def raveRC():
data= readRave()
jk= data['Jmag2']-data['Kmag2']-0.17*numpy.exp(data['Av'])
z= isodist.FEH2Z(data['[M/H]K'],zsolar=0.017)
logg= data['loggK']
indx= (jk < 0.8)*(jk >= 0.5)\
*(z <= 0.06)\
*(z <= rcmodel.jkzcut(jk,upper=True))\
*(z >= rcmodel.jkzcut(jk))\
*(logg >= rcmodel.loggteffcut(data['TeffK'],z,upper=False))\
*(logg <= rcmodel.loggteffcut(data['TeffK'],z,upper=True))
data= data[indx]
#To allow for XY pixelization
data= esutil.numpy_util.add_fields(data,[('RC_GALR', float),
('RC_GALPHI', float),
('RC_GALZ', float),
('VHELIO_AVG', float)])
XYZ= bovy_coords.lbd_to_XYZ(data['GLON'],
data['GLAT'],
data['Dist'],
degree=True)
R,phi,Z= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],
XYZ[:,1],
XYZ[:,2],
Xsun=8.,Zsun=0.025)
data['RC_GALR']= R
data['RC_GALPHI']= phi
data['RC_GALZ']= Z
data['VHELIO_AVG']= data['HRV']
return data
def write_matches(savefilename):
apokasc= match_apokasc_saga()
#Perform RC selection
logg= apokasc['KASC_RG_LOGG_SCALE_2']
teff= apokasc['TEFF']
z= 0.017*10.**apokasc['METALS']
jk= apokasc['J0']-apokasc['K0']
indx= (logg >= 1.8)\
*(logg <= 0.0018*(teff+382.5*apokasc['METALS']-4607)+2.5)\
*(jk < 0.8)\
*(jk >= 0.5)\
*(z <= 0.06)\
*(z <= rcmodel.jkzcut(jk,upper=True))\
*(z >= rcmodel.jkzcut(jk))
print "Found %i RC stars in APOKASC" % numpy.sum(indx)
rcdists= numpy.zeros(len(apokasc))-1
rcd= rcdist('../data/rcmodel_mode_jkz_ks_parsec_newlogg.sav')
rcdists[indx]= rcd(jk[indx],z[indx],apokasc['K0'][indx])
pindx= (rcdists > 0.)*(apokasc['DIST_SEISMO'] > 0.)
apokasc= apokasc[pindx]
rcdists= rcdists[pindx]
print "Found %i RC stars in APOKASC with seismic distances" % numpy.sum(pindx)
savefile= open(savefilename,'w')
savefile.write('#ID,RCDIST(pc),SEISMODIST(SCALE,MO,pc),(SEISMODIST-RCDIST)/RCDIST\n')
csvwriter = csv.writer(savefile, delimiter=',')
for ii in range(len(apokasc)):
csvwriter.writerow([apokasc['KEPLER ID'][ii],
rcdists[ii]*1000.,
apokasc['DIST_SEISMO'][ii]*1000.,
(apokasc['DIST_SEISMO'][ii]-rcdists[ii])/rcdists[ii]])
savefile.close()
def plot_apogee_jkz(plotfilename,sample):
if sample == 'disk':
data= readData()
elif sample == 'halo':
data= readData(halo=True)
elif sample == 'bulge':
data= readData(bulge=True)
ntotal= len(data)
#logg cut
data= data[(data['LOGG_AVG'] < 2.8)*(data['LOGG_AVG'] > 1.8)]
npass= len(data)
#Deredden
aj= data['AK_WISE']*2.5
ah= data['AK_WISE']*1.55
j0= data['JMAG']-aj
h0= data['HMAG']-ah
k0= data['KMAG']-data['AK_WISE']
#calculate Zs
zsolar= 0.019
z= isodist.FEH2Z(data['METALS_AVG'])/zsolar
#Plot
bovy_plot.bovy_print()
if sample == 'bulge':
bovy_plot.bovy_plot(j0-k0,z,'ko',
xrange=[0.5,0.75],
yrange=[0.,0.03/zsolar],
xlabel=r'$(J-K_s)_0$',
ylabel=r'$Z/Z_\odot$',ms=5.)
else:
bovy_plot.scatterplot(j0-k0,z,'k,',
xrange=[0.5,0.75],
yrange=[0.,0.03/zsolar],
xlabel=r'$(J-K_s)_0$',
ylabel=r'$Z/Z_\odot$',
bins=21)
#Overplot cuts
jks= numpy.linspace(0.5,0.75,1001)
bovy_plot.bovy_plot(jks,rcmodel.jkzcut(jks)/zsolar,
'k--',lw=2.,overplot=True)
bovy_plot.bovy_plot(jks,rcmodel.jkzcut(jks,upper=True)/zsolar,
'k--',lw=2.,overplot=True)
#Data between the cuts
indx= (j0-k0 < 0.75)*(j0-k0 > 0.5)\
*(z <= rcmodel.jkzcut(j0-k0,upper=True)/zsolar)\
*(z >= rcmodel.jkzcut(j0-k0)/zsolar)
# j0= j0[indx]
# h0= h0[indx]
# k0= k0[indx]
# z= z[indx]
# bovy_plot.bovy_plot(j0-k0,z,'w.',
# overplot=True,
# mec='w',
# ms=.5)
bovy_plot.bovy_text(r'$\mathrm{APOGEE\ ' + sample + '\ sample}$',
title=True)
bovy_plot.bovy_text(r'$%i/%i/%i$' % (numpy.sum(indx),npass,ntotal),
bottom_right=True,size=14.)
