# This is a shell archive.  Save it in a file, remove anything before
# this line, and then unpack it by entering "sh file".  Note, it may
# create directories; files and directories will be owned by you and
# have default permissions.
#
# This archive contains:
#
#	py-numba
#	py-numba/Makefile
#	py-numba/distinfo
#	py-numba/pkg-descr
#
echo c - py-numba
mkdir -p py-numba > /dev/null 2>&1
echo x - py-numba/Makefile
sed 's/^X//' >py-numba/Makefile << '7cec429fbe32ef0fd086b0c2eeb5d417'
X# Created by David Kalliecharan <dave@dal.ca>
X# $FreeBSD$
X
XPORTNAME=	numba
XPORTVERSION=	0.27.0
XCATEGORIES=	devel python
XMASTER_SITES=	CHEESESHOP
XPKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
X
XMAINTAINER=	dave@dal.ca
XCOMMENT=	Numba is an optimizing compiler for Python, using LLVM 
X
XLICENSE=	BSD
XLICENSE_FILE=	${WRKSRC}/LICENSE
X
X# Uses Python 2.7, 3.4+
XUSES=		python
XUSE_PYTHON=	distutils autoplist
X
X.include <bsd.port.pre.mk>
X
XBUILD_DEPENDS+=	${PYTHON_PKGNAMEPREFIX}numpy>1.7:${PORTSDIR}/math/py-numpy
X
X# Requried for Python 2.7
X.if ${PYTHON_REL} < 3400
XRUN_DEPENDS+=	${PYTHON_PKGNAMEPREFIX}enum34>0:${PORTSDIR}/devel/py-enum34 \
X		${PYTHON_PKGNAMEPREFIX}singledispatch>0:${PORTSDIR}/devel/py-singledispatch \
X		${PYTHON_PKGNAMEPREFIX}funcsigs>0:${PORTSDIR}/devel/py-funcsigs
X.endif
X
XRUN_DEPENDS+=	${PYTHON_PKGNAMEPREFIX}llvmlite>=0.12:${PORTSDIR}/devel/py-llvmlite \
X		${PYTHON_PKGNAMEPREFIX}argparse>0:${PORTSDIR}/devel/py-argparse
X
X.include <bsd.port.post.mk>
7cec429fbe32ef0fd086b0c2eeb5d417
echo x - py-numba/distinfo
sed 's/^X//' >py-numba/distinfo << 'f797cd4ab57f8ee15d55230f0ebf4cd1'
XSHA256 (numba-0.27.0.tar.gz) = 5fc8069cdc839b8b44ac6c54260902f60cbd77bd027b20999970a81cce7008ba
XSIZE (numba-0.27.0.tar.gz) = 1094160
f797cd4ab57f8ee15d55230f0ebf4cd1
echo x - py-numba/pkg-descr
sed 's/^X//' >py-numba/pkg-descr << '437ad8edf45d6b8b7231626ee7f66711'
XNumba gives you the power to speed up your applications with high performance functions written directly in Python. With a few annotations, array-oriented and math-heavy Python code can be just-in-time compiled to native machine instructions, similar in performance to C, C++ and Fortran, without having to switch languages or Python interpreters.
X
XWWW: http://numba.pydata.org/
437ad8edf45d6b8b7231626ee7f66711
exit