def time_test_cluster(): from popstar import synthetic as syn from popstar import atmospheres as atm from popstar import evolution from popstar import reddening from popstar.imf import imf from popstar.imf import multiplicity logAge = 6.7 AKs = 2.7 distance = 4000 cluster_mass = 10**4 startTime = time.time() evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atm.get_merged_atmosphere red_law = reddening.RedLawNishiyama09() filt_list = ['nirc2,J', 'nirc2,Kp'] iso = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo, atm_func=atm_func, red_law=red_law, filters=filt_list) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) imf_limits = np.array([0.07, 0.5, 150]) imf_powers = np.array([-1.3, -2.35]) multi = multiplicity.MultiplicityUnresolved() my_imf = imf.IMF_broken_powerlaw(imf_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) cluster = syn.ResolvedCluster(iso, my_imf, cluster_mass) print('Constructed cluster: %d seconds' % (time.time() - startTime)) return
def test_ifmr_multiplicity(): from popstar import synthetic as syn from popstar import atmospheres as atm from popstar import evolution from popstar import reddening from popstar import ifmr from popstar.imf import imf from popstar.imf import multiplicity # Define cluster parameters logAge = 9.7 AKs = 0.0 distance = 1000 cluster_mass = 1e6 mass_sampling = 5 # Test all filters filt_list = ['nirc2,Kp', 'nirc2,H', 'nirc2,J'] startTime = time.time() evo = evolution.MISTv1() atm_func = atm.get_merged_atmosphere ifmr_obj = ifmr.IFMR() red_law = reddening.RedLawNishiyama09() iso = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo, atm_func=atm_func, red_law=red_law, filters=filt_list, mass_sampling=mass_sampling) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) # Now to create the cluster. imf_mass_limits = np.array([0.07, 0.5, 1, np.inf]) imf_powers = np.array([-1.3, -2.3, -2.3]) ########## # Start without multiplicity and IFMR ########## my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=None) print('Constructed IMF: %d seconds' % (time.time() - startTime)) cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass, ifmr=ifmr_obj) clust1 = cluster1.star_systems print('Constructed cluster: %d seconds' % (time.time() - startTime)) ########## # Test with multiplicity and IFMR ########## multi = multiplicity.MultiplicityUnresolved() my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=ifmr_obj) clust2 = cluster2.star_systems comps2 = cluster2.companions print('Constructed cluster with multiples: %d seconds' % (time.time() - startTime)) ########## # Tests ########## # Check that we have black holes, neutron stars, and white dwarfs in both. assert len(np.where(clust1['phase'] == 101)) > 0 # WD assert len(np.where(clust2['phase'] == 101)) > 0 assert len(np.where(clust1['phase'] == 102)) > 0 # NS assert len(np.where(clust2['phase'] == 102)) > 0 assert len(np.where(clust1['phase'] == 103)) > 0 # BH assert len(np.where(clust2['phase'] == 103)) > 0 # Now check that we have companions that are WDs, NSs, and BHs assert len(np.where(comps2['phase'] == 101)) > 0 assert len(np.where(comps2['phase'] == 102)) > 0 assert len(np.where(comps2['phase'] == 103)) > 0 # Make sure no funky phase designations (due to interpolation effects) # slipped through idx = np.where((clust1['phase'] > 5) & (clust1['phase'] < 101) & (clust1['phase'] != 9)) idx2 = np.where((comps2['phase'] > 5) & (comps2['phase'] < 101) & (comps2['phase'] != 9)) assert len(idx[0]) == 0 return
