def stage0(**kwargs):
ps = PlotSequence('cfht')
decals = CfhtDecals()
B = decals.get_bricks()
print('Bricks:')
B.about()
ra, dec = 190.0, 11.0
#bands = 'ugri'
bands = 'gri'
B.cut(np.argsort(degrees_between(ra, dec, B.ra, B.dec)))
print('Nearest bricks:', B.ra[:5], B.dec[:5], B.brickid[:5])
brick = B[0]
pixscale = 0.186
#W,H = 1024,1024
#W,H = 2048,2048
#W,H = 3600,3600
W, H = 4800, 4800
targetwcs = wcs_for_brick(brick, pixscale=pixscale, W=W, H=H)
ccdfn = 'cfht-ccds.fits'
if os.path.exists(ccdfn):
T = fits_table(ccdfn)
else:
T = get_ccd_list()
T.writeto(ccdfn)
print(len(T), 'CCDs')
T.cut(ccds_touching_wcs(targetwcs, T))
print(len(T), 'CCDs touching brick')
T.cut(np.array([b in bands for b in T.filter]))
print(len(T), 'in bands', bands)
ims = []
for t in T:
im = CfhtImage(t)
# magzp = hdr['PHOT_C'] + 2.5 * np.log10(hdr['EXPTIME'])
# fwhm = t.seeing / (pixscale * 3600)
# print '-> FWHM', fwhm, 'pix'
im.seeing = t.seeing
im.pixscale = t.pixscale
print('seeing', t.seeing)
print('pixscale', im.pixscale * 3600, 'arcsec/pix')
im.run_calibs(t.ra, t.dec, im.pixscale, W=t.width, H=t.height)
ims.append(im)
# Read images, clip to ROI
targetrd = np.array([
targetwcs.pixelxy2radec(x, y)
for x, y in [(1, 1), (W, 1), (W, H), (1, H), (1, 1)]
])
keepims = []
tims = []
for im in ims:
print()
print('Reading expnum', im.expnum, 'name', im.extname, 'band', im.band,
'exptime', im.exptime)
band = im.band
wcs = im.read_wcs()
imh, imw = wcs.imageh, wcs.imagew
imgpoly = [(1, 1), (1, imh), (imw, imh), (imw, 1)]
ok, tx, ty = wcs.radec2pixelxy(targetrd[:-1, 0], targetrd[:-1, 1])
tpoly = zip(tx, ty)
clip = clip_polygon(imgpoly, tpoly)
clip = np.array(clip)
#print 'Clip', clip
if len(clip) == 0:
continue
x0, y0 = np.floor(clip.min(axis=0)).astype(int)
x1, y1 = np.ceil(clip.max(axis=0)).astype(int)
slc = slice(y0, y1 + 1), slice(x0, x1 + 1)
## FIXME -- it seems I got lucky and the cross product is
## negative == clockwise, as required by clip_polygon. One
## could check this and reverse the polygon vertex order.
# dx0,dy0 = tx[1]-tx[0], ty[1]-ty[0]
# dx1,dy1 = tx[2]-tx[1], ty[2]-ty[1]
# cross = dx0*dy1 - dx1*dy0
# print 'Cross:', cross
print('Image slice: x [%i,%i], y [%i,%i]' % (x0, x1, y0, y1))
print('Reading image from', im.imgfn, 'HDU', im.hdu)
img, imghdr = im.read_image(header=True, slice=slc)
goodpix = (img != 0)
print('Number of pixels == 0:', np.sum(img == 0))
print('Number of pixels != 0:', np.sum(goodpix))
if np.sum(goodpix) == 0:
continue
# print 'Image shape', img.shape
print('Image range', img.min(), img.max())
print('Goodpix image range:', (img[goodpix]).min(),
(img[goodpix]).max())
if img[goodpix].min() == img[goodpix].max():
print('No dynamic range in image')
