def test_params_named():
#Test that the named tags correspond to the correct values in param according to PARAM_SYMBOL
assert numpy.all(
numpy.fabs(_DATA['PARAM'][:, paramIndx('teff')] - _DATA['TEFF']) < 10.
**-10.), 'PARAM TEFF does not correspond to tag TEFF'
assert numpy.all(
numpy.fabs(_DATA['PARAM'][:, paramIndx('logg')] - _DATA['LOGG']) < 10.
**-10.), 'PARAM LOGG does not correspond to tag LOGG'
cnanIndx = (True - numpy.isnan(
numpy.sqrt(_DATA['PARAM_COV'][:,
paramIndx('teff'),
paramIndx('teff')])))
if numpy.sum(cnanIndx) > 0:
assert numpy.all(
numpy.fabs(
numpy.sqrt(_DATA['PARAM_COV'][cnanIndx,
paramIndx('teff'),
paramIndx('teff')]) -
_DATA['TEFF_ERR'][cnanIndx]) < 10.**-10.
), 'PARAM_COV TEFF does not correspond to tag TEFF_ERR'
cnanIndx = (True - numpy.isnan(
numpy.sqrt(_DATA['PARAM_COV'][:,
paramIndx('logg'),
paramIndx('logg')])))
if numpy.sum(cnanIndx) > 0:
assert numpy.all(
numpy.fabs(
numpy.sqrt(_DATA['PARAM_COV'][cnanIndx,
paramIndx('logg'),
paramIndx('logg')]) -
_DATA['LOGG_ERR'][cnanIndx]) < 10.**-10.
), 'PARAM_COV LOGG does not correspond to tag LOGG_ERR'
return None
def test_elem_named():
#Test that the named tags for the elements correspond to the correct values in elem according to ELEM_SYMBOL
elems= ['C','N','O','Mg','Si','S','Ca','Ti',
'Ni','Fe','Al','K','Na','V','Mn']
ferreOverM= ['C','N','O','Mg','Si','S','Ca','Ti']
for ii,elem in enumerate(elems):
elemval= copy.copy(_DATA['ELEM'][:,elemIndx(elem)])
if elem in ferreOverM: elemval+= _DATA['FPARAM'][:,paramIndx('metals')]
#BOVY: What about the following?
goodIndx= _DATA['FPARAM'][:,paramIndx('metals')] != -9999.
assert numpy.all(numpy.fabs(elemval[goodIndx]-_DATA[elem.upper()+'_H'][goodIndx]) < 10.**-10.), 'ELEM value for %s_H does not agree with named tag' % elem
return None
def test_elem_named():
#Test that the named tags for the elements correspond to the correct values in elem according to ELEM_SYMBOL
from apogee.tools import _ELEM_SYMBOL
elems= [e.capitalize() for e in _ELEM_SYMBOL if e != 'ci' and e != 'tiii']
ferreOverM= ['C','N','O','Mg','Si','S','Ca','Ti']
for ii,elem in enumerate(elems):
if elem == 'C' or elem == 'N' or elem == 'O': continue
elemval= copy.copy(_DATA['ELEM'][:,elemIndx(elem)])
if elem in ferreOverM: elemval+= _DATA['FPARAM'][:,paramIndx('metals')]
#BOVY: What about the following?
goodIndx= (_DATA['FPARAM'][:,paramIndx('metals')] != -9999.)\
*(_DATA[elem.upper()+'_H'] != -9999.)
assert numpy.all(numpy.fabs(elemval[goodIndx]-_DATA[elem.upper()+'_H'][goodIndx]) < 10.**-10.), 'ELEM value for %s_H does not agree with named tag' % elem
return None
def test_elem_named():
#Test that the named tags for the elements correspond to the correct values in elem according to ELEM_SYMBOL
from apogee.tools import _ELEM_SYMBOL
elems= [e.capitalize() for e in _ELEM_SYMBOL if e != 'ci' and e != 'tiii']
ferreOverM= ['C','N','O','Mg','Si','S','Ca','Ti']
for ii,elem in enumerate(elems):
if elem == 'C' or elem == 'N' or elem == 'O': continue
elemval= copy.copy(_DATA['ELEM'][:,elemIndx(elem)])
if elem in ferreOverM: elemval+= _DATA['FPARAM'][:,paramIndx('metals')]
#BOVY: What about the following?
goodIndx= (_DATA['FPARAM'][:,paramIndx('metals')] != -9999.)\
*(_DATA[elem.upper()+'_H'] != -9999.)
assert numpy.all(numpy.fabs(elemval[goodIndx]-_DATA[elem.upper()+'_H'][goodIndx]) < 10.**-10.), 'ELEM value for %s_H does not agree with named tag' % elem
return None
def test_elem_calib_outsiderange_dwarfs():
#Test that the elem calibration does not extend outside of the calibration
#temperature range
from apogee.tools import _ELEM_SYMBOL
elems = [e.capitalize() for e in _ELEM_SYMBOL if e != 'ci' and e != 'tiii']
TeffMin = 3800.
TeffMax = 7500.
dwarfs= (_DATA['FPARAM'][:,paramIndx('logg')] >= (2./1300.\
*(_DATA['FPARAM'][:,paramIndx('teff')]-3500.)+2.))\
+(_DATA['FPARAM'][:,paramIndx('logg')] >= 4.)\
+(_DATA['FPARAM'][:,paramIndx('teff')] >= 7000.)
for elem in elems:
calibDiff= _DATA['FELEM'][:,elemIndx(elem)]\
-_DATA['ELEM'][:,elemIndx(elem)]
#Only consider good stars for this element
indx= ((_DATA['ASPCAPFLAG'] & 2**23) == 0)\
*(_DATA['FPARAM'][:,paramIndx('teff')] > -1000.)\
*dwarfs\
*(_DATA['FELEM'][:,elemIndx(elem)] > -1000.)\
*(_DATA['ELEM'][:,elemIndx(elem)] > -1000.)
try:
loTIndx = numpy.argmin(
numpy.fabs(_DATA['FPARAM'][indx, paramIndx('teff')] - TeffMin))
except ValueError:
pass
else:
assert numpy.all(
numpy.fabs(calibDiff[indx][
_DATA['FPARAM'][indx, paramIndx('teff')] < TeffMin] -
calibDiff[indx][loTIndx]) < 10.**-3.
), 'Calibration offset does not saturate below the minimum calibration temperature of %i for element %s' % (
TeffMin, elem)
try:
hiTIndx = numpy.argmin(
numpy.fabs(_DATA['FPARAM'][indx, paramIndx('teff')] - TeffMax))
except ValueError:
pass
else:
assert numpy.all(
numpy.fabs(calibDiff[indx][
_DATA['FPARAM'][indx, paramIndx('teff')] > TeffMax] -
calibDiff[indx][hiTIndx]) < 10.**-2.
), 'Calibration offset does not saturate above the maximum calibration temperature of %i for element %s' % (
TeffMax, elem)
return None
def test_params_named():
#Test that the named tags correspond to the correct values in param according to PARAM_SYMBOL
assert numpy.all(numpy.fabs(_DATA['PARAM'][:,paramIndx('teff')]
-_DATA['TEFF']) < 10.**-10.), 'PARAM TEFF does not correspond to tag TEFF'
assert numpy.all(numpy.fabs(_DATA['PARAM'][:,paramIndx('logg')]
-_DATA['LOGG']) < 10.**-10.), 'PARAM LOGG does not correspond to tag LOGG'
cnanIndx= (True-numpy.isnan(numpy.sqrt(_DATA['PARAM_COV'][:,paramIndx('teff'),paramIndx('teff')])))
if numpy.sum(cnanIndx) > 0:
assert numpy.all(numpy.fabs(numpy.sqrt(_DATA['PARAM_COV'][cnanIndx,paramIndx('teff'),paramIndx('teff')])
-_DATA['TEFF_ERR'][cnanIndx]) < 10.**-10.), 'PARAM_COV TEFF does not correspond to tag TEFF_ERR'
cnanIndx= (True-numpy.isnan(numpy.sqrt(_DATA['PARAM_COV'][:,paramIndx('logg'),paramIndx('logg')])))
if numpy.sum(cnanIndx) > 0:
assert numpy.all(numpy.fabs(numpy.sqrt(_DATA['PARAM_COV'][cnanIndx,paramIndx('logg'),paramIndx('logg')])
-_DATA['LOGG_ERR'][cnanIndx]) < 10.**-10.), 'PARAM_COV LOGG does not correspond to tag LOGG_ERR'
return None
def test_elem_calib_outsiderange():
#Test that the elem calibration does not extend outside of the calibration
#temperature range
elems= ['C','N','O','Mg','Si','S','Ca','Ti',
'Ni','Fe','Al','K','Na','V','Mn']
TeffMin= 3800.
TeffMax= 5250.
for elem in elems:
calibDiff= _DATA['FELEM'][:,elemIndx(elem)]\
-_DATA['ELEM'][:,elemIndx(elem)]
#Only consider good stars for this element
indx= ((_DATA['ASPCAPFLAG'] & 2**23) == 0)\
*(_DATA['FPARAM'][:,paramIndx('teff')] > -1000.)\
*(_DATA['FELEM'][:,elemIndx(elem)] > -1000.)\
*(_DATA['ELEM'][:,elemIndx(elem)] > -1000.)
loTIndx= numpy.argmin(numpy.fabs(_DATA['FPARAM'][indx,
paramIndx('teff')]
-TeffMin))
hiTIndx= numpy.argmin(numpy.fabs(_DATA['FPARAM'][indx,
paramIndx('teff')]
-TeffMax))
assert numpy.all(numpy.fabs(calibDiff[indx][_DATA['FPARAM'][indx,paramIndx('teff')] < TeffMin]-calibDiff[indx][loTIndx]) < 10.**-3.), 'Calibration offset does not saturate below the minimum calibration temperature of %i for element %s' % (TeffMin,elem)
assert numpy.all(numpy.fabs(calibDiff[indx][_DATA['FPARAM'][indx,paramIndx('teff')] > TeffMax]-calibDiff[indx][hiTIndx]) < 10.**-2.), 'Calibration offset does not saturate above the maximum calibration temperature of %i for element %s' % (TeffMax,elem)
return None
def test_elem_calib_outsiderange():
#Test that the elem calibration does not extend outside of the calibration
#temperature range
elems= ['C','N','O','Mg','Si','S','Ca','Ti',
'Ni','Fe','Al','K','Na','V','Mn']
TeffMin= 3800.
TeffMax= 5250.
for elem in elems:
calibDiff= _DATA['FELEM'][:,elemIndx(elem)]\
-_DATA['ELEM'][:,elemIndx(elem)]
#Only consider good stars for this element
indx= ((_DATA['ASPCAPFLAG'] & 2**23) == 0)\
*(_DATA['FPARAM'][:,paramIndx('teff')] > -1000.)\
*(_DATA['FELEM'][:,elemIndx(elem)] > -1000.)\
*(_DATA['ELEM'][:,elemIndx(elem)] > -1000.)
loTIndx= numpy.argmin(numpy.fabs(_DATA['FPARAM'][indx,
paramIndx('teff')]
-TeffMin))
hiTIndx= numpy.argmin(numpy.fabs(_DATA['FPARAM'][indx,
paramIndx('teff')]
-TeffMax))
assert numpy.all(numpy.fabs(calibDiff[indx][_DATA['FPARAM'][indx,paramIndx('teff')] < TeffMin]-calibDiff[indx][loTIndx]) < 10.**-3.), 'Calibration offset does not saturate below the minimum calibration temperature of %i for element %s' % (TeffMin,elem)
assert numpy.all(numpy.fabs(calibDiff[indx][_DATA['FPARAM'][indx,paramIndx('teff')] > TeffMax]-calibDiff[indx][hiTIndx]) < 10.**-2.), 'Calibration offset does not saturate above the maximum calibration temperature of %i for element %s' % (TeffMax,elem)
return None
def test_elem_calib_outsiderange_dwarfs():
#Test that the elem calibration does not extend outside of the calibration
#temperature range
from apogee.tools import _ELEM_SYMBOL
elems= [e.capitalize() for e in _ELEM_SYMBOL if e != 'ci' and e != 'tiii']
TeffMin= 3800.
TeffMax= 7500.
dwarfs= (_DATA['FPARAM'][:,paramIndx('logg')] >= (2./1300.\
*(_DATA['FPARAM'][:,paramIndx('teff')]-3500.)+2.))\
+(_DATA['FPARAM'][:,paramIndx('logg')] >= 4.)\
+(_DATA['FPARAM'][:,paramIndx('teff')] >= 7000.)
for elem in elems:
calibDiff= _DATA['FELEM'][:,elemIndx(elem)]\
-_DATA['ELEM'][:,elemIndx(elem)]
#Only consider good stars for this element
indx= ((_DATA['ASPCAPFLAG'] & 2**23) == 0)\
*(_DATA['FPARAM'][:,paramIndx('teff')] > -1000.)\
*dwarfs\
*(_DATA['FELEM'][:,elemIndx(elem)] > -1000.)\
*(_DATA['ELEM'][:,elemIndx(elem)] > -1000.)
try:
loTIndx= numpy.argmin(numpy.fabs(_DATA['FPARAM'][indx,
paramIndx('teff')]
-TeffMin))
except ValueError:
pass
else:
assert numpy.all(numpy.fabs(calibDiff[indx][_DATA['FPARAM'][indx,paramIndx('teff')] < TeffMin]-calibDiff[indx][loTIndx]) < 10.**-3.), 'Calibration offset does not saturate below the minimum calibration temperature of %i for element %s' % (TeffMin,elem)
try:
hiTIndx= numpy.argmin(numpy.fabs(_DATA['FPARAM'][indx,
paramIndx('teff')]
-TeffMax))
except ValueError:
pass
else:
assert numpy.all(numpy.fabs(calibDiff[indx][_DATA['FPARAM'][indx,paramIndx('teff')] > TeffMax]-calibDiff[indx][hiTIndx]) < 10.**-2.), 'Calibration offset does not saturate above the maximum calibration temperature of %i for element %s' % (TeffMax,elem)
return None
def windows(*args, **kwargs):
"""
NAME:
windows
PURPOSE:
Generate model APOGEE spectra using MOOG in selected wavelength windows (but the whole APOGEE spectral range is returned): this is a general routine that generates the non-continuum-normalized spectrum, convolves with the LSF and macrotubulence, and optionally continuum normalizes the output; use 'moogsynth' for a direct interface to MOOG
INPUT ARGUMENTS:
Windows specification: Provide one of
(1) Element string: the APOGEE windows for this element will be loaded
(2) startindxs, endindxs= start and end indexes of the windows on the apStar wavelength grid
(3) startlams, endlams= start and end wavelengths in \AA
lists with abundance differences wrt the atmosphere (they don't all have to have the same length, missing ones are filled in with zeros):
[Atomic number1,diff1_1,diff1_2,diff1_3,...,diff1_N]
[Atomic number2,diff2_1,diff2_2,diff2_3,...,diff2_N]
...
[Atomic numberM,diffM_1,diffM_2,diffM_3,...,diffM_N]
INPUT KEYWORDS:
BASELINE: you can specify the baseline spectrum to not always re-compute it
baseline= baseline c-normalized spectrum on MOOG wavelength grid (obtained from moogsynth)
mwav= MOOG wavelength grid (obtained from moogsynth)
cflux= continuum flux from MOOG
Typically, you can obtain these three keywords by doing (kwargs are the keywords you provide to this function as well)
>>> baseline= moogsynth(**kwargs)[1]
>>> mwav, cflux= moogsynth(doflux=True,**kwargs)
LSF:
lsf= ('all') LSF to convolve with; output of apogee.spec.lsf.eval; sparsify for efficiency; if 'all' or 'combo' a pre-computed version will be downloaded from the web
Either:
xlsf= (None) pixel offset grid on which the LSF is computed (see apogee.spec.lsf.eval); unnecessary if lsf=='all' or 'combo'
dxlsf= (None) spacing of pixel offsets
vmacro= (6.) macroturbulence to apply
CONTINUUM:
cont= ('aspcap') continuum-normalization to apply:
None: no continuum normalization
'true': Use the true continuum
'aspcap': Use the continuum normalization method of ASPCAP DR12
'cannon': Normalize using continuum pixels derived from the Cannon
SYNTHESIS:
linelist= (None) linelist to use; if this is None, the code looks for a weed-out version of the linelist appropriate for the given model atmosphere
run_weedout= (False) if True, run MOOG weedout on the linelist first
wmin, wmax, dw, width= (15000.000, 17000.000, 0.10000000, 7.0000000) spectral synthesis limits *for the whole spectrum* (not just the windows), step, and width of calculation (see MOOG)
MODEL ATMOSPHERE PARAMETERS:
Specify one of the following:
(a) modelatm= (None) can be set to the filename of a model atmosphere or to a model-atmosphere instance (if filename, needs to end in .mod)
(b) parameters of a KURUCZ model atmosphere:
(1) teff= (4500) Teff
logg= (2.5) logg
metals= (0.) metallicity
cm= (0.) carbon-enhancement
am= (0.) alpha-enhancement
(2) fparam= standard ASPCAP output format (
lib= ('kurucz_filled') model atmosphere library
dr= (None) use model atmospheres from this data release
vmicro= (2.) microturbulence (only used if the MOOG-formatted atmosphere is not found) (can also be part of fparam)
MISCELLANEOUS:
dr= return the path corresponding to this data release
OUTPUT:
spectra (nspec,nwave)
HISTORY:
2015-03-18 - Written - Bovy (IAS)
"""
# Pop some kwargs
run_weedout = kwargs.pop('run_weedout', False)
baseline = kwargs.pop('baseline', None)
mwav = kwargs.pop('mwav', None)
cflux = kwargs.pop('cflux', None)
# Check that we have the LSF and store the relevant keywords
lsf = kwargs.pop('lsf', 'all')
if isinstance(lsf, str):
xlsf, lsf = aplsf._load_precomp(dr=kwargs.get('dr', None), fiber=lsf)
dxlsf = None
else:
xlsf = kwargs.pop('xlsf', None)
dxlsf = kwargs.pop('dxlsf', None)
if xlsf is None and dxlsf is None:
raise ValueError(
'xlsf= or dxlsf= input needs to be given if the LSF is given as an array'
)
vmacro = kwargs.pop('vmacro', 6.)
