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here is the log from the commit of package python-scipy for openSUSE:Factory
checked in at 2023-08-06 16:29:16
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Comparing /work/SRC/openSUSE:Factory/python-scipy (Old)
and /work/SRC/openSUSE:Factory/.python-scipy.new.22712 (New)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "python-scipy"
Sun Aug 6 16:29:16 2023 rev:65 rq:1101324 version:1.11.1
Changes:
--------
--- /work/SRC/openSUSE:Factory/python-scipy/python-scipy.changes
2023-07-05 15:30:34.850573704 +0200
+++ /work/SRC/openSUSE:Factory/.python-scipy.new.22712/python-scipy.changes
2023-08-06 16:29:20.283611737 +0200
@@ -1,0 +2,20 @@
+Thu Jul 27 13:31:24 UTC 2023 - Markéta Machová <mmach...@suse.com>
+
+- Add upstream intc.patch to fix gh#scipy/scipy#18603
+
+-------------------------------------------------------------------
+Tue Jul 25 10:04:04 UTC 2023 - Markéta Machová <mmach...@suse.com>
+
+- Update to 1.11.1
+ * Several scipy.sparse array API improvements, including sparse.sparray,
+ a new public base class distinct from the older sparse.spmatrix class,
+ proper 64-bit index support, and numerous deprecations paving the way
+ to a modern sparse array experience.
+ * scipy.stats added tools for survival analysis, multiple hypothesis
+ testing, sensitivity analysis, and working with censored data.
+ * A new function was added for quasi-Monte Carlo integration, and linear
+ algebra functions det and lu now accept nD-arrays.
+ * An axes argument was added broadly to ndimage functions, facilitating
+ analysis of stacked image data.
+
+-------------------------------------------------------------------
Old:
----
scipy-1.10.1.tar.gz
New:
----
intc.patch
scipy-1.11.1.tar.gz
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Other differences:
------------------
++++++ python-scipy.spec ++++++
--- /var/tmp/diff_new_pack.07KTdX/_old 2023-08-06 16:29:22.127623548 +0200
+++ /var/tmp/diff_new_pack.07KTdX/_new 2023-08-06 16:29:22.135623599 +0200
@@ -18,7 +18,7 @@
%{?sle15_python_module_pythons}
%global flavor @BUILD_FLAVOR@%{nil}
-%define _ver 1_10_1
+%define _ver 1_11_1
%define shortname scipy
%define pname python-%{shortname}
%define hpc_upcase_trans_hyph() %(echo %{**} | tr [a-z] [A-Z] | tr '-' '_')
@@ -93,7 +93,7 @@
# TODO explore debundling Boost for standard and hpc
Name: %{package_name}
-Version: 1.10.1
+Version: 1.11.1
Release: 0
Summary: Scientific Tools for Python
License: BSD-3-Clause AND LGPL-2.0-or-later AND BSL-1.0
@@ -102,6 +102,8 @@
Source0:
https://files.pythonhosted.org/packages/source/s/scipy/scipy-%{version}.tar.gz
# Create with pooch: `python3 scipy-%{version}/scipy/datasets/_download_all.py
scipy-datasets/scipy-data; tar czf scipy-datasets.tar.gz scipy-datasets`
Source1: scipy-datasets.tar.gz
+#PATCH-FIX-UPSTREAM
https://github.com/scipy/scipy/commit/8501b7c2fb8a7121aeef94489ece988043c463d0
BUG: sparse.linalg: Cast index arrays to intc before calling SuperLU functions
+Patch: intc.patch
BuildRequires: %{python_module Cython >= 0.29.32}
BuildRequires: %{python_module devel >= 3.8}
BuildRequires: %{python_module meson-python >= 0.9.0}
@@ -262,6 +264,7 @@
