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 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 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 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 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 runTarget(gridParam):
locationID = gridParam.locationID
apogeeID = gridParam.apogeeID
badheader, header = apread.apStar(locationID, apogeeID, ext=0, header=True, dr='13')
specs = apread.apStar(locationID, apogeeID, ext=1, header=False, dr='13')
specerrs = apread.apStar(locationID, apogeeID, ext=2, header=False, dr='13')
nvisits = header['NVISITS']
gridParamVists = []
for visit in range(1, nvisits + 1):
print('Visit ' + str(visit) + '/' + str(nvisits))
if nvisits is 1:
spec = specs
specerr = specerrs
else:
spec = specs[1 + nvisits]
specerr = specerrs[ 1+ nvisits]
aspec= np.reshape(spec,(1, len(spec)))
aspecerr= np.reshape(specerr,(1, len(specerr)))
cont= spec / continuum.fit(aspec, aspecerr, type='aspcap')[0]
conterr = specerr / continuum.fit(aspec, aspecerr, type='aspcap')[0]
gridParam = GridParam(locationID, apogeeID)
gridParam.constructParams()
gridParam.spec = bm.shiftFlux(cont, header['VHELIO' + str(visit)])
gridParam.specErr = bm.shiftFlux(conterr, header['VHELIO' + str(visit)])
gridParam.getRVs(visit)
gridParam.visit = visit
nSteps = 200
sampler = MCMC(gridParam, nSteps=nSteps)
circular_samples = sampler.chain[:, :, :].reshape((-1, 5))
results = np.asarray(list(map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]),
zip(*np.percentile(circular_samples, [16, 50, 84], axis=0)))))
fig, ax = plt.subplots(5, 1, sharex='col')
for i in range(5):
for j in range(len(sampler.chain[:, 0, i])):
ax[i].plot(np.linspace(0, nSteps, num=nSteps), sampler.chain[j, :, i], 'k', alpha=0.2)
ax[i].plot(np.linspace(0, nSteps, num=nSteps) , np.ones(nSteps)*results[i][0], 'b', lw=2)
fig.set_figheight(20)
fig.set_figwidth(15)
if not os.path.exists('plots/walker/' + str(locationID) + '/' + str(apogeeID) + '/'):
os.makedirs('plots/walker/' + str(locationID) + '/' + str(apogeeID) + '/')
plt.savefig('plots/walker/' + str(locationID) + '/' + str(apogeeID) + '/' + str(visit) + '.png')
plt.close('all')
gridParam.modelParamA.teff = results[0][0]
gridParam.modelParamB.teff = results[1][0]
gridParam.modelParamB.fluxRatio = results[2][0]
gridParam.modelParamA.rv = results[3][0]
gridParam.modelParamB.rv = results[4][0]
gridParam.chi2 = -1.0 * fitModel(None, gridParam, plot=True)
gridParamVists.append(gridParam)
if not os.path.exists('lists/chi2/' + str(locationID) + '/'):
os.makedirs('lists/chi2/' + str(locationID) + '/')
filename = 'lists/chi2/' + str(locationID) + '/' + str(apogeeID) + '.tbl'
writeGridToFile(gridParamVists, filename=filename)
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 targetGrid(gridParam, minimizedVisitParams, plot=True):
'''
The grid tests against ranging effective temperatures for both stars and the flux ratio of the
secondary component. This is done by target.
:param gridParam: [in/out] The GridParam of the target
:param gridRes: [out] The visits that have the same paramters as the minimized chi2 visit
:param plot: [in] If true makes plots to see intermediate steps (default=True)
'''
locationID = gridParam.locationID
apogeeID = gridParam.apogeeID
badheader, header = apread.apStar(locationID, apogeeID, ext=0, header=True)
specs = apread.apStar(locationID, apogeeID, ext=1, header=False)
specerrs = apread.apStar(locationID, apogeeID, ext=2, header=False)
nvisits = header['NVISITS']
# chi2 = np.full((nvisits, nrangeTeffA, nrangeTeffB, nrangeFluxRatio), -1.)
#chi2 = np.full((nvisits, nrangeTeffA, nrangeTeffB, nrangeFluxRatio, nrangeRVA, nrangeRVB), -1.)
