line_labels['mn'] = r'$\mathrm{Mn\kern 0.1em I}$'
line_labels['s'] = r'$\mathrm{S\kern 0.1em I}$'
line_labels['v'] = r'$\mathrm{V\kern 0.1em I}$'
line_labels['cob'] = r'$\mathrm{Co\kern 0.1em I}$'
line_labels['cu'] = r'$\mathrm{Cu\kern 0.1em I}$'
line_labels['oh'] = r'$\mathrm{OH}$'
line_labels['co'] = r'$^{12}\!\mathrm{CO}$'
line_labels['cn'] = r'$\mathrm{CN}$'
line_labels['13co'] = r'$^{13}\!\mathrm{CO}$'
line_labels['hbrpi'] = r'$\mathrm{Br-}\pi$'
line_labels['hbrla'] = r'$\mathrm{Br-}\lambda$'
line_labels['hbr'] = r'$\mathrm{H[Br]}$'
line_labels['dib'] = r'$\mathrm{DIB}$'
# From Table 2 in Smith et al. (2013)
_FEI_lines = [
air2vac(l) for l in [
15194.492, 15207.526, 15395.718, 15490.339, 15648.510, 15964.867,
16040.657, 16153.247, 16165.032
]
]
_FEI_lines.append(16697.635) # one more from Shetrone
# From Table 5
_MGI_lines = [
air2vac(l)
for l in [15740.716, 15748.9, 15765.8, 15879.5, 15886.2, 15954.477]
]
_ALI_lines = [air2vac(l) for l in [16718.957, 16750.564286, 16763.359]]
_SII_lines = [
air2vac(l) for l in [
15361.161, 15376.831, 15833.602, 15960.063, 16060.009, 16094.787,
16215.670, 16680.770, 16828.159
line_labels['na']= r'$\mathrm{Na\kern 0.1em I}$'
line_labels['mn']= r'$\mathrm{Mn\kern 0.1em I}$'
line_labels['s']= r'$\mathrm{S\kern 0.1em I}$'
line_labels['v']= r'$\mathrm{V\kern 0.1em I}$'
line_labels['cob']= r'$\mathrm{Co\kern 0.1em I}$'
line_labels['cu']= r'$\mathrm{Cu\kern 0.1em I}$'
line_labels['oh']= r'$\mathrm{OH}$'
line_labels['co']= r'$^{12}\!\mathrm{CO}$'
line_labels['cn']= r'$\mathrm{CN}$'
line_labels['13co']= r'$^{13}\!\mathrm{CO}$'
line_labels['hbrpi']= r'$\mathrm{Br-}\pi$'
line_labels['hbrla']= r'$\mathrm{Br-}\lambda$'
line_labels['hbr']= r'$\mathrm{H[Br]}$'
line_labels['dib']= r'$\mathrm{DIB}$'
# From Table 2 in Smith et al. (2013)
_FEI_lines= [air2vac(l) for l in [15194.492,15207.526,15395.718,15490.339,
15648.510,15964.867,16040.657,16153.247,
16165.032]]
_FEI_lines.append(16697.635) # one more from Shetrone
# From Table 5
_MGI_lines= [air2vac(l) for l in [15740.716,15748.9,15765.8,15879.5,
15886.2,15954.477]]
_ALI_lines= [air2vac(l) for l in [16718.957,16750.564286,16763.359]]
_SII_lines= [air2vac(l) for l in [15361.161,15376.831,15833.602,15960.063,
16060.009,16094.787,16215.670,16680.770,
16828.159]]
_KI_lines= [air2vac(l) for l in [15163.067,15168.376]]
_CAI_lines= [air2vac(l) for l in [16136.823,16150.763,16155.236,16157.364]]
_TII_lines= [air2vac(l) for l in [15543.756,15602.842,15698.979,15715.573,
16635.161]]
_VI_lines= [air2vac(15925.)]
