Source code for halotools.empirical_models.factories.hod_model_factory

"""
Module storing the `~halotools.empirical_models.HodModelFactory`,
the primary factory responsible for building
HOD-style models of the galaxy-halo connection.
"""

import numpy as np
from copy import copy
from warnings import warn
import collections

from .model_factory_template import ModelFactory
from .hod_mock_factory import HodMockFactory

from .. import model_helpers

from ..occupation_models import OccupationComponent
from ...sim_manager import sim_defaults
from ...custom_exceptions import HalotoolsError

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


[docs]class HodModelFactory(ModelFactory): """ Class used to build HOD-style models of the galaxy-halo connection. See :ref:`hod_modeling_tutorial0` for an in-depth description of how to build HOD models, demonstrated by a sequence of increasingly complex examples. If you do not wish to build your own model but want to use one provided by Halotools, instead see `~halotools.empirical_models.PrebuiltHodModelFactory`. All HOD-style composite models can directly populate catalogs of dark matter halos. For an in-depth description of how Halotools implements this mock-generation, see :ref:`hod_mock_factory_source_notes`. The arguments passed to the `HodModelFactory` constructor determine the features of the model that are returned by the factory. This works in one of two ways, both of which have explicit examples provided below. 1. Building a new model from scratch. You can build a model from scratch by passing in a sequence of ``model_features``, each of which are instances of component models. The factory then composes these independently-defined components into a composite model. 2. Building a new model from an existing model. It is also possible to add/swap new features to a previously built composite model instance, allowing you to create new models from existing ones. To do this, you pass in a ``baseline_model_instance`` and any set of ``model_features``. Any ``model_feature`` keyword that matches a feature name of the ``baseline_model_instance`` will replace that feature in the ``baseline_model_instance``; all other ``model_features`` that you pass in will augment the ``baseline_model_instance`` with new behavior. Regardless what set of features you use to build your model, the returned object can be used to directly populate a halo catalog with mock galaxies using the `~halotools.empirical_models.HodModelFactory.populate_mock` method, as shown in the example below. """ def __init__(self, **kwargs): """ Parameters ---------- *model_features : sequence of keyword arguments, optional Each keyword you use will be interpreted as the name of a feature in the composite model, e.g. 'stellar_mass' or 'star_formation_rate'; the value bound to each keyword must be an instance of a component model governing the behavior of that feature. See the examples section below. baseline_model_instance : `SubhaloModelFactory` instance, optional If passed to the constructor, the ``model_dictionary`` bound to the ``baseline_model_instance`` will be treated as the baseline dictionary. Any additional keyword arguments passed to the constructor that appear in the baseline dictionary will be treated as model features that replace the corresponding component model in the baseline dictionary. Any model features passed to the constructor that do not appear in the baseline dictionary will be treated as new features that augment the baseline model with new behavior. See the examples section below. model_feature_calling_sequence : list, optional Determines the order in which your component features will be called during mock population. Some component models may have explicit dependence upon the value of some other galaxy property being modeled. In such a case, you must pass a ``model_feature_calling_sequence`` list, ordered in the desired calling sequence. A classic example is if the stellar-to-halo-mass relation has explicit dependence on the star formation rate of the galaxy (active or quiescent). For this example, the ``model_feature_calling_sequence`` would be model_feature_calling_sequence = ['sfr_designation', 'stellar_mass', ...]. Default behavior is to assume that no model feature has explicit dependence upon any other, in which case the component models appearing in the ``model_features`` keyword arguments will be called in random order, giving primacy to the potential presence of `stellar_mass` and/or `luminosity` features. gal_type_list : list, optional List of strings providing the names of the galaxy types in the composite model. This is only necessary to provide if you have a gal_type in your model that is neither ``centrals`` nor ``satellites``. For example, if you have entirely separate models for ``red_satellites`` and ``blue_satellites``, then your ``gal_type_list`` might be, gal_type_list = ['centrals', 'red_satellites', 'blue_satellites']. Another possible example would be gal_type_list = ['centrals', 'satellites', 'orphans']. redshift: float, optional Redshift of the model galaxies. Must be compatible with the redshift of all component models, and with the redshift of the snapshot of the simulation used to populate mocks. Default is None. halo_selection_func : function object, optional Function object used to place a cut on the input ``table``. If the ``halo_selection_func`` keyword argument is passed, the input to the function must be a single positional argument storing a length-N structured numpy array or Astropy table; the function output must be a length-N boolean array that will be used as a mask. Halos that are masked will be entirely neglected during mock population. Examples --------- As described above, there are two different ways to build