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wip/py-efmlrs: import py37-efmlrs-2.1.3 

EFMlrs EFMlrs is a Python package combined with a designated workflow that
makes it easy for users to calculate elementary flux modes (EFMs) using a
new approach in metabolic modelling - the [mplrs algorithm](http://
cgm.cs.mcgill.ca/~avis/C/lrs.html). Besides this new approach also [efmtool]
(https://csb.ethz.ch/tools/software/efmtool.html) is supported, as it uses
the most common and established algorithm for calculating EFMs - the double
description method.

The mplrs algorithm can make use of up to two thousand threads in parallel
so that for the first time EFMs can be calculated for models that are too
big to be calculated using tools e.g. efmtool that are based on the double
description method. Furthermore the EFMlrs package provides a method that
makes it possible to integrate reaction bounds from the sbml files, so that
subsets of EFMs can be calculated. This feature is compatible with both
algorithms, the mplrs algorithm and the efmtool. EFMlrs can be used as a
stand alone console python program but is flexible enough to be integrated
in already established workflows
This commit is contained in:
K.I.A.Derouiche 2020-09-14 01:44:00 +01:00
parent d9044fa561
commit 08b75e498f
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EFMlrs EFMlrs is a Python package combined with a designated workflow that
makes it easy for users to calculate elementary flux modes (EFMs) using a
new approach in metabolic modelling - the [mplrs algorithm](http://
cgm.cs.mcgill.ca/~avis/C/lrs.html). Besides this new approach also [efmtool]
(https://csb.ethz.ch/tools/software/efmtool.html) is supported, as it uses
the most common and established algorithm for calculating EFMs - the double
description method.
The mplrs algorithm can make use of up to two thousand threads in parallel
so that for the first time EFMs can be calculated for models that are too
big to be calculated using tools e.g. efmtool that are based on the double
description method. Furthermore the EFMlrs package provides a method that
makes it possible to integrate reaction bounds from the sbml files, so that
subsets of EFMs can be calculated. This feature is compatible with both
algorithms, the mplrs algorithm and the efmtool. EFMlrs can be used as a
stand alone console python program but is flexible enough to be integrated
in already established workflows

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py-efmlrs/Makefile Normal file
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# $NetBSD$
DISTNAME= efmlrs-2.1.3
PKGNAME= ${PYPKGPREFIX}-${DISTNAME}
CATEGORIES= math python
MASTER_SITES= https://files.pythonhosted.org/packages/ee/f0/ab1af72bb71db7fd769a42b0bf4ffc34ebf1d8a3d1baf80abd240bc290a5/
MAINTAINER= jihbed.research@gmail.com
HOMEPAGE= https://github.com/BeeAnka/EFMlrs
COMMENT= Extracts EFMs from result file of mplrs and decompresses EFMs
LICENSE= gnu-gpl-v3
DEPENDS+= ${PYPKGPREFIX}-pandas>=0.25.3:../../math/py-pandas
DEPENDS+= ${PYPKGPREFIX}-sympy>=1.5.1:../../math/py-sympy
BUILDLINK_API_DEPENDS.py-numpy+= ${PYPKGPREFIX}-numpy>=1.0
.include "../../math/py-numpy/buildlink3.mk"
.include "../../lang/python/egg.mk"
.include "../../mk/bsd.pkg.mk"