bovy_plot.bovy_end_print(plotfilename)
return None
def indiv_calc(ii,zs,options,njks,jks,nmks,mks,logpz):
print zs[ii]
out= numpy.empty((2,njks,nmks))
rc= rcmodel.rcmodel(Z=zs[ii],loggmin=1.8,loggmax='custom',
band=options.band,basti=options.basti,
imfmodel=options.imfmodel,
expsfh=options.expsfh,
parsec=options.parsec,
stage=options.stage)
for jj in range(njks):
if zs[ii] > rcmodel.jkzcut(jks[jj],upper=True) \
or zs[ii] < rcmodel.jkzcut(jks[jj]):
out[0,jj,:]= -numpy.finfo(numpy.dtype(numpy.float64)).max
out[1,jj,:]= -numpy.finfo(numpy.dtype(numpy.float64)).max
continue
for kk in range(nmks):
out[0,jj,kk]= logpz[ii]+rc(jks[jj],mks[kk])+numpy.log(-mks[kk])
out[1,jj,kk]= logpz[ii]+rc(jks[jj],mks[kk])
return out
def plot_vs_jkz(parser):
options,args= parser.parse_args()
if options.basti:
zs= numpy.array([0.004,0.008,0.01,0.0198,0.03,0.04])
elif options.parsec:
zs= numpy.arange(0.0005,0.06005,0.0005)
else:
zs= numpy.arange(0.0005,0.03005,0.0005)
if os.path.exists(args[0]):
savefile= open(args[0],'rb')
plotthis= pickle.load(savefile)
jks= pickle.load(savefile)
zs= pickle.load(savefile)
savefile.close()
else:
njks= 101
jks= numpy.linspace(0.5,0.8,njks)
plotthis= numpy.zeros((njks,len(zs)))
funcargs= (zs,options,njks,jks)
multOut= multi.parallel_map((lambda x: indiv_calc(x,
*funcargs)),
range(len(zs)),
numcores=numpy.amin([64,len(zs),
multiprocessing.cpu_count()]))
for ii in range(len(zs)):
plotthis[:,ii]= multOut[ii]
#Save
save_pickles(args[0],plotthis,jks,zs)
#Plot
if options.type == 'sig':
if options.band.lower() == 'age':
if options.relative:
raise NotImplementedError("relative age not implemented yet")
else:
vmin, vmax= 0.,.5
zlabel= r'$\mathrm{FWHM} / 2\sqrt{2\,\ln 2}$'
else:
if options.relative:
vmin, vmax= 0.8,1.2
zlabel= r'$\mathrm{FWHM}/\mathrm{FWHM}_{\mathrm{fiducial}}$'
else:
vmin, vmax= 0., 0.4
zlabel= r'$\mathrm{FWHM} / 2\sqrt{2\,\ln 2}$'
elif options.type == 'mode':
if options.band.lower() == 'age':
if options.relative:
raise NotImplementedError("relative age not implemented yet")
else:
vmin, vmax= 0.,1.
zlabel= r'$\Delta\displaystyle\arg\!\max_{\substack{\log_{10}\mathrm{Age}}}{p(\log_{10}\mathrm{Age}|[J-K_s]_0)}$'
else:
if options.relative:
vmin, vmax= -0.05,0.05
zlabel= r'$\Delta\displaystyle\arg\!\max_{\substack{K_s}}{p(M_{K_s}|[J-K_s]_0)}$'
else:
vmin, vmax= -1.8, -1.5
if options.band.lower() == 'h':
zlabel= r'$\displaystyle\arg\!\max_{\substack{H}}{p(M_{H}|[J-K_s]_0)}$'
else:
zlabel= r'$\displaystyle\arg\!\max_{\substack{K_s}}{p(M_{K_s}|[J-K_s]_0)}$'
if options.basti:#Remap the Zs
zs= numpy.array([0.004,0.008,0.01,0.0198,0.03,0.04])
regularzs= numpy.arange(0.0005,0.04005,0.0005)
njks= len(jks)
regularplotthis= numpy.zeros((njks,len(regularzs)))
for jj in range(len(regularzs)):
#Find z
thisindx= numpy.argmin(numpy.fabs(regularzs[jj]-zs))
for ii in range(njks):
regularplotthis[ii,jj]= plotthis[ii,thisindx]
zs= regularzs
plotthis= regularplotthis
if options.relative and os.path.exists(options.infilename):
savefile= open(options.infilename,'rb')
plotthisrel= pickle.load(savefile)
savefile.close()
if options.basti:
plotthisrel= plotthisrel[:,:80]
elif not options.parsec:
plotthisrel= plotthisrel[:,:60]
if options.type == 'mode':
plotthis-= plotthisrel
elif options.type == 'sig':
plotthis/= plotthisrel
bovy_plot.bovy_print()
if options.type == 'sig':
plotthis[numpy.isnan(plotthis)]= vmax
if options.relative:
#Only plot between the cuts
for ii in range(plotthis.shape[0]):
indx= zs >= rcmodel.jkzcut(jks[ii],upper=True)
indx+= zs <= rcmodel.jkzcut(jks[ii],upper=False)
plotthis[ii,indx]= numpy.nan
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
xrange=[jks[0],jks[-1]],
yrange=[zs[0],zs[-1]],
vmin=vmin,vmax=vmax,
xlabel=r'$(J-K_s)_0$',
ylabel=r'$Z$',
interpolation='nearest',
colorbar=True,
shrink=0.78,
zlabel=zlabel)
#Overplot cuts
bovy_plot.bovy_plot(jks,rcmodel.jkzcut(jks),
'w--',lw=2.,overplot=True)
bovy_plot.bovy_plot(jks,rcmodel.jkzcut(jks,upper=True),
'w--',lw=2.,overplot=True)
if options.basti:
pyplot.annotate(r'$\mathrm{BaSTI}$',
(0.5,1.08),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=16.)
elif not options.parsec:
pyplot.annotate(r'$\mathrm{Padova}$',
(0.5,1.08),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=16.)
elif options.imfmodel == 'kroupa2003':
pyplot.annotate(r'$\mathrm{Kroupa\ (2003)\ IMF}$',
(0.5,1.08),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=16.)
elif 'expsfh' in args[0]:
pyplot.annotate(r'$\mathrm{p(\mathrm{Age}) \propto e^{\mathrm{Age}/(8\,\mathrm{Gyr})}}$',
(0.5,1.08),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=16.)
elif not options.eta is None:
pyplot.annotate(r'$\eta_{\mathrm{Reimers}} = %.1f$' % options.eta,
(0.5,1.08),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=16.)
elif False:
pyplot.annotate(r'$\mathrm{Padova}$',
(0.5,1.08),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=16.)