models using the `HodModelFactory`. Here we give demonstrations of each in turn. In the first example we'll show how to build a model from scratch using the ``model_features`` option. For illustration purposes, we'll pick a particularly simple HOD-style model based on Zheng et al. (2007). As described in `~halotools.empirical_models.zheng07_model_dictionary`, in this model there are two galaxy populations, 'centrals' and 'satellites'; centrals sit at the center of dark matter halos, and satellites follow an NFW profile. We'll start with the features for the population of centrals: >>> from halotools.empirical_models import TrivialPhaseSpace, Zheng07Cens >>> cens_occ_model = Zheng07Cens() >>> cens_prof_model = TrivialPhaseSpace() Now for the satellites: >>> from halotools.empirical_models import NFWPhaseSpace, Zheng07Sats >>> sats_occ_model = Zheng07Sats() >>> sats_prof_model = NFWPhaseSpace() At this point we have our component model instances. The following call to the factory uses the ``model_features`` option described above: >>> model_instance = HodModelFactory(centrals_occupation = cens_occ_model, centrals_profile = cens_prof_model, satellites_occupation = sats_occ_model, satellites_profile = sats_prof_model) The feature names we have chosen are 'centrals_occupation' and 'centrals_profile', 'satellites_occupation' and 'satellites_profile'. The first substring of each feature name informs the factory of the name of the galaxy population, the second substring identifies the type of feature; to each feature we have attached a component model instance. Whatever features your composite model has, you can use the `~HodModelFactory.populate_mock` method to create Monte Carlo realization of the model by populating any dark matter halo catalog in your cache directory: >>> from halotools.sim_manager import CachedHaloCatalog >>> halocat = CachedHaloCatalog(simname = 'bolshoi', redshift = 0.5) # doctest: +SKIP >>> model_instance.populate_mock(halocat) # doctest: +SKIP Your ``model_instance`` now has a ``mock`` attribute storing a synthetic galaxy population. See the `~HodModelFactory.populate_mock` docstring for details. There also convenience functions for estimating the clustering signal predicted by the model. For example, the following method repeatedly populates the Bolshoi simulation with galaxies, computes the 3-d galaxy clustering signal of each mock, computes the median clustering signal in each bin, and returns the result: >>> r, xi = model_instance.compute_average_galaxy_clustering(num_iterations = 5, simname = 'bolshoi', redshift = 0.5) # doctest: +SKIP In this next example we'll show how to build a new model from an existing one using the ``baseline_model_instance`` option. We will start from the composite model built in Example 1 above. Here we'll build a new model which is identical the ``model_instance`` above, only we instead use the `AssembiasZheng07Cens` class to introduce assembly bias into the occupation statistics of central galaxies. >>> from halotools.empirical_models import AssembiasZheng07Cens >>> new_cen_occ_model = AssembiasZheng07Cens() >>> new_model_instance = HodModelFactory(baseline_model_instance = model_instance, centrals_occupation = new_cen_occ_model) The ``new_model_instance`` and the original ``model_instance`` are identical in every respect except for the assembly bias of central galaxy occupation. See also --------- :ref:`hod_model_factory_source_notes` :ref:`hod_mock_factory_source_notes` """ input_model_dictionary, supplementary_kwargs = self._parse_constructor_kwargs( **kwargs) super(HodModelFactory, self).__init__(input_model_dictionary, **supplementary_kwargs) self.mock_factory = HodMockFactory self.model_factory = HodModelFactory self._model_feature_calling_sequence = ( self.build_model_feature_calling_sequence(supplementary_kwargs)) self.model_dictionary = collections.OrderedDict() for key in self._model_feature_calling_sequence: # Making a copy is not strictly necessary, but we do it here to emphasize # at the syntax-level that the model_dictionary and _input_model_dictionary # are fully independent, not pointers to the same locations in memory self.model_dictionary[key] = copy(self._input_model_dictionary[key]) self._test_censat_occupation_consistency(self.model_dictionary) # Build up and bind several lists from the component models self.set_gal_types() self.build_prim_sec_haloprop_list() self.build_prof_param_keys() self.build_publication_list() self.build_dtype_list() self.build_new_haloprop_func_dict() self.set_warning_suppressions() self.set_inherited_methods() self.set_model_redshift() self.build_init_param_dict() # Create a set of bound methods with specific names # that will be called by the mock factory self.set_primary_behaviors() self.set_calling_sequence() self._test_dictionary_consistency() ############################################################ def _parse_constructor_kwargs(self, **kwargs): """ Method used to parse the arguments passed to the constructor into a model dictionary and supplementary arguments. `parse_constructor_kwargs` examines the keyword arguments passed to `__init__`, and identifies the possible presence of ``galaxy_selection_func``, ``halo_selection_func``, ``model_feature_calling_sequence`` and ``gal_type_list``; all other keyword arguments will be treated as component models, and it is enforced that the values bound to all such arguments at the very least have a ``_methods_to_inherit`` attribute. Parameters ----------- **kwargs : optional keyword arguments keywords will be interpreted as the ``feature name``; values must be instances of Halotools component models Returns -------- input_model_dictionary : dict Model dictionary defining the composite model. supplementary_kwargs : dict Dictionary