sacc 0.2.1

SACC - the LSST/DESC summary statistic data format library

SACC (Save All Correlations and Covariances) is a format and reference library for general storage of summary statistic measurements for the Dark Energy Science Collaboration (DESC) within the Large Synoptic Survey Telescope (LSST) project.

Installation

You can install with the command:

pip install sacc

(For local installation you might need to add –user)

Or for development versions you can download the repository with git and install from there using python setup.py install

Examples

The examples directory on github contains ipython notebooks showing various ways of constructing sacc data, manipulating it, saving it, and loading it.

Conceptual Summary

Sacc models summary statistics using the following concepts:

• a Sacc dataset, containing all the information needed to construct likelihoods of some data.

• Tracers, objects usually corresponding to groups of astrophysical objects and the metadata needed to make predictions for theoretical quantities based on them.

• Windows, objects describing the mapping from a range of theory measurements to individual binned statistics

• Data Points, statistical measurements of some observable quantity, each of which has one or more Tracers and optionally some Windows.

• Covariances, describing the statistic covariance between data points.

Documentation

Documentation can be found [on ReadTheDocs](https://sacc.readthedocs.io/en/latest/).