps is not None:
plt.clf()
dimshow(img, **self.imgkwa)
plt.title('Trimmed image')
ps.savefig()
M = 200
plt.clf()
plt.subplot(2, 2, 1)
dimshow(img[-M:, :M], ticks=False, **self.imgkwa)
plt.subplot(2, 2, 2)
dimshow(img[-M:, -M:], ticks=False, **self.imgkwa)
plt.subplot(2, 2, 3)
dimshow(img[:M, :M], ticks=False, **self.imgkwa)
plt.subplot(2, 2, 4)
dimshow(img[:M, -M:], ticks=False, **self.imgkwa)
plt.suptitle('Trimmed corners')
ps.savefig()
band = self.get_band(primhdr)
exptime = primhdr['EXPTIME']
airmass = primhdr['AIRMASS']
print('Band', band, 'Exptime', exptime, 'Airmass', airmass)
zp0 = self.nom.zeropoint(band, ext=self.ext)
sky0 = self.nom.sky(band)
kx = self.nom.fiducial_exptime(band).k_co
# Find the sky value and noise level
sky, sig1 = self.get_sky_and_sigma(img)
sky1 = np.median(sky)
skybr = -2.5 * np.log10(sky1 / pixsc / pixsc / exptime) + zp0
print('Sky brightness: %8.3f mag/arcsec^2' % skybr)
print('Fiducial: %8.3f mag/arcsec^2' % sky0)
img -= sky
self.remove_sky_gradients(img)
# Post sky-sub
mn, mx = np.percentile(img.ravel(), [25, 98])
self.imgkwa = dict(vmin=mn, vmax=mx, cmap='gray')
if ps is not None:
plt.clf()
dimshow(img, **self.imgkwa)
plt.title('Sky-sub image: %s-%s' % (os.path.basename(fn).replace(
'.fits', '').replace('.fz', ''), ext))
plt.colorbar()
ps.savefig()
# Read WCS header and compute boresight
wcs = self.get_wcs(hdr)
ra_ccd, dec_ccd = wcs.pixelxy2radec((fullW + 1) / 2., (fullH + 1) / 2.)
# Detect stars
psfsig = self.nominal_fwhm / 2.35
detsn = self.detection_map(img, sig1, psfsig, ps)
slices = self.detect_sources(detsn, self.det_thresh, ps)
print(len(slices), 'sources detected')
if len(slices) < 20:
slices = self.detect_sources(detsn, 10., ps)
print(len(slices), 'sources detected')
ndetected = len(slices)
camera = primhdr.get('INSTRUME', '').strip().lower()
# -> "decam" / "mosaic3"
meas = dict(band=band,
airmass=airmass,
skybright=skybr,
pixscale=pixsc,
primhdr=primhdr,
hdr=hdr,
wcs=wcs,
ra_ccd=ra_ccd,
dec_ccd=dec_ccd,
extension=ext,
camera=camera,
ndetected=ndetected)
if ndetected == 0:
print('NO SOURCES DETECTED')
return meas
xx, yy = [], []
fx, fy = [], []
mx2, my2, mxy = [], [], []
wmx2, wmy2, wmxy = [], [], []
# "Peak" region to centroid
P = momentsize
H, W = img.shape
for i, slc in enumerate(slices):
y0 = slc[0].start
x0 = slc[1].start
subimg = detsn[slc]
imax = np.argmax(subimg)
y, x = np.unravel_index(imax, subimg.shape)
if (x0 + x) < P or (x0 + x) > W - 1 - P or (y0 + y) < P or (
y0 + y) > H - 1 - P:
#print('Skipping edge peak', x0+x, y0+y)
continue
xx.append(x0 + x)
yy.append(y0 + y)
pkarea = detsn[y0 + y - P:y0 + y + P + 1,
x0 + x - P:x0 + x + P + 1]
from scipy.ndimage.measurements import center_of_mass
cy, cx = center_of_mass(pkarea)
#print('Center of mass', cx,cy)
fx.append(x0 + x - P + cx)
fy.append(y0 + y - P + cy)
#print('x,y', x0+x, y0+y, 'vs centroid', x0+x-P+cx, y0+y-P+cy)
### HACK -- measure source ellipticity
# go back to the image (not detection map)
#subimg = img[slc]
subimg = img[y0 + y - P:y0 + y + P + 1,
x0 + x - P:x0 + x + P + 1].copy()
subimg /= subimg.sum()
ph, pw = subimg.shape
px, py = np.meshgrid(np.arange(pw), np.arange(ph))
mx2.append(np.sum(subimg * (px - cx)**2))
my2.append(np.sum(subimg * (py - cy)**2))
mxy.append(np.sum(subimg * (px - cx) * (py - cy)))
# Gaussian windowed version
s = 1.
