Source code for halotools.empirical_models.factories.hod_mock_factory

"""
Module containing the ~halotools.empirical_models.HodMockFactory class,
the primary class used to construct mock galaxy populations
based on HOD-style models.

The ~halotools.empirical_models.HodMockFactory class
provides an abstract interface between halo catalogs
and Halotools models.
"""

import numpy as np
from copy import copy
from astropy.table import Table
from astropy.utils.misc import NumpyRNGContext

from .mock_factory_template import MockFactory

from .. import model_helpers

from ...sim_manager import sim_defaults
from ...utils.table_utils import SampleSelector
from ...custom_exceptions import HalotoolsError

__all__ = ['HodMockFactory']
__author__ = ['Andrew Hearin']

unavailable_haloprop_msg = ("Your model requires that the %s key appear in the halo catalog,\n"
"but this column is not available in the catalog you attempted to populate.\n")

missing_halo_upid_msg = ("All HOD-style models populate host halos with mock galaxies.\n"
"The way Halotools distinguishes host halos from subhalos is via the halo_upid column,\n"
"with halo_upid = -1 for host halos and !=-1 for subhalos.\n"
"The halo catalog you passed to the HodMockFactory does not have the halo_upid column.\n")

[docs]class HodMockFactory(MockFactory):
""" Class responsible for populating a simulation with a
population of mock galaxies based on an HOD-style model
built by the ~halotools.empirical_models.HodModelFactory class.

Can be thought of as a factory that takes a model
and simulation halocat as input,
and generates a mock galaxy population.
The returned collection of galaxies possesses whatever
attributes were requested by the model, such as xyz position,
central/satellite designation, star-formation rate, etc.

See :ref:hod_mock_factory_source_notes
for an in-depth tutorial on the mock-making algorithm.

"""

def __init__(self, Num_ptcl_requirement=sim_defaults.Num_ptcl_requirement,
halo_mass_column_key='halo_mvir', **kwargs):
"""
Parameters
----------
halocat : object, keyword argument
Object containing the halo catalog and other associated data.
Produced by ~halotools.sim_manager.CachedHaloCatalog

model : object, keyword argument
A model built by a sub-class of ~halotools.empirical_models.HodModelFactory.

populate : boolean, optional
If set to False, the class will perform all pre-processing tasks
but will not call the model to populate the galaxy_table
with mock galaxies and their observable properties. Default is True.

Num_ptcl_requirement : int, optional
Requirement on the number of dark matter particles in the halo.
The column defined by the halo_mass_column_key string will have a cut placed on it:
all halos with halocat.halo_table[halo_mass_column_key] < Num_ptcl_requirement*halocat.particle_mass
Default value is set in ~halotools.sim_defaults.Num_ptcl_requirement.

halo_mass_column_key : string, optional
This string must be a column of the input halo catalog.
The column defined by this string will have a cut placed on it:
all halos with halocat.halo_table[halo_mass_column_key] < Num_ptcl_requirement*halocat.particle_mass
Default is 'halo_mvir'

"""

MockFactory.__init__(self, **kwargs)

halocat = kwargs['halocat']
self.Num_ptcl_requirement = Num_ptcl_requirement
self.halo_mass_column_key = halo_mass_column_key

self.preprocess_halo_catalog(halocat)

[docs]    def preprocess_halo_catalog(self, halocat):
""" Method to pre-process a halo catalog upon instantiation of
the mock object. This pre-processing includes identifying the
catalog columns that will be used by the model to create the mock,
building lookup tables associated with the halo profile,
and possibly creating new halo properties.

Parameters
----------
logrmin : float, optional
Minimum radius used to build the lookup table for the halo profile.
Default is set in ~halotools.empirical_models.model_defaults.

logrmax : float, optional
Maximum radius used to build the lookup table for the halo profile.
Default is set in ~halotools.empirical_models.model_defaults.

Number of control points used in the lookup table for the halo profile.
Default is set in ~halotools.empirical_models.model_defaults.