of any possible remaining keyword arguments passed to the `__init__` constructor that are not part of the composite model dictionary, e.g., ``model_feature_calling_sequence``. """ if len(kwargs) == 0: msg = ("You did not pass any model features to the factory") raise HalotoolsError(msg) try: self._factory_constructor_redshift = kwargs.pop('redshift') except KeyError: pass if 'baseline_model_instance' in kwargs: baseline_model_dictionary = kwargs['baseline_model_instance'].model_dictionary input_model_dictionary = copy(kwargs) del input_model_dictionary['baseline_model_instance'] # First parse the supplementary keyword arguments, # such as 'model_feature_calling_sequence', # from the keywords that are bound to component model instances, # such as 'centrals_occupation' possible_supplementary_kwargs = ( 'halo_selection_func', 'model_feature_calling_sequence', 'gal_type_list' ) supplementary_kwargs = {} for key in possible_supplementary_kwargs: try: supplementary_kwargs[key] = copy(input_model_dictionary[key]) del input_model_dictionary[key] except KeyError: pass if 'gal_type_list' not in supplementary_kwargs: supplementary_kwargs['gal_type_list'] = None if 'model_feature_calling_sequence' not in supplementary_kwargs: supplementary_kwargs['model_feature_calling_sequence'] = None new_model_dictionary = copy(baseline_model_dictionary) for key, value in list(input_model_dictionary.items()): new_model_dictionary[key] = value return new_model_dictionary, supplementary_kwargs else: input_model_dictionary = copy(kwargs) # First parse the supplementary keyword arguments, # such as 'model_feature_calling_sequence', # from the keywords that are bound to component model instances, # such as 'centrals_occupation' possible_supplementary_kwargs = ( 'halo_selection_func', 'model_feature_calling_sequence', 'gal_type_list' ) supplementary_kwargs = {} for key in possible_supplementary_kwargs: try: supplementary_kwargs[key] = copy(input_model_dictionary[key]) del input_model_dictionary[key] except KeyError: pass if 'gal_type_list' not in supplementary_kwargs: supplementary_kwargs['gal_type_list'] = None if 'model_feature_calling_sequence' not in supplementary_kwargs: supplementary_kwargs['model_feature_calling_sequence'] = None return input_model_dictionary, supplementary_kwargs
[docs] def build_model_feature_calling_sequence(self, supplementary_kwargs): """ Method uses the ``model_feature_calling_sequence`` passed to __init__, if available. If no such argument was passed, the default sequence will be to first call ``occupation`` features, then call all other features in a random order, always calling features associated with a ``centrals`` population first (if presesent). Parameters ----------- supplementary_kwargs : dict Dictionary storing all keyword arguments passed to the `__init__` constructor that were not part of the input model dictionary. Returns ------- model_feature_calling_sequence : list List of strings specifying the order in which the component models will be called upon during mock population to execute their methods. See also --------- :ref:`model_feature_calling_sequence_mechanism` """ ######################## # Require that all elements of the input model_feature_calling_sequence # were also keyword arguments to the __init__ constructor try: model_feature_calling_sequence = list(supplementary_kwargs['model_feature_calling_sequence']) for model_feature in model_feature_calling_sequence: try: assert model_feature in list(self._input_model_dictionary.keys()) except AssertionError: msg = ("\nYour input ``model_feature_calling_sequence`` has a ``%s`` element\n" "that does not appear in the keyword arguments you passed to the HodModelFactory.\n" "For every element of the input ``model_feature_calling_sequence``, " "there must be a corresponding \n" "keyword argument to which a component model instance is bound.\n") raise HalotoolsError(msg % model_feature) except TypeError: # The supplementary_kwargs['model_feature_calling_sequence'] was None, triggering a TypeError, # so we will use the default calling sequence instead # The default sequence will be to first use the centrals_occupation (if relevant), # then any possible additional occupation features, then any possible remaining features model_feature_calling_sequence = [] occupation_keys = [key for key in self._input_model_dictionary if 'occupation' in key] centrals_occupation_keys = [key for key in occupation_keys if 'central' in key] remaining_occupation_keys = [key for key in occupation_keys if key not in centrals_occupation_keys] model_feature_calling_sequence.extend(centrals_occupation_keys) model_feature_calling_sequence.extend(remaining_occupation_keys) remaining_model_dictionary_keys = ( [key for key in self._input_model_dictionary if key not in model_feature_calling_sequence] ) model_feature_calling_sequence.extend(remaining_model_dictionary_keys) ######################## ######################## # Now conversely require that all remaining __init__ constructor keyword arguments # appear in the model_feature_calling_sequence for constructor_kwarg in self._input_model_dictionary: try: assert constructor_kwarg in model_feature_calling_sequence except AssertionError: msg = ("\nYou passed ``%s`` as a keyword argument to the HodModelFactory constructor.\n" "This keyword argument does not appear in your input ``model_feature_calling_sequence``\n" "and is otherwise not recognized.\n") raise HalotoolsError(msg % constructor_kwarg) ######################## gal_type_list = supplementary_kwargs['gal_type_list'] self._test_model_feature_calling_sequence_consistency( model_feature_calling_sequence, gal_type_list) return model_feature_calling_sequence
