This code implements the Gaussian Streaming Model using components from Convolution Lagrangian Effective Field Theory as described in:

Z.Vlah, E.Castorina, M.White

The Gaussian streaming model and Convolution Lagrangian effective field theory

JCAP 12(2016)007, [https://arxiv.org/abs/1609.02908]

The code is written (mostly) in C++. It can be run from the command line, or called from Python (wrappers provided).

The C++ version in "config2pt" currently only implements the configuration-space statistics (i.e. the correlation function). The Fortran routines in "ps_fortran" provide an implementation of the power spectrum routines, but we recommend using the Python versions instead.

We provide fast and simple Python routines for computing the power spectrum in the ZEFT, Halo-Zeldovich and GSM models including real-space auto- and cross-correlations of biased tracers as described in

C.Modi, M.White, Z.Vlah

Modeling CMB Lensing Cross Correlations with CLEFT

JCAP, 08(2017)009, [https://arxiv.org/abs/1706.03173]

This code is available in the ps_python3 directory.

A fast, pure Python package (VelociLEPTors) to compute real- and redshift-space power spectra and correlation functions using both EPT and LPT is also available at

https://github.com/sfschen/velocileptors

This package has all of the functionality of CLEFT_GSM, plus an extended bias expansion and Fourier-space statistics.