def calc_elemvar_table(savename,_mywindows=False,_no_indiv_windows=False):
if _mywindows: import window
# Restore the file with the synthetic spectra with all variations
if os.path.exists(savename):
with open(savename,'rb') as savefile:
baseline= pickle.load(savefile)
elem_synspec= pickle.load(savefile)
else:
raise IOError("File %s with synthetic spectra does not exists; compute this first with apogee/test/test_windows.py" % savename)
# Compute the whole table
elems= ['C','N','O','Na','Mg','Al','Si','S','K','Ca','Ti','V','Mn','Fe',
'Ni']
table= []
# Loop through synthetic spectra varying one element
for elem in elems:
col= []
# Now look at the windows for each element
for elemc in elems:
if not _no_indiv_windows:
si, ei= apwindow.waveregions(elemc,asIndex=True,pad=0,dr='13')
if _mywindows:
elemWeights= window.read(elemc,dr='13')
else:
elemWeights= apwindow.read(elemc,dr='13')
elemWeights/= numpy.nansum(elemWeights)
# Start with total
col.append(numpy.sqrt(numpy.nansum((elemWeights\
*(elem_synspec[elem][1]-elem_synspec[elem][0])**2.))/numpy.nansum(elemWeights)))
if not _no_indiv_windows:
for s,e in zip(si,ei):
col.append(numpy.sqrt(numpy.nansum((elemWeights\
*(elem_synspec[elem][1]-elem_synspec[elem][0])**2.)[s:e])/numpy.nansum(elemWeights[s:e])))
table.append(col)
return table
def bad(elem):
"""
NAME:
bad
PURPOSE:
return the index (on the apStar grid) of bad windows
INPUT:
elem - element
OUTPUT:
boolean array
HISTORY:
2015-09-02 - Written - Bovy (UofT)
"""
si,ei= apwindow.waveregions(elem,asIndex=True,pad=0,dr='13')
out= numpy.zeros(8575,dtype='bool')
if elem.lower() == 'k': return out
for ii,(s,e) in enumerate(zip(si,ei)):
# First do the special cases
if elem.lower() == 'c':
if ref[elem.lower()][ii] <= 0.01 \
or numpy.any(numpy.array(oth[elem.lower()][ii]) > 100.):
out[s:e]= True
elif elem.lower() == 'n':
if ref[elem.lower()][ii] <= 0.01 \
or numpy.any(numpy.array(oth[elem.lower()][ii]) > 200.):
out[s:e]= True
elif elem.lower() == 'o':
if ref[elem.lower()][ii] <= 0.005 \
or numpy.any(numpy.array(oth[elem.lower()][ii]) > 500.):
out[s:e]= True
elif elem.lower() == 'na':
if numpy.any(numpy.array(oth[elem.lower()][ii]) > 34.):
out[s:e]= True
elif elem.lower() == 'ti':
if numpy.any(numpy.array(oth[elem.lower()][ii]) > 30.):
out[s:e]= True
elif elem.lower() == 'v':
if numpy.any(numpy.array(oth[elem.lower()][ii]) > 40.):
out[s:e]= True
elif elem.lower() == 'mn':
if numpy.any(numpy.array(oth[elem.lower()][ii]) > 25.):
out[s:e]= True
elif elem.lower() == 'ni':
if numpy.any(numpy.array(oth[elem.lower()][ii]) > 50.):
out[s:e]= True
elif ref[elem.lower()][ii] <= 0.01 \
or numpy.any(numpy.array(oth[elem.lower()][ii]) > 34.):
out[s:e]= True
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 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 test_windows(options):
elems = [
'C', 'N', 'O', 'Na', 'Mg', 'Al', 'Si', 'S', 'K', 'Ca', 'Ti', 'V', 'Mn',
'Fe', 'Ni', 'Ce', 'Co', 'Cr', 'Cu', 'Ge', 'Nd', 'P', 'Rb', 'Y'
]
if options.savefilename is None or \
not os.path.exists(options.savefilename):
# Set default linelist for Turbospectrum or MOOG
if options.linelist is None and options.moog:
linelist = 'moog.201312161124.vac'
elif options.linelist is None:
linelist = 'turbospec.201312161124'
else:
linelist = options.linelist
# set up a model atmosphere for the requested atmospheric parameters
if options.arcturus:
options.teff = 4286.
