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plots.py
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plots.py
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import toy
import os
import testXD
from astroML.plotting import setup_text_plots
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import corner
from xdgmm import XDGMM
import drawEllipse
import comparePrior
import scipy.interpolate
import astropy.visualization as av
from matplotlib.colors import LinearSegmentedColormap
def figsize_and_margins(plotsize,subplots=(1,1),**absolute_margins):
'''Determine figure size and margins from plot size and absolute margins
Parameters:
plotsize: (width, height) of plot area
subplots: (nrows, ncols) of subplots
left, right, top, bottom: absolute margins around plot area
wspace, hspace: width and height spacing between subplots
Returns:
size: figure size for figsize argument of figure()
margins: relative margins dict suitable for subplots_adjust()
Example: making 2x2 grid of 3" square plots with specific spacings:
sz, rm = figsize_and_margins((3,3), (2,2), left=1, right=.5,
top=.5, bottom=1,
wspace=.5, hspace=.5)
figure(figsize=sz)
subplots_adjust(**rm)
subplot(221); subplot(222)
subplot(223); subplot(224)
'''
#from matplotlib import rcParams
pw,ph = plotsize
nr,nc = subplots
amarg = absolute_margins
#dictionary for relative margins
# initialize from rcParams with margins not in amarg
rmarg = dict((m, mpl.rcParams['figure.subplot.' + m])
for m in ('left','right','top','bottom','wspace','hspace')
if m not in amarg
)
#subplots_adjust wants wspace and hspace relative to plotsize:
if 'wspace' in amarg: rmarg['wspace'] = float(amarg['wspace']) / pw
if 'hspace' in amarg: rmarg['hspace'] = float(amarg['hspace']) / ph
#in terms of the relative margins:
#width * (right - left)
# = ncols * plot_width + (ncols - 1) * wspace * plot_width
#height * (top - bottom)
# = nrows * plot_height + (nrows - 1) * hspace * plot_height
#solve for width and height, using absolute margins as necessary:
#print(nc, rmarg['wspace'], pw, amarg.get('lef', 0), amarg.get('right', 0), rmarg.get('right', 1), rmarg.get('left', 0))
width = float((nc + (nc - 1) * rmarg['wspace']) * pw \
+ amarg.get('left',0) + amarg.get('right',0)) \
/ (rmarg.get('right',1) - rmarg.get('left',0))
height = float((nr + (nr - 1) * rmarg['hspace']) * ph \
+ amarg.get('top',0) + amarg.get('bottom',0)) \
/ (rmarg.get('top',1) - rmarg.get('bottom',0))
#now we can get any remaining relative margins
if 'left' in amarg: rmarg['left'] = float(amarg['left']) / width
if 'right' in amarg: rmarg['right'] = 1 - float(amarg['right']) / width
if 'top' in amarg: rmarg['top'] = 1 - float(amarg['top']) / height
if 'bottom' in amarg: rmarg['bottom'] = float(amarg['bottom']) / height
#return figure size and relative margins
return (width, height), rmarg
#Example usage: make 2 side-by-side 3" square figures
#from pylab import *
#fsize, margins = figsize_and_margins(plotsize=(3,3),subplots=(1,2))
#figure('My figure', figsize=fsize)
#adjust_subplots(**margins)
#subplot(121)
# ... plot something ...
#subplot(122)
# ... plot something ...
#show()
def gaussian(mean, sigma, array, amplitude=1.0):
return amplitude/np.sqrt(2.*np.pi*sigma**2.)*np.exp(-(array - mean)**2./(2.*sigma**2.))
