def get_apogee(dr='16',use_astroNN=True):
"""
dr: dr to select.
astronn: boolean flag for whether to use astroNN abundances, defaults
to ASPCAP abundances or not.
Returns APOGEE allStar file without duplicates.
"""
dr = str(dr)
# Change to relevant data release
change_dr(dr)
# Only use astroNN values if they are available for this data release.
if use_astroNN:
if int(dr)<14:
use_astroNN=False
elif int(dr)>=14:
allStar=apread.allStar(rmcommissioning=True,
rmdups=False,
use_astroNN=True)
if not use_astroNN:
allStar=apread.allStar(rmcommissioning=True,
rmdups=False,
use_astroNN=False)
# Manually remove duplicates
apids,inds=np.unique(allStar['APOGEE_ID'],return_index=True)
return allStar[inds]
def getAllTargets(): ''' Returns all targets from the allStar file :return: locationIDs, apogeeIDs ''' data = apread.allStar(dr='13') return data['APOGEE_ID'], data['LOCATION_ID']
def apogee(**kwargs):
"""
PURPOSE:
read the APOGEE allStar file
INPUT:
IF the apogee package is not installed:
dr= (13) SDSS data release
ELSE you can use the same keywords as apogee.tools.read.allstar:
rmcommissioning= (default: True) if True, only use data obtained after commissioning
main= (default: False) if True, only select stars in the main survey
exclude_star_bad= (False) if True, remove stars with the STAR_BAD flag set in ASPCAPFLAG
exclude_star_warn= (False) if True, remove stars with the STAR_WARN flag set in ASPCAPFLAG
ak= (default: True) only use objects for which dereddened mags exist
akvers= 'targ' (default) or 'wise': use target AK (AK_TARG) or AK derived from all-sky WISE (AK_WISE)
rmnovisits= (False) if True, remove stars with no good visits (to go into the combined spectrum); shouldn't be necessary
adddist= (default: False) add distances (DR10/11 Hayden distances, DR12 combined distances)
distredux= (default: DR default) reduction on which the distances are based
rmdups= (False) if True, remove duplicates (very slow)
raw= (False) if True, just return the raw file, read w/ fitsio
OUTPUT:
allStar data
HISTORY:
2013-09-06 - Written - Bovy (IAS)
"""
if not _APOGEE_LOADED:
warnings.warn("Falling back on simple APOGEE interface; for more functionality, install the jobovy/apogee package")
dr= kwargs.get('dr',13)
filePath= path.apogeePath(dr=dr)
if not os.path.exists(filePath):
download.apogee(dr=dr)
return fitsio.read(filePath,1)
else:
return apread.allStar(**kwargs)
def read_caldata(filename='../cldata/aj485195t4_mrt.txt'):
data = astropy.io.ascii.read(filename)
data.rename_column('Cluster', 'CLUSTER')
data.remove_column('Teff')
data.rename_column('TeffC', 'TEFF')
data.remove_column('logg')
data.rename_column('loggC', 'LOGG')
data.remove_column('[M/H]')
data.rename_column('[M/H]C', 'FEH')
data.rename_column('2MASS', 'ID')
# Now match to allStar to get the location_ids, and abundances
alldata = apread.allStar(raw=True)
locids = numpy.empty(len(data), dtype='int')
fehs = numpy.empty(len(data), dtype='float')
afes = numpy.empty(len(data), dtype='float')
for ii in range(len(data)):
if 'Pleiades' in data['CLUSTER'][ii]: continue
indx = alldata['APOGEE_ID'] == data['ID'][ii]
if numpy.sum(indx) == 0:
raise ValueError('allStar match for %s not found ...' %
(data['ID'][ii]))
if len(list(set(alldata['LOCATION_ID'][indx]))) > 1:
raise ValueError('Multiple matches found for for %s ...' %
(data['ID'][ii]))
locids[ii] = alldata['LOCATION_ID'][indx][0]
fehs[ii] = alldata['FE_H'][indx][0]
afes[ii] = define_rcsample.avg_alphafe(alldata[indx])[0]
data['LOCATION_ID'] = locids
data['FE_H'] = fehs
data[define_rcsample._AFETAG] = afes
return data
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 readAndHackHoltz():
alldata = apread.allStar(adddist=True, distredux="v402")
jk = alldata["J0"] - alldata["K0"]
data = alldata[(jk > 0.8) * (alldata["DISO_GAL"] > 0.0)]
# To allow for XY pixelization, we will hack these
data = esutil.numpy_util.add_fields(data, [("RC_GALR", float), ("RC_GALPHI", float), ("RC_GALZ", float)])
XYZ = bovy_coords.lbd_to_XYZ(data["GLON"], data["GLAT"], data["DISO_GAL"], degree=True)
R, phi, Z = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], Xsun=8.0, Zsun=0.025)
data["RC_GALR"] = R
data["RC_GALPHI"] = phi
data["RC_GALZ"] = Z
return data
def read_meszarosgcdata(filename=os.path.join(
os.path.dirname(os.path.realpath(__file__)), '..', 'data', 'clusterdata',
'aj509073t2_mrt.txt')):
"""
NAME:
read_meszarosgcdata
PURPOSE:
Read the data on globular clusters from Meszaros et al. (2015)
INPUT:
filename= Name of the file that has the ApJ machine-readable table
OUTPUT:
data structure with the data
HISTORY:
2015-02-11 - Started - Bovy (IAS@KITP)
2015-08-13 - Re-written for new data format - Bovy (UofT)
"""
data = astropy.io.ascii.read(filename)
data.rename_column('Clust', 'CLUSTER')
data.rename_column('Teff', 'TEFF')
data.rename_column('log(g)', 'LOGG')
data.rename_column('[Fe/H]', 'FEH')
data.rename_column('2MASS', 'ID')
# Now match to allStar to get the location_ids and H magnitudes
alldata = apread.allStar(raw=True)
locids = numpy.zeros(len(data), dtype='int') - 1
hmags = numpy.zeros(len(data), dtype='float') - 1
# and match to allVisit for the fibers that each star was observed in
allvdata = apread.allVisit(raw=True)
fibers = numpy.zeros(
(len(data), numpy.nanmax(alldata['NVISITS'])), dtype='int') - 1
for ii in range(len(data)):
if 'Pleiades' in data['CLUSTER'][ii]: continue
indx = alldata['APOGEE_ID'] == data['ID'][ii]
if numpy.sum(indx) == 0:
raise ValueError('allStar match for %s not found ...' %
(data['ID'][ii]))
if len(list(set(alldata['LOCATION_ID'][indx]))) > 1:
raise ValueError('Multiple matches found for for %s ...' %
(data['ID'][ii]))
locids[ii] = alldata['LOCATION_ID'][indx][0]
hmags[ii] = alldata['H'][indx][0]
for jj in range(alldata['NVISITS'][indx][0]):
fibers[ii, jj] = allvdata[alldata['VISIT_PK'][indx][0,
jj]]['FIBERID']
data['LOCATION_ID'] = locids
data['H'] = hmags