options.logg = 1.66
options.metals = -0.52
options.am = 0.4
options.cm = 0.09
options.vm = 1.7
atm = atlas9.Atlas9Atmosphere(teff=options.teff,
logg=options.logg,
metals=options.metals,
am=options.am,
cm=options.cm)
# create baseline
if options.moog:
baseline= \
apogee.modelspec.moog.synth(modelatm=atm,
linelist=linelist,
lsf='all',cont='aspcap',
vmacro=6.,isotopes='arcturus',
vmicro=options.vm)
else:
baseline= \
apogee.modelspec.turbospec.synth(modelatm=atm,
linelist=linelist,
lsf='all',cont='aspcap',
vmacro=6.,isotopes='arcturus',
vmicro=options.vm)
# Loop through elements
elem_synspec = {}
# Run through once to simulate all differences
for elem in elems:
# First check that this element has windows
elemPath = apwindow.path(elem, dr=options.dr)
if not os.path.exists(elemPath): continue
# Simulate deltaAbu up and down
print "Working on %s" % (elem.capitalize())
abu = [atomic_number(elem), -options.deltaAbu, options.deltaAbu]
if options.moog:
synspec= \
apogee.modelspec.moog.synth(abu,
modelatm=atm,
linelist=linelist,
lsf='all',cont='aspcap',
vmacro=6.,
isotopes='arcturus',
vmicro=options.vm)
else:
synspec= \
apogee.modelspec.turbospec.synth(abu,
modelatm=atm,
linelist=linelist,
lsf='all',cont='aspcap',
vmacro=6.,
isotopes='arcturus',
vmicro=options.vm)
elem_synspec[elem] = synspec
if not options.savefilename is None:
save_pickles(options.savefilename, baseline, elem_synspec)
else:
with open(options.savefilename, 'rb') as savefile:
baseline = pickle.load(savefile)
elem_synspec = pickle.load(savefile)
# Now run through the different elements again and plot windows for each
# with elements that vary significantly
colors = sns.color_palette("colorblind")
plotelems = [
elem if not elem in ['C', 'N', 'O', 'Fe'] else '%s1' % elem
for elem in elems
]
plotelems.extend(['C2', 'N2', 'O2', 'Fe2'])
for pelem in plotelems:
if '1' in pelem or '2' in pelem: elem = pelem[:-1]
else: elem = pelem
if not elem in elem_synspec: continue
# Figure out which elements have significant variations in these
# windows and always plot the element that should vary
elemIndx = apwindow.tophat(elem, dr=options.dr)
elemWeights = apwindow.read(elem, dr=options.dr)
elemWeights /= numpy.nansum(elemWeights)
# Start with the element in question
splot.windows(1. + options.amplify *
(elem_synspec[elem][0] - baseline[0]),
pelem,
color=colors[0],
yrange=[0., 1.4],
plot_weights=True,
zorder=len(elems))
splot.windows(1. + options.amplify *
(elem_synspec[elem][1] - baseline[0]),
pelem,
color=colors[0],
overplot=True,
zorder=len(elems))
elem_shown = [elem]
# Run through the rest to figure out the order
elemVar = numpy.zeros(len(elems))
for ii, altElem in enumerate(elems):
if altElem == elem: continue
if not altElem in elem_synspec: continue
elemVar[ii] = 0.5 * numpy.nansum(
(elem_synspec[altElem][0] - baseline[0])**2. * elemWeights)
elemVar[ii] += 0.5 * numpy.nansum(
(elem_synspec[altElem][1] - baseline[0])**2. * elemWeights)
jj = 0
sortindx = numpy.argsort(elemVar)[::-1]
for altElem in numpy.array(elems)[sortindx]:
if altElem == elem: continue
if not altElem in elem_synspec: continue
if numpy.fabs(\
numpy.nanmax([(elem_synspec[altElem][0]-baseline[0])[elemIndx],
(elem_synspec[altElem][1]-baseline[0])[elemIndx]]))\
> options.varthreshold:
jj += 1
if jj >= len(colors): jj = len(colors) - 1
elem_shown.append(altElem)
splot.windows(1. + options.amplify *
(elem_synspec[altElem][0] - baseline[0]),
pelem,
color=colors[jj],
overplot=True,
zorder=len(elems) - jj)
splot.windows(1. + options.amplify *
(elem_synspec[altElem][1] - baseline[0]),
pelem,
color=colors[jj],
overplot=True,
zorder=len(elems) - jj)
t = pyplot.gca().transData
fig = pyplot.gcf()
for s, c in zip(elem_shown, colors[:jj + 1]):
xc = 0.05
if elem == 'K' or elem == 'Ce' or elem == 'Ge' or elem == 'Nd' \
or elem == 'Rb':
xc = apwindow.waveregions(elem, dr=options.dr,
pad=3)[0][0] - 15000. + 1.
