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tightPosterior.py
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tightPosterior.py
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import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import comparePrior
import testXD
from xdgmm import XDGMM
import testXD
import stellarTwins as st
np.random.seed(42)
def distanceFilename(ngauss, quantile, iter, survey, dataFilename):
return 'distanceQuantiles.' + str(ngauss) + 'gauss.dQ' + str(quantile) + '.' + iter + '.' + survey + '.' + dataFilename
def dustFilename(ngauss, quantile, iter, survey, dataFilename):
return 'dustCorrection.' + str(ngauss) + 'gauss.dQ' + str(quantile) + '.' + iter + '.' + survey + '.' + dataFilename
tgas, twoMass, Apass, bandDictionary, indices = testXD.dataArrays()
ngauss = 128
survey = '2MASS'
quantile = 0.05
dataFilename = 'All.npz'
norm = mpl.colors.Normalize(vmin=-1, vmax=5)
iter = '8th'
if survey == 'APASS':
mag1 = 'B'
mag2 = 'V'
absmag = 'G'
xlabel='B-V'
ylabel = r'M$_\mathrm{G}$'
xlim = [-0.2, 2]
ylim = [9, -2]
if survey == '2MASS':
mag1 = 'J'
mag2 = 'K'
absmag = 'J'
xlabel = 'J-K$_s$'
ylabel = r'M$_\mathrm{J}$'
xlim = [-0.25, 1.25]
ylim = [6, -6]
trueColor = 'darkred'
priorColor = 'darkgreen'
posteriorColor='royalblue'
#posteriorMap = mpl.cm.get_cmap('Blues')
dataColor = 'black'
tgas, twoMass, Apass, bandDictionary, indices = testXD.dataArrays()
dustFile = dustFilename(ngauss, quantile, iter, survey, dataFilename)
data = np.load(dustFile)
dustEBV = data['ebv']
color = testXD.colorArray(mag1, mag2, dustEBV, bandDictionary)
absMagKinda, apparentMagnitude = testXD.absMagKindaArray(absmag, dustEBV, bandDictionary, tgas['parallax'])
color_err = np.sqrt(bandDictionary[mag1]['array'][bandDictionary[mag1]['err_key']]**2. + bandDictionary[mag2]['array'][bandDictionary[mag2]['err_key']]**2.)
absMagKinda_err = tgas['parallax_error']*10.**(0.2*bandDictionary[absmag]['array'][bandDictionary[absmag]['key']])
colorBins = [0.0, 0.2, 0.4, 0.7, 1.0]
digit = np.digitize(color, colorBins)
debug = False
ndim = 2
nPosteriorPoints = 1000 #number of elements in the posterior array
projectedDimension = 1 #which dimension to project the prior onto
ndim = 2
xparallaxMAS = np.linspace(0, 10, nPosteriorPoints)
y = absMagKinda
yplus = y + absMagKinda_err
yminus = y - absMagKinda_err
#parallaxErrGoesNegative = yminus < 0
absMagYMinus = testXD.absMagKinda2absMag(yminus)
absMagYPlus = testXD.absMagKinda2absMag(yplus)
absMag = testXD.absMagKinda2absMag(y)
yerr_minus = absMag - absMagYMinus
yerr_plus = absMagYPlus - absMag
yerr_minus[y < 0] = 100.
yerr_plus[y < 0] = 100.
yerr_minus[np.isnan(yerr_minus)] = 100.
