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stellarTwins.py
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stellarTwins.py
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from astropy.io import fits
#import h5py
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import ckdtree as kdtree
import itertools
import matplotlib as mpl
from astroML.density_estimation import XDGMM
from astroML.plotting.tools import draw_ellipse
from matplotlib.patches import Ellipse
import pdb
from dustmaps.sfd import SFDQuery
from dustmaps.bayestar import BayestarQuery
from dustmaps.iphas import IPHASQuery
from dustmaps.marshall import MarshallQuery
from dustmaps.chen2014 import Chen2014Query
from astropy.coordinates import SkyCoord
import astropy.units as units
from scipy.integrate import cumtrapz
import sys
params = {'legend.fontsize': 'x-large',
'figure.figsize': (15, 5),
'axes.labelsize': 'x-large',
'axes.titlesize':'x-large',
'xtick.labelsize':'x-large',
'ytick.labelsize':'x-large'}
plt.rcParams.update(params)
def distMetric(sourceInd, matchedInd, apassMagnitudes, varMuMatched, p=False):
colorChiSq = 0.0
colorSigs = 0.0
colors = [B_V, g_r, r_i]
color_errors = [['e_bmag', 'e_vmag'], ['e_gmag', 'e_rmag'], ['e_rmag', 'e_imag']]
for color, error in zip(colors, color_errors):
colorChiSq += (color[sourceInd] - color[matchedInd])**2./(apassMagnitudes[error[0]][sourceInd]**2. + apassMagnitudes[error[1]][sourceInd]**2. + apassMagnitudes[error[0]][matchedInd]**2.+ apassMagnitudes[error[1]][matchedInd]**2.)
colorSigs += np.log(apassMagnitudes[error[0]][matchedInd]**2. + apassMagnitudes[error[1]][matchedInd]**2. + apassMagnitudes[error[0]][sourceInd]**2. + apassMagnitudes[error[1]][sourceInd]**2.)
absMagChiSq = (M_V[sourceInd] - M_V[matchedInd])**2./(apassMagnitudes['e_vmag'][sourceInd]**2. + apassMagnitudes['e_vmag'][matchedInd]**2. + varMuMatched[sourceInd] + varMuMatched[matchedInd])
absMagSigs = np.log(apassMagnitudes['e_vmag'][sourceInd]**2. + apassMagnitudes['e_vmag'][matchedInd]**2. + varMuMatched[sourceInd] + varMuMatched[matchedInd])
totChiSq = colorChiSq + absMagChiSq + colorSigs + absMagSigs
if p:
print( 'the total chi2: ',totChiSq[0:5])
print( 'the color chi2: ',colorChiSq[0:5])
print( 'the color sigs: ', colorSigs[0:5])
print( 'the absmag chi2:',absMagChiSq[0:5])
print( 'the absmag sigs:',absMagSigs[0:5])
return totChiSq
def findNeighborsAndCalculateChisqs(sourceIndex, pts, tree, apassMagnitudes, varMuMatched, nNeighbors=200, printChi=False):
treeDistNum, treeIndex = tree.query(pts, k=nNeighbors)
treeIndNum = treeIndex[1:]
chisq = distMetric(sourceIndex, treeIndNum, apassMagnitudes, varMuMatched, p=printChi)
return chisq, treeIndNum
def raveChisq(raveSourceIndex, raveTwinIndex, raveCutMatched):
chisq = (raveCutMatched['TEFF'][raveSourceIndex] - raveCutMatched['TEFF'][raveTwinIndex])**2./(raveCutMatched['E_TEFF'][raveSourceIndex]**2. + raveCutMatched['E_TEFF'][raveTwinIndex]**2.) + (raveCutMatched['LOGG'][raveSourceIndex] - raveCutMatched['LOGG'][raveTwinIndex])**2./(raveCutMatched['E_LOGG'][raveSourceIndex]**2. + raveCutMatched['E_LOGG'][raveTwinIndex]**2.) + (raveCutMatched['FE_H'][raveSourceIndex] - raveCutMatched['FE_H'][raveTwinIndex])**2./(raveCutMatched['E_FE_H'][raveSourceIndex]**2. + raveCutMatched['E_FE_H'][raveTwinIndex]**2.) + np.log(raveCutMatched['E_TEFF'][raveSourceIndex]**2. + raveCutMatched['E_TEFF'][raveTwinIndex]**2.) + np.log(raveCutMatched['E_LOGG'][raveSourceIndex]**2. + raveCutMatched['E_LOGG'][raveTwinIndex]**2.) + np.log(raveCutMatched['E_FE_H'][raveSourceIndex]**2. + raveCutMatched['E_FE_H'][raveTwinIndex]**2.)
return chisq
def neff(weights):
return np.sum(weights)**2./np.sum(weights**2.)
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 pdf(mean, sigma, area, array):
return np.sum(area/np.sqrt(2.*np.pi*sigma**2.)*np.exp(-(array - mean)**2./(2.*sigma**2.)), axis=1)
def plotPDFs(axes, raveTwinIndex, raveSourceIndex, raveCutMatched, chisqApass, npoints=1000):
teff_prob = np.zeros(npoints)
logg_prob = np.zeros(npoints)
feh_prob = np.zeros(npoints)
#guassian weight for each twin
gaussianArea = np.exp(-chisqApass/2.)
neff = np.sum(gaussianArea)**2./np.sum(gaussianArea**2.)
