def plot_nonaxi(parser): (options, args) = parser.parse_args() if len(args) == 0 or options.plotfilename is None: parser.print_help() return #Read the data print "Reading the data ..." data = readVclosData( postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=options.bmax, ak=True, validfeh=options.indivfeh, #if indivfeh, we need validfeh cutmultiples=options.cutmultiples, jkmax=options.jkmax) #data= data[0:20] #HACK indx = (data['J0MAG'] - data['K0MAG'] < 0.5) data['J0MAG'][indx] = 0.5 + data['K0MAG'][indx] indx = (data['GLON'] <= 30.) data['GLON'][ indx] = 30.05 #Hack because the non-axi models don't go below Ro/2 #Cut outliers #data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)] #Set up the isochrone if not options.isofile is None and os.path.exists(options.isofile): print "Loading the isochrone model ..." isofile = open(options.isofile, 'rb') iso = pickle.load(isofile) if options.indivfeh: zs = pickle.load(isofile) elif options.varfeh: locl = pickle.load(isofile) isofile.close() else: print "Setting up the isochrone model ..." if options.indivfeh: #Load all isochrones iso = [] zs = numpy.arange(0.0005, 0.03005, 0.0005) for ii in range(len(zs)): iso.append( isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii])) elif options.varfeh: locs = list(set(data['LOCATION'])) iso = [] for ii in range(len(locs)): indx = (data['LOCATION'] == locs[ii]) locl = numpy.mean(data['GLON'][indx] * _DEGTORAD) iso.append( isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl)) else: iso = isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, expsfh=options.expsfh) if options.dwarf: iso = [ iso, isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, dwarf=True, expsfh=options.expsfh) ] else: iso = [iso] if not options.isofile is None: isofile = open(options.isofile, 'wb') pickle.dump(iso, isofile) if options.indivfeh: pickle.dump(zs, isofile) elif options.varfeh: pickle.dump(locl, isofile) isofile.close() df = None print "Pre-calculating isochrone distance prior ..." logpiso = numpy.zeros((len(data), _BINTEGRATENBINS)) ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS) dm = _dm(ds) for ii in range(len(data)): mh = data['H0MAG'][ii] - dm if options.indivfeh: #Find closest Z thisZ = isodist.FEH2Z(data[ii]['FEH']) indx = numpy.argmin(numpy.fabs(thisZ - zs)) logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + (data['J0MAG'] - data['K0MAG'])[ii], mh) elif options.varfeh: #Find correct iso indx = (locl == data[ii]['LOCATION']) logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + (data['J0MAG'] - data['K0MAG'])[ii], mh) else: logpiso[ii, :] = iso[0](numpy.zeros(_BINTEGRATENBINS) + (data['J0MAG'] - data['K0MAG'])[ii], mh) if options.dwarf: logpisodwarf = numpy.zeros((len(data), _BINTEGRATENBINS)) dwarfds = numpy.linspace(_BINTEGRATEDMIN_DWARF, _BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm = _dm(dwarfds) for ii in range(len(data)): mh = data['H0MAG'][ii] - dm logpisodwarf[ii, :] = iso[1](numpy.zeros(_BINTEGRATENBINS) + (data['J0MAG'] - data['K0MAG'])[ii], mh) else: logpisodwarf = None """ #Does not matter anyway #clean logpiso dataindx= [] for ii in range(len(data)): thislogpiso= logpiso[ii,:]-logsumexp(logpiso[ii,:]) if numpy.all(thislogpiso == 0.): dataindx.append(False) else: dataindx.append(True) dataindx= numpy.array(dataindx,dtype='bool') data= data[dataindx] logpiso= logpiso[dataindx,:] logpisodwarf= logpisodwarf[dataindx,:] """ #Calculate data means etc. #Calculate means locations = list(set(data['LOCATION'])) nlocs = len(locations) l_plate = numpy.zeros(nlocs) avg_plate = numpy.zeros(nlocs) sig_plate = numpy.zeros(nlocs) siga_plate = numpy.zeros(nlocs) for ii in range(nlocs): indx = (data['LOCATION'] == locations[ii]) l_plate[ii] = numpy.mean(data['GLON'][indx]) avg_plate[ii] = numpy.mean(data['VHELIO'][indx]) sig_plate[ii] = numpy.std(data['VHELIO'][indx]) siga_plate[ii] = numpy.std(data['VHELIO'][indx]) / numpy.sqrt( numpy.sum(indx)) #Calculate plate means and variances from the model #Load initial parameters from file savefile = open(args[0], 'rb') params = pickle.load(savefile) savefile.close() #First calculate fiducial model if not options.dwarf: logpisodwarf = None avg_plate_model_fid = calc_model(params, options, data, logpiso, logpisodwarf, df, nlocs, locations, iso) print l_plate, avg_plate_model_fid #Plot everything bovy_plot.bovy_print(fig_height=6., fig_width=7.) dx = 0.8 / 5. left, bottom, width, height = 0.1, 0.9 - dx, 0.8, dx axTop = pyplot.axes([left, bottom, width, height]) allaxes = [axTop] fig = pyplot.gcf() fig.sca(axTop) bovy_plot.bovy_plot([0., 360.], [0., 0.], '-', color='0.5', overplot=True) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid, 'ko', overplot=True) pyplot.errorbar(l_plate, avg_plate - avg_plate_model_fid, yerr=siga_plate, marker='o', color='k', linestyle='none', elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{fiducial}$', top_right=True, size=14.) thisax = pyplot.gca() thisax.set_ylim(-14.5, 14.5) pyplot.xlim(0., 360.) bovy_plot._add_ticks() nullfmt = NullFormatter() # no labels axTop.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0., 360.) bovy_plot._add_ticks() #Second is bar print "Reading bar file ..." barfile = '/work/bovy/data/bovy/nonaximw/bar/bar_rect_vr_tform-150_tsteady-4pi_51_101.sav' avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs, locations, 0.) print avg_plate_bar left, bottom, width, height = 0.1, 0.9 - 2. * dx, 0.8, dx thisax = pyplot.axes([left, bottom, width, height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0., 360.], [0., 0.], '-', color='0.5', overplot=True, zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid, 'o', overplot=True, color='0.6') pyplot.errorbar(l_plate, avg_plate - avg_plate_model_fid, yerr=siga_plate, marker='o', color='0.6', linestyle='none', elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, 'o', overplot=True, color='k') pyplot.errorbar(l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, yerr=siga_plate, marker='o', color='k', linestyle='none', elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{bar}$', top_right=True, size=14.) #pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') thisax.set_ylim(-14.5, 14.5) pyplot.xlim(0., 360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0., 360.) bovy_plot._add_ticks() #Third is spiral print "Reading spiral file ..." barfile = '/work/bovy/data/bovy/nonaximw/spiral/spiral_rect_vr_51_101.sav' avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs, locations, 0.) print avg_plate_bar left, bottom, width, height = 0.1, 0.9 - 3. * dx, 0.8, dx thisax = pyplot.axes([left, bottom, width, height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0., 360.], [0., 0.], '-', color='0.5', overplot=True, zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid, 'o', overplot=True, color='0.6') pyplot.errorbar(l_plate, avg_plate - avg_plate_model_fid, yerr=siga_plate, marker='o', color='0.6', linestyle='none', elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, 'o', overplot=True, color='k') pyplot.errorbar(l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, yerr=siga_plate, marker='o', color='k', linestyle='none', elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{spiral}$', top_right=True, size=14.) pyplot.ylabel( r'$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}} \ [\mathrm{km\ s}^{-1}]$' ) thisax.set_ylim(-14.5, 14.5) pyplot.xlim(0., 360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0., 360.) #Fourth is first elliptical print "Reading elliptical file ..." barfile = '/work/bovy/data/bovy/nonaximw/elliptical/el_rect_so_0.2_res_51_grid_101_tform_-150._tsteady_125._cp_0.05_nsigma_4.sav' avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs, locations, 0.) print avg_plate_bar left, bottom, width, height = 0.1, 0.9 - 4. * dx, 0.8, dx thisax = pyplot.axes([left, bottom, width, height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0., 360.], [0., 0.], '-', color='0.5', overplot=True, zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid, 'o', overplot=True, color='0.6') pyplot.errorbar(l_plate, avg_plate - avg_plate_model_fid, yerr=siga_plate, marker='o', color='0.6', linestyle='none', elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, 'o', overplot=True, color='k') pyplot.errorbar(l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, yerr=siga_plate, marker='o', color='k', linestyle='none', elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{elliptical}$', top_right=True, size=14.) #pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') thisax.set_ylim(-14.5, 14.5) pyplot.xlim(0., 360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0., 360.) bovy_plot._add_ticks() #Fourth is first elliptical print "Reading elliptical file ..." barfile = '/work/bovy/data/bovy/nonaximw/elliptical/el_rect_so_0.2_res_51_grid_101_tform_-150._tsteady_125._cp_0.05_nsigma_4.sav' avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs, locations, -45. * _DEGTORAD) print avg_plate_bar left, bottom, width, height = 0.1, 0.9 - 5. * dx, 0.8, dx thisax = pyplot.axes([left, bottom, width, height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0., 360.], [0., 0.], '-', color='0.5', overplot=True, zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid, 'o', overplot=True, color='0.6') pyplot.errorbar(l_plate, avg_plate - avg_plate_model_fid, yerr=siga_plate, marker='o', color='0.6', linestyle='none', elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, 'o', overplot=True, color='k') pyplot.errorbar(l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, yerr=siga_plate, marker='o', color='k', linestyle='none', elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{elliptical}$', top_right=True, size=14.) #pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') thisax.set_ylim(-14.5, 14.5) pyplot.xlim(0., 360.) bovy_plot._add_ticks() #thisax.xaxis.set_major_formatter(nullfmt) pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0., 360.) bovy_plot._add_ticks() #Save bovy_plot.bovy_end_print(options.plotfilename) return None