bovy_plot.bovy_end_print(options.outfilename)
return None
def compare_seismic_distances(plotfilename):
apokasc= match_apokasc_saga()
#Perform RC selection
logg= apokasc['KASC_RG_LOGG_SCALE_2']
teff= apokasc['TEFF']
z= 0.017*10.**apokasc['METALS']
jk= apokasc['J0']-apokasc['K0']
indx= (logg >= 1.8)\
*(logg <= 0.0018*(teff+382.5*apokasc['METALS']-4607)+2.5)\
*(jk < 0.8)\
*(jk >= 0.5)\
*(z <= 0.06)\
*(z <= rcmodel.jkzcut(jk,upper=True))\
*(z >= rcmodel.jkzcut(jk))#\
#*(apokasc['SEISMO EVOL'] == 'CLUMP')
print "Found %i RC stars in APOKASC" % numpy.sum(indx)
rcdists= numpy.zeros(len(apokasc))-1
rcd= rcdist('../data/rcmodel_mode_jkz_ks_parsec_newlogg.sav')
rcdists[indx]= rcd(jk[indx],z[indx],apokasc['K0'][indx])
pindx= (rcdists > 0.)*(apokasc['DIST_SEISMO'] > 0.)
print "Found %i RC stars in APOKASC with seismic distances" % numpy.sum(pindx)
#Setup plot
bovy_plot.bovy_print(fig_height=7.)
dx= 0.6
left, bottom, width, height= 0.1, 0.9-dx, 0.8, dx
axTop= pyplot.axes([left,bottom,width,height])
fig= pyplot.gcf()
fig.sca(axTop)
bovy_plot.bovy_plot([0.,20.],[0.,20.],'k-',lw=2.,color='0.4',
overplot=True,zorder=0)
bovy_plot.bovy_plot(rcdists[pindx],apokasc['DIST_SEISMO'][pindx],
'k.',overplot=True,zorder=10)
if False:
pyplot.errorbar(rcdists[pindx],
apokasc['DIST_SEISMO'][pindx],
xerr=0.05*rcdists[pindx],
yerr=apokasc['E_DIST_SEISMO'][pindx],
marker=',',color='k',
linestyle='none')
thisax= pyplot.gca()
thisax.set_ylim(0.,5.)
pyplot.xlim(0.,5.)
bovy_plot._add_ticks()
nullfmt = NullFormatter() # no labels
axTop.xaxis.set_major_formatter(nullfmt)
bovy_plot._add_ticks()
pyplot.ylabel(r'$\mathrm{seismic\ distance}\,(\mathrm{kpc})$')
#Second plot
left, bottom, width, height= 0.1, 0.1, 0.8, 0.8-dx
thisax= pyplot.axes([left,bottom,width,height])
fig.sca(thisax)
bovy_plot.bovy_plot([0.,20.],[0.,0.],'k-',lw=2.,color='0.4',
overplot=True,zorder=0)
bovy_plot.bovy_plot(rcdists[pindx],
(apokasc['DIST_SEISMO'][pindx]-rcdists[pindx])/rcdists[pindx],
'k.',overplot=True,zorder=10)
thisax= pyplot.gca()
thisax.set_ylim(-0.2,0.2)
pyplot.xlim(0.,5.)
bovy_plot._add_ticks()
nullfmt = NullFormatter() # no labels
bovy_plot._add_ticks()
pyplot.ylabel(r'$\mathrm{relative\ differene}$')
pyplot.xlabel(r'$\mathrm{RC\ distance}\,(\mathrm{kpc})$')
medoffset= numpy.median((apokasc['DIST_SEISMO'][pindx]-rcdists[pindx])/rcdists[pindx])
medsig= 1.4826*numpy.median(numpy.fabs((apokasc['DIST_SEISMO'][pindx]-rcdists[pindx])/rcdists[pindx]-medoffset))
bovy_plot.bovy_text(2.75,-0.125,r'$\mathrm{diff} = %.3f\pm%.3f$' % \
(medoffset,medsig),size=14.)
bovy_plot.bovy_end_print(plotfilename)
def _calc_one(z, options, nages, lages, dlages):
print z
if options.allapogee or options.redapogee:
rc = rcmodel.rcmodel(
Z=z,
loggmax=3.5,
band=options.band,
basti=options.basti,
imfmodel=options.imfmodel,
parsec=options.parsec,
eta=options.eta,
)
else:
rc = rcmodel.rcmodel(
Z=z,
loggmin=1.8,
loggmax="custom",
band=options.band,
basti=options.basti,
imfmodel=options.imfmodel,
parsec=options.parsec,
eta=options.eta,
)
out = numpy.zeros(nages)
for jj in range(nages):
jk = rc._jks
aindx = (rc._lages <= lages[jj] + dlages) * (rc._lages > lages[jj] - dlages)
if options.allapogee:
aindx *= jk > 0.5
elif options.redapogee:
aindx *= jk > 0.8
else:
rcd = rcmodel.rcdist("../../rcdist-apogee/data/rcmodel_mode_jkz_ks_parsec_newlogg.sav")
predH = numpy.array([rcd(j, z) for j in jk])
predH = numpy.reshape(predH, len(jk))
aindx *= (
(jk < 0.8)
* (jk > 0.5)
* (z <= rcmodel.jkzcut(jk, upper=True))
* (z >= rcmodel.jkzcut(jk))
* (z <= 0.06)
* (rc._sample[:, 1] > (predH - 0.4))
* (rc._sample[:, 1] < (predH + 0.4))
* (rc._sample[:, 1] > -3.0)
* (rc._loggs[:, 0] <= 3.5)
)
if options.type == "omega":
try:
out[jj] = numpy.mean(rc._massweights[aindx])
except ValueError:
out[jj] = numpy.nan
elif options.type == "numfrac":
try:
out[jj] = numpy.mean(rc._weights[aindx])
except ValueError:
out[jj] = numpy.nan
elif options.type == "mass":
try:
out[jj] = numpy.sum(rc._masses[aindx] * rc._weights[aindx]) / numpy.sum(rc._weights[aindx])
except ValueError:
out[jj] = numpy.nan
return out
def plot_Z_jk_apokasc(parser):
options, args = parser.parse_args()
# Setup Zs
if os.path.exists(args[0]):
