def _det_one((cowcs, fn, wcsfn, do_img, wise)):
print 'Image', fn
F = fitsio.FITS(fn)[0]
imginf = F.get_info()
hdr = F.read_header()
H,W = imginf['dims']
wcs = Sip(wcsfn)
if wise:
# HACK -- assume, incorrectly, single Gaussian ~diffraction limited
psf_sigma = 0.873
else:
pixscale = wcs.pixel_scale()
seeing = hdr['SEEING']
print 'Seeing', seeing
psf_sigma = seeing / pixscale / 2.35
print 'Sigma:', psf_sigma
psfnorm = 1./(2. * np.sqrt(np.pi) * psf_sigma)
if False:
# Compare PSF models with Peter's "SEEING" card
# (units: arcsec FWHM)
from tractor.psfex import *
psffn = fn.replace('.p.w.fits', '.p.w.cat.psf')
print 'PSF', psffn
psfmod = PsfEx(psffn, W, H)
psfim = psfmod.instantiateAt(W/2, H/2)
print 'psfim', psfim.shape
ph,pw = psfim.shape
plt.clf()
plt.plot(psfim[ph/2,:], 'r-')
plt.plot(psfim[:,pw/2], 'b-')
xx = np.arange(pw)
cc = pw/2
G = np.exp(-0.5 * (xx - cc)**2 / sig**2)
plt.plot(G * sum(psfim[:,pw/2]) / sum(G), 'k-')
ps.savefig()
# Read full image and estimate noise
img = F.read()
print 'Image', img.shape
if wise:
ivfn = fn.replace('img-m.fits', 'invvar-m.fits.gz')
iv = fitsio.read(ivfn)
sig1 = 1./np.sqrt(np.median(iv))
print 'Per-pixel noise estimate:', sig1
mask = (iv == 0)
else:
# Estimate and subtract background
bg = sky_subtract(img, 512, gradient=False)
img -= bg
diffs = img[:-5:10,:-5:10] - img[5::10,5::10]
mad = np.median(np.abs(diffs).ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print 'Per-pixel noise estimate:', sig1
# Read bad pixel mask
maskfn = fn.replace('.p.w.fits', '.p.w.bpm.fits')
mask = fitsio.read(maskfn)
# FIXME -- mask edge pixels -- some seem to be bad and unmasked
mask[:2 ,:] = 1
mask[-2:,:] = 1
mask[:, :2] = 1
mask[:,-2:] = 1
# FIXME -- patch image?
img[mask != 0] = 0.
# Get image zeropoint
for zpkey in ['MAG_ZP', 'UB1_ZP']:
zp = hdr.get(zpkey, None)
if zp is not None:
break
zpscale = NanoMaggies.zeropointToScale(zp)
# Scale image to nanomaggies
img /= zpscale
sig1 /= zpscale
# Produce detection map
detmap = gaussian_filter(img, psf_sigma, mode='constant') / psfnorm**2
detmap_sig1 = sig1 / psfnorm
print 'Detection map sig1', detmap_sig1
# Lanczos resample
print 'Resampling...'
L = 3
try:
lims = [detmap]
if do_img:
lims.append(img)
Yo,Xo,Yi,Xi,rims = resample_with_wcs(cowcs, wcs, lims, L)
rdetmap = rims[0]
except OverlapError:
return None
print 'Resampled'
detmap_iv = (mask[Yo,Xo] == 0) * 1./detmap_sig1**2
if do_img:
rimg = rims[1]
else:
rimg = None
return Yo,Xo,rdetmap,detmap_iv,rimg
def get_tractor_image(self,
slc=None,
radecpoly=None,
gaussPsf=False,
pixPsf=False,
hybridPsf=False,
splinesky=False,
nanomaggies=True,
subsky=True,
tiny=10,
dq=True,
invvar=True,
pixels=True,
constant_invvar=False):
'''
Returns a tractor.Image ("tim") object for this image.
Options describing a subimage to return:
- *slc*: y,x slice objects
- *radecpoly*: numpy array, shape (N,2), RA,Dec polygon describing
bounding box to select.
Options determining the PSF model to use:
- *gaussPsf*: single circular Gaussian PSF based on header FWHM value.