def plot_distanceprior(parser): (options,args)= parser.parse_args() #Read the data print "Reading the data ..." data= readVclosData(postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=options.bmax, ak=True, cutmultiples=options.cutmultiples, validfeh=options.indivfeh, #if indivfeh, we need validfeh jkmax=options.jkmax, datafilename=options.fakedata) l= data['GLON']*_DEGTORAD b= data['GLAT']*_DEGTORAD sinl= numpy.sin(l) cosl= numpy.cos(l) sinb= numpy.sin(b) cosb= numpy.cos(b) jk= data['J0MAG']-data['K0MAG'] jk[(jk < 0.5)]= 0.5 #BOVY: FIX THIS HACK BY EMAILING GAIL h= data['H0MAG'] #Set up the isochrone if not options.isofile is None and os.path.exists(options.isofile): print "Loading the isochrone model ..." isofile= open(options.isofile,'rb') iso= pickle.load(isofile) if options.indivfeh: zs= pickle.load(isofile) elif options.varfeh: locl= pickle.load(isofile) isofile.close() else: print "Setting up the isochrone model ..." if options.indivfeh: #Load all isochrones iso= [] zs= numpy.arange(0.0005,0.03005,0.0005) for ii in range(len(zs)): iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii])) elif options.varfeh: locs= list(set(data['LOCATION'])) iso= [] for ii in range(len(locs)): indx= (data['LOCATION'] == locs[ii]) locl= numpy.mean(data['GLON'][indx]*_DEGTORAD) iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl)) else: iso= isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z, expsfh=options.expsfh) #Set up polar grid res= 51 xgrid= numpy.linspace(0.,2.*math.pi*(1.-1./res/2.), 2*res) ygrid= numpy.linspace(0.5,2.8,res) plotxgrid= numpy.linspace(xgrid[0]-(xgrid[1]-xgrid[0])/2., xgrid[-1]+(xgrid[1]-xgrid[0])/2., len(xgrid)+1) plotygrid= numpy.linspace(ygrid[0]-(ygrid[1]-ygrid[0])/2., ygrid[-1]+(ygrid[1]-ygrid[0])/2., len(ygrid)+1) plotthis= numpy.zeros((2*res,res,len(data)))-numpy.finfo(numpy.dtype(numpy.float64)).max #_BINTEGRATENBINS= 11 #For quick testing ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX,_BINTEGRATENBINS) logpiso= numpy.zeros((len(data),_BINTEGRATENBINS)) dm= _dm(ds) for ii in range(len(data)): mh= h[ii]-dm if options.indivfeh: #Find closest Z thisZ= isodist.FEH2Z(data[ii]['FEH']) indx= numpy.argmin(numpy.fabs(thisZ-zs)) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+jk[ii],mh) elif options.varfeh: #Find correct iso indx= (locl == data[ii]['LOCATION']) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+jk[ii],mh) else: logpiso[ii,:]= iso(numpy.zeros(_BINTEGRATENBINS)+jk[ii],mh) for jj in range(_BINTEGRATENBINS): d= ds[jj]/_REFR0 R= numpy.sqrt(1.+d**2.-2.*d*cosl) indx= (R == 0.) R[indx]+= 0.0001 theta= numpy.arcsin(d/R*sinl) indx= (1./cosl < d)*(cosl > 0.) theta[indx]= numpy.pi-theta[indx] indx= (theta < 0.) theta[indx]+= 2.*math.pi thisout= _logpd([0.,1.],d,None,None, None,None,None, options,R,theta, 1.,0.,logpiso[:,jj]) #Find bin to which these contribute thetabin= numpy.floor((theta-xgrid[0])/(xgrid[1]-xgrid[0])+0.5) Rbin= numpy.floor((R-plotygrid[0])/(plotygrid[1]-plotygrid[0])) indx= (thetabin < 0) thetabin[indx]= 0 Rbin[indx]= 0 thisout[indx]= -numpy.finfo(numpy.dtype(numpy.float64)).max indx= (thetabin >= 2*res) thetabin[indx]= 0. #Has to be #Rbin[indx]= 0 #thisout[indx]= -numpy.finfo(numpy.dtype(numpy.float64)).max indx= (Rbin < 0) thetabin[indx]= 0 Rbin[indx]= 0 thisout[indx]= -numpy.finfo(numpy.dtype(numpy.float64)).max indx= (Rbin >= res) thetabin[indx]= 0 Rbin[indx]= 0 thisout[indx]= -numpy.finfo(numpy.dtype(numpy.float64)).max thetabin= thetabin.astype('int') Rbin= Rbin.astype('int') for ii in range(len(data)): plotthis[thetabin,Rbin,ii]= thisout[ii] #Normalize for ii in range(2*res): for jj in range(res): plotthis[ii,jj,0]= logsumexp(plotthis[ii,jj,:]) plotthis= plotthis[:,:,0] plotthis-= numpy.amax(plotthis) plotthis= numpy.exp(plotthis) plotthis[(plotthis == 0.)]= numpy.nan #Get los locations= list(set(data['LOCATION'])) nlocs= len(locations) l_plate= numpy.zeros(nlocs) for ii in range(nlocs): indx= (data['LOCATION'] == locations[ii]) l_plate[ii]= numpy.mean(data['GLON'][indx]) bovy_plot.bovy_print() ax= pyplot.subplot(111,projection='galpolar')#galpolar is in bovy_plot vmin, vmax= 0., 1. out= ax.pcolor(plotxgrid,plotygrid,plotthis.T,cmap='gist_yarg', vmin=vmin,vmax=vmax,zorder=2) #Overlay los for ii in range(nlocs): lds= numpy.linspace(0.,2.95,501) lt= numpy.zeros(len(lds)) lr= numpy.zeros(len(lds)) lr= numpy.sqrt(1.+lds**2.-2.*lds*numpy.cos(l_plate[ii]*_DEGTORAD)) lt= numpy.arcsin(lds/lr*numpy.sin(l_plate[ii]*_DEGTORAD)) indx= (1./numpy.cos(l_plate[ii]*_DEGTORAD) < lds)*(numpy.cos(l_plate[ii]*_DEGTORAD) > 0.) lt[indx]= numpy.pi-lt[indx] ax.plot(lt,lr, ls='--',color='w',zorder=3) from matplotlib.patches import Arrow, FancyArrowPatch arr= FancyArrowPatch(posA=(-math.pi/2.,1.8), posB=(-math.pi/4.,1.8), arrowstyle='->', connectionstyle='arc3,rad=%4.2f' % (-math.pi/16.), shrinkA=2.0, shrinkB=2.0, mutation_scale=20.0, mutation_aspect=None,fc='k') ax.add_patch(arr) bovy_plot.bovy_text(-math.pi/2.,1.97,r'$\mathrm{Galactic\ rotation}$', rotation=-22.5) radii= numpy.array([0.5,1.,1.5,2.,2.5]) labels= [] for r in radii: ax.plot(numpy.linspace(0.,2.*math.pi,501,), numpy.zeros(501)+r,ls='-',color='0.65',zorder=1,lw=0.5) labels.append(r'$%i$' % int(r*8.)) pyplot.rgrids(radii,labels=labels,angle=-32.5) bovy_plot.bovy_text(5.785,2.82,r'$\mathrm{kpc}$') azs= numpy.array([0.,45.,90.,135.,180.,225.,270.,315.])*_DEGTORAD for az in azs: ax.plot(numpy.zeros(501)+az, numpy.linspace(0.,2.8,501),'-',color='0.6',lw=0.5,zorder=1) #Sun bovy_plot.bovy_text(0.065,.9075,r'$\odot$') pyplot.ylim(0.,2.8) bovy_plot.bovy_end_print(options.plotfile)
def plot_chi2(parser): (options,args)= parser.parse_args() if len(args) == 0 or options.plotfilename is None: parser.print_help() return #Read the data print "Reading the data ..." data= readVclosData(postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=options.bmax, ak=True, cutmultiples=options.cutmultiples, validfeh=options.indivfeh, #if indivfeh, we need validfeh jkmax=options.jkmax, datafilename=options.fakedata) #HACK indx= (data['J0MAG']-data['K0MAG'] < 0.5) data['J0MAG'][indx]= 0.5+data['K0MAG'][indx] #Cut inner disk locations #data= data[(data['GLON'] > 75.)] #Cut outliers #data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)] print "Using %i data points ..." % len(data) #Set up the isochrone if not options.isofile is None and os.path.exists(options.isofile): print "Loading the isochrone model ..." isofile= open(options.isofile,'rb') iso= pickle.load(isofile) if options.indivfeh: zs= pickle.load(isofile) if options.varfeh: locl= pickle.load(isofile) isofile.close() else: print "Setting up the isochrone model ..." if options.indivfeh: #Load all isochrones iso= [] zs= numpy.arange(0.0005,0.03005,0.0005) for ii in range(len(zs)): iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii])) elif options.varfeh: locs= list(set(data['LOCATION'])) iso= [] for ii in range(len(locs)): indx= (data['LOCATION'] == locs[ii]) locl= numpy.mean(data['GLON'][indx]*_DEGTORAD) iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl)) else: iso= isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z, expsfh=options.expsfh) if options.dwarf: iso= [iso, isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z, dwarf=True,expsfh=options.expsfh)] else: iso= [iso] if not options.isofile is None: isofile= open(options.isofile,'wb') pickle.dump(iso,isofile) if options.indivfeh: pickle.dump(zs,isofile) elif options.varfeh: pickle.dump(locl,isofile) isofile.close() df= None print "Pre-calculating isochrone distance prior ..." logpiso= numpy.zeros((len(data),_BINTEGRATENBINS)) ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX, _BINTEGRATENBINS) dm= _dm(ds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm if options.indivfeh: #Find closest Z thisZ= isodist.FEH2Z(data[ii]['FEH']) indx= numpy.argmin((thisZ-zs)) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) elif options.varfeh: #Find correct iso indx= (locl == data[ii]['LOCATION']) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) if options.dwarf: logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS)) dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm= _dm(dwarfds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpisodwarf= None #Load initial parameters from file savefile= open(args[0],'rb') params= pickle.load(savefile) if not options.index is None: params= params[options.index] savefile.close() #params[0]= 245./235. #params[1]= 8.5/8. #Calculate data means etc. #Calculate means locations= list(set(data['LOCATION'])) nlocs= len(locations) l_plate= numpy.zeros(nlocs) avg_plate= numpy.zeros(nlocs) sig_plate= numpy.zeros(nlocs) siga_plate= numpy.zeros(nlocs) sigerr_plate= numpy.zeros(nlocs) fidlogl= logl.logl(init=params,data=data,options=options) logl_plate= numpy.zeros(nlocs) for ii in range(nlocs): indx= (data['LOCATION'] == locations[ii]) l_plate[ii]= numpy.mean(data['GLON'][indx]) avg_plate[ii]= numpy.mean(data['VHELIO'][indx]) sig_plate[ii]= numpy.std(data['VHELIO'][indx]) siga_plate[ii]= numpy.std(data['VHELIO'][indx])/numpy.sqrt(numpy.sum(indx)) sigerr_plate[ii]= bootstrap_sigerr(data['VHELIO'][indx]) #Logl logl_plate[ii]= -2.*(numpy.sum(fidlogl[indx])-numpy.sum(fidlogl)/len(indx)*numpy.sum(indx)) #Calculate plate means and variances from the model avg_plate_model= numpy.zeros(nlocs) sig_plate_model= numpy.zeros(nlocs) for ii in range(nlocs): #Calculate vlos | los indx= (data['LOCATION'] == locations[ii]) thesedata= data[indx] thislogpiso= logpiso[indx,:] if options.dwarf: thislogpisodwarf= logpisodwarf[indx,:] else: thislogpisodwarf= None vlos= numpy.linspace(-200.,200.,options.nvlos) pvlos= numpy.zeros(options.nvlos) if not options.multi is None: pvlos= multi.parallel_map((lambda x: pvlosplate(params,vlos[x], thesedata, df,options, thislogpiso, thislogpisodwarf,iso)), range(options.nvlos), numcores=numpy.amin([len(vlos),multiprocessing.cpu_count(),options.multi])) else: for jj in range(options.nvlos): print jj pvlos[jj]= pvlosplate(params,vlos[jj],thesedata,df,options, thislogpiso,thislogpisodwarf,iso) pvlos-= logsumexp(pvlos) pvlos= numpy.exp(pvlos) #Calculate mean and velocity dispersion avg_plate_model[ii]= numpy.sum(vlos*pvlos) sig_plate_model[ii]= numpy.sqrt(numpy.sum(vlos**2.*pvlos)\ -avg_plate_model[ii]**2.) #Plot everything left, bottom, width, height= 0.1, 0.4, 0.8, 0.3 axTop= pyplot.axes([left,bottom,width,height]) left, bottom, width, height= 0.1, 0.1, 0.8, 0.3 axChi2= pyplot.axes([left,bottom,width,height]) #left, bottom, width, height= 0.1, 0.1, 0.8, 0.2 #axSig= pyplot.axes([left,bottom,width,height]) fig= pyplot.gcf() #Plot the difference fig.sca(axTop) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True) bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model, 'ko',overplot=True) pyplot.errorbar(l_plate,avg_plate-avg_plate_model, yerr=siga_plate,marker='o',color='k',linestyle='none',elinestyle='-') pyplot.ylabel(r'$\langle v_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle v_{\mathrm{los}}\rangle_{\mathrm{model}}$') pyplot.ylim(-14.5,14.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() nullfmt = NullFormatter() # no labels axTop.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Plot the chi2 fig.sca(axChi2) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True) bovy_plot.bovy_plot(l_plate, logl_plate, 'ko',overplot=True) pyplot.ylabel(r'$\Delta \chi^2$') #pyplot.ylim(numpy.amin(logl_plate),numpy.amax(logl_plate)) pyplot.ylim(-150.,150.) pyplot.xlim(0.,360.) bovy_plot._add_ticks() pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Save bovy_plot.bovy_end_print(options.plotfilename) return None
def plot_distanceprior(parser): (options, args) = parser.parse_args() #Read the data print "Reading the data ..." data = readVclosData( postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=options.bmax, ak=True, cutmultiples=options.cutmultiples, validfeh=options.indivfeh, #if indivfeh, we need validfeh jkmax=options.jkmax, datafilename=options.fakedata) l = data['GLON'] * _DEGTORAD b = data['GLAT'] * _DEGTORAD sinl = numpy.sin(l) cosl = numpy.cos(l) sinb = numpy.sin(b) cosb = numpy.cos(b) jk = data['J0MAG'] - data['K0MAG'] jk[(jk < 0.5)] = 0.5 #BOVY: FIX THIS HACK BY EMAILING GAIL h = data['H0MAG'] #Set up the isochrone if not options.isofile is None and os.path.exists(options.isofile): print "Loading the isochrone model ..." isofile = open(options.isofile, 'rb') iso = pickle.load(isofile) if options.indivfeh: zs = pickle.load(isofile) elif options.varfeh: locl = pickle.load(isofile) isofile.close() else: print "Setting up the isochrone model ..." if options.indivfeh: #Load all isochrones iso = [] zs = numpy.arange(0.0005, 0.03005, 0.0005) for ii in range(len(zs)): iso.append( isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii])) elif options.varfeh: locs = list(set(data['LOCATION'])) iso = [] for ii in range(len(locs)): indx = (data['LOCATION'] == locs[ii]) locl = numpy.mean(data['GLON'][indx] * _DEGTORAD) iso.append( isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl)) else: iso = isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, expsfh=options.expsfh) #Set up polar grid res = 51 xgrid = numpy.linspace(0., 2. * math.pi * (1. - 1. / res / 2.), 2 * res) ygrid = numpy.linspace(0.5, 2.8, res) plotxgrid = numpy.linspace(xgrid[0] - (xgrid[1] - xgrid[0]) / 2., xgrid[-1] + (xgrid[1] - xgrid[0]) / 2., len(xgrid) + 1) plotygrid = numpy.linspace(ygrid[0] - (ygrid[1] - ygrid[0]) / 2., ygrid[-1] + (ygrid[1] - ygrid[0]) / 2., len(ygrid) + 