def raveRC(): data= readRave() jk= data['Jmag2']-data['Kmag2']-0.17*numpy.exp(data['Av']) z= isodist.FEH2Z(data['[M/H]K'],zsolar=0.017) logg= data['loggK'] indx= (jk < 0.8)*(jk >= 0.5)\ *(z <= 0.06)\ *(z <= rcmodel.jkzcut(jk,upper=True))\ *(z >= rcmodel.jkzcut(jk))\ *(logg >= rcmodel.loggteffcut(data['TeffK'],z,upper=False))\ *(logg <= rcmodel.loggteffcut(data['TeffK'],z,upper=True)) data= data[indx] #To allow for XY pixelization data= esutil.numpy_util.add_fields(data,[('RC_GALR', float), ('RC_GALPHI', float), ('RC_GALZ', float), ('VHELIO_AVG', float)]) XYZ= bovy_coords.lbd_to_XYZ(data['GLON'], data['GLAT'], data['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 data['VHELIO_AVG']= data['HRV'] return data
def get_rcsample(): """ NAME: get_rcsample PURPOSE: get the RC sample INPUT: None so far OUTPUT: sample HISTORY: 2015-02-10 - Started - Bovy (IAS@KITP) """ data = apread.rcsample() # Cut to statistical sample data = data[data['STAT'] == 1] # Add the M_H-based distances data = esutil.numpy_util.add_fields(data, [('RC_DIST_H', float), ('RC_DM_H', float), ('RC_GALR_H', float), ('RC_GALPHI_H', float), ('RC_GALZ_H', float)]) rcd = rcdist() jk = data['J0'] - data['K0'] z = isodist.FEH2Z(data['METALS'], zsolar=0.017) data['RC_DIST_H'] = rcd(jk, z, appmag=data['H0'], mh=True) data['RC_DM_H'] = 5. * numpy.log10(data['RC_DIST_H']) + 10. XYZ = bovy_coords.lbd_to_XYZ(data['GLON'], data['GLAT'], data['RC_DIST_H'], degree=True) R, phi, Z = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], Xsun=8., Zsun=0.025) data['RC_GALR_H'] = R data['RC_GALPHI_H'] = phi data['RC_GALZ_H'] = Z # Add the average alpha/Fe data = esutil.numpy_util.add_fields(data, [('AVG_ALPHAFE', float)]) data['AVG_ALPHAFE'] = avg_alphafe(data) # Apply -0.1 offset in [Fe/H] data[_FEHTAG] += -0.10 # Remove locations outside of the Pan-STARRS dust map # In the Southern hemisphere data = data[data['LOCATION_ID'] != 4266] #240,-18 data = data[data['LOCATION_ID'] != 4331] #5.5,-14.2 data = data[data['LOCATION_ID'] != 4381] #5.2,-12.2 data = data[data['LOCATION_ID'] != 4332] #1,-4 data = data[data['LOCATION_ID'] != 4329] #0,-5 data = data[data['LOCATION_ID'] != 4351] #0,-2 data = data[data['LOCATION_ID'] != 4353] #358,0 data = data[data['LOCATION_ID'] != 4385] #358.6,1.4 # Close to the ecliptic pole where there's no data (is it the ecliptic pole? data = data[data['LOCATION_ID'] != 4528] #120,30 data = data[data['LOCATION_ID'] != 4217] #123,22.4 # Remove stars w/ DM < 8.49, because for standard candle RC, these cannot be in the sample data = data[data['RC_DM_H'] > 8.49] return data
def _setup_effvol(locations,effsel,distmods): # First restore the APOGEE selection function (assumed pre-computed) selectFile= '../savs/selfunc-nospdata.sav' if os.path.exists(selectFile): with open(selectFile,'rb') as savefile: apo= pickle.load(savefile) # Now compute the necessary coordinate transformations ds= 10.**(distmods/5-2.) Rgrid, phigrid, zgrid= [], [], [] for loc in locations: lcen, bcen= apo.glonGlat(loc) XYZ= bovy_coords.lbd_to_XYZ(lcen*numpy.ones_like(ds), bcen*numpy.ones_like(ds), ds, degree=True) Rphiz= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],XYZ[:,1],XYZ[:,2], Xsun=define_rcsample._R0, Ysun=0., Zsun=define_rcsample._Z0) Rgrid.append(Rphiz[0]) phigrid.append(Rphiz[1]) zgrid.append(Rphiz[2]) Rgrid= numpy.array(Rgrid) phigrid= numpy.array(phigrid) zgrid= numpy.array(zgrid) # Also need to multiply in distance factors effsel*= numpy.tile(ds**3.*(distmods[1]-distmods[0]),(effsel.shape[0],1)) return (effsel,Rgrid,phigrid,zgrid)
def _calc_lnprob(loc,nls,nbs,ds,distmods,H0,densfunc): lcen, bcen= apo.glonGlat(loc) rad= apo.radius(loc) ls= numpy.linspace(lcen-rad,lcen+rad,nls) bs= numpy.linspace(bcen-rad,bcen+rad,nbs) # Tile these tls= numpy.tile(ls,(len(ds),len(bs),1)) tbs= numpy.swapaxes(numpy.tile(bs,(len(ds),len(ls),1)),1,2) tds= numpy.tile(ds,(len(ls),len(bs),1)).T XYZ= bovy_coords.lbd_to_XYZ(tls.flatten(), tbs.flatten(), tds.flatten(), degree=True) Rphiz= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],XYZ[:,1],XYZ[:,2], Xsun=define_rcsample._R0, Ysun=0., Zsun=define_rcsample._Z0) # Evaluate probability density tH= numpy.tile(distmods.T,(1,len(ls),len(bs),1))[0].T for ii in range(tH.shape[1]): for jj in range(tH.shape[2]): try: tH[:,ii,jj]+= dmap(ls[jj],bs[ii],ds) except (IndexError, TypeError,ValueError): try: tH[:,ii,jj]+= dmapg15(ls[jj],bs[ii],ds) except IndexError: # assume zero outside pass tH= tH.flatten()+H0[0] ps= densfunc(Rphiz[0],Rphiz[1],Rphiz[2])*apo(loc,tH)\ *numpy.fabs(numpy.cos(tbs.flatten()/180.*numpy.pi))\ *tds.flatten()**3. return numpy.log(numpy.reshape(ps,(len(distmods),nbs,nls))\ +10.**-8.)
def _setup_effvol(locations, effsel, distmods): # First restore the APOGEE selection function (assumed pre-computed) selectFile = '../savs/selfunc-nospdata.sav' if os.path.exists(selectFile): with open(selectFile, 'rb') as savefile: apo = pickle.load(savefile) # Now compute the necessary coordinate transformations ds = 10.**(distmods / 5 - 2.) Rgrid, phigrid, zgrid = [], [], [] for loc in locations: lcen, bcen = apo.glonGlat(loc) XYZ = bovy_coords.lbd_to_XYZ(lcen * numpy.ones_like(ds), bcen * numpy.ones_like(ds), ds, degree=True) Rphiz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], Xsun=define_rcsample._R0, Ysun=0., Zsun=define_rcsample._Z0) Rgrid.append(Rphiz[0]) phigrid.append(Rphiz[1]) zgrid.append(Rphiz[2]) Rgrid = numpy.array(Rgrid) phigrid = numpy.array(phigrid) zgrid = numpy.array(zgrid) # Also need to multiply in distance factors effsel *= numpy.tile(ds**3. * (distmods[1] - distmods[0]), (effsel.shape[0], 1)) return (effsel, Rgrid, phigrid, zgrid)
def force_pal5(pot: PotentialType, dpal5: float, ro: float = REFR0, vo: float = REFV0) -> Tuple[float]: """Return the force at Pal5. Parameters ---------- pot: Potential, list dpal5: float ro, vo: float Return ------ force: tuple [fx, fy, fz] """ from galpy import potential from galpy.util import bovy_coords # First compute the location based on the distance l5, b5 = bovy_coords.radec_to_lb(229.018, -0.124, degree=True) X5, Y5, Z5 = bovy_coords.lbd_to_XYZ(l5, b5, dpal5, degree=True) R5, p5, Z5 = bovy_coords.XYZ_to_galcencyl(X5, Y5, Z5, Xsun=ro, Zsun=0.025) args: list = [pot, R5 / ro, Z5 / ro] kws: dict = {"phi": p5, "use_physical": True, "ro": ro, "vo": vo} return ( potential.evaluateRforces(*args, **kws), potential.evaluatezforces(*args, **kws), potential.evaluatephiforces(*args, **kws), )
def read_mgiant(self): dpath = "/Users/htian/Documents/GitHub/rothalo/data/dr4_mgiant.csv" ra, dec, rv, dist, sn, feh = np.loadtxt(dpath, skiprows=1, usecols=(0, 1, 2, 9, 10, 11), delimiter=',', unpack=True) ind_o = (rv > -10000000) & (sn > 10) & (feh > self.min_feh) & ( feh < self.max_feh) & (dist > -1000) print(len(sn[sn < 10]), len(sn)) print("there are ", len(ra[ind_o]), " stars readout!") # ra_o = data_halo[ind_o, 1] # dec_o = data_halo[ind_o, 2] lb = gub.radec_to_lb(ra, dec, degree=True) l = lb[:, 0] b = lb[:, 1] xyz = gub.lbd_to_XYZ(l, b, dist, degree=True) self.l_o = l[ind_o] self.b_o = b[ind_o] self.feh_o = feh[ind_o] self.rv_o = rv[ind_o] self.dist_o = dist[ind_o] self.Z_o = xyz[ind_o, 2] self.R_o = np.sqrt((8 - xyz[ind_o, 0])**2 + xyz[ind_o, 1]**2) self.name = "DR4_mgiant"
def get_rcsample(): """ NAME: get_rcsample PURPOSE: get the RC sample INPUT: None so far OUTPUT: sample HISTORY: 2015-02-10 - Started - Bovy (IAS@KITP) """ data= apread.rcsample() # Cut to statistical sample data= data[data['STAT'] == 1] # Add the M_H-based distances data= esutil.numpy_util.add_fields(data,[('RC_DIST_H', float), ('RC_DM_H', float), ('RC_GALR_H', float), ('RC_GALPHI_H', float), ('RC_GALZ_H', float)]) rcd= rcdist() jk= data['J0']-data['K0'] z= isodist.FEH2Z(data['METALS'],zsolar=0.017) data['RC_DIST_H']= rcd(jk,z,appmag=data['H0'],mh=True) data['RC_DM_H']= 5.*numpy.log10(data['RC_DIST_H'])+10. XYZ= bovy_coords.lbd_to_XYZ(data['GLON'], data['GLAT'], data['RC_DIST_H'], degree=True) R,phi,Z= bovy_coords.XYZ_to_galcencyl(XYZ[:,0], XYZ[:,1], XYZ[:,2], Xsun=8.,Zsun=0.025) data['RC_GALR_H']= R data['RC_GALPHI_H']= phi data['RC_GALZ_H']= Z # Add the average alpha/Fe data= esutil.numpy_util.add_fields(data,[('AVG_ALPHAFE', float)]) data['AVG_ALPHAFE']= avg_alphafe(data) # Apply -0.1 offset in [Fe/H] data[_FEHTAG]+= -0.10 # Remove locations outside of the Pan-STARRS dust map # In the Southern hemisphere data= data[data['LOCATION_ID'] != 4266] #240,-18 data= data[data['LOCATION_ID'] != 4331] #5.5,-14.2 data= data[data['LOCATION_ID'] != 4381] #5.2,-12.2 data= data[data['LOCATION_ID'] != 4332] #1,-4 data= data[data['LOCATION_ID'] != 4329] #0,-5 data= data[data['LOCATION_ID'] != 4351] #0,-2 data= data[data['LOCATION_ID'] != 4353] #358,0 data= data[data['LOCATION_ID'] != 4385] #358.6,1.4 # Close to the ecliptic pole where there's no data (is it the ecliptic pole? data= data[data['LOCATION_ID'] != 4528] #120,30 data= data[data['LOCATION_ID'] != 4217] #123,22.4 # Remove stars w/ DM < 8.49, because for standard candle RC, these cannot be in the sample data= data[data['RC_DM_H'] > 8.49] return data
def glon_wrapper(*args,**kwargs): if kwargs.pop('glon',False): XYZ= bovy_coords.lbd_to_XYZ(args[0],args[1],args[2],degree=False) R,phi,z= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],XYZ[:,1],XYZ[:,2], Xsun=_R0,Zsun=_Zsun) else: R,phi,z= args[0],args[1],args[2] return func(R,phi,z,*args[3:],**kwargs)
