def make_sim_cluster():
work_dir = '/u/jlu/work/gc/jwst/2018_03_19/'
ages = [4e6, 1e8, 8e9]
cluster_mass = [1e4, 1e4, 1e7]
AKs = 2.7
deltaAKs = 1.0
distance = 8000
mass_sampling = 5
isochrones = []
clusters = []
evo = evolution.MergedBaraffePisaEkstromParsec()
atm_func = atm.get_merged_atmosphere
red_law = reddening.RedLawHosek18()
multi = multiplicity.MultiplicityUnresolved()
imf_mass_limits = np.array([0.07, 0.5, 1, np.inf])
imf_powers_old = np.array([-1.3, -2.3, -2.3])
imf_powers_yng = np.array([-1.3, -1.8, -1.8])
my_imf_old = imf.IMF_broken_powerlaw(imf_mass_limits,
imf_powers_old,
multiplicity=multi)
my_imf_yng = imf.IMF_broken_powerlaw(imf_mass_limits,
imf_powers_yng,
multiplicity=multi)
# Test all filters
filt_list = [
'wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m', 'acs,wfc1,f814w',
'wfc3,ir,f125w', 'wfc3,ir,f160w', 'jwst,F090W', 'jwst,F115W',
'jwst,F164N', 'jwst,F187N', 'jwst,F212N', 'jwst,F323N', 'jwst,F405N',
'jwst,F466N', 'jwst,F470N', 'jwst,F140M', 'jwst,F162M', 'jwst,F182M',
'jwst,F210M', 'jwst,F250M', 'jwst,F300M', 'jwst,F335M', 'jwst,F360M',
'jwst,F410M', 'jwst,F430M', 'jwst,F460M', 'jwst,F480M', 'nirc2,J',
'nirc2,H', 'nirc2,Kp', 'nirc2,Lp', 'nirc2,Ms'
]
startTime = time.time()
for ii in range(len(ages)):
logAge = np.log10(ages[ii])
iso = syn.IsochronePhot(logAge,
AKs,
distance,
evo_model=evo,
atm_func=atm_func,
red_law=red_law,
filters=filt_list,
mass_sampling=mass_sampling,
iso_dir=work_dir)
print('Constructed isochrone: %d seconds' % (time.time() - startTime))
if ii < 2:
imf_ii = my_imf_yng
else:
imf_ii = my_imf_old
cluster = syn.ResolvedClusterDiffRedden(iso, imf_ii, cluster_mass[ii],
deltaAKs)
# Save generated clusters to file.
save_file_fmt = '{0}/clust_{1:.2f}_{2:4.2f}_{3:4s}.fits'
save_file_txt = save_file_fmt.format(work_dir, logAge, AKs,
str(distance).zfill(5))
save_file = open(save_file_txt, 'wb')
pickle.dump(cluster, save_file)
return
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()
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_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 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)
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)
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)
##########
# 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