H, W = 100, 100
pixscale = 0.262
ra, dec = 40., 10.
psf_sigma = 1.4 # pixels
v = psf_sigma**2
ps = pixscale / 3600.
wcs = Tan(ra, dec, W / 2. + 0.5, H / 2. + 0.5, -ps, 0., 0., ps, float(W),
float(H))
tim = tractor.Image(data=np.zeros((H, W), np.float32),
inverr=np.ones((H, W), np.float32),
psf=tractor.GaussianMixturePSF(1., 0., 0., v, v, 0.),
wcs=tractor.ConstantFitsWcs(wcs))
src = RexGalaxy(tractor.RaDecPos(ra, dec), tractor.Flux(100.),
LogRadius(0.))
tr = tractor.Tractor([tim], [src])
mod = tr.getModelImage(0)
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower')
plt.savefig('rex.png')
# add noise with std 1.
noisy = mod + np.random.normal(size=mod.shape)
# make that the tim's data
tim.data = noisy
# reset the source params
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
FWHM = 1.0 # arcsec
# Need it in pixels:
FWHM = FWHM / (3600.0 * hdr['CDELT1'])
print "PS1_IQ: WARNING: FWHM = NaN in header, setting to 1.0 arcsec =", FWHM, "pixels"
return FWHM
# ============================================================================
if __name__ == '__main__':
if False:
# Testing LensPlaneWCS:
ra, dec = 310.0, 0.0
pos = tractor.RaDecPos(ra, dec)
lenswcs = lenstractor.LensPlaneWCS(pos)
print lenswcs
dt = 1 / 360.0
print lenswcs.positionToPixel(tractor.RaDecPos(ra, dec))
print lenswcs.positionToPixel(tractor.RaDecPos(ra + dt, dec))
print lenswcs.positionToPixel(tractor.RaDecPos(ra - dt, dec))
print lenswcs.positionToPixel(tractor.RaDecPos(ra, dec + dt))
print lenswcs.positionToPixel(tractor.RaDecPos(ra, dec - dt))
# ============================================================================
psf = tractor.psf.PixelizedPSF(psf.array)
# sky_level_pixel = sky_level * pixel_scale**2
sky_level_pixel = float(sky_levels_pixel[0]) # [counts / pixel]
gmag = 23.0
## gflux = exptime * 10.**((zpt - gmag) / 2.5) # [Total counts on the image].
gflux = 10**(-0.4 * (gmag - 22.5)) # [Nanomaggies].
gre = 0.40 # [arcsec].
## https://github.com/dstndstn/tractor/blob/13d3239500c5af873935c81d079c928f4cdf0b1d/doc/galsim.rst
## _gflux = tractor.Fluxes(g=gflux, r=0.0, z=0.0)
_gflux = tractor.NanoMaggies(**{'g': gflux, 'r': 0.0, 'z': 0.0})
src = RexGalaxy(tractor.RaDecPos(ra, dec), _gflux, LogRadius(gre))
wcs = Tan(ra, dec, W / 2. + 0.5, H / 2. + 0.5, -ps, 0., 0., ps, float(W),
float(H))
wcs = tractor.ConstantFitsWcs(wcs)
tims = []
for band in ['g', 'r', 'z']:
## photcal = tractor.LinearPhotoCal(1., band=band)
photcal = tractor.MagsPhotoCal(band, zpt)
csky_level_sig = photcal.brightnessToCounts(sky_level_sig)
## The rms of the noise in ADU.
## noise = galsim.PoissonNoise(rng, sky_level=sky_level_pixel)
def read_wise_level1b(basefn, radecroi=None, radecrad=None, filtermap=None,
nanomaggies=False, mask_gz=False, unc_gz=False,
sipwcs=False, constantInvvar=False,
roi=None,
zrsigs=[-3, 10],
):
if filtermap is None:
filtermap = {}
intfn = basefn + '-int-1b.fits'
maskfn = basefn + '-msk-1b.fits'
if mask_gz:
maskfn = maskfn + '.gz'
uncfn = basefn + '-unc-1b.fits'
if unc_gz:
uncfn = uncfn + '.gz'
logger.debug('intensity image %s' % intfn)
logger.debug('mask image %s' % maskfn)
logger.debug('uncertainty image %s' % uncfn)
if sipwcs:
wcs = Sip(intfn, 0)
twcs = tractor.ConstantFitsWcs(wcs)
else:
twcs = tractor.ConstantFitsWcs(intfn)
# Read enough of the image to get its size
Fint = fitsio.FITS(intfn)
H, W = Fint[0].get_info()['dims']
if radecrad is not None:
r, d, rad = radecrad
x, y = twcs.positionToPixel(tractor.RaDecPos(r, d))
pixrad = rad / (twcs.pixel_scale() / 3600.)