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@comment $NetBSD$
bin/efmlrs_post
bin/efmlrs_pre
${PYSITELIB}/${EGG_INFODIR}/PKG-INFO
${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt
${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt
${PYSITELIB}/${EGG_INFODIR}/entry_points.txt
${PYSITELIB}/${EGG_INFODIR}/requires.txt
${PYSITELIB}/${EGG_INFODIR}/top_level.txt
${PYSITELIB}/efmlrs/__init__.py
${PYSITELIB}/efmlrs/__init__.pyc
${PYSITELIB}/efmlrs/__init__.pyo
${PYSITELIB}/efmlrs/_version.py
${PYSITELIB}/efmlrs/_version.pyc
${PYSITELIB}/efmlrs/_version.pyo
${PYSITELIB}/efmlrs/post.py
${PYSITELIB}/efmlrs/post.pyc
${PYSITELIB}/efmlrs/post.pyo
${PYSITELIB}/efmlrs/postprocessing/__init__.py
${PYSITELIB}/efmlrs/postprocessing/__init__.pyc
${PYSITELIB}/efmlrs/postprocessing/__init__.pyo
${PYSITELIB}/efmlrs/postprocessing/decompressing.py
${PYSITELIB}/efmlrs/postprocessing/decompressing.pyc
${PYSITELIB}/efmlrs/postprocessing/decompressing.pyo
${PYSITELIB}/efmlrs/postprocessing/decompressions/__init__.py
${PYSITELIB}/efmlrs/postprocessing/decompressions/__init__.pyc
${PYSITELIB}/efmlrs/postprocessing/decompressions/__init__.pyo
${PYSITELIB}/efmlrs/postprocessing/decompressions/deadend.py
${PYSITELIB}/efmlrs/postprocessing/decompressions/deadend.pyc
${PYSITELIB}/efmlrs/postprocessing/decompressions/deadend.pyo
${PYSITELIB}/efmlrs/postprocessing/decompressions/null_space.py
${PYSITELIB}/efmlrs/postprocessing/decompressions/null_space.pyc
${PYSITELIB}/efmlrs/postprocessing/decompressions/null_space.pyo
${PYSITELIB}/efmlrs/postprocessing/decompressions/one2many.py
${PYSITELIB}/efmlrs/postprocessing/decompressions/one2many.pyc
${PYSITELIB}/efmlrs/postprocessing/decompressions/one2many.pyo
${PYSITELIB}/efmlrs/postprocessing/get_data.py
${PYSITELIB}/efmlrs/postprocessing/get_data.pyc
${PYSITELIB}/efmlrs/postprocessing/get_data.pyo
${PYSITELIB}/efmlrs/pre.py
${PYSITELIB}/efmlrs/pre.pyc
${PYSITELIB}/efmlrs/pre.pyo
${PYSITELIB}/efmlrs/preprocessing/__init__.py
${PYSITELIB}/efmlrs/preprocessing/__init__.pyc
${PYSITELIB}/efmlrs/preprocessing/__init__.pyo
${PYSITELIB}/efmlrs/preprocessing/boundaries.py
${PYSITELIB}/efmlrs/preprocessing/boundaries.pyc
${PYSITELIB}/efmlrs/preprocessing/boundaries.pyo
${PYSITELIB}/efmlrs/preprocessing/compressions/__init__.py
${PYSITELIB}/efmlrs/preprocessing/compressions/__init__.pyc
${PYSITELIB}/efmlrs/preprocessing/compressions/__init__.pyo
${PYSITELIB}/efmlrs/preprocessing/compressions/deadend.py
${PYSITELIB}/efmlrs/preprocessing/compressions/deadend.pyc
${PYSITELIB}/efmlrs/preprocessing/compressions/deadend.pyo
${PYSITELIB}/efmlrs/preprocessing/compressions/echelon.py
${PYSITELIB}/efmlrs/preprocessing/compressions/echelon.pyc
${PYSITELIB}/efmlrs/preprocessing/compressions/echelon.pyo
${PYSITELIB}/efmlrs/preprocessing/compressions/null_space.py
${PYSITELIB}/efmlrs/preprocessing/compressions/null_space.pyc
${PYSITELIB}/efmlrs/preprocessing/compressions/null_space.pyo
${PYSITELIB}/efmlrs/preprocessing/compressions/one2many.py
${PYSITELIB}/efmlrs/preprocessing/compressions/one2many.pyc
${PYSITELIB}/efmlrs/preprocessing/compressions/one2many.pyo
${PYSITELIB}/efmlrs/preprocessing/get_data.py
${PYSITELIB}/efmlrs/preprocessing/get_data.pyc
${PYSITELIB}/efmlrs/preprocessing/get_data.pyo
${PYSITELIB}/efmlrs/preprocessing/mplrs_output.py
${PYSITELIB}/efmlrs/preprocessing/mplrs_output.pyc
${PYSITELIB}/efmlrs/preprocessing/mplrs_output.pyo
${PYSITELIB}/efmlrs/util/__init__.py
${PYSITELIB}/efmlrs/util/__init__.pyc
${PYSITELIB}/efmlrs/util/__init__.pyo
${PYSITELIB}/efmlrs/util/convert_matrix.py
${PYSITELIB}/efmlrs/util/convert_matrix.pyc
${PYSITELIB}/efmlrs/util/convert_matrix.pyo
${PYSITELIB}/efmlrs/util/data.py
${PYSITELIB}/efmlrs/util/data.pyc
${PYSITELIB}/efmlrs/util/data.pyo
${PYSITELIB}/efmlrs/util/log.py
${PYSITELIB}/efmlrs/util/log.pyc
${PYSITELIB}/efmlrs/util/log.pyo

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$NetBSD$
SHA1 (efmlrs-2.1.3.tar.gz) = d4f3fb10815e9d8ea3df3257068e8cb2e3f1ffca
RMD160 (efmlrs-2.1.3.tar.gz) = 40c0f5d55f5568a6758c7c7085e166cf6f85954d
SHA512 (efmlrs-2.1.3.tar.gz) = 0a7c512fa1016aa99f31efeb2ccc0bdc89e827875bba8a9bb4746632edffeb58e2e8cac1de6d041fc51ff7c04107c0814e6aae4774c431d73af62c6137db7462
Size (efmlrs-2.1.3.tar.gz) = 1827401 bytes