def _meisner_psf_models():
global plotslice
# Meisner's PSF models
# for band in [4]:
for band in [1, 2, 3, 4]:
print()
print('W%i' % band)
print()
#pix = fitsio.read('wise-psf-avg-pix.fits', ext=band-1)
pix = fitsio.read('wise-psf-avg-pix-bright.fits', ext=band - 1)
fit = fits_table('wise-psf-avg.fits', hdu=band)
#fit = fits_table('psf-allwise-con3.fits', hdu=band)
scale = 1.
print('Pix shape', pix.shape)
h, w = pix.shape
xx, yy = np.meshgrid(np.arange(w), np.arange(h))
cx, cy = np.sum(xx * pix) / np.sum(pix), np.sum(yy * pix) / np.sum(pix)
print('Centroid:', cx, cy)
#S = 100
S = h / 2
slc = slice(h / 2 - S, h / 2 + S + 1), slice(w / 2 - S, w / 2 + S + 1)
plotss = 30
plotslice = slice(S - plotss,
-(S - plotss)), slice(S - plotss, -(S - plotss))
opix = pix
print('Original pixels sum:', opix.sum())
pix /= pix.sum()
pix = pix[slc]
print('Sliced pix sum', pix.sum())
psf = GaussianMixturePSF(fit.amp, fit.mean * scale, fit.var * scale**2)
psfmodel = psf.getPointSourcePatch(0., 0., radius=h / 2)
mod = psfmodel.patch
print('Orig mod sum', mod.sum())
mod = mod[slc]
print('Sliced mod sum', mod.sum())
print('Amps:', np.sum(psf.mog.amp))
_plot_psf(pix, mod, psf)
plt.suptitle('W%i: Orig' % band)
ps.savefig()
# Lanczos sub-sample
if band == 4:
lpix = _lanczos_subsample(opix, 2)
pix = lpix
h, w = pix.shape
#S = 140
slc = slice(h / 2 - S,
h / 2 + S + 1), slice(w / 2 - S, w / 2 + S + 1)
print('Resampled pix sum', pix.sum())
pix = pix[slc]
print('sliced pix sum', pix.sum())
psf = GaussianMixturePSF(fit.amp, fit.mean * scale,
fit.var * scale**2)
psfmodel = psf.getPointSourcePatch(0., 0., radius=h / 2)
mod = psfmodel.patch
print('Scaled mod sum', mod.sum())
mod = mod[slc]
print('Sliced mod sum', mod.sum())
_plot_psf(pix, mod, psf)
plt.suptitle('W%i: Scaled' % band)
ps.savefig()
plotslice = slice(S - plotss,
-(S - plotss)), slice(S - plotss, -(S - plotss))
psfx = GaussianMixturePSF.fromStamp(pix,
P0=(fit.amp, fit.mean * scale,
fit.var * scale**2))
psfmodel = psfx.getPointSourcePatch(0., 0., radius=h / 2)
mod = psfmodel.patch
print('From stamp: mod sum', mod.sum())
mod = mod[slc]
print('Sliced mod sum:', mod.sum())
_plot_psf(pix, mod, psfx)
plt.suptitle('W%i: fromStamp: %g = %s, res %.3f' %
(band, np.sum(psfx.mog.amp), ','.join(
['%.3f' % a
for a in psfx.mog.amp]), np.sum(pix - mod)))
ps.savefig()
print('Stamp-Fit PSF params:', psfx)
# class MyGaussianMixturePSF(GaussianMixturePSF):
# def getLogPrior(self):
# if np.any(self.mog.amp < 0.):
# return -np.inf
# for k in range(self.mog.K):
# if np.linalg.det(self.mog.var[k]) <= 0:
# return -np.inf
# return 0
#
# @property
# def amp(self):
# return self.mog.amp
#
# mypsf = MyGaussianMixturePSF(psfx.mog.amp, psfx.mog.mean, psfx.mog.var)
# mypsf.radius = sh/2
sh, sw = pix.shape
# Initialize from original fit params
psfx = psf
# Try concentric gaussian PSF
sigmas = []
for k in range(psfx.mog.K):
v = psfx.mog.var[k, :, :]
sigmas.append(np.sqrt(np.sqrt(np.abs(v[0, 0] * v[1, 1]))))
print('Initializing concentric Gaussian PSF with sigmas', sigmas)
gpsf = NCircularGaussianPSF(sigmas, psfx.mog.amp)
gpsf.radius = sh / 2
mypsf = gpsf
tim = Image(data=pix, invvar=1e6 * np.ones_like(pix), psf=mypsf)
tim.modelMinval = 1e-16
# xx,yy = np.meshgrid(np.arange(sw), np.arange(sh))
# cx,cy = np.sum(xx*pix)/np.sum(pix), np.sum(yy*pix)/np.sum(pix)
# print 'Centroid:', cx,cy
# print 'Pix midpoint:', sw/2, sh/2
cx, cy = sw / 2, sh / 2
src = PointSource(PixPos(cx, cy), Flux(1.0))
tractor = Tractor([tim], [src])
tim.freezeAllBut('psf')
# tractor.freezeParam('catalog')
src.freezeAllBut('pos')
# src.freezeAllBut('brightness')
# tractor.freezeParam('images')
# tractor.optimize_forced_photometry()
# tractor.thawParam('images')
# print 'Source flux after forced-photom fit:', src.getBrightness()
print('Optimizing Params:')
tractor.printThawedParams()
for i in range(200):
#dlnp,X,alpha = tractor.optimize(damp=0.1)
dlnp, X, alpha = tractor.optimize(damp=1)
print(i, 'dlnp %.3g' % dlnp, 'psf', gpsf)
if dlnp < 1e-6:
break
tractor.freezeParam('catalog')
gpsf.sigmas.stepsize = len(gpsf.sigmas) * [1e-6]
gpsf.weights.stepsize = len(gpsf.sigmas) * [1e-6]
for i in range(200):
#dlnp,X,alpha = tractor.optimize(damp=0.1)
dlnp, X, alpha = tractor.optimize(damp=1)
print(i, 'dlnp %.3g' % dlnp, 'psf', gpsf)
if dlnp < 1e-6:
break
print('PSF3(opt): flux', src.brightness)
print('PSF amps:', np.sum(mypsf.amp))
print('PSF amps * Source brightness:',
src.brightness.getValue() * np.sum(mypsf.amp))
print('pix sum:', pix.sum())
print('Optimize source:', src)
print('Optimized PSF:', mypsf)
mod = tractor.getModelImage(0)
print('Mod sum:', mod.sum())
_plot_psf(pix, mod, mypsf, flux=src.brightness.getValue())
plt.suptitle('W%i psf3 (opt): %g = %s, resid %.3f' %
(band, np.sum(mypsf.amp), ','.join(
['%.3f' % a for a in mypsf.amp]), np.sum(pix - mod)))
ps.savefig()