savefile = open(args[0], "rb")
outhist = pickle.load(savefile)
hists = pickle.load(savefile)
edgess = pickle.load(savefile)
data = pickle.load(savefile)
savefile.close()
else:
if _PREDICT:
zs = numpy.arange(0.0005, 0.06005, 0.0005)
if _DEBUG:
zs = numpy.arange(0.0005, 0.06005, 0.005)
# Load the RC models for each feh individually
rcms = []
hists = []
edgess = []
for z in zs:
print z
trc = rcmodel.rcmodel(
Z=z,
loggmin=1.8,
loggmax=2.8,
band=options.band,
basti=options.basti,
imfmodel=options.imfmodel,
parsec=options.parsec,
)
rcms.append(trc)
sample = numpy.vstack([trc._sample[:, 0], z * numpy.ones(trc._sample.shape[0])]).T
weights = trc._weights
hist, edges = numpy.histogramdd(
sample, weights=weights, bins=12, range=[[0.5, 0.8], [0.0, 0.06]] # 12*(10-_DEBUG*9),
)
hists.append(hist)
edgess.append(edges)
# Load APOKASC data
data = apread.apokasc()
indx = (data["KASC_RG_LOGG_SCALE_2"] > 2.25) * (data["KASC_RG_LOGG_SCALE_2"] < 2.65)
print "Using %i APOKASC objects ..." % (numpy.sum(indx))
data = data[indx]
if _PREDICT:
# Stack predictions
outhist = numpy.zeros_like(hists[0])
for ii in range(len(hists)):
outhist += hists[ii]
save_pickles(args[0], outhist, hists, edgess, data)
if _PREDICT:
# Normalize each color
pass
# for ii in range(len(outhist[:,0])):
# outhist[ii,:]/= numpy.nanmax(outhist[ii,:])/numpy.nanmax(outhist)
# for ii in range(len(outhist[0,:])):
# outhist[:,ii]/= numpy.nanmax(outhist[:,ii])/numpy.nanmax(outhist)
# Plot everything
bovy_plot.bovy_print()
if _PREDICT:
bovy_plot.bovy_dens2d(
outhist.T,
origin="lower",
cmap="gist_yarg",
xrange=[edgess[0][0][0], edgess[0][0][-1]],
yrange=[edgess[0][1][0], edgess[0][1][-1]],
aspect=(edgess[0][0][-1] - edgess[0][0][0]) / float(edgess[0][1][-1] - edgess[0][1][0]),
xlabel=r"$(J-K_s)_0\ [\mathrm{mag}]$",
ylabel=r"$Z$",
shrink=0.78,
interpolation="nearest",
)
# Overplot APOKASC data
# Load APOKASC data
data = apread.apokasc()
indx = (data["KASC_RG_LOGG_SCALE_2"] > 1.8) * (data["KASC_RG_LOGG_SCALE_2"] < 2.8)
print "Using %i APOKASC objects ..." % (numpy.sum(indx))
data = data[indx]
bovy_plot.bovy_plot(
data["J0"] - data["K0"],
options.zsolar * 10.0 ** data["METALS"],
c=data["KASC_RG_LOGG_SCALE_2"] - 2.45,
s=20.0,
edgecolors="none",
scatter=True,
colorbar=True,
overplot=True,
)
# mec='none',ms=3.)
# Overplot cuts
jks = numpy.linspace(0.5, 0.8, 201)
bovy_plot.bovy_plot(jks, rcmodel.jkzcut(jks), "k--", lw=2.0, overplot=True)
bovy_plot.bovy_plot(jks, rcmodel.jkzcut(jks, upper=True), "k--", lw=2.0, overplot=True)
bovy_plot.bovy_end_print(options.outfilename)
return None
def make_rcsample(parser):
options,args= parser.parse_args()
savefilename= options.savefilename
if savefilename is None:
#Create savefilename if not given
savefilename= os.path.join(appath._APOGEE_DATA,
'rcsample_'+appath._APOGEE_REDUX+'.fits')
print "Saving to %s ..." % savefilename
#Read the base-sample
data= apread.allStar(adddist=_ADDHAYDENDIST,rmdups=options.rmdups)
#Remove a bunch of fields that we do not want to keep
data= esutil.numpy_util.remove_fields(data,
['TARGET_ID',
'FILE',
'AK_WISE',
'SFD_EBV',
'SYNTHVHELIO_AVG',
'SYNTHVSCATTER',
'SYNTHVERR',
'SYNTHVERR_MED',
'RV_TEFF',
'RV_LOGG',
'RV_FEH',
'RV_CCFWHM',
'RV_AUTOFWHM',
'SYNTHSCATTER',
'CHI2_THRESHOLD',
'APSTAR_VERSION',
'ASPCAP_VERSION',
'RESULTS_VERSION',
'REDUCTION_ID',
'SRC_H',
'PM_SRC'])
if int(appath._APOGEE_REDUX[1:]) < 500:
data= esutil.numpy_util.remove_fields(data,
['ELEM'])
#Select red-clump stars
jk= data['J0']-data['K0']
z= isodist.FEH2Z(data['METALS'],zsolar=0.017)
if int(appath._APOGEE_REDUX[1:]) > 600:
from apogee.tools import paramIndx
if False:
#Use my custom logg calibration that's correct for the RC
logg= (1.-0.042)*data['FPARAM'][:,paramIndx('logg')]-0.213
lowloggindx= data['FPARAM'][:,paramIndx('logg')] < 1.
logg[lowloggindx]= data['FPARAM'][lowloggindx,paramIndx('logg')]-0.255
hiloggindx= data['FPARAM'][:,paramIndx('logg')] > 3.8
logg[hiloggindx]= data['FPARAM'][hiloggindx,paramIndx('logg')]-0.3726
else:
#Use my custom logg calibration that's correct on average
logg= (1.+0.03)*data['FPARAM'][:,paramIndx('logg')]-0.37
lowloggindx= data['FPARAM'][:,paramIndx('logg')] < 1.