def test_ResolvedClusterDiffRedden(): from popstar import synthetic as syn from popstar import atmospheres as atm from popstar import evolution from popstar import reddening from popstar.imf import imf from popstar.imf import multiplicity logAge = 6.7 AKs = 2.4 distance = 4000 cluster_mass = 10**5. deltaAKs = 0.05 mass_sampling = 5 # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] startTime = time.time() evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atm.get_merged_atmosphere red_law = reddening.RedLawNishiyama09() iso = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo, atm_func=atm_func, red_law=red_law, filters=filt_list, mass_sampling=mass_sampling) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) imf_mass_limits = np.array([0.07, 0.5, 1, np.inf]) imf_powers = np.array([-1.3, -2.3, -2.3]) ########## # Start without multiplicity ########## my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=None) print('Constructed IMF: %d seconds' % (time.time() - startTime)) cluster1 = syn.ResolvedClusterDiffRedden(iso, my_imf1, cluster_mass, deltaAKs) clust1 = cluster1.star_systems print('Constructed cluster: %d seconds' % (time.time() - startTime)) assert len(clust1) > 0 plt.figure(3) plt.clf() plt.plot(clust1['m_nirc2_J'] - clust1['m_nirc2_Kp'], clust1['m_nirc2_J'], 'r.') plt.plot(iso.points['m_nirc2_J'] - iso.points['m_nirc2_Kp'], iso.points['m_nirc2_J'], 'c.') plt.gca().invert_yaxis() # *** Visual Inspections: *** # - check that points (red) fall between isochrone points (blue) ########## # Test with multiplicity ########## multi = multiplicity.MultiplicityUnresolved() my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) cluster2 = syn.ResolvedClusterDiffRedden(iso, my_imf2, cluster_mass, deltaAKs) clust2 = cluster2.star_systems print('Constructed cluster with multiples: %d seconds' % (time.time() - startTime)) assert len(clust2) > 0 assert len(cluster2.companions) > 0 assert np.sum(clust2['N_companions']) == len(cluster2.companions) ########## # Plots ########## # Plot an IR CMD and compare cluster members to isochrone. plt.figure(1) plt.clf() plt.plot(clust1['m_nirc2_J'] - clust1['m_nirc2_Kp'], clust1['m_nirc2_J'], 'r.') plt.plot(clust2['m_nirc2_J'] - clust2['m_nirc2_Kp'], clust2['m_nirc2_J'], 'b.') plt.plot(iso.points['m_nirc2_J'] - iso.points['m_nirc2_Kp'], iso.points['m_nirc2_J'], 'c-') plt.gca().invert_yaxis() plt.xlabel('J - Kp (mag)') plt.ylabel('J (mag') # Plot a mass-magnitude relationship. plt.figure(2) plt.clf() plt.semilogx(clust1['mass'], clust1['m_nirc2_J'], 'r.') plt.semilogx(clust2['mass'], clust2['m_nirc2_J'], 'r.') plt.gca().invert_yaxis() plt.xlabel('Mass (Msun)') plt.ylabel('J (mag)') return
def test_IsochronePhot(plot=False): from popstar import synthetic as syn from popstar import evolution, atmospheres, reddening logAge = 6.7 AKs = 2.7 distance = 4000 filt_list = ['wfc3,ir,f127m', 'nirc2,J'] mass_sampling = 1 iso_dir = 'iso/' evo_model = evolution.MISTv1() atm_func = atmospheres.get_merged_atmosphere redlaw = reddening.RedLawNishiyama09() startTime = time.time() iso = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo_model, atm_func=atm_func, red_law=redlaw, filters=filt_list, mass_sampling=mass_sampling, iso_dir=iso_dir) endTime = time.time() print('IsochronePhot generated in: %d seconds' % (endTime - startTime)) # Typically takes 120 seconds if file is regenerated. # Limited by pysynphot.Icat call in atmospheres.py assert iso.points.meta['LOGAGE'] == logAge assert iso.points.meta['AKS'] == AKs assert iso.points.meta['DISTANCE'] == distance assert len(iso.points) > 100 assert 'm_nirc2_J' in iso.points.colnames if plot: plt.figure(1) iso.plot_CMD('mag814w', 'mag160w') plt.figure(2) iso.plot_mass_magnitude('mag160w') # Finally, let's test the isochronePhot file generation assert os.path.exists('{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}_p00.fits'.format( iso_dir, logAge, AKs, str(distance).zfill(5))) # Check 1: If we try to remake the isochrone, does it read the file rather than # making a new one iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo_model, atm_func=atm_func, red_law=redlaw, filters=filt_list, mass_sampling=mass_sampling, iso_dir=iso_dir) assert iso_new.recalc == False # Check 2: If we change evo model, atmo model, or redlaw, # does IsochronePhot regenerate the isochrone and overwrite the existing one? evo2 = evolution.MergedBaraffePisaEkstromParsec() mass_sampling = 20 iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo2, atm_func=atm_func, red_law=redlaw, filters=filt_list, mass_sampling=mass_sampling, iso_dir=iso_dir) assert iso_new.recalc == True redlaw2 = reddening.RedLawHosek18b() iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo2, atm_func=atm_func, red_law=redlaw2, filters=filt_list, mass_sampling=mass_sampling, iso_dir=iso_dir) assert iso_new.recalc == True atm2 = atmospheres.get_castelli_atmosphere iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo2, atm_func=atm2, red_law=redlaw2, filters=filt_list, mass_sampling=mass_sampling, iso_dir=iso_dir) assert iso_new.recalc == True return
def test_ResolvedCluster(): from popstar import synthetic as syn from popstar import atmospheres as atm from popstar import evolution from popstar import reddening from popstar.imf import imf from popstar.imf import multiplicity # Define cluster parameters logAge = 6.7 AKs = 2.4 distance = 4000 cluster_mass = 10**5. mass_sampling=5 # Test all filters filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m', 'acs,wfc1,f814w', 'wfc3,ir,f125w', 'wfc3,ir,f160w', 'decam,y', 'decam,i', 'decam,z', 'decam,u', 'decam,g', 'decam,r', 'vista,Y', 'vista,Z', 'vista,J', 'vista,H', 'vista,Ks', 'ps1,z', 'ps1,g', 'ps1,r', 'ps1,i', 'ps1,y', 'jwst,F090W', 'jwst,F164N', 'jwst,F212N', 'jwst,F323N', 'jwst,F466N', 'nirc2,J', 'nirc2,H', 'nirc2,Kp', 'nirc2,K', 'nirc2,Lp', 'nirc2,Ms', 'nirc2,Hcont', 'nirc2,FeII', 'nirc2,Brgamma', 'jg,J', 'jg,H', 'jg,K'] startTime = time.time() evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atm.get_merged_atmosphere red_law = reddening.RedLawNishiyama09() iso = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo, atm_func=atm_func, red_law=red_law, filters=filt_list, mass_sampling=mass_sampling) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) # Now to create the cluster. imf_mass_limits = np.array([0.07, 0.5, 1, np.inf]) imf_powers = np.array([-1.3, -2.3, -2.3]) ########## # Start without multiplicity ########## my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=None) print('Constructed IMF: %d seconds' % (time.time() - startTime)) cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass) clust1 = cluster1.star_systems print('Constructed cluster: %d seconds' % (time.time() - startTime)) plt.figure(3) plt.clf() plt.plot(clust1['m_nirc2_J'] - clust1['m_nirc2_Kp'], clust1['m_nirc2_J'], 'r.') plt.plot(iso.points['m_nirc2_J'] - iso.points['m_nirc2_Kp'], iso.points['m_nirc2_J'], 'c.') plt.gca().invert_yaxis() # *** Visual Inspections: *** # - check that points (red) fall between isochrone points (blue) ########## # Test with multiplicity ########## multi = multiplicity.MultiplicityUnresolved() my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass) clust2 = cluster2.star_systems print('Constructed cluster with multiples: %d seconds' % (time.time() - startTime)) ########## # Plots ########## # Plot an IR CMD and compare cluster members to isochrone. plt.figure(1) plt.clf() plt.plot(clust1['m_nirc2_J'] - clust1['m_nirc2_Kp'], clust1['m_nirc2_J'], 'r.') plt.plot(clust2['m_nirc2_J'] - clust2['m_nirc2_Kp'], clust2['m_nirc2_J'], 'b.') plt.plot(iso.points['m_nirc2_J'] - iso.points['m_nirc2_Kp'], iso.points['m_nirc2_J'], 'c-') plt.gca().invert_yaxis() plt.xlabel('J - Kp (mag)') plt.ylabel('J (mag') # Plot a mass-magnitude relationship. plt.figure(2) plt.clf() plt.semilogx(clust1['mass'], clust1['m_nirc2_J'], 'r.') plt.semilogx(clust2['mass'], clust2['m_nirc2_J'], 'r.') plt.gca().invert_yaxis() plt.xlabel('Mass (Msun)') plt.ylabel('J (mag)') # # Plot the spectrum of the most massive star # idx = cluster.mass.argmax() # plt.clf() # plt.plot(cluster.stars[idx].wave, cluster.stars[idx].flux, 'k.') # # Plot an integrated spectrum of the whole cluster. # wave, flux = cluster.get_integrated_spectrum() # plt.clf() # plt.plot(wave, flux, 'k.') return
