math/py-numba-stats: New port: Numba-accelerated implementations of common probability distributions

math/Makefile

@@ -978,6 +978,7 @@
     SUBDIR += py-networkx
     SUBDIR += py-nevergrad
     SUBDIR += py-nlopt
+    SUBDIR += py-numba-stats
     SUBDIR += py-numdifftools
     SUBDIR += py-numexpr
     SUBDIR += py-numpoly

math/py-numba-stats/Makefile

@@ -0,0 +1,28 @@
+PORTNAME=	numba-stats
+PORTVERSION=	1.2.0
+CATEGORIES=	math python
+MASTER_SITES=	PYPI
+PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
+
+MAINTAINER=	yuri@FreeBSD.org
+COMMENT=	Numba-accelerated implementations of common probability distributions
+WWW=		https://github.com/HDembinski/numba-stats
+
+LICENSE=	MIT
+LICENSE_FILE=	${WRKSRC}/LICENSE
+
+BUILD_DEPENDS=	${PYTHON_PKGNAMEPREFIX}setuptools>0:devel/py-setuptools@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}setuptools_scm>0:devel/py-setuptools_scm@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}wheel>0:devel/py-wheel@${PY_FLAVOR}
+RUN_DEPENDS=	${PYTHON_PKGNAMEPREFIX}numba>0:devel/py-numba@${PY_FLAVOR}
+
+USES=		python
+USE_PYTHON=	pep517 autoplist pytest # several tests fail, see https://github.com/HDembinski/numba-stats/issues/74
+
+TEST_ENV=	${MAKE_ENV} PYTHONPATH=${STAGEDIR}${PYTHONPREFIX_SITELIBDIR}
+
+NO_ARCH=	yes
+
+BINARY_ALIAS=	python=${PYTHON_CMD} # for tests
+
+.include <bsd.port.mk>

math/py-numba-stats/distinfo

@@ -0,0 +1,3 @@
+TIMESTAMP = 1687500930
+SHA256 (numba-stats-1.2.0.tar.gz) = 0a335f943121dce707ce827098d3062d28b98cc4e4d8cb1f7fe29f76a26f0431
+SIZE (numba-stats-1.2.0.tar.gz) = 207230

math/py-numba-stats/pkg-descr

@@ -0,0 +1,16 @@
+numba-stats provides numba-accelerated implementations of statistical functions
+for common probability distributions.
+
+* Uniform
+* (Truncated) Normal
+* Log-normal
+* Poisson
+* (Truncated) Exponential
+* Student's t
+* Voigtian
+* Crystal Ball
+* Generalised double-sided Crystal Ball
+* Tsallis-Hagedorn, a model for the minimum bias pT distribution
+* Q-Gaussian
+* Bernstein density
+* Cruijff density