# Parse continuum-normalization keywords
cont = kwargs.pop('cont', 'aspcap')
# Parse the wavelength regions
apWave = apStarWavegrid()
if isinstance(args[0], str): #element string given
si, ei = apwindow.waveregions(args[0], pad=3, asIndex=True)
args = args[1:]
else:
if isinstance(args[0][0], int): # assume index
si, ei = args[0], args[1]
else: # assume wavelengths in \AA
sl, el = args[0], args[1]
# Convert to index
si, ei = [], []
for s, e in zip(sl, el):
# Find closest index into apWave
si.append(numpy.argmin(numpy.fabs(s - apWave)))
ei.append(numpy.argmin(numpy.fabs(e - apWave)))
args = args[2:]
# Setup the model atmosphere
modelatm = kwargs.pop('modelatm', None)
tmpModelAtmDir = False
# Parse fparam, if present
fparam = kwargs.pop('fparam', None)
if not fparam is None:
kwargs['teff'] = fparam[0, paramIndx('TEFF')]
kwargs['logg'] = fparam[0, paramIndx('LOGG')]
kwargs['metals'] = fparam[0, paramIndx('METALS')]
kwargs['am'] = fparam[0, paramIndx('ALPHA')]
kwargs['cm'] = fparam[0, paramIndx('C')]
kwargs['vm'] = 10.**fparam[0, paramIndx('LOG10VDOP')]
if modelatm is None: # Setup a model atmosphere
modelatm = atlas9.Atlas9Atmosphere(teff=kwargs.get('teff', 4500.),
logg=kwargs.get('logg', 2.5),
metals=kwargs.get('metals', 0.),
am=kwargs.get('am', 0.),
cm=kwargs.get('cm', 0.),
dr=kwargs.get('dr', None))
if isinstance(modelatm, str) and os.path.exists(modelatm):
modelfilename = modelatm
elif isinstance(modelatm, str):
raise ValueError('modelatm= input is a non-existing filename')
else: # model atmosphere instance
# Need to write this instance to a file; we will run in a temp
# subdirectory of the current directory
tmpDir = tempfile.mkdtemp(dir=os.getcwd())
tmpModelAtmDir = True # need to remove this later
modelfilename = os.path.join(tmpDir, 'modelatm.mod')
modelatm.writeto(modelfilename)
kwargs['modelatm'] = modelfilename
try:
# Check whether a MOOG version of the model atmosphere exists
if not os.path.exists(modelfilename.replace('.mod', '.org')):
# Convert to MOOG format
convert_modelAtmosphere(**kwargs)
# Run weedout on the linelist first if requested
if run_weedout:
linelistfilename = modelfilename.replace('.mod', '.lines')
if not os.path.exists(linelistfilename):
weedout(**kwargs)
kwargs['linelist'] = linelistfilename
# Run MOOG synth for the whole wavelength range as a baseline, also contin
if baseline is None:
baseline = moogsynth(**kwargs)[1]
elif isinstance(baseline,
tuple): #probably accidentally gave wav as well
baseline = baseline[1]
if mwav is None or cflux is None:
mwav, cflux = moogsynth(doflux=True, **kwargs)
# Convert the apStarWavegrid windows to moogWavegrid regions
sm, em = [], []
for start, end in zip(si, ei):
sm.append(numpy.argmin(numpy.fabs(apWave[start] - mwav)))
em.append(numpy.argmin(numpy.fabs(apWave[end] - mwav)))
# Run MOOG synth for all abundances and all windows
if len(args) == 0: #special case that there are *no* differences
args = ([26, 0.], )
nsynths = numpy.array([len(args[ii]) - 1 for ii in range(len(args))])
nsynth = numpy.amax(nsynths) #Take the longest abundance list
out = numpy.tile(baseline, (nsynth, 1))
# Run all windows
for start, end in zip(sm, em):
kwargs['wmin'] = mwav[start]
kwargs['wmax'] = mwav[end]
# Check whether the number of syntheses is > 5 and run multiple
# MOOG instances if necessary, bc MOOG only does 5 at a time
ninstances = int(numpy.ceil(nsynth / 5.))
for ii in range(ninstances):
newargs = ()
for jj in range(len(args)):
tab = [args[jj][0]]
if len(args[jj][5 * ii + 1:5 * (ii + 1) + 1]) > 0:
tab.extend(args[jj][5 * ii + 1:5 * (ii + 1) + 1])
newargs = newargs + (tab, )
out[5 * ii:5 * (ii + 1),
start:end + 1] = moogsynth(*newargs, **kwargs)[1]
except:
raise
finally:
if tmpModelAtmDir: # need to remove this temporary directory
os.remove(modelfilename)
moogmodelfilename = modelfilename.replace('.mod', '.org')
if os.path.exists(moogmodelfilename):
os.remove(moogmodelfilename)
if run_weedout:
os.remove(modelfilename.replace('.mod', '.lines'))
os.rmdir(tmpDir)
# Now multiply each continuum-normalized spectrum with the continuum
out *= numpy.tile(cflux, (nsynth, 1))
# Now convolve with the LSF
out = aplsf.convolve(mwav,
out,
lsf=lsf,
xlsf=xlsf,
dxlsf=dxlsf,
vmacro=vmacro)
# Now continuum-normalize
if cont.lower() == 'true':
# Get the true continuum on the apStar wavelength grid
apWave = apStarWavegrid()
baseline = numpy.polynomial.Polynomial.fit(mwav, cflux, 4)
ip = interpolate.InterpolatedUnivariateSpline(mwav,
cflux / baseline(mwav),
k=3)
cflux = baseline(apWave) * ip(apWave)
# Divide it out
out /= numpy.tile(cflux, (nsynth, 1))
elif not cont is None:
cflux = apcont.fit(out, numpy.ones_like(out), type=cont)
out[cflux > 0.] /= cflux[cflux > 0.]
out[cflux <= 0.] = numpy.nan
return out
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
def calibrate_logg_dr12(rgb=False):
"""Calibrate, using RC or RGV when rgb=True (the latter should reproduce the official calibration"""
#Read the calibration file, APOKASC, and match them
caldata= fitsio.read(os.path.join(os.getenv('APOGEE_DATA'),'cal_%s.fits' % os.getenv('APOGEE_REDUX')),1)
apokasc= apread.apokasc()
h=esutil.htm.HTM()
m1,m2,d12 = h.match(caldata['RA'],caldata['DEC'],
apokasc['RA'],apokasc['DEC'],
2./3600.,maxmatch=1)
caldata= caldata[m1]
apokasc= apokasc[m2]
#Select a decent calibration sample
# Use stars that are definitely RGB or RC
seismoState= numpy.char.strip(apokasc['SEISMO EVOL'])
if rgb:
indx= (seismoState == 'RGB') \
+ (seismoState == 'DWARF/SUBGIANT')
else:
indx= (seismoState == 'CLUMP')
#Add low-logg giants of any kind
indx+= (apokasc['KASC_RG_LOGG_SCALE_2'] < 2.)
#rm bad data
indx= (caldata['FPARAM'][:,paramIndx('logg')] > -1000.)
indx*= (apokasc['KASC_RG_LOGG_SCALE_2'] > -1000.)
#Apply limits
indx*= (caldata['FPARAM'][:,paramIndx('logg')] > 1.)\
*(caldata['FPARAM'][:,paramIndx('logg')] < 3.8)
print "Using %i stars to calibrate logg ..." % numpy.sum(indx)
#Now fit the difference
fitOut= numpy.polyfit(caldata['FPARAM'][indx,paramIndx('logg')],
caldata['FPARAM'][indx,paramIndx('logg')]\
-apokasc['KASC_RG_LOGG_SCALE_2'][indx],
1)
print fitOut
if True:
bovy_plot.bovy_print()
bovy_plot.bovy_plot(caldata['FPARAM'][indx,paramIndx('logg')],
caldata['FPARAM'][indx,paramIndx('logg')]\
-apokasc['KASC_RG_LOGG_SCALE_2'][indx],
'k.',
xrange=[0.,5.],yrange=[-0.5,1.],
xlabel=r'$\log g_{\mathrm{ASPCAP}}$',
ylabel=r'$\log g_{\mathrm{ASPCAP}}-\log g_{\mathrm{seismo}}$')
if not rgb:
plotindx= (seismoState == 'RGB') \
+ (seismoState == 'DWARF/SUBGIANT')
else:
plotindx= (seismoState == 'CLUMP')
plotindx*= (caldata['FPARAM'][:,paramIndx('logg')] > -1000.)
plotindx*= (apokasc['KASC_RG_LOGG_SCALE_2'] > -1000.)
bovy_plot.bovy_plot(caldata['FPARAM'][plotindx,paramIndx('logg')],
caldata['FPARAM'][plotindx,paramIndx('logg')]\
-apokasc['KASC_RG_LOGG_SCALE_2'][plotindx],
'.',color='0.65',overplot=True)
xs= numpy.linspace(1.,3.8,1001)
bovy_plot.bovy_plot(xs,fitOut[0]*xs+fitOut[1],'k-',overplot=True)
bovy_plot.bovy_plot(xs,-0.14*xs+0.588,'k--',overplot=True)
print numpy.amax(numpy.fabs(fitOut[0]*xs+fitOut[1]-(-0.14*xs+0.588)))
print numpy.amax(numpy.fabs(fitOut[0]*xs+fitOut[1]-(-0.14*xs+0.588))[xs < 2.])
bovy_plot.bovy_text(r'$\mathrm{diff} = %.3f \log g_{\mathrm{ASPCAP}} + %.3f$' % (fitOut[0],fitOut[1]),
bottom_left=True,fontsize=14.)
bovy_plot.bovy_end_print('/Users/bovy/Desktop/test.png')
return None
def 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 synth(*args,**kwargs):
"""
NAME:
synth
PURPOSE:
Generate model APOGEE spectra using Turbospectrum: this is a general routine that generates the non-continuum-normalized spectrum, convolves with the LSF and macrotubulence, and optionally continuum normalizes the output; use 'turbosynth' for a direct interface to Turbospectrum
INPUT ARGUMENTS:
lists with abundances differences wrt the atmosphere (they don't all have to have the same length, missing ones are filled in with zeros):
[Atomic number1,diff1_1,diff1_2,diff1_3,...,diff1_N]
[Atomic number2,diff2_1,diff2_2,diff2_3,...,diff2_N]
...
[Atomic numberM,diffM_1,diffM_2,diffM_3,...,diffM_N]
INPUT KEYWORDS:
LSF:
lsf= ('all') LSF to convolve with; output of apogee.spec.lsf.eval; sparsify for efficiency; if 'all' or 'combo' a pre-computed version will be downloaded from the web
Either:
xlsf= (None) pixel offset grid on which the LSF is computed (see apogee.spec.lsf.eval); unnecessary if lsf=='all' or 'combo'
dxlsf= (None) spacing of pixel offsets
vmacro= (6.) macroturbulence to apply
CONTINUUM:
cont= ('aspcap') continuum-normalization to apply:
None: no continuum normalization
'true': Use the true continuum
'aspcap': Use the continuum normalization method of ASPCAP DR12
'cannon': Normalize using continuum pixels derived from the Cannon
SYNTHESIS:
air= (True) if True, perform the synthesis in air wavelengths (output is still in vacuum); set to False at your own risk, as Turbospectrum expects the linelist in air wavelengths!)
Hlinelist= (None) Hydrogen linelists to use; can be set to the path of a linelist file or to the name of an APOGEE linelist; if None, then we first search for the Hlinedata.vac in the APOGEE linelist directory (if air=False) or we use the internal Turbospectrum Hlinelist (if air=True)
linelist= (None) molecular and atomic linelists to use; can be set to the path of a linelist file or to the name of an APOGEE linelist, or lists of such files; if a single filename is given, the code will first search for files with extensions '.atoms', '.molec' or that start with 'turboatoms.' and 'turbomolec.'
wmin, wmax, dw= (15000.000, 17000.000, 0.10000000) spectral synthesis limits and step
costheta= (1.) cosine of the viewing angle
lib= ('kurucz_filled') spectral library
MODEL ATMOSPHERE PARAMETERS:
Specify one of the following:
(a) modelatm= (None) model-atmosphere instance
(b) parameters of a KURUCZ model atmosphere:
(1) teff= (4500) Teff
logg= (2.5) logg
metals= (0.) metallicity
cm= (0.) carbon-enhancement
am= (0.) alpha-enhancement
(2) fparam= standard ASPCAP output format
lib= ('kurucz_filled') model atmosphere library
vmicro= (2.) microturbulence (only used if the MOOG-formatted atmosphere is not found) (can also be part of fparam)
MISCELLANEOUS:
dr= return the path corresponding to this data release
OUTPUT:
spectra (nspec,nwave)
HISTORY:
2015-04-16 - Written - Bovy (IAS)
"""
# Check that we have the LSF and store the relevant keywords
lsf= kwargs.pop('lsf','all')
if isinstance(lsf,str):
xlsf, lsf= aplsf._load_precomp(dr=kwargs.get('dr',None),fiber=lsf)
dxlsf= None
else:
xlsf= kwargs.pop('xlsf',None)
dxlsf= kwargs.pop('dxlsf',None)
if xlsf is None and dxlsf is None: raise ValueError('xlsf= or dxlsf= input needs to be given if the LSF is given as an array')
vmacro= kwargs.pop('vmacro',6.)
# Parse continuum-normalization keywords
cont= kwargs.pop('cont','aspcap')
# Setup the model atmosphere
modelatm= kwargs.pop('modelatm',None)
# Parse fparam, if present
fparam= kwargs.pop('fparam',None)
if not fparam is None:
kwargs['teff']= fparam[paramIndx('TEFF')]
kwargs['logg']= fparam[paramIndx('LOGG')]
kwargs['metals']= fparam[paramIndx('METALS')]
kwargs['am']= fparam[paramIndx('ALPHA')]
kwargs['cm']= fparam[paramIndx('C')]
kwargs['vmicro']= 10.**fparam[paramIndx('LOG10VDOP')]
# Need to pass a model atmosphere instance to turbosynth (needs to be made
# more efficient, because now turbosynth always write the atmosphere
if modelatm is None: # Setup a model atmosphere
modelatm= atlas9.Atlas9Atmosphere(teff=kwargs.get('teff',4500.),
logg=kwargs.get('logg',2.5),
metals=kwargs.get('metals',0.),
am=kwargs.get('am',0.),
cm=kwargs.get('cm',0.),
dr=kwargs.get('dr',None))
if isinstance(modelatm,str) and os.path.exists(modelatm):
raise ValueError('modelatm= input is an existing filename, but you need to give an Atmosphere object instead')
elif isinstance(modelatm,str):
raise ValueError('modelatm= input needs to be an Atmosphere instance')
# Check temperature
if modelatm._teff > 7000.:
warnings.warn('Turbospectrum does not include all necessary physics to model stars hotter than about 7000 K; proceed with caution',RuntimeWarning)
kwargs['modelatm']= modelatm
try:
# Run turbosynth for all abundances
if len(args) == 0: #special case that there are *no* differences
args= ([26,0.],)
nsynths= numpy.array([len(args[ii])-1 for ii in range(len(args))])
nsynth= numpy.amax(nsynths) #Take the longest abundance list
nturbowav= int((kwargs.get('wmax',_WMAX_DEFAULT)\
-kwargs.get('wmin',_WMIN_DEFAULT))\
/kwargs.get('dw',_DW_DEFAULT)+1)
out= numpy.empty((nsynth,nturbowav))
for ii in range(nsynth):
newargs= ()
for jj in range(len(args)):
tab= [args[jj][0]]
if len(args[jj]) > ii+1:
tab.append(args[jj][ii+1])
newargs= newargs+(tab,)
tmpOut= turbosynth(*newargs,**kwargs)
out[ii]= tmpOut[2] # incl. continuum
# wavelength grid from final one
mwav= tmpOut[0]
except: raise
# If the synthesis was done in air, convert wavelength array
if kwargs.get('air',True):
mwav= numpy.array([air2vac(w) for w in list(mwav)])
# Now convolve with the LSF
out= aplsf.convolve(mwav,out,
lsf=lsf,xlsf=xlsf,dxlsf=dxlsf,vmacro=vmacro)
# Now continuum-normalize
if cont.lower() == 'true':
# Get the true continuum on the apStar wavelength grid
apWave= apStarWavegrid()
baseline= numpy.polynomial.Polynomial.fit(mwav,tmpOut[2]/tmpOut[1],4)
ip= interpolate.InterpolatedUnivariateSpline(mwav,
tmpOut[2]/tmpOut[1]\
/baseline(mwav),
k=3)
cflux= baseline(apWave)*ip(apWave)
# Divide it out
out/= numpy.tile(cflux,(nsynth,1))
elif not cont is None:
cflux= apcont.fit(out,numpy.ones_like(out),type=cont)
out[cflux > 0.]/= cflux[cflux > 0.]
out[cflux <= 0.]= numpy.nan
return out
def synth(*args,**kwargs):
"""
NAME:
synth
PURPOSE:
Generate model APOGEE spectra using MOOG: this is a general routine that generates the non-continuum-normalized spectrum, convolves with the LSF and macrotubulence, and optionally continuum normalizes the output; use 'moogsynth' for a direct interface to MOOG
INPUT ARGUMENTS:
lists with abundances wrt the atmosphere (they don't all have to have the same length, missing ones are filled in with zeros):
[Atomic number1,diff1_1,diff1_2,diff1_3,...,diff1_N]
[Atomic number2,diff2_1,diff2_2,diff2_3,...,diff2_N]
...
