def comparePrior():
ngauss = [512, 128]
iter = ['1st', '6th']
color = ['k', 'red']
label = ['512 Gaussians', '128 Gaussians']
fig, ax = plt.subplots(1,2, figsize=(12,5))
for n, i, c, l in zip(ngauss, iter, color, label):
xdgmmFilename = 'xdgmm.' + str(n) + 'gauss.dQ0.05.' + i + '.2MASS.All.npz.fit'
xdgmm = XDGMM(filename=xdgmmFilename)
for gg in range(xdgmm.n_components):
if xdgmm.weights[gg] == np.max(xdgmm.weights):
lab = l
else:
lab = None
points = drawEllipse.plotvector(xdgmm.mu[gg], xdgmm.V[gg])
ax[0].plot(points[0,:],testXD.absMagKinda2absMag(points[1,:]), c, lw=1, alpha=xdgmm.weights[gg]/np.max(xdgmm.weights))
ax[1].plot(points[0,:], points[1,:], c, lw=1, alpha=xdgmm.weights[gg]/np.max(xdgmm.weights), label=lab)
for a in ax:
a.set_xlim(-0.5, 1.5)
a.set_xlabel(r'$(J - K)^C$')
ax[0].set_ylabel(r'$M_J^C$')
ax[1].set_ylabel(r'$\varpi 10^{0.2\,m_J}$')
ax[0].set_ylim(6, -6)
ax[1].set_ylim(1100, -100)
ax[1].legend(loc='lower left', fontsize=10)
plt.tight_layout()
fig.savefig('priorNgaussComparison.png')
def prior(xdgmm, ax):
for gg in range(xdgmm.n_components):
points = drawEllipse.plotvector(xdgmm.mu[gg], xdgmm.V[gg])
ax[0].plot(points[0, :],
testXD.absMagKinda2absMag(points[1, :]),
c,
lw=1,
alpha=xdgmm.weights[gg] / np.max(xdgmm.weights))
def priorSample(ngauss=128, quantile=0.5, iter='8th', survey='2MASS', dataFilename='All.npz', Nsamples=1.2e6, xdgmmFilename='xdgmm.fit', xlabel='X', ylabel='Y', contourColor='k'):
setup_text_plots(fontsize=16, usetex=True)
xdgmm = XDGMM(filename=xdgmmFilename)
figPrior = plt.figure(figsize=(12, 5.5))
figPrior.subplots_adjust(left=0.1, right=0.95,
bottom=0.15, top=0.95,
wspace=0.1, hspace=0.1)
sample = xdgmm.sample(Nsamples)
negParallax = sample[:,1] < 0
nNegP = np.sum(negParallax)
while nNegP > 0:
sampleNew = xdgmm.sample(nNegP)
sample[negParallax] = sampleNew
negParallax = sample[:,1] < 0
nNegP = np.sum(negParallax)
samplex = sample[:,0]
sampley = testXD.absMagKinda2absMag(sample[:,1])
ax3 = figPrior.add_subplot(121)
alpha = 0.1
xlim = [-0.25, 1.25]
ylim = [6, -6]
levels = 1.0 - np.exp(-0.5 * np.arange(1.0, 2.1, 1.0) ** 2)
corner.hist2d(samplex, sampley, ax=ax3, levels=levels, bins=200, plot_datapoints=False, no_fill_contours=True, plot_density=False, color=contourColor)
ax3.scatter(samplex, sampley, s=1, lw=0, c='k', alpha=alpha)
ax4 = figPrior.add_subplot(122)
for i in range(xdgmm.n_components):
points = drawEllipse.plotvector(xdgmm.mu[i], xdgmm.V[i])
ax4.plot(points[0, :], testXD.absMagKinda2absMag(points[1,:]), 'k-', alpha=xdgmm.weights[i]/np.max(xdgmm.weights))
titles = ["Extreme Deconvolution\n resampling",
"Extreme Deconvolution\n cluster locations"]
ax = [ax3, ax4]
for i in range(2):
ax[i].set_xlim(xlim)
ax[i].set_ylim(ylim[0], ylim[1]*1.1)
ax[i].text(0.05, 0.95, titles[i],
ha='left', va='top', transform=ax[i].transAxes, fontsize=18)
ax[i].set_xlabel(xlabel, fontsize = 18)
if i in (1, 3):
ax[i].yaxis.set_major_formatter(plt.NullFormatter())
else:
ax[i].set_ylabel(ylabel, fontsize = 18)
figPrior.savefig('prior_ngauss' + str(ngauss) +'.png')
def plotPrior(xdgmm, ax, c='black', lw=1, stretch=False):
for gg in range(xdgmm.n_components):
points = drawEllipse.plotvector(xdgmm.mu[gg], xdgmm.V[gg])
if stretch:
Stretch = av.PowerStretch(1. / 5)
alpha = np.power(xdgmm.weights[gg] / np.max(xdgmm.weights),
1. / 10)
else:
alpha = xdgmm.weights[gg] / np.max(xdgmm.weights)
ax.plot(points[0, :],
testXD.absMagKinda2absMag(points[1, :]),
c,
lw=lw,
alpha=alpha)
def plot_sample(x,
y,
samplex,
sampley,
xdgmm,
xlabel='x',
ylabel='y',
xerr=None,
yerr=None,
ylim=(6, -6),
xlim=(0.5, 1.5),
errSubsample=1.2e6,
thresholdScatter=0.1,
binsScatter=200,
c='black',
norm=None,
cmap=None,
contourColor='k',