"""
try:
assert 'halo_upid' in list(halocat.halo_table.keys())
except AssertionError:
raise HalotoolsError(missing_halo_upid_msg)

# Make cuts on halo catalog #
# Select host halos only, since this is an HOD-style model
halo_table, subhalo_table = SampleSelector.host_halo_selection(
table=halocat.halo_table, return_subhalos=True)

# make a (possibly trivial) completeness cut
cutoff_mvir = self.Num_ptcl_requirement*self.particle_mass
mass_cut = halo_table[self.halo_mass_column_key] > cutoff_mvir
max_column_value = np.max(halo_table[self.halo_mass_column_key])
halo_table = halo_table[mass_cut]
if len(halo_table) == 0:
msg = ("During the pre-processing phase of HOD mock-making \n"
"controlled by the preprocess_halo_catalog method of the HodMockFactory,\n"
"a cut on halo completeness is made. The column used in this cut\n"
"is determined by the value of the halo_mass_column_key string \n"
"passed to the HodMockFactory constructor, which in this case was %s.\n"
"The largest value of this column in the uncut catalog is %.2e.\n"
"Your mass cut of M > %.2e resulted in zero halos in the halo catalog;\n"
"for reference, your halo catalog has a particle mass of m_p = %.2e.\n"
"Such a cut is not permissible. \nThis could indicate a problem in "
"the processing of the halo catalog,\n"
"for example, an incorrect column number and/or dtype.\n"
"Alternatively, the value of Num_ptcl_requirement = %.2e \n"
"that was passed to the HodMockFactory constructor could be the problem.\n")
raise HalotoolsError(msg % (
self.halo_mass_column_key, max_column_value, cutoff_mvir,
self.particle_mass, self.Num_ptcl_requirement))

# If any component model needs the subhalo_table, bind it to the mock object
for component_model in list(self.model.model_dictionary.values()):
try:
f = getattr(component_model, 'preprocess_subhalo_table')
halo_table, self.subhalo_table = f(halo_table, subhalo_table)
except AttributeError:
pass

############################################################

# Create new columns of the halo catalog, if applicable
try:
d = self.model.new_haloprop_func_dict
for new_haloprop_key, new_haloprop_func in list(d.items()):
halo_table[new_haloprop_key] = new_haloprop_func(table=halo_table)
except AttributeError:
pass

self._orig_halo_table = Table()
try:
self._orig_halo_table[key] = halo_table[key][:]
except KeyError:
raise HalotoolsError(unavailable_haloprop_msg % key)

self.model.build_lookup_tables()

[docs]    def populate(self, seed=None, **kwargs):
"""
Method populating host halos with mock galaxies.

By calling the populate method of your mock, you will repopulate
the halo catalog with a new realization of the model based on
whatever values of the model parameters are currently stored in the
param_dict of the model.

For an in-depth discussion of how this method is implemented,
see the :ref:hod_mock_factory_source_notes section of the documentation.

Parameters
------------
Function object used to place a mask on the halo table prior to
calling the mock generating functions. Calling signature of the
function should be to accept a single positional argument storing
a table, and returning a boolean numpy array that will be used
as a fancy indexing mask. All masked halos will be ignored during
mock population. Default is None.

enforce_PBC : bool, optional
If set to True, after galaxy positions are assigned the
model_helpers.enforce_periodicity_of_box will re-map
satellite galaxies whose positions spilled over the edge
of the periodic box. Default is True. This variable should only
ever be set to False when using the masking_function to
populate a specific spatial subvolume, as in that case PBCs
no longer apply.

seed : int, optional
Random number seed used in the Monte Carlo realization.
Default is None, which will produce stochastic results.

Notes
-----
Note the difference between the
halotools.empirical_models.HodMockFactory.populate method and the
closely related method
halotools.empirical_models.HodModelFactory.populate_mock.
The ~halotools.empirical_models.HodModelFactory.populate_mock method
is bound to a composite model instance and is called the *first* time
a composite model is used to generate a mock. Calling the
~halotools.empirical_models.HodModelFactory.populate_mock method creates
the ~halotools.empirical_models.HodMockFactory instance and binds it to
composite model. From then on, if you want to *repopulate* a new Universe
with the same composite model, you should instead call the
~halotools.empirical_models.HodMockFactory.populate method
bound to model.mock. The reason for this distinction is that
calling ~halotools.empirical_models.HodModelFactory.populate_mock
triggers a large number of relatively expensive pre-processing steps
and self-consistency checks that need only be carried out once.
See the Examples section below for an explicit demonstration.

In particular, if you are running an MCMC type analysis,
you will choose your halo catalog and completeness cuts, and call
halotools.empirical_models.ModelFactory.populate_mock
with the appropriate arguments. Thereafter, you can
explore parameter space by changing the values stored in the
param_dict dictionary attached to the model, and then calling the
~halotools.empirical_models.MockFactory.populate method
bound to model.mock. Any changes to the param_dict of the
model will automatically propagate into the behavior of
the ~halotools.empirical_models.MockFactory.populate method.

Normally, repeated calls to the
~halotools.empirical_models.HodMockFactory.populate method
should not increase the RAM usage of halotools because a new mock
catalog is created and the old one
deleted. However, on certain machines the memory usage was found to
increase over time. If this is the case and memory usage is critical you
can try calling gc.collect() immediately following the call to
mock.populate to manually invoke python's garbage collection.

