Date: Tue, 18 Apr 2023 18:08:11 GMT From: Po-Chuan Hsieh <sunpoet@FreeBSD.org> To: ports-committers@FreeBSD.org, dev-commits-ports-all@FreeBSD.org, dev-commits-ports-main@FreeBSD.org Subject: git: 6aed6e90482e - main - devel/py-mystic: Add py-mystic 0.4.0 Message-ID: <202304181808.33II8BIL078413@gitrepo.freebsd.org>
next in thread | raw e-mail | index | archive | help
The branch main has been updated by sunpoet: URL: https://cgit.FreeBSD.org/ports/commit/?id=6aed6e90482ef4198bf509c154b1c33b4c2932b9 commit 6aed6e90482ef4198bf509c154b1c33b4c2932b9 Author: Po-Chuan Hsieh <sunpoet@FreeBSD.org> AuthorDate: 2023-04-18 17:51:35 +0000 Commit: Po-Chuan Hsieh <sunpoet@FreeBSD.org> CommitDate: 2023-04-18 18:00:41 +0000 devel/py-mystic: Add py-mystic 0.4.0 The mystic framework provides a collection of optimization algorithms and tools that allows the user to more robustly (and easily) solve hard optimization problems. All optimization algorithms included in mystic provide workflow at the fitting layer, not just access to the algorithms as function calls. mystic gives the user fine-grained power to both monitor and steer optimizations as the fit processes are running. Optimizers can advance one iteration with Step, or run to completion with Solve. Users can customize optimizer stop conditions, where both compound and user-provided conditions may be used. Optimizers can save state, can be reconfigured dynamically, and can be restarted from a saved solver or from a results file. All solvers can also leverage parallel computing, either within each iteration or as an ensemble of solvers. --- devel/Makefile | 1 + devel/py-mystic/Makefile | 27 +++++++++++++++++++++++++++ devel/py-mystic/distinfo | 3 +++ devel/py-mystic/pkg-descr | 11 +++++++++++ 4 files changed, 42 insertions(+) diff --git a/devel/Makefile b/devel/Makefile index 921fb473867e..3041613f3668 100644 --- a/devel/Makefile +++ b/devel/Makefile @@ -4939,6 +4939,7 @@ SUBDIR += py-mypy-boto3-s3 SUBDIR += py-mypy-protobuf SUBDIR += py-mypy_extensions + SUBDIR += py-mystic SUBDIR += py-naiveBayesClassifier SUBDIR += py-nanotime SUBDIR += py-natsort diff --git a/devel/py-mystic/Makefile b/devel/py-mystic/Makefile new file mode 100644 index 000000000000..c029b6a9ed9a --- /dev/null +++ b/devel/py-mystic/Makefile @@ -0,0 +1,27 @@ +PORTNAME= mystic +PORTVERSION= 0.4.0 +CATEGORIES= devel python +MASTER_SITES= PYPI +PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} + +MAINTAINER= sunpoet@FreeBSD.org +COMMENT= Highly-constrained non-convex optimization and uncertainty quantification +WWW= https://github.com/uqfoundation/mystic + +LICENSE= BSD3CLAUSE +LICENSE_FILE= ${WRKSRC}/LICENSE + +BUILD_DEPENDS= ${PYTHON_PKGNAMEPREFIX}setuptools>=42:devel/py-setuptools@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}wheel>=0:devel/py-wheel@${PY_FLAVOR} +RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}dill>=0.3.6:devel/py-dill@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}klepto>=0.2.3:devel/py-klepto@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}mpmath>=0.19:math/py-mpmath@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}numpy>=1.0,1:math/py-numpy@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}sympy>=0.6.7:math/py-sympy@${PY_FLAVOR} + +USES= python:3.7+ +USE_PYTHON= autoplist concurrent cython pep517 + +NO_ARCH= yes + +.include <bsd.port.mk> diff --git a/devel/py-mystic/distinfo b/devel/py-mystic/distinfo new file mode 100644 index 000000000000..84eec4c609fb --- /dev/null +++ b/devel/py-mystic/distinfo @@ -0,0 +1,3 @@ +TIMESTAMP = 1681052886 +SHA256 (mystic-0.4.0.tar.gz) = 04027c856f776b1724b18deab9dd222f0602c20b54dbaadbff7a255006cfadce +SIZE (mystic-0.4.0.tar.gz) = 556775 diff --git a/devel/py-mystic/pkg-descr b/devel/py-mystic/pkg-descr new file mode 100644 index 000000000000..606c10e85719 --- /dev/null +++ b/devel/py-mystic/pkg-descr @@ -0,0 +1,11 @@ +The mystic framework provides a collection of optimization algorithms and tools +that allows the user to more robustly (and easily) solve hard optimization +problems. All optimization algorithms included in mystic provide workflow at the +fitting layer, not just access to the algorithms as function calls. mystic gives +the user fine-grained power to both monitor and steer optimizations as the fit +processes are running. Optimizers can advance one iteration with Step, or run to +completion with Solve. Users can customize optimizer stop conditions, where both +compound and user-provided conditions may be used. Optimizers can save state, +can be reconfigured dynamically, and can be restarted from a saved solver or +from a results file. All solvers can also leverage parallel computing, either +within each iteration or as an ensemble of solvers.
Want to link to this message? Use this URL: <https://mail-archive.FreeBSD.org/cgi/mid.cgi?202304181808.33II8BIL078413>