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CVS commit: pkgsrc/math/py-scipy
Module Name: pkgsrc
Committed By: adam
Date: Wed Jul 12 16:26:29 UTC 2023
Modified Files:
pkgsrc/math/py-scipy: Makefile PLIST distinfo
Log Message:
py-scipy: updated to 1.11.1
SciPy 1.11.1
Issues closed for 1.11.1
BUG: run method of scipy.odr.ODR class fails when delta0 parameter...
BUG: segfault in \`scipy.linalg.lu\` on x86_64 windows and macos...
BUG: factorial return type inconsistent for 0-dim arrays
determinant of a 1x1 matrix returns an array, not a scalar
Licensing concern
Pull requests for 1.11.1
BUG: Fix work array construction for various weight shapes.
REL, MAINT: prep for 1.11.1
BUG: fix handling for \`factorial(..., exact=False)\` for 0-dim...
FIX:linalg.lu:Guard against permute_l out of bound behavior
MAINT:linalg.det:Return scalars for singleton inputs
MAINT: fix unuran licensing
SciPy 1.11.0 is the culmination of 6 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with ``python -Wd`` and check for ``DeprecationWarning`` s).
Our development attention will now shift to bug-fix releases on the
1.11.x branch, and on adding new features on the main branch.
This release requires Python 3.9+ and NumPy 1.21.6 or greater.
For running on PyPy, PyPy3 6.0+ is required.
**************************
Highlights of this release
**************************
- 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.
************
New features
************
`scipy.integrate` improvements
==============================
- Added `scipy.integrate.qmc_quad` for quasi-Monte Carlo integration.
- For an even number of points, `scipy.integrate.simpson` now calculates
a parabolic segment over the last three points which gives improved
accuracy over the previous implementation.
`scipy.cluster` improvements
============================
- ``disjoint_set`` has a new method ``subset_size`` for providing the size
of a particular subset.
`scipy.constants` improvements
================================
- The ``quetta``, ``ronna``, ``ronto``, and ``quecto`` SI prefixes were added.
`scipy.linalg` improvements
===========================
- `scipy.linalg.det` is improved and now accepts nD-arrays.
- `scipy.linalg.lu` is improved and now accepts nD-arrays. With the new
``p_indices`` switch the output permutation argument can be 1D ``(n,)``
permutation index instead of the full ``(n, n)`` array.
`scipy.ndimage` improvements
============================
- ``axes`` argument was added to ``rank_filter``, ``percentile_filter``,
``median_filter``, ``uniform_filter``, ``minimum_filter``,
``maximum_filter``, and ``gaussian_filter``, which can be useful for
processing stacks of image data.
`scipy.optimize` improvements
=============================
- `scipy.optimize.linprog` now passes unrecognized options directly to HiGHS.
- `scipy.optimize.root_scalar` now uses Newton's method to be used without
providing ``fprime`` and the ``secant`` method to be used without a second
guess.
- `scipy.optimize.lsq_linear` now accepts ``bounds`` arguments of type
`scipy.optimize.Bounds`.
- `scipy.optimize.minimize` ``method='cobyla'`` now supports simple bound
constraints.
- Users can opt into a new callback interface for most methods of
`scipy.optimize.minimize`: If the provided callback callable accepts
a single keyword argument, ``intermediate_result``, `scipy.optimize.minimize`
now passes both the current solution and the optimal value of the objective
function to the callback as an instance of `scipy.optimize.OptimizeResult`.
It also allows the user to terminate optimization by raising a
``StopIteration`` exception from the callback function.
`scipy.optimize.minimize` will return normally, and the latest solution
information is provided in the result object.
- `scipy.optimize.curve_fit` now supports an optional ``nan_policy`` argument.
- `scipy.optimize.shgo` now has parallelization with the ``workers`` argument,
symmetry arguments that can improve performance, class-based design to
improve usability, and generally improved performance.