logg[lowloggindx]= data['FPARAM'][lowloggindx,paramIndx('logg')]-0.34
hiloggindx= data['FPARAM'][:,paramIndx('logg')] > 3.8
logg[hiloggindx]= data['FPARAM'][hiloggindx,paramIndx('logg')]-0.256
else:
logg= data['LOGG']
indx= (jk < 0.8)*(jk >= 0.5)\
*(z <= 0.06)\
*(z <= rcmodel.jkzcut(jk,upper=True))\
*(z >= rcmodel.jkzcut(jk))\
*(logg >= rcmodel.loggteffcut(data['TEFF'],z,upper=False))\
*(logg <= rcmodel.loggteffcut(data['TEFF'],z,upper=True))
data= data[indx]
#Add more aggressive flag cut
data= esutil.numpy_util.add_fields(data,[('ADDL_LOGG_CUT',numpy.int32)])
data['ADDL_LOGG_CUT']= ((data['TEFF']-4800.)/1000.+2.75) > data['LOGG']
if options.loggcut:
data= data[data['ADDL_LOGG_CUT'] == 1]
print "Making catalog of %i objects ..." % len(data)
#Add distances
data= esutil.numpy_util.add_fields(data,[('RC_DIST', float),
('RC_DM', float),
('RC_GALR', float),
('RC_GALPHI', float),
('RC_GALZ', float)])
rcd= rcmodel.rcdist('../../rcdist-apogee/data/rcmodel_mode_jkz_ks_parsec_newlogg.sav')
jk= data['J0']-data['K0']
z= isodist.FEH2Z(data['METALS'],zsolar=0.017)
data['RC_DIST']= rcd(jk,z,appmag=data['K0'])*options.distfac
data['RC_DM']= 5.*numpy.log10(data['RC_DIST'])+10.
XYZ= bovy_coords.lbd_to_XYZ(data['GLON'],
data['GLAT'],
data['RC_DIST'],
degree=True)
R,phi,Z= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],
XYZ[:,1],
XYZ[:,2],
Xsun=8.,Zsun=0.025)
data['RC_GALR']= R
data['RC_GALPHI']= phi
data['RC_GALZ']= Z
#Save
fitsio.write(savefilename,data,clobber=True)
if not options.nostat:
#Determine statistical sample and add flag
apo= apogee.select.apogeeSelect()
statIndx= apo.determine_statistical(data)
mainIndx= apread.mainIndx(data)
data= esutil.numpy_util.add_fields(data,[('STAT',numpy.int32),
('INVSF',float)])
data['STAT']= 0
data['STAT'][statIndx*mainIndx]= 1
for ii in range(len(data)):
if (statIndx*mainIndx)[ii]:
data['INVSF'][ii]= 1./apo(data['LOCATION_ID'][ii],
data['H'][ii])
else:
data['INVSF'][ii]= -1.
if options.nopm:
fitsio.write(savefilename,data,clobber=True)
return None
#Get proper motions
from astroquery.vizier import Vizier
import astroquery
from astropy import units as u
import astropy.coordinates as coord
pmfile= savefilename.split('.')[0]+'_pms.fits'
if os.path.exists(pmfile):
pmdata= fitsio.read(pmfile,1)
else:
pmdata= numpy.recarray(len(data),
formats=['f8','f8','f8','f8','f8','f8','i4'],
names=['RA','DEC','PMRA','PMDEC',
'PMRA_ERR','PMDEC_ERR','PMMATCH'])
rad= u.Quantity(4./3600.,u.degree)
v= Vizier(columns=['RAJ2000','DEJ2000','pmRA','pmDE','e_pmRA','e_pmDE'])
for ii in range(len(data)):
#if ii > 100: break
sys.stdout.write('\r'+"Getting pm data for point %i / %i" % (ii+1,len(data)))
sys.stdout.flush()
pmdata.RA[ii]= data['RA'][ii]
pmdata.DEC[ii]= data['DEC'][ii]
co= coord.ICRS(ra=data['RA'][ii],
dec=data['DEC'][ii],
unit=(u.degree, u.degree))
trying= True
while trying:
try:
tab= v.query_region(co,rad,catalog='I/322') #UCAC-4 catalog
except astroquery.exceptions.TimeoutError:
pass
else:
trying= False
if len(tab) == 0:
pmdata.PMMATCH[ii]= 0
print "Didn't find a match for %i ..." % ii
continue
else:
pmdata.PMMATCH[ii]= len(tab)
if len(tab[0]['pmRA']) > 1:
print "Found more than 1 match for %i ..." % ii
try:
pmdata.PMRA[ii]= float(tab[0]['pmRA'])
except TypeError:
jj= 1
while len(tab[0]['pmRA']) > 1 and jj < 4:
trad= u.Quantity((4.-jj)/3600.,u.degree)
trying= True
while trying:
try:
tab= v.query_region(co,trad,catalog='I/322') #UCAC-4 catalog
except astroquery.exceptions.TimeoutError:
pass
else:
trying= False
jj+= 1
if len(tab) == 0:
pmdata.PMMATCH[ii]= 0
print "Didn't find a unambiguous match for %i ..." % ii
continue
pmdata.PMRA[ii]= float(tab[0]['pmRA'])
pmdata.PMDEC[ii]= float(tab[0]['pmDE'])
pmdata.PMRA_ERR[ii]= float(tab[0]['e_pmRA'])
pmdata.PMDEC_ERR[ii]= float(tab[0]['e_pmDE'])
if numpy.isnan(float(tab[0]['pmRA'])): pmdata.PMMATCH[ii]= 0
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
fitsio.write(pmfile,pmdata,clobber=True)
#To make sure we're using the same format below
pmdata= fitsio.read(pmfile,1)
#Match proper motions
try: #These already exist currently, but may not always exist
data= esutil.numpy_util.remove_fields(data,['PMRA','PMDEC'])