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 makeFigureInstance(x=1, y=1, left=1.0, right=0.25, top=0.25, bottom=0.75, wspace=0.5, hspace=0.5, figureSize=(3,3)):#, figsize=None, fontsize=12):
sz, rm = figsize_and_margins(figureSize, (y,x), left=left, right=right,
top=top, bottom=bottom,
wspace=wspace, hspace=hspace)
fig, ax = plt.subplots(y, x, figsize=sz)#, figsize=figsize)
if (x > 1) or (y > 1): ax = ax.flatten()
fig.subplots_adjust(**rm)
#fig.subplots_adjust(left=0.1, right=0.9,
# bottom=0.1, top=0.9,
# wspace=0.4, hspace=0.5)
#setup_text_plots(fontsize=fontsize, usetex=True)
return fig, ax
def plot_samples(x, y, xerr, yerr, ind, contourColor='black', rasterized=True, plot_contours=True, dataColor='black', titles=None, xlim=(0,1), ylim=(0,1), xlabel=None, ylabel=None, prior=False, xdgmm=None, pdf=False):#, annotateTextSize=18, figsize2x1 = (12, 5.5)):
#setup_text_plots(fontsize=, usetex=True)
plt.clf()
alpha = 0.1
alpha_points = 0.01
fig, ax = makeFigureInstance(x=2, y=1)#, figsize=figsize2x1)
#fig = plt.figure(figsize=figsize2x1)
#fig.subplots_adjust(left=0.1, right=0.95,
# bottom=0.15, top=0.95,
# wspace=0.1, hspace=0.1)
#ax1 = fig.add_subplot(121)
#ax2 = fig.add_subplot(122)
levels = 1.0 - np.exp(-0.5 * np.arange(1.0, 2.1, 1.0) ** 2)
im = corner.hist2d(x, y, ax=ax[0], levels=levels, bins=200, no_fill_contours=True, plot_density=False, color=contourColor, rasterized=True, plot_contours=plot_contours, plot_datapoints=False)
ax[0].scatter(x, y, s=1, lw=0, c=dataColor, alpha=alpha, zorder=0, rasterized=True)
if prior:
plotPrior(xdgmm, ax[1], c=dataColor)
else:
ax[1].errorbar(x[ind], y[ind], xerr=xerr[ind], yerr=[yerr[0][ind], yerr[1][ind]], fmt="none", zorder=0, mew=0, ecolor=dataColor, alpha=0.5, elinewidth=0.5)
for i, axis in enumerate(ax):
axis.set_xlim(xlim)
axis.set_ylim(ylim[0], ylim[1]*1.1)
axis.text(0.05, 0.95, titles[i],
ha='left', va='top', transform=axis.transAxes)#, fontsize=annotateTextSize)
axis.set_xlabel(xlabel)
if i in [1]: #(1, 3):
axis.yaxis.set_major_formatter(plt.NullFormatter())
else:
axis.set_ylabel(ylabel)
if pdf: fig.savefig('plot_sample.pdf', dpi=400)
fig.savefig('plot_sample.png')
plt.close(fig)
def absMagError(parallax, parallax_err, apparentMag, absMag):
absMag_errPlus = testXD.absMagKinda2absMag((parallax + parallax_err)*10.**(0.2*apparentMag)) - absMag
absMag_errMinus = absMag - testXD.absMagKinda2absMag((parallax - parallax_err)*10.**(0.2*apparentMag))
return [absMag_errMinus, absMag_errPlus]
def sampleXDGMM(xdgmm, Nsamples):
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])
return samplex, sampley
def likePriorPost(color, absMagKinda, color_err, absMagKinda_err, apparentMagnitude, xdgmm, xparallaxMAS, ndim=2, nPosteriorPoints=1000, projectedDimension=1):
meanData, covData = testXD.matrixize(color, absMagKinda, color_err, absMagKinda_err)
meanPrior, covPrior = testXD.matrixize(color, absMagKinda, color_err, 1e5)
meanData = meanData[0]
covData = covData[0]
meanPrior = meanPrior[0]
covPrior = covPrior[0]
xabsMagKinda = testXD.parallax2absMagKinda(xparallaxMAS, apparentMagnitude)
xColor = np.linspace(-2, 4, nPosteriorPoints)
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)
allMeansColor, allAmpsColor, allCovsColor, summedPosteriorColor = testXD.absMagKindaPosterior(xdgmm, ndim, meanData, covData, xColor, projectedDimension=0, nPosteriorPoints=1000, prior=False)
posteriorParallax = summedPosteriorAbsmagKinda*10.**(0.2*apparentMagnitude)
priorParallax = summedPriorAbsMagKinda*10.**(0.2*apparentMagnitude)
likeParallax = gaussian(absMagKinda/10.**(0.2*apparentMagnitude), absMagKinda_err/10.**(0.2*apparentMagnitude), xparallaxMAS)
return likeParallax, priorParallax, posteriorParallax, summedPosteriorColor
def main():
# for label, style in zip(['paper', 'talk'],['seaborn-paper', 'seaborn-talk']):
pdf = True
plot_data = True
plot_dust = False
plot_prior = False
plot_m67 = True
plot_compare = False
plot_expectation = True
plot_odd_examples = False
plot_examples = False
plot_delta = False
plot_deltacdf = False
plot_nobias = False
plot_wtf = False
plot_toy = False
#figsize2x1 = (12, 5.5)
#figsize2x2 = (12, 11)
#figsize3x2 = (18, 11)
style = 'seaborn-paper'
#plt.style.use(style)
#fontsize = 12
#annotateTextSize = 12
#legendTextSize = 12
params = {
'axes.labelsize' : 9,
'font.size' : 9,
'legend.fontsize': 9,
'xtick.labelsize': 9,
'ytick.labelsize': 9,
'text.usetex': False,
'figure.figsize': [4.5, 4.5]
}
mpl.rcParams.update(params)
#mpl.rcParams['xtick.labelsize'] = fontsize
#mpl.rcParams['ytick.labelsize'] = fontsize
#mpl.rcParams['axes.labelsize'] = fontsize
#mpl.rcParams['font.size'] = fontsize
nsubsamples = 1024
np.random.seed(0)
trueColor='#FF8D28'
priorColor= '#7bccc4'#'#6baed6' #'#9ebcda' #'#9ecae1' #'royalblue'
priorColor = '#6FB8B0'
cmap_prior = 'Blues'
posteriorColor= '#0977C4' #'#0868ac' #'#984ea3' #'#7a0177' #'#8856a7' #'#810f7c' #'#08519c' #'darkblue'
dataColor='black'
posteriorMapColor = 'Blues'
annotationColor = '#FF2412'
color1 = np.array((240,249,232))/255.