donttest+=" or (test_rotation and test_align_vectors_single_vector)"
donttest+=" or (test_lobpcg and test_tolerance_float32)"
donttest+=" or (test_iterative and test_maxiter_worsening)"
+donttest+=" or (test_resampling and test_bootstrap_alternative)"
%ifarch %ix86
donttest+=" or (test_solvers and test_solve_generalized_discrete_are)"
# Skip the following tests that fail with GCC 13 due to the excess precision
change:
++++++ intc.patch ++++++
>From 8501b7c2fb8a7121aeef94489ece988043c463d0 Mon Sep 17 00:00:00 2001
From: CJ Carey <perimosocord...@gmail.com>
Date: Sun, 2 Jul 2023 16:29:22 -0400
Subject: [PATCH] BUG: sparse.linalg: Cast index arrays to intc before calling
SuperLU functions (#18644)
* BUG: sparse.linalg: Cast to intc before SuperLU
---------
Co-authored-by: Stefan van der Walt <stef...@berkeley.edu>
Co-authored-by: Dan Schult <dsch...@colgate.edu>
---
scipy/sparse/linalg/_dsolve/linsolve.py | 27 +++++++++++--
.../linalg/_dsolve/tests/test_linsolve.py | 38 ++++++++++---------
2 files changed, 44 insertions(+), 21 deletions(-)
diff --git a/scipy/sparse/linalg/_dsolve/linsolve.py
b/scipy/sparse/linalg/_dsolve/linsolve.py
index 0c408f737266..78ba447bcd44 100644
--- a/scipy/sparse/linalg/_dsolve/linsolve.py
+++ b/scipy/sparse/linalg/_dsolve/linsolve.py
@@ -123,6 +123,21 @@ def _get_umf_family(A):
return family, A_new
+def _safe_downcast_indices(A):
+ # check for safe downcasting
+ max_value = np.iinfo(np.intc).max
+
+ if A.indptr[-1] > max_value: # indptr[-1] is max b/c indptr always sorted
+ raise ValueError("indptr values too large for SuperLU")
+
+ if max(*A.shape) > max_value: # only check large enough arrays
+ if np.any(A.indices > max_value):
+ raise ValueError("indices values too large for SuperLU")
+
+ indices = A.indices.astype(np.intc, copy=False)
+ indptr = A.indptr.astype(np.intc, copy=False)
+ return indices, indptr
+
def spsolve(A, b, permc_spec=None, use_umfpack=True):
"""Solve the sparse linear system Ax=b, where b may be a vector or a
matrix.
@@ -269,8 +284,10 @@ def spsolve(A, b, permc_spec=None, use_umfpack=True):
else:
flag = 0 # CSR format
+ indices = A.indices.astype(np.intc, copy=False)
+ indptr = A.indptr.astype(np.intc, copy=False)
options = dict(ColPerm=permc_spec)
- x, info = _superlu.gssv(N, A.nnz, A.data, A.indices, A.indptr,
+ x, info = _superlu.gssv(N, A.nnz, A.data, indices, indptr,
b, flag, options=options)
if info != 0:
warn("Matrix is exactly singular", MatrixRankWarning)
@@ -402,6 +419,8 @@ def csc_construct_func(*a, cls=type(A)):
if (M != N):
raise ValueError("can only factor square matrices") # is this true?
+ indices, indptr = _safe_downcast_indices(A)
+
_options = dict(DiagPivotThresh=diag_pivot_thresh, ColPerm=permc_spec,
PanelSize=panel_size, Relax=relax)
if options is not None:
@@ -411,7 +430,7 @@ def csc_construct_func(*a, cls=type(A)):
if (_options["ColPerm"] == "NATURAL"):
_options["SymmetricMode"] = True
- return _superlu.gstrf(N, A.nnz, A.data, A.indices, A.indptr,
+ return _superlu.gstrf(N, A.nnz, A.data, indices, indptr,
csc_construct_func=csc_construct_func,
ilu=False, options=_options)
@@ -495,6 +514,8 @@ def csc_construct_func(*a, cls=type(A)):
if (M != N):
raise ValueError("can only factor square matrices") # is this true?
