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Pep8 changes to model_factory_template.py module
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@aphearin
aphearin committed Feb 19, 2021
1 parent f60a8aa commit bee45fc
Showing 1 changed file with 110 additions and 71 deletions.
181 changes: 110 additions & 71 deletions halotools/empirical_models/factories/model_factory_template.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,31 +15,39 @@
from ...sim_manager import sim_defaults
from ...custom_exceptions import HalotoolsError

__all__ = ['ModelFactory']
__author__ = ['Andrew Hearin']
__all__ = ["ModelFactory"]
__author__ = ["Andrew Hearin"]


inconsistent_redshift_error_msg = ("Inconsistency between the redshift "
inconsistent_redshift_error_msg = (
"Inconsistency between the redshift "
"already bound to the existing mock = ``%f`` "
"and the redshift passed as a keyword argument = ``%f``.\n"
"You should instantiate a new model object if you wish to switch halo catalogs.")
inconsistent_simname_error_msg = ("Inconsistency between the simname "
"You should instantiate a new model object if you wish to switch halo catalogs."
)
inconsistent_simname_error_msg = (
"Inconsistency between the simname "
"already bound to the existing mock = ``%s`` "
"and the simname passed as a keyword argument = ``%s``.\n"
"You should instantiate a new model object if you wish to switch halo catalogs.")
inconsistent_halo_finder_error_msg = ("Inconsistency between the halo-finder "
"You should instantiate a new model object if you wish to switch halo catalogs."
)
inconsistent_halo_finder_error_msg = (
"Inconsistency between the halo-finder "
"already bound to the existing mock = ``%s`` "
"and the halo-finder passed as a keyword argument = ``%s``.\n"
"You should instantiate a new model object if you wish to switch halo catalogs.")
inconsistent_version_name_error_msg = ("Inconsistency between the version_name "
"You should instantiate a new model object if you wish to switch halo catalogs."
)
inconsistent_version_name_error_msg = (
"Inconsistency between the version_name "
"already bound to the existing mock = ``%s`` "
"and the version_name passed as a keyword argument = ``%s``.\n"
"You should instantiate a new model object if you wish to switch halo catalogs.")
"You should instantiate a new model object if you wish to switch halo catalogs."
)


@six.add_metaclass(ABCMeta)
class ModelFactory(object):
r""" Abstract container class used to build
r"""Abstract container class used to build
any composite model of the galaxy-halo connection.
See `~halotools.empirical_models.SubhaloModelFactory` for
Expand Down Expand Up @@ -77,18 +85,18 @@ def __init__(self, input_model_dictionary, **kwargs):
self._input_model_dictionary = input_model_dictionary

try:
self.galaxy_selection_func = kwargs['galaxy_selection_func']
self.galaxy_selection_func = kwargs["galaxy_selection_func"]
except KeyError:
pass

try:
self.halo_selection_func = kwargs['halo_selection_func']
self.halo_selection_func = kwargs["halo_selection_func"]
except KeyError:
pass

def populate_mock(self, halocat,
Num_ptcl_requirement=sim_defaults.Num_ptcl_requirement,
**kwargs):
def populate_mock(
self, halocat, Num_ptcl_requirement=sim_defaults.Num_ptcl_requirement, **kwargs
):
r"""
Method used to populate a simulation
with a Monte Carlo realization of a model.
Expand Down Expand Up @@ -217,20 +225,33 @@ def populate_mock(self, halocat,
:ref:`mock_making_tutorials`
"""
if hasattr(self, 'redshift'):
if hasattr(self, "redshift"):
if abs(self.redshift - halocat.redshift) > 0.05:
raise HalotoolsError("Inconsistency between the model redshift = %.2f"
" and the halocat redshift = %.2f" % (self.redshift, halocat.redshift))

mock_factory_init_args = (
{'halocat': halocat, 'model': self, 'Num_ptcl_requirement': Num_ptcl_requirement})
raise HalotoolsError(
"Inconsistency between the model redshift = %.2f"
" and the halocat redshift = %.2f"
% (self.redshift, halocat.redshift)
)

mock_factory_init_args = {
"halocat": halocat,
"model": self,
"Num_ptcl_requirement": Num_ptcl_requirement,
}
try:
mock_factory_init_args['halo_mass_column_key'] = kwargs['halo_mass_column_key']
mock_factory_init_args["halo_mass_column_key"] = kwargs[
"halo_mass_column_key"
]
except KeyError:
pass
self.mock = self.mock_factory(**mock_factory_init_args)