color1 = np.array((255,255,255))/255.
#color2 = np.array((123,204,196))/255.
color2 = np.array((112, 186, 179))/255.
colors = [color1, color2]
cm = LinearSegmentedColormap.from_list('my_color', colors, N=100)
cmap_prior = LinearSegmentedColormap.from_list('my_color', colors, N=100)
color2 = np.array((6, 82, 135))/255.
colors = [color1, color2]
cmap_posterior = LinearSegmentedColormap.from_list('my_color', colors, N=100)
mag1 = 'J'
mag2 = 'K'
absmag = 'J'
xlabel_cmd = r'$(J-K_s)^C$'
ylabel_cmd = r'$M_J^C$'
xlim_cmd = [-0.25, 1.25]
ylim_cmd = [6, -6]
dustFile = 'dustCorrection.128gauss.dQ0.05.10th.2MASS.All.npz'
xdgmmFile = 'xdgmm.128gauss.dQ0.05.10th.2MASS.All.npz.fit'
posteriorFile = 'posteriorParallax.128gauss.dQ0.05.10th.2MASS.All.npz'
xdgmm = XDGMM(filename=xdgmmFile)
#generate toy model plot
mtrue=-1.37
btrue=0.2
ttrue=0.8
nexamples=5
if plot_toy:
fig, ax = makeFigureInstance(x=2, y=2, wspace=0.75)
toy.makeplots(mtrue=mtrue, btrue=btrue, ttrue=ttrue, nexamples=nexamples,
trueColor=trueColor, priorColor=priorColor, posteriorColor=posteriorColor, dataColor=dataColor, posteriorMapColor=posteriorMapColor, fig=fig, axes=ax)
os.rename('toy.paper.pdf', 'paper/toy.pdf')
#----------------------------------------------
#generate raw data plot
tgas, twoMass, Apass, bandDictionary, indices = testXD.dataArrays()
posterior = np.load(posteriorFile)
mean = posterior['mean']
sigma = np.sqrt(posterior['var'])
positive = (tgas['parallax'] > 0.) & (mean > 0.)
ind = np.random.randint(0, len(tgas[positive]), nsubsamples)
dustEBV = 0.0
absMagKinda, apparentMagnitude = testXD.absMagKindaArray(absmag, dustEBV, bandDictionary, tgas['parallax'])
absMagKinda_err = tgas['parallax_error']*10.**(0.2*apparentMagnitude)
color = testXD.colorArray(mag1, mag2, dustEBV, bandDictionary)[positive]
color_err = np.sqrt(bandDictionary[mag1]['array'][bandDictionary[mag1]['err_key']]**2. + bandDictionary[mag2]['array'][bandDictionary[mag2]['err_key']]**2.)[positive]
absMag = testXD.absMagKinda2absMag(tgas['parallax'][positive]*10.**(0.2*apparentMagnitude[positive]))
absMag_err = absMagError(tgas['parallax'][positive], tgas['parallax_error'][positive], apparentMagnitude[positive], absMag)
titles = ["Observed Distribution", "Obs+Noise Distribution"]
if plot_data:
plot_samples(color, absMag, color_err, absMag_err, ind, contourColor='grey', rasterized=True, plot_contours=True, dataColor=dataColor, titles=titles, xlim=xlim_cmd, ylim=ylim_cmd, xlabel=xlabel_cmd, ylabel=ylabel_cmd, pdf=pdf) #, annotateTextSize=annotateTextSize, figsize2x1=figsize2x1)
if pdf: os.rename('plot_sample.pdf', 'paper/data.pdf')
os.rename('plot_sample.png', 'data.png')
#color_raw = color
#color_err_raw = color_err
#absMag_raw = absMag
#absMag_err_raw = absMag_err
#absMagKinda_raw = absMagKinda
#absMagKinda_err_raw = absMagKinda_err
#-------------------------------------------------------
#dust plot
if plot_dust:
fig, ax = makeFigureInstance(figureSize = (6,3),left=0.75)
comparePrior.dustViz(ngauss=128, quantile=0.05, iter='10th', survey='2MASS', dataFilename='All.npz', ax=ax, tgas=tgas)
fig.savefig('paper/dust.pdf', dpi=400)
fig.savefig('dust.png')
plt.close(fig)
#-------------------------------------------------------
#generate prior plot
if plot_prior:
samplex, sampley = sampleXDGMM(xdgmm, len(tgas))
titles = ["Extreme Deconvolution\n resampling", "Extreme Deconvolution\n cluster locations"]
