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