Exemplo n.º 1
0
def distanceAreaIntegrand(dist, cosb, Gsamples, rmcenter, onlygreen):
    # Calculate the density
    densmap = densprofiles.healpixelate(dist,
                                        densprofiles.expdisk,
                                        [1. / 3., 1. / 0.3],
                                        nside=_NSIDE,
                                        nest=False)
    # Load the dust map
    if onlygreen:
        combinedmap = dust.load_green15(dist, nest=False, nside_out=_NSIDE)
        combinedmap[combinedmap == healpy.UNSEEN] = 0.
    else:
        combinedmap = dust.load_combined(dist, nest=False, nside_out=_NSIDE)
    # Sample over the distribution of MG
    combinedmask = numpy.zeros_like(combinedmap)
    G0 = 0.68 + dust.dist2distmod(dist)
    if dust.dist2distmod(dist) == 9.5 or dust.dist2distmod(dist) == 10.5:
        print numpy.sum(numpy.isnan(combinedmap))
    for jj in range(_NGSAMPLES):
        combinedmask+= ((combinedmap > (_GMIN-G0-Gsamples[jj]+0.68))\
                            *(combinedmap < (_GMAX-G0-Gsamples[jj]+0.68))).astype('float')
    combinedmask /= _NGSAMPLES
    if dust.dist2distmod(dist) == 9.5 or dust.dist2distmod(dist) == 10.5:
        print numpy.sum(combinedmask)
        print dust.dist2distmod(dist), numpy.sum(cosb * densmap * combinedmask)
    # If rmcenter, rm the center of the MW
    if rmcenter:
        theta, phi = healpy.pixelfunc.pix2ang(
            _NSIDE,
            numpy.arange(healpy.pixelfunc.nside2npix(_NSIDE)),
            nest=False)
        combinedmask[((phi < 25.*_DEGTORAD)+(phi > (360.-25.)*_DEGTORAD))\
                         *(numpy.fabs(numpy.pi/2.-theta) < 25.*_DEGTORAD)]= 0.
    # Compute cross correlation
    return (cosb * densmap * combinedmask)
def distanceAreaIntegrand(dist,cosb,Gsamples,rmcenter,onlygreen):
    # Calculate the density
    densmap= densprofiles.healpixelate(dist,densprofiles.expdisk,
                                       [1./3.,1./0.3],nside=_NSIDE,
                                       nest=False)
    # Load the dust map
    if onlygreen:
        combinedmap= dust.load_green15(dist,nest=False,nside_out=_NSIDE)
        combinedmap[combinedmap == healpy.UNSEEN]= 0.
    else:
        combinedmap= dust.load_combined(dist,nest=False,nside_out=_NSIDE)
    # Sample over the distribution of MG
    combinedmask= numpy.zeros_like(combinedmap)
    G0= 0.68+dust.dist2distmod(dist)
    if dust.dist2distmod(dist) == 9.5 or dust.dist2distmod(dist) == 10.5:
        print numpy.sum(numpy.isnan(combinedmap))
    for jj in range(_NGSAMPLES):
        combinedmask+= ((combinedmap > (_GMIN-G0-Gsamples[jj]+0.68))\
                            *(combinedmap < (_GMAX-G0-Gsamples[jj]+0.68))).astype('float')
    combinedmask/= _NGSAMPLES
    if dust.dist2distmod(dist) == 9.5 or dust.dist2distmod(dist) == 10.5:
        print numpy.sum(combinedmask)
        print dust.dist2distmod(dist), numpy.sum(cosb*densmap*combinedmask)
    # If rmcenter, rm the center of the MW
    if rmcenter:
        theta, phi= healpy.pixelfunc.pix2ang(_NSIDE,
                                             numpy.arange(healpy.pixelfunc.nside2npix(_NSIDE)),
                                             nest=False)
        combinedmask[((phi < 25.*_DEGTORAD)+(phi > (360.-25.)*_DEGTORAD))\
                         *(numpy.fabs(numpy.pi/2.-theta) < 25.*_DEGTORAD)]= 0.