ipg = ferre.Interpolator(lib='GK')
ipf = ferre.Interpolator(lib='F')
# Create file to store all the chi2 values
path = 'lists/all_chi2/' + str(locationID) + '/'
if not os.path.exists(path):
os.makedirs(path)
fn = open(path + apogeeID + '.lis', 'w')
fn.write(gridParam.toStringHeader())
timer = Timer()
timeSum = 0.0
allChi2 = []
visitGridParamsBuffer = []
for visit in range(1, nvisits + 1):
timer.start()
if (nvisits != 1):
spec = specs[1+visit]
specerr = specerrs[1+visit]
else:
spec = specs
specerr = specerrs
if (len(minimizedVisitParams) == 0):
gridParam = GridParam(locationID, apogeeID)
gridParam.constructParams()
gridParam.getRVs(visit)
else:
gridParam = minimizedVisitParams[visit - 1]
visitGridParamsBuffer.append(gridParam)
# Prepare grid ranges
rangeTeffA = np.arange(gridParam.minTeffA, gridParam.maxTeffA, gridParam.teffStepA)
rangeTeffB = np.arange(gridParam.minTeffB, gridParam.maxTeffB, gridParam.teffStepB)
rangeFluxRatio = np.arange(gridParam.minFluxRatio, gridParam.maxFluxRatio, gridParam.fluxStep)
rangeRVA = np.arange(gridParam.minRVA, gridParam.maxRVA, gridParam.rvAStep)
rangeRVB = np.arange(gridParam.minRVB, gridParam.maxRVB, gridParam.rvBStep)
nrangeTeffA = len(rangeTeffA)
nrangeTeffB = len(rangeTeffB)
nrangeFluxRatio = len(rangeFluxRatio)
nrangeRVA =len(rangeRVA)
nrangeRVB =len(rangeRVB)
chi2 = np.full((nrangeTeffA, nrangeTeffB, nrangeFluxRatio, nrangeRVA, nrangeRVB), -1.)
print('Visit: ' + str(visit) ,'Grid dimensions: ' + str(chi2.shape))
# Prep Spectra
aspec= np.reshape(spec,(1, len(spec)))
aspecerr= np.reshape(specerr,(1, len(specerr)))
cont= spec / continuum.fit(aspec, aspecerr, type='aspcap')[0]
conterr = specerr / continuum.fit(aspec, aspecerr, type='aspcap')[0]
shiftedSpec = bm.shiftFlux(cont, header['VHELIO' + str(visit)])
# Run grid
for i in range(nrangeTeffA):
gridParam.modelParamA.teff = rangeTeffA[i]
componentA = bm.genComponent(gridParam.modelParamA, ipf, ipg)
for j in range(nrangeTeffB):
gridParam.modelParamB.teff = rangeTeffB[j]
componentB = bm.genComponent(gridParam.modelParamB, ipf, ipg)
for k in range(nrangeFluxRatio):
gridParam.modelParamB.fluxRatio = rangeFluxRatio[k]
componentBR = componentB * rangeFluxRatio[k]
for l in range(nrangeRVA):
gridParam.modelParamA.rv = rangeRVA[l]
componentAS = bm.shiftFlux(componentA, rangeRVA[l])
for m in range(nrangeRVB):
gridParam.modelParamB.rv = rangeRVB[m]
componentBS = bm.shiftFlux(componentBR, rangeRVB[m])
binaryFlux = bm.combineFlux(componentAS, componentBS)
chi2[i][j][k][l][m] = calcChi2(binaryFlux, shiftedSpec, conterr) / (len(binaryFlux) - 5.0)
gridParam.chi2 = chi2[i][j][k][l][m]
fn.write(gridParam.toString())
if (plot is True):
restLambda = splot.apStarWavegrid()
BinPlot.plotDeltaVCheck(locationID, apogeeID, visit,
[ [ restLambda, binaryFlux, 'blue', 'model' ],
[ restLambda, cont, 'orange', 'unshifted' ],
[ restLambda, shiftedSpec, 'green', 'shifted' ]],
