def G(age, Z, iso):
"""G for a given age and metallicity"""
Zs = iso.Zs()
tZ = Zs[numpy.argmin(numpy.fabs(Z - Zs))]
p = iso(age, tZ)
jk = p["J"] - p["Ks"]
indx = (
(jk < 0.8)
* (jk > 0.5)
* (tZ <= 0.06)
* (tZ <= rc.jkzcut(jk, upper=True))
* (tZ >= rc.jkzcut(jk))
* (p["logg"] >= rc.loggteffcut(10.0 ** p["logTe"], tZ, upper=False))
* (p["logg"] <= rc.loggteffcut(10.0 ** p["logTe"], tZ, upper=True))
)
outG = G_jordi(p["g"], p["g"] - p["z"])
# Average over the IMF
sindx = numpy.argsort(p["M_ini"])
outG = outG[sindx]
int_IMF = p["int_IMF"][sindx]
w = (numpy.roll(int_IMF, -1) - int_IMF) / (int_IMF[-1] - int_IMF[0])
w = w[indx]
outG = outG[indx]
return (
numpy.nansum(w * outG) / numpy.nansum(w),
numpy.sqrt(numpy.nansum(w * outG ** 2.0) / numpy.nansum(w) - (numpy.nansum(w * outG) / numpy.nansum(w)) ** 2.0),
)
def rgsample(dr='13'):
data= apread.allStar(main=True,exclude_star_bad=True,exclude_star_warn=True,rmdups=True)
jk= data['J0']-data['K0']
z= isodist.FEH2Z(data['METALS'],zsolar=0.017)
z[z > 0.024]= 0.024
logg= data['LOGG']
indx= ((jk >= 0.8)#+(z < rcmodel.jkzcut(jk-0.1,upper=False))
+(logg > rcmodel.loggteffcut(data['TEFF'],z,upper=True)))
rgindx=indx*(data['METALS'] > -.8)
return data[rgindx]
def rgsample(dr="13"):
data = apread.allStar(main=True, exclude_star_bad=True, exclude_star_warn=True, dr=dr) # rmdups=True,dr=dr)
jk = data["J0"] - data["K0"]
z = isodist.FEH2Z(data["METALS"], zsolar=0.017)
z[z > 0.024] = 0.024
logg = data["LOGG"]
indx = (jk >= 0.8) + ( # +(z < rcmodel.jkzcut(jk-0.1,upper=False))
logg > rcmodel.loggteffcut(data["TEFF"], z, upper=True)
)
rgindx = indx * (data["METALS"] > -0.8)
return data[rgindx]
def G(age, Z, iso):
"""G for a given age and metallicity"""
Zs = iso.Zs()
tZ = Zs[numpy.argmin(numpy.fabs(Z - Zs))]
p = iso(age, tZ)
jk = p['J'] - p['Ks']
indx= (jk < 0.8)*(jk > 0.5)\
*(tZ <= 0.06)\
*(tZ <= rc.jkzcut(jk,upper=True))\
*(tZ >= rc.jkzcut(jk))\
*(p['logg'] >= rc.loggteffcut(10.**p['logTe'],tZ,upper=False))\
*(p['logg'] <= rc.loggteffcut(10.**p['logTe'],tZ,upper=True))
outG = G_jordi(p['g'], p['g'] - p['z'])
# Average over the IMF
sindx = numpy.argsort(p['M_ini'])
outG = outG[sindx]
int_IMF = p['int_IMF'][sindx]
w = (numpy.roll(int_IMF, -1) - int_IMF) / (int_IMF[-1] - int_IMF[0])
w = w[indx]
outG = outG[indx]
return (numpy.nansum(w * outG) / numpy.nansum(w),
numpy.sqrt(
numpy.nansum(w * outG**2.) / numpy.nansum(w) -
(numpy.nansum(w * outG) / numpy.nansum(w))**2.))
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 not appath._APOGEE_REDUX.lower() == 'current' \
and 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 appath._APOGEE_REDUX.lower() == 'current' \
or 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()
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
(numpy.sum(rcclumpseismo) + numpy.sum(rcnoclumpseismo)))
print(
"%i / %i = %i%% APOKASC CLUMP stars are in the RC catalog (COMPLETENESS)"
% (numpy.sum(rcclumpseismo), numpy.sum(clumpseismo),
float(numpy.sum(rcclumpseismo)) / numpy.sum(clumpseismo) * 100.))
print(
"%i / %i = %i%% APOKASC non-CLUMP stars out of all stars in the RC catalog with evolutionary measurements are in the RC catalog (CONTAMINATION)"
% (numpy.sum(rcnoclumpseismo), numpy.sum(rcnoclumpseismo) +
numpy.sum(rcclumpseismo), float(numpy.sum(rcnoclumpseismo)) /
(numpy.sum(rcnoclumpseismo) + numpy.sum(rcclumpseismo)) * 100.))
