This page details the methods and classes provided by the tabcorr module.

Top-Level Interface

class tabcorr.TabCorr

Class to tabulate halo and predict galaxy correlation functions.

predict(model, separate_gal_type=False, n_gauss_prim=10, check_consistency=True, **occ_kwargs)

Predict the number density and correlation function for a model.

Parameters:

modelHodModelFactory or numpy.ndarray

Instance of halotools.empirical_models.HodModelFactory describing the model for which predictions are made or a numpy array containing the mean occupation in each halo bin. The latter option is mainly used internally.

separate_gal_typebool, optional

If True, the return values are dictionaries divided by each galaxy types contribution to the output result. Default is False.

n_gauss_primint, optional

The number of points used in the Gaussian quadrature to calculate the mean occupation averaged over the primary halo property in each halo bin. Default is 10.

check_consistency: bool, optional

Whether to enforce consistency in the redshift, primary halo property and secondary halo property between the model and the TabCorr instance. Default is True.

**occ_kwargsdict, optional

Keyword arguments passed to the mean_occupation functions of the model.

Returns:

ngalfloat or dict

Galaxy number density. If separate_gal_type is True, this is a dictionary splitting contributions by galaxy type.

xinumpy.ndarray

Correlation function values. If separate_gal_type is True, this is a dictionary splitting contributions by galaxy type.

classmethod read(fname)

Read tabulated correlation functions from the disk.

Parameters:

fnamestring or h5py.Group

Name of the file or h5py group containing the TabCorr object.

Returns:

TabCorr object.

classmethod tabulate(halocat, tpcf, *tpcf_args, mode='auto', Num_ptcl_requirement=300, prim_haloprop_key='halo_mvir', prim_haloprop_bins=30, sec_haloprop_key='halo_nfw_conc', sec_haloprop_percentile_bins=None, sats_per_prim_haloprop=3e-12, downsample=1.0, verbose=False, redshift_space_distortions=True, cens_prof_model=None, sats_prof_model=None, project_xyz=False, cosmology_obs=None, num_threads=1, **tpcf_kwargs)

Tabulate correlation functions for halos.

Parameters:

halocathalotools.sim_manager.CachedHaloCatalog or halotools.sim_manager.UserSuppliedHaloCatalog

Halo catalog used to tabulate correlation functions.

tpcffunction

The halotools correlation function for which values are tabulated. Can also be a custom function as long as it follows the halotools syntax.

*tpcf_argstuple, optional

Positional arguments passed to the tpcf function.

modestring, optional

Whether an auto- (‘auto’) or a cross-correlation (‘cross’) function is tabulated.

Num_ptcl_requirementint, optional

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

prim_haloprop_keystring, optional

Name of the primary halo property governing the occupation statistics of galaxies. Default value is specified in the halotools.empirical_models.model_defaults.

prim_haloprop_binsint or list, optional

Integer determining how many (logarithmic) bins in primary halo property will be used. If a list or numpy array is provided, these will be used as bins directly. Default is 30.

sec_haloprop_keystring, optional

String giving the column name of the secondary halo property governing the assembly bias. Default value is specified in the halotools.empirical_models.model_defaults module.

sec_haloprop_percentile_binsint, float or None, optional

If an integer, determines how many evenly spaced bins in the secondary halo property percentiles are going to be used. If a float between 0 and 1, it determines the split. If None is provided, no binning is applied. Default is None.

sats_per_prim_halopropfloat, optional

Determines how many satellites sample each halo. For each halo, the number is drawn from a Poisson distribution with an expectation value of sats_per_prim_haloprop times the primary halo property. Default is 3e12.

downsamplefloat or function, optional

Fraction between 0 and 1 used to downsample the total sample used to tabulate correlation functions. Values below unity can be used to reduce the computation time. It should not result in biases but the resulting correlation functions will be less accurate. If float, the same value is applied to all halos. If function, it should return the fraction is a function of the primary halo property. Default is 1.0.

verbosebool, optional

Whether the progress should be displayed. Default is False.

redshift_space_distortionsbool, optional

Whether redshift space distortions should be applied to halos/galaxies. Default is True.

cens_prof_modelobject, optional

Instance of halotools.empirical_models.MonteCarloGalProf that determines the phase space coordinates of centrals. If None, halotools.empirical_models.TrivialPhaseSpace will be used. Default is None.