1) plotthis = numpy.zeros((2 * res, res, len(data))) - numpy.finfo( numpy.dtype(numpy.float64)).max #_BINTEGRATENBINS= 11 #For quick testing ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS) logpiso = numpy.zeros((len(data), _BINTEGRATENBINS)) dm = _dm(ds) for ii in range(len(data)): mh = h[ii] - dm if options.indivfeh: #Find closest Z thisZ = isodist.FEH2Z(data[ii]['FEH']) indx = numpy.argmin(numpy.fabs(thisZ - zs)) logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + jk[ii], mh) elif options.varfeh: #Find correct iso indx = (locl == data[ii]['LOCATION']) logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + jk[ii], mh) else: logpiso[ii, :] = iso(numpy.zeros(_BINTEGRATENBINS) + jk[ii], mh) for jj in range(_BINTEGRATENBINS): d = ds[jj] / _REFR0 R = numpy.sqrt(1. + d**2. - 2. * d * cosl) indx = (R == 0.) R[indx] += 0.0001 theta = numpy.arcsin(d / R * sinl) indx = (1. / cosl < d) * (cosl > 0.) theta[indx] = numpy.pi - theta[indx] indx = (theta < 0.) theta[indx] += 2. * math.pi thisout = _logpd([0., 1.], d, None, None, None, None, None, options, R, theta, 1., 0., logpiso[:, jj]) #Find bin to which these contribute thetabin = numpy.floor((theta - xgrid[0]) / (xgrid[1] - xgrid[0]) + 0.5) Rbin = numpy.floor((R - plotygrid[0]) / (plotygrid[1] - plotygrid[0])) indx = (thetabin < 0) thetabin[indx] = 0 Rbin[indx] = 0 thisout[indx] = -numpy.finfo(numpy.dtype(numpy.float64)).max indx = (thetabin >= 2 * res) thetabin[indx] = 0. #Has to be #Rbin[indx]= 0 #thisout[indx]= -numpy.finfo(numpy.dtype(numpy.float64)).max indx = (Rbin < 0) thetabin[indx] = 0 Rbin[indx] = 0 thisout[indx] = -numpy.finfo(numpy.dtype(numpy.float64)).max indx = (Rbin >= res) thetabin[indx] = 0 Rbin[indx] = 0 thisout[indx] = -numpy.finfo(numpy.dtype(numpy.float64)).max thetabin = thetabin.astype('int') Rbin = Rbin.astype('int') for ii in range(len(data)): plotthis[thetabin, Rbin, ii] = thisout[ii] #Normalize for ii in range(2 * res): for jj in range(res): plotthis[ii, jj, 0] = logsumexp(plotthis[ii, jj, :]) plotthis = plotthis[:, :, 0] plotthis -= numpy.amax(plotthis) plotthis = numpy.exp(plotthis) plotthis[(plotthis == 0.)] = numpy.nan #Get los locations = list(set(data['LOCATION'])) nlocs = len(locations) l_plate = numpy.zeros(nlocs) for ii in range(nlocs): indx = (data['LOCATION'] == locations[ii]) l_plate[ii] = numpy.mean(data['GLON'][indx]) bovy_plot.bovy_print() ax = pyplot.subplot(111, projection='galpolar') #galpolar is in bovy_plot vmin, vmax = 0., 1. out = ax.pcolor(plotxgrid, plotygrid, plotthis.T, cmap='gist_yarg', vmin=vmin, vmax=vmax, zorder=2) #Overlay los for ii in range(nlocs): lds = numpy.linspace(0., 2.95, 501) lt = numpy.zeros(len(lds)) lr = numpy.zeros(len(lds)) lr = numpy.sqrt(1. + lds**2. - 2. * lds * numpy.cos(l_plate[ii] * _DEGTORAD)) lt = numpy.arcsin(lds / lr * numpy.sin(l_plate[ii] * _DEGTORAD)) indx = (1. / numpy.cos(l_plate[ii] * _DEGTORAD) < lds) * (numpy.cos( l_plate[ii] * _DEGTORAD) > 0.) lt[indx] = numpy.pi - lt[indx] ax.plot(lt, lr, ls='--', color='w', zorder=3) from matplotlib.patches import Arrow, FancyArrowPatch arr = FancyArrowPatch(posA=(-math.pi / 2., 1.8), posB=(-math.pi / 4., 1.8), arrowstyle='->', connectionstyle='arc3,rad=%4.2f' % (-math.pi / 16.), shrinkA=2.0, shrinkB=2.0, mutation_scale=20.0, mutation_aspect=None, fc='k') ax.add_patch(arr) bovy_plot.bovy_text(-math.pi / 2., 1.97, r'$\mathrm{Galactic\ rotation}$', rotation=-22.5) radii = numpy.array([0.5, 1., 1.5, 2., 2.5]) labels = [] for r in radii: ax.plot(numpy.linspace( 0., 2. * math.pi, 501, ), numpy.zeros(501) + r, ls='-', color='0.65', zorder=1, lw=0.5) labels.append(r'$%i$' % int(r * 8.)) pyplot.rgrids(radii, labels=labels, angle=-32.5) bovy_plot.bovy_text(5.785, 2.82, r'$\mathrm{kpc}$') azs = numpy.array([0., 45., 90., 135., 180., 225., 270., 315.]) * _DEGTORAD for az in azs: ax.plot(numpy.zeros(501) + az, numpy.linspace(0., 2.8, 501), '-', color='0.6', lw=0.5, zorder=1) #Sun bovy_plot.bovy_text(0.065, .9075, r'$\odot$') pyplot.ylim(0., 2.8) bovy_plot.bovy_end_print(options.plotfile)
def plot_bestfit(parser): (options, args) = parser.parse_args() if len(args) == 0 or options.plotfilename is None: parser.print_help() return # Read the data print "Reading the data ..." data = readVclosData( postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=options.bmax, ak=True, cutmultiples=options.cutmultiples, validfeh=options.indivfeh, # if indivfeh, we need validfeh jkmax=options.jkmax, datafilename=options.fakedata, ) # HACK indx = data["J0MAG"] - data["K0MAG"] < 0.5 data["J0MAG"][indx] = 0.5 + data["K0MAG"][indx] # Cut inner disk locations # data= data[(data['GLON'] > 75.)] # Cut outliers # data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)] print "Using %i data points ..." % len(data) # Set up the isochrone if not options.isofile is None and os.path.exists(options.isofile): print "Loading the isochrone model ..." isofile = open(options.isofile, "rb") iso = pickle.load(isofile) if options.indivfeh: zs = pickle.load(isofile) if options.varfeh: locl = pickle.load(isofile) isofile.close() else: print "Setting up the isochrone model ..." if options.indivfeh: # Load all isochrones iso = [] zs = numpy.arange(0.0005, 0.03005, 0.0005) for ii in range(len(zs)): iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii])) elif options.varfeh: locs = list(set(data["LOCATION"])) iso = [] for ii in range(len(locs)): indx = data["LOCATION"] == locs[ii] locl = numpy.mean(data["GLON"][indx] * _DEGTORAD) iso.append( isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl) ) else: iso = isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, expsfh=options.expsfh) if options.dwarf: iso = [iso, isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, dwarf=True, expsfh=options.expsfh)] else: iso = [iso] if not options.isofile is None: isofile = open(options.isofile, "wb") pickle.dump(iso, isofile) if options.indivfeh: pickle.dump(zs, isofile) elif options.varfeh: pickle.dump(locl, isofile) isofile.close() df = None print "Pre-calculating isochrone distance prior ..." logpiso = numpy.zeros((len(data), _BINTEGRATENBINS)) ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS) dm = _dm(ds) for ii in range(len(data)): mh = data["H0MAG"][ii] - dm if options.indivfeh: # Find closest Z thisZ = isodist.FEH2Z(data[ii]["FEH"]) indx = numpy.argmin(numpy.fabs(thisZ - zs)) logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + (data["J0MAG"] - data["K0MAG"])[ii], mh) elif options.varfeh: # Find correct iso indx = locl == data[ii]["LOCATION"] logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + (data["J0MAG"] - data["K0MAG"])[ii], mh) else: logpiso[ii, :] = iso[0](numpy.zeros(_BINTEGRATENBINS) + (data["J0MAG"] - data["K0MAG"])[ii], mh) if options.dwarf: logpisodwarf = numpy.zeros((len(data), _BINTEGRATENBINS)) dwarfds = numpy.linspace(_BINTEGRATEDMIN_DWARF, _BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm = _dm(dwarfds) for ii in range(len(data)): mh = data["H0MAG"][ii] - dm logpisodwarf[ii, :] = iso[1](numpy.zeros(_BINTEGRATENBINS) + (data["J0MAG"] - data["K0MAG"])[ii], mh) else: logpisodwarf = None # Calculate data means etc. # Calculate means locations = list(set(data["LOCATION"])) nlocs = len(locations) l_plate = numpy.zeros(nlocs) avg_plate = numpy.zeros(nlocs) sig_plate = numpy.zeros(nlocs) siga_plate = numpy.zeros(nlocs) sigerr_plate = numpy.zeros(nlocs) for ii in range(nlocs): indx = data["LOCATION"] == locations[ii] l_plate[ii] = numpy.mean(data["GLON"][indx]) avg_plate[ii] = numpy.mean(data["VHELIO"][indx]) sig_plate[ii] = numpy.std(data["VHELIO"][indx]) siga_plate[ii] = numpy.std(data["VHELIO"][indx]) / numpy.sqrt(numpy.sum(indx)) sigerr_plate[ii] = bootstrap_sigerr(data["VHELIO"][indx]) # Calculate plate means and variances from the model # Load initial parameters from file savefile = open(args[0], "rb") params = pickle.load(savefile) if not options.index is None: params = params[options.index] savefile.close() # params[0]= 245./235. # params[1]= 8.5/8. avg_plate_model = numpy.zeros(nlocs) sig_plate_model = numpy.zeros(nlocs) for ii in range(nlocs): # Calculate vlos | los indx = data["LOCATION"] == locations[ii] thesedata = data[indx] thislogpiso = logpiso[indx, :] if options.dwarf: thislogpisodwarf = logpisodwarf[indx, :] else: thislogpisodwarf = None vlos = numpy.linspace(-200.0, 200.0, options.nvlos) pvlos = numpy.zeros(options.nvlos) if not options.multi is None: pvlos = multi.parallel_map( (lambda x: pvlosplate(params, vlos[x], thesedata, df, options, thislogpiso, thislogpisodwarf, iso)), range(options.nvlos), numcores=numpy.amin([len(vlos), multiprocessing.cpu_count(), options.multi]), ) else: for jj in range(options.nvlos): print jj pvlos[jj] = pvlosplate(params, vlos[jj], thesedata, df, options, thislogpiso, thislogpisodwarf, iso) pvlos -= logsumexp(pvlos) pvlos = numpy.exp(pvlos) # Calculate mean and velocity dispersion avg_plate_model[ii] = numpy.sum(vlos * pvlos) sig_plate_model[ii] = numpy.sqrt(numpy.sum(vlos ** 2.0 * pvlos) - avg_plate_model[ii] ** 2.0) # Plot everything left, bottom, width, height = 0.1, 0.4, 0.8, 0.5 axTop = pyplot.axes([left, bottom, width, height]) left, bottom, width, height = 0.1, 0.1, 0.8, 0.3 axMean = pyplot.axes([left, bottom, width, height]) # left, bottom, width, height= 0.1, 0.1, 0.8, 0.2 # axSig= pyplot.axes([left,bottom,width,height]) fig = pyplot.gcf() fig.sca(axTop) pyplot.ylabel(r"$\mathrm{Heliocentric\ velocity}\ [\mathrm{km\ s}^{-1}]$") pyplot.xlabel(r"$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$") pyplot.xlim(0.0, 360.0) pyplot.ylim(-200.0, 200.0) nullfmt = NullFormatter() # no labels axTop.xaxis.set_major_formatter(nullfmt) bovy_plot.bovy_plot(data["GLON"], data["VHELIO"], "k,", yrange=[-200.0, 200.0], xrange=[0.0, 360.0], overplot=True) ndata_t = int(math.floor(len(data) / 1000.0)) ndata_h = len(data) - ndata_t * 1000 bovy_plot.bovy_plot(l_plate, avg_plate, "o", overplot=True, mfc="0.5", mec="none") bovy_plot.bovy_plot(l_plate, avg_plate_model, "x", overplot=True, ms=10.0, mew=1.5, color="0.7") # Legend bovy_plot.bovy_plot([260.0], [150.0], "k,", overplot=True) bovy_plot.bovy_plot([260.0], [120.0], "o", mfc="0.5", mec="none", overplot=True) bovy_plot.bovy_plot([260.0], [90.0], "x", ms=10.0, mew=1.5, color="0.7", overplot=True) bovy_plot.bovy_text(270.0, 145.0, r"$\mathrm{data}$") bovy_plot.bovy_text(270.0, 115.0, r"$\mathrm{data\ mean}$") bovy_plot.bovy_text(270.0, 85.0, r"$\mathrm{model\ mean}$") bovy_plot._add_ticks() # Now plot the difference fig.sca(axMean) bovy_plot.bovy_plot([0.0, 360.0], [0.0, 0.0], "-", color="0.5", overplot=True) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model, "ko", overplot=True) pyplot.errorbar( l_plate, avg_plate - avg_plate_model, yerr=siga_plate, marker="o", color="k", linestyle="none", elinestyle="-" ) pyplot.ylabel(r"$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}}$") pyplot.ylim(-14.5, 14.5) pyplot.xlim(0.0, 360.0) bovy_plot._add_ticks() # axMean.xaxis.set_major_formatter(nullfmt) pyplot.xlabel(r"$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$") pyplot.xlim(0.0, 360.0) bovy_plot._add_ticks() # Save bovy_plot.bovy_end_print(options.plotfilename) return None # Sigma fig.sca(axSig) pyplot.plot([0.0, 360.0], [1.0, 1.0], "-", color="0.5") bovy_plot.bovy_plot(l_plate, sig_plate / sig_plate_model, "ko", overplot=True) pyplot.errorbar( l_plate, sig_plate / sig_plate_model, yerr=sigerr_plate / sig_plate_model, marker="o", color="k", linestyle="none", elinestyle="-", ) pyplot.ylabel(r"$\sigma_{\mathrm{los}}^{\mathrm{data}}/ \sigma_{\mathrm{los}}^{\mathrm{model}}$") pyplot.ylim(0.5, 1.5)