def lbd_to_galcencyl(l, b, d, degree=True): xyz = bovy_coords.lbd_to_XYZ(l, b, d, degree=degree) Rphiz = bovy_coords.XYZ_to_galcencyl(xyz[:, 0], xyz[:, 1], xyz[:, 2], Xsun=1., Zsun=0.) return (Rphiz[:, 0], Rphiz[:, 1], Rphiz[:, 2])
def test_lbd_to_XYZ(): l,b,d= 90., 30.,1. XYZ= bovy_coords.lbd_to_XYZ(l,b,d,degree=True) assert numpy.fabs(XYZ[0]) <10.**-10., 'lbd_to_XYZ conversion does not work as expected' assert numpy.fabs(XYZ[1]-numpy.sqrt(3.)/2.) < 10.**-10., 'lbd_to_XYZ conversion does not work as expected' assert numpy.fabs(XYZ[2]-0.5) < 10.**-10., 'lbd_to_XYZ conversion does not work as expected' # Also test for degree=False XYZ= bovy_coords.lbd_to_XYZ(l/180.*numpy.pi,b/180.*numpy.pi,d,degree=False) assert numpy.fabs(XYZ[0]) <10.**-10., 'lbd_to_XYZ conversion does not work as expected' assert numpy.fabs(XYZ[1]-numpy.sqrt(3.)/2.) < 10.**-10., 'lbd_to_XYZ conversion does not work as expected' assert numpy.fabs(XYZ[2]-0.5) < 10.**-10., 'lbd_to_XYZ conversion does not work as expected' # Also test for arrays os= numpy.ones(2) XYZ= bovy_coords.lbd_to_XYZ(os*l/180.*numpy.pi,os*b/180.*numpy.pi, os*d,degree=False) assert numpy.all(numpy.fabs(XYZ[:,0]) <10.**-10.), 'lbd_to_XYZ conversion does not work as expected' assert numpy.all(numpy.fabs(XYZ[:,1]-numpy.sqrt(3.)/2.) < 10.**-10.), 'lbd_to_XYZ conversion does not work as expected' assert numpy.all(numpy.fabs(XYZ[:,2]-0.5) < 10.**-10.), 'lbd_to_XYZ conversion does not work as expected' return None
def cut_indx_vol(gaia, rcut, zcut): XYZ = bovy_coords.lbd_to_XYZ(gaia['l'], gaia['b'], 1. / gaia['parallax'], degree=True) r_cyl = np.sqrt(XYZ[:, 0]**2. + XYZ[:, 1]**2.) z_cyl = XYZ[:, 2] return [(r_cyl < r_cyl_cut) * (np.abs(z_cyl) < z_cyl_cut)]
def measure_kinematics_onepop(tgas,twomass,jk,dm,mj,spii,zbins,options, csvwriter,csvout,maxcovar=30.): # Compute XYZ lb= bovy_coords.radec_to_lb(tgas['ra'],tgas['dec'],degree=True,epoch=None) XYZ= bovy_coords.lbd_to_XYZ(lb[:,0],lb[:,1],1./tgas['parallax'], degree=True) # Generate vradec and projection matrix vradec= numpy.array([bovy_coords._K/tgas['parallax']*tgas['pmra'], bovy_coords._K/tgas['parallax']*tgas['pmdec']]) proj= compute_projection(tgas) # Sample from the joint (parallax,proper motion) uncertainty distribution # to get the covariance matrix of the vradec, using MC sims nmc= 10001 vradec_cov= compute_vradec_cov_mc(tgas,nmc) # Fit each zbin if spii == options.start: startz= options.startz else: startz= 0 for ii in tqdm.trange(startz,len(zbins)-1): indx= (XYZ[:,2] > zbins[ii])\ *(XYZ[:,2] <= zbins[ii+1])\ *(numpy.sqrt(XYZ[:,0]**2.+XYZ[:,1]**2.) < 0.2) nstar= numpy.sum(indx) if numpy.sum(indx) < 30: continue # Basic XD fit ydata= vradec.T[indx] ycovar= numpy.zeros_like(vradec.T)[indx] initamp= numpy.random.uniform(size=options.ngauss) initamp/= numpy.sum(initamp) m= numpy.zeros(3) s= numpy.array([40.,40.,20.]) initmean= [] initcovar= [] for jj in range(options.ngauss): initmean.append(m+numpy.random.normal(size=3)*s) initcovar.append(4.*s**2.*numpy.diag(numpy.ones(3))) initcovar= numpy.array(initcovar) initmean= numpy.array(initmean) lnL= extreme_deconvolution(ydata,ycovar,initamp,initmean,initcovar, projection=proj[indx]) sig2z= combined_sig2(initamp,initmean[:,2],initcovar[:,2,2], maxcovar=maxcovar) kurtz= combined_k(initamp,initmean[:,2],initcovar[:,2,2], maxcovar=maxcovar) sam= bootstrap(options.nboot, vradec.T[indx],vradec_cov[indx],proj[indx], ngauss=options.ngauss,maxcovar=maxcovar) sig2z_err= 1.4826*numpy.median(numpy.fabs(sam[0]-numpy.median(sam[0]))) kurtz_err= 1.4826*numpy.median(numpy.fabs(sam[1]-numpy.median(sam[1]))) sig2kurtz_corr= numpy.corrcoef(sam)[0,1] csvwriter.writerow([spii,ii,nstar, sig2z,sig2z_err,kurtz,kurtz_err,sig2kurtz_corr]) csvout.flush() return None
def xyz(req_dict): #get heliocentric position x_hc, y_hc, z_hc = b_c.lbd_to_XYZ(req_dict['l'], req_dict['b'], req_dict['dist'], degree=True) #get galactocentric position x_gc, y_gc, z_gc = b_c.XYZ_to_galcenrect(x_hc, y_hc, z_hc, Xsun, Zsun) return float(x_gc), y_gc, float(z_gc)
def force_pal5(pot,dpal5,ro,vo): """Return the force at Pal5""" # First compute the location based on the distance l5, b5= bovy_coords.radec_to_lb(229.018,-0.124,degree=True) X5,Y5,Z5= bovy_coords.lbd_to_XYZ(l5,b5,dpal5,degree=True) R5,p5,Z5= bovy_coords.XYZ_to_galcencyl(X5,Y5,Z5,Xsun=ro,Zsun=0.025) return (potential.evaluateRforces(pot,R5/ro,Z5/ro,phi=p5, use_physical=True,ro=ro,vo=vo), potential.evaluatezforces(pot,R5/ro,Z5/ro,phi=p5, use_physical=True,ro=ro,vo=vo), potential.evaluatephiforces(pot,R5/ro,Z5/ro,phi=p5, use_physical=True,ro=ro,vo=vo))
def readAndHackHoltz(): alldata = apread.allStar(adddist=True, distredux="v402") jk = alldata["J0"] - alldata["K0"] data = alldata[(jk > 0.8) * (alldata["DISO_GAL"] > 0.0)] # To allow for XY pixelization, we will hack these data = esutil.numpy_util.add_fields(data, [("RC_GALR", float), ("RC_GALPHI", float), ("RC_GALZ", float)]) XYZ = bovy_coords.lbd_to_XYZ(data["GLON"], data["GLAT"], data["DISO_GAL"], degree=True) R, phi, Z = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], Xsun=8.0, Zsun=0.025) data["RC_GALR"] = R data["RC_GALPHI"] = phi data["RC_GALZ"] = Z return data
def test_XYZ_to_lbd(): l,b,d= 90., 30.,1. XYZ= bovy_coords.lbd_to_XYZ(l,b,d,degree=True) lt,bt,dt= bovy_coords.XYZ_to_lbd(XYZ[0],XYZ[1],XYZ[2],degree=True) assert numpy.fabs(lt-l) <10.**-10., 'XYZ_to_lbd conversion does not work as expected' assert numpy.fabs(bt-b) < 10.**-10., 'XYZ_to_lbd conversion does not work as expected' assert numpy.fabs(dt-d) < 10.**-10., 'XYZ_to_lbd conversion does not work as expected' # Also test for degree=False XYZ= bovy_coords.lbd_to_XYZ(l/180.*numpy.pi,b/180.*numpy.pi,d,degree=False) lt,bt,dt= bovy_coords.XYZ_to_lbd(XYZ[0],XYZ[1],XYZ[2],degree=False) assert numpy.fabs(lt-l/180.*numpy.pi) <10.**-10., 'XYZ_to_lbd conversion does not work as expected' assert numpy.fabs(bt-b/180.*numpy.pi) < 10.**-10., 'XYZ_to_lbd conversion does not work as expected' assert numpy.fabs(dt-d) < 10.**-10., 'XYZ_to_lbd conversion does not work as expected' # Also test for arrays os= numpy.ones(2) XYZ= bovy_coords.lbd_to_XYZ(os*l/180.*numpy.pi,os*b/180.*numpy.pi, os*d,degree=False) lbdt= bovy_coords.XYZ_to_lbd(XYZ[:,0],XYZ[:,1],XYZ[:,2],degree=False) assert numpy.all(numpy.fabs(lbdt[:,0]-l/180.*numpy.pi) <10.**-10.), 'XYZ_to_lbd conversion does not work as expected' assert numpy.all(numpy.fabs(lbdt[:,1]-b/180.*numpy.pi) < 10.**-10.), 'XYZ_to_lbd conversion does not work as expected' assert numpy.all(numpy.fabs(lbdt[:,2]-d) < 10.**-10.), 'XYZ_to_lbd conversion does not work as expected' return None
def to_space_velocties(self): """ Wrapper around galpy, and wrapper to go through steps to calc UVW """ self.conv_pmrapmdec_to_pmllpmbb() self.calc_covar_pmrapmdec() self.calc_covar_pmllpmbb() self.XYZ = np.array(bcoords.lbd_to_XYZ(self.get_col('GLON'), self.get_col('GLAT'), self.get_col('RC_DIST'), degree=True)) self.calc_spacevel() self.calc_spacevel_uncer_var_tensor()
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 xyzgrid(apo, distmods): """ Generates a grid of x, y, z for each location in apo at the distance moduli supplied """ ds = 10**(distmods / 5. - 2.) xgrid = np.zeros((len(apo._locations), len(ds))) ygrid = np.zeros((len(apo._locations), len(ds))) zgrid = np.zeros((len(apo._locations), len(ds))) for i in range(len(apo._locations)): glon, glat = apo.glonGlat(apo._locations[i]) glon = np.ones(len(ds)) * glon[0] glat = np.ones(len(ds)) * glat[0] xyz = bovy_coords.lbd_to_XYZ(glon, glat, ds, degree=True) xgrid[i] = xyz[:, 0] ygrid[i] = xyz[:, 1] zgrid[i] = xyz[:, 2] return zgrid, ygrid, zgrid
def Rphizgrid(apo,distmods): """ Generates a grid of R, phi, z for each location in apo at the distance moduli supplied """ ds = 10**(distmods/5.-2.) Rgrid = np.zeros((len(apo._locations),len(ds))) phigrid = np.zeros((len(apo._locations),len(ds))) zgrid = np.zeros((len(apo._locations),len(ds))) for i in range(len(apo._locations)): glon,glat = apo.glonGlat(apo._locations[i]) glon = np.ones(len(ds))*glon[0] glat = np.ones(len(ds))*glat[0] xyz = bovy_coords.lbd_to_XYZ(glon,glat,ds, degree=True) rphiz = bovy_coords.XYZ_to_galcencyl(xyz[:,0], xyz[:,1], xyz[:,2], Xsun=8., Zsun=0.02) Rgrid[i] = rphiz[:,0] phigrid[i] = rphiz[:,1] zgrid[i] = rphiz[:,2] return Rgrid, phigrid, zgrid
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 _calc_lnprob(loc, nls, nbs, ds, distmods, H0, densfunc): lcen, bcen = apo.glonGlat(loc) rad = apo.radius(loc) ls = numpy.linspace(lcen - rad, lcen + rad, nls) bs = numpy.linspace(bcen - rad, bcen + rad, nbs) # Tile these tls = numpy.tile(ls, (len(ds), len(bs), 1)) tbs = numpy.swapaxes(numpy.tile(bs, (len(ds), len(ls), 1)), 1, 2) tds = numpy.tile(ds, (len(ls), len(bs), 1)).T XYZ = bovy_coords.lbd_to_XYZ(tls.flatten(), tbs.flatten(), tds.flatten(), degree=True) Rphiz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], Xsun=define_rcsample._R0, Ysun=0., Zsun=define_rcsample._Z0) # Evaluate probability density tH = numpy.tile(distmods.T, (1, len(ls), len(bs), 1))[0].T for ii in range(tH.shape[1]): for jj in range(tH.shape[2]): try: tH[:, ii, jj] += dmap(ls[jj], bs[ii], ds) except (IndexError, TypeError, ValueError): try: tH[:, ii, jj] += dmapg15(ls[jj], bs[ii], ds) except IndexError: # assume zero outside pass tH = tH.flatten() + H0[0] ps= densfunc(Rphiz[0],Rphiz[1],Rphiz[2])*apo(loc,tH)\ *numpy.fabs(numpy.cos(tbs.flatten()/180.*numpy.pi))\ *tds.flatten()**3. return numpy.log(numpy.reshape(ps,(len(distmods),nbs,nls))\ +10.**-8.)
