mirror of
https://github.com/NixOS/nixpkgs.git
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87 lines
2.5 KiB
Nix
87 lines
2.5 KiB
Nix
{ stdenv
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, lib
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, buildPythonPackage
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, fetchPypi
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, isPyPy
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, python
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, openblas
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, blas
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, lapack # build segfaults with 64-bit blas
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, suitesparse
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, unittestCheckHook
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, glpk ? null
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, gsl ? null
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, fftw ? null
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, withGlpk ? true
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, withGsl ? true
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, withFftw ? true
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}:
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assert (!blas.isILP64) && (!lapack.isILP64);
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buildPythonPackage rec {
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pname = "cvxopt";
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version = "1.3.0";
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disabled = isPyPy; # hangs at [translation:info]
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src = fetchPypi {
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inherit pname version;
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hash = "sha256-ALGyMvnR+QLVeKnXWBS2f6AgdY1a5CLijKjO9iafpcY=";
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};
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buildInputs = (if stdenv.isDarwin then [ openblas ] else [ blas lapack ]);
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doCheck = !stdenv.isDarwin;
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# similar to Gsl, glpk, fftw there is also a dsdp interface
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# but dsdp is not yet packaged in nixpkgs
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preConfigure = (if stdenv.isDarwin then
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''
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export CVXOPT_BLAS_LIB=openblas
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export CVXOPT_LAPACK_LIB=openblas
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''
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else
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''
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export CVXOPT_BLAS_LIB=blas
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export CVXOPT_LAPACK_LIB=lapack
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'') +
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''
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export CVXOPT_BUILD_DSDP=0
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export CVXOPT_SUITESPARSE_LIB_DIR=${lib.getLib suitesparse}/lib
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export CVXOPT_SUITESPARSE_INC_DIR=${lib.getDev suitesparse}/include
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'' + lib.optionalString withGsl ''
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export CVXOPT_BUILD_GSL=1
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export CVXOPT_GSL_LIB_DIR=${lib.getLib gsl}/lib
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export CVXOPT_GSL_INC_DIR=${lib.getDev gsl}/include
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'' + lib.optionalString withGlpk ''
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export CVXOPT_BUILD_GLPK=1
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export CVXOPT_GLPK_LIB_DIR=${lib.getLib glpk}/lib
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export CVXOPT_GLPK_INC_DIR=${lib.getDev glpk}/include
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'' + lib.optionalString withFftw ''
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export CVXOPT_BUILD_FFTW=1
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export CVXOPT_FFTW_LIB_DIR=${lib.getLib fftw}/lib
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export CVXOPT_FFTW_INC_DIR=${lib.getDev fftw}/include
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'';
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nativeCheckInputs = [ unittestCheckHook ];
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unittestFlagsArray = [ "-s" "tests" ];
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meta = with lib; {
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homepage = "http://cvxopt.org/";
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description = "Python Software for Convex Optimization";
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longDescription = ''
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CVXOPT is a free software package for convex optimization based on the
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Python programming language. It can be used with the interactive
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Python interpreter, on the command line by executing Python scripts,
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or integrated in other software via Python extension modules. Its main
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purpose is to make the development of software for convex optimization
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applications straightforward by building on Python's extensive
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standard library and on the strengths of Python as a high-level
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programming language.
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'';
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maintainers = with maintainers; [ edwtjo ];
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license = licenses.gpl3Plus;
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};
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}
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