rcindx = data['RC'] == 1
#Statistics using simple seismo logg cut
kascLoggTag = 'KASC_RG_LOGG_SCALE_2'
try:
clumplogg= (data[kascLoggTag] > 1.8)\
*(data[kascLoggTag] < rcmodel.loggteffcut(data['TEFF'],
isodist.FEH2Z(data['METALS'],zsolar=0.017),upper=True))#2.8)
except ValueError:
kascLoggTag = 'LOGGRG'
clumplogg= (data[kascLoggTag] > 1.8)\
*(data[kascLoggTag] < rcmodel.loggteffcut(data['TEFF'],
isodist.FEH2Z(data['METALS'],zsolar=0.017),upper=True))#2.8)
if False:
rcclumplogg = clumplogg * (data['RC'] == 1)
print("%i / %i = %i%% APOKASC logg clump stars are in the RC catalog" %
(numpy.sum(rcclumplogg), numpy.sum(clumplogg),
float(numpy.sum(rcclumplogg)) / numpy.sum(clumplogg) * 100))
rcnoclumplogg = (True ^ clumplogg) * (data['RC'] == 1)
print(
"%i / %i = %i%% APOKASC logg non-clump stars are in the RC catalog"
% (numpy.sum(rcnoclumplogg), numpy.sum(True ^ clumplogg),
float(numpy.sum(rcnoclumplogg)) / numpy.sum(True ^ clumplogg) *
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_ALPHA', 'RV_CARB', 'RV_CCFWHM', 'RV_AUTOFWHM',
'SYNTHSCATTER', 'STABLERV_CHI2', 'STABLERV_RCHI2',
'STABLERV_CHI2_PROB', 'CHI2_THRESHOLD', 'APSTAR_VERSION',
'ASPCAP_VERSION', 'RESULTS_VERSION', 'WASH_M', 'WASH_M_ERR', 'WASH_T2',
'WASH_T2_ERR', 'DDO51', 'DDO51_ERR', 'IRAC_3_6', 'IRAC_3_6_ERR',
'IRAC_4_5', 'IRAC_4_5_ERR', 'IRAC_5_8', 'IRAC_5_8_ERR', 'IRAC_8_0',
'IRAC_8_0_ERR', 'WISE_4_5', 'WISE_4_5_ERR', 'TARG_4_5', 'TARG_4_5_ERR',
'WASH_DDO51_GIANT_FLAG', 'WASH_DDO51_STAR_FLAG', 'REDUCTION_ID',
'SRC_H', 'PM_SRC'
])
if not appath._APOGEE_REDUX.lower() == 'current' \
and not 'l30' in appath._APOGEE_REDUX \
and 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 'l30' in appath._APOGEE_REDUX:
logg = data['LOGG']
elif appath._APOGEE_REDUX.lower() == 'current' \
or 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()
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)
# Add Tycho-2 matches
if options.tyc2:
data = esutil.numpy_util.add_fields(data, [('TYC2MATCH', numpy.int32),
('TYC1', numpy.int32),
('TYC2', numpy.int32),
('TYC3', numpy.int32)])
data['TYC2MATCH'] = 0
data['TYC1'] = -1
data['TYC2'] = -1
data['TYC3'] = -1
# Write positions
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=2', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:Tycho2',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Directly match on input RA
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
usecols=(1, 2, 7, 8, 9))
iis = numpy.arange(len(data))
mai = [iis[data['RA'] == ma[ii, 0]][0] for ii in range(len(ma))]
data['TYC2MATCH'][mai] = 1
data['TYC1'][mai] = ma[:, 2]
data['TYC2'][mai] = ma[:, 3]
data['TYC3'][mai] = ma[:, 4]
os.remove(posfilename)
os.remove(resultfilename)
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, in a somewhat roundabout way
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'
])
# Write positions, again ...
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=4', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:UCAC4',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Match back and only keep the closest one
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
converters={
15: lambda s: float(s.strip() or -9999),
16: lambda s: float(s.strip() or -9999),
17: lambda s: float(s.strip() or -9999),
18: lambda s: float(s.strip() or -9999)
},
usecols=(4, 5, 15, 16, 17, 18))
h = esutil.htm.HTM()
m1, m2, d12 = h.match(data['RA'],
data['DEC'],
ma[:, 0],
ma[:, 1],
4. / 3600.,
maxmatch=1)
pmdata['PMMATCH'] = 0
pmdata['RA'] = data['RA']
pmdata['DEC'] = data['DEC']
pmdata['PMMATCH'][m1] = 1
pmdata['PMRA'][m1] = ma[m2, 2]
pmdata['PMDEC'][m1] = ma[m2, 3]
pmdata['PMRA_ERR'][m1] = ma[m2, 4]
pmdata['PMDEC_ERR'][m1] = ma[m2, 5]
pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \
+(pmdata['PMDEC'] == -9999) \
+(pmdata['PMRA_ERR'] == -9999) \
+(pmdata['PMDEC_ERR'] == -9999)]= 0
fitsio.write(pmfile, pmdata, clobber=True)
#To make sure we're using the same format below
pmdata = fitsio.read(pmfile, 1)
os.remove(posfilename)
os.remove(resultfilename)
#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 PPMXL proper motions, in a somewhat roundabout way
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'
])
