Index: head/math/py-numexpr/Makefile
===================================================================
--- head/math/py-numexpr/Makefile	(revision 381853)
+++ head/math/py-numexpr/Makefile	(revision 381854)
@@ -1,22 +1,26 @@
 # Created by: Ju Pengfei <ju.pengfei@gmail.com>
 # $FreeBSD$
 
 PORTNAME=	numexpr
-PORTVERSION=	2.2.2
-PORTREVISION=	3
+PORTVERSION=	2.4
 CATEGORIES=	math python
-MASTER_SITES=	GOOGLE_CODE
 PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
 
 MAINTAINER=	sunpoet@FreeBSD.org
 COMMENT=	Fast numerical array expression evaluator for Python and NumPy
 
 LICENSE=	MIT
 
 BUILD_DEPENDS=	${PYNUMPY}
 RUN_DEPENDS:=	${BUILD_DEPENDS}
 
 USE_PYTHON=	autoplist distutils
 USES=		python
+WRKSRC=		${WRKDIR}/${PORTNAME}-${PORTVERSION}
+
+GH_ACCOUNT=	pydata
+GH_PROJECT=	${PORTNAME}
+GH_TAGNAME=	v${PORTVERSION}
+USE_GITHUB=	yes
 
 .include <bsd.port.mk>
Index: head/math/py-numexpr/distinfo
===================================================================
--- head/math/py-numexpr/distinfo	(revision 381853)
+++ head/math/py-numexpr/distinfo	(revision 381854)
@@ -1,2 +1,2 @@
-SHA256 (numexpr-2.2.2.tar.gz) = 47d3435785f59a72dc06a1c0257b83f0f656d4974e804c30a6ef8c0de293d9ee
-SIZE (numexpr-2.2.2.tar.gz) = 75680
+SHA256 (pydata-numexpr-2.4-v2.4_GH0.tar.gz) = b3d5f998d1e489c6e3e672d75c2e6b4a871d67fefc6b6154b90795010bee14c0
+SIZE (pydata-numexpr-2.4-v2.4_GH0.tar.gz) = 89752
Index: head/math/py-numexpr/pkg-descr
===================================================================
--- head/math/py-numexpr/pkg-descr	(revision 381853)
+++ head/math/py-numexpr/pkg-descr	(revision 381854)
@@ -1,16 +1,16 @@
 numexpr - Fast numerical array expression evaluator for Python and NumPy. 
 
 The numexpr package evaluates multiple-operator array expressions many times
 faster than NumPy can. It accepts the expression as a string, analyzes it,
 rewrites it more efficiently, and compiles it to faster Python code on the fly.
 It's the next best thing to writing the expression in C and compiling it with
 a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler
 at runtime.  
 
 Also, numexpr has support for the Intel VML (Vector Math Library) -- integrated
 in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing
 transcendental functions (like trigonometrical, exponentials...) on top of
 Intel-compatible platforms. This support also allows to use multiple cores in
 your computations. 
 
-WWW: http://code.google.com/p/numexpr/
+WWW: https://github.com/pydata/numexpr