def psf_minus_model_hists(epoch, model, features, filters, fgal=0.5,
idx=-1):
"""
Plot the histogram of psf-model for the data and denoised data.
"""
fs = 5
w = 0.1
nb = 50
mags = [19.5, 20.5, 21.5]
Xsingle, Xsinglecov = fetch_prepped_s82data(epoch, fgal, features, filters)
Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch, fgal, features, filters,
use_single=False)
f = pl.figure(figsize=(3 * fs, fs))
for i in range(len(mags)):
ind = (Xsingle[:, 0] > mags[i] - w) & (Xsingle[:, 0] < mags[i] + w)
#ind = ind & (Xsingle[:, idx] < 0.3)
ind = ind & (Xcoadd[:, idx] < 0.03)
a, m, v = model.posterior(Xsingle[ind], Xsinglecov[ind])
posts = np.zeros_like(Xsingle[ind])
for j in range(Xsingle[ind].shape[0]):
posts[j] = np.median(model.sample(a[j], m[j], v[j], size=1000),
axis=0)
print i, mags[i]
pl.subplot(1, 3, i + 1)
pl.hist(Xsingle[ind, idx], nb, alpha=0.3, color='k')
pl.hist(posts[:, idx], nb, alpha=0.3, color='r')
pl.title('$r=%0.1f$' % mags[i])
pl.xlim(-0.2, 0.2)
pl.xlabel('psf - model')
f.savefig('../../plots/phot_and_morph/foo.png')
def psf_minus_model_hists(epoch, model, features, filters, fgal=0.5, idx=-1):
"""
Plot the histogram of psf-model for the data and denoised data.
"""
fs = 5
w = 0.1
nb = 50
mags = [19.5, 20.5, 21.5]
Xsingle, Xsinglecov = fetch_prepped_s82data(epoch, fgal, features, filters)
Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch,
fgal,
features,
filters,
use_single=False)
f = pl.figure(figsize=(3 * fs, fs))
for i in range(len(mags)):
ind = (Xsingle[:, 0] > mags[i] - w) & (Xsingle[:, 0] < mags[i] + w)
#ind = ind & (Xsingle[:, idx] < 0.3)
ind = ind & (Xcoadd[:, idx] < 0.03)
a, m, v = model.posterior(Xsingle[ind], Xsinglecov[ind])
posts = np.zeros_like(Xsingle[ind])
for j in range(Xsingle[ind].shape[0]):
posts[j] = np.median(model.sample(a[j], m[j], v[j], size=1000),
axis=0)
print i, mags[i]
pl.subplot(1, 3, i + 1)
pl.hist(Xsingle[ind, idx], nb, alpha=0.3, color='k')
pl.hist(posts[:, idx], nb, alpha=0.3, color='r')
pl.title('$r=%0.1f$' % mags[i])
pl.xlim(-0.2, 0.2)
pl.xlabel('psf - model')
f.savefig('../../plots/phot_and_morph/foo.png')
def plot_contours_and_data(epoch, model, features, filters, fgal=0.5, idx=-1):
"""
Plot the data and the contours for stars and galaxies.
