예제 #1
0
def write_matches(savefilename):
    apokasc= match_apokasc_saga()
    #Perform RC selection
    logg= apokasc['KASC_RG_LOGG_SCALE_2']
    teff= apokasc['TEFF']
    z= 0.017*10.**apokasc['METALS']
    jk= apokasc['J0']-apokasc['K0']
    indx= (logg >= 1.8)\
        *(logg <= 0.0018*(teff+382.5*apokasc['METALS']-4607)+2.5)\
        *(jk < 0.8)\
        *(jk >= 0.5)\
        *(z <= 0.06)\
        *(z <= rcmodel.jkzcut(jk,upper=True))\
        *(z >= rcmodel.jkzcut(jk))
    print "Found %i RC stars in APOKASC" % numpy.sum(indx)
    rcdists= numpy.zeros(len(apokasc))-1
    rcd= rcdist('../data/rcmodel_mode_jkz_ks_parsec_newlogg.sav')
    rcdists[indx]= rcd(jk[indx],z[indx],apokasc['K0'][indx])
    pindx= (rcdists > 0.)*(apokasc['DIST_SEISMO'] > 0.)
    apokasc= apokasc[pindx]
    rcdists= rcdists[pindx]
    print "Found %i RC stars in APOKASC with seismic distances" % numpy.sum(pindx)
    savefile= open(savefilename,'w')
    savefile.write('#ID,RCDIST(pc),SEISMODIST(SCALE,MO,pc),(SEISMODIST-RCDIST)/RCDIST\n')
    csvwriter = csv.writer(savefile, delimiter=',')
    for ii in range(len(apokasc)):
        csvwriter.writerow([apokasc['KEPLER ID'][ii],
                            rcdists[ii]*1000.,
                            apokasc['DIST_SEISMO'][ii]*1000.,
                            (apokasc['DIST_SEISMO'][ii]-rcdists[ii])/rcdists[ii]])
    savefile.close()
예제 #2
0
def compare_seismic_distances(plotfilename):
    apokasc= match_apokasc_saga()
    #Perform RC selection
    logg= apokasc['KASC_RG_LOGG_SCALE_2']
    teff= apokasc['TEFF']
    z= 0.017*10.**apokasc['METALS']
    jk= apokasc['J0']-apokasc['K0']
    indx= (logg >= 1.8)\
        *(logg <= 0.0018*(teff+382.5*apokasc['METALS']-4607)+2.5)\
        *(jk < 0.8)\
        *(jk >= 0.5)\
        *(z <= 0.06)\
        *(z <= rcmodel.jkzcut(jk,upper=True))\
        *(z >= rcmodel.jkzcut(jk))#\
        #*(apokasc['SEISMO EVOL'] == 'CLUMP')
    print "Found %i RC stars in APOKASC" % numpy.sum(indx)
    rcdists= numpy.zeros(len(apokasc))-1
    rcd= rcdist('../data/rcmodel_mode_jkz_ks_parsec_newlogg.sav')
    rcdists[indx]= rcd(jk[indx],z[indx],apokasc['K0'][indx])
    pindx= (rcdists > 0.)*(apokasc['DIST_SEISMO'] > 0.)
    print "Found %i RC stars in APOKASC with seismic distances" % numpy.sum(pindx)
    #Setup plot
    bovy_plot.bovy_print(fig_height=7.)
    dx= 0.6
    left, bottom, width, height= 0.1, 0.9-dx, 0.8, dx
    axTop= pyplot.axes([left,bottom,width,height])
    fig= pyplot.gcf()
    fig.sca(axTop)
    bovy_plot.bovy_plot([0.,20.],[0.,20.],'k-',lw=2.,color='0.4',
                        overplot=True,zorder=0)
    bovy_plot.bovy_plot(rcdists[pindx],apokasc['DIST_SEISMO'][pindx],
                        'k.',overplot=True,zorder=10)
    if False:
        pyplot.errorbar(rcdists[pindx],
                        apokasc['DIST_SEISMO'][pindx],
                        xerr=0.05*rcdists[pindx],
                        yerr=apokasc['E_DIST_SEISMO'][pindx],
                        marker=',',color='k',
                        linestyle='none')
    thisax= pyplot.gca()
    thisax.set_ylim(0.,5.)
    pyplot.xlim(0.,5.)