[gridParam.modelParamA.teff,gridParam.modelParamB.teff, gridParam.modelParamB.fluxRatio],
'Delta V Shift', folder='grid_deltaVCheck')
timeSum+=timer.end()
allChi2.append(chi2)
fn.close()
print('Average visit time: ' + str(round(timeSum/nvisits, 2)) + str('s'))
# Get minized values for each visit
indices = None
for i in range(nvisits):
inds = getMinIndicies(allChi2[i])
rangeTeffA = np.arange(visitGridParamsBuffer[i].minTeffA, visitGridParamsBuffer[i].maxTeffA, visitGridParamsBuffer[i].teffStepA)
rangeTeffB = np.arange(visitGridParamsBuffer[i].minTeffB, visitGridParamsBuffer[i].maxTeffB, visitGridParamsBuffer[i].teffStepB)
rangeFluxRatio = np.arange(visitGridParamsBuffer[i].minFluxRatio, visitGridParamsBuffer[i].maxFluxRatio, visitGridParamsBuffer[i].fluxStep)
rangeRVA = np.arange(visitGridParamsBuffer[i].minRVA, visitGridParamsBuffer[i].maxRVA, visitGridParamsBuffer[i].rvAStep)
rangeRVB = np.arange(visitGridParamsBuffer[i].minRVB, visitGridParamsBuffer[i].maxRVB, visitGridParamsBuffer[i].rvBStep)
nrangeTeffA = len(rangeTeffA)
nrangeTeffB = len(rangeTeffB)
nrangeFluxRatio = len(rangeFluxRatio)
nrangeRVA =len(rangeRVA)
nrangeRVB =len(rangeRVB)
visitGridParamsBuffer[i].setParams(i + 1, rangeTeffA[inds[0]], rangeTeffB[inds[1]], rangeFluxRatio[inds[2]],
rangeRVA[inds[3]], rangeRVB[inds[4]], allChi2[i][inds[0]][inds[1]][inds[2]][inds[3]][inds[4]])
if (indices is None):
indices = [i + 1, inds, allChi2[i][inds[0]][inds[1]][inds[2]][inds[3]][inds[4]]]
if (allChi2[i][inds[0]][inds[1]][inds[2]][inds[3]][inds[4]] < indices[2]):
indices = [i + 1, inds, allChi2[i][inds[0]][inds[1]][inds[2]][inds[3]][inds[4]]]
inds = getMinIndicies(allChi2)
gridParam = visitGridParamsBuffer[inds[0]]
return gridParam, visitGridParamsBuffer
def read_spectra(cluster,
teffmin=4000.,
teffmax=5000.,
cont_type='cannon',
cont_deg=4):
"""
NAME:
read_spectra
PURPOSE:
Read the APOGEE spectra and their errors for stars in a given cluster
INPUT:
cluster - Name of the cluster (name in one of the data files)
teffmin= (4000.) minimum temperature
teffmax= (5000.) maximum temperature
cont_type = ('cannon') type of continuum normalization to perform
cont_deg= (4) degree polynomial to fit for continuum normalization
OUTPUT:
(data, spec, specerr) - (full data structure, spectra [nspec,nlam], spectral uncertainties [nspec,nlam]) nlam=7214 on ASPCAP grid
HISTORY:
2015-08-13 - Written based on some older code - Bovy (UofT)
"""
if cluster.upper() in _GCS:
data = read_meszarosgcdata()
else:
data = read_caldata()
# Cut to just this cluster and temperature range
if 'rc' in cluster.lower():
# Only for NGC 6819
rc = True
cluster = cluster[:-2]
else:
rc = False
data = data[data['CLUSTER'] == cluster.upper()]
data= data[(data['TEFF'] < teffmax)\
*(data['TEFF'] > teffmin)]
if cluster.lower() == 'n6819':
g4CN = good4CN(cluster, data)
g4CN[10] = False # another one, by hand!
if rc:
data = data[True - g4CN] # Just those!
else:
data = data[g4CN] # Just those!