[Atomic numberM,diffM_1,diffM_2,diffM_3,...,diffM_N]
INPUT KEYWORDS:
LSF:
lsf= ('all') LSF to convolve with; output of apogee.spec.lsf.eval; sparsify for efficiency; if 'all' or 'combo' a pre-computed version will be downloaded from the web
Either:
xlsf= (None) pixel offset grid on which the LSF is computed (see apogee.spec.lsf.eval); unnecessary if lsf=='all' or 'combo'
dxlsf= (None) spacing of pixel offsets
vmacro= (6.) macroturbulence to apply
CONTINUUM:
cont= ('aspcap') continuum-normalization to apply:
None: no continuum normalization
'true': Use the true continuum
'aspcap': Use the continuum normalization method of ASPCAP DR12
'cannon': Normalize using continuum pixels derived from the Cannon
SYNTHESIS:
linelist= (None) linelist to use; can be set to the path of a linelist file or to the name of an APOGEE linelist
run_weedout= (False) if True, run MOOG weedout on the linelist first
wmin, wmax, dw, width= (15000.000, 17000.000, 0.10000000, 7.0000000) spectral synthesis limits, step, and width of calculation (see MOOG)
lib= ('kurucz_filled') spectral library
MODEL ATMOSPHERE PARAMETERS:
Specify one of the following:
(a) modelatm= (None) can be set to the filename of a model atmosphere or to a model-atmosphere instance (if filename, needs to end in .mod)
(b) parameters of a KURUCZ model atmosphere:
(1) teff= (4500) Teff
logg= (2.5) logg
metals= (0.) metallicity
cm= (0.) carbon-enhancement
am= (0.) alpha-enhancement
(2) fparam= standard ASPCAP output format
lib= ('kurucz_filled') model atmosphere library
dr= (None) use model atmospheres from this data release
vmicro= (2.) microturbulence (only used if the MOOG-formatted atmosphere is not found) (can also be part of fparam)
MISCELLANEOUS:
dr= return the path corresponding to this data release
OUTPUT:
spectra (nspec,nwave)
HISTORY:
2015-03-15 - Written - Bovy (IAS)
"""
run_weedout= kwargs.pop('run_weedout',False)
# Check that we have the LSF and store the relevant keywords
lsf= kwargs.pop('lsf','all')
if isinstance(lsf,str):
xlsf, lsf= aplsf._load_precomp(dr=kwargs.get('dr',None),fiber=lsf)
dxlsf= None
else:
xlsf= kwargs.pop('xlsf',None)
dxlsf= kwargs.pop('dxlsf',None)
if xlsf is None and dxlsf is None: raise ValueError('xlsf= or dxlsf= input needs to be given if the LSF is given as an array')
vmacro= kwargs.pop('vmacro',6.)
# Parse continuum-normalization keywords
cont= kwargs.pop('cont','aspcap')
# Setup the model atmosphere
modelatm= kwargs.pop('modelatm',None)
tmpModelAtmDir= False
# Parse fparam, if present
fparam= kwargs.pop('fparam',None)
if not fparam is None:
kwargs['teff']= fparam[paramIndx('TEFF')]
kwargs['logg']= fparam[paramIndx('LOGG')]
kwargs['metals']= fparam[paramIndx('METALS')]
kwargs['am']= fparam[paramIndx('ALPHA')]
kwargs['cm']= fparam[paramIndx('C')]
kwargs['vm']= 10.**fparam[paramIndx('LOG10VDOP')]
if modelatm is None: # Setup a model atmosphere
modelatm= atlas9.Atlas9Atmosphere(teff=kwargs.get('teff',4500.),
logg=kwargs.get('logg',2.5),
metals=kwargs.get('metals',0.),
am=kwargs.get('am',0.),
cm=kwargs.get('cm',0.),
dr=kwargs.get('dr',None))
if isinstance(modelatm,str) and os.path.exists(modelatm):
modelfilename= modelatm
elif isinstance(modelatm,str):
raise ValueError('modelatm= input is a non-existing filename')
else: # model atmosphere instance
# Need to write this instance to a file; we will run in a temp
# subdirectory of the current directory
tmpDir= tempfile.mkdtemp(dir=os.getcwd())
tmpModelAtmDir= True # need to remove this later
modelfilename= os.path.join(tmpDir,'modelatm.mod')
modelatm.writeto(modelfilename)
kwargs['modelatm']= modelfilename
try:
# Check whether a MOOG version of the model atmosphere exists
if not os.path.exists(modelfilename.replace('.mod','.org')):
# Convert to MOOG format
convert_modelAtmosphere(**kwargs)
# Run weedout on the linelist first if requested
if run_weedout:
linelistfilename= modelfilename.replace('.mod','.lines')
if not os.path.exists(linelistfilename):
weedout(**kwargs)
kwargs['linelist']= linelistfilename
# Run MOOG synth for all abundances
if len(args) == 0: #special case that there are *no* differences
args= ([26,0.],)
nsynths= numpy.array([len(args[ii])-1 for ii in range(len(args))])
nsynth= numpy.amax(nsynths) #Take the longest abundance list
nmoogwav= int((kwargs.get('wmax',_WMAX_DEFAULT)\
-kwargs.get('wmin',_WMIN_DEFAULT))\
/kwargs.get('dw',_DW_DEFAULT)+1)
out= numpy.empty((nsynth,nmoogwav))
# Check whether the number of syntheses is > 5 and run multiple
# MOOG instances if necessary, bc MOOG only does 5 at a time
ninstances= int(numpy.ceil(nsynth/5.))
for ii in range(ninstances):
newargs= ()
for jj in range(len(args)):
tab= [args[jj][0]]
if len(args[jj][5*ii+1:5*(ii+1)+1]) > 0:
tab.extend(args[jj][5*ii+1:5*(ii+1)+1])
newargs= newargs+(tab,)
out[5*ii:5*(ii+1)]= moogsynth(*newargs,**kwargs)[1]
# We'll grab the wavelength grid from the continuum below
# Now compute the continuum and multiply each c-norm spectrum with it
mwav, cflux= moogsynth(doflux=True,**kwargs)
except: raise
finally:
if tmpModelAtmDir: # need to remove this temporary directory
os.remove(modelfilename)
moogmodelfilename= modelfilename.replace('.mod','.org')
if os.path.exists(moogmodelfilename):
os.remove(moogmodelfilename)
if run_weedout:
os.remove(modelfilename.replace('.mod','.lines'))
os.rmdir(tmpDir)
out*= numpy.tile(cflux,(nsynth,1))
# Now convolve with the LSF
out= aplsf.convolve(mwav,out,
lsf=lsf,xlsf=xlsf,dxlsf=dxlsf,vmacro=vmacro)
# Now continuum-normalize
if cont.lower() == 'true':
# Get the true continuum on the apStar wavelength grid
apWave= apStarWavegrid()
baseline= numpy.polynomial.Polynomial.fit(mwav,cflux,4)
ip= interpolate.InterpolatedUnivariateSpline(mwav,
cflux/baseline(mwav),
k=3)
cflux= baseline(apWave)*ip(apWave)
# Divide it out
out/= numpy.tile(cflux,(nsynth,1))
elif not cont is None:
cflux= apcont.fit(out,numpy.ones_like(out),type=cont)
out[cflux > 0.]/= cflux[cflux > 0.]
out[cflux <= 0.]= numpy.nan
return out
def allStar(rmcommissioning=True,
main=False,
exclude_star_bad=False,
exclude_star_warn=False,
ak=True,
akvers='targ',
rmnovisits=False,
adddist=False,
distredux=None,
rmdups=False,
raw=False):
"""
NAME:
allStar
PURPOSE:
read the 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)
"""
filePath = path.allStarPath()
if not os.path.exists(filePath):
download.allStar()
#read allStar file
data = fitsio.read(path.allStarPath())
if raw: return data
#Remove duplicates, cache
if rmdups:
dupsFilename = path.allStarPath().replace('.fits', '-nodups.fits')
if os.path.exists(dupsFilename):
data = fitsio.read(dupsFilename)
else:
sys.stdout.write(
'\r' +
"Removing duplicates (might take a while) and caching the duplicate-free file ...\r"
)
sys.stdout.flush()
data = remove_duplicates(data)
#Cache this file for subsequent use of rmdups
fitsio.write(dupsFilename, data, clobber=True)
sys.stdout.write('\r' + _ERASESTR + '\r')
sys.stdout.flush()
#Some cuts
if rmcommissioning:
indx = numpy.array(
['apogee.n.c'.encode('utf-8') in s for s in data['APSTAR_ID']])
indx += numpy.array(
['apogee.s.c'.encode('utf-8') in s for s in data['APSTAR_ID']])
data = data[True - indx]
if rmnovisits:
indx = numpy.array([s.strip() != '' for s in data['VISITS']])
data = data[indx]
if main:
indx = mainIndx(data)
data = data[indx]
if akvers.lower() == 'targ':
aktag = 'AK_TARG'
elif akvers.lower() == 'wise':
aktag = 'AK_WISE'
if ak:
data = data[True - numpy.isnan(data[aktag])]
data = data[(data[aktag] > -50.)]
if exclude_star_bad:
data = data[(data['ASPCAPFLAG'] & 2**23) == 0]
if exclude_star_warn:
data = data[(data['ASPCAPFLAG'] & 2**7) == 0]
#Add dereddened J, H, and Ks
aj = data[aktag] * 2.5
ah = data[aktag] * 1.55
if _ESUTIL_LOADED:
data = esutil.numpy_util.add_fields(data,
[('J0', float), ('H0', float),
('K0', float)])
data['J0'] = data['J'] - aj
data['H0'] = data['H'] - ah
data['K0'] = data['K'] - data[aktag]
data['J0'][(data[aktag] <= -50.)] = -9999.9999
data['H0'][(data[aktag] <= -50.)] = -9999.9999
data['K0'][(data[aktag] <= -50.)] = -9999.9999
else:
warnings.warn(
"Extinction-corrected J,H,K not added because esutil is not installed",
RuntimeWarning)
#Add distances
if adddist and _ESUTIL_LOADED:
dist = fitsio.read(path.distPath(), 1)
h = esutil.htm.HTM()
m1, m2, d12 = h.match(dist['RA'],
dist['DEC'],
data['RA'],
data['DEC'],
2. / 3600.,
maxmatch=1)
data = data[m2]
dist = dist[m1]
distredux = path._redux_dr()
if distredux.lower() == 'v302' or distredux.lower() == path._DR10REDUX:
data = esutil.numpy_util.add_fields(data, [('DM05', float),
('DM16', float),
('DM50', float),
('DM84', float),
('DM95', float),
('DMPEAK', float),
('DMAVG', float),
('SIG_DM', float),
('DIST_SOL', float),
('SIG_DISTSOL', float)])
data['DM05'] = dist['DM05']
data['DM16'] = dist['DM16']
data['DM50'] = dist['DM50']
data['DM84'] = dist['DM84']
data['DM95'] = dist['DM95']
data['DMPEAK'] = dist['DMPEAK']
data['DMAVG'] = dist['DMAVG']
data['SIG_DM'] = dist['SIG_DM']
data['DIST_SOL'] = dist['DIST_SOL'] / 1000.
data['SIG_DISTSOL'] = dist['SIG_DISTSOL'] / 1000.
elif distredux.lower() == path._DR11REDUX:
data = esutil.numpy_util.add_fields(data, [('DISO', float),
('DMASS', float),
('DISO_GAL', float),
('DMASS_GAL', float)])
data['DISO'] = dist['DISO'][:, 1]
data['DMASS'] = dist['DMASS'][:, 1]
data['DISO_GAL'] = dist['DISO_GAL'][:, 1]
data['DMASS_GAL'] = dist['DMASS_GAL'][:, 1]
elif distredux.lower() == path._DR12REDUX:
data = esutil.numpy_util.add_fields(
data, [('HIP_PLX', float), ('HIP_E_PLX', float),
('RC_DIST', float), ('APOKASC_DIST_DIRECT', float),
('BPG_DIST1_MEAN', float), ('HAYDEN_DIST_PEAK', float),
('SCHULTHEIS_DIST', float)])
data['HIP_PLX'] = dist['HIP_PLX']
data['HIP_E_PLX'] = dist['HIP_E_PLX']
data['RC_DIST'] = dist['RC_dist_pc']
data[
'APOKASC_DIST_DIRECT'] = dist['APOKASC_dist_direct_pc'] / 1000.
data['BPG_DIST1_MEAN'] = dist['BPG_dist1_mean']
data['HAYDEN_DIST_PEAK'] = 10.**(dist['HAYDEN_distmod_PEAK'] / 5. -
2.)
data['SCHULTHEIS_DIST'] = dist['SCHULTHEIS_dist']
elif adddist:
warnings.warn(
"Distances not added because matching requires the uninstalled esutil module",
RuntimeWarning)
if _ESUTIL_LOADED and (path._APOGEE_REDUX.lower() == 'current' \
or 'l30' in path._APOGEE_REDUX.lower() \
or int(path._APOGEE_REDUX[1:]) > 600):
data = esutil.numpy_util.add_fields(data, [('METALS', float),
('ALPHAFE', float)])
data['METALS'] = data['PARAM'][:, paramIndx('metals')]
data['ALPHAFE'] = data['PARAM'][:, paramIndx('alpha')]
return data
def fit(spec,specerr,
teff=4750.,logg=2.5,metals=0.,am=0.,nm=0.,cm=0.,vm=None,
fixteff=False,fixlogg=False,fixmetals=False,fixam=False,fixcm=False,
fixnm=False,fixvm=False,
lib='GK',pca=True,sixd=True,dr=None,
offile=None,
inter=3,f_format=1,f_access=None,
errbar=1,indini=[1,1,1,2,2,3],init=1,initcannon=False,
verbose=False):
"""
NAME:
fit
PURPOSE:
Fit a model spectrum to a given data spectrum
INPUT:
Either:
(1) location ID - single or list/array of location IDs
APOGEE ID - single or list/array of APOGEE IDs; loads aspcapStar
(2) spec - spectrum: can be (nwave) or (nspec,nwave)
specerr - spectrum errors: can be (nwave) or (nspec,nwave)
Input parameters (can be 1D arrays); only used when init=0
teff= (4750.) Effective temperature (K)
logg= (2.5) log10 surface gravity / cm s^-2
metals= (0.) overall metallicity
am= (0.) [alpha/M]
nm= (0.) [N/M]
cm= (0.) [C/M]
vm= if using the 7D library, also specify the microturbulence
Fit options:
fixteff= (False) if True, fix teff at the input value
fixlogg= (False) if True, fix logg at the input value
fixmetals= (False) if True, fix metals at the input value
fixam= (False) if True, fix am at the input value
fixcm= (False) if True, fix cm at the input value
fixnm= (False) if True, fix nm at the input value
fixvm= (False) if True, fix vm at the input value (only if sixd is False)
Library options:
lib= ('GK') spectral library
pca= (True) if True, use a PCA compressed library
sixd= (True) if True, use the 6D library (w/o vm)
dr= data release
FERRE options:
inter= (3) order of the interpolation
errbar= (1) method for calculating the error bars
indini= ([1,1,1,2,2,3]) how to initialize the search (int or array/list with ndim entries)
init= (1) if 0, initialize the search at the parameters in the pfile
f_format= (1) file format (0=ascii, 1=unf)
f_access= (None) 0: load whole library, 1: use direct access (for small numbers of interpolations), None: automatically determine a good value (currently, 1)
Other options:
initcannon= (False) If True, initialize a single run by first running the Cannon using the default Cannon fit (sets init=0)
Output options:
offile= (None) if offile is set, the FERRE OFFILE is saved to this file, otherwise this file is removed
verbose= (False) if True, run FERRE in verbose mode
OUTPUT:
best-fit parameters (nspec,nparams); in the same order as the FPARAM APOGEE data product
HISTORY:
2015-01-29 - Written - Bovy (IAS)
"""