- *pixPsf*: pixelized PsfEx model.
- *hybridPsf*: combo pixelized PsfEx + Gaussian approx.
Options determining the sky model to use:
- *splinesky*: median filter chunks of the image, then spline those.
Options determining the units of the image:
- *nanomaggies*: convert the image to be in units of NanoMaggies;
*tim.zpscale* contains the scale value the image was divided by.
- *subsky*: instantiate and subtract the initial sky model,
leaving a constant zero sky model?
'''
get_dq = dq
get_invvar = invvar
band = self.band
imh, imw = self.get_image_shape()
wcs = self.get_wcs()
x0, y0 = 0, 0
x1 = x0 + imw
y1 = y0 + imh
# Clip to RA,Dec polygon?
if slc is None and radecpoly is not None:
from astrometry.util.miscutils import clip_polygon
imgpoly = [(1, 1), (1, imh), (imw, imh), (imw, 1)]
ok, tx, ty = wcs.radec2pixelxy(radecpoly[:-1, 0], radecpoly[:-1,
1])
tpoly = zip(tx, ty)
clip = clip_polygon(imgpoly, tpoly)
clip = np.array(clip)
if len(clip) == 0:
return None
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)
if y1 - y0 < tiny or x1 - x0 < tiny:
print('Skipping tiny subimage')
return None
# Slice?
if slc is not None:
sy, sx = slc
y0, y1 = sy.start, sy.stop
x0, x1 = sx.start, sx.stop
# Is part of this image bad?
old_extent = (x0, x1, y0, y1)
new_extent = self.get_good_image_slice((x0, x1, y0, y1),
get_extent=True)
if new_extent != old_extent:
x0, x1, y0, y1 = new_extent
print('Applying good subregion of CCD: slice is', x0, x1, y0, y1)
if x0 >= x1 or y0 >= y1:
return None
slc = slice(y0, y1), slice(x0, x1)
# Read image pixels
if pixels:
print('Reading image slice:', slc)
img, imghdr = self.read_image(header=True, slice=slc)
self.check_image_header(imghdr)
else:
img = np.zeros((imh, imw), np.float32)
imghdr = self.read_image_header()
if slc is not None:
img = img[slc]
assert (np.all(np.isfinite(img)))
# Read inverse-variance (weight) map
if get_invvar:
invvar = self.read_invvar(slice=slc, clipThresh=0.)
else:
invvar = np.ones_like(img)
assert (np.all(np.isfinite(invvar)))
if np.all(invvar == 0.):
print('Skipping zero-invvar image')
return None
# Negative invvars (from, eg, fpack decompression noise) cause havoc
assert (np.all(invvar >= 0.))
# Read data-quality (flags) map and zero out the invvars of masked pixels
if get_dq:
dq = self.read_dq(slice=slc)
if dq is not None:
invvar[dq != 0] = 0.
if np.all(invvar == 0.):
print('Skipping zero-invvar image (after DQ masking)')
return None
# header 'FWHM' is in pixels
assert (self.fwhm > 0)
psf_fwhm = self.fwhm
psf_sigma = psf_fwhm / 2.35
primhdr = self.read_image_primary_header()
sky = self.read_sky_model(splinesky=splinesky,
slc=slc,
primhdr=primhdr,
imghdr=imghdr)
skysig1 = getattr(sky, 'sig1', None)
midsky = 0.
if subsky:
print('Instantiating and subtracting sky model')
from tractor.sky import ConstantSky
skymod = np.zeros_like(img)
sky.addTo(skymod)
img -= skymod
midsky = np.median(skymod)
zsky = ConstantSky(0.)
zsky.version = getattr(sky, 'version', '')
zsky.plver = getattr(sky, 'plver', '')
del skymod
sky = zsky
del zsky
orig_zpscale = zpscale = NanoMaggies.zeropointToScale(self.ccdzpt)
if nanomaggies:
# Scale images to Nanomaggies
img /= zpscale
invvar = invvar * zpscale**2
if not subsky:
sky.scale(1. / zpscale)
zpscale = 1.
