def get_tractor_image_dr7(run, camcol, field, bandname,
sdssobj=None, release='DR7',
retrieve=True, curl=False,
psf='kl-gm', useMags=False,
roi=None,
roiradecsize=None,
roiradecbox=None,
nanomaggies=False,
savepsfimg=None, zrange=[-3, 10]):
'''
Creates a tractor.Image given an SDSS field identifier.
If not None, roi = (x0, x1, y0, y1) defines a region-of-interest
in the image, in zero-indexed pixel coordinates. x1,y1 are
NON-inclusive; roi=(0,100,0,100) will yield a 100 x 100 image.
psf can be:
"dg" for double-Gaussian
"kl-gm" for SDSS KL-decomposition approximated as a Gaussian mixture
"roiradecsize" = (ra, dec, half-size in pixels) indicates that you
want to grab a ROI around the given RA,Dec.
Returns:
(tractor.Image, dict)
dict contains useful details like:
'sky'
'skysig'
'''
from astrometry.sdss import DR7, band_index
if sdssobj is None:
# Ugly
if release != 'DR7':
raise RuntimeError('We only support DR7 currently')
sdss = DR7(curl=curl)
else:
sdss = sdssobj
valid_psf = ['dg', 'kl-gm']
if psf not in valid_psf:
raise RuntimeError('PSF must be in ' + str(valid_psf))
# FIXME
rerun = 0
bandnum = band_index(bandname)
_check_sdss_files(sdss, run, camcol, field, bandname,
['fpC', 'tsField', 'psField', 'fpM'],
retrieve=retrieve)
fpC = sdss.readFpC(run, camcol, field, bandname)
hdr = fpC.getHeader()
fpC = fpC.getImage()
fpC = fpC.astype(np.float32) - sdss.softbias
image = fpC
(H, W) = image.shape
info = dict()
tai = hdr.get('TAI')
stripe = hdr.get('STRIPE')
strip = hdr.get('STRIP')
obj = hdr.get('OBJECT')
info.update(tai=tai, stripe=stripe, strip=strip, object=obj, hdr=hdr)
tsf = sdss.readTsField(run, camcol, field, rerun)
astrans = tsf.getAsTrans(bandnum)
wcs = SdssWcs(astrans)
#print('Created SDSS Wcs:', wcs)
X = interpret_roi(wcs, (H, W), roi=roi, roiradecsize=roiradecsize,
roiradecbox=roiradecbox)
if X is None:
return None, None
roi, hasroi = X
info.update(roi=roi)
x0, x1, y0, y1 = roi
# Mysterious half-pixel shift. asTrans pixel coordinates?
wcs.setX0Y0(x0 + 0.5, y0 + 0.5)
if nanomaggies:
zp = tsf.get_zeropoint(bandnum)
photocal = LinearPhotoCal(NanoMaggies.zeropointToScale(zp),
band=bandname)
elif useMags:
photocal = SdssMagsPhotoCal(tsf, bandname)
else:
photocal = SdssFluxPhotoCal()
psfield = sdss.readPsField(run, camcol, field)
sky = psfield.getSky(bandnum)
skysig = sqrt(sky)
skyobj = ConstantSky(sky)
zr = sky + np.array(zrange) * skysig
info.update(sky=sky, skysig=skysig, zr=zr)
fpM = sdss.readFpM(run, camcol, field, bandname)
gain = psfield.getGain(bandnum)
darkvar = psfield.getDarkVariance(bandnum)
skyerr = psfield.getSkyErr(bandnum)
invvar = sdss.getInvvar(fpC, fpM, gain, darkvar, sky, skyerr)
dgpsf = psfield.getDoubleGaussian(bandnum, normalize=True)
info.update(dgpsf=dgpsf)
if roi is not None:
roislice = (slice(y0, y1), slice(x0, x1))
image = image[roislice].copy()
invvar = invvar[roislice].copy()
if psf == 'kl-gm':
from emfit import em_fit_2d
from fitpsf import em_init_params
# Create Gaussian mixture model PSF approximation.
H, W = image.shape
klpsf = psfield.getPsfAtPoints(bandnum, x0 + W / 2, y0 + H / 2)
S = klpsf.shape[0]
# number of Gaussian components
K = 3
w, mu, sig = em_init_params(K, None, None, None)
II = klpsf.copy()
II /= II.sum()
# HIDEOUS HACK
II = np.maximum(II, 0)
#print('Multi-Gaussian PSF fit...')
