def LensPlaneWCS(pos):
'''
The "local" WCS -- useful when you need to work on the sky, but in
small offsets from RA, Dec in arcsec. Initialisation is with the
coordinates of the central pixel, which is set to be the origin of
the "pixel coordinate" system. Return a WCS object.
'''
onearcsec = 1.0 / 3600.0
return tractor.FitsWcs(
util.Tan(pos.ra, pos.dec, 1.0, 1.0, -onearcsec, 0.0, 0.0, onearcsec, 0,
0))
def PS1WCS(hdr):
'''
Return a WCS object initialised from a PS1 file header.
WARNING: PC matrix not being used, determinant may be wrong sign...
Need to check with literature image of H1413
'''
t = util.Tan()
t.set_crpix(hdr['CRPIX1'], hdr['CRPIX2'])
t.set_crval(hdr['CRVAL1'], hdr['CRVAL2'])
t.set_cd(hdr['CDELT1'], 0., 0., hdr['CDELT2'])
t.set_imagesize(hdr['NAXIS1'], hdr['NAXIS2'])
return tractor.FitsWcs(t)
def main():
# In LSST meas-deblend (on lsst6):
# python examples/suprimePlot.py --data ~dstn/lsst/ACT-data -v 126969 -c 5 --data-range -100 300 --roi 0 500 0 500 --psf psf.fits --image img.fits --sources srcs.fits
from optparse import OptionParser
import sys
parser = OptionParser(usage=('%prog <img> <psf> <srcs>'))
parser.add_option('-v',
'--verbose',
dest='verbose',
action='count',
default=0,
help='Make more verbose')
opt, args = parser.parse_args()
if len(args) != 3:
parser.print_help()
sys.exit(-1)
if opt.verbose == 0:
lvl = logging.INFO
else:
lvl = logging.DEBUG
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
imgfn, psffn, srcfn = args
pimg = pyfits.open(imgfn)
if len(pimg) != 4:
print('Image must have 3 extensions')
sys.exit(-1)
img = pimg[1].data
mask = pimg[2].data
maskhdr = pimg[2].header
var = pimg[3].data
del pimg
print('var', var.shape)
#print var
print('mask', mask.shape)
#print mask
print('img', img.shape)
#print img
mask = mask.astype(np.int16)
for bit in range(16):
on = ((mask & (1 << bit)) != 0)
print('Bit', bit, 'has', np.sum(on), 'pixels set')
'''
MP_BAD = 0
Bit 0 has 2500 pixels set
MP_SAT = 1
Bit 1 has 5771 pixels set
MP_INTRP= 2
Bit 2 has 11269 pixels set
MP_CR = 3
Bit 3 has 136 pixels set
MP_EDGE = 4
Bit 4 has 11856 pixels set
HIERARCH MP_DETECTED = 5
Bit 5 has 37032 pixels set
'''
print('Mask header:', maskhdr)
maskplanes = {}
print('Mask planes:')
for card in maskhdr.ascardlist():
if not card.key.startswith('MP_'):
continue
print(card.value, card.key)
maskplanes[card.key[3:]] = card.value
print('Variance range:', var.min(), var.max())
print('Image median:', np.median(img.ravel()))
invvar = 1. / var
invvar[var == 0] = 0.
invvar[var < 0] = 0.
sig = np.sqrt(np.median(var))
H, W = img.shape
for k, v in maskplanes.items():
plt.clf()
I = ((mask & (1 << v)) != 0)
rgb = np.zeros((H, W, 3))
clipimg = np.clip((img - (-3. * sig)) / (13. * sig), 0, 1)
cimg = clipimg.copy()
cimg[I] = 1
rgb[:, :, 0] = cimg
cimg = clipimg.copy()
cimg[I] = 0
rgb[:, :, 1] = cimg
rgb[:, :, 2] = cimg
plt.imshow(rgb, interpolation='nearest', origin='lower')
plt.title(k)
plt.savefig('mask-%s.png' % k.lower())
badmask = sum([(1 << maskplanes[k])
for k in ['BAD', 'SAT', 'INTRP', 'CR']])
# HACK -- left EDGE sucks
badmask += (1 << maskplanes['EDGE'])
#badmask = (1 << 0) | (1 << 1) | (1 << 2) | (1 << 3)
#badmask |= (1 << 4)
print('Masking out: 0x%x' % badmask)
invvar[(mask & badmask) != 0] = 0.
