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 \(\xi^{2h}(r)\).
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.

Galaxy Group Statistics

FoFGroups(positions, b_perp, b_para[, …]) Friends-of-friends (FoF) groups class.

Isolation Criteria

spherical_isolation(sample1, sample2, r_max) Determine whether a set of points, sample1, is isolated, i.e.
cylindrical_isolation(sample1, sample2, …) Determine whether a set of points, sample1, is isolated, i.e.
conditional_spherical_isolation(sample1, …) Determine whether a set of points, sample1, is isolated, i.e.
conditional_cylindrical_isolation(sample1, …) Determine whether a set of points, sample1, is isolated, i.e.

Pairwise Velocities

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), \(\sigma_{z12}(r_p)\).

Radial Profiles

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 Statistics

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

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.

Tensor Calculations

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.