def halocat_init(halo_catalog, z_median):
"""
Initial population of halo catalog using populate_mock function
Parameters
----------
halo_catalog: string
Path to halo catalog
z_median: float
Median redshift of survey
Returns
---------
model: halotools model instance
Model based on behroozi 2010 SMHM
"""
halocat = CachedHaloCatalog(fname=halo_catalog, update_cached_fname=True)
"""
prim_haloprop_key : String giving the column name of the primary halo
property governing stellar mass.
"""
model = PrebuiltSubhaloModelFactory('behroozi10', redshift=z_median, \
prim_haloprop_key='halo_macc')
model.populate_mock(halocat, seed=5)
return model
def populate_mock(key,halocat):
"""
Populates mock halo catalog with galaxies using Behroozi 2010 relation
Parameters
----------
key: string
Halo mass property used to populate mock (halo_mvir or halo_macc)
halocat: hdf5 file
Mock halo catalog
Returns
---------
model:
subhalo-based composite model
"""
model = PrebuiltSubhaloModelFactory('behroozi10',redshift=0.0186,\
prim_haloprop_key=key)
model.populate_mock(halocat)
return model
def halocat_init(halo_cat, z):
"""
Initial population of halo catalog using populate_mock function
Parameters
----------
halo_cat: string
Path to halo catalog
z: float
Median redshift of survey
Returns
---------
model: halotools model instance
Model based on behroozi 2010 SMHM
"""
halocat = CachedHaloCatalog(fname=halo_cat, update_cached_fname=True)
model = PrebuiltSubhaloModelFactory('behroozi10', redshift=z, \
prim_haloprop_key='halo_macc')
model.populate_mock(halocat, seed=5)
return model
eco_nobuff = eco_buff.loc[(eco_buff.grpcz.values >= 3000) & \
(eco_buff.grpcz.values <= 7000) & (eco_buff.absrmag.values <= -17.33) &\
(eco_buff.logmstar.values >= 8.9)]
cvar = 0.125
volume = 151829.26 # Survey volume without buffer [Mpc/h]^3
# BMF
eco_nobuff_mstellar = eco_nobuff.logmstar.values
eco_nobuff_mgas = eco_nobuff.logmgas.values
eco_nobuff_mbary = calc_bary(eco_nobuff_mstellar, eco_nobuff_mgas)
maxis, phi, err, bins = diff_bmf(eco_nobuff_mbary, volume, cvar, False)
halocat = CachedHaloCatalog(fname=halo_catalog, update_cached_fname=True)
z_model = np.median(eco_nobuff.grpcz.values) / (3 * 10**5)
model = PrebuiltSubhaloModelFactory('behroozi10', redshift=z_model,
prim_haloprop_key='halo_macc')
model.populate_mock(halocat)
# ensure reproducibility
rseed = 12
np.random.seed(rseed)
behroozi10_param_vals = [12.35,10.72,0.44,0.57,0.15]
nwalkers = 250
ndim = 5
p0 = behroozi10_param_vals + 0.1*np.random.rand(ndim*nwalkers).\
reshape((nwalkers,ndim))
with Pool(processes=20) as pool:
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, args=(phi, err)\
,pool=pool)
# plt.title('Stellar mass function')
counter = 0
nbins = bins
volume_sim = 130**3
Mhalo_characteristic = np.linspace(11.5, 13.0, 30)
Mstellar_characteristic = np.linspace(9.5, 11.0, 30)
Mlow_slope = np.linspace(0.35, 0.50, 30)
Mhigh_slope = np.linspace(0.50, 0.65, 30)
Mstellar_scatter = np.linspace(0.1, 0.2, 30)
print('Setting up models')
###Models
model1 = PrebuiltSubhaloModelFactory('behroozi10',
redshift=z_median,
prim_haloprop_key='halo_macc')
model2 = PrebuiltSubhaloModelFactory('behroozi10',
redshift=z_median,
prim_haloprop_key='halo_macc')
