Guillaume Girol
33afbf39f6
checkInputs used to be added to nativeBuildInputs. Now we have nativeCheckInputs to do that instead. Doing this treewide change allows to keep hashes identical to before the introduction of nativeCheckInputs.
75 lines
2.2 KiB
Nix
75 lines
2.2 KiB
Nix
{ 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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, 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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sha256 = "sha256-ALGyMvnR+QLVeKnXWBS2f6AgdY1a5CLijKjO9iafpcY=";
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};
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buildInputs = [ blas lapack ];
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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 = ''
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export CVXOPT_BLAS_LIB=blas
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export CVXOPT_LAPACK_LIB=lapack
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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=${gsl}/lib
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export CVXOPT_GSL_INC_DIR=${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=${glpk}/lib
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export CVXOPT_GLPK_INC_DIR=${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=${fftw}/lib
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export CVXOPT_FFTW_INC_DIR=${fftw.dev}/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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