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)
def __init__(self, vxvv=None, uvw=False, lb=False, radec=False, vo=235., ro=8.5, zo=0.025, solarmotion='hogg'): """ NAME: __init__ PURPOSE: Initialize an Orbit instance INPUT: vxvv - initial conditions 3D can be either 1) in Galactocentric cylindrical coordinates [R,vR,vT(,z,vz,phi)] 2) [ra,dec,d,mu_ra, mu_dec,vlos] in [deg,deg,kpc,mas/yr,mas/yr,km/s] (all J2000.0; mu_ra = mu_ra * cos dec) 3) [ra,dec,d,U,V,W] in [deg,deg,kpc,km/s,km/s,kms] 4) (l,b,d,mu_l, mu_b, vlos) in [deg,deg,kpc,mas/yr,mas/yr,km/s) (all J2000.0; mu_l = mu_l * cos b) 5) [l,b,d,U,V,W] in [deg,deg,kpc,km/s,km/s,kms] 4) and 5) also work when leaving out b and mu_b/W OPTIONAL INPUTS: radec - if True, input is 2) (or 3) above uvw - if True, velocities are UVW lb - if True, input is 4) or 5) above vo - circular velocity at ro ro - distance from vantage point to GC (kpc) zo - offset toward the NGP of the Sun wrt the plane (kpc) solarmotion - 'hogg' or 'dehnen', or 'schoenrich', or value in [-U,V,W] OUTPUT: instance HISTORY: 2010-07-20 - Written - Bovy (NYU) """ if isinstance(solarmotion, str) and solarmotion.lower() == 'hogg': vsolar = nu.array([-10.1, 4.0, 6.7]) / vo elif isinstance(solarmotion, str) and solarmotion.lower() == 'dehnen': vsolar = nu.array([-10., 5.25, 7.17]) / vo elif isinstance(solarmotion,str) \ and solarmotion.lower() == 'schoenrich': vsolar = nu.array([-11.1, 12.24, 7.25]) / vo else: vsolar = nu.array(solarmotion) / vo if radec or lb: if radec: l, b = coords.radec_to_lb(vxvv[0], vxvv[1], degree=True) elif len(vxvv) == 4: l, b = vxvv[0], 0. else: l, b = vxvv[0], vxvv[1] if uvw: X, Y, Z = coords.lbd_to_XYZ(l, b, vxvv[2], degree=True) vx = vxvv[3] vy = vxvv[4] vz = vxvv[5] else: if radec: pmll, pmbb = coords.pmrapmdec_to_pmllpmbb(vxvv[3], vxvv[4], vxvv[0], vxvv[1], degree=True) d, vlos = vxvv[2], vxvv[5] elif len(vxvv) == 4: pmll, pmbb = vxvv[2], 0. d, vlos = vxvv[1], vxvv[3] else: pmll, pmbb = vxvv[3], vxvv[4] d, vlos = vxvv[2], vxvv[5] X, Y, Z, vx, vy, vz = coords.sphergal_to_rectgal(l, b, d, vlos, pmll, pmbb, degree=True) X /= ro Y /= ro Z /= ro vx /= vo vy /= vo vz /= vo vsun = nu.array([ 0., 1., 0., ]) + vsolar R, phi, z = coords.XYZ_to_galcencyl(X, Y, Z, Zsun=zo / ro) vR, vT, vz = coords.vxvyvz_to_galcencyl(vx, vy, vz, R, phi, z, vsun=vsun, galcen=True) if lb and len(vxvv) == 4: vxvv = [R, vR, vT, phi] else: vxvv = [R, vR, vT, z, vz, phi] self.vxvv = vxvv if len(vxvv) == 2: self._orb = linearOrbit(vxvv=vxvv) elif len(vxvv) == 3: self._orb = planarROrbit(vxvv=vxvv) elif len(vxvv) == 4: self._orb = planarOrbit(vxvv=vxvv) elif len(vxvv) == 5: self._orb = RZOrbit(vxvv=vxvv) elif len(vxvv) == 6: self._orb = FullOrbit(vxvv=vxvv)
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 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
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 generate(locations, type='exp', sample='lowlow', extmap='green15', nls=101, nmock=1000, H0=-1.49, _dmapg15=None, ncpu=1): """ NAME: generate PURPOSE: generate mock data following a given density INPUT: locations - locations to be included in the sample type= ('exp') type of density profile to sample from sample= ('lowlow') for selecting mock parameters extmap= ('green15') extinction map to use ('marshall06' and others use Green15 to fill in unobserved regions) nls= (101) number of longitude bins to use for each field nmock= (1000) number of mock data points to generate H0= (-1.49) absolute magnitude (can be array w/ sampling spread) ncpu= (1) number of cpus to use to compute the probability OUTPUT: mockdata recarray with tags 'RC_GALR_H', 'RC_GALPHI_H', 'RC_GALZ_H' HISTORY: 2015-04-03 - Written - Bovy (IAS) """ if isinstance(H0,float): H0= [H0] # Setup the density function and its initial parameters rdensfunc= fitDens._setup_densfunc(type) mockparams= _setup_mockparams_densfunc(type,sample) densfunc= lambda x,y,z: rdensfunc(x,y,z,params=mockparams) # Setup the extinction map global dmap global dmapg15 if _dmapg15 is None: dmapg15= mwdust.Green15(filter='2MASS H') else: dmapg15= _dmapg15 if isinstance(extmap,mwdust.DustMap3D.DustMap3D): dmap= extmap elif extmap.lower() == 'green15': dmap= dmapg15 elif extmap.lower() == 'marshall06': dmap= mwdust.Marshall06(filter='2MASS H') elif extmap.lower() == 'sale14': dmap= mwdust.Sale14(filter='2MASS H') elif extmap.lower() == 'drimmel03': dmap= mwdust.Drimmel03(filter='2MASS H') # Use brute-force rejection sampling to make no approximations # First need to estimate the max probability to use in rejection; # Loop through all locations and compute sampling probability on grid in # (l,b,D) # First restore the APOGEE selection function (assumed pre-computed) global apo selectFile= '../savs/selfunc-nospdata.sav' if os.path.exists(selectFile): with open(selectFile,'rb') as savefile: apo= pickle.load(savefile) # Now compute the necessary coordinate transformations and evaluate the # maximum probability distmods= numpy.linspace(7.,15.5,301) ds= 10.**(distmods/5-2.) nbs= nls lnprobs= numpy.empty((len(locations),len(distmods),nbs,nls)) radii= [] lcens, bcens= [], [] lnprobs= multi.parallel_map(lambda x: _calc_lnprob(locations[x],nls,nbs, ds,distmods, H0, densfunc), range(len(locations)), numcores=numpy.amin([len(locations), multiprocessing.cpu_count(),ncpu])) lnprobs= numpy.array(lnprobs) for ll, loc in enumerate(locations): lcen, bcen= apo.glonGlat(loc) rad= apo.radius(loc) radii.append(rad) # save for later lcens.append(lcen[0]) bcens.append(bcen[0]) maxp= (numpy.exp(numpy.nanmax(lnprobs))-10.**-8.)*1.1 # Just to be sure # Now generate mock data using rejection sampling nout= 0 arlocations= numpy.array(locations) arradii= numpy.array(radii) arlcens= numpy.array(lcens) arbcens= numpy.array(bcens) out= numpy.recarray((nmock,), dtype=[('RC_DIST_H','f8'), ('RC_DM_H','f8'), ('RC_GALR_H','f8'), ('RC_GALPHI_H','f8'), ('RC_GALZ_H','f8')]) while nout < nmock: nnew= 2*(nmock-nout) # nnew new locations locIndx= numpy.floor(numpy.random.uniform(size=nnew)*len(locations)).astype('int') newlocations= arlocations[locIndx] # Point within these locations newds_coord= numpy.random.uniform(size=nnew) newds= 10.**((newds_coord*(numpy.amax(distmods)-numpy.amin(distmods))\ +numpy.amin(distmods))/5.-2.) newdls_coord= numpy.random.uniform(size=nnew) newdls= newdls_coord*2.*arradii[locIndx]\ -arradii[locIndx] newdbs_coord= numpy.random.uniform(size=nnew) newdbs= newdbs_coord*2.*arradii[locIndx]\ -arradii[locIndx] newr2s= newdls**2.+newdbs**2. keepIndx= newr2s < arradii[locIndx]**2. newlocations= newlocations[keepIndx] newds_coord= newds_coord[keepIndx] newdls_coord= newdls_coord[keepIndx] newdbs_coord= newdbs_coord[keepIndx] newds= newds[keepIndx] newdls= newdls[keepIndx] newdbs= newdbs[keepIndx] newls= newdls+arlcens[locIndx][keepIndx] newbs= newdbs+arbcens[locIndx][keepIndx] # Reject? tps= numpy.zeros_like(newds) for nloc in list(set(newlocations)): lindx= newlocations == nloc pindx= arlocations == nloc coord= numpy.array([newds_coord[lindx]*(len(distmods)-1.), newdbs_coord[lindx]*(nbs-1.), newdls_coord[lindx]*(nls-1.)]) tps[lindx]= \ numpy.exp(ndimage.interpolation.map_coordinates(\ lnprobs[pindx][0], coord,cval=-10., order=1))-10.**-8. XYZ= bovy_coords.lbd_to_XYZ(newls,newbs,newds,degree=True) Rphiz= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],XYZ[:,1],XYZ[:,2], Xsun=define_rcsample._R0, Ysun=0., Zsun=define_rcsample._Z0) testp= numpy.random.uniform(size=len(newds))*maxp keepIndx= tps > testp if numpy.sum(keepIndx) > nmock-nout: rangeIndx= numpy.zeros(len(keepIndx),dtype='int') rangeIndx[keepIndx]= numpy.arange(numpy.sum(keepIndx)) keepIndx*= (rangeIndx < nmock-nout) out['RC_DIST_H'][nout:nout+numpy.sum(keepIndx)]= newds[keepIndx] out['RC_DM_H'][nout:nout+numpy.sum(keepIndx)]= newds_coord[keepIndx]*(numpy.amax(distmods)-numpy.amin(distmods))\ +numpy.amin(distmods) out['RC_GALR_H'][nout:nout+numpy.sum(keepIndx)]= Rphiz[0][keepIndx] out['RC_GALPHI_H'][nout:nout+numpy.sum(keepIndx)]= Rphiz[1][keepIndx] out['RC_GALZ_H'][nout:nout+numpy.sum(keepIndx)]= Rphiz[2][keepIndx] nout= nout+numpy.sum(keepIndx) return (out,lnprobs)