    bovy_plot._add_ticks()
    nullfmt   = NullFormatter()         # no labels
    axTop.xaxis.set_major_formatter(nullfmt)
    bovy_plot._add_ticks()
    pyplot.ylabel(r'$\mathrm{seismic\ distance}\,(\mathrm{kpc})$')
    #Second plot
    left, bottom, width, height= 0.1, 0.1, 0.8, 0.8-dx
    thisax= pyplot.axes([left,bottom,width,height])
    fig.sca(thisax)
    bovy_plot.bovy_plot([0.,20.],[0.,0.],'k-',lw=2.,color='0.4',
                        overplot=True,zorder=0)
    bovy_plot.bovy_plot(rcdists[pindx],
                        (apokasc['DIST_SEISMO'][pindx]-rcdists[pindx])/rcdists[pindx],
                        'k.',overplot=True,zorder=10)
    thisax= pyplot.gca()
    thisax.set_ylim(-0.2,0.2)
    pyplot.xlim(0.,5.)
    bovy_plot._add_ticks()
    nullfmt   = NullFormatter()         # no labels
    bovy_plot._add_ticks()
    pyplot.ylabel(r'$\mathrm{relative\ differene}$')
    pyplot.xlabel(r'$\mathrm{RC\ distance}\,(\mathrm{kpc})$')
    medoffset= numpy.median((apokasc['DIST_SEISMO'][pindx]-rcdists[pindx])/rcdists[pindx])
    medsig= 1.4826*numpy.median(numpy.fabs((apokasc['DIST_SEISMO'][pindx]-rcdists[pindx])/rcdists[pindx]-medoffset))
    bovy_plot.bovy_text(2.75,-0.125,r'$\mathrm{diff} = %.3f\pm%.3f$' % \
                            (medoffset,medsig),size=14.)
    bovy_plot.bovy_end_print(plotfilename)
예제 #3
0
def _calc_one(z, options, nages, lages, dlages):
    print z
    if options.allapogee or options.redapogee:
        rc = rcmodel.rcmodel(
            Z=z,
            loggmax=3.5,
            band=options.band,
            basti=options.basti,
            imfmodel=options.imfmodel,
            parsec=options.parsec,
            eta=options.eta,
        )
    else:
        rc = rcmodel.rcmodel(
            Z=z,
            loggmin=1.8,
            loggmax="custom",
            band=options.band,
            basti=options.basti,
            imfmodel=options.imfmodel,
            parsec=options.parsec,
            eta=options.eta,
        )
    out = numpy.zeros(nages)
    for jj in range(nages):
        jk = rc._jks
        aindx = (rc._lages <= lages[jj] + dlages) * (rc._lages > lages[jj] - dlages)
        if options.allapogee:
            aindx *= jk > 0.5
        elif options.redapogee:
            aindx *= jk > 0.8
        else:
            rcd = rcmodel.rcdist("../../rcdist-apogee/data/rcmodel_mode_jkz_ks_parsec_newlogg.sav")
            predH = numpy.array([rcd(j, z) for j in jk])
            predH = numpy.reshape(predH, len(jk))
            aindx *= (
                (jk < 0.8)
                * (jk > 0.5)
                * (z <= rcmodel.jkzcut(jk, upper=True))
                * (z >= rcmodel.jkzcut(jk))
                * (z <= 0.06)
                * (rc._sample[:, 1] > (predH - 0.4))
                * (rc._sample[:, 1] < (predH + 0.4))
                * (rc._sample[:, 1] > -3.0)
                * (rc._loggs[:, 0] <= 3.5)
            )
        if options.type == "omega":
            try:
                out[jj] = numpy.mean(rc._massweights[aindx])
            except ValueError:
                out[jj] = numpy.nan
        elif options.type == "numfrac":
            try:
                out[jj] = numpy.mean(rc._weights[aindx])
            except ValueError:
                out[jj] = numpy.nan
        elif options.type == "mass":
            try:
                out[jj] = numpy.sum(rc._masses[aindx] * rc._weights[aindx]) / numpy.sum(rc._weights[aindx])
            except ValueError:
                out[jj] = numpy.nan
    return out
예제 #4
0
def make_rcsample(parser):
    options,args= parser.parse_args()
    savefilename= options.savefilename
    if savefilename is None:
        #Create savefilename if not given
        savefilename= os.path.join(appath._APOGEE_DATA,
                                   'rcsample_'+appath._APOGEE_REDUX+'.fits')
        print "Saving to %s ..." % savefilename
    #Read the base-sample