# Load all the spectra
nspec = len(data)
spec = numpy.zeros((nspec, 7214))
specerr = numpy.zeros((nspec, 7214))
# Setup bad pixel mask
badcombpixmask= bitmask.badpixmask()\
+2**bitmask.apogee_pixmask_int("SIG_SKYLINE")
for ii in range(nspec):
sys.stdout.write('\r' + "Loading spectrum %i / %i ...\r" %
(ii + 1, nspec))
sys.stdout.flush()
spec[ii] = apread.apStar(data['LOCATION_ID'][ii],
data['ID'][ii],
ext=1,
header=False,
aspcapWavegrid=True)[_COMBINED_INDEX]
specerr[ii] = apread.apStar(data['LOCATION_ID'][ii],
data['ID'][ii],
ext=2,
header=False,
aspcapWavegrid=True)[_COMBINED_INDEX]
# Inflate uncertainties for bad pixels
mask = apread.apStar(data['LOCATION_ID'][ii],
data['ID'][ii],
ext=3,
header=False,
aspcapWavegrid=True)[_COMBINED_INDEX]
specerr[ii,(mask & (badcombpixmask)) != 0]+=\
100.*numpy.mean(spec[ii,True-numpy.isnan(spec[ii])])
# Also inflate pixels with high SNR to 0.5%
highsnr = spec[ii] / specerr[ii] > 200.
specerr[ii, highsnr] = 0.005 * numpy.fabs(spec[ii, highsnr])
# Continuum-normalize
cont = continuum.fit(spec[ii],
specerr[ii],
type=cont_type,
deg=cont_deg)
spec[ii] /= cont
specerr[ii] /= cont
specerr[ii, highsnr] = 0.005 # like standard APOGEE reduction
sys.stdout.write('\r' + _ERASESTR + '\r')
sys.stdout.flush()
return (data, spec, specerr)
nan_vals = [i for i in range(len(spec[0])) if np.isnan(spec[0][i])] nan_ranges = [(nan_vals[i] + 1, nan_vals[i+1]) for i in range(len(nan_vals) - 1) if nan_vals[i+1]!=nan_vals[i]+1] print(nan_ranges) specChunk = spec[0][nan_ranges[0][0]:nan_ranges[0][1]] errChunk = err[0][nan_ranges[0][0]:nan_ranges[0][1]]''' aspec= apread.apStar(locationID, apogeeID, ext=1, header=False)[1] aspecerr= apread.apStar(locationID, apogeeID, ext=2, header=False)[1] # Input needs to be (nspec,nwave) aspec= np.reshape(aspec,(1,len(aspec))) aspecerr= np.reshape(aspecerr,(1,len(aspecerr))) # Fit the continuum from apogee.spec import continuum cont= continuum.fit(aspec,aspecerr,type='aspcap') cspec= apread.aspcapStar(locationID, apogeeID,ext=1,header=False) import apogee.spec.plot as splot splot.waveregions(aspec[0]/cont[0]) splot.waveregions(cspec,overplot=True) '''params = ferre.fit(locationID, apogeeID, teff=teff1, fixteff=True, logg=logg, fixlogg=True, metals=metals, fixmetals=False, am=am, fixam=False, nm=nm, fixnm=False, cm=cm, fixcm=False, verbose=True) '''
def read_spectra(cluster,teffmin=4000.,teffmax=5000.,cont_type='cannon',
cont_deg=4):
"""
NAME:
read_spectra
PURPOSE:
Read the APOGEE spectra and their errors for stars in a given cluster
INPUT:
cluster - Name of the cluster (name in one of the data files)
teffmin= (4000.) minimum temperature
teffmax= (5000.) maximum temperature
cont_type = ('cannon') type of continuum normalization to perform
cont_deg= (4) degree polynomial to fit for continuum normalization
OUTPUT:
(data, spec, specerr) - (full data structure, spectra [nspec,nlam], spectral uncertainties [nspec,nlam]) nlam=7214 on ASPCAP grid
HISTORY:
2015-08-13 - Written based on some older code - Bovy (UofT)
"""
if cluster.upper() in _GCS:
data= read_meszarosgcdata()
else:
data= read_caldata()
# Cut to just this cluster and temperature range
if 'rc' in cluster.lower():
# Only for NGC 6819
rc= True
cluster= cluster[:-2]
else:
rc= False
data= data[data['CLUSTER'] == cluster.upper()]
data= data[(data['TEFF'] < teffmax)\
*(data['TEFF'] > teffmin)]
if cluster.lower() == 'n6819':
g4CN= good4CN(cluster,data)
g4CN[10]= False # another one, by hand!