xm, ym = -(S / 2), -(S / 2)
if savepsfimg is not None:
plt.clf()
plt.imshow(II, interpolation='nearest', origin='lower')
plt.title('PSF image to fit with EM')
plt.savefig(savepsfimg)
res = em_fit_2d(II, xm, ym, w, mu, sig)
print('em_fit_2d result:', res)
if res == 0:
# print('w,mu,sig', w,mu,sig)
mypsf = GaussianMixturePSF(w, mu, sig)
mypsf.computeRadius()
else:
# Failed! Return 'dg' model instead?
print('PSF model fit', psf, 'failed! Returning DG model instead')
psf = 'dg'
if psf == 'dg':
print('Creating double-Gaussian PSF approximation')
(a, s1, b, s2) = dgpsf
mypsf = NCircularGaussianPSF([s1, s2], [a, b])
timg = Image(data=image, invvar=invvar, psf=mypsf, wcs=wcs,
sky=skyobj, photocal=photocal,
name=('SDSS (r/c/f/b=%i/%i/%i/%s)' %
(run, camcol, field, bandname)))
timg.zr = zr
return timg, info
def get_tractor_image(run, camcol, field, bandname,
sdssobj=None, release='DR7',
retrieve=True, curl=False,
psf='kl-gm', useMags=False,
roi=None,
roiradecsize=None,
roiradecbox=None,
nanomaggies=False,
savepsfimg=None, zrange=[-3,10]):
'''
Creates a tractor.Image given an SDSS field identifier.
If not None, roi = (x0, x1, y0, y1) defines a region-of-interest
in the image, in zero-indexed pixel coordinates. x1,y1 are
NON-inclusive; roi=(0,100,0,100) will yield a 100 x 100 image.
psf can be:
"dg" for double-Gaussian
"kl-gm" for SDSS KL-decomposition approximated as a Gaussian mixture
"roiradecsize" = (ra, dec, half-size in pixels) indicates that you
want to grab a ROI around the given RA,Dec.
Returns:
(tractor.Image, dict)
dict contains useful details like:
'sky'
'skysig'
'''
from astrometry.sdss import DR7, band_index
if sdssobj is None:
# Ugly
if release != 'DR7':
raise RuntimeError('We only support DR7 currently')
sdss = DR7(curl=curl)
else:
sdss = sdssobj
valid_psf = ['dg', 'kl-gm']
if psf not in valid_psf:
raise RuntimeError('PSF must be in ' + str(valid_psf))
# FIXME
rerun = 0
bandnum = band_index(bandname)
_check_sdss_files(sdss, run, camcol, field, bandname,
['fpC', 'tsField', 'psField', 'fpM'],
retrieve=retrieve)
fpC = sdss.readFpC(run, camcol, field, bandname)
hdr = fpC.getHeader()
fpC = fpC.getImage()
fpC = fpC.astype(np.float32) - sdss.softbias
image = fpC
(H,W) = image.shape
info = dict()
tai = hdr.get('TAI')
stripe = hdr.get('STRIPE')
strip = hdr.get('STRIP')
obj = hdr.get('OBJECT')
info.update(tai=tai, stripe=stripe, strip=strip, object=obj, hdr=hdr)
tsf = sdss.readTsField(run, camcol, field, rerun)
astrans = tsf.getAsTrans(bandnum)
wcs = SdssWcs(astrans)
#print('Created SDSS Wcs:', wcs)
X = interpret_roi(wcs, (H,W), roi=roi, roiradecsize=roiradecsize,
roiradecbox=roiradecbox)
if X is None:
return None,None
roi,hasroi = X
info.update(roi=roi)