`scipy.signal` improvements
===========================
- ``istft`` has an improved warning message when the NOLA condition fails.
`scipy.sparse` improvements
===========================
- A new public base class `scipy.sparse.sparray` was introduced, allowing further
extension of the sparse array API (such as the support for 1-dimensional
sparse arrays) without breaking backwards compatibility.
`isinstance(x, scipy.sparse.sparray)` to select the new sparse array classes,
while `isinstance(x, scipy.sparse.spmatrix)` selects only the old sparse
matrix classes.
- Division of sparse arrays by a dense array now returns sparse arrays.
- `scipy.sparse.isspmatrix` now only returns `True` for the sparse matrices instances.
`scipy.sparse.issparse` now has to be used instead to check for instances of sparse
arrays or instances of sparse matrices.
- Sparse arrays constructed with int64 indices will no longer automatically
downcast to int32.
- The ``argmin`` and ``argmax`` methods now return the correct result when explicit
zeros are present.
`scipy.sparse.linalg` improvements
==================================
- dividing ``LinearOperator`` by a number now returns a
``_ScaledLinearOperator``
- ``LinearOperator`` now supports right multiplication by arrays
- ``lobpcg`` should be more efficient following removal of an extraneous
QR decomposition.
`scipy.spatial` improvements
============================
- Usage of new C++ backend for additional distance metrics, the majority of
which will see substantial performance improvements, though a few minor
regressions are known. These are focused on distances between boolean
arrays.
`scipy.special` improvements
============================
- The factorial functions ``factorial``, ``factorial2`` and ``factorialk``
were made consistent in their behavior (in terms of dimensionality,
errors etc.). Additionally, ``factorial2`` can now handle arrays with
``exact=True``, and ``factorialk`` can handle arrays.
`scipy.stats` improvements
==========================
New Features
------------
- `scipy.stats.sobol_indices`, a method to compute Sobol' sensitivity indices.
- `scipy.stats.dunnett`, which performs Dunnett's test of the means of multiple
experimental groups against the mean of a control group.
- `scipy.stats.ecdf` for computing the empirical CDF and complementary
CDF (survival function / SF) from uncensored or right-censored data. This
function is also useful for survival analysis / Kaplan-Meier estimation.
- `scipy.stats.logrank` to compare survival functions underlying samples.
- `scipy.stats.false_discovery_control` for adjusting p-values to control the
false discovery rate of multiple hypothesis tests using the
Benjamini-Hochberg or Benjamini-Yekutieli procedures.
- `scipy.stats.CensoredData` to represent censored data. It can be used as
input to the ``fit`` method of univariate distributions and to the new
``ecdf`` function.
- Filliben's goodness of fit test as ``method='Filliben'`` of
`scipy.stats.goodness_of_fit`.
- `scipy.stats.ttest_ind` has a new method, ``confidence_interval`` for
computing a confidence interval of the difference between means.
- `scipy.stats.MonteCarloMethod`, `scipy.stats.PermutationMethod`, and
`scipy.stats.BootstrapMethod` are new classes to configure resampling and/or
Monte Carlo versions of hypothesis tests. They can currently be used with
`scipy.stats.pearsonr`.
To generate a diff of this commit:
cvs rdiff -u -r1.66 -r1.67 pkgsrc/math/py-scipy/Makefile
cvs rdiff -u -r1.24 -r1.25 pkgsrc/math/py-scipy/PLIST
cvs rdiff -u -r1.43 -r1.44 pkgsrc/math/py-scipy/distinfo
Please note that diffs are not public domain; they are subject to the
copyright notices on the relevant files.