def plot_nonaxi(parser): (options, args) = parser.parse_args() if len(args) == 0 or options.plotfilename is None: parser.print_help() return # Read the data print "Reading the data ..." data = readVclosData( postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=options.bmax, ak=True, validfeh=options.indivfeh, # if indivfeh, we need validfeh cutmultiples=options.cutmultiples, jkmax=options.jkmax, ) # data= data[0:20] # HACK indx = data["J0MAG"] - data["K0MAG"] < 0.5 data["J0MAG"][indx] = 0.5 + data["K0MAG"][indx] indx = data["GLON"] <= 30.0 data["GLON"][indx] = 30.05 # Hack because the non-axi models don't go below Ro/2 # Cut outliers # data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)] # Set up the isochrone if not options.isofile is None and os.path.exists(options.isofile): print "Loading the isochrone model ..." isofile = open(options.isofile, "rb") iso = pickle.load(isofile) if options.indivfeh: zs = pickle.load(isofile) elif options.varfeh: locl = pickle.load(isofile) isofile.close() else: print "Setting up the isochrone model ..." if options.indivfeh: # Load all isochrones iso = [] zs = numpy.arange(0.0005, 0.03005, 0.0005) for ii in range(len(zs)): iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii])) elif options.varfeh: locs = list(set(data["LOCATION"])) iso = [] for ii in range(len(locs)): indx = data["LOCATION"] == locs[ii] locl = numpy.mean(data["GLON"][indx] * _DEGTORAD) iso.append( isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl) ) else: iso = isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, expsfh=options.expsfh) if options.dwarf: iso = [iso, isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, dwarf=True, expsfh=options.expsfh)] else: iso = [iso] if not options.isofile is None: isofile = open(options.isofile, "wb") pickle.dump(iso, isofile) if options.indivfeh: pickle.dump(zs, isofile) elif options.varfeh: pickle.dump(locl, isofile) isofile.close() df = None print "Pre-calculating isochrone distance prior ..." logpiso = numpy.zeros((len(data), _BINTEGRATENBINS)) ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS) dm = _dm(ds) for ii in range(len(data)): mh = data["H0MAG"][ii] - dm if options.indivfeh: # Find closest Z thisZ = isodist.FEH2Z(data[ii]["FEH"]) indx = numpy.argmin(numpy.fabs(thisZ - zs)) logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + (data["J0MAG"] - data["K0MAG"])[ii], mh) elif options.varfeh: # Find correct iso indx = locl == data[ii]["LOCATION"] logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + (data["J0MAG"] - data["K0MAG"])[ii], mh) else: logpiso[ii, :] = iso[0](numpy.zeros(_BINTEGRATENBINS) + (data["J0MAG"] - data["K0MAG"])[ii], mh) if options.dwarf: logpisodwarf = numpy.zeros((len(data), _BINTEGRATENBINS)) dwarfds = numpy.linspace(_BINTEGRATEDMIN_DWARF, _BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm = _dm(dwarfds) for ii in range(len(data)): mh = data["H0MAG"][ii] - dm logpisodwarf[ii, :] = iso[1](numpy.zeros(_BINTEGRATENBINS) + (data["J0MAG"] - data["K0MAG"])[ii], mh) else: logpisodwarf = None """ #Does not matter anyway #clean logpiso dataindx= [] for ii in range(len(data)): thislogpiso= logpiso[ii,:]-logsumexp(logpiso[ii,:]) if numpy.all(thislogpiso == 0.): dataindx.append(False) else: dataindx.append(True) dataindx= numpy.array(dataindx,dtype='bool') data= data[dataindx] logpiso= logpiso[dataindx,:] logpisodwarf= logpisodwarf[dataindx,:] """ # Calculate data means etc. # Calculate means locations = list(set(data["LOCATION"])) nlocs = len(locations) l_plate = numpy.zeros(nlocs) avg_plate = numpy.zeros(nlocs) sig_plate = numpy.zeros(nlocs) siga_plate = numpy.zeros(nlocs) for ii in range(nlocs): indx = data["LOCATION"] == locations[ii] l_plate[ii] = numpy.mean(data["GLON"][indx]) avg_plate[ii] = numpy.mean(data["VHELIO"][indx]) sig_plate[ii] = numpy.std(data["VHELIO"][indx]) siga_plate[ii] = numpy.std(data["VHELIO"][indx]) / numpy.sqrt(numpy.sum(indx)) # Calculate plate means and variances from the model # Load initial parameters from file savefile = open(args[0], "rb") params = pickle.load(savefile) savefile.close() # First calculate fiducial model if not options.dwarf: logpisodwarf = None avg_plate_model_fid = calc_model(params, options, data, logpiso, logpisodwarf, df, nlocs, locations, iso) print l_plate, avg_plate_model_fid # Plot everything bovy_plot.bovy_print(fig_height=6.0, fig_width=7.0) dx = 0.8 / 5.0 left, bottom, width, height = 0.1, 0.9 - dx, 0.8, dx axTop = pyplot.axes([left, bottom, width, height]) allaxes = [axTop] fig = pyplot.gcf() fig.sca(axTop) bovy_plot.bovy_plot([0.0, 360.0], [0.0, 0.0], "-", color="0.5", overplot=True) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid, "ko", overplot=True) pyplot.errorbar( l_plate, avg_plate - avg_plate_model_fid, yerr=siga_plate, marker="o", color="k", linestyle="none", elinestyle="-", ) bovy_plot.bovy_text(r"$\mathrm{fiducial}$", top_right=True, size=14.0) thisax = pyplot.gca() thisax.set_ylim(-14.5, 14.5) pyplot.xlim(0.0, 360.0) bovy_plot._add_ticks() nullfmt = NullFormatter() # no labels axTop.xaxis.set_major_formatter(nullfmt) # pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.0, 360.0) bovy_plot._add_ticks() # Second is bar print "Reading bar file ..." barfile = "/work/bovy/data/bovy/nonaximw/bar/bar_rect_vr_tform-150_tsteady-4pi_51_101.sav" avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs, locations, 0.0) print avg_plate_bar left, bottom, width, height = 0.1, 0.9 - 2.0 * dx, 0.8, dx thisax = pyplot.axes([left, bottom, width, height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.0, 360.0], [0.0, 0.0], "-", color="0.5", overplot=True, zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid, "o", overplot=True, color="0.6") pyplot.errorbar( l_plate, avg_plate - avg_plate_model_fid, yerr=siga_plate, marker="o", color="0.6", linestyle="none", elinestyle="-", ) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, "o", overplot=True, color="k") pyplot.errorbar( l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, yerr=siga_plate, marker="o", color="k", linestyle="none", elinestyle="-", ) bovy_plot.bovy_text(r"$\mathrm{bar}$", top_right=True, size=14.0) # pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') thisax.set_ylim(-14.5, 14.5) pyplot.xlim(0.0, 360.0) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) # pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.0, 360.0) bovy_plot._add_ticks() # Third is spiral print "Reading spiral file ..." barfile = "/work/bovy/data/bovy/nonaximw/spiral/spiral_rect_vr_51_101.sav" avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs, locations, 0.0) print avg_plate_bar left, bottom, width, height = 0.1, 0.9 - 3.0 * dx, 0.8, dx thisax = pyplot.axes([left, bottom, width, height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.0, 360.0], [0.0, 0.0], "-", color="0.5", overplot=True, zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid, "o", overplot=True, color="0.6") pyplot.errorbar( l_plate, avg_plate - avg_plate_model_fid, yerr=siga_plate, marker="o", color="0.6", linestyle="none", elinestyle="-", ) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, "o", overplot=True, color="k") pyplot.errorbar( l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, yerr=siga_plate, marker="o", color="k", linestyle="none", elinestyle="-", ) bovy_plot.bovy_text(r"$\mathrm{spiral}$", top_right=True, size=14.0) pyplot.ylabel(r"$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}} \ [\mathrm{km\ s}^{-1}]$") thisax.set_ylim(-14.5, 14.5) pyplot.xlim(0.0, 360.0) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) # pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.0, 360.0) # Fourth is first elliptical print "Reading elliptical file ..." barfile = "/work/bovy/data/bovy/nonaximw/elliptical/el_rect_so_0.2_res_51_grid_101_tform_-150._tsteady_125._cp_0.05_nsigma_4.sav" avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs, locations, 0.0) print avg_plate_bar left, bottom, width, height = 0.1, 0.9 - 4.0 * dx, 0.8, dx thisax = pyplot.axes([left, bottom, width, height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.0, 360.0], [0.0, 0.0], "-", color="0.5", overplot=True, zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid, "o", overplot=True, color="0.6") pyplot.errorbar( l_plate, avg_plate - avg_plate_model_fid, yerr=siga_plate, marker="o", color="0.6", linestyle="none", elinestyle="-", ) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, "o", overplot=True, color="k") pyplot.errorbar( l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, yerr=siga_plate, marker="o", color="k", linestyle="none", elinestyle="-", ) bovy_plot.bovy_text(r"$\mathrm{elliptical}$", top_right=True, size=14.0) # pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') thisax.set_ylim(-14.5, 14.5) pyplot.xlim(0.0, 360.0) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) # pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.0, 360.0) bovy_plot._add_ticks() # Fourth is first elliptical print "Reading elliptical file ..." barfile = "/work/bovy/data/bovy/nonaximw/elliptical/el_rect_so_0.2_res_51_grid_101_tform_-150._tsteady_125._cp_0.05_nsigma_4.sav" avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs, locations, -45.0 * _DEGTORAD) print avg_plate_bar left, bottom, width, height = 0.1, 0.9 - 5.0 * dx, 0.8, dx thisax = pyplot.axes([left, bottom, width, height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.0, 360.0], [0.0, 0.0], "-", color="0.5", overplot=True, zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid, "o", overplot=True, color="0.6") pyplot.errorbar( l_plate, avg_plate - avg_plate_model_fid, yerr=siga_plate, marker="o", color="0.6", linestyle="none", elinestyle="-", ) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, "o", overplot=True, color="k") pyplot.errorbar( l_plate, avg_plate - avg_plate_model_fid - avg_plate_bar, yerr=siga_plate, marker="o", color="k", linestyle="none", elinestyle="-", ) bovy_plot.bovy_text(r"$\mathrm{elliptical}$", top_right=True, size=14.0) # pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') thisax.set_ylim(-14.5, 14.5) pyplot.xlim(0.0, 360.0) bovy_plot._add_ticks() # thisax.xaxis.set_major_formatter(nullfmt) pyplot.xlabel(r"$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$") pyplot.xlim(0.0, 360.0) bovy_plot._add_ticks() # Save bovy_plot.bovy_end_print(options.plotfilename) return None