x0,x1,y0,y1 = roi
# Mysterious half-pixel shift. asTrans pixel coordinates?
wcs.setX0Y0(x0 + 0.5, y0 + 0.5)
if nanomaggies:
zp = tsf.get_zeropoint(bandnum)
photocal = LinearPhotoCal(NanoMaggies.zeropointToScale(zp),
band=bandname)
elif useMags:
photocal = SdssMagsPhotoCal(tsf, bandname)
else:
photocal = SdssFluxPhotoCal()
psfield = sdss.readPsField(run, camcol, field)
sky = psfield.getSky(bandnum)
skysig = sqrt(sky)
skyobj = ConstantSky(sky)
zr = sky + np.array(zrange) * skysig
info.update(sky=sky, skysig=skysig, zr=zr)
fpM = sdss.readFpM(run, camcol, field, bandname)
gain = psfield.getGain(bandnum)
darkvar = psfield.getDarkVariance(bandnum)
skyerr = psfield.getSkyErr(bandnum)
invvar = sdss.getInvvar(fpC, fpM, gain, darkvar, sky, skyerr)
dgpsf = psfield.getDoubleGaussian(bandnum, normalize=True)
info.update(dgpsf=dgpsf)
if roi is not None:
roislice = (slice(y0,y1), slice(x0,x1))
image = image[roislice].copy()
invvar = invvar[roislice].copy()
if psf == 'kl-gm':
from emfit import em_fit_2d
from fitpsf import em_init_params
# Create Gaussian mixture model PSF approximation.
H,W = image.shape
klpsf = psfield.getPsfAtPoints(bandnum, x0+W/2, y0+H/2)
S = klpsf.shape[0]
# number of Gaussian components
K = 3
w,mu,sig = em_init_params(K, None, None, None)
II = klpsf.copy()
II /= II.sum()
# HIDEOUS HACK
II = np.maximum(II, 0)
#print('Multi-Gaussian PSF fit...')
xm,ym = -(S/2), -(S/2)
if savepsfimg is not None:
plt.clf()
plt.imshow(II, interpolation='nearest', origin='lower')
plt.title('PSF image to fit with EM')
plt.savefig(savepsfimg)
res = em_fit_2d(II, xm, ym, w, mu, sig)
print('em_fit_2d result:', res)
if res == 0:
# print('w,mu,sig', w,mu,sig)
mypsf = GaussianMixturePSF(w, mu, sig)
mypsf.computeRadius()
else:
# Failed! Return 'dg' model instead?
print('PSF model fit', psf, 'failed! Returning DG model instead')
psf = 'dg'
if psf == 'dg':
print('Creating double-Gaussian PSF approximation')
(a,s1, b,s2) = dgpsf
mypsf = NCircularGaussianPSF([s1, s2], [a, b])
timg = Image(data=image, invvar=invvar, psf=mypsf, wcs=wcs,
sky=skyobj, photocal=photocal,
name=('SDSS (r/c/f/b=%i/%i/%i/%s)' %
(run, camcol, field, bandname)))
timg.zr = zr
return timg,info
def _get_tractor_image_dr8(run, camcol, field, bandname, sdss=None,
roi=None, psf='kl-gm', roiradecsize=None,
roiradecbox=None,
savepsfimg=None, curl=False,
nanomaggies=False,
zrange=[-3,10],
invvarIgnoresSourceFlux=False,
invvarAtCenter=False,
invvarAtCenterImage=False,
imargs={}):
# retry_retrieve=True,
'''
Creates a tractor.Image given an SDSS field identifier.
If not None, roi = (x0, x1, y0, y1) defines a region-of-interest
in the image, in zero-indexed pixel coordinates. x1,y1 are
NON-inclusive; roi=(0,100,0,100) will yield a 100 x 100 image.
psf can be:
"dg" for double-Gaussian
"kl-gm" for SDSS KL-decomposition approximated as a Gaussian mixture
"bright-*", "*" one of the above PSFs, with special handling at
the bright end.