print('Tractor WCS:', twcs)
print('RA,Dec,rad', r, d, rad, 'becomes x,y,pixrad', x, y, pixrad)
roi = (x - pixrad, x + pixrad + 1, y - pixrad, y + pixrad + 1)
if 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)
roi = (x0, x1 + 1, y0, y1 + 1)
if roi is not None:
x0, x1, y0, y1 = roi
x0 = int(np.floor(x0))
x1 = int(np.ceil(x1))
y0 = int(np.floor(y0))
y1 = int(np.ceil(y1))
roi = (x0, x1, y0, y1)
# Clip to image size...
x0 = np.clip(x0, 0, W)
x1 = np.clip(x1, 0, W)
y0 = np.clip(y0, 0, H)
y1 = np.clip(y1, 0, H)
if x0 == x1 or y0 == y1:
print('ROI is empty')
return None
assert(x0 < x1)
assert(y0 < y1)
#roi = (x0,x1,y0,y1)
twcs.setX0Y0(x0, y0)
else:
x0, x1, y0, y1 = 0, W, 0, H
roi = (x0, x1, y0, y1)
ihdr = Fint[0].read_header()
data = Fint[0][y0:y1, x0:x1]
logger.debug('Read %s intensity' % (str(data.shape)))
band = ihdr['BAND']
F = fitsio.FITS(uncfn)
assert(F[0].get_info()['dims'] == [H, W])
unc = F[0][y0:y1, x0:x1]
F = fitsio.FITS(maskfn)
assert(F[0].get_info()['dims'] == [H, W])
mask = F[0][y0:y1, x0:x1]
# HACK -- circular Gaussian PSF of fixed size...
# in arcsec
fwhms = {1: 6.1, 2: 6.4, 3: 6.5, 4: 12.0}
# -> sigma in pixels
sig = fwhms[band] / 2.35 / twcs.pixel_scale()
# print 'PSF sigma', sig, 'pixels'
tpsf = tractor.NCircularGaussianPSF([sig], [1.])
filter = 'w%i' % band
if filtermap:
filter = filtermap.get(filter, filter)
zp = ihdr['MAGZP']
if nanomaggies:
photocal = tractor.LinearPhotoCal(tractor.NanoMaggies.zeropointToScale(zp),
band=filter)
else:
photocal = tractor.MagsPhotoCal(filter, zp)
# print 'Image median:', np.median(data)
# print 'unc median:', np.median(unc)
sky = np.median(data)
tsky = tractor.ConstantSky(sky)
name = 'WISE ' + ihdr['FRSETID'] + ' W%i' % band
# Mask bits, from
# http://wise2.ipac.caltech.edu/docs/release/allsky/expsup/sec4_4a.html#maskdef
# 0 from static mask: excessively noisy due to high dark current alone
# 1 from static mask: generally noisy [includes bit 0]
# 2 from static mask: dead or very low responsivity
# 3 from static mask: low responsivity or low dark current
# 4 from static mask: high responsivity or high dark current
# 5 from static mask: saturated anywhere in ramp
# 6 from static mask: high, uncertain, or unreliable non-linearity
# 7 from static mask: known broken hardware pixel or excessively noisy responsivity estimate [may include bit 1]
# 8 reserved
# 9 broken pixel or negative slope fit value (downlink value = 32767)
# 10 saturated in sample read 1 (down-link value = 32753)
# 11 saturated in sample read 2 (down-link value = 32754)
# 12 saturated in sample read 3 (down-link value = 32755)
# 13 saturated in sample read 4 (down-link value = 32756)
# 14 saturated in sample read 5 (down-link value = 32757)
# 15 saturated in sample read 6 (down-link value = 32758)
# 16 saturated in sample read 7 (down-link value = 32759)
# 17 saturated in sample read 8 (down-link value = 32760)
# 18 saturated in sample read 9 (down-link value = 32761)
# 19 reserved
# 20 reserved
# 21 new/transient bad pixel from dynamic masking
# 22 reserved
# 23 reserved
# 24 reserved
# 25 reserved
# 26 non-linearity correction unreliable
# 27 contains cosmic-ray or outlier that cannot be classified (from temporal outlier rejection in multi-frame pipeline)
# 28 contains positive or negative spike-outlier
# 29 reserved
# 30 reserved
# 31 not used: sign bit
goodmask = ((mask & sum([1 << bit for bit in [0, 1, 2, 3, 4, 5, 6, 7, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18,
21, 26, 27, 28]])) == 0)
sigma1 = np.median(unc[goodmask])
zr = np.array(zrsigs) * sigma1 + sky
# constant
cinvvar = np.zeros_like(data)
cinvvar[goodmask] = 1. / (sigma1**2)
# varying
vinvvar = np.zeros_like(data)
vinvvar[goodmask] = 1. / (unc[goodmask])**2
bad = np.flatnonzero(np.logical_not(np.isfinite(vinvvar)))
if len(bad):
vinvvar.flat[bad] = 0.