def createFakeData(parser): options, args= parser.parse_args() if len(args) == 0: parser.print_help() return if os.path.exists(options.plotfile): print "Outfile "+options.plotfile+" exists ..." print "Returning ..." return None #Read the data numpy.random.seed(options.seed) print "Reading the data ..." data= readVclosData(postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=options.bmax, validfeh=options.indivfeh, #if indivfeh, we need validfeh ak=True, cutmultiples=options.cutmultiples, jkmax=options.jkmax) #HACK indx= (data['J0MAG']-data['K0MAG'] < 0.5) data['J0MAG'][indx]= 0.5+data['K0MAG'][indx] #Set up the isochrone #Set up the isochrone if not options.isofile is None and os.path.exists(options.isofile): print "Loading the isochrone model ..." isofile= open(options.isofile,'rb') iso= pickle.load(isofile) if options.indivfeh: zs= pickle.load(isofile) elif options.varfeh: locl= pickle.load(isofile) isofile.close() else: print "Setting up the isochrone model ..." if options.indivfeh: #Load all isochrones iso= [] zs= numpy.arange(0.0005,0.03005,0.0005) for ii in range(len(zs)): iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii])) elif options.varfeh: locs= list(set(data['LOCATION'])) iso= [] for ii in range(len(locs)): indx= (data['LOCATION'] == locs[ii]) locl= numpy.mean(data['GLON'][indx]*_DEGTORAD) iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl)) else: iso= isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z, expsfh=options.expsfh) if options.dwarf: iso= [iso, isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z, dwarf=True,expsfh=options.expsfh)] else: iso= [iso] if not options.isofile is None: isofile= open(options.isofile,'wb') pickle.dump(iso,isofile) if options.indivfeh: pickle.dump(zs,isofile) elif options.varfeh: pickle.dump(locl,isofile) isofile.close() df= None print "Pre-calculating isochrone distance prior ..." logpiso= numpy.zeros((len(data),_BINTEGRATENBINS)) ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX, _BINTEGRATENBINS) dm= _dm(ds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm if options.indivfeh: #Find closest Z thisZ= isodist.FEH2Z(data[ii]['FEH']) indx= numpy.argmin((thisZ-zs)) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) elif options.varfeh: #Find correct iso indx= (locl == data[ii]['LOCATION']) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) if options.dwarf: logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS)) dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm= _dm(dwarfds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpisodwarf= None #Load initial parameters from file savefile= open(args[0],'rb') params= pickle.load(savefile) savefile.close() #Prep data l= data['GLON']*_DEGTORAD b= data['GLAT']*_DEGTORAD sinl= numpy.sin(l) cosl= numpy.cos(l) sinb= numpy.sin(b) cosb= numpy.cos(b) jk= data['J0MAG']-data['K0MAG'] jk[(jk < 0.5)]= 0.5 #BOVY: FIX THIS HACK BY EMAILING GAIL h= data['H0MAG'] #Re-sample vlos= numpy.linspace(-200.,200.,options.nvlos) pvlos= numpy.zeros((len(data),options.nvlos)) if options.dwarf: thislogpisodwarf= logpisodwarf else: thislogpisodwarf= None if not options.multi is None and options.multi > 1: thismulti= options.multi options.multi= 1 #To avoid conflict thispvlos= multi.parallel_map((lambda x: -mloglike(params, numpy.zeros(len(data))+vlos[x], l, b, jk, h, df,options, sinl, cosl, cosb, sinb, logpiso, thislogpisodwarf, True, None,None,None)), range(options.nvlos), numcores=numpy.amin([len(vlos),multiprocessing.cpu_count(),thismulti])) for jj in range(options.nvlos): pvlos[:,jj]= thispvlos[jj] else: for jj in range(options.nvlos): pvlos[:,jj]= -mloglike(params,numpy.zeros(len(data))+vlos[jj], l, b, jk, h, df,options, sinl, cosl, cosb, sinb, logpiso, thislogpisodwarf,True,None,None,None) """ for jj in range(options.nvlos): pvlos[:,jj]= -mloglike(params,numpy.zeros(len(data))+vlos[jj], l, b, jk, h, df,options, sinl, cosl, cosb, sinb, logpiso, thislogpisodwarf,True,None,None,None) """ for ii in range(len(data)): pvlos[ii,:]-= logsumexp(pvlos[ii,:]) pvlos[ii,:]= numpy.exp(pvlos[ii,:]) pvlos[ii,:]= numpy.cumsum(pvlos[ii,:]) pvlos[ii,:]/= pvlos[ii,-1] #Draw randindx= numpy.random.uniform() kk= 0 while pvlos[ii,kk] < randindx: kk+= 1 data['VHELIO'][ii]= vlos[kk] #Dump raw fitsio.write(options.plotfile,data,clobber=True)
def logl(init=None,data=None,options=None): if options is None: parser= get_options() options, args= parser.parse_args([]) if data is None: #Read data data= readVclosData(lmin=25., bmax=2., ak=True, validfeh=options.indivfeh, #if indivfeh, we need validfeh correctak=options.correctak, jkmax=1.1) #HACK indx= (data['J0MAG']-data['K0MAG'] < 0.5) data['J0MAG'][indx]= 0.5+data['K0MAG'][indx] #Set up the isochrone if not options.isofile is None and os.path.exists(options.isofile): print "Loading the isochrone model ..." isofile= open(options.isofile,'rb') iso= pickle.load(isofile) if options.indivfeh: zs= pickle.load(isofile) elif options.varfeh: locl= pickle.load(isofile) isofile.close() else: if options.indivfeh: #Load all isochrones iso= [] zs= numpy.arange(0.0005,0.03005,0.0005) for ii in range(len(zs)): iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii])) elif options.varfeh: locs= list(set(data['LOCATION'])) iso= [] for ii in range(len(locs)): indx= (data['LOCATION'] == locs[ii]) locl= numpy.mean(data['GLON'][indx]*_DEGTORAD) iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl)) else: iso= isomodel.isomodel(Z=0.019) if options.dwarf: iso= [iso, isomodel.isomodel(Z=0.019, dwarf=True)] else: iso= [iso] if not options.isofile is None: isofile= open(options.isofile,'wb') pickle.dump(iso,isofile) if options.indivfeh: pickle.dump(zs,isofile) elif options.varfeh: pickle.dump(locl,isofile) isofile.close() df= None #Pre-calculate distance prior logpiso= numpy.zeros((len(data),_BINTEGRATENBINS)) ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX, _BINTEGRATENBINS) dm= _dm(ds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm if options.indivfeh: #Find closest Z thisZ= isodist.FEH2Z(data[ii]['FEH']) indx= numpy.argmin((thisZ-zs)) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) elif options.varfeh: #Find correct iso indx= (locl == data[ii]['LOCATION']) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) if options.dwarf: logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS)) dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm= _dm(dwarfds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpisodwarf= None if isinstance(init,str): #FILE #Load initial parameters from file savefile= open(init,'rb') params= pickle.load(savefile) savefile.close() else: #Array params= init #Prep data l= data['GLON']*_DEGTORAD b= data['GLAT']*_DEGTORAD sinl= numpy.sin(l) cosl= numpy.cos(l) sinb= numpy.sin(b) cosb= numpy.cos(b) jk= data['J0MAG']-data['K0MAG'] try: jk[(jk < 0.5)]= 0.5 #BOVY: FIX THIS HACK BY EMAILING GAIL except TypeError: pass #HACK h= data['H0MAG'] options.multi= 1 out= -mloglike(params,data['VHELIO'], l, b, jk, h, df,options, sinl, cosl, cosb, sinb, logpiso, logpisodwarf,True,None,iso,data['FEH']) #None iso for now return out
] else: iso = [iso] if not options.isofile is None: isofile = open(options.isofile, 'wb') pickle.dump(iso, isofile) if options.indivfeh: pickle.dump(zs, isofile) elif options.varfeh: pickle.dump(locl, isofile) isofile.close() df = None print "Pre-calculating isochrone distance prior ..." logpiso = numpy.zeros((len(data), _BINTEGRATENBINS)) ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS) dm = _dm(ds) for ii in range(len(data)): if options.fitah: ah = params[5 - options.nooutliermean + (options.rotcurve.lower() == 'linear') + (options.rotcurve.lower() == 'powerlaw') + 2 * (options.rotcurve.lower() == 'quadratic') + 3 * (options.rotcurve.lower() == 'cubic') + 2 * options.fitvpec + options.dwarf + options.fitsratio + 2 * options.fitsratioinnerouter] if options.fitahinnerouter and data[ii]['GLON'] < 35.: ah = params[5 - options.nooutliermean + (options.rotcurve.lower() == 'linear') + (options.rotcurve.lower() == 'powerlaw') + 2 * (options.rotcurve.lower() == 'quadratic') + 3 * (options.rotcurve.lower() == 'cubic') +
def createFakeData(parser): options, args = parser.parse_args() if len(args) == 0: parser.print_help() return if os.path.exists(options.plotfile): print "Outfile " + options.plotfile + " exists ..." print "Returning ..." return None #Read the data numpy.random.seed(options.seed) print "Reading the data ..." data = readVclosData( postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=options.bmax, validfeh=options.indivfeh, #if indivfeh, we need validfeh ak=True, cutmultiples=options.cutmultiples, jkmax=options.jkmax) #HACK indx = (data['J0MAG'] - data['K0MAG'] < 0.5) data['J0MAG'][indx] = 0.5 + data['K0MAG'][indx] #Set up the isochrone #Set up the isochrone if not options.isofile is None and os.path.exists(options.isofile): print "Loading the isochrone model ..." isofile = open(options.isofile, 'rb') iso = pickle.load(isofile) if options.indivfeh: zs = pickle.load(isofile) elif options.varfeh: locl = pickle.load(isofile) isofile.close() else: print "Setting up the isochrone model ..." if options.indivfeh: #Load all isochrones iso = [] zs = numpy.arange(0.0005, 0.03005, 0.0005) for ii in range(len(zs)): iso.append( isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii])) elif options.varfeh: locs = list(set(data['LOCATION'])) iso = [] for ii in range(len(locs)): indx = (data['LOCATION'] == locs[ii]) locl = numpy.mean(data['GLON'][indx] * _DEGTORAD) iso.append( isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl)) else: iso = isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, expsfh=options.expsfh) if options.dwarf: iso = [ iso, isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, dwarf=True, expsfh=options.expsfh) ] else: iso = [iso] if not options.isofile is None: isofile = open(options.isofile, 'wb') pickle.dump(iso, isofile) if options.indivfeh: pickle.dump(zs, isofile) elif options.varfeh: pickle.dump(locl, isofile) isofile.close() df = None print "Pre-calculating isochrone distance prior ..." logpiso = numpy.zeros((len(data), _BINTEGRATENBINS)) ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS) dm = _dm(ds) for ii in range(len(data)): mh = data['H0MAG'][ii] - dm if options.indivfeh: #Find closest Z thisZ = isodist.FEH2Z(data[ii]['FEH']) indx = numpy.argmin((thisZ - zs)) logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + (data['J0MAG'] - data['K0MAG'])[ii], mh) elif options.varfeh: #Find correct iso indx = (locl == data[ii]['LOCATION']) logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + (data['J0MAG'] - data['K0MAG'])[ii], mh) else: logpiso[ii, :] = iso[0](numpy.zeros(_BINTEGRATENBINS) + (data['J0MAG'] - data['K0MAG'])[ii], mh) if options.dwarf: logpisodwarf = numpy.zeros((len(data), _BINTEGRATENBINS)) dwarfds = numpy.linspace(_BINTEGRATEDMIN_DWARF, _BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm = _dm(dwarfds) for ii in range(len(data)): mh = data['H0MAG'][ii] - dm logpisodwarf[ii, :] = iso[1](numpy.zeros(_BINTEGRATENBINS) + (data['J0MAG'] - data['K0MAG'])[ii], mh) else: logpisodwarf = None #Load initial parameters from file savefile = open(args[0], 'rb') params = pickle.load(savefile) savefile.close() #Prep data l = data['GLON'] * _DEGTORAD b = data['GLAT'] * _DEGTORAD sinl = numpy.sin(l) cosl = numpy.cos(l) sinb = numpy.sin(b) cosb = numpy.cos(b) jk = data['J0MAG'] - data['K0MAG'] jk[(jk < 0.5)] = 0.5 #BOVY: FIX THIS HACK BY EMAILING GAIL h = data['H0MAG'] #Re-sample vlos = numpy.linspace(-200., 200., options.nvlos) pvlos = numpy.zeros((len(data), options.nvlos)) if options.dwarf: thislogpisodwarf = logpisodwarf else: thislogpisodwarf = None if not options.multi is None and options.multi > 1: thismulti = options.multi options.multi = 1 #To avoid conflict thispvlos = multi.parallel_map( (lambda x: -mloglike(params, numpy.zeros(len(data)) + vlos[x], l, b, jk, h, df, options, sinl, cosl, cosb, sinb, logpiso, thislogpisodwarf, True, None, None, None)), range(options.nvlos), numcores=numpy.amin( [len(vlos), multiprocessing.cpu_count(), thismulti])) for jj in range(options.nvlos): pvlos[:, jj] = thispvlos[jj] else: for jj in range(options.nvlos): pvlos[:, jj] = -mloglike( params, numpy.zeros(len(data)) + vlos[jj], l, b, jk, h, df, options, sinl, cosl, cosb, sinb, logpiso, thislogpisodwarf, True, None, None, None) """ for jj in range(options.nvlos): pvlos[:,jj]= -mloglike(params,numpy.zeros(len(data))+vlos[jj], l, b, jk, h, df,options, sinl, cosl, cosb, sinb, logpiso, thislogpisodwarf,True,None,None,None) """ for ii in range(len(data)): pvlos[ii, :] -= logsumexp(pvlos[ii, :]) pvlos[ii, :] = numpy.exp(pvlos[ii, :]) pvlos[ii, :] = numpy.cumsum(pvlos[ii, :]) pvlos[ii, :] /= pvlos[ii, -1] #Draw randindx = numpy.random.uniform() kk = 0 while pvlos[ii, kk] < randindx: kk += 1 data['VHELIO'][ii] = vlos[kk] #Dump raw fitsio.write(options.plotfile, data, clobber=True)