wimg = subimg * np.exp(-0.5 * ((px - cx)**2 + (py - cy)**2) / s**2)
wimg /= np.sum(wimg)
wmx2.append(np.sum(wimg * (px - cx)**2))
wmy2.append(np.sum(wimg * (py - cy)**2))
wmxy.append(np.sum(wimg * (px - cx) * (py - cy)))
mx2 = np.array(mx2)
my2 = np.array(my2)
mxy = np.array(mxy)
wmx2 = np.array(wmx2)
wmy2 = np.array(wmy2)
wmxy = np.array(wmxy)
# semi-major/minor axes and position angle
theta = np.rad2deg(np.arctan2(2 * mxy, mx2 - my2) / 2.)
theta = np.abs(theta) * np.sign(mxy)
s = np.sqrt(((mx2 - my2) / 2.)**2 + mxy**2)
a = np.sqrt((mx2 + my2) / 2. + s)
b = np.sqrt((mx2 + my2) / 2. - s)
ell = 1. - b / a
wtheta = np.rad2deg(np.arctan2(2 * wmxy, wmx2 - wmy2) / 2.)
wtheta = np.abs(wtheta) * np.sign(wmxy)
ws = np.sqrt(((wmx2 - wmy2) / 2.)**2 + wmxy**2)
wa = np.sqrt((wmx2 + wmy2) / 2. + ws)
wb = np.sqrt((wmx2 + wmy2) / 2. - ws)
well = 1. - wb / wa
fx = np.array(fx)
fy = np.array(fy)
xx = np.array(xx)
yy = np.array(yy)
if ps is not None:
plt.clf()
dimshow(detsn, vmin=-3, vmax=50, cmap='gray')
ax = plt.axis()
plt.plot(fx, fy, 'go', mec='g', mfc='none', ms=10)
plt.colorbar()
plt.title('Detected sources')
plt.axis(ax)
ps.savefig()
