pycorr

pycorr is a wrapper for correlation function estimation, designed to handle different two-point counter engines (currently only Corrfunc). It currently supports:

• theta (angular), s, s-mu, rp-pi binning schemes
• analytical two-point counts with periodic boundary conditions
• inverse bitwise weights (in any integer format) and (angular) upweighting
• MPI parallelization (further requires mpi4py and pmesh)
• jackknife estimate of the correlation function covariance matrix

A typical auto-correlation function estimation is as simple as:

import numpy as np
from pycorr import TwoPointCorrelationFunction

edges = (np.linspace(1, 101, 51), np.linspace(0, 50, 51))
# pass e.g. mpicomm = MPI.COMM_WORLD if input positions and weights are MPI-scattered
result = TwoPointCorrelationFunction('rppi', edges, data_positions1=data_positions1, data_weights1=data_weights1,
                                     randoms_positions1=randoms_positions1, randoms_weights1=randoms_weights1,
                                     engine='corrfunc', nthreads=4)
# separation array in result.sep
# correlation function in result.corr

Example notebooks presenting most use cases are provided in directory nb/.

Documentation

Documentation is hosted on Read the Docs, pycorr docs. As mentioned above, you may find more practical guidance in the example notebooks directory.

Requirements

Strict requirements are:

• numpy
• scipy

To use Corrfunc as two-point-counting engine (only engine linked so far):

• https://github.com/adematti/Corrfunc (desi branch)

To run with MPI:

• mpi4py
• pmesh

Installation

See pycorr docs.

License

pycorr is free software distributed under a BSD3 license. For details see the LICENSE.

Acknowledgments

• Lehman Garrison and Manodeep Sinha for advice when implementing linear binning, and PIP and angular weights into Corrfunc.
• Davide Bianchi for cross-checks of two-point counts with PIP weights.
• Svyatoslav Trusov for script to compute jackknife covariance estimates based on https://arxiv.org/pdf/2109.07071.pdf: https://github.com/theonefromnowhere/JK_pycorr/blob/main/CF_JK_ST_conf.py.
• Enrique Paillas and Seshadri Nadathur for suggestions about reconstructed 2pcf measurements
• Craig Warner for GPU-izing Corrfunc 'smu' counts
• Craig Warner, James Lasker, Misha Rashkovetskyi and Edmond Chaussidon for spotting typos / bug reports

Citations

If you use pycorr with the Corrfunc engine (default one) for research, please cite the MNRAS Corrfunc code papers with the following bibtex entries:

@ARTICLE{2020MNRAS.491.3022S,
    author = {{Sinha}, Manodeep and {Garrison}, Lehman H.},
    title = "{CORRFUNC - a suite of blazing fast correlation functions on
    the CPU}",
    journal = {\mnras},
    keywords = {methods: numerical, galaxies: general, galaxies:
    haloes, dark matter, large-scale structure of Universe, cosmology:
    theory},
    year = "2020",
    month = "Jan",
    volume = {491},
    number = {2},
    pages = {3022-3041},
    doi = {10.1093/mnras/stz3157},
    adsurl =
    {https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.3022S},
    adsnote = {Provided by the SAO/NASA
    Astrophysics Data System}
}


@InProceedings{10.1007/978-981-13-7729-7_1,
    author="Sinha, Manodeep and Garrison, Lehman",
    editor="Majumdar, Amit and Arora, Ritu",
    title="CORRFUNC: Blazing Fast Correlation Functions with AVX512F SIMD Intrinsics",
    booktitle="Software Challenges to Exascale Computing",
    year="2019",
    publisher="Springer Singapore",
    address="Singapore",
    pages="3--20",
    isbn="978-981-13-7729-7",
    url={https://doi.org/10.1007/978-981-13-7729-7_1}
}