def sky_coords(cluster): """Get the sky coordinates of every star in the cluster Parameters ---------- cluster : class StarCluster Returns ------- ra,dec,d0,pmra,pmdec,vr0 : float on-sky positions and velocities of cluster stars History ------- 2018 - Written - Webb (UofT) """ origin0 = cluster.origin if origin0 != "galaxy": cluster.to_galaxy() x0, y0, z0 = bovy_coords.galcenrect_to_XYZ(cluster.x, cluster.y, cluster.z, Xsun=8.0, Zsun=0.025).T vx0, vy0, vz0 = bovy_coords.galcenrect_to_vxvyvz( cluster.vx, cluster.vy, cluster.vz, Xsun=8.0, Zsun=0.025, vsun=[-11.1, 244.0, 7.25], ).T l0, b0, d0 = bovy_coords.XYZ_to_lbd(x0, y0, z0, degree=True).T ra, dec = bovy_coords.lb_to_radec(l0, b0, degree=True).T vr0, pmll0, pmbb0 = bovy_coords.vxvyvz_to_vrpmllpmbb(vx0, vy0, vz0, l0, b0, d0, degree=True).T pmra, pmdec = bovy_coords.pmllpmbb_to_pmrapmdec(pmll0, pmbb0, l0, b0, degree=True).T if origin0 == "centre": cluster.to_centre() elif origin0 == "cluster": cluster.to_cluster() return ra, dec, d0, pmra, pmdec, vr0
def convertHelioCentricToRADEC(xyzuvw_hc, kpc=False): """ Generate astrometry values from cartesian coordinates centred on sun Parameters ---------- xyzuvw_hc : [6] array [kpc, kpc, kpc, km/s, km/s, km/s] Returns ------- astrometry : [6] array [RA, DEC, pi, pm_ra, pm_dec, vr] """ # if not kpc: # xyzuvw_hc = xyzuvw_hc.copy() # xyzuvw_hc[:3] /= 1e3 logging.debug("Positions is: {}".format(xyzuvw_hc[:3])) logging.debug("Velocity is: {}".format(xyzuvw_hc[3:])) lbdist = convertHelioCentricTolbdist(xyzuvw_hc) radec = bovy_coords.lb_to_radec(lbdist[0], lbdist[1], degree=True) vrpmllpmbb = bovy_coords.vxvyvz_to_vrpmllpmbb(xyzuvw_hc[3], xyzuvw_hc[4], xyzuvw_hc[5], lbdist[0], lbdist[1], lbdist[2], degree=True) pmrapmdec = bovy_coords.pmllpmbb_to_pmrapmdec(vrpmllpmbb[1], vrpmllpmbb[2], lbdist[0], lbdist[1], degree=True) return [ radec[0], radec[1], 1.0 / lbdist[2], pmrapmdec[0], pmrapmdec[1], vrpmllpmbb[0] ]
def example_integrate_orbit(): # Use 'MWPotential2014' as an example my_potential = MWPotential2014 # integration time in units of (_R0/_V0) time_start = 0. time_end = 100. my_time_step = numpy.linspace(time_start, time_end, 1001) # read 6D astrometric data # (0) [mas] parallax # (1) [deg] ell # (2) [deg] b # (3) [km/s] heliocentric line-of-sight velocity # (4) [mas/yr] proper motion along ell direction (mu_ellstar = mu_ell * cos(b)) # (5) [mas/yr] proper motion along b direction (mu_b) my_filename = 'parallax_ell_b_heliocentricLineOfSightVelocity_properMotionEllStar_properMotionB.txt' my_file = open(my_filename, 'r') my_data = numpy.loadtxt(my_file, comments='#') my_parallax_mas = my_data[:, 0] my_ell_deg = my_data[:, 1] my_b_deg = my_data[:, 2] my_hlosv_kms = my_data[:, 3] my_muellstar_masyr = my_data[:, 4] my_mub_masyr = my_data[:, 5] # count sample size my_sample_size = len(my_ell_deg) for i in range(my_sample_size): print('star ID=%d' % (i)) # convert parallax to distance distance_kpc = 1. / my_parallax_mas[i] # convert (ell, b) to (RA, DEC) RA_deg, DEC_deg = bovy_coords.lb_to_radec(my_ell_deg[i], my_b_deg[i], degree=True, epoch=_my_epoch) # heliocentric line-of-sight velocity hlosv_kms = my_hlosv_kms[i] # convert (mu_ellstar, mu_b) to (mu_RAstar, mu_DEC) muRAstar_masyr, muDEC_masyr = bovy_coords.pmllpmbb_to_pmrapmdec( my_muellstar_masyr[i], my_mub_masyr[i], my_ell_deg[i], my_b_deg[i], degree=True, epoch=_my_epoch) # create orbit instance obs_6D = Orbit(vxvv=[ RA_deg, DEC_deg, distance_kpc, muRAstar_masyr, muDEC_masyr, hlosv_kms ], radec=True, ro=_R0, vo=_V0, zo=0., solarmotion=[_Usun, _Vsun, _Wsun]) # integrate orbit in my_potential obs_6D.integrate(my_time_step, my_potential) # file on which we write 6D data at each time step outfile_i = open("t_x_y_z_vx_vy_vz_R__orbitID%03d.txt" % (i), 'w') # For illustrative purpose, I use an explicit expression to access 6D data at each time. for j in range(len(my_time_step)): # Here I assume that # (a) Galactic Center is located at (x,y)=(0,0) kpc # (b) Sun is located at (x,y)=(-8,0) kpc # (c) Nearby disc stars with circular orbits move towards (vx,vy)=(0,220) km/s # This is why I add a minus sign (-) to x and vx. x = -obs_6D.x(my_time_step[j]) y = obs_6D.y(my_time_step[j]) z = obs_6D.z(my_time_step[j]) vx = -obs_6D.vx(my_time_step[j]) vy = obs_6D.vy(my_time_step[j]) vz = obs_6D.vz(my_time_step[j]) R = obs_6D.R(my_time_step[j]) printline = '%lf %lf %lf %lf %lf %lf %lf %lf\n' % ( my_time_step[j], x, y, z, vx, vy, vz, R) outfile_i.write(printline) # close file outfile_i.close() return None