model3 = PrebuiltSubhaloModelFactory('behroozi10',
redshift=z_median,
prim_haloprop_key='halo_macc')
model4 = PrebuiltSubhaloModelFactory('behroozi10',
redshift=z_median,
prim_haloprop_key='halo_macc')
model5 = PrebuiltSubhaloModelFactory('behroozi10',
redshift=z_median,
prim_haloprop_key='halo_macc')
print('Setting up halocats')
mdef = args.mdef
radmin = args.radmin
inc = args.inc
z = args.z
# set cosmology
redshift = 1/scale - 1
cosmo = Planck15
h = Planck15.h
conc = 5
# needs to run first to initialize package
# halotools.test_installation()
# determine dark mass and virial radius
model = PrebuiltSubhaloModelFactory('behroozi10', redshift = redshift)
stars_mass = 60850451172.24926
# stars_mass in units Msun from simulation
log_stars_mass = np.log10(stars_mass)
log_dark_mass = model.mean_log_halo_mass(log_stellar_mass=log_stars_mass)
dm_stellar = 10**log_dark_mass
virial = halo_mass_to_halo_radius(dm_stellar, cosmo, redshift, mdef)
virial_kpc = (virial * u.Mpc).to('kpc') / h
virial_rad = np.arange(radmin, virial_kpc.value, inc)
scaled_rad = virial_rad / np.max(virial_rad)
# calculate the density threshold for dimensionless mass density calculation
nfw = NFWProfile()
rho_thresh = density_threshold(cosmo, redshift, mdef)
# rho_thresh in units Msun*h^2/Mpc^3
rho_units = (rho_thresh * u.Msun / u.Mpc**3).to('Msun/kpc3') * h**2
def plot_SMHM(halocat_galcat_merged,mass_to_plot_key,populate_mock_key):
"""
Plots SM-HM relation
Parameters
----------
halocat_galcat_merged: Pandas dataframe
Merged dataframe containing halo and galaxy information
mass_to_plot_key: string
Halo mass property that will be plotted on the X-axis
populate_mock_key: string
Halo mass property used to populate mock
"""
print(' -> Separating centrals and satellites')
cens = halocat_galcat_merged.loc[halocat_galcat_merged.C_S.values == 1]
sats = halocat_galcat_merged.loc[halocat_galcat_merged.C_S.values == 0]
print(' -> Overplotting Behroozi 2010 relation for centrals (halotools)')
mstar_arr = np.linspace(cens.stellar_mass.values.min(),\
cens.stellar_mass.values.max(),\
1000000)
log_mstar_arr_B10 = np.log10(mstar_arr)
model = PrebuiltSubhaloModelFactory('behroozi10',redshift=0.0186,\
prim_haloprop_key=populate_mock_key)
log_halo_mass_B10 = model.mean_log_halo_mass(log_stellar_mass=\
log_mstar_arr_B10,\
redshift=0.0186)
if mass_to_plot_key == 'halo_mvir':
print(' -> Calculating statistics using {0}'.format\
(mass_to_plot_key))
stats_cens = stats_cens_func(cens,mass_to_plot_key)
print(' -> Plotting')
fig1 = plt.figure()
plt.scatter(np.log10(sats.halo_mvir_host_halo.values),\
np.log10(sats.stellar_mass.values),color='g',s=5,\
alpha=0.5,label='Satellites')
plt.plot(log_halo_mass_B10,log_mstar_arr_B10,'-k',\
label='Behroozi 2010 cosmoutils')
plt.errorbar(stats_cens[0],stats_cens[1],yerr=stats_cens[2],color='r',\
label='Centrals')
plt.xlabel(r'$\mathrm{Halo\ mass\ (mvir)}/\mathrm{[\frac{M_\odot}{h}]'\
'})$')
plt.ylabel(r'$\mathrm{Stellar\ mass}/\mathrm{[\frac{M_\odot}{h}]})$')
plt.title('SM-HM relation using {0} to populate mocks'.format\
(populate_mock_key.split('_')[1]))
elif mass_to_plot_key == 'halo_macc':
print(' -> Calculating statistics using {0}'.format\
(mass_to_plot_key))
stats_cens = stats_cens_func(cens,mass_to_plot_key)