Modified files:
Index: pkgsrc/math/py-scipy/Makefile
diff -u pkgsrc/math/py-scipy/Makefile:1.66 pkgsrc/math/py-scipy/Makefile:1.67
--- pkgsrc/math/py-scipy/Makefile:1.66 Sat Jul 1 08:37:41 2023
+++ pkgsrc/math/py-scipy/Makefile Wed Jul 12 16:26:29 2023
@@ -1,6 +1,6 @@
-# $NetBSD: Makefile,v 1.66 2023/07/01 08:37:41 wiz Exp $
+# $NetBSD: Makefile,v 1.67 2023/07/12 16:26:29 adam Exp $
-DISTNAME= scipy-1.10.1
+DISTNAME= scipy-1.11.1
PKGNAME= ${PYPKGPREFIX}-${DISTNAME}
CATEGORIES= math python
MASTER_SITES= ${MASTER_SITE_PYPI:=s/scipy/}
@@ -39,12 +39,7 @@ FFLAGS+= -fPIC
LDFLAGS.Darwin+= -headerpad_max_install_names
-PYTHON_VERSIONS_INCOMPATIBLE= 27 37 38
-
-# Prefer boost-headers from PkgSrc which are properly patched.
-post-configure:
- ${RM} -f -r ${WRKSRC}/scipy/_lib/boost/boost
- ${LN} -s ${BUILDLINK_DIR}/include/boost ${WRKSRC}/scipy/_lib/boost/boost
+PYTHON_VERSIONS_INCOMPATIBLE= 27 38
.include "../../mk/bsd.prefs.mk"
@@ -67,14 +62,12 @@ do-test:
cd ${WRKDIR} && \
${PYTHONBIN} -c "import scipy; scipy.test('full')"
-BUILDLINK_API_DEPENDS.boost-headers+= boost-headers>=1.77.0nb1
-.include "../../devel/boost-headers/buildlink3.mk"
.include "../../devel/py-cython/buildlink3.mk"
.include "../../devel/py-pybind11/buildlink3.mk"
.include "../../lang/python/application.mk"
.include "../../lang/python/egg.mk"
.include "../../math/fftw/buildlink3.mk"
-BUILDLINK_API_DEPENDS.py-numpy+= ${PYPKGPREFIX}-numpy>=1.18.5
+BUILDLINK_API_DEPENDS.py-numpy+= ${PYPKGPREFIX}-numpy>=1.21.6
.include "../../math/py-numpy/buildlink3.mk"
.include "../../math/py-pythran/buildlink3.mk"
.include "../../mk/bsd.pkg.mk"
Index: pkgsrc/math/py-scipy/PLIST
diff -u pkgsrc/math/py-scipy/PLIST:1.24 pkgsrc/math/py-scipy/PLIST:1.25
--- pkgsrc/math/py-scipy/PLIST:1.24 Fri May 5 14:59:25 2023
+++ pkgsrc/math/py-scipy/PLIST Wed Jul 12 16:26:29 2023
@@ -1,12 +1,10 @@
-@comment $NetBSD: PLIST,v 1.24 2023/05/05 14:59:25 adam Exp $
+@comment $NetBSD: PLIST,v 1.25 2023/07/12 16:26:29 adam Exp $
${PYSITELIB}/${EGG_INFODIR}/PKG-INFO
${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt
${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt
${PYSITELIB}/${EGG_INFODIR}/not-zip-safe
${PYSITELIB}/${EGG_INFODIR}/requires.txt
${PYSITELIB}/${EGG_INFODIR}/top_level.txt
-${PYSITELIB}/scipy/HACKING.rst.txt
-${PYSITELIB}/scipy/INSTALL.rst.txt
${PYSITELIB}/scipy/LICENSE.txt
${PYSITELIB}/scipy/LICENSES_bundled.txt
${PYSITELIB}/scipy/__config__.py
@@ -24,9 +22,6 @@ ${PYSITELIB}/scipy/_build_utils/_fortran
${PYSITELIB}/scipy/_build_utils/compiler_helper.py