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 propagate_initial(self, potential, index, dt=0.01 * u.Myr, size=100, deltav=5. * u.km / u.s, deltat=0.5 * u.Myr): ''' Propagates the index-th star and _size_ more stars with random spread in initial velocity and flight time dictated by deltav, deltat. This overwrites the original catalogue! Parameters ---------- index : int Index of the star to propagate this in. size : int dv, dt : Quantity Spread in initial velocity and See self.propagate() for the other parameters. ''' from galpy.orbit import Orbit from galpy.util.bovy_coords import pmllpmbb_to_pmrapmdec, lb_to_radec, vrpmllpmbb_to_vxvyvz, lbd_to_XYZ self.r0, self.theta0, self.phi0, self.v0, self.phiv0, self.thetav0, self.tflight, self.tage, self.m = np.ones(size)*self.r0[index], np.ones(size)*self.theta0[index], \ np.ones(size)*self.phi0[index], np.ones(size)*self.v0[index], np.ones(size)*self.phiv0[index], np.ones(size)*self.thetav0[index], \ np.ones(size)*self.tflight[index], np.ones(size)*self.tage[index], np.ones(size)*self.m[index] self.v0 = self.v0 + np.random.normal(size=size) * deltav self.tflight = self.tflight + np.random.normal(size=size) * deltat self.size = size # Integration time step self.dt = dt nsteps = np.ceil((self.tflight / self.dt).to('1').value) nsteps[nsteps < 100] = 100 # Initialize position in cylindrical coords rho = self.r0 * np.sin(self.theta0) z = self.r0 * np.cos(self.theta0) phi = self.phi0 #... and velocity vR = self.v0 * np.sin(self.thetav0) * np.cos(self.phiv0) vT = self.v0 * np.sin(self.thetav0) * np.sin(self.phiv0) vz = self.v0 * np.cos(self.thetav0) # Initialize empty arrays to save orbit data and integration steps self.pmll, self.pmbb, self.ll, self.bb, self.vlos, self.dist, self.energy_var = \ (np.zeros(self.size) for i in xrange(7)) self.orbits = [None] * self.size #Integration loop for the self.size orbits for i in xrange(self.size): ts = np.linspace(0, 1, nsteps[i]) * self.tflight[i] self.orbits[i] = Orbit( vxvv=[rho[i], vR[i], vT[i], z[i], vz[i], phi[i]], solarmotion=self.solarmotion) self.orbits[i].integrate(ts, potential, method='dopr54_c') # Export the final position self.dist[i], self.ll[i], self.bb[i], self.pmll[i], self.pmbb[i], self.vlos[i] = \ self.orbits[i].dist(self.tflight[i], use_physical=True), \ self.orbits[i].ll(self.tflight[i], use_physical=True), \ self.orbits[i].bb(self.tflight[i], use_physical=True), \ self.orbits[i].pmll(self.tflight[i], use_physical=True) , \ self.orbits[i].pmbb(self.tflight[i], use_physical=True) , \ self.orbits[i].vlos(self.tflight[i], use_physical=True) # Radial velocity and distance + distance modulus self.vlos, self.dist = self.vlos * u.km / u.s, self.dist * u.kpc # Sky coordinates and proper motion data = pmllpmbb_to_pmrapmdec( self.pmll, self.pmbb, self.ll, self.bb, degree=True) * u.mas / u.year self.pmra, self.pmdec = data[:, 0], data[:, 1] data = lb_to_radec(self.ll, self.bb, degree=True) * u.deg self.ra, self.dec = data[:, 0], data[:, 1] # Done propagating self.cattype = 1
def propagate(self, potential, dt=0.01 * u.Myr, threshold=None): ''' Propagates the sample in the Galaxy, changes cattype from 0 to 1. Parameters ---------- potential : galpy potential instance Potential instance of the galpy library used to integrate the orbits dt : Quantity Integration timestep. Defaults to 0.01 Myr threshold : float Maximum relative energy difference between the initial energy and the energy at any point needed to consider an integration step an energy outliar. E.g. for threshold=0.01, any excess or deficit of 1% (or more) of the initial energy is enough to be registered as outliar. A table E_data.fits is created in the working directory containing for every orbit the percentage of outliar points (pol) ''' from galpy.orbit import Orbit from galpy.util.bovy_coords import pmllpmbb_to_pmrapmdec, lb_to_radec, vrpmllpmbb_to_vxvyvz, lbd_to_XYZ if (self.cattype > 0): raise RuntimeError('This sample is already propagated!') if (threshold is None): check = False else: check = True # Integration time step self.dt = dt nsteps = np.ceil((self.tflight / self.dt).to('1').value) nsteps[nsteps < 100] = 100 # Initialize position in cylindrical coords rho = self.r0 * np.sin(self.theta0) z = self.r0 * np.cos(self.theta0) phi = self.phi0 #... and velocity vR = self.v0 * np.sin(self.thetav0) * np.cos(self.phiv0) vT = self.v0 * np.sin(self.thetav0) * np.sin(self.phiv0) vz = self.v0 * np.cos(self.thetav0) # Initialize empty arrays to save orbit data and integration steps self.pmll, self.pmbb, self.ll, self.bb, self.vlos, self.dist, self.energy_var = \ (np.zeros(self.size) for i in xrange(7)) self.orbits = [None] * self.size #Integration loop for the self.size orbits for i in xrange(self.size): ts = np.linspace(0, 1, nsteps[i]) * self.tflight[i] self.orbits[i] = Orbit( vxvv=[rho[i], vR[i], vT[i], z[i], vz[i], phi[i]], solarmotion=self.solarmotion) self.orbits[i].integrate(ts, potential, method='dopr54_c') # Export the final position self.dist[i], self.ll[i], self.bb[i], self.pmll[i], self.pmbb[i], self.vlos[i] = \ self.orbits[i].dist(self.tflight[i], use_physical=True), \ self.orbits[i].ll(self.tflight[i], use_physical=True), \ self.orbits[i].bb(self.tflight[i], use_physical=True), \ self.orbits[i].pmll(self.tflight[i], use_physical=True) , \ self.orbits[i].pmbb(self.tflight[i], use_physical=True) , \ self.orbits[i].vlos(self.tflight[i], use_physical=True) # Energy check if (check): energy_array = self.orbits[i].E(ts) idx_energy = np.absolute(energy_array / energy_array[0] - 1) > threshold self.energy_var[i] = float( idx_energy.sum()) / nsteps[i] # percentage of outliars # Radial velocity and distance + distance modulus self.vlos, self.dist = self.vlos * u.km / u.s, self.dist * u.kpc # Sky coordinates and proper motion data = pmllpmbb_to_pmrapmdec( self.pmll, self.pmbb, self.ll, self.bb, degree=True) * u.mas / u.year self.pmra, self.pmdec = data[:, 0], data[:, 1] data = lb_to_radec(self.ll, self.bb, degree=True) * u.deg self.ra, self.dec = data[:, 0], data[:, 1] # Done propagating self.cattype = 1 # Save the energy check information if (check): from astropy.table import Table e_data = Table([self.m, self.tflight, self.energy_var], names=['m', 'tflight', 'pol']) e_data.write('E_data.fits', overwrite=True)
def to_radec(cluster, do_order=False, do_key_params=False, ro=8.0, vo=220.0): """Convert to 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) ro : float galpy radius scaling parameter vo : float galpy velocity scaling parameter Returns ------- None History: ------- 2018 - Written - Webb (UofT) """ if len(cluster.ra) == len(cluster.x): cluster.x = copy(cluster.ra) cluster.y = copy(cluster.dec) cluster.z = copy(cluster.dist) cluster.vx = copy(cluster.pmra) cluster.vy = copy(cluster.pmdec) cluster.vz = copy(cluster.vlos) cluster.units = "radec" cluster.origin = "sky" else: units0, origin0 = cluster.units, cluster.origin cluster.to_galaxy() cluster.to_kpckms() x0, y0, z0 = bovy_coords.galcenrect_to_XYZ(cluster.x, cluster.y, cluster.z, Xsun=8.0, Zsun=0.025).T cluster.dist = np.sqrt(x0**2.0 + y0**2.0 + z0**2.0) vx0, vy0, vz0 = bovy_coords.galcenrect_to_vxvyvz( cluster.vx, cluster.vy, cluster.vz, Xsun=8.0, Zsun=0.025, vsun=[-11.1, 244.0, 7.25], ).T cluster.vlos = (vx0 * x0 + vy0 * y0 + vz0 * z0) / np.sqrt(x0**2.0 + y0**2.0 + z0**2.0) l0, b0, cluster.dist = bovy_coords.XYZ_to_lbd(x0, y0, z0, degree=True).T cluster.ra, cluster.dec = bovy_coords.lb_to_radec(l0, b0, degree=True).T vr0, pmll0, pmbb0 = bovy_coords.vxvyvz_to_vrpmllpmbb(vx0, vy0, vz0, l0, b0, cluster.dist, degree=True).T cluster.pmra, cluster.pmdec = bovy_coords.pmllpmbb_to_pmrapmdec( pmll0, pmbb0, l0, b0, degree=True).T x0, y0, z0 = bovy_coords.galcenrect_to_XYZ(cluster.xgc, cluster.ygc, cluster.zgc, Xsun=8.0, Zsun=0.025) vx0, vy0, vz0 = bovy_coords.galcenrect_to_vxvyvz( cluster.vxgc, cluster.vygc, cluster.vzgc, Xsun=8.0, Zsun=0.025, vsun=[-11.1, 244.0, 7.25], ) cluster.vlos_gc = (vx0 * x0 + vy0 * y0 + vz0 * z0) / np.sqrt(x0**2.0 + y0**2.0 + z0**2.0) l0, b0, cluster.dist_gc = bovy_coords.XYZ_to_lbd(x0, y0, z0, degree=True) cluster.ra_gc, cluster.dec_gc = bovy_coords.lb_to_radec(l0, b0, degree=True) vr0, pmll0, pmbb0 = bovy_coords.vxvyvz_to_vrpmllpmbb(vx0, vy0, vz0, l0, b0, cluster.dist_gc, degree=True) cluster.pmra_gc, cluster.pmdec_gc = bovy_coords.pmllpmbb_to_pmrapmdec( pmll0, pmbb0, l0, b0, degree=True) cluster.x = copy(cluster.ra) cluster.y = copy(cluster.dec) cluster.z = copy(cluster.dist) cluster.vx = copy(cluster.pmra) cluster.vy = copy(cluster.pmdec) cluster.vz = copy(cluster.vlos) cluster.xgc = copy(cluster.ra_gc) cluster.ygc = copy(cluster.dec_gc) cluster.zgc = copy(cluster.dist_gc) cluster.vxgc = copy(cluster.pmra_gc) cluster.vygc = copy(cluster.pmdec_gc) cluster.vzgc = copy(cluster.vlos_gc) cluster.units = "radec" cluster.origin = "sky" cluster.rv3d() if do_key_params: cluster.key_params(do_order=do_order)
