Permalink
Branch: master
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
79 lines (46 sloc) 2.74 KB

Getting started with Corrfunc

Corrfunc is a set of high-performance routines to measure clustering statistics. The codes are divided conceptually into two different segments:

  • theory - calculates clustering statistics on simulation volumes. Input positions are expected to be Cartesian X/Y/Z. Periodic boundary conditions are supported. Relevant C codes are in directory theory/
  • mocks - calculates clustering statistics on observation volumes. Input positions are assumed to be in obverser frame, Right Ascension, Declination and SpeedofLight*Redshift (where required; \omega(\theta) only needs RA and DEC). Relevant C codes are in directory mocks/

This getting-started guide assumes you have already followed the :ref:`step_by_step_install` section of the documentation to get the package and its dependencies set up on your machine.

If you want to compute correlation functions and have installed the python extensions, then see :ref:`function_usage_examples` for typical tasks. Otherwise, read on for the various interfaces available within Corrfunc.

Computing Clustering Statistics with Corrfunc

Corrfunc supports three separate mechanisms to compute the clustering statistics:

  • Via python (if you have python and numpy installed)

    Pros: Fully flexible API to modulate code behaviour at runtime. For instance, calculations can be performed in double-precision simply by passing arrays of doubles (rather than floats).

    Cons: Has fixed python overhead. For low particle numbers, can be as much as 20% slower compared to the command-line executables.

    See :ref:`python-interface` for details on how to use the python interface.

  • Via static libraries directly in C codes

    Pros: Fully flexible API to modulate code behaviour at runtime. All features supported by the python extensions are also supported here.

    Cons: Requires coding in C. See example C codes invoking the theory and mocks in the directories: theory/examples/run_correlations.c and mocks/examples/run_correlations_mocks.c.

    See :ref:`staticlibrary-interface` for details on how to use the static library interface.

  • Command-line executables

    Pros: Fastest possible implementations of all clustering statistics

    Cons: API is fixed. Any changes require full re-compilation.

    See :ref:`commandline-interface` for details on how to use the command-line executables.

Available Corrfunc interfaces

.. toctree::
   :maxdepth: 1

   python-interface
   staticlibrary-interface
   commandline-interface
   all-interfaces