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)