def main(args: Optional[list] = None, opts: Optional[Namespace] = None): """Fit MWPotential2014 Script Function. Parameters ---------- args : list, optional an optional single argument that holds the sys.argv list, except for the script name (e.g., argv[1:]) opts : Namespace, optional pre-constructed results of parsed args if not None, used ONLY if args is None """ if opts is not None and args is None: pass else: if opts is not None: warnings.warn("Not using `opts` because `args` are given") parser = make_parser() opts = parser.parse_args(args) fpath = opts.fpath + "/" if not opts.fpath.endswith("/") else opts.fpath opath = opts.opath + "/" if not opts.opath.endswith("/") else opts.opath # plot chains print("Plotting Chains") fig = mcmc_util.plot_chains(opath) fig.savefig(fpath + "chains.pdf") plt.close(fig) print("Need to continue chains:", end=" ") for i in range(32): ngood = mcmc_util.determine_nburn( filename=opath + f"mwpot14-fitsigma-{i:02}.dat", return_nsamples=True, ) if ngood < 4000: print(f"{i:02} (N={ngood})", end=", ") ############################################################### # RESULTING PDFS data, _, weights, _ = read_mcmc(nburn=None, skip=1, evi_func=lambda x: 1.0) plot_corner(data, weights=weights) plt.savefig("figures/PDFs/corner.pdf") plt.close() # -------------- savefilename = "output/pal5_forces_mcmc.pkl" if not os.path.exists(savefilename): data_wf, index_wf, weights_wf, evi_wf = read_mcmc( nburn=None, addforces=True, skip=1, evi_func=lambda x: 1.0 ) save_pickles(savefilename, data_wf, index_wf, weights_wf, evi_wf) else: with open(savefilename, "rb") as savefile: data_wf = pickle.load(savefile) index_wf = pickle.load(savefile) weights_wf = pickle.load(savefile) evi_wf = pickle.load(savefile) # -------------- plot_corner(data_wf, weights=weights_wf, addvcprior=False) plt.savefig("figures/PDFs/corner_wf.pdf") plt.close() # -------------- # Which potential is preferred? data_noforce, potindx, weights, evidences = read_mcmc(evi_func=evi_harmonic) fig = plt.figure(figsize=(6, 4)) bovy_plot.bovy_plot( potindx, np.log(evidences), "o", xrange=[-1, 34], yrange=[-35, -22], xlabel=r"$\mathrm{Potential\ index}$", ylabel=r"$\ln\ \mathrm{evidence}$", ) data_noforce, potindx, weights, evidences = read_mcmc(evi_func=evi_laplace) bovy_plot.bovy_plot(potindx, np.log(evidences) - 30.0, "d", overplot=True) data_noforce, potindx, weights, evidences = read_mcmc( evi_func=lambda x: np.exp(np.amax(x[:, -1])) ) bovy_plot.bovy_plot(potindx, np.log(evidences) - 8.0, "s", overplot=True) data_noforce, potindx, weights, evidences = read_mcmc( evi_func=lambda x: np.exp(-25.0) if (np.log(evi_harmonic(x)) > -25.0) else np.exp(-50.0) ) bovy_plot.bovy_plot(potindx, np.log(evidences), "o", overplot=True) plt.savefig("figures/PDFs/preferred_pot.pdf") plt.close() ############################################################### # Look at the results for individual potentials # -------------- # The flattening $c$ npot = 32 nwalkers = 12 plt.figure(figsize=(16, 6)) cmap = cm.plasma maxl = np.zeros((npot, 2)) for en, ii in enumerate(range(npot)): data_ip, _, weights_ip, evi_ip = read_mcmc(singlepot=ii, evi_func=evi_harmonic) try: maxl[en, 0] = np.amax(data_ip[:, -1]) maxl[en, 1] = np.log(evi_ip[0]) except ValueError: maxl[en] = -10000000.0 plt.subplot(2, 4, en // 4 + 1) bovy_plot.bovy_hist( data_ip[:, 0], range=[0.5, 2.0], bins=26, histtype="step", color=cmap((en % 4) / 3.0), normed=True, xlabel=r"$c$", lw=1.5, overplot=True, ) if en % 4 == 0: bovy_plot.bovy_text( r"$\mathrm{Potential\ %i\ to\ % i}$" % (en, en + 3), size=17.0, top_left=True, ) plt.tight_layout() plt.savefig("figures/flattening_c.pdf") plt.close() ############################################################### # What is the effective prior in $(F_R,F_Z)$? frfzprior_savefilename = "frfzprior.pkl" if not os.path.exists(frfzprior_savefilename): # Compute for each potential separately nvoc = 10000 ro = 8.0 npot = 32 fs = np.zeros((2, nvoc, npot)) for en, ii in tqdm(enumerate(range(npot))): fn = f"output/fitsigma/mwpot14-fitsigma-{i:02}.dat" # Read the potential parameters with open(fn, "r") as savefile: line1 = savefile.readline() potparams = [float(s) for s in (line1.split(":")[1].split(","))] for jj in range(nvoc): c = np.random.uniform() * 1.5 + 0.5 tvo = np.random.uniform() * 50.0 + 200.0 pot = mw_pot.setup_potential(potparams, c, False, False, REFR0, tvo) fs[:, jj, ii] = np.array(pal5_util.force_pal5(pot, 23.46, REFR0, tvo))[ :2 ] save_pickles(frfzprior_savefilename, fs) else: with open(frfzprior_savefilename, "rb") as savefile: fs = pickle.load(savefile) # -------------- plt.figure(figsize=(6, 6)) bovy_plot.scatterplot( fs[0].flatten(), fs[1].flatten(), "k,", xrange=[-1.75, -0.25], yrange=[-2.5, -1.2], xlabel=r"$F_R(\mathrm{Pal\ 5})$", ylabel=r"$F_Z(\mathrm{Pal\ 5})$", onedhists=True, ) bovy_plot.scatterplot( data_wf[:, 7], data_wf[:, 8], weights=weights_wf, bins=26, xrange=[-1.75, -0.25], yrange=[-2.5, -1.2], justcontours=True, cntrcolors="w", overplot=True, onedhists=True, ) plt.axvline(-0.81, color=sns.color_palette()[0]) plt.axhline(-1.85, color=sns.color_palette()[0]) plt.savefig("figures/effective_force_prior.pdf") plt.close() # -------------- # The ratio of the posterior and the prior bovy_plot.bovy_print( axes_labelsize=17.0, text_fontsize=12.0, xtick_labelsize=14.0, ytick_labelsize=14.0, ) plt.figure(figsize=(12.5, 4)) def axes_white(): for k, spine in plt.gca().spines.items(): # ax.spines is a dictionary spine.set_color("w") plt.gca().tick_params(axis="x", which="both", colors="w") plt.gca().tick_params(axis="y", which="both", colors="w") [t.set_color("k") for t in plt.gca().xaxis.get_ticklabels()] [t.set_color("k") for t in plt.gca().yaxis.get_ticklabels()] return None bins = 32 trange = [[-1.75, -0.25], [-2.5, -1.2]] tw = copy.deepcopy(weights_wf) tw[index_wf == 14] = 0.0 # Didn't converge properly H_prior, xedges, yedges = np.histogram2d( fs[0].flatten(), fs[1].flatten(), bins=bins, range=trange, normed=True ) H_post, xedges, yedges = np.histogram2d( data_wf[:, 7], data_wf[:, 8], weights=tw, bins=bins, range=trange, normed=True, ) H_like = H_post / H_prior H_like[H_prior == 0.0] = 0.0 plt.subplot(1, 3, 1) bovy_plot.bovy_dens2d( H_prior.T, origin="lower", cmap="viridis", interpolation="nearest", xrange=[xedges[0], xedges[-1]], yrange=[yedges[0], yedges[-1]], xlabel=r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$", ylabel=r"$F_Z(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$", gcf=True, ) bovy_plot.bovy_text(r"$\mathbf{Prior}$", top_left=True, size=19.0, color="w") axes_white() plt.subplot(1, 3, 2) bovy_plot.bovy_dens2d( H_post.T, origin="lower", cmap="viridis", interpolation="nearest", xrange=[xedges[0], xedges[-1]], yrange=[yedges[0], yedges[-1]], xlabel=r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$", gcf=True, ) bovy_plot.bovy_text(r"$\mathbf{Posterior}$", top_left=True, size=19.0, color="w") axes_white() plt.subplot(1, 3, 3) bovy_plot.bovy_dens2d( H_like.T, origin="lower", cmap="viridis", interpolation="nearest", vmin=0.1, vmax=4.0, xrange=[xedges[0], xedges[-1]], yrange=[yedges[0], yedges[-1]], xlabel=r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$", gcf=True, ) bovy_plot.bovy_text(r"$\mathbf{Likelihood}$", top_left=True, size=19.0, color="w") axes_white() def qline(FR, q=0.95): return 2.0 * FR / q ** 2.0 q = 0.94 plt.plot([-1.25, -0.2], [qline(-1.25, q=q), qline(-0.2, q=q)], "w--") bovy_plot.bovy_text(-1.7, -2.2, r"$q_\Phi = 0.94$", size=16.0, color="w") plt.plot((-1.25, -1.02), (-2.19, qline(-1.02, q=q)), "w-", lw=0.8) plt.tight_layout() plt.savefig( "figures/pal5post.pdf", bbox_inches="tight", ) plt.close() # -------------- # Projection onto the direction perpendicular to constant $q = 0.94$: frs = np.tile(0.5 * (xedges[:-1] + xedges[1:]), (len(yedges) - 1, 1)).T fzs = np.tile(0.5 * (yedges[:-1] + yedges[1:]), (len(xedges) - 1, 1)) plt.figure(figsize=(6, 4)) txlabel = r"$F_\perp$" dum = bovy_plot.bovy_hist( (-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten(), weights=H_prior.flatten(), bins=21, histtype="step", lw=2.0, xrange=[-1.5, 