def plot_bestfit(parser): (options, args) = parser.parse_args() if len(args) == 0 or options.plotfilename is None: parser.print_help() return #Read the data print "Reading the data ..." data = readVclosData( postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=options.bmax, ak=True, cutmultiples=options.cutmultiples, validfeh=options.indivfeh, #if indivfeh, we need validfeh jkmax=options.jkmax, datafilename=options.fakedata) #HACK indx = (data['J0MAG'] - data['K0MAG'] < 0.5) data['J0MAG'][indx] = 0.5 + data['K0MAG'][indx] #Cut inner disk locations #data= data[(data['GLON'] > 75.)] #Cut outliers #data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)] print "Using %i data points ..." % len(data) #Set up the isochrone if not options.isofile is None and os.path.exists(options.isofile): print "Loading the isochrone model ..." isofile = open(options.isofile, 'rb') iso = pickle.load(isofile) if options.indivfeh: zs = pickle.load(isofile) if options.varfeh: locl = pickle.load(isofile) isofile.close() else: print "Setting up the isochrone model ..." if options.indivfeh: #Load all isochrones iso = [] zs = numpy.arange(0.0005, 0.03005, 0.0005) for ii in range(len(zs)): iso.append( isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii])) elif options.varfeh: locs = list(set(data['LOCATION'])) iso = [] for ii in range(len(locs)): indx = (data['LOCATION'] == locs[ii]) locl = numpy.mean(data['GLON'][indx] * _DEGTORAD) iso.append( isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl)) else: iso = isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, expsfh=options.expsfh) if options.dwarf: iso = [ iso, isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, dwarf=True, expsfh=options.expsfh) ] else: iso = [iso] if not options.isofile is None: isofile = open(options.isofile, 'wb') pickle.dump(iso, isofile) if options.indivfeh: pickle.dump(zs, isofile) elif options.varfeh: pickle.dump(locl, isofile) isofile.close() df = None print "Pre-calculating isochrone distance prior ..." logpiso = numpy.zeros((len(data), _BINTEGRATENBINS)) ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS) dm = _dm(ds) for ii in range(len(data)): mh = data['H0MAG'][ii] - dm if options.indivfeh: #Find closest Z thisZ = isodist.FEH2Z(data[ii]['FEH']) indx = numpy.argmin(numpy.fabs(thisZ - zs)) logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + (data['J0MAG'] - data['K0MAG'])[ii], mh) elif options.varfeh: #Find correct iso indx = (locl == data[ii]['LOCATION']) logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + (data['J0MAG'] - data['K0MAG'])[ii], mh) else: logpiso[ii, :] = iso[0](numpy.zeros(_BINTEGRATENBINS) + (data['J0MAG'] - data['K0MAG'])[ii], mh) if options.dwarf: logpisodwarf = numpy.zeros((len(data), _BINTEGRATENBINS)) dwarfds = numpy.linspace(_BINTEGRATEDMIN_DWARF, _BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm = _dm(dwarfds) for ii in range(len(data)): mh = data['H0MAG'][ii] - dm logpisodwarf[ii, :] = iso[1](numpy.zeros(_BINTEGRATENBINS) + (data['J0MAG'] - data['K0MAG'])[ii], mh) else: logpisodwarf = None #Calculate data means etc. #Calculate means locations = list(set(data['LOCATION'])) nlocs = len(locations) l_plate = numpy.zeros(nlocs) avg_plate = numpy.zeros(nlocs) sig_plate = numpy.zeros(nlocs) siga_plate = numpy.zeros(nlocs) sigerr_plate = numpy.zeros(nlocs) for ii in range(nlocs): indx = (data['LOCATION'] == locations[ii]) l_plate[ii] = numpy.mean(data['GLON'][indx]) avg_plate[ii] = numpy.mean(data['VHELIO'][indx]) sig_plate[ii] = numpy.std(data['VHELIO'][indx]) siga_plate[ii] = numpy.std(data['VHELIO'][indx]) / numpy.sqrt( numpy.sum(indx)) sigerr_plate[ii] = bootstrap_sigerr(data['VHELIO'][indx]) #Calculate plate means and variances from the model #Load initial parameters from file savefile = open(args[0], 'rb') params = pickle.load(savefile) if not options.index is None: params = params[options.index] savefile.close() #params[0]= 245./235. #params[1]= 8.5/8. avg_plate_model = numpy.zeros(nlocs) sig_plate_model = numpy.zeros(nlocs) for ii in range(nlocs): #Calculate vlos | los indx = (data['LOCATION'] == locations[ii]) thesedata = data[indx] thislogpiso = logpiso[indx, :] if options.dwarf: thislogpisodwarf = logpisodwarf[indx, :] else: thislogpisodwarf = None vlos = numpy.linspace(-200., 200., options.nvlos) pvlos = numpy.zeros(options.nvlos) if not options.multi is None: pvlos = multi.parallel_map( (lambda x: pvlosplate(params, vlos[x], thesedata, df, options, thislogpiso, thislogpisodwarf, iso)), range(options.nvlos), numcores=numpy.amin( [len(vlos), multiprocessing.cpu_count(), options.multi])) else: for jj in range(options.nvlos): print jj pvlos[jj] = pvlosplate(params, vlos[jj], thesedata, df, options, thislogpiso, thislogpisodwarf, iso) pvlos -= logsumexp(pvlos) pvlos = numpy.exp(pvlos) #Calculate mean and velocity dispersion avg_plate_model[ii] = numpy.sum(vlos * pvlos) sig_plate_model[ii]= numpy.sqrt(numpy.sum(vlos**2.*pvlos)\ -avg_plate_model[ii]**2.) #Plot everything left, bottom, width, height = 0.1, 0.4, 0.8, 0.5 axTop = pyplot.axes([left, bottom, width, height]) left, bottom, width, height = 0.1, 0.1, 0.8, 0.3 axMean = pyplot.axes([left, bottom, width, height]) #left, bottom, width, height= 0.1, 0.1, 0.8, 0.2 #axSig= pyplot.axes([left,bottom,width,height]) fig = pyplot.gcf() fig.sca(axTop) pyplot.ylabel(r'$\mathrm{Heliocentric\ velocity}\ [\mathrm{km\ s}^{-1}]$') pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0., 360.) pyplot.ylim(-200., 200.) nullfmt = NullFormatter() # no labels axTop.xaxis.set_major_formatter(nullfmt) bovy_plot.bovy_plot(data['GLON'], data['VHELIO'], 'k,', yrange=[-200., 200.], xrange=[0., 360.], overplot=True) ndata_t = int(math.floor(len(data) / 1000.)) ndata_h = len(data) - ndata_t * 1000 bovy_plot.bovy_plot(l_plate, avg_plate, 'o', overplot=True, mfc='0.5', mec='none') bovy_plot.bovy_plot(l_plate, avg_plate_model, 'x', overplot=True, ms=10., mew=1.5, color='0.7') #Legend bovy_plot.bovy_plot([260.], [150.], 'k,', overplot=True) bovy_plot.bovy_plot([260.], [120.], 'o', mfc='0.5', mec='none', overplot=True) bovy_plot.bovy_plot([260.], [90.], 'x', ms=10., mew=1.5, color='0.7', overplot=True) bovy_plot.bovy_text(270., 145., r'$\mathrm{data}$') bovy_plot.bovy_text(270., 115., r'$\mathrm{data\ mean}$') bovy_plot.bovy_text(270., 85., r'$\mathrm{model\ mean}$') bovy_plot._add_ticks() #Now plot the difference fig.sca(axMean) bovy_plot.bovy_plot([0., 360.], [0., 0.], '-', color='0.5', overplot=True) bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model, 'ko', overplot=True) pyplot.errorbar(l_plate, avg_plate - avg_plate_model, yerr=siga_plate, marker='o', color='k', linestyle='none', elinestyle='-') pyplot.ylabel(r'$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}}$') pyplot.ylim(-14.5, 14.5) pyplot.xlim(0., 360.) bovy_plot._add_ticks() #axMean.xaxis.set_major_formatter(nullfmt) pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0., 360.) bovy_plot._add_ticks() #Save bovy_plot.bovy_end_print(options.plotfilename) return None #Sigma fig.sca(axSig) pyplot.plot([0., 360.], [1., 1.], '-', color='0.5') bovy_plot.bovy_plot(l_plate, sig_plate / sig_plate_model, 'ko', overplot=True) pyplot.errorbar(l_plate, sig_plate / sig_plate_model, yerr=sigerr_plate / sig_plate_model, marker='o', color='k', linestyle='none', elinestyle='-') pyplot.ylabel( r'$\sigma_{\mathrm{los}}^{\mathrm{data}}/ \sigma_{\mathrm{los}}^{\mathrm{model}}$' ) pyplot.ylim(0.5, 1.5)
else: iso= [iso] if not options.isofile is None: isofile= open(options.isofile,'wb') pickle.dump(iso,isofile) if options.indivfeh: pickle.dump(zs,isofile) elif options.varfeh: pickle.dump(locl,isofile) isofile.close() df= None print "Pre-calculating isochrone distance prior ..." logpiso= numpy.zeros((len(data),_BINTEGRATENBINS)) ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX, _BINTEGRATENBINS) dm= _dm(ds) for ii in range(len(data)): if options.fitah: ah= params[5-options.nooutliermean+(options.rotcurve.lower() == 'linear') +(options.rotcurve.lower() == 'powerlaw') + 2*(options.rotcurve.lower() == 'quadratic')+3*(options.rotcurve.lower() == 'cubic')+2*options.fitvpec+options.dwarf+options.fitsratio+2*options.fitsratioinnerouter] if options.fitahinnerouter and data[ii]['GLON'] < 35.: ah= params[5-options.nooutliermean+(options.rotcurve.lower() == 'linear') +(options.rotcurve.lower() == 'powerlaw') + 2*(options.rotcurve.lower() == 'quadratic')+3*(options.rotcurve.lower() == 'cubic')+2*options.fitvpec+options.dwarf+options.fitsratio+2*options.fitsratioinnerouter+options.fiths+options.fitsrinnerouter+options.dwarfinnerouter+options.fitah+options.fitdm] mh= data['H0MAG'][ii]-dm+ah if options.indivfeh: #Find closest Z thisZ= isodist.FEH2Z(data[ii]['FEH']) indx= numpy.argmin(numpy.fabs(thisZ-zs)) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) elif options.varfeh: #Find correct iso indx= (locl == data[ii]['LOCATION']) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) else:
def plot_internalcomparison(parser): (options,args)= parser.parse_args() if len(args) == 0 or options.plotfilename is None: parser.print_help() return #Read the data print "Reading the data ..." data= readVclosData(postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=options.bmax, ak=True, cutmultiples=options.cutmultiples, validfeh=options.indivfeh, #if indivfeh, we need validfeh jkmax=options.jkmax, datafilename=options.fakedata) #data= data[0:20] #HACK indx= (data['J0MAG']-data['K0MAG'] < 0.5) data['J0MAG'][indx]= 0.5+data['K0MAG'][indx] #Cut outliers #data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)] print "Using %i data points ..." % len(data) #Set up the isochrone if not options.isofile is None and os.path.exists(options.isofile): print "Loading the isochrone model ..." isofile= open(options.isofile,'rb') iso= pickle.load(isofile) if options.indivfeh: zs= pickle.load(isofile) if options.varfeh: locl= pickle.load(isofile) isofile.close() else: print "Setting up the isochrone model ..." if options.indivfeh: #Load all isochrones iso= [] zs= numpy.arange(0.0005,0.03005,0.0005) for ii in range(len(zs)): iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii])) elif options.varfeh: locs= list(set(data['LOCATION'])) iso= [] for ii in range(len(locs)): indx= (data['LOCATION'] == locs[ii]) locl= numpy.mean(data['GLON'][indx]*_DEGTORAD) iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl)) else: iso= isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z, expsfh=options.expsfh) if options.dwarf: iso= [iso, isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z, dwarf=True,expsfh=options.expsfh)] else: iso= [iso] if not options.isofile is None: isofile= open(options.isofile,'wb') pickle.dump(iso,isofile) if options.indivfeh: pickle.dump(zs,isofile) elif options.varfeh: pickle.dump(locl,isofile) isofile.close() df= None print "Pre-calculating isochrone distance prior ..." logpiso= numpy.zeros((len(data),_BINTEGRATENBINS)) ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX, _BINTEGRATENBINS) dm= _dm(ds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm if options.indivfeh: #Find closest Z thisZ= isodist.FEH2Z(data[ii]['FEH']) indx= numpy.argmin((thisZ-zs)) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) elif options.varfeh: #Find correct iso indx= (locl == data[ii]['LOCATION']) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) if options.dwarf: logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS)) dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm= _dm(dwarfds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpisodwarf= None #Calculate data means etc. #Calculate means locations= list(set(data['LOCATION'])) nlocs= len(locations) l_plate= numpy.zeros(nlocs) avg_plate= numpy.zeros(nlocs) sig_plate= numpy.zeros(nlocs) siga_plate= numpy.zeros(nlocs) for ii in range(nlocs): indx= (data['LOCATION'] == locations[ii]) l_plate[ii]= numpy.mean(data['GLON'][indx]) avg_plate[ii]= numpy.mean(data['VHELIO'][indx]) sig_plate[ii]= numpy.std(data['VHELIO'][indx]) siga_plate[ii]= numpy.std(data['VHELIO'][indx])/numpy.sqrt(numpy.sum(indx)) #Calculate plate means and variances from the model #Load initial parameters from file savefile= open(args[0],'rb') params= pickle.load(savefile) if not options.index is None: params= params[options.index] savefile.close() #First calculate fiducial model if not options.dwarf: logpisodwarf= None avg_plate_model_fid= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) #Plot everything bovy_plot.bovy_print(fig_height=10.,fig_width=7.) dx= 0.8/9. left, bottom, width, height= 0.1, 0.9, 0.8, dx axTop= pyplot.axes([left,bottom,width,height]) allaxes= [axTop] fig= pyplot.gcf() fig.sca(axTop) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True) bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model_fid, 'ko',overplot=True) pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{fiducial}$',top_right=True,size=14.) pyplot.ylim(-14.5,14.