${PYSITELIB}/scipy/_build_utils/compiler_helper.pyc
${PYSITELIB}/scipy/_build_utils/compiler_helper.pyo
-${PYSITELIB}/scipy/_build_utils/gcc_build_bitness.py
-${PYSITELIB}/scipy/_build_utils/gcc_build_bitness.pyc
-${PYSITELIB}/scipy/_build_utils/gcc_build_bitness.pyo
${PYSITELIB}/scipy/_build_utils/setup.py
${PYSITELIB}/scipy/_build_utils/setup.pyc
${PYSITELIB}/scipy/_build_utils/setup.pyo
@@ -866,6 +861,8 @@ ${PYSITELIB}/scipy/linalg/_decomp_ldl.py
${PYSITELIB}/scipy/linalg/_decomp_lu.py
${PYSITELIB}/scipy/linalg/_decomp_lu.pyc
${PYSITELIB}/scipy/linalg/_decomp_lu.pyo
+${PYSITELIB}/scipy/linalg/_decomp_lu_cython.pyi
+${PYSITELIB}/scipy/linalg/_decomp_lu_cython.so
${PYSITELIB}/scipy/linalg/_decomp_polar.py
${PYSITELIB}/scipy/linalg/_decomp_polar.pyc
${PYSITELIB}/scipy/linalg/_decomp_polar.pyo
@@ -1000,6 +997,7 @@ ${PYSITELIB}/scipy/linalg/tests/test_dec
${PYSITELIB}/scipy/linalg/tests/test_decomp_cholesky.py
${PYSITELIB}/scipy/linalg/tests/test_decomp_cossin.py
${PYSITELIB}/scipy/linalg/tests/test_decomp_ldl.py
+${PYSITELIB}/scipy/linalg/tests/test_decomp_lu.py
${PYSITELIB}/scipy/linalg/tests/test_decomp_polar.py
${PYSITELIB}/scipy/linalg/tests/test_decomp_update.py
${PYSITELIB}/scipy/linalg/tests/test_fblas.py
@@ -1281,10 +1279,13 @@ ${PYSITELIB}/scipy/optimize/_shgo.pyo
${PYSITELIB}/scipy/optimize/_shgo_lib/__init__.py
${PYSITELIB}/scipy/optimize/_shgo_lib/__init__.pyc
${PYSITELIB}/scipy/optimize/_shgo_lib/__init__.pyo
+${PYSITELIB}/scipy/optimize/_shgo_lib/_complex.py
+${PYSITELIB}/scipy/optimize/_shgo_lib/_complex.pyc
+${PYSITELIB}/scipy/optimize/_shgo_lib/_complex.pyo
+${PYSITELIB}/scipy/optimize/_shgo_lib/_vertex.py
+${PYSITELIB}/scipy/optimize/_shgo_lib/_vertex.pyc
+${PYSITELIB}/scipy/optimize/_shgo_lib/_vertex.pyo
${PYSITELIB}/scipy/optimize/_shgo_lib/meson.build
-${PYSITELIB}/scipy/optimize/_shgo_lib/triangulation.py
-${PYSITELIB}/scipy/optimize/_shgo_lib/triangulation.pyc
-${PYSITELIB}/scipy/optimize/_shgo_lib/triangulation.pyo
${PYSITELIB}/scipy/optimize/_slsqp.so
${PYSITELIB}/scipy/optimize/_slsqp_py.py
${PYSITELIB}/scipy/optimize/_slsqp_py.pyc
@@ -1577,9 +1578,6 @@ ${PYSITELIB}/scipy/signal/windows/window
${PYSITELIB}/scipy/sparse/__init__.py
${PYSITELIB}/scipy/sparse/__init__.pyc
${PYSITELIB}/scipy/sparse/__init__.pyo
-${PYSITELIB}/scipy/sparse/_arrays.py
-${PYSITELIB}/scipy/sparse/_arrays.pyc
-${PYSITELIB}/scipy/sparse/_arrays.pyo
${PYSITELIB}/scipy/sparse/_base.py
${PYSITELIB}/scipy/sparse/_base.pyc
${PYSITELIB}/scipy/sparse/_base.pyo
@@ -1623,6 +1621,9 @@ ${PYSITELIB}/scipy/sparse/_index.pyo
${PYSITELIB}/scipy/sparse/_lil.py