"roiradecsize" = (ra, dec, half-size in pixels) indicates that you
want to grab a ROI around the given RA,Dec.
"roiradecbox" = (ra0, ra1, dec0, dec1) indicates that you
want to grab a ROI containing the given RA,Dec ranges.
"invvarAtCentr" -- get a scalar constant inverse-variance
"invvarAtCenterImage" -- get a scalar constant inverse-variance
but still make an image out of it.
Returns: (tractor.Image, dict)
dict contains useful details like:
'sky'
'skysig'
'''
from astrometry.sdss import band_index
origpsf = psf
if psf.startswith('bright-'):
psf = psf[7:]
brightpsf = True
print('Setting bright PSF handling')
else:
brightpsf = False
valid_psf = ['dg', 'kl-gm', 'kl-pix']
if psf not in valid_psf:
raise RuntimeError('PSF must be in ' + str(valid_psf))
if sdss is None:
from astrometry.sdss import DR8
sdss = DR8(curl=curl)
bandnum = band_index(bandname)
for ft in ['psField', 'fpM']:
fn = sdss.retrieve(ft, run, camcol, field, bandname)
fn = sdss.retrieve('frame', run, camcol, field, bandname)
# http://data.sdss3.org/datamodel/files/BOSS_PHOTOOBJ/frames/RERUN/RUN/CAMCOL/frame.html
frame = sdss.readFrame(run, camcol, field, bandname, filename=fn)
#image = frame.getImage().astype(np.float32)
#(H,W) = image.shape
H,W = frame.getImageShape()
info = dict()
hdr = frame.getHeader()
tai = hdr.get('TAI')
stripe = hdr.get('STRIPE')
strip = hdr.get('STRIP')
obj = hdr.get('OBJECT')
info.update(tai=tai, stripe=stripe, strip=strip, object=obj, hdr=hdr)
astrans = frame.getAsTrans()
wcs = SdssWcs(astrans)
#print('Created SDSS Wcs:', wcs)
#print('(x,y) = 1,1 -> RA,Dec', wcs.pixelToPosition(1,1))
X = interpret_roi(wcs, (H,W), roi=roi, roiradecsize=roiradecsize,
roiradecbox=roiradecbox)
if X is None:
return None,None
roi,hasroi = X
info.update(roi=roi)
x0,x1,y0,y1 = roi
# Mysterious half-pixel shift. asTrans pixel coordinates?
wcs.setX0Y0(x0 + 0.5, y0 + 0.5)
if nanomaggies:
photocal = LinearPhotoCal(1., band=bandname)
else:
photocal = SdssNanomaggiesPhotoCal(bandname)
sky = 0.
skyobj = ConstantSky(sky)
calibvec = frame.getCalibVec()
invvarAtCenter = invvarAtCenter or invvarAtCenterImage
psfield = sdss.readPsField(run, camcol, field)
iva = dict(ignoreSourceFlux=invvarIgnoresSourceFlux)
if invvarAtCenter:
if hasroi:
iva.update(constantSkyAt=((x0+x1)/2., (y0+y1)/2.))
else:
iva.update(constantSkyAt=(W/2., H/2.))