    data= apread.allStar(adddist=_ADDHAYDENDIST,rmdups=options.rmdups)
    #Remove a bunch of fields that we do not want to keep
    data= esutil.numpy_util.remove_fields(data,
                                          ['TARGET_ID',
                                           'FILE',
                                           'AK_WISE',
                                           'SFD_EBV',
                                           'SYNTHVHELIO_AVG',
                                           'SYNTHVSCATTER',
                                           'SYNTHVERR',
                                           'SYNTHVERR_MED',
                                           'RV_TEFF',
                                           'RV_LOGG',
                                           'RV_FEH',
                                           'RV_CCFWHM',
                                           'RV_AUTOFWHM',
                                           'SYNTHSCATTER',
                                           'CHI2_THRESHOLD',
                                           'APSTAR_VERSION',
                                           'ASPCAP_VERSION',
                                           'RESULTS_VERSION',
                                           'REDUCTION_ID',
                                           'SRC_H',
                                           'PM_SRC'])
    if int(appath._APOGEE_REDUX[1:]) < 500:
        data= esutil.numpy_util.remove_fields(data,
                                              ['ELEM'])
    #Select red-clump stars
    jk= data['J0']-data['K0']
    z= isodist.FEH2Z(data['METALS'],zsolar=0.017)
    if int(appath._APOGEE_REDUX[1:]) > 600:
        from apogee.tools import paramIndx
        if False:
            #Use my custom logg calibration that's correct for the RC
            logg= (1.-0.042)*data['FPARAM'][:,paramIndx('logg')]-0.213
            lowloggindx= data['FPARAM'][:,paramIndx('logg')] < 1.
            logg[lowloggindx]= data['FPARAM'][lowloggindx,paramIndx('logg')]-0.255
            hiloggindx= data['FPARAM'][:,paramIndx('logg')] > 3.8
            logg[hiloggindx]= data['FPARAM'][hiloggindx,paramIndx('logg')]-0.3726
        else:
            #Use my custom logg calibration that's correct on average
            logg= (1.+0.03)*data['FPARAM'][:,paramIndx('logg')]-0.37
            lowloggindx= data['FPARAM'][:,paramIndx('logg')] < 1.
            logg[lowloggindx]= data['FPARAM'][lowloggindx,paramIndx('logg')]-0.34
            hiloggindx= data['FPARAM'][:,paramIndx('logg')] > 3.8
            logg[hiloggindx]= data['FPARAM'][hiloggindx,paramIndx('logg')]-0.256
    else:
        logg= data['LOGG']
    indx= (jk < 0.8)*(jk >= 0.5)\
        *(z <= 0.06)\
        *(z <= rcmodel.jkzcut(jk,upper=True))\
        *(z >= rcmodel.jkzcut(jk))\
        *(logg >= rcmodel.loggteffcut(data['TEFF'],z,upper=False))\
        *(logg <= rcmodel.loggteffcut(data['TEFF'],z,upper=True))
    data= data[indx]
    #Add more aggressive flag cut
    data= esutil.numpy_util.add_fields(data,[('ADDL_LOGG_CUT',numpy.int32)])
    data['ADDL_LOGG_CUT']= ((data['TEFF']-4800.)/1000.+2.75) > data['LOGG']
    if options.loggcut:
        data= data[data['ADDL_LOGG_CUT'] == 1]
    print "Making catalog of %i objects ..." % len(data)
    #Add distances
    data= esutil.numpy_util.add_fields(data,[('RC_DIST', float),
                                             ('RC_DM', float),
                                             ('RC_GALR', float),
                                             ('RC_GALPHI', float),
                                             ('RC_GALZ', float)])
    rcd= rcmodel.rcdist('../../rcdist-apogee/data/rcmodel_mode_jkz_ks_parsec_newlogg.sav')
    jk= data['J0']-data['K0']
    z= isodist.FEH2Z(data['METALS'],zsolar=0.017)
    data['RC_DIST']= rcd(jk,z,appmag=data['K0'])*options.distfac
    data['RC_DM']= 5.*numpy.log10(data['RC_DIST'])+10.
