def plot_psd_red(): data= readAndHackHoltz.readAndHackHoltz() dx= 1. binsize= .735 pix= pixelize_sample.pixelXY(data,xmin=5.,xmax=13, ymin=-3.,ymax=7., dx=dx,dy=dx) resv= pix.plot(lambda x: dvlosgal(x),returnz=True,justcalc=True) resvunc= pix.plot('VHELIO_AVG', func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)), returnz=True,justcalc=True) psd1d= bovy_psd.psd1d(resv,dx,binsize=binsize) print psd1d #Simulations for constant 3.25 km/s nnoise= _NNOISE noisepsd= numpy.empty((nnoise,len(psd1d[0])-3)) for ii in range(nnoise): newresv= numpy.random.normal(size=resv.shape)*3.25 noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][0:-3] scale= 4.*numpy.pi#3.25/numpy.median(numpy.sqrt(noisepsd)) print scale #Simulations for the actual noise nnoise= _NNOISE noisepsd= numpy.empty((nnoise,len(psd1d[0])-3)) for ii in range(nnoise): newresv= numpy.random.normal(size=resv.shape)*resvunc noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][0:-3] ks= psd1d[0][0:-3] bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psd1d[1][0:-3] -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),'ko',mew=2., mfc='none',overplot=True) pyplot.errorbar(ks,scale*numpy.sqrt(psd1d[1][0:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)), yerr=scale*0.5*(psd1d[2][0:-3]**2. +_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][0:-3]), marker='None',ls='none',color='k') if False: interpindx= True-numpy.isnan(psd1d[1][0:-3]) interpspec= interpolate.InterpolatedUnivariateSpline(ks[interpindx], scale*numpy.sqrt(psd1d[1][0:-3])[interpindx], k=3) pks= numpy.linspace(ks[0],ks[-1],201) bovy_plot.bovy_plot(pks,interpspec(pks),'k-',overplot=True) #Simulations for the actual noise nnoise= _NNOISE noisepsd= numpy.empty((nnoise,len(psd1d[0])-3)) for ii in range(nnoise): newresv= numpy.random.normal(size=resv.shape)*resvunc noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][0:-3] if _PLOTBAND: bovy_plot.bovy_plot(ks, scale*numpy.median(numpy.sqrt(noisepsd),axis=0), '-',lw=8.,zorder=9, color='0.65',overplot=True) return None
def large_scale_power(pix,vsolar,vc=220.,dx=None,beta=0.,hs=33.3,hR=3./8.): """Determine the power on large scales in the residuals for different solarmotions""" out= numpy.empty_like(vsolar) binsize= 0.8 scale= 4.*numpy.pi #0.522677552224 for ii in range(len(vsolar)): resv= pix.plot(lambda x: dvlosgal(x,vc=vc,vtsun=vc+vsolar[ii], beta=beta,hR=hR,hs=hs), returnz=True,justcalc=True) psd1d= bovy_psd.psd1d(resv,dx,binsize=binsize) indx= (psd1d[0] > 0.2)*(psd1d[0] < 0.9) out[ii]= scale*numpy.sqrt(numpy.sum(psd1d[1][indx]*(psd1d[1][indx]/psd1d[2][indx])**2.)/numpy.sum((psd1d[1][indx]/psd1d[2][indx])**2.)) return out
def plot_psd(plotfilename): data= apread.rcsample() if _ADDLLOGGCUT: data= data[data['ADDL_LOGG_CUT'] == 1] #Cut indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.) print "Using %i stars for low-Z 2D kinematics analysis" % numpy.sum(indx) data= data[indx] #Get residuals dx= _RCDX binsize= .8#.765 pix= pixelize_sample.pixelXY(data, xmin=_RCXMIN,xmax=_RCXMAX, ymin=_RCYMIN,ymax=_RCYMAX, dx=dx,dy=dx) resv= pix.plot(lambda x: dvlosgal(x,vtsun=220.+22.5), returnz=True,justcalc=True) resvunc= pix.plot('VHELIO_AVG', func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)), returnz=True,justcalc=True) psd1d= bovy_psd.psd1d(resv,dx,binsize=binsize) print psd1d #Simulations for constant 3.25 km/s nnoise= _NNOISE noisepsd= numpy.empty((nnoise,len(psd1d[0])-4)) for ii in range(nnoise): newresv= numpy.random.normal(size=resv.shape)*3.25 noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][1:-3] scale= 4.