text = pyplot.text(xc,
1.2,
" " + (r"$\mathrm{%s}$" % s) + " ",
color=c,
transform=t,
size=16.,
backgroundcolor='w')
text.draw(fig.canvas.get_renderer())
ex = text.get_window_extent()
t = transforms.offset_copy(text._transform,
x=1.5 * ex.width,
units='dots')
# Save
bovy_plot.bovy_end_print(options.plotfilename.replace('ELEM', pelem))
return None
def windows(*args, **kwargs):
"""
NAME:
windows
PURPOSE:
plot the spectral windows for a given element
INPUT:
Either:
(a) wavelength, spectrum (\AA,spectrum units)
(b) spectrum (assumed on standard APOGEE re-sampled wavelength grid)
(c) location ID, APOGEE ID (default loads aspcapStar, loads extension ext(=1); apStar=True loads apStar spectrum)
+element string (e.g., 'Al'); Adding 1 and 2 splits the windows into two
KEYWORDS:
plot_weights= (False) if True, also plot the weights for the windows (assumes that the spectrum is on the apStarWavegrid)
markLines= mark the location of 'lines' (see apogee.spec.window.lines)
apogee.spec.plot.waveregions keywords
OUTPUT:
plot to output
The final axes allow one to put additional labels on the plot, e.g., for adding the APOGEE ID:
bovy_plot.bovy_text(r'$\mathrm{%s}$' % '2M02420597+0837017',top_left=True)
Note that an ID (e.g., the apogee ID) and Teff, logg, metallicity, and alpha-enhancement labels can be added using the keywords label* above
HISTORY:
2015-01-26 - Written (based on older code) - Bovy (IAS)
"""
pad = kwargs.pop('pad', 3)
try:
si, ei = apwindow.waveregions(args[2], pad=pad, asIndex=True)
except IOError:
try:
si, ei = apwindow.waveregions(args[2][:-1], pad=pad, asIndex=True)
except IOError:
raise IOError(
"Windows for element %s could not be loaded, please specify an existing APOGEE element"
% ((args[2].lower().capitalize())))
if args[2][-1] == '1':
si = si[:len(si) // 2]
ei = ei[:len(ei) // 2]
else:
si = si[len(si) // 2:]
ei = ei[len(ei) // 2:]
# Remove the number from the element
newargs = (args[0], args[1], args[2][:-1])
for ii in range(len(args) - 3):
newargs = newargs + (args[ii + 3], )
args = newargs
# Also get the number and total width of all of the windows
dlam = apwindow.total_dlambda(args[2], pad=pad)
numw = apwindow.num(args[2])
# Set spacing between windows
if numw > 20:
kwargs['skipdx'] = 0.003
kwargs['_noskipdiags'] = True
elif numw > 15:
kwargs['skipdx'] = 0.01
# Set initial space to zero
kwargs['_startendskip'] = 0
# Set initial figure width
if not kwargs.get('overplot', False) and not 'fig_width' in kwargs:
if dlam > 150.:
kwargs['fig_width'] = 8.4
else:
kwargs['fig_width'] = 4.2
# Don't tick x
kwargs['_noxticks'] = True
# Label the largest wavelength in angstrom
kwargs['_labelwav'] = True
# Don't label the lines unless explicitly asked for
kwargs['labelLines'] = kwargs.get('labelLines', False)
# Plot the weights as well
if kwargs.pop('plot_weights', False):
kwargs['_plotw'] = apwindow.read(args[2], apStarWavegrid=True)
if kwargs.get('apStar', False):
kwargs['yrange'] = kwargs.get('yrange',
[0., 1.1 * numpy.nanmax(args[1])])
else:
kwargs['yrange'] = kwargs.get('yrange', [0., 1.2])
# mark the 'lines'
markLines = kwargs.get('markLines', not 'overplot' in kwargs)
if markLines and not '_markwav' in kwargs:
kwargs['_markwav'] = apwindow.lines(args[2])
# Plot
waveregions(args[0],
args[1],
startindxs=si,
endindxs=ei,
*args[3:],
**kwargs)
# Add label
bovy_plot.bovy_text(r'$\mathrm{%s}$' % ((args[2].lower().capitalize())),
top_left=True,
fontsize=10,
backgroundcolor='w')
return None
def windows(*args,**kwargs):
"""
NAME:
windows
PURPOSE:
plot the spectral windows for a given element
