Source: uncertainties
Maintainer: Debian Python Team <team+python@tracker.debian.org>
Uploaders: Federico Ceratto <federico@debian.org>, David Paleino <dapal@debian.org>
Section: python
Priority: optional
Build-Depends: debhelper-compat (= 13)
Build-Depends-Indep: dh-sequence-numpy3,
                     dh-sequence-python3,
                     dh-sequence-sphinxdoc <!nodoc>,
                     python3-all,
                     python3-nose,
                     python3-numpy,
                     python3-setuptools,
                     python3-sphinx
Standards-Version: 4.6.2
Vcs-Browser: https://salsa.debian.org/python-team/packages/python-uncertainties
Vcs-Git: https://salsa.debian.org/python-team/packages/python-uncertainties.git
Homepage: https://packages.python.org/uncertainties/
Rules-Requires-Root: no

Package: python3-uncertainties
Architecture: all
Depends: ${misc:Depends},
         ${python3:Depends}
Recommends: python3-numpy
Suggests: python-uncertainties-doc
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.

Package: python-uncertainties-doc
Architecture: all
Multi-Arch: foreign
Section: doc
Depends: ${misc:Depends},
         ${sphinxdoc:Depends},
         libjs-jquery,
         libjs-underscore
Suggests: python3-uncertainties
Description: Python3 module for calculations with uncertainties: documentation
 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.
 .
 This package contains documentation for the python3-uncertainties package