# Write before normalizing!
T = fits_table()
T.amp = mypsf.mog.amp
T.mean = mypsf.mog.mean
T.var = mypsf.mog.var
T.writeto('psf3-w%i.fits' % band)
T.writeto('psf3.fits', append=(band != 1))
mypsf.weights.setParams(
np.array(mypsf.weights.getParams()) /
sum(mypsf.weights.getParams()))
print('Normalized PSF weights:', mypsf)
mod = tractor.getModelImage(0)
print('Mod sum:', mod.sum())
_plot_psf(pix, mod, mypsf, flux=src.brightness.getValue())
plt.suptitle('W%i psf3 (opt): %g = %s, resid %.3f' %
(band, np.sum(mypsf.amp), ','.join(
['%.3f' % a for a in mypsf.amp]), np.sum(pix - mod)))
ps.savefig()
class MyGaussianPSF(NCircularGaussianPSF):
'''
A PSF model with strictly positive weights that sum to
unity.
'''
def __init__(self, sigmas, weights):
ww = np.array(weights)
ww = np.log(ww)
super(MyGaussianPSF, self).__init__(sigmas, ww)
@staticmethod
def getNamedParams():
return dict(sigmas=0, logweights=1)
def __str__(self):
return ('MyGaussianPSF: sigmas [ ' +
', '.join(['%.3f' % s for s in self.mysigmas]) +
' ], weights [ ' +
', '.join(['%.3f' % w for w in self.myweights]) + ' ]')
@property
def myweights(self):
ww = np.exp(self.logweights.getAllParams())
ww /= ww.sum()
return ww
@property
def weights(self):
ww = np.exp(self.logweights.getParams())
wsum = np.sum(np.exp(self.logweights.getAllParams()))
return ww / wsum
if band == 4:
# HACK
mypsf = MyGaussianPSF([1.7, 6.4, 15.0], [0.333, 0.666, 0.1])
else:
mypsf = MyGaussianPSF(gpsf.sigmas, gpsf.amp)
mypsf.radius = sh / 2
tim.psf = mypsf
print('Optimizing Params:')
tractor.printThawedParams()
for i in range(200):
#dlnp,X,alpha = tractor.optimize(damp=0.1)
dlnp, X, alpha = tractor.optimize(damp=1)
print(i, 'dlnp %.3g' % dlnp, 'psf', mypsf)
if dlnp < 1e-6:
break
mypsf.sigmas.stepsize = len(mypsf.sigmas) * [1e-6]
mypsf.logweights.stepsize = len(mypsf.sigmas) * [1e-6]
for i in range(200):
#dlnp,X,alpha = tractor.optimize(damp=0.1)
dlnp, X, alpha = tractor.optimize(damp=1)
print(i, 'dlnp %.3g' % dlnp, 'psf', mypsf)
if dlnp < 1e-6:
break
print('PSF amps:', np.sum(mypsf.amp))
print('pix sum:', pix.sum())
print('Optimize source:', src)
print('Optimized PSF:', mypsf)
mod = tractor.getModelImage(0)
print('Mod sum:', mod.sum())
_plot_psf(pix, mod, mypsf, flux=src.brightness.getValue())
plt.suptitle('W%i psf3 (opt2): %g = %s, resid %.3f' %
(band, np.sum(mypsf.amp), ','.join(
['%.3f' % a for a in mypsf.amp]), np.sum(pix - mod)))
ps.savefig()
# Write
mog = mypsf.getMixtureOfGaussians()
T = fits_table()
T.amp = mog.amp
T.mean = mog.mean
T.var = mog.var
T.writeto('psf4.fits', append=(band != 1))
def simult_photom(cat0=None, WW=None, band=None, tims=None,
allrois=None, badrois=None, groups=None,
tgroups=None, minsb=None,
gslices=None, cat=None,
opt=None, ps=None):
def _plot_grid(ims, kwas):
N = len(ims)
C = int(np.ceil(np.sqrt(N)))
R = int(np.ceil(N / float(C)))
plt.clf()
for i,(im,kwa) in enumerate(zip(ims, kwas)):
plt.subplot(R,C, i+1)
#print 'plotting grid cell', i, 'img shape', im.shape
plt.imshow(im, **kwa)
plt.gray()
plt.xticks([]); plt.yticks([])
return R,C
def _plot_grid2(ims, cat, tims, kwas, ptype='mod'):
xys = []
stamps = []
for (img,mod,chi,roi),tim in zip(ims, tims):
if ptype == 'mod':
stamps.append(mod)
elif ptype == 'chi':
stamps.append(chi)
wcs = tim.getWcs()
y0,x0 = roi[0].start, roi[1].start
xy = []
for src in cat:
xi,yi = wcs.positionToPixel(src.getPosition())
xy.append((xi - x0, yi - y0))
xys.append(xy)
#print 'X,Y source positions in stamp of shape', stamps[-1].shape
#print ' ', xy
R,C = _plot_grid(stamps, kwas)
for i,xy in enumerate(xys):
plt.subplot(R, C, i+1)
ax = plt.axis()
xy = np.array(xy)
plt.plot(xy[:,0], xy[:,1], 'r+', lw=2)
plt.axis(ax)
# Simultaneous photometry
if opt.osources:
O = fits_table(opt.osources)
ocat = Catalog()
print 'Other catalog:'
for i in range(len(O)):
w1 = O.wiseflux[i, 0]
s = PointSource(RaDecPos(O.ra[i], O.dec[i]), NanoMaggies(w1=w1))
ocat.append(s)
print ocat
ocat.freezeParamsRecursive('*')
ocat.thawPathsTo(band)