    XYZ= bovy_coords.lbd_to_XYZ(data['GLON'],
                                data['GLAT'],
                                data['RC_DIST'],
                                degree=True)
    R,phi,Z= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],
                                          XYZ[:,1],
                                          XYZ[:,2],
                                          Xsun=8.,Zsun=0.025)
    data['RC_GALR']= R
    data['RC_GALPHI']= phi
    data['RC_GALZ']= Z
    #Save
    fitsio.write(savefilename,data,clobber=True)
    if not options.nostat:
        #Determine statistical sample and add flag
        apo= apogee.select.apogeeSelect()
        statIndx= apo.determine_statistical(data)
        mainIndx= apread.mainIndx(data)
        data= esutil.numpy_util.add_fields(data,[('STAT',numpy.int32),
                                                 ('INVSF',float)])
        data['STAT']= 0
        data['STAT'][statIndx*mainIndx]= 1
        for ii in range(len(data)):
            if (statIndx*mainIndx)[ii]:
                data['INVSF'][ii]= 1./apo(data['LOCATION_ID'][ii],
                                          data['H'][ii])
            else:
                data['INVSF'][ii]= -1.
    if options.nopm:
        fitsio.write(savefilename,data,clobber=True)       
        return None
    #Get proper motions
    from astroquery.vizier import Vizier
    import astroquery
    from astropy import units as u
    import astropy.coordinates as coord
    pmfile= savefilename.split('.')[0]+'_pms.fits'
    if os.path.exists(pmfile):
        pmdata= fitsio.read(pmfile,1)
    else:
        pmdata= numpy.recarray(len(data),
                               formats=['f8','f8','f8','f8','f8','f8','i4'],
                               names=['RA','DEC','PMRA','PMDEC',
                                      'PMRA_ERR','PMDEC_ERR','PMMATCH'])
        rad= u.Quantity(4./3600.,u.degree)
        v= Vizier(columns=['RAJ2000','DEJ2000','pmRA','pmDE','e_pmRA','e_pmDE'])
        for ii in range(len(data)):
            #if ii > 100: break
            sys.stdout.write('\r'+"Getting pm data for point %i / %i" % (ii+1,len(data)))
            sys.stdout.flush()
            pmdata.RA[ii]= data['RA'][ii]
            pmdata.DEC[ii]= data['DEC'][ii]
            co= coord.ICRS(ra=data['RA'][ii],
                           dec=data['DEC'][ii],
                           unit=(u.degree, u.degree))
            trying= True
            while trying:
                try:
                    tab= v.query_region(co,rad,catalog='I/322') #UCAC-4 catalog
                except astroquery.exceptions.TimeoutError:
                    pass
                else:
                    trying= False
            if len(tab) == 0:
                pmdata.PMMATCH[ii]= 0
                print "Didn't find a match for %i ..." % ii
                continue
            else:
                pmdata.PMMATCH[ii]= len(tab)
                if len(tab[0]['pmRA']) > 1:
                    print "Found more than 1 match for %i ..." % ii
            try:
                pmdata.PMRA[ii]= float(tab[0]['pmRA'])
            except TypeError:
                jj= 1
                while len(tab[0]['pmRA']) > 1 and jj < 4: 
                    trad= u.Quantity((4.-jj)/3600.,u.degree)
                    trying= True
                    while trying:
                        try:
                            tab= v.query_region(co,trad,catalog='I/322') #UCAC-4 catalog
                        except astroquery.exceptions.TimeoutError:
                            pass
                        else:
                            trying= False
                    jj+= 1
                if len(tab) == 0:
                    pmdata.PMMATCH[ii]= 0
                    print "Didn't find a unambiguous match for %i ..." % ii
                    continue               
                pmdata.PMRA[ii]= float(tab[0]['pmRA'])
            pmdata.PMDEC[ii]= float(tab[0]['pmDE'])
            pmdata.PMRA_ERR[ii]= float(tab[0]['e_pmRA'])
            pmdata.PMDEC_ERR[ii]= float(tab[0]['e_pmDE'])
            if numpy.isnan(float(tab[0]['pmRA'])): pmdata.PMMATCH[ii]= 0
        sys.stdout.write('\r'+_ERASESTR+'\r')
        sys.stdout.flush()
        fitsio.write(pmfile,pmdata,clobber=True)
        #To make sure we're using the same format below
        pmdata= fitsio.read(pmfile,1)