1.5], xlabel=txlabel, normed=True, ) dum = bovy_plot.bovy_hist( (-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten(), weights=H_post.flatten(), bins=21, histtype="step", lw=2.0, overplot=True, xrange=[-1.5, 1.5], normed=True, ) dum = bovy_plot.bovy_hist( (-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten(), weights=H_like.flatten(), bins=21, histtype="step", lw=2.0, overplot=True, xrange=[-1.5, 1.5], normed=True, ) plt.savefig("figures/PDFs/projection_perp_to_q0p94.pdf") plt.close() mq = ( np.sum( (-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten() * H_like.flatten() ) ) / np.sum(H_like.flatten()) print( mq, np.sqrt( ( np.sum( ((-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten() - mq) ** 2.0 * H_like.flatten() ) ) / np.sum(H_like.flatten()) ), ) # -------------- # Projection onto the direction parallel to constant $q = 0.94$: frs = np.tile(0.5 * (xedges[:-1] + xedges[1:]), (len(yedges) - 1, 1)).T fzs = np.tile(0.5 * (yedges[:-1] + yedges[1:]), (len(xedges) - 1, 1)) plt.figure(figsize=(6, 4)) txlabel = r"$F_\parallel$" dum = bovy_plot.bovy_hist( (0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten(), weights=H_prior.flatten(), bins=21, histtype="step", lw=2.0, xrange=[-2.5, 2.5], xlabel=txlabel, normed=True, ) dum = bovy_plot.bovy_hist( (0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten(), weights=H_post.flatten(), bins=21, histtype="step", lw=2.0, overplot=True, xrange=[-2.5, 2.5], normed=True, ) dum = bovy_plot.bovy_hist( (0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten(), weights=H_like.flatten(), bins=21, histtype="step", lw=2.0, overplot=True, xrange=[-2.5, 2.5], normed=True, ) plt.savefig("figures/PDFs/projection_prll_to_q0p94.pdf") plt.close() mq = ( np.sum( (0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten() * H_like.flatten() ) ) / np.sum(H_like.flatten()) print( mq, np.sqrt( ( np.sum( ((0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten() - mq) ** 2.0 * H_like.flatten() ) ) / np.sum(H_like.flatten()) ), ) # -------------- # Thus, there is only a weak constraint on $F_\parallel$. nrow = int(np.ceil(npot / 4.0)) plt.figure(figsize=(16, nrow * 4)) for en, ii in enumerate(range(npot)): plt.subplot(nrow, 4, en + 1) if ii % 4 == 0: tylabel = r"$F_Z(\mathrm{Pal\ 5})$" else: tylabel = None if ii // 4 == nrow - 1: txlabel = r"$F_R(\mathrm{Pal\ 5})$" else: txlabel = None bovy_plot.scatterplot( fs[0][:, en], fs[1][:, en], "k,", xrange=[-1.75, -0.25], yrange=[-2.5, -1.0], xlabel=txlabel, ylabel=tylabel, gcf=True, ) bovy_plot.scatterplot( data_wf[:, 7], data_wf[:, 8], weights=weights_wf, bins=26, xrange=[-1.75, -0.25], yrange=[-2.5, -1.0], justcontours=True, cntrcolors="w", overplot=True, ) bovy_plot.scatterplot( data_wf[index_wf == ii, 7], data_wf[index_wf == ii, 8], weights=weights_wf[index_wf == ii], bins=26, xrange=[-1.75, -0.25], yrange=[-2.5, -1.0], justcontours=True, # cntrcolors=sns.color_palette()[2], overplot=True, ) plt.axvline(-0.80, color=sns.color_palette()[0]) plt.axhline(-1.83, color=sns.color_palette()[0]) bovy_plot.bovy_text(r"$\mathrm{Potential}\ %i$" % ii, size=17.0, top_left=True) plt.savefig("figures/PDFs/constain_F_prll.pdf") plt.close() # -------------- # Let's plot four representative ones for the paper: bovy_plot.bovy_print( axes_labelsize=17.0, text_fontsize=12.0, xtick_labelsize=14.0, ytick_labelsize=14.0, ) nullfmt = NullFormatter() nrow = 1 plt.figure(figsize=(15, nrow * 4)) for en, ii in enumerate([0, 15, 24, 25]): plt.subplot(nrow, 4, en + 1) if en % 4 == 0: tylabel = ( r"$F_Z(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$" ) else: tylabel = None if en // 4 == nrow - 1: txlabel = ( r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$" ) else: txlabel = None bovy_plot.scatterplot( fs[0][:, ii], fs[1][:, ii], "k,", bins=31, xrange=[-1.75, -0.25], yrange=[-2.5, -1.0], xlabel=txlabel, ylabel=tylabel, gcf=True, ) bovy_plot.scatterplot( data_wf[:, 7], data_wf[:, 8], weights=weights_wf, bins=21, xrange=[-1.75, -0.25], yrange=[-2.5, -1.0], justcontours=True, cntrcolors=sns.color_palette("colorblind")[2], cntrls="--", cntrlw=2.0, overplot=True, ) bovy_plot.scatterplot( data_wf[index_wf == ii, 7], data_wf[index_wf == ii, 8], weights=weights_wf[index_wf == ii], bins=21, xrange=[-1.75, -0.25], yrange=[-2.5, -1.0], justcontours=True, cntrcolors=sns.color_palette("colorblind")[0], cntrlw=2.5, overplot=True, ) if en > 0: plt.gca().yaxis.set_major_formatter(nullfmt) plt.tight_layout() plt.savefig( "figures/pal5post_examples.pdf", bbox_inches="tight", ) plt.close() ############################################################### # What about $q_\Phi$? bins = 47 plt.figure(figsize=(6, 4)) dum = bovy_plot.bovy_hist( np.sqrt(2.0 * fs[0].flatten() / fs[1].flatten()), histtype="step", lw=2.0, bins=bins, xlabel=r"$q_\mathrm{\Phi}$", xrange=[0.7, 1.25], normed=True, ) dum = bovy_plot.bovy_hist( np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]), weights=weights_wf, histtype="step", lw=2.0, bins=bins, overplot=True, xrange=[0.7, 1.25], normed=True, ) mq = np.sum( np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]) * weights_wf ) / np.sum(weights_wf) sq = np.sqrt( np.sum( (np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]) - mq) ** 2.0 * weights_wf ) / np.sum(weights_wf) ) print("From posterior samples: q = %.3f +/- %.3f" % (mq, sq)) Hq_post, xedges = np.histogram( np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]), weights=weights_wf, bins=bins, range=[0.7, 1.25], normed=True, ) Hq_prior, xedges = np.histogram( np.sqrt(2.0 * fs[0].flatten() / fs[1].flatten()), bins=bins, range=[0.7, 1.25], normed=True, ) qs = 0.5 * (xedges[:-1] + xedges[1:]) Hq_like = Hq_post / Hq_prior Hq_like[Hq_post == 0.0] = 0.0 mq = np.sum(qs * Hq_like) / np.sum(Hq_like) sq = np.sqrt(np.sum((qs - mq) ** 2.0 * Hq_like) / np.sum(Hq_like)) print("From likelihood of samples: q = %.3f +/- %.3f" % (mq, sq)) plt.savefig("figures/q_phi.pdf") plt.close() # It appears that $q_\Phi$ is the quantity that is the most strongly constrained by the Pal 5 data. ############################################################### # A sampling of tracks from the MCMC savefilename = "mwpot14-pal5-mcmcTracks.pkl" pmdecpar = 2.257 / 2.296 pmdecperp = -2.296 / 2.257 if os.path.exists(savefilename): with open(savefilename, "rb") as savefile: pal5_track_samples = pickle.load(savefile) forces = pickle.load(savefile) all_potparams = pickle.load(savefile) all_params = pickle.load(savefile) else: np.random.seed(1) ntracks = 21 multi = 8 pal5_track_samples = np.zeros((ntracks, 2, 6, pal5varyc[0].shape[1])) forces = np.zeros((ntracks, 2)) all_potparams = np.zeros((ntracks, 5)) all_params = np.zeros((ntracks, 7)) for ii in range(ntracks): # Pick a random potential from among the set, but leave 14 out pindx = 14 while pindx == 14: pindx = np.random.permutation(32)[0] # Load this potential fn = f"output/fitsigma/mwpot14-fitsigma-{pindx:02}.dat" with open(fn, "r") as savefile: line1 = savefile.readline() potparams = [float(s) for s in (line1.split(":")[1].split(","))] all_potparams[ii] = potparams # Now pick a random sample from this MCMC chain tnburn = mcmc_util.determine_nburn(fn) tdata = np.loadtxt(fn, comments="#", delimiter=",") tdata = tdata[tnburn::] tdata = tdata[np.random.permutation(len(tdata))[0]] all_params[ii] = tdata tvo = tdata[1] * REFV0 pot = mw_pot.setup_potential(potparams, tdata[0], False, False, REFR0, tvo) forces[ii, :] = pal5_util.force_pal5(pot, 23.46, REFR0, tvo)[:2] # Now compute the stream model for this setup dist = tdata[2] * 22.0 pmra = -2.296 + tdata[3] + tdata[4] pmdecpar = 2.257 / 2.296 pmdecperp = -2.296 / 2.257 pmdec = -2.257 + tdata[3] * pmdecpar + tdata[4] * pmdecperp vlos = -58.7 sigv = 0.4 * np.exp(tdata[5]) prog = Orbit( [229.018, -0.124, dist, pmra, pmdec, vlos], radec=True, ro=REFR0, vo=tvo, solarmotion=[-11.1, 24.0, 7.25], ) tsdf_trailing, tsdf_leading = pal5_util.setup_sdf( pot, prog, sigv, 10.0, REFR0, tvo, multi=multi, nTrackChunks=8, trailing_only=False, verbose=True, useTM=False, ) # Compute the track for jj, sdf in enumerate([tsdf_trailing, tsdf_leading]): trackRADec = bovy_coords.lb_to_radec( sdf._interpolatedObsTrackLB[:, 0], sdf._interpolatedObsTrackLB[:, 1], degree=True, ) trackpmRADec = bovy_coords.pmllpmbb_to_pmrapmdec( sdf._interpolatedObsTrackLB[:, 4], sdf._interpolatedObsTrackLB[:, 5], sdf._interpolatedObsTrackLB[:, 0], sdf._interpolatedObsTrackLB[:, 1], degree=True, ) # Store the track pal5_track_samples[ii, jj, 0] = trackRADec[:, 0] pal5_track_samples[ii, jj, 1] = trackRADec[:, 