# Write positions, again ...
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=4', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:PPMXL',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Match back and only keep the closest one
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
converters={
15: lambda s: float(s.strip() or -9999),
16: lambda s: float(s.strip() or -9999),
17: lambda s: float(s.strip() or -9999),
18: lambda s: float(s.strip() or -9999)
},
usecols=(4, 5, 15, 16, 19, 20))
h = esutil.htm.HTM()
m1, m2, d12 = h.match(data['RA'],
data['DEC'],
ma[:, 0],
ma[:, 1],
4. / 3600.,
maxmatch=1)
pmdata['PMMATCH'] = 0
pmdata['RA'] = data['RA']
pmdata['DEC'] = data['DEC']
pmdata['PMMATCH'][m1] = 1
pmdata['PMRA'][m1] = ma[m2, 2]
pmdata['PMDEC'][m1] = ma[m2, 3]
pmdata['PMRA_ERR'][m1] = ma[m2, 4]
pmdata['PMDEC_ERR'][m1] = ma[m2, 5]
pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \
+(pmdata['PMDEC'] == -9999) \
+(pmdata['PMRA_ERR'] == -9999) \
+(pmdata['PMDEC_ERR'] == -9999)]= 0
fitsio.write(pmfile, pmdata, clobber=True)
#To make sure we're using the same format below
pmdata = fitsio.read(pmfile, 1)
os.remove(posfilename)
os.remove(resultfilename)
#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 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_ALPHA',
'RV_CARB',
'RV_CCFWHM',
'RV_AUTOFWHM',
'SYNTHSCATTER',
'STABLERV_CHI2',
'STABLERV_RCHI2',
'STABLERV_CHI2_PROB',
'CHI2_THRESHOLD',
'APSTAR_VERSION',
'ASPCAP_VERSION',
'RESULTS_VERSION',
'WASH_M',
'WASH_M_ERR',
'WASH_T2',
'WASH_T2_ERR',
'DDO51',
'DDO51_ERR',
'IRAC_3_6',
'IRAC_3_6_ERR',
'IRAC_4_5',
'IRAC_4_5_ERR',
'IRAC_5_8',
'IRAC_5_8_ERR',
'IRAC_8_0',
'IRAC_8_0_ERR',
'WISE_4_5',
'WISE_4_5_ERR',
'TARG_4_5',
'TARG_4_5_ERR',
'WASH_DDO51_GIANT_FLAG',
'WASH_DDO51_STAR_FLAG',
'REDUCTION_ID',
'SRC_H',
'PM_SRC'])
if not appath._APOGEE_REDUX.lower() == 'current' \
and not 'l30' in appath._APOGEE_REDUX \
and 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 'l30' in appath._APOGEE_REDUX:
logg= data['LOGG']
elif appath._APOGEE_REDUX.lower() == 'current' \
or 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()
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)
# Add Tycho-2 matches
if options.tyc2:
data= esutil.numpy_util.add_fields(data,[('TYC2MATCH',numpy.int32),
('TYC1',numpy.int32),
('TYC2',numpy.int32),
('TYC3',numpy.int32)])
data['TYC2MATCH']= 0
data['TYC1']= -1
data['TYC2']= -1
data['TYC3']= -1
# Write positions
posfilename= tempfile.mktemp('.csv',dir=os.getcwd())
resultfilename= tempfile.mktemp('.csv',dir=os.getcwd())
with open(posfilename,'w') as csvfile:
wr= csv.writer(csvfile,delimiter=',',quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA','DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'],data[ii]['DEC']])
# Send to CDS for matching
result= open(resultfilename,'w')
try:
subprocess.check_call(['curl',
'-X','POST',
'-F','request=xmatch',
'-F','distMaxArcsec=2',
'-F','RESPONSEFORMAT=csv',
'-F','cat1=@%s' % os.path.basename(posfilename),
'-F','colRA1=RA',
'-F','colDec1=DEC',
'-F','cat2=vizier:Tycho2',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Directly match on input RA
ma= numpy.loadtxt(resultfilename,delimiter=',',skiprows=1,
usecols=(1,2,7,8,9))
iis= numpy.arange(len(data))
mai= [iis[data['RA'] == ma[ii,0]][0] for ii in range(len(ma))]
data['TYC2MATCH'][mai]= 1
data['TYC1'][mai]= ma[:,2]
data['TYC2'][mai]= ma[:,3]
data['TYC3'][mai]= ma[:,4]
os.remove(posfilename)
os.remove(resultfilename)
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, in a somewhat roundabout way
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'])
# Write positions, again ...
posfilename= tempfile.mktemp('.csv',dir=os.getcwd())
resultfilename= tempfile.mktemp('.csv',dir=os.getcwd())
with open(posfilename,'w') as csvfile:
wr= csv.writer(csvfile,delimiter=',',quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA','DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'],data[ii]['DEC']])
# Send to CDS for matching
result= open(resultfilename,'w')
try:
subprocess.check_call(['curl',
'-X','POST',
'-F','request=xmatch',
'-F','distMaxArcsec=4',
'-F','RESPONSEFORMAT=csv',
'-F','cat1=@%s' % os.path.basename(posfilename),
'-F','colRA1=RA',
'-F','colDec1=DEC',
'-F','cat2=vizier:UCAC4',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Match back and only keep the closest one
ma= numpy.loadtxt(resultfilename,delimiter=',',skiprows=1,
converters={15: lambda s: float(s.strip() or -9999),
16: lambda s: float(s.strip() or -9999),
17: lambda s: float(s.strip() or -9999),
18: lambda s: float(s.strip() or -9999)},
usecols=(4,5,15,16,17,18))
h=esutil.htm.HTM()
m1,m2,d12 = h.match(data['RA'],data['DEC'],
ma[:,0],ma[:,1],4./3600.,maxmatch=1)
pmdata['PMMATCH']= 0
pmdata['RA']= data['RA']
pmdata['DEC']= data['DEC']
pmdata['PMMATCH'][m1]= 1
pmdata['PMRA'][m1]= ma[m2,2]
pmdata['PMDEC'][m1]= ma[m2,3]
pmdata['PMRA_ERR'][m1]= ma[m2,4]
pmdata['PMDEC_ERR'][m1]= ma[m2,5]
pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \
+(pmdata['PMDEC'] == -9999) \
+(pmdata['PMRA_ERR'] == -9999) \
+(pmdata['PMDEC_ERR'] == -9999)]= 0
fitsio.write(pmfile,pmdata,clobber=True)
#To make sure we're using the same format below
pmdata= fitsio.read(pmfile,1)
os.remove(posfilename)
os.remove(resultfilename)
#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 PPMXL proper motions, in a somewhat roundabout way
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'])