    # Compute cross correlation
    return (cosb*densmap*combinedmask)
Exemplo n.º 3
0
def plot_powspec(dist,basename,plotname):
    # Density
    G0= 0.68+dust.dist2distmod(dist)
    densname= basename+'_D%.1f_denscl.sav' % dist
    if not os.path.exists(densname):
        densmap= densprofiles.healpixelate(dist,densprofiles.expdisk,
                                           [1./3.,1./0.3],nside=_NSIDE,
                                           nest=False)
        denscl= healpy.sphtfunc.anafast(densmap,pol=False)
        densmap2= densprofiles.healpixelate(dist,densprofiles.expdisk,
                                            [1./2.,1./0.9],nside=_NSIDE,
                                            nest=False)
        denscl2= healpy.sphtfunc.anafast(densmap2,pol=False)
        ell= numpy.arange(len(denscl))
        save_pickles(densname,ell,denscl,densmap,denscl2,densmap2)
    else:
        with open(densname,'rb') as savefile:
            ell= pickle.load(savefile)
            denscl= pickle.load(savefile)
            densmap= pickle.load(savefile)
            denscl2= pickle.load(savefile)
            densmap2= pickle.load(savefile)
    # dust map Cl and cross-power with dens
    combinedname= basename+'_D%.1f_combinedcl.sav' % dist
    bestfitloaded= False
    if os.path.exists(combinedname):
        with open(combinedname,'rb') as savefile:
            ell= pickle.load(savefile)
            combinedcl= pickle.load(savefile)
            combinedcr= pickle.load(savefile)
            combinedmcl= pickle.load(savefile)
            combinedmcr= pickle.load(savefile)
            combinedmcr2= pickle.load(savefile)
            bestfitloaded= True
    if not bestfitloaded:
        # do the best-fit
        combinedmap= dust.load_combined(dist,nest=False,nside_out=_NSIDE)
        print numpy.sum(numpy.isnan(combinedmap))
        combinedmap[numpy.isnan(combinedmap)]= 0.
        # Sample over the distribution of MG
        combinedmask= numpy.zeros_like(combinedmap)
        iso= gaia_rc.load_iso()
        Gsamples= gaia_rc.sample_Gdist(iso,n=_NGSAMPLES)
        print "Computing effective selection function"
        for jj in range(_NGSAMPLES):
            combinedmask+= ((combinedmap > (_GMIN-G0-Gsamples[jj]+0.68))\
                                *(combinedmap < (_GMAX-G0-Gsamples[jj]+0.68))).astype('float')
        combinedmask/= _NGSAMPLES
        print "Computing Cl of extinction map"
        combinedcl= healpy.sphtfunc.anafast(combinedmap,pol=False)
        print "Computing cross of extinction map w/ densmap"
        combinedcr= healpy.sphtfunc.anafast(combinedmap,map2=densmap,pol=False)
        print "Computing Cl of effective selection function"
        combinedmcl= healpy.sphtfunc.anafast(combinedmask,pol=False)
        print "Computing cross of effective selection function w/ densmap"
        combinedmcr= healpy.sphtfunc.anafast(combinedmask,map2=densmap,pol=False)
        print "Computing cross of effective selection function w/ densmap2"
        combinedmcr2= healpy.sphtfunc.anafast(combinedmask,map2=densmap2,pol=False)
        # Save
        save_pickles(combinedname,ell,combinedcl,combinedcr,
                     combinedmcl,combinedmcr,combinedmcr2)
        gc.collect()
    # Plot (2l+1)Cl!!
    # Can smooth the masked power spectrum, perhaps underplot the non-smoothed in gray
    # sp= interpolate.UnivariateSpline(numpy.log(ell)[1:],numpy.log(combinedmcl)[1:],k=3,s=300.)
    # sp= interpolate.UnivariateSpline(numpy.log(ell)[1:],numpy.log(numpy.fabs(combinedmcr))[1:],k=3,s=10000.)
    # First plot the power-spectrum, then the cross-correlation, then the
    # cumulative sum
    bovy_plot.bovy_print(fig_height=3.)
