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here is the log from the commit of package python-scikit-umfpack for
openSUSE:Factory checked in at 2022-04-04 19:26:32
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Comparing /work/SRC/openSUSE:Factory/python-scikit-umfpack (Old)
and /work/SRC/openSUSE:Factory/.python-scikit-umfpack.new.1900 (New)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "python-scikit-umfpack"
Mon Apr 4 19:26:32 2022 rev:6 rq:966736 version:0.3.2
Changes:
--------
---
/work/SRC/openSUSE:Factory/python-scikit-umfpack/python-scikit-umfpack.changes
2021-09-09 23:07:55.072856981 +0200
+++
/work/SRC/openSUSE:Factory/.python-scikit-umfpack.new.1900/python-scikit-umfpack.changes
2022-04-04 19:26:54.696180121 +0200
@@ -1,0 +2,7 @@
+Sat Apr 2 20:13:01 UTC 2022 - Ben Greiner <[email protected]>
+
+- Add scikit-umfpack-pr68-scipy-sparse-linalg.patch
+ gh#scikit-umfpack/scikit-umfpack#68
+- x86_64 is the only platform where this works
+
+-------------------------------------------------------------------
New:
----
scikit-umfpack-pr68-scipy-sparse-linalg.patch
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Other differences:
------------------
++++++ python-scikit-umfpack.spec ++++++
--- /var/tmp/diff_new_pack.vrMJTi/_old 2022-04-04 19:26:55.224174067 +0200
+++ /var/tmp/diff_new_pack.vrMJTi/_new 2022-04-04 19:26:55.228174021 +0200
@@ -1,7 +1,7 @@
#
# spec file for package python-scikit-umfpack
#
-# Copyright (c) 2021 SUSE LLC
+# Copyright (c) 2022 SUSE LLC
#
# All modifications and additions to the file contributed by third parties
# remain the property of their copyright owners, unless otherwise agreed
@@ -17,7 +17,6 @@
%{?!python_module:%define python_module() python-%{**} python3-%{**}}
-%global skip_python36 1
%define oldpython python
Name: python-scikit-umfpack
Version: 0.3.2
@@ -27,6 +26,8 @@
URL: https://github.com/scikit-umfpack/scikit-umfpack
Source0:
https://files.pythonhosted.org/packages/source/s/scikit-umfpack/scikit-umfpack-%{version}.tar.gz
Patch0: do-not-use-numpy-decorators.patch
+# PATCH-FIX-UPSTREAM scikit-umfpack-pr68-scipy-sparse-linalg.patch --
gh#scikit-umfpack/scikit-umfpack68
+Patch1: scikit-umfpack-pr68-scipy-sparse-linalg.patch
BuildRequires: %{python_module devel}
BuildRequires: %{python_module numpy-devel >= 1.14.3}
BuildRequires: %{python_module scipy >= 1.0.0rc1}
@@ -40,7 +41,7 @@
BuildRequires: swig
Requires: python-numpy >= 1.14.3
Requires: python-scipy >= 1.0.0rc1
-ExcludeArch: aarch64 ppc64 ppc64le
+ExclusiveArch: x86_64
# SECTION test requirements
BuildRequires: %{python_module pytest}
# /SECTION
@@ -81,6 +82,6 @@
%license LICENSE
%dir %{python_sitearch}/scikits/
%{python_sitearch}/scikits/umfpack/
-%{python_sitearch}/scikit_umfpack-%{version}-py*.egg-info
+%{python_sitearch}/scikit_umfpack-%{version}*-info
%changelog
++++++ scikit-umfpack-pr68-scipy-sparse-linalg.patch ++++++
Index: scikit-umfpack-0.3.2/scikits/umfpack/tests/test_umfpack.py
===================================================================
--- scikit-umfpack-0.3.2.orig/scikits/umfpack/tests/test_umfpack.py
+++ scikit-umfpack-0.3.2/scikits/umfpack/tests/test_umfpack.py
@@ -11,8 +11,7 @@ import warnings