# Write positions, again ...
posfilename= tempfile.mktemp('.csv',dir=os.getcwd())
resultfilename= tempfile.mktemp('.csv',dir=os.getcwd())
with open(posfilename,'w') as csvfile:
wr= csv.writer(csvfile,delimiter=',',quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA','DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'],data[ii]['DEC']])
# Send to CDS for matching
result= open(resultfilename,'w')
try:
subprocess.check_call(['curl',
'-X','POST',
'-F','request=xmatch',
'-F','distMaxArcsec=4',
'-F','RESPONSEFORMAT=csv',
'-F','cat1=@%s' % os.path.basename(posfilename),
'-F','colRA1=RA',
'-F','colDec1=DEC',
'-F','cat2=vizier:PPMXL',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Match back and only keep the closest one
ma= numpy.loadtxt(resultfilename,delimiter=',',skiprows=1,
converters={15: lambda s: float(s.strip() or -9999),
16: lambda s: float(s.strip() or -9999),
17: lambda s: float(s.strip() or -9999),
18: lambda s: float(s.strip() or -9999)},
usecols=(4,5,15,16,19,20))
h=esutil.htm.HTM()
m1,m2,d12 = h.match(data['RA'],data['DEC'],
ma[:,0],ma[:,1],4./3600.,maxmatch=1)
pmdata['PMMATCH']= 0
pmdata['RA']= data['RA']
pmdata['DEC']= data['DEC']
pmdata['PMMATCH'][m1]= 1
pmdata['PMRA'][m1]= ma[m2,2]
pmdata['PMDEC'][m1]= ma[m2,3]
pmdata['PMRA_ERR'][m1]= ma[m2,4]
pmdata['PMDEC_ERR'][m1]= ma[m2,5]
pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \
+(pmdata['PMDEC'] == -9999) \
+(pmdata['PMRA_ERR'] == -9999) \
+(pmdata['PMDEC_ERR'] == -9999)]= 0
fitsio.write(pmfile,pmdata,clobber=True)
#To make sure we're using the same format below
pmdata= fitsio.read(pmfile,1)
os.remove(posfilename)
os.remove(resultfilename)
#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 __init__(self,
loggmin=None,loggmax=None,
imfmodel='lognormalChabrier2001',
Z=None,
expsfh=False,band='Ks',
dontgather=False,
basti=False,
parsec=True,
minage=None,
maxage=10.,
jkmin=None,
stage=None,
eta=None):
"""
NAME:
__init__
PURPOSE:
initialize isocmodel
INPUT:
Z= metallicity (if not set, use flat prior in Z over all Z; can be list)
loggmin= if set, cut logg at this minimum
loggmax= if set, cut logg at this maximum, if 'rc', then this is the function of teff and z appropriate for the APOGEE RC sample
imfmodel= (default: 'lognormalChabrier2001') IMF model to use (see isodist.imf code for options)
band= band to use for M_X (JHK)
expsfh= if True, use an exponentially-declining star-formation history (can be set to a decay time-scale in Gyr)
dontgather= if True, don't gather surrounding Zs
basti= if True, use Basti isochrones (if False, use PARSEC)
parsec= if True (=default), use PARSEC isochrones, if False, use Padova
stage= if True, only use this evolutionary stage
minage= (None) minimum log10 of age/yr
maxage= (10.) maximum log10 of age/yr
jkmin= (None) if set, only consider J-Ks greater than this
eta= (None) mass-loss efficiency parameter
OUTPUT:
object
HISTORY:
2012-11-07 - Written - Bovy (IAS)
"""
self._band= band
self._loggmin= loggmin
self._loggmax= loggmax
if isinstance(expsfh,(int,float,numpy.float32,numpy.float64)):
self._expsfh= expsfh
elif expsfh:
self._expsfh= 8.
else:
self._expsfh= False
self._Z= Z
self._imfmodel= imfmodel
self._basti= basti
self._eta= eta
if isinstance(loggmax,str) and loggmax.lower() == 'rc':
from apogee.samples.rc import loggteffcut
#Read isochrones
if basti:
zs= numpy.array([0.0001,0.0003,0.0006,0.001,0.002,0.004,0.008,
0.01,0.0198,0.03,0.04])
elif parsec:
zs= numpy.arange(0.0005,0.06005,0.0005)
else:
zs= numpy.arange(0.0005,0.03005,0.0005)
if Z is None:
Zs= zs
elif isinstance(Z,float):
if basti or dontgather:
Zs= [Z]
elif Z < 0.001 or Z > 0.0295:
Zs= [Z]
elif Z < 0.0015 or Z > 0.029:
Zs= [Z-0.0005,Z,Z+0.0005] #build up statistics
elif Z < 0.01:
Zs= [Z-0.001,Z-0.0005,Z,Z+0.0005,Z+0.001] #build up statistics
else:
Zs= [Z-0.0005,Z,Z+0.0005] #build up statistics
if basti:
p= isodist.BastiIsochrone(Z=Zs,eta=eta)
else:
p= isodist.PadovaIsochrone(Z=Zs,parsec=parsec,eta=eta)
if basti:
#Force BaSTI to have equal age sampling
lages= list(numpy.log10(numpy.arange(0.1,1.,0.1))+9.)
lages.extend(list(numpy.log10(numpy.arange(1.0,10.5,0.5))+9.))