temp = raveCutMatched['TEFF'][raveTwinIndex]
temp_err = raveCutMatched['E_TEFF'][raveTwinIndex]
teff_pdf = pdf(temp, temp_err, gaussianArea, teff_array[:,None])
logg = raveCutMatched['LOGG'][raveTwinIndex]
logg_err = raveCutMatched['E_LOGG'][raveTwinIndex]
logg_pdf = pdf(logg, logg_err, gaussianArea, logg_array[:,None])
feh = raveCutMatched['FE_H'][raveTwinIndex]
feh_err = raveCutMatched['E_FE_H'][raveTwinIndex]
feh_pdf = pdf(feh, feh_err, gaussianArea, feh_array[:,None])
ax = axes[0]
ax.plot(teff_array/1000., teff_pdf/np.max(teff_pdf), lw=2, label='PDF', alpha=alpha)
ax.set_title('{:.2e}'.format(neff))
star_gauss = gaussian(raveCutMatched['TEFF'][raveSourceIndex], raveCutMatched['E_TEFF'][raveSourceIndex], teff_array)
ax.plot(teff_array/1000., star_gauss/np.max(star_gauss), lw=2, label='RAVE', alpha=alpha)
ax.set_ylim(0, 1.1)
ax.set_xlabel('T$_\mathrm{eff}$ [kK]', fontsize=20)
ax.legend()
ax = axes[1]
ax.plot(logg_array, logg_pdf/np.max(logg_pdf), lw=2, label='PDF', alpha=alpha)
star_gauss = gaussian(raveCutMatched['LOGG'][raveSourceIndex], raveCutMatched['E_LOGG'][raveSourceIndex], logg_array)
ax.plot(logg_array, star_gauss/np.max(star_gauss), lw=2, label='RAVE', alpha=alpha)
ax.set_ylim(0, 1.1)
ax.set_xlabel('log g')
ax.legend()
ax = axes[2]
ax.plot(feh_array, feh_pdf/np.max(feh_pdf), lw=2, label='PDF', alpha=alpha)
star_gauss = gaussian(raveCutMatched['FE_H'][raveSourceIndex], raveCutMatched['E_FE_H'][raveSourceIndex], feh_array)
ax.plot(feh_array, star_gauss/np.max(star_gauss), lw=2, label='RAVE', alpha=alpha)
ax.set_ylim(0, 1.1)
ax.set_xlabel('Fe/H')
ax.legend()
def plotRainbows(axes, apassTwinIndex, apassSourceIndex, raveTwinIndex, raveSourceIndex, apassCutMatched, raveCutMatched, varmuCutMatched, chisqApass, vmax=5):
alpha_points = 0.3
alpha_bars = 0.25
vmax = 5
b_v_lim = [0.25, 1.5]
g_r_lim = None #[0, 1.5]
r_i_lim = None #[-0.25, 0.75]
M_v_lim = None #[10, 2]
teff_lim = [7, 4] #kK
log_g_lim = [6, 3]
#plot B-V vs g-r for the source and twins
"""
ax = axes[0]
ax.set_title('Tycho2 ID: ' + tgasMatched['tycho2_id'][apassSourceIndex])
ax.scatter(g_r[apassTwinIndex], r_i[apassTwinIndex], c=chisqApass - np.min(chisqApass), cmap='plasma',
norm=mpl.colors.Normalize(vmax=vmax), lw=0, zorder=100, alpha=alpha_points)
ax.errorbar(g_r[apassTwinIndex], r_i[apassTwinIndex],
xerr = np.sqrt(apassCutMatched['e_gmag'][apassTwinIndex]**2. + apassCutMatched['e_rmag'][apassTwinIndex]**2.),
yerr = np.sqrt(apassCutMatched['e_rmag'][apassTwinIndex]**2. + apassCutMatched['e_imag'][apassTwinIndex]**2.),
fmt="none", ecolor='black', zorder=0, lw=0.5, mew=0, alpha=alpha_bars)
ax.errorbar(g_r[star], r_i[star],
xerr = np.sqrt(apassCutMatched['e_bmag'][apassSourceIndex]**2. + apassCutMatched['e_vmag'][apassSourceIndex]**2.),
yerr = np.sqrt(apassCutMatched['e_gmag'][apassSourceIndex]**2. + apassCutMatched['e_rmag'][apassSourceIndex]**2.), fmt='o', color='black',lw=4)
ax.scatter(g_r[apassSourceIndex], r_i[apassSourceIndex], s=400, c='black')
ax.set_xlabel('g - r')
ax.set_ylabel('r - i')
ax.set_xlim(g_r_lim)
ax.set_ylim(r_i_lim)
ax.grid()
"""
#plot B-V and M_v for the source and twins
ax = axes[0]
ax.scatter(B_V[apassTwinIndex], M_V[apassTwinIndex], c=chisqApass - np.min(chisqApass), cmap='plasma',
norm=mpl.colors.Normalize(vmax=vmax), lw=0, zorder=100, alpha=alpha_points)
ax.errorbar(B_V[apassTwinIndex], M_V[apassTwinIndex],
xerr = np.sqrt(apassCutMatched['e_bmag'][apassTwinIndex]**2. + apassCutMatched['e_vmag'][apassTwinIndex]**2.),
yerr = np.sqrt(apassCutMatched['e_vmag'][apassTwinIndex]**2. + varmuCutMatched[apassTwinIndex]),
fmt="none", ecolor='black', zorder=0, lw=0.5, mew=0, alpha=alpha_bars)
ax.errorbar(B_V[apassSourceIndex], M_V[apassSourceIndex],
xerr = np.sqrt(apassCutMatched['e_bmag'][apassSourceIndex]**2. + apassCutMatched['e_vmag'][apassSourceIndex]**2.),
yerr = np.sqrt(apassCutMatched['e_vmag'][apassSourceIndex]**2. + varmuCutMatched[apassSourceIndex]),
fmt='o', color='black', lw=4)
ax.scatter(B_V[apassSourceIndex], M_V[apassSourceIndex], s=400, c='black')
ax.set_xlabel('B - V')
ax.set_ylabel('M_V')