*numpy.pi#3.25/numpy.median(numpy.sqrt(noisepsd)) print scale #Simulations for the actual noise nnoise= _NNOISE noisepsd= numpy.empty((nnoise,len(psd1d[0])-4)) for ii in range(nnoise): newresv= numpy.random.normal(size=resv.shape)*resvunc noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][1:-3] ks= psd1d[0][1:-3] if _ADDGCS: xrange=[.03,110.] else: xrange= [0.,1.] yrange= [0.,11.9] if _PROPOSAL: bovy_plot.bovy_print(fig_width=7.5,fig_height=3.) else: bovy_plot.bovy_print(fig_width=7.5,fig_height=4.5) apop= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psd1d[1][1:-3] -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)), 'ko',lw=2., zorder=12, xlabel=r'$k\,(\mathrm{kpc}^{-1})$', ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1})$', semilogx=_ADDGCS, xrange=xrange,yrange=yrange) if _DUMP2FILE: with open('bovy-apogee-psd.dat','w') as csvfile: writer= csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL) csvfile.write('#APOGEE\n') for ii in range(len(ks)): writer.writerow([ks[ii], (scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)))[ii], (scale*0.5*(psd1d[2][1:-3]**2.+_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][1:-3]))[ii]]) if _PROPOSAL: pyplot.gcf().subplots_adjust(bottom=0.15) pyplot.errorbar(ks,scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)), yerr=scale*0.5*(psd1d[2][1:-3]**2. +_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][1:-3]), marker='None',ls='none',color='k') if _PLOTBAND: # bovy_plot.bovy_plot(ks, # scale*numpy.median(numpy.sqrt(noisepsd),axis=0), # '--',lw=2.,zorder=10, # color='0.85',overplot=True) # bovy_plot.bovy_plot(ks, # scale*numpy.sqrt(numpy.sort(noisepsd # -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0),axis=0)[int(numpy.floor(0.99*_NNOISE)),:]), # zorder=1,ls='--',overplot=True, # color='0.65') pyplot.fill_between(ks, scale*numpy.sqrt(numpy.sort(noisepsd -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0),axis=0)[int(numpy.floor(_SIGNIF*_NNOISE)),:]), zorder=0, color='0.65') #Add a simple model of a spiral potential if _ADDSIMPLESPIRAL: if False: alpha= -12.5 spvlos= simulate_vlos_spiral(alpha=alpha, gamma=1.2, xmin=_RCXMIN,xmax=_RCXMAX, ymin=_RCYMIN,ymax=_RCYMAX, dx=0.01) potscale= 1.35*1.2 print numpy.arctan(2./alpha)/numpy.pi*180., numpy.sqrt(0.035/numpy.fabs(alpha)/2.)*potscale*220., numpy.sqrt(0.035/numpy.fabs(alpha))*potscale*220. simpsd1d= bovy_psd.psd1d(spvlos*220.*potscale,0.01,binsize=binsize) tks= simpsd1d[0][1:-3] bovy_plot.bovy_plot(tks, scale*numpy.sqrt(simpsd1d[1][1:-3]), 'k--',lw=2.,overplot=True) #A better simulation # spvlos= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_alpha-14.sav') spvlos= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_hivres.sav') potscale= 1. simpsd1d= bovy_psd.psd1d(spvlos*220.*potscale,0.33333333,binsize=binsize) tks= simpsd1d[0][1:-3] # alpha=-14. # print numpy.arctan(2./-14.)/numpy.pi*180., numpy.sqrt(0.075/numpy.fabs(alpha)/2.)*potscale*220., numpy.sqrt(0.075/numpy.fabs(alpha))*potscale*220. line1= bovy_plot.bovy_plot(tks, scale*numpy.sqrt(simpsd1d[1][1:-3]), 'k--',lw=2.,overplot=True) if _DUMP2FILE: with open('bovy-bar-psd.dat','w') as csvfile: writer= csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL) for ii in range(len(ks)): writer.writerow([tks[ii],(scale*numpy.sqrt(simpsd1d[1][1:-3]))[ii]]) #bovy_plot.bovy_plot(tks[tks > 0.7], # scale*numpy.sqrt(simpsd1d[1][1:-3][tks > 0.7]+4./scale**2.*(1.-numpy.tanh(-(tks[tks > 0.7]-0.9)/0.1))/2.), # 'k-.',lw=2.,overplot=True) #line2= bovy_plot.bovy_plot(tks, # scale*numpy.sqrt(simpsd1d[1][1:-3]+4./scale**2.), # 'k-.',lw=2.,overplot=True,dashes=(10,5,3,5)) l1= pyplot.legend((line1[0],), # (r'$\mathrm{Spiral}:\ \delta \phi_{\mathrm{rms}} = (10\,\mathrm{km\,s}^{-1})^2,$'+'\n'+r'$\mathrm{pitch\ angle} = 8^\circ$'+'\n'+r'$\mathrm{Sun\ near\ 2\!:\!1\ Lindblad\ resonance}$',), (r'$\mathrm{Bar}:\ F_{R,\mathrm{bar}} / F_{R,\mathrm{axi}} = 1.5\%,$'+'\n'+r'$\mathrm{angle} = 25^\circ,$'+'\n'+r'$\mathrm{Sun\ near\ 2\!:\!1\ Lindblad\ resonance}$',), loc='upper right',#bbox_to_anchor=(.91,.375), numpoints=8, prop={'size':14}, frameon=False) #Add the lopsided