data['FIBERID'] = fibers
data['APOGEE_ID'] = data['ID']
data['FE_H'] = data['FEH']
return data
def apogee(xmatch=None, **kwargs):
"""
PURPOSE:
read the APOGEE allStar file
INPUT:
IF the apogee package is not installed:
dr= (14) SDSS data release
ELSE you can use the same keywords as apogee.tools.read.allstar:
rmcommissioning= (default: True) if True, only use data obtained after commissioning
main= (default: False) if True, only select stars in the main survey
exclude_star_bad= (False) if True, remove stars with the STAR_BAD flag set in ASPCAPFLAG
exclude_star_warn= (False) if True, remove stars with the STAR_WARN flag set in ASPCAPFLAG
ak= (default: True) only use objects for which dereddened mags exist
akvers= 'targ' (default) or 'wise': use target AK (AK_TARG) or AK derived from all-sky WISE (AK_WISE)
rmnovisits= (False) if True, remove stars with no good visits (to go into the combined spectrum); shouldn't be necessary
adddist= (default: False) add distances (DR10/11 Hayden distances, DR12 combined distances)
distredux= (default: DR default) reduction on which the distances are based
rmdups= (False) if True, remove duplicates (very slow)
raw= (False) if True, just return the raw file, read w/ fitsio
ALWAYS ALSO
xmatch= (None) if set, cross-match against a Vizier catalog (e.g., vizier:I/345/gaia2 for Gaia DR2) using gaia_tools.xmatch.cds and return the overlap
+gaia_tools.xmatch.cds keywords
OUTPUT:
allStar data[,xmatched table]
HISTORY:
2013-09-06 - Written - Bovy (IAS)
2018-05-09 - Add xmatch - Bovy (UofT)
"""
if not _APOGEE_LOADED:
warnings.warn(
"Falling back on simple APOGEE interface; for more functionality, install the jobovy/apogee package"
)
dr = kwargs.get('dr', 14)
filePath = path.apogeePath(dr=dr)
if not os.path.exists(filePath):
download.apogee(dr=dr)
data = fitsread(filePath, 1)
if not xmatch is None:
ma, mai = _xmatch_cds(data, xmatch, filePath, **kwargs)
return (data[mai], ma)
else:
return data
else:
kwargs['xmatch'] = xmatch
return apread.allStar(**kwargs)
def read_meszarosgcdata(filename='../clusterdata/aj509073t2_mrt.txt'):
"""
NAME:
read_meszarosgcdata
PURPOSE:
Read the data on globular clusters from Meszaros et al. (2015)
INPUT:
filename= Name of the file that has the ApJ machine-readable table
OUTPUT:
data structure with the data
HISTORY:
2015-02-11 - Started - Bovy (IAS@KITP)
2015-08-13 - Re-written for new data format - Bovy (UofT)
"""
data= astropy.io.ascii.read(filename)
data.rename_column('Clust','CLUSTER')
data.rename_column('Teff','TEFF')
data.rename_column('log(g)','LOGG')
data.rename_column('[Fe/H]','FEH')
data.rename_column('2MASS','ID')
# Now match to allStar to get the location_ids and H magnitudes
alldata= apread.allStar(raw=True)
locids= numpy.zeros(len(data),dtype='int')-1
hmags= numpy.zeros(len(data),dtype='float')-1
# and match to allVisit for the fibers that each star was observed in
allvdata= apread.allVisit(raw=True)
fibers= numpy.zeros((len(data),numpy.nanmax(alldata['NVISITS'])),
dtype='int')-1
for ii in range(len(data)):
if 'Pleiades' in data['CLUSTER'][ii]: continue
indx= alldata['APOGEE_ID'] == data['ID'][ii]
if numpy.sum(indx) == 0:
raise ValueError('allStar match for %s not found ...' % (data['ID'][ii]))
if len(list(set(alldata['LOCATION_ID'][indx]))) > 1:
raise ValueError('Multiple matches found for for %s ...' % (data['ID'][ii]))
locids[ii]= alldata['LOCATION_ID'][indx][0]
hmags[ii]= alldata['H'][indx][0]
for jj in range(alldata['NVISITS'][indx][0]):
fibers[ii,jj]= allvdata[alldata['VISIT_PK'][indx][0,jj]]['FIBERID']
data['LOCATION_ID']= locids
data['H']= hmags
data['FIBERID']= fibers
data['APOGEE_ID'] = data['ID']
data['FE_H'] = data['FEH']
return data
def read_apogee_catalog():
'''
read in the catalog of info for all stars in a data release.
'''
all_star_catalog = apread.allStar(rmcommissioning=False,
rmdups=False,
main=False,
raw=True)
# and match to allVisit for the fibers that each star was observed in
allvdata = apread.allVisit(raw=True)
fibers = np.zeros(
(len(all_star_catalog), np.nanmax(all_star_catalog['NVISITS'])),
dtype='int') - 1
for ii in range(len(all_star_catalog)):
for jj in range(all_star_catalog['NVISITS'][ii]):
fibers[ii, jj] = allvdata[all_star_catalog['VISIT_PK'][ii]
[jj]]['FIBERID']
return all_star_catalog, fibers
def apogee(**kwargs):
"""
PURPOSE:
read the APOGEE allStar file
INPUT:
rmcommissioning= (default: True) if True, only use data obtained after commissioning
main= (default: False) if True, only select stars in the main survey
exclude_star_bad= (False) if True, remove stars with the STAR_BAD flag set in ASPCAPFLAG
exclude_star_warn= (False) if True, remove stars with the STAR_WARN flag set in ASPCAPFLAG
ak= (default: True) only use objects for which dereddened mags exist
akvers= 'targ' (default) or 'wise': use target AK (AK_TARG) or AK derived from all-sky WISE (AK_WISE)
rmnovisits= (False) if True, remove stars with no good visits (to go into the combined spectrum); shouldn't be necessary
adddist= (default: False) add distances (DR10/11 Hayden distances, DR12 combined distances)
distredux= (default: DR default) reduction on which the distances are based
rmdups= (False) if True, remove duplicates (very slow)
raw= (False) if True, just return the raw file, read w/ fitsio
OUTPUT:
allStar data
HISTORY:
2013-09-06 - Written - Bovy (IAS)
"""
if not _APOGEE_LOADED:
raise ImportError("Loading the APOGEE data requires the jobovy/apogee module to be installed")
return apread.allStar(**kwargs)
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 read_caldata(filename='../clusterdata/aj485195t4_mrt.txt',dr='13'):
"""
NAME:
read_caldata
PURPOSE:
Read the data on calibration clusters from Meszaros et al. (2013)
INPUT:
filename= Name of the file that has the ApJ machine-readable table
OUTPUT:
data structure with the data
HISTORY:
2015-02-11 - Written - Bovy (IAS@KITP)
"""