# Compute 'sig1', scalar typical per-pixel noise
if get_invvar:
sig1 = 1. / np.sqrt(np.median(invvar[invvar > 0]))
elif skysig1 is not None:
sig1 = skysig1
if nanomaggies:
# skysig1 is in the native units
sig1 /= orig_zpscale
else:
# 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(img[slice1] - img[slice2]).ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print('sig1 estimate:', sig1)
invvar *= (1. / sig1**2)
assert (np.isfinite(sig1))
if constant_invvar:
print('Setting constant invvar', 1. / sig1**2)
invvar[invvar > 0] = 1. / sig1**2
if subsky:
# Warn if the subtracted sky doesn't seem to work well
# (can happen, eg, if sky calibration product is inconsistent with
# the data)
imgmed = np.median(img[invvar > 0])
if np.abs(imgmed) > sig1:
print('WARNING: image median', imgmed, 'is more than 1 sigma',
'away from zero!')
# tractor WCS object
twcs = self.get_tractor_wcs(wcs,
x0,
y0,
primhdr=primhdr,
imghdr=imghdr)
if hybridPsf:
pixPsf = False
psf = self.read_psf_model(x0,
y0,
gaussPsf=gaussPsf,
pixPsf=pixPsf,
hybridPsf=hybridPsf,
psf_sigma=psf_sigma,
cx=(x0 + x1) / 2.,
cy=(y0 + y1) / 2.)
tim = Image(img,
invvar=invvar,
wcs=twcs,
psf=psf,
photocal=LinearPhotoCal(zpscale, band=band),
sky=sky,
name=self.name + ' ' + band)
assert (np.all(np.isfinite(tim.getInvError())))
# PSF norm
psfnorm = self.psf_norm(tim)
# Galaxy-detection norm
tim.band = band
galnorm = self.galaxy_norm(tim)
# CP (DECam) images include DATE-OBS and MJD-OBS, in UTC.
import astropy.time
mjd_tai = astropy.time.Time(self.mjdobs, format='mjd',
scale='utc').tai.mjd
tim.time = TAITime(None, mjd=mjd_tai)
tim.slice = slc
tim.zpscale = orig_zpscale
tim.midsky = midsky
tim.sig1 = sig1
tim.psf_fwhm = psf_fwhm
tim.psf_sigma = psf_sigma
tim.propid = self.propid
tim.psfnorm = psfnorm
tim.galnorm = galnorm
tim.sip_wcs = wcs
tim.x0, tim.y0 = int(x0), int(y0)
tim.imobj = self
tim.primhdr = primhdr
tim.hdr = imghdr
tim.plver = primhdr.get('PLVER', '').strip()
tim.skyver = (getattr(sky, 'version', ''), getattr(sky, 'plver', ''))
tim.wcsver = (getattr(wcs, 'version', ''), getattr(wcs, 'plver', ''))
tim.psfver = (getattr(psf, 'version', ''), getattr(psf, 'plver', ''))
if get_dq:
tim.dq = dq
tim.dq_saturation_bits = self.dq_saturation_bits
subh, subw = tim.shape
tim.subwcs = tim.sip_wcs.get_subimage(tim.x0, tim.y0, subw, subh)
return tim
def _det_one((cowcs, fn, wcsfn, do_img, wise)):
print('Image', fn)
F = fitsio.FITS(fn)[0]
imginf = F.get_info()
hdr = F.read_header()
H, W = imginf['dims']
wcs = Sip(wcsfn)
if wise:
# HACK -- assume, incorrectly, single Gaussian ~diffraction limited
psf_sigma = 0.873
else:
pixscale = wcs.pixel_scale()
seeing = hdr['SEEING']
print('Seeing', seeing)
psf_sigma = seeing / pixscale / 2.35
print('Sigma:', psf_sigma)
psfnorm = 1. / (2. * np.sqrt(np.pi) * psf_sigma)
if False:
# Compare PSF models with Peter's "SEEING" card
# (units: arcsec FWHM)
from tractor.psfex import *
psffn = fn.replace('.p.w.fits', '.p.w.cat.psf')