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 get_rgbsample(loggcut=[1.8, 3.0], teffcut=[0, 10000], add_ages=False, agetype='Martig', apply_corrections=False, distance_correction=False, verbose=False): """ Get a clean sample of dr12 APOGEE data with Michael Haydens distances --- INPUT: None OUTPUT: Clean rgb sample with added distances HISTORY: Started - Mackereth 02/06/16 """ #get the allStar catalogue using apogee python (exlude all bad flags etc) allStar = apread.allStar(rmcommissioning=True, exclude_star_bad=True, exclude_star_warn=True, main=True, ak=True, adddist=False) #cut to a 'sensible' logg range (giants which are not too high on the RGB) allStar = allStar[(allStar['LOGG'] > loggcut[0]) & (allStar['LOGG'] < loggcut[1]) & (allStar['TEFF'] > teffcut[0]) & (allStar['TEFF'] < teffcut[1])] if verbose == True: print str( len(allStar )) + ' Stars before Distance catalogue join (after Log(g) cut)' #load the distance VAC dists = fits.open(catpath + 'DR12_DIST_R-GC.fits')[1].data #convert to astropy Table allStar_tab = Table(data=allStar) dists_tab = Table(data=dists) #join table tab = join(allStar_tab, dists_tab, keys='APOGEE_ID', uniq_col_name='{col_name}{table_name}', table_names=['', '2']) data = tab.as_array() data = esutil.numpy_util.add_fields(data, [('M_J', float), ('M_H', float), ('M_K', float), ('MH50_DIST', float), ('MH50_GALR', float), ('MH50_GALZ', float), ('MH50_GALPHI', float), ('AVG_ALPHAFE', float)]) data['MH50_DIST'] = (10**((data['HAYDEN_DISTMOD_50'] + 5) / 5)) / 1e3 if distance_correction == True: data['MH50_DIST'] *= 1.05 XYZ = bovy_coords.lbd_to_XYZ(data['GLON'], data['GLAT'], data['MH50_DIST'], degree=True) RphiZ = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], Xsun=8., Zsun=0.025) data['MH50_GALR'] = RphiZ[:, 0] data['MH50_GALPHI'] = RphiZ[:, 1] data['MH50_GALZ'] = RphiZ[:, 2] data['M_J'] = data['J0'] - data['HAYDEN_DISTMOD_50'] data['M_H'] = data['H0'] - data['HAYDEN_DISTMOD_50'] data['M_K'] = data['K0'] - data['HAYDEN_DISTMOD_50'] data['AVG_ALPHAFE'] = avg_alphafe_dr12(data) data[_FEHTAG] += -0.1 #remove locations not in the apogee selection function (FIND OUT WHATS UP HERE) data = data[np.in1d(data['LOCATION_ID'], apo.list_fields())] # Remove locations outside of the Pan-STARRS dust map # In the Southern hemisphere data = data[data['LOCATION_ID'] != 4266] #240,-18 data = data[data['LOCATION_ID'] != 4331] #5.5,-14.2 data = data[data['LOCATION_ID'] != 4381] #5.2,-12.2 data = data[data['LOCATION_ID'] != 4332] #1,-4 data = data[data['LOCATION_ID'] != 4329] #0,-5 data = data[data['LOCATION_ID'] != 4351] #0,-2 data = data[data['LOCATION_ID'] != 4353] #358,0 data = data[data['LOCATION_ID'] != 4385] #358.6,1.4 # Close to the ecliptic pole where there's no data (is it the ecliptic pole? data = data[data['LOCATION_ID'] != 4528] #120,30 data = data[data['LOCATION_ID'] != 4217] #123,22.4 #remove any non-finite magnitudes data = data[np.isfinite(data['M_H'])] if verbose == True: print str(len( data)) + ' Stars with distance measures (and in good fields...)' if add_ages == True: if agetype == 'Martig': ages = fits.open(catpath + 'DR12_martigages_vizier.fits')[1].data idtag = '2MASS_ID' if agetype == 'Cannon': ages = fits.open(catpath + 'RGB_Cannon_Ages.fits')[1].data ages = esutil.numpy_util.add_fields(ages, [('Age', float)]) ages['Age'] = np.exp(ages['ln_age']) idtag = 'ID' ages_tab = Table(data=ages) ages_tab.rename_column(idtag, 'APOGEE_ID') tab = join(ages_tab, data, keys='APOGEE_ID', uniq_col_name='{col_name}{table_name}', table_names=['', '2']) allStar_full = tab.as_array() data = allStar_full if verbose == True: print str(len(data)) + ' Stars with ages' if apply_corrections == True: #martig1 = np.genfromtxt(catpath+'martig2016_table1.txt', dtype=None, names=True, skip_header=2) martig1 = fits.open(catpath + 'martig_table1.fits') fit = lowess(np.log10(martig1['Age_out']), np.log10(martig1['Age_in'])) xs = np.linspace(-0.3, 1.2, 100) xsinterpolate = interp1d(xs, xs) fys = fit[:, 0] - xsinterpolate(fit[:, 1]) interp = UnivariateSpline(fit[:, 1], fys) corr_age = np.log10(data['Age']) + (interp(np.log10(data['Age']))) corr_age = 10**corr_age data['Age'] = corr_age 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 calc_normalisation(params, nbin, iso_grid, fehbin=[-0.1, 0.0], agebin=[1., 3.], loggcut=[1.8, 3.0], teffcut=[4000, 5000], type='brokenexpflare', verbose=True, fitIndx=None, weights='padova', distance_cut=False, lowermass=None): #first get the values necessary from the isochrone grid #make a mask for giant stars (+ J-K cut) if teffcut == None: giants = (iso_grid[:, 3] >= loggcut[0]) & ( iso_grid[:, 3] < loggcut[1]) & (iso_grid[:, 5] > 0.5) else: giants = (iso_grid[:, 3] >= loggcut[0]) & ( iso_grid[:, 3] < loggcut[1]) & (iso_grid[:, 5] > 0.5) & ( 10**iso_grid[:, 7] >= teffcut[0]) & (10**iso_grid[:, 7] < teffcut[1]) #make a mask for the age and feh bin if agebin == None: bin = (10**iso_grid[:,0] >= 0.)&(10**iso_grid[:,0] < 13.)&\ (Z2FEH(iso_grid[:,1]) >= fehbin[0])&(Z2FEH(iso_grid[:,1]) < fehbin[1]) else: bin = (10**iso_grid[:,0] >= agebin[0])&(10**iso_grid[:,0] < agebin[1])&\ (Z2FEH(iso_grid[:,1]) >= fehbin[0])&(Z2FEH(iso_grid[:,1]) < fehbin[1]) if lowermass != None: giants *= iso_grid[:, 2] >= lowermass bin *= iso_grid[:, 2] >= lowermass if len(iso_grid[:, 0][bin]) < 1: fehs = np.unique(Z2FEH(iso_grid[:, 1])) cfehbin = fehbin[0] + ((fehbin[1] - fehbin[0]) / 2) feh_offsets = np.fabs(fehs - cfehbin) ind = np.argmin(feh_offsets) cfeh = fehs[ind] bin = (10**iso_grid[:,0] >= agebin[0])&(10**iso_grid[:,0] < agebin[1])&\ (Z2FEH(iso_grid[:,1]) == cfeh) #find the average giant mass mass = iso_grid[:, 2] if weights == 'padova': weight = iso_grid[:, 6] * (10**iso_grid[:, 0] / iso_grid[:, 1]) if weights == 'basti': weight = iso_grid[:, 6] av_mass = np.sum(mass[giants & bin] * weight[giants & bin]) / np.sum( weight[giants & bin]) #find the ratio between giants and the total stellar pop. for this bin mass_total = mass[bin] weight_total = weight[bin] mass_bin = mass[giants & bin] weight_bin = weight[giants & bin] m_ratio = np.sum(mass_bin * weight_bin) / np.sum(mass_total * weight_total) #now compute and sum the rate for this density function #load the raw selection function selectFile = '../savs/selfunc-nospdata.sav' if os.path.exists(selectFile): with open(selectFile, 'rb') as savefile: apo = pickle.load(savefile) #load the effective selection function if agebin == None: with open( '../essf/maps/essf_rgb_green15_modelmh_feh' + str(round(fehbin[0], 1)) + '.sav', 'rb') as savefile: locations = pickle.load(savefile) effsel = pickle.load(savefile) distmods = pickle.load(savefile) with open( '../essf/maps/essf_rgb_marshall06_modelmh_feh' + str(round(fehbin[0], 1)) + '.sav', 'rb') as savefile: mlocations = pickle.load(savefile) meffsel = pickle.load(savefile) mdistmods = pickle.load(savefile) if agebin != None: if agebin[0] < 1.: with open( '../essf/maps/essf_rgb_green15_modelmh_feh' + str(round(fehbin[0], 1)) + '_age' + str(round(1.0, 1)) + '.sav', 'rb') as savefile: locations = pickle.load(savefile) effsel = pickle.load(savefile) distmods = pickle.load(savefile) with open( '../essf/maps/essf_rgb_marshall06_modelmh_feh' + str(round(fehbin[0], 1)) + '_age' + str(round(1.0, 1)) + '.sav', 'rb') as savefile: mlocations = pickle.load(savefile) meffsel = pickle.load(savefile) mdistmods = pickle.load(savefile) if agebin[0] > 0.9: with open( '../essf/maps/essf_rgb_green15_modelmh_feh' + str(round(fehbin[0], 1)) + '_age' + str(round(agebin[0], 1)) + '.sav', 'rb') as savefile: locations = pickle.load(savefile) effsel = pickle.load(savefile) distmods = pickle.load(savefile) with open( '../essf/maps/essf_rgb_marshall06_modelmh_feh' + str(round(fehbin[0], 1)) + '_age' + str(round(agebin[0], 1)) + '.sav', 'rb') as savefile: mlocations = pickle.load(savefile) meffsel = pickle.load(savefile) mdistmods = pickle.load(savefile) # Fill in regions not covered by Marshall map meffsel[meffsel < -0.5] = effsel[meffsel < -0.5] if fitIndx is None: fitIndx = numpy.ones(len(mlocations), dtype='bool') #True-betwDiskIndx locations, effsel, distmods = np.array(mlocations)[fitIndx], np.array( meffsel)[fitIndx], mdistmods #get the density function and set it up to find the normalisation (surfdens=True) rdensfunc = _setup_densfunc(type) densfunc = lambda x: rdensfunc(x, None, None, params=params, surfdens=True) #evaluate surface density at R0 for the density normalisation (always 1. if R_b > R0) R0 = densprofiles._R0 Rb = np.exp(params[3]) dens_norm = densfunc(densprofiles._R0) #set up the density function again with surfdens=False for the rate calculation rdensfunc = _setup_densfunc(type) densfunc = lambda x, y, z: rdensfunc( x, y, z, params=params, surfdens=False) ds = 10.**(distmods / 5. - 2.) #imply the distance cut if distance_cut == True if distance_cut == True: distmods = distmods[ds <= 3.] ds = ds[ds <= 3.] effsel = effsel[:, :len(ds)] #Compute the grid of R, phi and Z for each location Rgrid, phigrid, zgrid = [], [], [] for loc in locations: lcen, bcen = apo.glonGlat(loc) XYZ = bovy_coords.lbd_to_XYZ(lcen * numpy.ones_like(ds), bcen * numpy.ones_like(ds), ds, degree=True) Rphiz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], Xsun=define_rgbsample._R0, Zsun=define_rgbsample._Z0) Rgrid.append(Rphiz[:, 0]) phigrid.append(Rphiz[:, 1]) zgrid.append(Rphiz[:, 2]) Rgrid = numpy.array(Rgrid) phigrid = numpy.array(phigrid) zgrid = numpy.array(zgrid) # Now compute rate(R) for each location and combine effsel *= numpy.tile( ds**2. * (distmods[1] - distmods[0]) * (ds * np.log(10) / 5.), (effsel.shape[0], 1)) tdens = densfunc(Rgrid, phigrid, zgrid) / dens_norm rate = tdens * effsel sumrate = np.sum(rate) #calculate normalisation N(R0) norm = (nbin / sumrate) #convert units (Kpc^2 > pc^2, deg > rad etc) norm *= 1e-6 * (180 / np.pi)**2 #compute mass in bin using values from isochrones bin_mass = (norm * av_mass) / m_ratio if verbose == True: print bin_mass return bin_mass, norm, m_ratio, (av_mass * 1e-6 * (180 / np.pi)**2) / (sumrate * m_ratio)
