Two-point Correlation Functions

There are currently two different classes for calculating the possible two-point correlation functions:

Each of the above classes is a sub-class of the base class npcf, so they have a number of features in common about how they are constructed. The common features are documented here.

class orpheus.npcf_base.BinnedNPCF(order, spins, n_cfs, min_sep, max_sep, nbinsr=None, binsize=None, nbinsphi=100, nmaxs=30, method='DoubleTree', multicountcorr=True, shuffle_pix=0, tree_alpha=None, tree_mincellsize=0.1, tree_maxcellsize=4.0, tree_resos=[0, 0.25, 0.5, 1.0, 2.0], tree_redges=None, rmin_pixsize=20, resoshift_leafs=0, minresoind_leaf=None, maxresoind_leaf=None, methods_avail=['Discrete', 'Tree', 'BaseTree', 'DoubleTree'], verbosity=0, nthreads=16)[source]

Class of a binned N-point correlation function of various arbitrary tracer catalogs. This class contains attributes and methods that can be used across any of its children.

order

The order of the correlation function.

Type: int

spins

The spins of the tracer fields of which the NPCF is computed.

Type: list

n_cfs

The number of independent components of the NPCF.

Type: int

min_sep

The smallest distance of each vertex for which the NPCF is computed.

Type: float

max_sep

The largest distance of each vertex for which the NPCF is computed.

Type: float

nbinsr

The number of radial bins for each vertex of the NPCF. If set to None this attribute is inferred from the binsize attribute.

Type: int, optional

binsize

The logarithmic size of the radial bins for each vertex of the NPCF. If set to None this attribute is inferred from the nbinsr attribute.

Type: float, optional

nbinsphi

The number of angular bins for the NPCF in the real-space basis. Defaults to 100.

Type: float, optional

nmaxs

The largest multipole component considered for the NPCF in the multipole basis. Defaults to 30.

Type: list, optional

method

The method to be employed for the estimator. Defaults to DoubleTree.

Type: str, optional

multicountcorr

Flag on whether to subtract multiplets in which the same tracer appears more than once. Defaults to True.

Type: bool, optional

shuffle_pix

Choice of how to define centers of the cells in the spatial hash structure. Defaults to 0, i.e. position at pixel center of mass.

Type: int, optional

tree_alpha

Parameter used for autosetting tree resolutions given a catalog.

Type: float, optional

tree_mincellsize

Smallest allowed sidelength for cell in tree. Defaults to 0.1.

Type: float

tree_maxcellsize

Largest allowed sidelength for cell in tree. Defaults to 4.

Type: float

tree_resos

The cell sizes of the hierarchical spatial hash structure

Type: list, optional

tree_redges

List of radii where the tree changes resolution.

Type: list, optional

rmin_pixsize

The limiting radial distance relative to the cell of the spatial hash after which one switches to the next hash in the hierarchy. Defaults to 20.

Type: int, optional

resoshift_leafs

Allows for a difference in how the hierarchical spatial hash is traversed for pixels at the base of the NPCF and pixels at leafs. I.e. positive values indicate that leafs will be evaluated at coarser resolutions than the base. Defaults to 0.

Type: int, optional

minresoind_leaf

Sets the smallest resolution in the spatial hash hierarchy which can be used to access tracers at leaf positions. If set to None uses the smallest specified cell size. Defaults to None.

Type: int, optional

maxresoind_leaf

Sets the largest resolution in the spatial hash hierarchy which can be used to access tracers at leaf positions. If set to None uses the largest specified cell size. Defaults to None.

Type: int, optional

verbosity

The level of verbosity during the computation. Level 0: No verbosity, 1: Progress verbosity on python layer, 2: Progress verbosity also on C level, 3: Debug verbosity. Defaults to 0.

Type: int, optional

nthreads

The number of openmp threads used for the reduction procedure. Defaults to 16.

Type: int, optional

bin_centers

The centers of the radial bins for each combination of tomographic redshifts.

Type: numpy.ndarray

bin_centers_mean

The centers of the radial bins averaged over all combination of tomographic redshifts.

Type: numpy.ndarray

phis

The bin centers for the N-2 angles describing the NPCF in the real-space basis.

Type: list

npcf

The natural components of the NPCF in the real space basis. The different axes are specified as follows: (component, zcombi, rbin_1, ..., rbin_N-1, phibin_1, phibin_N-2).

Type: numpy.ndarray

npcf_norm

The normalization of the natural components of the NPCF in the real space basis. The different axes are specified as follows: (zcombi, rbin_1, ..., rbin_N-1, phiin_1, phibin_N-2).

Type: numpy.ndarray

npcf_multipoles

The natural components of the NPCF in the multipole basis. The different axes are specified as follows: (component, zcombi, multipole_1, ..., multipole_N-2, rbin_1, ..., rbin_N-1).

Type: numpy.ndarray

npcf_multipoles_norm

The normalization of the natural components of the NPCF in the multipole basis. The different axes are specified as follows: (zcombi, multipole_1, ..., multipole_N-2, rbin_1, ..., rbin_N-1).

Type: numpy.ndarray

is_edge_corrected

Flag signifying on whether the NPCF multipoles have been edge-corrected. Defaults to False.

Type: bool

autoset_tree(cat, dpix_grid=2.0, nside_grid=2048)[source]

_initprojections(child)[source]

_projectnpcf(child, projection)[source]: Projects npcf to a new basis.

_print_npcfprojections_avail(child)[source]

_checkcats(cats, spins)[source]

_updatetree(new_resos, include_shifts=True)[source]