posterior=False,
ind=None,
plot_contours=True,
sdss5=False,
whatsThatFeature=False,
rasterized=False):
prng = np.random.RandomState(1234567890)
setup_text_plots(fontsize=16, usetex=True)
plt.clf()
alpha = 0.1
alpha_points = 0.01
figData = plt.figure(figsize=(12, 5.5))
figPrior = plt.figure(figsize=(12, 5.5))
for fig in [figData, figPrior]:
fig.subplots_adjust(left=0.1,
right=0.95,
bottom=0.15,
top=0.95,
wspace=0.1,
hspace=0.1)
ax1 = figData.add_subplot(121)
levels = 1.0 - np.exp(-0.5 * np.arange(1.0, 2.1, 1.0)**2)
cNorm = plt.matplotlib.colors.LogNorm(vmin=3, vmax=1e5)
#ax1.hist2d(x, y, bins=100, norm=cNorm, cmap='Greys')
if sdss5: plot_contours = False
im = corner.hist2d(x,
y,
ax=ax1,
levels=levels,
bins=200,
no_fill_contours=True,
plot_density=False,
color=contourColor,
rasterized=rasterized,
plot_contours=plot_contours)
#im = ax1.scatter(x, y, s=1, lw=0, c=c, alpha=alpha, norm=norm, cmap=cmap)
ax2 = figData.add_subplot(122)
if ind is None: ind = prng.randint(0, len(x), size=errSubsample)
#ax2.scatter(x[ind], y[ind], s=1, lw=0, c='black', alpha=alpha_points)
ax2.errorbar(x[ind],
y[ind],
xerr=xerr[ind],
yerr=[yerr[0][ind], yerr[1][ind]],
fmt="none",
zorder=0,
mew=0,
ecolor='black',
alpha=0.5,
elinewidth=0.5)
ax3 = figPrior.add_subplot(121)
#ax3.hist2d(x, y, bins=100, norm=cNorm, cmap='Greys')
#kdeDensity(ax3, samplex, sampley, threshold=thresholdScatter, bins=binsScatter, s=1, lw=0, alpha=alpha)
corner.hist2d(samplex,
sampley,
ax=ax3,
levels=levels,
bins=200,
no_fill_contours=True,
plot_density=False,
color=contourColor,
rasterized=rasterized,
plot_contours=False)
ax3.scatter(samplex, sampley, s=1, lw=0, c='k', alpha=alpha)
ax4 = figPrior.add_subplot(122)
for i in range(xdgmm.n_components):
points = drawEllipse.plotvector(xdgmm.mu[i], xdgmm.V[i])
ax4.plot(points[0, :],
absMagKinda2absMag(points[1, :]),
'k-',
alpha=xdgmm.weights[i] / np.max(xdgmm.weights))
#draw_ellipse(xdgmm.mu[i], xdgmm.V[i], scales=[2], ax=ax4,
# ec='None', fc='gray', alpha=xdgmm.weights[i]/np.max(xdgmm.weights)*0.1)
#xlim = ax4.get_xlim()
#ylim = ylim #ax3.get_ylim()
titles = [
"Observed Distribution", "Obs+Noise Distribution",
"Extreme Deconvolution\n resampling",
"Extreme Deconvolution\n cluster locations"
]
if posterior:
titles = [
"De-noised Expectation Values", "Posterior Distributions",
"Extreme Deconvolution\n resampling",
"Extreme Deconvolution\n cluster locations"
]
if sdss5:
titles = ['', '', '', '']
ax = [ax1, ax2, ax3, ax4]
for i in range(4):
ax[i].set_xlim(xlim)
ax[i].set_ylim(ylim[0], ylim[1] * 1.1)
#ax[i].xaxis.set_major_locator(plt.MultipleLocator([-1, 0, 1]))
#ax[i].yaxis.set_major_locator(plt.MultipleLocator([3, 4, 5, 6]))
ax[i].text(0.05,
0.95,
titles[i],
ha='left',
va='top',
transform=ax[i].transAxes,
fontsize=18)
#if i in (0, 1):
# ax[i].xaxis.set_major_formatter(plt.NullFormatter())
#else:
ax[i].set_xlabel(xlabel, fontsize=18)
if i in (1, 3):
ax[i].yaxis.set_major_formatter(plt.NullFormatter())
else:
ax[i].set_ylabel(ylabel, fontsize=18)
#ax[3].text(0.05, 0.95, titles[3],
# ha='left', va='top', transform=ax[3].transAxes)
#ax[3].set_ylabel(r'$\varpi10^{0.2*m_G}$', fontsize=18)
#ax[3].set_xlim(-2, 3)
#ax[3].set_ylim(3, -1)
#ax[3].yaxis.tick_right()
#ax[3].yaxis.set_label_position("right")
#plt.tight_layout()
"""
if norm is not None:
figData.subplots_adjust(left=0.2, right=0.95)
cbar_ax = figData.add_axes([0.01, 0.125, 0.02, 0.75])
cb = figData.colorbar(im, cax=cbar_ax)
#cb = plt.colorbar(im, ax=axes[2])
cb.set_label(r'$ln \, \tilde{\sigma}_{\varpi}^2 - ln \, \sigma_{\varpi}^2$', fontsize=20)
cb.set_clim(-7, 2)
"""
figData.savefig('plot_sample.data.pdf', format='pdf')
figPrior.savefig('plot_sample.prior.pdf', format='pdf')