data= astropy.io.ascii.read(filename)
data.rename_column('Cluster','CLUSTER')
data.remove_column('Teff')
data.rename_column('TeffC','TEFF')
data.remove_column('logg')
data.rename_column('loggC','LOGG')
data.remove_column('[M/H]')
data.rename_column('[M/H]C','FEH')
data.rename_column('2MASS','ID')
# Now match to allStar to get the location_ids
alldata= apread.allStar(raw=True,dr=dr)
locids= numpy.zeros(len(data),dtype='int')-1
hmags= numpy.zeros(len(data),dtype='float')-1
snrs = numpy.zeros(len(data),dtype='float')-1
ras= numpy.zeros(len(data),dtype='float')-1
decs= numpy.zeros(len(data),dtype='float')-1
# and match to allVisit for the fibers that each star was observed in
allvdata= apread.allVisit(raw=True,dr=dr)
fibers= numpy.zeros((len(data),numpy.nanmax(alldata['NVISITS'])),
dtype='int')-1
inds = []
for ii in range(len(data)):
if 'Pleiades' in data['CLUSTER'][ii]:
inds.append(0)
continue
indx= alldata['APOGEE_ID'] == data['ID'][ii]
success = numpy.where(indx==True)[0]
if success.size==0 or success.size>1:
inds.append(0)
elif success.size==1:
inds.append(success[0])
if numpy.sum(indx) == 0:
raise ValueError('allStar match for %s not found ...' % (data['ID'][ii]))
if len(list(set(alldata['LOCATION_ID'][indx]))) > 1:
raise ValueError('Multiple matches found for for %s ...' % (data['ID'][ii]))
locids[ii]= alldata['LOCATION_ID'][indx][0]
hmags[ii]= alldata['H'][indx][0]
snrs[ii] = alldata['SNR'][indx][0]
ras[ii] = alldata['RA'][indx][0]
decs[ii] = alldata['DEC'][indx][0]
for jj in range(alldata['NVISITS'][indx][0]):
fibers[ii,jj]= allvdata[alldata['VISIT_PK'][indx][0,jj]]['FIBERID']
inds = (numpy.array(inds),)
data['LOCATION_ID']= locids
data['H']= hmags
data['FIBERID']= fibers
data['SNR'] = snrs
data['APOGEE_ID'] = data['ID']
data['RA'] = ras
data['DEC'] = decs
data['index'] = inds[0]
data['M_H'] = data['FEH']
data['FE_H'] = alldata['FE_H'][inds]
if dr == '13':
rel = 'FE'
if dr != '13':
rel = 'H'
data['C_{0}'.format(rel)] = alldata['C_{0}'.format(rel)][inds]
data['N_{0}'.format(rel)] = alldata['N_{0}'.format(rel)][inds]
data['O_{0}'.format(rel)] = alldata['O_{0}'.format(rel)][inds]
data['NA_{0}'.format(rel)] = alldata['NA_{0}'.format(rel)][inds]
data['MG_{0}'.format(rel)] = alldata['MG_{0}'.format(rel)][inds]
data['AL_{0}'.format(rel)] = alldata['AL_{0}'.format(rel)][inds]
data['SI_{0}'.format(rel)] = alldata['SI_{0}'.format(rel)][inds]
data['S_{0}'.format(rel)] = alldata['S_{0}'.format(rel)][inds]
data['K_{0}'.format(rel)] = alldata['K_{0}'.format(rel)][inds]
data['CA_{0}'.format(rel)] = alldata['CA_{0}'.format(rel)][inds]
data['TI_{0}'.format(rel)] = alldata['TI_{0}'.format(rel)][inds]
data['V_{0}'.format(rel)] = alldata['V_{0}'.format(rel)][inds]
data['MN_{0}'.format(rel)] = alldata['MN_{0}'.format(rel)][inds]
data['NI_{0}'.format(rel)] = alldata['NI_{0}'.format(rel)][inds]
return data
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
def get_rgbsample(loggcut=[1.8, 3.0],
teffcut=[0, 10000],
add_ages=False,
agetype='Martig',
apply_corrections=False,
distance_correction=False,
verbose=False):
"""
Get a clean sample of dr12 APOGEE data with Michael Haydens distances
---
INPUT:
None
OUTPUT:
Clean rgb sample with added distances
HISTORY:
Started - Mackereth 02/06/16
"""
#get the allStar catalogue using apogee python (exlude all bad flags etc)
allStar = apread.allStar(rmcommissioning=True,
exclude_star_bad=True,
exclude_star_warn=True,
main=True,
ak=True,
adddist=False)
#cut to a 'sensible' logg range (giants which are not too high on the RGB)
allStar = allStar[(allStar['LOGG'] > loggcut[0])
& (allStar['LOGG'] < loggcut[1]) &
(allStar['TEFF'] > teffcut[0]) &
(allStar['TEFF'] < teffcut[1])]
if verbose == True:
print str(
len(allStar
)) + ' Stars before Distance catalogue join (after Log(g) cut)'
#load the distance VAC
dists = fits.open(catpath + 'DR12_DIST_R-GC.fits')[1].data
#convert to astropy Table
allStar_tab = Table(data=allStar)
dists_tab = Table(data=dists)
#join table
tab = join(allStar_tab,
dists_tab,
keys='APOGEE_ID',
uniq_col_name='{col_name}{table_name}',
table_names=['', '2'])
data = tab.as_array()
data = esutil.numpy_util.add_fields(data, [('M_J', float), ('M_H', float),
('M_K', float),
('MH50_DIST', float),
('MH50_GALR', float),
('MH50_GALZ', float),
('MH50_GALPHI', float),
('AVG_ALPHAFE', float)])
data['MH50_DIST'] = (10**((data['HAYDEN_DISTMOD_50'] + 5) / 5)) / 1e3
if distance_correction == True:
data['MH50_DIST'] *= 1.05
XYZ = bovy_coords.lbd_to_XYZ(data['GLON'],
data['GLAT'],
data['MH50_DIST'],
degree=True)
RphiZ = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Xsun=8.,
Zsun=0.025)
data['MH50_GALR'] = RphiZ[:, 0]
data['MH50_GALPHI'] = RphiZ[:, 1]
data['MH50_GALZ'] = RphiZ[:, 2]
data['M_J'] = data['J0'] - data['HAYDEN_DISTMOD_50']
data['M_H'] = data['H0'] - data['HAYDEN_DISTMOD_50']
data['M_K'] = data['K0'] - data['HAYDEN_DISTMOD_50']
data['AVG_ALPHAFE'] = avg_alphafe_dr12(data)
data[_FEHTAG] += -0.1
#remove locations not in the apogee selection function (FIND OUT WHATS UP HERE)
data = data[np.in1d(data['LOCATION_ID'], apo.list_fields())]
# Remove locations outside of the Pan-STARRS dust map
# In the Southern hemisphere
data = data[data['LOCATION_ID'] != 4266] #240,-18
data = data[data['LOCATION_ID'] != 4331] #5.5,-14.2
data = data[data['LOCATION_ID'] != 4381] #5.2,-12.2
data = data[data['LOCATION_ID'] != 4332] #1,-4
data = data[data['LOCATION_ID'] != 4329] #0,-5
data = data[data['LOCATION_ID'] != 4351] #0,-2
data = data[data['LOCATION_ID'] != 4353] #358,0
data = data[data['LOCATION_ID'] != 4385] #358.6,1.4
# Close to the ecliptic pole where there's no data (is it the ecliptic pole?
data = data[data['LOCATION_ID'] != 4528] #120,30
data = data[data['LOCATION_ID'] != 4217] #123,22.4
#remove any non-finite magnitudes
data = data[np.isfinite(data['M_H'])]
if verbose == True:
print str(len(
data)) + ' Stars with distance measures (and in good fields...)'
