--- science/py-scikit-sparse/Makefile	(revision 391243)
+++ science/py-scikit-sparse/Makefile	(working copy)
@@ -3,6 +3,7 @@ 
 
 PORTNAME=	scikit-sparse
 PORTVERSION=	0.2
+PORTREVISION=	1
 CATEGORIES=	science python
 MASTER_SITES=	CHEESESHOP
 PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
--- science/py-scikit-sparse/files/patch-scikits_sparse_test__cholmod.py	(revision 0)
+++ science/py-scikit-sparse/files/patch-scikits_sparse_test__cholmod.py	(working copy)
@@ -0,0 +1,33 @@ 
+--- scikits/sparse/test_cholmod.py.orig	2015-06-24 10:39:06 UTC
++++ scikits/sparse/test_cholmod.py
+@@ -53,17 +53,17 @@ def test_integer_size():
+ def test_cholesky_smoke_test():
+     f = cholesky(sparse.eye(10, 10) * 1.)
+     d = np.arange(20).reshape(10, 2)
+-    print "dense"
++    print("dense")
+     assert np.allclose(f(d), d)
+-    print "sparse"
++    print("sparse")
+     s_csc = sparse.csc_matrix(np.eye(10)[:, :2] * 1.)
+     assert sparse.issparse(f(s_csc))
+     assert np.allclose(f(s_csc).todense(), s_csc.todense())
+-    print "csr"
++    print("csr")
+     s_csr = s_csc.tocsr()
+     assert sparse.issparse(f(s_csr))
+     assert np.allclose(f(s_csr).todense(), s_csr.todense())
+-    print "extract"
++    print("extract")
+     assert np.all(f.P() == np.arange(10))
+ 
+ def real_matrix():
+@@ -193,7 +193,7 @@ def test_cholesky_matrix_market():
+             f3.cholesky_AAt_inplace(X.T)
+             assert np.allclose(f3(Xty), answer)
+ 
+-            print problem, mode
++            print(problem, mode)
+             for f in (f1, f2, f3, f4):
+                 pXtX = XtX.todense()[f.P()[:, np.newaxis],
+                                      f.P()[np.newaxis, :]]
--- science/py-scikit-sparse/pkg-descr	(revision 391243)
+++ science/py-scikit-sparse/pkg-descr	(working copy)
@@ -1,3 +1,3 @@ 
 scikits-sparse is a Python module for sparse matrix calculations.
 
-WWW: https://scikits.appspot.com/sparse/
+WWW: https://github.com/njsmith/scikits-sparse