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py-scipy: updated to 1.15.2

Issues closed for 1.15.2

BUG: _lib: crash in uarray on interpreter exit with a free-threaded...
BUG: special: Unchecked calls of malloc() and calloc() in specfun.h
BUG: Segmentation Fault when passing a special array to ``scipy.cluster.hierarch``...
BUG: median_filter on 1D array crashes with memory corruption...
BUG: special: Potential memory leak in the function ``besy()``...
BUG: ndimage.median_filter: Heap corruption with scipy 1.15
BUG: signal.medfilt and ndimage.median_filter not returning correct...
BUG: special: Unchecked malloc in stirling2.h
BUG: Matrix multiplication of a coo_matrix with an invalid type...
BLD: libhighs.a static library is installed
BUG: interpolate test_bary_rational.TestAAA.test_basic_functions...
BUG: ``scipy.stats.zmap`` returns incorrect values for complex...
BUG: spatial.transform: ``Rotation.from_matrix()`` uses inaccurate...
BUG: io.loadmat thinks a file with a lot of zeros is valid
BUG: Slicing sparse matrix with ``None`` gives result different...
MAINT: scipy.stats: test_regressZEROX SIMD warning for certain...

Pull requests for 1.15.2

BUG: immortalize uarray global strings in order to prevent negative...
BUG: special: Fix unchecked memory allocations in ``specfun.h``
BUG: cluster: ``cophenet`` intercept invalid linkage matrix count
REL, MAINT: prep for 1.15.2
MAINT: stats.Mixture: make return type consistent when ``shape``...
MAINT: stats.Mixture: fix inverse functions when mean is undefined
BUG: special: Fix unchecked malloc in stirling2.h
TST: turn off dtype check due to endianness
BUG: sparse: fix selecting wrong dtype for coo coords
TST: interpolate: small tolerance bump to TestAAA.test_basic_functions
MAINT: Stop installing ``libhighs``
BUG: sparse.linalg.norm: add test for and fix return type
BUG: sparse: revert NotImplemented return values in dot and matmul
DOC: sparse.linalg: add two recent functions to namespace and...
BUG: wrap median_filter stability
MAINT: stats.zmap: restore support for complex data
MAINT: integrate.cumulative_simpson: bump test tolerance
BUG: scipy.spatial: Fix inaccurate orthonormalization in ``Rotation.from_matrix(``...
BUG: special: Fix a memory leak in the AMOS function besy().
CI: migrate Linux aarch64 jobs to GitHub Actions, add cp313t...
BUG: io.loadmat: throw error on file containing all zeros
BUG: sparse: fix spmatrix indexing with None and implicit padding...
MAINT: stats: pearsonr SIMD-related shim

math/py-scipy: Add patch for proper use of <complex> in C++ mode

Patch originally by Patrick Welche, munged and tested by me.

py-scipy: updated to 1.15.1

SciPy 1.15.1

REL, MAINT: prep for 1.15.1
MAINT: fix url for array-api-extra git submodule
BLD: fix some issues with undeclared internal build dependencies
TST: fix thread safety issue in interpolate.bsplines memmap test
TST: stats.Normal: bump tolerance on test of logcdf
MAINT: Update highs subproject commit

py-scipy: Avoid WRAP conflict from sys/termios.h on illumos.

py-scipy: updated to 1.15.0

1.15.0

Highlights of this release

Sparse arrays are now fully functional for 1-D and 2-D arrays. We recommend that all new code use sparse arrays instead of sparse matrices and that developers start to migrate their existing code from sparse matrix to sparse array: Migration from spmatrix to sparray. Both sparse.linalg and sparse.csgraph work with either sparse matrix or sparse array and work internally with sparse array.
Sparse arrays now provide basic support for n-D arrays in the COO format including add, subtract, reshape, transpose, matmul, dot, tensordot and others. More functionality is coming in future releases.
Preliminary support for free-threaded Python 3.13.
New probability distribution features in scipy.stats can be used to improve the speed and accuracy of existing continuous distributions and perform new probability calculations.
Several new features support vectorized calculations with Python Array API Standard compatible input (see “Array API Standard Support” below):
scipy.differentiate is a new top-level submodule for accurate estimation of derivatives of black box functions.
scipy.optimize.elementwise contains new functions for root-finding and minimization of univariate functions.
scipy.integrate offers new functions cubature, tanhsinh, and nsum for multivariate integration, univariate integration, and univariate series summation, respectively.
scipy.interpolate.AAA adds the AAA algorithm for barycentric rational approximation of real or complex functions.
scipy.special adds new functions offering improved Legendre function implementations with a more consistent interface.

py-scipy: updated to 1.14.1

Issues closed for 1.14.1

BUG: doccer: \`test_decorator\` fails with Python 3.13 due to...
BUG: open_memstream unavailable with glibc >= 2.10 + C99
ENH: 3.13 wheels
BUG: spsolve prints "dgstrf info" to stdout on singular matrices
BUG: \`special.pro_rad1\`: incorrect results
BUG: sparse: \`hstack/vstack\` between a sparse and ndarray breaks...
MAINT: \`cluster\`/\`stats\`: array API test failures in main
BUG: unable to securely deploy app as scipy 1.14.0 requires write...
BUG: signal: crash in \`signaltools\` on free-threaded Python,...
CI: documentation build failing?
BUG: \`fft.hfftn\` fails on list inputs
BUG: Files in SuperLU under LGPL license
BUG: io/scipy.sparse.csgraph: refguide check failure in main
DOC: \`sampling_tdr.rst\` failing in CircleCI smoke-tutorials...
BUG: dtype changed for argument to \`rv_discrete._pmf\`
BUG: odr: pickling is not possible
DOC: build failing in CI
BLD, CI: Cirrus 3.13 wheels?

Pull requests for 1.14.1

BLD: make cp313 wheels [wheel build]
REL, MAINT: prep for 1.14.1
BUG: special: Fixes for pro_rad1
BUG: special: remove type punning to avoid warnings in LTO builds
MAINT: uarray: fix typo in \`small_dynamic_array.h\`
MAINT: adapt to array-api-strict 2.0
BLD: Enable \`open_memstream()\` on newer glibc
MAINT: Unskip \`scipy.misc.test.test_decorator\` for Python 3.13+
DOC: add release note for 1.14 sparse section about sparse array...
BUG: sparse: fix 1D for vstack/hstack and Improve 1D error msgs...
BUG: signal: fix crash under free-threaded CPython in medfilt2d
BUG: sparse.linalg: Update \`SuperLU\` to fix unfilterable output...
DOC: Fix type of \`\`html_sidebars\`\` value in \`\`conf.py\`\`
BUG: fft: fix array-like input
BUG: sparse: fix failing doctests in io and csgraph that print...
DOC: stats: silence the doctest error
MAINT: sparse.linalg: update \`SuperLU/colamd.c\` to fix license...
BUG: stats: adapt to \`np.floor\` type promotion removal
MAINT: \`stats.bartlett\`: ensure statistic is non-negative
CI: Update to cibuildwheel 2.20.0
BUG: odr: fix pickling
DOC: Don't use Sphinx 8.0.0 until gh-21323 is resolved.
BUG: sparse: Fix 1D specialty hstack codes
MAINT: special: Accommodate changed integer handling in NumPy...
BLD: cp313 wheels on \`manylinux_aarch64\`

py-scipy: updated to 1.14.0

SciPy 1.14.0 is the culmination of 3 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
1.14.x branch, and on adding new features on the main branch.