    yrange=[10.**-12.,20.],
    line1= bovy_plot.bovy_plot(ell[1:],
                               (2.*ell[1:]+1.)*combinedcl[1:],
                               'k-',loglog=True,
                               ylabel=r'$(2\ell+1)\,C_\ell$',
                               xrange=[0.5,20000],
                               yrange=yrange,
                               zorder=3)
    line2= bovy_plot.bovy_plot(ell[2::2],
                               (2.*ell[2::2]+1.)*denscl[2::2],
                               'b-',overplot=True)
    line3= bovy_plot.bovy_plot(ell[1:],
                               (2.*ell[1:]+1.)*combinedmcl[1:],
                               'r-',overplot=True)
    # Add legend
    if dist == 5.:
        pyplot.legend((line2[0],line1[0],line3[0]),
                      (r'$\mathrm{exp.\ disk\ w/}$'+'\n'+r'$h_R = 3\,\mathrm{kpc},$'+'\n'+r'$h_Z = 0.3\,\mathrm{kpc}$',
                       r'$\mathrm{extinction\ map}$',
                       r'$\mathrm{effective\ selection\ function}$'),
                      loc='lower left',bbox_to_anchor=(.02,.02),
                      numpoints=8,
                      prop={'size':14},
                      frameon=False) 
        bovy_plot.bovy_text(r'$\mathrm{power\ spectrum}$',top_right=True,size=16.)
    nullfmt   = NullFormatter()         # no labels
    pyplot.gca().xaxis.set_major_formatter(nullfmt)
    bovy_plot.bovy_end_print(plotname)
    # Cross-correlation
    bovy_plot.bovy_print(fig_height=3.)
    line1= bovy_plot.bovy_plot(ell[1:],
                               (2.*ell[1:]+1.)*numpy.fabs(combinedcr[1:]),
                               'k-',loglog=True,
                               ylabel=r'$(2\ell+1)\,C^{\mathrm{cross}}_\ell$',
                               xrange=[0.5,20000],
                               yrange=yrange,
                               zorder=1)
    line2= bovy_plot.bovy_plot(ell[1:],
                               (2.*ell[1:]+1.)*numpy.fabs(combinedmcr[1:]),
                               'r-',overplot=True,zorder=2)
    # Add legend
    if dist == 5.:
        pyplot.legend((line1[0],line2[0]),
                      (r'$\mathrm{extinction\ map}$',
                       r'$\mathrm{effective\ selection}$'+'\n'+r'$\mathrm{function}$'),
                      loc='lower left',bbox_to_anchor=(.02,.02),
                      numpoints=8,
                      prop={'size':14},
                      frameon=False) 
        bovy_plot.bovy_text(r'$\mathrm{cross\ power\ spectrum\ w/\ density}$',top_right=True,size=16.)
