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 plot_afe_spectra(savename,plotname):
# Load the data
data= define_rcsample.get_rcsample()
data= data[data['SNR'] > 200.]
fehindx= (data['FE_H'] <= -0.35)*(data['FE_H'] > -0.45)
fehdata= data[fehindx]
# First compute the residuals and do the EM-PCA smoothing
if not os.path.exists(savename):
nspec= len(fehdata)
spec= numpy.zeros((nspec,7214))
specerr= numpy.zeros((nspec,7214))
for ii in range(nspec):
sys.stdout.write('\r'+"Loading spectrum %i / %i ...\r" % (ii+1,nspec))
sys.stdout.flush()
spec[ii]= apread.aspcapStar(fehdata['LOCATION_ID'][ii],
fehdata['APOGEE_ID'][ii],
ext=1,header=False,aspcapWavegrid=True)
specerr[ii]= apread.aspcapStar(fehdata['LOCATION_ID'][ii],
fehdata['APOGEE_ID'][ii],
ext=2,header=False,
aspcapWavegrid=True)
teffs= fehdata['FPARAM'][:,paramIndx('teff')]
loggs= fehdata['FPARAM'][:,paramIndx('logg')]
metals= fehdata[define_rcsample._FEHTAG]
cf, s, r= apcannon.quadfit(spec,specerr,
teffs-4800.,loggs-2.85,metals+0.3,
return_residuals=True)
pr= numpy.zeros_like(r)
# Deal w/ bad data
_MAXERR= 0.02
npca= 8
pca_input= r
pca_weights= (1./specerr**2.)
pca_weights[pca_weights < 1./_MAXERR**2.]= 0.
nanIndx= numpy.isnan(pca_input) + numpy.isnan(pca_weights)
pca_weights[nanIndx]= 0.
pca_input[nanIndx]= 0.
# Run EM-PCA
m= empca.empca(pca_input,pca_weights,nvec=npca,niter=25)#,silent=False)
for jj in range(nspec):
for kk in range(npca):
pr[jj]+= m.coeff[jj,kk]*m.eigvec[kk]
save_pickles(savename,pr,r,cf)
else:
with open(savename,'rb') as savefile:
pr= pickle.load(savefile)
# Now plot the various elements
colormap= cm.seismic
colorFunc= lambda afe: afe/0.25
widths= [3.5,2.]
yranges= [[-0.05,0.02],[-0.03,0.01]]
for ee, elem in enumerate(['S','Ca1']):
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),elem,)
kwargs= {'markLines':ii==4,
'yrange':yranges[ee],
'ylabel':'',
'cleanZero':False,
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':widths[ee]}
if ii>0: kwargs.pop('fig_width')
splot.windows(*args,**kwargs)
bovy_plot.bovy_end_print(plotname.replace('ELEM',
elem.lower().capitalize()))
# Also do Mg
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startindxs':[3012,3120,3990],
'endindxs':[3083,3158,4012],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':[15745.017,15753.189,15770.055,15958.836],
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':4.5,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{Mg}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','Mg'))
# Also do Si
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startindxs':[4469, 4624,5171, 7205, 7843],
'endindxs':[4488, 4644,5182, 7243, 7871],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':apwindow.lines('Si'),
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':6.,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{Si}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','Si2'))
# Also do Oxygen
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startlams':[15558,16242,16536,16720],
'endlams':[15566,16250,16544,16728],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':[15562,16246,16539,16723.5],
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':5.,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{O}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','O'))
# Also do Ti
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startindxs':[1116,2100,2899],
'endindxs':[1146,2124,2922],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':apwindow.lines('Ti'),
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':3.5,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{Ti}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','Ti'))
return None
