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new file: h/h5py/h5py-2.7.0-intel-2017a-Python-2.7.13-base.eb new file: k/Keras/Keras-2.0.5-intel-2017a-Python-3.6.1.eb new file: l/libxslt/libxslt-1.1.29.eb new file: l/libyaml/libyaml-0.1.7.eb new file: l/lxml/lxml-3.8.0-Python-2.7.13-base.eb new file: m/matplotlib/matplotlib-2.0.2-Python-2.7.13-base.eb new file: m/mpi4py/mpi4py-2.0.0-intel-2017a-Python-2.7.13-base.eb new file: n/numpy/numpy-1.12.1-Python-2.7.13-base.eb new file: n/numpy/numpy-1.13.0-Python-2.7.13-base.eb new file: p/PyYAML/PyYAML-3.12-Python-2.7.13-base.eb new file: p/Python/Python-2.7.13-base.eb new file: p/phonopy/phonopy-1.11.12.5-intel-2017a-Python-2.7.13-base.eb new file: s/scipy/scipy-0.19.1-Python-2.7.13-base.eb new file: t/Tensorflow/Tensorflow-1.2.0-GCC-7.1.0-2.28.eb deleted: t/Tensorflow/Tensorflow-1.2.0-Python-3.6.1.eb
27 lines
1.0 KiB
Plaintext
27 lines
1.0 KiB
Plaintext
name = 'numpy'
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version = '1.12.1'
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versionsuffix = '-Python-%(pyver)s-base'
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homepage = 'http://www.numpy.org'
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description = """NumPy is the fundamental package for scientific computing with Python. It contains among other things:
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a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran
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code, useful linear algebra, Fourier transform, and random number capabilities. Besides its obvious scientific uses,
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NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be
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defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases."""
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toolchain = {'name': 'dummy', 'version': ''}
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source_urls = [PYPI_SOURCE]
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sources = [SOURCE_ZIP]
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#patches = ['numpy-1.12.0-mkl.patch']
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dependencies = [
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('Python', '2.7.13', '-base'),
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]
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# check that numpy is present in python and in proper version
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sanity_check_commands = [('python', '-c "import numpy; assert(numpy.__version__ == \'%(version)s\')"')]
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moduleclass = 'math'
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