invvar = frame.getInvvar(psfield, bandnum, **iva)
invvar = invvar.astype(np.float32)
if not invvarAtCenter:
assert(invvar.shape == (H,W))
# Could get this from photoField instead
# http://data.sdss3.org/datamodel/files/BOSS_PHOTOOBJ/RERUN/RUN/photoField.html
gain = psfield.getGain(bandnum)
darkvar = psfield.getDarkVariance(bandnum)
meansky = np.mean(frame.sky)
meancalib = np.mean(calibvec)
skysig = sqrt((meansky / gain) + darkvar) * meancalib
info.update(sky=sky, skysig=skysig)
zr = np.array(zrange)*skysig + sky
info.update(zr=zr)
# http://data.sdss3.org/datamodel/files/PHOTO_REDUX/RERUN/RUN/objcs/CAMCOL/fpM.html
fpM = sdss.readFpM(run, camcol, field, bandname)
if not hasroi:
image = frame.getImage()
else:
roislice = (slice(y0,y1), slice(x0,x1))
image = frame.getImageSlice(roislice).astype(np.float32)
if invvarAtCenterImage:
invvar = invvar + np.zeros(image.shape, np.float32)
elif invvarAtCenter:
pass
else:
invvar = invvar[roislice].copy()
H,W = image.shape
if (not invvarAtCenter) or invvarAtCenterImage:
for plane in [ 'INTERP', 'SATUR', 'CR', 'GHOST' ]:
fpM.setMaskedPixels(plane, invvar, 0, roi=roi)
dgpsf = psfield.getDoubleGaussian(bandnum, normalize=True)
info.update(dgpsf=dgpsf)
if psf == 'kl-pix':
# Pixelized KL-PSF
klpsf = psfield.getPsfAtPoints(bandnum, x0+W/2, y0+H/2)
# Trim symmetric zeros
sh,sw = klpsf.shape
while True:
if (np.all(klpsf[0,:] == 0.) and
np.all(klpsf[:,0] == 0.) and
np.all(klpsf[-1,:] == 0.) and
np.all(klpsf[:,-1] == 0.)):
klpsf = klpsf[1:-1, 1:-1]
else:
break
mypsf = PixelizedPSF(klpsf)
elif psf == 'kl-gm':
from emfit import em_fit_2d
from fitpsf import em_init_params
# Create Gaussian mixture model PSF approximation.
klpsf = psfield.getPsfAtPoints(bandnum, x0+W/2, y0+H/2)
S = klpsf.shape[0]
# number of Gaussian components
K = 3
w,mu,sig = em_init_params(K, None, None, None)
II = klpsf.copy()
II /= II.sum()
# HIDEOUS HACK
II = np.maximum(II, 0)
#print('Multi-Gaussian PSF fit...')
xm,ym = -(S/2), -(S/2)
if savepsfimg is not None:
plt.clf()
plt.imshow(II, interpolation='nearest', origin='lower')
plt.title('PSF image to fit with EM')
plt.savefig(savepsfimg)
res = em_fit_2d(II, xm, ym, w, mu, sig)
#print('em_fit_2d result:', res)
if res == 0:
# print('w,mu,sig', w,mu,sig)
mypsf = GaussianMixturePSF(w, mu, sig)
mypsf.computeRadius()
else:
# Failed! Return 'dg' model instead?
print('PSF model fit', psf, 'failed! Returning DG model instead')
psf = 'dg'
if psf == 'dg':
print('Creating double-Gaussian PSF approximation')
(a,s1, b,s2) = dgpsf
mypsf = NCircularGaussianPSF([s1, s2], [a, b])
if brightpsf:
print('Wrapping PSF in SdssBrightPSF')
(a1,s1, a2,s2, a3,sigmap,beta) = psfield.getPowerLaw(bandnum)
mypsf = SdssBrightPSF(mypsf, a1,s1,a2,s2,a3,sigmap,beta)
print('PSF:', mypsf)
timg = Image(data=image, invvar=invvar, psf=mypsf, wcs=wcs,
sky=skyobj, photocal=photocal,
name=('SDSS (r/c/f/b=%i/%i/%i/%s)' %
(run, camcol, field, bandname)),
time=TAITime(tai),
**imargs)
timg.zr = zr
return timg,info
def _get_tractor_image_dr8(run,
camcol,
field,
bandname,
sdss=None,
roi=None,
psf='kl-gm',
roiradecsize=None,
roiradecbox=None,
savepsfimg=None,
curl=False,
nanomaggies=False,
zrange=[-3, 10],
invvarIgnoresSourceFlux=False,
invvarAtCenter=False,
invvarAtCenterImage=False,
imargs={}):
# retry_retrieve=True,
'''
Creates a tractor.Image given an SDSS field identifier.