continue
# print 'Reading invvar from', im.wtfn, 'HDU', im.hdu
# invvar = im.read_invvar(slice=slc)
# # print 'Invvar shape', invvar.shape
# # print 'Invvar range:', invvar.min(), invvar.max()
# invvar[goodpix == 0] = 0.
# if np.all(invvar == 0.):
# print 'Skipping zero-invvar image'
# continue
# assert(np.all(np.isfinite(img)))
# assert(np.all(np.isfinite(invvar)))
# assert(not(np.all(invvar == 0.)))
# # Estimate per-pixel noise via Blanton's 5-pixel MAD
# slice1 = (slice(0,-5,10),slice(0,-5,10))
# slice2 = (slice(5,None,10),slice(5,None,10))
# # print 'sliced shapes:', img[slice1].shape, img[slice2].shape
# # print 'good shape:', (goodpix[slice1] * goodpix[slice2]).shape
# # print 'good values:', np.unique(goodpix[slice1] * goodpix[slice2])
# # print 'sliced[good] shapes:', (img[slice1] - img[slice2])[goodpix[slice1] * goodpix[slice2]].shape
# mad = np.median(np.abs(img[slice1] - img[slice2])[goodpix[slice1] * goodpix[slice2]].ravel())
# sig1 = 1.4826 * mad / np.sqrt(2.)
# print 'MAD sig1:', sig1
# # invvar was 1 or 0
# invvar *= (1./(sig1**2))
# medsky = np.median(img[goodpix])
# Read full image for sig1 and sky estimate
fullimg = im.read_image()
fullgood = (fullimg != 0)
# Estimate per-pixel noise via Blanton's 5-pixel MAD
slice1 = (slice(0, -5, 10), slice(0, -5, 10))
slice2 = (slice(5, None, 10), slice(5, None, 10))
mad = np.median(
np.abs(fullimg[slice1] -
fullimg[slice2])[fullgood[slice1] *
fullgood[slice2]].ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print('MAD sig1:', sig1)
medsky = np.median(fullimg[fullgood])
invvar = np.zeros_like(img)
invvar[goodpix] = 1. / sig1**2
# Median-smooth sky subtraction
plt.clf()
dimshow(np.round((img - medsky) / sig1), vmin=-3, vmax=5)
plt.title('Scalar median: %s' % im.name)
ps.savefig()
# medsky = np.zeros_like(img)
# # astrometry.util.util
# median_smooth(img, np.logical_not(goodpix), 256, medsky)
fullmed = np.zeros_like(fullimg)
median_smooth(fullimg - medsky, np.logical_not(fullgood), 256, fullmed)
fullmed += medsky
medimg = fullmed[slc]
plt.clf()
dimshow(np.round((img - medimg) / sig1), vmin=-3, vmax=5)
plt.title('Median filtered: %s' % im.name)
ps.savefig()
#print 'Subtracting median:', medsky
#img -= medsky
img -= medimg
primhdr = im.read_image_primary_header()
magzp = decals.get_zeropoint_for(im)
print('magzp', magzp)
zpscale = NanoMaggies.zeropointToScale(magzp)
print('zpscale', zpscale)
# Scale images to Nanomaggies
img /= zpscale
sig1 /= zpscale
invvar *= zpscale**2
orig_zpscale = zpscale
zpscale = 1.
assert (np.sum(invvar > 0) > 0)
print('After scaling:')
print('sig1', sig1)
print('invvar range', invvar.min(), invvar.max())
print('image range', img.min(), img.max())
assert (np.all(np.isfinite(img)))
assert (np.all(np.isfinite(invvar)))
assert (np.isfinite(sig1))
plt.clf()
lo, hi = -5 * sig1, 10 * sig1
n, b, p = plt.hist(img[goodpix].ravel(),
100,
range=(lo, hi),
histtype='step',
color='k')
xx = np.linspace(lo, hi, 200)
plt.plot(xx, max(n) * np.exp(-xx**2 / (2. * sig1**2)), 'r-')
plt.xlim(lo, hi)
plt.title('Pixel histogram: %s' % im.name)
ps.savefig()
twcs = ConstantFitsWcs(wcs)
if x0 or y0:
twcs.setX0Y0(x0, y0)
info = im.get_image_info()
fullh, fullw = info['dims']
# read fit PsfEx model
psfex = PsfEx.fromFits(im.psffitfn)
print('Read', psfex)
# HACK -- highly approximate PSF here!
#psf_fwhm = imghdr['FWHM']
#psf_fwhm = im.seeing
psf_fwhm = im.seeing / (im.pixscale * 3600)
print('PSF FWHM', psf_fwhm, 'pixels')
psf_sigma = psf_fwhm / 2.35
psf = NCircularGaussianPSF([psf_sigma], [1.])
print('img type', img.dtype)
tim = Image(img,
invvar=invvar,
wcs=twcs,
psf=psf,
photocal=LinearPhotoCal(zpscale, band=band),
sky=ConstantSky(0.),
name=im.name + ' ' + band)
tim.zr = [-3. * sig1, 10. * sig1]
tim.sig1 = sig1
tim.band = band
tim.psf_fwhm = psf_fwhm
tim.psf_sigma = psf_sigma
tim.sip_wcs = wcs
tim.x0, tim.y0 = int(x0), int(y0)
tim.psfex = psfex
tim.imobj = im
mn, mx = tim.zr
tim.ima = dict(interpolation='nearest',
origin='lower',
cmap='gray',
vmin=mn,
vmax=mx)
tims.append(tim)
keepims.append(im)
ims = keepims
print('Computing resampling...')
# save resampling params
for tim in tims:
wcs = tim.sip_wcs
subh, subw = tim.shape
subwcs = wcs.get_subimage(tim.x0, tim.y0, subw, subh)
tim.subwcs = subwcs
try:
Yo, Xo, Yi, Xi, rims = resample_with_wcs(targetwcs, subwcs, [], 2)
except OverlapError:
print('No overlap')
continue
if len(Yo) == 0:
continue
tim.resamp = (Yo, Xo, Yi, Xi)
print('Creating coadds...')
