def test_vrpmllpmbb_to_vxvyvz(): l,b,d= 90., 0.,1. vr,pmll,pmbb= 10.,20./4.74047,-10./4.74047 vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(vr,pmll,pmbb,l,b,d, degree=True,XYZ=False) assert numpy.fabs(vxvyvz[0]+20.) < 10.**-10., 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' assert numpy.fabs(vxvyvz[1]-10.) < 10.**-10., 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' assert numpy.fabs(vxvyvz[2]+10.) < 10.**-10., 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(vr,pmll,pmbb,l/180.*numpy.pi, b/180.*numpy.pi,d, degree=False,XYZ=False) assert numpy.fabs(vxvyvz[0]+20.) < 10.**-10., 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' assert numpy.fabs(vxvyvz[1]-10.) < 10.**-10., 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' assert numpy.fabs(vxvyvz[2]+10.) < 10.**-10., 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(vr,pmll,pmbb,0.,1,0., XYZ=True) assert numpy.fabs(vxvyvz[0]+20.) < 10.**-10., 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' assert numpy.fabs(vxvyvz[1]-10.) < 10.**-10., 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' assert numpy.fabs(vxvyvz[2]+10.) < 10.**-10., 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(vr,pmll,pmbb,0.,1,0., XYZ=True,degree=True) assert numpy.fabs(vxvyvz[0]+20.) < 10.**-10., 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' assert numpy.fabs(vxvyvz[1]-10.) < 10.**-10., 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' assert numpy.fabs(vxvyvz[2]+10.) < 10.**-10., 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' #Also test for arrays os= numpy.ones(2) vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(os*vr,os*pmll,os*pmbb,os*l,os*b, os*d,degree=True,XYZ=False) assert numpy.all(numpy.fabs(vxvyvz[:,0]+20.) < 10.**-10.), 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' assert numpy.all(numpy.fabs(vxvyvz[:,1]-10.) < 10.**-10.), 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' assert numpy.all(numpy.fabs(vxvyvz[:,2]+10.) < 10.**-10.), 'vrpmllpmbb_to_vxvyvz conversion did not work as expected' return None
def calc_spacevel(self): """ Calculates U,V,W """ uvw = bcoords.vrpmllpmbb_to_vxvyvz(self.get_col('VHELIO_AVG'), self.pmll_pmbb[:, 0], self.pmll_pmbb[:, 1], self.get_col('GLON'), self.get_col('GLAT'), self.get_col('RC_DIST'), degree=self.degree) self.spacevels = uvw
def uvw(req_dict): #get vx, vy, vz (heliocentric) in R-handed coord. syst. u, v, w = b_c.vrpmllpmbb_to_vxvyvz(req_dict['rv'], req_dict['pml'], req_dict['pmb'], req_dict['l'], req_dict['b'], req_dict['dist'], XYZ=False, degree=True) #get vx, vy, vz (galactocentric) in L-handed coord syst (corrected for sun & disk motion) vx_gc, vy_gc, vz_gc = b_c.vxvyvz_to_galcenrect(u, v, w, vsun1, Xsun, Zsun) return u, v, w, float(vx_gc), vy_gc, float(vz_gc)
def test_coords(): from galpy.util import bovy_coords ra, dec, dist = 161., 50., 8.5 pmra, pmdec, vlos = -6.8, -10., -115. # Convert to Galactic and then to rect. Galactic ll, bb = bovy_coords.radec_to_lb(ra, dec, degree=True) pmll, pmbb = bovy_coords.pmrapmdec_to_pmllpmbb(pmra, pmdec, ra, dec, degree=True) X, Y, Z = bovy_coords.lbd_to_XYZ(ll, bb, dist, degree=True) vX, vY, vZ = bovy_coords.vrpmllpmbb_to_vxvyvz(vlos, pmll, pmbb, X, Y, Z, XYZ=True) # Convert to cylindrical Galactocentric # Assuming Sun's distance to GC is (8,0.025) in (R,z) R, phi, z = bovy_coords.XYZ_to_galcencyl(X, Y, Z, Xsun=8., Zsun=0.025) vR, vT, vz = bovy_coords.vxvyvz_to_galcencyl(vX, vY, vZ, R, phi, Z, vsun=[-10.1, 244., 6.7], galcen=True) # 5/12/2016: test weakened, because improved galcen<->heliocen # transformation has changed these, but still close print(R, phi, z, vR, vT, vz) assert numpy.fabs(R - 12.51328515156942 ) < 10.**-1., 'Coordinate transformation has changed' assert numpy.fabs(phi - 0.12177409073433249 ) < 10.**-1., 'Coordinate transformation has changed' assert numpy.fabs(z - 7.1241282354856228 ) < 10.**-1., 'Coordinate transformation has changed' assert numpy.fabs(vR - 78.961682923035966 ) < 10.**-1., 'Coordinate transformation has changed' assert numpy.fabs(vT + 241.49247772351964 ) < 10.**-1., 'Coordinate transformation has changed' assert numpy.fabs(vz + 102.83965442188689 ) < 10.**-1., 'Coordinate transformation has changed' return None
def test_coords(): from galpy.util import bovy_coords ra, dec, dist= 161., 50., 8.5 pmra, pmdec, vlos= -6.8, -10., -115. # Convert to Galactic and then to rect. Galactic ll, bb= bovy_coords.radec_to_lb(ra,dec,degree=True) pmll, pmbb= bovy_coords.pmrapmdec_to_pmllpmbb(pmra,pmdec,ra,dec,degree=True) X,Y,Z= bovy_coords.lbd_to_XYZ(ll,bb,dist,degree=True) vX,vY,vZ= bovy_coords.vrpmllpmbb_to_vxvyvz(vlos,pmll,pmbb,X,Y,Z,XYZ=True) # Convert to cylindrical Galactocentric # Assuming Sun's distance to GC is (8,0.025) in (R,z) R,phi,z= bovy_coords.XYZ_to_galcencyl(X,Y,Z,Xsun=8.,Zsun=0.025) vR,vT,vz= bovy_coords.vxvyvz_to_galcencyl(vX,vY,vZ,R,phi,Z,vsun=[-10.1,244.,6.7],galcen=True) assert numpy.fabs(R-12.51328515156942) < 10.**-4., 'Coordinate transformation has changed' assert numpy.fabs(phi-0.12177409073433249) < 10.**-4., 'Coordinate transformation has changed' assert numpy.fabs(z-7.1241282354856228) < 10.**-4., 'Coordinate transformation has changed' assert numpy.fabs(vR-78.961682923035966) < 10.**-4., 'Coordinate transformation has changed' assert numpy.fabs(vT+241.49247772351964) < 10.**-4., 'Coordinate transformation has changed' assert numpy.fabs(vz+102.83965442188689) < 10.**-4., 'Coordinate transformation has changed' return None
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 not appath._APOGEE_REDUX.lower() == 'current' \ and 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 appath._APOGEE_REDUX.lower() == 'current' \ or 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() 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
def action(ra_deg, dec_deg, d_kpc, pm_ra_masyr, pm_dec_masyr, v_los_kms, verbose=False): """ parameters: ---------- ra_deg: (float) RA in degrees. dec_deg: (float) Dec in degress. d_kpc: (float) Distance in kpc. pm_ra_masyr: (float) RA proper motion in mas/yr. pm_decmasyr: (float) Dec proper motion in mas/yr. v_los_kms: (float) RV in kms. returns: ------ R_kpc, phi_rad, z_kpc, vR_kms, vT_kms, vz_kms jR: (float) Radial action. lz: (float) Vertical ang mom. jz: (float) Vertical action. """ ra_rad = ra_deg * (np.pi / 180.) # RA [rad] dec_rad = dec_deg * (np.pi / 180.) # dec [rad] # Galactocentric position of the Sun: X_gc_sun_kpc = 8. # [kpc] Z_gc_sun_kpc = 0.025 # [kpc] # Galactocentric velocity of the Sun: vX_gc_sun_kms = -9.58 # = -U [kms] vY_gc_sun_kms = 10.52 + 220. # = V+v_circ(R_Sun) [kms] vZ_gc_sun_kms = 7.01 # = W [kms] # a. convert spatial coordinates (ra,dec,d) to (R,z,phi) # (ra,dec) --> Galactic coordinates (l,b): lb = bovy_coords.radec_to_lb(ra_rad, dec_rad, degree=False, epoch=2000.0) # l_rad = lb[:, 0] # b_rad = lb[:, 1] l_rad = lb[0] b_rad = lb[1] # (l,b,d) --> Galactocentric cartesian coordinates (x,y,z): xyz = bovy_coords.lbd_to_XYZ(l_rad, b_rad, d_kpc, degree=False) # x_kpc = xyz[:, 0] # y_kpc = xyz[:, 1] # z_kpc = xyz[:, 2] x_kpc = xyz[0] y_kpc = xyz[1] z_kpc = xyz[2] # (x,y,z) --> Galactocentric cylindrical coordinates (R,z,phi): Rzphi = bovy_coords.XYZ_to_galcencyl(x_kpc, y_kpc, z_kpc, Xsun=X_gc_sun_kpc, Zsun=Z_gc_sun_kpc) # R_kpc = Rzphi[:, 0] # phi_rad = Rzphi[:, 1] # z_kpc = Rzphi[:, 2] R_kpc = Rzphi[0] phi_rad = Rzphi[1] z_kpc = Rzphi[2] # b. convert velocities (pm_ra,pm_dec,vlos) to (vR,vz,vT) # (pm_ra,pm_dec) --> (pm_l,pm_b): pmlpmb = bovy_coords.pmrapmdec_to_pmllpmbb(pm_ra_masyr, pm_dec_masyr, ra_rad, dec_rad, degree=False, epoch=2000.0) # pml_masyr = pmlpmb[:, 0] # pmb_masyr = pmlpmb[:, 1] pml_masyr = pmlpmb[0] pmb_masyr = pmlpmb[1] # (v_los,pm_l,pm_b) & (l,b,d) --> (vx,vy,vz): vxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(v_los_kms, pml_masyr, pmb_masyr, l_rad, b_rad, d_kpc, XYZ=False, degree=False) # vx_kms = vxvyvz[:, 0] # vy_kms = vxvyvz[:, 1] # vz_kms = vxvyvz[:, 2] vx_kms = vxvyvz[0] vy_kms = vxvyvz[1] vz_kms = vxvyvz[2] # (vx,vy,vz) & (x,y,z) --> (vR,vT,vz): vRvTvZ = bovy_coords.vxvyvz_to_galcencyl(vx_kms, vy_kms, vz_kms, R_kpc, phi_rad, z_kpc, vsun=[vX_gc_sun_kms, vY_gc_sun_kms, vZ_gc_sun_kms], galcen=True) # vR_kms = vRvTvZ[:, 0] # vT_kms = vRvTvZ[:, 1] # vz_kms = vRvTvZ[:, 2] vR_kms = vRvTvZ[0] vT_kms = vRvTvZ[1] vz_kms = vRvTvZ[2] if verbose: print("R = ", R_kpc, "\t kpc") print("phi = ", phi_rad, "\t rad") print("z = ", z_kpc, "\t kpc") print("v_R = ", vR_kms, "\t km/s") print("v_T = ", vT_kms, "\t km/s") print("v_z = ", vz_kms, "\t km/s") jR, lz, jz = calc_actions(R_kpc, phi_rad, z_kpc, vR_kms, vT_kms, vz_kms) return R_kpc, phi_rad, z_kpc, vR_kms, vT_kms, vz_kms, jR, lz, jz