If not None, roi = (x0, x1, y0, y1) defines a region-of-interest
in the image, in zero-indexed pixel coordinates. x1,y1 are
NON-inclusive; roi=(0,100,0,100) will yield a 100 x 100 image.
psf can be:
"dg" for double-Gaussian
"kl-gm" for SDSS KL-decomposition approximated as a Gaussian mixture
"bright-*", "*" one of the above PSFs, with special handling at
the bright end.
"roiradecsize" = (ra, dec, half-size in pixels) indicates that you
want to grab a ROI around the given RA,Dec.
"roiradecbox" = (ra0, ra1, dec0, dec1) indicates that you
want to grab a ROI containing the given RA,Dec ranges.
"invvarAtCentr" -- get a scalar constant inverse-variance
"invvarAtCenterImage" -- get a scalar constant inverse-variance
but still make an image out of it.
Returns: (tractor.Image, dict)
dict contains useful details like:
'sky'
'skysig'
'''
from astrometry.sdss import band_index
origpsf = psf
if psf.startswith('bright-'):
psf = psf[7:]
brightpsf = True
print('Setting bright PSF handling')
else:
brightpsf = False
valid_psf = ['dg', 'kl-gm', 'kl-pix']
if psf not in valid_psf:
raise RuntimeError('PSF must be in ' + str(valid_psf))
if sdss is None:
from astrometry.sdss import DR8
sdss = DR8(curl=curl)
bandnum = band_index(bandname)
for ft in ['psField', 'fpM']:
fn = sdss.retrieve(ft, run, camcol, field, bandname)
fn = sdss.retrieve('frame', run, camcol, field, bandname)
# http://data.sdss3.org/datamodel/files/BOSS_PHOTOOBJ/frames/RERUN/RUN/CAMCOL/frame.html
frame = sdss.readFrame(run, camcol, field, bandname, filename=fn)
#image = frame.getImage().astype(np.float32)
#(H,W) = image.shape
H, W = frame.getImageShape()
info = dict()
hdr = frame.getHeader()
tai = hdr.get('TAI')
stripe = hdr.get('STRIPE')
strip = hdr.get('STRIP')
obj = hdr.get('OBJECT')
info.update(tai=tai, stripe=stripe, strip=strip, object=obj, hdr=hdr)
astrans = frame.getAsTrans()
wcs = SdssWcs(astrans)
#print('Created SDSS Wcs:', wcs)
#print('(x,y) = 1,1 -> RA,Dec', wcs.pixelToPosition(1,1))
X = interpret_roi(wcs, (H, W),
roi=roi,
roiradecsize=roiradecsize,
roiradecbox=roiradecbox)
if X is None:
return None, None
roi, hasroi = X
info.update(roi=roi)
x0, x1, y0, y1 = roi
# Mysterious half-pixel shift. asTrans pixel coordinates?
wcs.setX0Y0(x0 + 0.5, y0 + 0.5)
if nanomaggies:
photocal = LinearPhotoCal(1., band=bandname)
else:
photocal = SdssNanomaggiesPhotoCal(bandname)
sky = 0.
skyobj = ConstantSky(sky)
calibvec = frame.getCalibVec()
invvarAtCenter = invvarAtCenter or invvarAtCenterImage
psfield = sdss.readPsField(run, camcol, field)
iva = dict(ignoreSourceFlux=invvarIgnoresSourceFlux)
if invvarAtCenter:
if hasroi:
iva.update(constantSkyAt=((x0 + x1) / 2., (y0 + y1) / 2.))
else:
iva.update(constantSkyAt=(W / 2., H / 2.))