# Initialize using the Cannon?
if initcannon:
init= 0 # just run one fit using the Cannon as a starting guess
initparams= cannon.polylabels(spec,specerr)
if not fixteff: teff= initparams[:,0]
if not fixlogg: logg= initparams[:,1]
if not fixmetals: metals= initparams[:,2]
if not fixam: am= initparams[:,3]
if not fixcm: cm= numpy.zeros_like(initparams[:,0])
if not fixnm: nm= numpy.zeros_like(initparams[:,0])
# Make sure the Teff etc. have the right dimensionality
if len(spec.shape) == 1:
nspec= 1
else:
nspec= spec.shape[0]
if nspec > 1 and isinstance(teff,float):
teff= teff*numpy.ones(nspec)
if nspec > 1 and isinstance(logg,float):
logg= logg*numpy.ones(nspec)
if nspec > 1 and isinstance(metals,float):
metals= metals*numpy.ones(nspec)
if nspec > 1 and isinstance(am,float):
am= am*numpy.ones(nspec)
if nspec > 1 and isinstance(nm,float):
nm= nm*numpy.ones(nspec)
if nspec > 1 and isinstance(cm,float):
cm= cm*numpy.ones(nspec)
if nspec > 1 and not vm is None and isinstance(vm,float):
vm= vm*numpy.ones(nspec)
if dr is None: dr= appath._default_dr()
# Fix any of the parameters?
indv= []
indini= copy.copy(indini) # need to copy bc passed by reference
if isinstance(indini,numpy.ndarray) and \
((not sixd and fixvm) or fixcm or fixnm or fixam or fixmetals \
or fixlogg or fixteff):
indini= list(indini)
if not sixd and not fixvm:
indv.append(1)
elif not sixd:
if isinstance(indini,list): indini[0]= -1
if not fixcm:
indv.append(2-sixd)
else:
if isinstance(indini,list): indini[1-sixd]= -1
if not fixnm:
indv.append(3-sixd)
else:
if isinstance(indini,list): indini[2-sixd]= -1
if not fixam:
indv.append(4-sixd)
else:
if isinstance(indini,list): indini[3-sixd]= -1
if not fixmetals:
indv.append(5-sixd)
else:
if isinstance(indini,list): indini[4-sixd]= -1
if not fixlogg:
indv.append(6-sixd)
else:
if isinstance(indini,list): indini[5-sixd]= -1
if not fixteff:
indv.append(7-sixd)
else:
if isinstance(indini,list): indini[6-sixd]= -1
if isinstance(indini,list):
while -1 in indini: indini.remove(-1)
# Setup temporary directory to run FERRE from
tmpDir= tempfile.mkdtemp(dir='./')
try:
# First write the ipf file with the parameters
write_ipf(tmpDir,teff,logg,metals,am,nm,cm,vm=vm)
# Write the file with the fluxes and the flux errors
write_ffile(tmpDir,spec,specerr=specerr)
# Now write the input.nml file
if f_access is None:
f_access= 1
write_input_nml(tmpDir,'input.ipf','output.dat',ndim=7-sixd,
nov=7-sixd-fixcm-fixnm-fixam-fixmetals\
-fixlogg-fixteff,
indv=indv,
synthfile=appath.ferreModelLibraryPath\
(lib=lib,pca=pca,sixd=sixd,dr=dr,
header=True,unf=False),
ffile='input.frd',erfile='input.err',
opfile='output.opf',
inter=inter,f_format=f_format,
errbar=errbar,indini=indini,init=init,
f_access=f_access)
# Run FERRE
run_ferre(tmpDir,verbose=verbose)
# Read the output
cols= (1,2,3,4,5,6)
tmpOut= numpy.loadtxt(os.path.join(tmpDir,'output.opf'),usecols=cols)
if len(spec.shape) == 1 or spec.shape[0] == 1:
out= numpy.zeros((1,7))
tmpOut= numpy.reshape(tmpOut,(1,7-sixd))
else:
out= numpy.zeros((nspec,7))
out[:,paramIndx('TEFF')]= tmpOut[:,-1]
out[:,paramIndx('LOGG')]= tmpOut[:,-2]
out[:,paramIndx('METALS')]= tmpOut[:,-3]
out[:,paramIndx('ALPHA')]= tmpOut[:,-4]
out[:,paramIndx('N')]= tmpOut[:,-5]
out[:,paramIndx('C')]= tmpOut[:,-6]
if sixd and dr == '12':
out[:,paramIndx('LOG10VDOP')]=\
numpy.log10(2.478-0.325*out[:,paramIndx('LOGG')])
else:
out[:,paramIndx('LOG10VDOP')]= tmpOut[:,0]
if not offile is None:
os.rename(os.path.join(tmpDir,'output.dat'),offile)
finally:
# Clean up
if os.path.exists(os.path.join(tmpDir,'input.ipf')):
os.remove(os.path.join(tmpDir,'input.ipf'))
if os.path.exists(os.path.join(tmpDir,'input.frd')):
os.remove(os.path.join(tmpDir,'input.frd'))
if os.path.exists(os.path.join(tmpDir,'input.err')):
os.remove(os.path.join(tmpDir,'input.err'))
if os.path.exists(os.path.join(tmpDir,'input.nml')):
os.remove(os.path.join(tmpDir,'input.nml'))
if os.path.exists(os.path.join(tmpDir,'output.dat')):
os.remove(os.path.join(tmpDir,'output.dat'))
if os.path.exists(os.path.join(tmpDir,'output.opf')):
os.remove(os.path.join(tmpDir,'output.opf'))
os.rmdir(tmpDir)
return out
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 allStar(rmcommissioning=True,
main=False,
exclude_star_bad=False,
exclude_star_warn=False,
ak=True,
akvers='targ',
rmnovisits=False,
adddist=False,
distredux=None,
rmdups=False,
raw=False):
"""
NAME:
allStar
PURPOSE:
read the 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)
"""
filePath= path.allStarPath()
if not os.path.exists(filePath):
download.allStar()
#read allStar file
data= fitsio.read(path.allStarPath())
if raw: return data
#Remove duplicates, cache
if rmdups:
dupsFilename= path.allStarPath().replace('.fits','-nodups.fits')
if os.path.exists(dupsFilename):
data= fitsio.read(dupsFilename)
else:
sys.stdout.write('\r'+"Removing duplicates (might take a while) and caching the duplicate-free file ...\r")
sys.stdout.flush()
data= remove_duplicates(data)
#Cache this file for subsequent use of rmdups
fitsio.write(dupsFilename,data,clobber=True)
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
#Some cuts
if rmcommissioning:
indx= numpy.array(['apogee.n.c' in s for s in data['APSTAR_ID']])
indx+= numpy.array(['apogee.s.c' in s for s in data['APSTAR_ID']])
data= data[True-indx]
if rmnovisits:
indx= numpy.array([s.strip() != '' for s in data['VISITS']])
data= data[indx]
if main:
indx= mainIndx(data)
data= data[indx]
if akvers.lower() == 'targ':
aktag= 'AK_TARG'
elif akvers.lower() == 'wise':
aktag= 'AK_WISE'
if ak:
data= data[True-numpy.isnan(data[aktag])]
data= data[(data[aktag] > -50.)]
if exclude_star_bad:
data= data[(data['ASPCAPFLAG'] & 2**23) == 0]
if exclude_star_warn:
data= data[(data['ASPCAPFLAG'] & 2**7) == 0]
#Add dereddened J, H, and Ks
aj= data[aktag]*2.5
ah= data[aktag]*1.55
data= esutil.numpy_util.add_fields(data,[('J0', float),
('H0', float),
('K0', float)])
data['J0']= data['J']-aj
data['H0']= data['H']-ah
data['K0']= data['K']-data[aktag]
data['J0'][(data[aktag] <= -50.)]= -9999.9999
data['H0'][(data[aktag] <= -50.)]= -9999.9999
data['K0'][(data[aktag] <= -50.)]= -9999.9999
#Add distances
if adddist:
dist= fitsio.read(path.distPath(),1)
h=esutil.htm.HTM()
m1,m2,d12 = h.match(dist['RA'],dist['DEC'],
data['RA'],data['DEC'],
2./3600.,maxmatch=1)
data= data[m2]
dist= dist[m1]
distredux= path._redux_dr()
if distredux.lower() == 'v302' or distredux.lower() == path._DR10REDUX:
data= esutil.numpy_util.add_fields(data,[('DM05', float),
('DM16', float),
('DM50', float),
('DM84', float),
('DM95', float),
('DMPEAK', float),
('DMAVG', float),
('SIG_DM', float),
('DIST_SOL', float),
('SIG_DISTSOL', float)])
data['DM05']= dist['DM05']
data['DM16']= dist['DM16']
data['DM50']= dist['DM50']
data['DM84']= dist['DM84']
data['DM95']= dist['DM95']
data['DMPEAK']= dist['DMPEAK']
data['DMAVG']= dist['DMAVG']
data['SIG_DM']= dist['SIG_DM']
data['DIST_SOL']= dist['DIST_SOL']/1000.
data['SIG_DISTSOL']= dist['SIG_DISTSOL']/1000.
elif distredux.lower() == path._DR11REDUX:
data= esutil.numpy_util.add_fields(data,[('DISO', float),
('DMASS', float),
('DISO_GAL', float),
('DMASS_GAL', float)])
data['DISO']= dist['DISO'][:,1]
data['DMASS']= dist['DMASS'][:,1]
data['DISO_GAL']= dist['DISO_GAL'][:,1]
data['DMASS_GAL']= dist['DMASS_GAL'][:,1]
elif distredux.lower() == path._DR12REDUX:
data= esutil.numpy_util.add_fields(data,[('HIP_PLX', float),
('HIP_E_PLX', float),
('RC_DIST', float),
('APOKASC_DIST_DIRECT', float),
('BPG_DIST1_MEAN', float),
('HAYDEN_DIST_PEAK', float),
('SCHULTHEIS_DIST', float)])
data['HIP_PLX']= dist['HIP_PLX']
data['HIP_E_PLX']= dist['HIP_E_PLX']
data['RC_DIST']= dist['RC_dist_pc']
data['APOKASC_DIST_DIRECT']= dist['APOKASC_dist_direct_pc']/1000.
data['BPG_DIST1_MEAN']= dist['BPG_dist1_mean']
data['HAYDEN_DIST_PEAK']= 10.**(dist['HAYDEN_distmod_PEAK']/5.-2.)
data['SCHULTHEIS_DIST']= dist['SCHULTHEIS_dist']
if path._APOGEE_REDUX.lower() == 'current' \
or int(path._APOGEE_REDUX[1:]) > 600:
data= esutil.numpy_util.add_fields(data,[('METALS', float),
('ALPHAFE', float)])
data['METALS']= data['PARAM'][:,paramIndx('metals')]
data['ALPHAFE']= data['PARAM'][:,paramIndx('alpha')]
return data
def allStar(rmcommissioning=True,
main=False,
exclude_star_bad=False,
exclude_star_warn=False,
ak=True,
akvers='targ',
rmnovisits=False,
use_astroNN=False,
use_astroNN_abundances=False,
use_astroNN_distances=False,
use_astroNN_ages=False,
adddist=False,
distredux=None,
rmdups=False,
raw=False,
mjd=58104,
xmatch=None,**kwargs):
"""
NAME:
allStar
PURPOSE:
read the 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
use_astroNN= (False) if True, swap in astroNN (Leung & Bovy 2019a) parameters (get placed in, e.g., TEFF and TEFF_ERR), astroNN distances (Leung & Bovy 2019b), and astroNN ages (Mackereth, Bovy, Leung, et al. (2019)
use_astroNN_abundances= (False) only swap in astroNN parameters and abundances, not distances and ages
use_astroNN_distances= (False) only swap in astroNN distances, not parameters and abundances and ages
use_astroNN_ages= (False) only swap in astroNN ages, not parameters and abundances and distances
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
mjd= (58104) MJD of version for monthly internal pipeline runs
xmatch= (None) uses gaia_tools.xmatch.cds to x-match to an external catalog (eg., Gaia DR2 for xmatch='vizier:I/345/gaia2') and caches the result for re-use; requires jobovy/gaia_tools
+gaia_tools.xmatch.cds keywords
OUTPUT:
allStar data[,xmatched table]
HISTORY:
2013-09-06 - Written - Bovy (IAS)
2018-01-22 - Edited for new monthly pipeline runs - Bovy (UofT)
2018-05-09 - Add xmatch - Bovy (UofT)
2018-10-20 - Add use_astroNN option - Bovy (UofT)
2018-02-15 - Add astroNN distances and corresponding options - Bovy (UofT)
2018-02-16 - Add astroNN ages and corresponding options - Bovy (UofT)
"""
filePath= path.allStarPath(mjd=mjd)
if not os.path.exists(filePath):
download.allStar(mjd=mjd)
#read allStar file
data= fitsread(path.allStarPath(mjd=mjd))
#Add astroNN? astroNN file matched line-by-line to allStar, so match here
# [ages file not matched line-by-line]
if use_astroNN or kwargs.get('astroNN',False) or use_astroNN_abundances:
_warn_astroNN_abundances()
astroNNdata= astroNN()
data= _swap_in_astroNN(data,astroNNdata)
if use_astroNN or kwargs.get('astroNN',False) or use_astroNN_distances:
_warn_astroNN_distances()
astroNNdata= astroNNDistances()
data= _add_astroNN_distances(data,astroNNdata)
if use_astroNN or kwargs.get('astroNN',False) or use_astroNN_ages:
_warn_astroNN_ages()
astroNNdata= astroNNAges()
data= _add_astroNN_ages(data,astroNNdata)
if raw: return data
#Remove duplicates, cache
if rmdups:
dupsFilename= path.allStarPath(mjd=mjd).replace('.fits','-nodups.fits')
if os.path.exists(dupsFilename):
data= fitsread(dupsFilename)
else:
sys.stdout.write('\r'+"Removing duplicates (might take a while) and caching the duplicate-free file ...\r")
sys.stdout.flush()
data= remove_duplicates(data)
#Cache this file for subsequent use of rmdups
fitswrite(dupsFilename,data,clobber=True)
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
if not xmatch is None:
from gaia_tools.load import _xmatch_cds
if rmdups:
matchFilePath= dupsFilename
else:
matchFilePath= filePath
if use_astroNN_ages:
matchFilePath= matchFilePath.replace('rc-','rc-astroNN-ages-')
ma,mai= _xmatch_cds(data,xmatch,filePath,**kwargs)
data= data[mai]
#Some cuts
if rmcommissioning:
try:
indx= numpy.array(['apogee.n.c'.encode('utf-8') in s for s in data['APSTAR_ID']])
indx+= numpy.array(['apogee.s.c'.encode('utf-8') in s for s in data['APSTAR_ID']])
except TypeError:
indx= numpy.array(['apogee.n.c' in s for s in data['APSTAR_ID']])
indx+= numpy.array(['apogee.s.c' in s for s in data['APSTAR_ID']])
data= data[True^indx]
if not xmatch is None: ma= ma[True^indx]
if rmnovisits:
indx= numpy.array([s.strip() != '' for s in data['VISITS']])
data= data[indx]
if not xmatch is None: ma= ma[indx]
if main:
indx= mainIndx(data)
data= data[indx]
if not xmatch is None: ma= ma[indx]
if akvers.lower() == 'targ':
aktag= 'AK_TARG'
elif akvers.lower() == 'wise':
aktag= 'AK_WISE'
if ak:
if not xmatch is None: ma= ma[True^numpy.isnan(data[aktag])]
data= data[True^numpy.isnan(data[aktag])]
if not xmatch is None: ma= ma[(data[aktag] > -50.)]
data= data[(data[aktag] > -50.)]
if exclude_star_bad:
if not xmatch is None: ma= ma[(data['ASPCAPFLAG'] & 2**23) == 0]
data= data[(data['ASPCAPFLAG'] & 2**23) == 0]
if exclude_star_warn:
if not xmatch is None: ma= ma[(data['ASPCAPFLAG'] & 2**7) == 0]
data= data[(data['ASPCAPFLAG'] & 2**7) == 0]
#Add dereddened J, H, and Ks
aj= data[aktag]*2.5
ah= data[aktag]*1.55
if _ESUTIL_LOADED:
data= esutil.numpy_util.add_fields(data,[('J0', float),
('H0', float),
('K0', float)])
data['J0']= data['J']-aj
data['H0']= data['H']-ah
data['K0']= data['K']-data[aktag]
data['J0'][(data[aktag] <= -50.)]= -9999.9999
data['H0'][(data[aktag] <= -50.)]= -9999.9999
data['K0'][(data[aktag] <= -50.)]= -9999.9999
else:
warnings.warn("Extinction-corrected J,H,K not added because esutil is not installed",RuntimeWarning)
#Add distances
if adddist and _ESUTIL_LOADED:
dist= fitsread(path.distPath(),1)
h=esutil.htm.HTM()
m1,m2,d12 = h.match(dist['RA'],dist['DEC'],
data['RA'],data['DEC'],
2./3600.,maxmatch=1)
data= data[m2]
if not xmatch is None: ma= ma[m2]
dist= dist[m1]
distredux= path._redux_dr()
if distredux.lower() == 'v302' or distredux.lower() == path._DR10REDUX:
data= esutil.numpy_util.add_fields(data,[('DM05', float),
('DM16', float),
('DM50', float),
('DM84', float),
('DM95', float),
('DMPEAK', float),
('DMAVG', float),
('SIG_DM', float),
('DIST_SOL', float),
('SIG_DISTSOL', float)])
data['DM05']= dist['DM05']
data['DM16']= dist['DM16']
data['DM50']= dist['DM50']
data['DM84']= dist['DM84']
data['DM95']= dist['DM95']
data['DMPEAK']= dist['DMPEAK']
data['DMAVG']= dist['DMAVG']
data['SIG_DM']= dist['SIG_DM']
data['DIST_SOL']= dist['DIST_SOL']/1000.
