Package 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 Jackknifes sections.
Dependencies¶
RascalC requires the following packages:
- C compiler: Tested with gcc 5.4.O
- 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)
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.