CorrHOD.cubic#

class CorrHOD.cubic.CorrHOD_cubic(path2config: str, HOD_params: Optional[dict] = None, los: str = 'z', boxsize: float = 2000, cosmo: int = 0)[source][source]#

Bases: object

This class is used to compute the 2PCF and the autocorrelation and cross-correlation of the quantiles of the DensitySplit. It takes HOD parameters and a cosmology as input and uses AbacusHOD to generate a cubic mock. It then uses DensitySplit to compute the density field and the quantiles. Finally, it uses pycorr to compute the 2PCF and the autocorrelation and cross-correlation of the quantiles.

Initialises the CorrHOD object. Note that the ‘tracer’ parameter is fixed to ‘LRG’ by default. It can be changed later by manually reassigning the parameter. This tracer is chosen to study LRG and BGS. It can be changed but the code is not guaranteed to work.

Parameters:

  • path2config (str) – Path to the config file of AbacusHOD. It is used to load the simulation. See the documentation for more details.

  • HOD_params (dict, optional) – Dictionary containing the HOD parameters. The keys must be the same as the ones used in AbacusHOD. If not provided, the default parameters of AbacusHOD are used. Defaults to None.

  • los (str, optional) – Line-of-sight direction in which to apply the RSD. Defaults to ‘z’.

  • boxsize (float, optional) – Size of the simulation box in Mpc/h. Defaults to 2000.

  • cosmo (int, optional) – Index of the cosmology to use. This index is used to load the cosmology from the AbacusSummit object. Defaults to 0. See the documentation of AbacusSummit for more details.

Raises:

ValueError – If the simulation is not precomputed. See the documentation of AbacusHOD for more details.

initialize_halo(nthread: int = 16)[source][source]#

Initializes the AbacusHOD object and loads the simulation.

Parameters:

nthread (int, optional) – Number of threads to use. Defaults to 16.

Returns:

Ball – AbacusHOD object containing the simulation.

Return type:

AbacusHOD object

populate_halos(nthread: int = 16, remove_Ball: bool = False)[source][source]#

Populates the halos with galaxies using the HOD parameters.

Parameters:

  • nthread (int, optional) – Number of threads to use. Defaults to 16.

  • remove_Ball (bool, optional) – If True, the Ball object (Dark matter simulation loaded from AbacusHOD) is removed after populating the halos. This is useful to save memory. Defaults to False.

Returns:

cubic_dict – Dictionary containing the HOD catalogue. It contains for each tracer : * ‘x’, ‘y’, ‘z’ : the positions of the galaxies, * ‘vx’, ‘vy’, ‘vz’ : their velocities, * ‘id’ and ‘mass’ : the ID and mass of the halo they belong to, * ‘Ncent’ : the number of central galaxies in the simulation. The first Ncent galaxies are the centrals.

Return type:

dict

set_cubic(positions: ndarray, velocities: ndarray, masses: ndarray, halo_id: ndarray, Ncent: int, tracer: str = 'LRG')[source][source]#

A method to set a tracer data in the cubic dictionary. This will overwrite the tracer data if it already exists. Otherwise, it will create it.

Parameters:

  • positions (np.ndarray) – The positions of the galaxies. It can be a (N,3) array or a dictionary with keys ‘x’, ‘y’, ‘z’.

  • velocities (np.ndarray) – The velocities of the galaxies. It can be a (N,3) array or a dictionary with keys ‘vx’, ‘vy’, ‘vz’.

  • masses (np.ndarray) – The masses of the galaxies. It must be a (N,) array.

  • halo_id (np.ndarray) – The ID of the halo each galaxy belongs to. It must be a (N,) array.

  • Ncent (int) – The number of central galaxies in the simulation. The first Ncent galaxies are the centrals.

  • tracer (str, optional) – The tracer type. Defaults to ‘LRG’.

Raises:

ValueError – If an array is not of the right shape or if a dictionary does not have the right keys.

get_tracer_positions()[source][source]#

Get the positions of the galaxies and apply RSD.

Returns:

data_positions – The positions of the galaxies after RSD. It is a (N,3) array.