1] pal5_track_samples[ii, jj, 2] = sdf._interpolatedObsTrackLB[:, 2] pal5_track_samples[ii, jj, 3] = sdf._interpolatedObsTrackLB[:, 3] pal5_track_samples[ii, jj, 4] = trackpmRADec[:, 0] pal5_track_samples[ii, jj, 5] = trackpmRADec[:, 1] save_pickles( savefilename, pal5_track_samples, forces, all_potparams, all_params ) # -------------- bovy_plot.bovy_print( axes_labelsize=17.0, text_fontsize=12.0, xtick_labelsize=14.0, ytick_labelsize=14.0, ) plt.figure(figsize=(12, 8)) gs = gridspec.GridSpec(2, 2, wspace=0.225, hspace=0.1, right=0.94) ntracks = pal5_track_samples.shape[0] cmap = cm.plasma alpha = 0.7 for ii in range(ntracks): tc = cmap((forces[ii, 1] + 2.5) / 1.0) for jj in range(2): # RA, Dec plt.subplot(gs[0]) plt.plot( pal5_track_samples[ii, jj, 0], pal5_track_samples[ii, jj, 1], "-", color=tc, alpha=alpha, ) # RA, Vlos plt.subplot(gs[1]) plt.plot( pal5_track_samples[ii, jj, 0], pal5_track_samples[ii, jj, 3], "-", color=tc, alpha=alpha, ) # RA, Dist plt.subplot(gs[2]) plt.plot( pal5_track_samples[ii, jj, 0], pal5_track_samples[ii, jj, 2] - all_params[ii, 2] * 22.0, "-", color=tc, alpha=alpha, ) # RA, pm_parallel plt.subplot(gs[3]) plt.plot( pal5_track_samples[ii, jj, 0, : 500 + 500 * (1 - jj)], np.sqrt(1.0 + (2.257 / 2.296) ** 2.0) * ( (pal5_track_samples[ii, jj, 4, : 500 + 500 * (1 - jj)] + 2.296) * pmdecperp - (pal5_track_samples[ii, jj, 5, : 500 + 500 * (1 - jj)] + 2.257) ) / (pmdecpar - pmdecperp), "-", color=tc, alpha=alpha, ) plot_data_add_labels( p1=(gs[0],), p2=(gs[1],), noxlabel_dec=True, noxlabel_vlos=True ) plt.subplot(gs[2]) plt.xlim(250.0, 221.0) plt.ylim(-3.0, 1.5) bovy_plot._add_ticks() plt.xlabel(r"$\mathrm{RA}\,(\mathrm{degree})$") plt.ylabel(r"$\Delta\mathrm{Distance}\,(\mathrm{kpc})$") plt.subplot(gs[3]) plt.xlim(250.0, 221.0) plt.ylim(-0.5, 0.5) bovy_plot._add_ticks() plt.xlabel(r"$\mathrm{RA}\,(\mathrm{degree})$") plt.ylabel(r"$\Delta\mu_\parallel\,(\mathrm{mas\,yr}^{-1})$") # Colorbar gs2 = gridspec.GridSpec(2, 1, wspace=0.0, left=0.95, right=0.975) plt.subplot(gs2[:, -1]) sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=-2.5, vmax=-1.5)) sm._A = [] CB1 = plt.colorbar(sm, orientation="vertical", cax=plt.gca()) CB1.set_label( r"$F_Z(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$", fontsize=18.0, ) plt.savefig( "figures/pal5tracksamples.pdf", bbox_inches="tight", ) plt.close() ############################################################### # What about Kuepper et al. (2015)? # Can we recover the Kuepper et al. (2015) result as prior + $q_\Phi = # 0.94 \pm 0.05$ + $V_c(R_0)$ between 200 and 280 km/s? For simplicity # we will not vary $R_0$, which should not have a big impact. s = sample_kuepper_flattening_post(50000, 0.94, 0.05) plot_kuepper_samples(s) plt.savefig("figures/kuepper_samples.pdf") plt.close() # The constraint on the potential flattening gets you far, but there # is more going on (the constraint on the halo mass and scale # parameter appear to come completely from the $V_c(R_0)$ constraint). # Let's add the weak constraint on the sum of the forces, scaled to # Kuepper et al.'s best-fit acceleration (which is higher than ours): s = sample_kuepper_flatteningforce_post(50000, 0.94, 0.05, -0.83, 0.36) plot_kuepper_samples(s) plt.savefig("figures/kuepper_samples_flatF.pdf") plt.close() # This gets a tight relation between $M_\mathrm{halo}$ and the scale # parameter of the halo, but does not lead to a constraint on either # independently; the halo potential flattening constraint is that of # Kuepper et al. Based on this, it appears that the information that # ties down $M_\mathrm{halo}$ comes from the overdensities, which may # be spurious (Thomas et al. 2016) and whose use in dynamical modeling # is dubious anyway. # What is Kuepper et al.'s prior on $a_{\mathrm{Pal\ 5}}$? apal5s = [] ns = 100000 for ii in range(ns): Mh = np.random.uniform() * (10.0 - 0.001) + 0.001 a = np.random.uniform() * (100.0 - 0.1) + 0.1 q = np.random.uniform() * (1.8 - 0.2) + 0.2 pot = setup_potential_kuepper(Mh, a) FR, FZ = force_pal5_kuepper(pot, q) apal5s.append(np.sqrt(FR ** 2.0 + FZ ** 2.0)) apal5s = np.array(apal5s) plt.figure(figsize=(6, 4)) dum = bovy_plot.bovy_hist( apal5s, range=[0.0, 10.0], lw=2.0, bins=51, histtype="step", normed=True, ) plt.savefig("figures/kuepper_samples_prior.pdf") plt.close()