assert (all(np.isfinite(img.ravel())))
assert (all(np.isfinite(invvar.ravel())))
psf = pyfits.open(psffn)[0].data
print('psf', psf.shape)
psf /= psf.sum()
from tractor.emfit import em_fit_2d
from tractor.fitpsf import em_init_params
# Create Gaussian mixture model PSF approximation.
S = psf.shape[0]
# number of Gaussian components
K = 3
w, mu, sig = em_init_params(K, None, None, None)
II = psf.copy()
II /= II.sum()
# HIDEOUS HACK
II = np.maximum(II, 0)
print('Multi-Gaussian PSF fit...')
xm, ym = -(S / 2), -(S / 2)
em_fit_2d(II, xm, ym, w, mu, sig)
print('w,mu,sig', w, mu, sig)
mypsf = tractor.GaussianMixturePSF(w, mu, sig)
P = mypsf.getPointSourcePatch(S / 2, S / 2)
mn, mx = psf.min(), psf.max()
ima = dict(interpolation='nearest', origin='lower', vmin=mn, vmax=mx)
plt.clf()
plt.subplot(1, 2, 1)
plt.imshow(psf, **ima)
plt.subplot(1, 2, 2)
pimg = np.zeros_like(psf)
P.addTo(pimg)
plt.imshow(pimg, **ima)
plt.savefig('psf.png')
sig = np.sqrt(np.median(var))
plt.clf()
plt.hist(img.ravel(), 100, range=(-3. * sig, 3. * sig))
plt.savefig('imghist.png')
srcs = fits_table(srcfn)
print('Initial:', len(srcs), 'sources')
# Trim sources with x=0 or y=0
srcs = srcs[(srcs.x != 0) * (srcs.y != 0)]
print('Trim on x,y:', len(srcs), 'sources left')
# Zero out nans & infs
for c in ['theta', 'a', 'b']:
I = np.logical_not(np.isfinite(srcs.get(c)))
srcs.get(c)[I] = 0.
# Set sources with flux=NaN to something more sensible...
I = np.logical_not(np.isfinite(srcs.flux))
srcs.flux[I] = 1.
# Sort sources by flux.
srcs = srcs[np.argsort(-srcs.flux)]
# Trim sources that are way outside the image.
margin = 8. * np.maximum(srcs.a, srcs.b)
H, W = img.shape
srcs = srcs[(srcs.x > -margin) * (srcs.y > -margin) *
(srcs.x < (W + margin) * (srcs.y < (H + margin)))]
print('Trim out-of-bounds:', len(srcs), 'sources left')
wcs = tractor.FitsWcs(Sip(imgfn, 1))
#wcs = tractor.NullWCS()
timg = tractor.Image(data=img,
invvar=invvar,
psf=mypsf,
wcs=wcs,
sky=tractor.ConstantSky(0.),
photocal=tractor.NullPhotoCal(),
name='image')
inverr = timg.getInvError()
assert (all(np.isfinite(inverr.ravel())))
tsrcs = []
for s in srcs:
#pos = tractor.PixPos(s.x, s.y)
pos = tractor.RaDecPos(s.ra, s.dec)
if s.a > 0 and s.b > 0:
eflux = tractor.Flux(s.flux / 2.)
dflux = tractor.Flux(s.flux / 2.)
re, ab, phi = s.a, s.b / s.a, 90. - s.theta
eshape = gal.GalaxyShape(re, ab, phi)
dshape = gal.GalaxyShape(re, ab, phi)
print('Fluxes', eflux, dflux)
tsrc = gal.CompositeGalaxy(pos, eflux, eshape, dflux, dshape)
else:
flux = tractor.Flux(s.flux)
print('Flux', flux)
tsrc = tractor.PointSource(pos, flux)
tsrcs.append(tsrc)
chug = tractor.Tractor([timg])
for src in tsrcs:
if chug.getModelPatch(timg, src) is None:
print('Dropping non-overlapping source:', src)
continue
chug.addSource(src)
print('Kept a total of', len(chug.catalog), 'sources')
ima = dict(interpolation='nearest',
origin='lower',
vmin=-3. * sig,
vmax=10. * sig)
chia = dict(interpolation='nearest', origin='lower', vmin=-5., vmax=5.)