print('PSF', psffn)
psfmod = PsfEx(psffn, W, H)
psfim = psfmod.instantiateAt(W / 2, H / 2)
print('psfim', psfim.shape)
ph, pw = psfim.shape
plt.clf()
plt.plot(psfim[ph / 2, :], 'r-')
plt.plot(psfim[:, pw / 2], 'b-')
xx = np.arange(pw)
cc = pw / 2
G = np.exp(-0.5 * (xx - cc)**2 / sig**2)
plt.plot(G * sum(psfim[:, pw / 2]) / sum(G), 'k-')
ps.savefig()
# Read full image and estimate noise
img = F.read()
print('Image', img.shape)
if wise:
ivfn = fn.replace('img-m.fits', 'invvar-m.fits.gz')
iv = fitsio.read(ivfn)
sig1 = 1. / np.sqrt(np.median(iv))
print('Per-pixel noise estimate:', sig1)
mask = (iv == 0)
else:
# Estimate and subtract background
bg = sky_subtract(img, 512, gradient=False)
img -= bg
diffs = img[:-5:10, :-5:10] - img[5::10, 5::10]
mad = np.median(np.abs(diffs).ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print('Per-pixel noise estimate:', sig1)
# Read bad pixel mask
maskfn = fn.replace('.p.w.fits', '.p.w.bpm.fits')
mask = fitsio.read(maskfn)
# FIXME -- mask edge pixels -- some seem to be bad and unmasked
mask[:2, :] = 1
mask[-2:, :] = 1
mask[:, :2] = 1
mask[:, -2:] = 1
# FIXME -- patch image?
img[mask != 0] = 0.
# Get image zeropoint
for zpkey in ['MAG_ZP', 'UB1_ZP']:
zp = hdr.get(zpkey, None)
if zp is not None:
break
zpscale = NanoMaggies.zeropointToScale(zp)
# Scale image to nanomaggies
img /= zpscale
sig1 /= zpscale
# Produce detection map
detmap = gaussian_filter(img, psf_sigma, mode='constant') / psfnorm**2
detmap_sig1 = sig1 / psfnorm
print('Detection map sig1', detmap_sig1)
# Lanczos resample
print('Resampling...')
L = 3
try:
lims = [detmap]
if do_img:
lims.append(img)
Yo, Xo, Yi, Xi, rims = resample_with_wcs(cowcs, wcs, lims, L)
rdetmap = rims[0]
except OverlapError:
return None
print('Resampled')
detmap_iv = (mask[Yo, Xo] == 0) * 1. / detmap_sig1**2
if do_img:
rimg = rims[1]
else:
rimg = None
return Yo, Xo, rdetmap, detmap_iv, rimg
def get_tractor_image(self, slc=None, radecpoly=None,
gaussPsf=False, const2psf=False, pixPsf=False,
splinesky=False,
nanomaggies=True, subsky=True, tiny=5,
dq=True, invvar=True, pixels=True):
'''
Returns a tractor.Image ("tim") object for this image.
Options describing a subimage to return:
- *slc*: y,x slice objects
- *radecpoly*: numpy array, shape (N,2), RA,Dec polygon describing bounding box to select.
Options determining the PSF model to use:
- *gaussPsf*: single circular Gaussian PSF based on header FWHM value.
- *const2Psf*: 2-component general Gaussian fit to PsfEx model at image center.
- *pixPsf*: pixelized PsfEx model at image center.
Options determining the sky model to use:
- *splinesky*: median filter chunks of the image, then spline those.
Options determining the units of the image:
- *nanomaggies*: convert the image to be in units of NanoMaggies;
*tim.zpscale* contains the scale value the image was divided by.
- *subsky*: instantiate and subtract the initial sky model,
leaving a constant zero sky model?