py-scipy: updated to 1.13.1

Issues closed for 1.13.1

BUG: \`scipy.ndimage.value_indices\` returns empty dict for \`intc\`/\`uintc\` dtype on Windows
DOC, MAINT: .jupyterlite.doit.db shows up untracked
DOC: optimize.root(method='lm') option
BUG: csr_array can no longer be initialized with 1D array
BUG: \`TestEig.test_falker\` fails on windows + MKL as well as...
BUG: Cannot find \`OpenBLAS\` on Cygwin
BUG: special.spherical_in: derivative at \`z=0, n=1\` incorrect
BUG: \`eigh\` fails for size 1 array with driver=evd
BUG: warning from \`optimize.least_squares\` for astropy with...
BUG: spatial: error in \`Rotation.align_vectors()\` with an infinite...
MAINT, TST: two types of failures observed on maintenance/1.13.x...
BUG: scipy.special.factorial2 doesn't handle \`uint32\` dtypes
BUG: scipy.stats.wilcoxon in 1.13 fails on 2D array with nan...
BUG: scipy.spatial.Delaunay, scipy.interpolate.LinearNDInterpolator...
BUG: stats.yulesimon: incorrect kurtosis values
BUG: incorrect origin tuple handling in ndimage \`minimum_filter\`...
BUG: spatial: \`Rotation.align_vectors()\` incorrect for anti-parallel...
BUG: sparse matrix creation in 1.13 with indices not summing...
BUG: stats.zipf: incorrect pmf values
CI: scipy installation failing in umfpack tests


Pull requests for 1.13.1

MAINT: added doc/source/.jupyterlite.doit.db to .gitignore See...
BUG: sparse: align dok_array.pop() to dict.pop() for case with...
BUG: sync pocketfft again
REL, MAINT: prep for 1.13.1
DOC: optimize: fix wrong optional argument name in \`root(method="lm")\`.
DOC: add missing deprecations from 1.13.0 release notes
MAINT/DOC: fix syntax in 1.13.0 release notes
BUG: sparse: Clean up 1D input handling to sparse array/matrix...
DOC: remove spurious backtick from release notes
BUG: linalg: fix ordering of complex conj gen eigenvalues
TST: tolerance bumps for the conda-forge builds
TST: compare absolute values of U and VT in pydata-sparse SVD...
BUG: Include Python.h before system headers.
BUG: linalg: fix eigh(1x1 array, driver='evd') f2py check
BUG: \`spherical_in\` for \`n=0\` and \`z=0\`
BLD: Fix error message for f2py generation fail
TST: Adapt to \`__array__(copy=True)\`
BLD: Move Python-including files to start of source.
REV: 1.13.x: revert changes to f2py and tempita handling in meson.build...
update openblas to 0.3.27
BUG: Fix error with 180 degree rotation in Rotation.align_vectors()...
MAINT: optimize.linprog: fix bug when integrality is a list of...
MAINT: stats.wilcoxon: fix failure with multidimensional \`x\`...
MAINT: lint: temporarily disable UP031
BUG: handle uint arrays in factorial{,2,k}
BUG: prevent QHull message stream being closed twice
MAINT/DEV: lint: disable UP032
BUG: fix Vor/Delaunay segfaults
BUG: ndimage.value_indices: deal with unfixed types
BUG: ndimage: fix origin handling for \`{minimum, maximum}_filter\`
MAINT: stats.yulesimon: fix kurtosis
BUG: sparse: Fix summing duplicates for CSR/CSC creation from...
BUG: stats: Fix \`zipf.pmf\` and \`zipfian.pmf\` for int32 \`k\`
CI: pin Python for MacOS conda

py-scipy: updated to 1.13.0

SciPy 1.13.0 is the culmination of 3 months of hard work. This
out-of-band release aims to support NumPy ``2.0.0``, and is backwards
compatible to NumPy ``1.22.4``. The version of OpenBLAS used to build
the PyPI wheels has been increased to ``0.3.26.dev``.

This release requires Python 3.9+ and NumPy 1.22.4 or greater.

For running on PyPy, PyPy3 6.0+ is required.


**************************
Highlights of this release
**************************
- Support for NumPy ``2.0.0``.
- Interactive examples have been added to the documentation, allowing users
  to run the examples locally on embedded Jupyterlite notebooks in their
  browser.
- Preliminary 1D array support for the COO and DOK sparse formats.
- Several `scipy.stats` functions have gained support for additional
  ``axis``, ``nan_policy``, and ``keepdims`` arguments. `scipy.stats` also
  has several performance and accuracy improvements.

************
New features
************

`scipy.integrate` improvements
==============================
- The ``terminal`` attribute of `scipy.integrate.solve_ivp` ``events``
  callables now additionally accepts integer values to specify a number
  of occurrences required for termination, rather than the previous restriction
  of only accepting a ``bool`` value to terminate on the first registered
  event.


`scipy.io` improvements
=======================
- `scipy.io.wavfile.write` has improved ``dtype`` input validation.


`scipy.interpolate` improvements
================================
- The Modified Akima Interpolation has been added to
  ``interpolate.Akima1DInterpolator``, available via the new ``method``
  argument.
- New method ``BSpline.insert_knot`` inserts a knot into a ``BSpline`` instance.
  This routine is similar to the module-level `scipy.interpolate.insert`
  function, and works with the BSpline objects instead of ``tck`` tuples.
- ``RegularGridInterpolator`` gained the functionality to compute derivatives
  in place. For instance, ``RegularGridInterolator((x, y), values,
  method="cubic")(xi, nu=(1, 1))`` evaluates the mixed second derivative,
  :math:`\partial^2 / \partial x \partial y` at ``xi``.
- Performance characteristics of tensor-product spline methods of
  ``RegularGridInterpolator`` have been changed: evaluations should be
  significantly faster, while construction might be slower. If you experience
  issues with construction times, you may need to experiment with optional
  keyword arguments ``solver`` and ``solver_args``. Previous behavior (fast
  construction, slow evaluations) can be obtained via `"*_legacy"` methods:
  ``method="cubic_legacy"`` is exactly equivalent to ``method="cubic"`` in
  previous releases. See ``gh-19633`` for details.