from numpy.testing import assert_array_almost_equal, run_module_suite
from scipy import rand, matrix, diag, eye
-from scipy.sparse import csc_matrix, spdiags, SparseEfficiencyWarning
-from scipy.sparse.linalg import linsolve
+from scipy.sparse import csc_matrix, linalg, spdiags, SparseEfficiencyWarning
import numpy as np
import scikits.umfpack as um
@@ -45,51 +44,51 @@ class TestScipySolvers(_DeprecationAccep
def test_solve_complex_umfpack(self):
# Solve with UMFPACK: double precision complex
- linsolve.use_solver(useUmfpack=True)
+ linalg.use_solver(useUmfpack=True)
a = self.a.astype('D')
b = self.b
- x = linsolve.spsolve(a, b)
+ x = linalg.spsolve(a, b)
assert_array_almost_equal(a*x, b)
@unittest.skipIf(_is_32bit_platform, reason="requires 64 bit platform")
def test_solve_complex_long_umfpack(self):
# Solve with UMFPACK: double precision complex, long indices
- linsolve.use_solver(useUmfpack=True)
+ linalg.use_solver(useUmfpack=True)
a = _to_int64(self.a.astype('D'))
b = self.b
- x = linsolve.spsolve(a, b)
+ x = linalg.spsolve(a, b)
assert_array_almost_equal(a*x, b)
def test_solve_umfpack(self):
# Solve with UMFPACK: double precision
- linsolve.use_solver(useUmfpack=True)
+ linalg.use_solver(useUmfpack=True)
a = self.a.astype('d')
b = self.b
- x = linsolve.spsolve(a, b)
+ x = linalg.spsolve(a, b)
assert_array_almost_equal(a*x, b)
@unittest.skipIf(_is_32bit_platform, reason="requires 64 bit platform")
def test_solve_long_umfpack(self):
# Solve with UMFPACK: double precision
- linsolve.use_solver(useUmfpack=True)
+ linalg.use_solver(useUmfpack=True)
a = _to_int64(self.a.astype('d'))
b = self.b
- x = linsolve.spsolve(a, b)
+ x = linalg.spsolve(a, b)
assert_array_almost_equal(a*x, b)
def test_solve_sparse_rhs(self):
# Solve with UMFPACK: double precision, sparse rhs
- linsolve.use_solver(useUmfpack=True)
+ linalg.use_solver(useUmfpack=True)
a = self.a.astype('d')
b = csc_matrix(self.b).T
- x = linsolve.spsolve(a, b)
+ x = linalg.spsolve(a, b)
assert_array_almost_equal(a*x, self.b)
def test_factorized_umfpack(self):
# Prefactorize (with UMFPACK) matrix for solving with multiple rhs
- linsolve.use_solver(useUmfpack=True)
+ linalg.use_solver(useUmfpack=True)
a = self.a.astype('d')
- solve = linsolve.factorized(a)
+ solve = linalg.factorized(a)
x1 = solve(self.b)
assert_array_almost_equal(a*x1, self.b)
@@ -99,9 +98,9 @@ class TestScipySolvers(_DeprecationAccep
@unittest.skipIf(_is_32bit_platform, reason="requires 64 bit platform")
def test_factorized_long_umfpack(self):
# Prefactorize (with UMFPACK) matrix for solving with multiple rhs
- linsolve.use_solver(useUmfpack=True)
+ linalg.use_solver(useUmfpack=True)
a = _to_int64(self.a.astype('d'))
- solve = linsolve.factorized(a)
+ solve = linalg.factorized(a)
x1 = solve(self.b)
assert_array_almost_equal(a*x1, self.b)
@@ -110,9 +109,9 @@ class TestScipySolvers(_DeprecationAccep
def test_factorized_without_umfpack(self):
# Prefactorize matrix for solving with multiple rhs
- linsolve.use_solver(useUmfpack=False)
+ linalg.use_solver(useUmfpack=False)
a = self.a.astype('d')
- solve = linsolve.factorized(a)
+ solve = linalg.factorized(a)
x1 = solve(self.b)
assert_array_almost_equal(a*x1, self.b)