def _test_model_feature_calling_sequence_consistency(self, model_feature_calling_sequence, gal_type_list): """ """ for model_feature_calling_sequence_element in model_feature_calling_sequence: try: component_model = self._input_model_dictionary[model_feature_calling_sequence_element] except KeyError: msg = ("\nYour input ``model_feature_calling_sequence`` has a ``%s`` element\n" "that does not appear in the keyword arguments passed to \n" "the constructor of the HodModelFactory.\n") raise HalotoolsError(msg % model_feature_calling_sequence_element) component_model_class_name = component_model.__class__.__name__ gal_type, feature_name = self._infer_gal_type_and_feature_name( model_feature_calling_sequence_element, gal_type_list) try: component_model_gal_type = component_model.gal_type except AttributeError: self._input_model_dictionary[model_feature_calling_sequence_element].gal_type = gal_type component_model_gal_type = gal_type try: component_model_feature_name = component_model.feature_name except AttributeError: self._input_model_dictionary[model_feature_calling_sequence_element].feature_name = feature_name component_model_feature_name = feature_name try: assert gal_type == component_model_gal_type except AssertionError: msg = ("\nThe ``%s`` component model instance has ``gal_type`` = %s.\n" "However, you used a keyword argument = ``%s`` when passing this component model \n" "to the constructor of the HodModelFactory, " "\nfrom which it was inferred that your intended" "``gal_type`` = %s, which is inconsistent.\n" "If this inferred ``gal_type`` seems incorrect,\n" "please raise an Issue on https://github.com/astropy/halotools.\n" "Otherwise, either change the ``%s`` keyword argument " "to conform to the Halotools convention \n" "to use keyword arguments that are composed of a " "``gal_type`` and ``feature_name`` substring,\n" "separated by a '_', in that order.\n") raise HalotoolsError(msg % (component_model_class_name, component_model_gal_type, model_feature_calling_sequence_element, gal_type, model_feature_calling_sequence_element)) try: assert feature_name == component_model_feature_name except AssertionError: msg = ("\nThe ``%s`` component model instance has ``feature_name`` = %s.\n" "However, you used a keyword argument = ``%s`` when passing this component model \n" "to the constructor of the HodModelFactory, \nfrom which it was inferred that your intended" "``feature_name`` = %s, which is inconsistent.\n" "If this inferred ``feature_name`` seems incorrect,\n" "please raise an Issue on https://github.com/astropy/halotools.\n" "Otherwise, either change the ``%s`` keyword argument " "to conform to the Halotools convention \n" "to use keyword arguments that are composed of a " "``gal_type`` and ``feature_name`` substring,\n" "separated by a '_', in that order.\n") raise HalotoolsError(msg % (component_model_class_name, component_model_feature_name, model_feature_calling_sequence_element, feature_name, model_feature_calling_sequence_element)) def _infer_gal_type_and_feature_name(self, model_dictionary_key, gal_type_list, known_gal_type=None, known_feature_name=None): processed_key = model_dictionary_key.lower() if known_gal_type is not None: gal_type = known_gal_type # Ensure that the gal_type appears first in the string if processed_key[0:len(gal_type)] != gal_type: msg = ("\nThe first substring of each key of the ``model_dictionary`` \n" "must be the ``gal_type`` substring. So the first substring of the ``%s`` key \n" "should be %s") raise HalotoolsError(msg % (model_dictionary_key, gal_type)) # Remove the gal_type substring processed_key = processed_key.replace(gal_type, '') # Ensure that the gal_type and feature_name were separated by a '_' if processed_key[0] != '_': msg = ("\nThe model_dictionary key ``%s`` must be comprised of \n" "the ``gal_type`` and ``feature_name`` substrings, separated by a '_', in that order.\n") raise HalotoolsError(msg % model_dictionary_key) else: processed_key = processed_key[1:] feature_name = processed_key return gal_type, feature_name elif known_feature_name is not None: feature_name = known_feature_name # Ensure that the feature_name appears last in the string feature_name_first_idx = processed_key.find(feature_name) if processed_key[feature_name_first_idx:] != feature_name: msg = ("\nThe second substring of each key of the ``model_dictionary`` \n" "must be the ``feature_name`` substring. So the second substring of the ``%s`` key \n" "should be %s") raise HalotoolsError(msg % (model_dictionary_key, feature_name)) # Remove the feature_name substring processed_key = processed_key.replace(feature_name, '') # Ensure that the gal_type and feature_name were separated by a '_' if processed_key[-1] != '_': msg = ("\nThe model_dictionary key ``%s`` must be comprised of \n" "the ``gal_type`` and ``feature_name`` substrings, separated by a '_', in that order.\n") raise HalotoolsError(msg % model_dictionary_key) else: processed_key = processed_key[:-1] gal_type = processed_key return gal_type, feature_name else: if gal_type_list is not None: gal_type_guess_list = gal_type_list else: gal_type_guess_list = ('centrals', 'satellites') for gal_type_guess in gal_type_guess_list: if gal_type_guess in processed_key: known_gal_type = gal_type_guess gal_type, feature_name = self._infer_gal_type_and_feature_name( processed_key, gal_type_guess_list, known_gal_type=known_gal_type) return gal_type, feature_name msg = ("\nThe ``_infer_gal_type_and_feature_name`` method was unable to identify\n" "the name of your galaxy population from the ``%s`` key of the model_dictionary.\n" "If you are modeling a population whose name is neither ``centrals`` nor ``satellites``,\n" "then you must provide a ``gal_type_list`` keyword argument to \n" "the constructor of the HodModelFactory.\n") raise HalotoolsError(msg % model_dictionary_key)
