gvar · PyPI

Utilities for manipulating correlated Gaussian random variables.

Project description

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

Project details

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13.1.1

Jul 6, 2024

13.1

May 21, 2024

13.0.2

Mar 8, 2024

13.0.1

Mar 5, 2024

This version

13.0

Mar 4, 2024

12.1

Dec 4, 2023

12.0

Nov 2, 2023

11.11.17

Oct 12, 2023

11.11.15

Sep 28, 2023

11.11.14

Sep 27, 2023

11.11.13

Aug 28, 2023

11.11.12

Jul 22, 2023

11.11.11

Jun 11, 2023

11.11.10

May 11, 2023

11.11.6

May 10, 2023

11.11.5

May 10, 2023

11.11.4

May 10, 2023

11.11.3

May 10, 2023

11.11.2

Mar 3, 2023

11.11.1

Nov 11, 2022

11.11

Nov 2, 2022

11.10.1

Aug 2, 2022

11.10

May 12, 2022

11.9.7

May 1, 2022

11.9.6

Mar 13, 2022

11.9.5

Dec 13, 2021

11.9.4

Sep 6, 2021

11.9.3

Aug 14, 2021

11.9.2

Apr 15, 2021

11.9.1

Nov 25, 2020

11.9

Nov 22, 2020

11.8

Nov 17, 2020

11.7

Jul 16, 2020

11.6

Jun 11, 2020

11.5.2

May 9, 2020

11.5.1

May 9, 2020

11.5

May 7, 2020

11.4

Apr 29, 2020

11.2

Mar 18, 2020

11.1

Mar 1, 2020

11.0

Feb 27, 2020

10.1.2

Feb 24, 2020

10.1.1

Feb 23, 2020

10.1

Feb 23, 2020

10.0.2

Feb 4, 2020

10.0.1

Feb 3, 2020

10.0

Feb 2, 2020

9.2.1

Dec 4, 2019

9.2

May 8, 2019

9.1

Mar 28, 2019

9.0.3

Oct 3, 2018

9.0.2

Aug 3, 2018

9.0.1

Aug 1, 2018

9.0

Jul 27, 2018

8.5.1

Jul 24, 2018

8.5

Jul 22, 2018

8.4

Jul 15, 2018

8.3.7

Jul 8, 2018

8.3.6

Mar 18, 2018

8.3.5

Feb 16, 2018

8.3.4

Feb 16, 2018

8.3.3

Aug 4, 2017

8.3.2

Jun 23, 2017

8.3.1

Jun 17, 2017

8.3

Jun 3, 2017

8.2.3

Apr 30, 2017

8.2.2

Mar 17, 2017

8.2.1

Feb 11, 2017

8.2

Feb 7, 2017

8.1

Jan 9, 2017

8.0

Nov 5, 2016

7.3.1

Aug 17, 2016

7.3

Aug 15, 2016

7.2

Aug 10, 2015

7.1

Jul 24, 2015

7.0.3

Jun 17, 2015

7.0.2

Jun 17, 2015

7.0.1

Jun 17, 2015

7.0

Jun 16, 2015

6.0

Nov 20, 2014

5.0

Jun 28, 2014

4.8.5

May 29, 2014

4.8.4

May 23, 2014

4.8.2

May 16, 2014

4.8.1

May 14, 2014

4.8

May 12, 2014

4.7

Apr 29, 2014

4.6.1

Feb 2, 2014

4.5.3

Jan 7, 2014

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