ax.invert_yaxis()
ax.set_xlim(b_v_lim)
ax.set_ylim(M_v_lim)
ax.grid()
#plot Teff vs log g for the source and twins
raveTeff = raveCutMatched['TEFF'][raveTwinIndex]
raveLogG = raveCutMatched['LOGG'][raveTwinIndex]
ax = axes[1]
ax.scatter(raveTeff/1000., raveLogG, c=chisqApass - np.min(chisqApass), cmap='plasma',
norm=mpl.colors.Normalize(vmax=vmax), lw=0, zorder=100, alpha=alpha_points)
ax.errorbar(raveTeff/1000., raveLogG,
xerr=raveCutMatched['E_TEFF'][raveTwinIndex]/1000., yerr=raveCutMatched['E_LOGG'][raveTwinIndex],
fmt='none', ecolor='black', zorder=0, lw=0.5, mew=0, alpha=alpha_bars)
ax.scatter(raveCutMatched['TEFF'][raveSourceIndex]/1000., raveCutMatched['LOGG'][raveSourceIndex], s=400, c='black')
ax.errorbar(raveCutMatched['TEFF'][raveSourceIndex]/1000., raveCutMatched['LOGG'][raveSourceIndex],
xerr=raveCutMatched['E_TEFF'][raveSourceIndex]/1000., yerr=raveCutMatched['E_LOGG'][raveSourceIndex],
fmt='o', color='black', lw=4)
ax.set_ylabel('log g', fontsize=15)
ax.set_xlabel('Teff [kK]', fontsize=15)
ax.set_xlim(teff_lim)
ax.set_ylim(log_g_lim)
ax.grid()
#plot B-V vs Fe/H for the source and twins
ax = axes[2]
ax.errorbar(B_V[apassTwinIndex], raveCutMatched['FE_H'][raveTwinIndex], yerr=raveCutMatched['E_FE_H'][raveTwinIndex],
xerr = np.sqrt(apassCutMatched['e_bmag'][apassTwinIndex]**2. + apassCutMatched['e_vmag'][apassTwinIndex]**2.),
fmt='none', ecolor='black', zorder=0, lw=0.5, mew=0, alpha=alpha_bars)
ax.scatter(B_V[apassTwinIndex], raveCutMatched['FE_H'][raveTwinIndex], c=chisqApass - np.min(chisqApass), cmap='plasma',
norm=mpl.colors.Normalize(vmax=vmax), lw=0, zorder=100, alpha=alpha_points)
ax.scatter(B_V[apassSourceIndex], raveCutMatched['FE_H'][raveSourceIndex], s=400, c='black')
ax.errorbar(B_V[apassSourceIndex], raveCutMatched['FE_H'][raveSourceIndex], yerr=raveCutMatched['E_FE_H'][raveSourceIndex],
xerr = np.sqrt(apassCutMatched['e_bmag'][apassSourceIndex]**2. + apassCutMatched['e_vmag'][apassSourceIndex]**2.),
fmt='o', color='black', lw=4)
ax.set_xlabel('B - V', fontsize=15)
ax.set_ylabel('[Fe/H]', fontsize=15)
ax.grid()
def draw_ellipse(mu, C, scales=[1, 2, 3], ax=None, **kwargs):
if ax is None:
ax = plt.gca()
# find principal components and rotation angle of ellipse
sigma_x2 = C[0, 0]
sigma_y2 = C[1, 1]
sigma_xy = C[0, 1]
#print sigma_x2, sigma_y2, sigma_xy
alpha = 0.5 * np.arctan2(2 * sigma_xy,
(sigma_x2 - sigma_y2))
tmp1 = 0.5 * (sigma_x2 + sigma_y2)
tmp2 = np.sqrt(0.25 * (sigma_x2 - sigma_y2) ** 2 + sigma_xy ** 2)
#print tmp1, tmp2
sigma1 = np.sqrt(np.abs(tmp1 + tmp2))
sigma2 = np.sqrt(np.abs(tmp1 - tmp2))
#print sigma1, sigma2
for scale in scales:
ax.add_patch(Ellipse((mu[0], mu[1]),
2 * scale * sigma1, 2 * scale * sigma2,
alpha * 180. / np.pi,
**kwargs))
def XD(raveTwins, raveCutMatched, chisqApass, ngauss=2):
amp_guess = np.zeros(ngauss)[:,None] + 1.
mean_guess = np.array([4.5, 4.0, 0.0])[:,None] #np.array([[4.5, 4.0, 0], [4.5, 3.5, 0]])
temp = raveCutMatched['TEFF'][raveTwins]/1000.
temp_err = raveCutMatched['E_TEFF'][raveTwins]/1000.
logg = raveCutMatched['LOGG'][raveTwins]
logg_err = raveCutMatched['E_LOGG'][raveTwins]
feh = raveCutMatched['FE_H'][raveTwins]
feh_err = raveCutMatched['E_FE_H'][raveTwins]
gaussianArea = np.exp(-chisqApass/2.)
X = np.vstack([temp, logg, feh]).T
Xerr = np.zeros(X.shape + X.shape[-1:])
diag = np.arange(X.shape[-1])
Xerr[:,diag,diag] = np.vstack([temp_err**2., logg_err**2., feh_err**2.]).T
cov_guess = np.zeros(((ngauss,) + X.shape[-1:] + X.shape[-1:]))
cov_guess[:,diag,diag] = 1.0
ed(X, Xerr, amp_guess, mean_guess, cov_guess, weight=gaussianArea)
return amp_guess, mean_guess, cov_guess
def plotXD2d(ax, raveTwins, raveStar, raveCutMatched, chisqApass, mean, cov, ngauss=2, fracMaxPlot=0.01, size=50, alpha_points=1., alpha_ellipse=1.):
temp = raveCutMatched['TEFF'][raveTwins]/1000.
temp_err = raveCutMatched['E_TEFF'][raveTwins]/1000.
logg = raveCutMatched['LOGG'][raveTwins]
logg_err = raveCutMatched['E_LOGG'][raveTwins]
feh = raveCutMatched['FE_H'][raveTwins]
feh_err = raveCutMatched['E_FE_H'][raveTwins]
gaussianArea = np.exp(-chisqApass/2.)
