math/py-SQNomad: New port: Blackbox optimization software

2024Q1
Yuri Victorovich 2023-11-11 19:53:57 -08:00
parent d8c0758d48
commit 0a6f672ba6
5 changed files with 50 additions and 0 deletions

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@ -884,6 +884,7 @@
SUBDIR += py-PySCIPOpt
SUBDIR += py-PyWavelets
SUBDIR += py-Pyomo
SUBDIR += py-SQNomad
SUBDIR += py-affine
SUBDIR += py-algopy
SUBDIR += py-altgraph

31
math/py-SQNomad/Makefile Normal file
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@ -0,0 +1,31 @@
PORTNAME= SQNomad
DISTVERSION= 0.2.3
CATEGORIES= math python
MASTER_SITES= PYPI
PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
MAINTAINER= yuri@FreeBSD.org
COMMENT= Blackbox optimization software
WWW= https://www.gerad.ca/nomad/
LICENSE= LGPL3
LICENSE_FILE= ${WRKSRC}/LICENSE
BUILD_DEPENDS= ${PYNUMPY} \
${PY_SETUPTOOLS} \
${PYTHON_PKGNAMEPREFIX}wheel>0:devel/py-wheel@${PY_FLAVOR}
RUN_DEPENDS= ${PYNUMPY} \
${PYTHON_PKGNAMEPREFIX}SQCommon>0:misc/py-SQCommon@${PY_FLAVOR}
USES= python
USE_PYTHON= pep517 autoplist
post-install:
@${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/libsqnomad${PYTHON_EXT_SUFFIX}.so
# there are a lot of errors during build stemming from the numpy code, for example:
# cc: error: unknown argument: '-mavx5124fmaps'
# cc: error: unknown argument: '-mavx5124vnniw'
# but these erros are in the test coe nd they don't break the build
.include <bsd.port.mk>

3
math/py-SQNomad/distinfo Normal file
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TIMESTAMP = 1699747105
SHA256 (SQNomad-0.2.3.tar.gz) = 07e2b1998ddb6bc6d3ea9515b956fbd2ba053e9445961b2f895e6af7d84a8fa7
SIZE (SQNomad-0.2.3.tar.gz) = 455191

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--- setup.py.orig 2023-11-12 00:10:52 UTC
+++ setup.py
@@ -55,7 +55,7 @@ class my_build_extension(_build_ext):
ext.extra_compile_args += ['-I'+os.path.join(numpy.__path__[0], 'core/include/numpy')]
# force C++14
- if 'linux' in sys.platform or 'darwin' in sys.platform:
+ if 'linux' in sys.platform or 'freebsd' in sys.platform or 'darwin' in sys.platform:
ext.extra_compile_args += ['-std=c++14']
elif 'win32' in sys.platform:
# also define DLL_EXPORTS for sgtelib and NOMAD::Clock

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NOMAD is a C++ implementation of the Mesh Adaptive Direct Search algorithm
(MADS), designed for difficult blackbox optimization problems. These problems
occur when the functions defining the objective and constraints are the result
of costly computer simulations.