Return type:

np.ndarray

compute_DensitySplit(smooth_radius: float = 10, cellsize: float = 5, nquantiles: int = 10, sampling: str = 'randoms', randoms=None, filter_shape: str = 'Gaussian', return_density: bool = True, nthread=16)[source][source]#

Compute the density field and the quantiles using DensitySplit. (see epaillas/densitysplit for more details)

Parameters:

  • smooth_radius (float, optional) – The radius of the Gaussian smoothing in Mpc/h. Defaults to 10.

  • cellsize (float, optional) – The size of the cells in the mesh. Defaults to 10.

  • nquantiles (int, optional) – The number of quantiles to compute. Defaults to 10.

  • sampling (str, optional) – The sampling to use. Can be ‘randoms’ or ‘data’. Defaults to ‘randoms’.

  • randoms (np.ndarray, optional) – The positions of the randoms. It is a (N,3) array of points in the range [0, boxsize] for each coordinate. If set to none, a random array will be created of size nquantiles * len(data_positions). Defaults to None.

  • filter_shape (str, optional) – The shape of the filter to use. Can be ‘Gaussian’ or ‘TopHat’. Defaults to ‘Gaussian’.

  • return_density (bool, optional) – If True, the density field is returned. Defaults to True.

  • nthread (int, optional) – The number of threads to use. Defaults to 16.

Returns:

  • quantiles (np.ndarray) – The quantiles. It is a (nquantiles,N,3) array.

  • density (np.ndarray, optional) – The density field. It is a (N,3) array.

downsample_data(frac: Optional[float] = None, new_n: Optional[float] = None, npoints: Optional[int] = None)[source][source]#

Downsample the data to a new number of galaxies. The quantiles are also downsampled to the same number of galaxies. (They are generated to have the same number of points as the data in each quantile)

Parameters:

new_n (float) – The new number density in the box.

Returns:

  • data_positions (np.ndarray) – The positions of the galaxies after downsampling. It is a (N,3) array.

  • quantiles (np.ndarray, optional) – The quantiles after downsampling. It is a (nquantiles,N,3) array. Returns None if the quantiles have not been computed yet.

compute_auto_corr(quantile: int, mode: str = 'smu', edges: list = [np.linspace(0.1, 200, 50), np.linspace(-1, 1, 60)], mpicomm=None, mpiroot=None, nthread: int = 16)[source][source]#

Compute the auto-correlation of a quantile. The result will also be saved in the class, as self.CF[‘Auto’][f’DS{quantile}’]

Parameters:

  • quantile (int) – Index of the quantile to compute. It must be between 0 and nquantiles-1.

  • mode (str, optional) – The mode to use for the 2PCF. Defaults to ‘smu’. See pycorr for more details .

  • edges (list, optional) – The edges to use for the 2PCF. Defaults to [np.linspace(0.1, 200, 50), np.linspace(-1, 1, 60)]. See pycorr for more details.

  • mpicomm (_type_, optional) – The MPI communicator. Defaults to None.

  • mpiroot (_type_, optional) – The MPI root. Defaults to None.

  • nthread (int, optional) – The number of threads to use. Defaults to 16.

Returns:

xi_quantile – The auto-correlation of the quantile.

Return type:

pycorr.TwoPointCorrelationFunction

compute_cross_corr(quantile: int, mode: str = 'smu', edges: list = [np.linspace(0.1, 200, 50), np.linspace(-1, 1, 60)], mpicomm=None, mpiroot=None, nthread: int = 16)[source][source]#

Compute the cross-correlation of a quantile with the galaxies. The result will also be saved in the class, as self.CF[‘Cross’][f’DS{quantile}’]

Parameters:

  • quantile (int) – Index of the quantile to compute. It must be between 0 and nquantiles-1.

  • mode (str, optional) – The mode to use for the 2PCF. Defaults to ‘smu’. See pycorr for more details .

  • edges (list, optional) – The edges to use for the 2PCF. Defaults to [np.linspace(0.1, 200, 50), np.linspace(-1, 1, 60)]. See pycorr for more details.

  • mpicomm (_type_, optional) – The MPI communicator. Defaults to None.

  • mpiroot (_type_, optional) – The MPI root. Defaults to None.

  • nthread (int, optional) – The number of threads to use. Defaults to 16.

Returns:

xi_quantile – The cross-correlation of the quantile.