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_ALPHA', 'RV_CARB', 'RV_CCFWHM', 'RV_AUTOFWHM', 'SYNTHSCATTER', 'STABLERV_CHI2', 'STABLERV_RCHI2', 'STABLERV_CHI2_PROB', 'CHI2_THRESHOLD', 'APSTAR_VERSION', 'ASPCAP_VERSION', 'RESULTS_VERSION', 'WASH_M', 'WASH_M_ERR', 'WASH_T2', 'WASH_T2_ERR', 'DDO51', 'DDO51_ERR', 'IRAC_3_6', 'IRAC_3_6_ERR', 'IRAC_4_5', 'IRAC_4_5_ERR', 'IRAC_5_8', 'IRAC_5_8_ERR', 'IRAC_8_0', 'IRAC_8_0_ERR', 'WISE_4_5', 'WISE_4_5_ERR', 'TARG_4_5', 'TARG_4_5_ERR', 'WASH_DDO51_GIANT_FLAG', 'WASH_DDO51_STAR_FLAG', 'REDUCTION_ID', 'SRC_H', 'PM_SRC' ]) if not appath._APOGEE_REDUX.lower() == 'current' \ and not 'l30' in appath._APOGEE_REDUX \ and 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 'l30' in appath._APOGEE_REDUX: logg = data['LOGG'] elif appath._APOGEE_REDUX.lower() == 'current' \ or 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() 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) # Add Tycho-2 matches if options.tyc2: data = esutil.numpy_util.add_fields(data, [('TYC2MATCH', numpy.int32), ('TYC1', numpy.int32), ('TYC2', numpy.int32), ('TYC3', numpy.int32)]) data['TYC2MATCH'] = 0 data['TYC1'] = -1 data['TYC2'] = -1 data['TYC3'] = -1 # Write positions posfilename = tempfile.mktemp('.csv', dir=os.getcwd()) resultfilename = tempfile.mktemp('.csv', dir=os.getcwd()) with open(posfilename, 'w') as csvfile: wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) wr.writerow(['RA', 'DEC']) for ii in range(len(data)): wr.writerow([data[ii]['RA'], data[ii]['DEC']]) # Send to CDS for matching result = open(resultfilename, 'w') try: subprocess.check_call([ 'curl', '-X', 'POST', '-F', 'request=xmatch', '-F', 'distMaxArcsec=2', '-F', 'RESPONSEFORMAT=csv', '-F', 'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA', '-F', 'colDec1=DEC', '-F', 'cat2=vizier:Tycho2', 'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync' ], stdout=result) except subprocess.CalledProcessError: os.remove(posfilename) if os.path.exists(resultfilename): result.close() os.remove(resultfilename) result.close() # Directly match on input RA ma = numpy.loadtxt(resultfilename, delimiter=',', skiprows=1, usecols=(1, 2, 7, 8, 9)) iis = numpy.arange(len(data)) mai = [iis[data['RA'] == ma[ii, 0]][0] for ii in range(len(ma))] data['TYC2MATCH'][mai] = 1 data['TYC1'][mai] = ma[:, 2] data['TYC2'][mai] = ma[:, 3] data['TYC3'][mai] = ma[:, 4] os.remove(posfilename) os.remove(resultfilename) 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, in a somewhat roundabout way 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' ]) # Write positions, again ... posfilename = tempfile.mktemp('.csv', dir=os.getcwd()) resultfilename = tempfile.mktemp('.csv', dir=os.getcwd()) with open(posfilename, 'w') as csvfile: wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) wr.writerow(['RA', 'DEC']) for ii in range(len(data)): wr.writerow([data[ii]['RA'], data[ii]['DEC']]) # Send to CDS for matching result = open(resultfilename, 'w') try: subprocess.check_call([ 'curl', '-X', 'POST', '-F', 'request=xmatch', '-F', 'distMaxArcsec=4', '-F', 'RESPONSEFORMAT=csv', '-F', 'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA', '-F', 'colDec1=DEC', '-F', 'cat2=vizier:UCAC4', 'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync' ], stdout=result) except subprocess.CalledProcessError: os.remove(posfilename) if os.path.exists(resultfilename): result.close() os.remove(resultfilename) result.close() # Match back and only keep the closest one ma = numpy.loadtxt(resultfilename, delimiter=',', skiprows=1, converters={ 15: lambda s: float(s.strip() or -9999), 16: lambda s: float(s.strip() or -9999), 17: lambda s: float(s.strip() or -9999), 18: lambda s: float(s.strip() or -9999) }, usecols=(4, 5, 15, 16, 17, 18)) h = esutil.htm.HTM() m1, m2, d12 = h.match(data['RA'], data['DEC'], ma[:, 0], ma[:, 1], 4. / 3600., maxmatch=1) pmdata['PMMATCH'] = 0 pmdata['RA'] = data['RA'] pmdata['DEC'] = data['DEC'] pmdata['PMMATCH'][m1] = 1 pmdata['PMRA'][m1] = ma[m2, 2] pmdata['PMDEC'][m1] = ma[m2, 3] pmdata['PMRA_ERR'][m1] = ma[m2, 4] pmdata['PMDEC_ERR'][m1] = ma[m2, 5] pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \ +(pmdata['PMDEC'] == -9999) \ +(pmdata['PMRA_ERR'] == -9999) \ +(pmdata['PMDEC_ERR'] == -9999)]= 0 fitsio.write(pmfile, pmdata, clobber=True) #To make sure we're using the same format below pmdata = fitsio.read(pmfile, 1) os.remove(posfilename) os.remove(resultfilename) #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 PPMXL proper motions, in a somewhat roundabout way 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' ]) # Write positions, again ... posfilename = tempfile.mktemp('.csv', dir=os.getcwd()) resultfilename = tempfile.mktemp('.csv', dir=os.getcwd()) with open(posfilename, 'w') as csvfile: wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) wr.writerow(['RA', 'DEC']) for ii in range(len(data)): wr.writerow([data[ii]['RA'], data[ii]['DEC']]) # Send to CDS for matching result = open(resultfilename, 'w') try: subprocess.check_call([ 'curl', '-X', 'POST', '-F', 'request=xmatch', '-F', 'distMaxArcsec=4', '-F', 'RESPONSEFORMAT=csv', '-F', 'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA', '-F', 'colDec1=DEC', '-F', 'cat2=vizier:PPMXL', 'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync' ], stdout=result) except subprocess.CalledProcessError: os.remove(posfilename) if os.path.exists(resultfilename): result.close() os.remove(resultfilename) result.close() # Match back and only keep the closest one ma = numpy.loadtxt(resultfilename, delimiter=',', skiprows=1, converters={ 15: lambda s: float(s.strip() or -9999), 16: lambda s: float(s.strip() or -9999), 17: lambda s: float(s.strip() or -9999), 18: lambda s: float(s.strip() or -9999) }, usecols=(4, 5, 15, 16, 19, 20)) h = esutil.htm.HTM() m1, m2, d12 = h.match(data['RA'], data['DEC'], ma[:, 0], ma[:, 1], 4. / 3600., maxmatch=1) pmdata['PMMATCH'] = 0 pmdata['RA'] = data['RA'] pmdata['DEC'] = data['DEC'] pmdata['PMMATCH'][m1] = 1 pmdata['PMRA'][m1] = ma[m2, 2] pmdata['PMDEC'][m1] = ma[m2, 3] pmdata['PMRA_ERR'][m1] = ma[m2, 4] pmdata['PMDEC_ERR'][m1] = ma[m2, 5] pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \ +(pmdata['PMDEC'] == -9999) \ +(pmdata['PMRA_ERR'] == -9999) \ +(pmdata['PMDEC_ERR'] == -9999)]= 0 fitsio.write(pmfile, pmdata, clobber=True) #To make sure we're using the same format below pmdata = fitsio.read(pmfile, 1) os.remove(posfilename) os.remove(resultfilename) #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
glonradv = glonv * np.pi / 180.0 glatradv = glatv * np.pi / 180.0 # x, y position xposv = -rsun + np.cos(glonradv) * distv * np.cos(glatradv) yposv = np.sin(glonradv) * distv * np.cos(glatradv) zposv = distv * np.sin(glatradv) + zsun # rgal with Reid et al. value rgalv = np.sqrt(xposv**2 + yposv**2) # plx, pmra, pmdec -> vx,vy,vz # velocity Tpmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb( \ pmrav ,pmdecv ,rav ,decv, degree=True, epoch=None) Tvxvyvz=bovy_coords.vrpmllpmbb_to_vxvyvz( \ hrvv, Tpmllpmbb[:,0], Tpmllpmbb[:,1], glonv, glatv, distv, \ XYZ=False, degree=True) vxv = Tvxvyvz[:, 0] + usun vyv = Tvxvyvz[:, 1] + vsun vzv = Tvxvyvz[:, 2] + wsun # Vcirc at the radius of the stars, including dVc/dR vcircrv = vcirc + dvcdr * (rgalv - rsun) vyv = vyv + vcircrv # original velocity vxv0 = vxv vyv0 = vyv vzv0 = vzv # Galactic radius and velocities vradv0 = (vxv0 * xposv + vyv0 * yposv) / rgalv vrotv0 = (vxv0 * yposv - vyv0 * xposv) / rgalv # then subtract circular velocity contribution
def calc_actions(ra_deg, dec_deg, d_kpc, pm_ra_masyr, pm_dec_masyr, v_los_kms): ra_rad = ra_deg * (np.pi / 180.) # RA [rad] dec_rad = dec_deg * (np.pi / 180.) # dec [rad] # Galactocentric position of the Sun: X_gc_sun_kpc = 8. # [kpc] Z_gc_sun_kpc = 0.025 # [kpc] # Galactocentric velocity of the Sun: vX_gc_sun_kms = -9.58 # = -U [kms] vY_gc_sun_kms = 10.52 + 220. # = V+v_circ(R_Sun) [kms] vZ_gc_sun_kms = 7.01 # = W [kms] # a. convert spatial coordinates (ra,dec,d) to (R,z,phi) # (ra,dec) --> Galactic coordinates (l,b): lb = bovy_coords.radec_to_lb(ra_rad, dec_rad, degree=False, epoch=2000.0) l_rad = lb[:, 0] b_rad = lb[:, 1] # (l,b,d) --> Galactocentric cartesian coordinates (x,y,z): xyz = bovy_coords.lbd_to_XYZ(l_rad, b_rad, d_kpc, degree=False) x_kpc = xyz[:, 0] y_kpc = xyz[:, 1] z_kpc = xyz[:, 2] # (x,y,z) --> Galactocentric cylindrical coordinates (R,z,phi): Rzphi = bovy_coords.XYZ_to_galcencyl(x_kpc, y_kpc, z_kpc, Xsun=X_gc_sun_kpc, Zsun=Z_gc_sun_kpc) R_kpc = Rzphi[:, 0] phi_rad = Rzphi[:, 1] z_kpc = Rzphi[:, 2] # b. convert velocities (pm_ra,pm_dec,vlos) to (vR,vz,vT) # (pm_ra,pm_dec) --> (pm_l,pm_b): pmlpmb = bovy_coords.pmrapmdec_to_pmllpmbb(pm_ra_masyr, pm_dec_masyr, ra_rad, dec_rad, degree=False, epoch=2000.0) pml_masyr = pmlpmb[:, 0] pmb_masyr = pmlpmb[:, 1] # (v_los,pm_l,pm_b) & (l,b,d) --> (vx,vy,vz): vxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(v_los_kms, pml_masyr, pmb_masyr, l_rad, b_rad, d_kpc, XYZ=False, degree=False) vx_kms = vxvyvz[:, 0] vy_kms = vxvyvz[:, 1] vz_kms = vxvyvz[:, 2] # (vx,vy,vz) & (x,y,z) --> (vR,vT,vz): vRvTvZ = bovy_coords.vxvyvz_to_galcencyl( vx_kms, vy_kms, vz_kms, R_kpc, phi_rad, z_kpc, vsun=[vX_gc_sun_kms, vY_gc_sun_kms, vZ_gc_sun_kms], galcen=True) vR_kms = vRvTvZ[:, 0] vT_kms = vRvTvZ[:, 1] vz_kms = vRvTvZ[:, 2] print("R = ", R_kpc, "\t kpc") print("phi = ", phi_rad, "\t rad") print("z = ", z_kpc, "\t kpc") print("v_R = ", vR_kms, "\t km/s") print("v_T = ", vT_kms, "\t km/s") print("v_z = ", vz_kms, "\t km/s") return vz_kms
# mas/yr -> km/s vlons_obs = pmvconst * pmlons_obs * dists_obs vlats_obs = pmvconst * pmlats_obs * dists_obs # galactic position distxys_obs = dists_obs * np.cos(glatrads) xpos_obs = distxys_obs * np.cos(glonrads) ypos_obs = distxys_obs * np.sin(glonrads) zpos_obs = dists_obs * np.sin(glatrads) xposgals_obs = xpos_obs - rsun yposgals_obs = ypos_obs zposgals_obs = zpos_obs + zsun rgals_obs = np.sqrt(xposgals_obs**2 + yposgals_obs**2) # to vx vy vz Tvxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(\ hrvs_obs, Tpmllpmbb[:,0], Tpmllpmbb[:,1], \ glons, glats, dists_obs, XYZ=False, degree=True) vxs_obs = Tvxvyvz[:, 0] vys_obs = Tvxvyvz[:, 1] vzs_obs = Tvxvyvz[:, 2] + wsun # Galactocentric position and velcoity hrvxys_obs = hrvs_obs * np.cos(glatrads) vxgals_obs = vxs_obs + usun vygals_obs = vys_obs + vsun vrots_obs = (vxgals_obs*yposgals_obs-vygals_obs*xposgals_obs) \ /rgals_obs vrads_obs = (vxgals_obs*xposgals_obs+vygals_obs*yposgals_obs) \ /rgals_obs # fits output tbhdu = pyfits.BinTableHDU.from_columns([\
def findfriends(targname,radial_velocity,velocity_limit=5.0,search_radius=25.0,rvcut=5.0,radec=[None,None],output_directory = None,showplots=False,verbose=False,DoGALEX=True,DoWISE=True,DoROSAT=True): radvel= radial_velocity * u.kilometer / u.second if output_directory == None: outdir = './' + targname.replace(" ", "") + '_friends/' else: outdir = output_directory if os.path.isdir(outdir) == True: print('Output directory ' + outdir +' Already Exists!!') print('Either Move it, Delete it, or input a different [output_directory] Please!') return os.mkdir(outdir) if velocity_limit < 0.00001 : print('input velocity_limit is too small, try something else') print('velocity_limit: ' + str(velocity_limit)) if search_radius < 0.0000001: print('input search_radius is too small, try something else') print('search_radius: ' + str(search_radius)) # Search parameters vlim=velocity_limit * u.kilometer / u.second searchradpc=search_radius * u.parsec if (radec[0] != None) & (radec[1] != None): usera,usedec = radec[0],radec[1] else: ##use the target name to get simbad ra and dec. print('Asking Simbad for RA and DEC') result_table = Simbad.query_object(targname) usera,usedec = result_table['RA'][0],result_table['DEC'][0] if verbose == True: print('Target name: ',targname) print('Coordinates: ' + str(usera) +' '+str(usedec)) print() c = SkyCoord( ra=usera , dec=usedec , unit=(u.hourangle, u.deg) , frame='icrs') if verbose == True: print(c) # Find precise coordinates and distance from Gaia, define search radius and parallax cutoff print('Asking Gaia for precise coordinates') sqltext = "SELECT * FROM gaiaedr3.gaia_source WHERE CONTAINS( \ POINT('ICRS',gaiaedr3.gaia_source.ra,gaiaedr3.gaia_source.dec), \ CIRCLE('ICRS'," + str(c.ra.value) +","+ str(c.dec.value) +","+ str(6.0/3600.0) +"))=1;" job = Gaia.launch_job_async(sqltext , dump_to_file=False) Pgaia = job.get_results() if verbose == True: print(sqltext) print() print(Pgaia['source_id','ra','dec','phot_g_mean_mag','parallax','ruwe'].pprint_all()) print() minpos = Pgaia['phot_g_mean_mag'].tolist().index(min(Pgaia['phot_g_mean_mag'])) Pcoord = SkyCoord( ra=Pgaia['ra'][minpos]*u.deg , dec=Pgaia['dec'][minpos]*u.deg , \ distance=(1000.0/Pgaia['parallax'][minpos])*u.parsec , frame='icrs' , \ radial_velocity=radvel , \ pm_ra_cosdec=Pgaia['pmra'][minpos]*u.mas/u.year , pm_dec=Pgaia['pmdec'][minpos]*u.mas/u.year ) searchraddeg = np.arcsin(searchradpc/Pcoord.distance).to(u.deg) minpar = (1000.0 * u.parsec) / (Pcoord.distance + searchradpc) * u.mas if verbose == True: print(Pcoord) print() print('Search radius in deg: ',searchraddeg) print('Minimum parallax: ',minpar) # Query Gaia with search radius and parallax cut # Note, a cut on parallax_error was added because searches at low galactic latitude # return an overwhelming number of noisy sources that scatter into the search volume - ALK 20210325 print('Querying Gaia for neighbors') Pllbb = bc.radec_to_lb(Pcoord.ra.value , Pcoord.dec.value , degree=True) if ( np.abs(Pllbb[1]) > 10.0): plxcut = max( 0.5 , (1000.0/Pcoord.distance.value/10.0) ) else: plxcut = 0.5 print('Parallax cut: ',plxcut) if (searchradpc < Pcoord.distance): sqltext = "SELECT * FROM gaiaedr3.gaia_source WHERE CONTAINS( \ POINT('ICRS',gaiaedr3.gaia_source.ra,gaiaedr3.gaia_source.dec), \ CIRCLE('ICRS'," + str(Pcoord.ra.value) +","+ str(Pcoord.dec.value) +","+ str(searchraddeg.value) +"))\ =1 AND parallax>" + str(minpar.value) + " AND parallax_error<" + str(plxcut) + ";" if (searchradpc >= Pcoord.distance): sqltext = "SELECT * FROM gaiaedr3.gaia_source WHERE parallax>" + str(minpar.value) + " AND parallax_error<" + str(plxcut) + ";" print('Note, using all-sky search') if verbose == True: print(sqltext) print() job = Gaia.launch_job_async(sqltext , dump_to_file=False) r = job.get_results() if verbose == True: print('Number of records: ',len(r['ra'])) # Construct coordinates array for all stars returned in cone search gaiacoord = SkyCoord( ra=r['ra'] , dec=r['dec'] , distance=(1000.0/r['parallax'])*u.parsec , \ frame='icrs' , \ pm_ra_cosdec=r['pmra'] , pm_dec=r['pmdec'] ) sep = gaiacoord.separation(Pcoord) sep3d = gaiacoord.separation_3d(Pcoord) if verbose == True: print('Printing angular separations in degrees as sanity check') print(sep.degree) Pllbb = bc.radec_to_lb(Pcoord.ra.value , Pcoord.dec.value , degree=True) Ppmllpmbb = bc.pmrapmdec_to_pmllpmbb( Pcoord.pm_ra_cosdec.value , Pcoord.pm_dec.value , \ Pcoord.ra.value , Pcoord.dec.value , degree=True ) Pvxvyvz = bc.vrpmllpmbb_to_vxvyvz(Pcoord.radial_velocity.value , Ppmllpmbb[0] , Ppmllpmbb[1] , \ Pllbb[0] , Pllbb[1] , Pcoord.distance.value/1000.0 , XYZ=False , degree=True) if verbose == True: print('Science Target Name: ',targname) print('Science Target RA/DEC: ',Pcoord.ra.value,Pcoord.dec.value) print('Science Target Galactic Coordinates: ',Pllbb) print('Science Target UVW: ',Pvxvyvz) print() Gllbb = bc.radec_to_lb(gaiacoord.ra.value , gaiacoord.dec.value , degree=True) Gxyz = bc.lbd_to_XYZ( Gllbb[:,0] , Gllbb[:,1] , gaiacoord.distance/1000.0 , degree=True) Gvrpmllpmbb = bc.vxvyvz_to_vrpmllpmbb( \ Pvxvyvz[0]*np.ones(len(Gxyz[:,0])) , Pvxvyvz[1]*np.ones(len(Gxyz[:,1])) , Pvxvyvz[2]*np.ones(len(Gxyz[:,2])) , \ Gxyz[:,0] , Gxyz[:,1] , Gxyz[:,2] , XYZ=True) Gpmrapmdec = bc.pmllpmbb_to_pmrapmdec( Gvrpmllpmbb[:,1] , Gvrpmllpmbb[:,2] , Gllbb[:,0] , Gllbb[:,1] , degree=True) # Code in case I want to do chi^2 cuts someday Gvtanerr = 1.0 * np.ones(len(Gxyz[:,0])) Gpmerr = Gvtanerr * 206265000.0 * 3.154e7 / (gaiacoord.distance.value * 3.086e13) Gchi2 = ( (Gpmrapmdec[:,0]-gaiacoord.pm_ra_cosdec.value)**2 + (Gpmrapmdec[:,1]-gaiacoord.pm_dec.value)**2 )**0.5 Gchi2 = Gchi2 / Gpmerr if verbose == True: print('Predicted PMs if comoving:') print(Gpmrapmdec , "\n") print('Actual PMRAs from Gaia:') print(gaiacoord.pm_ra_cosdec.value , "\n") print('Actual PMDECs from Gaia:') print(gaiacoord.pm_dec.value , "\n") print('Predicted PM errors:') print(Gpmerr , "\n") print('Chi^2 values:') print(Gchi2) # Query external list(s) of RVs zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) ) yy = zz[0][np.argsort(sep3d[zz])] RV = np.empty(np.array(r['ra']).size) RVerr = np.empty(np.array(r['ra']).size) RVsrc = np.array([ ' None' for x in range(np.array(r['ra']).size) ]) RV[:] = np.nan RVerr[:] = np.nan print('Populating RV table') for x in range(0 , np.array(yy).size): if np.isnan(r['dr2_radial_velocity'][yy[x]]) == False: # First copy over DR2 RVs RV[yy[x]] = r['dr2_radial_velocity'][yy[x]] RVerr[yy[x]] = r['dr2_radial_velocity_error'][yy[x]] RVsrc[yy[x]] = 'Gaia DR2' if os.path.isfile('LocalRV.csv'): with open('LocalRV.csv') as csvfile: # Now check for a local RV that would supercede readCSV = csv.reader(csvfile, delimiter=',') for row in readCSV: ww = np.where(r['designation'] == row[0])[0] if (np.array(ww).size == 1): RV[ww] = row[2] RVerr[ww] = row[3] RVsrc[ww] = row[4] if verbose == True: print('Using stored RV: ',row) print(r['ra','dec','phot_g_mean_mag'][ww]) print(RV[ww]) print(RVerr[ww]) print(RVsrc[ww]) # Create Gaia CMD plot mamajek = np.loadtxt(datapath+'/sptGBpRp.txt') pleiades = np.loadtxt(datapath+'/PleGBpRp.txt') tuchor = np.loadtxt(datapath+'/TucGBpRp.txt') usco = np.loadtxt(datapath+'/UScGBpRp.txt') chai = np.loadtxt(datapath+'/ChaGBpRp.txt') zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (np.isnan(r['bp_rp']) == False) ) # Note, this causes an error because NaNs yy = zz[0][np.argsort(sep3d[zz])] zz2= np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) & \ (r['phot_bp_rp_excess_factor'] < (1.3 + 0.06*r['bp_rp']**2)) & \ (np.isnan(r['bp_rp']) == False) ) # Note, this causes an error because NaNs yy2= zz2[0][np.argsort((-Gchi2)[zz2])] figname=outdir + targname.replace(" ", "") + "cmd.png" if verbose == True: print(figname) fig,ax1 = plt.subplots(figsize=(12,8)) ax1.axis([ math.floor(min(r['bp_rp'][zz])) , \ math.ceil(max(r['bp_rp'][zz])), \ math.ceil(max((r['phot_g_mean_mag'][zz] - (5.0*np.log10(gaiacoord.distance[zz].value)-5.0))))+1, \ math.floor(min((r['phot_g_mean_mag'][zz] - (5.0*np.log10(gaiacoord.distance[zz].value)-5.0))))-1 ] ) ax1.set_xlabel(r'$B_p-R_p$ (mag)' , fontsize=16) ax1.set_ylabel(r'$M_G$ (mag)' , fontsize=16) ax1.tick_params(axis='both',which='major',labelsize=12) ax2 = ax1.twiny() ax2.set_xlim(ax1.get_xlim()) spttickvals = np.array([ -0.037 , 0.377 , 0.782 , 0.980 , 1.84 , 2.50 , 3.36 , 4.75 ]) sptticklabs = np.array([ 'A0' , 'F0' , 'G0' , 'K0' , 'M0' , 'M3' , 'M5' , 'M7' ]) xx = np.where( (spttickvals >= math.floor(min(r['bp_rp'][zz]))) & (spttickvals <= math.ceil(max(r['bp_rp'][zz]))) )[0] ax2.set_xticks(spttickvals[xx]) ax2.set_xticklabels( sptticklabs[xx] ) ax2.set_xlabel('SpT' , fontsize=16, labelpad=15) ax2.tick_params(axis='both',which='major',labelsize=12) ax1.plot( chai[:,1] , chai[:,0] , zorder=1 , label='Cha-I (0-5 Myr)') ax1.plot( usco[:,1] , usco[:,0] , zorder=2 , label='USco (11 Myr)') ax1.plot( tuchor[:,1] , tuchor[:,0] , zorder=3 , label='Tuc-Hor (40 Myr)') ax1.plot(pleiades[:,1] , pleiades[:,0] , zorder=4 , label='Pleiades (125 Myr)') ax1.plot( mamajek[:,2] , mamajek[:,1] , zorder=5 , label='Mamajek MS') for x in range(0 , np.array(yy2).size): msize = (17-12.0*(sep3d[yy2[x]].value/searchradpc.value))**2 mcolor = Gchi2[yy2[x]] medge = 'black' mzorder= 7 if (r['ruwe'][yy2[x]] < 1.2): mshape='o' if (r['ruwe'][yy2[x]] >= 1.2): mshape='s' if (np.isnan(rvcut) == False): if (np.isnan(RV[yy2[x]])==False) & (np.abs(RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) > rvcut): mshape='+' mcolor='black' mzorder=6 if (np.isnan(RV[yy2[x]])==False) & (np.abs(RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) <= rvcut): medge='blue' ccc = ax1.scatter(r['bp_rp'][yy2[x]] , (r['phot_g_mean_mag'][yy2[x]] - (5.0*np.log10(gaiacoord.distance[yy2[x]].value)-5.0)) , \ s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \ vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' ) temp1 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='o' , s=12**2 , label = 'RUWE < 1.2') temp2 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='s' , s=12**2 , label = 'RUWE >= 1.2') temp3 = ax1.scatter([] , [] , c='white' , edgecolors='blue' , marker='o' , s=12**2 , label = 'RV Comoving') temp4 = ax1.scatter([] , [] , c='black' , marker='+' , s=12**2 , label = 'RV Outlier') ax1.plot(r['bp_rp'][yy[0]] , (r['phot_g_mean_mag'][yy[0]] - (5.0*np.log10(gaiacoord.distance[yy[0]].value)-5.0)) , \ 'rx' , markersize=18 , mew=3 , markeredgecolor='red' , zorder=10 , label=targname) ax1.arrow( 1.3 , 2.5 , 0.374, 0.743 , length_includes_head=True , head_width=0.07 , head_length = 0.10 ) ax1.text( 1.4 , 2.3, r'$A_V=1$' , fontsize=12) ax1.legend(fontsize=11) cb = plt.colorbar(ccc , ax=ax1) cb.set_label(label='Velocity