`scipy.signal` improvements
===========================
- Many filter design functions now have improved input validation for the
  sampling frequency (``fs``).


`scipy.sparse` improvements
===========================
- ``coo_array`` now supports 1D shapes, and has additional 1D support for
  ``min``, ``max``, ``argmin``, and ``argmax``. The DOK format now has
  preliminary 1D support as well, though only supports simple integer indices
  at the time of writing.
- Experimental support has been added for ``pydata/sparse`` array inputs to
  `scipy.sparse.csgraph`.
- ``dok_array`` and ``dok_matrix`` now have proper implementations of
  ``fromkeys``.
- ``csr`` and ``csc`` formats now have improved ``setdiag`` performance.


`scipy.spatial` improvements
============================
- ``voronoi_plot_2d`` now draws Voronoi edges to infinity more clearly
  when the aspect ratio is skewed.


`scipy.special` improvements
============================
- All Fortran code, namely, ``AMOS``, ``specfun``, and ``cdflib`` libraries
  that the majority of special functions depend on, is ported to Cython/C.
- The function ``factorialk`` now also supports faster, approximate
  calculation using ``exact=False``.


`scipy.stats` improvements
==========================
- `scipy.stats.rankdata` and `scipy.stats.wilcoxon` have been vectorized,
  improving their performance and the performance of hypothesis tests that
  depend on them.
- ``stats.mannwhitneyu`` should now be faster due to a vectorized statistic
  calculation, improved caching, improved exploitation of symmetry, and a
  memory reduction. ``PermutationMethod`` support was also added.
- `scipy.stats.mood` now has ``nan_policy`` and ``keepdims`` support.
- `scipy.stats.brunnermunzel` now has ``axis`` and ``keepdims`` support.
- `scipy.stats.friedmanchisquare`, `scipy.stats.shapiro`,
  `scipy.stats.normaltest`, `scipy.stats.skewtest`,
  `scipy.stats.kurtosistest`, `scipy.stats.f_oneway`,
  `scipy.stats.alexandergovern`, `scipy.stats.combine_pvalues`, and
  `scipy.stats.kstest` have gained ``axis``, ``nan_policy`` and
  ``keepdims`` support.
- `scipy.stats.boxcox_normmax` has gained a ``ymax`` parameter to allow user
  specification of the maximum value of the transformed data.
- `scipy.stats.vonmises` ``pdf`` method has been extended to support
  ``kappa=0``. The ``fit`` method is also more performant due to the use of
  non-trivial bounds to solve for ``kappa``.
- High order ``moment`` calculations for `scipy.stats.powerlaw` are now more
  accurate.
- The ``fit`` methods of  `scipy.stats.gamma` (with ``method='mm'``) and
  `scipy.stats.loglaplace` are faster and more reliable.
- `scipy.stats.goodness_of_fit` now supports the use of a custom ``statistic``
  provided by the user.
- `scipy.stats.wilcoxon` now supports ``PermutationMethod``, enabling
  calculation of accurate p-values in the presence of ties and zeros.
- `scipy.stats.monte_carlo_test` now has improved robustness in the face of
  numerical noise.
- `scipy.stats.wasserstein_distance_nd` was introduced to compute the
  Wasserstein-1 distance between two N-D discrete distributions.


*******************
Deprecated features
*******************
- Complex dtypes in ``PchipInterpolator`` and ``Akima1DInterpolator`` have
  been deprecated and will raise an error in SciPy 1.15.0. If you are trying
  to use the real components of the passed array, use ``np.real`` on ``y``.



******************************
Backwards incompatible changes
******************************

*************
Other changes
*************
- The second argument of `scipy.stats.moment` has been renamed to ``order``
  while maintaining backward compatibility.

py-scipy: updated to 1.11.4

Issues closed for 1.11.4

Contradiction in \`pyproject.toml\` requirements?
Doc build fails with Python 3.11
BUG: upcasting of indices dtype from DIA to COO/CSR/BSR arrays
BUG: Regression in 1.11.3 can still fail for \`optimize.least_squares\`...
BUG: build failure with Xcode 15 linker
BUG: DiscreteAliasUrn construction fails with UNURANError for...
BUG: problem importing libgfortran.5.dylib on macOS Sonoma
BUG: scipy.sparse.lil_matrix division by complex number leads...
BUG: can't install scipy on mac m1 with poetry due to incompatible...
DOC: doc build failing
BUG: Python version constraints in releases causes issues for...

Pull requests for 1.11.4

DOC, MAINT: workaround for py311 docs
set idx_dtype in sparse dia_array.tocoo
MAINT: Prep 1.11.4
BLD: fix up version parsing issue in cythonize.py for setup.py...
DOC: stats.chisquare: result object contains attribute 'statistic'
BUG: fix pow method for sparrays with power zero
MAINT, BUG: stats: update the UNU.RAN submodule with DAU fix
BUG: Restore the original behavior of 'trf' from least_squares...
BLD: use classic linker on macOS 14 (Sonoma), the new linker...
BUG: Fix typecasting problem in scipy.sparse.lil_matrix truediv
DOC, MAINT: Bump CircleCI Python version to 3.11
MAINT, REL: unpin Python 1.11.x branch
MAINT, BLD: poetry loongarch shims

py-scipy: updated to 1.11.3

Issues closed for 1.11.3
------------------------
* BUG: scipy.optimize's trust-constr algorithm hangs when keep-feasible...
* freqz: suboptimal performance for worN=2\*\*n+1, include_nyquist=True...
* Bug in scipy.sparse.csgraph.min_weight_full_bipartite_matching
* BUG: Different results between numpy.fft.rfft and scipy.signal.freqz
* Buffer dtype mismatch, expected 'ITYPE_t' but got 'long'
* BUG: johnsonsu distribution no longer accepts integer \`b\` parameter
* BUG: dev.py has \`distutils\` usage
* BUG: mesonpy embeds random path in .pyx files
* BUG: Regression in 1.11.2: optimize.least_squares with method='trf'...
* BUG: Build fails on latest commit
* BUG: scipy.sparse.csgraph.laplacian raises AttributeError on...
* BUG: Incorrect sampling from zero rank covariance