and ellipticity constraints from Rix/Zaritsky if _ADDRIX: pyplot.errorbar([1./16.],[5.6], yerr=[5.6/2.], marker='d',color='0.6', mew=1.5,mfc='none',mec='0.6') pyplot.errorbar([1./8./numpy.sqrt(2.)],[6.4], yerr=numpy.reshape(numpy.array([6.4/0.045*0.02,6.4/0.045*0.03]),(2,1)), marker='d',color='0.6',mec='0.6', mew=1.5,mfc='none') if _ADDGCS: ks_gcs, psd_gcs, e_psd_gcs, gcsp= plot_psd_gcs() if _ADDRAVE: ks_rave, psd_rave, e_psd_rave, ravep= plot_psd_rave() if _ADDRED: plot_psd_red() l2= pyplot.legend((apop[0],ravep[0],gcsp[0]), (r'$\mathrm{APOGEE}$', r'$\mathrm{RAVE}$', r'$\mathrm{GCS}$'), loc='upper right',bbox_to_anchor=(.95,.750), numpoints=1, prop={'size':14}, frameon=False) pyplot.gca().add_artist(l1) pyplot.gca().add_artist(l2) #Plot an estimate of the noise, based on looking at the bands nks= numpy.linspace(2.,120.,2) if not 'mba23' in socket.gethostname(): pyplot.fill_between(nks,[2.,2.],hatch='/',color='k',facecolor=(0,0,0,0), lw=0.) # edgecolor=(0,0,0,0)) pyplot.plot(nks,[2.,2.],color='k',lw=1.5) nks= numpy.linspace(0.17,2.,2) def linsp(x): return .8/(numpy.log(0.17)-numpy.log(2.))*(numpy.log(x)-numpy.log(0.17))+2.8 if not 'mba23' in socket.gethostname(): pyplot.fill_between(nks,linsp(nks),hatch='/',color='k',facecolor=(0,0,0,0), lw=0.) # edgecolor=(0,0,0,0)) #Plot lines pyplot.plot([0.17,0.17],[0.,2.8],color='k',lw=1.5) pyplot.plot(nks,linsp(nks)+0.01,color='k',lw=1.5) bovy_plot.bovy_text(0.19,.5,r'$95\,\%\,\mathrm{noise\ range}$', bbox=dict(facecolor='w',edgecolor='w'),fontsize=14.) if _INTERP: interpks= list(ks[:-5]) interppsd= list((scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)))[:-5]) interppsd_w= (scale*0.5*psd1d[2][1:-3]/numpy.sqrt(psd1d[1][1:-3]))[:-5] interppsd_w[:8]*= 0.025 #fiddling to get a decent fit interppsd_w[3:5]*= 0.001 interppsd_w= list(interppsd_w) interpks.append(0.025) interppsd.append(10.**-5.) interppsd_w.append(0.00001) interpks.append(110.) interppsd.append(1.) interppsd_w.append(0.00001) if _ADDGCS: interpks.extend(ks_gcs) interppsd.extend(psd_gcs) interppsd_w.extend(e_psd_gcs) if _ADDRAVE: interpks.extend(ks_rave[5:]) interppsd.extend(psd_rave[5:]) interppsd_w.extend(e_psd_rave[5:]) interpks= numpy.array(interpks) sortindx= numpy.argsort(interpks) interpks= interpks[sortindx] interppsd= numpy.array(interppsd)[sortindx] interppsd_w= interppsd/numpy.array(interppsd_w)[sortindx] interpindx= True-numpy.isnan(interppsd) #interpspec= interpolate.InterpolatedUnivariateSpline(interpks[interpindx], interpspec= interpolate.UnivariateSpline(numpy.log(interpks[interpindx]), numpy.log(interppsd[interpindx]/3.), w=interppsd_w, k=3,s=len(interppsd_w)*0.8) pks= numpy.linspace(interpks[0],interpks[-1],201) #bovy_plot.bovy_plot(pks, # 3.*numpy.exp(interpspec(numpy.log(pks))), # 'k-',overplot=True) def my_formatter(x, pos): return r'$%g$' % x def my_formatter2(x, pos): return r'$%g$' % (1./x) major_formatter = FuncFormatter(my_formatter) major_formatter2 = FuncFormatter(my_formatter2) ax= pyplot.gca() ax.xaxis.set_major_formatter(major_formatter) if not _PROPOSAL: ax2= pyplot.twiny() xmin, xmax= ax.xaxis.get_view_interval() ax2.set_xscale('log') ax2.xaxis.set_view_interval(1./xmin,1./xmax,ignore=True) ax2.set_xlabel('$\mathrm{Approximate\ scale}\,(\mathrm{kpc})$', fontsize=12.,ha='center',x=0.5) ax2.xaxis.set_major_formatter(major_formatter) bovy_plot.bovy_end_print(plotfilename,dpi=300) return None