if add_ages == True:
if agetype == 'Martig':
ages = fits.open(catpath + 'DR12_martigages_vizier.fits')[1].data
idtag = '2MASS_ID'
if agetype == 'Cannon':
ages = fits.open(catpath + 'RGB_Cannon_Ages.fits')[1].data
ages = esutil.numpy_util.add_fields(ages, [('Age', float)])
ages['Age'] = np.exp(ages['ln_age'])
idtag = 'ID'
ages_tab = Table(data=ages)
ages_tab.rename_column(idtag, 'APOGEE_ID')
tab = join(ages_tab,
data,
keys='APOGEE_ID',
uniq_col_name='{col_name}{table_name}',
table_names=['', '2'])
allStar_full = tab.as_array()
data = allStar_full
if verbose == True:
print str(len(data)) + ' Stars with ages'
if apply_corrections == True:
#martig1 = np.genfromtxt(catpath+'martig2016_table1.txt', dtype=None, names=True, skip_header=2)
martig1 = fits.open(catpath + 'martig_table1.fits')
fit = lowess(np.log10(martig1['Age_out']), np.log10(martig1['Age_in']))
xs = np.linspace(-0.3, 1.2, 100)
xsinterpolate = interp1d(xs, xs)
fys = fit[:, 0] - xsinterpolate(fit[:, 1])
interp = UnivariateSpline(fit[:, 1], fys)
corr_age = np.log10(data['Age']) + (interp(np.log10(data['Age'])))
corr_age = 10**corr_age
data['Age'] = corr_age
return data
def get_rgbsample(cuts=True,
add_dist=False,
astronn_dist=True,
add_ages=False,
rm_bad_dist=True,
no_gaia=False,
distkey='BPG_meandist',
verbose=True,
alternate_ages=False,
rmdups=True):
"""
Get a clean sample of dr14 APOGEE data with Gaia (!) parallaxes and PMs
---
INPUT:
None
OUTPUT:
Clean rgb sample with added parallaxes and PMs
HISTORY:
Started - Mackereth 24/04/18
"""
if cuts:
allStar = apread.allStar(rmcommissioning=True,
exclude_star_bad=True,
exclude_star_warn=True,
main=False,
ak=True,
rmdups=True,
adddist=False)
allStar = allStar[(allStar['LOGG'] > 1.8) & (allStar['LOGG'] < 3.0)]
else:
allStar = apread.allStar(rmcommissioning=True,
main=False,
ak=True,
rmdups=True,
adddist=False)
if verbose:
print('%i Objects meeting quality cuts in APOGEE DR14' % len(allStar))
if not no_gaia:
gaia_xmatch = fits.open(
'../sav/allStar_l31c2_GaiaDR2_crossmatch_withpms.fits')
gaia_xmatch = gaia_xmatch[1].data
gaia_xmatch = Table(data=gaia_xmatch)
allStar_tab = Table(data=allStar)
tab = join(allStar_tab,
gaia_xmatch,
keys='APOGEE_ID',
uniq_col_name='{col_name}{table_name}',
table_names=['', '_xmatch'])
dat = tab.as_array()
if verbose:
print('%i Matched Objects in Gaia DR2' % len(dat))
else:
dat = allStar
dat = esutil.numpy_util.add_fields(dat, [('AVG_ALPHAFE', float)])
dat['AVG_ALPHAFE'] = avg_alphafe(dat)
if add_dist:
if astronn_dist:
dists = fits.open(astronn_dists)[1].data
distkey = 'pc'
else:
dists = fits.open(
'/gal/astjmack/apogee/catalogues/apogee_distances-DR14.fits'
)[1].data
allStar_tab = Table(data=dat)
dists_tab = Table(data=dists)
#join table
tab = join(allStar_tab,
dists_tab,
keys='APOGEE_ID',
uniq_col_name='{col_name}{table_name}',
table_names=['', '_dist_table'])
dat = tab.as_array()
if rm_bad_dist:
mask = np.isfinite(dat[distkey])
dat = dat[mask]
if verbose:
print('%i Matched Objects in APOGEE distance VAC' % len(dat))
if add_ages:
allStar_tab = Table(data=dat)
if alternate_ages:
ages = np.load(corr_agecat)
ages_tab = Table(data=ages)
ages_tab.rename_column('astroNN_age', 'Age')
else:
ages = np.genfromtxt(agecat, names=True, dtype=None)
ages_tab = Table(data=ages)
ages_tab.rename_column('2MASS_ID', 'APOGEE_ID')
tab = join(allStar_tab,
ages_tab,
keys='APOGEE_ID',
uniq_col_name='{col_name}{table_name}',
table_names=['', '_ages'])
dat = tab.as_array()
if rmdups:
print('removing duplicates...')
dat = remove_duplicates(dat)
if verbose:
print('%i Matched Objects in Age Catalogue' % len(dat))
return dat
import vsEnvironSetup vsEnvironSetup.setVariables() import apogee.tools.read as apread data = apread.allStar(dr='13') apogeeIDs = data['APOGEE_ID'] locationIDs = data['LOCATION_ID'] targetCount = len(apogeeIDs) filename = 'lists/all.csv' f = open(filename, 'w') for i in range(targetCount): f.write(str(locationIDs[i]) + ',' + str(apogeeIDs[i]) + '\n') f.close()
import copy
import numpy
import apogee.tools.read as apread
from apogee.tools import bitmask, paramIndx, elemIndx
_DATA= apread.allStar(raw=True) #such that we can re-use it in different tests
from _util import known_failure
def test_telescope():
#Test the telescope tag against the APSTAR_ID
onemIndx= numpy.array(['apogee.apo1m' in s for s in _DATA['APSTAR_ID']])
telescopeIndx= numpy.array(['apo1m' in d for d in _DATA['TELESCOPE']],
dtype='bool')
assert numpy.sum(onemIndx*(True-telescopeIndx)) == 0,\
'TELESCOPE tag does not correspond to APSTAR_ID for 1m data'
return None
def test_targflags_apogee_target1():
# Test that TARGFLAGS corresponds to the bits in APOGEE_TARGET
targ1bits= range(31) #don't check 31, bc always set
targ1bits.pop(14) #14 not populated
for targbit in targ1bits:
name= bitmask.apogee_target1_string(targbit)
targindx= numpy.array([name in s for s in _DATA['TARGFLAGS']],
dtype='bool')
if targbit == 0:
targindx*= \
numpy.array([not 'APOGEE_FAINT_EXTRA' in s for s in _DATA['TARGFLAGS']],
dtype='bool')
badindx= ((_DATA['APOGEE_TARGET1'] & 2**targbit) != 0)*(True-targindx)
assert numpy.sum(badindx) == 0, 'Some objects with bit %i set in apogee_target1 do not have the corresponding flag name in TARGFLAGS set' % targbit
return None
def read_caldata(filename=os.path.join(
os.path.dirname(os.path.realpath(__file__)), 'aj485195t4_mrt.txt'),
dr='12'):
"""
NAME:
read_caldata
PURPOSE:
Read the data on calibration clusters from Meszaros et al. (2013)
INPUT:
filename= Name of the file that has the ApJ machine-readable table
OUTPUT:
data structure with the data
HISTORY:
2015-02-11 - Written - Bovy (IAS@KITP)
"""
data = astropy.io.ascii.read(filename)
data.rename_column('Cluster', 'CLUSTER')
data.remove_column('Teff')
data.rename_column('TeffC', 'TEFF')
data.remove_column('logg')
data.rename_column('loggC', 'LOGG')
data.remove_column('[M/H]')
data.rename_column('[M/H]C', 'FEH')
data.rename_column('2MASS', 'ID')
# Now match to allStar to get the location_ids
alldata = apread.allStar(raw=True)
locids = numpy.zeros(len(data), dtype='int') - 1
hmags = numpy.zeros(len(data), dtype='float') - 1
snrs = numpy.zeros(len(data), dtype='float') - 1
ras = numpy.zeros(len(data), dtype='float') - 1
decs = numpy.zeros(len(data), dtype='float') - 1
# and match to allVisit for the fibers that each star was observed in
allvdata = apread.allVisit(raw=True)
fibers = numpy.zeros(
(len(data), numpy.nanmax(alldata['NVISITS'])), dtype='int') - 1
inds = []
for ii in range(len(data)):
if 'Pleiades' in data['CLUSTER'][ii]:
inds.append(0)
continue
indx = alldata['APOGEE_ID'] == data['ID'][ii]
success = numpy.where(indx == True)[0]
if success.size == 0 or success.size > 1:
inds.append(0)
elif success.size == 1:
inds.append(success[0])
print(indx)
# if numpy.sum(indx) == 0:
# raise ValueError('allStar match for %s not found ...' % (data['ID'][ii]))
# if len(list(set(alldata['LOCATION_ID'][indx]))) > 1:
# raise ValueError('Multiple matches found for for %s ...' % (data['ID'][ii]))
locids[ii] = alldata['LOCATION_ID'][indx][0]
hmags[ii] = alldata['H'][indx][0]
snrs[ii] = alldata['SNR'][indx][0]
ras[ii] = alldata['RA'][indx][0]
decs[ii] = alldata['DEC'][indx][0]
for jj in range(alldata['NVISITS'][indx][0]):
fibers[ii, jj] = allvdata[alldata['VISIT_PK'][indx][0,
jj]]['FIBERID']
inds = (numpy.array(inds), )
data['LOCATION_ID'] = locids
data['H'] = hmags
data['FIBERID'] = fibers
data['SNR'] = snrs
data['APOGEE_ID'] = data['ID']
data['RA'] = ras
data['DEC'] = decs
data['index'] = inds[0]
data['M_H'] = data['FEH']
data['FE_H'] = alldata['FE_H'][inds]
if int(dr) > 12:
rel = 'FE'
if int(dr) <= 12:
rel = 'H'
data['C_{0}'.format(rel)] = alldata['C_{0}'.format(rel)][inds]
data['N_{0}'.format(rel)] = alldata['N_{0}'.format(rel)][inds]
data['O_{0}'.format(rel)] = alldata['O_{0}'.format(rel)][inds]
data['NA_{0}'.format(rel)] = alldata['NA_{0}'.format(rel)][inds]
data['MG_{0}'.format(rel)] = alldata['MG_{0}'.format(rel)][inds]
data['AL_{0}'.format(rel)] = alldata['AL_{0}'.format(rel)][inds]
data['SI_{0}'.format(rel)] = alldata['SI_{0}'.format(rel)][inds]
data['S_{0}'.format(rel)] = alldata['S_{0}'.format(rel)][inds]
data['K_{0}'.format(rel)] = alldata['K_{0}'.format(rel)][inds]
data['CA_{0}'.format(rel)] = alldata['CA_{0}'.format(rel)][inds]
data['TI_{0}'.format(rel)] = alldata['TI_{0}'.format(rel)][inds]
data['V_{0}'.format(rel)] = alldata['V_{0}'.format(rel)][inds]
data['MN_{0}'.format(rel)] = alldata['MN_{0}'.format(rel)][inds]
data['NI_{0}'.format(rel)] = alldata['NI_{0}'.format(rel)][inds]
return numpy.array(data)
from astropy.io import fits
import apogee.tools.read as apread
import numpy as np
allStarDR14 = apread.allStar(rmcommissioning=True,
main=False,
ak=True,
akvers='targ',
adddist=False)
locationIDs = allStarDR14['LOCATION_ID']
apogeeIDs = allStarDR14['APOGEE_ID']
apogeeIDs = [s.decode('utf-8') for s in apogeeIDs] #remove bit from string
# R calculation
def calcR401(x, pos1=0, pos2=401, peakLoc=201):
'''
Calculates the value of R with the given array x
Returns: The value of R for whole CCF
Assupmtion: the center peak lies in CCF lag space 201
'''
# if peakLoc is near the edges just skip it
if (peakLoc <= 10) or (peakLoc >= 390):
return np.nan
Mirror = (x[peakLoc:pos2])[::-1]
sigmaA = np.sqrt(1.0 / (2.0 * len(Mirror)) * np.sum(
(x[pos1:peakLoc] - Mirror)**2))
r401 = np.max(x) / (np.sqrt(2.0) * sigmaA)
return r401
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
import copy
import numpy
import apogee.tools.read as apread
from apogee.tools import bitmask, paramIndx, elemIndx
_DATA= apread.allStar(raw=True) #such that we can re-use it in different tests
from _util import known_failure
def test_telescope():
#Test the telescope tag against the APSTAR_ID
onemIndx= numpy.array(['apogee.apo1m' in s for s in _DATA['APSTAR_ID']])
telescopeIndx= numpy.array(['apo1m' in d for d in _DATA['TELESCOPE']],
dtype='bool')
assert numpy.sum(onemIndx*(True-telescopeIndx)) == 0,\
'TELESCOPE tag does not correspond to APSTAR_ID for 1m data'
return None
def test_targflags_apogee_target1():
# Test that TARGFLAGS corresponds to the bits in APOGEE_TARGET
targ1bits= range(31) #don't check 31, bc always set
targ1bits.pop(14) #14 not populated
for targbit in targ1bits:
name= bitmask.apogee_target1_string(targbit)
targindx= numpy.array([name in s for s in _DATA['TARGFLAGS']],
dtype='bool')
if targbit == 0:
targindx*= \
numpy.array([not 'APOGEE_FAINT_EXTRA' in s for s in _DATA['TARGFLAGS']],
dtype='bool')
badindx= ((_DATA['APOGEE_TARGET1'] & 2**targbit) != 0)*(True-targindx)
assert numpy.sum(badindx) == 0, 'Some objects with bit %i set in apogee_target1 do not have the corresponding flag name in TARGFLAGS set' % targbit
return None
def plot_distcomparisons(plotfilename):
data= apread.allStar(adddist=True)
plotKASCDiffs= []
plotKASCDiffErrs= []
plotRCDiffs= []
plotRCDiffErrs= []
# RC vs. APOKASC
rcDiff= distDiff(data['APOKASC_DIST_DIRECT'],data['RC_DIST'])
plotKASCDiffs.append(rcDiff[0])
plotKASCDiffErrs.append(rcDiff[1])
# RC vs. RC
plotRCDiffs.append(0.)
plotRCDiffErrs.append(0.)
# BPG Dist1 vs. APOKASC
apokascMinDist= 1.
bpgDiff= distDiff(data['APOKASC_DIST_DIRECT'],data['BPG_DIST1_MEAN'],
minDist=apokascMinDist)
plotKASCDiffs.append(bpgDiff[0])
plotKASCDiffErrs.append(bpgDiff[1])
# BPG vs. RC
bpgDiff= distDiff(data['RC_DIST'],data['BPG_DIST1_MEAN'])
plotRCDiffs.append(bpgDiff[0])
plotRCDiffErrs.append(bpgDiff[1])
# Hayden peak vs. APOKASC
haydenDiff= distDiff(data['APOKASC_DIST_DIRECT'],data['HAYDEN_DIST_PEAK'],
minDist=apokascMinDist)
plotKASCDiffs.append(haydenDiff[0])
plotKASCDiffErrs.append(haydenDiff[1])
# Hayden peak vs. RC
haydenDiff= distDiff(data['RC_DIST'],data['HAYDEN_DIST_PEAK'])
plotRCDiffs.append(haydenDiff[0])
plotRCDiffErrs.append(haydenDiff[1])
# Schultheis vs. APOKASC
schultheisDiff= distDiff(data['APOKASC_DIST_DIRECT'],data['SCHULTHEIS_DIST'],
minDist=apokascMinDist)
plotKASCDiffs.append(schultheisDiff[0])
plotKASCDiffErrs.append(schultheisDiff[1])
# Schultheis vs. RC
schultheisDiff= distDiff(data['RC_DIST'],data['SCHULTHEIS_DIST'])
plotRCDiffs.append(schultheisDiff[0])
plotRCDiffErrs.append(schultheisDiff[1])
# plot
bovy_plot.bovy_print(fig_width=7.,
text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=24.)
ms= 8.
line1= bovy_plot.bovy_plot([1,2,3,4],plotKASCDiffs,'bo',ms=ms,
xrange=[0,5],
yrange=[-0.11,0.11],
ylabel=r'$\mathrm{distance\ modulus\ offset}$')
pyplot.errorbar([1,2,3,4],plotKASCDiffs,yerr=plotKASCDiffErrs,
ls='none',marker='o',color='b',ms=ms)
line2= bovy_plot.bovy_plot([1,2,3,4],plotRCDiffs,'ro',ms=ms,
overplot=True)
pyplot.errorbar([1,2,3,4],plotRCDiffs,yerr=plotRCDiffErrs,
ls='none',marker='o',color='r',ms=ms)
pyplot.legend([line1[0],line2[0]],
[r'$\mathrm{wrt\ APOKASC}$',
r'$\mathrm{wrt\ RC}$'],
loc='lower left',fontsize=14.,frameon=False,numpoints=1)
#Put labels and rotate them
pyplot.xticks([1,2,3,4],
[r'$\mathrm{RC}$',
r"$\mathrm{BPG\ dist1}$",
r"$\mathrm{Hayden\ peak}$",
r"$\mathrm{Schultheis}$"],size=16.,
rotation=45.)
bovy_plot.bovy_end_print(plotfilename)
return None
def get_spectra(
name, red_clump, location
): ###Function to read the allStar file and get the spectra, correct spectra for
###small and large uncertainties, remove red clump stars
"""Return cluster data, spectra, spectral errors, photometric Teffs, and bitmask from APOGEE.