Pull requests for 1.11.3
------------------------
* BUG: add infeasibility checks to min_weight_full_bipartite_matching
* BUG: Allow johnsonsu parameters to be floats
* BUG: sparse.csgraph: Support int64 indices in traversal.pyx
* BUG: Fix python3.12 distutils dev.py build
* BUG: trust-constr Bounds exclusive
* MAINT: should not be using np.float64() on arrays
* REL, MAINT: prep for 1.11.3
* BUG: Fixes 19103 by adding back make_strictly_feasible to lsq...
* BLD: Avoid absolute pathnames in .pyx files
* MAINT: signal: Remove the cval parameter from the private function...
* BLD: revert to using published wheels [wheel build]
* BUG: Support sparse arrays in scipy.sparse.csgraph.laplacian
* MAINT: stats.CovViaEigendecomposition: fix \`_LA\` attribute...
* TST: fix \`TestODR.test_implicit\` test failure with tolerance...
* BUG: freqz rfft grid fix
* MAINT: newton, make sure x0 is an inexact type
* BUG: stats: fix build failure due to incorrect Boost policies...
* BLD: add float.h include to \`_fpumode.c\`, fixes Clang on Windows...
* MAINT: fix libquadmath licence

py-scipy: move patch for _ISOC99_SOURCE issue to meson.build

py-scipy: updated to 1.11.2

SciPy 1.11.2

Issues closed for 1.11.2

special.jn_zeros(281, 6) hangs
Complex matrix square root of positive semi-definite matrix
BUG: \`loadmat\` fails to load matlab structures with anonymous...
BUG: Floating point CSC with int64 indices doesn't work with...
BUG: \`scipy.optimize.minimize\` fails when \`dtype=float32\`...
DOC: Broken link to installation instructions in README.rst
BUG: Build failure with Cython 3.0.0b3 if scipy is already installed
BUG: optimize.least_squares with method='trf' yields wrong result...
BUG: cKDtree.query no longer accepts DataFrame as input
Spalde error with scipy 1.10: 0-th dimension must be fixed
BUG: <Compilation of scipy 1.11 falls with python3.12>
BUG: Compilation of scipy 1.10.1 and 1.11.1 fails with Python...

Pull requests for 1.11.2

BUG: Fix error in linalg/_matfuncs_sqrtm.py
BUG: sparse.linalg: Cast index arrays to intc before calling...
Allow johnsonsu parameters to be floats
MAINT: stats: fix NumPy DeprecationWarnings
REL, MAINT: prep for 1.11.2
DOC: Fix broken link to installation guide in README
BUG: Ensure cKDtree.query does not pass Pandas DataFrame to np.isfinite
CI, MAINT: 32-bit Pillow pin
BLD: copy \`cython_optimize.pxd\` to build dir
BUG: make \`L-BFGS-B\` optimizer work with single precision gradient
BUG: io/matlab: Fix loading of mat files containing fn handles...
BUG: make Bessel-roots function not hang and not skip roots
DOC: linking interp1d docstring to tutorial
BUG: lsq trf gives x=1e-10 if x0 is near a bound
CI/BLD: create cp312 wheels
DOC: Fix installation instructions using venv/pip
CI: move the musllinux Cirrus job to GHA, optimize other jobs
CI: reduce Cirrus CI usage during wheel builds
BUG: interpolate: fix spalde with len(c) < len(t)
BUG: pass unused xrtol in fmin_bfgs to _minimize_bfgs
BLD: rename \`setup.py\` to \`_setup.py\` to signal it should...
MAINT: NumPy 1.25.x deprecations
MAINT: ensure cobyla objective returns scalar

py-scipy: update patch comment

py-scipy: revert removal of patch-scipy_spatial___ckdtree.pyx

This patch is still needed. Updated patch comment to clarify.

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`.

py-scipy: fix missing isnan/isinf prototypes

py-scipy: updated to 1.10.1

SciPy 1.10.1 is a bug-fix release with no new features
compared to 1.10.0.


SciPy 1.10.0 Release Notes
==========================

SciPy 1.10.0 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
1.10.x branch, and on adding new features on the main branch.

This release requires Python 3.8+ and NumPy 1.19.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.


**************************
Highlights of this release
**************************

- A new dedicated datasets submodule (`scipy.datasets`) has been added, and is
  now preferred over usage of `scipy.misc` for dataset retrieval.
- A new `scipy.interpolate.make_smoothing_spline` function was added. This
  function constructs a smoothing cubic spline from noisy data, using the
  generalized cross-validation (GCV) criterion to find the tradeoff between
  smoothness and proximity to data points.
- `scipy.stats` has three new distributions, two new hypothesis tests, three
  new sample statistics, a class for greater control over calculations
  involving covariance matrices, and many other enhancements.

************
New features
************

`scipy.datasets` introduction
=============================
- A new dedicated ``datasets`` submodule has been added. The submodules
  is meant for datasets that are relevant to other SciPy submodules ands
  content (tutorials, examples, tests), as well as contain a curated
  set of datasets that are of wider interest. As of this release, all
  the datasets from `scipy.misc` have been added to `scipy.datasets`
  (and deprecated in `scipy.misc`).
- The submodule is based on [Pooch](https://www.fatiando.org/pooch/latest/)
  (a new optional dependency for SciPy), a Python package to simplify fetching
  data files. This move will, in a subsequent release, facilitate SciPy
  to trim down the sdist/wheel sizes, by decoupling the data files and
  moving them out of the SciPy repository, hosting them externally and
  downloading them when requested. After downloading the datasets once,
  the files are cached to avoid network dependence and repeated usage.
- Added datasets from ``scipy.misc``: `scipy.datasets.face`,
  `scipy.datasets.ascent`, `scipy.datasets.electrocardiogram`
- Added download and caching functionality:

  - `scipy.datasets.download_all`: a function to download all the `scipy.datasets`
    associated files at once.
  - `scipy.datasets.clear_cache`: a simple utility function to clear cached dataset
    files from the file system.
  - ``scipy/datasets/_download_all.py`` can be run as a standalone script for
    packaging purposes to avoid any external dependency at build or test time.
    This can be used by SciPy packagers (e.g., for Linux distros) which may
    have to adhere to rules that forbid downloading sources from external
    repositories at package build time.

`scipy.integrate` improvements
==============================
- Added parameter ``complex_func`` to `scipy.integrate.quad`, which can be set
  ``True`` to integrate a complex integrand.


`scipy.interpolate` improvements
================================
- `scipy.interpolate.interpn` now supports tensor-product interpolation methods
  (``slinear``, ``cubic``, ``quintic`` and ``pchip``)
- Tensor-product interpolation methods (``slinear``, ``cubic``, ``quintic`` and
  ``pchip``) in `scipy.interpolate.interpn` and
  `scipy.interpolate.RegularGridInterpolator` now allow values with trailing
  dimensions.
- `scipy.interpolate.RegularGridInterpolator` has a new fast path for
  ``method="linear"`` with 2D data, and ``RegularGridInterpolator`` is now
  easier to subclass
- `scipy.interpolate.interp1d` now can take a single value for non-spline
  methods.
- A new ``extrapolate`` argument is available to `scipy.interpolate.BSpline.design_matrix`,
  allowing extrapolation based on the first and last intervals.
- A new function `scipy.interpolate.make_smoothing_spline` has been added. It is an
  implementation of the generalized cross-validation spline smoothing
  algorithm. The ``lam=None`` (default) mode of this function is a clean-room
  reimplementation of the classic ``gcvspl.f`` Fortran algorithm for
  constructing GCV splines.
- A new ``method="pchip"`` mode was aded to
  `scipy.interpolate.RegularGridInterpolator`. This mode constructs an
  interpolator using tensor products of C1-continuous monotone splines
  (essentially, a `scipy.interpolate.PchipInterpolator` instance per
  dimension).



