File python-kcbo.spec of Package python-kcbo

#
# spec file for package python-kcbo
#
# Copyright (c) 2014 SUSE LINUX Products GmbH, Nuernberg, Germany.
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# license for the pristine package is not an Open Source License, in which
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# 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/
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Name:           python-kcbo
Version:        0.0.1
Release:        0
Summary:        A Bayesian data analysis library in Python
License:        MIT
Group:          Development/Languages/Python
Url:            https://github.com/HHammond/kcbo
Source:         https://pypi.python.org/packages/source/k/kcbo/kcbo-%{version}.tar.gz
BuildRequires:  python-devel
BuildRequires:  python-numpy >= 1.8
BuildRequires:  python-pandas >= 0.14
BuildRequires:  python-pymc
BuildRequires:  python-scipy
BuildRequires:  python-setuptools
BuildRequires:  python-tabulate
Requires:       python-numpy >= 1.8
Requires:       python-pandas >= 0.14
Requires:       python-pymc
Requires:       python-scipy
Requires:       python-tabulate
BuildRoot:      %{_tmppath}/%{name}-%{version}-build
%if 0%{?suse_version} && 0%{?suse_version} <= 1110
%{!?python_sitelib: %global python_sitelib %(python -c "from distutils.sysconfig import get_python_lib; print get_python_lib()")}
%else
BuildArch:      noarch
%endif

%description
The goal of KCBO is to provide an easy to use, Bayesian framework to
the masses.

The Bayesian philosophy and framework provide an excellent structure
for both asking and answering questions. Bayesian statistics allow us
to ask questions in a more natural manner and derive incredibly
powerful solutions.

Researchers and analysts shouldn't spend hours reading academic papers
and finding which conjugate priors they need, which type of sampler
their MCMC should have, or when to use MC or MCMC. Software should
take care of that computing and researchers should take care of
producing insights.

The world is ready for a good, clean, and easy to use Bayesian
framework. The goal of KCBO is to provide that framework.

%prep
%setup -q -n kcbo-%{version}

%build
python setup.py build

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

%files
%defattr(-,root,root,-)
%doc README.rst
%{python_sitelib}/*

%changelog