plot_samples(samplex, sampley, None, None, ind, contourColor='black', rasterized=True, plot_contours=True, dataColor=priorColor, titles=titles, xlim=xlim_cmd, ylim=ylim_cmd, xlabel=xlabel_cmd, ylabel=ylabel_cmd, prior=True, xdgmm=xdgmm, pdf=pdf) #, annotateTextSize=annotateTextSize, figsize2x1=figsize2x1)
if pdf: os.rename('plot_sample.pdf', 'paper/prior.pdf')
os.rename('plot_sample.png', 'prior.png')
#-------------------------------------------------------
data = np.load(dustFile)
dustEBV = data['ebv']
absMagKinda, apparentMagnitude = testXD.absMagKindaArray(absmag, dustEBV, bandDictionary, tgas['parallax'])
color = testXD.colorArray(mag1, mag2, dustEBV, bandDictionary)
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*apparentMagnitude)
#-------------------------------------------------------
#M67 plot
if plot_m67:
fig, ax = makeFigureInstance(x=2, y=2, hspace=1.0, wspace=1.0, figureSize=(2.5, 2.5))
#setup_text_plots(fontsize=fontsize, usetex=True)
#fig, ax = plt.subplots(2,2, figsize=figsize2x2)
#fig.subplots_adjust(left=0.1, right=0.95,
# bottom=0.1, top=0.95,
# wspace=0.25, hspace=0.25)
#ax = ax.flatten()
nPosteriorPoints = 1000
print(dataColor)
#def distanceTest(tgas, xdgmm, nPosteriorPoints, data1, data2, err1, err2, xlim, ylim, plot2DPost=False, dataColor='black', priorColor='green', truthColor='red', posteriorColor='blue', dl=0.1, db=0.1):
testXD.distanceTest(tgas, xdgmm, nPosteriorPoints, color, absMagKinda, color_err, absMagKinda_err, xlim_cmd, ylim_cmd, bandDictionary, absmag, dataColor=dataColor, priorColor=priorColor, truthColor=trueColor, posteriorColor=posteriorColor, figDist=fig, axDist=ax, xlabel=xlabel_cmd, ylabel=ylabel_cmd, dl=0.075, db=0.075)
plt.tight_layout()
if pdf: fig.savefig('paper/m67.pdf', dpi=400)
fig.savefig('m67.png')
plt.close(fig)
#-------------------------------------------------------
color = color[positive]
color_err = color_err[positive]
apparentMagnitude = apparentMagnitude[positive]
absMagKinda_dust = absMagKinda[positive]
absMagKinda_dust_err = absMagKinda_err[positive]
absMag_dust = testXD.absMagKinda2absMag(absMagKinda[positive])
absMag_dust_err = absMagError(tgas['parallax'][positive], tgas['parallax_error'][positive], apparentMagnitude, absMag_dust)
#generate comparison prior plot
if plot_compare:
#setup_text_plots(fontsize=fontsize, usetex=True)
plt.clf()
alpha = 0.1
alpha_points = 0.01
fig, ax = makeFigureInstance(x=2, y=1)#, figsize=figsize2x1)
#fig = plt.figure(figsize=figsize2x1)
#fig.subplots_adjust(left=0.1, right=0.95,
# bottom=0.15, top=0.95,
# wspace=0.1, hspace=0.1)
#ax1 = fig.add_subplot(121)
#ax2 = fig.add_subplot(122)
#ax = [ax1, ax2]
titles = ['Exp Dec Sp \nDen Prior', 'CMD Prior']
for i, file in enumerate(['posteriorSimple.npz', posteriorFile]):
data = np.load(file)
posterior = data['posterior']
sigma = np.sqrt(data['var'])
mean = data['mean']
absMag = testXD.absMagKinda2absMag(mean[positive]*10.**(0.2*apparentMagnitude))
absMag_err = absMagError(mean[positive], sigma[positive], apparentMagnitude, absMag)
if plot_compare: #ax[i].scatter(color[ind], absMag[ind], c=posteriorColor, s=1, lw=0, alpha=alpha, zorder=0)
ax[i].errorbar(color[ind], absMag[ind], xerr=color_err[ind], yerr=[absMag_err[0][ind], absMag_err[1][ind]], fmt="none", zorder=0, mew=0, ecolor=posteriorColor, alpha=0.5, elinewidth=0.5, color=posteriorColor)
ax[i].set_xlim(xlim_cmd)
ax[i].set_ylim(ylim_cmd[0], ylim_cmd[1]*1.1)
ax[i].text(0.05, 0.95, titles[i], ha='left', va='top', transform=ax[i].transAxes) #, fontsize=annotateTextSize)
ax[i].set_xlabel(xlabel_cmd)
if i in [1]:
ax[i].yaxis.set_major_formatter(plt.NullFormatter())
else:
ax[i].set_ylabel(ylabel_cmd)
if plot_compare:
if pdf: fig.savefig('paper/comparePrior.pdf', dpi=400)
fig.savefig('comparePrior.png')
plt.close(fig)
#-------------------------------------------------------
#generate expectation plot
absMag = testXD.absMagKinda2absMag(mean[positive]*10.**(0.2*apparentMagnitude))
absMag_err = absMagError(mean[positive], sigma[positive], apparentMagnitude, absMag)
titles = ["De-noised Expectation \nValues", "Posterior Distributions"]
if plot_expectation:
plot_samples(color, absMag, color_err, absMag_err, ind, contourColor='black', rasterized=True, plot_contours=True, dataColor=posteriorColor, titles=titles, xlim=xlim_cmd, ylim=ylim_cmd, xlabel=xlabel_cmd, ylabel=ylabel_cmd, pdf=pdf)#, annotateTextSize=annotateTextSize, figsize2x1=figsize2x1)
if pdf: os.rename('plot_sample.pdf', 'paper/posteriorCMD.pdf')
os.rename('plot_sample.png', 'posteriorCMD.png')
#-------------------------------------------------------
#posterior example plot
if plot_examples:
colorBins = [0.0, 0.2, 0.4, 0.7, 1.0]
digit = np.digitize(color, colorBins)
ndim = 2
nPosteriorPoints = 1000 #number of elements in the posterior array
projectedDimension = 1 #which dimension to project the prior onto
xparallaxMAS = np.linspace(0, 10, nPosteriorPoints)
#plot likelihood and posterior in each axes
for iteration in np.arange(20, 40):
fig, ax = makeFigureInstance(x=3, y=2, hspace=0.75, figureSize=(2,2)) #, figsize=figsize3x2)
#fig, ax = plt.subplots(2, 3, figsize=figsize3x2)
#ax = ax.flatten()
#fig.subplots_adjust(left=0.1, right=0.9,
# bottom=0.1, top=0.8,
# wspace=0.4, hspace=0.5)
plotPrior(xdgmm, ax[0], c=priorColor, lw=1)
ax[0].set_xlim(xlim_cmd)
ax[0].set_ylim(ylim_cmd)
ax[0].set_xlabel(xlabel_cmd)
ax[0].set_ylabel(ylabel_cmd)
for i in range(np.max(digit)):
currentInd = np.where((digit == i))[0]
index = currentInd[np.random.randint(0, high=len(currentInd))]
ax[0].scatter(color[index], absMag_dust[index], c=dataColor, s=20)
ax[0].errorbar(color[index], absMag_dust[index], xerr=[[color_err[index], color_err[index]]], yerr=[[absMag_dust_err[0][index], absMag_dust_err[1][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]+0.075, absMag_dust[index]+0.175))#, fontsize=annotateTextSize)
#print len(color), len(absMagKinda_dust), len(color_err), len(absMagKinda_dust_err), len(apparentMagnitude)
likeParallax, priorParallax, posteriorParallax, posteriorColor = likePriorPost(color[index], absMagKinda_dust[index], color_err[index], absMagKinda_dust_err[index], apparentMagnitude[index], xdgmm, xparallaxMAS, ndim=2, nPosteriorPoints=1000, projectedDimension=1)
l1, = ax[i+1].plot(xparallaxMAS, likeParallax*np.max(posteriorParallax)/np.max(likeParallax), lw=1, color=dataColor, zorder=100)
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)
maxInd = posteriorParallax == np.max(posteriorParallax)
maxPar = xparallaxMAS[maxInd]
maxY = posteriorParallax[maxInd]
if maxPar < 5: annX = 9
else: annX = 0
if i == 1: annY = 0.75*maxY
else: annY = maxY/1.1
ax[i+1].text(annX, annY, str(i+1))
ax[i+1].set_xlabel(r'$\varpi$ [mas]')
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:
leg = fig.legend((l1, l2, l3), ('likelihood', 'prior', 'posterior'), 'upper right') #, fontsize=legendTextSize)
leg.get_frame().set_alpha(1.0)
#plt.tight_layout()
if pdf: fig.savefig('posterior_' + str(iteration) + '.pdf', dpi=400)
fig.savefig('paper/posterior.pdf', dpi=400)
fig.tight_layout()
fig.savefig('posterior.png')
plt.close(fig)
#-------------------------------------------------------
#odd posterior example plot