INPUT:
Either:
(a) wavelength, spectrum (\AA,spectrum units)
(b) spectrum (assumed on standard APOGEE re-sampled wavelength grid)
(c) location ID, APOGEE ID (default loads aspcapStar, loads extension ext(=1); apStar=True loads apStar spectrum)
+element string (e.g., 'Al'); Adding 1 and 2 splits the windows into two
KEYWORDS:
plot_weights= (False) if True, also plot the weights for the windows (assumes that the spectrum is on the apStarWavegrid)
markLines= mark the location of 'lines' (see apogee.spec.window.lines)
apogee.spec.plot.waveregions keywords
OUTPUT:
plot to output
The final axes allow one to put additional labels on the plot, e.g., for adding the APOGEE ID:
bovy_plot.bovy_text(r'$\mathrm{%s}$' % '2M02420597+0837017',top_left=True)
Note that an ID (e.g., the apogee ID) and Teff, logg, metallicity, and alpha-enhancement labels can be added using the keywords label* above
HISTORY:
2015-01-26 - Written (based on older code) - Bovy (IAS)
"""
pad= kwargs.pop('pad',3)
try:
si,ei= apwindow.waveregions(args[2],pad=pad,asIndex=True)
except IOError:
try:
si, ei= apwindow.waveregions(args[2][:-1],pad=pad,asIndex=True)
except IOError:
raise IOError("Windows for element %s could not be loaded, please specify an existing APOGEE element" % ((args[2].lower().capitalize())))
if args[2][-1] == '1':
si= si[:len(si)//2]
ei= ei[:len(ei)//2]
else:
si= si[len(si)//2:]
ei= ei[len(ei)//2:]
# Remove the number from the element
newargs= (args[0],args[1],args[2][:-1])
for ii in range(len(args)-3):
newargs= newargs+(args[ii+3],)
args= newargs
# Also get the number and total width of all of the windows
dlam= apwindow.total_dlambda(args[2],pad=pad)
numw= apwindow.num(args[2])
# Set spacing between windows
if numw > 20:
kwargs['skipdx']= 0.003
kwargs['_noskipdiags']= True
elif numw > 15:
kwargs['skipdx']= 0.01
# Set initial space to zero
kwargs['_startendskip']= 0
# Set initial figure width
if not kwargs.get('overplot',False) and not 'fig_width' in kwargs:
if dlam > 150.:
kwargs['fig_width']= 8.4
else:
kwargs['fig_width']= 4.2
# Don't tick x
kwargs['_noxticks']= True
# Label the largest wavelength in angstrom
kwargs['_labelwav']= True
# Don't label the lines unless explicitly asked for
kwargs['labelLines']= kwargs.get('labelLines',False)
# Plot the weights as well
if kwargs.pop('plot_weights',False):
kwargs['_plotw']= apwindow.read(args[2],apStarWavegrid=True)
if kwargs.get('apStar',False):
kwargs['yrange']= kwargs.get('yrange',
[0.,1.1*numpy.nanmax(args[1])])
else:
kwargs['yrange']= kwargs.get('yrange',[0.,1.2])
# mark the 'lines'
markLines= kwargs.get('markLines',not 'overplot' in kwargs)
if markLines and not '_markwav' in kwargs:
kwargs['_markwav']= apwindow.lines(args[2])
# Plot
waveregions(args[0],args[1],startindxs=si,endindxs=ei,
*args[3:],**kwargs)
# Add label
bovy_plot.bovy_text(r'$\mathrm{%s}$' % ((args[2].lower().capitalize())),
top_left=True,fontsize=10,backgroundcolor='w')
return None
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 test_windows(options):
elems= ['C','N','O','Na','Mg','Al','Si','S','K','Ca','Ti','V','Mn','Fe',
'Ni','Ce','Co','Cr','Cu','Ge','Nd','P','Rb','Y']
if options.savefilename is None or \
not os.path.exists(options.savefilename):
# Set default linelist for Turbospectrum or MOOG
if options.linelist is None and options.moog:
linelist= 'moog.201312161124.vac'
elif options.linelist is None:
linelist= 'turbospec.201312161124'
else:
linelist= options.linelist
# set up a model atmosphere for the requested atmospheric parameters
if options.arcturus:
options.teff= 4286.