Difference (km/s)',fontsize=14) plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200) if showplots == True: plt.show() plt.close('all') # Create PM plot zz2= np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) ) yy2= zz2[0][np.argsort((-Gchi2)[zz2])] zz3= np.where( (sep3d.value < searchradpc.value) & (sep.degree > 0.00001) ) figname=outdir + targname.replace(" ", "") + "pmd.png" fig,ax1 = plt.subplots(figsize=(12,8)) ax1.axis([ (max(r['pmra'][zz2]) + 0.05*np.ptp(r['pmra'][zz2]) ) , \ (min(r['pmra'][zz2]) - 0.05*np.ptp(r['pmra'][zz2]) ) , \ (min(r['pmdec'][zz2])- 0.05*np.ptp(r['pmra'][zz2]) ) , \ (max(r['pmdec'][zz2])+ 0.05*np.ptp(r['pmra'][zz2]) ) ] ) ax1.tick_params(axis='both',which='major',labelsize=16) if ((max(r['pmra'][zz2]) + 0.05*np.ptp(r['pmra'][zz2])) > 0.0) & \ ((min(r['pmra'][zz2]) - 0.05*np.ptp(r['pmra'][zz2])) < 0.0) & \ ((min(r['pmdec'][zz2])- 0.05*np.ptp(r['pmra'][zz2])) < 0.0) & \ ((max(r['pmdec'][zz2])+ 0.05*np.ptp(r['pmra'][zz2])) > 0.0): ax1.plot( [0.0,0.0] , [-1000.0,1000.0] , 'k--' , linewidth=1 ) ax1.plot( [-1000.0,1000.0] , [0.0,0.0] , 'k--' , linewidth=1 ) ax1.errorbar( (r['pmra'][yy2]) , (r['pmdec'][yy2]) , \ yerr=(r['pmdec_error'][yy2]) , xerr=(r['pmra_error'][yy2]) , fmt='none' , ecolor='k' ) ax1.scatter( (r['pmra'][zz3]) , (r['pmdec'][zz3]) , \ s=(0.5)**2 , marker='o' , c='black' , zorder=2 , label='Field' ) for x in range(0 , np.array(yy2).size): msize = (17-12.0*(sep3d[yy2[x]].value/searchradpc.value))**2 mcolor = Gchi2[yy2[x]] medge = 'black' mzorder= 7 if (r['ruwe'][yy2[x]] < 1.2): mshape='o' if (r['ruwe'][yy2[x]] >= 1.2): mshape='s' if (np.isnan(rvcut) == False): if (np.isnan(RV[yy2[x]])==False) & (np.abs(RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) > rvcut): mshape='+' mcolor='black' mzorder=6 if (np.isnan(RV[yy2[x]])==False) & (np.abs(RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) <= rvcut): medge='blue' ccc = ax1.scatter(r['pmra'][yy2[x]] , r['pmdec'][yy2[x]] , \ s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \ vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' ) temp1 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='o' , s=12**2 , label = 'RUWE < 1.2') temp2 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='s' , s=12**2 , label = 'RUWE >= 1.2') temp3 = ax1.scatter([] , [] , c='white' , edgecolors='blue' , marker='o' , s=12**2 , label = 'RV Comoving') temp4 = ax1.scatter([] , [] , c='black' , marker='+' , s=12**2 , label = 'RV Outlier') ax1.plot( Pgaia['pmra'][minpos] , Pgaia['pmdec'][minpos] , \ 'rx' , markersize=18 , mew=3 , markeredgecolor='red' , zorder=3 , label=targname) ax1.set_xlabel(r'$\mu_{RA}$ (mas/yr)' , fontsize=22 , labelpad=10) ax1.set_ylabel(r'$\mu_{DEC}$ (mas/yr)' , fontsize=22 , labelpad=10) ax1.legend(fontsize=12) cb = plt.colorbar(ccc , ax=ax1) cb.set_label(label='Tangential Velocity Difference (km/s)',fontsize=18 , labelpad=10) plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200) if showplots == True: plt.show() plt.close('all') # Create RV plot zz2= np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) & \ (np.isnan(RV) == False) ) yy2= zz2[0][np.argsort((-Gchi2)[zz2])] zz3= np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) & \ (np.isnan(RV) == False) & (np.isnan(r['phot_g_mean_mag']) == False) & \ (np.abs(RV-Gvrpmllpmbb[:,0]) < 20.0) ) # Just to set Y axis fig,ax1 = plt.subplots(figsize=(12,8)) ax1.axis([ -20.0 , +20.0, \ max( np.append( np.array(r['phot_g_mean_mag'][zz3] - (5.0*np.log10(gaiacoord.distance[zz3].value)-5.0)) , 0.0 )) + 0.3 , \ min( np.append( np.array(r['phot_g_mean_mag'][zz3] - (5.0*np.log10(gaiacoord.distance[zz3].value)-5.0)) , 15.0 )) - 0.3 ]) ax1.tick_params(axis='both',which='major',labelsize=16) ax1.plot( [0.0,0.0] , [-20.0,25.0] , 'k--' , linewidth=1 ) ax1.errorbar( (RV[yy2]-Gvrpmllpmbb[yy2,0]) , \ (r['phot_g_mean_mag'][yy2] - (5.0*np.log10(gaiacoord.distance[yy2].value)-5.0)) , \ yerr=None,xerr=(RVerr[yy2]) , fmt='none' , ecolor='k' ) for x in range(0 , np.array(yy2).size): msize = (17-12.0*(sep3d[yy2[x]].value/searchradpc.value))**2 mcolor = Gchi2[yy2[x]] medge = 'black' mzorder= 2 if (r['ruwe'][yy2[x]] < 1.2): mshape='o' if (r['ruwe'][yy2[x]] >= 1.2): mshape='s' ccc = ax1.scatter( (RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) , \ (r['phot_g_mean_mag'][yy2[x]] - (5.0*np.log10(gaiacoord.distance[yy2[x]].value)-5.0)) , \ s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \ vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' ) temp1 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='o' , s=12**2 , label = 'RUWE < 1.2') temp2 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='s' , s=12**2 , label = 'RUWE >= 1.2') temp3 = ax1.scatter([] , [] , c='white' , edgecolors='blue' , marker='o' , s=12**2 , label = 'RV Comoving') if ( (Pgaia['phot_g_mean_mag'][minpos] - (5.0*np.log10(Pcoord.distance.value)-5.0)) < \ (max( np.append( np.array(r['phot_g_mean_mag'][zz3] - (5.0*np.log10(gaiacoord.distance[zz3].value)-5.0)) , 0.0 )) + 0.3) ): ax1.plot( [0.0] , (Pgaia['phot_g_mean_mag'][minpos] - (5.0*np.log10(Pcoord.distance.value)-5.0)) , \ 'rx' , markersize=18 , mew=3 , markeredgecolor='red' , zorder=3 , label=targname) ax1.set_ylabel(r'$M_G$ (mag)' , fontsize=22 , labelpad=10) ax1.set_xlabel(r'$v_{r,obs}-v_{r,pred}$ (km/s)' , fontsize=22 , labelpad=10) ax1.legend(fontsize=12) cb = plt.colorbar(ccc , ax=ax1) cb.set_label(label='Tangential Velocity Difference (km/s)',fontsize=18 , labelpad=10) figname=outdir + targname.replace(" ", "") + "drv.png" plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200) if showplots == True: plt.show() plt.close('all') # Create XYZ plot Pxyz = bc.lbd_to_XYZ( Pllbb[0] , Pllbb[1] , Pcoord.distance.value/1000.0 , degree=True) fig,axs = plt.subplots(2,2) fig.set_figheight(16) fig.set_figwidth(16) fig.subplots_adjust(hspace=0.03,wspace=0.03) zz2= np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) ) yy2= zz2[0][np.argsort((-Gchi2)[zz2])] for x in range(0 , np.array(yy2).size): msize = (17-12.0*(sep3d[yy2[x]].value/searchradpc.value))**2 mcolor = Gchi2[yy2[x]] medge = 'black' mzorder= 3 if (r['ruwe'][yy2[x]] < 1.2): mshape='o' if (r['ruwe'][yy2[x]] >= 1.2): mshape='s' if (np.isnan(rvcut) == False): if (np.isnan(RV[yy2[x]])==False) & (np.abs(RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) > rvcut): mshape='+' mcolor='black' mzorder=2 if (np.isnan(RV[yy2[x]])==False) & (np.abs(RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) <= rvcut): medge='blue' ccc = axs[0,0].scatter( 1000.0*Gxyz[yy2[x],0] , 1000.0*Gxyz[yy2[x],1] , \ s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \ vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' ) ccc = axs[0,1].scatter( 1000.0*Gxyz[yy2[x],2] , 1000.0*Gxyz[yy2[x],1] , \ s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \ vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' ) ccc = axs[1,0].scatter( 1000.0*Gxyz[yy2[x],0] , 1000.0*Gxyz[yy2[x],2] , \ s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \ vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' ) temp1 = axs[0,0].scatter([] , [] , c='white' , edgecolors='black', marker='o' , s=12**2 , label = 'RUWE < 1.2') temp2 = axs[0,0].scatter([] , [] , c='white' , edgecolors='black', marker='s' , s=12**2 , label = 'RUWE >= 1.2') temp3 = axs[0,0].scatter([] , [] , c='white' , edgecolors='blue' , marker='o' , s=12**2 , label = 'RV Comoving') temp4 = axs[0,0].scatter([] , [] , c='black' , marker='+' , s=12**2 , label = 'RV Outlier') axs[0,0].plot( 1000.0*Pxyz[0] , 1000.0*Pxyz[1] , 'rx' , markersize=18 , mew=3 , markeredgecolor='red') axs[0,1].plot( 1000.0*Pxyz[2] , 1000.0*Pxyz[1] , 'rx' , markersize=18 , mew=3 , markeredgecolor='red') axs[1,0].plot( 1000.0*Pxyz[0] , 1000.0*Pxyz[2] , 'rx' , markersize=18 , mew=3 , markeredgecolor='red' , zorder=1 , label = targname) axs[0,0].set_xlim( [1000.0*Pxyz[0]-(search_radius+1.0) , 1000.0*Pxyz[0]+(search_radius+1.0)] ) axs[0,0].set_ylim( [1000.0*Pxyz[1]-(search_radius+1.0) , 1000.0*Pxyz[1]+(search_radius+1.0)] ) axs[0,1].set_xlim( [1000.0*Pxyz[2]-(search_radius+1.0) , 1000.0*Pxyz[2]+(search_radius+1.0)] ) axs[0,1].set_ylim( [1000.0*Pxyz[1]-(search_radius+1.0) , 1000.0*Pxyz[1]+(search_radius+1.0)] ) axs[1,0].set_xlim( [1000.0*Pxyz[0]-(search_radius+1.0) , 1000.0*Pxyz[0]+(search_radius+1.0)] ) axs[1,0].set_ylim( [1000.0*Pxyz[2]-(search_radius+1.0) , 1000.0*Pxyz[2]+(search_radius+1.0)] ) axs[0,0].set_xlabel(r'$X$ (pc)',fontsize=20,labelpad=10) axs[0,0].set_ylabel(r'$Y$ (pc)',fontsize=20,labelpad=10) axs[1,0].set_xlabel(r'$X$ (pc)',fontsize=20,labelpad=10) axs[1,0].set_ylabel(r'$Z$ (pc)',fontsize=20,labelpad=10) axs[0,1].set_xlabel(r'$Z$ (pc)',fontsize=20,labelpad=10) axs[0,1].set_ylabel(r'$Y$ (pc)',fontsize=20,labelpad=10) axs[0,0].xaxis.set_ticks_position('top') axs[0,1].xaxis.set_ticks_position('top') axs[0,1].yaxis.set_ticks_position('right') axs[0,0].xaxis.set_label_position('top') axs[0,1].xaxis.set_label_position('top') axs[0,1].yaxis.set_label_position('right') for aa in [0,1]: for bb in [0,1]: axs[aa,bb].tick_params(top=True,bottom=True,left=True,right=True,direction='in',labelsize=18) fig.delaxes(axs[1][1]) strsize = 26 if (len(targname) > 12.0): strsize = np.floor(24 / (len(targname)/14.5)) fig.legend( bbox_to_anchor=(0.92,0.37) , prop={'size':strsize}) cbaxes = fig.add_axes([0.55,0.14,0.02,0.34]) cb = plt.colorbar( ccc , cax=cbaxes ) cb.set_label( label='Velocity Difference (km/s)' , fontsize=24 , labelpad=20 ) cb.ax.tick_params(labelsize=18) figname=outdir + targname.replace(" ", "") + "xyz.png" plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200) if showplots == True: plt.show() plt.close('all') # Create sky map # Hacked from cartopy.mpl.gridliner _DEGREE_SYMBOL = u'\u00B0' def _east_west_formatted(longitude, num_format='g'): fmt_string = u'{longitude:{num_format}}{degree}' return fmt_string.format(longitude=(longitude if (longitude >= 0) else (longitude + 360)) , \ num_format=num_format,degree=_DEGREE_SYMBOL) def _north_south_formatted(latitude, num_format='g'): fmt_string = u'{latitude:{num_format}}{degree}' return fmt_string.format(latitude=latitude, num_format=num_format,degree=_DEGREE_SYMBOL) LONGITUDE_FORMATTER = mticker.FuncFormatter(lambda v, pos: _east_west_formatted(v)) LATITUDE_FORMATTER = mticker.FuncFormatter(lambda v, pos: _north_south_formatted(v)) zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) ) yy = zz[0][np.argsort((-Gchi2)[zz])] searchcircle = Pcoord.directional_offset_by( (np.arange(0,360)*u.degree) , searchraddeg*np.ones(360)) circleRA = searchcircle.ra.value circleDE = searchcircle.dec.value ww = np.where(circleRA > 180.0) circleRA[ww] = circleRA[ww] - 360.0 RAlist = gaiacoord.ra[yy].value DElist = gaiacoord.dec[yy].value ww = np.where( RAlist > 180.0 ) RAlist[ww] = RAlist[ww] - 360.0 polelat = ((Pcoord.dec.value+90) if (Pcoord.dec.value<0) else (90-Pcoord.dec.value)) polelong= (Pcoord.ra.value if (Pcoord.dec.value<0.0) else (Pcoord.ra.value+180.0)) polelong= (polelong if polelong < 180 else polelong - 360.0) if verbose == True: print('Alignment variables: ',polelat,polelong,Pcoord.ra.value) print(Pcoord.dec.value+searchraddeg.value) rotated_pole = ccrs.RotatedPole( \ pole_latitude=polelat , \ pole_longitude=polelong , \ central_rotated_longitude=90.0 )#\ # (Pcoord.ra.value if (Pcoord.dec.value > 0.0) else (Pcoord.ra.value+180.0)) ) fig = plt.figure(figsize=(8,8)) ax = fig.add_subplot(1, 1, 1, projection=rotated_pole) ax.gridlines(draw_labels=True,x_inline=True,y_inline=True, \ xformatter=LONGITUDE_FORMATTER,yformatter=LATITUDE_FORMATTER) ax.plot( circleRA , circleDE , c="gray" , ls="--" , transform=ccrs.Geodetic()) figname=outdir + targname.replace(" ", "") + "sky.png" base=plt.cm.get_cmap('cubehelix') for x in range(0 , np.array(yy).size): msize = (17-12.0*(sep3d[yy[x]].value/searchradpc.value)) mcolor = base(Gchi2[yy[x]]/vlim.value) medge = 'black' mzorder= 3 if (r['ruwe'][yy[x]] < 1.2): mshape='o' if (r['ruwe'][yy[x]] >= 1.2): mshape='s' if (np.isnan(rvcut) == False): if (np.isnan(RV[yy[x]])==False) & (np.abs(RV[yy[x]]-Gvrpmllpmbb[yy[x],0]) > rvcut): mshape='+' mcolor='black' mzorder=2 if (np.isnan(RV[yy[x]])==False) & (np.abs(RV[yy[x]]-Gvrpmllpmbb[yy[x],0]) <= rvcut): medge='blue' ccc = ax.plot( RAlist[x] , DElist[x] , marker=mshape , \ markeredgecolor=medge , ms = msize , mfc = mcolor , transform=ccrs.Geodetic() ) ax.plot( (Pcoord.ra.value-360.0) , Pcoord.dec.value , \ 'rx' , markersize=18 , mew=3 , transform=ccrs.Geodetic()) plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200) if showplots == True: plt.show() plt.close('all') ## Query GALEX and 2MASS data zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) ) yy = zz[0][np.argsort((-Gchi2)[zz])] NUVmag = np.empty(np.array(r['ra']).size) NUVerr = np.empty(np.array(r['ra']).size) NUVmag[:] = np.nan NUVerr[:] = np.nan print('Searching on neighbors in GALEX') ##suppress the stupid noresultswarning from the catalogs package warnings.filterwarnings("ignore",category=NoResultsWarning) for x in range(0 , np.array(yy).size): querystring=((str(gaiacoord.ra[yy[x]].value) if (gaiacoord.ra[yy[x]].value > 0) \ else str(gaiacoord.ra[yy[x]].value+360.0)) + " " + str(gaiacoord.dec[yy[x]].value)) print('GALEX query ',x,' of ',np.array(yy).size, end='\r') if verbose == True: print('GALEX query ',x,' of ',np.array(yy).size) if verbose == True: print(querystring) if (DoGALEX == True): galex = Catalogs.query_object(querystring , catalog="Galex" , radius=0.0028 , TIMEOUT=600) if ((np.where(galex['nuv_magerr'] > 0.0)[0]).size > 0): ww = np.where( (galex['nuv_magerr'] == min(galex['nuv_magerr'][np.where(galex['nuv_magerr'] > 0.0)]))) NUVmag[yy[x]] = galex['nuv_mag'][ww][0] NUVerr[yy[x]] = galex['nuv_magerr'][ww][0] if verbose == True: print(galex['distance_arcmin','ra','nuv_mag','nuv_magerr'][ww]) Jmag = np.empty(np.array(r['ra']).size) Jerr = np.empty(np.array(r['ra']).size) Jmag[:] = np.nan Jerr[:] = np.nan print('Searching on neighbors in 2MASS') for x in range(0 , np.array(yy).size): if ( np.isnan(NUVmag[yy[x]]) == False ): querycoord = SkyCoord((str(gaiacoord.ra[yy[x]].value) if (gaiacoord.ra[yy[x]].value > 0) else \ str(gaiacoord.ra[yy[x]].value+360.0)) , str(gaiacoord.dec[yy[x]].value) , \ unit=(u.deg,u.deg) , frame='icrs') print('2MASS query ',x,' of ',np.array(yy).size, end='\r') if verbose == True: print('2MASS query ',x,' of ',np.array(yy).size) if verbose == True: print(querycoord) tmass = [] if (DoGALEX == True): tmass = Irsa.query_region(querycoord , catalog='fp_psc' , radius='0d0m10s' ) if ((np.where(tmass['j_m'] > -10.0)[0]).size > 0): ww = np.where( (tmass['j_m'] == min(tmass['j_m'][np.where(tmass['j_m'] > 0.0)]))) Jmag[yy[x]] = tmass['j_m'][ww][0] Jerr[yy[x]] = tmass['j_cmsig'][ww][0] if verbose == True: print(tmass['j_m','j_cmsig'][ww]) # Create GALEX plots mamajek = np.loadtxt(datapath+'/sptGBpRp.txt') f = interp1d( mamajek[:,2] , mamajek[:,0] , kind='cubic') zz2 = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) ) yy2 = zz[0][np.argsort(sep3d[zz])] zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) ) yy = zz[0][np.argsort((-Gchi2)[zz])] fnuvj = (3631.0 * 10**6 * 10**(-0.4 * NUVmag)) / (1594.0 * 10**6 * 10**(-0.4 * Jmag)) spt = f(r['bp_rp'].filled(np.nan)) sptstring = ["nan" for x in range(np.array(r['bp_rp']).size)] for x in range(0 , np.array(zz2).size): if (round(spt[yy2[x]],1) >= 17.0) and (round(spt[yy2[x]],1) < 27.0): sptstring[yy2[x]] = 'M' + ('% 3.1f' % (round(spt[yy2[x]],1)-17.0)).strip() if (round(spt[yy2[x]],1) >= 16.0) and (round(spt[yy2[x]],1) < 17.0): sptstring[yy2[x]] = 'K' + ('% 3.1f' % (round(spt[yy2[x]],1)-9.0)).strip() if (round(spt[yy2[x]],1) >= 10.0) and (round(spt[yy2[x]],1) < 16.0): sptstring[yy2[x]] = 'K' + ('% 3.1f' % (round(spt[yy2[x]],1)-10.0)).strip() if (round(spt[yy2[x]],1) >= 0.0) and (round(spt[yy2[x]],1) < 10.0): sptstring[yy2[x]] = 'G' + ('% 3.1f' % (round(spt[yy2[x]],1)-0.0)).strip() if (round(spt[yy2[x]],1) >= -10.0) and (round(spt[yy2[x]],1) < 0.0): sptstring[yy2[x]] = 'F' + ('% 3.1f' % (round(spt[yy2[x]],1)+10.0)).strip() if (round(spt[yy2[x]],1) >= -20.0) and (round(spt[yy2[x]],1) < -10.0): sptstring[yy2[x]] = 'A' + ('% 3.1f' % (round(spt[yy2[x]],1)+20.0)).strip() if (round(spt[yy2[x]],1) >= -30.0) and (round(spt[yy2[x]],1) < -20.0): sptstring[yy2[x]] = 'B' + ('% 3.1f' % (round(spt[yy2[x]],1)+30.0)).strip() figname=outdir + targname.replace(" ", "") + "galex.png" if verbose == True: print(figname) ##Muck with the axis to get two x axes fig,ax1 = plt.subplots(figsize=(12,8)) ax1.set_yscale('log') ax1.axis([5.0 , 24.0 , 0.000004 , 0.02]) ax2 = ax1.twiny() ax2.set_xlim(ax1.get_xlim()) ax1.set_xticks(np.array([5.0 , 10.0 , 15.0 , 17.0 , 22.0 , 24.0])) ax1.set_xticklabels(['G5','K0','K5','M0','M5','M7']) ax1.set_xlabel('SpT' , fontsize=20, labelpad=15) ax1.tick_params(axis='both',which='major',labelsize=16) ax2.set_xticks(np.array([5.0 , 10.0 , 15.0 , 17.0 , 22.0 , 24.0])) ax2.set_xticklabels(['0.85','0.98','1.45','1.84','3.36','4.75']) ax2.set_xlabel(r'$B_p-R_p$ (mag)' , fontsize=20, labelpad=15) ax2.tick_params(axis='both',which='major',labelsize=16) ax1.set_ylabel(r'$F_{NUV}/F_{J}$' , fontsize=22, labelpad=0) ##Hyades hyades = readsav(datapath +'/HYsaved.sav') hyadesfnuvj = (3631.0 * 10**6 * 10**(-0.4 * hyades['clnuv'])) / (1594.0 * 10**6 * 10**(-0.4 * hyades['clJ'])) ax1.plot(hyades['clspt'] , hyadesfnuvj , 'x' , markersize=4 , mew=1 , markeredgecolor='black' , zorder=1 , label='Hyades' ) for x in range(0 , np.array(yy).size): msize = (17-12.0*(sep3d[yy[x]].value/searchradpc.value))**2 mcolor = Gchi2[yy[x]] medge = 'black' mzorder= 3 if (r['ruwe'][yy[x]] < 1.2): mshape='o' if (r['ruwe'][yy[x]] >= 1.2): mshape='s' if (np.isnan(rvcut) == False): if (np.isnan(RV[yy[x]])==False) & (np.abs(RV[yy[x]]-Gvrpmllpmbb[yy[x],0]) > rvcut): mshape='+' mcolor='black' mzorder=2 if (np.isnan(RV[yy[x]])==False) & (np.abs(RV[yy[x]]-Gvrpmllpmbb[yy[x],0]) <= rvcut): medge='blue' ccc = ax1.scatter( spt[yy[x]] , fnuvj[yy[x]] , \ s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \ vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' ) temp1 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='o' , s=12**2 , label = 'RUWE < 1.2') temp2 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='s' , s=12**2 , label = 'RUWE >= 1.2') temp3 = ax1.scatter([] , [] , c='white' , edgecolors='blue' , marker='o' , s=12**2 , label = 'RV Comoving') temp4 = ax1.scatter([] , [] , c='black' , marker='+' , s=12**2 , label = 'RV Outlier') # Plot science target if (spt[yy[0]] > 5): ax1.plot(spt[yy[0]] , fnuvj[yy[0]] , 'rx' , markersize=18 , mew=3 , markeredgecolor='red' , zorder=3 , label=targname ) ax1.legend(fontsize=16 , loc='lower left') cb = fig.colorbar(ccc , ax=ax1) cb.set_label(label='Velocity Offset (km/s)',fontsize=13) if (DoGALEX == True): plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200) if showplots == True: plt.show() plt.close('all') # Query CatWISE for W1+W2 and AllWISE for W3+W4 zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) ) yy = zz[0][np.argsort((-Gchi2)[zz])] WISEmag = np.empty([np.array(r['ra']).size,4]) WISEerr = np.empty([np.array(r['ra']).size,4]) WISEmag[:] = np.nan WISEerr[:] = np.nan print('Searching on neighbors in WISE') ##there's an annoying nan warning here, hide it for now as it's not a problem warnings.filterwarnings("ignore",category=UserWarning) for x in range(0 , np.array(yy).size): querycoord = SkyCoord((str(gaiacoord.ra[yy[x]].value) if (gaiacoord.ra[yy[x]].value > 0) else \ str(gaiacoord.ra[yy[x]].value+360.0)) , str(gaiacoord.dec[yy[x]].value) , \ unit=(u.deg,u.deg) , frame='icrs') print('WISE query ',x,' of ',np.array(yy).size, end='\r') if verbose == True: print('WISE query ',x,' of ',np.array(yy).size) if verbose == True: print(querycoord) wisecat = [] if (DoWISE == True): wisecat = Irsa.query_region(querycoord,catalog='catwise_2020' , radius='0d0m10s') if ((np.where(wisecat['w1mpro'] > -10.0)[0]).size > 0): ww = np.where( (wisecat['w1mpro'] == min( wisecat['w1mpro'][np.where(wisecat['w1mpro'] > -10.0)]) )) WISEmag[yy[x],0] = wisecat['w1mpro'][ww][0] WISEerr[yy[x],0] = wisecat['w1sigmpro'][ww][0] if ((np.where(wisecat['w2mpro'] > -10.0)[0]).size > 0): ww = np.where( (wisecat['w2mpro'] == min( wisecat['w2mpro'][np.where(wisecat['w2mpro'] > -10.0)]) )) WISEmag[yy[x],1] = wisecat['w2mpro'][ww][0] WISEerr[yy[x],1] = wisecat['w2sigmpro'][ww][0] if (DoWISE == True): wisecat = Irsa.query_region(querycoord,catalog='allwise_p3as_psd' , radius='0d0m10s') if ((np.where(wisecat['w1mpro'] > -10.0)[0]).size > 0): ww = np.where( (wisecat['w1mpro'] == min( wisecat['w1mpro'][np.where(wisecat['w1mpro'] > -10.0)]) )) if (np.isnan(WISEmag[yy[x],0]) == True) | (wisecat['w1mpro'][ww][0] < 11.0): # Note, only if CatWISE absent/saturated WISEmag[yy[x],0] = wisecat['w1mpro'][ww][0] WISEerr[yy[x],0] = wisecat['w1sigmpro'][ww][0] if ((np.where(wisecat['w2mpro'] > -10.0)[0]).size > 0): ww = np.where( (wisecat['w2mpro'] == min( wisecat['w2mpro'][np.where(wisecat['w2mpro'] > -10.0)]) )) if (np.isnan(WISEmag[yy[x],1]) == True) | (wisecat['w2mpro'][ww][0] < 11.0): # Note, only if CatWISE absent/saturated WISEmag[yy[x],1] = wisecat['w2mpro'][ww][0] WISEerr[yy[x],1] = wisecat['w2sigmpro'][ww][0] if ((np.where(wisecat['w3mpro'] > -10.0)[0]).size > 0): ww = np.where( (wisecat['w3mpro'] == min( wisecat['w3mpro'][np.where(wisecat['w3mpro'] > -10.0)]) )) WISEmag[yy[x],2] = wisecat['w3mpro'][ww][0] WISEerr[yy[x],2] = wisecat['w3sigmpro'][ww][0] if ((np.where(wisecat['w4mpro'] > -10.0)[0]).size > 0): ww = np.where( (wisecat['w4mpro'] == min( wisecat['w4mpro'][np.where(wisecat['w4mpro'] > -10.0)]) )) WISEmag[yy[x],3] = wisecat['w4mpro'][ww][0] WISEerr[yy[x],3] = wisecat['w4sigmpro'][ww][0] if verbose == True: print(yy[x],WISEmag[yy[x],:],WISEerr[yy[x],:]) # Create WISE plots W13 = WISEmag[:,0]-WISEmag[:,2] W13err = ( WISEerr[:,0]**2 + WISEerr[:,2]**2 )**0.5 zz = np.argwhere( np.isnan(W13err) ) W13[zz] = np.nan W13err[zz] = np.nan zz = np.where( (W13err > 0.15) ) W13[zz] = np.nan W13err[zz] = np.nan warnings.filterwarnings("default",category=UserWarning) zz2 = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value)) yy2 = zz[0][np.argsort(sep3d[zz])] zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) ) yy = zz[0][np.argsort((-Gchi2)[zz])] figname=outdir + targname.replace(" ", "") + "wise.png" if verbose == True: print(figname) plt.figure(figsize=(12,8)) if (verbose == True) & ((np.where(np.isfinite(W13+W13err))[0]).size > 0): print('Max y value: ' , (max((W13+W13err)[np.isfinite(W13+W13err)])+0.1) ) plt.axis([ 5.0 , 24.0 , \ max( [(min(np.append((W13-W13err)[ np.isfinite(W13-W13err) ],-0.1))-0.1) , -0.3]) , \ max( [(max(np.append((W13+W13err)[ np.isfinite(W13+W13err) ],+0.0))+0.2) , +0.6]) ]) ax1 = plt.gca() ax2 = ax1.twiny() ax2.set_xlim(5.0,24.0) ax1.set_xticks(np.array([5.0 , 10.0 , 15.0 , 17.0 , 22.0 , 24.0])) ax1.set_xticklabels(['G5','K0','K5','M0','M5','M7']) ax1.set_xlabel('SpT' , fontsize=20, labelpad=15) ax1.tick_params(axis='both',which='major',labelsize=16) ax2.set_xticks(np.array([5.0 , 10.0 , 15.0 , 17.0 , 22.0 , 24.0])) ax2.set_xticklabels(['0.85','0.98','1.45','1.84','3.36','4.75']) ax2.set_xlabel(r'$B_p-R_p$ (mag)' , fontsize=20, labelpad=15) ax2.tick_params(axis='both',which='major',labelsize=16) ax1.set_ylabel(r'$W1-W3$ (mag)' , fontsize=22, labelpad=0) # Plot field sequence from Tuc-Hor (Kraus et al. 2014) fldspt = [ 5 , 7 , 10 , 12 , 15 , 17 , 20 , 22 , 24 ] fldW13 = [ 0 , 0 , 0 , .02, .06, .12, .27, .40, .60] plt.plot(fldspt , fldW13 , zorder=0 , label='Photosphere') # Plot neighbors ax1.errorbar( spt[yy] , W13[yy] , yerr=W13err[yy] , fmt='none' , ecolor='k') for x in range(0 , np.array(yy).size): msize = (17-12.0*(sep3d[yy[x]].value/searchradpc.value))**2 mcolor = Gchi2[yy[x]] medge = 'black' mzorder= 3 if (r['ruwe'][yy[x]] < 1.2): mshape='o' if (r['ruwe'][yy[x]] >= 1.2): mshape='s' if (np.isnan(rvcut) == False): if (np.isnan(RV[yy[x]])==False) & (np.abs(RV[yy[x]]-Gvrpmllpmbb[yy[x],0]) > rvcut): mshape='+' mcolor='black' mzorder=2 if (np.isnan(RV[yy[x]])==False) & (np.abs(RV[yy[x]]-Gvrpmllpmbb[yy[x],0]) <= rvcut): medge='blue' ccc = ax1.scatter( spt[yy[x]] , W13[yy[x]] , \ s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \ vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' ) temp1 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='o' , s=12**2 , label = 'RUWE < 1.2') temp2 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='s' , s=12**2 , label = 'RUWE >= 1.2') temp3 = ax1.scatter([] , [] , c='white' , edgecolors='blue' , marker='o' , s=12**2 , label = 'RV Comoving') temp4 = ax1.scatter([] , [] , c='black' , marker='+' , s=12**2 , label = 'RV Outlier') # Plot science target if (spt[yy2[0]] > 5): plt.plot(spt[yy2[0]] , W13[yy2[0]] , 'rx' , markersize=18 , mew=3 , markeredgecolor='red' , zorder=3 , label=targname ) plt.legend(fontsize=16 , loc='upper left') cb = plt.colorbar(ccc , ax=ax1) cb.set_label(label='Velocity Offset (km/s)',fontsize=14) if (DoWISE == True): plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200) if showplots == True: plt.show() plt.close('all') # Cross-reference with ROSAT v = Vizier(columns=["**", "+_R"] , catalog='J/A+A/588/A103/cat2rxs' ) zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) ) yy = zz[0][np.argsort(sep3d[zz])] ROSATflux = np.empty([np.array(r['ra']).size]) ROSATflux[:] = np.nan print('Searching on neighbors in ROSAT') for x in range(0 , np.array(yy).size): querycoord = SkyCoord((str(gaiacoord.ra[yy[x]].value) if (gaiacoord.ra[yy[x]].value > 0) else \ str(gaiacoord.ra[yy[x]].value+360.0)) , str(gaiacoord.dec[yy[x]].value) , \ unit=(u.deg,u.deg) , frame='icrs') print('ROSAT query ',x,' of ',np.array(yy).size, end='\r') if verbose == True: print('ROSAT query ',x,' of ',np.array(yy).size) if verbose == True: print(querycoord) if (DoROSAT == True): rosatcat = v.query_region(querycoord , radius='0d1m0s' ) if (len(rosatcat) > 0): rosatcat = rosatcat['J/A+A/588/A103/cat2rxs'] if verbose == True: print(rosatcat) if ((np.where(rosatcat['CRate'] > -999)[0]).size > 0): ww = np.where( (rosatcat['CRate'] == max(rosatcat['CRate'][np.where(rosatcat['CRate'] > -999)]))) ROSATflux[yy[x]] = rosatcat['CRate'][ww][0] if verbose == True: print(x,yy[x],ROSATflux[yy[x]]) # Create output table with results print('Creating Output Tables with Results') if verbose == True: print('Reminder, there were this many input entries: ',len(Gxyz[:,0])) print('The search radius in velocity space is: ',vlim) print() zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) ) sortlist = np.argsort(sep3d[zz]) yy = zz[0][sortlist] fmt1 = "%11.7f %11.7f %6.3f %6.3f %11.3f %8.4f %8.4f %8.2f %8.2f %8.2f %8.3f %4s %8.6f %6.2f %7.3f %7.3f %35s" fmt2 = "%11.7f %11.7f %6.3f %6.3f %11.3f %8.4f %8.4f %8.2f %8.2f %8.2f %8.3f %4s %8.6f %6.2f %7.3f %7.3f %35s" filename=outdir + targname.replace(" ", "") + ".txt" warnings.filterwarnings("ignore",category=UserWarning) if verbose == True: print('Also creating SIMBAD query table') print(filename) print('RA DEC Gmag Bp-Rp Voff(km/s) Sep(deg) 3D(pc) Vr(pred) Vr(obs) Vrerr Plx(mas) SpT FnuvJ W1-W3 RUWE XCrate RVsrc') with open(filename,'w') as file1: file1.write('RA DEC Gmag Bp-Rp Voff(km/s) Sep(deg) 3D(pc) Vr(pred) Vr(obs) Vrerr Plx(mas) SpT FnuvJ W1-W3 RUWE XCrate RVsrc \n') for x in range(0 , np.array(zz).size): if verbose == True: print(fmt1 % (gaiacoord.ra[yy[x]].value,gaiacoord.dec[yy[x]].value, \ r['phot_g_mean_mag'][yy[x]], r['bp_rp'][yy[x]] , \ Gchi2[yy[x]] , sep[yy[x]].value , sep3d[yy[x]].value , \ Gvrpmllpmbb[yy[x],0] , RV[yy[x]] , RVerr[yy[x]] , \ r['parallax'][yy[x]], \ sptstring[yy[x]] , fnuvj[yy[x]] , W13[yy[x]] , r['ruwe'][yy[x]] , ROSATflux[yy[x]] , RVsrc[yy[x]]) ) with open(filename,'a') as file1: file1.write(fmt2 % (gaiacoord.ra[yy[x]].value,gaiacoord.dec[yy[x]].value, \ r['phot_g_mean_mag'][yy[x]], r['bp_rp'][yy[x]] , \ Gchi2[yy[x]],sep[yy[x]].value,sep3d[yy[x]].value , \ Gvrpmllpmbb[yy[x],0] , RV[yy[x]] , RVerr[yy[x]] , \ r['parallax'][yy[x]], \ sptstring[yy[x]] , fnuvj[yy[x]] , W13[yy[x]] , r['ruwe'][yy[x]] , ROSATflux[yy[x]] , RVsrc[yy[x]]) ) file1.write("\n") filename=outdir + targname.replace(" ", "") + ".csv" with open(filename,mode='w') as result_file: wr = csv.writer(result_file) wr.writerow(['RA','DEC','Gmag','Bp-Rp','Voff(km/s)','Sep(deg)','3D(pc)','Vr(pred)','Vr(obs)','Vrerr','Plx(mas)','SpT','FnuvJ','W1-W3','RUWE','XCrate','RVsrc']) for x in range(0 , np.array(zz).size): wr.writerow(( "{0:.7f}".format(gaiacoord.ra[yy[x]].value) , "{0:.7f}".format(gaiacoord.dec[yy[x]].value) , \ "{0:.3f}".format(r['phot_g_mean_mag'][yy[x]]), "{0:.3f}".format(r['bp_rp'][yy[x]]) , \ "{0:.3f}".format(Gchi2[yy[x]]) , "{0:.4f}".format(sep[yy[x]].value) , "{0:.4f}".format(sep3d[yy[x]].value) , \ "{0:.2f}".format(Gvrpmllpmbb[yy[x],0]) , "{0:.2f}".format(RV[yy[x]]) , "{0:.2f}".format(RVerr[yy[x]]) , \ "{0:.3f}".format(r['parallax'][yy[x]]), \ sptstring[yy[x]] , "{0:.6f}".format(fnuvj[yy[x]]) , "{0:.2f}".format(W13[yy[x]]) , \ "{0:.3f}".format(r['ruwe'][yy[x]]) , "{0:.3f}".format(ROSATflux[yy[x]]) , RVsrc[yy[x]].strip()) ) if verbose == True: print('All output can be found in ' + outdir) return outdir
pmlats_obs = Tpmllpmbb[:, 1] # mas/yr -> km/s vlons_obs = pmvconst * pmlons_obs * dists_obs vlats_obs = pmvconst * pmlats_obs * dists_obs # galactic position distxys_obs = dists_obs * np.cos(glatrads) xpos_obs = distxys_obs * np.cos(glonrads) ypos_obs = distxys_obs * np.sin(glonrads) zpos_obs = dists_obs * np.sin(glatrads) xposgals_obs = xpos_obs - rsun yposgals_obs = ypos_obs rgals_obs = np.sqrt(xposgals_obs**2 + yposgals_obs**2) if isamp == 0: Tvxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(\ hrvs_obs, Tpmllpmbb[:,0], Tpmllpmbb[:,1], \ glons, glats, dists_obs, XYZ=False, degree=True) vxs_obs = Tvxvyvz[:, 0] vys_obs = Tvxvyvz[:, 1] vzs_obs = Tvxvyvz[:, 2] + wsun # Galactocentric position and velcoity hrvxys_obs = hrvs_obs * np.cos(glatrads) vxgals_obs = vxs_obs + usun vygals_obs = vys_obs + vsun vrots_obs = (vxgals_obs*yposgals_obs-vygals_obs*xposgals_obs) \ /rgals_obs vrads_obs = (vxgals_obs*xposgals_obs+vygals_obs*yposgals_obs) \ /rgals_obs else: # approximation vrots_obs = np.copy(vlons_obs)