[docs] def set_gal_types(self): """ Private method binding the ``gal_types`` list attribute. If there are both centrals and satellites, method ensures that centrals will always be built first, out of consideration for satellite model components with explicit dependence on the central population. """ _gal_type_list = [] for component_model in list(self.model_dictionary.values()): _gal_type_list.append(component_model.gal_type) self.gal_types = sorted(list(set(_gal_type_list)))
[docs] def set_primary_behaviors(self): """ Creates names and behaviors for the primary methods of `HodModelFactory` that will be used by the outside world. Notes ----- The new methods created here are given standardized names, for consistent communication with the rest of the package. This consistency is particularly important for mock-making, so that the `HodMockFactory` can always call the same functions regardless of the complexity of the model. The behaviors of the methods created here are defined elsewhere; `set_primary_behaviors` just creates a symbolic link to those external behaviors. """ for component_model in list(self.model_dictionary.values()): gal_type = component_model.gal_type feature_name = component_model.feature_name try: component_model_galprop_dtype = component_model._galprop_dtypes_to_allocate except AttributeError: component_model_galprop_dtype = np.dtype([]) methods_to_inherit = list(set( component_model._methods_to_inherit)) for methodname in methods_to_inherit: new_method_name = methodname + '_' + gal_type new_method_behavior = self.update_param_dict_decorator( component_model, methodname) setattr(self, new_method_name, new_method_behavior) setattr(getattr(self, new_method_name), '_galprop_dtypes_to_allocate', component_model_galprop_dtype) setattr(getattr(self, new_method_name), 'gal_type', gal_type) setattr(getattr(self, new_method_name), 'feature_name', feature_name) docstring = getattr(component_model, methodname).__doc__ getattr(self, new_method_name).__doc__ = docstring if hasattr(component_model, '_additional_kwargs_dict'): additional_kwargs_dict = component_model._additional_kwargs_dict self._test_additional_kwargs_dict(additional_kwargs_dict) try: additional_kwargs = additional_kwargs_dict[methodname] setattr(getattr(self, new_method_name), 'additional_kwargs', additional_kwargs) except KeyError: pass attrs_to_inherit = list(set( component_model._attrs_to_inherit)) for attrname in attrs_to_inherit: new_attr_name = attrname + '_' + gal_type attr = getattr(component_model, attrname) setattr(self, new_attr_name, attr) # Repeatedly overwrite self.threshold # This is harmless provided that all gal_types are ensured to have the same threshold, # which is guaranteed by the _test_dictionary_consistency method if hasattr(component_model, 'threshold'): setattr(self, 'threshold_' + gal_type, component_model.threshold) self.threshold = getattr(self, 'threshold_' + gal_type)
[docs] def update_param_dict_decorator(self, component_model, func_name): """ Decorator used to propagate any possible changes in the composite model param_dict down to the appropriate component model param_dict. The behavior of the methods bound to the composite model are decorated versions of the methods defined in the component models. The decoration is done with `update_param_dict_decorator`. For each function that gets bound to the composite model, what this decorator does is search the param_dict of the component_model associated with the function, and update all matching keys in that param_dict with the param_dict of the composite. This way, all the user needs to do is make changes to the composite model param_dict. Then, when calling any method of the composite model, the changed values of the param_dict automatically propagate down to the component model before calling upon its behavior. This allows the composite_model to control behavior of functions that it does not define. Parameters ----------- component_model : obj Instance of the component model in which the behavior of the function is defined. func_name : string Name of the method in the component model whose behavior is being decorated. Returns -------- decorated_func : function Function object whose behavior is identical to the behavior of the function in the component model, except that the component model param_dict is first updated with any possible changes to corresponding parameters in the composite model param_dict. See also -------- :ref:`update_param_dict_decorator_mechanism` :ref:`param_dict_mechanism` """ return ModelFactory.update_param_dict_decorator(self, component_model, func_name)
[docs] def build_lookup_tables(self): """ Method to compute and load lookup tables for each of the phase space component models. """ for component_model in list(self.model_dictionary.values()): if hasattr(component_model, 'build_lookup_tables'): component_model.build_lookup_tables()
[docs] def build_init_param_dict(self): """ Create the ``param_dict`` attribute of the instance. The ``param_dict`` is a dictionary storing the full collection of parameters controlling the behavior of the composite model. The ``param_dict`` dictionary is determined by examining the ``param_dict`` attribute of every component model, and building up a composite dictionary from them. It is permissible for the same parameter name to appear more than once amongst a set of component models, but a warning will be issued in such cases. Notes ----- In MCMC applications, the items of ``param_dict`` defines the possible parameter set explored by the likelihood engine. Changing the values of the parameters in ``param_dict`` will propagate to the behavior of the component models when the relevant methods are called. See also --------- set_warning_suppressions :ref:`param_dict_mechanism` """ self.param_dict = {} try: suppress_warning = self._suppress_repeated_param_warning except AttributeError: suppress_warning = False msg = ("\n\nThe param_dict key %s appears in more than one component model.