except ValueError:
pass
data= esutil.numpy_util.add_fields(data,[('PMRA', numpy.float),
('PMDEC', numpy.float),
('PMRA_ERR', numpy.float),
('PMDEC_ERR', numpy.float),
('PMMATCH',numpy.int32)])
data['PMMATCH']= 0
h=esutil.htm.HTM()
m1,m2,d12 = h.match(pmdata['RA'],pmdata['DEC'],
data['RA'],data['DEC'],
2./3600.,maxmatch=1)
data['PMRA'][m2]= pmdata['PMRA'][m1]
data['PMDEC'][m2]= pmdata['PMDEC'][m1]
data['PMRA_ERR'][m2]= pmdata['PMRA_ERR'][m1]
data['PMDEC_ERR'][m2]= pmdata['PMDEC_ERR'][m1]
data['PMMATCH'][m2]= pmdata['PMMATCH'][m1].astype(numpy.int32)
pmindx= data['PMMATCH'] == 1
data['PMRA'][True-pmindx]= -9999.99
data['PMDEC'][True-pmindx]= -9999.99
data['PMRA_ERR'][True-pmindx]= -9999.99
data['PMDEC_ERR'][True-pmindx]= -9999.99
#Calculate Galactocentric velocities
data= esutil.numpy_util.add_fields(data,[('GALVR', numpy.float),
('GALVT', numpy.float),
('GALVZ', numpy.float)])
lb= bovy_coords.radec_to_lb(data['RA'],data['DEC'],degree=True)
XYZ= bovy_coords.lbd_to_XYZ(lb[:,0],lb[:,1],data['RC_DIST'],degree=True)
pmllpmbb= bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA'],data['PMDEC'],
data['RA'],data['DEC'],
degree=True)
vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:,0],
pmllpmbb[:,1],
lb[:,0],lb[:,1],data['RC_DIST'],
degree=True)
vR, vT, vZ= bovy_coords.vxvyvz_to_galcencyl(vxvyvz[:,0],
vxvyvz[:,1],
vxvyvz[:,2],
8.-XYZ[:,0],
XYZ[:,1],
XYZ[:,2]+0.025,
vsun=[-11.1,30.24*8.,7.25])#Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
data['GALVR']= vR
data['GALVT']= vT
data['GALVZ']= vZ
data['GALVR'][True-pmindx]= -9999.99
data['GALVT'][True-pmindx]= -9999.99
data['GALVZ'][True-pmindx]= -9999.99
#Get proper motions
pmfile= savefilename.split('.')[0]+'_pms_ppmxl.fits'
if os.path.exists(pmfile):
pmdata= fitsio.read(pmfile,1)
else:
pmdata= numpy.recarray(len(data),
formats=['f8','f8','f8','f8','f8','f8','i4'],
names=['RA','DEC','PMRA','PMDEC',
'PMRA_ERR','PMDEC_ERR','PMMATCH'])
rad= u.Quantity(4./3600.,u.degree)
v= Vizier(columns=['RAJ2000','DEJ2000','pmRA','pmDE','e_pmRA','e_pmDE'])
for ii in range(len(data)):
#if ii > 100: break
sys.stdout.write('\r'+"Getting pm data for point %i / %i" % (ii+1,len(data)))
sys.stdout.flush()
pmdata.RA[ii]= data['RA'][ii]
pmdata.DEC[ii]= data['DEC'][ii]
co= coord.ICRS(ra=data['RA'][ii],
dec=data['DEC'][ii],
unit=(u.degree, u.degree))
trying= True
while trying:
try:
tab= v.query_region(co,rad,catalog='I/317') #PPMXL catalog
except astroquery.exceptions.TimeoutError:
pass
else:
trying= False
if len(tab) == 0:
pmdata.PMMATCH[ii]= 0
print "Didn't find a match for %i ..." % ii
continue
else:
pmdata.PMMATCH[ii]= len(tab)
if len(tab[0]['pmRA']) > 1:
pass
#print "Found more than 1 match for %i ..." % ii
try:
pmdata.PMRA[ii]= float(tab[0]['pmRA'])
except TypeError:
#Find nearest
cosdists= numpy.zeros(len(tab[0]['pmRA']))
for jj in range(len(tab[0]['pmRA'])):
cosdists[jj]= cos_sphere_dist(tab[0]['RAJ2000'][jj],
tab[0]['DEJ2000'][jj],
data['RA'][ii],
data['DEC'][ii])
closest= numpy.argmax(cosdists)
pmdata.PMRA[ii]= float(tab[0]['pmRA'][closest])
pmdata.PMDEC[ii]= float(tab[0]['pmDE'][closest])
pmdata.PMRA_ERR[ii]= float(tab[0]['e_pmRA'][closest])
pmdata.PMDEC_ERR[ii]= float(tab[0]['e_pmDE'][closest])
if numpy.isnan(float(tab[0]['pmRA'][closest])): pmdata.PMMATCH[ii]= 0
else:
pmdata.PMDEC[ii]= float(tab[0]['pmDE'])
pmdata.PMRA_ERR[ii]= float(tab[0]['e_pmRA'])
pmdata.PMDEC_ERR[ii]= float(tab[0]['e_pmDE'])
if numpy.isnan(float(tab[0]['pmRA'])): pmdata.PMMATCH[ii]= 0
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
fitsio.write(pmfile,pmdata,clobber=True)
#To make sure we're using the same format below
pmdata= fitsio.read(pmfile,1)
#Match proper motions to ppmxl
data= esutil.numpy_util.add_fields(data,[('PMRA_PPMXL', numpy.float),
('PMDEC_PPMXL', numpy.float),
('PMRA_ERR_PPMXL', numpy.float),
('PMDEC_ERR_PPMXL', numpy.float),
('PMMATCH_PPMXL',numpy.int32)])
data['PMMATCH_PPMXL']= 0
h=esutil.htm.HTM()
m1,m2,d12 = h.match(pmdata['RA'],pmdata['DEC'],
data['RA'],data['DEC'],
2./3600.,maxmatch=1)
data['PMRA_PPMXL'][m2]= pmdata['PMRA'][m1]
data['PMDEC_PPMXL'][m2]= pmdata['PMDEC'][m1]