Return type:

pycorr.TwoPointCorrelationFunction

compute_2pcf(mode: str = 'smu', edges: list = [np.linspace(0.1, 200, 50), np.linspace(-1, 1, 60)], mpicomm=None, mpiroot=None, nthread: int = 16)[source][source]#

Compute the 2PCF of the galaxies. The result will also be saved in the class, as self.CF[‘2PCF’]

Parameters:

  • mode (str, optional) – The mode to use for the 2PCF. Defaults to ‘smu’. See pycorr for more details .

  • edges (list, optional) – The edges to use for the 2PCF. Defaults to [np.linspace(0.1, 200, 50), np.linspace(-1, 1, 60)]. See pycorr for more details.

  • mpicomm (_type_, optional) – The MPI communicator. Defaults to None.

  • mpiroot (_type_, optional) – The MPI root. Defaults to None.

  • nthread (int, optional) – The number of threads to use. Defaults to 16.

Returns:

xi – The 2PCF of the galaxies.

Return type:

pycorr.TwoPointCorrelationFunction

average_CF(average_on: list = ['x', 'y', 'z'])[source][source]#

Averages the 2PCF, autocorrelation and cross-correlation of the quantiles on the three lines of sight. available (x, y and z)

Parameters:

average_on (list, optional) – The lines of sight to average on. Defaults to [‘x’, ‘y’, ‘z’]. The CFs must have been computed on these lines of sight before calling this function.

Returns:

CF_average – Dictionary containing the averaged 2PCF, autocorrelation and cross-correlation of the quantiles on the lines of sight. It contains : * s : the separation bins (identical for all the lines of sight, as long as the same edges are used for the 2PCF), * 2PCF : the averaged poles of the 2PCF, * Auto : a dictionary containing the averaged autocorrelation of the quantiles. It contains for each quantile DS{quantile} as the poles of the autocorrelation, * Cross : a dictionary containing the averaged cross-correlation of the quantiles. It contains for each quantile DS{quantile} as the poles of the cross-correlation.

Return type:

dict

save(hod_indice: int = 0, path: Optional[str] = None, save_HOD: bool = True, save_pos: bool = True, save_density: bool = True, save_quantiles: bool = True, save_CF: bool = True, save_xi: bool = True, los: str = 'average', save_all: bool = False)[source][source]#

Save the results of the CorrHOD object. Some of the results are saved as dictionaries in numpy files. They can be accessed using the np.load function, and the .item() method of the loaded object.

Parameters:

  • hod_indice (int, optional) – The indice of the set of HOD parameters provided to the CorrHOD Object. Defaults to 0. Be careful to set the right indice if you want to save the HOD parameters !

  • path (str, optional) – The path where to save the results. If None, the path is set to the output_dir of config file. Defaults to None.

  • save_HOD (bool, optional) – If True, the HOD parameters are saved. File saved as hod{hod_indice}_c{cosmo}_p{phase}.npy. Defaults to True.

  • save_pos (bool, optional) – If True, the cubic mock dictionary is saved. File saved as pos_hod{hod_indice}_c{cosmo}_p{phase}.npy. Defaults to True.

  • save_density (bool, optional) – If True, the density PDF is saved. File saved as density_hod{hod_indice}_c{cosmo}_p{phase}.npy. Defaults to True.

  • save_quantiles (bool, optional) – If True, the quantiles of the densitysplit are saved. File saved as quantiles_hod{hod_indice}_c{cosmo}_p{phase}.npy. Defaults to True.

  • save_CF (bool, optional) – If True, the 2PCF, the autocorrelation and cross-correlation of the quantiles are saved. The 2PCF is saved as tpcf_hod{hod_indice}_c{cosmo}_p{phase}.npy. It contains the separation bins in the s key and the poles in the 2PCF key. The Auto and Corr dictionaries are saved as ds_auto_hod{hod_indice}_c{cosmo}_p{phase}.npy and ds_cross_hod{hod_indice}_c{cosmo}_p{phase}.npy. Each dictionnary contains DS{quantile} keys with the CF of the quantile. The s key contains the separation bins. Defaults to True.