weighty = gaussianArea > fracMaxPlot*np.max(gaussianArea)
X = np.vstack([temp, logg, feh]).T
ax[0].set_title(neff(gaussianArea))
ax[0].scatter(X[:,0][weighty], X[:,1][weighty], cmap='plasma_r', c = gaussianArea[weighty],
norm=mpl.colors.Normalize(), lw=0, alpha=alpha_points,s=size)
ax[0].errorbar(raveCutMatched['TEFF'][raveStar]/1000., raveCutMatched['LOGG'][raveStar],
xerr=raveCutMatched['E_TEFF'][raveStar]/1000., yerr=raveCutMatched['E_LOGG'][raveStar],fmt='o',lw=2, zorder=100)
for scale, alpha in zip([1, 2], [1.0, 0.5]):
for i in range(ngauss):
draw_ellipse(mean[i][0:2], cov[i][0:2], scales=[scale], ax=ax[0],
ec='k', fc="None", alpha=alpha_ellipse, zorder=99, lw=2)
ax[1].scatter(X[:,0][weighty], X[:,2][weighty], cmap='plasma_r', c = gaussianArea[weighty],
norm=mpl.colors.Normalize(), lw=0, alpha=alpha_points, s=size)
ax[1].errorbar(raveCutMatched['TEFF'][raveStar]/1000., raveCutMatched['FE_H'][raveStar],
xerr=raveCutMatched['E_TEFF'][raveStar]/1000., yerr=raveCutMatched['E_FE_H'][raveStar],fmt='o',lw=2, zorder=100)
for scale, alpha in zip([1, 2], [1.0, 0.5]):
for i in range(ngauss):
draw_ellipse(mean[i][[0,2]], cov[i][[0,2]], scales=[scale], ax=ax[1],
ec='k', fc="None", alpha=alpha_ellipse, zorder=99, lw=2)
ax[2].scatter(X[:,1][weighty], X[:,2][weighty], cmap='plasma_r', c = gaussianArea[weighty],
norm=mpl.colors.Normalize(), lw=0, alpha=alpha_points, s=size)
ax[2].errorbar(raveCutMatched['LOGG'][raveStar], raveCutMatched['FE_H'][raveStar],
xerr=raveCutMatched['E_LOGG'][raveStar], yerr=raveCutMatched['E_FE_H'][raveStar],fmt='o',lw=2, zorder=100)
for scale, alpha in zip([1, 2], [1.0, 0.5]):
for i in range(ngauss):
draw_ellipse(mean[i][1:3], cov[i][1:3], scales=[scale], ax=ax[2],
ec='k', fc="None", alpha=alpha_ellipse, zorder=99, lw=2)
ax[0].set_xlim(teff_lim)
ax[0].set_xlabel('T$_\mathrm{eff}$')
ax[1].set_xlim(teff_lim)
ax[1].set_xlabel('T$_\mathrm{eff}$')
ax[2].set_xlim(log_g_lim)
ax[2].set_xlabel('log g')
ax[0].set_ylim(log_g_lim)
ax[0].set_ylabel('log g')
ax[1].set_ylim(feh_lim)
ax[1].set_ylabel('Fe/H')
ax[2].set_ylim(feh_lim)
ax[2].set_ylabel('Fe/H')
plt.tight_layout()
def tgasDistance(ndist=1024):
#read in Adrian's distances from sampling the posterior
nfiles = 16
dist = None #np.zeros(len(tgasMatched), ndist)
for j in range(1,nfiles+1):
with h5py.File("distance-samples-{:02d}.hdf5".format(j)) as f:
if dist is None:
dist = f['distance'][:,:ndist]
else:
dist = np.concatenate((dist, f['distance'][:,:ndist]))
return dist
def observationsCutMatched(SNthreshold=1., filename='cutMatchedArrays.npz'):
#read in TGAS data for and matched sample for magnitudes
tgas = fits.getdata("stacked_tgas.fits", 1)
tgasRave = fits.getdata('tgas-rave.fits', 1)
tgasApass = fits.getdata('tgas-matched-apass-dr9.fits')
tgasWise = fits.getdata('tgas-matched-wise.fits')
tgas2mass = fits.getdata('tgas-matched-2mass.fits')
distances = tgasDistance(ndist=1024)
medianDist = np.median(distances, axis=1)
#cut out low logg and temperatures outside well populated area
nonNans = ~np.isnan(tgasRave['TEFF']) & ~np.isnan(tgasRave['LOGG']) & ~np.isnan(tgasRave['FE_H'])
#dwarfs = (tgasRave['LOGG'] < maxlogg) & (tgasRave['LOGG'] > minlogg) & (tgasRave['TEFF'] > mintemp)
tgasRave = tgasRave[nonNans] # & dwarfs]
#various cuts to select sample
magSN = SNthreshold
sigMax = 1.086/magSN
maxDist = 8000. #1000. #pc
fracErrorDistance = 1.
minDist = 0.0 #pc
parallaxSN = SNthreshold
galacticLatMin = 0. #degrees
galacticLatMax = None #degrees
#current cut: no magnitudes are NaNs, 0 < errors < sigMax,
noNans = ~np.isnan(tgasApass['bmag']) & ~np.isnan(tgasApass['vmag']) & ~np.isnan(tgasApass['gmag']) & ~np.isnan(tgasApass['rmag']) & ~np.isnan(tgasApass['imag'])
posErrors = (tgasApass['e_bmag'] > 0) & (tgasApass['e_vmag'] > 0) & (tgasApass['e_gmag'] > 0) & (tgasApass['e_rmag'] > 0) & (tgasApass['e_imag'] > 0)
lowPhotError = (tgasApass['e_bmag'] < sigMax) & (tgasApass['e_vmag'] < sigMax) & (tgasApass['e_gmag'] < sigMax) & (tgasApass['e_rmag'] < sigMax) & (tgasApass['e_imag'] < sigMax)
#lowPhotError_IR = (tgas2mass['j_cmsig'] < sigMax) & (tgas2mass['h_cmsig'] < sigMax) & (tgas2mass['k_cmsig'] < sigMax) & (tgasWise['w1sigmpro'] < sigMax) & (tgasWise['w2sigmpro'] < sigMax) & (tgasWise['w3sigmpro'] < sigMax)
lowPhotError_IR = (tgas2mass['j_cmsig'] < sigMax) & (tgas2mass['k_cmsig'] < sigMax)
noDust = (medianDist < maxDist) & (np.abs(tgas['b']) > galacticLatMin)
apassMatch = tgasApass['matched']
wiseMatch = tgasWise['matched']
twoMassMatch = tgas2mass['matched']
parallaxErr = np.sqrt(tgas['parallax_error']**2. + 0.3**2.)