lages= numpy.array(lages)
#Get relevant data
sample= []
weights= []
massweights= []
loggs= []
teffs= []
pmasses= []
plages= []
pjks= []
for logage in p.logages():
if logage > maxage: continue
if not minage is None and logage < minage: continue
if basti and numpy.sum((logage == lages)) == 0: continue
for zz in range(len(Zs)):
thisiso= p(logage,Zs[zz],asrecarray=True,stage=stage)
if len(thisiso.M_ini) == 0: continue
#Calculate int_IMF for this IMF model
if not imfmodel == 'lognormalChabrier2001': #That would be the default
if imfmodel == 'exponentialChabrier2001':
int_IMF= isodist.imf.exponentialChabrier2001(thisiso.M_ini,int=True)
elif imfmodel == 'kroupa2003':
int_IMF= isodist.imf.kroupa2003(thisiso.M_ini,int=True)
elif imfmodel == 'chabrier2003':
int_IMF= isodist.imf.chabrier2003(thisiso.M_ini,int=True)
else:
raise IOError("imfmodel option not understood (non-existing model)")
elif basti:
int_IMF= isodist.imf.lognormalChabrier2001(thisiso.M_ini,int=True)
else:
int_IMF= thisiso.int_IMF
dN= (numpy.roll(int_IMF,-1)-int_IMF)/(int_IMF[-1]-int_IMF[0])/10**(logage-7.)
dmass= thisiso.M_ini*(numpy.roll(int_IMF,-1)-int_IMF)/numpy.sum((thisiso.M_ini*(numpy.roll(int_IMF,-1)-int_IMF))[:-1])/10**(logage-7.)
for ii in range(1,len(int_IMF)-1):
if basti:
JK= 0.996*(thisiso.J[ii]-thisiso.K[ii])+0.00923
else:
JK= thisiso.J[ii]-thisiso.Ks[ii]
if not jkmin is None and JK < jkmin: continue
if band.lower() == 'h':
if basti:
raise NotImplementedError("'H' not implemented for BaSTI yet")
J= JK+thisiso.K[ii]-0.044
H= J-(0.980*(thisiso.J[ii]-thisiso.H[ii])-0.045)
else:
H= thisiso.H[ii]
elif band.lower() == 'j':
if basti:
raise NotImplementedError("'J' not implemented for BaSTI yet")
J= JK+thisiso.K[ii]-0.044
else:
H= thisiso.J[ii]
elif band.lower() == 'k' or band.lower() == 'ks':
if basti:
H= thisiso.K[ii]-0.046
else:
H= thisiso.Ks[ii]
if JK < 0.3 \
or (isinstance(loggmax,str) and loggmax == 'rc' and (thisiso['logg'][ii] > loggteffcut(10.**thisiso['logTe'][ii],Zs[zz],upper=True))) \
or (not isinstance(loggmax,str) and not loggmax is None and thisiso['logg'][ii] > loggmax) \
or (not loggmin is None and thisiso['logg'][ii] < loggmin):
continue
if dN[ii] > 0.:
sample.append([JK,H])
loggs.append([thisiso.logg[ii]])
teffs.append([10.**thisiso.logTe[ii]])
pmasses.append(thisiso.M_ini[ii])
plages.append(logage)
pjks.append(JK)
if basti: #BaSTI is sampled uniformly in age, not logage, but has a finer sampling below 1 Gyr
if logage < 9.:
if self._expsfh:
weights.append(dN[ii]/5.*numpy.exp((10.**(logage-7.))/self._expsfh/100.)) #e.g., Binney (2010)
massweights.append(dmass[ii]/5.*numpy.exp((10.**(logage-7.))/self._expsfh/100.)) #e.g., Binney (2010)
else:
weights.append(dN[ii]/5.)
massweights.append(dmass[ii]/5.)
else:
if self._expsfh:
weights.append(dN[ii]*numpy.exp((10.**(logage-7.))/self._expsfh/100.)) #e.g., Binney (2010)
massweights.append(dmass[ii]*numpy.exp((10.**(logage-7.))/self._expsfh/100.)) #e.g., Binney (2010)
else:
weights.append(dN[ii])
massweights.append(dmass[ii])
else:
if self._expsfh:
weights.append(dN[ii]*10**(logage-7.)*numpy.exp((10.**(logage-7.))/self._expsfh/100.)) #e.g., Binney (2010)
massweights.append(dmass[ii]*10**(logage-7.)*numpy.exp((10.**(logage-7.))/self._expsfh/100.)) #e.g., Binney (2010)
else:
weights.append(dN[ii]*10**(logage-7.))
massweights.append(dmass[ii]*10**(logage-7.))
else:
continue #no use in continuing here
#Form array
sample= numpy.array(sample)
loggs= numpy.array(loggs)
teffs= numpy.array(teffs)
pmasses= numpy.array(pmasses)
plages= numpy.array(plages)-9.