Examples
----------
>>> from halotools.empirical_models import PrebuiltHodModelFactory
>>> model_instance = PrebuiltHodModelFactory('zheng07')

Here we will use a fake simulation, but you can populate mocks
using any instance of ~halotools.sim_manager.CachedHaloCatalog or
~halotools.sim_manager.UserSuppliedHaloCatalog.

>>> from halotools.sim_manager import FakeSim
>>> halocat = FakeSim()
>>> model_instance.populate_mock(halocat)

Your model_instance now has a mock attribute bound to it,
which is an instance of the ~halotools.empirical_models.HodMockFactory class.
You can call the populate method bound to the mock,
which will repopulate the halo catalog with a new Monte Carlo
realization of the model.

>>> model_instance.mock.populate()

If you want to change the behavior of your model, just change the
values stored in the param_dict. The param_dict attribute
is a python dictionary storing the values of all parameters in the model.
Differences in the parameter values
will change the behavior of the mock-population.

>>> model_instance.param_dict['logMmin'] = 12.1
>>> model_instance.mock.populate()

---------
:ref:hod_mock_factory_source_notes

"""

# The _testing_mode keyword is for unit-testing only
# it has been intentionally left out of the docstring
try:
self._testing_mode = kwargs['_testing_mode']
except KeyError:
self._testing_mode = False

try:
self.enforce_PBC = kwargs['enforce_PBC']
except KeyError:
self.enforce_PBC = True

try:
except:
self.halo_table = self._orig_halo_table

self.allocate_memory(seed=seed)

# Loop over all gal_types in the model
for gal_type in self.gal_types:

# Retrieve the indices of our pre-allocated arrays
# that store the info pertaining to gal_type galaxies
gal_type_slice = self._gal_type_indices[gal_type]
# gal_type_slice is a slice object

# For the gal_type_slice indices of
# the pre-allocated array self.gal_type,
# set each string-type entry equal to the gal_type string
self.galaxy_table['gal_type'][gal_type_slice] = (
np.repeat(gal_type, self._total_abundance[gal_type], axis=0))

# Store all other relevant host halo properties into their
# appropriate pre-allocated array
self.galaxy_table[halocatkey][gal_type_slice] = np.repeat(
self.halo_table[halocatkey], self._occupation[gal_type], axis=0)

self.galaxy_table['x'] = self.galaxy_table['halo_x']
self.galaxy_table['y'] = self.galaxy_table['halo_y']
self.galaxy_table['z'] = self.galaxy_table['halo_z']
self.galaxy_table['vx'] = self.galaxy_table['halo_vx']
self.galaxy_table['vy'] = self.galaxy_table['halo_vy']
self.galaxy_table['vz'] = self.galaxy_table['halo_vz']

for method in self._remaining_methods_to_call:
func = getattr(self.model, method)
try:
d = {key: getattr(self, key) for key in func.additional_kwargs}
except AttributeError:
d = {}
gal_type_slice = self._gal_type_indices[func.gal_type]
if seed is not None:
seed += 1
func(table=self.galaxy_table[gal_type_slice], seed=seed, **d)

if self.enforce_PBC is True:
self.galaxy_table['x'], self.galaxy_table['vx'] = (
model_helpers.enforce_periodicity_of_box(
self.galaxy_table['x'], self.Lbox[0],
velocity=self.galaxy_table['vx'],
check_multiple_box_lengths=self._testing_mode)
)

self.galaxy_table['y'], self.galaxy_table['vy'] = (
model_helpers.enforce_periodicity_of_box(
self.galaxy_table['y'], self.Lbox[1],
velocity=self.galaxy_table['vy'],
check_multiple_box_lengths=self._testing_mode)
)

self.galaxy_table['z'], self.galaxy_table['vz'] = (
model_helpers.enforce_periodicity_of_box(
self.galaxy_table['z'], self.Lbox[2],
velocity=self.galaxy_table['vz'],
check_multiple_box_lengths=self._testing_mode)
)

if hasattr(self.model, 'galaxy_selection_func'):

[docs]    def allocate_memory(self, seed=None):
""" Method allocates the memory for all the numpy arrays
that will store the information about the mock.
These arrays are bound directly to the mock object.

The main bookkeeping devices generated by this method are
_occupation and _gal_type_indices.