def multinest_run(root_dir='/Users/jlu/work/wd1/analysis_2015_01_05/', data_tab='catalog_diffDered_NN_opt_10.fits', comp_tab='completeness_ccmd.fits', out_dir='multinest/fit_0001/'): if not os.path.exists(root_dir + out_dir): os.makedirs(root_dir + out_dir) # Input the observed data t = Table.read(root_dir + data_tab) # Input the completeness table and bins. completeness_map = pyfits.getdata(root_dir + comp_tab) completeness_map = completeness_map.T _in_bins = open(root_dir + comp_tab.replace('.fits', '_bins.pickle'), 'r') bins_mag = pickle.load(_in_bins) bins_col1 = pickle.load(_in_bins) bins_col2 = pickle.load(_in_bins) # Some components of our model are static. imf_multi = multiplicity.MultiplicityUnresolved() imf_mmin = 0.1 # msun imf_mmax = 150.0 # msun evo_model = evolution.MergedBaraffePisaEkstromParsec() red_law = reddening.RedLawNishiyama09() atm_func = atmospheres.get_merged_atmosphere Mcl_sim = 5.0e6 # Our data vs. model comparison will be done in # magnitude-color-color space. Models will be binned # to construct 3D probability density spaces. # These are the bin sizes for the models. # # Note Dimensions: # mag = m_2010_F160W # col1 = m_2005_F814W - m_2010_F160W # col2 = m_2010_F125W - m_2010_F160W # bins = np.array([bins_mag, bins_col1, bins_col2]) def priors(cube, ndim, nparams): return def likelihood(cube, ndim, nparams): ########## # Priors (I think order matters) ########## parName = [ 'distance', 'LogAge', 'AKs', 'dAKs', 'alpha1', 'alpha2', 'mbreak', 'Mcl' ] par, par_prior_logp = get_prior_info(cube, parName) sysMass = np.zeros(len(t)) ########## # Load up the model cluster. ########## imf_mass_limits = np.array([imf_mmin, par['mbreak'], imf_mmax]) imf_powers = np.array([par['alpha2'], par['alpha1']]) imf_multi = None new_imf = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, imf_multi) print 'Getting Isochrone' new_iso = synthetic.IsochronePhot(par['LogAge'], par['AKs'], par['distance'], evo_model=evo_model, atm_func=atm_func, red_law=red_law) print 'Getting Cluster' cluster = synthetic.ResolvedClusterDiffRedden(new_iso, new_imf, Mcl_sim, par['dAKs'], red_law=red_law) # Convert simulated cluster into agnitude-color-color histogram mag = cluster.star_systems['mag160w'] col1 = cluster.star_systems['mag814w'] - mag col2 = cluster.star_systems['mag125w'] - mag data = np.array([mag, col1, col2]).T bins = np.array([bins_mag, bins_col1, bins_col2]) H_sim_c, edges = np.histogramdd(data, bins=bins, normed=True) H_sim = H_sim_c * completeness_map # Convert Observed cluster into magnitude-color-color histogram mag = t['m_2010_F160W'] col1 = t['m_2005_F814W'] - t['m_2010_F160W'] col2 = t['m_2010_F125W'] - t['m_2010_F160W'] data = np.array([mag, col1, col2]).T bins = np.array([bins_mag, bins_col1, bins_col2]) H_obs, edges = np.histogramdd(data, bins=bins) # Plotting extent = (bins_col1[0], bins_col2[-1], bins_mag[0], bins_mag[-1]) py.figure(1) py.clf() py.imshow(H_sim_c.sum(axis=2), extent=extent) py.gca().invert_yaxis() py.colorbar() py.axis('tight') py.title('Sim Complete') py.figure(2) py.clf() py.imshow(H_sim.sum(axis=2), extent=extent) py.gca().invert_yaxis() py.colorbar() py.axis('tight') py.title('Sim Incomplete') py.figure(3) py.clf() py.imshow(H_obs.sum(axis=2), extent=extent) py.gca().invert_yaxis() py.colorbar() py.axis('tight') py.title('Obs Incomplete') py.figure(4) py.clf() py.imshow(completeness_map.mean(axis=2), extent=extent, vmin=0, vmax=1) py.gca().invert_yaxis() py.colorbar() py.axis('tight') py.title('Completeness Map') pdb.set_trace() mcc_cluster = 1 print likei.sum() return likei.sum() num_dims = 8 num_params = 8 ev_tol = 0.3 samp_eff = 0.8 n_live_points = 300 # pymultinest.run(likelihood, priors, num_dims, n_params=num_params, # outputfiles_basename=out_dir + 'test', # verbose=True, resume=False, evidence_tolerance=ev_tol, # sampling_efficiency=samp_eff, n_live_points=n_live_points, # multimodal=True, n_clustering_params=num_dims) cube_test = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5] likelihood(cube_test, num_dims, num_params)