invvar = frame.getInvvar(psfield, bandnum, **iva)
invvar = invvar.astype(np.float32)
if not invvarAtCenter:
assert (invvar.shape == (H, W))
# Could get this from photoField instead
# http://data.sdss3.org/datamodel/files/BOSS_PHOTOOBJ/RERUN/RUN/photoField.html
gain = psfield.getGain(bandnum)
darkvar = psfield.getDarkVariance(bandnum)
meansky = np.mean(frame.sky)
meancalib = np.mean(calibvec)
skysig = sqrt((meansky / gain) + darkvar) * meancalib
info.update(sky=sky, skysig=skysig)
zr = np.array(zrange) * skysig + sky
info.update(zr=zr)
# http://data.sdss3.org/datamodel/files/PHOTO_REDUX/RERUN/RUN/objcs/CAMCOL/fpM.html
fpM = sdss.readFpM(run, camcol, field, bandname)
if not hasroi:
image = frame.getImage()
else:
roislice = (slice(y0, y1), slice(x0, x1))
image = frame.getImageSlice(roislice).astype(np.float32)
if invvarAtCenterImage:
invvar = invvar + np.zeros(image.shape, np.float32)
elif invvarAtCenter:
pass
else:
invvar = invvar[roislice].copy()
H, W = image.shape
if (not invvarAtCenter) or invvarAtCenterImage:
for plane in ['INTERP', 'SATUR', 'CR', 'GHOST']:
fpM.setMaskedPixels(plane, invvar, 0, roi=roi)
dgpsf = psfield.getDoubleGaussian(bandnum, normalize=True)
info.update(dgpsf=dgpsf)
if psf == 'kl-pix':
# Pixelized KL-PSF
klpsf = psfield.getPsfAtPoints(bandnum, x0 + W / 2, y0 + H / 2)
# Trim symmetric zeros
sh, sw = klpsf.shape
while True:
if (np.all(klpsf[0, :] == 0.) and np.all(klpsf[:, 0] == 0.)
and np.all(klpsf[-1, :] == 0.)
and np.all(klpsf[:, -1] == 0.)):
klpsf = klpsf[1:-1, 1:-1]
else:
break
mypsf = PixelizedPSF(klpsf)
elif psf == 'kl-gm':
from emfit import em_fit_2d
from fitpsf import em_init_params
# Create Gaussian mixture model PSF approximation.
klpsf = psfield.getPsfAtPoints(bandnum, x0 + W / 2, y0 + H / 2)
S = klpsf.shape[0]
# number of Gaussian components
K = 3
w, mu, sig = em_init_params(K, None, None, None)
II = klpsf.copy()
II /= II.sum()
# HIDEOUS HACK
II = np.maximum(II, 0)
#print('Multi-Gaussian PSF fit...')
xm, ym = -(S / 2), -(S / 2)
if savepsfimg is not None:
plt.clf()
plt.imshow(II, interpolation='nearest', origin='lower')
plt.title('PSF image to fit with EM')
plt.savefig(savepsfimg)
res = em_fit_2d(II, xm, ym, w, mu, sig)
#print('em_fit_2d result:', res)
if res == 0:
# print('w,mu,sig', w,mu,sig)
mypsf = GaussianMixturePSF(w, mu, sig)
mypsf.computeRadius()
else:
# Failed! Return 'dg' model instead?
print('PSF model fit', psf, 'failed! Returning DG model instead')
psf = 'dg'
if psf == 'dg':
print('Creating double-Gaussian PSF approximation')
(a, s1, b, s2) = dgpsf
mypsf = NCircularGaussianPSF([s1, s2], [a, b])
if brightpsf:
print('Wrapping PSF in SdssBrightPSF')
(a1, s1, a2, s2, a3, sigmap, beta) = psfield.getPowerLaw(bandnum)
mypsf = SdssBrightPSF(mypsf, a1, s1, a2, s2, a3, sigmap, beta)
print('PSF:', mypsf)
timg = Image(data=image,
invvar=invvar,
psf=mypsf,
wcs=wcs,
sky=skyobj,
photocal=photocal,
name=('SDSS (r/c/f/b=%i/%i/%i/%s)' %
(run, camcol, field, bandname)),
time=TAITime(tai),
**imargs)
timg.zr = zr
return timg, info