    #sp= interpolate.UnivariateSpline(numpy.log(ell)[1:],
    #                                 numpy.log(numpy.fabs(combinedmcr))[1:],
    #                                 k=3,s=100000.)
    #bovy_plot.bovy_plot(ell[1:],
    #                    10.*(2.*ell[1:]+1.)*numpy.exp(sp(numpy.log(ell[1:]))),
    #                    'r-',overplot=True,zorder=2)
    pyplot.gca().xaxis.set_major_formatter(nullfmt)
    bovy_plot.bovy_end_print(plotname.replace('powspec','crosspowspec'))
    effvol= numpy.sum((2.*ell+1.)*combinedmcr)
    #effvol2= numpy.sum((2.*ell+1.)*combinedmcr2)
    matplotlib.rcParams['text.latex.preamble']=[r"\usepackage{yfonts}"]
    line1= bovy_plot.bovy_plot(ell[1:],
                               numpy.fabs(numpy.log(effvol)
                                          -numpy.log(numpy.cumsum((2.*ell+1.)*combinedmcr)))[1:],
                               'k-',loglog=True,
                               xlabel=r'$\ell$',
                               ylabel=r'$\left|\Delta\ln\sum_{\ell}\sum_{m}\nu_{*,\ell m}\,\textswab{S}_{\ell m}\right|$',
                               xrange=[0.5,20000],
                               yrange=[2.*10.**-13.,20.],
                               zorder=3)
    """
    line2= bovy_plot.bovy_plot(ell[1:],
                               numpy.fabs(numpy.log(effvol)
                                          -numpy.log(numpy.cumsum((2.*ell+1.)*combinedmcr))
                                          -numpy.log(effvol2)
                                          +numpy.log(numpy.cumsum((2.*ell+1.)*combinedmcr2)))[1:],
                               'k--',loglog=True,
                               overplot=True,zorder=2)
    # Add legend
    pyplot.legend((line1[0],line2[0]),
                  (r'$\mathrm{exp.\ disk\ top\ panel}$',
                   r'$\mathrm{relative\ wrt\ exp.\ disk\ w/}$'+'\n'+
                   r'$h_R = 2\,\mathrm{kpc}, h_Z = 0.9\,\mathrm{kpc}$'),
                   loc='lower right',#bbox_to_anchor=(.91,.375),
                   numpoints=8,
                   prop={'size':14},
                   frameon=False) 
    """
    if dist == 5.:
        bovy_plot.bovy_text(r'$\mathrm{error\ in}\ \ln\ \mathrm{effective\ area}$',top_right=True,size=16.)
    bovy_plot.bovy_end_print(plotname.replace('powspec','cumulcrosspowspec')) 
    return None
Exemplo n.º 4
0
def plot_powspec(dist, basename, plotname):
    # Density
    G0 = 0.68 + dust.dist2distmod(dist)
    densname = basename + '_D%.1f_denscl.sav' % dist
    if not os.path.exists(densname):
        densmap = densprofiles.healpixelate(dist,
                                            densprofiles.expdisk,
                                            [1. / 3., 1. / 0.3],
                                            nside=_NSIDE,
                                            nest=False)
        denscl = healpy.sphtfunc.anafast(densmap, pol=False)
        densmap2 = densprofiles.healpixelate(dist,
                                             densprofiles.expdisk,
                                             [1. / 2., 1. / 0.9],
                                             nside=_NSIDE,
                                             nest=False)
        denscl2 = healpy.sphtfunc.anafast(densmap2, pol=False)
        ell = numpy.arange(len(denscl))
        save_pickles(densname, ell, denscl, densmap, denscl2, densmap2)
    else:
        with open(densname, 'rb') as savefile:
            ell = pickle.load(savefile)
            denscl = pickle.load(savefile)
            densmap = pickle.load(savefile)
            denscl2 = pickle.load(savefile)
            densmap2 = pickle.load(savefile)
    # dust map Cl and cross-power with dens
    combinedname = basename + '_D%.1f_combinedcl.sav' % dist
    bestfitloaded = False
    if os.path.exists(combinedname):
        with open(combinedname, 'rb') as savefile:
            ell = pickle.load(savefile)
            combinedcl = pickle.load(savefile)
            combinedcr = pickle.load(savefile)
            combinedmcl = pickle.load(savefile)
            combinedmcr = pickle.load(savefile)
            combinedmcr2 = pickle.load(savefile)
            bestfitloaded = True
    if not bestfitloaded:
        # do the best-fit
        combinedmap = dust.load_combined(dist, nest=False, nside_out=_NSIDE)
        print numpy.sum(numpy.isnan(combinedmap))
        combinedmap[numpy.isnan(combinedmap)] = 0.
