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wip/py-gvar,
Utilities for manipulating Gaussian random variables
Branch: CURRENT,
Version: 4.6.1,
Package name: py312-gvar-4.6.1,
Maintainer: jihbed.researchThis package provides tools for creating and manipulating
Gaussian random variables from correlated multi-dimensional
Gaussian distributions. The Gaussian variables are represented
by objects of type :class:`gvar.GVar` which can be combined
with each other and with ordinary numbers in arbitrary
arithmetic expressions, including standard functions such
``sin`` and ``exp``, to create new :class:`gvar.GVar`\s whose
correlations with the original variables are tracked implicitly
(and handled correctly).
This package relies on ``numpy`` for efficient array arithmetic,
and on Cython to compile efficient core routines and interface code
Required to run:[
math/gsl] [
math/py-numpy] [
devel/py-cython] [
lang/python37]
Required to build:[
pkgtools/cwrappers]
Master sites:
RMD160: 497da8ff52659f8492d49f1c1ebeab3370f6d7be
Filesize: 522.652 KB
Version history: (Expand)
- (2024-09-19) Updated to version: py312-gvar-4.6.1
- (2024-09-19) Package has been reborn
- (2024-09-15) Package deleted from pkgsrc
- (2023-02-13) Updated to version: py310-gvar-4.6.1
- (2023-02-13) Package has been reborn
- (2021-10-08) Updated to version: py39-gvar-4.6.1
CVS history: (Expand)
2014-06-01 14:49:35 by Thomas Klausner | Files touched by this commit (208) |
Log message:
Remove FETCH_USING.
It is a user-defined variable and should NOT be set in Makefiles.
|
2014-02-02 21:30:58 by Kamel Derouiche | Files touched by this commit (4) |
Log message:
Import py27-gvar-4.6.1 as wip/py-gvar.
This package provides tools for creating and manipulating
Gaussian random variables from correlated multi-dimensional
Gaussian distributions. The Gaussian variables are represented
by objects of type :class:`gvar.GVar` which can be combined
with each other and with ordinary numbers in arbitrary
arithmetic expressions, including standard functions such
``sin`` and ``exp``, to create new :class:`gvar.GVar`\s whose
correlations with the original variables are tracked implicitly
(and handled correctly).
This package relies on ``numpy`` for efficient array arithmetic,
and on Cython to compile efficient core routines and interface code
|