# show centroids too
# plt.plot(xx, yy, 'go', mec='g', mfc='none', ms=8)
# plt.axis(ax)
# ps.savefig()
# if ps is not None:
# plt.clf()
# plt.subplot(2,1,1)
# mx = np.percentile(np.append(mx2,my2), 99)
# ha = dict(histtype='step', range=(0,mx), bins=50)
# plt.hist(mx2, color='b', label='mx2', **ha)
# plt.hist(my2, color='r', label='my2', **ha)
# plt.hist(mxy, color='g', label='mxy', **ha)
# plt.legend()
# plt.xlim(0,mx)
# plt.subplot(2,1,2)
# mx = np.percentile(np.append(wmx2,wmy2), 99)
# ha = dict(histtype='step', range=(0,mx), bins=50, lw=3, alpha=0.3)
# plt.hist(wmx2, color='b', label='wx2', **ha)
# plt.hist(wmy2, color='r', label='wy2', **ha)
# plt.hist(wmxy, color='g', label='wxy', **ha)
# plt.legend()
# plt.xlim(0,mx)
# plt.suptitle('Source moments')
# ps.savefig()
#
# #mx = np.percentile(np.abs(np.append(mxy,wmxy)), 99)
# plt.clf()
# plt.subplot(2,1,1)
# ha = dict(histtype='step', range=(0,1), bins=50)
# plt.hist(ell, color='g', label='ell', **ha)
# plt.hist(well, color='g', lw=3, alpha=0.3, label='windowed ell', **ha)
# plt.legend()
# plt.subplot(2,1,2)
# ha = dict(histtype='step', range=(-90,90), bins=50)
# plt.hist(theta, color='g', label='theta', **ha)
# plt.hist(wtheta, color='g', lw=3, alpha=0.3,
# label='windowed theta', **ha)
# plt.xlim(-90,90)
# plt.legend()
# plt.suptitle('Source ellipticities & angles')
# ps.savefig()
# Cut down to stars whose centroids are within 1 pixel of their peaks...
#keep = (np.hypot(fx - xx, fy - yy) < 2)
#print(sum(keep), 'of', len(keep), 'stars have centroids within 2 of peaks')
#print('mean dx', np.mean(fx-xx), 'dy', np.mean(fy-yy), 'pixels')
#assert(float(sum(keep)) / len(keep) > 0.9)
#fx = fx[keep]
#fy = fy[keep]
apxy = np.vstack((fx, fy)).T
ap = []
aprad_pix = self.aprad / pixsc
aper = photutils.CircularAperture(apxy, aprad_pix)
p = photutils.aperture_photometry(img, aper)
apflux = p.field('aperture_sum')
# Manual aperture photometry to get clipped means in sky annulus
sky_inner_r, sky_outer_r = [r / pixsc for r in self.skyrad]
sky = []
for xi, yi in zip(fx, fy):
ix = int(np.round(xi))
iy = int(np.round(yi))
skyR = int(np.ceil(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))
skypix = img[ylo:yhi, xlo:xhi][inannulus]
#print('ylo,yhi, xlo,xhi', ylo,yhi, xlo,xhi, 'img subshape', img[ylo:yhi, xlo:xhi].shape, 'inann shape', inannulus.shape)
s, nil = sensible_sigmaclip(skypix)
sky.append(s)
sky = np.array(sky)
apflux2 = apflux - sky * (np.pi * aprad_pix**2)
good = (apflux2 > 0) * (apflux > 0)
apflux = apflux[good]
apflux2 = apflux2[good]
fx = fx[good]
fy = fy[good]
# Read in the PS1 catalog, and keep those within 0.25 deg of CCD center
# and those with main sequence colors
pscat = ps1cat(ccdwcs=wcs)
stars = pscat.get_stars()
#print('Got PS1 stars:', len(stars))
# we add the color term later
ps1band = ps1cat.ps1band[band]
stars.mag = stars.median[:, ps1band]
ok, px, py = wcs.radec2pixelxy(stars.ra, stars.dec)
px -= 1
py -= 1
if ps is not None:
#kwa = dict(vmin=-3*sig1, vmax=50*sig1, cmap='gray')
# Add to the 'detected sources' plot
# mn,mx = np.percentile(img.ravel(), [50,99])
# kwa = dict(vmin=mn, vmax=mx, cmap='gray')
# plt.clf()
# dimshow(img, **kwa)
ax = plt.axis()
#plt.plot(fx, fy, 'go', mec='g', mfc='none', ms=10)
K = np.argsort(stars.mag)
plt.plot(px[K[:10]] - trim_x0,
py[K[:10]] - trim_y0,
'o',
mec='m',
mfc='none',
ms=12,
mew=2)
plt.plot(px[K[10:]] - trim_x0,
py[K[10:]] - trim_y0,
'o',
mec='m',
mfc='none',
ms=8)
plt.axis(ax)
plt.title('PS1 stars')
#plt.colorbar()
ps.savefig()
# we trimmed the image before running detection; re-add that margin
fullx = fx + trim_x0
fully = fy + trim_y0
# Match PS1 to our detections, find offset
radius = self.maxshift / pixsc
I, J, dx, dy = self.match_ps1_stars(px, py, fullx, fully, radius,
stars)
print(len(I), 'spatial matches with large radius', self.maxshift,
'arcsec,', radius, 'pix')
bins = 2 * int(np.ceil(radius))
#print('Histogramming with', bins, 'bins')
histo, xe, ye = np.histogram2d(dx,
dy,
bins=bins,
range=((-radius, radius), (-radius,
radius)))
# smooth histogram before finding peak -- fuzzy matching
from scipy.ndimage.filters import gaussian_filter
histo = gaussian_filter(histo, 1.)
histo = histo.T
mx = np.argmax(histo)
my, mx = np.unravel_index(mx, histo.shape)
shiftx = (xe[mx] + xe[mx + 1]) / 2.
shifty = (ye[my] + ye[my + 1]) / 2.