goodDistance = tgas['parallax']/parallaxErr > parallaxSN
hasDust = medianDist >= minDist
raveMatch = np.in1d(tgas['source_id'], tgasRave['source_id'])
if galacticLatMax: inDisk = np.abs(tgas['b'] <= galacticLatMax)
matched = goodDistance & lowPhotError_IR #& apassMatch
tgasMatched = tgas[matched]
magsMatched = tgasApass[matched]
wiseMatched = tgasWise[matched]
twoMassMatched = tgas2mass[matched]
distMatched = distances[matched]
raveInd = np.in1d(tgasRave['source_id'], tgasMatched['source_id'])
raveMatched = tgasRave[raveInd]
print( 'Number of tgas stars: ', len(tgas))
print( 'Number of matched stars: ', np.sum(matched))
print( 'Percent matched = ', 100 - (len(tgas) - np.sum(matched))/np.float(len(tgas))*100., '%')
np.savez(filename, tgasCutMatched=tgasMatched, apassCutMatched=magsMatched, raveCutMatched=raveMatched, twoMassCutMatched=twoMassMatched, wiseCutMatched=wiseMatched, distCutMatched=distMatched)
return tgasMatched, magsMatched, raveMatched, twoMassMatched, wiseMatched, distMatched
def crossMatchCheck(apassCutMatched, twoMassCutMatched, wiseCutMatched):
#plot broad colors to check that cross matching was done properly
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
fig.suptitle('Check Matching to IR surveys', fontsize=20, y=1.08)
ax[0].scatter(apassCutMatched['bmag'] - apassCutMatched['vmag'], twoMassCutMatched['j_mag'] - twoMassCutMatched['k_mag'], alpha=0.25, lw=0)
ax[0].set_xlabel('B - V')
ax[0].set_ylabel('J - K')
ax[1].scatter(apassCutMatched['bmag'] - apassCutMatched['vmag'], apassCutMatched['bmag'] - twoMassCutMatched['k_mag'], alpha=0.25, lw=0)
ax[1].set_xlabel('B - V')
ax[1].set_ylabel('B - K')
ax[2].scatter(apassCutMatched['bmag'] - apassCutMatched['vmag'], apassCutMatched['bmag'] - wiseCutMatched['w3mpro'], alpha=0.25, lw=0)
ax[2].set_xlabel('B - V')
ax[2].set_ylabel('B - W3')
plt.tight_layout()
plt.savefig('IRmatchCheck.png')
def distanceModulus(distCutMatched):
medianDistMatched = np.median(distCutMatched, axis=1)
muMatched = 5. * np.log10(distCutMatched / 10.) # 10 pc is mu = 0
meanMuMatched = np.mean(muMatched, axis=1)
varMuMatched = np.mean((muMatched - meanMuMatched[:,None]) ** 2, axis=1)
return meanMuMatched, varMuMatched
def dust(l, b, distance, plot=False, max_samples=2, mode='median', model='bayes'):
if model == 'sfd':
c = SkyCoord(l, b,
frame='galactic')
sfd = SFDQuery()
dust = sfd(c)
if model == 'bayes':
c = SkyCoord(l, b,
distance = distance,
frame='galactic')
bayes = BayestarQuery(max_samples=max_samples)
dust = bayes(c, mode=mode)
if model == 'iphas':
c = SkyCoord(l, b,
distance = distance,
frame='galactic')
iphas = IPHASQuery()
dust = iphas(c, mode=mode)
if model == 'marshall':
c = SkyCoord(l, b,
distance = distance,
frame='galactic')
marshall = MarshallQuery()
dust = marshall(c)
if model == 'chen':
c = SkyCoord(l, b,
distance = distance,
frame='galactic')
chen = Chen2014Query()
dust = chen(c)
#cNoDist = SkyCoord(l, b,
# frame='galactic')
#bayesDustNoDist = bayes(cNoDist, mode=mode)
#!!!!! Do something else than setting it equal to 0 !!!!!