# Keep track of params after simultaneous photometry...
cat.freezeParamsRecursive('*')
cat.thawPathsTo(band)
catsim = cat.getParams()
if opt.opt:
# ... and also after RA,Dec opt.
cat.thawPathsTo('ra','dec')
catopt = cat.getParams()
cat.freezeParamsRecursive('*')
cat.thawPathsTo(band)
for gi in range(len(gslices)):
gl = gi
gl += 1
if not gl in groups:
print 'Group', gl, 'not in groups array; skipping'
continue
gsrcs = groups[gl]
tsrcs = tgroups[gl]
print 'Group', gl
print 'gsrcs:', gsrcs
print 'tsrcs:', tsrcs
if (not gl in allrois) and (not gl in badrois):
print 'Group', gl, 'does not touch any images?'
continue
mytims = []
rois = []
if gl in allrois:
for imi,roi in allrois[gl].items():
mytims.append(tims[imi])
rois.append(roi)
mybadtims = []
mybadrois = []
if gl in badrois:
for imi,roi in badrois[gl].items():
mybadtims.append(tims[imi])
mybadrois.append(roi)
print 'Group', gl, 'touches', len(mytims), 'images and', len(mybadtims), 'bad ones'
tt = 'group %i: %i+%i sources' % (gl, len(gsrcs), len(tsrcs))
if len(mytims):
cat.setParams(catsim)
#print 'Restoring catsim:'
#cat.printThawedParams()
subcat = Catalog(*[cat[i] for i in gsrcs + tsrcs])
for i in range(len(tsrcs)):
subcat.freezeParam(len(gsrcs) + i)
tractor = Tractor(mytims, subcat)
tractor.freezeParam('images')
print len(gsrcs), 'sources unfrozen; total', len(subcat)
print 'Before fitting:'
for src in subcat[:len(gsrcs)]:
print ' ', src
t0 = Time()
ims0,ims1 = tractor.optimize_forced_photometry(minsb=minsb, mindlnp=1.,
rois=rois)
print 'optimize_forced_photometry took', Time()-t0
print 'After fitting:'
for src in subcat[:len(gsrcs)]:
print ' ', src
imas = [dict(interpolation='nearest', origin='lower',
vmin=tim.zr[0], vmax=tim.zr[1])
for tim in mytims]
imchi = dict(interpolation='nearest', origin='lower', vmin=-5, vmax=5)
imchis = [imchi] * len(mytims)
_plot_grid([img for (img, mod, chi, roi) in ims0], imas)
plt.suptitle('Data: ' + tt)
ps.savefig()
if ims1 is not None:
#_plot_grid([mod for (img, mod, chi, roi) in ims1], imas)
_plot_grid2(ims1, subcat, mytims, imas)
plt.suptitle('Forced-phot model: ' + tt)
ps.savefig()
#_plot_grid([chi for (img, mod, chi, roi) in ims1], imchis)
_plot_grid2(ims1, subcat, mytims, imchis, ptype='chi')
plt.suptitle('Forced-phot chi: ' + tt)
ps.savefig()
if opt.osources:
cc = tractor.catalog
tractor.catalog = ocat
nil,nil,ims3 = tractor.optimize_forced_photometry(minsb=minsb, rois=rois,
justims0=True)
tractor.catalog = cc
_plot_grid2(ims3, ocat, mytims, imas)
plt.suptitle("Schlegel's model: group %i" % gl)
ps.savefig()
_plot_grid2(ims3, ocat, mytims, imchis, ptype='chi')
plt.suptitle("Schlegel's chi: group %i" % gl)
ps.savefig()
if opt.opt:
op1 = ps.getnext()
op2 = ps.getnext()
#fits[gl] = (tractor, len(gsrcs), rois, op1, op2)
# print 'Plotting mods after simul photom'
# #_plot_grid([mod for (img, mod, chi, roi) in ims0], imas)
# _plot_grid2(ims0, subcat, mytims, imas)
# plt.suptitle('Initial model: ' + tt)
# ps.savefig()
#
# print 'Plotting chis after simul photom'
# #_plot_grid([chi for (img, mod, chi, roi) in ims0], imchis)
# _plot_grid2(ims0, subcat, mytims, imchis, ptype='chi')
# plt.suptitle('Initial chi: ' + tt)
# ps.savefig()
print 'After simultaneous photometry:'
subcat.printThawedParams()
# Copy updated params to "catsim"
catsim = cat.getParams()
#print 'Saving catsim:'
#cat.printThawedParams()
cat.freezeParamsRecursive('*')
cat.thawPathsTo(band)
WW.nmall = np.array([src.getBrightness().getBand(band) for src in cat])
if len(mytims) and opt.opt:
print 'Optimizing RA,Dec'
