File python3-dolfin-adjoint.spec of Package python3-dolfin-adjoint

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File python3-dolfin-adjoint.spec of Package python3-dolfin-adjoint

#
# spec file for package python-dolfin-adjoint
#
# Copyright (c) 2015 SUSE LINUX Products GmbH, Nuernberg, Germany.
#
# All modifications and additions to the file contributed by third parties
# remain the property of their copyright owners, unless otherwise agreed
# upon. The license for this file, and modifications and additions to the
# file, is the same license as for the pristine package itself (unless the
# license for the pristine package is not an Open Source License, in which
# case the license is the MIT License). An "Open Source License" is a
# license that conforms to the Open Source Definition (Version 1.9)
# published by the Open Source Initiative.

# Please submit bugfixes or comments via http://bugs.opensuse.org/
#

%define modname dolfin-adjoint
%define pyname  dolfin_adjoint

Name:           python3-%{modname}
Version:	2016.2.0
Release:	0
License:	LGPL-3.0
Summary:	Discrete adjoint and tangent linear computations for dolfin
Url:		http://dolfin-adjoint.org
Group:		Development/Languages/Python
Source:		https://bitbucket.org/dolfin-adjoint/dolfin-adjoint/downloads/%{modname}-%{version}.tar.gz
Patch0:         python3-fixes.patch
BuildRequires:  python3-devel
Requires:       python3-dolfin-openmpi = %{version}
Requires:       python3-libadjoint-openmpi = %{version}
Suggests:       python3-pyipopt
BuildArch:	noarch
BuildRoot:      %{_tmppath}/%{name}-%{version}-build

%description
The dolfin-adjoint project automatically derives the discrete adjoint 
and tangent linear models from a forward model written in the Python 
interface to dolfin.

These adjoint and tangent linear models are key ingredients in many 
important algorithms, such as data assimilation, optimal control, 
sensitivity analysis, design optimisation, and error estimation. Such 
models have made an enormous impact in fields such as meteorology and 
oceanography, but their use in other scientific fields has been hampered 
by the great practical difficulty of their derivation and implementation. 
In his recent book, Naumann (2011) states that

[T]he automatic generation of optimal (in terms of robustness and 
    efficiency) adjoint versions of large-scale simulation code is one 
    of the great open challenges in the field of High-Performance 
    Scientific Computing.

The dolfin-adjoint project aims to solve this problem for the case where 
the model is implemented in the Python interface to dolfin.

%prep
%setup -q -n %{modname}-%{version}
%patch0 -p1

%build
python3 setup.py build

%install
python3 setup.py install --root %{buildroot} --prefix=%{_prefix}

%files
%defattr(-,root,root)
%doc AUTHORS LICENSE README
%dir %{python3_sitelib}/%{pyname}
%{python3_sitelib}/%{pyname}/*
%{python3_sitelib}/%{pyname}-*.egg-info
%{python3_sitelib}/fenics_adjoint
%{python3_sitelib}/firedrake_adjoint

%changelog

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