If the data file for the specified cluster already exists locally,
import the data from the file (cluster data, spectra, spectral errors, bitmask).
If the data file does not exist, obtain the APOGEE spectra from a specified cluster
from the allStar catalogue, replacing ASPCAP abundances with astroNN abundances.
Parameters
----------
name : str
Name of desired cluster (i.e. 'NGC 2682')
red_clump : str
If the red clump stars in rcsample are to be removed, set to 'True'. If all stars are to be used,
set to 'False'.
location : str
If running locally, set to 'personal'. If running on the server, set to 'server'.
Returns
-------
apogee_cluster_data (all stars) or apogee_cluster_data_final (red clumps removed) : structured array
All cluster data from APOGEE
spectra_50 (all stars) or spectra_final (red clumps removed) : tuple
Array of floats representing the cleaned-up fluxes in the APOGEE spectra with red clump stars removed
spectra_err_50 (all stars) or spectra_err_final (red clumps removed) : tuple
Array of floats representing the cleaned-up spectral errors from the APOGEE spectra with red clump stars
removed
good_T (all stars) or T_final (red clumps removed) : tuple
Array of floats representing the effective temperatures of the stars in the cluster
between 4000K and 5000K
full_bitmask (all stars) or bitmask_final (red clumps removed) : tuple
Array of ints (1 or 0), cleaned in the same way as the spectra, representing the bad pixels
in the APOGEE_PIXMASK bitmask
"""
#Path, strip spaces in cluster name
if location == 'personal': ###If running on my Mac
path = '/Users/chloecheng/Personal/' + str(name).replace(
' ', '') + '.hdf5' ###Path to folder named after cluster
elif location == 'server': ###If running on the server
path = '/geir_data/scr/ccheng/AST425/Personal/' + str(name).replace(
' ', '') + '.hdf5' ###Path to cluster folder
#If the data file for this cluster exists, save the data to variables and return them
if glob.glob(path): ###If the file exists
if red_clump == 'False': ###If we're keeping all of the stars, read in the data
file = h5py.File(path, 'r')
apogee_cluster_data = file['apogee_cluster_data'][()]
spectra_50 = file['spectra'][()]
spectra_err_50 = file['spectra_errs'][()]
good_T = file['T'][()]
full_bitmask = file['bitmask'][()]
file.close()
print(name,
' complete.') ###Notification that this function is done
return apogee_cluster_data, spectra_50, spectra_err_50, good_T, full_bitmask
elif red_clump == 'True': ###If we're removing the red clumps, read in the data
file = h5py.File(path, 'r')
apogee_cluster_data_final = file['apogee_cluster_data'][()]
spectra_final = file['spectra'][()]
spectra_err_final = file['spectra_errs'][()]
T_final = file['T'][()]
bitmask_final = file['bitmask'][()]
file.close()
print(name,
' complete.') ###Notification that this function is done
return apogee_cluster_data_final, spectra_final, spectra_err_final, T_final, bitmask_final
#If the file does not exist, get the data from APOGEE
else: ###If the file does not exist
#Get red clump stars from rcsample
rc_data = rcsample(dr='14') ###Get the rcsample data for DR14
rc_stars = [] ###Empty list for the stars
for i in range(len(rc_data)): ###Iterate through the rcsample data
if location == 'personal': ###If running on Mac
rc_stars.append(
rc_data[i][2]) ###Append just the names of the stars
elif location == 'server': ###If running on server
rc_stars.append(
rc_data[i][2].decode('UTF-8')
) ###Append just the names of the stars (decode because on server the names are bitwise for some reason)
rc_stars = np.array(
rc_stars) ###Make list of red clump star names into array
#Read in APOGEE catalogue data, removing duplicated stars and replacing ASPCAP with astroNN abundances
apogee_cat = apread.allStar(
use_astroNN_abundances=True
) ###Read the allStar file, using the astroNN abundances
unique_apoids, unique_inds = np.unique(
apogee_cat['APOGEE_ID'], return_index=True) ###Get the APOGEE IDs
apogee_cat = apogee_cat[unique_inds] ###Get the APOGEE IDs
#Read in overall cluster information
cls = afits.open('occam_cluster-DR14.fits') ###Read in the OCCAM data
cls = cls[1].data ###Get the cluster information
#Read in information about cluster members
members = afits.open(
'occam_member-DR14.fits') ###Read in the OCCAM members data
members = members[1].data ###Get the member information
#Select all members of a given cluster
cluster_members = (members['CLUSTER'] == name) & (
members['MEMBER_FLAG'] == 'GM'
) #second part of the mask indicates to only use giant stars
member_list = members[
cluster_members] ###Make a list of all member stars in the cluster
#Find APOGEE entries for that cluster
#numpy.in1d finds the 1D intersection between two lists.
#In this case we're matching using the unique APOGEE ID assigned to each star
#The indices given by numpy.in1d are for the first argument, so in this case the apogee catalogue
cluster_inds = np.in1d((apogee_cat['APOGEE_ID']).astype('U100'),
member_list['APOGEE_ID']
) ###Get the indices of the cluster members
apogee_cluster_data = apogee_cat[
cluster_inds] ###Get the allStar data for these members
T = photometric_Teff(
apogee_cluster_data
) ###Compute the photometric effective temperature
#Mark red clump stars in the members of the cluster as NaNs
cluster_stars = member_list[
'APOGEE_ID'] ###Get a list of all the names of the member stars in the cluster
cluster_marked = np.copy(
cluster_stars
) ###Create a copy of this list to mark which stars are red clumps
for i in range(len(cluster_stars)
): ###Iterate through all of the stars in the cluster
for j in range(len(
rc_stars)): ###Iterate through all of the rcsample stars
if cluster_stars[i] in rc_stars[
j]: ###If a cluster member is also a member of the rcsample stars
cluster_marked[
i] = np.nan ###Replace the name of that star with a NaN to ignore it
#Get spectra, spectral errors, and bitmask for each star - apStar
#We can use the APOGEE package to read each star's spectrum
#We'll read in the ASPCAP spectra, which have combined all of the visits for each star and removed the spaces between the spectra
number_of_members = len(
member_list) ###Number of members in the cluster
spectra = np.zeros((number_of_members,
7514)) ###Create an empty array to add the spectra
spectra_errs = np.zeros(
(number_of_members,
7514)) ###Create an empty array to add the spectral errors
bitmask = np.zeros((number_of_members,
7514)) ###Create an empty array to add the bitmask
for s, star in enumerate(
apogee_cluster_data): ###Iterate through the allStar data
spectra[s] = apread.aspcapStar(
star['LOCATION_ID'],
star['APOGEE_ID'],
ext=1,
header=False,
dr='14',
aspcapWavegrid=True) ###Get the spectra
spectra_errs[s] = apread.aspcapStar(
star['LOCATION_ID'],
star['APOGEE_ID'],
ext=2,
header=False,
dr='14',
aspcapWavegrid=True) ###Get the spectral errors
bitmask[s] = apread.apStar(
star['LOCATION_ID'],
star['APOGEE_ID'],
ext=3,
header=False,
dr='14',
aspcapWavegrid=True)[1] ###Get the bitmask
#Set all entries in bitmask to integers
bitmask = bitmask.astype(
int) ###Set all entries in the bitmask to integers
bitmask_flip = np.zeros_like(
bitmask
) ###Create an empty array for the bitmask with flipped entries
for i in range(
len(spectra
)): ###Iterate through the number of stars in the cluster
for j in range(7514): ###Iterate through the wavelength range
