def good4CN(cluster,data):
"""
NAME:
good4CN
PURPOSE:
return the indices of stars that can be used to determine the spread in C/N
INPUT:
cluster - the cluster name
data - the data for this cluster
OUTPUT:
index
HISTORY:
2015-09-04 - Written - Bovy (UofT)
"""
if cluster.lower() == 'm67':
indx= (data['LOGG'] > _m67rccut(data['TEFF']))\
*(numpy.fabs(data['TEFF']-4600.)>3.)
elif cluster.lower() == 'n6819':
apokasc= apread.apokasc()
ma= numpy.zeros(len(data),dtype='int')-1
for ii in range(len(data)):
try:
ma[ii]= (numpy.arange(len(apokasc))[apokasc['APOGEE_ID'] == data['ID'][ii]])[0]
except IndexError: pass
indx= numpy.ones(len(data),dtype='bool')
for ii in range(len(data)):
if ma[ii] >= 0 \
and apokasc[ma[ii]]['SEISMO EVOL'] == 'CLUMP ':
indx[ii]= False
# Also the clump stars' friends, they are all suspicious
indx[numpy.fabs(data['TEFF']-4765.) < 60.]= False
else:
indx= numpy.ones(len(data),dtype='bool')
return indx
def match_apokasc_saga():
sagadata= read_saga()
ids= sagadata['KICID'].data
apokasc= apread.apokasc()
dists= numpy.zeros(len(apokasc))-1
edists= numpy.zeros(len(apokasc))-1
for ii in range(len(apokasc)):
indx= ids == apokasc['KEPLER ID'][ii]
if numpy.sum(indx) == 0: continue
dists[ii]= sagadata['Dis'][indx]
edists[ii]= 0.5*(sagadata['E_Dis'][indx]+sagadata['e_Dis'][indx])
apokasc= esutil.numpy_util.add_fields(apokasc,[('DIST_SEISMO', float),
('E_DIST_SEISMO', float),
])
apokasc['DIST_SEISMO']= dists/1000.
apokasc['E_DIST_SEISMO']= edists/1000.
return apokasc
def match_apokasc_rc(rcfile=None):
#First read apokasc
kascdata= apread.apokasc()
#Then read rc
if rcfile is None:
rcdata= apread.rcsample()
else:
rcdata= fitsio.read(rcfile)
print len(rcdata), numpy.sum(rcdata['ADDL_LOGG_CUT'])
rcdata= rcdata[rcdata['ADDL_LOGG_CUT'] == 1]
#Match
h=esutil.htm.HTM()
m1,m2,d12 = h.match(kascdata['RA'],kascdata['DEC'],
rcdata['RA'],rcdata['DEC'],
2./3600.,maxmatch=1)
kascdata= esutil.numpy_util.add_fields(kascdata,[('RC', int)])
kascdata['RC']= 0
kascdata['RC'][m1]= 1
return kascdata
def match_apokasc_rc(rcfile=None,addl_logg_cut=False):
#First read apokasc
kascdata= apread.apokasc()
#Then read rc
if rcfile is None:
rcdata= apread.rcsample()
else:
rcdata= fitsio.read(rcfile)
if addl_logg_cut:
rcdata= rcdata[rcdata['ADDL_LOGG_CUT'] == 1]
print "RC catalog has %i entries ..." % len(rcdata)
#Match
h=esutil.htm.HTM()
m1,m2,d12 = h.match(kascdata['RA'],kascdata['DEC'],
rcdata['RA'],rcdata['DEC'],
2./3600.,maxmatch=1)
kascdata= esutil.numpy_util.add_fields(kascdata,[('RC', int)])
kascdata['RC']= 0
kascdata['RC'][m1]= 1
return kascdata
def match_apokasc_rc(rcfile=None,addl_logg_cut=False):
#First read apokasc
kascdata= apread.apokasc()
#Then read rc
if rcfile is None:
rcdata= apread.rcsample()
else:
rcdata= fitsio.read(rcfile)
if addl_logg_cut:
rcdata= rcdata[rcdata['ADDL_LOGG_CUT'] == 1]
print("RC catalog has %i entries ..." % len(rcdata))
#Match
h=esutil.htm.HTM()
m1,m2,d12 = h.match(kascdata['RA'],kascdata['DEC'],
rcdata['RA'],rcdata['DEC'],
2./3600.,maxmatch=1)
kascdata= esutil.numpy_util.add_fields(kascdata,[('RC', int)])