#if len(bayesDust) > 1: bayesDust[np.isnan(bayesDust)] = 0.0
if plot:
fig, ax = plt.subplots(3, figsize=(5, 7.5))
ax[0].hist(np.log10(sfd(c)), bins=100, log=True, histtype='step')
ax[1].hist(np.log10(bayesDustNoDist[bayesDustNoDist>0]), bins=100, log=True, histtype='step')
ax[2].hist(np.log10(bayesDust[bayesDust >0]), bins=100, log=True, histtype='step')
ax[0].set_xlabel('SFD Dust Attenuation')
ax[1].set_xlabel('Bayestar Dust Attenuation No Distance')
ax[2].set_xlabel('Bayestar Dust Attenuation')
for a in ax: a.set_xlim(-4, 0.0)
hist, bins = np.histogram(magsMatched['bmag'], bins=100)
plt.hist(magsMatched['bmag'], bins=bins, histtype='step')
plt.hist(magsMatched['bmag'] - B_RedCoeff*bayesDust, bins=bins, histtype='step')
plt.tight_layout()
return dust
def dustTightenMS(B_V_dust, M_V_dust, B_V, M_V):
#check dust tightens main sequence
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
fig.suptitle('Check Dust Tightens MS', fontsize=20, y=1.08)
ax[0].scatter(B_V_dust, M_V_dust, alpha=0.1, lw=0)
ax[0].set_xlabel('B - V')
ax[0].set_ylabel('M_V')
ax[0].set_title('Dust Corrected')
ax[0].invert_yaxis()
ax[1].scatter(B_V, M_V, alpha=0.1, lw=0)
ax[1].set_xlabel('B - V')
ax[1].set_ylabel('M_V')
ax[1].set_title('No Dust Correction')
ax[1].invert_yaxis()
plt.tight_layout()
plt.savefig('dustCorrected.png')
def mainSequence(B_V, M_V, raveCutMatched):
alpha = 0.1
fig, axes = plt.subplots(3,2, figsize=(12.5, 17))
axes = axes.flatten()
axes[0].scatter(B_V,M_V, alpha=alpha, lw=0)
axes[0].invert_yaxis()
axes[0].set_xlabel('B-V')
axes[0].set_ylabel('M$_\mathrm{V}$')
axes[0].set_title('Apass HR Diagram')
axes[1].scatter(raveCutMatched['TEFF']/1000., raveCutMatched['LOGG'], alpha=alpha, lw=0)
axes[1].invert_yaxis()
axes[1].invert_xaxis()
axes[1].set_xlabel('T$_\mathrm{eff}$ [kK]')
axes[1].set_ylabel('log g')
axes[1].set_title('Rave HR Diagram')
axes[2].scatter(B_V, raveCutMatched['TEFF']/1000., alpha=alpha, lw=0)
#axes[2].invert_xaxis()
axes[2].set_ylabel('T$_\mathrm{eff}$ [kK]')
axes[2].set_xlabel('B - V')
axes[2].set_title('Apass Temp vs Rave Temp')
axes[3].scatter(M_V, raveCutMatched['LOGG'], alpha=alpha, lw=0)
axes[3].set_xlabel('M$_\mathrm{V}$')
axes[3].set_ylabel('log g')
axes[3].invert_yaxis()
axes[3].invert_xaxis()
axes[3].set_title('Apass log g vs Rave log g')
axes[4].scatter(M_V, raveCutMatched['FE_H'], alpha=alpha, lw=0)
axes[4].invert_xaxis()
axes[4].set_xlabel('M$_\mathrm{V}$')
axes[4].set_ylabel('Fe/H')
axes[5].scatter(B_V, raveCutMatched['FE_H'], alpha=alpha, lw=0)
#axes[5].invert_xaxis()
axes[5].set_xlabel('B - V')
axes[5].set_ylabel('Fe/H')
plt.tight_layout()
plt.savefig('AllStarsMatched.png')
def apassSourceTwinIndex(M_V, B_V, g_r, r_i, apassCutMatched, varmuCutMatched, nstars=100, nydim=100, nNeighbors=1024, printChi=False):
apassSourceIndex = np.zeros(nstars, dtype='int32')
apassTwinIndex = np.zeros((nstars, nydim-1), dtype='int32')
chisqApass = np.zeros((nstars, nydim-1))
for star in np.arange(nstars):
pts = [M_V[star], B_V[star], g_r[star], r_i[star]]
chisq, treeIndex = findNeighborsAndCalculateChisqs(star, pts, treeColor, apassCutMatched, varmuCutMatched, nNeighbors=nNeighbors, printChi=False)
sort = np.argsort(treeIndex)
chisqApass[star,:] = chisq[sort] #don't include self, zero is closest
#treeIndex = treeIndex[chisq < chisqThreshold]
apassTwinIndex[star,:] = treeIndex[sort]
apassSourceIndex[star] = star
return apassSourceIndex, apassTwinIndex, chisqApass
def raveSourceTwinIndex(apassSourceIndex, apassTwinIndex, raveCutMatched, tgasCutMatched, nstars=100, nydim=100):
raveSourceIndex = np.zeros(nstars, dtype='int32')
raveTwinIndex = np.zeros((nstars, nydim-1), dtype='int32')
chisqRave = np.zeros((nstars, nydim-1))
for i, (s, m) in enumerate(zip(apassSourceIndex, apassTwinIndex)):
raveSourceIndex[i] = np.where(np.in1d(raveCutMatched['source_id'], tgasCutMatched[s]['source_id']))[0]
raveTwinIndex[i, :] = np.where(np.in1d(raveCutMatched['source_id'], tgasCutMatched[m]['source_id']))[0]
if np.sum(raveCutMatched['source_id'][raveTwinIndex[i,:]] - tgasCutMatched[m]['source_id']) != 0: print( 'Rave not sorted like Apass')
chisqRave[i, :] = raveChisq(raveSourceIndex[i], raveTwinIndex[i,:], raveCutMatched)
return raveSourceIndex, raveTwinIndex, chisqRave
def plotChisqApassVsRave(chisqApass, chisqRave):
fig, ax = plt.subplots()
for chiA, chiR in zip(chisqApass, chisqRave):
ax.scatter(chiA, chiR, alpha=0.01, s=1)
#ax.set_xlim(0, 10)
#ax.set_ylim(0, 15)
ax.set_xlabel('Chisq Distance Apass')
ax.set_ylabel('Chisq Distance Rave')
plt.tight_layout()
plt.savefig('chisqApassVsRave.png')
def plotComparisons(indices, apassTwinIndex, apassSourceIndex, raveTwinIndex, raveSourceIndex, apassCutMatched, raveCutMatched, varmuCutMatched, chisqApass, nplot=10, filename='plot.png', ngauss=2):
fig, axes = plt.subplots(nplot, 6, figsize=(30, nplot*5))
for plotNumber, j in enumerate(indices):
try:
plotRainbows(axes[plotNumber][0:3], apassTwinIndex[j], apassSourceIndex[j], raveTwinIndex[j], raveSourceIndex[j], apassCutMatched, raveCutMatched, varmuCutMatched, chisqApass[j], vmax=10)
amp, mean, cov = XD(raveTwinIndex[j], raveCutMatched, chisqApass[j], ngauss=ngauss)
plotXD2d(axes[plotNumber][3:6], raveTwinIndex[j], raveSourceIndex[j], raveCutMatched, chisqApass[j], mean, cov, ngauss=ngauss, fracMaxPlot=0.01)
except ValueError:
pdb.set_trace()
plt.tight_layout()
plt.savefig(filename)
if __name__ == '__main__':
try: plot = np.bool(sys.argv[1])
except IndexError: plot = False
b_v_lim = [0.25, 1.5]
g_r_lim = None #[0, 1.5]
r_i_lim = None #[-0.25, 0.75]
M_v_lim = None #[10, 2]
teff_lim = [7, 4] #kKd
log_g_lim = [6, 3]
feh_lim = [-1.5, 1]
maxlogg = 20
minlogg = 1
mintemp = 100
SNthreshold = 4
filename = 'cutMatchedArrays.' + str(minlogg) + '_' + str(maxlogg) + '_' + str(mintemp) + '_' + str(SNthreshold) + '.npz'
try:
cutMatchedArrays = np.load(filename)
tgasCutMatched = cutMatchedArrays['tgasCutMatched']
apassCutMatched = cutMatchedArrays['apassCutMatched']
raveCutMatched = cutMatchedArrays['raveCutMatched']
twoMassCutMatched = cutMatchedArrays['twoMassCutMatched']
wiseCutMatched = cutMatchedArrays['wiseCutMatched']
distCutMatched = cutMatchedArrays['distCutMatched']
except IOError:
tgasCutMatched, apassCutMatched, raveCutMatched, twoMassCutMatched, wiseCutMatched, distCutMatched = observationsCutMatched(maxlogg=maxlogg, minlogg=minlogg, mintemp=mintemp, SNthreshold=SNthreshold, filename=filename)
print( 'Number of Matched stars is: ', len(tgasCutMatched))
#plot broad colors to check that cross matching was done properly
if plot: crossMatchCheck(apassCutMatched, twoMassCutMatched, wiseCutMatched)
meanMuMatched, varmuCutMatched = distanceModulus(distCutMatched)
apassMagKeys = ['bmag', 'gmag', 'vmag', 'rmag', 'imag']
apassErrorKeys = ['e_bmag', 'e_gmag', 'e_vmag', 'e_rmag', 'e_imag']
wavelength = [420., 475., 520., 658., 806.]