if bitmask[i][j] == 0: ###If the bitmask entry is set to 0
bitmask_flip[i][j] = 1 ###Set it to 1
else: ###If the bitmask entry is not set to 0
bitmask_flip[i][j] = 0 ###Set it to 0
###I do this part because the unmasked entries are always 0 in the original bitmask but I think before I was maybe adding in other values to include in the mask that may not have necessarily been 1 so I just set all masked bits to 0 and all unmasked bits to 1 (or maybe this just made more sense in my head for masked to be 0 and unmasked to be 1)
#Remove empty spectra
full_spectra = [
] ###Empty list for the spectra sans empty ones, list not array because we don't know how many stars will be eliminated
full_spectra_errs = [
] ###Empty list for the spectral errors sans empty spectra
full_bitmask = [] ###Empty list for bitmask sans empty spectra
full_T = [] ###Empty list for temperatures sans empty spectra
full_stars = [] ###Empty list for indices of stars sans empty spectra
for i in range(len(spectra)): ###Iterate through the number of stars
if any(spectra[i, :] != 0
): ###For all of the rows whose entries are not all 0
full_spectra.append(spectra[i]) ###Append those spectra
full_spectra_errs.append(
spectra_errs[i]) ###Append those spectral errors
full_bitmask.append(
bitmask_flip[i]) ###Append those bitmask rows
full_T.append(T[i]) ###Append those temperatures
full_stars.append(i) ###Append the indices of those stars
full_spectra = np.array(full_spectra) ###Make list into array
full_spectra_errs = np.array(
full_spectra_errs) ###Make list into array
full_bitmask = np.array(full_bitmask) ###Make list into array
full_T = np.array(full_T) ###Make list into array
full_stars = np.array(full_stars) ###Make list into array
full_marked_stars = cluster_marked[
full_stars] ###Use array of stars left to index marked stars so we know which ones are red clump stars
#Create array of NaNs to replace flagged values in spectra
masked_spectra = np.empty_like(
full_spectra
) ###Create an empty array that is the same shape as full_spectra
masked_spectra_errs = np.empty_like(
full_spectra_errs
) ###Create an empty array that is the same shape as full_spectra_errs
masked_spectra[:] = np.nan ###Set all of the entries to NaNs
masked_spectra_errs[:] = np.nan ###Set all of the entries to NaNs
#Mask the spectra
for i in range(
len(full_spectra)): ###Iterate through the number of stars
for j in range(7514): ###Iterate through the wavelength range
if full_bitmask[i][
j] != 0: ###If the bitmask is not 0 (i.e. if the bit is unmasked)
masked_spectra[i][j] = full_spectra[i][
j] ###Retain the value of the unmasked spectra here
masked_spectra_errs[i][j] = full_spectra_errs[i][
j] ###Retain the value of the unmasked spectral errors here
###All of the masked bits that were not captured by this if statement will remain NaNs and will thus be ignored
#Cut stars that are outside of the temperature limits
good_T_inds = (full_T > 4000) & (
full_T < 5000
) ###Get the indices of the temperatures that are between 4000K and 5000K
final_spectra = masked_spectra[
good_T_inds] ###Index the spectra to only keep stars that are within the temperature limits
final_spectra_errs = masked_spectra_errs[
good_T_inds] ###Index the spectral errors to only keep stars within Teff limits
good_T = full_T[
good_T_inds] ###Index the temperatures to keep only stars within Teff limits
apogee_cluster_data = apogee_cluster_data[
good_T_inds] ###Index the allStar data to keep stars only within Teff limits
full_bitmask = full_bitmask[
good_T_inds] ###Index the bitmask to keep stars only within Teff limits
final_stars = full_marked_stars[
good_T_inds] ###Index the array of red-clump-marked stars to keep only those within Teff limits
rgs = (final_stars != 'nan'
) #Get indices for final red giant stars to be used
#Want an SNR of 200 so set those errors that have a larger SNR to have an SNR of 200
spectra_err_200 = np.zeros_like(
final_spectra_errs
) ###Create an empty array to add corrected spectral errors to - shape will not change, just altering values
for i in range(len(final_spectra)): ###Iterate through the stars
for j in range(7514): ###Iterate through wavelength range
if final_spectra[i][j] / final_spectra_errs[i][
j] <= 200: ###If errors are of a reasonable size
spectra_err_200[i][j] = final_spectra_errs[i][
j] ###Leave them as they are
else: ###If errors are too small
spectra_err_200[i][j] = final_spectra[i][
j] / 200 ###Make them a bit bigger
#Cut errors with SNR of less than 50
spectra_50 = np.copy(
final_spectra
) ###Create a copy of the spectra to cut large error pixels
spectra_err_50 = np.copy(
spectra_err_200
) ###Create a copy of the spectral errors to cut large error pixels
for i in range(len(final_spectra)): ###Iterate through stars
for j in range(7514): ###Iterate through wavelength range
if final_spectra[i][j] / spectra_err_200[i][
j] <= 50: ###If an error is too big
spectra_50[i][
j] = np.nan ###Set the corresponding entry in the spectra to be a NaN, will be ignored
spectra_err_50[i][
j] = np.nan ###Set the corresponding entry in the spectral errors to be a NaN, will be ignored
#Cut red clumps
logg = apogee_cluster_data[
'LOGG'] ###Get the logg values for the cluster (all corrections have been applied)
apogee_cluster_data_final = apogee_cluster_data[
rgs] ###Get the allStar data for the RGB stars only (no red clumps)
spectra_final = spectra_50[
rgs] ###Get the spectra for the RGB stars only
spectra_err_final = spectra_err_50[
rgs] ###Get the spectral errors for the RGB stars only
T_final = good_T[rgs] ###Get the temperatures for the RGB stars only
bitmask_final = full_bitmask[
rgs] ###Get the bitmask for the RGB stars only
if red_clump == 'False': ###If we are looking at all of the stars, save all data before red clumps were cut to file
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = apogee_cluster_data
file['spectra'] = spectra_50
file['spectra_errs'] = spectra_err_50
file['T'] = good_T
file['bitmask'] = full_bitmask
file.close()
print(name, 'complete') ###Notification that this function is done
return apogee_cluster_data, spectra_50, spectra_err_50, good_T, full_bitmask
elif red_clump == 'True': ###If we are removing the red clump stars, save the data after red clumps cut to file
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = apogee_cluster_data_final
file['spectra'] = spectra_final
file['spectra_errs'] = spectra_err_final
file['T'] = T_final
file['bitmask'] = bitmask_final
file.close()
print(name, 'complete') ###Notification that this function is done
return apogee_cluster_data_final, spectra_final, spectra_err_final, T_final, bitmask_final
def get_spectra(name, red_clump, location):
"""Return cluster data, spectra, spectral errors, photometric Teffs, and bitmask from APOGEE.
If the data file for the specified cluster already exists locally,
import the data from the file (cluster data, spectra, spectral errors, bitmask).
If the data file does not exist, obtain the APOGEE spectra from a specified cluster
from the allStar catalogue, replacing ASPCAP abundances with astroNN abundances.
Parameters
----------
name : str
Name of desired cluster (i.e. 'NGC 2682')
red_clump : str
If the red clump stars in rcsample are to be removed, set to 'True'. If all stars are to be used,
set to 'False'.
location : str
If running locally, set to 'personal'. If running on the server, set to 'server'.