"""

self.galaxy_table = Table()

# We will keep track of the calling sequence with a list called _remaining_methods_to_call
# Each time a function in this list is called, we will remove that function from the list
# Mock generation will be complete when _remaining_methods_to_call is exhausted
self._remaining_methods_to_call = copy(self.model._mock_generation_calling_sequence)

# Call all composite model methods that should be called prior to mc_occupation
# All such function calls must be applied to the table, since we do not yet know
# how much memory we need for the mock galaxy_table
galprops_assigned_to_halo_table = []
for func_name in self.model._mock_generation_calling_sequence:
if 'mc_occupation' in func_name:
# exit when we encounter a mc_occupation_ function
break
else:
func = getattr(self.model, func_name)
try:
d = {key: getattr(self, key) for key in func.additional_kwargs}
except AttributeError:
d = {}
if seed is not None:
seed += 1
func(table=self.halo_table, seed=seed, **d)
galprops_assigned_to_halo_table_by_func = func._galprop_dtypes_to_allocate.names
galprops_assigned_to_halo_table.extend(galprops_assigned_to_halo_table_by_func)
self._remaining_methods_to_call.remove(func_name)
# Now update the list of additional_haloprops, if applicable
# This is necessary because each of the above function calls created new
# columns for the *halo_table*, not the *galaxy_table*. So we will need to use
# np.repeat inside mock.populate() so that mock galaxies inherit these newly-created columns
# Since there is already a loop over additional_haloprops inside mock.populate() that does this,
# then all we need to do is append to this list
galprops_assigned_to_halo_table = list(set(
galprops_assigned_to_halo_table))

self._occupation = {}
self._total_abundance = {}
self._gal_type_indices = {}

for gal_type in self.gal_types:
self.halo_table['halo_num_'+gal_type] = 0

first_galaxy_index = 0
for gal_type in self.gal_types:
occupation_func_name = 'mc_occupation_'+gal_type
occupation_func = getattr(self.model, occupation_func_name)
# Call the component model to get a Monte Carlo
# realization of the abundance of gal_type galaxies
if seed is not None:
seed += 1

self._occupation[gal_type] = occupation_func(table=self.halo_table, seed=seed)
self.halo_table['halo_num_'+gal_type][:] = self._occupation[gal_type]

# Now use the above result to set up the indexing scheme
self._total_abundance[gal_type] = (
self._occupation[gal_type].sum()
)
last_galaxy_index = first_galaxy_index + self._total_abundance[gal_type]
# Build a bookkeeping device to keep track of
# which array elements pertain to which gal_type.
self._gal_type_indices[gal_type] = slice(
first_galaxy_index, last_galaxy_index)
first_galaxy_index = last_galaxy_index
# Remove the mc_occupation function from the list of methods to call
self._remaining_methods_to_call.remove(occupation_func_name)

self.Ngals = np.sum(list(self._total_abundance.values()))

# Allocate memory for all additional halo properties,
# including profile parameters of the halos such as 'conc_NFWmodel'
self.galaxy_table[halocatkey] = np.zeros(self.Ngals,
dtype=self.halo_table[halocatkey].dtype)

# Separately allocate memory for the galaxy profile parameters
for galcatkey in self.model.halo_prof_param_keys:
self.galaxy_table[galcatkey] = 0.
for galcatkey in self.model.gal_prof_param_keys:
self.galaxy_table[galcatkey] = 0.

self.galaxy_table['gal_type'] = np.zeros(self.Ngals, dtype=object)

dt = self.model._galprop_dtypes_to_allocate
for key in dt.names:
self.galaxy_table[key] = np.zeros(self.Ngals, dtype=dt[key].type)

[docs]    def estimate_ngals(self, seed=None):
""" Method to estimate the number of galaxies produced by the
mock.populate() method. It runs one realization of all
mc_occupation methods and reports the total number of galaxies
produced. However, no extra memory is allocated for the
galaxy tables. Note that model.populate() will invoke a new call
to all mc_occupation methods and can produce a different number
of galaxies.

"""

# Call all composite model methods that should be called prior to mc_occupation
# All such function calls must be applied to the table.
halo_table = Table(np.copy(self.halo_table))
for func_name in self.model._mock_generation_calling_sequence:
if 'mc_occupation' in func_name:
# exit when we encounter a mc_occupation_ function
break
else:
func = getattr(self.model, func_name)
func(table=halo_table)

# Call the component model to get a Monte Carlo
# realization of the abundance of galaxies for all gal_type.
ngals = 0
for gal_type in self.gal_types:
occupation_func_name = 'mc_occupation_'+gal_type
occupation_func = getattr(self.model, occupation_func_name)
if seed is not None:
seed += 1
ngals = ngals + np.sum(occupation_func(table=halo_table, seed=seed))

return ngals