${PYSITELIB}/scipy/sparse/_lil.pyc
${PYSITELIB}/scipy/sparse/_lil.pyo
+${PYSITELIB}/scipy/sparse/_matrix.py
+${PYSITELIB}/scipy/sparse/_matrix.pyc
+${PYSITELIB}/scipy/sparse/_matrix.pyo
${PYSITELIB}/scipy/sparse/_matrix_io.py
${PYSITELIB}/scipy/sparse/_matrix_io.pyc
${PYSITELIB}/scipy/sparse/_matrix_io.pyo
@@ -1871,6 +1872,7 @@ ${PYSITELIB}/scipy/sparse/tests/test_bas
${PYSITELIB}/scipy/sparse/tests/test_construct.py
${PYSITELIB}/scipy/sparse/tests/test_csc.py
${PYSITELIB}/scipy/sparse/tests/test_csr.py
+${PYSITELIB}/scipy/sparse/tests/test_deprecations.py
${PYSITELIB}/scipy/sparse/tests/test_extract.py
${PYSITELIB}/scipy/sparse/tests/test_matrix_io.py
${PYSITELIB}/scipy/sparse/tests/test_sparsetools.py
@@ -2183,6 +2185,9 @@ ${PYSITELIB}/scipy/stats/_boost/setup.py
${PYSITELIB}/scipy/stats/_boost/setup.pyc
${PYSITELIB}/scipy/stats/_boost/setup.pyo
${PYSITELIB}/scipy/stats/_boost/skewnorm_ufunc.so
+${PYSITELIB}/scipy/stats/_censored_data.py
+${PYSITELIB}/scipy/stats/_censored_data.pyc
+${PYSITELIB}/scipy/stats/_censored_data.pyo
${PYSITELIB}/scipy/stats/_common.py
${PYSITELIB}/scipy/stats/_common.pyc
${PYSITELIB}/scipy/stats/_common.pyo
@@ -2244,6 +2249,9 @@ ${PYSITELIB}/scipy/stats/_mstats_basic.p
${PYSITELIB}/scipy/stats/_mstats_extras.py
${PYSITELIB}/scipy/stats/_mstats_extras.pyc
${PYSITELIB}/scipy/stats/_mstats_extras.pyo
+${PYSITELIB}/scipy/stats/_multicomp.py
+${PYSITELIB}/scipy/stats/_multicomp.pyc
+${PYSITELIB}/scipy/stats/_multicomp.pyo
${PYSITELIB}/scipy/stats/_multivariate.py
${PYSITELIB}/scipy/stats/_multivariate.pyc
${PYSITELIB}/scipy/stats/_multivariate.pyo
@@ -2259,6 +2267,9 @@ ${PYSITELIB}/scipy/stats/_qmc.pyc
${PYSITELIB}/scipy/stats/_qmc.pyo
${PYSITELIB}/scipy/stats/_qmc_cy.pyi
${PYSITELIB}/scipy/stats/_qmc_cy.so
+${PYSITELIB}/scipy/stats/_qmvnt.py
+${PYSITELIB}/scipy/stats/_qmvnt.pyc
+${PYSITELIB}/scipy/stats/_qmvnt.pyo
${PYSITELIB}/scipy/stats/_rcont/__init__.py
${PYSITELIB}/scipy/stats/_rcont/__init__.pyc
${PYSITELIB}/scipy/stats/_rcont/__init__.pyo
@@ -2278,6 +2289,9 @@ ${PYSITELIB}/scipy/stats/_result_classes
${PYSITELIB}/scipy/stats/_rvs_sampling.py
${PYSITELIB}/scipy/stats/_rvs_sampling.pyc
${PYSITELIB}/scipy/stats/_rvs_sampling.pyo
+${PYSITELIB}/scipy/stats/_sensitivity_analysis.py
+${PYSITELIB}/scipy/stats/_sensitivity_analysis.pyc
+${PYSITELIB}/scipy/stats/_sensitivity_analysis.pyo
${PYSITELIB}/scipy/stats/_sobol.pyi
${PYSITELIB}/scipy/stats/_sobol.so
${PYSITELIB}/scipy/stats/_sobol_direction_numbers.npz
@@ -2293,6 +2307,9 @@ ${PYSITELIB}/scipy/stats/_stats_pythran.