if ps is not None:
plt.clf()
plothist(dx, dy, range=((-radius, radius), (-radius, radius)))
plt.xlabel('dx (pixels)')
plt.ylabel('dy (pixels)')
plt.title('Offsets to PS1 stars')
ax = plt.axis()
plt.axhline(0, color='b')
plt.axvline(0, color='b')
plt.plot(shiftx, shifty, 'o', mec='m', mfc='none', ms=15, mew=3)
plt.axis(ax)
ps.savefig()
# Refine with smaller search radius
radius2 = 3. / pixsc
I, J, dx, dy = self.match_ps1_stars(px, py, fullx + shiftx,
fully + shifty, radius2, stars)
print(len(J), 'matches to PS1 with small radius', 3, 'arcsec')
shiftx2 = np.median(dx)
shifty2 = np.median(dy)
#print('Stage-1 shift', shiftx, shifty)
#print('Stage-2 shift', shiftx2, shifty2)
sx = shiftx + shiftx2
sy = shifty + shifty2
print('Astrometric shift (%.0f, %.0f) pixels' % (sx, sy))
if self.debug and ps is not None:
plt.clf()
plothist(dx, dy, range=((-radius2, radius2), (-radius2, radius2)))
plt.xlabel('dx (pixels)')
plt.ylabel('dy (pixels)')
plt.title('Offsets to PS1 stars')
ax = plt.axis()
plt.axhline(0, color='b')
plt.axvline(0, color='b')
plt.plot(shiftx2, shifty2, 'o', mec='m', mfc='none', ms=15, mew=3)
plt.axis(ax)
ps.savefig()
if ps is not None:
mn, mx = np.percentile(img.ravel(), [50, 99])
kwa2 = dict(vmin=mn, vmax=mx, cmap='gray')
plt.clf()
dimshow(img, **kwa2)
ax = plt.axis()
plt.plot(fx[J], fy[J], 'go', mec='g', mfc='none', ms=10, mew=2)
plt.plot(px[I] - sx - trim_x0,
py[I] - sy - trim_y0,
'm+',
ms=10,
mew=2)
plt.axis(ax)
plt.title('Matched PS1 stars')
plt.colorbar()
ps.savefig()
plt.clf()
dimshow(img, **kwa2)
ax = plt.axis()
plt.plot(fx[J], fy[J], 'go', mec='g', mfc='none', ms=10, mew=2)
K = np.argsort(stars.mag)
plt.plot(px[K[:10]] - sx - trim_x0,
py[K[:10]] - sy - trim_y0,
'o',
mec='m',
mfc='none',
ms=12,
mew=2)
plt.plot(px[K[10:]] - sx - trim_x0,
py[K[10:]] - sy - trim_y0,
'o',
mec='m',
mfc='none',
ms=8,
mew=2)
plt.axis(ax)
plt.title('All PS1 stars')
plt.colorbar()
ps.savefig()
# Now cut to just *stars* with good colors
stars.gicolor = stars.median[:, 0] - stars.median[:, 2]
keep = (stars.gicolor > 0.4) * (stars.gicolor < 2.7)
stars.cut(keep)
if len(stars) == 0:
print('No overlap or too few stars in PS1')
return None
px = px[keep]
py = py[keep]
# Re-match
I, J, dx, dy = self.match_ps1_stars(px, py, fullx + sx, fully + sy,
radius2, stars)
print('Cut to', len(stars), 'PS1 stars with good colors; matched',
len(I))
nmatched = len(I)
meas.update(dx=sx, dy=sy, nmatched=nmatched)
if focus:
meas.update(img=img,
hdr=hdr,
primhdr=primhdr,
fx=fx,
fy=fy,
px=px - trim_x0 - sx,
py=py - trim_y0 - sy,
sig1=sig1,
stars=stars,
moments=(mx2, my2, mxy, theta, a, b, ell),
wmoments=(wmx2, wmy2, wmxy, wtheta, wa, wb, well),
apflux=apflux,
apflux2=apflux2)
return meas
#print('Mean astrometric shift (arcsec): delta-ra=', -np.mean(dy)*0.263, 'delta-dec=', np.mean(dx)*0.263)
# Compute photometric offset compared to PS1
# as the PS1 minus observed mags
colorterm = self.colorterm_ps1_to_observed(stars.median, band)
stars.mag += colorterm
ps1mag = stars.mag[I]
if False and ps is not None:
plt.clf()
plt.semilogy(ps1mag, apflux2[J], 'b.')
plt.xlabel('PS1 mag')
plt.ylabel('DECam ap flux (with sky sub)')
ps.savefig()
plt.clf()
plt.semilogy(ps1mag, apflux[J], 'b.')