if plot_odd_examples:
#choose indices for odd plot_examples
#odd colors and magnitudes
#come back and do parallax negative
SN = tgas['parallax'][positive]/tgas['parallax_error'][positive]
oddIndicesWD_LowSN = np.where(np.logical_and((absMag_dust > 6.*color + 5.), (SN <= 5)))[0]
oddIndicesWD_HighSN = np.where(np.logical_and((absMag_dust > 6.*color + 5.), (SN > 5)))[0]#3.6)[0]
oddIndicesSSG = np.where(np.logical_and((absMag_dust < 7.5*color - 1.5), (absMag_dust > -8.1*color + 7.8)))[0]
oddIndicesPN_LowSN = np.where(np.logical_and(SN <= 5, np.logical_and((absMag_dust < 7.5*color - 4.25),(absMag_dust < -4.75*color - 0.6))))[0]
oddIndicesPN_HighSN = np.where(np.logical_and(SN > 5, np.logical_and((absMag_dust < 7.5*color - 4.25),(absMag_dust < -4.75*color - 0.6))))[0]
ndim = 2
nPosteriorPoints = 1000 #number of elements in the posterior array
projectedDimension = 1 #which dimension to project the prior onto
xparallaxMAS = np.linspace(0, 10, nPosteriorPoints)
xarray = np.logspace(-2, 2, 1000)
xColor = np.linspace(-2, 4, nPosteriorPoints)
samplex, sampley = sampleXDGMM(xdgmm, len(tgas)*10)
#plot likelihood and posterior in each axes
for iteration in np.arange(0, 10):
fig, ax = makeFigureInstance(x=3, y=2, hspace=0.75, figureSize=(2,2)) #, figsize=figsize3x2)
#fig, ax = plt.subplots(2, 3, figsize=figsize3x2)
#ax = ax.flatten()
#fig.subplots_adjust(left=0.1, right=0.9,
# bottom=0.1, top=0.8,
# wspace=0.4, hspace=0.5)
ax[0].hist2d(samplex, sampley, bins=500, norm=mpl.colors.LogNorm(), cmap=plt.get_cmap(cmap_prior), zorder=-1)
#plotPrior(xdgmm, ax[0], c=priorColor, lw=1, stretch=True)
ax[0].set_ylim(15, -10)
ax[0].set_xlim(-1.2, 2)
ax[0].set_ylim(ylim_cmd[0]+3, ylim_cmd[1]-3)
ax[0].set_xlabel(xlabel_cmd)
ax[0].set_ylabel(ylabel_cmd)
for i, indices in enumerate([oddIndicesWD_LowSN, oddIndicesWD_HighSN, oddIndicesSSG, oddIndicesPN_LowSN, oddIndicesPN_HighSN]):
print(len(indices), indices)
#if i == 0: index = indices[iteration]
#else: index = indices[np.random.randint(0, high=len(indices))]
index = indices[np.random.randint(0, high=len(indices))]
ax[0].scatter(color[index], absMag_dust[index], c=dataColor, s=20)
yplus = absMag_dust_err[0][index]
yminus = absMag_dust_err[1][index]
if np.isnan(yplus): yplus = 10.
if np.isnan(yminus): yminus = 10.
print(yplus, yminus)
ax[0].errorbar(color[index], absMag_dust[index], xerr=[[color_err[index]], [color_err[index]]], yerr=[[yplus], [yminus]], fmt="none", zorder=0, lw=2.0, mew=0, alpha=1.0, color=dataColor, ecolor=dataColor)
ax[0].annotate(str(i+1), (color[index]+0.075, absMag_dust[index]+0.175))#, fontsize=annotateTextSize)
#print len(color), len(absMagKinda_dust), len(color_err), len(absMagKinda_dust_err), len(apparentMagnitude)
likeParallax, priorParallax, posteriorParallax, posteriorColorArray = likePriorPost(color[index], absMagKinda_dust[index], color_err[index], absMagKinda_dust_err[index], apparentMagnitude[index], xdgmm, xparallaxMAS, ndim=2, nPosteriorPoints=1000, projectedDimension=1)
likeParallaxFull, priorParallaxFull, posteriorParallaxFull, posteriorColorFull = likePriorPost(color[index], absMagKinda_dust[index], color_err[index], absMagKinda_dust_err[index], apparentMagnitude[index], xdgmm, xarray, ndim=2, nPosteriorPoints=1000, projectedDimension=1)
meanPosteriorParallax = scipy.integrate.cumtrapz(posteriorParallaxFull*xarray, x=xarray)[-1]
x2PosteriorParallax = scipy.integrate.cumtrapz(posteriorParallaxFull*xarray**2., x=xarray)[-1]
varPosteriorParallax = x2PosteriorParallax - meanPosteriorParallax**2.