#include dust
#Assuming an R_V of 3.1, good assumption for Milky Way so say Schlafly+Finkbeiner
B_RedCoeff = 3.626
V_RedCoeff = 2.742
g_RedCoeff = 3.303
r_RedCoeff = 2.285
i_RedCoeff = 1.698
bayesDust = dust(tgasCutMatched['l']*units.deg, tgasCutMatched['b']*units.deg, np.median(distCutMatched, axis=1)*units.pc)
M_V = apassCutMatched['vmag'] - V_RedCoeff*bayesDust - meanMuMatched
B_V = apassCutMatched['bmag'] - B_RedCoeff*bayesDust - (apassCutMatched['vmag'] - V_RedCoeff*bayesDust)
g_r = apassCutMatched['gmag'] - g_RedCoeff*bayesDust - (apassCutMatched['rmag'] - r_RedCoeff*bayesDust)
r_i = apassCutMatched['rmag'] - r_RedCoeff*bayesDust - (apassCutMatched['imag'] - i_RedCoeff*bayesDust)
#plot mainsequence for all matched stars in both rave and apass
if plot: mainSequence(B_V, M_V, raveCutMatched)
#check dust tightens main sequence
if plot: dustTightenMS(B_V, M_V, apassCutMatched['bmag'] - apassCutMatched['vmag'], apassCutMatched['vmag'] - meanMuMatched)
#build tree on apass photometry + GAIA
treeColor = kdtree.cKDTree(data=zip(M_V, B_V, g_r, r_i))
treeRave = kdtree.cKDTree(data=zip(M_V, B_V, g_r, r_i, raveCutMatched['LOGG'], raveCutMatched['TEFF']))
#number of stars to find twins for
nstars = len(apassCutMatched)
#number of twins to grab for each source star
nNeighbors = 1024
if nNeighbors > nstars: nNeighbors = nstars
nydim = nNeighbors
#chisqThreshold = 100
apassSourceIndex, apassTwinIndex, chisqApass = apassSourceTwinIndex(M_V, B_V, g_r, r_i, apassCutMatched, varmuCutMatched, nstars=nstars, nydim=nydim, nNeighbors=nNeighbors)
raveSourceIndex, raveTwinIndex, chisqRave = raveSourceTwinIndex(apassSourceIndex, apassTwinIndex, raveCutMatched, tgasCutMatched, nstars=nstars, nydim=nydim)
nx = 10000
nmodel = 20
x_model = np.linspace(1, 10, nx)
mu = np.linspace(3, 6, nmodel)
sigma = np.linspace(0.01, 0.5, nmodel)
posterior = np.zeros((nmodel,nmodel))
for obsIndex in [6]:
x_obs = raveCutMatched[raveTwinIndex[obsIndex]]['TEFF']/1000.
sigma_obs = raveCutMatched[raveTwinIndex[obsIndex]]['E_TEFF']/1000.
x_rave = raveCutMatched[raveSourceIndex[obsIndex]]['TEFF']/1000.
sigma_rave = raveCutMatched[raveSourceIndex[obsIndex]]['E_TEFF']/1000.
plot=False
weight_obs = np.exp(-0.5*chisqApass[obsIndex])
for m, mean in enumerate(mu):
for s, sig in enumerate(sigma):
#integrand = gaussian(x_obs[:, None], sigma_obs[:, None], x_model, amplitude=weight_obs[:, None])*gaussian(mean, variance, x_model)
#integral = cumtrapz(integrand, x=x_model)
#loglikelihood = np.log(integral[:,-1])
loglikelihood = np.log(gaussian(mean, sig + sigma_obs, x_obs, amplitude=weight_obs))
posterior[m, s] = np.sum(loglikelihood)
#if plot:
# fig, ax = plt.subplots(2)
# for foo in np.arange(nNeighbors-1): ax[0].plot(x_model, integrand[foo], alpha=0.5, color='blue')
# ax[1].scatter(loglikelihood, np.log(weight_obs))
# plt.show()
fig, ax = plt.subplots(2)
maxIndex = np.where(posterior == np.max(posterior))
ax[0].plot(x_model, gaussian(mu[maxIndex[0]], sigma[maxIndex[1]], x_model, amplitude=1), color='black', lw=2, label='XD')
for foo in range(len(x_obs)): ax[1].plot(x_model, gaussian(x_obs[foo], sigma_obs[foo], x_model, amplitude = weight_obs[foo]), alpha=0.1, color='blue')
ax[0].plot(x_model, gaussian(x_rave, sigma_rave, x_model, amplitude=1), color='blue', linestyle='--', lw=2, label='Rave')
ax[1].set_xlabel('TEFF [kK]')
ax[1].set_yscale('log')
ax[1].set_ylim(1,)
# plt.tight_layout()
#print x_obs[np.argsort(weight_obs)[::-1]][0:100], sigma_obs[np.argsort(weight_obs)[::-1]][0:100]
plt.show()
pdb.set_trace()
neffRave = np.zeros(nstars)
neffApass = np.zeros(nstars)
for i in range(nstars):
neffRave[i] = neff(np.exp(-0.5*chisqRave[i]))
neffApass[i] = neff(np.exp(-0.5*chisqApass[i]))
maxNeffRave = 50.