data['SIG_DISTSOL']= dist['SIG_DISTSOL']/1000.
elif distredux.lower() == path._DR11REDUX:
data= esutil.numpy_util.add_fields(data,[('DISO', float),
('DMASS', float),
('DISO_GAL', float),
('DMASS_GAL', float)])
data['DISO']= dist['DISO'][:,1]
data['DMASS']= dist['DMASS'][:,1]
data['DISO_GAL']= dist['DISO_GAL'][:,1]
data['DMASS_GAL']= dist['DMASS_GAL'][:,1]
elif distredux.lower() == path._DR12REDUX:
data= esutil.numpy_util.add_fields(data,[('HIP_PLX', float),
('HIP_E_PLX', float),
('RC_DIST', float),
('APOKASC_DIST_DIRECT', float),
('BPG_DIST1_MEAN', float),
('HAYDEN_DIST_PEAK', float),
('SCHULTHEIS_DIST', float)])
data['HIP_PLX']= dist['HIP_PLX']
data['HIP_E_PLX']= dist['HIP_E_PLX']
data['RC_DIST']= dist['RC_dist_pc']
data['APOKASC_DIST_DIRECT']= dist['APOKASC_dist_direct_pc']/1000.
data['BPG_DIST1_MEAN']= dist['BPG_dist1_mean']
data['HAYDEN_DIST_PEAK']= 10.**(dist['HAYDEN_distmod_PEAK']/5.-2.)
data['SCHULTHEIS_DIST']= dist['SCHULTHEIS_dist']
elif adddist:
warnings.warn("Distances not added because matching requires the uninstalled esutil module",RuntimeWarning)
if _ESUTIL_LOADED and (path._APOGEE_REDUX.lower() == 'current' \
or 'l3' in path._APOGEE_REDUX.lower() \
or int(path._APOGEE_REDUX[1:]) > 600):
data= esutil.numpy_util.add_fields(data,[('METALS', float),
('ALPHAFE', float)])
data['METALS']= data['PARAM'][:,paramIndx('metals')]
data['ALPHAFE']= data['PARAM'][:,paramIndx('alpha')]
if not xmatch is None:
return (data,ma)
else:
return data
def synth(*args, **kwargs):
"""
NAME:
synth
PURPOSE:
Generate model APOGEE spectra using MOOG: this is a general routine that generates the non-continuum-normalized spectrum, convolves with the LSF and macrotubulence, and optionally continuum normalizes the output; use 'moogsynth' for a direct interface to MOOG
INPUT ARGUMENTS:
lists with abundances wrt the atmosphere (they don't all have to have the same length, missing ones are filled in with zeros):
[Atomic number1,diff1_1,diff1_2,diff1_3,...,diff1_N]
[Atomic number2,diff2_1,diff2_2,diff2_3,...,diff2_N]
...
[Atomic numberM,diffM_1,diffM_2,diffM_3,...,diffM_N]
INPUT KEYWORDS:
LSF:
lsf= ('all') LSF to convolve with; output of apogee.spec.lsf.eval; sparsify for efficiency; if 'all' or 'combo' a pre-computed version will be downloaded from the web
Either:
xlsf= (None) pixel offset grid on which the LSF is computed (see apogee.spec.lsf.eval); unnecessary if lsf=='all' or 'combo'
dxlsf= (None) spacing of pixel offsets
vmacro= (6.) macroturbulence to apply
CONTINUUM:
cont= ('aspcap') continuum-normalization to apply:
None: no continuum normalization
'true': Use the true continuum
'aspcap': Use the continuum normalization method of ASPCAP DR12
'cannon': Normalize using continuum pixels derived from the Cannon
SYNTHESIS:
linelist= (None) linelist to use; can be set to the path of a linelist file or to the name of an APOGEE linelist
run_weedout= (False) if True, run MOOG weedout on the linelist first
wmin, wmax, dw, width= (15000.000, 17000.000, 0.10000000, 7.0000000) spectral synthesis limits, step, and width of calculation (see MOOG)
lib= ('kurucz_filled') spectral library
MODEL ATMOSPHERE PARAMETERS:
Specify one of the following:
(a) modelatm= (None) can be set to the filename of a model atmosphere or to a model-atmosphere instance (if filename, needs to end in .mod)
(b) parameters of a KURUCZ model atmosphere:
(1) teff= (4500) Teff
logg= (2.5) logg
metals= (0.) metallicity
cm= (0.) carbon-enhancement
am= (0.) alpha-enhancement
(2) fparam= standard ASPCAP output format
lib= ('kurucz_filled') model atmosphere library
dr= (None) use model atmospheres from this data release
vmicro= (2.) microturbulence (only used if the MOOG-formatted atmosphere is not found) (can also be part of fparam)
MISCELLANEOUS:
dr= return the path corresponding to this data release
OUTPUT:
spectra (nspec,nwave)
HISTORY:
2015-03-15 - Written - Bovy (IAS)
"""
run_weedout = kwargs.pop('run_weedout', False)
# Check that we have the LSF and store the relevant keywords
lsf = kwargs.pop('lsf', 'all')
if isinstance(lsf, str):
xlsf, lsf = aplsf._load_precomp(dr=kwargs.get('dr', None), fiber=lsf)
dxlsf = None
else:
xlsf = kwargs.pop('xlsf', None)
dxlsf = kwargs.pop('dxlsf', None)
if xlsf is None and dxlsf is None:
raise ValueError(
'xlsf= or dxlsf= input needs to be given if the LSF is given as an array'
)
vmacro = kwargs.pop('vmacro', 6.)
# Parse continuum-normalization keywords
cont = kwargs.pop('cont', 'aspcap')
# Setup the model atmosphere
modelatm = kwargs.pop('modelatm', None)
tmpModelAtmDir = False
# Parse fparam, if present
fparam = kwargs.pop('fparam', None)
if not fparam is None:
kwargs['teff'] = fparam[paramIndx('TEFF')]
kwargs['logg'] = fparam[paramIndx('LOGG')]
kwargs['metals'] = fparam[paramIndx('METALS')]
kwargs['am'] = fparam[paramIndx('ALPHA')]
kwargs['cm'] = fparam[paramIndx('C')]
kwargs['vm'] = 10.**fparam[paramIndx('LOG10VDOP')]
if modelatm is None: # Setup a model atmosphere
modelatm = atlas9.Atlas9Atmosphere(teff=kwargs.get('teff', 4500.),
logg=kwargs.get('logg', 2.5),
metals=kwargs.get('metals', 0.),
am=kwargs.get('am', 0.),
cm=kwargs.get('cm', 0.),
dr=kwargs.get('dr', None))
if isinstance(modelatm, str) and os.path.exists(modelatm):
modelfilename = modelatm
elif isinstance(modelatm, str):
raise ValueError('modelatm= input is a non-existing filename')
else: # model atmosphere instance
# Need to write this instance to a file; we will run in a temp
# subdirectory of the current directory
tmpDir = tempfile.mkdtemp(dir=os.getcwd())
tmpModelAtmDir = True # need to remove this later
modelfilename = os.path.join(tmpDir, 'modelatm.mod')
modelatm.writeto(modelfilename)
kwargs['modelatm'] = modelfilename
try:
# Check whether a MOOG version of the model atmosphere exists
if not os.path.exists(modelfilename.replace('.mod', '.org')):
# Convert to MOOG format
convert_modelAtmosphere(**kwargs)
# Run weedout on the linelist first if requested
if run_weedout:
linelistfilename = modelfilename.replace('.mod', '.lines')
if not os.path.exists(linelistfilename):
weedout(**kwargs)
kwargs['linelist'] = linelistfilename
# Run MOOG synth for all abundances
if len(args) == 0: #special case that there are *no* differences
args = ([26, 0.], )
nsynths = numpy.array([len(args[ii]) - 1 for ii in range(len(args))])
nsynth = numpy.amax(nsynths) #Take the longest abundance list
nmoogwav= int((kwargs.get('wmax',_WMAX_DEFAULT)\
-kwargs.get('wmin',_WMIN_DEFAULT))\
/kwargs.get('dw',_DW_DEFAULT)+1)
out = numpy.empty((nsynth, nmoogwav))
# Check whether the number of syntheses is > 5 and run multiple
# MOOG instances if necessary, bc MOOG only does 5 at a time
ninstances = int(numpy.ceil(nsynth / 5.))
for ii in range(ninstances):
newargs = ()
for jj in range(len(args)):
tab = [args[jj][0]]
if len(args[jj][5 * ii + 1:5 * (ii + 1) + 1]) > 0:
tab.extend(args[jj][5 * ii + 1:5 * (ii + 1) + 1])
newargs = newargs + (tab, )
out[5 * ii:5 * (ii + 1)] = moogsynth(*newargs, **kwargs)[1]
# We'll grab the wavelength grid from the continuum below
# Now compute the continuum and multiply each c-norm spectrum with it
mwav, cflux = moogsynth(doflux=True, **kwargs)
except:
raise
finally:
if tmpModelAtmDir: # need to remove this temporary directory
os.remove(modelfilename)
moogmodelfilename = modelfilename.replace('.mod', '.org')
if os.path.exists(moogmodelfilename):
os.remove(moogmodelfilename)
if run_weedout:
os.remove(modelfilename.replace('.mod', '.lines'))
os.rmdir(tmpDir)
out *= numpy.tile(cflux, (nsynth, 1))
# Now convolve with the LSF
out = aplsf.convolve(mwav,
out,
lsf=lsf,
xlsf=xlsf,
dxlsf=dxlsf,
vmacro=vmacro)
# Now continuum-normalize
if cont.lower() == 'true':
# Get the true continuum on the apStar wavelength grid
apWave = apStarWavegrid()
baseline = numpy.polynomial.Polynomial.fit(mwav, cflux, 4)
ip = interpolate.InterpolatedUnivariateSpline(mwav,
cflux / baseline(mwav),
k=3)
cflux = baseline(apWave) * ip(apWave)
# Divide it out
out /= numpy.tile(cflux, (nsynth, 1))
elif not cont is None:
cflux = apcont.fit(out, numpy.ones_like(out), type=cont)
out[cflux > 0.] /= cflux[cflux > 0.]
out[cflux <= 0.] = numpy.nan
return out
def plot_afe_spectra(savename,plotname):
# Load the data
data= define_rcsample.get_rcsample()
data= data[data['SNR'] > 200.]
fehindx= (data['FE_H'] <= -0.35)*(data['FE_H'] > -0.45)
fehdata= data[fehindx]
# First compute the residuals and do the EM-PCA smoothing
if not os.path.exists(savename):
nspec= len(fehdata)
spec= numpy.zeros((nspec,7214))
specerr= numpy.zeros((nspec,7214))
for ii in range(nspec):
sys.stdout.write('\r'+"Loading spectrum %i / %i ...\r" % (ii+1,nspec))
sys.stdout.flush()
spec[ii]= apread.aspcapStar(fehdata['LOCATION_ID'][ii],
fehdata['APOGEE_ID'][ii],
ext=1,header=False,aspcapWavegrid=True)
specerr[ii]= apread.aspcapStar(fehdata['LOCATION_ID'][ii],
fehdata['APOGEE_ID'][ii],
ext=2,header=False,
aspcapWavegrid=True)
teffs= fehdata['FPARAM'][:,paramIndx('teff')]
loggs= fehdata['FPARAM'][:,paramIndx('logg')]
metals= fehdata[define_rcsample._FEHTAG]
cf, s, r= apcannon.quadfit(spec,specerr,
teffs-4800.,loggs-2.85,metals+0.3,
return_residuals=True)
pr= numpy.zeros_like(r)
# Deal w/ bad data
_MAXERR= 0.02
npca= 8
pca_input= r
pca_weights= (1./specerr**2.)
pca_weights[pca_weights < 1./_MAXERR**2.]= 0.
nanIndx= numpy.isnan(pca_input) + numpy.isnan(pca_weights)
pca_weights[nanIndx]= 0.
pca_input[nanIndx]= 0.
# Run EM-PCA
m= empca.empca(pca_input,pca_weights,nvec=npca,niter=25)#,silent=False)
for jj in range(nspec):
for kk in range(npca):
pr[jj]+= m.coeff[jj,kk]*m.eigvec[kk]
save_pickles(savename,pr,r,cf)
else:
with open(savename,'rb') as savefile:
pr= pickle.load(savefile)
# Now plot the various elements
colormap= cm.seismic
colorFunc= lambda afe: afe/0.25
widths= [3.5,2.]
yranges= [[-0.05,0.02],[-0.03,0.01]]
for ee, elem in enumerate(['S','Ca1']):
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),elem,)
kwargs= {'markLines':ii==4,
'yrange':yranges[ee],
'ylabel':'',
'cleanZero':False,
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':widths[ee]}
if ii>0: kwargs.pop('fig_width')
splot.windows(*args,**kwargs)
bovy_plot.bovy_end_print(plotname.replace('ELEM',
elem.lower().capitalize()))
# Also do Mg
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startindxs':[3012,3120,3990],
'endindxs':[3083,3158,4012],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':[15745.017,15753.189,15770.055,15958.836],
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':4.5,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{Mg}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','Mg'))
# Also do Si
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startindxs':[4469, 4624,5171, 7205, 7843],
'endindxs':[4488, 4644,5182, 7243, 7871],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':apwindow.lines('Si'),
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':6.,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{Si}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','Si2'))
# Also do Oxygen
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startlams':[15558,16242,16536,16720],
'endlams':[15566,16250,16544,16728],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':[15562,16246,16539,16723.5],
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':5.,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{O}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','O'))
# Also do Ti
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startindxs':[1116,2100,2899],
'endindxs':[1146,2124,2922],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':apwindow.lines('Ti'),
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':3.5,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{Ti}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','Ti'))
return None
def allStar(rmcommissioning=True,
main=False,
exclude_star_bad=False,
exclude_star_warn=False,
ak=True,
akvers='targ',
rmnovisits=False,
use_astroNN=False,
use_astroNN_abundances=False,
use_astroNN_distances=False,
use_astroNN_ages=False,
adddist=False,
distredux=None,
rmdups=False,
raw=False,
mjd=58104,
xmatch=None,
**kwargs):
"""
NAME:
allStar
PURPOSE:
read the 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
use_astroNN= (False) if True, swap in astroNN (Leung & Bovy 2019a) parameters (get placed in, e.g., TEFF and TEFF_ERR), astroNN distances (Leung & Bovy 2019b), and astroNN ages (Mackereth, Bovy, Leung, et al. (2019)
use_astroNN_abundances= (False) only swap in astroNN parameters and abundances, not distances and ages
use_astroNN_distances= (False) only swap in astroNN distances, not parameters and abundances and ages
use_astroNN_ages= (False) only swap in astroNN ages, not parameters and abundances and distances
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
mjd= (58104) MJD of version for monthly internal pipeline runs
xmatch= (None) uses gaia_tools.xmatch.cds to x-match to an external catalog (eg., Gaia DR2 for xmatch='vizier:I/345/gaia2') and caches the result for re-use; requires jobovy/gaia_tools
+gaia_tools.xmatch.cds keywords
OUTPUT:
allStar data[,xmatched table]
HISTORY:
2013-09-06 - Written - Bovy (IAS)
2018-01-22 - Edited for new monthly pipeline runs - Bovy (UofT)
2018-05-09 - Add xmatch - Bovy (UofT)
2018-10-20 - Add use_astroNN option - Bovy (UofT)
2018-02-15 - Add astroNN distances and corresponding options - Bovy (UofT)
2018-02-16 - Add astroNN ages and corresponding options - Bovy (UofT)
"""
filePath = path.allStarPath(mjd=mjd)
if not os.path.exists(filePath):
download.allStar(mjd=mjd)
#read allStar file
data = fitsread(path.allStarPath(mjd=mjd))
#Add astroNN? astroNN file matched line-by-line to allStar, so match here
# [ages file not matched line-by-line]
if use_astroNN or kwargs.get('astroNN', False) or use_astroNN_abundances:
_warn_astroNN_abundances()
astroNNdata = astroNN()
data = _swap_in_astroNN(data, astroNNdata)
if use_astroNN or kwargs.get('astroNN', False) or use_astroNN_distances:
_warn_astroNN_distances()
astroNNdata = astroNNDistances()
data = _add_astroNN_distances(data, astroNNdata)
if use_astroNN or kwargs.get('astroNN', False) or use_astroNN_ages:
_warn_astroNN_ages()
astroNNdata = astroNNAges()
data = _add_astroNN_ages(data, astroNNdata)
if raw: return data
#Remove duplicates, cache
if rmdups:
dupsFilename = path.allStarPath(mjd=mjd).replace(
'.fits', '-nodups.fits')
if os.path.exists(dupsFilename):
data = fitsread(dupsFilename)
else:
sys.stdout.write(
'\r' +
"Removing duplicates (might take a while) and caching the duplicate-free file ...\r"
)
sys.stdout.flush()
data = remove_duplicates(data)
#Cache this file for subsequent use of rmdups
fitswrite(dupsFilename, data, clobber=True)
sys.stdout.write('\r' + _ERASESTR + '\r')
sys.stdout.flush()
if not xmatch is None:
from gaia_tools.load import _xmatch_cds
if rmdups:
matchFilePath = dupsFilename
else:
matchFilePath = filePath
if use_astroNN_ages:
matchFilePath = matchFilePath.replace('rc-', 'rc-astroNN-ages-')
ma, mai = _xmatch_cds(data, xmatch, filePath, **kwargs)
data = data[mai]
#Some cuts
if rmcommissioning:
try:
indx = numpy.array(
['apogee.n.c'.encode('utf-8') in s for s in data['APSTAR_ID']])
indx += numpy.array(
['apogee.s.c'.encode('utf-8') in s for s in data['APSTAR_ID']])
except TypeError:
indx = numpy.array(['apogee.n.c' in s for s in data['APSTAR_ID']])
indx += numpy.array(['apogee.s.c' in s for s in data['APSTAR_ID']])
data = data[True ^ indx]
if not xmatch is None: ma = ma[True ^ indx]
if rmnovisits:
indx = numpy.array([s.strip() != '' for s in data['VISITS']])
data = data[indx]
if not xmatch is None: ma = ma[indx]
if main:
indx = mainIndx(data)
data = data[indx]
if not xmatch is None: ma = ma[indx]
if akvers.lower() == 'targ':
aktag = 'AK_TARG'
elif akvers.lower() == 'wise':
aktag = 'AK_WISE'
if ak:
if not xmatch is None: ma = ma[True ^ numpy.isnan(data[aktag])]
data = data[True ^ numpy.isnan(data[aktag])]
if not xmatch is None: ma = ma[(data[aktag] > -50.)]