`scipy.sparse.linalg` improvements
==================================
- The spectral 2-norm is now available in `scipy.sparse.linalg.norm`.
- The performance of `scipy.sparse.linalg.norm` for the default case (Frobenius
  norm) has been improved.
- LAPACK wrappers were added for ``trexc`` and ``trsen``.
- The `scipy.sparse.linalg.lobpcg` algorithm was rewritten, yielding
  the following improvements:

  - a simple tunable restart potentially increases the attainable
    accuracy for edge cases,
  - internal postprocessing runs one final exact Rayleigh-Ritz method
    giving more accurate and orthonormal eigenvectors,
  - output the computed iterate with the smallest max norm of the residual
    and drop the history of subsequent iterations,
  - remove the check for ``LinearOperator`` format input and thus allow
    a simple function handle of a callable object as an input,
  - better handling of common user errors with input data, rather
    than letting the algorithm fail.


`scipy.linalg` improvements
===========================
- `scipy.linalg.lu_factor` now accepts rectangular arrays instead of being restricted
  to square arrays.


`scipy.ndimage` improvements
============================
- The new `scipy.ndimage.value_indices` function provides a time-efficient method to
  search for the locations of individual values with an array of image data.
- A new ``radius`` argument is supported by `scipy.ndimage.gaussian_filter1d` and
  `scipy.ndimage.gaussian_filter` for adjusting the kernel size of the filter.


`scipy.optimize` improvements
=============================
- `scipy.optimize.brute` now coerces non-iterable/single-value ``args`` into a
  tuple.
- `scipy.optimize.least_squares` and `scipy.optimize.curve_fit` now accept
  `scipy.optimize.Bounds` for bounds constraints.
- Added a tutorial for `scipy.optimize.milp`.
- Improved the pretty-printing of `scipy.optimize.OptimizeResult` objects.
- Additional options (``parallel``, ``threads``, ``mip_rel_gap``) can now
  be passed to `scipy.optimize.linprog` with ``method='highs'``.


`scipy.signal` improvements
===========================
- The new window function `scipy.signal.windows.lanczos` was added to compute a
  Lanczos window, also known as a sinc window.


`scipy.sparse.csgraph` improvements
===================================
- the performance of `scipy.sparse.csgraph.dijkstra` has been improved, and
  star graphs in particular see a marked performance improvement


`scipy.special` improvements
============================
- The new function `scipy.special.powm1`, a ufunc with signature
  ``powm1(x, y)``, computes ``x**y - 1``. The function avoids the loss of
  precision that can result when ``y`` is close to 0 or when ``x`` is close to
  1.
- `scipy.special.erfinv` is now more accurate as it leverages the Boost equivalent under
  the hood.


`scipy.stats` improvements
==========================
- Added `scipy.stats.goodness_of_fit`, a generalized goodness-of-fit test for
  use with any univariate distribution, any combination of known and unknown
  parameters, and several choices of test statistic (Kolmogorov-Smirnov,
  Cramer-von Mises, and Anderson-Darling).
- Improved `scipy.stats.bootstrap`: Default method ``'BCa'`` now supports
  multi-sample statistics. Also, the bootstrap distribution is returned in the
  result object, and the result object can be passed into the function as
  parameter ``bootstrap_result`` to add additional resamples or change the
  confidence interval level and type.
- Added maximum spacing estimation to `scipy.stats.fit`.
- Added the Poisson means test ("E-test") as `scipy.stats.poisson_means_test`.
- Added new sample statistics.

  - Added `scipy.stats.contingency.odds_ratio` to compute both the conditional
    and unconditional odds ratios and corresponding confidence intervals for
    2x2 contingency tables.
  - Added `scipy.stats.directional_stats` to compute sample statistics of
    n-dimensional directional data.
  - Added `scipy.stats.expectile`, which generalizes the expected value in the
    same way as quantiles are a generalization of the median.

- Added new statistical distributions.

  - Added `scipy.stats.uniform_direction`, a multivariate distribution to
    sample uniformly from the surface of a hypersphere.
  - Added `scipy.stats.random_table`, a multivariate distribution to sample
    uniformly from m x n contingency tables with provided marginals.
  - Added `scipy.stats.truncpareto`, the truncated Pareto distribution.

- Improved the ``fit`` method of several distributions.

  - `scipy.stats.skewnorm` and `scipy.stats.weibull_min` now use an analytical
    solution when ``method='mm'``, which also serves a starting guess to
    improve the performance of ``method='mle'``.
  - `scipy.stats.gumbel_r` and `scipy.stats.gumbel_l`: analytical maximum
    likelihood estimates have been extended to the cases in which location or
    scale are fixed by the user.
  - Analytical maximum likelihood estimates have been added for
    `scipy.stats.powerlaw`.

- Improved random variate sampling of several distributions.

  - Drawing multiple samples from `scipy.stats.matrix_normal`,
    `scipy.stats.ortho_group`, `scipy.stats.special_ortho_group`, and
    `scipy.stats.unitary_group` is faster.
  - The ``rvs`` method of `scipy.stats.vonmises` now wraps to the interval
    ``[-np.pi, np.pi]``.
  - Improved the reliability of `scipy.stats.loggamma` ``rvs`` method for small
    values of the shape parameter.

- Improved the speed and/or accuracy of functions of several statistical
  distributions.

  - Added `scipy.stats.Covariance` for better speed, accuracy, and user control
    in multivariate normal calculations.
  - `scipy.stats.skewnorm` methods ``cdf``, ``sf``, ``ppf``, and ``isf``
    methods now use the implementations from Boost, improving speed while
    maintaining accuracy. The calculation of higher-order moments is also
    faster and more accurate.
  - `scipy.stats.invgauss` methods ``ppf`` and ``isf`` methods now use the
    implementations from Boost, improving speed and accuracy.
  - `scipy.stats.invweibull` methods ``sf`` and ``isf`` are more accurate for
    small probability masses.
  - `scipy.stats.nct` and `scipy.stats.ncx2` now rely on the implementations
    from Boost, improving speed and accuracy.
  - Implemented the ``logpdf`` method of `scipy.stats.vonmises` for reliability
    in extreme tails.
  - Implemented the ``isf`` method of `scipy.stats.levy` for speed and
    accuracy.
  - Improved the robustness of `scipy.stats.studentized_range` for large ``df``
    by adding an infinite degree-of-freedom approximation.
  - Added a parameter ``lower_limit`` to `scipy.stats.multivariate_normal`,
    allowing the user to change the integration limit from -inf to a desired
    value.
  - Improved the robustness of ``entropy`` of `scipy.stats.vonmises` for large
    concentration values.