cinvvar.flat[bad] = 0.
data.flat[bad] = sky
if constantInvvar:
invvar = cinvvar
else:
invvar = vinvvar
# avoid NaNs
data[np.logical_not(goodmask)] = sky
mjd = ihdr['MJD_OBS']
time = TAITime(None, mjd=mjd)
tim = tractor.Image(data=data, invvar=invvar, psf=tpsf, wcs=twcs,
sky=tsky, photocal=photocal, time=time, name=name, zr=zr,
domask=False)
tim.extent = [x0, x1, y0, y1]
tim.sigma1 = sigma1
#tim.roi = roi
# FIXME
tim.maskplane = mask
tim.uncplane = unc
tim.goodmask = goodmask
# carry both around for retrofitting
tim.vinvvar = vinvvar
tim.cinvvar = cinvvar
return tim
def read_wise_level3(basefn, radecroi=None, filtermap=None,
nanomaggies=False):
if filtermap is None:
filtermap = {}
intfn = basefn + '-int-3.fits'
uncfn = basefn + '-unc-3.fits'
print('intensity image', intfn)
print('uncertainty image', uncfn)
P = pyfits.open(intfn)
ihdr = P[0].header
data = P[0].data
print('Read', data.shape, 'intensity')
band = ihdr['BAND']
P = pyfits.open(uncfn)
unc = P[0].data
print('Read', unc.shape, 'uncertainty')
''' cov:
BAND = 1 / wavelength band number
WAVELEN = 3.368 / [microns] effective wavelength of band
COADDID = '3342p000_ab41' / atlas-image identifier
MAGZP = 20.5 / [mag] relative photometric zero point
MEDINT = 4.0289044380188 / [DN] median of intensity pixels
'''
''' int:
BUNIT = 'DN ' / image pixel units
CTYPE1 = 'RA---SIN' / Projection type for axis 1
CTYPE2 = 'DEC--SIN' / Projection type for axis 2
CRPIX1 = 2048.000000 / Axis 1 reference pixel at CRVAL1,CRVAL2
CRPIX2 = 2048.000000 / Axis 2 reference pixel at CRVAL1,CRVAL2
CDELT1 = -0.0003819444391411 / Axis 1 scale at CRPIX1,CRPIX2 (deg/pix)
CDELT2 = 0.0003819444391411 / Axis 2 scale at CRPIX1,CRPIX2 (deg/pix)
CROTA2 = 0.000000 / Image twist: +axis2 W of N, J2000.0 (deg)
'''
''' unc:
FILETYPE= '1-sigma uncertainty image' / product description
'''
twcs = tractor.WcslibWcs(intfn)
print('WCS', twcs)
# twcs.debug()
print('pixel scale', twcs.pixel_scale())
# HACK -- circular Gaussian PSF of fixed size...
# in arcsec
fwhms = {1: 6.1, 2: 6.4, 3: 6.5, 4: 12.0}
# -> sigma in pixels
sig = fwhms[band] / 2.35 / twcs.pixel_scale()
print('PSF sigma', sig, 'pixels')
tpsf = tractor.NCircularGaussianPSF([sig], [1.])
if 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)
data = data[y0:y1, x0:x1]
unc = unc[y0:y1, x0:x1]
twcs.setX0Y0(x0, y0)
print('Cut data to', data.shape)
else:
H, W = data.shape
x0, x1, y0, y1 = 0, W, 0, H
filt = 'w%i' % band
if filtermap:
filt = filtermap.get(filt, filt)
zp = ihdr['MAGZP']
if nanomaggies:
photocal = tractor.LinearPhotoCal(tractor.NanoMaggies.zeropointToScale(zp),
band=filt)
else:
photocal = tractor.MagsPhotoCal(filt, zp)
print('Image median:', np.median(data))
print('unc median:', np.median(unc))
sky = np.median(data)
tsky = tractor.ConstantSky(sky)
sigma1 = np.median(unc)
zr = np.array([-3, 10]) * sigma1 + sky
name = 'WISE ' + ihdr['COADDID'] + ' W%i' % band
tim = tractor.Image(data=data, invvar=1. / (unc**2), psf=tpsf, wcs=twcs,
sky=tsky, photocal=photocal, name=name, zr=zr,
domask=False)
tim.extent = [x0, x1, y0, y1]
return tim