# Produce per-band coadds, for plots
coimgs = []
cons = []
for ib, band in enumerate(bands):
coimg = np.zeros((H, W), np.float32)
con = np.zeros((H, W), np.uint8)
for tim in tims:
if tim.band != band:
continue
(Yo, Xo, Yi, Xi) = tim.resamp
if len(Yo) == 0:
continue
nn = (tim.getInvvar()[Yi, Xi] > 0)
coimg[Yo, Xo] += tim.getImage()[Yi, Xi] * nn
con[Yo, Xo] += nn
# print
# print 'tim', tim.name
# print 'number of resampled pix:', len(Yo)
# reim = np.zeros_like(coimg)
# ren = np.zeros_like(coimg)
# reim[Yo,Xo] = tim.getImage()[Yi,Xi] * nn
# ren[Yo,Xo] = nn
# print 'number of resampled pix with positive invvar:', ren.sum()
# plt.clf()
# plt.subplot(2,2,1)
# mn,mx = [np.percentile(reim[ren>0], p) for p in [25,95]]
# print 'Percentiles:', mn,mx
# dimshow(reim, vmin=mn, vmax=mx)
# plt.colorbar()
# plt.subplot(2,2,2)
# dimshow(con)
# plt.colorbar()
# plt.subplot(2,2,3)
# dimshow(reim, vmin=tim.zr[0], vmax=tim.zr[1])
# plt.colorbar()
# plt.subplot(2,2,4)
# plt.hist(reim.ravel(), 100, histtype='step', color='b')
# plt.hist(tim.getImage().ravel(), 100, histtype='step', color='r')
# plt.suptitle('%s: %s' % (band, tim.name))
# ps.savefig()
coimg /= np.maximum(con, 1)
coimgs.append(coimg)
cons.append(con)
plt.clf()
dimshow(get_rgb(coimgs, bands))
ps.savefig()
plt.clf()
for i, b in enumerate(bands):
plt.subplot(2, 2, i + 1)
dimshow(cons[i], ticks=False)
plt.title('%s band' % b)
plt.colorbar()
plt.suptitle('Number of exposures')
ps.savefig()
print('Grabbing SDSS sources...')
bandlist = [b for b in bands]
cat, T = get_sdss_sources(bandlist, targetwcs)
# record coordinates in target brick image
ok, T.tx, T.ty = targetwcs.radec2pixelxy(T.ra, T.dec)
T.tx -= 1
T.ty -= 1
T.itx = np.clip(np.round(T.tx).astype(int), 0, W - 1)
T.ity = np.clip(np.round(T.ty).astype(int), 0, H - 1)
plt.clf()
dimshow(get_rgb(coimgs, bands))
ax = plt.axis()
plt.plot(T.tx, T.ty, 'o', mec=green, mfc='none', ms=10, mew=1.5)
plt.axis(ax)
plt.title('SDSS sources')
ps.savefig()
print('Detmaps...')
# Render the detection maps
detmaps = dict([(b, np.zeros((H, W), np.float32)) for b in bands])
detivs = dict([(b, np.zeros((H, W), np.float32)) for b in bands])
for tim in tims:
iv = tim.getInvvar()
psfnorm = 1. / (2. * np.sqrt(np.pi) * tim.psf_sigma)
detim = tim.getImage().copy()
detim[iv == 0] = 0.
detim = gaussian_filter(detim, tim.psf_sigma) / psfnorm**2
detsig1 = tim.sig1 / psfnorm
subh, subw = tim.shape
detiv = np.zeros((subh, subw), np.float32) + (1. / detsig1**2)
detiv[iv == 0] = 0.
(Yo, Xo, Yi, Xi) = tim.resamp
detmaps[tim.band][Yo, Xo] += detiv[Yi, Xi] * detim[Yi, Xi]
detivs[tim.band][Yo, Xo] += detiv[Yi, Xi]
rtn = dict()
for k in [
'T', 'coimgs', 'cons', 'detmaps', 'detivs', 'targetrd', 'pixscale',
'targetwcs', 'W', 'H', 'bands', 'tims', 'ps', 'brick', 'cat'
]:
rtn[k] = locals()[k]
return rtn
def stage2(cat=None,
variances=None,
T=None,
bands=None,
ps=None,
targetwcs=None,
**kwargs):
print('kwargs:', kwargs.keys())
#print 'variances:', variances
from desi_common import prepare_fits_catalog
TT = T.copy()
hdr = None
fs = None
T2, hdr = prepare_fits_catalog(cat, 1. / np.array(variances), TT, hdr,
bands, fs)
T2.about()
T2.writeto('cfht.fits')
ccmap = dict(g='g', r='r', i='m')
bandlist = [b for b in bands]
scat, S = get_sdss_sources(bandlist, targetwcs)
S.about()
I, J, d = match_radec(T2.ra, T2.dec, S.ra, S.dec, 1. / 3600.)
