adisbladis
02dab4ab5c
Long term we should move everything over to `pyproject = true`, but in the mean time we can work towards deprecating the implicit `format` paremeter. cc https://github.com/NixOS/nixpkgs/issues/253154 cc @mweinelt @figsoda
78 lines
2.2 KiB
Nix
78 lines
2.2 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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, blas
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, lapack
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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 = "1.3.2";
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format = "setuptools";
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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-NGH6QsGyJAuk2h2YXKc1A5FBV/xMd0FzJ+1tfYWs2+Y=";
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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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env = {
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CVXOPT_BLAS_LIB = "blas";
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CVXOPT_LAPACK_LIB = "lapack";
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CVXOPT_BUILD_DSDP = "0";
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CVXOPT_SUITESPARSE_LIB_DIR = "${lib.getLib suitesparse}/lib";
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CVXOPT_SUITESPARSE_INC_DIR = "${lib.getDev suitesparse}/include";
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} // lib.optionalAttrs withGsl {
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CVXOPT_BUILD_GSL = "1";
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CVXOPT_GSL_LIB_DIR= "${lib.getLib gsl}/lib";
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CVXOPT_GSL_INC_DIR= "${lib.getDev gsl}/include";
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} // lib.optionalAttrs withGlpk {
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CVXOPT_BUILD_GLPK = "1";
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CVXOPT_GLPK_LIB_DIR = "${lib.getLib glpk}/lib";
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CVXOPT_GLPK_INC_DIR = "${lib.getDev glpk}/include";
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} // lib.optionalAttrs withFftw {
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CVXOPT_BUILD_FFTW = "1";
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CVXOPT_FFTW_LIB_DIR = "${lib.getLib fftw}/lib";
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CVXOPT_FFTW_INC_DIR = "${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 = "https://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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