Source: uncertainties
Maintainer: Debian Python Modules Team <python-modules-team@lists.alioth.debian.org>
Uploaders: David Paleino <dapal@debian.org>,
           Federico Ceratto <federico.ceratto@gmail.com>
Section: python
Testsuite: autopkgtest-pkg-python
Priority: optional
Build-Depends: debhelper (>= 11~),
               dh-python,
               python-all,
               python-nose,
               python-numpy,
               python-setuptools,
               python3-sphinx,
               python3-all,
               python3-nose,
               python3-numpy,
               python3-setuptools,
               2to3
Standards-Version: 4.2.1
Vcs-Browser: https://salsa.debian.org/debian/python-uncertainties
Vcs-Git: https://salsa.debian.org/debian/python-uncertainties.git
Homepage: http://packages.python.org/uncertainties/

Package: python-uncertainties
Architecture: all
Depends: ${misc:Depends},
         ${python:Depends}
Recommends: python-numpy
Provides: ${python:Provides}
Description: Python module for calculations with uncertainties
 uncertainties is a Python module, which allows calculations such as
 .
   (0.2 +/- 0.01) * 2 = 0.4 +/- 0.02
 .
 to be performed transparently; much more complex mathematical expressions
 involving numbers with uncertainties can also be evaluated transparently.
 .
 Correlations between expressions are correctly taken into account; x-x is
 thus exactly zero, for instance. The uncertainties produced by this module
 are what is predicted by error propagation theory.

Package: python3-uncertainties
Architecture: all
Depends: ${misc:Depends},
         ${python3:Depends}
Recommends: python3-numpy
Provides: ${python3:Provides}
Description: Python3 module for calculations with uncertainties
 uncertainties is a Python3 module, which allows calculations such as
 .
   (0.2 +/- 0.01) * 2 = 0.4 +/- 0.02
 .
 to be performed transparently; much more complex mathematical expressions
 involving numbers with uncertainties can also be evaluated transparently.
 .
 Correlations between expressions are correctly taken into account; x-x is
 thus exactly zero, for instance. The uncertainties produced by this module
 are what is predicted by error propagation theory.