- Enhanced `scipy.stats.gaussian_kde`.

  - Added `scipy.stats.gaussian_kde.marginal`, which returns the desired
    marginal distribution of the original kernel density estimate distribution.
  - The ``cdf`` method of `scipy.stats.gaussian_kde` now accepts a
    ``lower_limit`` parameter for integrating the PDF over a rectangular region.
  - Moved calculations for `scipy.stats.gaussian_kde.logpdf` to Cython,
    improving speed.
  - The global interpreter lock is released by the ``pdf`` method of
    `scipy.stats.gaussian_kde` for improved multithreading performance.
  - Replaced explicit matrix inversion with Cholesky decomposition for speed
    and accuracy.

- Enhanced the result objects returned by many `scipy.stats` functions

  - Added a ``confidence_interval`` method to the result object returned by
    `scipy.stats.ttest_1samp` and `scipy.stats.ttest_rel`.
  - The `scipy.stats` functions ``combine_pvalues``, ``fisher_exact``,
    ``chi2_contingency``, ``median_test`` and ``mood`` now return
    bunch objects rather than plain tuples, allowing attributes to be
    accessed by name.
  - Attributes of the result objects returned by ``multiscale_graphcorr``,
    ``anderson_ksamp``, ``binomtest``, ``crosstab``, ``pointbiserialr``,
    ``spearmanr``, ``kendalltau``, and ``weightedtau`` have been renamed to
    ``statistic`` and ``pvalue`` for consistency throughout `scipy.stats`.
    Old attribute names are still allowed for backward compatibility.
  - `scipy.stats.anderson` now returns the parameters of the fitted
    distribution in a `scipy.stats._result_classes.FitResult` object.
  - The ``plot`` method of `scipy.stats._result_classes.FitResult` now accepts
    a ``plot_type`` parameter; the options are ``'hist'`` (histogram, default),
    ``'qq'`` (Q-Q plot), ``'pp'`` (P-P plot), and ``'cdf'`` (empirical CDF
    plot).
  - Kolmogorov-Smirnov tests (e.g. `scipy.stats.kstest`) now return the
    location (argmax) at which the statistic is calculated and the variant
    of the statistic used.

- Improved the performance of several `scipy.stats` functions.

  - Improved the performance of `scipy.stats.cramervonmises_2samp` and
    `scipy.stats.ks_2samp` with ``method='exact'``.
  - Improved the performance of `scipy.stats.siegelslopes`.
  - Improved the performance of `scipy.stats.mstats.hdquantile_sd`.
  - Improved the performance of `scipy.stats.binned_statistic_dd` for several
    NumPy statistics, and binned statistics methods now support complex data.

- Added the ``scramble`` optional argument to `scipy.stats.qmc.LatinHypercube`.
  It replaces ``centered``, which is now deprecated.
- Added a parameter ``optimization`` to all `scipy.stats.qmc.QMCEngine`
  subclasses to improve characteristics of the quasi-random variates.
- Added tie correction to `scipy.stats.mood`.
- Added tutorials for resampling methods in `scipy.stats`.
- `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and
  `scipy.stats.monte_carlo_test` now automatically detect whether the provided
  ``statistic`` is vectorized, so passing the ``vectorized`` argument
  explicitly is no longer required to take advantage of vectorized statistics.
- Improved the speed of `scipy.stats.permutation_test` for permutation types
  ``'samples'`` and ``'pairings'``.
- Added ``axis``, ``nan_policy``, and masked array support to
  `scipy.stats.jarque_bera`.
- Added the ``nan_policy`` optional argument to `scipy.stats.rankdata`.


*******************
Deprecated features
*******************
- `scipy.misc` module and all the methods in ``misc`` are deprecated in v1.10
  and will be completely removed in SciPy v2.0.0. Users are suggested to
  utilize the `scipy.datasets` module instead for the dataset methods.
- `scipy.stats.qmc.LatinHypercube` parameter ``centered`` has been deprecated.
  It is replaced by the ``scramble`` argument for more consistency with other
  QMC engines.
- `scipy.interpolate.interp2d` class has been deprecated.  The docstring of the
  deprecated routine lists recommended replacements.

********************
Expired Deprecations
********************
- There is an ongoing effort to follow through on long-standing deprecations.
- The following previously deprecated features are affected:

  - Removed ``cond`` & ``rcond`` kwargs in ``linalg.pinv``
  - Removed wrappers ``scipy.linalg.blas.{clapack, flapack}``
  - Removed ``scipy.stats.NumericalInverseHermite`` and removed ``tol`` & ``max_intervals`` kwargs from ``scipy.stats.sampling.NumericalInverseHermite``
  - Removed ``local_search_options`` kwarg frrom ``scipy.optimize.dual_annealing``.


*************
Other changes
*************
- `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and
  `scipy.stats.monte_carlo_test` now automatically detect whether the provided
  ``statistic`` is vectorized by looking for an ``axis`` parameter in the
  signature of ``statistic``. If an ``axis`` parameter is present in
  ``statistic`` but should not be relied on for vectorized calls, users must
  pass option ``vectorized==False`` explicitly.
- `scipy.stats.multivariate_normal` will now raise a ``ValueError`` when the
  covariance matrix is not positive semidefinite, regardless of which method
  is called.

py-scipy: remove patch

This problem was solved differently in release 1.5, according to the
upstream bug report.

py-scipy: updated to 1.9.3

Issues closed for 1.9.3

scipy.interpolate.UnivariateSpline segfault
BUG: multivariate_normal returns a pdf for values outside its...
BUG: stats: inconsistency in docs and behavior of gmean and hmean
running scipy.interpolate.tests.test_fitpack::test_bisplev_integer_overflow...
test_bisplev_integer_overflow: Segmentation fault (core dumped)
Bug: setting iprint=0 hides all output from fmin_l_bfgs_b, but...
\`scipy.stats.mood\` does not correct for ties
ks_2samp throws \`RuntimeWarning: overflow encountered in double_scalars\`
\`shgo\` error since scipy 1.8.0.dev0+1529.803e52d
Input data validation for RectSphereBivariateSpline
BUG: binom.pmf - RuntimeWarning: divide by zero
BUG: scipy.optimize.minimize: Powell's method function evaluated...
BUG: lombscargle fails if argument is a view
BUG: Possible bug when using winsorize on pandas data instead...
BUG: stats.ttest_ind returns wrong p-values with permutations
odr.Model default meta value fails with __getattr__
BUG: Error in error message for incorrect sample dimension in...
BUG: dimension of isuppz in syevr is mistranslated
BUG: \`KDTree\`'s optional argument \`eps\` seems to have no...
dtype not preserved with operations on sparse arrays
BUG: \`stats.fit\` on \`boltzmann\` expects \`bound\` for \`lambda\`,...
BUG: Small oversight in sparse.linalg.lsmr?
BUG: Build failure due to problems with shebang line in cythoner.py
BUG: stats.rayleigh.fit: returns \`loc\` that is inconsistent...
BUG? Incorrect branch in \`LAMV\` / \`_specfunc.lamv\`
DOC: keepdims in stats.mode is incorrectly documented