subcat = tractor.catalog
# Copy updated forced-phot params from catsim to catopt.
#print 'Saving subcat forced-phot params:'
#subcat.printThawedParams()
fphot = subcat.getParams()
cat.thawPathsTo('ra','dec')
cat.setParams(catopt)
#print 'Copying forced-phot results to catopt:'
cat.freezeParamsRecursive('*')
cat.thawPathsTo(band)
cat.freezeAllBut(*gsrcs)
#cat.printThawedParams()
NP = cat.numberOfParams()
cat.setParams(fphot[:NP])
#print 'Result:'
#print 'Restoring catopt:'
#cat.printThawedParams()
cat.freezeParamsRecursive('*')
cat.thawPathsTo(band)
NG = len(gsrcs)
for i in range(NG):
subcat[i].thawPathsTo('ra','dec')
p0 = subcat.getParams()
print 'Optimizing params:'
subcat.printThawedParams()
thetims = tractor.images
subimgs = []
for i,img in enumerate(thetims):
roi = rois[i]
y0 = roi[0].start
x0 = roi[1].start
subwcs = ShiftedWcs(img.wcs, x0, y0)
subimg = Image(data=img.data[roi], invvar=img.invvar[roi],
psf=img.psf, wcs=subwcs, sky=img.sky,
photocal=img.photocal, name=img.name)
subimgs.append(subimg)
tractor.images = Images(*subimgs)
while True:
dlnp,X,alpha = tractor.optimize()
print 'dlnp', dlnp
print 'alpha', alpha
if dlnp < 0.1:
break
p1 = subcat.getParams()
print 'Param changes:'
for nm,pp0,pp1 in zip(subcat.getParamNames(), p0, p1):
print ' ', nm, pp0, 'to', pp1, '; delta', pp1-pp0
cat.thawPathsTo('ra','dec')
catopt = cat.getParams()
print 'Saving catopt:'
cat.printThawedParams()
cat.freezeParamsRecursive('ra', 'dec')
tractor.images = thetims
nil,nil,ims2 = tractor.optimize_forced_photometry(minsb=minsb, rois=rois,
justims0=True)
print 'Plotting mods after RA,Dec opt'
#_plot_grid([mod for (img, mod, chi, roi) in ims2], imas)
_plot_grid2(ims2, subcat, mytims, imas)
plt.suptitle('RA,Dec-opt model: ' + tt)
plt.savefig(op1)
print 'Plotting chis after RA,Dec opt'
#_plot_grid([chi for (img, mod, chi, roi) in ims2], imchis)
_plot_grid2(ims2, subcat, mytims, imchis, ptype='chi')
plt.suptitle('RA,Dec-opt chi: ' + tt)
plt.savefig(op2)
N = len(mybadtims)
if N and False:
C = int(np.ceil(np.sqrt(N)))
R = int(np.ceil(N / float(C)))
plt.clf()
for i,(tim,roi) in enumerate(zip(mybadtims, mybadrois)):
plt.subplot(R,C, i+1)
plt.imshow(tim.getImage()[roi], interpolation='nearest', origin='lower',
vmin=tim.zr[0], vmax=tim.zr[1])
plt.gray()
plt.suptitle('Data in bad regions')
ps.savefig()
plt.clf()
for i,(tim,roi) in enumerate(zip(mybadtims, mybadrois)):
plt.subplot(R,C, i+1)
plt.imshow(tim.getInvError()[roi], interpolation='nearest', origin='lower')
plt.gray()
plt.suptitle('Inverr in bad regions')
ps.savefig()
if gi == 0 and opt.plotmask:
alltims = mybadtims+mytims
_plot_grid([tim.uncplane[roi] for tim,roi in zip(alltims,
mybadrois + rois)],
[dict(interpolation='nearest', origin='lower')]*len(alltims))
plt.suptitle('Uncertainty plane')
ps.savefig()
for bit,txt in [
(0 , 'static: excessively noisy due to high dark current alone'),
(1 , 'static: generally noisy [includes bit 0]'),
(2 , 'static: dead or very low responsivity'),
(3 , 'static: low responsivity or low dark current'),
(4 , 'static: high responsivity or high dark current'),
(5 , 'static: saturated anywhere in ramp'),
(6 , 'static: high, uncertain, or unreliable non-linearity'),
(7 , 'static: known broken hardware pixel or excessively noisy responsivity estimate [may include bit 1]'),
(9 , 'broken pixel or negative slope fit value'),
(10, 'saturated in sample read 1'),
(11, 'saturated in sample read 2'),
(12, 'saturated in sample read 3'),
(13, 'saturated in sample read 4'),
(14, 'saturated in sample read 5'),
(15, 'saturated in sample read 6'),
(16, 'saturated in sample read 7'),
(17, 'saturated in sample read 8'),
(18, 'saturated in sample read 9'),
(21, 'new/transient bad pixel from dynamic masking'),
(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'),
]:
_plot_grid([tim.maskplane[roi] & (1 << bit)
for tim,roi in zip(alltims, mybadrois + rois)],
[dict(interpolation='nearest', origin='lower',
vmin=0, vmax=1)]*len(alltims))
plt.suptitle('Mask: ' + txt)
ps.savefig()
if opt.opt:
fn = opt.output % 998
WW.writeto(fn)
print 'Wrote', fn
cat.thawPathsTo('ra','dec')
cat.setParams(catopt)
WW.nmoptrd = np.array([src.getBrightness().getBand(band) for src in cat])
cat.freezeParamsRecursive(band, 'dec')
WW.raoptrd = np.array(cat.getParams())
cat.freezeParamsRecursive('ra')