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() nullfmt = NullFormatter() # no labels axTop.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Second is Dehnen fid_dfmodel= options.dfmodel options.dfmodel= 'dehnen' avg_plate_model= avg_plate_model_fid #avg_plate_model= calc_model(params,options,data, # logpiso,logpisodwarf, # df,nlocs,locations,iso) left, bottom, width, height= 0.1, 0.9-dx, 0.8, dx thisax= pyplot.axes([left,bottom,width,height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True, zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model_fid, 'o',overplot=True,color='0.6') pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid, yerr=siga_plate,marker='o',color='0.6', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model, 'o',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_model, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{Dehnen\ DF}$',top_right=True,size=14.) #pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') pyplot.ylim(-14.5,14.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Third = hR= 2. kpc fid_hr= options.hr options.dfmodel= fid_dfmodel options.hr= 2. avg_plate_model= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) left, bottom, width, height= 0.1, 0.9-2.*dx, 0.8, dx thisax= pyplot.axes([left,bottom,width,height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model_fid, 'o',overplot=True,color='0.6') pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid, yerr=siga_plate,marker='o',color='0.6', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model, 'o',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_model, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$h_R = 2\ \mathrm{kpc}$',top_right=True,size=14.) #pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') pyplot.ylim(-14.5,14.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Fourth = hR= 4. kpc options.dfmodel= fid_dfmodel options.hr= 4. avg_plate_model= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) left, bottom, width, height= 0.1, 0.9-3.*dx, 0.8, dx thisax= pyplot.axes([left,bottom,width,height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model_fid, 'o',overplot=True,color='0.6') pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid, yerr=siga_plate,marker='o',color='0.6', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model, 'o',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_model, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$h_R = 4\ \mathrm{kpc}$',top_right=True,size=14.) #pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') pyplot.ylim(-14.5,14.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Fifth = hs= 5. kpc fid_hs= options.hs options.hr= fid_hr options.hs= 5. avg_plate_model= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) left, bottom, width, height= 0.1, 0.9-4.*dx, 0.8, dx thisax= pyplot.axes([left,bottom,width,height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model_fid, 'o',overplot=True,color='0.6') pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid, yerr=siga_plate,marker='o',color='0.6', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model, 'o',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_model, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$h_\sigma = 5\ \mathrm{kpc}$',top_right=True,size=14.) pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}\ [\mathrm{km\ s}^{-1}]$') pyplot.ylim(-14.5,14.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Sixth: multiple pops options.hs= fid_hs options.dfmodel= 'multiplepops' fid_params=copy.copy(params) params[2]= -1.8 avg_plate_model= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) left, bottom, width, height= 0.1, 0.9-5.*dx, 0.8, dx thisax= pyplot.axes([left,bottom,width,height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model_fid, 'o',overplot=True,color='0.6') pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid, yerr=siga_plate,marker='o',color='0.6', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model, 'o',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_model, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{Multiple\ populations,\ SFR} = \exp\left( -t/8\ \mathrm{Gyr}\right)$',top_right=True,size=14.) #pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') pyplot.ylim(-14.5,14.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Seventh: cut multiples options.dfmodel= fid_dfmodel params= fid_params #Read the data print "Reading the data ..." data= readVclosData(postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=options.bmax, ak=True, cutmultiples=True, jkmax=options.jkmax) #HACK indx= (data['J0MAG']-data['K0MAG'] < 0.5) data['J0MAG'][indx]= 0.5+data['K0MAG'][indx] print "Pre-calculating isochrone distance prior ..." logpiso= numpy.zeros((len(data),_BINTEGRATENBINS)) ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX, _BINTEGRATENBINS) dm= _dm(ds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm if options.indivfeh: #Find closest Z thisZ= isodist.FEH2Z(data[ii]['FEH']) indx= numpy.argmin((thisZ-zs)) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) elif options.varfeh: #Find correct iso indx= (locl == data[ii]['LOCATION']) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) if options.dwarf: logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS)) dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm= _dm(dwarfds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpisodwarf= None l_plate_fid= l_plate avg_plate_fid= avg_plate siga_plate_fid= siga_plate locations= list(set(data['LOCATION'])) nlocs= len(locations) l_plate= numpy.zeros(nlocs) avg_plate= numpy.zeros(nlocs) sig_plate= numpy.zeros(nlocs) siga_plate= numpy.zeros(nlocs) for ii in range(nlocs): indx= (data['LOCATION'] == locations[ii]) l_plate[ii]= numpy.mean(data['GLON'][indx]) avg_plate[ii]= numpy.mean(data['VHELIO'][indx]) sig_plate[ii]= numpy.std(data['VHELIO'][indx]) siga_plate[ii]= numpy.std(data['VHELIO'][indx])/numpy.sqrt(numpy.sum(indx)) avg_plate_model= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) left, bottom, width, height= 0.1, 0.9-6.*dx, 0.8, dx thisax= pyplot.axes([left,bottom,width,height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1) bovy_plot.bovy_plot(l_plate_fid, avg_plate_fid-avg_plate_model_fid, 'o',overplot=True,color='0.6') pyplot.errorbar(l_plate_fid,avg_plate_fid-avg_plate_model_fid, yerr=siga_plate_fid,marker='o',color='0.6', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model, 'o',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_model, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{Multiple\ visit\ dispersion} < 1\ \mathrm{km\ s}^{-1}$',top_right=True,size=14.) #pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') pyplot.ylim(-14.5,14.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Eight b=2 options.dfmodel= fid_dfmodel params= fid_params #Read the data print "Reading the data ..." data= readVclosData(postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=10., meanb=4., ak=True, cutmultiples=options.cutmultiples, jkmax=options.jkmax) #HACK indx= (data['J0MAG']-data['K0MAG'] < 0.5) data['J0MAG'][indx]= 0.5+data['K0MAG'][indx] print "Pre-calculating isochrone distance prior ..." logpiso= numpy.zeros((len(data),_BINTEGRATENBINS)) ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX, _BINTEGRATENBINS) dm= _dm(ds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm if options.indivfeh: #Find closest Z thisZ= isodist.FEH2Z(data[ii]['FEH']) indx= numpy.argmin((thisZ-zs)) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) elif options.varfeh: #Find correct iso indx= (locl == data[ii]['LOCATION']) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) if options.dwarf: logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS)) dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm= _dm(dwarfds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpisodwarf= None locations= list(set(data['LOCATION'])) nlocs= len(locations) l_plate= numpy.zeros(nlocs) avg_plate= numpy.zeros(nlocs) sig_plate= numpy.zeros(nlocs) siga_plate= numpy.zeros(nlocs) for ii in range(nlocs): indx= (data['LOCATION'] == locations[ii]) l_plate[ii]= numpy.mean(data['GLON'][indx]) avg_plate[ii]= numpy.mean(data['VHELIO'][indx]) sig_plate[ii]= numpy.std(data['VHELIO'][indx]) siga_plate[ii]= numpy.std(data['VHELIO'][indx])/numpy.sqrt(numpy.sum(indx)) avg_plate_model= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) left, bottom, width, height= 0.1, 0.9-7.*dx, 0.8, dx thisax= pyplot.axes([left,bottom,width,height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1) bovy_plot.bovy_plot(l_plate_fid, avg_plate_fid-avg_plate_model_fid, 'o',overplot=True,color='0.6') pyplot.errorbar(l_plate_fid,avg_plate_fid-avg_plate_model_fid, yerr=siga_plate_fid,marker='o',color='0.6', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model, 'o',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_model, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$b = 4^\circ\ \mathrm{fields}$',top_right=True,size=14.) #pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') pyplot.ylim(-14.5,14.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Nintht b=-2 #Read the data print "Reading the data ..." data= readVclosData(postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=10., meanb=-4., ak=True, cutmultiples=options.cutmultiples, jkmax=options.jkmax) #HACK indx= (data['J0MAG']-data['K0MAG'] < 0.5) data['J0MAG'][indx]= 0.5+data['K0MAG'][indx] print "Pre-calculating isochrone distance prior ..." logpiso= numpy.zeros((len(data),_BINTEGRATENBINS)) ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX, _BINTEGRATENBINS) dm= _dm(ds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm if options.indivfeh: #Find closest Z thisZ= isodist.FEH2Z(data[ii]['FEH']) indx= numpy.argmin((thisZ-zs)) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) elif options.varfeh: #Find correct iso indx= (locl == data[ii]['LOCATION']) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) if options.dwarf: logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS)) dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm= _dm(dwarfds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpisodwarf= None locations= list(set(data['LOCATION'])) nlocs= len(locations) l_plate= numpy.zeros(nlocs) avg_plate= numpy.zeros(nlocs) sig_plate= numpy.zeros(nlocs) siga_plate= numpy.zeros(nlocs) for ii in range(nlocs): indx= (data['LOCATION'] == locations[ii]) l_plate[ii]= numpy.mean(data['GLON'][indx]) avg_plate[ii]= numpy.mean(data['VHELIO'][indx]) sig_plate[ii]= numpy.std(data['VHELIO'][indx]) siga_plate[ii]= numpy.std(data['VHELIO'][indx])/numpy.sqrt(numpy.sum(indx)) avg_plate_model= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) left, bottom, width, height= 0.1, 0.9-8.*dx, 0.8, dx thisax= pyplot.axes([left,bottom,width,height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1) bovy_plot.bovy_plot(l_plate_fid, avg_plate_fid-avg_plate_model_fid, 'o',overplot=True,color='0.6') pyplot.errorbar(l_plate_fid,avg_plate_fid-avg_plate_model_fid, yerr=siga_plate_fid,marker='o',color='0.6', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model, 'o',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_model, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$b = -4^\circ\ \mathrm{fields}$',top_right=True,size=14.) #pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') pyplot.ylim(-14.5,14.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() #thisax.xaxis.set_major_formatter(nullfmt) pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Save bovy_plot.bovy_end_print(options.plotfilename) return None