if rc:
data= data[True-g4CN] # Just those!
else:
data= data[g4CN] # Just those!
# Load all the spectra
nspec= len(data)
spec= numpy.zeros((nspec,7214))
specerr= numpy.zeros((nspec,7214))
# Setup bad pixel mask
badcombpixmask= bitmask.badpixmask()\
+2**bitmask.apogee_pixmask_int("SIG_SKYLINE")
for ii in range(nspec):
sys.stdout.write('\r'+"Loading spectrum %i / %i ...\r" % (ii+1,nspec))
sys.stdout.flush()
spec[ii]= apread.apStar(data['LOCATION_ID'][ii],
data['ID'][ii],
ext=1,header=False,
aspcapWavegrid=True)[_COMBINED_INDEX]
specerr[ii]= apread.apStar(data['LOCATION_ID'][ii],
data['ID'][ii],
ext=2,header=False,
aspcapWavegrid=True)[_COMBINED_INDEX]
# Inflate uncertainties for bad pixels
mask= apread.apStar(data['LOCATION_ID'][ii],
data['ID'][ii],
ext=3,header=False,
aspcapWavegrid=True)[_COMBINED_INDEX]
specerr[ii,(mask & (badcombpixmask)) != 0]+=\
100.*numpy.mean(spec[ii,True-numpy.isnan(spec[ii])])
# Also inflate pixels with high SNR to 0.5%
highsnr= spec[ii]/specerr[ii] > 200.
specerr[ii,highsnr]= 0.005*numpy.fabs(spec[ii,highsnr])
# Continuum-normalize
cont= continuum.fit(spec[ii],specerr[ii],type=cont_type,deg=cont_deg)
spec[ii]/= cont
specerr[ii]/= cont
specerr[ii,highsnr]= 0.005 # like standard APOGEE reduction
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
return (data,spec,specerr)
apogeeID = "2M03441568+3231282"
restLambda = splot.apStarWavegrid()
visit = 1
badheader, header = apread.apStar(locationID, apogeeID, ext=0, header=True)
gridParam = GridParam(locationID, apogeeID)
gridParam.constructParams()
spec = apread.apStar(locationID, apogeeID, ext=1, header=False)[1 + visit]
specerr = apread.apStar(locationID, apogeeID, ext=2, header=False)[1 + visit]
# plt.plot(restLambda, spec)
aspec = np.reshape(spec, (1, len(spec)))
aspecerr = np.reshape(specerr, (1, len(specerr)))
cont = spec / continuum.fit(aspec, aspecerr, type="aspcap")[0]
conterr = specerr / continuum.fit(aspec, aspecerr, type="aspcap")[0]
shiftedSpec = bm.shiftFlux(cont, header["VHELIO" + str(visit)])
conterr = bm.shiftFlux(cont, header["VHELIO" + str(visit)])
BinPlot.plotDeltaVCheck(
locationID,
apogeeID,
visit,
[[restLambda, cont, "blue", "Data"]],
[gridParam.modelParamA.teff, gridParam.modelParamB.teff],
"",
folder="model_gen",
)
# plt.plot(restLambda, cont)
# plt.show()
ipg = ferre.Interpolator(lib="GK")