plt.clf()
plt.imshow(img, **ima)
plt.colorbar()
plt.savefig('img.png')
plt.clf()
plt.imshow(invvar, interpolation='nearest', origin='lower')
plt.colorbar()
plt.savefig('invvar.png')
mod = chug.getModelImages()[0]
plt.clf()
plt.imshow(mod, **ima)
plt.colorbar()
plt.savefig('mod-0.png')
chi = chug.getChiImage(0)
plt.clf()
plt.imshow(chi, **chia)
plt.colorbar()
plt.savefig('chi-0.png')
for step in range(5):
cat = chug.getCatalog()
for src in cat:
if chug.getModelPatch(timg, src) is None:
print('Dropping non-overlapping source:', src)
chug.removeSource(src)
print('Kept a total of', len(chug.catalog), 'sources')
#cat = chug.getCatalog()
#for i,src in enumerate([]):
#for i,src in enumerate(chug.getCatalog()):
#for i in range(len(cat)):
i = 0
while i < len(cat):
src = cat[i]
#print 'Step', i
#for j,s in enumerate(cat):
# x,y = timg.getWcs().positionToPixel(s, s.getPosition())
# print ' ',
# if j == i:
# print '*',
# print '(%6.1f, %6.1f)'%(x,y), s
print('Optimizing source', i, 'of', len(cat))
x, y = timg.getWcs().positionToPixel(src.getPosition(), src)
print('(%6.1f, %6.1f)' % (x, y), src)
# pre = src.getModelPatch(timg)
s1 = str(src)
print('src1 ', s1)
dlnp1, X, a = chug.optimizeCatalogFluxes(srcs=[src])
s2 = str(src)
dlnp2, X, a = chug.optimizeCatalogAtFixedComplexityStep(srcs=[src],
sky=False)
s3 = str(src)
#post = src.getModelPatch(timg)
print('src1 ', s1)
print('src2 ', s2)
print('src3 ', s3)
print('dlnp', dlnp1, dlnp2)
if chug.getModelPatch(timg, src) is None:
print('After optimizing, no overlap!')
print('Removing source', src)
chug.removeSource(src)
i -= 1
i += 1
# plt.clf()
# plt.subplot(2,2,1)
# img = timg.getImage()
# (x0,x1,y0,y1) = pre.getExtent()
# plt.imshow(img, **ima)
# ax = plt.axis()
# plt.plot([x0,x0,x1,x1,x0], [y0,y1,y1,y0,y0], 'k-', lw=2)
# plt.axis(ax)
# plt.subplot(2,2,3)
# plt.imshow(pre.getImage(), **ima)
# plt.subplot(2,2,4)
# plt.imshow(post.getImage(), **ima)
# plt.savefig('prepost-s%i-s%03i.png' % (step, i))
#
# mod = chug.getModelImages()[0]
# plt.clf()
# plt.imshow(mod, **ima)
# plt.colorbar()
# plt.savefig('mod-s%i-s%03i.png' % (step, i))
# chi = chug.getChiImage(0)
# plt.clf()
# plt.imshow(chi, **chia)
# plt.colorbar()
# plt.savefig('chi-s%i-s%03i.png' % (step, i))
#dlnp,x,a = chug.optimizeCatalogFluxes()
#print 'fluxes: dlnp', dlnp
#dlnp,x,a = chug.optimizeCatalogAtFixedComplexityStep()
#print 'opt: dlnp', dlnp
mod = chug.getModelImages()[0]
plt.clf()
plt.imshow(mod, **ima)
plt.colorbar()
plt.savefig('mod-%i.png' % (step + 1))
chi = chug.getChiImage(0)
plt.clf()
plt.imshow(chi, **chia)
plt.colorbar()
plt.savefig('chi-%i.png' % (step + 1))
return
for step in range(5):
chug.optimizeCatalogFluxes()
mod = chug.getModelImages()[0]
plt.clf()
plt.imshow(mod, **ima)
plt.colorbar()
plt.savefig('mod-s%i.png' % step)
chi = chug.getChiImage(0)
plt.clf()
plt.imshow(chi, **chia)
plt.colorbar()
plt.savefig('chi-s%i.png' % step)
def read_cfht_coadd(imgfn, weightfn, roi=None, radecroi=None, filtermap=None):
'''
Given filenames for CFHT coadd image and weight files, produce
a tractor.Image object.
*roi*: (x0,x1, y0,y1): a region-of-interest in pixel space;
returns the subimage [x0,x1), [y0,y1).