data['PMRA_ERR_PPMXL'][m2]= pmdata['PMRA_ERR'][m1]
data['PMDEC_ERR_PPMXL'][m2]= pmdata['PMDEC_ERR'][m1]
data['PMMATCH_PPMXL'][m2]= pmdata['PMMATCH'][m1].astype(numpy.int32)
pmindx= data['PMMATCH_PPMXL'] == 1
data['PMRA_PPMXL'][True-pmindx]= -9999.99
data['PMDEC_PPMXL'][True-pmindx]= -9999.99
data['PMRA_ERR_PPMXL'][True-pmindx]= -9999.99
data['PMDEC_ERR_PPMXL'][True-pmindx]= -9999.99
#Calculate Galactocentric velocities
data= esutil.numpy_util.add_fields(data,[('GALVR_PPMXL', numpy.float),
('GALVT_PPMXL', numpy.float),
('GALVZ_PPMXL', numpy.float)])
lb= bovy_coords.radec_to_lb(data['RA'],data['DEC'],degree=True)
XYZ= bovy_coords.lbd_to_XYZ(lb[:,0],lb[:,1],data['RC_DIST'],degree=True)
pmllpmbb= bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA_PPMXL'],
data['PMDEC_PPMXL'],
data['RA'],data['DEC'],
degree=True)
vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:,0],
pmllpmbb[:,1],
lb[:,0],lb[:,1],data['RC_DIST'],
degree=True)
vR, vT, vZ= bovy_coords.vxvyvz_to_galcencyl(vxvyvz[:,0],
vxvyvz[:,1],
vxvyvz[:,2],
8.-XYZ[:,0],
XYZ[:,1],
XYZ[:,2]+0.025,
vsun=[-11.1,30.24*8.,7.25])#Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
data['GALVR_PPMXL']= vR
data['GALVT_PPMXL']= vT
data['GALVZ_PPMXL']= vZ
data['GALVR_PPMXL'][True-pmindx]= -9999.99
data['GALVT_PPMXL'][True-pmindx]= -9999.99
data['GALVZ_PPMXL'][True-pmindx]= -9999.99
#Save
fitsio.write(savefilename,data,clobber=True)
return None
def plot_gaiamag(plotfilename,plx=False):
Zs= [0.0035,0.017,0.035]
iso= isodist.PadovaIsochrone(type='sdss-2mass',parsec=True,
Z=Zs)
tages= [9.,9.3,9.5,9.7,9.85,10.,10.1]
overplot= False
bovy_plot.bovy_print()
hs= [12.2,12.8,13.3,13.8]
colors= ['r','y','g','b']
for jj in range(len(hs)):
h= hs[jj]
for age in tages:
for tZ in Zs:
p= iso(age,tZ)
jk= p['J']-p['Ks']
indx= (jk < 0.8)*(jk > 0.5)\
*(tZ <= 0.06)\
*(tZ <= rcmodel.jkzcut(jk,upper=True))\
*(tZ >= rcmodel.jkzcut(jk))\
*(p['logg'] >= 1.8)\
*(p['logg'] <= 2.8)
tG= G(p['g'][indx],p['g'][indx]-p['z'][indx])-p['H'][indx]+h
tdm= h+1.49
tdist= 10.**(tdm/5.-2.) #kpc
tplxerr= numpy.array([plxerr(tG[ii],
vifromgi(p['g'][indx][ii]-p['i'][indx][ii])) for ii in range(numpy.sum(indx))])
if plx:
bovy_plot.bovy_plot(tG,
tplxerr*tdist*100.,
marker='o',color=colors[jj],mec='none',
overplot=overplot,
xlabel=r'$Gaia\ G$',
ylabel=r'$\sigma_\pi/\pi\,(\%)$',
xrange=[11.,21.],
yrange=[0.,200.])
else:
bovy_plot.bovy_plot(tG,
4.74047*tdist*tplxerr*0.5,
marker='o',color=colors[jj],mec='none',
overplot=overplot,
xlabel=r'$Gaia\ G$',
ylabel=r'$\mathrm{transverse\ velocity\ uncertainty}\ (\mathrm{km\,s}^{-1})$',
xrange=[11.,21.],
yrange=[0.,8.])
overplot= True
#half a magnitude ah
tvi= numpy.array([vifromgi(p['g'][indx][ii]-p['i'][indx][ii]) for ii in range(numpy.sum(indx))])
ag= .5/0.18307*(0.8426-0.1187*tvi+0.0157*tvi**2.-0.0007*tvi**3.)
tG= tG-0.5
tplxerr= numpy.array([plxerr(tG[ii]+ag[ii],
vifromgi(p['g'][indx][ii]+.5/0.18307*1.20585-p['i'][indx][ii]-.5/0.18307*0.68319)) for ii in range(numpy.sum(indx))])
tdm= h+1.49-0.5
tdist= 10.**(tdm/5.-2.) #kpc
#print numpy.median(ag)/.5, numpy.median(tvi), numpy.std(tvi)
if plx:
bovy_plot.bovy_plot(tG+ag,
tdist*tplxerr*100.,
marker='o',color=colors[jj],mec='none',
overplot=overplot)
else:
bovy_plot.bovy_plot(tG+ag,
4.74047*tdist*tplxerr*0.5,
marker='o',color=colors[jj],mec='none',
overplot=overplot)
#Full magnitude of ah
tvi= numpy.array([vifromgi(p['g'][indx][ii]-p['i'][indx][ii]) for ii in range(numpy.sum(indx))])
ag= 1./0.18307*(0.8426-0.1187*tvi+0.0157*tvi**2.-0.0007*tvi**3.)
tG= tG-0.5
tplxerr= numpy.array([plxerr(tG[ii]+ag[ii],
vifromgi(p['g'][indx][ii]+1./0.18307*1.20585-p['i'][indx][ii]-1./0.18307*0.68319)) for ii in range(numpy.sum(indx))])
tdm= h+1.49-1.
tdist= 10.**(tdm/5.-2.) #kpc
if plx:
bovy_plot.bovy_plot(tG+ag,
tdist*tplxerr*100.,
marker='o',color=colors[jj],mec='none',
overplot=overplot)
else:
bovy_plot.bovy_plot(tG+ag,
4.74047*tdist*tplxerr*0.5,
marker='o',color=colors[jj],mec='none',
overplot=overplot)
if True:
#1.5 magnitudes of ah
tvi= numpy.array([vifromgi(p['g'][indx][ii]-p['i'][indx][ii]) for ii in range(numpy.sum(indx))])
ag= 1.5/0.18307*(0.8426-0.1187*tvi+0.0157*tvi**2.-0.0007*tvi**3.)