def plot_psd_rave(): data= fitsio.read(os.path.join(os.getenv('DATADIR'),'rave','ravedr4_rc.fits')) dx= _RAVEDX binsize= 0.8#.735 pix= pix= pixelize_sample.pixelXY(data, xmin=_RAVEXMIN,xmax=_RAVEXMAX, ymin=_RAVEYMIN,ymax=_RAVEYMAX, dx=dx,dy=dx) resv= pix.plot(lambda x: dvlosgal(x,vtsun=230.),returnz=True,justcalc=True) resvunc= pix.plot('VHELIO_AVG', func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)), returnz=True,justcalc=True) psd1d= bovy_psd.psd1d(resv,dx,binsize=binsize) print psd1d #Simulations for constant 3.25 km/s nnoise= _NNOISE noisepsd= numpy.empty((nnoise,len(psd1d[0])-4)) for ii in range(nnoise): newresv= numpy.random.normal(size=resv.shape)*3.25 noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][1:-3] scale= 4.*numpy.pi#3.25/numpy.median(numpy.sqrt(noisepsd)) print scale #Simulations for the actual noise nnoise= _NNOISE noisepsd= numpy.empty((nnoise,len(psd1d[0])-4)) for ii in range(nnoise): newresv= numpy.random.normal(size=resv.shape)*resvunc noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][1:-3] ks= psd1d[0][1:-3] ravep= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psd1d[1][1:-3] -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),'k+',mew=2., overplot=True) pyplot.errorbar(ks,scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)), yerr=scale*0.5*(psd1d[2][1:-3]**2. +_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][1:-3]), marker='None',ls='none',color='k') if _DUMP2FILE: with open('bovy-apogee-psd.dat','a') as csvfile: writer= csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL) csvfile.write('#RAVE\n') for ii in range(len(ks)): writer.writerow([ks[ii], (scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)))[ii], (scale*0.5*(psd1d[2][1:-3]**2.+_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][1:-3]))[ii]]) if False: interpindx= True-numpy.isnan(psd1d[1][1:-3]) interpspec= interpolate.InterpolatedUnivariateSpline(ks[interpindx], scale*numpy.sqrt(psd1d[1][1:-3])[interpindx], k=3) pks= numpy.linspace(ks[0],ks[-1],201) bovy_plot.bovy_plot(pks,interpspec(pks),'k-',overplot=True) if _PLOTBAND: #bovy_plot.bovy_plot(ks, # scale*numpy.median(numpy.sqrt(noisepsd),axis=0), # '-',lw=8.,zorder=9, # color='0.65',overplot=True) # bovy_plot.bovy_plot(ks, # scale*numpy.sqrt(numpy.sort(noisepsd # -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0),axis=0)[int(numpy.floor(0.99*_NNOISE)),:]), # zorder=1,ls='--',overplot=True, # color='0.65') pyplot.fill_between(ks, scale*numpy.sqrt(numpy.sort(noisepsd -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0),axis=0)[int(numpy.floor(_SIGNIF*_NNOISE)),:]), zorder=0, color='0.65') #bovy_plot.bovy_plot(numpy.tile(ks,(_NNOISE,1)).T, # scale*numpy.sqrt(noisepsd).T, # '-',zorder=0,alpha=0.5, # color='0.45',overplot=True) return (ks, scale*numpy.sqrt(psd1d[1][1:-3] -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)), scale*0.5*psd1d[2][1:-3]/numpy.sqrt(psd1d[1][1:-3]), ravep)
def plot_psd_gcs(): data= hackGCS.hackGCS() dx= _GCSDX binsize= .8 pix= pixelize_sample.pixelXY(data, xmin=_GCSXMIN,xmax=_GCSXMAX, ymin=_GCSYMIN,ymax=_GCSYMAX, dx=dx,dy=dx) resv= pix.plot('VVel',returnz=True,justcalc=True) resvunc= pix.plot('VVel', func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)), returnz=True,justcalc=True) psd1d= bovy_psd.psd1d(resv,dx,binsize=binsize) print psd1d #Simulations for constant 3.25 km/s nnoise= _NNOISE noisepsd= numpy.empty((nnoise,len(psd1d[0])-4)) for ii in range(nnoise): newresv= numpy.random.normal(size=resv.shape)*3.25 noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][1:-3] scale= 4.*numpy.pi#3.25/numpy.median(numpy.sqrt(noisepsd)) print scale #Simulations for the actual noise nnoise= _NNOISE noisepsd= numpy.empty((nnoise,len(psd1d[0])-4)) for ii in range(nnoise): newresv= numpy.random.normal(size=resv.shape)*resvunc noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][1:-3] ks= psd1d[0][1:-3] gcsp= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psd1d[1][1:-3] -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),'kx',mew=2., overplot=True) pyplot.errorbar(ks,scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)), yerr=scale*0.5*(psd1d[2][1:-3]**2. +_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][1:-3]), marker='None',ls='none',color='k')#,lolims=True) if _DUMP2FILE: with open('bovy-apogee-psd.dat','a') as csvfile: writer= csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL) csvfile.write('#GCS\n') for ii in range(len(ks)): writer.writerow([ks[ii], (scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)))[ii], (scale*0.5*(psd1d[2][1:-3]**2.