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 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
line_labels['na']= r'$\mathrm{Na\kern 0.1em I}$'
line_labels['mn']= r'$\mathrm{Mn\kern 0.1em I}$'
line_labels['s']= r'$\mathrm{S\kern 0.1em I}$'
line_labels['v']= r'$\mathrm{V\kern 0.1em I}$'
line_labels['cob']= r'$\mathrm{Co\kern 0.1em I}$'
line_labels['cu']= r'$\mathrm{Cu\kern 0.1em I}$'
line_labels['oh']= r'$\mathrm{OH}$'
line_labels['co']= r'$^{12}\!\mathrm{CO}$'
line_labels['cn']= r'$\mathrm{CN}$'
line_labels['13co']= r'$^{13}\!\mathrm{CO}$'
line_labels['hbrpi']= r'$\mathrm{Br-}\pi$'
line_labels['hbrla']= r'$\mathrm{Br-}\lambda$'
line_labels['hbr']= r'$\mathrm{H[Br]}$'
line_labels['dib']= r'$\mathrm{DIB}$'
# From Table 2 in Smith et al. (2013)
_FEI_lines= [air2vac(l) for l in [15194.492,15207.526,15395.718,15490.339,
15648.510,15964.867,16040.657,16153.247,
16165.032]]
_FEI_lines.append(16697.635) # one more from Shetrone
# From Table 5
_MGI_lines= [air2vac(l) for l in [15740.716,15748.9,15765.8,15879.5,
15886.2,15954.477]]
_ALI_lines= [air2vac(l) for l in [16718.957,16750.564286,16763.359]]
_SII_lines= [air2vac(l) for l in [15361.161,15376.831,15833.602,15960.063,
16060.009,16094.787,16215.670,16680.770,
16828.159]]
_KI_lines= [air2vac(l) for l in [15163.067,15168.376]]
_CAI_lines= [air2vac(l) for l in [16136.823,16150.763,16155.236,16157.364]]
_TII_lines= [air2vac(l) for l in [15543.756,15602.842,15698.979,15715.573,
16635.161]]
_VI_lines= [air2vac(15925.)]
15958.836]
_ALI_lines= [16723.524,16767.938]
_SII_lines= [15365.359,15381.033,15837.928,15964.424,16064.397,16099.184,
16220.100,16685.327,16832.756]
_KI_lines= [15167.211,15172.521]
_CAI_lines= [16155.176,16159.650,16161.778]
_TII_lines= [15548.003,15607.106,15703.269,15719.867,16639.705]
_CRI_lines= [15684.348,15864.548,15470.129]
_NII_lines= [15609.944,15636.926,16588.970,16593.827,16678.266,16820.064,
16823.354]
_NAI_lines= [16378.346633274852,16393.340725803333]
_MNI_lines= [15221.569]
#_MNI_lines= [15677.437,16712.565,15163,15221]
_SI_lines= [15406.540,15426.490,15474.043,15482.712]
_VI_lines= [15929.2]
_OH_lines= [air2vac(l) for l in [15279.5,15391.,15505.5,15570.5]]
_CO_lines= [air2vac(l) for l in [15582.,15780.5,15988.,16189.5]]
_CN_lines= [air2vac(l) for l in [15260.,15322.,15397.,15332.,15410.,
15447.,15466.,15472.,15482.]]
_13CO_lines= [air2vac(l) for l in [16122.5,16743.5]]
_DIB_lines= [15272.42] #from Zasowski et al. (2014)
def bovy_metallicity_gradient(plotfilename,savefilename,largewave=False):
# First read the RC catalog and cut it to stars near the plane
data= apread.rcsample()
if _HIZ:
indx= (numpy.fabs(data['RC_GALZ']) > 0.6)*(data['METALS'] > -1000.)
else:
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
data= data[indx]
# Now go through bins in R
Rmin, Rmax, dR= 5.5, 13., 0.1