def _add_proper_motions_pregaia(data, savefilename): #Get proper motions, in a somewhat roundabout way pmfile = savefilename.split('.')[0] + '_pms.fits' if os.path.exists(pmfile): pmdata = fitsread(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' ]) # Write positions, again ... posfilename = tempfile.mktemp('.csv', dir=os.getcwd()) resultfilename = tempfile.mktemp('.csv', dir=os.getcwd()) with open(posfilename, 'w') as csvfile: wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) wr.writerow(['RA', 'DEC']) for ii in range(len(data)): wr.writerow([data[ii]['RA'], data[ii]['DEC']]) # Send to CDS for matching result = open(resultfilename, 'w') try: subprocess.check_call([ 'curl', '-X', 'POST', '-F', 'request=xmatch', '-F', 'distMaxArcsec=4', '-F', 'RESPONSEFORMAT=csv', '-F', 'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA', '-F', 'colDec1=DEC', '-F', 'cat2=vizier:UCAC4', 'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync' ], stdout=result) except subprocess.CalledProcessError: os.remove(posfilename) if os.path.exists(resultfilename): result.close() os.remove(resultfilename) result.close() # Match back and only keep the closest one ma = numpy.loadtxt(resultfilename, delimiter=',', skiprows=1, converters={ 15: lambda s: float(s.strip() or -9999), 16: lambda s: float(s.strip() or -9999), 17: lambda s: float(s.strip() or -9999), 18: lambda s: float(s.strip() or -9999) }, usecols=(4, 5, 15, 16, 17, 18)) h = esutil.htm.HTM() m1, m2, d12 = h.match(data['RA'], data['DEC'], ma[:, 0], ma[:, 1], 4. / 3600., maxmatch=1) pmdata['PMMATCH'] = 0 pmdata['RA'] = data['RA'] pmdata['DEC'] = data['DEC'] pmdata['PMMATCH'][m1] = 1 pmdata['PMRA'][m1] = ma[m2, 2] pmdata['PMDEC'][m1] = ma[m2, 3] pmdata['PMRA_ERR'][m1] = ma[m2, 4] pmdata['PMDEC_ERR'][m1] = ma[m2, 5] pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \ +(pmdata['PMDEC'] == -9999) \ +(pmdata['PMRA_ERR'] == -9999) \ +(pmdata['PMDEC_ERR'] == -9999)]= 0 fitswrite(pmfile, pmdata, clobber=True) #To make sure we're using the same format below pmdata = fitsread(pmfile, 1) os.remove(posfilename) os.remove(resultfilename) #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) vRvTvZ = 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'] = vRvTvZ[:, 0] data['GALVT'] = vRvTvZ[:, 1] data['GALVZ'] = vRvTvZ[:, 2] data['GALVR'][True ^ pmindx] = -9999.99 data['GALVT'][True ^ pmindx] = -9999.99 data['GALVZ'][True ^ pmindx] = -9999.99 #Get HSOY proper motions, in a somewhat roundabout way pmfile = savefilename.split('.')[0] + '_pms_ppmxl.fits' if os.path.exists(pmfile): pmdata = fitsread(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' ]) # Write positions, again ... posfilename = tempfile.mktemp('.csv', dir=os.getcwd()) resultfilename = tempfile.mktemp('.csv', dir=os.getcwd()) with open(posfilename, 'w') as csvfile: wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) wr.writerow(['RA', 'DEC']) for ii in range(len(data)): wr.writerow([data[ii]['RA'], data[ii]['DEC']]) # Send to CDS for matching result = open(resultfilename, 'w') try: subprocess.check_call([ 'curl', '-X', 'POST', '-F', 'request=xmatch', '-F', 'distMaxArcsec=4', '-F', 'RESPONSEFORMAT=csv', '-F', 'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA', '-F', 'colDec1=DEC', '-F', 'cat2=vizier:I/339/hsoy', 'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync' ], stdout=result) except subprocess.CalledProcessError: os.remove(posfilename) if os.path.exists(resultfilename): result.close() os.remove(resultfilename) result.close() # Match back and only keep the closest one ma = numpy.loadtxt(resultfilename, delimiter=',', skiprows=1, converters={ 12: lambda s: float(s.strip() or -9999), 13: lambda s: float(s.strip() or -9999), 14: lambda s: float(s.strip() or -9999), 15: lambda s: float(s.strip() or -9999) }, usecols=(3, 4, 12, 13, 14, 15)) h = esutil.htm.HTM() m1, m2, d12 = h.match(data['RA'], data['DEC'], ma[:, 0], ma[:, 1], 4. / 3600., maxmatch=1) pmdata['PMMATCH'] = 0 pmdata['RA'] = data['RA'] pmdata['DEC'] = data['DEC'] pmdata['PMMATCH'][m1] = 1 pmdata['PMRA'][m1] = ma[m2, 2] pmdata['PMDEC'][m1] = ma[m2, 3] pmdata['PMRA_ERR'][m1] = ma[m2, 4] pmdata['PMDEC_ERR'][m1] = ma[m2, 5] pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \ +(pmdata['PMDEC'] == -9999) \ +(pmdata['PMRA_ERR'] == -9999) \ +(pmdata['PMDEC_ERR'] == -9999)]= 0 fitswrite(pmfile, pmdata, clobber=True) #To make sure we're using the same format below pmdata = fitsread(pmfile, 1) os.remove(posfilename) os.remove(resultfilename) #Match proper motions to ppmxl/HSOY data = esutil.numpy_util.add_fields(data, [('PMRA_HSOY', numpy.float), ('PMDEC_HSOY', numpy.float), ('PMRA_ERR_HSOY', numpy.float), ('PMDEC_ERR_HSOY', numpy.float), ('PMMATCH_HSOY', numpy.int32)]) data['PMMATCH_HSOY'] = 0 h = esutil.htm.HTM() m1, m2, d12 = h.match(pmdata['RA'], pmdata['DEC'], data['RA'], data['DEC'], 2. / 3600., maxmatch=1) data['PMRA_HSOY'][m2] = pmdata['PMRA'][m1] data['PMDEC_HSOY'][m2] = pmdata['PMDEC'][m1] data['PMRA_ERR_HSOY'][m2] = pmdata['PMRA_ERR'][m1] data['PMDEC_ERR_HSOY'][m2] = pmdata['PMDEC_ERR'][m1] data['PMMATCH_HSOY'][m2] = pmdata['PMMATCH'][m1].astype(numpy.int32) pmindx = data['PMMATCH_HSOY'] == 1 data['PMRA_HSOY'][True ^ pmindx] = -9999.99 data['PMDEC_HSOY'][True ^ pmindx] = -9999.99 data['PMRA_ERR_HSOY'][True ^ pmindx] = -9999.99 data['PMDEC_ERR_HSOY'][True ^ pmindx] = -9999.99 #Calculate Galactocentric velocities data = esutil.numpy_util.add_fields(data, [('GALVR_HSOY', numpy.float), ('GALVT_HSOY', numpy.float), ('GALVZ_HSOY', 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_HSOY'], data['PMDEC_HSOY'], 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) vRvTvZ = 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_HSOY'] = vRvTvZ[:, 0] data['GALVT_HSOY'] = vRvTvZ[:, 1] data['GALVZ_HSOY'] = vRvTvZ[:, 2] data['GALVR_HSOY'][True ^ pmindx] = -9999.99 data['GALVT_HSOY'][True ^ pmindx] = -9999.99 data['GALVZ_HSOY'][True ^ pmindx] = -9999.99 #Return return data return None
def _add_proper_motions_gaia(data): from gaia_tools import xmatch gaia2_matches, matches_indx = xmatch.cds(data, colRA='RA', colDec='DEC', xcat='vizier:I/345/gaia2') # Add matches 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, [('PLX', numpy.float), ('PMRA', numpy.float), ('PMDEC', numpy.float), ('PLX_ERR', numpy.float), ('PMRA_ERR', numpy.float), ('PMDEC_ERR', numpy.float), ('PMMATCH', numpy.int32)]) data['PMMATCH'] = 0 data['PMMATCH'][matches_indx] = 1 data['PLX'][matches_indx] = gaia2_matches['parallax'] data['PMRA'][matches_indx] = gaia2_matches['pmra'] data['PMDEC'][matches_indx] = gaia2_matches['pmdec'] data['PLX_ERR'][matches_indx] = gaia2_matches['parallax_error'] data['PMRA_ERR'][matches_indx] = gaia2_matches['pmra_error'] data['PMDEC_ERR'][matches_indx] = gaia2_matches['pmdec_error'] # Set values for those without match to -999 pmindx = data['PMMATCH'] == 1 data['PLX'][True ^ pmindx] = -9999.99 data['PMRA'][True ^ pmindx] = -9999.99 data['PMDEC'][True ^ pmindx] = -9999.99 data['PLX_ERR'][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) vRvTvZ = bovy_coords.vxvyvz_to_galcencyl( vxvyvz[:, 0], vxvyvz[:, 1], vxvyvz[:, 2], 8. - XYZ[:, 0], XYZ[:, 1], XYZ[:, 2] + 0.0208, vsun=[-11.1, 30.24 * 8.15, 7.25] ) #Assumes proper motion of Sgr A* and R0=8.15 kpc, zo= 20.8 pc (Bennett & Bovy 2019) data['GALVR'] = vRvTvZ[:, 0] data['GALVT'] = vRvTvZ[:, 1] data['GALVZ'] = vRvTvZ[:, 2] data['GALVR'][True ^ pmindx] = -9999.99 data['GALVT'][True ^ pmindx] = -9999.99 data['GALVZ'][True ^ pmindx] = -9999.99 return data
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_ALPHA', 'RV_CARB', 'RV_CCFWHM', 'RV_AUTOFWHM', 'SYNTHSCATTER', 'STABLERV_CHI2', 'STABLERV_RCHI2', 'STABLERV_CHI2_PROB', 'CHI2_THRESHOLD', 'APSTAR_VERSION', 'ASPCAP_VERSION', 'RESULTS_VERSION', 'WASH_M', 'WASH_M_ERR', 'WASH_T2', 'WASH_T2_ERR', 'DDO51', 'DDO51_ERR', 'IRAC_3_6', 'IRAC_3_6_ERR', 'IRAC_4_5', 'IRAC_4_5_ERR', 'IRAC_5_8', 'IRAC_5_8_ERR', 'IRAC_8_0', 'IRAC_8_0_ERR', 'WISE_4_5', 'WISE_4_5_ERR', 'TARG_4_5', 'TARG_4_5_ERR', 'WASH_DDO51_GIANT_FLAG', 'WASH_DDO51_STAR_FLAG', 'REDUCTION_ID', 'SRC_H', 'PM_SRC']) if not appath._APOGEE_REDUX.lower() == 'current' \ and not 'l30' in appath._APOGEE_REDUX \ and 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 'l30' in appath._APOGEE_REDUX: logg= data['LOGG'] elif appath._APOGEE_REDUX.lower() == 'current' \ or 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() 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) # Add Tycho-2 matches if options.tyc2: data= esutil.numpy_util.add_fields(data,[('TYC2MATCH',numpy.int32), ('TYC1',numpy.int32), ('TYC2',numpy.int32), ('TYC3',numpy.int32)]) data['TYC2MATCH']= 0 data['TYC1']= -1 data['TYC2']= -1 data['TYC3']= -1 # Write positions posfilename= tempfile.mktemp('.csv',dir=os.getcwd()) resultfilename= tempfile.mktemp('.csv',dir=os.getcwd()) with open(posfilename,'w') as csvfile: wr= csv.writer(csvfile,delimiter=',',quoting=csv.QUOTE_MINIMAL) wr.writerow(['RA','DEC']) for ii in range(len(data)): wr.writerow([data[ii]['RA'],data[ii]['DEC']]) # Send to CDS for matching result= open(resultfilename,'w') try: subprocess.check_call(['curl', '-X','POST', '-F','request=xmatch', '-F','distMaxArcsec=2', '-F','RESPONSEFORMAT=csv', '-F','cat1=@%s' % os.path.basename(posfilename), '-F','colRA1=RA', '-F','colDec1=DEC', '-F','cat2=vizier:Tycho2', 'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'], stdout=result) except subprocess.CalledProcessError: os.remove(posfilename) if os.path.exists(resultfilename): result.close() os.remove(resultfilename) result.close() # Directly match on input RA ma= numpy.loadtxt(resultfilename,delimiter=',',skiprows=1, usecols=(1,2,7,8,9)) iis= numpy.arange(len(data)) mai= [iis[data['RA'] == ma[ii,0]][0] for