+_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][1:-3]))[ii]]) if _PLOTBAND: # bovy_plot.bovy_plot(ks, # scale*numpy.median(numpy.sqrt(noisepsd),axis=0), # '-',lw=2.,zorder=9, # color='0.65',overplot=True) # bovy_plot.bovy_plot(ks, # scale*numpy.sqrt(numpy.sort(noisepsd # -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0),axis=0)[int(numpy.floor(0.99*_NNOISE)),:]), # zorder=1,ls='--',overplot=True, # color='0.65') pyplot.fill_between(ks, scale*numpy.sqrt(numpy.sort(noisepsd -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0),axis=0)[int(numpy.floor(_SIGNIF*_NNOISE)),:]), zorder=0, color='0.65') return (ks, scale*numpy.sqrt(psd1d[1][1:-3] -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)), scale*0.5*psd1d[2][1:-3]/numpy.sqrt(psd1d[1][1:-3]), gcsp)
def plot_psd_model(plotfilename,type): #Load fiducial # spvlos= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_alpha-14%s.sav' % _HIVRESSTR) spvlos= galpy_simulations.vlos('../sim/bar_rect_alpha0.015%s.sav' % _HIVRESSTR) potscale= 1. simpsd1d= bovy_psd.psd1d(spvlos*potscale,0.33333333,binsize=0.8) tks= simpsd1d[0][1:-3] xrange=[.08,4.] if type.lower() == 'elliptical': eres= 31 p= 0. vloscp= galpy_simulations.vlos_elliptical(res=eres,cp=0.02,sp=0.,p=p) vlossp= galpy_simulations.vlos_elliptical(res=eres,sp=0.02,cp=0.,p=p) vloscpsp= galpy_simulations.vlos_elliptical(res=eres,p=p, sp=0.02/numpy.sqrt(2.), cp=0.02/numpy.sqrt(2.)) p=2. vloscpp2= galpy_simulations.vlos_elliptical(res=eres,cp=0.01,sp=0.,p=p) vlosspp2= galpy_simulations.vlos_elliptical(res=eres,sp=0.01,cp=0.,p=p) vloscpspp2= galpy_simulations.vlos_elliptical(res=eres,p=p, sp=0.01/numpy.sqrt(2.), cp=0.01/numpy.sqrt(2.)) p=-3. vloscppm3= galpy_simulations.vlos_elliptical(res=eres,cp=0.05,sp=0.,p=p) vlossppm3= galpy_simulations.vlos_elliptical(res=eres,sp=0.05,cp=0.,p=p) vloscpsppm3= galpy_simulations.vlos_elliptical(res=eres,p=p, sp=0.05/numpy.sqrt(2.), cp=0.05/numpy.sqrt(2.)) xgrid= numpy.linspace((_RCXMIN-8.)/8.+_RCDX/8./2., (_RCXMAX-8.)/8.-_RCDX/8./2., eres) dx= (xgrid[1]-xgrid[0])*8. psdcp= bovy_psd.psd1d(vloscp,dx,binsize=0.8) psdsp= bovy_psd.psd1d(vlossp,dx,binsize=0.8) psdcpsp= bovy_psd.psd1d(vloscpsp,dx,binsize=0.8) psdcpp2= bovy_psd.psd1d(vloscpp2,dx,binsize=0.8) psdspp2= bovy_psd.psd1d(vlosspp2,dx,binsize=0.8) psdcpspp2= bovy_psd.psd1d(vloscpspp2,dx,binsize=0.8) psdcppm3= bovy_psd.psd1d(vloscppm3,dx,binsize=0.8) psdsppm3= bovy_psd.psd1d(vlossppm3,dx,binsize=0.8) psdcpsppm3= bovy_psd.psd1d(vloscpsppm3,dx,binsize=0.8) ks= psdcp[0][1:-3] scale= 4.*numpy.pi*220. bovy_plot.bovy_print(fig_width=8.,fig_height=4.5,axes_labelsize=20) line1= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdcp[1][1:-3]), 'k-',lw=2., semilogx=True, # xlabel=r'$k\,(\mathrm{kpc}^{-1})$', ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1})$', xrange=xrange, yrange=[0.,11.9],zorder=1) line2= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdsp[1][1:-3]), 'k--',lw=2., overplot=True,zorder=1) line3= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdcpsp[1][1:-3]), 'k-.',lw=2.,zorder=1, overplot=True) line4= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdcpp2[1][1:-3]), '-',lw=2.,color='c',zorder=1, overplot=True) line5= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdspp2[1][1:-3]), '--',lw=2.,color='c',zorder=1, overplot=True) line6= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdcpspp2[1][1:-3]), '-.',lw=2.,color='c',zorder=1, overplot=True) line7= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdcppm3[1][1:-3]), '-',lw=2.,color='r',zorder=1, overplot=True) line8= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdsppm3[1][1:-3]), '--',lw=2.,color='r',zorder=1, overplot=True) line9= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdcpsppm3[1][1:-3]), '-.',lw=2.,color='r',zorder=1, overplot=True) pyplot.annotate(r'$\mathrm{Elliptical\ perturbation}\ (m=2\ \mathrm{mode})$', (0.5,1.08),xycoords='axes fraction', horizontalalignment='center', verticalalignment='top',size=20.) l1= pyplot.legend((line1[0],line2[0],line3[0]), (r'$\phi_b = 0^\circ$', r'$\phi_b = 45^\circ$', r'$\phi_b = 90^\circ$'), loc='lower left',#bbox_to_anchor=(.91,.375), numpoints=8, prop={'size':16}, frameon=False) l2= pyplot.legend((line1[0],line4[0],line7[0]), (r'$\epsilon(R) = 0.02$', r'$\epsilon(R) = 0.01\,\left(\frac{R}{R_0}\right)^2$', r'$\epsilon(R) = 0.05\,\left(\frac{R}{R_0}\right)^{-3}$'), loc='upper right',#bbox_to_anchor=(.91,.375), numpoints=8, prop={'size':16}, frameon=False) pyplot.gca().add_artist(l1) pyplot.gca().add_artist(l2) elif type.lower() == 'bar': vlosbar= galpy_simulations.vlos('../sim/bar_rect_alpha0.015%s.sav' % _HIVRESSTR) vlosslowbar= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_slow%s.sav' % _HIVRESSTR) vlosbarsmallangle= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_angle10%s.sav' % _HIVRESSTR) vlosbarlargeangle= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_angle40%s.sav' % _HIVRESSTR) vlosbarsmallrolr= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_rolr0.85%s.sav' % _HIVRESSTR) vlosbarlargerolr= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_rolr0.95%s.sav' % _HIVRESSTR) eres= 19 xgrid= numpy.linspace((_RCXMIN-8.)/8.+_RCDX/8./2., (_RCXMAX-8.)/8.-_RCDX/8./2., eres) dx= (xgrid[1]-xgrid[0])*8. psdbar= bovy_psd.psd1d(vlosbar,dx,binsize=0.8) psdslowbar= bovy_psd.psd1d(vlosslowbar,dx,binsize=0.8) psdsbarsmallangle= bovy_psd.psd1d(vlosbarsmallangle,dx,binsize=0.8) psdsbarlargeangle= bovy_psd.psd1d(vlosbarlargeangle,dx,binsize=0.8) psdsbarsmallrolr= bovy_psd.psd1d(vlosbarsmallrolr,dx,binsize=0.8) psdsbarlargerolr= bovy_psd.psd1d(vlosbarlargerolr,dx,binsize=0.8) ks= psdbar[0][1:-3] scale= 4.*numpy.pi*220. bovy_plot.bovy_print(fig_width=8.,fig_height=4.5,axes_labelsize=20) line1= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdbar[1][1:-3]), '-',lw=2.,color='0.65', semilogx=True, # xlabel=r'$k\,(\mathrm{kpc}^{-1})$', ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1})$', xrange=xrange, yrange=[0.,11.9],zorder=1) line2= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdslowbar[1][1:-3]), 'r-',lw=2., overplot=True,zorder=1) line3= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdsbarsmallangle[1][1:-3])*0.9, '-',lw=2.,color='gold', overplot=True,zorder=1) line4= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdsbarlargeangle[1][1:-3])*1.1, 'b-',lw=2., overplot=True,zorder=1) line5= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdsbarsmallrolr[1][1:-3])*0.9, 'g-',lw=2., overplot=True,zorder=1) line6= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdsbarlargerolr[1][1:-3]), 'c-',lw=2., overplot=True,zorder=1) pyplot.annotate(r'$\mathrm{Bar\ perturbation\ (rotating}\ m=2\ \mathrm{mode})$', (0.5,1.08),xycoords='axes fraction', horizontalalignment='center', verticalalignment='top',size=20.) l1= pyplot.legend((line1[0],line2[0]), (r'$\mathrm{Fast\ bar\ growth}$', r'$\mathrm{Slow\ bar\ growth}$'), loc='upper right',#bbox_to_anchor=(.91,.375), numpoints=8, prop={'size':16}, frameon=False) l2= pyplot.legend((line3[0],line4[0],line5[0],line6[0]), (r'$\mathrm{Fast}\ \&\ \mathrm{bar\ angle} = 10^\circ$', r'$\mathrm{Fast}\ \&\ \mathrm{bar\ angle} = 40^\circ$', r'$\mathrm{Fast}\ \&\ R_{\mathrm{OLR}} = 0.85\,R_0$', r'$\mathrm{Fast}\ \&\ R_{\mathrm{OLR}} = 0.95\,R_0$'), loc='lower left',#bbox_to_anchor=(.91,.375), numpoints=8, prop={'size':16}, frameon=False) pyplot.gca().add_artist(l1) pyplot.gca().add_artist(l2) elif