def predict_spacedist(params, locations,effsel,distmods, type='exp', coord='Z'): """ NAME: predict_spacedist PURPOSE: predict the spatial distribution INPUT: params - parameters of the density profile locations - locations of the APOGEE effective selection function to consider effsel - array (nloc,nD) of the effective selection function, includes area of the field distmods - grid of distance moduli on which the effective selection function is pre-computed type= ('exp') type of density profile to fit coord= ('dm', 'X', or 'Z') OUTPUT: (R,model(R)) HISTORY: 2015-03-26 - Written - Bovy (IAS) """ if coord.lower() == 'x': # Grid in X Xs= numpy.linspace(0.,20.,301) elif coord.lower() == 'z': # Grid in X Xs= numpy.linspace(0.,20.,301) elif coord.lower() == 'dm': # Grid in X Xs= numpy.linspace(7.,15.5,301) # Setup the density function rdensfunc= _setup_densfunc(type) densfunc= lambda x,y,z: rdensfunc(x,y,z,params=params) # Restore the APOGEE selection function (assumed pre-computed) selectFile= '../savs/selfunc-nospdata.sav' if os.path.exists(selectFile): with open(selectFile,'rb') as savefile: apo= pickle.load(savefile) # Now compute the necessary coordinate transformations ds= 10.**(distmods/5-2.) Rgrid, phigrid, zgrid, Xgrid= [], [], [], [] for loc in locations: lcen, bcen= apo.glonGlat(loc) XYZ= bovy_coords.lbd_to_XYZ(lcen*numpy.ones_like(ds), bcen*numpy.ones_like(ds), ds, degree=True) Rphiz= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],XYZ[:,1],XYZ[:,2], Xsun=define_rcsample._R0, Ysun=0., Zsun=define_rcsample._Z0) Rgrid.append(Rphiz[0]) phigrid.append(Rphiz[1]) zgrid.append(Rphiz[2]) Xgrid.append(Rphiz[0]*numpy.cos(Rphiz[1])) Rgrid= numpy.array(Rgrid) phigrid= numpy.array(phigrid) zgrid= numpy.array(zgrid) Xgrid= numpy.array(Xgrid) # Now compute rate(R) for each location and combine effsel*= numpy.tile(ds**3.*(distmods[1]-distmods[0]),(effsel.shape[0],1)) tdens= densfunc(Rgrid,phigrid,zgrid) rate= tdens*effsel out= numpy.zeros((len(locations),len(Xs))) for ii in range(len(locations)): if coord.lower() == 'x': # Jacobian tjac= numpy.fabs((numpy.roll(distmods,-1)-distmods)/\ (numpy.roll(Xgrid[ii],-1)-Xgrid[ii])) tjac[-1]= tjac[-2] tXs= Xgrid[ii,rate[ii] > 0.] elif coord.lower() == 'z': # Jacobian tjac= numpy.fabs((numpy.roll(distmods,-1)-distmods)/\ (numpy.roll(zgrid[ii],-1)-zgrid[ii])) tjac[-1]= tjac[-2] tXs= zgrid[ii,rate[ii] > 0.] elif coord.lower() == 'dm': # Jacobian tjac= numpy.ones_like(Xs) tXs= distmods[rate[ii] > 0.] sindx= numpy.argsort(tXs) tXs= tXs[sindx] trate= rate[ii,rate[ii] > 0.][sindx] tjac= tjac[rate[ii] > 0.][sindx] ipthis= numpy.log(trate*tjac+10.**-8.) baseline= numpy.polynomial.Polynomial.fit(tXs,ipthis,4) ipthis= ipthis/baseline(tXs) sp= interpolate.InterpolatedUnivariateSpline(tXs,ipthis,k=3) tindx= (Xs >= numpy.amin(tXs))\ *(Xs <= numpy.amax(tXs)) out[ii,tindx]= (numpy.exp(sp(Xs[tindx])*baseline(Xs[tindx]))-10.**-8.) out[numpy.isinf(out)]= 0. return (Xs,out.sum(axis=0))
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 fakeDFData(binned,qdf,ii,params,fehs,afes,options, rmin,rmax, platelb, grmin,grmax, fehrange, colordist, fehdist,feh,sf, mapfehs,mapafes, ro=None,vo=None, ndata=None,#If set, supersedes binned, only to be used w/ returnlist=True returnlist=False): #last one useful for pixelFitDF normintstuff if ro is None: ro= get_ro(params,options) if vo is None: vo= get_vo(params,options,len(fehs)) thishr= qdf.estimate_hr(1.,z=0.125)*_REFR0*ro #qdf._hr*_REFR0*ro thishz= qdf.estimate_hz(1.,z=0.125)*_REFR0*ro if thishr < 0.: thishr= 10. #Probably close to flat if thishz < 0.1: thishz= 0.2 thissr= qdf._sr*_REFV0*vo thissz= qdf._sz*_REFV0*vo thishsr= qdf._hsr*_REFR0*ro thishsz= qdf._hsz*_REFR0*ro if True: if options.aAmethod.lower() == 'staeckel': #Make everything 10% larger thishr*= 1.2 thishz*= 1.2 thishsr*= 1.2 thishsz*= 1.2 thissr*= 2. thissz*= 2. else: #Make everything 20% larger thishr*= 1.2 thishz*= 1.2 thishsr*= 1.2 thishsz*= 1.2 thissr*= 2. thissz*= 2. #Find nearest mono-abundance bin that has a measurement abindx= numpy.argmin((fehs[ii]-mapfehs)**2./0.01 \ +(afes[ii]-mapafes)**2./0.0025) #Calculate the r-distribution for each plate nrs= 1001 ngr, nfeh= 11, 11 #BOVY: INCREASE? tgrs= numpy.linspace(grmin,grmax,ngr) tfehs= numpy.linspace(fehrange[0]+0.00001,fehrange[1]-0.00001,nfeh) #Calcuate FeH and gr distriutions fehdists= numpy.zeros(nfeh) for jj in range(nfeh): fehdists[jj]= fehdist(tfehs[jj]) fehdists= numpy.cumsum(fehdists) fehdists/= fehdists[-1] colordists= numpy.zeros(ngr) for jj in range(ngr): colordists[jj]= colordist(tgrs[jj]) colordists= numpy.cumsum(colordists) colordists/= colordists[-1] rs= numpy.linspace(rmin,rmax,nrs) rdists= numpy.zeros((len(sf.plates),nrs,ngr,nfeh)) #outlier model that we want to sample (not the one to aid in the sampling) srhalo= _SRHALO/vo/_REFV0 sphihalo= _SPHIHALO/vo/_REFV0 szhalo= _SZHALO/vo/_REFV0 logoutfrac= numpy.log(get_outfrac(params,ii,options)) loghalodens= numpy.log(ro*outDens(1.,0.,None)) #Calculate surface(R=1.) for relative outlier normalization logoutfrac+= numpy.log(qdf.surfacemass_z(1.,ngl=options.ngl)) if options.mcout: fidoutfrac= get_outfrac(params,ii,options) rdistsout= numpy.zeros((len(sf.plates),nrs,ngr,nfeh)) lagoutfrac= 0.15 #.0000000000000000000000001 #seems good #Setup density model use_real_dens= True if use_real_dens: #nrs, nzs= 101, 101 nrs, nzs= 64, 64 thisRmin, thisRmax= 4./_REFR0, 15./_REFR0 thiszmin, thiszmax= 0., .8 Rgrid= numpy.linspace(thisRmin,thisRmax,nrs) zgrid= numpy.linspace(thiszmin,thiszmax,nzs) surfgrid= numpy.empty((nrs,nzs)) for ll in range(nrs): for jj in range(nzs): sys.stdout.write('\r'+"Working on grid-point %i/%i" % (jj+ll*nzs+1,nzs*nrs)) sys.stdout.flush() surfgrid[ll,jj]= qdf.density(Rgrid[ll],zgrid[jj], nmc=options.nmcv, ngl=options.ngl) sys.stdout.write('\r'+_ERASESTR+'\r') sys.stdout.flush() if _SURFSUBTRACTEXPON: Rs= numpy.tile(Rgrid,(nzs,1)).T Zs= numpy.tile(zgrid,(nrs,1)) ehr= qdf.estimate_hr(1.,z=0.125) # ehz= qdf.estimate_hz(1.,zmin=0.5,zmax=0.7)#Get large z behavior right ehz= qdf.estimate_hz(1.,z=0.125) surfInterp= interpolate.RectBivariateSpline(Rgrid,zgrid, numpy.log(surfgrid) +Rs/ehr+numpy.fabs(Zs)/ehz, kx=3,ky=3, s=0.) # s=10.*float(nzs*nrs)) else: surfInterp= interpolate.RectBivariateSpline(Rgrid,zgrid, numpy.log(surfgrid), kx=3,ky=3, s=0.) # s=10.