'''
from astrometry.util.miscutils import clip_polygon
get_dq = dq
get_invvar = invvar
band = self.band
imh,imw = self.get_image_shape()
wcs = self.get_wcs()
x0,y0 = 0,0
x1 = x0 + imw
y1 = y0 + imh
if slc is None and radecpoly is not None:
imgpoly = [(1,1),(1,imh),(imw,imh),(imw,1)]
ok,tx,ty = wcs.radec2pixelxy(radecpoly[:-1,0], radecpoly[:-1,1])
tpoly = zip(tx,ty)
clip = clip_polygon(imgpoly, tpoly)
clip = np.array(clip)
if len(clip) == 0:
return None
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)
if y1 - y0 < tiny or x1 - x0 < tiny:
print('Skipping tiny subimage')
return None
if slc is not None:
sy,sx = slc
y0,y1 = sy.start, sy.stop
x0,x1 = sx.start, sx.stop
old_extent = (x0,x1,y0,y1)
new_extent = self.get_good_image_slice((x0,x1,y0,y1), get_extent=True)
if new_extent != old_extent:
x0,x1,y0,y1 = new_extent
print('Applying good subregion of CCD: slice is', x0,x1,y0,y1)
if x0 >= x1 or y0 >= y1:
return None
slc = slice(y0,y1), slice(x0,x1)
if pixels:
print('Reading image slice:', slc)
img,imghdr = self.read_image(header=True, slice=slc)
#print('SATURATE is', imghdr.get('SATURATE', None))
#print('Max value in image is', img.max())
# check consistency... something of a DR1 hangover
e = imghdr['EXTNAME']
assert(e.strip() == self.ccdname.strip())
else:
img = np.zeros((imh, imw))
imghdr = dict()
if slc is not None:
img = img[slc]
if get_invvar:
invvar = self.read_invvar(slice=slc, clipThresh=0.)
else:
invvar = np.ones_like(img)
if get_dq:
dq = self.read_dq(slice=slc)
invvar[dq != 0] = 0.
if np.all(invvar == 0.):
print('Skipping zero-invvar image')
return None
assert(np.all(np.isfinite(img)))
assert(np.all(np.isfinite(invvar)))
assert(not(np.all(invvar == 0.)))
# header 'FWHM' is in pixels
# imghdr['FWHM']
psf_fwhm = self.fwhm
psf_sigma = psf_fwhm / 2.35
primhdr = self.read_image_primary_header()
sky = self.read_sky_model(splinesky=splinesky, slc=slc)
midsky = 0.
if subsky:
print('Instantiating and subtracting sky model...')
from tractor.sky import ConstantSky
skymod = np.zeros_like(img)
sky.addTo(skymod)
img -= skymod
midsky = np.median(skymod)
zsky = ConstantSky(0.)
zsky.version = sky.version
zsky.plver = sky.plver
del skymod
del sky
sky = zsky
del zsky
magzp = self.decals.get_zeropoint_for(self)
orig_zpscale = zpscale = NanoMaggies.zeropointToScale(magzp)
if nanomaggies:
# Scale images to Nanomaggies
img /= zpscale
invvar *= zpscale**2
if not subsky:
sky.scale(1./zpscale)
zpscale = 1.
assert(np.sum(invvar > 0) > 0)
if get_invvar:
sig1 = 1./np.sqrt(np.median(invvar[invvar > 0]))
else:
# Estimate from the image?
# # 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(img[slice1] - img[slice2]).ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print('sig1 estimate:', sig1)
invvar *= (1. / sig1**2)
assert(np.all(np.isfinite(img)))
assert(np.all(np.isfinite(invvar)))
assert(np.isfinite(sig1))
if subsky:
##
imgmed = np.median(img[invvar>0])
if np.abs(imgmed) > sig1:
print('WARNING: image median', imgmed, 'is more than 1 sigma away from zero!')
# Boom!
#assert(False)
twcs = ConstantFitsWcs(wcs)
if x0 or y0:
twcs.setX0Y0(x0,y0)
psf = self.read_psf_model(x0, y0, gaussPsf=gaussPsf, pixPsf=pixPsf,
const2psf=const2psf, psf_sigma=psf_sigma,
cx=(x0+x1)/2., cy=(y0+y1)/2.)
tim = Image(img, invvar=invvar, wcs=twcs, psf=psf,
photocal=LinearPhotoCal(zpscale, band=band),
sky=sky, name=self.name + ' ' + band)
assert(np.all(np.isfinite(tim.getInvError())))
# PSF norm
psfnorm = self.psf_norm(tim)
print('PSF norm', psfnorm, 'vs Gaussian',
1./(2. * np.sqrt(np.pi) * psf_sigma))
# Galaxy-detection norm
tim.band = band
galnorm = self.galaxy_norm(tim)
print('Galaxy norm:', galnorm)
