Version 1.0 of SuperSCRAM: Super-Sample Covariance Reduction and Mitigation Described in arXiv:1904.12071
Author contact: Matthew C. Digman digman.12@osu.edu
Dependencies are: numpy scipy camb spherical_geometry matplotlib astropy dill (optional, makes viewing saved states from runs possible) pytest (for testing) basemap (optional)
the following creates a conda environment with the required dependencies:
conda create -n ssc_build_test -c conda-forge python=2.7.15 basemap scipy matplotlib=2.1.2 pytest numpy astropy cython mpmath future healpy spherical-geometry future pip install --upgrade numpy pip install spherical_geometry camb future pip install dill
To run a version of the demonstration case in the paper (arXiv:1904.12071),
python wfirst_embed_demo.py
The results of the highly converged run in the paper can be accessed using
python wfirst_demo_reconstitute
To run the unit tests,
python bundled_test.py
Develop repository for the super sample covariance project.