M = fits_table()
M.ra = S.ra[J]
M.dec = S.dec[J]
M.cfhtI = I
for band in bands:
mag = T2.get('decam_%s_mag' % band)[I]
sflux = np.array([s.getBrightness().getBand(band) for s in scat])[J]
smag = NanoMaggies.nanomaggiesToMag(sflux)
M.set('mag_%s' % band, mag)
M.set('smag_%s' % band, smag)
cc = ccmap[band]
plt.clf()
plt.subplot(2, 1, 1)
plt.plot(smag, mag, '.', color=cc, alpha=0.5)
lo, hi = 16, 24
plt.plot([lo, hi], [lo, hi], 'k-')
plt.xlabel('SDSS mag')
plt.ylabel('CFHT mag')
plt.axis([lo, hi, lo, hi])
plt.subplot(2, 1, 2)
plt.plot(smag, mag - smag, '.', color=cc, alpha=0.5)
lo, hi = 16, 24
plt.plot([lo, hi], [0, 0], 'k-')
plt.xlabel('SDSS mag')
plt.ylabel('CFHT - SDSS mag')
plt.axis([lo, hi, -1, 1])
plt.suptitle('%s band' % band)
ps.savefig()
M.writeto('cfht-matched.fits')
plt.clf()
lp, lt = [], []
for band in bands:
sn = T2.get('decam_%s_nanomaggies' % band) * np.sqrt(
T2.get('decam_%s_nanomaggies_invvar' % band))
#mag = T2.get('decam_%s_mag_corr' % band)
mag = T2.get('decam_%s_mag' % band)
print('band', band)
print('Mags:', mag)
print('SN:', sn)
cc = ccmap[band]
p = plt.semilogy(mag, sn, '.', color=cc, alpha=0.5)
lp.append(p[0])
lt.append('%s band' % band)
plt.xlabel('mag')
plt.ylabel('Flux Signal-to-Noise')
tt = [1, 2, 3, 4, 5, 10, 20, 30, 40, 50]
plt.yticks(tt, ['%i' % t for t in tt])
plt.axhline(5., color='k')
#plt.axis([21, 26, 1, 20])
plt.legend(lp, lt, loc='upper right')
plt.title('CFHT depth')
ps.savefig()
# ['tims', 'cons', 'pixscale', 'H', 'coimgs', 'detmaps', 'W', 'brick', 'detivs', 'targetrd']
return dict(T2=T2,
M=M,
tims=None,
detmaps=None,
detivs=None,
cons=None,
coimgs=None)
def main():
decals = Decals()
catpattern = 'pipebrick-cats/tractor-phot-b%06i.fits'
ra, dec = 242, 7
# Region-of-interest, in pixels: x0, x1, y0, y1
#roi = None
roi = [500, 1000, 500, 1000]
if roi is not None:
x0, x1, y0, y1 = roi
#expnum = 346623
#ccdname = 'N12'
#chips = decals.find_ccds(expnum=expnum, extname=ccdname)
#print 'Found', len(chips), 'chips for expnum', expnum, 'extname', ccdname
#if len(chips) != 1:
#return False
chips = decals.get_ccds()
D = np.argsort(np.hypot(chips.ra - ra, chips.dec - dec))
print('Closest chip:', chips[D[0]])
chips = [chips[D[0]]]
im = DecamImage(decals, chips[0])
print('Image:', im)
targetwcs = Sip(im.wcsfn)
if roi is not None:
targetwcs = targetwcs.get_subimage(x0, y0, x1 - x0, y1 - y0)
r0, r1, d0, d1 = targetwcs.radec_bounds()
# ~ 30-pixel margin
margin = 2e-3
if r0 > r1:
# RA wrap-around
TT = [
brick_catalog_for_radec_box(ra, rb, d0 - margin, d1 + margin,
decals, catpattern)
for (ra, rb) in [(0, r1 + margin), (r0 - margin, 360.)]
]
T = merge_tables(TT)
T._header = TT[0]._header
else:
T = brick_catalog_for_radec_box(r0 - margin, r1 + margin, d0 - margin,
d1 + margin, decals, catpattern)
print('Got', len(T), 'catalog entries within range')
cat = read_fits_catalog(T, T._header)
print('Got', len(cat), 'catalog objects')
print('Switching ellipse parameterizations')
switch_to_soft_ellipses(cat)
keepcat = []
for src in cat:
if not np.all(np.isfinite(src.getParams())):
print('Src has infinite params:', src)
continue
if isinstance(src, FixedCompositeGalaxy):
f = src.fracDev.getClippedValue()
if f == 0.:
src = ExpGalaxy(src.pos, src.brightness, src.shapeExp)
elif f == 1.:
src = DevGalaxy(src.pos, src.brightness, src.shapeDev)
keepcat.append(src)
cat = keepcat
slc = None
if roi is not None:
slc = slice(y0, y1), slice(x0, x1)
tim = im.get_tractor_image(slc=slc)
print('Got', tim)
tim.psfex.fitSavedData(*tim.psfex.splinedata)
tim.psfex.radius = 20
tim.psf = CachingPsfEx.fromPsfEx(tim.psfex)
tractor = Tractor([tim], cat)
print('Created', tractor)
mod = tractor.getModelImage(0)
plt.clf()
dimshow(tim.getImage(), **tim.ima)
plt.title('Image')
plt.savefig('1.png')
plt.clf()
dimshow(mod, **tim.ima)
plt.title('Model')
plt.savefig('2.png')
ok, x, y = targetwcs.radec2pixelxy([src.getPosition().ra for src in cat],
[src.getPosition().dec for src in cat])
ax = plt.axis()
plt.plot(x, y, 'rx')
#plt.savefig('3.png')
plt.axis(ax)
plt.title('Sources')
plt.savefig('3.png')
bands = [im.band]
import runbrick
runbrick.photoobjdir = '.'