print(' -> Plotting')
fig1 = plt.figure()
plt.scatter(np.log10(sats.halo_macc_host_halo.values),\
np.log10(sats.stellar_mass.values),color='g',s=5,\
alpha=0.5,label='Satellites')
plt.plot(log_halo_mass_B10,log_mstar_arr_B10,'-k',\
label='Behroozi 2010 cosmoutils')
plt.errorbar(stats_cens[0],stats_cens[1],yerr=stats_cens[2],color='r',\
label='Centrals')
plt.xlabel(r'$\mathrm{Halo\ mass\ (macc)}/\mathrm{[\frac{M_\odot}{h}]'\
'})$')
plt.ylabel(r'$\mathrm{Stellar\ mass}/\mathrm{[\frac{M_\odot}{h}]})$')
plt.title('SM-HM relation using {0} to populate mocks'.format\
(populate_mock_key.split('_')[1]))
plt.legend(loc='best',prop={'size': 6})
fig1.tight_layout()
print(' -> Saving figure')
fig1.savefig('../reports/figures/SMHM_{0}_hosthalo.png'.format\
(mass_to_plot_key.split('_')[1]))
def predict_model(param,
config,
obs_data,
sim_data,
smf_only=False,
ds_only=False):
"""Return all model predictions.
Parameters
----------
param: list, array, or tuple.
Input model parameters.
config : dict
Configurations of the data and model.
obs_data: dict
Dictionary for observed data.
sim_data: dict
Dictionary for UniverseMachine data.
constant_bin : boolen
Whether to use constant bin size for logMs_tot or not.
return_all : bool, optional
Return all model information.
show_smf : bool, optional
Show the comparison of SMF.
show_dsigma : bool, optional
Show the comparisons of WL.
"""
mass_x_field = config['sim_mass_x_field']
halotools = config['sim_halotools']
if halotools:
print("USING HALOTOOLS with halo_mvir")
# build_model and populate mock
if 'model' in sim_data: # save memory if model already exists
for i, model_param in enumerate(config['param_labels']):
sim_data['model'].param_dict[model_param] = param[i]
# set redshift dependence to 0
for i, model_param in enumerate(config['redshift_param_labels']):
sim_data['model'].param_dict[model_param] = 0
sim_data['model'].mock.populate()
print('mock.populate')
else:
sim_data['model'] = PrebuiltSubhaloModelFactory(
'behroozi10',
redshift=config['sim_z'],
scatter_abscissa=[12, 15],
scatter_ordinates=[param[0], param[1]])
for i, model_param in enumerate(config['param_labels']):
sim_data['model'].param_dict[model_param] = param[i]
# set redshift dependence to 0
for i, model_param in enumerate(config['redshift_param_labels']):
sim_data['model'].param_dict[model_param] = 0
# populate mock
# sim_data['model'].populate_mock(deepcopy(sim_data['halocat']))
sim_data['model'].populate_mock(sim_data['halocat'])
print('populate_mock')
print(sim_data['model'].param_dict)
stellar_masses = np.log10(
sim_data['model'].mock.galaxy_table['stellar_mass'])
else: #use Chris' code instead of halotools
print("USING CHRIS' CODE with {0}".format(mass_x_field))
stellar_masses = get_chris_stellar_masses(param, config, sim_data)
# Predict SMFs
smf_mass_bins, smf_log_phi = compute_SMF(stellar_masses, config, nbins=100)
print('SMF computed')
if smf_only:
return smf_mass_bins, smf_log_phi, None, None, stellar_masses
# Predict DeltaSigma profiles
wl_r, wl_ds = compute_deltaSigma(stellar_masses, config, obs_data,
sim_data)
print('DS computed')
if ds_only:
return None, None, wl_r, wl_ds, stellar_masses
return smf_mass_bins, smf_log_phi, wl_r, wl_ds, stellar_masses