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