scat, T = get_sdss_sources(bands, targetwcs, local=False)
print('Got', len(scat), 'SDSS sources in bounds')
stractor = Tractor([tim], scat)
print('Created', stractor)
smod = stractor.getModelImage(0)
plt.clf()
dimshow(smod, **tim.ima)
plt.title('SDSS model')
plt.savefig('4.png')
def stage2(cat=None, variances=None, T=None, bands=None, ps=None,
targetwcs=None, **kwargs):
print 'kwargs:', kwargs.keys()
#print 'variances:', variances
from desi_common import prepare_fits_catalog
TT = T.copy()
hdr = None
fs = None
T2,hdr = prepare_fits_catalog(cat, 1./np.array(variances), TT, hdr, bands, fs)
T2.about()
T2.writeto('cfht.fits')
ccmap = dict(g='g', r='r', i='m')
bandlist = [b for b in bands]
scat,S = get_sdss_sources(bandlist, targetwcs)
S.about()
I,J,d = match_radec(T2.ra, T2.dec, S.ra, S.dec, 1./3600.)
M = fits_table()
M.ra = S.ra[J]
M.dec = S.dec[J]
M.cfhtI = I
for band in bands:
mag = T2.get('decam_%s_mag' % band)[I]
sflux = np.array([s.getBrightness().getBand(band) for s in scat])[J]
smag = NanoMaggies.nanomaggiesToMag(sflux)
M.set('mag_%s' % band, mag)
M.set('smag_%s' % band, smag)
cc = ccmap[band]
plt.clf()
plt.subplot(2,1,1)
plt.plot(smag, mag, '.', color=cc, alpha=0.5)
lo,hi = 16,24
plt.plot([lo,hi],[lo,hi], 'k-')
plt.xlabel('SDSS mag')
plt.ylabel('CFHT mag')
plt.axis([lo,hi,lo,hi])
plt.subplot(2,1,2)
plt.plot(smag, mag - smag, '.', color=cc, alpha=0.5)
lo,hi = 16,24
plt.plot([lo,hi],[0, 0], 'k-')
plt.xlabel('SDSS mag')
plt.ylabel('CFHT - SDSS mag')
plt.axis([lo,hi,-1,1])
plt.suptitle('%s band' % band)
ps.savefig()
M.writeto('cfht-matched.fits')
plt.clf()
lp,lt = [],[]
for band in bands:
sn = T2.get('decam_%s_nanomaggies' % band) * np.sqrt(T2.get('decam_%s_nanomaggies_invvar' % band))
#mag = T2.get('decam_%s_mag_corr' % band)
mag = T2.get('decam_%s_mag' % band)
print 'band', band
print 'Mags:', mag
print 'SN:', sn
cc = ccmap[band]
p = plt.semilogy(mag, sn, '.', color=cc, alpha=0.5)
lp.append(p[0])
lt.append('%s band' % band)
plt.xlabel('mag')
plt.ylabel('Flux Signal-to-Noise')
tt = [1,2,3,4,5,10,20,30,40,50]
plt.yticks(tt, ['%i' % t for t in tt])
plt.axhline(5., color='k')
#plt.axis([21, 26, 1, 20])
plt.legend(lp, lt, loc='upper right')
plt.title('CFHT depth')
ps.savefig()
# ['tims', 'cons', 'pixscale', 'H', 'coimgs', 'detmaps', 'W', 'brick', 'detivs', 'targetrd']
return dict(T2=T2, M=M, tims=None, detmaps=None, detivs=None,
cons=None, coimgs=None)
def stage0(**kwargs):
ps = PlotSequence('cfht')
decals = CfhtDecals()
B = decals.get_bricks()
print 'Bricks:'
B.about()
ra,dec = 190.0, 11.0
#bands = 'ugri'
bands = 'gri'
B.cut(np.argsort(degrees_between(ra, dec, B.ra, B.dec)))
print 'Nearest bricks:', B.ra[:5], B.dec[:5], B.brickid[:5]
brick = B[0]
pixscale = 0.186
#W,H = 1024,1024
#W,H = 2048,2048
#W,H = 3600,3600
W,H = 4800,4800
targetwcs = wcs_for_brick(brick, pixscale=pixscale, W=W, H=H)
ccdfn = 'cfht-ccds.fits'
if os.path.exists(ccdfn):
T = fits_table(ccdfn)
else:
T = get_ccd_list()
T.writeto(ccdfn)
print len(T), 'CCDs'
T.cut(ccds_touching_wcs(targetwcs, T))
print len(T), 'CCDs touching brick'
T.cut(np.array([b in bands for b in T.filter]))
print len(T), 'in bands', bands
ims = []
for t in T:
im = CfhtImage(t)
# magzp = hdr['PHOT_C'] + 2.5 * np.log10(hdr['EXPTIME'])
# fwhm = t.seeing / (pixscale * 3600)
# print '-> FWHM', fwhm, 'pix'
im.seeing = t.seeing
im.pixscale = t.pixscale
print 'seeing', t.seeing
print 'pixscale', im.pixscale*3600, 'arcsec/pix'
im.run_calibs(t.ra, t.dec, im.pixscale, W=t.width, H=t.height)
ims.append(im)
# Read images, clip to ROI
targetrd = np.array([targetwcs.pixelxy2radec(x,y) for x,y in
[(1,1),(W,1),(W,H),(1,H),(1,1)]])
keepims = []
tims = []
for im in ims:
print
print 'Reading expnum', im.expnum, 'name', im.extname, 'band', im.band, 'exptime', im.exptime
band = im.band
wcs = im.read_wcs()
imh,imw = wcs.imageh,wcs.imagew
imgpoly = [(1,1),(1,imh),(imw,imh),(imw,1)]
ok,tx,ty = wcs.radec2pixelxy(targetrd[:-1,0], targetrd[:-1,1])
tpoly = zip(tx,ty)
clip = clip_polygon(imgpoly, tpoly)
clip = np.array(clip)
#print 'Clip', clip
if len(clip) == 0:
continue
x0,y0 = np.floor(clip.min(axis=0)).astype(int)
x1,y1 = np.ceil (clip.max(axis=0)).astype(int)
slc = slice(y0,y1+1), slice(x0,x1+1)