sats_prof_modelobject, optional

Instance of halotools.empirical_models.MonteCarloGalProf that determines the phase space coordinates of satellites. If None, halotools.empirical_models.NFWPhaseSpace will be used. Default is None.

project_xyzbool, optional

If True, the coordinates will be projected along all three spatial axes. If False, only the projection onto the z-axis is used. Default is False.

cosmology_obsobject, optional

Instance of an astropy astropy.cosmology. This can be used to correct coordinates in the simulation for the Alcock-Paczynski (AP) effect, i.e. a mismatch between the cosmology of the model (simulation) and the cosmology used to interpret observations. Note that the cosmology of the simulation is part of the halocat object. If None, no correction for the AP effect is applied. Also, a correction for the AP effect is only applied for auto-correlation functions. Default is None.

num_threadsint, optional

How many threads to use for the tabulation. Default is 1.

**tpcf_kwargsdict, optional

Keyword arguments passed to the tpcf function.

Returns:

halotabtabcorr.TabCorr

TabCorr object.

Raises:

ValueError

If invalid halo bins are given.

RuntimeError

If TabCorr encounters an internal error.

write(fname, overwrite=False, max_args_size=1000000, matrix_dtype=<class 'numpy.float32'>)

Write the tabulated correlation functions to the disk.

Parameters:

fnamestring or h5py.Group

Name of the file or h5py group the data is written to.

overwritebool, optional

If True, any existing file will be overwritten. Default is False.

max_args_sizeint, optional

By default, TabCorr writes all arguments passed to the correlation function when calling tabulate to file. However, arguments that are numpy arrays with more entries than max_args_size will be omitted. Default is 1000000.

matrix_dtypetype

The dtype used to write the correlation matrix to disk. Can be used to save space at the expense of precision.

class tabcorr.Interpolator(tabcorr_list, param_dict_table)

Initialize an interpolation of multiple TabCorr instances.

Parameters:

tabcorr_listlist or numpy.ndarray

TabCorr instances used to interpolate.

param_dict_tableastropy.table.Table

Table containing the keywords and values corresponding to each instance in the TabCorr list. Must have the same length and ordering as tabcorr_list.

Raises:

ValueError

If param_dict_table does not describe a grid.

predict(model, separate_gal_type=False, n_gauss_prim=10, extrapolate=False, check_consistency=True, **occ_kwargs)

Interpolate the predictions from multiple TabCorr instances.

The values of parameters to interpolate should be in the parameter dictionary of the model.

Parameters:

modelHodModelFactory

Instance of halotools.empirical_models.HodModelFactory describing the model for which predictions are made.

separate_gal_typebool, optional

If True, the return values are dictionaries divided by each galaxy types contribution to the output result. Default is False.

n_gauss_primint, optional

The number of points used in the Gaussian quadrature to calculate the mean occupation averaged over the primary halo property in each halo bin. Default is 10.

extrapolatebool, optional

Whether to allow extrapolation beyond points sampled by the input TabCorr instances. Default is False.

check_consistencybool, optional

Whether to enforce consistency in the redshift, primary halo property and secondary halo property between the model and the TabCorr instance. Default is True.

**occ_kwargsdict, optional

Keyword arguments passed to the mean_occupation functions of the model.

Returns:

ngalfloat

Galaxy number density.

xinumpy.ndarray

Correlation function values.

Raises:

ValueError

If extrapolate is set to True and values are outside the interpolation range.

classmethod read(fname)

Read a TabCorr interpolator from the disk.

Parameters:

fnamestring

Name of the file containing the interpolator object.

Returns:

Interpolator object.

write(fname, overwrite=False, max_args_size=1000000, matrix_dtype=<class 'numpy.float32'>)

Write the TabCorr interpolator to the disk.