ii in range(len(ma))] data['TYC2MATCH'][mai]= 1 data['TYC1'][mai]= ma[:,2] data['TYC2'][mai]= ma[:,3] data['TYC3'][mai]= ma[:,4] os.remove(posfilename) os.remove(resultfilename) 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, in a somewhat roundabout way 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']) # Write positions, again ... posfilename= tempfile.mktemp('.csv',dir=os.getcwd()) resultfilename= tempfile.mktemp('.csv',dir=os.getcwd()) with open(posfilename,'w') as csvfile: wr= csv.writer(csvfile,delimiter=',',quoting=csv.QUOTE_MINIMAL) wr.writerow(['RA','DEC']) for ii in range(len(data)): wr.writerow([data[ii]['RA'],data[ii]['DEC']]) # Send to CDS for matching result= open(resultfilename,'w') try: subprocess.check_call(['curl', '-X','POST', '-F','request=xmatch', '-F','distMaxArcsec=4', '-F','RESPONSEFORMAT=csv', '-F','cat1=@%s' % os.path.basename(posfilename), '-F','colRA1=RA', '-F','colDec1=DEC', '-F','cat2=vizier:UCAC4', 'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'], stdout=result) except subprocess.CalledProcessError: os.remove(posfilename) if os.path.exists(resultfilename): result.close() os.remove(resultfilename) result.close() # Match back and only keep the closest one ma= numpy.loadtxt(resultfilename,delimiter=',',skiprows=1, converters={15: lambda s: float(s.strip() or -9999), 16: lambda s: float(s.strip() or -9999), 17: lambda s: float(s.strip() or -9999), 18: lambda s: float(s.strip() or -9999)}, usecols=(4,5,15,16,17,18)) h=esutil.htm.HTM() m1,m2,d12 = h.match(data['RA'],data['DEC'], ma[:,0],ma[:,1],4./3600.,maxmatch=1) pmdata['PMMATCH']= 0 pmdata['RA']= data['RA'] pmdata['DEC']= data['DEC'] pmdata['PMMATCH'][m1]= 1 pmdata['PMRA'][m1]= ma[m2,2] pmdata['PMDEC'][m1]= ma[m2,3] pmdata['PMRA_ERR'][m1]= ma[m2,4] pmdata['PMDEC_ERR'][m1]= ma[m2,5] pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \ +(pmdata['PMDEC'] == -9999) \ +(pmdata['PMRA_ERR'] == -9999) \ +(pmdata['PMDEC_ERR'] == -9999)]= 0 fitsio.write(pmfile,pmdata,clobber=True) #To make sure we're using the same format below pmdata= fitsio.read(pmfile,1) os.remove(posfilename) os.remove(resultfilename) #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 PPMXL proper motions, in a somewhat roundabout way 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']) # Write positions, again ... posfilename= tempfile.mktemp('.csv',dir=os.getcwd()) resultfilename= tempfile.mktemp('.csv',dir=os.getcwd()) with open(posfilename,'w') as csvfile: wr= csv.writer(csvfile,delimiter=',',quoting=csv.QUOTE_MINIMAL) wr.writerow(['RA','DEC']) for ii in range(len(data)): wr.writerow([data[ii]['RA'],data[ii]['DEC']]) # Send to CDS for matching result= open(resultfilename,'w') try: subprocess.check_call(['curl', '-X','POST', '-F','request=xmatch', '-F','distMaxArcsec=4', '-F','RESPONSEFORMAT=csv', '-F','cat1=@%s' % os.path.basename(posfilename), '-F','colRA1=RA', '-F','colDec1=DEC', '-F','cat2=vizier:PPMXL', 'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'], stdout=result) except subprocess.CalledProcessError: os.remove(posfilename) if os.path.exists(resultfilename): result.close() os.remove(resultfilename) result.close() # Match back and only keep the closest one ma= numpy.loadtxt(resultfilename,delimiter=',',skiprows=1, converters={15: lambda s: float(s.strip() or -9999), 16: lambda s: float(s.strip() or -9999), 17: lambda s: float(s.strip() or -9999), 18: lambda s: float(s.strip() or -9999)}, usecols=(4,5,15,16,19,20)) h=esutil.htm.HTM() m1,m2,d12 = h.match(data['RA'],data['DEC'], ma[:,0],ma[:,1],4./3600.,maxmatch=1) pmdata['PMMATCH']= 0 pmdata['RA']= data['RA'] pmdata['DEC']= data['DEC'] pmdata['PMMATCH'][m1]= 1 pmdata['PMRA'][m1]= ma[m2,2] pmdata['PMDEC'][m1]= ma[m2,3] pmdata['PMRA_ERR'][m1]= ma[m2,4] pmdata['PMDEC_ERR'][m1]= ma[m2,5] pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \ +(pmdata['PMDEC'] == -9999) \ +(pmdata['PMRA_ERR'] == -9999) \ +(pmdata['PMDEC_ERR'] == -9999)]= 0 fitsio.write(pmfile,pmdata,clobber=True) #To make sure we're using the same format below pmdata= fitsio.read(pmfile,1) os.remove(posfilename) os.remove(resultfilename) #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
def comove_coords(t, lit_gaia): ###could add other outputs like Vr, pred, in addition to sep,sep3d,and Vtan off ra = t.target_df.squeeze()['ra'] * u.deg dec = t.target_df.squeeze()['dec'] * u.deg distance = (1000.0 / t.target_df.squeeze()['parallax']) * u.pc radvel = t.target_df.squeeze( )['dr2_radial_velocity'] * u.kilometer / u.second pmra = t.target_df.squeeze()['pmra'] * u.mas / u.year pmdec = t.target_df.squeeze()['pmdec'] * u.mas / u.year Pcoord = SkyCoord( ra=ra, dec=dec, \ distance=distance, frame='icrs' , \ radial_velocity=radvel , \ pm_ra_cosdec= pmra , pm_dec= pmdec ) # # Query Gaia with search radius and parallax cut # # Note, a cut on parallax_error was added because searches at low galactic latitude # # return an overwhelming number of noisy sources that scatter into the search volume - ALK 20210325 # print('Querying Gaia for neighbors') # if (searchradpc < Pcoord.distance): # sqltext = "SELECT * FROM gaiaedr3.gaia_source WHERE CONTAINS( \ # POINT('ICRS',gaiaedr3.gaia_source.ra,gaiaedr3.gaia_source.dec), \ # CIRCLE('ICRS'," + str(Pcoord.ra.value) +","+ str(Pcoord.dec.value) +","+ str(searchraddeg.value) +"))\ # =1 AND parallax>" + str(minpar.value) + " AND parallax_error<0.5;" # if (searchradpc >= Pcoord.distance): # sqltext = "SELECT * FROM gaiaedr3.gaia_source WHERE parallax>" + str(minpar.value) + " AND parallax_error<0.5;" # print('Note, using all-sky search') # if verbose == True: # print(sqltext) # print() # job = Gaia.launch_job_async(sqltext , dump_to_file=False) # r = job.get_results() # if verbose == True: print('Number of records: ',len(r['ra'])) # # Construct coordinates array for all stars returned in cone search # gaiacoord = SkyCoord( ra=r['ra'] , dec=r['dec'] , distance=(1000.0/r['parallax'])*u.parsec , \ # frame='icrs' , \ # pm_ra_cosdec=r['pmra'] , pm_dec=r['pmdec'] ) lit_sc = SkyCoord( ra=lit_gaia.ra.to_numpy(dtype='float') * u.deg, dec=lit_gaia.dec.to_numpy(dtype='float') * u.deg, pm_ra_cosdec=lit_gaia.pmra.to_numpy(dtype='float') * u.mas / u.yr, pm_dec=lit_gaia.pmdec.to_numpy(dtype='float') * u.mas / u.yr, distance=u.pc * (1000. / lit_gaia.parallax.to_numpy(dtype='float'))) sep = lit_sc.separation(Pcoord) #in degrees sep3d = lit_sc.separation_3d(Pcoord) #in parsec Pllbb = bc.radec_to_lb(Pcoord.ra.value, Pcoord.dec.value, degree=True) Ppmllpmbb = bc.pmrapmdec_to_pmllpmbb( Pcoord.pm_ra_cosdec.value , Pcoord.pm_dec.value , \ Pcoord.ra.value , Pcoord.dec.value , degree=True ) Pvxvyvz = bc.vrpmllpmbb_to_vxvyvz(Pcoord.radial_velocity.value , Ppmllpmbb[0] , Ppmllpmbb[1] , \ Pllbb[0] , Pllbb[1] , Pcoord.distance.value/1000.0 , XYZ=False , degree=True) Gllbb = bc.radec_to_lb(lit_sc.ra.value, lit_sc.dec.value, degree=True) Gxyz = bc.lbd_to_XYZ(Gllbb[:, 0], Gllbb[:, 1], lit_sc.distance / 1000.0, degree=True) Gvrpmllpmbb = bc.vxvyvz_to_vrpmllpmbb( \ Pvxvyvz[0]*np.ones(len(Gxyz[:,0])) , Pvxvyvz[1]*np.ones(len(Gxyz[:,1])) , Pvxvyvz[2]*np.ones(len(Gxyz[:,2])) , \ Gxyz[:,0] , Gxyz[:,1] , Gxyz[:,2] , XYZ=True) Gpmrapmdec = bc.pmllpmbb_to_pmrapmdec(Gvrpmllpmbb[:, 1], Gvrpmllpmbb[:, 2], Gllbb[:, 0], Gllbb[:, 1], degree=True) # Code in case I want to do chi^2 cuts someday Gvtanerr = 1.0 * np.ones(len(Gxyz[:, 0])) Gpmerr = Gvtanerr * 206265000.0 * 3.154e7 / (lit_sc.distance.value * 3.086e13) Gchi2 = ((Gpmrapmdec[:, 0] - lit_sc.pm_ra_cosdec.value)**2 + (Gpmrapmdec[:, 1] - lit_sc.pm_dec.value)**2)**0.5 vtanoff = Gchi2 / Gpmerr #this is reported Vtan,off(km/s) ##vr pred vr_pred = Gvrpmllpmbb[:, 0] #create results dataframe res = pd.DataFrame( data={ 'tic': lit_gaia.tic.to_numpy(dtype='str'), 'designation': lit_gaia.designation.to_numpy(dtype='str'), 'ra': lit_sc.ra.value, 'dec': lit_sc.dec.value, 'sep2D(deg)': sep.value, 'sep3D(pc)': sep3d.value, 'Vtan,off(km/s)': vtanoff, 'Vr,pred(km/s)': vr_pred }) return (res)
def from_radec(cluster, do_order=False, do_key_params=False): """Calculate galactocentric coordinates from on-sky position, proper motion, and radial velocity of cluster Parameters ---------- cluster : class StarCluster do_order : bool sort star by radius after coordinate change (default: False) do_key_params : bool call key_params to calculate key parameters after unit change (default: False) Returns ------- None History: ------- 2018 - Written - Webb (UofT) """ if cluster.units == "radec" and cluster.origin == "sky": origin0 = cluster.origin l, b = bovy_coords.radec_to_lb(cluster.ra, cluster.dec, degree=True).T x0, y0, z0 = bovy_coords.lbd_to_XYZ(l, b, cluster.dist, degree=True).T cluster.x, cluster.y, cluster.z = bovy_coords.XYZ_to_galcenrect( x0, y0, z0, Xsun=8.0, Zsun=0.025).T pml, pmb = bovy_coords.pmrapmdec_to_pmllpmbb(cluster.pmra, cluster.pmdec, cluster.ra, cluster.dec, degree=True).T vx0, vy0, vz0 = bovy_coords.vrpmllpmbb_to_vxvyvz(cluster.vlos, pml, pmb, l, b, cluster.dist, degree=True).T cluster.vx, cluster.vy, cluster.vz = bovy_coords.vxvyvz_to_galcenrect( vx0, vy0, vz0, vsun=[0.0, 220.0, 0.0], Xsun=8.0, Zsun=0.025, _extra_rot=True, ).T l_gc, b_gc = bovy_coords.radec_to_lb(cluster.ra_gc, cluster.dec_gc, degree=True) x0_gc, y0_gc, z0_gc = bovy_coords.lbd_to_XYZ(l_gc, b_gc, cluster.dist_gc, degree=True) cluster.xgc, cluster.ygc, cluster.zgc = bovy_coords.XYZ_to_galcenrect( x0_gc, y0_gc, z0_gc, Xsun=8.0, Zsun=0.025) pml_gc, pmb_gc = bovy_coords.pmrapmdec_to_pmllpmbb(cluster.pmra_gc, cluster.pmdec_gc, cluster.ra_gc, cluster.dec_gc, degree=True) vx0_gc, vy0_gc, vz0_gc = bovy_coords.vrpmllpmbb_to_vxvyvz( cluster.vlos_gc, pml_gc, pmb_gc, l_gc, b_gc, cluster.dist_gc, degree=True) cluster.vx_gc, cluster.vy_gc, cluster.vz_gc = bovy_coords.vxvyvz_to_galcenrect( vx0_gc, vy0_gc, vz0_gc, vsun=[0.0, 220.0, 0.0], Xsun=8.0, Zsun=0.025, _extra_rot=True, ) cluster.origin = "galaxy" cluster.units = "kpckms" cluster.rv3d() if do_key_params: cluster.key_params(do_order=do_order)