## FIXME -- it seems I got lucky and the cross product is
## negative == clockwise, as required by clip_polygon. One
## could check this and reverse the polygon vertex order.
# dx0,dy0 = tx[1]-tx[0], ty[1]-ty[0]
# dx1,dy1 = tx[2]-tx[1], ty[2]-ty[1]
# cross = dx0*dy1 - dx1*dy0
# print 'Cross:', cross
print 'Image slice: x [%i,%i], y [%i,%i]' % (x0,x1, y0,y1)
print 'Reading image from', im.imgfn, 'HDU', im.hdu
img,imghdr = im.read_image(header=True, slice=slc)
goodpix = (img != 0)
print 'Number of pixels == 0:', np.sum(img == 0)
print 'Number of pixels != 0:', np.sum(goodpix)
if np.sum(goodpix) == 0:
continue
# print 'Image shape', img.shape
print 'Image range', img.min(), img.max()
print 'Goodpix image range:', (img[goodpix]).min(), (img[goodpix]).max()
if img[goodpix].min() == img[goodpix].max():
print 'No dynamic range in image'
continue
# print 'Reading invvar from', im.wtfn, 'HDU', im.hdu
# invvar = im.read_invvar(slice=slc)
# # print 'Invvar shape', invvar.shape
# # print 'Invvar range:', invvar.min(), invvar.max()
# invvar[goodpix == 0] = 0.
# if np.all(invvar == 0.):
# print 'Skipping zero-invvar image'
# continue
# assert(np.all(np.isfinite(img)))
# assert(np.all(np.isfinite(invvar)))
# assert(not(np.all(invvar == 0.)))
# # Estimate per-pixel noise via Blanton's 5-pixel MAD
# slice1 = (slice(0,-5,10),slice(0,-5,10))
# slice2 = (slice(5,None,10),slice(5,None,10))
# # print 'sliced shapes:', img[slice1].shape, img[slice2].shape
# # print 'good shape:', (goodpix[slice1] * goodpix[slice2]).shape
# # print 'good values:', np.unique(goodpix[slice1] * goodpix[slice2])
# # print 'sliced[good] shapes:', (img[slice1] - img[slice2])[goodpix[slice1] * goodpix[slice2]].shape
# mad = np.median(np.abs(img[slice1] - img[slice2])[goodpix[slice1] * goodpix[slice2]].ravel())
# sig1 = 1.4826 * mad / np.sqrt(2.)
# print 'MAD sig1:', sig1
# # invvar was 1 or 0
# invvar *= (1./(sig1**2))
# medsky = np.median(img[goodpix])
# Read full image for sig1 and sky estimate
fullimg = im.read_image()
fullgood = (fullimg != 0)
# Estimate per-pixel noise via Blanton's 5-pixel MAD
slice1 = (slice(0,-5,10),slice(0,-5,10))
slice2 = (slice(5,None,10),slice(5,None,10))
mad = np.median(np.abs(fullimg[slice1] - fullimg[slice2])[fullgood[slice1] * fullgood[slice2]].ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print 'MAD sig1:', sig1
medsky = np.median(fullimg[fullgood])
invvar = np.zeros_like(img)
invvar[goodpix] = 1./sig1**2
# Median-smooth sky subtraction
plt.clf()
dimshow(np.round((img-medsky) / sig1), vmin=-3, vmax=5)
plt.title('Scalar median: %s' % im.name)
ps.savefig()
# medsky = np.zeros_like(img)
# # astrometry.util.util
# median_smooth(img, np.logical_not(goodpix), 256, medsky)
fullmed = np.zeros_like(fullimg)
median_smooth(fullimg - medsky, np.logical_not(fullgood), 256, fullmed)
fullmed += medsky
medimg = fullmed[slc]
plt.clf()
dimshow(np.round((img - medimg) / sig1), vmin=-3, vmax=5)
plt.title('Median filtered: %s' % im.name)
ps.savefig()
#print 'Subtracting median:', medsky
#img -= medsky
img -= medimg
primhdr = im.read_image_primary_header()
magzp = decals.get_zeropoint_for(im)
print 'magzp', magzp
zpscale = NanoMaggies.zeropointToScale(magzp)
print 'zpscale', zpscale
# Scale images to Nanomaggies
img /= zpscale
sig1 /= zpscale
invvar *= zpscale**2
orig_zpscale = zpscale
zpscale = 1.
