Index: head/devel/py-numba/Makefile
===================================================================
--- head/devel/py-numba/Makefile	(revision 463474)
+++ head/devel/py-numba/Makefile	(revision 463475)
@@ -1,33 +1,34 @@
 # Created by: David Kalliecharan <dave@dal.ca>
 # $FreeBSD$
 
 PORTNAME=	numba
-PORTVERSION=	0.29.0
-PORTREVISION=	3
+DISTVERSION=	0.37.0
 CATEGORIES=	devel python
 MASTER_SITES=	CHEESESHOP
 PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
 
 MAINTAINER=	dave@dal.ca
 COMMENT=	Optimizing compiler for Python using LLVM
 
-LICENSE=	BSD
+LICENSE=	BSD2CLAUSE
 LICENSE_FILE=	${WRKSRC}/LICENSE
 
 BUILD_DEPENDS=	${PYTHON_PKGNAMEPREFIX}numpy>1.7,1:math/py-numpy@${FLAVOR}
 RUN_DEPENDS=	${PY_ENUM34} \
 		${PYTHON_PKGNAMEPREFIX}llvmlite>=0.12:devel/py-llvmlite@${FLAVOR}
 
-# Uses Python 2.7, 3.4+
 USES=		python fortran
-USE_PYTHON=	distutils autoplist
+USE_PYTHON=	distutils concurrent autoplist
 
 .include <bsd.port.pre.mk>
 
 # Required for Python 2.7
 .if ${PYTHON_REL} < 3400
 RUN_DEPENDS+=	${PYTHON_PKGNAMEPREFIX}singledispatch>0:devel/py-singledispatch@${FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}funcsigs>0:devel/py-funcsigs@${FLAVOR}
 .endif
+
+post-install:
+	@${FIND} ${STAGEDIR}${PYTHON_SITELIBDIR} -name "*.so" | ${XARGS} ${STRIP_CMD}
 
 .include <bsd.port.post.mk>
Index: head/devel/py-numba/distinfo
===================================================================
--- head/devel/py-numba/distinfo	(revision 463474)
+++ head/devel/py-numba/distinfo	(revision 463475)
@@ -1,3 +1,3 @@
-TIMESTAMP = 1477331960
-SHA256 (numba-0.29.0.tar.gz) = 00ae294f3fb3a99e8f0a9f568213cebed26675bacc9c6f8d2e025b6d564e460d
-SIZE (numba-0.29.0.tar.gz) = 1146848
+TIMESTAMP = 1520067850
+SHA256 (numba-0.37.0.tar.gz) = c62121b2d384d8b4d244ef26c1cf8bb5cb819278a80b893bf41918ad6d391258
+SIZE (numba-0.37.0.tar.gz) = 1366942
Index: head/devel/py-numba/pkg-descr
===================================================================
--- head/devel/py-numba/pkg-descr	(revision 463474)
+++ head/devel/py-numba/pkg-descr	(revision 463475)
@@ -1,7 +1,7 @@
 Numba 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.
 
-WWW: http://numba.pydata.org/
+WWW: https://numba.pydata.org/