plt.subplot(1,2,1)
plt.imshow(true_mod, **ima)
plt.subplot(1,2,2)
plt.imshow(tim.data, **ima)
plt.title('S/N %f, r_e %f' % (sn, re))
ps.savefig()
## run one_blob code? Or shortcut?
src = PointSource(RaDecPos(0., 0.),
NanoMaggies(**{band: flux}))
nblob,iblob,Isrcs = 0, 1, np.array([0])
brickwcs = wcs
bx0, by0, blobw, blobh = 0, 0, sz, sz
blobmask = np.ones((sz,sz), bool)
timargs = [(tim.data, tim.getInvError(), tim.wcs, tim.wcs.wcs,
tim.getPhotoCal(), tim.getSky(), tim.psf,
'tim', 0, sz, 0, sz, band, sig1,
tim.modelMinval, None)]
srcs = [src]
bands = band
plots,psx = False, None
simul_opt, use_ceres, hastycho = False, False, False
X = (nblob, iblob, Isrcs, brickwcs, bx0, by0, blobw, blobh,
blobmask, timargs, srcs, bands, plots, psx, simul_opt,
use_ceres, hastycho)
R = one_blob(X)
#print('Got:', R)
print('Sources:', R.sources)
plt.subplot(1,2,1)
plt.imshow(true_mod, **ima)
plt.subplot(1,2,2)
plt.imshow(tim.data, **ima)
plt.title('S/N %f, r_e %f' % (sn, re))
ps.savefig()
## run one_blob code? Or shortcut?
src = PointSource(RaDecPos(0., 0.),
NanoMaggies(**{band: flux}))
nblob,iblob,Isrcs = 0, 1, np.array([0])
brickwcs = wcs
bx0, by0, blobw, blobh = 0, 0, sz, sz
blobmask = np.ones((sz,sz), bool)
timargs = [(tim.data, tim.getInvError(), tim.wcs, tim.wcs.wcs,
tim.getPhotoCal(), tim.getSky(), tim.psf,
'tim', 0, sz, 0, sz, band, sig1,
tim.modelMinval, None)]
srcs = [src]
bands = band
plots,psx = False, None
simul_opt, use_ceres, hastycho = False, False, False
X = (nblob, iblob, Isrcs, brickwcs, bx0, by0, blobw, blobh,
blobmask, timargs, srcs, bands, plots, psx, simul_opt,
use_ceres, hastycho)
R = one_blob(X)
#print('Got:', R)
print('Sources:', R.sources)
# lvl = logging.DEBUG
# logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
# Optimize the model.
for step in range(50):
print('Tractor params:')
tractor.printThawedParams()
dlnp,X,alpha = tractor.optimize(damp=1.)
print('dlnp', dlnp)
print('galaxy:', moggal)
#print('Mog', moggal.mog.getParams())
if dlnp == 0:
break
# Plot the model as we're optimizing...
mod = tractor.getModelImage(0)
chi = (tim.getImage() - mod) * tim.getInvError()
plt.clf()
plt.subplot(1,2,1)
plt.imshow(mod, interpolation='nearest', origin='lower')
plt.title('Model')
plt.subplot(1,2,2)
mx = np.abs(chi).max()
plt.imshow(chi, interpolation='nearest', origin='lower',
vmin=-mx, vmax=mx)
plt.colorbar()
plt.title('Chi residuals')
plt.suptitle('MoG model after optimization step %i' % step)
plt.savefig('mod-o%02i.png' % step)
