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openturns 1.14-1
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Source: openturns
Section: science
Priority: optional
Maintainer: Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
Uploaders: Denis Barbier <barbier@debian.org>
Build-Depends: debhelper (>= 10), dh-python, quilt, gfortran, g++ (>= 4:7),
 bison, flex, cmake,
 libmuparser-dev, libxml2-dev, liblapack-dev, libblas-dev,
 libboost-math-dev, libhmat-oss-dev, libnlopt-cxx-dev, libcminpack-dev,
 libtbb-dev [i386 amd64 ia64],
 python3-dev, swig (>= 2.0.9)
Standards-Version: 4.1.3
Homepage: http://www.openturns.org/
Vcs-Browser: https://salsa.debian.org/science-team/openturns
Vcs-Git: https://salsa.debian.org/science-team/openturns.git
X-Python-Version: current
X-Python3-Version: current

Package: libopenturns0.15
Section: libs
Architecture: any
Depends: ${shlibs:Depends}, ${misc:Depends}
Suggests: python3-openturns
Replaces: openturns-wrapper
Breaks: openturns-wrapper
Description: dynamic libraries for OpenTURNS
 OpenTURNS is a powerful and generic tool to treat and quantify
 uncertainties in numerical simulations in design, optimization and
 control. It allows both sensitivity and reliability analysis studies:
  * define the outputs of interest and decision criteria;
  * quantify and model the source of uncertainties;
  * propagate uncertainties and/or analyse sensitivity
  * rank the sources of uncertainty
 .
 Targeted users are all engineers who want to introduce the
 probabilistic dimension in their so far deterministic studies.
 .
 This package provides the dynamic libraries.

Package: libopenturns-dev
Section: libdevel
Architecture: any
Depends: ${misc:Depends}, libopenturns0.15 (= ${binary:Version}),
  libxml2-dev, libtbb-dev [i386 amd64 ia64], libhmat-oss-dev
Replaces: python-openturns-dev (<< 1.5-2)
Breaks: python-openturns-dev (<< 1.5-2)
Description: headers and development libraries for OpenTURNS
 OpenTURNS is a powerful and generic tool to treat and quantify
 uncertainties in numerical simulations in design, optimization and
 control. It allows both sensitivity and reliability analysis studies:
  * defining the outputs of interest and decision criterion;
  * quantify and model the source of uncertainties;
  * propagate uncertainties and/or analyse sensitivity and
  * rank the sources of uncertainty
 .
 Targeted users are all engineers who want to introduce the
 probabilistic dimension in their so far deterministic studies.
 .
 This package contains development files needed to build OpenTURNS applications.

Package: python3-openturns
Section: python
Architecture: any
Depends: ${shlibs:Depends}, ${misc:Depends}, libopenturns0.15 (= ${binary:Version}), ${python3:Depends}
Provides: ${python3:Provides}
Suggests: python3-matplotlib, python3-scipy
Description: Python3 front-end of OpenTURNS (aka TUI)
 OpenTURNS is a powerful and generic tool to treat and quantify
 uncertainties in numerical simulations in design, optimization and
 control. It allows both sensitivity and reliability analysis studies:
  * defining the outputs of interest and decision criterion;
  * quantify and model the source of uncertainties;
  * propagate uncertainties and/or analyse sensitivity and
  * rank the sources of uncertainty
 .
 Targeted users are all engineers who want to introduce the
 probabilistic dimension in their so far deterministic studies.
 .
 This package provides the Python3 bindings to the library.

Package: openturns-examples
Architecture: any
Depends: ${shlibs:Depends}, ${misc:Depends}, python, python3-openturns (= ${binary:Version}), libopenturns-dev
Replaces: openturns-wrapper
Breaks: openturns-wrapper
Description: examples of OpenTURNS functionalities
 OpenTURNS is a powerful and generic tool to treat and quantify
 uncertainties in numerical simulations in design, optimization and
 control. It allows both sensitivity and reliability analysis studies:
  * defining the outputs of interest and decision criterion;
  * quantify and model the source of uncertainties;
  * propagate uncertainties and/or analyse sensitivity and
  * rank the sources of uncertainty
 .
 Targeted users are all engineers who want to introduce the
 probabilistic dimension in their so far deterministic studies.
 .
 This package provides C++ and Python examples.