def _mean_galprop_fraction(self, **kwargs):
"""
Expectation value of the galprop for galaxies living in the input halos.
Parameters
----------
prim_haloprop : array, optional keyword argument
Array of mass-like variable upon which occupation statistics are based.
If ``prim_haloprop`` is not passed, then ``halo_table`` keyword argument must be passed.
halo_table : object, optional keyword argument
Data table storing halo catalog.
If ``halo_table`` is not passed, then ``prim_haloprop`` keyword argument must be passed.
Returns
-------
mean_galprop_fraction : array_like
Values of the galprop fraction evaluated at the input primary halo properties.
"""
# Retrieve the array storing the mass-like variable
if 'halo_table' in kwargs.keys():
prim_haloprop = kwargs['halo_table'][self.prim_haloprop_key]
elif 'prim_haloprop' in kwargs.keys():
prim_haloprop = kwargs['prim_haloprop']
else:
raise KeyError("Must pass one of the following keyword arguments to mean_occupation:\n"
"``halo_table`` or ``prim_haloprop``")
if self._logparam is True:
prim_haloprop = np.log10(prim_haloprop)
# Update self._abcissa, in case the user has changed it
self._abcissa = getattr(self, self.galprop_key+'_abcissa')
model_ordinates = [self.param_dict[ordinate_key] for ordinate_key in self._ordinates_keys]
if self._interpol_method=='polynomial':
mean_galprop_fraction = model_helpers.polynomial_from_table(
self._abcissa, model_ordinates, prim_haloprop)
elif self._interpol_method=='spline':
spline_function = model_helpers.custom_spline(
self._abcissa, model_ordinates,
k=self._spline_degree)
mean_galprop_fraction = spline_function(prim_haloprop)
else:
raise IOError("Input interpol_method must be 'polynomial' or 'spline'.")
# Enforce boundary conditions
mean_galprop_fraction[mean_galprop_fraction<0]=0
mean_galprop_fraction[mean_galprop_fraction>1]=1
return mean_galprop_fraction
def radprof_modfunc(self,profile_parameter_key,input_abcissa):
"""
Factor by which the halo profile parameters of gal_type galaxies
differ from the profile parameter of their underlying dark matter halo.
Parameters
----------
profile_parameter_key : string
Dictionary key of the profile parameter being modulated, e.g., 'halo_NFW_conc'.
input_abcissa : array_like
array of primary halo property
Returns
-------
output_profile_modulation : array_like
Values of the multiplicative factor that will be used to
modulate the halo profile parameters.
Notes
-----
Either assumes the profile parameters are modulated from those
of the underlying dark matter halo by a polynomial function
of the primary halo property, or is interpolated from a grid.
Either way, the behavior of this method is fully determined by
its values at the model's (abcissa, ordinates), specified by
self.abcissa_dict and self.ordinates_dict.
"""
model_abcissa, model_ordinates = (
self._retrieve_model_abcissa_ordinates(profile_parameter_key)
)
if self.interpol_method=='polynomial':
output_profile_modulation = model_helpers.polynomial_from_table(
model_abcissa,model_ordinates,input_abcissa)
elif self.interpol_method=='spline':
modulating_function = self.spline_function[profile_parameter_key]
output_profile_modulation = modulating_function(input_abcissa)
else:
raise IOError("Input interpol_method must be 'polynomial' or 'spline'.")
return output_profile_modulation
def radprof_modfunc(self, profile_parameter_key, input_abcissa):
"""
Factor by which the halo profile parameters of gal_type galaxies
differ from the profile parameter of their underlying dark matter halo.
Parameters
----------
profile_parameter_key : string
Dictionary key of the profile parameter being modulated, e.g., 'halo_NFW_conc'.
input_abcissa : array_like
array of primary halo property
Returns
-------
output_profile_modulation : array_like
Values of the multiplicative factor that will be used to
modulate the halo profile parameters.
Notes
-----
Either assumes the profile parameters are modulated from those
of the underlying dark matter halo by a polynomial function
of the primary halo property, or is interpolated from a grid.
Either way, the behavior of this method is fully determined by
its values at the model's (abcissa, ordinates), specified by
self.abcissa_dict and self.ordinates_dict.
"""
model_abcissa, model_ordinates = (
self._retrieve_model_abcissa_ordinates(profile_parameter_key))
if self.interpol_method == 'polynomial':
output_profile_modulation = model_helpers.polynomial_from_table(
model_abcissa, model_ordinates, input_abcissa)
elif self.interpol_method == 'spline':
modulating_function = self.spline_function[profile_parameter_key]
output_profile_modulation = modulating_function(input_abcissa)
else:
raise IOError(
"Input interpol_method must be 'polynomial' or 'spline'.")
return output_profile_modulation
def _mean_galprop_fraction(self, **kwargs):
"""
Expectation value of the galprop for galaxies living in the input halos.
Parameters
----------
prim_haloprop : array_like, optional keyword argument
Array of primary halo property, e.g., `mvir`,
at which the galprop fraction is being computed.