Pull requests for 1.9.3

BUG: multivariate_normal returns a pdf for values outside its...
Bug: setting iprint=0 hides all output from fmin_l_bfgs_b, but...
BUG: stats: Reformulate loggamma._rvs to handle c << 1.
BUG: fix out-of-bound evaluations in optimize.minimize, powell...
BUG: fix powell evaluated outside limits
BUG: fix stats.rv_histogram for non-uniform bins
stats.mood: correct for when ties are present
BUG: fix a crash in \`fpknot\`
MAINT: stats: fix _contains_nan on Pandas Series
Fix ttest permutations
MAINT: fix SHGO extra arguments
BUG: Fix error in error message for incorrect sample dimension...
MAINT: stats.ks_2samp: always emit warning when exact method...
BUG: fix syevr series segfault by explicitly specifying operator...
BUG: optimize: Fix differential_evolution error message.
FIX: \`odr.Model\` error with default \`meta\` value
FIX: stats: ignore divide-by-zero warnings from Boost binom impl
MAINT: stats.vonmises: wrap rvs to -pi, pi interval
BUG: eps param no effect fixed
MAINT: Ensure Pythran input for lombscargle are contiguous
Detect integer overflow in bivariate splines in fitpackmodule.c,...
BUG: sparse: Fix indexing sparse matrix with empty index arguments.
FIX: spurious divide error with \`gmean\`
BUG: fix mutable data types as default arguments in \`ord.{Data,RealData}\`
MAINT: stats.boltzmann: correct _shape_info typo
BUG: interpolate: sanity check x and y in make_interp_spline(x,...
MAINT: avoid \`func_data\`, it conflicts with system header on...
BUG: interpolate: work array sizes for RectSphereBivariateSpline
BUG: linalg: Fix the XSLOW test test_sgesdd_lwork_bug_workaround()
MAINT: fix small LSMR problem
MAINT: stats.rayleigh: enforce constraint on location
FIX: special: use intended branching for \`lamv\` implementation
MAINT: stats.rv_discrete.pmf: should be zero at non-integer argument
REL: Prep for SciPy 1.9.3
BUG: special: Fix two XSLOW test failures.
MAINT: update meson.build to make it work on IBM i system
BLD: fix issue with incomplete threads dependency handling
Keepdims incorrectly documneted fix
MAINT: Handle numpy's deprecation of accepting out-of-bound integers.
BLD: fix invalid shebang for build helper script

py-scipy: updated to 1.9.2

Issues closed for 1.9.2

BUG: 1.9.0rc1: \`OptimizeResult\` not populated when \`optimize.milp\`...
BUG: \`sparse.hstack\` returns incorrect result when the stack...
BUG: optimize.minimize backwards compatability in scipy 1.9
BUG: using msvc + meson to build scipy --> cl cannot be used...
BUG: error from \`scipy.stats.mode\` with \`NaN\`s, \`axis !=...
BUG: scipy 1.7.3 wheels on PyPI require numpy<1.23 in contradiction...
BUG: ncf_gen::ppf(..) causes segfault
Pearson3 PPF does not function properly with negative skew.
BUG: OSX-64 Test failure test_ppf_against_tables getting NaN

Pull requests for 1.9.2

FIX: Updated dtype resolution in \`_stack_along_minor_axis\`
FIX: milp: return feasible solutions if available on time out
ENH: cibuildwheel infrastructure
MAINT: minimize, restore squeezed ((1.0)) addresses 16898
REL: prep for SciPy 1.9.2
DOC: update version switcher for 1.9.1 and pin theme to 0.9
MAINT: cast \`linear_sum_assignment\` to PyCFunction
BLD: use compiler flags in a more portable way
MAINT: stats.mode: fix bug with \`axis!=1\`, \`nan_policy='omit'\`,...
MAINT: fix NumPy upper bound
BLD: fix usage of \`get_install_data\`, which defaults to purelib
DOC: Update numpy supported versions for 1.9.2
BLD: fixes for building with MSVC and Intel Fortran
Rudimentary test for manylinux_aarch64 with cibuildwheel
BLD: make MKL detection a little more robust, add notes on TODOs
CI: Update cibuildwheel to 2.10.1
MAINT: stats.pearson3: fix ppf for negative skew
BUG: Fix numerical precision error of \`truncnorm.logcdf\` when...
FIX: ensure a hold on GIL before raising warnings/errors
TST: stats.studentized_range: fix incorrect test
MAINT: pyproject.toml: Update build system requirements
MAINT: 1.9.2 backports

py-scipy: updated to 1.9.1

SciPy 1.9.1 is a bug-fix release with no new features
compared to 1.9.0. Notably, some important meson build
fixes are included.


SciPy 1.9.0 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
1.9.x branch, and on adding new features on the main branch.

This release requires Python 3.8-3.11 and NumPy 1.18.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

math/py-scipy: fix build on NetBSD/powerpc (at least!)

In the unuran part, omit defining _ISOC99_SOURCE.  I am told that
the ieeefp.h header should not be used with _ISOC99_SOURCE.  (Its
use comes from pyport.h.)  Lately I've seen this package fail to
build also for aarch64, have not verified that this fixes it, though
it's not entirely impossible.

Fixes what triggered PR#56892.

Bump PKGREVISION.

py-scipy: disable __builtin_prefetch completely for now

It failed with GCC too. There is some bad interaction with py-numpy,
probably related to patch-numpy_core_include_numpy_npy__common.h.
Unbreak the build until I have time to investigate this.

py-scipy: disable __builtin_prefetch with clang

py-scipy: updated to 1.8.1

SciPy 1.8.1 is a bug-fix release with no new features
compared to 1.8.0. Notably, usage of Pythran has been
restored for Windows builds/binaries.

py-scipy: redo NetBSD fix so it doesn't have side effects on other opsys

Previous workaround could fail to compile when double and long double are
effectively the same type.

py-scipy: work around undefined PLT symbol "log1pl" on NetBSD. Bump.

cipy: fix build on SunOS (system header conflict)

py-scipy: updated to 1.8.0

SciPy 1.8.0 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
1.8.x branch, and on adding new features on the master branch.

This release requires Python 3.8+ and NumPy 1.17.3 or greater.

For running on PyPy, PyPy3 6.0+ is required.