# CP (DECam) images include DATE-OBS and MJD-OBS, in UTC.
import astropy.time
#mjd_utc = mjd=primhdr.get('MJD-OBS', 0)
mjd_tai = astropy.time.Time(primhdr['DATE-OBS']).tai.mjd
tim.slice = slc
tim.time = TAITime(None, mjd=mjd_tai)
tim.zr = [-3. * sig1, 10. * sig1]
tim.zpscale = orig_zpscale
tim.midsky = midsky
tim.sig1 = sig1
tim.psf_fwhm = psf_fwhm
tim.psf_sigma = psf_sigma
tim.propid = self.propid
tim.psfnorm = psfnorm
tim.galnorm = galnorm
tim.sip_wcs = wcs
tim.x0,tim.y0 = int(x0),int(y0)
tim.imobj = self
tim.primhdr = primhdr
tim.hdr = imghdr
tim.plver = primhdr['PLVER'].strip()
tim.skyver = (sky.version, sky.plver)
tim.wcsver = (wcs.version, wcs.plver)
tim.psfver = (psf.version, psf.plver)
if get_dq:
tim.dq = dq
tim.dq_bits = CP_DQ_BITS
tim.saturation = imghdr.get('SATURATE', None)
tim.satval = tim.saturation or 0.
if subsky:
tim.satval -= midsky
if nanomaggies:
tim.satval /= orig_zpscale
subh,subw = tim.shape
tim.subwcs = tim.sip_wcs.get_subimage(tim.x0, tim.y0, subw, subh)
mn,mx = tim.zr
tim.ima = dict(interpolation='nearest', origin='lower', cmap='gray',
vmin=mn, vmax=mx)
return tim
def get_tractor_image(self, decals, slc=None, radecpoly=None, mock_psf=False):
'''
slc: y,x slices
'''
band = self.band
imh,imw = self.get_image_shape()
wcs = self.read_wcs()
x0,y0 = 0,0
if slc is None and radecpoly is not None:
imgpoly = [(1,1),(1,imh),(imw,imh),(imw,1)]
ok,tx,ty = wcs.radec2pixelxy(radecpoly[:-1,0], radecpoly[:-1,1])
tpoly = zip(tx,ty)
clip = clip_polygon(imgpoly, tpoly)
clip = np.array(clip)
if len(clip) == 0:
return None
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)
if y1 - y0 < 5 or x1 - x0 < 5:
print 'Skipping tiny subimage'
return None
if slc is not None:
sy,sx = slc
y0,y1 = sy.start, sy.stop
x0,x1 = sx.start, sx.stop
print 'Reading image from', self.imgfn, 'HDU', self.hdu
img,imghdr = self.read_image(header=True, slice=slc)
print 'Reading invvar from', self.wtfn, 'HDU', self.hdu
invvar = self.read_invvar(slice=slc, clip=True)
print 'Invvar range:', invvar.min(), invvar.max()
if np.all(invvar == 0.):
print 'Skipping zero-invvar image'
return None
assert(np.all(np.isfinite(img)))
assert(np.all(np.isfinite(invvar)))
assert(not(np.all(invvar == 0.)))
# header 'FWHM' is in pixels
psf_fwhm = imghdr['FWHM']
psf_sigma = psf_fwhm / 2.35
primhdr = self.read_image_primary_header()
magzp = decals.get_zeropoint_for(self)
print 'magzp', magzp
zpscale = NanoMaggies.zeropointToScale(magzp)
print 'zpscale', zpscale
sky = self.read_sky_model()
midsky = sky.getConstant()
img -= midsky
sky.subtract(midsky)
# Scale images to Nanomaggies
img /= zpscale
invvar *= zpscale**2
orig_zpscale = zpscale
zpscale = 1.