\n" "This is permissible, but if you are seeing this message you should be sure you " "understand it.\nIn particular, double-check that this parameter does not have " "conflicting meanings across components.\n" "\nIf you do not wish to see this message every time you instantiate, \n" "simply attach a _suppress_repeated_param_warning attribute \n" "to any of your component models and set this variable to ``True``.\n") for component_model in list(self.model_dictionary.values()): if not hasattr(component_model, 'param_dict'): component_model.param_dict = {} intersection = set(self.param_dict.keys()) & set(component_model.param_dict.keys()) if intersection != set(): for key in intersection: if suppress_warning is False: warn(msg % key) for key, value in list(component_model.param_dict.items()): self.param_dict[key] = value self._init_param_dict = copy(self.param_dict)
[docs] def restore_init_param_dict(self): """ Reset all values of the current ``param_dict`` to the values the class was instantiated with. Primary behaviors are reset as well, as this is how the inherited behaviors get bound to the values in ``param_dict``. """ self.param_dict = self._init_param_dict self.set_primary_behaviors() self.set_calling_sequence()
[docs] def set_model_redshift(self): """ """ zlist = list(model.redshift for model in list(self.model_dictionary.values()) if hasattr(model, 'redshift')) if len(set(zlist)) == 0: try: self.redshift = self._factory_constructor_redshift except AttributeError: self.redshift = sim_defaults.default_redshift elif len(set(zlist)) == 1: self.redshift = float(zlist[0]) else: msg = ("Inconsistency between the redshifts of the component models:\n\n") for model in list(self.model_dictionary.values()): gal_type = model.gal_type clname = model.__class__.__name__ if hasattr(model, 'redshift'): zs = str(model.redshift) msg += ("For gal_type = ``" + gal_type + "``, the " + clname+" instance has redshift = " + zs + "\n") raise HalotoolsError(msg) if hasattr(self, '_factory_constructor_redshift'): msg = ("You passed an input argument of ``redshift`` = {0} to the HodModelFactory\n" "that is inconsistent with the redshift z = {1} defined by " "the component models".format( self._factory_constructor_redshift, self.redshift)) assert self.redshift == self._factory_constructor_redshift, msg
[docs] def build_prim_sec_haloprop_list(self): """ Method builds the ``_haloprop_list`` of strings. This list stores the names of all halo catalog columns that appear as either ``prim_haloprop_key`` or ``sec_haloprop_key`` of any component model. For all strings appearing in ``_haloprop_list``, the mock ``galaxy_table`` will have a corresponding column storing the halo property inherited by the mock galaxy. """ haloprop_list = [] for component_model in list(self.model_dictionary.values()): if hasattr(component_model, 'prim_haloprop_key'): haloprop_list.append(component_model.prim_haloprop_key) if hasattr(component_model, 'sec_haloprop_key'): haloprop_list.append(component_model.sec_haloprop_key) if hasattr(component_model, 'halo_boundary_key'): haloprop_list.append(component_model.halo_boundary_key) if hasattr(component_model, 'list_of_haloprops_needed'): haloprop_list.extend(component_model.list_of_haloprops_needed) self._haloprop_list = list(set(haloprop_list))
[docs] def build_prof_param_keys(self): """ """ halo_prof_param_keys = [] gal_prof_param_keys = [] for component_model in list(self.model_dictionary.values()): if hasattr(component_model, 'halo_prof_param_keys'): halo_prof_param_keys.extend(component_model.halo_prof_param_keys) if hasattr(component_model, 'gal_prof_param_keys'): gal_prof_param_keys.extend(component_model.gal_prof_param_keys) self.halo_prof_param_keys = list(set(halo_prof_param_keys)) self.gal_prof_param_keys = list(set(gal_prof_param_keys))
[docs] def build_publication_list(self): """ """ pub_list = [] for component_model in list(self.model_dictionary.values()): if hasattr(component_model, 'publications'): pub_list.extend(component_model.publications) self.publications = list(set(pub_list))
[docs] def build_dtype_list(self): """ Create the `_galprop_dtypes_to_allocate` attribute that determines the name and data type of every galaxy property that will appear in the mock ``galaxy_table``. This attribute is determined by examining the `_galprop_dtypes_to_allocate` attribute of every component model, and building a composite set of all these dtypes, enforcing self-consistency in cases where the same galaxy property appears more than once. See also --------- :ref:`galprop_dtypes_to_allocate_mechanism` """ dtype_list = [] for component_model in list(self.model_dictionary.values()): # Column dtypes to add to mock galaxy_table if hasattr(component_model, '_galprop_dtypes_to_allocate'): dtype_list.append(component_model._galprop_dtypes_to_allocate) self._galprop_dtypes_to_allocate = model_helpers.create_composite_dtype(dtype_list)
[docs] def build_new_haloprop_func_dict(self): """ Method used to build a dictionary of functions, ``new_haloprop_func_dict``, that create new halo catalog columns during a pre-processing phase of mock population. See also --------- :ref:`new_haloprop_func_dict_mechanism` """ new_haloprop_func_dict = {} for component_model in list(self.model_dictionary.values()): feature_name, gal_type = component_model.feature_name, component_model.gal_type # Haloprop function dictionaries if hasattr(component_model, 'new_haloprop_func_dict'): dict_intersection = set(new_haloprop_func_dict).intersection( set(component_model.new_haloprop_func_dict)) if dict_intersection == set(): new_haloprop_func_dict = dict( list(new_haloprop_func_dict.items()) + list(component_model.new_haloprop_func_dict.items()) ) else: example_repeated_element = list(dict_intersection)[0] msg = ("The composite model received multiple " "component models \nwith a new_haloprop_func_dict that use " "the %s key. \nIgnoring the one that appears in the %s " "component for %s galaxies") warn(msg % (example_repeated_element, feature_name, gal_type)) self.new_haloprop_func_dict = new_haloprop_func_dict