"""
from astroML.plotting.tools import draw_ellipse
Xsingle, Xsinglecov = fetch_prepped_s82data(epoch, fgal, features, filters)
Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch, fgal, features, filters,
use_single=False)
sind = Xcoadd[:, idx] < 0.03
gind = Xcoadd[:, idx] > 0.03
fs = 5
ms = 1
f = pl.figure(figsize=(3 * fs, 2 * fs))
Nstar = len(np.where(model.fixed_means[:, idx] != np.inf)[0])
pl.subplot(231)
idx = [0, -1]
for i in range(Nstar):
print i, model.V[i, idx][:, idx]
draw_ellipse(model.mu[i, idx], model.V[i, idx][:, idx], scales=[2],
ec='k', fc='gray', alpha=0.2)
pl.plot(Xsingle[sind][::10, idx[0]], Xsingle[sind][::10, idx[1]], '.k',
ms=ms)
pl.xlim(18,22)
pl.ylim(-0.1, 0.5)
pl.subplot(232)
idx = [2, 1]
for i in range(Nstar):
print i, model.V[i, idx][:, idx]
draw_ellipse(model.mu[i, idx], model.V[i, idx][:, idx], scales=[2],
ec='k', fc='gray', alpha=0.2)
pl.plot(Xsingle[sind][::10, idx[0]], Xsingle[sind][::10, idx[1]], '.k',
ms=ms)
pl.xlim(-2, 3)
pl.ylim(-1, 6)
pl.subplot(233)
idx = [3, 4]
for i in range(Nstar):
draw_ellipse(model.mu[i, idx], model.V[i, idx][:, idx], scales=[2],
ec='k', fc='gray', alpha=0.2)
pl.plot(Xsingle[sind][::10, idx[0]], Xsingle[sind][::10, idx[1]], '.k',
ms=ms)
pl.xlim(-2, 3)
pl.ylim(-1, 3)
pl.subplot(234)
idx = [0, -1]
for i in range(Nstar, model.n_components):
print i, model.V[i, idx][:, idx]
draw_ellipse(model.mu[i, idx], model.V[i, idx][:, idx], scales=[2],
ec='k', fc='gray', alpha=0.2)
pl.plot(Xsingle[gind][::10, idx[0]], Xsingle[gind][::10, idx[1]], '.k',
ms=ms)
pl.xlim(18,22)
pl.ylim(-0.1, 0.5)
pl.subplot(235)
idx = [2, 1]
for i in range(Nstar, model.n_components):
print i, model.V[i, idx][:, idx]
draw_ellipse(model.mu[i, idx], model.V[i, idx][:, idx], scales=[2],
ec='k', fc='gray', alpha=0.2)
pl.plot(Xsingle[gind][::10, idx[0]], Xsingle[gind][::10, idx[1]], '.k',
ms=ms)
pl.xlim(-2, 3)
pl.ylim(-1, 6)
pl.subplot(236)
idx = [3, 4]
for i in range(Nstar, model.n_components):
draw_ellipse(model.mu[i, idx], model.V[i, idx][:, idx], scales=[2],
ec='k', fc='gray', alpha=0.2)
pl.plot(Xsingle[gind][::10, idx[0]], Xsingle[gind][::10, idx[1]], '.k',
ms=ms)
pl.xlim(-2, 3)
pl.ylim(-1, 3)
f.savefig('../../plots/phot_and_morph/foo.png')
fname = 'xdmodel_%s_%d_%d_%d_%d_%s.pkl' % (data, Nr, K, n_iter, Nstar,
message)
f = open(os.environ['sgdata'] + fname, 'rb')
model = cPickle.load(f)
f.close()
if True:
plot_contours_and_data(epoch, model, features, filters, idx=-1)
if False:
psf_minus_model_hists(epoch, model, features, filters, idx=-1)
if False:
epoch = 10
Xsingle, Xsinglecov = fetch_prepped_s82data(epoch)
Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch, use_single=False)
N = 5000
Xsingle = Xsingle[:N]
Xsinglecov = Xsinglecov[:N]
Xcoadd = Xcoadd[:N]
Xcoaddcov = Xcoaddcov[:N]
f = open(os.environ['sgdata'] + 'xd_w_32_10_2.pkl')
model = cPickle.load(f)
f.close()
post_alpha, post_mu, post_V = model.posterior(Xsingle, Xsinglecov)
posts = np.zeros_like(Xsingle)
for i in range(N):
def plot_contours_and_data(epoch, model, features, filters, fgal=0.5, idx=-1):
"""
Plot the data and the contours for stars and galaxies.