+ indices, indptr = _safe_downcast_indices(A)
+
_options = dict(ILU_DropRule=drop_rule, ILU_DropTol=drop_tol,
ILU_FillFactor=fill_factor,
DiagPivotThresh=diag_pivot_thresh, ColPerm=permc_spec,
@@ -506,7 +527,7 @@ def csc_construct_func(*a, cls=type(A)):
if (_options["ColPerm"] == "NATURAL"):
_options["SymmetricMode"] = True
- return _superlu.gstrf(N, A.nnz, A.data, A.indices, A.indptr,
+ return _superlu.gstrf(N, A.nnz, A.data, indices, indptr,
csc_construct_func=csc_construct_func,
ilu=True, options=_options)
diff --git a/scipy/sparse/linalg/_dsolve/tests/test_linsolve.py
b/scipy/sparse/linalg/_dsolve/tests/test_linsolve.py
index 4740685a3dd7..00e0d1886dcc 100644
--- a/scipy/sparse/linalg/_dsolve/tests/test_linsolve.py
+++ b/scipy/sparse/linalg/_dsolve/tests/test_linsolve.py
@@ -220,8 +220,11 @@ def test_singular_gh_3312(self):
except RuntimeError:
pass
- def test_twodiags(self):
- A = spdiags([[1, 2, 3, 4, 5], [6, 5, 8, 9, 10]], [0, 1], 5, 5)
+ @pytest.mark.parametrize('format', ['csc', 'csr'])
+ @pytest.mark.parametrize('idx_dtype', [np.int32, np.int64])
+ def test_twodiags(self, format: str, idx_dtype: np.dtype):
+ A = spdiags([[1, 2, 3, 4, 5], [6, 5, 8, 9, 10]], [0, 1], 5, 5,
+ format=format)
b = array([1, 2, 3, 4, 5])
# condition number of A
@@ -230,13 +233,12 @@ def test_twodiags(self):
for t in ['f','d','F','D']:
eps = finfo(t).eps # floating point epsilon
b = b.astype(t)
+ Asp = A.astype(t)
+ Asp.indices = Asp.indices.astype(idx_dtype, copy=False)
+ Asp.indptr = Asp.indptr.astype(idx_dtype, copy=False)
- for format in ['csc','csr']:
- Asp = A.astype(t).asformat(format)
-
- x = spsolve(Asp,b)
-
- assert_(norm(b - Asp@x) < 10 * cond_A * eps)
+ x = spsolve(Asp, b)
+ assert_(norm(b - Asp@x) < 10 * cond_A * eps)
def test_bvector_smoketest(self):
Adense = array([[0., 1., 1.],
@@ -442,16 +444,18 @@ def setup_method(self):
n = 40
d = arange(n) + 1
self.n = n
- self.A = spdiags((d, 2*d, d[::-1]), (-3, 0, 5), n, n)
+ self.A = spdiags((d, 2*d, d[::-1]), (-3, 0, 5), n, n, format='csc')
random.seed(1234)
- def _smoketest(self, spxlu, check, dtype):
+ def _smoketest(self, spxlu, check, dtype, idx_dtype):
if np.issubdtype(dtype, np.complexfloating):
A = self.A + 1j*self.A.T
else:
A = self.A
A = A.astype(dtype)
+ A.indices = A.indices.astype(idx_dtype, copy=False)
+ A.indptr = A.indptr.astype(idx_dtype, copy=False)
lu = spxlu(A)
rng = random.RandomState(1234)
@@ -489,10 +493,9 @@ def check(A, b, x, msg=""):
r = A @ x
assert_(abs(r - b).max() < 1e3*eps, msg)
- self._smoketest(splu, check, np.float32)
- self._smoketest(splu, check, np.float64)
- self._smoketest(splu, check, np.complex64)
- self._smoketest(splu, check, np.complex128)
+ for dtype in [np.float32, np.float64, np.complex64, np.complex128]:
+ for idx_dtype in [np.int32, np.int64]:
+ self._smoketest(splu, check, dtype, idx_dtype)
@sup_sparse_efficiency
def test_spilu_smoketest(self):
@@ -508,10 +511,9 @@ def check(A, b, x, msg=""):
if b.dtype in (np.float64, np.complex128):
errors.append(err)
- self._smoketest(spilu, check, np.float32)
- self._smoketest(spilu, check, np.float64)
- self._smoketest(spilu, check, np.complex64)
- self._smoketest(spilu, check, np.complex128)
+ for dtype in [np.float32, np.float64, np.complex64, np.complex128]:
+ for idx_dtype in [np.int32, np.int64]:
+ self._smoketest(spilu, check, dtype, idx_dtype)
assert_(max(errors) > 1e-5)
++++++ scipy-1.10.1.tar.gz -> scipy-1.11.1.tar.gz ++++++
/work/SRC/openSUSE:Factory/python-scipy/scipy-1.10.1.tar.gz
/work/SRC/openSUSE:Factory/.python-scipy.new.22712/scipy-1.11.1.tar.gz differ:
char 5, line 1