data = data[(data[aktag] > -50.)]
if exclude_star_bad:
if not xmatch is None: ma = ma[(data['ASPCAPFLAG'] & 2**23) == 0]
data = data[(data['ASPCAPFLAG'] & 2**23) == 0]
if exclude_star_warn:
if not xmatch is None: ma = ma[(data['ASPCAPFLAG'] & 2**7) == 0]
data = data[(data['ASPCAPFLAG'] & 2**7) == 0]
#Add dereddened J, H, and Ks
aj = data[aktag] * 2.5
ah = data[aktag] * 1.55
if _ESUTIL_LOADED:
data = esutil.numpy_util.add_fields(data,
[('J0', float), ('H0', float),
('K0', float)])
data['J0'] = data['J'] - aj
data['H0'] = data['H'] - ah
data['K0'] = data['K'] - data[aktag]
data['J0'][(data[aktag] <= -50.)] = -9999.9999
data['H0'][(data[aktag] <= -50.)] = -9999.9999
data['K0'][(data[aktag] <= -50.)] = -9999.9999
else:
warnings.warn(
"Extinction-corrected J,H,K not added because esutil is not installed",
RuntimeWarning)
#Add distances
if adddist and _ESUTIL_LOADED:
dist = fitsread(path.distPath(), 1)
h = esutil.htm.HTM()
m1, m2, d12 = h.match(dist['RA'],
dist['DEC'],
data['RA'],
data['DEC'],
2. / 3600.,
maxmatch=1)
data = data[m2]
if not xmatch is None: ma = ma[m2]
dist = dist[m1]
distredux = path._redux_dr()
if distredux.lower() == 'v302' or distredux.lower() == path._DR10REDUX:
data = esutil.numpy_util.add_fields(data, [('DM05', float),
('DM16', float),
('DM50', float),
('DM84', float),
('DM95', float),
('DMPEAK', float),
('DMAVG', float),
('SIG_DM', float),
('DIST_SOL', float),
('SIG_DISTSOL', float)])
data['DM05'] = dist['DM05']
data['DM16'] = dist['DM16']
data['DM50'] = dist['DM50']
data['DM84'] = dist['DM84']
data['DM95'] = dist['DM95']
data['DMPEAK'] = dist['DMPEAK']
data['DMAVG'] = dist['DMAVG']
data['SIG_DM'] = dist['SIG_DM']
data['DIST_SOL'] = dist['DIST_SOL'] / 1000.
data['SIG_DISTSOL'] = dist['SIG_DISTSOL'] / 1000.
elif distredux.lower() == path._DR11REDUX:
data = esutil.numpy_util.add_fields(data, [('DISO', float),
('DMASS', float),
('DISO_GAL', float),
('DMASS_GAL', float)])
data['DISO'] = dist['DISO'][:, 1]
data['DMASS'] = dist['DMASS'][:, 1]
data['DISO_GAL'] = dist['DISO_GAL'][:, 1]
data['DMASS_GAL'] = dist['DMASS_GAL'][:, 1]
elif distredux.lower() == path._DR12REDUX:
data = esutil.numpy_util.add_fields(
data, [('HIP_PLX', float), ('HIP_E_PLX', float),
('RC_DIST', float), ('APOKASC_DIST_DIRECT', float),
('BPG_DIST1_MEAN', float), ('HAYDEN_DIST_PEAK', float),
('SCHULTHEIS_DIST', float)])
data['HIP_PLX'] = dist['HIP_PLX']
data['HIP_E_PLX'] = dist['HIP_E_PLX']
data['RC_DIST'] = dist['RC_dist_pc']
data[
'APOKASC_DIST_DIRECT'] = dist['APOKASC_dist_direct_pc'] / 1000.
data['BPG_DIST1_MEAN'] = dist['BPG_dist1_mean']
data['HAYDEN_DIST_PEAK'] = 10.**(dist['HAYDEN_distmod_PEAK'] / 5. -
2.)
data['SCHULTHEIS_DIST'] = dist['SCHULTHEIS_dist']
elif adddist:
warnings.warn(
"Distances not added because matching requires the uninstalled esutil module",
RuntimeWarning)
if _ESUTIL_LOADED and (path._APOGEE_REDUX.lower() == 'current' \
or 'l3' in path._APOGEE_REDUX.lower() \
or int(path._APOGEE_REDUX[1:]) > 600):
data = esutil.numpy_util.add_fields(data, [('METALS', float),
('ALPHAFE', float)])
data['METALS'] = data['PARAM'][:, paramIndx('metals')]
data['ALPHAFE'] = data['PARAM'][:, paramIndx('alpha')]
if not xmatch is None:
return (data, ma)
else:
return data
def mcmc(spec,specerr,
fparam=None,
teff=4750.,logg=2.5,metals=0.,am=0.,nm=0.,cm=0.,vm=None,
nsamples=1000,nwalkers=40,
initsig=[20.,0.05,0.025,0.025,0.025,0.025,0.025],
fixteff=False,fixlogg=False,fixmetals=False,fixam=False,fixcm=False,
fixnm=False,fixvm=False,
lib='GK',pca=True,sixd=True,dr=None,
inter=3,f_format=1,f_access=None,
verbose=False):
"""
NAME:
mcmc
PURPOSE:
Perform MCMC of the 6/7 parameter global fit of model spectra to a given data spectrum
INPUT:
Either:
(1) location ID - single or list/array of location IDs
APOGEE ID - single or list/array of APOGEE IDs; loads aspcapStar
(2) spec - spectrum: can be (nwave) or (nspec,nwave)
specerr - spectrum errors: can be (nwave) or (nspec,nwave)
Input parameters (can be 1D arrays)
Either:
(1) fparam= (None) output of ferre.fit
(2) teff= (4750.) Effective temperature (K)
logg= (2.5) log10 surface gravity / cm s^-2
metals= (0.) overall metallicity
am= (0.) [alpha/M]
nm= (0.) [N/M]
cm= (0.) [C/M]
vm= if using the 7D library, also specify the microturbulence
MCMC options:
nsamples= (1000) number of samples to get
nwalkers= (40) number of ensemble walkers to use in the MCMC
initsig= std. dev. of the initial ball of walkers around
[Teff,logg,log10vmicro,metals,cm,nm,am]
specify log10vmicro even when using a 6D library
fixteff= (True) if True, fix teff at the input value
fixlogg= (True) if True, fix logg at the input value
fixvm= (True) if True, fix vm at the input value (only if sixd is False)
fixmetals= (None) if True, fix metals at the input value
fixam= (None) if True, fix am at the input value
fixcm= (None) if True, fix cm at the input value
fixnm= (None) if True, fix nm at the input value
Library options:
lib= ('GK') spectral library
pca= (True) if True, use a PCA compressed library
sixd= (True) if True, use the 6D library (w/o vm)
dr= data release
FERRE options:
inter= (3) order of the interpolation
f_format= (1) file format (0=ascii, 1=unf)
f_access= (None) 0: load whole library, 1: use direct access (for small numbers of interpolations), None: automatically determine a good value (currently, 1)
verbose= (False) if True, run FERRE in verbose mode
OUTPUT:
TBD
HISTORY:
2015-04-08 - Written - Bovy (IAS)
"""
# Parse fparam
if not fparam is None:
teff= fparam[:,paramIndx('TEFF')]
logg= fparam[:,paramIndx('LOGG')]
metals= fparam[:,paramIndx('METALS')]
am= fparam[:,paramIndx('ALPHA')]
nm= fparam[:,paramIndx('N')]
cm= fparam[:,paramIndx('C')]
if sixd:
vm= None
else:
vm= fparam[:,paramIndx('LOG10VDOP')]
# Make sure the Teff etc. have the right dimensionality
if len(spec.shape) == 1:
nspec= 1
else:
raise ValueError("apogee.modelspec.ferre.mcmc only works for a single spectrum")
nspec= spec.shape[0]
if nspec > 1 and isinstance(teff,float):
teff= teff*numpy.ones(nspec)
if nspec > 1 and isinstance(logg,float):
logg= logg*numpy.ones(nspec)
if nspec > 1 and isinstance(metals,float):
metals= metals*numpy.ones(nspec)
if nspec > 1 and isinstance(am,float):
am= am*numpy.ones(nspec)
if nspec > 1 and isinstance(nm,float):
nm= nm*numpy.ones(nspec)
if nspec > 1 and isinstance(cm,float):
cm= cm*numpy.ones(nspec)
if nspec > 1 and not vm is None and isinstance(vm,float):
vm= vm*numpy.ones(nspec)
if dr is None: dr= appath._default_dr()
# Setup the walkers
ndim= 7-sixd-fixvm+sixd*fixvm-fixteff-fixlogg-fixmetals-fixam-fixcm-fixnm
p0= []
for ii in range(nwalkers):
tp0= []
# Teff
if not fixteff:
tteff= teff+numpy.random.normal()*initsig[0]
if tteff > 6000.: tteff= 6000.
if tteff < 3500.: tteff= 3500.
tp0.append(tteff)
# logg
if not fixlogg:
tlogg= logg+numpy.random.normal()*initsig[1]
if logg > 5.: tlogg= 5.
if logg < 0.: tlogg= 0.
tp0.append(tlogg)
# log10vmicro
if not (fixvm or sixd):
tvm= numpy.log10(vm)+numpy.random.normal()*initsig[2]
if tvm > numpy.log10(8.0): tvm= numpy.log10(8.0)
if tvm < numpy.log10(0.5): tvm= numpy.log10(0.5)
tp0.append(tvm)
# metals
if not fixmetals:
tmetals= metals+numpy.random.normal()*initsig[3]
if metals > 0.5: tmetals= 0.5
if metals < -2.5: tmetals= -2.5
tp0.append(tmetals)
# cm
if not fixcm:
tcm= cm+numpy.random.normal()*initsig[4]
if cm > 1.0: tcm= 1.0
if cm < -1.0: tcm= -1.
tp0.append(tcm)
# nm
if not fixnm:
tnm= nm+numpy.random.normal()*initsig[5]
if nm > 0.5: tnm= 1.0
if nm < -1.0: tnm= -1.0
tp0.append(tnm)
# am
if not fixam:
tam= am+numpy.random.normal()*initsig[6]
if am > 0.5: tam= 1.0
if am < -1.0: tam= -1.0
tp0.append(tam)
p0.append(numpy.array(tp0)[:,0])
p0= numpy.array(p0)
# Prepare the data
dspec= numpy.tile(spec,(nwalkers,1))
dspecerr= numpy.tile(specerr,(nwalkers,1))
# Run MCMC
sampler= apogee.util.emcee.EnsembleSampler(nwalkers,ndim,_mcmc_lnprob,
args=[dspec,dspecerr,
teff,logg,vm,metals,
cm,nm,am,
fixteff,fixlogg,fixvm,
fixmetals,fixcm,fixnm,
fixam,sixd,pca,
dr,lib,
inter,f_format,f_access])
# Burn-in 10% of nsamples
pos, prob, state = sampler.run_mcmc(p0,nsamples//10)
sampler.reset()
# Run main MCMC
sampler.run_mcmc(pos,nsamples)
return sampler.flatchain
def elemfitall(*args,**kwargs):
"""
NAME:
elemfitall
PURPOSE:
Fit a model spectrum to a given data spectrum for all element windows
INPUT:
Either:
(1) location ID - single or list/array of location IDs
APOGEE ID - single or list/array of APOGEE IDs; loads aspcapStar
(2) spec - spectrum: can be (nwave) or (nspec,nwave)
specerr - spectrum errors: can be (nwave) or (nspec,nwave)
estimate_err= (False) if True, estimate the error from Delta chi^2=1; only works for errors <~ 0.3 dex, code returns numpy.nan when error is larger
Input parameters (can be 1D arrays); only used when init=0
Either:
(1) fparam= (None) output of ferre.fit
(2) teff= (4750.) Effective temperature (K)
logg= (2.5) log10 surface gravity / cm s^-2
metals= (0.) overall metallicity
am= (0.) [alpha/M]
nm= (0.) [N/M]
cm= (0.) [C/M]
vm= if using the 7D library, also specify the microturbulence
Fit options:
fixteff= (True) if True, fix teff at the input value
fixlogg= (True) if True, fix logg at the input value
fixvm= (True) if True, fix vm at the input value (only if sixd is False)
The following are set to False based on the element being fit (C -> fixcm=False, N -> fixnm=False, O,Mg,S,Si,Ca,Ti -> fixam=False, rest -> fixmetals=False)
fixmetals= (None) if True, fix metals at the input value
fixam= (None) if True, fix am at the input value
fixcm= (None) if True, fix cm at the input value
fixnm= (None) if True, fix nm at the input value
Library options:
lib= ('GK') spectral library
pca= (True) if True, use a PCA compressed library
sixd= (True) if True, use the 6D library (w/o vm)
dr= data release
FERRE options:
inter= (3) order of the interpolation
errbar= (1) method for calculating the error bars
indini= ([1,1,1,2,2,3]) how to initialize the search (int or array/list with ndim entries)
init= (0) if 0, initialize the search at the parameters in the pfile
f_format= (1) file format (0=ascii, 1=unf)
f_access= (None) 0: load whole library, 1: use direct access (for small numbers of interpolations), None: automatically determine a good value (currently, 1)
Output options:
offile= (None) if offile is set, the FERRE OFFILE is saved to this file, otherwise this file is removed
verbose= (False) if True, run FERRE in verbose mode
OUTPUT:
dictionary with best-fit ELEM_H (nspec), contains e_ELEM when estimate_err; this does not include the (potentially correlated) error on METALS for those elements fit relative to Fe
HISTORY:
2015-03-01 - Written - Bovy (IAS)
"""
# METALS for normalization
if not kwargs.get('fparam',None) is None:
metals= kwargs.get('fparam')[:,paramIndx('METALS')]
else:
metals= kwargs.get('metals',0.)
# Run through and fit all elements
out= {}
for elem in _ELEM_SYMBOL:
targs= args+(elem.capitalize(),)
tefit= elemfit(*targs,**kwargs)
if kwargs.get('estimate_err',False):
tefit, terr= tefit
out['e_'+elem.capitalize()]= terr
if elem.lower() == 'c':
tout= tefit[:,paramIndx('C')]+metals
elif elem.lower() == 'n':
tout= tefit[:,paramIndx('N')]+metals
elif elem.lower() in ['o','mg','s','si','ca','ti']:
tout= tefit[:,paramIndx('ALPHA')]+metals
else:
tout= tefit[:,paramIndx('METALS')]
out[elem.capitalize()]= tout
return out
def windows(*args,**kwargs):
"""
NAME:
windows
PURPOSE:
Generate model APOGEE spectra using Turbospectrum in selected wavelength windows (but the whole APOGEE spectral range is returned): this is a general routine that generates the non-continuum-normalized spectrum, convolves with the LSF and macrotubulence, and optionally continuum normalizes the output; use 'turbosynth' for a direct interface to Turbospectrum
INPUT ARGUMENTS:
Windows specification: Provide one of
(1) Element string: the APOGEE windows for this element will be loaded
(2) startindxs, endindxs= start and end indexes of the windows on the apStar wavelength grid
(3) startlams, endlams= start and end wavelengths in \AA
lists with abundance differences wrt the atmosphere (they don't all have to have the same length, missing ones are filled in with zeros):
[Atomic number1,diff1_1,diff1_2,diff1_3,...,diff1_N]
[Atomic number2,diff2_1,diff2_2,diff2_3,...,diff2_N]
...
