Two-Point Correlation Functions¶

All of these can be imported from halotools.mock_observables.

`tpcf`(sample1, rbins[, sample2, randoms, ...])	Calculate the real space two-point correlation function, \(\xi(r)\).
`wp`(sample1, rp_bins, pi_max[, sample2, ...])	Calculate the projected two point correlation function, \(w_{p}(r_p)\), where \(r_p\) is the separation perpendicular to the line-of-sight (LOS).
`rp_pi_tpcf`(sample1, rp_bins, pi_bins[, ...])	Calculate the redshift space correlation function, \(\xi(r_{p}, \pi)\)
`tpcf_multipole`(s_mu_tcpf_result, mu_bins[, ...])	Calculate the multipoles of the two point correlation function after first computing `s_mu_tpcf`.
`s_mu_tpcf`(sample1, s_bins, mu_bins[, ...])	Calculate the redshift space correlation function, \(\xi(s, \mu)\)
`tpcf_jackknife`(sample1, randoms, rbins[, ...])	Calculate the two-point correlation function, \(\xi(r)\) and the covariance matrix, \({C}_{ij}\), between ith and jth radial bin.
`tpcf_one_two_halo_decomp`(sample1, ...[, ...])	Calculate the real space one-halo and two-halo decomposed two-point correlation functions, \(\xi^{1h}(r)\) and \(\xi^{2h}(r)\).
`mean_delta_sigma`(galaxies, particles, ...[, ...])	Calculate \(\Delta\Sigma(r_p)\), the galaxy-galaxy lensing signal as a function of projected distance.
`marked_tpcf`(sample1, rbins[, sample2, ...])	Calculate the real space marked two-point correlation function, \(\mathcal{M}(r)\).

Calculating the HOD¶

hod_from_mock(haloprop_galaxies, haloprop_halos)

Calculate the HOD of a mock galaxy sample.

FoFGroups(positions, b_perp, b_para[, ...])

Friends-of-friends (FoF) groups class.

`spherical_isolation`(sample1, sample2, r_max)	Determine whether a set of points, `sample1`, is isolated, i.e. does not have a neighbor in `sample2` within an user specified spherical volume centered at each point in `sample1`.
`cylindrical_isolation`(sample1, sample2, ...)	Determine whether a set of points, `sample1`, is isolated, i.e. does not have a neighbor in `sample2` within an user specified cylindrical volume centered at each point in `sample1`.
`conditional_spherical_isolation`(sample1, ...)	Determine whether a set of points, `sample1`, is isolated, i.e. does not have a neighbor in `sample2` within an user specified spherical volume centered at each point in `sample1`, where various additional conditions may be applied to judge whether a matching point is considered to be a neighbor.
`conditional_cylindrical_isolation`(sample1, ...)	Determine whether a set of points, `sample1`, is isolated, i.e. does not have a neighbor in `sample2` within an user specified cylindrical volume centered at each point in `sample1`, where various additional conditions may be applied to judge whether a matching point is considered to be a neighbor.

`mean_radial_velocity_vs_r`(sample1, velocities1)	Calculate the mean pairwise velocity, \(\bar{v}_{12}(r)\).
`mean_los_velocity_vs_rp`(sample1, ...[, ...])	Calculate the mean pairwise line-of-sight (LOS) velocity as a function of projected separation, \(\bar{v}_{z,12}(r_p)\).
`radial_pvd_vs_r`(sample1, velocities1[, ...])	Calculate the pairwise radial velocity dispersion as a function of absolute distance, or as a function of \(s = r / R_{\rm vir}\).
`los_pvd_vs_rp`(sample1, velocities1, rp_bins, ...)	Calculate the pairwise line-of-sight (LOS) velocity dispersion (PVD), as a function of radial distance from `sample1` \(\sigma_{z12}(r_p)\).

radial_profile_3d(sample1, sample2, ...[, ...])

Function used to calculate the mean value of some quantity in sample2 as a function of 3d distance from the points in sample1.

`void_prob_func`(sample1, rbins[, n_ran, ...])	Calculate the void probability function (VPF), \(P_0(r)\), defined as the probability that a random sphere of radius r contains zero points in the input sample.
`underdensity_prob_func`(sample1, rbins[, ...])	Calculate the underdensity probability function (UPF), \(P_U(r)\).

`large_scale_density_spherical_volume`(sample, ...)	Calculate the mean density of the input `sample` from an input set of tracer particles using a sphere centered on each point in the input `sample` as the tracer volume.
`large_scale_density_spherical_annulus`(...[, ...])	Calculate the mean density of the input `sample` from an input set of tracer particles using a spherical annulus centered on each point in the input `sample` as the tracer volume.

`inertia_tensor_per_object`(sample1, sample2, ...)	For each point in `sample1`, identify all `sample2` points within the input `smoothing_scale`; using those points together with the input `weights2`, the `inertia_tensor_per_object` function calculates the inertia tensor of the mass distribution surrounding each point in `sample1`.
`principal_axes_from_inertia_tensors`(...)	Calculate the principal eigenvector of each of the input inertia tensors.