kascdata['RC']= 0
kascdata['RC'][m1]= 1
return kascdata
def match_apokasc_rodrigues(median=False,dteff=False,scale=False):
rdata= read_rodrigues(median=median,dteff=dteff,scale=scale)
ids= numpy.array([str(int(id)) for id in rdata[:,0]])
apokasc= apread.apokasc()
dists= numpy.zeros(len(apokasc))-1
edists= numpy.zeros(len(apokasc))-1
ages= numpy.zeros(len(apokasc))-1
eages= numpy.zeros(len(apokasc))-1
llages= numpy.zeros(len(apokasc))-1
ulages= numpy.zeros(len(apokasc))-1
for ii in range(len(apokasc)):
indx= ids == apokasc['KEPLER ID'][ii]
if numpy.sum(indx) == 0: continue
if median:
dists[ii]= rdata[indx,107]
edists[ii]= 0.5*(rdata[indx,109]-rdata[indx,108])
else:
dists[ii]= rdata[indx,109]
edists[ii]= 0.5*(rdata[indx,111]-rdata[indx,110])
ages[ii]= rdata[indx,1]
eages[ii]= 0.5*(rdata[indx,3]-rdata[indx,2])
llages[ii]= rdata[indx,4]
ulages[ii]= rdata[indx,5]
apokasc= esutil.numpy_util.add_fields(apokasc,[('DIST_SEISMO', float),
('E_DIST_SEISMO', float),
('AGE_SEISMO', float),
('E_AGE_SEISMO', float),
('LL_AGE_SEISMO', float),
('UL_AGE_SEISMO', float),
])
apokasc['DIST_SEISMO']= dists/1000.
apokasc['E_DIST_SEISMO']= edists/1000.
apokasc['AGE_SEISMO']= ages
apokasc['E_AGE_SEISMO']= eages
apokasc['LL_AGE_SEISMO']= llages
apokasc['UL_AGE_SEISMO']= ulages
return apokasc
def good4CN(cluster, data):
"""
NAME:
good4CN
PURPOSE:
return the indices of stars that can be used to determine the spread in C/N
INPUT:
cluster - the cluster name
data - the data for this cluster
OUTPUT:
index
HISTORY:
2015-09-04 - Written - Bovy (UofT)
"""
if cluster.lower() == 'm67':
indx= (data['LOGG'] > _m67rccut(data['TEFF']))\
*(numpy.fabs(data['TEFF']-4600.)>3.)
elif cluster.lower() == 'n6819':
apokasc = apread.apokasc()
ma = numpy.zeros(len(data), dtype='int') - 1
for ii in range(len(data)):
try:
ma[ii] = (numpy.arange(
len(apokasc))[apokasc['APOGEE_ID'] == data['ID'][ii]])[0]
except IndexError:
pass
indx = numpy.ones(len(data), dtype='bool')
for ii in range(len(data)):
if ma[ii] >= 0 \
and apokasc[ma[ii]]['SEISMO EVOL'] == 'CLUMP ':
indx[ii] = False
# Also the clump stars' friends, they are all suspicious
indx[numpy.fabs(data['TEFF'] - 4765.) < 60.] = False
else:
indx = numpy.ones(len(data), dtype='bool')
return indx
def calibrate_logg_dr12(rgb=False):
"""Calibrate, using RC or RGV when rgb=True (the latter should reproduce the official calibration"""
#Read the calibration file, APOKASC, and match them
caldata= fitsio.read(os.path.join(os.getenv('APOGEE_DATA'),'cal_%s.fits' % os.getenv('APOGEE_REDUX')),1)
apokasc= apread.apokasc()
h=esutil.htm.HTM()
m1,m2,d12 = h.match(caldata['RA'],caldata['DEC'],
apokasc['RA'],apokasc['DEC'],
2./3600.,maxmatch=1)
caldata= caldata[m1]
apokasc= apokasc[m2]
#Select a decent calibration sample
# Use stars that are definitely RGB or RC
seismoState= numpy.char.strip(apokasc['SEISMO EVOL'])
if rgb:
indx= (seismoState == 'RGB') \
+ (seismoState == 'DWARF/SUBGIANT')
else:
indx= (seismoState == 'CLUMP')
#Add low-logg giants of any kind
indx+= (apokasc['KASC_RG_LOGG_SCALE_2'] < 2.)