  • save_xi (bool, optional) – If True, the pycorr objects are saved in an unique dictionary. The 2PCF can be accessed using the xi[los][‘2PCF’] key. The auto and cross-correlations of the quantiles can be accessed using the xi[los][‘Auto’][‘DS{i}’] and xi[los][‘Cross’][‘DS{i}’] keys. Defaults to True.

  • los (str, optional) – The line of sight along which to save the 2PCF, the autocorrelation and cross-correlation of the quantiles. Can be ‘x’, ‘y’, ‘z’ or ‘average’. Defaults to ‘average’.

  • save_all (bool, optional) – If True, all the results are saved. This overrides the other options. Defaults to False.

run_all(is_populated: bool = False, exit_on_underpopulated: bool = False, los_to_compute='average', downsample_to: Optional[float] = None, remove_Ball: bool = False, smooth_radius: float = 10, cellsize: float = 5, nquantiles: int = 10, sampling: str = 'randoms', filter_shape: str = 'Gaussian', edges=[np.linspace(0.1, 200, 50), np.linspace(-1, 1, 60)], mpicomm=None, mpiroot=None, nthread: int = 16, hod_indice: int = 0, path: Optional[str] = None, **kwargs)[source][source]#

Run all the steps of the CorrHOD object. See tests/test_corrHOD.py for an example of how to use it and its contents.

Note that the **kwargs are used to set the parameters of the save function. If nothing is provided, nothing will be saved. See the documentation of the save function for more details.

Times will be saved in the times_dict attribute of the CorrHOD object and displayed in the logs.

Parameters:

  • los_to_compute (str, optional) – The line of sight along which to compute the 2PCF, the autocorrelation and cross-correlation of the quantiles. If set to ‘average’, the 2PCF, the autocorrelation and cross-correlation of the quantiles will be averaged on the three lines of sight. Defaults to ‘average’.

  • is_populated (bool, optional) – If True, the halos have already been populated before running this method. Defaults to False.

  • exit_on_underpopulated (bool, optional) – If True, the function will exit if the halos are underpopulated compared to the requested number density. Defaults to False.

  • downsample_to (float, optional) – If provided, the galaxies will be randomly downsampled to the provided number density in h^3/Mpc^3 before computing the CFs. This can be useful to reduce the computation time. Defaults to None.

  • remove_Ball (bool, optional) – If True, the Ball object (Dark matter simulation loaded from AbacusHOD) is removed after populating the halos. This is useful to save memory. Defaults to False.

  • smooth_radius (float, optional) – The radius of the Gaussian smoothing in Mpc/h used in the densitysplit. See epaillas/densitysplit for more details. Defaults to 10.

  • cellsize (float, optional) – The size of the cells in the mesh used in the densitysplit. See epaillas/densitysplit for more details. Defaults to 10.

  • nquantiles (int, optional) – The number of quantiles to define in the densitysplit. see epaillas/densitysplit for more details. Defaults to 10.

  • sampling (str, optional) – The type of sampling to use in the densitysplit. See epaillas/densitysplit for more details. Defaults to ‘randoms’.

  • filter_shape (str, optional) – The shape of the smoothing filter to use in the densitysplit. see epaillas/densitysplit for more details. Defaults to ‘Gaussian’.

  • edges (list, optional) – The edges of the s, mu bins to use for the 2PCF, autocorrelation and cross-correlations. Defaults to [np.linspace(0.1, 200, 50), np.linspace(-1, 1, 60)].

  • mpicomm (_type_, optional) – The MPI communicator used in the 2PCF, autocorrelation and cross-correlations. Defaults to None.

  • mpiroot (_type_, optional) – The MPI root used in the 2PCF, autocorrelation and cross-correlations. Defaults to None.

  • nthread (int, optional) – The number of threads to use in the 2PCF, autocorrelation and cross-correlations. Defaults to 16.

  • hod_indice (int, optional) – The indice of the set of HOD parameters provided to the CorrHOD Object. Defaults to 0. Be careful to set the right indice if you want to save the HOD parameters !

  • path (str, optional) – The path where to save the results. If None, the path is set to the output_dir of config file. Defaults to None.

  • **kwargs (dict, optional) – The parameters to pass to the save function. See the documentation of the save function for more details. If nothing is provided, nothing will be saved.