Returns
-------
apogee_cluster_data (all stars) or apogee_cluster_data_final (red clumps removed) : structured array
All cluster data from APOGEE
spectra_50 (all stars) or spectra_final (red clumps removed) : tuple
Array of floats representing the cleaned-up fluxes in the APOGEE spectra with red clump stars removed
spectra_err_50 (all stars) or spectra_err_final (red clumps removed) : tuple
Array of floats representing the cleaned-up spectral errors from the APOGEE spectra with red clump stars
removed
good_T (all stars) or T_final (red clumps removed) : tuple
Array of floats representing the effective temperatures of the stars in the cluster
between 4000K and 5000K
full_bitmask (all stars) or bitmask_final (red clumps removed) : tuple
Array of ints (1 or 0), cleaned in the same way as the spectra, representing the bad pixels
in the APOGEE_PIXMASK bitmask
"""
#Path, strip spaces in cluster name
if location == 'personal':
path = '/Users/chloecheng/Personal/' + str(name).replace(' ', '') + '.hdf5'
elif location == 'server':
path = '/geir_data/scr/ccheng/AST425/Personal/' + str(name).replace(' ', '') + '.hdf5'
#If the data file for this cluster exists, save the data to variables
if glob.glob(path):
if red_clump == 'False':
file = h5py.File(path, 'r')
apogee_cluster_data = file['apogee_cluster_data'][()]
spectra_50 = file['spectra'][()]
spectra_err_50 = file['spectra_errs'][()]
good_T = file['T'][()]
full_bitmask = file['bitmask'][()]
file.close()
print(name, ' complete.')
return apogee_cluster_data, spectra_50, spectra_err_50, good_T, full_bitmask
elif red_clump == 'True':
file = h5py.File(path, 'r')
apogee_cluster_data_final = file['apogee_cluster_data'][()]
spectra_final = file['spectra'][()]
spectra_err_final = file['spectra_errs'][()]
T_final = file['T'][()]
bitmask_final = file['bitmask'][()]
file.close()
print(name, ' complete.')
return apogee_cluster_data_final, spectra_final, spectra_err_final, T_final, bitmask_final
#If the file does not exist, get the data from APOGEE
else:
#Get red clump stars from rcsample
rc_data = rcsample(dr='14')
rc_stars = []
for i in range(len(rc_data)):
#rc_stars.append(rc_data[i][2]) - REMOVE IN FINAL VERSION
rc_stars.append(rc_data[i][2].decode('UTF-8'))
rc_stars = np.array(rc_stars)
#Read in APOGEE catalogue data, removing duplicated stars and replacing ASPCAP with astroNN abundances
apogee_cat = apread.allStar(use_astroNN_abundances=True)
unique_apoids,unique_inds = np.unique(apogee_cat['APOGEE_ID'],return_index=True)
apogee_cat = apogee_cat[unique_inds]
#Read in overall cluster information
cls = afits.open('occam_cluster-DR14.fits')
cls = cls[1].data
#Read in information about cluster members
members = afits.open('occam_member-DR14.fits')
members = members[1].data
#Select all members of a given cluster
cluster_members = (members['CLUSTER']==name) & (members['MEMBER_FLAG']=='GM') #second part of the mask indicates to only use giant stars
member_list = members[cluster_members]
#Find APOGEE entries for that cluster
#numpy.in1d finds the 1D intersection between two lists.
#In this case we're matching using the unique APOGEE ID assigned to each star
#The indices given by numpy.in1d are for the first argument, so in this case the apogee catalogue
cluster_inds = np.in1d((apogee_cat['APOGEE_ID']).astype('U100'),member_list['APOGEE_ID'])
apogee_cluster_data = apogee_cat[cluster_inds]
T = photometric_Teff(apogee_cluster_data)
#Mark red clump stars in the members of the cluster as NaNs
cluster_stars = member_list['APOGEE_ID']
cluster_marked = np.copy(cluster_stars)
for i in range(len(cluster_stars)):
for j in range(len(rc_stars)):
if cluster_stars[i] == rc_stars[j]:
cluster_marked[i] = np.nan
#Get spectra, spectral errors, and bitmask for each star - apStar
#We can use the APOGEE package to read each star's spectrum
#We'll read in the ASPCAP spectra, which have combined all of the visits for each star and removed the spaces between the spectra
number_of_members = len(member_list)
spectra = np.zeros((number_of_members, 7514))
spectra_errs = np.zeros((number_of_members, 7514))
bitmask = np.zeros((number_of_members, 7514))
for s,star in enumerate(apogee_cluster_data):
spectra[s] = apread.aspcapStar(star['LOCATION_ID'],star['APOGEE_ID'],ext=1,header=False,dr='14',aspcapWavegrid=True)
spectra_errs[s] = apread.aspcapStar(star['LOCATION_ID'],star['APOGEE_ID'],ext=2,header=False,dr='14',aspcapWavegrid=True)
bitmask[s] = apread.apStar(star['LOCATION_ID'],star['APOGEE_ID'],ext=3,header=False,dr='14', aspcapWavegrid=True)[1]
#Set all entries in bitmask to integers
bitmask = bitmask.astype(int)
bitmask_flip = np.zeros_like(bitmask)
for i in range(len(spectra)):
for j in range(7514):
if bitmask[i][j] == 0:
bitmask_flip[i][j] = 1
else:
bitmask_flip[i][j] = 0
#Remove empty spectra
full_spectra = []
full_spectra_errs = []
full_bitmask = []
full_T = []
full_stars = []
for i in range(len(spectra)):
if any(spectra[i,:] != 0):
full_spectra.append(spectra[i])
full_spectra_errs.append(spectra_errs[i])
full_bitmask.append(bitmask_flip[i])
full_T.append(T[i])
full_stars.append(i)
full_spectra = np.array(full_spectra)
full_spectra_errs = np.array(full_spectra_errs)
full_bitmask = np.array(full_bitmask)
full_T = np.array(full_T)
full_stars = np.array(full_stars)
full_marked_stars = cluster_marked[full_stars]
#Create array of NaNs to replace flagged values in spectra
masked_spectra = np.empty_like(full_spectra)
masked_spectra_errs = np.empty_like(full_spectra_errs)
masked_spectra[:] = np.nan
masked_spectra_errs[:] = np.nan
#Mask the spectra
for i in range(len(full_spectra)):
for j in range(7514):
if full_bitmask[i][j] != 0:
masked_spectra[i][j] = full_spectra[i][j]
masked_spectra_errs[i][j] = full_spectra_errs[i][j]
#Cut stars that are outside of the temperature limits
good_T_inds = (full_T > 4000) & (full_T < 5000)
final_spectra = masked_spectra[good_T_inds]
final_spectra_errs = masked_spectra_errs[good_T_inds]
good_T = full_T[good_T_inds]
apogee_cluster_data = apogee_cluster_data[good_T_inds]
full_bitmask = full_bitmask[good_T_inds]
final_stars = full_marked_stars[good_T_inds]
rgs = (final_stars != 'nan') #Get indices for final red giant stars to be used
#Want an SNR of 200 so set those errors that have a larger SNR to have an SNR of 200
spectra_err_200 = np.zeros_like(final_spectra_errs)
for i in range(len(final_spectra)):
for j in range(7514):
if final_spectra[i][j]/final_spectra_errs[i][j] <= 200:
spectra_err_200[i][j] = final_spectra_errs[i][j]
else:
spectra_err_200[i][j] = final_spectra[i][j]/200
#Cut errors with SNR of less than 50
spectra_50 = np.copy(final_spectra)
spectra_err_50 = np.copy(spectra_err_200)
for i in range(len(final_spectra)):
for j in range(7514):
if final_spectra[i][j]/spectra_err_200[i][j] <= 50:
spectra_50[i][j] = np.nan
spectra_err_50[i][j] = np.nan
#Cut red clumps
logg = apogee_cluster_data['LOGG']
apogee_cluster_data_final = apogee_cluster_data[rgs]
spectra_final = spectra_50[rgs]
spectra_err_final = spectra_err_50[rgs]
T_final = good_T[rgs]
bitmask_final = full_bitmask[rgs]
if red_clump == 'False':
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = apogee_cluster_data
file['spectra'] = spectra_50
file['spectra_errs'] = spectra_err_50
file['T'] = good_T
file['bitmask'] = full_bitmask
file.close()
print(name, 'complete')
return apogee_cluster_data, spectra_50, spectra_err_50, good_T, full_bitmask
elif red_clump == 'True':
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = apogee_cluster_data_final
file['spectra'] = spectra_final
file['spectra_errs'] = spectra_err_final
file['T'] = T_final
file['bitmask'] = bitmask_final
file.close()
print(name, 'complete')
return apogee_cluster_data_final, spectra_final, spectra_err_final, T_final, bitmask_final