type.lower() == 'spiral': vlosfid= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_alpha-14%s.sav' % _HIVRESSTR) vloslpitch= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_alpha-7%s.sav' % _HIVRESSTR) vlosdiffgamma= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_gamma0.3%s.sav' % _HIVRESSTR) vlosdiffomegas= galpy_simulations.vlos('../sim/spiral_rect_alpha-14%s.sav' % _HIVRESSTR) vlosdiffm= galpy_simulations.vlos('../sim/spiral_rect_m4_alpha-14%s.sav' % _HIVRESSTR) vlosdiffm2= galpy_simulations.vlos('../sim/spiral_rect_m4_alpha-14_gamma0.4%s.sav' % _HIVRESSTR) potscale= 0.85 eres= 19 xgrid= numpy.linspace((_RCXMIN-8.)/8.+_RCDX/8./2., (_RCXMAX-8.)/8.-_RCDX/8./2., eres) dx= (xgrid[1]-xgrid[0])*8. psdfid= bovy_psd.psd1d(vlosfid,dx,binsize=0.8) psdlpitch= bovy_psd.psd1d(vloslpitch,dx,binsize=0.8) psddiffgamma= bovy_psd.psd1d(vlosdiffgamma,dx,binsize=0.8) psddiffomegas= bovy_psd.psd1d(vlosdiffomegas,dx,binsize=0.8) psddiffm= bovy_psd.psd1d(vlosdiffm,dx,binsize=0.8) psddiffm2= bovy_psd.psd1d(vlosdiffm2,dx,binsize=0.8) ks= psdfid[0][1:-3] scale= 4.*numpy.pi*220. bovy_plot.bovy_print(fig_width=8.,fig_height=4.5,axes_labelsize=20) line1= bovy_plot.bovy_plot(ks,potscale*scale*numpy.sqrt(psdfid[1][1:-3]), 'k-',lw=2, semilogx=True, # xlabel=r'$k\,(\mathrm{kpc}^{-1})$', ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1})$', xrange=xrange, yrange=[0.,11.9],zorder=1) potscale= 0.25 line2= bovy_plot.bovy_plot(ks,potscale*scale*numpy.sqrt(psdlpitch[1][1:-3]), '-',lw=2.,zorder=1,color='gold', overplot=True) potscale= 0.5 line3= bovy_plot.bovy_plot(ks,potscale*scale*numpy.sqrt(psddiffgamma[1][1:-3]), 'r-',lw=2.,zorder=1, overplot=True) line4= bovy_plot.bovy_plot(ks,4.*scale*numpy.sqrt(psddiffomegas[1][1:-3]), 'b-',lw=2.,zorder=1, overplot=True) line5= bovy_plot.bovy_plot(ks,10./7.*scale*numpy.sqrt(psddiffm[1][1:-3]), 'g-',lw=2.,zorder=1, overplot=True) line6= bovy_plot.bovy_plot(ks,10./6.*scale*numpy.sqrt(psddiffm2[1][1:-3]), 'c-',lw=2.,zorder=1, overplot=True) pyplot.annotate(r'$\mathrm{Spiral\ perturbation}$', (0.5,1.08),xycoords='axes fraction', horizontalalignment='center', verticalalignment='top',size=20.) l1= pyplot.legend((line1[0],line2[0],line3[0], line4[0],line5[0],line6[0]), (r'$\mathrm{Fiducial\ spiral}$', r'$\mathrm{Pitch\ angle} = 16^\circ$', r'$\gamma = 17^\circ$'), loc='upper right',#bbox_to_anchor=(.91,.375), numpoints=8, prop={'size':16}, frameon=False) l2= pyplot.legend((line4[0],line5[0],line6[0]), (r'$\Omega_s = 0.65\,\Omega_0$', r'$\Omega_s = 0.65\,\Omega_0\ \&\ m=4$', r'$\Omega_s = 0.65\,\Omega_0\ \&\ m=4\ \&\ \gamma = 23^\circ$'), loc='lower left',#bbox_to_anchor=(.91,.375), numpoints=8, prop={'size':16}, frameon=False) pyplot.gca().add_artist(l1) pyplot.gca().add_artist(l2) elif type.lower() == 'bird': _nSims= 8 #Read the Bird data birdData= numpy.load('../pecvel/dr12_1_5gyr_8pos.npz') #Get residuals for all simulations dx= _RCDX binsize= .8#.765 scale= 4.*numpy.pi tmp= bovy_psd.psd1d(birdData['dVlos1'],dx,binsize=binsize) #just to get the size ks= tmp[0][1:-3] psds= numpy.zeros((len(tmp[1]),_nSims)) if True: import apogee.tools.read as apread import pixelize_sample data= apread.rcsample() data= data[data['ADDL_LOGG_CUT'] == 1] #Cut indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.) print "Using %i stars for low-Z 2D kinematics analysis" % numpy.sum(indx) data= data[indx] #Get residuals dx= _RCDX pix= pixelize_sample.pixelXY(data, xmin=_RCXMIN,xmax=_RCXMAX, ymin=_RCYMIN,ymax=_RCYMAX, dx=dx,dy=dx) resv= pix.plot('VHELIO_AVG',returnz=True,justcalc=True) rc_mask= numpy.ones(resv.shape,dtype='bool') rc_mask[True-numpy.isnan(resv)]= False if _SUBTRACTERRORS: for ii in range(_nSims): sim= ii+1 tmpPsd= bovy_psd.psd1d(birdData['dVlos%i' % sim], dx,binsize=binsize)[1] #Simulations for the noise nnoise= _NNOISE