Dmitry Roshchin
abc952fcdb
This is an update to a new version of dolfin-adjoint as well as a migration from python2 to python3. This package provides adjoint computation for dolfin models in conjunction wiht libadjoint package. OBS-URL: https://build.opensuse.org/request/show/480865 OBS-URL: https://build.opensuse.org/package/show/science/python3-dolfin-adjoint?expand=0&rev=1
78 lines
2.8 KiB
RPMSpec
78 lines
2.8 KiB
RPMSpec
#
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# spec file for package python-dolfin-adjoint
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#
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# Copyright (c) 2015 SUSE LINUX Products GmbH, Nuernberg, Germany.
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#
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# All modifications and additions to the file contributed by third parties
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# remain the property of their copyright owners, unless otherwise agreed
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# upon. The license for this file, and modifications and additions to the
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# file, is the same license as for the pristine package itself (unless the
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# license for the pristine package is not an Open Source License, in which
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# case the license is the MIT License). An "Open Source License" is a
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# license that conforms to the Open Source Definition (Version 1.9)
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# published by the Open Source Initiative.
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# Please submit bugfixes or comments via http://bugs.opensuse.org/
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#
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%define modname dolfin-adjoint
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%define pyname dolfin_adjoint
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Name: python3-%{modname}
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Version: 2016.2.0
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Release: 0
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License: LGPL-3.0
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Summary: Discrete adjoint and tangent linear computations for dolfin
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Url: http://dolfin-adjoint.org
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Group: Development/Languages/Python
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Source: https://bitbucket.org/dolfin-adjoint/dolfin-adjoint/downloads/%{modname}-%{version}.tar.gz
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Patch0: python3-fixes.patch
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BuildRequires: python3-devel
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Requires: python3-dolfin-openmpi = %{version}
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Requires: python3-libadjoint-openmpi = %{version}
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Suggests: python3-pyipopt
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BuildArch: noarch
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BuildRoot: %{_tmppath}/%{name}-%{version}-build
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%description
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The dolfin-adjoint project automatically derives the discrete adjoint
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and tangent linear models from a forward model written in the Python
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interface to dolfin.
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These adjoint and tangent linear models are key ingredients in many
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important algorithms, such as data assimilation, optimal control,
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sensitivity analysis, design optimisation, and error estimation. Such
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models have made an enormous impact in fields such as meteorology and
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oceanography, but their use in other scientific fields has been hampered
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by the great practical difficulty of their derivation and implementation.
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In his recent book, Naumann (2011) states that
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[T]he automatic generation of optimal (in terms of robustness and
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efficiency) adjoint versions of large-scale simulation code is one
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of the great open challenges in the field of High-Performance
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Scientific Computing.
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The dolfin-adjoint project aims to solve this problem for the case where
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the model is implemented in the Python interface to dolfin.
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%prep
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%setup -q -n %{modname}-%{version}
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%patch0 -p1
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%build
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python3 setup.py build
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%install
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python3 setup.py install --root %{buildroot} --prefix=%{_prefix}
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%files
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%defattr(-,root,root)
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%doc AUTHORS LICENSE README
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%dir %{python3_sitelib}/%{pyname}
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%{python3_sitelib}/%{pyname}/*
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%{python3_sitelib}/%{pyname}-*.egg-info
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%{python3_sitelib}/fenics_adjoint
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%{python3_sitelib}/firedrake_adjoint
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%changelog
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