${PYSITELIB}/scipy/stats/_stats_pythran.pyc
${PYSITELIB}/scipy/stats/_stats_pythran.pyo
${PYSITELIB}/scipy/stats/_stats_pythran.so
+${PYSITELIB}/scipy/stats/_survival.py
+${PYSITELIB}/scipy/stats/_survival.pyc
+${PYSITELIB}/scipy/stats/_survival.pyo
${PYSITELIB}/scipy/stats/_tukeylambda_stats.py
${PYSITELIB}/scipy/stats/_tukeylambda_stats.pyc
${PYSITELIB}/scipy/stats/_tukeylambda_stats.pyo
@@ -2355,6 +2372,7 @@ ${PYSITELIB}/scipy/stats/stats.pyc
${PYSITELIB}/scipy/stats/stats.pyo
${PYSITELIB}/scipy/stats/tests/__init__.py
${PYSITELIB}/scipy/stats/tests/common_tests.py
+${PYSITELIB}/scipy/stats/tests/data/_mvt.py
${PYSITELIB}/scipy/stats/tests/data/fisher_exact_results_from_r.py
${PYSITELIB}/scipy/stats/tests/data/levy_stable/meson.build
${PYSITELIB}/scipy/stats/tests/data/levy_stable/stable-Z1-cdf-sample-data.npy
@@ -2375,13 +2393,16 @@ ${PYSITELIB}/scipy/stats/tests/data/nist
${PYSITELIB}/scipy/stats/tests/data/nist_anova/meson.build
${PYSITELIB}/scipy/stats/tests/data/nist_linregress/Norris.dat
${PYSITELIB}/scipy/stats/tests/data/nist_linregress/meson.build
+${PYSITELIB}/scipy/stats/tests/data/rel_breitwigner_pdf_sample_data_ROOT.npy
${PYSITELIB}/scipy/stats/tests/data/studentized_range_mpmath_ref.json
${PYSITELIB}/scipy/stats/tests/meson.build
${PYSITELIB}/scipy/stats/tests/test_axis_nan_policy.py
${PYSITELIB}/scipy/stats/tests/test_binned_statistic.py
${PYSITELIB}/scipy/stats/tests/test_boost_ufuncs.py
+${PYSITELIB}/scipy/stats/tests/test_censored_data.py
${PYSITELIB}/scipy/stats/tests/test_contingency.py
${PYSITELIB}/scipy/stats/tests/test_continuous_basic.py
+${PYSITELIB}/scipy/stats/tests/test_continuous_fit_censored.py
${PYSITELIB}/scipy/stats/tests/test_crosstab.py
${PYSITELIB}/scipy/stats/tests/test_discrete_basic.py
${PYSITELIB}/scipy/stats/tests/test_discrete_distns.py
@@ -2390,12 +2411,15 @@ ${PYSITELIB}/scipy/stats/tests/test_entr
${PYSITELIB}/scipy/stats/tests/test_fit.py
${PYSITELIB}/scipy/stats/tests/test_generation/generate_fisher_exact_results_from_r.R
${PYSITELIB}/scipy/stats/tests/test_generation/meson.build
+${PYSITELIB}/scipy/stats/tests/test_generation/reference_distribution_infrastructure_tests.py
+${PYSITELIB}/scipy/stats/tests/test_generation/reference_distributions.py
${PYSITELIB}/scipy/stats/tests/test_generation/studentized_range_mpmath_ref.py