def plot_externalcomparison(parser): (options,args)= parser.parse_args() if len(args) == 0 or options.plotfilename is None: parser.print_help() return #Read the data print "Reading the data ..." data= readVclosData(postshutdown=options.postshutdown, fehcut=options.fehcut, cohort=options.cohort, lmin=options.lmin, bmax=options.bmax, ak=True, cutmultiples=options.cutmultiples, validfeh=options.indivfeh, #if indivfeh, we need validfeh jkmax=options.jkmax, datafilename=options.fakedata) #data= data[0:20] #HACK indx= (data['J0MAG']-data['K0MAG'] < 0.5) data['J0MAG'][indx]= 0.5+data['K0MAG'][indx] #Cut outliers #data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)] #Set up the isochrone if not options.isofile is None and os.path.exists(options.isofile): print "Loading the isochrone model ..." isofile= open(options.isofile,'rb') iso= pickle.load(isofile) if options.indivfeh: zs= pickle.load(isofile) elif options.varfeh: locl= pickle.load(isofile) isofile.close() else: print "Setting up the isochrone model ..." if options.indivfeh: #Load all isochrones iso= [] zs= numpy.arange(0.0005,0.03005,0.0005) for ii in range(len(zs)): iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii])) elif options.varfeh: locs= list(set(data['LOCATION'])) iso= [] for ii in range(len(locs)): indx= (data['LOCATION'] == locs[ii]) locl= numpy.mean(data['GLON'][indx]*_DEGTORAD) iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl)) else: iso= isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z, expsfh=options.expsfh) if options.dwarf: iso= [iso, isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z, dwarf=True,expsfh=options.expsfh)] else: iso= [iso] if not options.isofile is None: isofile= open(options.isofile,'wb') pickle.dump(iso,isofile) if options.indivfeh: pickle.dump(zs,isofile) elif options.varfeh: pickle.dump(locl,isofile) isofile.close() df= None print "Pre-calculating isochrone distance prior ..." logpiso= numpy.zeros((len(data),_BINTEGRATENBINS)) ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX, _BINTEGRATENBINS) dm= _dm(ds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm if options.indivfeh: #Find closest Z thisZ= isodist.FEH2Z(data[ii]['FEH']) indx= numpy.argmin((thisZ-zs)) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) elif options.varfeh: #Find correct iso indx= (locl == data[ii]['LOCATION']) logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) if options.dwarf: logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS)) dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS) dm= _dm(dwarfds) for ii in range(len(data)): mh= data['H0MAG'][ii]-dm logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS) +(data['J0MAG']-data['K0MAG'])[ii],mh) else: logpisodwarf= None #Calculate data means etc. #Calculate means locations= list(set(data['LOCATION'])) nlocs= len(locations) l_plate= numpy.zeros(nlocs) avg_plate= numpy.zeros(nlocs) sig_plate= numpy.zeros(nlocs) siga_plate= numpy.zeros(nlocs) for ii in range(nlocs): indx= (data['LOCATION'] == locations[ii]) l_plate[ii]= numpy.mean(data['GLON'][indx]) avg_plate[ii]= numpy.mean(data['VHELIO'][indx]) sig_plate[ii]= numpy.std(data['VHELIO'][indx]) siga_plate[ii]= numpy.std(data['VHELIO'][indx])/numpy.sqrt(numpy.sum(indx)) #Calculate plate means and variances from the model #Load initial parameters from file savefile= open(args[0],'rb') params= pickle.load(savefile) if not options.index is None: params= params[options.index] savefile.close() #First calculate fiducial model if not options.dwarf: logpisodwarf= None fid_logl= numpy.sum(logl.logl(init=params,data=data,options=copy.copy(options))) #Copy options, since logl sets multi=1 avg_plate_model_fid= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) #Plot everything bovy_plot.bovy_print(fig_height=6.,fig_width=7.) dx= 0.8/5. left, bottom, width, height= 0.1, 0.9-dx, 0.8, dx axTop= pyplot.axes([left,bottom,width,height]) allaxes= [axTop] fig= pyplot.gcf() fig.sca(axTop) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True) bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model_fid, 'ko',overplot=True) pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{fiducial}$',top_right=True,size=14.) thisax= pyplot.gca() thisax.set_ylim(-19.5,19.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() nullfmt = NullFormatter() # no labels axTop.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Second is powerlaw with beta=0.15 #fid_slope= params[5-options.nooutliermean+options.dwarf] #params[5-options.nooutliermean+options.dwarf]= 0. fid_params= copy.copy(params) if options.rotcurve.lower() == 'flat': params= list(params) params.insert(6,0.15) params= numpy.array(params) fid_rotcurve= options.rotcurve options.rotcurve= 'powerlaw' elif options.rotcurve.lower() == 'powerlaw': params[6]= 0.15 fid_rotcurve= options.rotcurve avg_plate_model= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) #params[5-options.nooutliermean+options.dwarf]= fid_slope #Undo changes params= fid_params options.rotcurve= fid_rotcurve left, bottom, width, height= 0.1, 0.9-2.*dx, 0.8, dx thisax= pyplot.axes([left,bottom,width,height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True, zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model_fid, 'o',overplot=True,color='0.6') pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid, yerr=siga_plate,marker='o',color='0.6', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model, 'o',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_model, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{power\!\!-\!\!law\ rotation\ curve}, \beta = 0.15$', top_right=True,size=14.) #pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') thisax.set_ylim(-19.5,19.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Third is powerlaw with beta=-0.15 #fid_slope= params[5-options.nooutliermean+options.dwarf] #params[5-options.nooutliermean+options.dwarf]= 0. fid_params= copy.copy(params) if options.rotcurve.lower() == 'flat': params= list(params) params.insert(6,-0.15) params= numpy.array(params) fid_rotcurve= options.rotcurve options.rotcurve= 'powerlaw' elif options.rotcurve.lower() == 'powerlaw': params[6]= -0.15 fid_rotcurve= options.rotcurve avg_plate_model= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) #params[5-options.nooutliermean+options.dwarf]= fid_slope #Undo changes params= fid_params options.rotcurve= fid_rotcurve left, bottom, width, height= 0.1, 0.9-3.*dx, 0.8, dx thisax= pyplot.axes([left,bottom,width,height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True, zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model_fid, 'o',overplot=True,color='0.6') pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid, yerr=siga_plate,marker='o',color='0.6', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model, 'o',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_model, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{power\!\!-\!\!law\ rotation\ curve}, \beta = -0.15$', top_right=True,size=14.) pyplot.ylabel(r'$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}}$') thisax.set_ylim(-19.5,19.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Second is vc=250 #Load initial parameters from file savefile= open(options.vo250file,'rb') params= pickle.load(savefile) savefile.close() options.fixvo= 250. options.fixro= 8.4 vo250_logl= numpy.sum(logl.logl(init=params,data=data,options=copy.copy(options))) avg_plate_modelr84= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) #Load initial parameters from file savefile= open(options.vo250r8file,'rb') paramsr8= pickle.load(savefile) savefile.close() options.fixvo= 250. options.fixro= 8. vo250r8_logl= numpy.sum(logl.logl(init=paramsr8,data=data,options=copy.copy(options))) avg_plate_modelr8= calc_model(paramsr8,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) options.fixro= None options.fixvo= None left, bottom, width, height= 0.1, 0.9-4.*dx, 0.8, dx thisax= pyplot.axes([left,bottom,width,height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True, zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model_fid, 'o',overplot=True,color='0.6') pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid, yerr=siga_plate,marker='o',color='0.6', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_modelr84, 'x',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_modelr84, yerr=siga_plate,marker='x',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_modelr8, 'o',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_modelr8, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') #top_right=True,size=14.) bovy_plot.bovy_text(r'$\mathrm{best\!\!-\!\!fit}\ V_c(R_0) = 250\ \mathrm{km\ s}^{-1}, R_0 = %.1f\,\mathrm{kpc}, \Delta \chi^2 = %.0f\ (%.1f\sigma)$' % (8.,2.*(fid_logl-vo250r8_logl),special.erfinv(stats.chi2.cdf(2.*(fid_logl-vo250r8_logl),10.))*numpy.sqrt(2.)), top_right=True,size=13.) bovy_plot.bovy_text(r'$\mathrm{best\!\!-\!\!fit}\ V_c(R_0) = 250\ \mathrm{km\ s}^{-1}, R_0 = %.1f\,\mathrm{kpc}, \Delta \chi^2 = %.0f\ (%.1f\sigma)$' % (8.4,2.*(fid_logl-vo250_logl),special.erfinv(stats.chi2.cdf(2.*(fid_logl-vo250_logl),10.))*numpy.sqrt(2.)), bottom_right=True,size=13.) bovy_plot.bovy_plot([40.],[-15.],'kx',overplot=True) bovy_plot.bovy_plot([40.],[12.],'ko',overplot=True) thisax.set_ylim(-19.5,19.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() thisax.xaxis.set_major_formatter(nullfmt) #pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Fourth = vpec=SBD10 #Load sbd fit #Load initial parameters from file savefile= open(options.sbdfile,'rb') params= pickle.load(savefile) savefile.close() options.sbdvpec= True options.fitvpec= False sbd_logl= numpy.nansum(logl.logl(init=params,data=data,options=copy.copy(options))) avg_plate_model= calc_model(params,options,data, logpiso,logpisodwarf, df,nlocs,locations,iso) left, bottom, width, height= 0.1, 0.9-5.*dx, 0.8, dx thisax= pyplot.axes([left,bottom,width,height]) allaxes.append(thisax) fig.sca(thisax) bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1) bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model_fid, 'o',overplot=True,color='0.6') pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid, yerr=siga_plate,marker='o',color='0.6', linestyle='none',elinestyle='-') bovy_plot.bovy_plot(l_plate, avg_plate-avg_plate_model, 'o',overplot=True,color='k') pyplot.errorbar(l_plate,avg_plate-avg_plate_model, yerr=siga_plate,marker='o',color='k', linestyle='none',elinestyle='-') bovy_plot.bovy_text(r'$\mathrm{best\!\!-\!\!fit}\ \vec{V}_\odot = \vec{V}_c(R_0) + \mathrm{SBD10}, \Delta \chi^2 = %.0f$' % (2.*(fid_logl-sbd_logl)),#,special.erfinv(stats.chi2.cdf(2.*(fid_logl-sbd_logl),10.))*numpy.sqrt(2.)), top_right=True,size=14.) #pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$') thisax.set_ylim(-19.5,19.5) pyplot.xlim(0.,360.) bovy_plot._add_ticks() #thisax.xaxis.set_major_formatter(nullfmt) pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$') pyplot.xlim(0.,360.) bovy_plot._add_ticks() #Save bovy_plot.bovy_end_print(options.plotfilename) print fid_logl, sbd_logl, 2.*(fid_logl-sbd_logl),special.erfinv(stats.chi2.cdf(2.*(fid_logl-sbd_logl),10.))*numpy.sqrt(2.) return None