additional_potential_kwargs = ('masking_function', '_testing_mode', 'enforce_PBC', 'seed')
additional_potential_kwargs = (
"masking_function",
"_testing_mode",
"enforce_PBC",
"seed",
)
mockpop_keys = set(additional_potential_kwargs) & set(kwargs)
mockpop_kwargs = {key: kwargs[key] for key in mockpop_keys}
self.mock.populate(**mockpop_kwargs)
Expand Down Expand Up @@ -281,6 +302,7 @@ def update_param_dict_decorator(self, component_model, func_name):
# passing `self` causes a cycle reference when
# the function is assigned as attributes of self.
__param_dict__ = self.param_dict

def decorated_func(*args, **kwargs):

# Update the param_dict as necessary
Expand All @@ -293,7 +315,9 @@ def decorated_func(*args, **kwargs):

return decorated_func

def compute_average_galaxy_clustering(self, num_iterations=5, summary_statistic='median', **kwargs):
def compute_average_galaxy_clustering(
self, num_iterations=5, summary_statistic="median", **kwargs
):
r"""
Method repeatedly populates a simulation with a mock galaxy catalog, computes the clustering
signal of each Monte Carlo realization, and returns a summary statistic of the clustering
Expand Down Expand Up @@ -430,23 +454,23 @@ def compute_average_galaxy_clustering(self, num_iterations=5, summary_statistic=
`~halotools.mock_observables.tpcf` after placing a cut on the
``galaxy_table``, as demonstrated in :ref:`galaxy_catalog_analysis_tutorial2`.
"""
if summary_statistic == 'mean':
if summary_statistic == "mean":
summary_func = np.mean
else:
summary_func = np.median

halocat_kwargs = {}
if 'simname' in kwargs:
halocat_kwargs['simname'] = kwargs['simname']
if 'redshift' in kwargs:
halocat_kwargs['redshift'] = kwargs['redshift']
elif hasattr(self, 'redshift'):
halocat_kwargs['redshift'] = self.redshift
if 'halo_finder' in kwargs:
halocat_kwargs['halo_finder'] = kwargs['halo_finder']
if "simname" in kwargs:
halocat_kwargs["simname"] = kwargs["simname"]
if "redshift" in kwargs:
halocat_kwargs["redshift"] = kwargs["redshift"]
elif hasattr(self, "redshift"):
halocat_kwargs["redshift"] = self.redshift
if "halo_finder" in kwargs:
halocat_kwargs["halo_finder"] = kwargs["halo_finder"]

try:
assert kwargs['simname'] == 'fake'
assert kwargs["simname"] == "fake"
use_fake_sim = True
except (AssertionError, KeyError):
use_fake_sim = False
Expand All @@ -456,43 +480,51 @@ def compute_average_galaxy_clustering(self, num_iterations=5, summary_statistic=
else:
halocat = CachedHaloCatalog(preload_halo_table=True, **halocat_kwargs)

if 'rbins' in kwargs:
rbins = kwargs['rbins']
if "rbins" in kwargs:
rbins = kwargs["rbins"]
else:
rbins = model_defaults.default_rbins

if 'include_crosscorr' in list(kwargs.keys()):
include_crosscorr = kwargs['include_crosscorr']
if "include_crosscorr" in list(kwargs.keys()):
include_crosscorr = kwargs["include_crosscorr"]
else:
include_crosscorr = False

if include_crosscorr is True:

xi_coll = np.zeros(
(len(rbins)-1)*num_iterations*3).reshape(3, num_iterations, len(rbins)-1)
xi_coll = np.zeros((len(rbins) - 1) * num_iterations * 3).reshape(
3, num_iterations, len(rbins) - 1
)

for i in range(num_iterations):
self.populate_mock(halocat=halocat)
rbin_centers, xi_coll[0, i, :], xi_coll[1, i, :], xi_coll[2, i, :] = (
self.mock.compute_galaxy_clustering(**kwargs)
)
(
rbin_centers,
xi_coll[0, i, :],
xi_coll[1, i, :],
xi_coll[2, i, :],
) = self.mock.compute_galaxy_clustering(**kwargs)
xi_11 = summary_func(xi_coll[0, :], axis=0)
xi_12 = summary_func(xi_coll[1, :], axis=0)
xi_22 = summary_func(xi_coll[2, :], axis=0)
return rbin_centers, xi_11, xi_12, xi_22
else:

xi_coll = np.zeros(
(len(rbins)-1)*num_iterations).reshape(num_iterations, len(rbins)-1)
xi_coll = np.zeros((len(rbins) - 1) * num_iterations).reshape(
num_iterations, len(rbins) - 1
)

for i in range(num_iterations):
self.populate_mock(halocat=halocat)
rbin_centers, xi_coll[i, :] = self.mock.compute_galaxy_clustering(**kwargs)
rbin_centers, xi_coll[i, :] = self.mock.compute_galaxy_clustering(
**kwargs
)
xi = summary_func(xi_coll, axis=0)
return rbin_centers, xi

def compute_average_galaxy_matter_cross_clustering(self, num_iterations=5,
summary_statistic='median', **kwargs):
def compute_average_galaxy_matter_cross_clustering(
self, num_iterations=5, summary_statistic="median", **kwargs
):
r"""
Method repeatedly populates a simulation with a mock galaxy catalog,
computes the galaxy-matter cross-correlation
Expand Down Expand Up @@ -635,23 +667,23 @@ def compute_average_galaxy_matter_cross_clustering(self, num_iterations=5,
rather than calling the `~halotools.mock_observables.mean_delta_sigma` function.
"""
if summary_statistic == 'mean':
if summary_statistic == "mean":
summary_func = np.mean
else:
summary_func = np.median

halocat_kwargs = {}
if 'simname' in kwargs:
halocat_kwargs['simname'] = kwargs['simname']
if 'redshift' in kwargs:
halocat_kwargs['redshift'] = kwargs['redshift']
elif hasattr(self, 'redshift'):
halocat_kwargs['redshift'] = self.redshift
if 'halo_finder' in kwargs:
halocat_kwargs['halo_finder'] = kwargs['halo_finder']
if "simname" in kwargs:
halocat_kwargs["simname"] = kwargs["simname"]
if "redshift" in kwargs:
halocat_kwargs["redshift"] = kwargs["redshift"]
elif hasattr(self, "redshift"):
halocat_kwargs["redshift"] = self.redshift
if "halo_finder" in kwargs:
halocat_kwargs["halo_finder"] = kwargs["halo_finder"]

try:
assert kwargs['simname'] == 'fake'
assert kwargs["simname"] == "fake"
use_fake_sim = True
except (AssertionError, KeyError):
use_fake_sim = False
Expand All @@ -661,36 +693,43 @@ def compute_average_galaxy_matter_cross_clustering(self, num_iterations=5,
else:
halocat = CachedHaloCatalog(preload_halo_table=True, **halocat_kwargs)

if 'rbins' in kwargs:
rbins = kwargs['rbins']
if "rbins" in kwargs:
rbins = kwargs["rbins"]
else:
rbins = model_defaults.default_rbins

if 'include_complement' in list(kwargs.keys()):
include_complement = kwargs['include_complement']
if "include_complement" in list(kwargs.keys()):
include_complement = kwargs["include_complement"]
else:
include_complement = False

if include_complement is True:

xi_coll = np.zeros(
(len(rbins)-1)*num_iterations*2).reshape(2, num_iterations, len(rbins)-1)
xi_coll = np.zeros((len(rbins) - 1) * num_iterations * 2).reshape(
2, num_iterations, len(rbins) - 1
)

for i in range(num_iterations):
self.populate_mock(halocat=halocat)
rbin_centers, xi_coll[0, i, :], xi_coll[1, i, :] = (
self.mock.compute_galaxy_matter_cross_clustering(**kwargs)
)
(
rbin_centers,
xi_coll[0, i, :],
xi_coll[1, i, :],
) = self.mock.compute_galaxy_matter_cross_clustering(**kwargs)
xi_11 = summary_func(xi_coll[0, :], axis=0)
xi_22 = summary_func(xi_coll[1, :], axis=0)
return rbin_centers, xi_11, xi_22
else:

xi_coll = np.zeros(
(len(rbins)-1)*num_iterations).reshape(num_iterations, len(rbins)-1)
xi_coll = np.zeros((len(rbins) - 1) * num_iterations).reshape(
num_iterations, len(rbins) - 1
)

for i in range(num_iterations):
self.populate_mock(halocat=halocat)
rbin_centers, xi_coll[i, :] = self.mock.compute_galaxy_matter_cross_clustering(**kwargs)
(
rbin_centers,
xi_coll[i, :],
) = self.mock.compute_galaxy_matter_cross_clustering(**kwargs)
xi = summary_func(xi_coll, axis=0)
return rbin_centers, xi

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