Parameters:

fnamestring or h5py.Group

Name of the file the data is written to.

overwritebool, optional

If True, any existing file will be overwritten. Default is False.

max_args_sizeint, optional

By default, TabCorr writes all arguments passed to the correlation function when calling tabulate to file. However, arguments that are numpy arrays with more entries than max_args_size will be omitted. Default is 1000000.

matrix_dtypetype

The dtype used to write the correlation matrix to disk. Can be used to save space at the expense of precision.

tabcorr.database

Module providing database capabilities.

class tabcorr.database.TabCorrFlatw0waCDM(H0: ~astropy.cosmology._src.parameter.core.Parameter, Om0: ~astropy.cosmology._src.parameter.core.Parameter, Tcmb0: ~astropy.cosmology._src.parameter.core.Parameter = Parameter(default=<Quantity 0. K>, derived=False, unit=Unit("K"), equivalencies=[], fvalidate='scalar', doc='Temperature of the CMB at z=0.'), Neff: ~astropy.cosmology._src.parameter.core.Parameter = Parameter(default=3.04, derived=False, unit=None, equivalencies=[], fvalidate='non-negative', doc='Number of effective neutrino species.'), m_nu: ~astropy.cosmology._src.parameter.core.Parameter = Parameter(default=<Quantity 0. eV>, derived=False, unit=Unit("eV"), equivalencies=[(Unit("kg"), Unit("J"), <function mass_energy.<locals>.<lambda>>, <function mass_energy.<locals>.<lambda>>), (Unit("kg / m2"), Unit("J / m2"), <function mass_energy.<locals>.<lambda>>, <function mass_energy.<locals>.<lambda>>), (Unit("kg / m3"), Unit("J / m3"), <function mass_energy.<locals>.<lambda>>, <function mass_energy.<locals>.<lambda>>), (Unit("kg / s"), Unit("J / s"), <function mass_energy.<locals>.<lambda>>, <function mass_energy.<locals>.<lambda>>)], fvalidate=<function FLRW.m_nu>, doc='Mass of neutrino species.'), Ob0: ~astropy.cosmology._src.parameter.core.Parameter = Parameter(default=0.0, derived=False, unit=None, equivalencies=[], fvalidate=<function FLRW.Ob0>, doc='Omega baryon; baryonic matter density/critical density at z=0.'), w0: ~astropy.cosmology._src.parameter.core.Parameter = Parameter(default=-1.0, derived=False, unit=None, equivalencies=[], fvalidate='float', doc='Dark energy equation of state at z=0.'), wa: ~astropy.cosmology._src.parameter.core.Parameter = Parameter(default=0.0, derived=False, unit=None, equivalencies=[], fvalidate='float', doc='Negative derivative of dark energy equation of state w.r.t. a.'), *, name: ~astropy.cosmology._src.core._NameField = None, meta: ~astropy.utils.metadata.core.MetaData = None)

class tabcorr.database.TabCorrFlatwCDM(H0: ~astropy.cosmology._src.parameter.core.Parameter, Om0: ~astropy.cosmology._src.parameter.core.Parameter, Tcmb0: ~astropy.cosmology._src.parameter.core.Parameter = Parameter(default=<Quantity 0. K>, derived=False, unit=Unit("K"), equivalencies=[], fvalidate='scalar', doc='Temperature of the CMB at z=0.'), Neff: ~astropy.cosmology._src.parameter.core.Parameter = Parameter(default=3.04, derived=False, unit=None, equivalencies=[], fvalidate='non-negative', doc='Number of effective neutrino species.'), m_nu: ~astropy.cosmology._src.parameter.core.Parameter = Parameter(default=<Quantity 0. eV>, derived=False, unit=Unit("eV"), equivalencies=[(Unit("kg"), Unit("J"), <function mass_energy.<locals>.<lambda>>, <function mass_energy.<locals>.<lambda>>), (Unit("kg / m2"), Unit("J / m2"), <function mass_energy.<locals>.<lambda>>, <function mass_energy.<locals>.<lambda>>), (Unit("kg / m3"), Unit("J / m3"), <function mass_energy.<locals>.<lambda>>, <function mass_energy.<locals>.<lambda>>), (Unit("kg / s"), Unit("J / s"), <function mass_energy.<locals>.<lambda>>, <function mass_energy.<locals>.<lambda>>)], fvalidate=<function FLRW.m_nu>, doc='Mass of neutrino species.'), Ob0: ~astropy.cosmology._src.parameter.core.Parameter = Parameter(default=0.0, derived=False, unit=None, equivalencies=[], fvalidate=<function FLRW.Ob0>, doc='Omega baryon; baryonic matter density/critical density at z=0.'), w0: ~astropy.cosmology._src.parameter.core.Parameter = Parameter(default=-1.0, derived=False, unit=None, equivalencies=[], fvalidate='float', doc='Dark energy equation of state.'), *, name: ~astropy.cosmology._src.core._NameField = None, meta: ~astropy.utils.metadata.core.MetaData = None)

tabcorr.database.configuration(config_str)

Describe the tabulation configuration used.