        # Sample over the distribution of MG
        combinedmask = numpy.zeros_like(combinedmap)
        iso = gaia_rc.load_iso()
        Gsamples = gaia_rc.sample_Gdist(iso, n=_NGSAMPLES)
        print "Computing effective selection function"
        for jj in range(_NGSAMPLES):
            combinedmask+= ((combinedmap > (_GMIN-G0-Gsamples[jj]+0.68))\
                                *(combinedmap < (_GMAX-G0-Gsamples[jj]+0.68))).astype('float')
        combinedmask /= _NGSAMPLES
        print "Computing Cl of extinction map"
        combinedcl = healpy.sphtfunc.anafast(combinedmap, pol=False)
        print "Computing cross of extinction map w/ densmap"
        combinedcr = healpy.sphtfunc.anafast(combinedmap,
                                             map2=densmap,
                                             pol=False)
        print "Computing Cl of effective selection function"
        combinedmcl = healpy.sphtfunc.anafast(combinedmask, pol=False)
        print "Computing cross of effective selection function w/ densmap"
        combinedmcr = healpy.sphtfunc.anafast(combinedmask,
                                              map2=densmap,
                                              pol=False)
        print "Computing cross of effective selection function w/ densmap2"
        combinedmcr2 = healpy.sphtfunc.anafast(combinedmask,
                                               map2=densmap2,
                                               pol=False)
        # Save
        save_pickles(combinedname, ell, combinedcl, combinedcr, combinedmcl,
                     combinedmcr, combinedmcr2)
        gc.collect()
    # Plot (2l+1)Cl!!
    # Can smooth the masked power spectrum, perhaps underplot the non-smoothed in gray
    # sp= interpolate.UnivariateSpline(numpy.log(ell)[1:],numpy.log(combinedmcl)[1:],k=3,s=300.)
    # sp= interpolate.UnivariateSpline(numpy.log(ell)[1:],numpy.log(numpy.fabs(combinedmcr))[1:],k=3,s=10000.)
    # First plot the power-spectrum, then the cross-correlation, then the
    # cumulative sum
    bovy_plot.bovy_print(fig_height=3.)
    yrange = [10.**-12., 20.],
    line1 = bovy_plot.bovy_plot(ell[1:], (2. * ell[1:] + 1.) * combinedcl[1:],
                                'k-',
                                loglog=True,
                                ylabel=r'$(2\ell+1)\,C_\ell$',
                                xrange=[0.5, 20000],
                                yrange=yrange,
                                zorder=3)
    line2 = bovy_plot.bovy_plot(ell[2::2],
                                (2. * ell[2::2] + 1.) * denscl[2::2],
                                'b-',
                                overplot=True)
    line3 = bovy_plot.bovy_plot(ell[1:], (2. * ell[1:] + 1.) * combinedmcl[1:],
                                'r-',
                                overplot=True)
    # Add legend
    if dist == 5.:
        pyplot.legend(
            (line2[0], line1[0], line3[0]),
            (r'$\mathrm{exp.\ disk\ w/}$' + '\n' +
             r'$h_R = 3\,\mathrm{kpc},$' + '\n' + r'$h_Z = 0.3\,\mathrm{kpc}$',
             r'$\mathrm{extinction\ map}$',
             r'$\mathrm{effective\ selection\ function}$'),
            loc='lower left',
            bbox_to_anchor=(.02, .02),
            numpoints=8,
            prop={'size': 14},
            frameon=False)
        bovy_plot.bovy_text(r'$\mathrm{power\ spectrum}$',
                            top_right=True,
                            size=16.)
    nullfmt = NullFormatter()  # no labels
    pyplot.gca().xaxis.set_major_formatter(nullfmt)
    bovy_plot.bovy_end_print(plotname)
    # Cross-correlation
    bovy_plot.bovy_print(fig_height=3.)