*float(nzs*nrs)) if _SURFSUBTRACTEXPON: compare_func= lambda x,y,du: numpy.exp(surfInterp.ev(x/ro/_REFR0,numpy.fabs(y)/ro/_REFR0)-x/ro/_REFR0/ehr-numpy.fabs(y)/ehz/ro/_REFR0) else: compare_func= lambda x,y,du: numpy.exp(surfInterp.ev(x/ro/_REFR0,numpy.fabs(y)/ro/_REFR0)) else: compare_func= lambda x,y,z: fidDens(x,y,thishr,thishz,z) for jj in range(len(sf.plates)): p= sf.plates[jj] sys.stdout.write('\r'+"Working on plate %i (%i/%i)" % (p,jj+1,len(sf.plates))) sys.stdout.flush() rdists[jj,:,:,:]= _predict_rdist_plate(rs, compare_func, None,rmin,rmax, platelb[jj,0],platelb[jj,1], grmin,grmax, fehrange[0],fehrange[1],feh, colordist, fehdist,sf,sf.plates[jj], dontmarginalizecolorfeh=True, ngr=ngr,nfeh=nfeh) if options.mcout: rdistsout[jj,:,:,:]= _predict_rdist_plate(rs, lambda x,y,z: outDens(x,y,z), None,rmin,rmax, platelb[jj,0],platelb[jj,1], grmin,grmax, fehrange[0],fehrange[1],feh, colordist, fehdist,sf,sf.plates[jj], dontmarginalizecolorfeh=True, ngr=ngr,nfeh=nfeh) sys.stdout.write('\r'+_ERASESTR+'\r') sys.stdout.flush() numbers= numpy.sum(rdists,axis=3) numbers= numpy.sum(numbers,axis=2) numbers= numpy.sum(numbers,axis=1) numbers= numpy.cumsum(numbers) if options.mcout: totfid= numbers[-1] numbers/= numbers[-1] rdists= numpy.cumsum(rdists,axis=1) for ll in range(len(sf.plates)): for jj in range(ngr): for kk in range(nfeh): rdists[ll,:,jj,kk]/= rdists[ll,-1,jj,kk] if options.mcout: numbersout= numpy.sum(rdistsout,axis=3) numbersout= numpy.sum(numbersout,axis=2) numbersout= numpy.sum(numbersout,axis=1) numbersout= numpy.cumsum(numbersout) totout= fidoutfrac*numbersout[-1] totnumbers= totfid+totout totfid/= totnumbers totout/= totnumbers if _DEBUG: print totfid, totout numbersout/= numbersout[-1] rdistsout= numpy.cumsum(rdistsout,axis=1) for ll in range(len(sf.plates)): for jj in range(ngr): for kk in range(nfeh): rdistsout[ll,:,jj,kk]/= rdistsout[ll,-1,jj,kk] #Now sample thisout= [] newrs= [] newls= [] newbs= [] newplate= [] newgr= [] newfeh= [] newds= [] newzs= [] newvas= [] newRs= [] newphi= [] newvr= [] newvt= [] newvz= [] newlogratio= [] newfideval= [] newqdfeval= [] newpropeval= [] if ndata is None: thisdata= binned(fehs[ii],afes[ii]) thisdataIndx= binned.callIndx(fehs[ii],afes[ii]) ndata= len(thisdata) #First sample from spatial density for ll in range(ndata): #First sample a plate ran= numpy.random.uniform() kk= 0 while numbers[kk] < ran: kk+= 1 #Also sample a FeH and a color ran= numpy.random.uniform() ff= 0 while fehdists[ff] < ran: ff+= 1 ran= numpy.random.uniform() cc= 0 while colordists[cc] < ran: cc+= 1 #plate==kk, feh=ff,color=cc; now sample from the rdist of this plate ran= numpy.random.uniform() jj= 0 if options.mcout and numpy.random.uniform() < totout: #outlier while rdistsout[kk,jj,cc,ff] < ran: jj+= 1 thisoutlier= True else: while rdists[kk,jj,cc,ff] < ran: jj+= 1 thisoutlier= False #r=jj newrs.append(rs[jj]) newls.append(platelb[kk,0]) newbs.append(platelb[kk,1]) newplate.append(sf.plates[kk]) newgr.append(tgrs[cc]) newfeh.append(tfehs[ff]) dist= _ivezic_dist(tgrs[cc],rs[jj],tfehs[ff]) newds.append(dist) #calculate R,z XYZ= bovy_coords.lbd_to_XYZ(platelb[kk,0],platelb[kk,1], dist,degree=True) R= ((_REFR0-XYZ[0])**2.+XYZ[1]**2.)**(0.5) newRs.append(R) phi= numpy.arcsin(XYZ[1]/R) if (_REFR0-XYZ[0]) < 0.: phi= numpy.pi-phi newphi.append(phi) z= XYZ[2]+_ZSUN newzs.append(z) newrs= numpy.array(newrs) newls= numpy.array(newls) newbs= numpy.array(newbs) newplate= numpy.array(newplate) newgr= numpy.array(newgr) newfeh= numpy.array(newfeh) newds= numpy.array(newds) newRs= numpy.array(newRs) newzs= numpy.array(newzs) newphi= numpy.array(newphi) #Add mock velocities newvr= numpy.empty_like(newrs) newvt= numpy.empty_like(newrs) newvz= numpy.empty_like(newrs) use_sampleV= True if use_sampleV: for kk in range(ndata): newv= qdf.sampleV(newRs[kk]/_REFR0,newzs[kk]/_REFR0,n=1) newvr[kk]= newv[0,0]*_REFV0*vo newvt[kk]= newv[0,1]*_REFV0*vo newvz[kk]= newv[0,2]*_REFV0*vo else: accept_v= numpy.zeros(ndata,dtype='bool') naccept= numpy.sum(accept_v) sigz= thissz*numpy.exp(-(newRs-_REFR0)/thishsz) sigr= thissr*numpy.exp(-(newRs-_REFR0)/thishsr) va= numpy.empty_like(newrs) sigphi= numpy.empty_like(newrs) maxqdf= numpy.empty_like(newrs) nvt= 101 tvt= numpy.linspace(0.1,1.2,nvt) for kk in range(ndata): #evaluate qdf for vt pvt= qdf(newRs[kk]/ro/_REFR0+numpy.zeros(nvt), numpy.zeros(nvt), tvt, newzs[kk]/ro/_REFR0+numpy.zeros(nvt), numpy.zeros(nvt),log=True) pvt_maxindx= numpy.argmax(pvt) va[kk]= (1.-tvt[pvt_maxindx])*_REFV0*vo if options.aAmethod.lower() == 'adiabaticgrid' and options.flatten >= 0.9: maxqdf[kk]= pvt[pvt_maxindx]+numpy.log(250.) elif options.aAmethod.lower() == 'adiabaticgrid' and options.flatten >= 0.8: maxqdf[kk]= pvt[pvt_maxindx]+numpy.log(250.) else: maxqdf[kk]= pvt[pvt_maxindx]+numpy.log(40.) sigphi[kk]= _REFV0*vo*4.*numpy.sqrt(numpy.sum(numpy.exp(pvt)*tvt**2.)/numpy.sum(numpy.exp(pvt))-(numpy.sum(numpy.exp(pvt)*tvt)/numpy.sum(numpy.exp(pvt)))**2.) ntries= 0 ngtr1= 0 while naccept < ndata: sys.stdout.write('\r %i %i %i \r' % (ntries,naccept,ndata)) sys.stdout.flush() #print ntries, naccept, ndata ntries+= 1 accept_v_comp= True-accept_v prop_vr= numpy.random.normal(size=ndata)*sigr prop_vt= numpy.random.normal(size=ndata)*sigphi+vo*_REFV0-va prop_vz= numpy.random.normal(size=ndata)*sigz qoverp= numpy.zeros(ndata)-numpy.finfo(numpy.dtype(numpy.float64)).max qoverp[accept_v_comp]= (qdf(newRs[accept_v_comp]/ro/_REFR0, prop_vr[accept_v_comp]/vo/_REFV0, prop_vt[accept_v_comp]/vo/_REFV0, newzs[accept_v_comp]/ro/_REFR0, prop_vz[accept_v_comp]/vo/_REFV0,log=True) -maxqdf[accept_v_comp] #normalize max to 1 -(-0.5*(prop_vr[accept_v_comp]**2./sigr[accept_v_comp]**2.+prop_vz[accept_v_comp]**2./sigz[accept_v_comp]**2.+(prop_vt[accept_v_comp]-_REFV0*vo+va[accept_v_comp])**2./sigphi[accept_v_comp]**2.))) if numpy.any(qoverp > 0.): ngtr1+= numpy.sum((qoverp > 0.)) if ngtr1 > 5: qindx= (qoverp > 0.) print naccept, ndata, newRs[qindx], newzs[qindx], prop_vr[qindx], va[qindx], sigphi[qindx], prop_vt[qindx], prop_vz[qindx], qoverp[qindx] raise RuntimeError("max qoverp = %f > 1, but shouldn't be" % (numpy.exp(numpy.amax(qoverp)))) accept_these= numpy.log(numpy.random.uniform(size=ndata)) #print accept_these, (accept_these < qoverp) accept_these= (accept_these < qoverp) newvr[accept_these]= prop_vr[accept_these] newvt[accept_these]= prop_vt[accept_these] newvz[accept_these]= prop_vz[accept_these] accept_v[accept_these]= True naccept= numpy.sum(accept_v) sys.stdout.write('\r'+_ERASESTR+'\r') sys.stdout.flush() """ ntot= 0 nsamples= 0 itt= 0 fracsuccess= 0. fraccomplete= 0. while fraccomplete < 1.: if itt == 0: nthis= numpy.amax([ndata,_NMIN]) else: nthis= int(numpy.ceil((1-fraccomplete)/fracsuccess*ndata)) itt+= 1 count= 0 while count < nthis: count+= 1 sigz= thissz*numpy.exp(-(R-_REFR0)/thishsz) sigr= thissr*numpy.exp(-(R-_REFR0)/thishsr) sigphi= sigr #/numpy.sqrt(2.) #BOVY: FOR NOW #Estimate asymmetric drift va= sigr**2./2./_REFV0/vo\ *(-.5+R*(1./thishr+2./thishsr))+10.*numpy.fabs(z) newvas.append(va) if options.mcout and thisoutlier: #Sample from outlier gaussian newvz.append(numpy.random.normal()*_SZHALOFAKE*2.) newvr.append(numpy.random.normal()*_SRHALOFAKE*2.) newvt.append(numpy.random.normal()*_SPHIHALOFAKE*2.) elif numpy.random.uniform() < lagoutfrac: #Sample from lagging gaussian newvz.append(numpy.random.normal()*_SZHALOFAKE) newvr.append(numpy.random.normal()*_SRHALOFAKE) newvt.append(numpy.random.normal()*_SPHIHALOFAKE*2.+_REFV0*vo/4.) else: #Sample from disk gaussian newvz.append(numpy.random.normal()*sigz) newvr.append(numpy.random.normal()*sigr) newvt.append(numpy.random.normal()*sigphi+_REFV0*vo-va) newlogratio= list(newlogratio) fidlogeval= numpy.log(1.-lagoutfrac)\ -numpy.log(sigr)-numpy.log(sigphi)-numpy.log(sigz)-0.5*(newvr[-1]**2./sigr**2.+newvz[-1]**2./sigz**2.+(newvt[-1]-_REFV0*vo+va)**2./sigphi**2.) lagoutlogeval= numpy.log(lagoutfrac)\ -numpy.log(_SRHALOFAKE)\ -numpy.log(_SPHIHALOFAKE*2.)\ -numpy.log(_SZHALOFAKE)\ -0.5*(newvr[-1]**2./_SRHALOFAKE**2.+newvz[-1]**2./_SZHALOFAKE**2.+(newvt[-1]-_REFV0*vo/4.)**2./_SPHIHALOFAKE**2./4.) if use_real_dens: fidlogeval+= numpy.log(compare_func(R,z,None)[0]) lagoutlogeval+= numpy.log(compare_func(R,z,None)[0]) else: fidlogeval+= numpy.log(fidDens(R,z,thishr,thishz,None)) lagoutlogeval+= numpy.log(fidDens(R,z,thishr,thishz,None)) newfideval.append(fidlogeval) if options.mcout: fidoutlogeval= numpy.log(fidoutfrac)\ +numpy.log(outDens(R,z,None))\ -numpy.log(_SRHALOFAKE*2.)\ -numpy.log(_SPHIHALOFAKE*2.)\ -numpy.log(_SZHALOFAKE*2.)\ -0.5*(newvr[-1]**2./_SRHALOFAKE**2./4.+newvz[-1]**2./_SZHALOFAKE**2./4.+newvt[-1]**2./_SPHIHALOFAKE**2./4.) newpropeval.append(logsumexp([fidoutlogeval,fidlogeval, lagoutlogeval])) else: newpropeval.append(logsumexp([lagoutlogeval,fidlogeval])) qdflogeval= qdf(R/ro/_REFR0,newvr[-1]/vo/_REFV0,newvt[-1]/vo/_REFV0,z/ro/_REFR0,newvz[-1]/vo/_REFV0,log=True) if isinstance(qdflogeval,(list,numpy.ndarray)): qdflogeval= qdflogeval[0] if options.mcout: outlogeval= logoutfrac+loghalodens\ -numpy.log(srhalo)-numpy.log(sphihalo)-numpy.log(szhalo)\ -0.5*((newvr[-1]/vo/_REFV0)**2./srhalo**2.+(newvz[-1]/vo/_REFV0)**2./szhalo**2.+(newvt[-1]/vo/_REFV0)**2./sphihalo**2.)\ -1.5*numpy.log(2.