This package facilitates the creation and manipulation of arbitrarily
complicated (correlated) multi-dimensional Gaussian random variables.
The random variables are represented by a new data type (gvar.GVar)
that can be used in arithmetic expressions and pure Python functions. Such
expressions/functions create new Gaussian random variables
while automatically tracking statistical correlations between the new
and old variables. This data type is useful for simple error propagation,
but also is heavily used by the Bayesian least-squares fitting module
lsqfit.py to define priors and specify fit results, while accounting
for correlations between all variables. Documentation can is in the
doc/ subdirectory: see doc/html/index.html
or look online at https://gvar.readthedocs.io.
These packages use numpy for efficient array arithmetic, and cython
to compile efficient code. gvar uses automatic differentiation to
track covariances through arbitrary arithmetic.
Information on how to install the components is in the INSTALLATION file.
To test the libraries try make tests. Some
examples are give in the examples/ subdirectory.
gvar version numbers have the form major.minor.patch where:
incompatible changes are signaled by incrementing the major version
number, the minor number signals new features, and the patch number
signals bug fixes.
| Created by G. Peter Lepage (Cornell University) 2008 | Copyright (c) 2008-2020 G. Peter Lepage
.. image:: https://zenodo.org/badge/37556070.svg :target: https://zenodo.org/badge/latestdoi/37556070
FAQs
Utilities for manipulating correlated Gaussian random variables.
We found that gvar demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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