Parameters:

config_strstr

String describing the configuration. You can specify a mixture of different configuations by separating them with a _. In this case, the first configurations in the list always take precedence in case multiple configurations apply to a parameter.

Returns:

config_dictdict

Dictionary descibing the configuration, i.e. radial binning, number of satellites per halo etc.

Raises:

ValueError

If an unkown configuration was requested.

tabcorr.database.cosmology(suite, i_cosmo=0)

Return the cosmology of a given simulation.

Parameters:

suitestr

The simulation suite.

i_cosmoint, optional

Number corresponding to the cosmology. Default is 0.

Returns:

cosmoastropy.cosmology.Cosmology

The cosmology associated with the simulation. This is a regular astropy cosmology object with sigma8 added as an extra attribute.

Raises:

ValueError

If the an unkown simulation or cosmology number is requested.

tabcorr.database.directory(suite, redshift, i_cosmo=0, i_phase=0, config=None)

Return the directory where all data for a simulation snapshot is stored.

Parameters:

suitestr

The simulation suite.

redshiftfloat

The redshift of the simulation output.

i_cosmoint, optional

If applicable, number corresponding to the cosmology. Default is 0.

i_phaseint, optional

If applicable, number corresponding to the simulation phase. Default is 0.

configstr, optional

Simulation configuration. Only applicable to AbacusSummit. If None, will default to ‘base’ for AbacusSummit. Default is None.

Returns:

pathpathlib.Path

The directory where all data for a simulation snapshot is stored.

Raises:

RuntimeError

If the TABCORR_DATABASE environment variable is not set.

tabcorr.database.read(suite, redshift, tpcf, i_cosmo=0, i_phase=0, sim_config=None, tab_config='default')

Read the TabCorr tabulation for a given simulation, redshift etc.

Parameters:

suitestr

The simulation suite.

redshiftfloat

The redshift of the simulation output.

tpcfstr

String describing the two-point correlation function.

i_cosmoint, optional

If applicable, number corresponding to the cosmology. Default is 0.

i_phaseint, optional

If applicable, number corresponding to the simulation phase. Default is 0.

sim_configstr, optional

Simulation configuration. Only applicable to AbacusSummit. If None, will default to ‘base’ for AbacusSummit. Default is None.

tab_configconfig_strstr, optional

String describing the configuration of the tabulation, i.e. binning, cosmology etc.

Returns:

halotabtabcorr.TabCorr or tabcorr.Interpolator

The tabcorr tabulation.

tabcorr.database.simulation_name(suite, i_cosmo=0, i_phase=0, config=None)

Return the name of a given simulation.

Parameters:

suitestr

The simulation suite.

i_cosmoint, optional

If applicable, number corresponding to the cosmology. Default is 0.

i_phaseint, optional

If applicable, number corresponding to the simulation phase. Default is 0.

configstr, optional

Simulation configuration. Only applicable to AbacusSummit. If None, will default to ‘base’ for AbacusSummit. Default is None.

Returns:

namestr

The name of the simulation.

Raises:

ValueError

If the an unkown simulation, simulation number or phase number is requested.

tabcorr.database.tabcorr(suite, redshift, tpcf, i_cosmo=0, i_phase=0, sim_config=None, tab_config='default')

Read the TabCorr tabulation for a given simulation, redshift etc.

Parameters:

suitestr

The simulation suite.

redshiftfloat

The redshift of the simulation output.

tpcfstr

String describing the two-point correlation function.

i_cosmoint, optional

If applicable, number corresponding to the cosmology. Default is 0.

i_phaseint, optional

If applicable, number corresponding to the simulation phase. Default is 0.

sim_configstr, optional

Simulation configuration. Only applicable to AbacusSummit. If None, will default to ‘base’ for AbacusSummit. Default is None.

tab_configconfig_strstr, optional

String describing the configuration of the tabulation, i.e. binning, cosmology etc.

Returns:

halotabtabcorr.TabCorr or tabcorr.Interpolator

The tabcorr tabulation.