A Rapid Sampler For Large Cross-Covariance Matrices in C++
C++ code to simulate correlation function covariance matrices from large surveys, using a grid and jackknife based approach. This can be used to find covariances of (a) the angularly binned anisotropic 2PCF, (b) the Legendre-binned anisotropic 2PCF and (c) the Legendre-binned isotropic 3PCF in arbitrary survey geometries. For (a) we can also compute the jackknife covariance matrix, which can be used to fit our non-Gaussianity model. There is additionally functionality to compute multi-tracer cross-covariances for the 2PCF.
For full usage, see the ReadTheDocs documentation.
Any usage of this code should cite Philcox et al. 2019 (for the angularly binned 2PCF) and Philcox & Eisenstein 2019 (for the Legendre-binned 2PCF and 3PCF).
New for version 2: Legendre moment covariances and the 3PCF
Python interface and Jupyter notebook tutorial (alpha-testing before version 3.0)
Recommended to use with cosmodesi environment.
In particular, load it before installing:
source /global/common/software/desi/users/adematti/cosmodesi_environment.sh main
pip install -e /global/common/software/desi/users/mrash/RascalC
This installs the library from my software folder in the development mode, so that after I update it e.g. with some fix, you will have the new version without the need to re-install or any other action.
git clone https://github.com/misharash/RascalC
cd RascalC
python3 -m pip install .
Make sure to reinstall after pulling updates. At this early stage, the fixes might be needed quite often.
Or you can install with just one command:
python3 -m pip install https://github.com/misharash/RascalC.git
The code requires pycorr to deal with pair counts and data correlation function estimators.
To compute pair counts of catalogs, you need a custom version of Corrfunc (see also pycorr installation instructions).
Both can be installed quickly via
python3 -m pip install 'git+https://github.com/cosmodesi/pycorr#egg=pycorr[corrfunc]'
import RascalC
result = RascalC.run_cov(...)
run_cov is the main function for the covariance matrix computation.
Use help(RascalC.run_cov) to learn more about the inputs and outputs; many of them are similar to pycorr TwoPointCorrelationFunction and some others are pycorr.TwoPointEstimators.
An example pipeline is showcased in a tutorial notebook (will be smoothed out soon).
It is strongly recommended NOT to use multi-threaded operations in the python process before launching RascalC.run_cov – this may cause the code to run effectively single-threaded.
E.g. at NERSC this would mean not setting OMP_* and other *_THREADS environment variables; the code will set them by itself according to the number of threads you passed.
This caveat does not seem to be unique for RascalC – different multi-threading backends can interfere.
Some specific examples are also available in the new separate script gallery: https://github.com/misharash/RascalC-scripts.
More documentation is coming, in the meantime please contact Michael 'Misha' Rashkovetskyi mrashkovetskyi@cfa.harvard.edu with any questions. Please also feel free to open GitHub issues both for problems and clarification requests.
- Oliver Philcox (Columbia / Simons Foundation)
- Daniel Eisenstein (Harvard)
- Ross O'Connell (Pittsburgh)
- Alexander Wiegand (Garching)
- Misha Rashkovetskyi (Harvard)
We thank Yuting Wang and Ryuichiro Hada for pointing out and fixing a number of issues with the code and its documentation. We are particularly grateful to Uendert Andrade for finding a wide variety of improvements and bugs, and to Jiaxi Yu for feedback on the Python interface and Jupyter notebook tutorial!