pjks= numpy.array(pjks)
weights= numpy.array(weights)
massweights= numpy.array(massweights)
#Cut out low weights
if False:
indx= (weights > 10.**-5.*numpy.sum(weights))
else:
indx= numpy.ones(len(weights),dtype='bool')
self._sample= sample[indx,:]
self._weights= weights[indx]
self._massweights= massweights[indx]
self._loggs= loggs[indx]
self._teffs= teffs[indx]
self._masses= pmasses[indx]
self._lages= plages[indx]
self._jks= pjks[indx]
#Setup KDE
self._kde= dens_kde.densKDE(self._sample,w=self._weights,
h=2.*self._sample.shape[0]**(-1./5.),#h='scott',
kernel='biweight',
variable=True,variablenitt=3,
variableexp=0.5)
return None
def __init__(self,
loggmin=None,
loggmax=None,
imfmodel='lognormalChabrier2001',
Z=None,
expsfh=False,
band='Ks',
dontgather=False,
basti=False,
parsec=True,
minage=None,
maxage=10.,
jkmin=None,
stage=None,
eta=None):
"""
NAME:
__init__
PURPOSE:
initialize isocmodel
INPUT:
Z= metallicity (if not set, use flat prior in Z over all Z; can be list)
loggmin= if set, cut logg at this minimum
loggmax= if set, cut logg at this maximum, if 'rc', then this is the function of teff and z appropriate for the APOGEE RC sample
imfmodel= (default: 'lognormalChabrier2001') IMF model to use (see isodist.imf code for options)
band= band to use for M_X (JHK)
expsfh= if True, use an exponentially-declining star-formation history (can be set to a decay time-scale in Gyr)
dontgather= if True, don't gather surrounding Zs
basti= if True, use Basti isochrones (if False, use PARSEC)
parsec= if True (=default), use PARSEC isochrones, if False, use Padova
stage= if True, only use this evolutionary stage
minage= (None) minimum log10 of age/yr
maxage= (10.) maximum log10 of age/yr
jkmin= (None) if set, only consider J-Ks greater than this
eta= (None) mass-loss efficiency parameter
OUTPUT:
object
HISTORY:
2012-11-07 - Written - Bovy (IAS)
"""
self._band = band
self._loggmin = loggmin
self._loggmax = loggmax
if isinstance(expsfh, (int, float, numpy.float32, numpy.float64)):
self._expsfh = expsfh
elif expsfh:
self._expsfh = 8.
else:
self._expsfh = False
self._Z = Z
self._imfmodel = imfmodel
self._basti = basti
self._eta = eta
if isinstance(loggmax, str) and loggmax.lower() == 'rc':
from apogee.samples.rc import loggteffcut
#Read isochrones
if basti:
zs = numpy.array([
0.0001, 0.0003, 0.0006, 0.001, 0.002, 0.004, 0.008, 0.01,
0.0198, 0.03, 0.04
])
elif parsec:
zs = numpy.arange(0.0005, 0.06005, 0.0005)
else:
zs = numpy.arange(0.0005, 0.03005, 0.0005)
if Z is None:
Zs = zs
elif isinstance(Z, float):
if basti or dontgather:
Zs = [Z]
elif Z < 0.001 or Z > 0.0295:
Zs = [Z]
elif Z < 0.0015 or Z > 0.029:
Zs = [Z - 0.0005, Z, Z + 0.0005] #build up statistics
elif Z < 0.01:
Zs = [Z - 0.001, Z - 0.0005, Z, Z + 0.0005,
Z + 0.001] #build up statistics
else:
Zs = [Z - 0.0005, Z, Z + 0.0005] #build up statistics
if basti:
p = isodist.BastiIsochrone(Z=Zs, eta=eta)
else:
p = isodist.PadovaIsochrone(Z=Zs, parsec=parsec, eta=eta)
if basti:
#Force BaSTI to have equal age sampling
lages = list(numpy.log10(numpy.arange(0.1, 1., 0.1)) + 9.)
lages.extend(list(numpy.log10(numpy.arange(1.0, 10.5, 0.5)) + 9.))
lages = numpy.array(lages)
#Get relevant data
sample = []
weights = []
massweights = []
loggs = []
teffs = []
pmasses = []
plages = []
pjks = []
for logage in p.logages():
if logage > maxage: continue
if not minage is None and logage < minage: continue
if basti and numpy.sum((logage == lages)) == 0: continue
for zz in range(len(Zs)):
thisiso = p(logage, Zs[zz], asrecarray=True, stage=stage)
if len(thisiso.M_ini) == 0: continue
#Calculate int_IMF for this IMF model
if not imfmodel == 'lognormalChabrier2001': #That would be the default
if imfmodel == 'exponentialChabrier2001':
int_IMF = isodist.imf.exponentialChabrier2001(
thisiso.M_ini, int=True)
elif imfmodel == 'kroupa2003':
int_IMF = isodist.imf.kroupa2003(thisiso.M_ini,
int=True)
elif imfmodel == 'chabrier2003':
int_IMF = isodist.imf.chabrier2003(thisiso.M_ini,
int=True)
else:
raise IOError(
"imfmodel option not understood (non-existing model)"
)
elif basti:
int_IMF = isodist.imf.lognormalChabrier2001(thisiso.M_ini,
int=True)
else:
int_IMF = thisiso.int_IMF
dN = (numpy.roll(int_IMF, -1) -
int_IMF) / (int_IMF[-1] - int_IMF[0]) / 10**(logage - 7.)
dmass = thisiso.M_ini * (
numpy.roll(int_IMF, -1) - int_IMF) / numpy.sum(
(thisiso.M_ini *
(numpy.roll(int_IMF, -1) - int_IMF))[:-1]) / 10**(
logage - 7.)