*numpy.pi) newqdfeval.append(logsumexp([qdflogeval,outlogeval])) else: newqdfeval.append(qdflogeval) newlogratio.append(qdflogeval -newpropeval[-1])#logsumexp([fidlogeval,fidoutlogeval])) newlogratio= numpy.array(newlogratio) thisnewlogratio= copy.copy(newlogratio) maxnewlogratio= numpy.amax(thisnewlogratio) if False: argsort_thisnewlogratio= numpy.argsort(thisnewlogratio)[::-1] thisnewlogratio-= thisnewlogratio[argsort_thisnewlogratio[2]] #3rd largest else: thisnewlogratio-= numpy.amax(thisnewlogratio) thisnewratio= numpy.exp(thisnewlogratio) if len(thisnewratio.shape) > 1 and thisnewratio.shape[1] == 1: thisnewratio= numpy.reshape(thisnewratio,(thisnewratio.shape[0])) #Rejection sample accept= numpy.random.uniform(size=len(thisnewratio)) accept= (accept < thisnewratio) fraccomplete= float(numpy.sum(accept))/ndata fracsuccess= float(numpy.sum(accept))/len(thisnewratio) if _DEBUG: print fraccomplete, fracsuccess, ndata print numpy.histogram(thisnewratio,bins=16) indx= numpy.argmax(thisnewratio) print numpy.array(newvr)[indx], \ numpy.array(newvt)[indx], \ numpy.array(newvz)[indx], \ numpy.array(newrs)[indx], \ numpy.array(newds)[indx], \ numpy.array(newls)[indx], \ numpy.array(newbs)[indx], \ numpy.array(newfideval)[indx] bovy_plot.bovy_print() bovy_plot.bovy_plot(numpy.array(newvt), numpy.exp(numpy.array(newqdfeval)),'b,', xrange=[-300.,500.],yrange=[0.,1.]) bovy_plot.bovy_plot(newvt, numpy.exp(numpy.array(newpropeval+maxnewlogratio)), 'g,', overplot=True) bovy_plot.bovy_plot(numpy.array(newvt), numpy.exp(numpy.array(newlogratio-maxnewlogratio)), 'b,', xrange=[-300.,500.], # xrange=[0.,20.], # xrange=[0.,3.], # xrange=[6.,9.], yrange=[0.001,1.],semilogy=True) bovy_plot.bovy_end_print('/home/bovy/public_html/segue-local/test.png') #Now collect the samples newrs= numpy.array(newrs)[accept][0:ndata] newls= numpy.array(newls)[accept][0:ndata] newbs= numpy.array(newbs)[accept][0:ndata] newplate= numpy.array(newplate)[accept][0:ndata] newgr= numpy.array(newgr)[accept][0:ndata] newfeh= numpy.array(newfeh)[accept][0:ndata] newvr= numpy.array(newvr)[accept][0:ndata] newvt= numpy.array(newvt)[accept][0:ndata] newvz= numpy.array(newvz)[accept][0:ndata] newphi= numpy.array(newphi)[accept][0:ndata] newds= numpy.array(newds)[accept][0:ndata] newqdfeval= numpy.array(newqdfeval)[accept][0:ndata] """ vx, vy, vz= bovy_coords.galcencyl_to_vxvyvz(newvr,newvt,newvz,newphi, vsun=[_VRSUN,_VTSUN,_VZSUN]) vrpmllpmbb= bovy_coords.vxvyvz_to_vrpmllpmbb(vx,vy,vz,newls,newbs,newds, XYZ=False,degree=True) pmrapmdec= bovy_coords.pmllpmbb_to_pmrapmdec(vrpmllpmbb[:,1], vrpmllpmbb[:,2], newls,newbs, degree=True) #Dump everything for debugging the coordinate transformation from galpy.util import save_pickles save_pickles('dump.sav', newds,newls,newbs,newphi, newvr,newvt,newvz, vx, vy, vz, vrpmllpmbb, pmrapmdec) if returnlist: out= [] for ii in range(ndata): out.append([newrs[ii], newgr[ii], newfeh[ii], newls[ii], newbs[ii], newplate[ii], newds[ii], False, #outlier? vrpmllpmbb[ii,0], vrpmllpmbb[ii,1], vrpmllpmbb[ii,2]])#, # newqdfeval[ii]]) return out #Load into data binned.data.feh[thisdataIndx]= newfeh oldgr= thisdata.dered_g-thisdata.dered_r oldr= thisdata.dered_r if options.noerrs: binned.data.dered_r[thisdataIndx]= newrs else: binned.data.dered_r[thisdataIndx]= newrs\ +numpy.random.normal(size=numpy.sum(thisdataIndx))\ *ivezic_dist_gr(oldgr,0., #g-r is all that counts binned.data.feh[thisdataIndx], dg=binned.data[thisdataIndx].g_err, dr=binned.data[thisdataIndx].r_err, dfeh=binned.data[thisdataIndx].feh_err, return_error=True, _returndmr=True) binned.data.dered_r[(binned.data.dered_r >= rmax)]= rmax #tweak to make sure everything stays within the observed range if False: binned.data.dered_r[(binned.data.dered_r <= rmin)]= rmin binned.data.dered_g[thisdataIndx]= oldgr+binned.data[thisdataIndx].dered_r #Also change plate and l and b binned.data.plate[thisdataIndx]= newplate radec= bovy_coords.lb_to_radec(newls,newbs,degree=True) binned.data.ra[thisdataIndx]= radec[:,0] binned.data.dec[thisdataIndx]= radec[:,1] binned.data.l[thisdataIndx]= newls binned.data.b[thisdataIndx]= newbs if options.noerrs: binned.data.vr[thisdataIndx]= vrpmllpmbb[:,0] binned.data.pmra[thisdataIndx]= pmrapmdec[:,0] binned.data.pmdec[thisdataIndx]= pmrapmdec[:,1] else: binned.data.vr[thisdataIndx]= vrpmllpmbb[:,0]+numpy.random.normal(size=numpy.sum(thisdataIndx))*binned.data.vr_err[thisdataIndx] binned.data.pmra[thisdataIndx]= pmrapmdec[:,0]+numpy.random.normal(size=numpy.sum(thisdataIndx))*binned.data.pmra_err[thisdataIndx] binned.data.pmdec[thisdataIndx]= pmrapmdec[:,1]+numpy.random.normal(size=numpy.sum(thisdataIndx))*binned.data.pmdec_err[thisdataIndx] return binned
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 lbd_to_galcencyl(l,b,d,degree=True): xyz=bovy_coords.lbd_to_XYZ(l,b,d,degree=degree) ## These are in physical units, DUMB DUMB DUMB!!! Rphiz=bovy_coords.XYZ_to_galcencyl(xyz[:,0]/ro,xyz[:,1]/ro,xyz[:,2]/ro,Xsun=1.,Zsun=0.) return (Rphiz[:,0]*ro,Rphiz[:,1],Rphiz[:,2]*ro)
def scatterData(options,args): if options.png: ext= 'png' else: ext= 'ps' #Load sf sf= segueSelect.segueSelect(sample=options.sample,sn=True, type_bright='sharprcut', type_faint='sharprcut') if options.fake: fakefile= open(options.fakefile,'rb') fakedata= pickle.load(fakefile) fakefile.close() #Calculate distance ds, ls, bs, rs, grs, fehs= [], [], [], [], [], [] for ii in range(len(fakedata)): ds.append(_ivezic_dist(fakedata[ii][1],fakedata[ii][0],fakedata[ii][2])) ls.append(fakedata[ii][3]+(2*numpy.random.uniform()-1.)\ *1.49) bs.append(fakedata[ii][4]+(2*numpy.random.uniform()-1.)\ *1.49) rs.append(fakedata[ii][0]) grs.append(fakedata[ii][1]) fehs.append(fakedata[ii][2]) ds= numpy.array(ds) ls= numpy.array(ls) bs= numpy.array(bs) rs= numpy.array(rs) grs= numpy.array(grs) fehs= numpy.array(fehs) XYZ= bovy_coords.lbd_to_XYZ(ls,bs,ds,degree=True) else: #Load data XYZ,vxvyvz,cov_vxvyvz,data= readData(metal=options.metal, select=options.select, sample=options.sample) #Cut out bright stars on faint plates and vice versa indx= [] for ii in range(len(data.feh)): if sf.platebright[str(data[ii].plate)] and data[ii].dered_r >= 17.8: indx.append(False) elif not sf.platebright[str(data[ii].plate)] and data[ii].dered_r < 17.8: indx.append(False) else: indx.append(True) indx= numpy.array(indx,dtype='bool') data= data[indx] XYZ= XYZ[indx,:] vxvyvz= vxvyvz[indx,:] cov_vxvyvz= cov_vxvyvz[indx,:] R= ((8.-XYZ[:,0])**2.+XYZ[:,1]**2.)**0.5 bovy_plot.bovy_print() if options.type.lower() == 'dataxy': bovy_plot.bovy_plot(XYZ[:,0],XYZ[:,1],'k,', xlabel=r'$X\ [\mathrm{kpc}]$', ylabel=r'$Y\ [\mathrm{kpc}]$', xrange=[5,-5],yrange=[5,-5], onedhists=True) elif options.type.lower() == 'datarz': bovy_plot.bovy_plot(R,XYZ[:,2],'k,', xlabel=r'$R\ [\mathrm{kpc}]$', ylabel=r'$Z\ [\mathrm{kpc}]$', xrange=[5,14], yrange=[-4,4], onedhists=True) if options.fake: bovy_plot.bovy_end_print(os.path.join(args[0],options.type+'_' +'fake_'+ options.sample+'_'+ options.metal+'.'+ext)) else: bovy_plot.bovy_end_print(os.path.join(args[0],options.type+'_' +options.sample+'_'+ options.metal+'.'+ext))
def volume(self, vol_func, xyz=False, MJ=None, JK=None, ndists=101, linearDist=False, relative=False, ncpu=None): """ NAME: volume PURPOSE: Compute the effective volume of a spatial volume under this effective selection function INPUT: vol_func - function of (a) (ra/deg,dec/deg,dist/kpc) (b) heliocentric Galactic X,Y,Z if xyz that returns 1. inside the spatial volume under consideration and 0. outside of it, should be able to take array input of a certain shape and return an array with the same shape xyz= (False) if True, vol_func is a function of X,Y,Z (see above) MJ= (object-wide default) absolute magnitude in J or an array of samples of absolute magnitudes in J for the tracer population JK= (object-wide default) J-Ks color or an array of samples of the J-Ks color relative= (False) if True, compute the effective volume completeness = effective volume / true volume; computed using the same integration grid, so will be more robust against integration errors (especially due to the finite HEALPix grid for the angular integration). For simple volumes, a more precise effective volume can be computed by using relative=True and multiplying in the correct true volume ndists= (101) number of distances to use in the distance integration linearDist= (False) if True, integrate in distance rather than distance modulus ncpu= (None) if set to an integer, use this many CPUs to compute the effective selection function (only for non-zero extinction) OUTPUT effective volume HISTORY: 2017-01-18 - Written - Bovy (UofT/CCA) """ # Pre-compute coordinates for integrand evaluation if not hasattr(self,'_ra_cen_4vol') or \ (hasattr(self,'_ndists_4vol') and (ndists != self._ndists_4vol or linearDist != self._linearDist_4vol)): theta,phi= healpy.pix2ang(\ _BASE_NSIDE,numpy.arange(_BASE_NPIX)\ [True^self._tgasSel._exclude_mask_skyonly],nest=True) self._ra_cen_4vol = 180. / numpy.pi * phi self._dec_cen_4vol = 90. - 180. / numpy.pi * theta if linearDist: dists = numpy.linspace(0.001, 10., ndists) dms = 5. * numpy.log10(dists) + 10. self._deltadm_4vol = dists[1] - dists[0] else: dms = numpy.linspace(0., 18., ndists) self._deltadm_4vol = (dms[1] - dms[0]) * numpy.log(10.) / 5. self._dists_4vol = 10.