options.logg= 1.66
options.metals= -0.52
options.am= 0.4
options.cm= 0.09
options.vm= 1.7
atm= atlas9.Atlas9Atmosphere(teff=options.teff,logg=options.logg,
metals=options.metals,
am=options.am,cm=options.cm)
# create baseline
if options.moog:
baseline= \
apogee.modelspec.moog.synth(modelatm=atm,
linelist=linelist,
lsf='all',cont='aspcap',
vmacro=6.,isotopes='arcturus',
vmicro=options.vm)
else:
baseline= \
apogee.modelspec.turbospec.synth(modelatm=atm,
linelist=linelist,
lsf='all',cont='aspcap',
vmacro=6.,isotopes='arcturus',
vmicro=options.vm)
# Loop through elements
elem_synspec= {}
# Run through once to simulate all differences
for elem in elems:
# First check that this element has windows
elemPath= apwindow.path(elem,dr=options.dr)
if not os.path.exists(elemPath): continue
# Simulate deltaAbu up and down
print "Working on %s" % (elem.capitalize())
abu= [atomic_number(elem),-options.deltaAbu,options.deltaAbu]
if options.moog:
synspec= \
apogee.modelspec.moog.synth(abu,
modelatm=atm,
linelist=linelist,
lsf='all',cont='aspcap',
vmacro=6.,
isotopes='arcturus',
vmicro=options.vm)
else:
synspec= \
apogee.modelspec.turbospec.synth(abu,
modelatm=atm,
linelist=linelist,
lsf='all',cont='aspcap',
vmacro=6.,
isotopes='arcturus',
vmicro=options.vm)
elem_synspec[elem]= synspec
if not options.savefilename is None:
save_pickles(options.savefilename,baseline,elem_synspec)
else:
with open(options.savefilename,'rb') as savefile:
baseline= pickle.load(savefile)
elem_synspec= pickle.load(savefile)
# Now run through the different elements again and plot windows for each
# with elements that vary significantly
colors= sns.color_palette("colorblind")
plotelems= [elem if not elem in ['C','N','O','Fe'] else '%s1' % elem
for elem in elems]
plotelems.extend(['C2','N2','O2','Fe2'])
for pelem in plotelems:
if '1' in pelem or '2' in pelem: elem = pelem[:-1]
else: elem= pelem
if not elem in elem_synspec: continue
# Figure out which elements have significant variations in these
# windows and always plot the element that should vary
elemIndx= apwindow.tophat(elem,dr=options.dr)
elemWeights= apwindow.read(elem,dr=options.dr)
elemWeights/= numpy.nansum(elemWeights)
# Start with the element in question
splot.windows(1.+options.amplify*(elem_synspec[elem][0]-baseline[0]),
pelem,
color=colors[0],
yrange=[0.,1.4],
plot_weights=True,
zorder=len(elems))
splot.windows(1.+options.amplify*(elem_synspec[elem][1]-baseline[0]),
pelem,
color=colors[0],overplot=True,
zorder=len(elems))
elem_shown= [elem]
# Run through the rest to figure out the order
elemVar= numpy.zeros(len(elems))
for ii,altElem in enumerate(elems):
if altElem == elem: continue
if not altElem in elem_synspec: continue
elemVar[ii]= 0.5*numpy.nansum((elem_synspec[altElem][0]-baseline[0])**2.*elemWeights)
elemVar[ii]+= 0.5*numpy.nansum((elem_synspec[altElem][1]-baseline[0])**2.*elemWeights)
jj= 0
sortindx= numpy.argsort(elemVar)[::-1]
for altElem in numpy.array(elems)[sortindx]:
if altElem == elem: continue
if not altElem in elem_synspec: continue
if numpy.fabs(\
numpy.nanmax([(elem_synspec[altElem][0]-baseline[0])[elemIndx],
(elem_synspec[altElem][1]-baseline[0])[elemIndx]]))\
> options.varthreshold:
jj+= 1
if jj >= len(colors): jj= len(colors)-1
elem_shown.append(altElem)
splot.windows(1.+options.amplify*(elem_synspec[altElem][0]-baseline[0]),
pelem,
color=colors[jj],overplot=True,
zorder=len(elems)-jj)
splot.windows(1.+options.amplify*(elem_synspec[altElem][1]-baseline[0]),
pelem,
color=colors[jj],overplot=True,
zorder=len(elems)-jj)
t = pyplot.gca().transData
fig= pyplot.gcf()
for s,c in zip(elem_shown,colors[:jj+1]):
xc= 0.05
if elem == 'K' or elem == 'Ce' or elem == 'Ge' or elem == 'Nd' \
or elem == 'Rb':
xc= apwindow.waveregions(elem,dr=options.dr,
pad=3)[0][0]-15000.+1.
text = pyplot.text(xc,1.2," "+(r"$\mathrm{%s}$" % s)+" ",color=c,
transform=t,size=16.,backgroundcolor='w')
text.draw(fig.canvas.get_renderer())
ex= text.get_window_extent()
t= transforms.offset_copy(text._transform,x=1.5*ex.width,
units='dots')
# Save
bovy_plot.bovy_end_print(options.plotfilename.replace('ELEM',pelem))
return None