for ii in range(1, len(int_IMF) - 1):
if basti:
JK = 0.996 * (thisiso.J[ii] - thisiso.K[ii]) + 0.00923
else:
JK = thisiso.J[ii] - thisiso.Ks[ii]
if not jkmin is None and JK < jkmin: continue
if band.lower() == 'h':
if basti:
raise NotImplementedError(
"'H' not implemented for BaSTI yet")
J = JK + thisiso.K[ii] - 0.044
H = J - (0.980 *
(thisiso.J[ii] - thisiso.H[ii]) - 0.045)
else:
H = thisiso.H[ii]
elif band.lower() == 'j':
if basti:
raise NotImplementedError(
"'J' not implemented for BaSTI yet")
J = JK + thisiso.K[ii] - 0.044
else:
H = thisiso.J[ii]
elif band.lower() == 'k' or band.lower() == 'ks':
if basti:
H = thisiso.K[ii] - 0.046
else:
H = thisiso.Ks[ii]
if JK < 0.3 \
or (isinstance(loggmax,str) and loggmax == 'rc' and (thisiso['logg'][ii] > loggteffcut(10.**thisiso['logTe'][ii],Zs[zz],upper=True))) \
or (not isinstance(loggmax,str) and not loggmax is None and thisiso['logg'][ii] > loggmax) \
or (not loggmin is None and thisiso['logg'][ii] < loggmin):
continue
if dN[ii] > 0.:
sample.append([JK, H])
loggs.append([thisiso.logg[ii]])
teffs.append([10.**thisiso.logTe[ii]])
pmasses.append(thisiso.M_ini[ii])
plages.append(logage)
pjks.append(JK)
if basti: #BaSTI is sampled uniformly in age, not logage, but has a finer sampling below 1 Gyr
if logage < 9.:
if self._expsfh:
weights.append(dN[ii] / 5. * numpy.exp(
(10.**(logage - 7.)) / self._expsfh /
100.)) #e.g., Binney (2010)
massweights.append(
dmass[ii] / 5. * numpy.exp(
(10.**(logage - 7.)) / self._expsfh
/ 100.)) #e.g., Binney (2010)
else:
weights.append(dN[ii] / 5.)
massweights.append(dmass[ii] / 5.)
else:
if self._expsfh:
weights.append(dN[ii] * numpy.exp(
(10.**(logage - 7.)) / self._expsfh /
100.)) #e.g., Binney (2010)
massweights.append(dmass[ii] * numpy.exp(
(10.**(logage - 7.)) / self._expsfh /
100.)) #e.g., Binney (2010)
else:
weights.append(dN[ii])
massweights.append(dmass[ii])
else:
if self._expsfh:
weights.append(
dN[ii] * 10**(logage - 7.) * numpy.exp(
(10.**(logage - 7.)) / self._expsfh /
100.)) #e.g., Binney (2010)
massweights.append(
dmass[ii] * 10**(logage - 7.) * numpy.exp(
(10.**(logage - 7.)) / self._expsfh /
100.)) #e.g., Binney (2010)
else:
weights.append(dN[ii] * 10**(logage - 7.))
massweights.append(dmass[ii] *
10**(logage - 7.))
else:
continue #no use in continuing here
#Form array
sample = numpy.array(sample)
loggs = numpy.array(loggs)
teffs = numpy.array(teffs)
pmasses = numpy.array(pmasses)
plages = numpy.array(plages) - 9.
pjks = numpy.array(pjks)
weights = numpy.array(weights)
massweights = numpy.array(massweights)
#Cut out low weights
if False:
indx = (weights > 10.**-5. * numpy.sum(weights))
else:
indx = numpy.ones(len(weights), dtype='bool')
self._sample = sample[indx, :]
self._weights = weights[indx]
self._massweights = massweights[indx]
self._loggs = loggs[indx]
self._teffs = teffs[indx]
self._masses = pmasses[indx]
self._lages = plages[indx]
self._jks = pjks[indx]
#Setup KDE
self._kde = dens_kde.densKDE(
self._sample,
w=self._weights,
h=2. * self._sample.shape[0]**(-1. / 5.), #h='scott',
kernel='biweight',
variable=True,
variablenitt=3,
variableexp=0.5)
return None
noseismo= seismoState == 'UNKNOWN'
noclumpseismo= (seismoState == 'RGB') \
+ (seismoState == 'DWARF/SUBGIANT')
rcclumpseismo= clumpseismo*(data['RC'] == 1)#*(((data['TEFF']-4800.)/1000.+2.75) > data['LOGG'])
rcnoclumpseismo= noclumpseismo*(data['RC'] == 1)#*(((data['TEFF']-4800.)/1000.+2.75) > data['LOGG'])
#Statistics using evolutionary state measurements
print "%i APOKASC stars have evolutionary state measurements" % (numpy.sum(clumpseismo)+numpy.sum(noclumpseismo))
print "%i APOKASC RC stars have evolutionary state measurements" % (numpy.sum(rcclumpseismo)+numpy.sum(rcnoclumpseismo))
print "%i / %i = %i%% APOKASC CLUMP stars are in the RC catalog (COMPLETENESS)" % (numpy.sum(rcclumpseismo),numpy.sum(clumpseismo),float(numpy.sum(rcclumpseismo))/numpy.sum(clumpseismo)*100.)
print "%i / %i = %i%% APOKASC non-CLUMP stars out of all stars in the RC catalog with evolutionary measurements are in the RC catalog (CONTAMINATION)" % (numpy.sum(rcnoclumpseismo),numpy.sum(rcnoclumpseismo)+numpy.sum(rcclumpseismo),float(numpy.sum(rcnoclumpseismo))/(numpy.sum(rcnoclumpseismo)+numpy.sum(rcclumpseismo))*100.)