    #Match proper motions
    try: #These already exist currently, but may not always exist
        data= esutil.numpy_util.remove_fields(data,['PMRA','PMDEC'])
    except ValueError:
        pass
    data= esutil.numpy_util.add_fields(data,[('PMRA', numpy.float),
                                             ('PMDEC', numpy.float),
                                             ('PMRA_ERR', numpy.float),
                                             ('PMDEC_ERR', numpy.float),
                                             ('PMMATCH',numpy.int32)])
    data['PMMATCH']= 0
    h=esutil.htm.HTM()
    m1,m2,d12 = h.match(pmdata['RA'],pmdata['DEC'],
                        data['RA'],data['DEC'],
                        2./3600.,maxmatch=1)
    data['PMRA'][m2]= pmdata['PMRA'][m1]
    data['PMDEC'][m2]= pmdata['PMDEC'][m1]
    data['PMRA_ERR'][m2]= pmdata['PMRA_ERR'][m1]
    data['PMDEC_ERR'][m2]= pmdata['PMDEC_ERR'][m1]
    data['PMMATCH'][m2]= pmdata['PMMATCH'][m1].astype(numpy.int32)
    pmindx= data['PMMATCH'] == 1
    data['PMRA'][True-pmindx]= -9999.99
    data['PMDEC'][True-pmindx]= -9999.99
    data['PMRA_ERR'][True-pmindx]= -9999.99
    data['PMDEC_ERR'][True-pmindx]= -9999.99
    #Calculate Galactocentric velocities
    data= esutil.numpy_util.add_fields(data,[('GALVR', numpy.float),
                                             ('GALVT', numpy.float),
                                             ('GALVZ', numpy.float)])
    lb= bovy_coords.radec_to_lb(data['RA'],data['DEC'],degree=True)
    XYZ= bovy_coords.lbd_to_XYZ(lb[:,0],lb[:,1],data['RC_DIST'],degree=True)
    pmllpmbb= bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA'],data['PMDEC'],
                                                data['RA'],data['DEC'],
                                                degree=True)
    vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
                                             pmllpmbb[:,0],
                                             pmllpmbb[:,1],
                                             lb[:,0],lb[:,1],data['RC_DIST'],
                                             degree=True)
    vR, vT, vZ= bovy_coords.vxvyvz_to_galcencyl(vxvyvz[:,0],
                                                vxvyvz[:,1],
                                                vxvyvz[:,2],
                                                8.-XYZ[:,0],
                                                XYZ[:,1],
                                                XYZ[:,2]+0.025,
                                                vsun=[-11.1,30.24*8.,7.25])#Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
    data['GALVR']= vR
    data['GALVT']= vT
    data['GALVZ']= vZ
    data['GALVR'][True-pmindx]= -9999.99
    data['GALVT'][True-pmindx]= -9999.99
    data['GALVZ'][True-pmindx]= -9999.99
    #Get proper motions
    pmfile= savefilename.split('.')[0]+'_pms_ppmxl.fits'
    if os.path.exists(pmfile):
        pmdata= fitsio.read(pmfile,1)
    else:
        pmdata= numpy.recarray(len(data),
                               formats=['f8','f8','f8','f8','f8','f8','i4'],
                               names=['RA','DEC','PMRA','PMDEC',
                                      'PMRA_ERR','PMDEC_ERR','PMMATCH'])
        rad= u.Quantity(4./3600.,u.degree)
        v= Vizier(columns=['RAJ2000','DEJ2000','pmRA','pmDE','e_pmRA','e_pmDE'])
        for ii in range(len(data)):
            #if ii > 100: break
            sys.stdout.write('\r'+"Getting pm data for point %i / %i" % (ii+1,len(data)))
            sys.stdout.flush()
            pmdata.RA[ii]= data['RA'][ii]
            pmdata.DEC[ii]= data['DEC'][ii]
            co= coord.ICRS(ra=data['RA'][ii],
                           dec=data['DEC'][ii],
                           unit=(u.degree, u.degree))
            trying= True
            while trying:
                try:
                    tab= v.query_region(co,rad,catalog='I/317') #PPMXL catalog
                except astroquery.exceptions.TimeoutError:
                    pass
                else:
                    trying= False
            if len(tab) == 0:
                pmdata.PMMATCH[ii]= 0
                print "Didn't find a match for %i ..." % ii
                continue
            else:
                pmdata.PMMATCH[ii]= len(tab)
                if len(tab[0]['pmRA']) > 1:
                    pass
                    #print "Found more than 1 match for %i ..." % ii