[docs] def set_warning_suppressions(self): """ Method used to determine whether a warning should be issued if the `build_init_param_dict` method detects the presence of multiple appearances of the same parameter name. If *any* of the component model instances have a ``_suppress_repeated_param_warning`` attribute that is set to the boolean True value, then no warning will be issued even if there are multiple appearances of the same parameter name. This allows the user to not be bothered with warning messages for cases where it is understood that there will be no conflicting behavior. See also --------- build_init_param_dict """ self._suppress_repeated_param_warning = False for component_model in list(self.model_dictionary.values()): if hasattr(component_model, '_suppress_repeated_param_warning'): self._suppress_repeated_param_warning += component_model._suppress_repeated_param_warning
[docs] def set_inherited_methods(self): """ Each component model *should* have a ``_mock_generation_calling_sequence`` attribute that provides the sequence of method names to call during mock population. Additionally, each component *should* have a ``_methods_to_inherit`` attribute that determines which methods will be inherited by the composite model. The ``_mock_generation_calling_sequence`` list *should* be a subset of ``_methods_to_inherit``. If any of the above conditions fail, no exception will be raised during the construction of the composite model. Instead, an empty list will be forcibly attached to each component model for which these lists may have been missing. Also, for each component model, if there are any elements of ``_mock_generation_calling_sequence`` that were missing from ``_methods_to_inherit``, all such elements will be forcibly added to that component model's ``_methods_to_inherit``. Finally, each component model *should* have an ``_attrs_to_inherit`` attribute that determines which attributes will be inherited by the composite model. If any component models did not implement the ``_attrs_to_inherit``, an empty list is forcibly added to the component model. After calling the set_inherited_methods method, it will be therefore be entirely safe to run a for loop over each component model's ``_methods_to_inherit`` and ``_attrs_to_inherit``, even if these lists were forgotten or irrelevant to that particular component. """ for component_model in list(self.model_dictionary.values()): # Ensure that all methods in the calling sequence are inherited try: mock_making_methods = component_model._mock_generation_calling_sequence except AttributeError: mock_making_methods = [] try: inherited_methods = component_model._methods_to_inherit except AttributeError: inherited_methods = [] component_model._methods_to_inherit = [] missing_methods = (set(mock_making_methods) - set(inherited_methods).intersection(set(mock_making_methods))) for methodname in missing_methods: component_model._methods_to_inherit.append(methodname) if not hasattr(component_model, '_attrs_to_inherit'): component_model._attrs_to_inherit = []
[docs] def set_calling_sequence(self): """ Method used to determine the sequence of function calls that will be made during mock population. The methods of each component model will be called one after the other; the order in which the component models are called upon is determined by ``_model_feature_calling_sequence``. When each component model is called, the sequence of methods that are called for that component is determined by the ``_mock_generation_calling_sequence`` attribute bound to the component model instance. See :ref:`model_feature_calling_sequence_mechanism` for further details. """ # model_feature_calling_sequence self._mock_generation_calling_sequence = [] missing_calling_sequence_msg = ("\nComponent models typically have a list attribute called " "_mock_generation_calling_sequence.\nThis list determines the methods that are called " "by the mock factory, and the order in which they are called.\n" "The ``%s`` component of the gal_type = ``%s`` population has no such method.\n" "Only ignore this warning if you are sure this is not an error.\n") for model_feature in self._model_feature_calling_sequence: component_model = self.model_dictionary[model_feature] if hasattr(component_model, '_mock_generation_calling_sequence'): component_method_list = ( [name + '_' + component_model.gal_type for name in component_model._mock_generation_calling_sequence] ) self._mock_generation_calling_sequence.extend(component_method_list) else: warn(missing_calling_sequence_msg % (component_model.feature_name, component_model.gal_type))