cat.thawPathsTo('dec')
WW.decoptrd = np.array(cat.getParams())
cat.freezeParamsRecursive('dec')
fn = opt.output % 999
WW.writeto(fn)
print 'Wrote', fn
def _allwise_psf_models():
global plotslice
# AllWISE PSF models
for band in [1, 2, 3, 4]:
print()
print('W%i' % band)
print()
pix = reduce(np.add, [
fitsio.read('wise-w%i-psf-wpro-09x09-%02ix%02i.fits' %
(band, 1 + (i / 9), 1 + (i % 9))) for i in range(9 * 9)
])
print('Pix', pix.shape)
h, w = pix.shape
print('Pix sum', pix.sum())
print('Pix max', pix.max())
pix /= pix.sum()
psf = GaussianMixturePSF.fromStamp(pix)
print('Fit PSF', psf)
psfmodel = psf.getPointSourcePatch(0., 0., radius=h / 2)
mod = psfmodel.patch
print('Orig mod sum', mod.sum())
S = h / 2
plotss = 30
plotslice = slice(S - plotss,
-(S - plotss)), slice(S - plotss, -(S - plotss))
_plot_psf(pix, mod, psf)
plt.suptitle('W%i: from stamp' % band)
ps.savefig()
# Try concentric gaussian PSF
sh, sw = pix.shape
sigmas = []
for k in range(psf.mog.K):
v = psf.mog.var[k, :, :]
sigmas.append(np.sqrt(np.sqrt(v[0, 0] * v[1, 1])))
print('Initializing concentric Gaussian model with sigmas', sigmas)
print('And weights', psf.mog.amp)
# (Initial) W1:
# sig [7.1729391870564569, 24.098952864976805, 108.78869923786333]
# weights [ 0.80355109 0.15602835 0.04195982]
# sigmas [ 6.928, 17.091, 45.745 ], weights [ 0.760, 0.154, 0.073 ]
# W2:
# sig [8.2356371659198189, 25.741694812001221, 110.17431488810806]
# weights [ 0.80636191 0.1563742 0.03926182]
# sigmas [ 7.177, 10.636, 30.247 ], weights [ 0.500, 0.341, 0.134 ]
# W3:
# sig [6.860124763919889, 19.160849966251824, 134.20812055907825]
# weights [ 0.28227047 0.55080156 0.16706357]
# sigmas [ 6.850, 18.922, 109.121 ], weights [ 0.276, 0.554, 0.148 ]
# W4:
# [4.6423676603462001, 5.31542132669962, 18.375512539373585]
# weights [ 0.10764341 0.26915295 0.62320374]
# (Opt)
# sigmas [ 6.293, 6.314, 20.932 ], weights [ 0.129, 0.309, 0.598 ]
weights = psf.mog.amp
# HACK
if band == 4:
sigmas = [2., 6., 25.]
weights = [0.5, 0.25, 0.25]
else:
sigmas = [8., 24., 100.]
weights = [0.5, 0.25, 0.25]
gpsf = NCircularGaussianPSF(sigmas, weights)
gpsf.radius = sh / 2
mypsf = gpsf
tim = Image(data=pix, invvar=1e6 * np.ones_like(pix), psf=mypsf)
cx, cy = sw / 2, sh / 2
src = PointSource(PixPos(cx, cy), Flux(1.0))
tractor = Tractor([tim], [src])
tim.freezeAllBut('psf')
# tractor.freezeParam('catalog')
src.freezeAllBut('pos')
print('Optimizing Params:')
tractor.printThawedParams()
for i in range(100):
dlnp, X, alpha = tractor.optimize(damp=0.1)
print('dlnp', dlnp)
print('PSF', mypsf)
if dlnp < 0.001:
break
print('PSF3(opt): flux', src.brightness)
print('PSF amps:', np.sum(mypsf.amp))
print('PSF amps * Source brightness:',
src.brightness.getValue() * np.sum(mypsf.amp))
print('pix sum:', pix.sum())
print('Optimize source:', src)
print('Optimized PSF:', mypsf)
mod = tractor.getModelImage(0)
print('Mod sum:', mod.sum())
print('Mod min:', mod.min())
print('Mod max:', mod.max())
_plot_psf(pix, mod, mypsf, flux=src.brightness.getValue())
plt.suptitle('W%i psf3 (opt): %g = %s, resid %.3f' %
(band, np.sum(mypsf.amp), ','.join(
['%.3f' % a for a in mypsf.amp]), np.sum(pix - mod)))
ps.savefig()
# These postage stamps are subsampled at 8x the 2.75"/pix,
# except for the W4 one, which is at 4x that.
scale = 8
if band == 4:
scale = 4
T = fits_table()
T.amp = mypsf.mog.amp
T.mean = mypsf.mog.mean / scale
T.var = mypsf.mog.var / scale**2
T.writeto('psf-allwise-con3-w%i.fits' % band)
T.writeto('psf-allwise-con3.fits', append=(band != 1))
mypsf.weights.setParams(
np.array(mypsf.weights.getParams()) /
sum(mypsf.weights.getParams()))
print('Normalized PSF weights:', mypsf)
mod = tractor.getModelImage(0)
print('Mod sum:', mod.sum())
_plot_psf(pix, mod, mypsf, flux=src.brightness.getValue())
plt.suptitle('W%i psf3 (opt): %g = %s, resid %.3f' %
(band, np.sum(mypsf.amp), ','.join(
['%.3f' % a for a in mypsf.amp]), np.sum(pix - mod)))
ps.savefig()
mypsf.mog.mean[:2, :], mypsf.mog.var[:2, :, :])
psf2.radius = sh / 2
tim.psf = psf2
mod = tractor.getModelImage(0)
_plot_psf(lpix, mod, psf2, flux=src.brightness.getValue())
plt.suptitle('psf2 (init)')
ps.savefig()
src.freezeAllBut('brightness')
tractor.freezeParam('catalog')
for i in range(100):
print('Optimizing PSF:')
tractor.printThawedParams()
dlnp, X, alpha = tractor.optimize(damp=1.)
