New port: math/py-algopy: Algorithmic Differentiation (AD) and Taylor polynomial approximations

svn path=/head/; revision=467660

math/Makefile

@@ -645,6 +645,7 @@
     SUBDIR += py-PySCIPOpt
     SUBDIR += py-PyWavelets
     SUBDIR += py-Pyomo
+    SUBDIR += py-algopy
     SUBDIR += py-altgraph
     SUBDIR += py-apgl
     SUBDIR += py-basemap

math/py-algopy/Makefile

@@ -0,0 +1,20 @@
+# $FreeBSD$
+
+PORTNAME=	algopy
+DISTVERSION=	0.5.7
+CATEGORIES=	math python
+MASTER_SITES=	CHEESESHOP
+PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
+
+MAINTAINER=	yuri@FreeBSD.org
+COMMENT=	Algorithmic Differentiation (AD) and Taylor polynomial approximations
+
+LICENSE=	BSD3CLAUSE
+
+RUN_DEPENDS=	${PYNUMPY}
+
+USES=		python zip
+USE_PYTHON=	distutils autoplist
+NO_ARCH=	yes
+
+.include <bsd.port.mk>

math/py-algopy/distinfo

@@ -0,0 +1,3 @@
+TIMESTAMP = 1524003219
+SHA256 (algopy-0.5.7.zip) = 6955f676fce3858fa3585cb7f3f7e1796cb93377d24016419b6699291584b7df
+SIZE (algopy-0.5.7.zip) = 189516

math/py-algopy/pkg-descr

@@ -0,0 +1,10 @@
+The purpose of AlgoPy is the evaluation of higher-order derivatives in the
+forward and reverse mode of Algorithmic Differentiation (AD) of functions
+that are implemented as Python programs. Particular focus are functions that
+contain numerical linear algebra functions as they often appear in statistically
+motivated functions. The intended use of AlgoPy is for easy prototyping at
+reasonable execution speeds. More precisely, for a typical program a directional
+derivative takes order 10 times as much time as time as the function evaluation.
+This is approximately also true for the gradient.
+
+WWW: https://pythonhosted.org/algopy/