            try:
                pmdata.PMRA[ii]= float(tab[0]['pmRA'])
            except TypeError:
                #Find nearest
                cosdists= numpy.zeros(len(tab[0]['pmRA']))
                for jj in range(len(tab[0]['pmRA'])):
                    cosdists[jj]= cos_sphere_dist(tab[0]['RAJ2000'][jj],
                                                  tab[0]['DEJ2000'][jj],
                                                  data['RA'][ii],
                                                  data['DEC'][ii])
                closest= numpy.argmax(cosdists)
                pmdata.PMRA[ii]= float(tab[0]['pmRA'][closest])
                pmdata.PMDEC[ii]= float(tab[0]['pmDE'][closest])
                pmdata.PMRA_ERR[ii]= float(tab[0]['e_pmRA'][closest])
                pmdata.PMDEC_ERR[ii]= float(tab[0]['e_pmDE'][closest])
                if numpy.isnan(float(tab[0]['pmRA'][closest])): pmdata.PMMATCH[ii]= 0
            else:
                pmdata.PMDEC[ii]= float(tab[0]['pmDE'])
                pmdata.PMRA_ERR[ii]= float(tab[0]['e_pmRA'])
                pmdata.PMDEC_ERR[ii]= float(tab[0]['e_pmDE'])
                if numpy.isnan(float(tab[0]['pmRA'])): pmdata.PMMATCH[ii]= 0
        sys.stdout.write('\r'+_ERASESTR+'\r')
        sys.stdout.flush()
        fitsio.write(pmfile,pmdata,clobber=True)
        #To make sure we're using the same format below
        pmdata= fitsio.read(pmfile,1)
    #Match proper motions to ppmxl
    data= esutil.numpy_util.add_fields(data,[('PMRA_PPMXL', numpy.float),
                                             ('PMDEC_PPMXL', numpy.float),
                                             ('PMRA_ERR_PPMXL', numpy.float),
                                             ('PMDEC_ERR_PPMXL', numpy.float),
                                             ('PMMATCH_PPMXL',numpy.int32)])
    data['PMMATCH_PPMXL']= 0
    h=esutil.htm.HTM()
    m1,m2,d12 = h.match(pmdata['RA'],pmdata['DEC'],
                        data['RA'],data['DEC'],
                        2./3600.,maxmatch=1)
    data['PMRA_PPMXL'][m2]= pmdata['PMRA'][m1]
    data['PMDEC_PPMXL'][m2]= pmdata['PMDEC'][m1]
    data['PMRA_ERR_PPMXL'][m2]= pmdata['PMRA_ERR'][m1]
    data['PMDEC_ERR_PPMXL'][m2]= pmdata['PMDEC_ERR'][m1]
    data['PMMATCH_PPMXL'][m2]= pmdata['PMMATCH'][m1].astype(numpy.int32)
    pmindx= data['PMMATCH_PPMXL'] == 1
    data['PMRA_PPMXL'][True-pmindx]= -9999.99
    data['PMDEC_PPMXL'][True-pmindx]= -9999.99
    data['PMRA_ERR_PPMXL'][True-pmindx]= -9999.99
    data['PMDEC_ERR_PPMXL'][True-pmindx]= -9999.99
    #Calculate Galactocentric velocities
    data= esutil.numpy_util.add_fields(data,[('GALVR_PPMXL', numpy.float),
                                             ('GALVT_PPMXL', numpy.float),
                                             ('GALVZ_PPMXL', numpy.float)])
    lb= bovy_coords.radec_to_lb(data['RA'],data['DEC'],degree=True)
    XYZ= bovy_coords.lbd_to_XYZ(lb[:,0],lb[:,1],data['RC_DIST'],degree=True)
    pmllpmbb= bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA_PPMXL'],
                                                data['PMDEC_PPMXL'],
                                                data['RA'],data['DEC'],
                                                degree=True)
    vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
                                             pmllpmbb[:,0],
                                             pmllpmbb[:,1],
                                             lb[:,0],lb[:,1],data['RC_DIST'],
                                             degree=True)
    vR, vT, vZ= bovy_coords.vxvyvz_to_galcencyl(vxvyvz[:,0],
                                                vxvyvz[:,1],
                                                vxvyvz[:,2],
                                                8.-XYZ[:,0],
                                                XYZ[:,1],
                                                XYZ[:,2]+0.025,
                                                vsun=[-11.1,30.24*8.,7.25])#Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
    data['GALVR_PPMXL']= vR
    data['GALVT_PPMXL']= vT
    data['GALVZ_PPMXL']= vZ
    data['GALVR_PPMXL'][True-pmindx]= -9999.99
    data['GALVT_PPMXL'][True-pmindx]= -9999.99
    data['GALVZ_PPMXL'][True-pmindx]= -9999.99
    #Save
    fitsio.write(savefilename,data,clobber=True)
    return None