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A Python implementation of the fast super-sample covariance from arXiv:1809.05437
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A Python implementation of the fast Super-Sample Covariance (SSC) from Lacasa & Grain 2018 arXiv:1809.05437

Dependencies: math, numpy, scipy, classy (Python wrapper of CLASS,


  • : the module to compute the SSC
  • plots-article.ipynb : a jupyter notebook showcasing how the computation works and reproducing the plots of the article
  • examples.ipynb : a jupyter notebook with example applications using the module

The module is You can place it in your Python path and import it with $ import PySSC

The module contains functions to compute the Sij matrix (defined in the article) that allows to easily build the SSC covariance matrix.

  • PySSC.turboSij() : computes Sij with sharp disjoint redshift bins
  • PySSC.Sij() : computes Sij with more general window functions
  • PySSC.Sij_alt() : alternative to PySSC.Sij(), computation through a different route for comparison. Generally slower for high number of redshift integration points.
  • PySSC.Sijkl() : computes the most general case, for the covariance not only of power spectra but also of cross-spectra
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