minNeffRave = 150.
maxNeffApass = 25.
minNeffApass = 100.
if plot:
logg = raveCutMatched['LOGG'][raveSourceIndex]
varpi = tgasCutMatched['parallax'][apassSourceIndex]
teff = raveCutMatched['TEFF'][raveSourceIndex]
dwarfs = (logg < 5.) & (logg > 4.2) & (teff > 4500)
warm = teff > 4500
fig, ax = plt.subplots(figsize=(7, 5))
points = ax.scatter(neffApass, neffRave, lw=0, alpha=0.5, c=logg, norm=mpl.colors.Normalize(), cmap='cool')
ax.fill_between([np.min(neffApass), maxNeffApass], [np.max(neffRave), np.max(neffRave)], y2 = [minNeffRave, minNeffRave], color='black', alpha=0.1)
ax.fill_between([minNeffApass, np.max(neffApass)],[maxNeffRave, maxNeffRave], color='black', alpha=0.1)
fig.colorbar(points)
ax.set_xlabel('Apass Neff')
ax.set_ylabel('Rave Neff')
plt.tight_layout()
fig.savefig('neffApassVsTgas.png')
#spot check dwarfs with lots of Apass neighbors but few Rave neighbors
manyApassfewRave = np.where((neffRave < maxNeffRave) & (neffApass > minNeffApass))[0]
manyRavefewApass = np.where((neffRave > minNeffRave) & (neffApass < maxNeffApass))[0]
print( 'Lots of Apass, few Rave: ', manyApassfewRave)
print( 'Few Apass, lots of Rave: ', manyRavefewApass)
try:
data = np.load('gaussianArrays.npz')
gaussAmplitudes = data['gaussAmplitudes']
gaussMeans = data['gaussMeans']
gaussCov = data['gaussCov']
randarray = data['index']
except IOError:
nValues = 3 #logg, Teff, Fe/H
ngauss = 2
nstars = 5
gaussAmplitudes = np.zeros((nstars, ngauss))
gaussMeans = np.zeros((nstars, ngauss, nValues))
gaussCov = np.zeros((nstars, ngauss, nValues, nValues))
randarray = np.random.randint(0, high=len(apassCutMatched), size=nstars)
for i, j in enumerate(randarray):
gaussAmplitudes[i], gaussMeans[i], gaussCov[i] = XD(raveTwinIndex[j], raveCutMatched, chisqApass[j], ngauss=ngauss)
np.savez('gaussianArrays.npz', gaussAmplitudes=gaussAmplitudes, gaussMeans=gaussMeans, gaussCov=gaussCov, index=randarray)
fig, ax = plt.subplots(3)
for i, (key, keyerror) in enumerate(zip(['TEFF', 'LOGG', 'FE_H'],['E_TEFF', 'E_LOGG', 'E_FE_H'])):
mean = (gaussMeans[:,0,i]*gaussAmplitudes[:,0] + gaussMeans[:,1, i]*gaussAmplitudes[:,1])/(gaussAmplitudes[:,0] + gaussAmplitudes[:,1])
cov = (gaussCov[:,0, i, i] + gaussCov[:,1,i,i])
if key == 'TEFF':
mean = 1000.*mean
cov = 1000.*cov
#ax[i].scatter(raveCutMatched[raveSourceIndex[randarray]][key], gaussMeans[:,0,i])
ax[i].scatter(raveCutMatched[raveSourceIndex[randarray]][key], mean)
ax[i].errorbar(raveCutMatched[raveSourceIndex[randarray]][key], mean, xerr=raveCutMatched[raveSourceIndex[randarray]][keyerror], yerr=np.sqrt(cov), fmt="none", ecolor='black', zorder=0, lw=0.5, mew=0)
xmin, xmax = ax[i].get_xlim()
plotarray = np.linspace(xmin, xmax, 100)
ax[i].plot(plotarray, plotarray)
plt.show()
#nplot = 10
#randarray = np.random.randint(0, high=len(apassCutMatched), size=nplot)
#plotComparisons(randarray, apassTwinIndex, apassSourceIndex, raveTwinIndex, raveSourceIndex, apassCutMatched, raveCutMatched, varmuCutMatched, chisqApass, nplot=nplot, filename='random.png', ngauss=2)
#randarray = randarray[np.argsort(M_V[sources][randarray])
#nplot = len(manyApassfewRave)
#print 'The number of many Apass but few Rave neighbors: ', nplot
#plotComparisons(manyApassfewRave, apassTwinIndex, apassSourceIndex, raveTwinIndex, raveSourceIndex, apassCutMatched, raveCutMatched, varmuCutMatched, chisqApass, nplot=nplot, filename='manyApassFewRave.png')
#nplot = len(manyRavefewApass)
#print 'The number of many Rave but few Apass neighbors: ', nplot
#plotComparisons(manyRavefewApass, apassTwinIndex, apassSourceIndex, raveTwinIndex, raveSourceIndex, apassCutMatched, raveCutMatched, varmuCutMatched, chisqApass, nplot=nplot, filename='manyRavefewApass.png')