def bg_only():
psf = PSF([1.0, 0.6], [1.0, 4.0])
sf = StarField(psf, 200, 200)
sf.generate_bg_stars(100, p=0, f0=1)
sf.save("bg.h5")
def fit_bg():
psf = PSF([0.5, 0.5], [1.0, 25.0])
nx = 100
Nstars = 10
f0 = 1.0
nwalkers = 50
sf = StarField(psf, nx, nx)
sf.generate_bg_stars(Nstars, p=1e20, f0=f0)
truth = sf.image(noise=False)
data = sf.image()
# sf = StarField(psf, nx, nx)
sampler, r = sf.sample(Nstars, data, nwalkers=nwalkers)
fig = pl.figure(figsize=(10,10))
ax = fig.add_subplot(221, aspect="equal")
ax.imshow(-data, cmap="gray")
ax = fig.add_subplot(222, aspect="equal")
ax.imshow(-truth, cmap="gray")
ax1 = fig.add_subplot(223, aspect="equal")
ax2 = fig.add_subplot(224, aspect="equal")
for i, (pos,lnprob,state) in enumerate(sampler.sample(*r,
iterations=500)):
if i%10 == 0:
w = np.random.randint(nwalkers)
catalog = Catalog(x=pos[w,:Nstars], y=pos[w,Nstars:],
flux=f0*np.ones(Nstars))
sf = StarField(psf, nx, nx, catalog=catalog)
ax1.cla()
ax2.cla()
ax1.imshow(-sf.image(noise=False), cmap="gray")
ax2.imshow(data-sf.image(noise=False), cmap="gray")
pl.savefig("fit/%d-%d.png"%(i, w))
def gaussian_and_bg():
psf = PSF([1.0, 0.6], [1.0, 4.0])
sf = StarField(psf, 200, 200)
sf.generate_bg_stars(100)
sf.generate_gaussian_stars(50, 95, 83.5, [[8.**2, 0], [0, 8.**2]])
s = time.time()
img = sf.image()
print sf.lnlike(img)
print "Generating an image w/ %d stars took: %.4f seconds"\
%(len(sf.catalog), time.time()-s)
fig = pl.figure(figsize=(8,8))
ax = fig.add_subplot(111, aspect="equal")
ax.pcolor(-img, cmap="gray")
pl.savefig("cluster.png")
sf.save("cluster.h5")
sf.save("cluster.fits")