assert(np.sum(invvar > 0) > 0)
print 'After scaling:'
print 'sig1', sig1
print 'invvar range', invvar.min(), invvar.max()
print 'image range', img.min(), img.max()
assert(np.all(np.isfinite(img)))
assert(np.all(np.isfinite(invvar)))
assert(np.isfinite(sig1))
plt.clf()
lo,hi = -5*sig1, 10*sig1
n,b,p = plt.hist(img[goodpix].ravel(), 100, range=(lo,hi), histtype='step', color='k')
xx = np.linspace(lo, hi, 200)
plt.plot(xx, max(n)*np.exp(-xx**2 / (2.*sig1**2)), 'r-')
plt.xlim(lo,hi)
plt.title('Pixel histogram: %s' % im.name)
ps.savefig()
twcs = ConstantFitsWcs(wcs)
if x0 or y0:
twcs.setX0Y0(x0,y0)
info = im.get_image_info()
fullh,fullw = info['dims']
# read fit PsfEx model
psfex = PsfEx.fromFits(im.psffitfn)
print 'Read', psfex
# HACK -- highly approximate PSF here!
#psf_fwhm = imghdr['FWHM']
#psf_fwhm = im.seeing
psf_fwhm = im.seeing / (im.pixscale * 3600)
print 'PSF FWHM', psf_fwhm, 'pixels'
psf_sigma = psf_fwhm / 2.35
psf = NCircularGaussianPSF([psf_sigma],[1.])
print 'img type', img.dtype
tim = Image(img, invvar=invvar, wcs=twcs, psf=psf,
photocal=LinearPhotoCal(zpscale, band=band),
sky=ConstantSky(0.), name=im.name + ' ' + band)
tim.zr = [-3. * sig1, 10. * sig1]
tim.sig1 = sig1
tim.band = band
tim.psf_fwhm = psf_fwhm
tim.psf_sigma = psf_sigma
tim.sip_wcs = wcs
tim.x0,tim.y0 = int(x0),int(y0)
tim.psfex = psfex
tim.imobj = im
mn,mx = tim.zr
tim.ima = dict(interpolation='nearest', origin='lower', cmap='gray',
vmin=mn, vmax=mx)
tims.append(tim)
keepims.append(im)
ims = keepims
print 'Computing resampling...'
# save resampling params
for tim in tims:
wcs = tim.sip_wcs
subh,subw = tim.shape
subwcs = wcs.get_subimage(tim.x0, tim.y0, subw, subh)
tim.subwcs = subwcs
try:
Yo,Xo,Yi,Xi,rims = resample_with_wcs(targetwcs, subwcs, [], 2)
except OverlapError:
print 'No overlap'
continue
if len(Yo) == 0:
continue
tim.resamp = (Yo,Xo,Yi,Xi)
print 'Creating coadds...'
# Produce per-band coadds, for plots
coimgs = []
cons = []
for ib,band in enumerate(bands):
coimg = np.zeros((H,W), np.float32)
con = np.zeros((H,W), np.uint8)
for tim in tims:
if tim.band != band:
continue
(Yo,Xo,Yi,Xi) = tim.resamp
if len(Yo) == 0:
continue
nn = (tim.getInvvar()[Yi,Xi] > 0)
coimg[Yo,Xo] += tim.getImage ()[Yi,Xi] * nn
con [Yo,Xo] += nn
# print
# print 'tim', tim.name
# print 'number of resampled pix:', len(Yo)
# reim = np.zeros_like(coimg)
# ren = np.zeros_like(coimg)
# reim[Yo,Xo] = tim.getImage()[Yi,Xi] * nn
# ren[Yo,Xo] = nn
# print 'number of resampled pix with positive invvar:', ren.sum()
# plt.clf()
# plt.subplot(2,2,1)
# mn,mx = [np.percentile(reim[ren>0], p) for p in [25,95]]
# print 'Percentiles:', mn,mx
# dimshow(reim, vmin=mn, vmax=mx)
# plt.colorbar()
# plt.subplot(2,2,2)
# dimshow(con)
# plt.colorbar()
# plt.subplot(2,2,3)
# dimshow(reim, vmin=tim.zr[0], vmax=tim.zr[1])
# plt.colorbar()
# plt.subplot(2,2,4)
# plt.hist(reim.ravel(), 100, histtype='step', color='b')
# plt.hist(tim.getImage().ravel(), 100, histtype='step', color='r')
# plt.suptitle('%s: %s' % (band, tim.name))
# ps.savefig()
coimg /= np.maximum(con,1)
coimgs.append(coimg)
cons .append(con)
plt.clf()
dimshow(get_rgb(coimgs, bands))
ps.savefig()
plt.clf()
for i,b in enumerate(bands):
plt.subplot(2,2,i+1)
dimshow(cons[i], ticks=False)
plt.title('%s band' % b)
plt.colorbar()
plt.suptitle('Number of exposures')
ps.savefig()
print 'Grabbing SDSS sources...'
bandlist = [b for b in bands]
cat,T = get_sdss_sources(bandlist, targetwcs)
# record coordinates in target brick image
ok,T.tx,T.ty = targetwcs.radec2pixelxy(T.ra, T.dec)
T.tx -= 1
T.ty -= 1
T.itx = np.clip(np.round(T.tx).astype(int), 0, W-1)
T.ity = np.clip(np.round(T.ty).astype(int), 0, H-1)
plt.clf()
dimshow(get_rgb(coimgs, bands))
ax = plt.axis()
plt.plot(T.tx, T.ty, 'o', mec=green, mfc='none', ms=10, mew=1.5)
plt.axis(ax)
plt.title('SDSS sources')
ps.savefig()
print 'Detmaps...'