    line1 = bovy_plot.bovy_plot(ell[1:], (2. * ell[1:] + 1.) *
                                numpy.fabs(combinedcr[1:]),
                                'k-',
                                loglog=True,
                                ylabel=r'$(2\ell+1)\,C^{\mathrm{cross}}_\ell$',
                                xrange=[0.5, 20000],
                                yrange=yrange,
                                zorder=1)
    line2 = bovy_plot.bovy_plot(ell[1:], (2. * ell[1:] + 1.) *
                                numpy.fabs(combinedmcr[1:]),
                                'r-',
                                overplot=True,
                                zorder=2)
    # Add legend
    if dist == 5.:
        pyplot.legend((line1[0], line2[0]),
                      (r'$\mathrm{extinction\ map}$',
                       r'$\mathrm{effective\ selection}$' + '\n' +
                       r'$\mathrm{function}$'),
                      loc='lower left',
                      bbox_to_anchor=(.02, .02),
                      numpoints=8,
                      prop={'size': 14},
                      frameon=False)
        bovy_plot.bovy_text(r'$\mathrm{cross\ power\ spectrum\ w/\ density}$',
                            top_right=True,
                            size=16.)
    #sp= interpolate.UnivariateSpline(numpy.log(ell)[1:],
    #                                 numpy.log(numpy.fabs(combinedmcr))[1:],
    #                                 k=3,s=100000.)
    #bovy_plot.bovy_plot(ell[1:],
    #                    10.*(2.*ell[1:]+1.)*numpy.exp(sp(numpy.log(ell[1:]))),
    #                    'r-',overplot=True,zorder=2)
    pyplot.gca().xaxis.set_major_formatter(nullfmt)
    bovy_plot.bovy_end_print(plotname.replace('powspec', 'crosspowspec'))
    effvol = numpy.sum((2. * ell + 1.) * combinedmcr)
    #effvol2= numpy.sum((2.*ell+1.)*combinedmcr2)
    matplotlib.rcParams['text.latex.preamble'] = [r"\usepackage{yfonts}"]
    line1 = bovy_plot.bovy_plot(
        ell[1:],
        numpy.fabs(
            numpy.log(effvol) -
            numpy.log(numpy.cumsum((2. * ell + 1.) * combinedmcr)))[1:],
        'k-',
        loglog=True,
        xlabel=r'$\ell$',
        ylabel=
        r'$\left|\Delta\ln\sum_{\ell}\sum_{m}\nu_{*,\ell m}\,\textswab{S}_{\ell m}\right|$',
        xrange=[0.5, 20000],
        yrange=[2. * 10.**-13., 20.],
        zorder=3)
    """
    line2= bovy_plot.bovy_plot(ell[1:],
                               numpy.fabs(numpy.log(effvol)
                                          -numpy.log(numpy.cumsum((2.*ell+1.)*combinedmcr))
                                          -numpy.log(effvol2)
                                          +numpy.log(numpy.cumsum((2.*ell+1.)*combinedmcr2)))[1:],
                               'k--',loglog=True,
                               overplot=True,zorder=2)
    # Add legend
    pyplot.legend((line1[0],line2[0]),
                  (r'$\mathrm{exp.\ disk\ top\ panel}$',
                   r'$\mathrm{relative\ wrt\ exp.\ disk\ w/}$'+'\n'+
                   r'$h_R = 2\,\mathrm{kpc}, h_Z = 0.9\,\mathrm{kpc}$'),
                   loc='lower right',#bbox_to_anchor=(.91,.375),
                   numpoints=8,
                   prop={'size':14},
                   frameon=False) 
    """
    if dist == 5.:
        bovy_plot.bovy_text(
            r'$\mathrm{error\ in}\ \ln\ \mathrm{effective\ area}$',
            top_right=True,
            size=16.)
    bovy_plot.bovy_end_print(plotname.replace('powspec', 'cumulcrosspowspec'))
    return None