*radecroi*: (ra0, ra1, dec0, dec1): a region-of-interest in RA,Dec space;
returns the subimage bounding the given RA,Dec box [ra0,ra1], [dec0,dec1].
*filtermap*: dict, eg, { 'i.MP9701': 'i' }, to map from the FILTER header keyword to
a standard filter name.
'''
P = pyfits.open(imgfn)
print 'Read', P[0].data.shape, 'image'
img = P[0].data
imgheader = P[0].header
# WCS: the image file has a WCS header
# we should be able to do:
#twcs = tractor.FitsWcs(imgfn)
# ARGH! Memory issues reading the file; HACK: copy header...
f, tempfn = tempfile.mkstemp()
os.close(f)
pyfits.writeto(tempfn, None, header=imgheader, clobber=True)
twcs = tractor.FitsWcs(tempfn)
# Cut down to the region-of-interest, if given.
if roi is not None:
x0, x1, y0, y1 = roi
elif radecroi is not None:
ralo, rahi, declo, dechi = radecroi
xy = [
twcs.positionToPixel(tractor.RaDecPos(r, d))
for r, d in [(ralo, declo), (ralo, dechi), (rahi,
declo), (rahi, dechi)]
]
xy = np.array(xy)
x0, x1 = xy[:, 0].min(), xy[:, 0].max()
y0, y1 = xy[:, 1].min(), xy[:, 1].max()
print 'RA,Dec ROI', ralo, rahi, declo, dechi, 'becomes x,y ROI', x0, x1, y0, y1
# Clip to image size...
H, W = data.shape
x0 = max(0, min(x0, W - 1))
x1 = max(0, min(x1, W))
y0 = max(0, min(y0, H - 1))
y1 = max(0, min(y1, H))
print ' clipped to', x0, x1, y0, y1
else:
H, W = img.shape
x0, x1, y0, y1 = 0, W, 0, H
if roi is not None or radecroi is not None:
# Actually cut the pixels
img = img[y0:y1, x0:x1].copy()
# Also tell the WCS to apply an offset.
twcs.setX0Y0(x0, y0)
print 'Image:', img.shape
# HACK, tell the WCS how big the image is...
# (needed because of the previous HACK, copying the header)
twcs.wcs.set_imagesize(x1 - x0, y1 - y0)
print twcs
# Argh, this doesn't work: the files are .fz compressed
#P = pyfits.open(weightfn)
#weight = P[1].data[y0:y1, x0:x1]
# HACK: use "imcopy" to uncompress to a temp file!
#print 'Writing to temp file', tempfn
cmd = "imcopy '%s[%i:%i,%i:%i]' '!%s'" % (weightfn, x0 + 1, x1, y0 + 1, y1,
tempfn)
print 'running', cmd
os.system(cmd)
P = pyfits.open(tempfn)
weight = P[0].data
print 'Read', weight.shape, 'weight image'
# PSF model: FAKE IT for now
tpsf = tractor.GaussianMixturePSF(np.array([0.9, 0.1]), np.zeros((2, 2)),
np.array([1, 2]))
# SKY level: assume zero
#sky = np.median(img)
#print 'Image median value:', sky
sky = 0.
tsky = tractor.ConstantSky(sky)
# Photometric calibration: the FITS header says:
'''
FILTER = 'r.MP9601' / Filter
PHOTZP = 30.000 / photometric zeropoint
COMMENT AB magnitude = -2.5 * log10(flux) + PHOTZP
COMMENT r.MP9601=r_SDSS-0.024*(g_SDSS-r_SDSS)
'''
# Grab the filter name, and apply the filtermap (if given)
filter = imgheader['FILTER']
if filtermap:
filter = filtermap.get(filter, filter)
zp = imgheader['PHOTZP']
# Simple photocal object
photocal = tractor.MagsPhotoCal(filter, zp)
# For plotting: find the approximate standard deviation
#print 'Median weight:', np.median(weight)
sigma1 = 1. / np.sqrt(np.median(weight))
zr = np.array([-3, 10]) * sigma1 + sky
name = 'CFHT ' + imgheader.get('OBJECT', '')
tim = tractor.Image(data=img,
invvar=weight,
psf=tpsf,
wcs=twcs,
sky=tsky,
photocal=photocal,
name=name,
zr=zr)
tim.extent = [x0, x1, y0, y1]
return tim