#rm bad data
indx= (caldata['FPARAM'][:,paramIndx('logg')] > -1000.)
indx*= (apokasc['KASC_RG_LOGG_SCALE_2'] > -1000.)
#Apply limits
indx*= (caldata['FPARAM'][:,paramIndx('logg')] > 1.)\
*(caldata['FPARAM'][:,paramIndx('logg')] < 3.8)
print "Using %i stars to calibrate logg ..." % numpy.sum(indx)
#Now fit the difference
fitOut= numpy.polyfit(caldata['FPARAM'][indx,paramIndx('logg')],
caldata['FPARAM'][indx,paramIndx('logg')]\
-apokasc['KASC_RG_LOGG_SCALE_2'][indx],
1)
print fitOut
if True:
bovy_plot.bovy_print()
bovy_plot.bovy_plot(caldata['FPARAM'][indx,paramIndx('logg')],
caldata['FPARAM'][indx,paramIndx('logg')]\
-apokasc['KASC_RG_LOGG_SCALE_2'][indx],
'k.',
xrange=[0.,5.],yrange=[-0.5,1.],
xlabel=r'$\log g_{\mathrm{ASPCAP}}$',
ylabel=r'$\log g_{\mathrm{ASPCAP}}-\log g_{\mathrm{seismo}}$')
if not rgb:
plotindx= (seismoState == 'RGB') \
+ (seismoState == 'DWARF/SUBGIANT')
else:
plotindx= (seismoState == 'CLUMP')
plotindx*= (caldata['FPARAM'][:,paramIndx('logg')] > -1000.)
plotindx*= (apokasc['KASC_RG_LOGG_SCALE_2'] > -1000.)
bovy_plot.bovy_plot(caldata['FPARAM'][plotindx,paramIndx('logg')],
caldata['FPARAM'][plotindx,paramIndx('logg')]\
-apokasc['KASC_RG_LOGG_SCALE_2'][plotindx],
'.',color='0.65',overplot=True)
xs= numpy.linspace(1.,3.8,1001)
bovy_plot.bovy_plot(xs,fitOut[0]*xs+fitOut[1],'k-',overplot=True)
bovy_plot.bovy_plot(xs,-0.14*xs+0.588,'k--',overplot=True)
print numpy.amax(numpy.fabs(fitOut[0]*xs+fitOut[1]-(-0.14*xs+0.588)))
print numpy.amax(numpy.fabs(fitOut[0]*xs+fitOut[1]-(-0.14*xs+0.588))[xs < 2.])
bovy_plot.bovy_text(r'$\mathrm{diff} = %.3f \log g_{\mathrm{ASPCAP}} + %.3f$' % (fitOut[0],fitOut[1]),
bottom_left=True,fontsize=14.)