${PYSITELIB}/scipy/stats/tests/test_hypotests.py
${PYSITELIB}/scipy/stats/tests/test_kdeoth.py
${PYSITELIB}/scipy/stats/tests/test_morestats.py
${PYSITELIB}/scipy/stats/tests/test_mstats_basic.py
${PYSITELIB}/scipy/stats/tests/test_mstats_extras.py
+${PYSITELIB}/scipy/stats/tests/test_multicomp.py
${PYSITELIB}/scipy/stats/tests/test_multivariate.py
${PYSITELIB}/scipy/stats/tests/test_odds_ratio.py
${PYSITELIB}/scipy/stats/tests/test_qmc.py
@@ -2403,7 +2427,9 @@ ${PYSITELIB}/scipy/stats/tests/test_rank
${PYSITELIB}/scipy/stats/tests/test_relative_risk.py
${PYSITELIB}/scipy/stats/tests/test_resampling.py
${PYSITELIB}/scipy/stats/tests/test_sampling.py
+${PYSITELIB}/scipy/stats/tests/test_sensitivity_analysis.py
${PYSITELIB}/scipy/stats/tests/test_stats.py
+${PYSITELIB}/scipy/stats/tests/test_survival.py
${PYSITELIB}/scipy/stats/tests/test_tukeylambda_stats.py
${PYSITELIB}/scipy/stats/tests/test_variation.py
${PYSITELIB}/scipy/version.py
Index: pkgsrc/math/py-scipy/distinfo
diff -u pkgsrc/math/py-scipy/distinfo:1.43 pkgsrc/math/py-scipy/distinfo:1.44
--- pkgsrc/math/py-scipy/distinfo:1.43 Mon May 8 20:39:18 2023
+++ pkgsrc/math/py-scipy/distinfo Wed Jul 12 16:26:29 2023
@@ -1,9 +1,8 @@
-$NetBSD: distinfo,v 1.43 2023/05/08 20:39:18 tnn Exp $
+$NetBSD: distinfo,v 1.44 2023/07/12 16:26:29 adam Exp $
-BLAKE2s (scipy-1.10.1.tar.gz) = 0ba22381e66ccc4017de0042a14410af34e44455f4c4a12b80ed3f670c0d2d72
-SHA512 (scipy-1.10.1.tar.gz) = 767e1dee6285d98e4d58d8d8d1e3654b00e3216e9ad55455fc9473f1d044bef80ecce749bcd24d8f331367b882c9c3e6b652a6e901bb4296ddc7cc44699215b6
-Size (scipy-1.10.1.tar.gz) = 42407997 bytes
-SHA1 (patch-scipy_spatial___ckdtree.pyx) = 0739d06bc1d1cb75761dd0f43c47a76f2a90327b
+BLAKE2s (scipy-1.11.1.tar.gz) = 1fe93584ad30db4c1e2686cb74ad291e3f62ffe49c4d8afcc6d1d5bccfd26268
+SHA512 (scipy-1.11.1.tar.gz) = 2859fc73c4fef713497f450b76a57d274bb46477a08ac909714823225f065a13c1e42a61ce67575cfc475e1e4612090cc4ca0920411fe32370c5b5c476352f97
+Size (scipy-1.11.1.tar.gz) = 56031509 bytes
SHA1 (patch-scipy_spatial_ckdtree_src_ckdtree__decl.h) = c5684427731eef00c012b1ebc7fba00da560f493
SHA1 (patch-scipy_special_Faddeeva.hh) = 9f6c0bb808fc9be0a1c1b7e65395d29f13821233
SHA1 (patch-scipy_special_sf__error.c) = 416489193a6ff90caaa95f489a452f23f0a27bbe
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