tractor.freezeParam('images')
tractor.thawParam('catalog')
print('Optimizing flux:')
tractor.printThawedParams()
tractor.optimize_forced_photometry()
tractor.thawParam('images')
tractor.freezeParam('catalog')
mod = tractor.getModelImage(0)
_plot_psf(lpix, mod, psf2, flux=src.brightness.getValue())
plt.suptitle('psf2 (opt)')
ps.savefig()
def _meisner_psf_models():
global plotslice
# Meisner's PSF models
#for band in [4]:
for band in [1,2,3,4]:
print
print 'W%i' % band
print
#pix = fitsio.read('wise-psf-avg-pix.fits', ext=band-1)
pix = fitsio.read('wise-psf-avg-pix-bright.fits', ext=band-1)
fit = fits_table('wise-psf-avg.fits', hdu=band)
#fit = fits_table('psf-allwise-con3.fits', hdu=band)
scale = 1.
print 'Pix shape', pix.shape
h,w = pix.shape
xx,yy = np.meshgrid(np.arange(w), np.arange(h))
cx,cy = np.sum(xx*pix)/np.sum(pix), np.sum(yy*pix)/np.sum(pix)
print 'Centroid:', cx,cy
#S = 100
S = h/2
slc = slice(h/2-S, h/2+S+1), slice(w/2-S, w/2+S+1)
plotss = 30
plotslice = slice(S-plotss, -(S-plotss)), slice(S-plotss, -(S-plotss))
opix = pix
print 'Original pixels sum:', opix.sum()
pix /= pix.sum()
pix = pix[slc]
print 'Sliced pix sum', pix.sum()
psf = GaussianMixturePSF(fit.amp, fit.mean * scale, fit.var * scale**2)
psfmodel = psf.getPointSourcePatch(0., 0., radius=h/2)
mod = psfmodel.patch
print 'Orig mod sum', mod.sum()
mod = mod[slc]
print 'Sliced mod sum', mod.sum()
print 'Amps:', np.sum(psf.mog.amp)
_plot_psf(pix, mod, psf)
plt.suptitle('W%i: Orig' % band)
ps.savefig()
# Lanczos sub-sample
if band == 4:
lpix = _lanczos_subsample(opix, 2)
pix = lpix
h,w = pix.shape
#S = 140
slc = slice(h/2-S, h/2+S+1), slice(w/2-S, w/2+S+1)
print 'Resampled pix sum', pix.sum()
pix = pix[slc]
print 'sliced pix sum', pix.sum()
psf = GaussianMixturePSF(fit.amp, fit.mean * scale, fit.var * scale**2)
psfmodel = psf.getPointSourcePatch(0., 0., radius=h/2)
mod = psfmodel.patch
print 'Scaled mod sum', mod.sum()
mod = mod[slc]
print 'Sliced mod sum', mod.sum()
_plot_psf(pix, mod, psf)
plt.suptitle('W%i: Scaled' % band)
ps.savefig()
plotslice = slice(S-plotss,-(S-plotss)),slice(S-plotss,-(S-plotss))
psfx = GaussianMixturePSF.fromStamp(pix, P0=(fit.amp, fit.mean*scale, fit.var*scale**2))
psfmodel = psfx.getPointSourcePatch(0., 0., radius=h/2)
mod = psfmodel.patch
print 'From stamp: mod sum', mod.sum()
mod = mod[slc]
print 'Sliced mod sum:', mod.sum()
_plot_psf(pix, mod, psfx)
plt.suptitle('W%i: fromStamp: %g = %s, res %.3f' %
(band, np.sum(psfx.mog.amp), ','.join(['%.3f'%a for a in psfx.mog.amp]),
np.sum(pix - mod)))
ps.savefig()
print 'Stamp-Fit PSF params:', psfx
# class MyGaussianMixturePSF(GaussianMixturePSF):
# def getLogPrior(self):
# if np.any(self.mog.amp < 0.):
# return -np.inf
# for k in range(self.mog.K):
# if np.linalg.det(self.mog.var[k]) <= 0:
# return -np.inf
# return 0
#
# @property
# def amp(self):
# return self.mog.amp
#
# mypsf = MyGaussianMixturePSF(psfx.mog.amp, psfx.mog.mean, psfx.mog.var)
# mypsf.radius = sh/2
sh,sw = pix.shape
# Initialize from original fit params
psfx = psf
# Try concentric gaussian PSF
sigmas = []
for k in range(psfx.mog.K):
v = psfx.mog.var[k,:,:]
sigmas.append(np.sqrt(np.sqrt(np.abs(v[0,0] * v[1,1]))))
print 'Initializing concentric Gaussian PSF with sigmas', sigmas
gpsf = NCircularGaussianPSF(sigmas, psfx.mog.amp)
gpsf.radius = sh/2
mypsf = gpsf
tim = Image(data=pix, invvar=1e6 * np.ones_like(pix), psf=mypsf)
tim.modelMinval = 1e-16
# xx,yy = np.meshgrid(np.arange(sw), np.arange(sh))
# cx,cy = np.sum(xx*pix)/np.sum(pix), np.sum(yy*pix)/np.sum(pix)
# print 'Centroid:', cx,cy
# print 'Pix midpoint:', sw/2, sh/2
cx,cy = sw/2, sh/2
src = PointSource(PixPos(cx, cy), Flux(1.0))
tractor = Tractor([tim], [src])
tim.freezeAllBut('psf')
#tractor.freezeParam('catalog')
src.freezeAllBut('pos')
# src.freezeAllBut('brightness')
# tractor.freezeParam('images')
# tractor.optimize_forced_photometry()
# tractor.thawParam('images')
# print 'Source flux after forced-photom fit:', src.getBrightness()
print 'Optimizing Params:'
tractor.printThawedParams()
for i in range(200):
#dlnp,X,alpha = tractor.optimize(damp=0.1)
dlnp,X,alpha = tractor.optimize(damp=1)
print i,'dlnp %.3g' % dlnp, 'psf', gpsf
if dlnp < 1e-6:
break
tractor.freezeParam('catalog')
gpsf.sigmas.stepsize = len(gpsf.sigmas) * [1e-6]
gpsf.weights.stepsize = len(gpsf.sigmas) * [1e-6]
for i in range(200):
#dlnp,X,alpha = tractor.optimize(damp=0.1)
dlnp,X,alpha = tractor.optimize(damp=1)
print i,'dlnp %.3g' % dlnp, 'psf', gpsf
if dlnp < 1e-6:
break
print 'PSF3(opt): flux', src.brightness
print 'PSF amps:', np.sum(mypsf.amp)
print 'PSF amps * Source brightness:', src.brightness.getValue() * np.sum(mypsf.amp)
print 'pix sum:', pix.sum()
print 'Optimize source:', src
print 'Optimized PSF:', mypsf
mod = tractor.getModelImage(0)
print 'Mod sum:', mod.sum()
_plot_psf(pix, mod, mypsf, flux=src.brightness.getValue())
plt.suptitle('W%i psf3 (opt): %g = %s, resid %.3f' %
(band, np.sum(mypsf.amp), ','.join(['%.3f'%a for a in mypsf.amp]), np.sum(pix-mod)))
ps.savefig()