Functionality is equivalent to using the prim_haloprop keyword argument.
halos : table, optional keyword argument
Astropy Table containing a halo catalog.
If the ``halos`` keyword argument is passed,
``self.prim_haloprop_key`` must be a column of the halo catalog.
galaxy_table : table, optional keyword argument
Astropy Table containing a galaxy catalog.
If the ``galaxy_table`` keyword argument is passed,
``self.prim_haloprop_key`` must be a column of the galaxy catalog.
input_param_dict : dict, optional keyword argument
If passed, the model will first update
its param_dict with input_param_dict, altering the behavior of the model.
Returns
-------
mean_galprop_fraction : array_like
Values of the galprop fraction evaluated at the input primary halo properties.
"""
model_helpers.update_param_dict(self, **kwargs)
# Retrieve the array storing the mass-like variable
if 'galaxy_table' in kwargs.keys():
key = model_defaults.host_haloprop_prefix+self.prim_haloprop_key
prim_haloprop = kwargs['galaxy_table'][key]
elif 'halos' in kwargs.keys():
prim_haloprop = kwargs['halos'][self.prim_haloprop_key]
elif 'prim_haloprop' in kwargs.keys():
prim_haloprop = kwargs['prim_haloprop']
else:
raise KeyError("Must pass one of the following keyword arguments to mean_occupation:\n"
"``halos``, ``prim_haloprop``, or ``galaxy_table``")
if self._logparam is True:
prim_haloprop = np.log10(prim_haloprop)
# Update self._abcissa, in case the user has changed it
self._abcissa = getattr(self, self.galprop_key+'_abcissa')
model_ordinates = [self.param_dict[ordinate_key] for ordinate_key in self._ordinates_keys]
if self._interpol_method=='polynomial':
mean_galprop_fraction = model_helpers.polynomial_from_table(
self._abcissa, model_ordinates, prim_haloprop)
elif self._interpol_method=='spline':
spline_function = model_helpers.custom_spline(
self._abcissa, model_ordinates,
k=self._spline_degree)
mean_galprop_fraction = spline_function(prim_haloprop)
else:
raise IOError("Input interpol_method must be 'polynomial' or 'spline'.")
# Enforce boundary conditions
mean_galprop_fraction[mean_galprop_fraction<0]=0
mean_galprop_fraction[mean_galprop_fraction>1]=1
return mean_galprop_fraction
def _mean_galprop_fraction(self, **kwargs):
"""
Expectation value of the galprop for galaxies living in the input halos.
Parameters
----------
prim_haloprop : array_like, optional keyword argument
Array of primary halo property, e.g., `mvir`,
at which the galprop fraction is being computed.
Functionality is equivalent to using the prim_haloprop keyword argument.
halos : table, optional keyword argument
Astropy Table containing a halo catalog.
If the ``halos`` keyword argument is passed,
``self.prim_haloprop_key`` must be a column of the halo catalog.
galaxy_table : table, optional keyword argument
Astropy Table containing a galaxy catalog.
If the ``galaxy_table`` keyword argument is passed,
``self.prim_haloprop_key`` must be a column of the galaxy catalog.
input_param_dict : dict, optional keyword argument
If passed, the model will first update
its param_dict with input_param_dict, altering the behavior of the model.
Returns
-------
mean_galprop_fraction : array_like
Values of the galprop fraction evaluated at the input primary halo properties.
"""
model_helpers.update_param_dict(self, **kwargs)
# Retrieve the array storing the mass-like variable
if 'galaxy_table' in kwargs.keys():
key = model_defaults.host_haloprop_prefix + self.prim_haloprop_key
prim_haloprop = kwargs['galaxy_table'][key]
elif 'halos' in kwargs.keys():
prim_haloprop = kwargs['halos'][self.prim_haloprop_key]
elif 'prim_haloprop' in kwargs.keys():
prim_haloprop = kwargs['prim_haloprop']
else:
raise KeyError(
"Must pass one of the following keyword arguments to mean_occupation:\n"
"``halos``, ``prim_haloprop``, or ``galaxy_table``")
if self._logparam is True:
prim_haloprop = np.log10(prim_haloprop)
# Update self._abcissa, in case the user has changed it
self._abcissa = getattr(self, self.galprop_key + '_abcissa')
model_ordinates = [
self.param_dict[ordinate_key]
for ordinate_key in self._ordinates_keys
]
if self._interpol_method == 'polynomial':
mean_galprop_fraction = model_helpers.polynomial_from_table(
self._abcissa, model_ordinates, prim_haloprop)
elif self._interpol_method == 'spline':
spline_function = model_helpers.custom_spline(
self._abcissa, model_ordinates, k=self._spline_degree)
mean_galprop_fraction = spline_function(prim_haloprop)
else:
raise IOError(
"Input interpol_method must be 'polynomial' or 'spline'.")
# Enforce boundary conditions
mean_galprop_fraction[mean_galprop_fraction < 0] = 0
mean_galprop_fraction[mean_galprop_fraction > 1] = 1
return mean_galprop_fraction