**(0.2 * dms - 2.) self._tiled_dists3_4vol= numpy.tile(\ self._dists_4vol**(3.-linearDist),(len(self._ra_cen_4vol),1)) self._tiled_ra_cen_4vol = numpy.tile(self._ra_cen_4vol, (len(self._dists_4vol), 1)).T self._tiled_dec_cen_4vol = numpy.tile(self._dec_cen_4vol, (len(self._dists_4vol), 1)).T lb = bovy_coords.radec_to_lb(phi, numpy.pi / 2. - theta) l = numpy.tile(lb[:, 0], (len(self._dists_4vol), 1)).T.flatten() b = numpy.tile(lb[:, 1], (len(self._dists_4vol), 1)).T.flatten() XYZ_4vol= \ bovy_coords.lbd_to_XYZ(l,b, numpy.tile(self._dists_4vol, (len(self._ra_cen_4vol),1)).flatten()) self._X_4vol = numpy.reshape( XYZ_4vol[:, 0], (len(self._ra_cen_4vol), len(self._dists_4vol))) self._Y_4vol = numpy.reshape( XYZ_4vol[:, 1], (len(self._ra_cen_4vol), len(self._dists_4vol))) self._Z_4vol = numpy.reshape( XYZ_4vol[:, 2], (len(self._ra_cen_4vol), len(self._dists_4vol))) # Cache effective-selection function MJ, JK = self._parse_mj_jk(MJ, JK) new_hash = hashlib.md5(numpy.array([MJ, JK])).hexdigest() if not hasattr(self,'_vol_MJ_hash') or new_hash != self._vol_MJ_hash \ or (hasattr(self,'_ndists_4vol') and (ndists != self._ndists_4vol or linearDist != self._linearDist_4vol)): # Need to update the effective-selection function if isinstance(self._dmap3d, mwdust.Zero): #easy bc same everywhere effsel_4vol = self(self._dists_4vol, self._ra_cen_4vol[0], self._dec_cen_4vol[0], MJ=MJ, JK=JK) self._effsel_4vol = numpy.tile(effsel_4vol, (len(self._ra_cen_4vol), 1)) else: # Need to treat each los separately if ncpu is None: self._effsel_4vol = numpy.empty( (len(self._ra_cen_4vol), len(self._dists_4vol))) for ii,(ra_cen, dec_cen) \ in enumerate(tqdm.tqdm(zip(self._ra_cen_4vol, self._dec_cen_4vol))): self._effsel_4vol[ii] = self(self._dists_4vol, ra_cen, dec_cen, MJ=MJ, JK=JK) else: multiOut= multi.parallel_map(\ lambda x: self(self._dists_4vol, self._ra_cen_4vol[x], self._dec_cen_4vol[x],MJ=MJ,JK=JK), range(len(self._ra_cen_4vol)), numcores=ncpu) self._effsel_4vol = numpy.array(multiOut) self._vol_MJ_hash = new_hash self._ndists_4vol = ndists self._linearDist_4vol = linearDist out = 0. if xyz: out= numpy.sum(\ self._effsel_4vol\ *vol_func(self._X_4vol,self._Y_4vol,self._Z_4vol)\ *self._tiled_dists3_4vol) else: out= numpy.sum(\ self._effsel_4vol\ *vol_func(self._ra_cen_4vol,self._dec_cen_4vol, self._dists_4vol)\ *self._tiled_dists3_4vol) if relative: if not hasattr(self, '_tgasEffSelUniform'): tgasSelUniform = tgasSelectUniform(comp=1.) self._tgasEffSelUniform = tgasEffectiveSelect(tgasSelUniform) true_volume = self._tgasEffSelUniform.volume(vol_func, xyz=xyz, ndists=ndists, linearDist=linearDist, relative=False) else: true_volume = 1. return out*healpy.nside2pixarea(_BASE_NSIDE)*self._deltadm_4vol\ /true_volume
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' ]) # More if appath._APOGEE_REDUX.lower() == 'l33': data = esutil.numpy_util.remove_fields(data, [ 'GAIA_SOURCE_ID', 'GAIA_PARALLAX', 'GAIA_PARALLAX_ERROR', 'GAIA_PMRA', 'GAIA_PMRA_ERROR', 'GAIA_PMDEC', 'GAIA_PMDEC_ERROR', 'GAIA_PHOT_G_MEAN_MAG', 'GAIA_PHOT_BP_MEAN_MAG', 'GAIA_PHOT_RP_MEAN_MAG', 'GAIA_RADIAL_VELOCITY', 'GAIA_RADIAL_VELOCITY_ERROR', 'GAIA_R_EST', 'GAIA_R_LO', 'GAIA_R_HI', 'TEFF_SPEC', 'LOGG_SPEC' ]) if not appath._APOGEE_REDUX.lower() == 'current' \ and not 'l3' 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 'l31' in appath._APOGEE_REDUX: logg = data['LOGG'] elif 'l30' in appath._APOGEE_REDUX: logg = data['LOGG'] elif appath._APOGEE_REDUX.lower() == 'current' \ or int(appath._APOGEE_REDUX[1:]) > 600: 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+0.1*('l31' in appath._APOGEE_REDUX or 'l33' in appath._APOGEE_REDUX) \ <= 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) RphiZ = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], Xsun=8.15, Zsun=0.0208) R = RphiZ[:, 0] phi = RphiZ[:, 1] Z = RphiZ[:, 2] data['RC_GALR'] = R data['RC_GALPHI'] = phi data['RC_GALZ'] = Z #Save fitswrite(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: fitswrite(savefilename, data, clobber=True) return None data = _add_proper_motions(data, savefilename) # Save fitswrite(savefilename, data, clobber=True) return None
def __init__(self,vxvv=None,uvw=False,lb=False, radec=False,vo=235.,ro=8.5,zo=0.025, solarmotion='hogg'): """ NAME: __init__ PURPOSE: Initialize an Orbit instance INPUT: vxvv - initial conditions 3D can be either 1) in Galactocentric cylindrical coordinates [R,vR,vT(,z,vz,phi)] 2) [ra,dec,d,mu_ra, mu_dec,vlos] in [deg,deg,kpc,mas/yr,mas/yr,km/s] (all J2000.0; mu_ra = mu_ra * cos dec) 3) [ra,dec,d,U,V,W] in [deg,deg,kpc,km/s,km/s,kms] 4) (l,b,d,mu_l, mu_b, vlos) in [deg,deg,kpc,mas/yr,mas/yr,km/s) (all J2000.0; mu_l = mu_l * cos b) 5) [l,b,d,U,V,W] in [deg,deg,kpc,km/s,km/s,kms] 4) and 5) also work when leaving out b and mu_b/W OPTIONAL INPUTS: radec - if True, input is 2) (or 3) above uvw - if True, velocities are UVW lb - if True, input is 4) or 5) above vo - circular velocity at ro ro - distance from vantage point to GC (kpc) zo - offset toward the NGP of the Sun wrt the plane (kpc) solarmotion - 'hogg' or 'dehnen', or 'schoenrich', or value in [-U,V,W] OUTPUT: instance HISTORY: 2010-07-20 - Written - Bovy (NYU) """ if isinstance(solarmotion,str) and solarmotion.lower() == 'hogg': vsolar= nu.array([-10.1,4.0,6.7])/vo elif isinstance(solarmotion,str) and solarmotion.lower() == 'dehnen': vsolar= nu.array([-10.,5.25,7.17])/vo elif isinstance(solarmotion,str) \ and solarmotion.lower() == 'schoenrich': vsolar= nu.array([-11.1,12.24,7.25])/vo else: vsolar= nu.array(solarmotion)/vo if radec or lb: if radec: l,b= coords.radec_to_lb(vxvv[0],vxvv[1],degree=True) elif len(vxvv) == 4: l, b= vxvv[0], 0. else: l,b= vxvv[0],vxvv[1] if uvw: X,Y,Z= coords.lbd_to_XYZ(l,b,vxvv[2],degree=True) vx= vxvv[3] vy= vxvv[4] vz= vxvv[5] else: if radec: pmll, pmbb= coords.pmrapmdec_to_pmllpmbb(vxvv[3],vxvv[4], vxvv[0],vxvv[1], degree=True) d, vlos= vxvv[2], vxvv[5] elif len(vxvv) == 4: pmll, pmbb= vxvv[2], 0. d, vlos= vxvv[1], vxvv[3] else: pmll, pmbb= vxvv[3], vxvv[4] d, vlos= vxvv[2], vxvv[5] X,Y,Z,vx,vy,vz= coords.sphergal_to_rectgal(l,b,d, vlos,pmll, pmbb, degree=True) X/= ro Y/= ro Z/= ro vx/= vo vy/= vo vz/= vo vsun= nu.array([0.,1.,0.,])+vsolar R, phi, z= coords.XYZ_to_galcencyl(X,Y,Z,Zsun=zo/ro) vR, vT,vz= coords.vxvyvz_to_galcencyl(vx,vy,vz, R,phi,z, vsun=vsun,galcen=True) if lb and len(vxvv) == 4: vxvv= [R,vR,vT,phi] else: vxvv= [R,vR,vT,z,vz,phi] self.vxvv= vxvv if len(vxvv) == 2: self._orb= linearOrbit(vxvv=vxvv) elif len(vxvv) == 3: self._orb= planarROrbit(vxvv=vxvv) elif len(vxvv) == 4: self._orb= planarOrbit(vxvv=vxvv) elif len(vxvv) == 5: self._orb= RZOrbit(vxvv=vxvv) elif len(vxvv) == 6: self._orb= FullOrbit(vxvv=vxvv)
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
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)
print("optical cut completed!") radeg = stars_categorized['ra'] decdeg = stars_categorized['dec'] pmra = stars_categorized['pmra'] pmdec = stars_categorized['pmdec'] pml, pmb = ProperMotionTransform(radeg, decdeg, pmra, pmdec) hipID = stars_categorized['hip'] ldeg = stars_categorized['l'] bdeg = stars_categorized['b'] plx = stars_categorized['parallax'] e_plx = stars_categorized['parallax_error'] XYZ = bovy_coords.lbd_to_XYZ(ldeg, bdeg, 1. / plx, degree=True) z_cyl = XYZ[:, 2] z_pc = XYZ[:, 2] * 1000. zRangeList_upper = zRangeList + zStepWidth / 2 zRangeList_lower = zRangeList - zStepWidth / 2 evfs_weight = np.array([]) for i, z_i in enumerate(z_cyl): zPosition = (z_i < zRangeList_upper) * (z_i >= zRangeList_lower) if np.sum(zPosition) == 0: print((z_i < zRangeList_upper), (z_i >= zRangeList_lower)) evfs_weight = np.append(evfs_weight, 1. / evfs_out[zPosition]) if len(evfs_weight) != len(z_cyl): print("Need a more inclusive evfs volumn! (larger z evfs range)")