meanPosteriorColor = scipy.integrate.cumtrapz(posteriorColorFull*xColor, x=xColor)[-1]
x2PosteriorColor = scipy.integrate.cumtrapz(posteriorColorFull*xColor**2., x=xColor)[-1]
varPosteriorColor = x2PosteriorColor - meanPosteriorColor**2.
absMagPost = testXD.absMagKinda2absMag(meanPosteriorParallax*10.**(0.2*apparentMagnitude[index]))
absMag_errPost = absMagError(meanPosteriorParallax, np.sqrt(varPosteriorParallax), apparentMagnitude[index], absMagPost)
yplus = absMag_dust_err[0][index]
yminus = absMag_dust_err[1][index]
if np.isnan(yplus): yplus = 10.
if np.isnan(yminus): yminus = 10.
l1, = ax[i+1].plot(xparallaxMAS, likeParallax*np.max(posteriorParallax)/np.max(likeParallax), lw=2, color=dataColor, zorder=100)
l2, = ax[i+1].plot(xparallaxMAS, priorParallax*np.max(posteriorParallax)/np.max(priorParallax), lw=2, color=priorColor, linestyle='--')
l3, = ax[i+1].plot(xparallaxMAS, posteriorParallax, lw=2, color=posteriorColor)
ax[0].scatter(meanPosteriorColor, absMagPost, c=posteriorColor, s=20)
ax[0].errorbar(meanPosteriorColor, absMagPost, xerr=[[np.sqrt(varPosteriorColor)], [np.sqrt(varPosteriorColor)]], yerr=[[yplus], [yminus]], fmt="none", zorder=0, lw=2.0, mew=0, alpha=1.0, color=posteriorColor, ecolor=posteriorColor)
maxInd = np.where(posteriorParallax == np.max(posteriorParallax))[0]
maxPar = xparallaxMAS[maxInd]
maxY = posteriorParallax[maxInd]
if maxPar < 5: annX = 9
else: annX = 0
if i == 1: annY = 0.75*maxY
else: annY = maxY/1.1
ax[i+1].text(annX, annY, str(i+1))
ax[i+1].set_xlabel(r'$\varpi$ [mas]')
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:
leg = fig.legend((l1, l2, l3), ('likelihood', 'prior', 'posterior'), 'upper right') #, fontsize=legendTextSize)
leg.get_frame().set_alpha(1.0)
#plt.tight_layout()
if pdf: fig.savefig('posterior_' + str(iteration) + '_odd.pdf', dpi=400)
fig.savefig('paper/posterior_odd.pdf', dpi=400)
fig.tight_layout()
fig.savefig('posterior_odd.png')
plt.close(fig)
#-------------------------------------
#delta plot
label = r'$\mathrm{ln} \, \tilde{\sigma}_{\varpi}^2 - \mathrm{ln} \, \sigma_{\varpi}^2$'
contourColor = '#1f77b4'
color = testXD.colorArray(mag1, mag2, dustEBV, bandDictionary)
color_err = np.sqrt(bandDictionary[mag1]['array'][bandDictionary[mag1]['err_key']]**2. + bandDictionary[mag2]['array'][bandDictionary[mag2]['err_key']]**2.)
x = color
y = np.log(sigma**2.) - np.log(tgas['parallax_error']**2.)
colorDeltaVar = y
notnans = ~np.isnan(sigma) & ~np.isnan(tgas['parallax_error']) & ~np.isnan(color)
if plot_delta:
fig, ax = makeFigureInstance(x=2, y=1, wspace=1.0) # , figsize=figsize2x1)
#fig, ax = plt.subplots(1, 2, figsize=figsize2x1)
levels = 1.0 - np.exp(-0.5 * np.arange(1.0, 2.1, 1.0) ** 2)
norm = plt.matplotlib.colors.Normalize(vmin=-1.5, vmax=1)
cmap = 'inferno'
ax[0].scatter(x[notnans], y[notnans], c=y[notnans], s=1, lw=0, alpha=0.05, norm=norm, cmap=cmap, rasterized=True)
#corner.hist2d(x[notnans], y[notnans], bins=200, ax=ax[0], levels=levels, no_fill_contours=True, plot_density=False, plot_data=False, color=contourColor, rasterized=True)
ax[0].set_xlabel(xlabel_cmd)
ax[0].set_ylim(-6, 2)
ax[0].set_xlim(-0.5, 2)
ax[0].set_ylabel(label)
cNorm = plt.matplotlib.colors.Normalize(vmin=0.1, vmax=2)
ax[1].scatter(x[positive], absMag, s=1, lw=0, c=y[positive], alpha=0.05, norm=norm, cmap=cmap, rasterized=True)
ax[1].set_xlim(xlim_cmd)
ax[1].set_ylim(ylim_cmd)
ax[1].set_xlabel(xlabel_cmd)
ax[1].set_ylabel(ylabel_cmd)
if pdf: fig.savefig('paper/delta.pdf', dpi=400)
fig.savefig('delta.png')
plt.close(fig)
#delta cdf plot
ratioCmd = sigma[notnans]**2./tgas['parallax_error'][notnans]**2.
