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 obs_to_galcen(ra, dec, dist, pmra, pmdec, rv, ro=_R0, vo=_v0, zo=_z0): vxvv = np.dstack([ra, dec, dist, pmra, pmdec, rv])[0] ra, dec = vxvv[:, 0], vxvv[:, 1] lb = bovy_coords.radec_to_lb(ra, dec, degree=True) pmra, pmdec = vxvv[:, 3], vxvv[:, 4] pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(pmra, pmdec, ra, dec, degree=True) d, vlos = vxvv[:, 2], vxvv[:, 5] rectgal = bovy_coords.sphergal_to_rectgal(lb[:, 0], lb[:, 1], d, vlos, pmllpmbb[:, 0], pmllpmbb[:, 1], degree=True) vsolar = np.array([-11.1, 245.7, 7.25]) vsun = vsolar / vo X = rectgal[:, 0] / ro Y = rectgal[:, 1] / ro Z = rectgal[:, 2] / ro vx = rectgal[:, 3] / vo vy = rectgal[:, 4] / vo vz = rectgal[:, 5] / vo XYZ = np.dstack([X, Y, Z])[0] vxyz = np.dstack([vx, vy, vz])[0] Rpz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], Zsun=zo / ro) vRvTvz = bovy_coords.vxvyvz_to_galcencyl(vxyz[:, 0], vxyz[:, 1], vxyz[:, 2], Rpz[:, 0], Rpz[:, 1], Rpz[:, 2], vsun=vsun, Xsun=1., Zsun=zo / ro, galcen=True) return XYZ, vxyz, Rpz, vRvTvz
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 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 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 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 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 _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 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 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 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 __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 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 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 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 calc_eccentricity(args, options): table = os.path.join(args[0], 'table2.dat') readme = os.path.join(args[0], 'ReadMe') dierickx = ascii.read(table, readme=readme) vxvv = np.dstack([ dierickx['RAdeg'], dierickx['DEdeg'], dierickx['Dist'] / 1e3, dierickx['pmRA'], dierickx['pmDE'], dierickx['HRV'] ])[0] ro, vo, zo = 8., 220., 0.025 ra, dec = vxvv[:, 0], vxvv[:, 1] lb = bovy_coords.radec_to_lb(ra, dec, degree=True) pmra, pmdec = vxvv[:, 3], vxvv[:, 4] pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(pmra, pmdec, ra, dec, degree=True) d, vlos = vxvv[:, 2], vxvv[:, 5] rectgal = bovy_coords.sphergal_to_rectgal(lb[:, 0], lb[:, 1], d, vlos, pmllpmbb[:, 0], pmllpmbb[:, 1], degree=True) vsolar = np.array([-10.1, 4.0, 6.7]) vsun = np.array([ 0., 1., 0., ]) + vsolar / vo X = rectgal[:, 0] / ro Y = rectgal[:, 1] / ro Z = rectgal[:, 2] / ro vx = rectgal[:, 3] / vo vy = rectgal[:, 4] / vo vz = rectgal[:, 5] / vo vsun = np.array([ 0., 1., 0., ]) + vsolar / vo Rphiz = bovy_coords.XYZ_to_galcencyl(X, Y, Z, Zsun=zo / ro) vRvTvz = bovy_coords.vxvyvz_to_galcencyl(vx, vy, vz, Rphiz[:, 0], Rphiz[:, 1], Rphiz[:, 2], vsun=vsun, Xsun=1., Zsun=zo / ro, galcen=True) #do the integration and individual analytic estimate for each object ts = np.linspace(0., 20., 10000) lp = LogarithmicHaloPotential(normalize=1.) e_ana = numpy.zeros(len(vxvv)) e_int = numpy.zeros(len(vxvv)) print( 'Performing orbit integration and analytic parameter estimates for Dierickx et al. sample...' ) for