depot/third_party/nixpkgs/pkgs/development/python-modules/cvxopt/default.nix
Default email 504525a148 Project import generated by Copybara.
GitOrigin-RevId: bd645e8668ec6612439a9ee7e71f7eac4099d4f6
2024-01-02 12:29:13 +01:00

77 lines
2.2 KiB
Nix

{ stdenv
, lib
, buildPythonPackage
, fetchPypi
, isPyPy
, python
, blas
, lapack
, suitesparse
, unittestCheckHook
, glpk ? null
, gsl ? null
, fftw ? null
, withGlpk ? true
, withGsl ? true
, withFftw ? true
}:
assert (!blas.isILP64) && (!lapack.isILP64);
buildPythonPackage rec {
pname = "cvxopt";
version = "1.3.2";
format = "setuptools";
disabled = isPyPy; # hangs at [translation:info]
src = fetchPypi {
inherit pname version;
hash = "sha256-NGH6QsGyJAuk2h2YXKc1A5FBV/xMd0FzJ+1tfYWs2+Y=";
};
buildInputs = [ blas lapack ];
# similar to Gsl, glpk, fftw there is also a dsdp interface
# but dsdp is not yet packaged in nixpkgs
env = {
CVXOPT_BLAS_LIB = "blas";
CVXOPT_LAPACK_LIB = "lapack";
CVXOPT_BUILD_DSDP = "0";
CVXOPT_SUITESPARSE_LIB_DIR = "${lib.getLib suitesparse}/lib";
CVXOPT_SUITESPARSE_INC_DIR = "${lib.getDev suitesparse}/include";
} // lib.optionalAttrs withGsl {
CVXOPT_BUILD_GSL = "1";
CVXOPT_GSL_LIB_DIR= "${lib.getLib gsl}/lib";
CVXOPT_GSL_INC_DIR= "${lib.getDev gsl}/include";
} // lib.optionalAttrs withGlpk {
CVXOPT_BUILD_GLPK = "1";
CVXOPT_GLPK_LIB_DIR = "${lib.getLib glpk}/lib";
CVXOPT_GLPK_INC_DIR = "${lib.getDev glpk}/include";
} // lib.optionalAttrs withFftw {
CVXOPT_BUILD_FFTW = "1";
CVXOPT_FFTW_LIB_DIR = "${lib.getLib fftw}/lib";
CVXOPT_FFTW_INC_DIR = "${lib.getDev fftw}/include";
};
nativeCheckInputs = [ unittestCheckHook ];
unittestFlagsArray = [ "-s" "tests" ];
meta = with lib; {
homepage = "https://cvxopt.org/";
description = "Python Software for Convex Optimization";
longDescription = ''
CVXOPT is a free software package for convex optimization based on the
Python programming language. It can be used with the interactive
Python interpreter, on the command line by executing Python scripts,
or integrated in other software via Python extension modules. Its main
purpose is to make the development of software for convex optimization
applications straightforward by building on Python's extensive
standard library and on the strengths of Python as a high-level
programming language.
'';
maintainers = with maintainers; [ edwtjo ];
license = licenses.gpl3Plus;
};
}