# Write before normalizing!
T = fits_table()
T.amp = mypsf.mog.amp
T.mean = mypsf.mog.mean
T.var = mypsf.mog.var
T.writeto('psf3-w%i.fits' % band)
T.writeto('psf3.fits', append=(band != 1))
mypsf.weights.setParams(np.array(mypsf.weights.getParams()) /
sum(mypsf.weights.getParams()))
print 'Normalized PSF weights:', mypsf
mod = tractor.getModelImage(0)
print 'Mod sum:', mod.sum()
_plot_psf(pix, mod, mypsf, flux=src.brightness.getValue())
plt.suptitle('W%i psf3 (opt): %g = %s, resid %.3f' %
(band, np.sum(mypsf.amp), ','.join(['%.3f'%a for a in mypsf.amp]), np.sum(pix-mod)))
ps.savefig()
class MyGaussianPSF(NCircularGaussianPSF):
'''
A PSF model with strictly positive weights that sum to
unity.
'''
def __init__(self, sigmas, weights):
ww = np.array(weights)
ww = np.log(ww)
super(MyGaussianPSF, self).__init__(sigmas, ww)
@staticmethod
def getNamedParams():
return dict(sigmas=0, logweights=1)
def __str__(self):
return ('MyGaussianPSF: sigmas [ ' +
', '.join(['%.3f'%s for s in self.mysigmas]) +
' ], weights [ ' +
', '.join(['%.3f'%w for w in self.myweights]) +
' ]')
@property
def myweights(self):
ww = np.exp(self.logweights.getAllParams())
ww /= ww.sum()
return ww
@property
def weights(self):
ww = np.exp(self.logweights.getParams())
wsum = np.sum(np.exp(self.logweights.getAllParams()))
return ww / wsum
if band == 4:
# HACK
mypsf = MyGaussianPSF([1.7, 6.4, 15.0], [0.333, 0.666, 0.1])
else:
mypsf = MyGaussianPSF(gpsf.sigmas, gpsf.amp)
mypsf.radius = sh/2
tim.psf = mypsf
print 'Optimizing Params:'
tractor.printThawedParams()
for i in range(200):
#dlnp,X,alpha = tractor.optimize(damp=0.1)
dlnp,X,alpha = tractor.optimize(damp=1)
print i,'dlnp %.3g' % dlnp, 'psf', mypsf
if dlnp < 1e-6:
break
mypsf.sigmas.stepsize = len(mypsf.sigmas) * [1e-6]
mypsf.logweights.stepsize = len(mypsf.sigmas) * [1e-6]
for i in range(200):
#dlnp,X,alpha = tractor.optimize(damp=0.1)
dlnp,X,alpha = tractor.optimize(damp=1)
print i,'dlnp %.3g' % dlnp, 'psf', mypsf
if dlnp < 1e-6:
break
print 'PSF amps:', np.sum(mypsf.amp)
print 'pix sum:', pix.sum()
print 'Optimize source:', src
print 'Optimized PSF:', mypsf
mod = tractor.getModelImage(0)
print 'Mod sum:', mod.sum()
_plot_psf(pix, mod, mypsf, flux=src.brightness.getValue())
plt.suptitle('W%i psf3 (opt2): %g = %s, resid %.3f' %
(band, np.sum(mypsf.amp), ','.join(['%.3f'%a for a in mypsf.amp]), np.sum(pix-mod)))
ps.savefig()
# Write
mog = mypsf.getMixtureOfGaussians()
T = fits_table()
T.amp = mog.amp
T.mean = mog.mean
T.var = mog.var
T.writeto('psf4.fits', append=(band != 1))
def _allwise_psf_models():
global plotslice
# AllWISE PSF models
for band in [1,2,3,4]:
print
print 'W%i' % band
print
pix = reduce(np.add,
[fitsio.read('wise-w%i-psf-wpro-09x09-%02ix%02i.fits' %
(band, 1+(i/9), 1+(i%9)))
for i in range(9*9)])
print 'Pix', pix.shape
h,w = pix.shape
print 'Pix sum', pix.sum()
print 'Pix max', pix.max()
pix /= pix.sum()
psf = GaussianMixturePSF.fromStamp(pix)
print 'Fit PSF', psf
psfmodel = psf.getPointSourcePatch(0., 0., radius=h/2)
mod = psfmodel.patch
print 'Orig mod sum', mod.sum()
S = h/2
plotss = 30
plotslice = slice(S-plotss, -(S-plotss)), slice(S-plotss, -(S-plotss))
_plot_psf(pix, mod, psf)
plt.suptitle('W%i: from stamp' % band)
ps.savefig()
# Try concentric gaussian PSF
sh,sw = pix.shape
sigmas = []
for k in range(psf.mog.K):
v = psf.mog.var[k,:,:]
sigmas.append(np.sqrt(np.sqrt(v[0,0] * v[1,1])))
print 'Initializing concentric Gaussian model with sigmas', sigmas
print 'And weights', psf.mog.amp