import pdb; pdb.set_trace()
#plot likelihood and posterior in each axes
for iteration in np.arange(20, 40):
fig, ax = plt.subplots(2, 3, figsize=(15, 9))
ax = ax.flatten()
fig.subplots_adjust(left=0.1, right=0.9,
bottom=0.1, top=0.9,
wspace=0.4, hspace=0.5)
#plot prior in upper left
xdgmmFilename = 'xdgmm.' + str(ngauss) + 'gauss.dQ' + str(quantile) + '.' + iter + '.2MASS.All.npz.fit'
xdgmm = XDGMM(filename=xdgmmFilename)
testXD.plotPrior(xdgmm, ax[0], c=priorColor, lw=1)
ax[0].set_xlim(xlim)
ax[0].set_ylim(ylim)
ax[0].set_xlabel('$(J-K)^C$', fontsize=18)
ax[0].set_ylabel('$M_J^C$', fontsize=18)
for i in range(np.max(digit)):
currentInd = np.where((digit == i))[0]
index = currentInd[np.random.randint(0, high=len(currentInd))]
print 'yerr minus: ' + str(yerr_minus[index]) + ' yerr plus: ' + str(yerr_plus[index])
ax[0].scatter(color[index], testXD.absMagKinda2absMag(absMagKinda[index]), c=dataColor)
ax[0].errorbar(color[index], testXD.absMagKinda2absMag(absMagKinda[index]), xerr=[[color_err[index], color_err[index]]], yerr=[[yerr_minus[index], yerr_plus[index]]], fmt="none", zorder=0, lw=2.0, mew=0, alpha=1.0, color=dataColor, ecolor=dataColor)
ax[0].annotate(str(i+1), (color[index]+0.05, testXD.absMagKinda2absMag(absMagKinda[index])+0.15), fontsize=18)
meanData, covData = testXD.matrixize(color[index], absMagKinda[index], color_err[index], absMagKinda_err[index])
meanPrior, covPrior = testXD.matrixize(color[index], absMagKinda[index], color_err[index], 1e5)
meanData = meanData[0]
covData = covData[0]
meanPrior = meanPrior[0]
covPrior = covPrior[0]
xabsMagKinda = testXD.parallax2absMagKinda(xparallaxMAS, apparentMagnitude[index])
if debug:
windowFactor = 15. #the number of sigma to sample in mas for plotting
minParallaxMAS = tgas['parallax'][index] - windowFactor*tgas['parallax_error'][index]
maxParallaxMAS = tgas['parallax'][index] + windowFactor*tgas['parallax_error'][index]
xparallaxMAS, xabsMagKinda = testXD.plotXarrays(minParallaxMAS, maxParallaxMAS, apparentMagnitude[index], nPosteriorPoints=nPosteriorPoints)
xabsMagKinda = xabsMagKinda[::-1]
xparallaxMAS = xparallaxMAS[::-1]
positive = xparallaxMAS > 0.
if np.sum(positive) == 0:
print str(index) + ' has no positive distance values'
continue
logDistance = np.log10(1./xparallaxMAS[positive])
allMeans, allAmps, allCovs, summedPosteriorAbsmagKinda = testXD.absMagKindaPosterior(xdgmm, ndim, meanData, covData, xabsMagKinda, projectedDimension=1, nPosteriorPoints=nPosteriorPoints)
allMeansPrior, allAmpsPrior, allCovsPrior, summedPriorAbsMagKinda = testXD.absMagKindaPosterior(xdgmm, ndim, meanPrior, covPrior, xabsMagKinda, projectedDimension=1, nPosteriorPoints=nPosteriorPoints)
#print np.min(summedPriorAbsMagKinda), np.max(summedPriorAbsMagKinda)
posteriorParallax = summedPosteriorAbsmagKinda*10.**(0.2*apparentMagnitude[index])
priorParallax = summedPriorAbsMagKinda*10.**(0.2*apparentMagnitude[index])
likeParallax = st.gaussian(absMagKinda[index]/10.**(0.2*apparentMagnitude[index]), absMagKinda_err[index]/10.**(0.2*apparentMagnitude[index]), xparallaxMAS)
l1, = ax[i+1].plot(xparallaxMAS, likeParallax*np.max(posteriorParallax)/np.max(likeParallax), lw=2, color=dataColor)
l2, = ax[i+1].plot(xparallaxMAS, priorParallax*np.max(posteriorParallax)/np.max(priorParallax), lw=0.5, color=priorColor)
l3, = ax[i+1].plot(xparallaxMAS, posteriorParallax, lw=2, color=posteriorColor)
ax[i+1].set_title(str(i+1))
ax[i+1].set_xlabel(r'$\varpi$ [mas]', fontsize=18)
ax[i+1].tick_params(
axis='y', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off') # labels along the bottom edge are off
if i+1 == 1: fig.legend((l1, l2, l3), ('likelihood', 'prior', 'posterior'), 'upper right', fontsize=15)
#plt.tight_layout()
fig.savefig('tightPosterior_' + str(iteration) + '.png')
plt.close(fig)