"""
from astroML.plotting.tools import draw_ellipse
Xsingle, Xsinglecov = fetch_prepped_s82data(epoch, fgal, features, filters)
Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch,
fgal,
features,
filters,
use_single=False)
sind = Xcoadd[:, idx] < 0.03
gind = Xcoadd[:, idx] > 0.03
fs = 5
ms = 1
f = pl.figure(figsize=(3 * fs, 2 * fs))
Nstar = len(np.where(model.fixed_means[:, idx] != np.inf)[0])
pl.subplot(231)
idx = [0, -1]
for i in range(Nstar):
print i, model.V[i, idx][:, idx]
draw_ellipse(model.mu[i, idx],
model.V[i, idx][:, idx],
scales=[2],
ec='k',
fc='gray',
alpha=0.2)
pl.plot(Xsingle[sind][::10, idx[0]],
Xsingle[sind][::10, idx[1]],
'.k',
ms=ms)
pl.xlim(18, 22)
pl.ylim(-0.1, 0.5)
pl.subplot(232)
idx = [2, 1]
for i in range(Nstar):
print i, model.V[i, idx][:, idx]
draw_ellipse(model.mu[i, idx],
model.V[i, idx][:, idx],
scales=[2],
ec='k',
fc='gray',
alpha=0.2)
pl.plot(Xsingle[sind][::10, idx[0]],
Xsingle[sind][::10, idx[1]],
'.k',
ms=ms)
pl.xlim(-2, 3)
pl.ylim(-1, 6)
pl.subplot(233)
idx = [3, 4]
for i in range(Nstar):
draw_ellipse(model.mu[i, idx],
model.V[i, idx][:, idx],
scales=[2],
ec='k',
fc='gray',
alpha=0.2)
pl.plot(Xsingle[sind][::10, idx[0]],
Xsingle[sind][::10, idx[1]],
'.k',
ms=ms)
pl.xlim(-2, 3)
pl.ylim(-1, 3)
pl.subplot(234)
idx = [0, -1]
for i in range(Nstar, model.n_components):
print i, model.V[i, idx][:, idx]
draw_ellipse(model.mu[i, idx],
model.V[i, idx][:, idx],
scales=[2],
ec='k',
fc='gray',
alpha=0.2)
pl.plot(Xsingle[gind][::10, idx[0]],
Xsingle[gind][::10, idx[1]],
'.k',
ms=ms)
pl.xlim(18, 22)
pl.ylim(-0.1, 0.5)
pl.subplot(235)
idx = [2, 1]
for i in range(Nstar, model.n_components):
print i, model.V[i, idx][:, idx]
draw_ellipse(model.mu[i, idx],
model.V[i, idx][:, idx],
scales=[2],
ec='k',
fc='gray',
alpha=0.2)
pl.plot(Xsingle[gind][::10, idx[0]],
Xsingle[gind][::10, idx[1]],
'.k',
ms=ms)
pl.xlim(-2, 3)
pl.ylim(-1, 6)
pl.subplot(236)
idx = [3, 4]
for i in range(Nstar, model.n_components):
draw_ellipse(model.mu[i, idx],
model.V[i, idx][:, idx],
scales=[2],
ec='k',
fc='gray',
alpha=0.2)
pl.plot(Xsingle[gind][::10, idx[0]],
Xsingle[gind][::10, idx[1]],
'.k',
ms=ms)
pl.xlim(-2, 3)
pl.ylim(-1, 3)
f.savefig('../../plots/phot_and_morph/foo.png')
fname = 'xdmodel_%s_%d_%d_%d_%d_%s.pkl' % (data, Nr, K, n_iter, Nstar,
message)
f = open(os.environ['sgdata'] + fname, 'rb')
model = cPickle.load(f)
f.close()
if True:
plot_contours_and_data(epoch, model, features, filters, idx=-1)
if False:
psf_minus_model_hists(epoch, model, features, filters, idx=-1)
if False:
epoch = 10
Xsingle, Xsinglecov = fetch_prepped_s82data(epoch)
Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch, use_single=False)
N = 5000
Xsingle = Xsingle[:N]
Xsinglecov = Xsinglecov[:N]
Xcoadd = Xcoadd[:N]
Xcoaddcov = Xcoaddcov[:N]
f = open(os.environ['sgdata'] + 'xd_w_32_10_2.pkl')
model = cPickle.load(f)
f.close()
post_alpha, post_mu, post_V = model.posterior(Xsingle, Xsinglecov)
posts = np.zeros_like(Xsingle)
for i in range(N):