plt.xlabel('PS1 mag')
plt.ylabel('DECam ap flux (no sky sub)')
ps.savefig()
apmag2 = -2.5 * np.log10(apflux2) + zp0 + 2.5 * np.log10(exptime)
apmag = -2.5 * np.log10(apflux) + zp0 + 2.5 * np.log10(exptime)
if ps is not None:
plt.clf()
plt.plot(ps1mag, apmag[J], 'b.', label='No sky sub')
plt.plot(ps1mag, apmag2[J], 'r.', label='Sky sub')
# ax = plt.axis()
# mn = min(ax[0], ax[2])
# mx = max(ax[1], ax[3])
# plt.plot([mn,mx], [mn,mx], 'k-', alpha=0.1)
# plt.axis(ax)
plt.xlabel('PS1 mag')
plt.ylabel('DECam ap mag')
plt.legend(loc='upper left')
plt.title('Zeropoint')
ps.savefig()
dm = ps1mag - apmag[J]
dmag, dsig = sensible_sigmaclip(dm, nsigma=2.5)
print('Mag offset: %8.3f' % dmag)
print('Scatter: %8.3f' % dsig)
if not np.isfinite(dmag) or not np.isfinite(dsig):
print('FAILED TO GET ZEROPOINT!')
meas.update(zp=None)
return meas
from scipy.stats import sigmaclip
goodpix, lo, hi = sigmaclip(dm, low=3, high=3)
dmagmed = np.median(goodpix)
print(len(goodpix), 'stars used for zeropoint median')
print('Using median zeropoint:')
zp_med = zp0 + dmagmed
trans_med = 10.**(-0.4 * (zp0 - zp_med - kx * (airmass - 1.)))
print('Zeropoint %6.3f' % zp_med)
print('Transparency: %.3f' % trans_med)
dm = ps1mag - apmag2[J]
dmag2, dsig2 = sensible_sigmaclip(dm, nsigma=2.5)
#print('Sky-sub mag offset', dmag2)
#print('Scatter', dsig2)
if ps is not None:
plt.clf()
plt.plot(ps1mag,
apmag[J] + dmag - ps1mag,
'b.',
label='No sky sub')
plt.plot(ps1mag, apmag2[J] + dmag2 - ps1mag, 'r.', label='Sky sub')
plt.xlabel('PS1 mag')
plt.ylabel('DECam ap mag - PS1 mag')
plt.legend(loc='upper left')
plt.ylim(-0.25, 0.25)
plt.axhline(0, color='k', alpha=0.25)
plt.title('Zeropoint')
ps.savefig()
zp_obs = zp0 + dmag
transparency = 10.**(-0.4 * (zp0 - zp_obs - kx * (airmass - 1.)))
meas.update(zp=zp_obs, transparency=transparency)
print('Zeropoint %6.3f' % zp_obs)
print('Fiducial %6.3f' % zp0)
print('Transparency: %.3f' % transparency)
# print('Using sky-subtracted values:')
# zp_sky = zp0 + dmag2
# trans_sky = 10.**(-0.4 * (zp0 - zp_sky - kx * (airmass - 1.)))
# print('Zeropoint %6.3f' % zp_sky)
# print('Transparency: %.3f' % trans_sky)
fwhms = []
psf_r = 15
if n_fwhm not in [0, None]:
Jf = J[:n_fwhm]
for i, (xi, yi, fluxi) in enumerate(zip(fx[Jf], fy[Jf], apflux[Jf])):
#print('Fitting source', i, 'of', len(Jf))
ix = int(np.round(xi))
iy = int(np.round(yi))
xlo = max(0, ix - psf_r)
xhi = min(W, ix + psf_r + 1)
ylo = max(0, iy - psf_r)
yhi = min(H, iy + psf_r + 1)
xx, yy = np.meshgrid(np.arange(xlo, xhi), np.arange(ylo, yhi))
r2 = (xx - xi)**2 + (yy - yi)**2
keep = (r2 < psf_r**2)
pix = img[ylo:yhi, xlo:xhi].copy()
ie = np.zeros_like(pix)
ie[keep] = 1. / sig1
#print('fitting source at', ix,iy)
#print('number of active pixels:', np.sum(ie > 0), 'shape', ie.shape)
psf = tractor.NCircularGaussianPSF([4.], [1.])
tim = tractor.Image(data=pix, inverr=ie, psf=psf)
src = tractor.PointSource(tractor.PixPos(xi - xlo, yi - ylo),
tractor.Flux(fluxi))
tr = tractor.Tractor([tim], [src])
#print('Posterior before prior:', tr.getLogProb())
src.pos.addGaussianPrior('x', 0., 1.)