# Render the detection maps
detmaps = dict([(b, np.zeros((H,W), np.float32)) for b in bands])
detivs = dict([(b, np.zeros((H,W), np.float32)) for b in bands])
for tim in tims:
iv = tim.getInvvar()
psfnorm = 1./(2. * np.sqrt(np.pi) * tim.psf_sigma)
detim = tim.getImage().copy()
detim[iv == 0] = 0.
detim = gaussian_filter(detim, tim.psf_sigma) / psfnorm**2
detsig1 = tim.sig1 / psfnorm
subh,subw = tim.shape
detiv = np.zeros((subh,subw), np.float32) + (1. / detsig1**2)
detiv[iv == 0] = 0.
(Yo,Xo,Yi,Xi) = tim.resamp
detmaps[tim.band][Yo,Xo] += detiv[Yi,Xi] * detim[Yi,Xi]
detivs [tim.band][Yo,Xo] += detiv[Yi,Xi]
rtn = dict()
for k in ['T', 'coimgs', 'cons', 'detmaps', 'detivs',
'targetrd', 'pixscale', 'targetwcs', 'W','H',
'bands', 'tims', 'ps', 'brick', 'cat']:
rtn[k] = locals()[k]
return rtn
def main():
decals = Decals()
catpattern = 'pipebrick-cats/tractor-phot-b%06i.fits'
ra,dec = 242, 7
# Region-of-interest, in pixels: x0, x1, y0, y1
#roi = None
roi = [500, 1000, 500, 1000]
if roi is not None:
x0,x1,y0,y1 = roi
#expnum = 346623
#ccdname = 'N12'
#chips = decals.find_ccds(expnum=expnum, extname=ccdname)
#print 'Found', len(chips), 'chips for expnum', expnum, 'extname', ccdname
#if len(chips) != 1:
#return False
chips = decals.get_ccds()
D = np.argsort(np.hypot(chips.ra - ra, chips.dec - dec))
print('Closest chip:', chips[D[0]])
chips = [chips[D[0]]]
im = DecamImage(decals, chips[0])
print('Image:', im)
targetwcs = Sip(im.wcsfn)
if roi is not None:
targetwcs = targetwcs.get_subimage(x0, y0, x1-x0, y1-y0)
r0,r1,d0,d1 = targetwcs.radec_bounds()
# ~ 30-pixel margin
margin = 2e-3
if r0 > r1:
# RA wrap-around
TT = [brick_catalog_for_radec_box(ra,rb, d0-margin,d1+margin,
decals, catpattern)
for (ra,rb) in [(0, r1+margin), (r0-margin, 360.)]]
T = merge_tables(TT)
T._header = TT[0]._header
else:
T = brick_catalog_for_radec_box(r0-margin,r1+margin,d0-margin,
d1+margin, decals, catpattern)
print('Got', len(T), 'catalog entries within range')
cat = read_fits_catalog(T, T._header)
print('Got', len(cat), 'catalog objects')
print('Switching ellipse parameterizations')
switch_to_soft_ellipses(cat)
keepcat = []
for src in cat:
if not np.all(np.isfinite(src.getParams())):
print('Src has infinite params:', src)
continue
if isinstance(src, FixedCompositeGalaxy):
f = src.fracDev.getClippedValue()
if f == 0.:
src = ExpGalaxy(src.pos, src.brightness, src.shapeExp)
elif f == 1.:
src = DevGalaxy(src.pos, src.brightness, src.shapeDev)
keepcat.append(src)
cat = keepcat
slc = None
if roi is not None:
slc = slice(y0,y1), slice(x0,x1)
tim = im.get_tractor_image(slc=slc)
print('Got', tim)
tim.psfex.fitSavedData(*tim.psfex.splinedata)
tim.psfex.radius = 20
tim.psf = CachingPsfEx.fromPsfEx(tim.psfex)
tractor = Tractor([tim], cat)
print('Created', tractor)
mod = tractor.getModelImage(0)
plt.clf()
dimshow(tim.getImage(), **tim.ima)
plt.title('Image')
plt.savefig('1.png')
plt.clf()
dimshow(mod, **tim.ima)
plt.title('Model')
plt.savefig('2.png')
ok,x,y = targetwcs.radec2pixelxy([src.getPosition().ra for src in cat],
[src.getPosition().dec for src in cat])
ax = plt.axis()
plt.plot(x, y, 'rx')
#plt.savefig('3.png')
plt.axis(ax)
plt.title('Sources')
plt.savefig('3.png')
bands = [im.band]
import runbrick
runbrick.photoobjdir = '.'
scat,T = get_sdss_sources(bands, targetwcs, local=False)
print('Got', len(scat), 'SDSS sources in bounds')
stractor = Tractor([tim], scat)
print('Created', stractor)
smod = stractor.getModelImage(0)
plt.clf()
dimshow(smod, **tim.ima)
plt.title('SDSS model')
plt.savefig('4.png')
