# Correlation function

Pylians provides several routines to compute auto- and cross-correlation functions.

Note

For the time being, Pylians routines to compute correlation functions only support 3D fields.

## Auto-correlation function

Pylians can be used to efficiently compute correlation functions of a generic field (e.g. total matter, CDM, gas, halos, neutrinos, CDM+gas, galaxies…etc). The ingredients needed are:

• `delta`. This is the density contrast field. It should be a 3 dimensional float numpy array such `delta = np.zeros((grid, grid, grid), dtype=np.float32)`. See Density fields on how to compute density fields using Pylians.

• `BoxSize`. Size of the periodic box. The units of the output radii will depend on this.

• `MAS`. Mass-assignment scheme used to generate the density field, if any. Possible options are `'NGP'`, `'CIC'`, `'TSC'`, `'PCS'`. If the density field has not been generated with any of these set it to `'None'`.

• `axis`. Axis along which compute the quadrupole, hexadecapole. If the field is in real-space set `axis=0`. If the field is in redshift-space set `axis=0`, `axis=1` or `axis=2` if the redshift-space distortions have been placed along the x-axis, y-axis or z-axis, respectively.

• `threads`. This routine is openmp parallelized. Set this to the maximum number of cores per node available in your machine.

An example on how to use the routine is this:

```import numpy as np
import Pk_library as PKL

# correlation function parameters
BoxSize = 1000.0 #Mpc/h
MAS     = 'CIC'
axis    = 0

# compute the correlation function
CF     = PKL.Xi(delta, BoxSize, MAS, axis, threads)

# get the attributes
r      = CF.r3D      #radii in Mpc/h
xi0    = CF.xi[:,0]  #correlation function (monopole)
xi2    = CF.xi[:,1]  #correlation function (quadrupole)
xi4    = CF.xi[:,2]  #correlation function (hexadecapole)
Nmodes = CF.Nmodes3D #number of modes
```

Note

This routine uses a FFT approach that allows a very computationally efficient calculation of the correlation function. However, if the number density of the tracers is very low (i.e. the density field is very sparse) this function may produce strange results. In this case it is better to use the traditional Landy-Szalay routine also available in Pylians.

## Cross-correlation function

The routine `XXi` can be used to compute the cross-correlation function between two generic fields. The ingredients needed are:

• `delta1`. A 3D numpy float32 array containing the value of the first density field; e.g. `delta1 = np.zeros((128,128,128), dtype=np.float32)`.

• `delta2`. A 3D numpy float32 array containing the value of the second density field.

• `BoxSize`. Size of the periodic box. The units of the output radii will depend on this. Note that both fields have to have the same size.

• `MAS`. Mass-assignment scheme used to generate the 3D density field, if any. Possible options are `'NGP'`, `'CIC'`, `'TSC'`, `'PCS'`. If the density field has not been generated with any of these set it to `'None'`. In this case, this variable should be a tuple with 2 values, once for each field.

• `axis`. Axis along which compute the quadrupole, hexadecapole. If the field is in real-space set `axis=0`. If the field is in redshift-space set `axis=0`, `axis=1` or `axis=2` if the redshift-space distortions have been placed along the x-axis, y-axis or z-axis, respectively.

• `threads`. Number of openmp threads to be used.

An example of the usage of this routine is this:

```import numpy as np
import Pk_library as PKL

# correlation function parameters
BoxSize = 1000.0 #Mpc/h
MAS     = ['CIC', 'None']
axis    = 0

# compute cross-correlaton function of the two fields
CCF = PKL.XXi(delta1, delta2, BoxSize, MAS, axis, threads)

# get the attributes
r      = CCF.r3D      #radii in Mpc/h
xxi0   = CCF.xi[:,0]  #monopole