Installation

To install RascalC, simply clone the Github repository and compile the C++ code (see Dependencies below). This is done as follows:

git clone https://github.com/oliverphilcox/RascalC.git
cd RascalC
make

NB: RascalC can be run in various modes by adding compiler flags in the Makefile. See Getting Started and Covariance Matrix Estimation for more information.

Once RascalC is installed, see the Getting Started and Tutorial: Aperiodic Data and Jackknives sections.

Dependencies

RascalC requires the following packages:

C compiler: Tested with 5.4 or later
Gnu Scientific Library (GSL): Any recent version
Corrfunc: 2.0 or later
(Optional but encouraged) OpenMP: Any recent version (required for parallelization)

Corrfunc can be installed using pip install corrfunc and is used for efficient pair counting.

For the Python pre- and post-processing we require:

Python: 2.7 or later, 3.4 or later
Numpy: 1.10 or later
(Optional) Healpy: any recent version. (Necessary if using HealPix jackknife regions)

These can be simply installed with pip or conda.

Acknowledgements

Main Authors:

Oliver H. E. Philcox (Princeton / Harvard)
Daniel J. Eisenstein (Harvard)
Ross O’Connell (Pittsburgh)
Alexander Wiegand (Garching)
Misha Rashkovetskyi (Harvard)

Please cite O’Connell et al. 2016, O’Connell & Eisenstein 2018 , Philcox et al. 2019 and Philcox & Eisenstein 2019 when using this code in your research.

Note that many of the code modules and convenience functions are shared with the small-scale power spectrum estimator code HIPSTER, developed by Oliver Philcox and Daniel Eisenstein.