[Atomic numberM,diffM_1,diffM_2,diffM_3,...,diffM_N]
INPUT KEYWORDS:
BASELINE: you can specify the baseline spectrum and the continuous opacity to not always re-compute it
baseline= baseline c-normalized spectrum on Turbospectrum wavelength grid (obtained from turbosynth)
mwav= Turbospectrum wavelength grid (obtained from turbosynth)
cflux= continuum flux from Turbospectrum
modelopac= (None)
(a) if set to an existing filename: assume babsma_lu has already been run and use this continuous opacity in bsyn_lu
(b) if set to a non-existing filename: store the continuous opacity in this file
Typically, you can obtain these three keywords by doing (kwargs are the keywords you provide to this function as well, and includes modelopac='SOME FILENAME')
>>> baseline= turbosynth(**kwargs)
>>> mwav= baseline[0]
>>> cflux= baseline[2]/baseline[1]
>>> baseline= baseline[1]
LSF:
lsf= ('all') LSF to convolve with; output of apogee.spec.lsf.eval; sparsify for efficiency; if 'all' or 'combo' a pre-computed version will be downloaded from the web
Either:
xlsf= (None) pixel offset grid on which the LSF is computed (see apogee.spec.lsf.eval); unnecessary if lsf=='all' or 'combo'
dxlsf= (None) spacing of pixel offsets
vmacro= (6.) macroturbulence to apply
CONTINUUM:
cont= ('aspcap') continuum-normalization to apply:
None: no continuum normalization
'true': Use the true continuum
'aspcap': Use the continuum normalization method of ASPCAP DR12
'cannon': Normalize using continuum pixels derived from the Cannon
SYNTHESIS:
air= (True) if True, perform the synthesis in air wavelengths (output is still in vacuum); set to False at your own risk, as Turbospectrum expects the linelist in air wavelengths!)
Hlinelist= (None) Hydrogen linelists to use; can be set to the path of a linelist file or to the name of an APOGEE linelist; if None, then we first search for the Hlinedata.vac in the APOGEE linelist directory (if air=False) or we use the internal Turbospectrum Hlinelist (if air=True)
linelist= (None) molecular and atomic linelists to use; can be set to the path of a linelist file or to the name of an APOGEE linelist, or lists of such files; if a single filename is given, the code will first search for files with extensions '.atoms', '.molec' or that start with 'turboatoms.' and 'turbomolec.'
wmin, wmax, dw= (15000.000, 17000.000, 0.10000000, 7.0000000) spectral synthesis limits, step, and width of calculation (see MOOG)
costheta= (1.) cosine of the viewing angle
MODEL ATMOSPHERE PARAMETERS:
Specify one of the following:
(a) modelatm= (None) model-atmosphere instance
(b) parameters of a KURUCZ model atmosphere:
(1) teff= (4500) Teff
logg= (2.5) logg
metals= (0.) metallicity
cm= (0.) carbon-enhancement
am= (0.) alpha-enhancement
(2) fparam= standard ASPCAP output format
lib= ('kurucz_filled') model atmosphere library
vmicro= (2.) microturbulence (only used if the MOOG-formatted atmosphere is not found) (can also be part of fparam)
MISCELLANEOUS:
dr= return the path corresponding to this data release
raw= (False) if True, return the raw turbosynth output
OUTPUT:
spectra (nspec,nwave)
(wavelengths,cont-norm. spectrum, spectrum (nwave)) if raw == True
HISTORY:
2015-04-17 - Written - Bovy (IAS)
"""
# Pop some kwargs
baseline= kwargs.pop('baseline',None)
mwav= kwargs.pop('mwav',None)
cflux= kwargs.pop('cflux',None)
raw= kwargs.pop('raw',False)
# Check that we have the LSF and store the relevant keywords
lsf= kwargs.pop('lsf','all')
if isinstance(lsf,str):
xlsf, lsf= aplsf._load_precomp(dr=kwargs.get('dr',None),fiber=lsf)
dxlsf= None
else:
xlsf= kwargs.pop('xlsf',None)
dxlsf= kwargs.pop('dxlsf',None)
if xlsf is None and dxlsf is None: raise ValueError('xlsf= or dxlsf= input needs to be given if the LSF is given as an array')
vmacro= kwargs.pop('vmacro',6.)
# Parse continuum-normalization keywords
cont= kwargs.pop('cont','aspcap')
# Parse the wavelength regions
apWave= apStarWavegrid()
if isinstance(args[0],str): #element string given
si,ei= apwindow.waveregions(args[0],pad=3,asIndex=True)
args= args[1:]
else:
if isinstance(args[0][0],int): # assume index
si,ei= args[0], args[1]
else: # assume wavelengths in \AA
sl,el= args[0], args[1]
# Convert to index
si, ei= [], []
for s,e in zip(sl,el):
# Find closest index into apWave
si.append(numpy.argmin(numpy.fabs(s-apWave)))
ei.append(numpy.argmin(numpy.fabs(e-apWave)))
args= args[2:]
# Setup the model atmosphere
modelatm= kwargs.pop('modelatm',None)
# Parse fparam, if present
fparam= kwargs.pop('fparam',None)
if not fparam is None:
kwargs['teff']= fparam[0,paramIndx('TEFF')]
kwargs['logg']= fparam[0,paramIndx('LOGG')]
kwargs['metals']= fparam[0,paramIndx('METALS')]
kwargs['am']= fparam[0,paramIndx('ALPHA')]
kwargs['cm']= fparam[0,paramIndx('C')]
kwargs['vmicro']= 10.**fparam[0,paramIndx('LOG10VDOP')]
# Need to pass a model atmosphere instance to turbosynth (needs to be made
# more efficient, because now turbosynth always write the atmosphere
if modelatm is None: # Setup a model atmosphere
modelatm= atlas9.Atlas9Atmosphere(teff=kwargs.get('teff',4500.),
logg=kwargs.get('logg',2.5),
metals=kwargs.get('metals',0.),
am=kwargs.get('am',0.),
cm=kwargs.get('cm',0.),
dr=kwargs.get('dr',None))
if isinstance(modelatm,str) and os.path.exists(modelatm):
raise ValueError('modelatm= input is an existing filename, but you need to give an Atmosphere object instead')
elif isinstance(modelatm,str):
raise ValueError('modelatm= input needs to be an Atmosphere instance')
# Check temperature
if modelatm._teff > 7000.:
warnings.warn('Turbospectrum does not include all necessary physics to model stars hotter than about 7000 K; proceed with caution',RuntimeWarning)
kwargs['modelatm']= modelatm
try:
rmModelopac= False
if not 'modelopac' in kwargs:
rmModelopac= True
kwargs['modelopac']= tempfile.mktemp('mopac')
# Make sure opacity is first calculated over the full wav. range
kwargs['babsma_wmin']= 15000.
kwargs['babsma_wmax']= 17000.
elif 'modelopac' in kwargs and not isinstance(kwargs['modelopac'],str):
raise ValueError('modelopac needs to be set to a filename')
# Run synth for the whole wavelength range as a baseline
if baseline is None or mwav is None or cflux is None:
baseline= turbosynth(**kwargs)
mwav= baseline[0]
cflux= baseline[2]/baseline[1]
baseline= baseline[1]
elif isinstance(baseline,tuple): #probably accidentally gave the entire output of turbosynth
mwav= baseline[0]
cflux= baseline[2]/baseline[1]
baseline= baseline[1]
# Convert the apStarWavegrid windows to turboWavegrid regions
sm,em= [], []
for start,end in zip(si,ei):
if kwargs.get('air',True):
sm.append(numpy.argmin(numpy.fabs(vac2air(apWave[start])-mwav)))
em.append(numpy.argmin(numpy.fabs(vac2air(apWave[end])-mwav)))
else:
sm.append(numpy.argmin(numpy.fabs(apWave[start]-mwav)))
em.append(numpy.argmin(numpy.fabs(apWave[end]-mwav)))
# Run Turbospectrum synth for all abundances and all windows
if len(args) == 0: #special case that there are *no* differences
args= ([26,0.],)
nsynths= numpy.array([len(args[ii])-1 for ii in range(len(args))])
nsynth= numpy.amax(nsynths) #Take the longest abundance list
out= numpy.tile(baseline,(nsynth,1))
# Run all windows
for start, end in zip(sm,em):
kwargs['wmin']= mwav[start]
kwargs['wmax']= mwav[end]+0.001
for ii in range(nsynth):
newargs= ()
for jj in range(len(args)):
tab= [args[jj][0]]
if len(args[jj]) > ii+1:
tab.append(args[jj][ii+1])
newargs= newargs+(tab,)
tmpOut= turbosynth(*newargs,**kwargs)
if numpy.isnan(tmpOut[1][-1]):
# NaN returned for reasons that I don't understand
out[ii,start:end]= tmpOut[1][:-1]
else:
out[ii,start:end+1]= tmpOut[1]
except: raise
finally:
if rmModelopac and os.path.exists(kwargs['modelopac']):
os.remove(kwargs['modelopac'])
kwargs.pop('modelopac')
# Now multiply each continuum-normalized spectrum with the continuum
out*= numpy.tile(cflux,(nsynth,1))
if raw: return (mwav,out/numpy.tile(cflux,(nsynth,1)),out)
# If the synthesis was done in air, convert wavelength array
if kwargs.get('air',True):
mwav= numpy.array([air2vac(w) for w in list(mwav)])
# Now convolve with the LSF
out= aplsf.convolve(mwav,out,
lsf=lsf,xlsf=xlsf,dxlsf=dxlsf,vmacro=vmacro)
# Now continuum-normalize
if cont.lower() == 'true':
# Get the true continuum on the apStar wavelength grid
apWave= apStarWavegrid()
baseline= numpy.polynomial.Polynomial.fit(mwav,cflux,4)
ip= interpolate.InterpolatedUnivariateSpline(mwav,
cflux/baseline(mwav),
k=3)
cflux= baseline(apWave)*ip(apWave)
# Divide it out
out/= numpy.tile(cflux,(nsynth,1))
elif not cont is None:
cflux= apcont.fit(out,numpy.ones_like(out),type=cont)
out[cflux > 0.]/= cflux[cflux > 0.]
out[cflux <= 0.]= numpy.nan
return out
def windows(*args,**kwargs):
"""
NAME:
windows
PURPOSE:
Generate model APOGEE spectra using MOOG in selected wavelength windows (but the whole APOGEE spectral range is returned): this is a general routine that generates the non-continuum-normalized spectrum, convolves with the LSF and macrotubulence, and optionally continuum normalizes the output; use 'moogsynth' for a direct interface to MOOG
INPUT ARGUMENTS:
Windows specification: Provide one of
(1) Element string: the APOGEE windows for this element will be loaded
(2) startindxs, endindxs= start and end indexes of the windows on the apStar wavelength grid
(3) startlams, endlams= start and end wavelengths in \AA
lists with abundance differences wrt the atmosphere (they don't all have to have the same length, missing ones are filled in with zeros):
[Atomic number1,diff1_1,diff1_2,diff1_3,...,diff1_N]
[Atomic number2,diff2_1,diff2_2,diff2_3,...,diff2_N]
...
[Atomic numberM,diffM_1,diffM_2,diffM_3,...,diffM_N]
INPUT KEYWORDS:
BASELINE: you can specify the baseline spectrum to not always re-compute it
baseline= baseline c-normalized spectrum on MOOG wavelength grid (obtained from moogsynth)
mwav= MOOG wavelength grid (obtained from moogsynth)
cflux= continuum flux from MOOG
Typically, you can obtain these three keywords by doing (kwargs are the keywords you provide to this function as well)
>>> baseline= moogsynth(**kwargs)[1]
>>> mwav, cflux= moogsynth(doflux=True,**kwargs)
LSF:
lsf= ('all') LSF to convolve with; output of apogee.spec.lsf.eval; sparsify for efficiency; if 'all' or 'combo' a pre-computed version will be downloaded from the web
Either:
xlsf= (None) pixel offset grid on which the LSF is computed (see apogee.spec.lsf.eval); unnecessary if lsf=='all' or 'combo'
dxlsf= (None) spacing of pixel offsets
vmacro= (6.) macroturbulence to apply
CONTINUUM:
cont= ('aspcap') continuum-normalization to apply:
None: no continuum normalization
'true': Use the true continuum
'aspcap': Use the continuum normalization method of ASPCAP DR12
'cannon': Normalize using continuum pixels derived from the Cannon
SYNTHESIS:
linelist= (None) linelist to use; if this is None, the code looks for a weed-out version of the linelist appropriate for the given model atmosphere
run_weedout= (False) if True, run MOOG weedout on the linelist first
wmin, wmax, dw, width= (15000.000, 17000.000, 0.10000000, 7.0000000) spectral synthesis limits *for the whole spectrum* (not just the windows), step, and width of calculation (see MOOG)
MODEL ATMOSPHERE PARAMETERS:
Specify one of the following:
(a) modelatm= (None) can be set to the filename of a model atmosphere or to a model-atmosphere instance (if filename, needs to end in .mod)
(b) parameters of a KURUCZ model atmosphere:
(1) teff= (4500) Teff
logg= (2.5) logg
metals= (0.) metallicity
cm= (0.) carbon-enhancement
am= (0.) alpha-enhancement
(2) fparam= standard ASPCAP output format (
lib= ('kurucz_filled') model atmosphere library
dr= (None) use model atmospheres from this data release
vmicro= (2.) microturbulence (only used if the MOOG-formatted atmosphere is not found) (can also be part of fparam)
MISCELLANEOUS:
dr= return the path corresponding to this data release
OUTPUT:
spectra (nspec,nwave)
HISTORY:
2015-03-18 - Written - Bovy (IAS)
"""
# Pop some kwargs
run_weedout= kwargs.pop('run_weedout',False)
baseline= kwargs.pop('baseline',None)
mwav= kwargs.pop('mwav',None)
cflux= kwargs.pop('cflux',None)
# Check that we have the LSF and store the relevant keywords
lsf= kwargs.pop('lsf','all')
if isinstance(lsf,str):
xlsf, lsf= aplsf._load_precomp(dr=kwargs.get('dr',None),fiber=lsf)
dxlsf= None
else:
xlsf= kwargs.pop('xlsf',None)
dxlsf= kwargs.pop('dxlsf',None)
if xlsf is None and dxlsf is None: raise ValueError('xlsf= or dxlsf= input needs to be given if the LSF is given as an array')
vmacro= kwargs.pop('vmacro',6.)
# Parse continuum-normalization keywords
cont= kwargs.pop('cont','aspcap')
# Parse the wavelength regions
apWave= apStarWavegrid()
if isinstance(args[0],str): #element string given
si,ei= apwindow.waveregions(args[0],pad=3,asIndex=True)
args= args[1:]
else:
if isinstance(args[0][0],int): # assume index
si,ei= args[0], args[1]
else: # assume wavelengths in \AA
sl,el= args[0], args[1]
# Convert to index
si, ei= [], []
for s,e in zip(sl,el):
# Find closest index into apWave
si.append(numpy.argmin(numpy.fabs(s-apWave)))
ei.append(numpy.argmin(numpy.fabs(e-apWave)))
args= args[2:]
# Setup the model atmosphere
modelatm= kwargs.pop('modelatm',None)
tmpModelAtmDir= False
# Parse fparam, if present
fparam= kwargs.pop('fparam',None)
if not fparam is None:
kwargs['teff']= fparam[0,paramIndx('TEFF')]
kwargs['logg']= fparam[0,paramIndx('LOGG')]
kwargs['metals']= fparam[0,paramIndx('METALS')]
kwargs['am']= fparam[0,paramIndx('ALPHA')]
kwargs['cm']= fparam[0,paramIndx('C')]
kwargs['vm']= 10.**fparam[0,paramIndx('LOG10VDOP')]
if modelatm is None: # Setup a model atmosphere
modelatm= atlas9.Atlas9Atmosphere(teff=kwargs.get('teff',4500.),
logg=kwargs.get('logg',2.5),
metals=kwargs.get('metals',0.),
am=kwargs.get('am',0.),
cm=kwargs.get('cm',0.),
dr=kwargs.get('dr',None))
if isinstance(modelatm,str) and os.path.exists(modelatm):
modelfilename= modelatm
elif isinstance(modelatm,str):
raise ValueError('modelatm= input is a non-existing filename')
else: # model atmosphere instance
# Need to write this instance to a file; we will run in a temp
# subdirectory of the current directory
tmpDir= tempfile.mkdtemp(dir=os.getcwd())
tmpModelAtmDir= True # need to remove this later
modelfilename= os.path.join(tmpDir,'modelatm.mod')
modelatm.writeto(modelfilename)
kwargs['modelatm']= modelfilename
try:
# Check whether a MOOG version of the model atmosphere exists
if not os.path.exists(modelfilename.replace('.mod','.org')):
# Convert to MOOG format
convert_modelAtmosphere(**kwargs)
# Run weedout on the linelist first if requested
if run_weedout:
linelistfilename= modelfilename.replace('.mod','.lines')
if not os.path.exists(linelistfilename):
weedout(**kwargs)
kwargs['linelist']= linelistfilename
# Run MOOG synth for the whole wavelength range as a baseline, also contin
if baseline is None:
baseline= moogsynth(**kwargs)[1]
elif isinstance(baseline,tuple): #probably accidentally gave wav as well
baseline= baseline[1]
if mwav is None or cflux is None:
mwav, cflux= moogsynth(doflux=True,**kwargs)
# Convert the apStarWavegrid windows to moogWavegrid regions
sm,em= [], []
for start,end in zip(si,ei):
sm.append(numpy.argmin(numpy.fabs(apWave[start]-mwav)))
em.append(numpy.argmin(numpy.fabs(apWave[end]-mwav)))
# Run MOOG synth for all abundances and all windows
if len(args) == 0: #special case that there are *no* differences
args= ([26,0.],)
nsynths= numpy.array([len(args[ii])-1 for ii in range(len(args))])
nsynth= numpy.amax(nsynths) #Take the longest abundance list
out= numpy.tile(baseline,(nsynth,1))
# Run all windows
for start, end in zip(sm,em):
kwargs['wmin']= mwav[start]
kwargs['wmax']= mwav[end]
# Check whether the number of syntheses is > 5 and run multiple
# MOOG instances if necessary, bc MOOG only does 5 at a time
ninstances= int(numpy.ceil(nsynth/5.))