assert(np.sum(invvar > 0) > 0)
sig1 = 1./np.sqrt(np.median(invvar[invvar > 0]))
assert(np.all(np.isfinite(img)))
assert(np.all(np.isfinite(invvar)))
assert(np.isfinite(sig1))
twcs = ConstantFitsWcs(wcs)
if x0 or y0:
twcs.setX0Y0(x0,y0)
if mock_psf:
from tractor.basics import NCircularGaussianPSF
psfex = None
psf = NCircularGaussianPSF([1.5], [1.0])
print 'WARNING: using mock PSF:', psf
else:
# read fit PsfEx model -- with ellipse representation
psfex = PsfEx.fromFits(self.psffitellfn)
print 'Read', psfex
psf = psfex
tim = Image(img, invvar=invvar, wcs=twcs, psf=psf,
photocal=LinearPhotoCal(zpscale, band=band),
sky=sky, name=self.name + ' ' + band)
assert(np.all(np.isfinite(tim.getInvError())))
tim.zr = [-3. * sig1, 10. * sig1]
tim.midsky = midsky
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 = self
mn,mx = tim.zr
subh,subw = tim.shape
tim.subwcs = tim.sip_wcs.get_subimage(tim.x0, tim.y0, subw, subh)
tim.ima = dict(interpolation='nearest', origin='lower', cmap='gray',
vmin=mn, vmax=mx)
return tim
def get_tractor_image(self,
slc=None,
radecpoly=None,
gaussPsf=False,
const2psf=False,
pixPsf=False,
splinesky=False,
nanomaggies=True,
subsky=True,
tiny=5,
dq=True,
invvar=True,
pixels=True):
'''
Returns a tractor.Image ("tim") object for this image.
Options describing a subimage to return:
- *slc*: y,x slice objects
- *radecpoly*: numpy array, shape (N,2), RA,Dec polygon describing bounding box to select.
Options determining the PSF model to use:
- *gaussPsf*: single circular Gaussian PSF based on header FWHM value.
- *const2Psf*: 2-component general Gaussian fit to PsfEx model at image center.
- *pixPsf*: pixelized PsfEx model at image center.
Options determining the sky model to use:
- *splinesky*: median filter chunks of the image, then spline those.
Options determining the units of the image:
- *nanomaggies*: convert the image to be in units of NanoMaggies;
*tim.zpscale* contains the scale value the image was divided by.
- *subsky*: instantiate and subtract the initial sky model,
leaving a constant zero sky model?
'''
from astrometry.util.miscutils import clip_polygon
get_dq = dq
get_invvar = invvar
band = self.band
imh, imw = self.get_image_shape()
wcs = self.get_wcs()
x0, y0 = 0, 0
x1 = x0 + imw
y1 = y0 + imh
if slc is None and radecpoly is not None:
imgpoly = [(1, 1), (1, imh), (imw, imh), (imw, 1)]
ok, tx, ty = wcs.radec2pixelxy(radecpoly[:-1, 0], radecpoly[:-1,
1])
tpoly = zip(tx, ty)
clip = clip_polygon(imgpoly, tpoly)
clip = np.array(clip)
if len(clip) == 0:
return None
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)
if y1 - y0 < tiny or x1 - x0 < tiny:
print('Skipping tiny subimage')
return None
if slc is not None:
sy, sx = slc
y0, y1 = sy.start, sy.stop
x0, x1 = sx.start, sx.stop
old_extent = (x0, x1, y0, y1)
new_extent = self.get_good_image_slice((x0, x1, y0, y1),
get_extent=True)
if new_extent != old_extent:
x0, x1, y0, y1 = new_extent
print('Applying good subregion of CCD: slice is', x0, x1, y0, y1)
if x0 >= x1 or y0 >= y1:
return None
slc = slice(y0, y1), slice(x0, x1)
if pixels:
print('Reading image slice:', slc)
img, imghdr = self.read_image(header=True, slice=slc)
#print('SATURATE is', imghdr.get('SATURATE', None))
#print('Max value in image is', img.max())
# check consistency... something of a DR1 hangover
e = imghdr['EXTNAME']
assert (e.strip() == self.ccdname.strip())
else:
img = np.zeros((imh, imw))
imghdr = dict()
if slc is not None:
img = img[slc]
if get_invvar:
invvar = self.read_invvar(slice=slc, clipThresh=0.)
else:
invvar = np.ones_like(img)
if get_dq:
dq = self.read_dq(slice=slc)
invvar[dq != 0] = 0.
if np.all(invvar == 0.):
print('Skipping zero-invvar image')
return None
assert (np.all(np.isfinite(img)))
assert (np.all(np.isfinite(invvar)))
assert (not (np.all(invvar == 0.)))