lnratio = np.log(ratioCmd)
if plot_deltacdf:
plt.clf()
fig, ax = makeFigureInstance(left=0.75)
N = len(lnratio)
ys = np.arange(0+0.5/N, 1, 1.0/N)
sinds = np.argsort(lnratio)
f = scipy.interpolate.interp1d(lnratio[sinds], ys)
f_inv = scipy.interpolate.interp1d(ys, lnratio[sinds])
ax.plot(lnratio[sinds], ys, 'k-', lw=2)
fac2 = np.log(1/4.)
fac1 = 0.
ax.plot([fac2, fac2],[-1, f(fac2)], 'k--', lw=2)
ax.plot([-6, fac2],[f(fac2), f(fac2)], 'k--' ,lw=2)
ax.plot([fac1, fac1], [-1, f(fac1)], 'k--', lw=2)
ax.plot([-6, fac1], [f(fac1), f(fac1)], 'k--', lw=2)
ax.plot([f_inv(0.5), f_inv(0.5)], [-1, 0.5], 'k--', lw=2)
ax.plot([-6, f_inv(0.5)], [0.5, 0.5], 'k--', lw=2)
ax.set_xlabel(label)
ax.set_ylabel('cumulative fraction')
ax.set_xlim(-6, 2)
ax.set_ylim(-0.05, 1.05)
if pdf: fig.savefig('paper/deltaCDF.pdf', dpi=400)
fig.savefig('deltaCDF.png')
plt.close(fig)
print('fraction of stars which decreased in variance: ', f(fac1))
#delta mean vs gaia uncertainty
y = mean - tgas['parallax']
x = tgas['parallax_error']
good = ~np.isnan(y) & ~np.isnan(x)
if plot_nobias:
plt.clf()
fig, ax = makeFigureInstance(left=0.75)
levels = 1.0 - np.exp(-0.5 * np.arange(1.0, 2.1, 1.0) ** 2)
contourColor = '#1f77b4'
contourColor = 'black'
#corner.hist2d(x[good], y[good], bins=200, ax=ax, levels=levels, no_fill_contours=True, plot_density=False, plot_data=False, color=contourColor, rasterized=True)
#norm = plt.matplotlib.colors.Normalize(vmin=0.0, vmax=1)
ax.scatter(x[notnans], y[notnans], c=colorDeltaVar[notnans], s=1, lw=0, alpha=0.05, norm=norm, cmap=cmap, rasterized=True)
#ax.scatter(x[good], y[good], c=sigma[good], s=1, lw=0, alpha=0.05, norm=norm, cmap=cmap, rasterized=True)
#ax.scatter(x[good], y[good], c=np.sqrt(sigma[good]), s=1, rasterized=True, zorder=0, alpha=0.1, cmap=cmap, norm=norm)
ax.plot([0, 1.1], [0,0], 'k--', lw=1)
ax.set_xlim(0.15, 1.05)
ax.set_ylim(-2.5, 2.5)
ylabel = r'$\mathrm{Posterior \, Expectation \, Value} - \varpi_n$'
xlabel = r'$\sigma_{\varpi,n}$'
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
if pdf: fig.savefig('paper/deltaParallax.pdf', dpi=400)
fig.savefig('deltaParallax.png')
plt.close(fig)
#what's that feature plot
if plot_wtf:
fig, ax = makeFigureInstance(left=0.75)
ax.scatter(color[positive], absMag, s=1, lw=0, c=dataColor, alpha=0.01, zorder=0, rasterized=True)
ax.set_xlim(xlim_cmd)
ax.set_ylim(ylim_cmd)
ax.set_xlabel(xlabel_cmd)
ax.set_ylabel(ylabel_cmd)
lowerMainSequence = (0.4, 5.5)
upperMainSequence = (-0.225, 2)
binarySequence = (0.65, 4)
redClump = (0.35, -2)
redGiantBranch = (1.0, -2)
turnOff = (-0.15, 3.5)
features = [lowerMainSequence, upperMainSequence, binarySequence, redClump, redGiantBranch, turnOff]
labels = ['lower MS', 'upper MS', 'binary sequence', 'red clump', 'RGB', 'MS turn off', 'subgiant branch']
for l, f in zip(labels, features): ax.text(f[0], f[1], l) #, fontsize=annotateTextSize)
if pdf: fig.savefig('paper/whatsThatFeature.pdf', dpi=400)
fig.savefig('whatsThatFeature.png')
plt.close(fig)
if __name__ == '__main__':
main()