i in tqdm(range(len(vxvv))): try: orbit = Orbit(vxvv[i], radec=True, vo=220., ro=8.) e_ana[i] = orbit.e(analytic=True, pot=lp, c=True) except UnboundError: e_ana[i] = np.nan orbit.integrate(ts, lp) e_int[i] = orbit.e(analytic=False) fig = plt.figure() fig.set_size_inches(1.5 * columnwidth, 1.5 * columnwidth) plt.scatter(e_int, e_ana, s=1, color='Black', lw=0.) plt.xlabel(r'$\mathrm{galpy\ integrated}\ e$') plt.ylabel(r'$\mathrm{galpy\ analytic}\ e$') plt.xlim(0., 1.) plt.ylim(0., 1.) fig.tight_layout() plt.savefig(os.path.join(args[0], 'dierickx-integratedeanalytice.png'), format='png', dpi=200) fig = plt.figure() fig.set_size_inches(1.5 * columnwidth, 1.5 * columnwidth) plt.hist(e_int, bins=30) plt.xlim(0., 1.) plt.xlabel(r'$\mathrm{galpy}\ e$') fig.tight_layout() plt.savefig(os.path.join(args[0], 'dierickx-integratedehist.png'), format='png', dpi=200) fig = plt.figure() fig.set_size_inches(1.5 * columnwidth, 1.5 * columnwidth) plt.scatter(dierickx['e'], e_int, s=1, color='Black', lw=0.) plt.xlabel(r'$\mathrm{Dierickx\ et\ al.}\ e$') plt.ylabel(r'$\mathrm{galpy\ integrated}\ e$') plt.xlim(0., 1.) plt.ylim(0., 1.) fig.tight_layout() plt.savefig(os.path.join(args[0], 'dierickx-integratedee.png'), format='png', dpi=200) fig = plt.figure() fig.set_size_inches(1.5 * columnwidth, 1.5 * columnwidth) plt.scatter(dierickx['e'], e_ana, s=1, color='Black', lw=0.) plt.xlabel(r'$\mathrm{Dierickx\ et\ al.}\ e$') plt.ylabel(r'$\mathrm{galpy\ estimated}\ e$') plt.xlim(0., 1.) plt.ylim(0., 1.) fig.tight_layout() plt.savefig(os.path.join(args[0], 'dierickx-analyticee.png'), format='png', dpi=200) arr = numpy.recarray(len(e_ana), dtype=[('analytic_e', float), ('integrated_e', float)]) arr['analytic_e'] = e_ana arr['integrated_e'] = e_int with open(os.path.join(args[0], 'eccentricities.dat'), 'w') as file: pickle.dump(arr, file) file.close()
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 dat_to_galcen( dat, return_cov=True, return_rphiz=True, verbose=False, ro=8., vo=220., zo=0.025, keys=['ra', 'dec', 'BPG_meandist', 'pmra', 'pmdec', 'VHELIO_AVG'], cov_keys=[ 'pmra_error', 'pmdec_error', 'pmra_pmdec_corr', 'BPG_diststd', 'VERR' ], parallax=False): vxvv = np.dstack([dat[keys[i]] for i in range(len(keys))])[0] ra, dec = vxvv[:, 0], vxvv[:, 1] lb = bovy_coords.radec_to_lb(ra, dec, degree=True) pmra, pmdec = vxvv[:, 3], vxvv[:, 4] pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(pmra, pmdec, ra, dec, degree=True) d, vlos = vxvv[:, 2], vxvv[:, 5] if parallax: d = 1. / d rectgal = bovy_coords.sphergal_to_rectgal(lb[:, 0], lb[:, 1], d, vlos, pmllpmbb[:, 0], pmllpmbb[:, 1], degree=True) vsolar = np.array([-11.1, 245.6, 7.25]) #use SBD10 vR and vZ and SGR proper motion vT vsun = np.array([ 0., 0., 0., ]) + vsolar / vo X = rectgal[:, 0] / ro Y = rectgal[:, 1] / ro Z = rectgal[:, 2] / ro vx = rectgal[:, 3] / vo vy = rectgal[:, 4] / vo vz = rectgal[:, 5] / vo XYZ = np.dstack([X, Y, Z])[0] vxyz = np.dstack([vx, vy, vz])[0] if return_rphiz: Rpz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], Zsun=zo / ro) vRvTvz = bovy_coords.vxvyvz_to_galcencyl(vxyz[:, 0], vxyz[:, 1], vxyz[:, 2], Rpz[:, 0], Rpz[:, 1], Rpz[:, 2], vsun=vsun, Xsun=1., Zsun=zo / ro, galcen=True) if return_cov == True: cov_pmradec = np.array([[[ dat[cov_keys[0]][i]**2, dat[cov_keys[2]][i] * dat[cov_keys[0]][i] * dat[cov_keys[1]][i] ], [ dat[cov_keys[2]][i] * dat[cov_keys[0]][i] * dat[cov_keys[1]][i], dat[cov_keys[1]][i]**2 ]] for i in range(len(dat))]) if verbose: print('propagating covariance in pmra pmdec -> pmll pmbb') cov_pmllbb = bovy_coords.cov_pmrapmdec_to_pmllpmbb(cov_pmradec, vxvv[:, 0], vxvv[:, 1], degree=True, epoch='J2015') if verbose: print('propagating covariance in pmll pmbb -> vx vy vz') cov_vxyz = bovy_coords.cov_dvrpmllbb_to_vxyz(vxvv[:, 2], dat[cov_keys[3]], dat[cov_keys[4]], pmllpmbb[:, 0], pmllpmbb[:, 1], cov_pmllbb, lb[:, 0], lb[:, 1]) if not return_rphiz: return XYZ, vxyz, cov_vxyz if verbose: print('propagating covariance in vx vy vz -> vR vT vz') cov_galcencyl = bovy_coords.cov_vxyz_to_galcencyl(cov_vxyz, Rpz[:, 1], Xsun=1., Zsun=zo / ro) return XYZ, vxyz, cov_vxyz, Rpz, vRvTvz, cov_galcencyl if not return_rphiz: return XYZ, vxyz return XYZ, vxyz, Rpz, vRvTvz
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 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 obs_to_galcen(ra, dec, dist, pmra, pmdec, rv, pmra_err, pmdec_err, pmra_pmdec_corr, dist_err, rv_err, return_cov=True, verbose=True, return_rphiz=True, ro=8., vo=220., zo=0.025, parallax=False): vxvv = np.dstack([ra, dec, dist, pmra, pmdec, rv])[0] ra, dec = vxvv[:, 0], vxvv[:, 1] lb = bovy_coords.radec_to_lb(ra, dec, degree=True) pmra, pmdec = vxvv[:, 3], vxvv[:, 4] pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(pmra, pmdec, ra, dec, degree=True) d, vlos = vxvv[:, 2], vxvv[:, 5] if parallax: d = 1. / d rectgal = bovy_coords.sphergal_to_rectgal(lb[:, 0], lb[:, 1], d, vlos, pmllpmbb[:, 0], pmllpmbb[:, 1], degree=True) vsolar = np.array([-10.1, 4.0, 6.7]) vsun = np.array([ 0., 1., 0., ]) + vsolar / vo X = rectgal[:, 0] / ro Y = rectgal[:, 1] / ro Z = rectgal[:, 2] / ro vx = rectgal[:, 3] / vo vy = rectgal[:, 4] / vo vz = rectgal[:, 5] / vo XYZ = np.dstack([X, Y, Z])[0] vxyz = np.dstack([vx, vy, vz])[0] if return_rphiz: Rpz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], Zsun=zo / ro) vRvTvz = bovy_coords.vxvyvz_to_galcencyl(vxyz[:, 0], vxyz[:, 1], vxyz[:, 2], Rpz[:, 0], Rpz[:, 1], Rpz[:, 2], vsun=vsun, Xsun=1., Zsun=zo / ro, galcen=True) if return_cov == True: cov_pmradec = np.empty([len(pmra_err), 2, 2]) cov_pmradec[:, 0, 0] = pmra_err**2 cov_pmradec[:, 1, 1] = pmdec_err**2 cov_pmradec[:, 0, 1] = pmra_pmdec_corr * pmra_err * pmdec_err cov_pmradec[:, 1, 0] = pmra_pmdec_corr * pmra_err * pmdec_err if verbose: print('propagating covariance in pmra pmdec -> pmll pmbb') cov_pmllbb = bovy_coords.cov_pmrapmdec_to_pmllpmbb(cov_pmradec, vxvv[:, 0], vxvv[:, 1], degree=True, epoch='J2015') if verbose: print('propagating covariance in pmll pmbb -> vx vy vz') cov_vxyz = bovy_coords.cov_dvrpmllbb_to_vxyz(vxvv[:, 2], dist_err, rv_err, pmllpmbb[:, 0], pmllpmbb[:, 1], cov_pmllbb, lb[:, 0], lb[:, 1]) if not return_rphiz: return XYZ, vxyz, cov_vxyz if verbose: print('propagating covariance in vx vy vz -> vR vT vz') cov_galcencyl = bovy_coords.cov_vxyz_to_galcencyl(cov_vxyz, Rpz[:, 1], Xsun=1., Zsun=zo / ro) return XYZ, vxyz, cov_vxyz, Rpz, vRvTvz, cov_galcencyl if not return_rphiz: return XYZ, vxyz return XYZ, vxyz, Rpz, vRvTvz