noisepsd= numpy.empty((nnoise,len(tmpPsd))) for jj in range(nnoise): newresv= \ numpy.random.normal(size=birdData['dVlos%i' % sim].shape)\ *birdData['sig_dVlos%i' % sim].reshape((9,9))\ *(True-rc_mask) # *(True-birdData['rc_mask']) noisepsd[jj,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1] psds[:,ii]= tmpPsd-numpy.median(noisepsd,axis=0) #Calculate median PSD and spread around this medPsd= scale*numpy.median(numpy.sqrt(psds),axis=1)[1:-3] flucPsd=\ 1.4826*scale*numpy.median(numpy.fabs(numpy.sqrt(psds)[1:-3] -numpy.tile(medPsd/scale, (psds.shape[1],1)).T),axis=1) bovy_plot.bovy_print(fig_width=8.,fig_height=4.5,axes_labelsize=20) def my_formatter(x, pos): return r'$%g$' % x major_formatter = FuncFormatter(my_formatter) line1= bovy_plot.bovy_plot(ks,medPsd, 'k-',lw=2, semilogx=True, xlabel=r'$k\,(\mathrm{kpc}^{-1})$', ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1})$', xrange=xrange, yrange=[0.,11.9],zorder=1) pyplot.gca().xaxis.set_major_formatter(major_formatter) goodIndx= True-numpy.isnan(flucPsd) pyplot.fill_between(ks[goodIndx],(medPsd-flucPsd)[goodIndx], y2=(medPsd+flucPsd)[goodIndx], color='0.45',zorder=-1) pyplot.annotate(r'$\mathrm{Cosmological\ simulation}$', (0.5,1.08),xycoords='axes fraction', horizontalalignment='center', verticalalignment='top',size=20.) bovy_plot.bovy_text(r'$\mathrm{Median\ and\ range\ from}$'+'\n' +r'$\mathrm{8\ APOGEE\!-\!like\ volumes}$', top_right=True,size=16.) bovy_plot.bovy_plot([0.4,0.65],[7.55,10.],'k-',overplot=True) #Also plot fiducial scale= 4.*numpy.pi*220. bovy_plot.bovy_plot(tks, scale*numpy.sqrt(simpsd1d[1][1:-3]), '-',color='0.65',lw=4.,overplot=True,zorder=0) if not type.lower() == 'bird': nullfmt = NullFormatter() # no labels pyplot.gca().xaxis.set_major_formatter(nullfmt) bovy_plot.bovy_end_print(plotfilename) return None
def plot_bird_psd(plotfilename): #Read the Bird data birdData= numpy.load('../pecvel/pecvel.npz') #Get residuals for all simulations dx= _RCDX binsize= .8#.765 scale= 4.*numpy.pi tmp= bovy_psd.psd1d(birdData['dVlos1'],dx,binsize=binsize) #just to get the size ks= tmp[0][1:-3] psds= numpy.zeros((len(tmp[1]),_nSims)) if _SUBTRACTERRORS: for ii in range(_nSims): sim= ii+1 tmpPsd= bovy_psd.psd1d(birdData['dVlos%i' % sim], dx,binsize=binsize)[1] #Simulations for the noise nnoise= _NNOISE noisepsd= numpy.empty((nnoise,len(tmpPsd))) for jj in range(nnoise): newresv= \ numpy.random.normal(size=birdData['dVlos%i' % sim].shape)\ *birdData['sig_dVlos%i' % sim].reshape((9,9))\ *(True-birdData['rc_mask']) noisepsd[jj,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1] psds[:,ii]= tmpPsd-numpy.median(noisepsd,axis=0) #Calculate median PSD and spread around this medPsd= scale*numpy.median(numpy.sqrt(psds),axis=1)[1:-3] flucPsd=\ 1.4826*scale*numpy.median(numpy.fabs(numpy.sqrt(psds)[1:-3] -numpy.tile(medPsd/scale, (psds.shape[1],1)).T),axis=1) print medPsd, flucPsd #Now plot xrange=[.03,3.] yrange= [0.,11.9] bovy_plot.bovy_print(fig_width=5.5,fig_height=4.5) bovy_plot.bovy_plot(ks,medPsd, 'k-',lw=2., zorder=12, xlabel=r'$k\,(\mathrm{kpc}^{-1})$', ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1})$', semilogx=True, xrange=xrange,yrange=yrange) goodIndx= True-numpy.isnan(flucPsd) pyplot.fill_between(ks[goodIndx],(medPsd-flucPsd)[goodIndx], y2=(medPsd+flucPsd)[goodIndx], color='0.65',zorder=1) if _PLOTINDIV: for ii in range(_nSims): bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psds[:,ii])[1:-3], '-',color='0.8',overplot=True) if _ADDDATALINE: alpha= -12.5 spvlos= simulate_vlos_spiral(alpha=alpha, gamma=1.2, xmin=_RCXMIN,xmax=_RCXMAX, ymin=_RCYMIN,ymax=_RCYMAX, dx=0.01) potscale= 1.35 simpsd1d= bovy_psd.psd1d(spvlos*220.*potscale,0.01,binsize=binsize) tks= simpsd1d[0][1:-3] line1= bovy_plot.bovy_plot(tks, scale*numpy.sqrt(simpsd1d[1][1:-3]), 'k--',lw=2.,overplot=True) bovy_plot.bovy_end_print(plotfilename) return None