bovy_plot.bovy_end_print('/Users/bovy/Desktop/test.png')
return None
def plot_logg_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:
zs= numpy.arange(0.0005,0.03005,0.0005)
if _DEBUG:
zs= numpy.arange(0.0005,0.03005,0.005)
fehs= isodist.Z2FEH(zs,zsolar=0.017)#0.017 for APOGEE analysis
zs= zs[numpy.fabs(fehs-options.feh) < 0.2]
if _DEBUG:
zs= [zs[numpy.fabs(fehs-options.feh) < 0.2][0]]
fehs= isodist.Z2FEH(zs,zsolar=0.017)
#Load the RC models for each feh individually
rcms= []
hists= []
edgess= []
for z in zs:
print z
trc= rcmodel.rcmodel(Z=z,loggmin=1.,loggmax=3.5,
band=options.band,basti=options.basti,
imfmodel=options.imfmodel,
parsec=options.parsec)
rcms.append(trc)
sample= numpy.vstack([trc._sample[:,0],trc._loggs[:,0]]).T
weights= trc._weights
hist, edges= numpy.histogramdd(sample,weights=weights,bins=31,
range=[[0.5,0.8],[1.,3.5]])
hists.append(hist)
edgess.append(edges)
#Load APOKASC data
data= apread.apokasc()
indx= (data['KASC_RG_LOGG_SCALE_2'] > 1.)\
*(data['KASC_RG_LOGG_SCALE_2'] < 3.5)\
*(data['METALS'] > options.feh-0.2)\
*(data['METALS'] <= options.feh+0.2)
print "Using %i APOKASC objects ..." % (numpy.sum(indx))
data= data[indx]
ndata= numpy.sum(indx)
#Stack predictions
outhist= numpy.zeros_like(hists[0])
for ii in range(ndata):
zindx= numpy.argmin(numpy.fabs(fehs-data['METALS'][ii]))
outhist+= hists[zindx]
save_pickles(args[0],outhist,hists,edgess,data)
#Normalize each J-K
for ii in range(len(outhist[:,0])):
outhist[ii,:]/= numpy.nanmax(outhist[ii,:])/numpy.nanmax(outhist)
rev= copy.copy(outhist[ii,::-1]) #reverse, but in one go does not always work
outhist[ii,:]= rev
if True:
#Reload apokasc data
data= apread.apokasc()
indx= (data['KASC_RG_LOGG_SCALE_2'] > 1.)\
*(data['KASC_RG_LOGG_SCALE_2'] < 3.5)\
*(data['METALS'] > options.feh-0.2)\
*(data['METALS'] <= options.feh+0.2)
data= data[indx]
#Plot everything
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(outhist.T,origin='lower',cmap='gist_yarg',
xrange=[edgess[0][0][0],edgess[0][0][-1]],
yrange=[edgess[0][1][-1],edgess[0][1][0]],
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'$\mathrm{Seismic}\ \log g$',
shrink=0.78,
interpolation='nearest')
#Overplot APOKASC data
noseismo= data['SEISMO EVOL'] == 'UNKNOWN'
if numpy.sum(noseismo) > 0:
bovy_plot.bovy_plot(data['J0'][noseismo]-data['K0'][noseismo],
data['KASC_RG_LOGG_SCALE_2'][noseismo],'bo',
overplot=True,
mec='none',ms=3.)
clumpseismo= data['SEISMO EVOL'] == 'CLUMP'
if numpy.sum(clumpseismo) > 0:
bovy_plot.bovy_plot(data['J0'][clumpseismo]-data['K0'][clumpseismo],
data['KASC_RG_LOGG_SCALE_2'][clumpseismo],'yo',
overplot=True,
mec='none',ms=4.5)
noclumpseismo= (data['SEISMO EVOL'] == 'RGB') \
+ (data['SEISMO EVOL'] == 'DWARF/SUBGIANT')
if numpy.sum(noclumpseismo) > 0:
bovy_plot.bovy_plot(data['J0'][noclumpseismo]-data['K0'][noclumpseismo],
data['KASC_RG_LOGG_SCALE_2'][noclumpseismo],'ro',
overplot=True,
mec='none',ms=3.)
bovy_plot.bovy_text(r'$%.1f < [\mathrm{M/H}] \leq %.1f$' % (options.feh-0.2,options.feh+0.2),
top_left=True,size=14.)
bovy_plot.bovy_end_print(options.outfilename)
return None
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