rcindx= data['RC'] == 1
#Statistics using simple seismo logg cut
kascLoggTag= 'KASC_RG_LOGG_SCALE_2'
try:
clumplogg= (data[kascLoggTag] > 1.8)\
*(data[kascLoggTag] < rcmodel.loggteffcut(data['TEFF'],
isodist.FEH2Z(data['METALS'],zsolar=0.017),upper=True))#2.8)
except ValueError:
kascLoggTag= 'LOGGRG'
clumplogg= (data[kascLoggTag] > 1.8)\
*(data[kascLoggTag] < rcmodel.loggteffcut(data['TEFF'],
isodist.FEH2Z(data['METALS'],zsolar=0.017),upper=True))#2.8)
if False:
rcclumplogg= clumplogg*(data['RC'] == 1)
print "%i / %i = %i%% APOKASC logg clump stars are in the RC catalog" % (numpy.sum(rcclumplogg),numpy.sum(clumplogg),float(numpy.sum(rcclumplogg))/numpy.sum(clumplogg)*100)
rcnoclumplogg= (True-clumplogg)*(data['RC'] == 1)
print "%i / %i = %i%% APOKASC logg non-clump stars are in the RC catalog" % (numpy.sum(rcnoclumplogg),numpy.sum(True-clumplogg),float(numpy.sum(rcnoclumplogg))/numpy.sum(True-clumplogg)*100.)
print "%i / %i = %i%% APOKASC logg non-clump stars out of all stars are in the RC catalog" % (numpy.sum(rcnoclumplogg),numpy.sum(data['RC'] == 1),float(numpy.sum(rcnoclumplogg))/numpy.sum(data['RC'] == 1)*100.)
bloggindx= (data['LOGG'] >= 1.8)*\
(data['LOGG'] <= rcmodel.loggteffcut(data['TEFF'],data['METALS'],
upper=True))
gloggindx= (data[kascLoggTag] < 1.8)+\
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_ALPHA', 'RV_CARB', 'RV_CCFWHM', 'RV_AUTOFWHM',
'SYNTHSCATTER', 'STABLERV_CHI2', 'STABLERV_RCHI2',
'STABLERV_CHI2_PROB', 'CHI2_THRESHOLD', 'APSTAR_VERSION',
'ASPCAP_VERSION', 'RESULTS_VERSION', 'WASH_M', 'WASH_M_ERR', 'WASH_T2',
'WASH_T2_ERR', 'DDO51', 'DDO51_ERR', 'IRAC_3_6', 'IRAC_3_6_ERR',
'IRAC_4_5', 'IRAC_4_5_ERR', 'IRAC_5_8', 'IRAC_5_8_ERR', 'IRAC_8_0',
'IRAC_8_0_ERR', 'WISE_4_5', 'WISE_4_5_ERR', 'TARG_4_5', 'TARG_4_5_ERR',
'WASH_DDO51_GIANT_FLAG', 'WASH_DDO51_STAR_FLAG', 'REDUCTION_ID',
'SRC_H', 'PM_SRC'
])
# More
if appath._APOGEE_REDUX.lower() == 'l33':
data = esutil.numpy_util.remove_fields(data, [
'GAIA_SOURCE_ID', 'GAIA_PARALLAX', 'GAIA_PARALLAX_ERROR',
'GAIA_PMRA', 'GAIA_PMRA_ERROR', 'GAIA_PMDEC', 'GAIA_PMDEC_ERROR',
'GAIA_PHOT_G_MEAN_MAG', 'GAIA_PHOT_BP_MEAN_MAG',
'GAIA_PHOT_RP_MEAN_MAG', 'GAIA_RADIAL_VELOCITY',
'GAIA_RADIAL_VELOCITY_ERROR', 'GAIA_R_EST', 'GAIA_R_LO',
'GAIA_R_HI', 'TEFF_SPEC', 'LOGG_SPEC'
])
if not appath._APOGEE_REDUX.lower() == 'current' \
and not 'l3' in appath._APOGEE_REDUX \
and 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 'l31' in appath._APOGEE_REDUX:
logg = data['LOGG']
elif 'l30' in appath._APOGEE_REDUX:
logg = data['LOGG']
elif appath._APOGEE_REDUX.lower() == 'current' \
or int(appath._APOGEE_REDUX[1:]) > 600:
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+0.1*('l31' in appath._APOGEE_REDUX
or 'l33' in appath._APOGEE_REDUX) \
<= 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()
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)
RphiZ = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Xsun=8.15,
Zsun=0.0208)
R = RphiZ[:, 0]
phi = RphiZ[:, 1]
Z = RphiZ[:, 2]
data['RC_GALR'] = R
data['RC_GALPHI'] = phi
data['RC_GALZ'] = Z
#Save
fitswrite(savefilename, data, clobber=True)
# Add Tycho-2 matches
if options.tyc2:
data = esutil.numpy_util.add_fields(data, [('TYC2MATCH', numpy.int32),
('TYC1', numpy.int32),
('TYC2', numpy.int32),
('TYC3', numpy.int32)])
data['TYC2MATCH'] = 0
data['TYC1'] = -1
data['TYC2'] = -1
data['TYC3'] = -1
# Write positions
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=2', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:Tycho2',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Directly match on input RA
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
usecols=(1, 2, 7, 8, 9))
iis = numpy.arange(len(data))
mai = [iis[data['RA'] == ma[ii, 0]][0] for ii in range(len(ma))]
data['TYC2MATCH'][mai] = 1
data['TYC1'][mai] = ma[:, 2]
data['TYC2'][mai] = ma[:, 3]
data['TYC3'][mai] = ma[:, 4]
os.remove(posfilename)
os.remove(resultfilename)
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:
fitswrite(savefilename, data, clobber=True)
return None
data = _add_proper_motions(data, savefilename)
# Save
fitswrite(savefilename, data, clobber=True)
return None