for ii in range(ninstances):
newargs= ()
for jj in range(len(args)):
tab= [args[jj][0]]
if len(args[jj][5*ii+1:5*(ii+1)+1]) > 0:
tab.extend(args[jj][5*ii+1:5*(ii+1)+1])
newargs= newargs+(tab,)
out[5*ii:5*(ii+1),start:end+1]= moogsynth(*newargs,**kwargs)[1]
except: raise
finally:
if tmpModelAtmDir: # need to remove this temporary directory
os.remove(modelfilename)
moogmodelfilename= modelfilename.replace('.mod','.org')
if os.path.exists(moogmodelfilename):
os.remove(moogmodelfilename)
if run_weedout:
os.remove(modelfilename.replace('.mod','.lines'))
os.rmdir(tmpDir)
# Now multiply each continuum-normalized spectrum with the continuum
out*= numpy.tile(cflux,(nsynth,1))
# Now convolve with the LSF
out= aplsf.convolve(mwav,out,
lsf=lsf,xlsf=xlsf,dxlsf=dxlsf,vmacro=vmacro)
# Now continuum-normalize
if cont.lower() == 'true':
# Get the true continuum on the apStar wavelength grid
apWave= apStarWavegrid()
baseline= numpy.polynomial.Polynomial.fit(mwav,cflux,4)
ip= interpolate.InterpolatedUnivariateSpline(mwav,
cflux/baseline(mwav),
k=3)
cflux= baseline(apWave)*ip(apWave)
# Divide it out
out/= numpy.tile(cflux,(nsynth,1))
elif not cont is None:
cflux= apcont.fit(out,numpy.ones_like(out),type=cont)
out[cflux > 0.]/= cflux[cflux > 0.]
out[cflux <= 0.]= numpy.nan
return out
def elemfit(spec,specerr,elem,
fparam=None,
teff=4750.,logg=2.5,metals=0.,am=0.,nm=0.,cm=0.,vm=None,
fixteff=True,fixlogg=True,fixmetals=None,fixam=None,
fixcm=None,
fixnm=None,fixvm=True,
estimate_err=False,
lib='GK',pca=True,sixd=True,dr=None,
offile=None,
inter=3,f_format=1,f_access=None,
errbar=1,indini=[1,1,1,2,2,3],init=0,
verbose=False):
"""
NAME:
elemfit
PURPOSE:
Fit a model spectrum to a given data spectrum for a given element window
INPUT:
Either:
(1) location ID - single or list/array of location IDs
APOGEE ID - single or list/array of APOGEE IDs; loads aspcapStar
(2) spec - spectrum: can be (nwave) or (nspec,nwave)
specerr - spectrum errors: can be (nwave) or (nspec,nwave)
elem - element to fit (e.g., 'Al')
estimate_err= (False) if True, estimate the error from Delta chi^2=1; only works for errors <~ 0.3 dex, code returns numpy.nan when error is larger
Input parameters (can be 1D arrays); only used when init=0
Either:
(1) fparam= (None) output of ferre.fit
(2) teff= (4750.) Effective temperature (K)
logg= (2.5) log10 surface gravity / cm s^-2
metals= (0.) overall metallicity
am= (0.) [alpha/M]
nm= (0.) [N/M]
cm= (0.) [C/M]
vm= if using the 7D library, also specify the microturbulence
Fit options:
fixteff= (True) if True, fix teff at the input value
fixlogg= (True) if True, fix logg at the input value
fixvm= (True) if True, fix vm at the input value (only if sixd is False)
The following are set to False based on the element being fit (C -> fixcm=False, N -> fixnm=False, O,Mg,S,Si,Ca,Ti -> fixam=False, rest -> fixmetals=False)
fixmetals= (None) if True, fix metals at the input value
fixam= (None) if True, fix am at the input value
fixcm= (None) if True, fix cm at the input value
fixnm= (None) if True, fix nm at the input value
Library options:
lib= ('GK') spectral library
pca= (True) if True, use a PCA compressed library
sixd= (True) if True, use the 6D library (w/o vm)
dr= data release
FERRE options:
inter= (3) order of the interpolation
errbar= (1) method for calculating the error bars
indini= ([1,1,1,2,2,3]) how to initialize the search (int or array/list with ndim entries)
init= (0) if 0, initialize the search at the parameters in the pfile
f_format= (1) file format (0=ascii, 1=unf)
f_access= (None) 0: load whole library, 1: use direct access (for small numbers of interpolations), None: automatically determine a good value (currently, 1)
Output options:
offile= (None) if offile is set, the FERRE OFFILE is saved to this file, otherwise this file is removed
verbose= (False) if True, run FERRE in verbose mode
OUTPUT:
best-fit parameters (nspec,nparams); in the same order as the FPARAM APOGEE data product (fixed inputs are repeated in the output)
if estimate_err: tuple with best-fit (see above) and error on the element abundance
HISTORY:
2015-02-27 - Written - Bovy (IAS)
"""
# Parse fparam
if not fparam is None:
teff= fparam[:,paramIndx('TEFF')]
logg= fparam[:,paramIndx('LOGG')]
metals= fparam[:,paramIndx('METALS')]
am= fparam[:,paramIndx('ALPHA')]
nm= fparam[:,paramIndx('N')]
cm= fparam[:,paramIndx('C')]
if sixd:
vm= None
else:
vm= fparam[:,paramIndx('LOG10VDOP')]
# Make sure the Teff etc. have the right dimensionality
if len(spec.shape) == 1:
nspec= 1
else:
nspec= spec.shape[0]
if nspec > 1 and isinstance(teff,float):
teff= teff*numpy.ones(nspec)
if nspec > 1 and isinstance(logg,float):
logg= logg*numpy.ones(nspec)
if nspec > 1 and isinstance(metals,float):
metals= metals*numpy.ones(nspec)
if nspec > 1 and isinstance(am,float):
am= am*numpy.ones(nspec)
if nspec > 1 and isinstance(nm,float):
nm= nm*numpy.ones(nspec)
if nspec > 1 and isinstance(cm,float):
cm= cm*numpy.ones(nspec)
if nspec > 1 and not vm is None and isinstance(vm,float):
vm= vm*numpy.ones(nspec)
if dr is None: dr= appath._default_dr()
# Set fixXX based on element being fit, first set None to True
if fixcm is None: fixcm= True
if fixnm is None: fixnm= True
if fixam is None: fixam= True
if fixmetals is None: fixmetals= True
if elem.lower() == 'c':
fixcm= False
elif elem.lower() == 'n':
fixnm= False
elif elem.lower() in ['o','mg','s','si','ca','ti']:
fixam= False
else:
fixmetals= False
# Fix any of the parameters?
indv= []
indini= copy.copy(indini) # need to copy bc passed by reference
if isinstance(indini,numpy.ndarray) and \
((not sixd and fixvm) or fixcm or fixnm or fixam or fixmetals \
or fixlogg or fixteff):
indini= list(indini)
if not sixd and not fixvm:
indv.append(1)
elif not sixd:
if isinstance(indini,list): indini[0]= -1
if not fixcm:
indv.append(2-sixd)
else:
if isinstance(indini,list): indini[1-sixd]= -1
if not fixnm:
indv.append(3-sixd)
else:
if isinstance(indini,list): indini[2-sixd]= -1
if not fixam:
indv.append(4-sixd)
else:
if isinstance(indini,list): indini[3-sixd]= -1
if not fixmetals:
indv.append(5-sixd)
else:
if isinstance(indini,list): indini[4-sixd]= -1
if not fixlogg:
indv.append(6-sixd)
else:
if isinstance(indini,list): indini[5-sixd]= -1
if not fixteff:
indv.append(7-sixd)
else:
if isinstance(indini,list): indini[6-sixd]= -1
if isinstance(indini,list):
while -1 in indini: indini.remove(-1)
if init == 0: indini= 0
# Setup temporary directory to run FERRE from
tmpDir= tempfile.mkdtemp(dir='./')
try:
# First write the ipf file with the parameters
write_ipf(tmpDir,teff,logg,metals,am,nm,cm,vm=vm)
# Write the file with the fluxes and the flux errors
write_ffile(tmpDir,spec,specerr=specerr)
# Now write the input.nml file
if f_access is None:
f_access= 1
write_input_nml(tmpDir,'input.ipf','output.dat',ndim=7-sixd,
nov=7-sixd-fixcm-fixnm-fixam-fixmetals\
-fixlogg-fixteff,
indv=indv,
synthfile=appath.ferreModelLibraryPath\
(lib=lib,pca=pca,sixd=sixd,dr=dr,
header=True,unf=False),
ffile='input.frd',erfile='input.err',
opfile='output.opf',
inter=inter,f_format=f_format,
errbar=errbar,indini=indini,init=init,
f_access=f_access,
filterfile=apwindow.path(elem,dr=dr))
# Run FERRE
run_ferre(tmpDir,verbose=verbose)
# Read the output
cols= (1,2,3,4,5,6)
tmpOut= numpy.loadtxt(os.path.join(tmpDir,'output.opf'),usecols=cols)
if len(spec.shape) == 1 or spec.shape[0] == 1:
out= numpy.zeros((1,7))
tmpOut= numpy.reshape(tmpOut,(1,7-sixd))
else:
out= numpy.zeros((nspec,7))
out[:,paramIndx('TEFF')]= tmpOut[:,-1]
out[:,paramIndx('LOGG')]= tmpOut[:,-2]
out[:,paramIndx('METALS')]= tmpOut[:,-3]
out[:,paramIndx('ALPHA')]= tmpOut[:,-4]
out[:,paramIndx('N')]= tmpOut[:,-5]
out[:,paramIndx('C')]= tmpOut[:,-6]
if sixd and dr == '12':
out[:,paramIndx('LOG10VDOP')]=\
numpy.log10(2.478-0.325*out[:,paramIndx('LOGG')])
else:
out[:,paramIndx('LOG10VDOP')]= tmpOut[:,0]
if not offile is None:
os.rename(os.path.join(tmpDir,'output.dat'),offile)
finally:
# Clean up
if os.path.exists(os.path.join(tmpDir,'input.ipf')):
os.remove(os.path.join(tmpDir,'input.ipf'))
if os.path.exists(os.path.join(tmpDir,'input.frd')):
os.remove(os.path.join(tmpDir,'input.frd'))
if os.path.exists(os.path.join(tmpDir,'input.err')):
os.remove(os.path.join(tmpDir,'input.err'))
if os.path.exists(os.path.join(tmpDir,'input.nml')):
os.remove(os.path.join(tmpDir,'input.nml'))
if os.path.exists(os.path.join(tmpDir,'output.dat')):
os.remove(os.path.join(tmpDir,'output.dat'))
if os.path.exists(os.path.join(tmpDir,'output.opf')):
os.remove(os.path.join(tmpDir,'output.opf'))
os.rmdir(tmpDir)
if estimate_err:
# Determine the chi2 and the error
elem_linspace= (-0.5,0.5,51)
elems= numpy.linspace(*elem_linspace)
c2= elemchi2(spec,specerr,elem,
elem_linspace=elem_linspace,
fparam=out,lib=lib,pca=pca,sixd=sixd,dr=dr,
inter=inter,f_format=f_format,f_access=f_access,
verbose=verbose)
from scipy import interpolate, optimize
outerr= numpy.empty(nspec)
for ii in range(nspec):
spl= \
interpolate.InterpolatedUnivariateSpline(elems,
c2[ii]-numpy.amin(c2[ii]))
try:
ul= optimize.brentq(lambda x: spl(x)-1.,
0.,elem_linspace[1]-0.01)
ll= optimize.brentq(lambda x: spl(x)-1.,
elem_linspace[0]+0.01,0.)
outerr[ii]= (ul-ll)/2.
except ValueError:
outerr[ii]= numpy.nan
return (out,outerr)
else:
return out
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 elemchi2(spec,specerr,
elem,elem_linspace=(-0.5,0.5,11),tophat=False,
fparam=None,
teff=4750.,logg=2.5,metals=0.,am=0.,nm=0.,cm=0.,vm=None,
lib='GK',pca=True,sixd=True,dr=None,
offile=None,
inter=3,f_format=1,f_access=None,
verbose=False):
"""
NAME:
elemchi2
PURPOSE:
Calculate the chi^2 for a given element
INPUT:
Either:
(1) location ID - single or list/array of location IDs
APOGEE ID - single or list/array of APOGEE IDs; loads aspcapStar
(2) spec - spectrum: can be (nwave) or (nspec,nwave)
specerr - spectrum errors: can be (nwave) or (nspec,nwave)
elem - element to consider (e.g., 'Al')
elem_linspace= ((-0.5,0.5,11)) numpy.linspace range of abundance, relative to the relevant value in fparam / metals,am,nm,cm
tophat= (False) if True, don't use the value of weights, just use them to define windows that have weight equal to one
Input parameters (can be 1D arrays)
Either:
(1) fparam= (None) output of ferre.fit
(2) teff= (4750.) Effective temperature (K)
logg= (2.5) log10 surface gravity / cm s^-2
metals= (0.) overall metallicity
am= (0.) [alpha/M]
nm= (0.) [N/M]
cm= (0.) [C/M]
vm= if using the 7D library, also specify the microturbulence
Library options:
lib= ('GK') spectral library
pca= (True) if True, use a PCA compressed library
sixd= (True) if True, use the 6D library (w/o vm)
dr= data release
FERRE options:
inter= (3) order of the interpolation
f_format= (1) file format (0=ascii, 1=unf)
f_access= (None) 0: load whole library, 1: use direct access (for small numbers of interpolations), None: automatically determine a good value (currently, 1)
verbose= (False) if True, run FERRE in verbose mode
OUTPUT:
chi^2
HISTORY:
2015-03-12 - Written - Bovy (IAS)
"""
# Parse fparam
if not fparam is None:
teff= fparam[:,paramIndx('TEFF')]
logg= fparam[:,paramIndx('LOGG')]
metals= fparam[:,paramIndx('METALS')]
am= fparam[:,paramIndx('ALPHA')]
nm= fparam[:,paramIndx('N')]
cm= fparam[:,paramIndx('C')]
if sixd:
vm= None
else:
vm= fparam[:,paramIndx('LOG10VDOP')]
# parse spec, specerr input
nspec= len(teff)
if len(spec.shape) == 1:
spec= numpy.reshape(spec,(1,spec.shape[0]))
specerr= numpy.reshape(specerr,(1,specerr.shape[0]))
# Read the weights
if tophat:
weights= apwindow.tophat(elem,apStarWavegrid=False,dr=dr)
else:
weights= apwindow.read(elem,apStarWavegrid=False,dr=dr)
weights/= numpy.sum(weights)
# Decide which parameter to vary
nvelem= elem_linspace[2]
var_elem= numpy.tile(numpy.linspace(*elem_linspace),(nspec,1))
if elem.lower() == 'c':
cm= var_elem+numpy.tile(cm,(nvelem,1)).T
elif elem.lower() == 'n':
nm= var_elem+numpy.tile(nm,(nvelem,1)).T
elif elem.lower() in ['o','mg','s','si','ca','ti']:
am= var_elem+numpy.tile(am,(nvelem,1)).T
else:
metals= var_elem+numpy.tile(metals,(nvelem,1)).T
# Upgrade dimensionality of other parameters for interpolate input
teff= numpy.tile(teff,(nvelem,1)).T
logg= numpy.tile(logg,(nvelem,1)).T
if not sixd:
vm= numpy.tile(vm,(nvelem,1)).T.flatten()
if not elem.lower() == 'c':
cm= numpy.tile(cm,(nvelem,1)).T
if not elem.lower() == 'n':
nm= numpy.tile(nm,(nvelem,1)).T
if not elem.lower() in ['o','mg','s','si','ca','ti']:
am= numpy.tile(am,(nvelem,1)).T
if elem.lower() in ['c','n','o','mg','s','si','ca','ti']:
metals= numpy.tile(metals,(nvelem,1)).T
# Get interpolated spectra, [nspec,nwave]
ispec= interpolate(teff.flatten(),logg.flatten(),metals.flatten(),
am.flatten(),nm.flatten(),cm.flatten(),vm=vm,
lib=lib,pca=pca,sixd=sixd,dr=dr,
inter=inter,f_format=f_format,f_access=f_access,
verbose=verbose,apStarWavegrid=False)
dspec= numpy.tile(spec,(1,nvelem)).reshape((nspec*nvelem,spec.shape[1]))
dspecerr= numpy.tile(specerr,
(1,nvelem)).reshape((nspec*nvelem,spec.shape[1]))
tchi2= _chi2(ispec,dspec,dspecerr,numpy.tile(weights,(nspec*nvelem,1)))
return numpy.reshape(tchi2,(nspec,nvelem))