# (Initial) W1:
# sig [7.1729391870564569, 24.098952864976805, 108.78869923786333]
# weights [ 0.80355109 0.15602835 0.04195982]
# sigmas [ 6.928, 17.091, 45.745 ], weights [ 0.760, 0.154, 0.073 ]
# W2:
# sig [8.2356371659198189, 25.741694812001221, 110.17431488810806]
# weights [ 0.80636191 0.1563742 0.03926182]
# sigmas [ 7.177, 10.636, 30.247 ], weights [ 0.500, 0.341, 0.134 ]
# W3:
# sig [6.860124763919889, 19.160849966251824, 134.20812055907825]
# weights [ 0.28227047 0.55080156 0.16706357]
# sigmas [ 6.850, 18.922, 109.121 ], weights [ 0.276, 0.554, 0.148 ]
# W4:
# [4.6423676603462001, 5.31542132669962, 18.375512539373585]
# weights [ 0.10764341 0.26915295 0.62320374]
# (Opt)
# sigmas [ 6.293, 6.314, 20.932 ], weights [ 0.129, 0.309, 0.598 ]
weights = psf.mog.amp
# HACK
if band == 4:
sigmas = [ 2., 6., 25. ]
weights = [ 0.5, 0.25, 0.25 ]
else:
sigmas = [ 8., 24., 100. ]
weights = [ 0.5, 0.25, 0.25 ]
gpsf = NCircularGaussianPSF(sigmas, weights)
gpsf.radius = sh/2
mypsf = gpsf
tim = Image(data=pix, invvar=1e6 * np.ones_like(pix), psf=mypsf)
cx,cy = sw/2, sh/2
src = PointSource(PixPos(cx, cy), Flux(1.0))
tractor = Tractor([tim], [src])
tim.freezeAllBut('psf')
#tractor.freezeParam('catalog')
src.freezeAllBut('pos')
print 'Optimizing Params:'
tractor.printThawedParams()
for i in range(100):
dlnp,X,alpha = tractor.optimize(damp=0.1)
print 'dlnp', dlnp
print 'PSF', mypsf
if dlnp < 0.001:
break
print 'PSF3(opt): flux', src.brightness
print 'PSF amps:', np.sum(mypsf.amp)
print 'PSF amps * Source brightness:', src.brightness.getValue() * np.sum(mypsf.amp)
print 'pix sum:', pix.sum()
print 'Optimize source:', src
print 'Optimized PSF:', mypsf
mod = tractor.getModelImage(0)
print 'Mod sum:', mod.sum()
print 'Mod min:', mod.min()
print 'Mod max:', mod.max()
_plot_psf(pix, mod, mypsf, flux=src.brightness.getValue())
plt.suptitle('W%i psf3 (opt): %g = %s, resid %.3f' %
(band, np.sum(mypsf.amp), ','.join(['%.3f'%a for a in mypsf.amp]), np.sum(pix-mod)))
ps.savefig()
# These postage stamps are subsampled at 8x the 2.75"/pix,
# except for the W4 one, which is at 4x that.
scale = 8
if band == 4:
scale = 4
T = fits_table()
T.amp = mypsf.mog.amp
T.mean = mypsf.mog.mean / scale
T.var = mypsf.mog.var / scale**2
T.writeto('psf-allwise-con3-w%i.fits' % band)
T.writeto('psf-allwise-con3.fits', append=(band != 1))
mypsf.weights.setParams(np.array(mypsf.weights.getParams()) /
sum(mypsf.weights.getParams()))
print 'Normalized PSF weights:', mypsf
mod = tractor.getModelImage(0)
print 'Mod sum:', mod.sum()
_plot_psf(pix, mod, mypsf, flux=src.brightness.getValue())
plt.suptitle('W%i psf3 (opt): %g = %s, resid %.3f' %
(band, np.sum(mypsf.amp), ','.join(['%.3f'%a for a in mypsf.amp]), np.sum(pix-mod)))
ps.savefig()
mypsf.mog.mean[:2,:], mypsf.mog.var[:2,:,:])
psf2.radius = sh/2
tim.psf = psf2
mod = tractor.getModelImage(0)
_plot_psf(lpix, mod, psf2, flux=src.brightness.getValue())
plt.suptitle('psf2 (init)')
ps.savefig()
src.freezeAllBut('brightness')
tractor.freezeParam('catalog')
for i in range(100):
print 'Optimizing PSF:'
tractor.printThawedParams()
dlnp,X,alpha = tractor.optimize(damp=1.)
tractor.freezeParam('images')
tractor.thawParam('catalog')
print 'Optimizing flux:'
tractor.printThawedParams()
tractor.optimize_forced_photometry()
tractor.thawParam('images')
tractor.freezeParam('catalog')
mod = tractor.getModelImage(0)
_plot_psf(lpix, mod, psf2, flux=src.brightness.getValue())
plt.suptitle('psf2 (opt)')
ps.savefig()