# header 'FWHM' is in pixels
# imghdr['FWHM']
psf_fwhm = self.fwhm
psf_sigma = psf_fwhm / 2.35
primhdr = self.read_image_primary_header()
sky = self.read_sky_model(splinesky=splinesky, slc=slc)
midsky = 0.
if subsky:
print('Instantiating and subtracting sky model...')
from tractor.sky import ConstantSky
skymod = np.zeros_like(img)
sky.addTo(skymod)
img -= skymod
midsky = np.median(skymod)
zsky = ConstantSky(0.)
zsky.version = sky.version
zsky.plver = sky.plver
del skymod
del sky
sky = zsky
del zsky
magzp = self.decals.get_zeropoint_for(self)
orig_zpscale = zpscale = NanoMaggies.zeropointToScale(magzp)
if nanomaggies:
# Scale images to Nanomaggies
img /= zpscale
invvar *= zpscale**2
if not subsky:
sky.scale(1. / zpscale)
zpscale = 1.
assert (np.sum(invvar > 0) > 0)
if get_invvar:
sig1 = 1. / np.sqrt(np.median(invvar[invvar > 0]))
else:
# Estimate from the image?
# # 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(img[slice1] - img[slice2]).ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print('sig1 estimate:', sig1)
invvar *= (1. / sig1**2)
assert (np.all(np.isfinite(img)))
assert (np.all(np.isfinite(invvar)))
assert (np.isfinite(sig1))
if subsky:
##
imgmed = np.median(img[invvar > 0])
if np.abs(imgmed) > sig1:
print('WARNING: image median', imgmed,
'is more than 1 sigma away from zero!')
# Boom!
#assert(False)
twcs = ConstantFitsWcs(wcs)
if x0 or y0:
twcs.setX0Y0(x0, y0)
psf = self.read_psf_model(x0,
y0,
gaussPsf=gaussPsf,
pixPsf=pixPsf,
const2psf=const2psf,
psf_sigma=psf_sigma,
cx=(x0 + x1) / 2.,
cy=(y0 + y1) / 2.)
tim = Image(img,
invvar=invvar,
wcs=twcs,
psf=psf,
photocal=LinearPhotoCal(zpscale, band=band),
sky=sky,
name=self.name + ' ' + band)
assert (np.all(np.isfinite(tim.getInvError())))
# PSF norm
psfnorm = self.psf_norm(tim)
print('PSF norm', psfnorm, 'vs Gaussian',
1. / (2. * np.sqrt(np.pi) * psf_sigma))
# Galaxy-detection norm
tim.band = band
galnorm = self.galaxy_norm(tim)
print('Galaxy norm:', galnorm)
# CP (DECam) images include DATE-OBS and MJD-OBS, in UTC.
import astropy.time
#mjd_utc = mjd=primhdr.get('MJD-OBS', 0)
mjd_tai = astropy.time.Time(primhdr['DATE-OBS']).tai.mjd
tim.slice = slc
tim.time = TAITime(None, mjd=mjd_tai)
tim.zr = [-3. * sig1, 10. * sig1]
tim.zpscale = orig_zpscale
tim.midsky = midsky
tim.sig1 = sig1
tim.psf_fwhm = psf_fwhm
tim.psf_sigma = psf_sigma
tim.propid = self.propid
tim.psfnorm = psfnorm
tim.galnorm = galnorm
tim.sip_wcs = wcs
tim.x0, tim.y0 = int(x0), int(y0)
tim.imobj = self
tim.primhdr = primhdr
tim.hdr = imghdr
tim.plver = primhdr['PLVER'].strip()
tim.skyver = (sky.version, sky.plver)
tim.wcsver = (wcs.version, wcs.plver)
tim.psfver = (psf.version, psf.plver)
if get_dq:
tim.dq = dq
tim.dq_bits = CP_DQ_BITS
tim.saturation = imghdr.get('SATURATE', None)
tim.satval = tim.saturation or 0.
if subsky:
tim.satval -= midsky
if nanomaggies:
tim.satval /= orig_zpscale
subh, subw = tim.shape
tim.subwcs = tim.sip_wcs.get_subimage(tim.x0, tim.y0, subw, subh)
mn, mx = tim.zr
tim.ima = dict(interpolation='nearest',
origin='lower',
cmap='gray',
vmin=mn,
vmax=mx)
return tim