tG= tG-.5
tplxerr= numpy.array([plxerr(tG[ii]+ag[ii],
vifromgi(p['g'][indx][ii]+1./0.18307*1.20585-p['i'][indx][ii]-1./0.18307*0.68319)) for ii in range(numpy.sum(indx))])
tdm= h+1.49-1.5
tdist= 10.**(tdm/5.-2.) #kpc
if plx:
bovy_plot.bovy_plot(tG+ag,
tdist*tplxerr*100.,
marker='o',color=colors[jj],mec='none',
overplot=overplot)
else:
bovy_plot.bovy_plot(tG+ag,
4.74047*tdist*tplxerr*0.5,
marker='o',color=colors[jj],mec='none',
overplot=overplot)
bovy_plot.bovy_plot([20.,20.],[0.,300.],'k:',overplot=True)
#Work on legend
dot2= bovy_plot.bovy_plot([-10.],[-10.],'o',color=colors[0],overplot=True,mec='none')
dot3= bovy_plot.bovy_plot([-10.],[-10.],'o',color=colors[1],overplot=True,mec='none')
dot4= bovy_plot.bovy_plot([-10.],[-10.],'o',color=colors[2],overplot=True,mec='none')
dot5= bovy_plot.bovy_plot([-10.],[-10.],'o',color=colors[3],overplot=True,mec='none')
pyplot.legend((dot2[0],dot3[0],dot4[0],dot5[0]),
(r'$H = 12.2$',
r'$H = 12.8 \ \ \ \ \mathrm{each\ incl.}$',
r'$H = 13.3 \ \ \ A_H= 0, 0.5,$',
r'$H = 13.8 \ \ \ \ \ \ \ \ \ \ \ \ \, 1, 1.5$'),
loc='upper left',#bbox_to_anchor=(.91,.375),
numpoints=1,
prop={'size':16},
frameon=False)
if plx:
bovy_plot.bovy_plot([5.,25.],[10.,10.],'k--',lw=2.,overplot=True)
bovy_plot.bovy_text(11.25,2.,r'$\mathrm{spectro-photometric\ RC\ precision}$',
size=16.)
from matplotlib.patches import FancyArrowPatch
ax=pyplot.gca()
ax.add_patch(FancyArrowPatch((20.5,10.),(20.5,-1.),
arrowstyle='->',mutation_scale=15,
fill=True,
lw=1.25,color='k'))
#Add twin x axis w/ distsance
ax= pyplot.gca()
def my_formatter(x, pos):
"""distance in kpc for zero reddening"""
xs= 10.**((x-0.71)/5.-2.)
xs2= 10.**((x-0.71-4.)/5.-2.)
if xs2 > 1:
return r'$%.0f/%.0f$' % (xs,xs2)
else:
return r'$%.0f/%.1f$' % (xs,xs2)
ax2= pyplot.twiny()
major_formatter = FuncFormatter(my_formatter)
ax2.xaxis.set_major_formatter(major_formatter)
xstep= ax.xaxis.get_majorticklocs()
xstep= xstep[1]-xstep[0]
ax2.xaxis.set_minor_locator(MultipleLocator(xstep/5.))
ax2.xaxis.tick_top()
ax2.xaxis.set_label_position('top')
xmin, xmax= ax.xaxis.get_view_interval()
ax2.xaxis.set_view_interval(xmin,xmax,ignore=True)
ax2.set_xlabel('$\mathrm{distance\ for}\ A_H = 0/1\,(\mathrm{kpc})$')
ystep= ax.yaxis.get_majorticklocs()
ystep= ystep[1]-ystep[0]
ax2.yaxis.set_minor_locator(MultipleLocator(ystep/5.))
#Save
bovy_plot.bovy_end_print(plotfilename)
return None
upper=True))
print "Current contamination in logg range %i / % i = %i%%" % (numpy.sum(bloggindx*gloggindx),len(data),float(numpy.sum(bloggindx*gloggindx))*100./len(data))
nlogg= data['KASC_RG_LOGG_SCALE_2']+numpy.random.normal(size=len(data))*0.2
bloggindx= (nlogg >= 1.8)*(nlogg <= rcmodel.loggteffcut(data['TEFF'],
data['METALS'],
upper=True))
print "Future contamination w/ good logg (unbiased, errors 0.2) in logg range %i / % i = %i%%" % (numpy.sum(bloggindx*gloggindx),len(data),float(numpy.sum(bloggindx*gloggindx))*100./len(data))
print "Future contamination w/ good logg (unbiased, errors 0.2) for just RC in logg range %i / % i = %i%%" % (numpy.sum(bloggindx*gloggindx*rcindx),numpy.sum(rcindx),float(numpy.sum(bloggindx*gloggindx*rcindx))*100./numpy.sum(rcindx))
#Select stars to be in the RC from the APOKASC data, then check against
#evolutionary state
jk= data['J0']-data['K0']
z= isodist.FEH2Z(data['METALS'],zsolar=0.017)#*(0.638*10.**data['ALPHAFE']+0.372)
logg= data['KASC_RG_LOGG_SCALE_2']+numpy.random.normal(size=len(data))*0. #can adjust this to look at errors
indx= (jk < 0.8)*(jk >= 0.5)\
*(z <= 0.06)\
*(z <= rcmodel.jkzcut(jk,upper=True))\
*(z >= rcmodel.jkzcut(jk))\
*(logg >= 1.8)\
*(logg <= rcmodel.loggteffcut(data['TEFF'],z,upper=True))
#indx*= ((data['TEFF']-4800.)/1000.+2.75) > logg
#*(logg <= 2.8)
rckascdata= data[indx]
rcseismoState= numpy.char.strip(rckascdata['SEISMO EVOL'])
seismo= True-((rcseismoState == 'UNKNOWN'))
norcseismo= (rcseismoState == 'RGB') \
+ (rcseismoState == 'DWARF/SUBGIANT')
print "%i / %i = %i%% APOKASC non-CLUMP stars out of all RC stars would be included with good logg" % (numpy.sum(norcseismo),numpy.sum(seismo),float(numpy.sum(norcseismo))/numpy.sum(seismo)*100.)
#Now, how many of the stars in our RC cut have evol and how many of RGB?
indx= (jk < 0.8)*(jk >= 0.5)\
*(logg >= 1.8)\
*(logg <= rcmodel.loggteffcut(data['TEFF'],z,upper=True))