#print('Posterior after prior:', tr.getLogProb())
doplot = (i < 5) * (ps is not None)
if doplot:
mod0 = tr.getModelImage(0)
tim.freezeAllBut('psf')
psf.freezeAllBut('sigmas')
# print('Optimizing params:')
# tr.printThawedParams()
#print('Parameter step sizes:', tr.getStepSizes())
optargs = dict(priors=False, shared_params=False)
for step in range(50):
dlnp, x, alpha = tr.optimize(**optargs)
#print('dlnp', dlnp)
#print('src', src)
#print('psf', psf)
if dlnp == 0:
break
# Now fit only the PSF size
tr.freezeParam('catalog')
# print('Optimizing params:')
# tr.printThawedParams()
for step in range(50):
dlnp, x, alpha = tr.optimize(**optargs)
#print('dlnp', dlnp)
#print('src', src)
#print('psf', psf)
if dlnp == 0:
break
fwhms.append(psf.sigmas[0] * 2.35 * pixsc)
if doplot:
mod1 = tr.getModelImage(0)
chi1 = tr.getChiImage(0)
plt.clf()
plt.subplot(2, 2, 1)
plt.title('Image')
dimshow(pix, **self.imgkwa)
plt.subplot(2, 2, 2)
plt.title('Initial model')
dimshow(mod0, **self.imgkwa)
plt.subplot(2, 2, 3)
plt.title('Final model')
dimshow(mod1, **self.imgkwa)
plt.subplot(2, 2, 4)
plt.title('Final chi')
dimshow(chi1, vmin=-10, vmax=10)
plt.suptitle('PSF fit')
ps.savefig()
fwhms = np.array(fwhms)
fwhm = np.median(fwhms)
print('Median FWHM: %.3f' % np.median(fwhms))
meas.update(seeing=fwhm)
if False and ps is not None:
lo, hi = np.percentile(fwhms, [5, 95])
lo -= 0.1
hi += 0.1
plt.clf()
plt.hist(fwhms, 25, range=(lo, hi), histtype='step', color='b')
plt.xlabel('FWHM (arcsec)')
ps.savefig()
if ps is not None:
plt.clf()
for i, (xi, yi) in enumerate(zip(fx[J], fy[J])[:50]):
ix = int(np.round(xi))
iy = int(np.round(yi))
xlo = max(0, ix - psf_r)
xhi = min(W, ix + psf_r + 1)
ylo = max(0, iy - psf_r)
yhi = min(H, iy + psf_r + 1)
pix = img[ylo:yhi, xlo:xhi]
slc = pix[iy - ylo, :].copy()
slc /= np.sum(slc)
p1 = plt.plot(slc, 'b-', alpha=0.2)
slc = pix[:, ix - xlo].copy()
slc /= np.sum(slc)
p2 = plt.plot(slc, 'r-', alpha=0.2)
ph, pw = pix.shape
cx, cy = pw / 2, ph / 2
if i == 0:
xx = np.linspace(0, pw, 300)
dx = xx[1] - xx[0]
sig = fwhm / pixsc / 2.35
yy = np.exp(-0.5 * (xx - cx)**2 / sig**2) # * np.sum(pix)
yy /= (np.sum(yy) * dx)
p3 = plt.plot(xx, yy, 'k-', zorder=20)
#plt.ylim(-0.2, 1.0)
plt.legend([p1[0], p2[0], p3[0]],
['image slice (y)', 'image slice (x)', 'fit'])
plt.title('PSF fit')
ps.savefig()
return meas
if __name__ == '__main__':
expnum, ccdname = 292604, 'N4'
survey = LegacySurveyData(output_dir='onechip-civ')
ccds = survey.find_ccds(expnum=expnum, ccdname=ccdname)
print('Found', len(ccds), 'CCD')
# HACK -- cut to JUST that ccd.
survey.ccds = ccds
ccd = ccds[0]
im = survey.get_image_object(ccd)
wcs = survey.get_approx_wcs(ccd)
rc,dc = wcs.radec_center()
# Read PS-1 catalog to find out which blobs to process.
ps1 = ps1cat(ccdwcs=wcs)
stars = ps1.get_stars()
print('Read', len(stars), 'PS1 stars')
brickname = ('custom-%06i%s%05i' %
(int(1000*rc), 'm' if dc < 0 else 'p', int(1000*np.abs(dc))))
outfn = survey.find_file('tractor', brick=brickname, output=True)
print('Output catalog:', outfn)
if not os.path.exists(outfn):
run_brick(None, survey, radec=(rc,dc),
width=ccd.height, height=ccd.width, # CCDs are rotated
bands=im.band,
wise=False,
blobradec=zip(stars.ra, stars.dec),