def _test_dictionary_consistency(self): """ Impose the following requirements on the dictionary: * All occupation components have the same threshold. * Each element in _mock_generation_calling_sequence is included in _methods_to_inherit """ threshold_list = [getattr(self, 'threshold_' + gal_type) for gal_type in self.gal_types] if len(threshold_list) > 1: d = np.diff(threshold_list) if np.any(d != 0): threshold_msg = '' for gal_type in self.gal_types: threshold_msg += '\n' + gal_type + ' threshold = ' + str(getattr(self, 'threshold_' + gal_type)) msg = ("Inconsistency in the threshold of the " "component occupation models:\n" + threshold_msg + "\n") raise HalotoolsError(msg) missing_method_msg1 = ("\nAll component models have a " "``_mock_generation_calling_sequence`` attribute,\n" "which is a list of method names that are called by the " "``populate_mock`` method of the mock factory.\n" "All component models also have a ``_methods_to_inherit`` attribute, \n" "which determines which methods of the component model are inherited by the composite model.\n" "The former must be a subset of the latter. However, for ``gal_type`` = %s,\n" "the following method was not inherited:\n%s") for component_model in list(self.model_dictionary.values()): mock_generation_methods = set(component_model._mock_generation_calling_sequence) inherited_methods = set(component_model._methods_to_inherit) overlap = mock_generation_methods.intersection(inherited_methods) missing_methods = mock_generation_methods - overlap if missing_methods != set(): some_missing_method = list(missing_methods)[0] raise HalotoolsError(missing_method_msg1 % (component_model.gal_type, some_missing_method)) missing_method_msg2 = ("\nAll component models have a " "``_mock_generation_calling_sequence`` attribute,\n" "which is a list of method names that are called by the " "``populate_mock`` method of the mock factory.\n" "The HodModelFactory builds a composite ``_mock_generation_calling_sequence`` " "from each of these lists.\n" "However, the following method does not appear to have been created during this process:\n%s\n" "This is likely a bug in Halotools - " "please raise an Issue on https://github.com/astropy/halotools\n") for method in self._mock_generation_calling_sequence: if not hasattr(self, method): raise HalotoolsError(missing_method_msg2) def _test_censat_occupation_consistency(self, model_dictionary): """ This private method searches each OccupationComponent instance for a ``central_occupation_model`` attribute. If detected, a check is made on the self-consistency between the class of the object bound to that attribute, and the class of the occupation model actually bound to the centrals population. """ occu_model_list = list(obj for obj in list(model_dictionary.values()) if isinstance(obj, OccupationComponent)) actual_cenocc_model_exists = False for i, occu_model in enumerate(occu_model_list): try: gal_type = occu_model.gal_type if gal_type == 'centrals': actual_cenocc_model = occu_model actual_cenocc_model_exists = True except AttributeError: pass if not actual_cenocc_model_exists: # There is no central occupation model to be inconsistent with return else: for component_model in occu_model_list: try: subordinate_cenocc_model = getattr(component_model, 'central_occupation_model') assert isinstance(subordinate_cenocc_model, actual_cenocc_model.__class__) try: assert set(subordinate_cenocc_model.param_dict) == set(actual_cenocc_model.param_dict) except AttributeError: raise HalotoolsError("The ``centrals`` occupation model " "must have a ``param_dict`` attribute\n") except AttributeError: pass except AssertionError: msg = ("The occupation component of gal_type = ``{0}`` galaxies \n" "has a ``central_occupation_model`` attribute with an inconsistent \n" "implementation with the {1} class controlling the " "occupation statistics of the ``centrals`` population.\n" "If you use the ``cenocc_model`` feature, you must build a \n" "composite model with a self-consistent population of centrals.\n".format( component_model.gal_type, component_model.__class__.__name__)) raise HalotoolsError(msg) def _test_additional_kwargs_dict(self, _additional_kwargs_dict): """ """ assert 'table' not in list(_additional_kwargs_dict.keys()) assert 'seed' not in list(_additional_kwargs_dict.keys())
[docs] def populate_mock(self, halocat, **kwargs): """ Method used to populate a simulation with a Monte Carlo realization of a model. After calling this method, the model instance will have a new ``mock`` attribute. You can then access the galaxy population via ``model.mock.galaxy_table``, an Astropy `~astropy.table.Table`. See :ref:`hod_mock_factory_source_notes` for an in-depth tutorial on the mock-making algorithm. Parameters ---------- halocat : object Either an instance of `~halotools.sim_manager.CachedHaloCatalog` or `~halotools.sim_manager.UserSuppliedHaloCatalog`. 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 will be thrown out immediately after reading the original halo catalog in memory. Default value is set in `~halotools.sim_defaults.Num_ptcl_requirement`. Currently only supported for instances of `~halotools.empirical_models.HodModelFactory`. 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 will be thrown out immediately after reading the original halo catalog in memory. Default is 'halo_mvir'. Currently only supported for instances of `~halotools.empirical_models.HodModelFactory`. masking_function : function, optional 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. Currently only supported for instances of `~halotools.empirical_models.HodModelFactory`. 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 `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. Examples ---------- Here we'll use a pre-built model to demonstrate basic usage. The syntax shown below is the same for all composite models, whether they are pre-built by Halotools or built by you with `HodModelFactory`. >>> 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. You can call the `~halotools.empirical_models.HodMockFactory.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``. Differences in the parameter values will change the behavior of the mock-population. >>> model_instance.param_dict['logMmin'] = 12.1 >>> model_instance.mock.populate() See also -------- :ref:`hod_mock_factory_source_notes` """ ModelFactory.populate_mock(self, halocat, **kwargs)
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