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python-Biggus.spec
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File python-Biggus.spec of Package python-Biggus
# # spec file for package python-Biggus # # Copyright (c) 2015 SUSE LINUX 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/ # Name: python-Biggus Version: 0.10.0 Release: 0 Summary: Virtual large arrays and lazy evaluation License: LGPL-3.0 Group: Development/Languages/Python Url: https://github.com/SciTools/biggus Source: https://pypi.python.org/packages/source/B/Biggus/Biggus-%{version}.tar.gz BuildRequires: python-devel BuildRequires: python-mock BuildRequires: python-nose BuildRequires: python-numpy-devel BuildRequires: python-pep8 Requires: python-numpy 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 Virtual large arrays and lazy evaluation. For example, we can combine multiple array data sources into a single virtual array:: >>> first_time_series = OrthoArrayAdapter(hdf_var_a) >>> second_time_series = OrthoArrayAdapater(hdf_var_b) >>> print first_time_series.shape, second_time_series.shape (52000, 800, 600) (56000, 800, 600) >>> time_series = biggus.LinearMosaic([first_time_series, second_time_series], axis=0) >>> time_series <LinearMosaic shape=(108000, 800, 600) dtype=dtype('float32')> *Any* biggus Array can then be indexed, independent of underlying data sources:: >>> time_series[51999:52001, 10, 12] <LinearMosaic shape=(2,) dtype=dtype('float32')> And an Array can be converted to a numpy ndarray on demand:: >>> time_series[51999:52001, 10, 12].ndarray() array([ 0.72151309, 0.54654914], dtype=float32) %prep %setup -q -n Biggus-%{version} %build python setup.py build %install python setup.py install --prefix=%{_prefix} --root=%{buildroot} # test fails on pep8 errors # %check # nosetests -e test_pep8 %files %defattr(-,root,root,-) %doc COPYING COPYING.LESSER README.rst %{python_sitelib}/biggus/ %{python_sitelib}/Biggus-%{version}-py*.egg-info %changelog
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