**************************
Highlights of this release
**************************

- A sparse array API has been added for early testing and feedback; this
  work is ongoing, and users should expect minor API refinements over
  the next few releases.
- The sparse SVD library PROPACK is now vendored with SciPy, and an interface
  is exposed via `scipy.sparse.svds` with ``solver='PROPACK'``. It is currently
  default-off due to potential issues on Windows that we aim to
  resolve in the next release, but can be optionally enabled at runtime for
  friendly testing with an environment variable setting of ``USE_PROPACK=1``.
- A new `scipy.stats.sampling` submodule that leverages the ``UNU.RAN`` C
  library to sample from arbitrary univariate non-uniform continuous and
  discrete distributions
- All namespaces that were private but happened to miss underscores in
  their names have been deprecated.

py-scipy: updated to 1.7.3

SciPy 1.7.3 is a bug-fix release that provides binary wheels
for MacOS arm64 with Python 3.8, 3.9, and 3.10. The MacOS arm64 wheels
are only available for MacOS version 12.0 and greater, as explained
in Issue 14688, linked below.

Issues closed for 1.7.3
-----------------------
* Segmentation fault on import of scipy.integrate on Apple M1 ARM...
* BUG: ARPACK's eigsh & OpenBLAS from Apple Silicon M1 (arm64)...
* four CI failures on pre-release job
* Remaining test failures for macOS arm64 wheel
* BUG: Segmentation fault caused by scipy.stats.qmc.qmc.update_discrepancy

Pull requests for 1.7.3
-----------------------
* BLD: update pyproject.toml for Python 3.10 changes
* BUG: out of bounds indexing in stats.qmc.update_discrepancy
* MAINT: skip a few failing tests in \`1.7.x\` for macOS arm64

py-scipy: updated to 1.7.2

SciPy 1.7.2 is a bug-fix release with no new features
compared to 1.7.1. Notably, the release includes wheels
for Python 3.10, and wheels are now built with a newer
version of OpenBLAS, 0.3.17. Python 3.10 wheels are provided
for MacOS x86_64 (thin, not universal2 or arm64 at this time),
and Windows/Linux 64-bit. Many wheels are now built with newer
versions of manylinux, which may require newer versions of pip.

py-scipy: updated to 1.7.1

SciPy 1.7.1 is a bug-fix release with no new features compared to 1.7.0.

1.7.0:

A new submodule for quasi-Monte Carlo, scipy.stats.qmc, was added
The documentation design was updated to use the same PyData-Sphinx theme as NumPy and other ecosystem libraries.
We now vendor and leverage the Boost C++ library to enable numerous improvements for long-standing weaknesses in scipy.stats
scipy.stats has six new distributions, eight new (or overhauled) hypothesis tests, a new function for bootstrapping, a class that enables fast random variate sampling and percentile point function evaluation, and many other enhancements.
cdist and pdist distance calculations are faster for several metrics, especially weighted cases, thanks to a rewrite to a new C++ backend framework
A new class for radial basis function interpolation, RBFInterpolator, was added to address issues with the Rbf class.

math: Replace RMD160 checksums with BLAKE2s checksums

All checksums have been double-checked against existing RMD160 and
SHA512 hashes

math: Remove SHA1 hashes for distfiles

py-scipy: updated to 1.6.3

Issues closed for 1.6.3
-----------------------
* Divide by zero in distance.yule
* prerelease_deps failures
* spatial rotation failure in (1.6.3) wheels repo (ARM64)

Pull requests for 1.6.3
-----------------------
* fix the matplotlib warning emitted during builing docs
* Divide by zero in yule dissimilarity of constant vectors
* deprecated np.typeDict
* substitute np.math.factorial with math.factorial
* add random seeds in Rotation module

py-scipy: update to 1.6.2

Highlights of this release

scipy.ndimage improvements: Fixes and ehancements to boundary extension
  modes for interpolation functions. Support for complex-valued inputs in many
  filtering and interpolation functions. New grid_mode option for
  scipy.ndimage.zoom to enable results consistent with scikit-image's
  rescale.
scipy.optimize.linprog has fast, new methods for large, sparse problems
  from the HiGHS library.
scipy.stats improvements including new distributions, a new test, and
  enhancements to existing distributions and tests

Deprecated features

scipy.spatial changes
  Calling KDTree.query with k=None to find all neighbours is deprecated.
  Use KDTree.query_ball_point instead.
distance.wminkowski was deprecated; use distance.minkowski and supply
  weights with the w keyword instead.
Backwards incompatible changes
  Using scipy.fft as a function aliasing numpy.fft.fft was removed after
  being deprecated in SciPy 1.4.0. As a result, the scipy.fft submodule
  must be explicitly imported now, in line with other SciPy subpackages.
  scipy.signal changes
The output of decimate, lfilter_zi, lfiltic, sos2tf, and
  sosfilt_zi have been changed to match numpy.result_type of their inputs.
The window function slepian was removed.
The frechet_l and frechet_r distributions were removed.

py-scipy: update to 1.5.2

Done to fix build w/ gfortran 10. "make test" was mostly OK except for
three tests that returned nan where inf was expected ...

Highlights of this release:
  wrappers for more than a dozen new LAPACK routines are now available
  in scipy.linalg.lapack
  Improved support for leveraging 64-bit integer size from linear algebra
  backends
  addition of the probability distribution for two-sided one-sample
  Kolmogorov-Smirnov tests

New features:
  Too many; see release notes at github.

Backwards incompatible changes:
  The output signatures of ?syevr, ?heevr have been changed from
  w, v, info to w, v, m, isuppz, info
  The order of output arguments w, v of <sy/he>{gv, gvd, gvx} is
  swapped.
  The output length of scipy.signal.upfirdn has been corrected, resulting
  outputs may now be shorter for some combinations of up/down ratios and
  input signal and filter lengths.
  scipy.signal.resample now supports a domain keyword argument for
  specification of time or frequency domain input.

py-scipy: updated to 1.4.1

SciPy 1.4.1 is a bug-fix release with no new features
compared to 1.4.0. Importantly, it aims to fix a problem
where an older version of pybind11 may cause a segmentation
fault when imported alongside incompatible libraries.

SciPy 1.4.0 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
1.4.x branch, and on adding new features on the master branch.

py-scipy: updated to 1.3.0

SciPy 1.3.0 Release Notes

SciPy 1.3.0 is the culmination of 5 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been some 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
1.3.x branch, and on adding new features on the master branch.

This release requires Python 3.5+ and NumPy 1.13.3 or greater.

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

Highlights of this release
- Three new stats functions, a rewrite of pearsonr, and an exact
  computation of the Kolmogorov-Smirnov two-sample test
- A new Cython API for bounded scalar-function root-finders in scipy.optimize
- Substantial CSR and CSC sparse matrix indexing performance
  improvements
- Added support for interpolation of rotations with continuous angular
  rate and acceleration in RotationSpline


SciPy 1.2.0 Release Notes

SciPy 1.2.0 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
1.2.x branch, and on adding new features on the master branch.

This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.

This will be the last SciPy release to support Python 2.7.
Consequently, the 1.2.x series will be a long term support (LTS)
release; we will backport bug fixes until 1 Jan 2020.

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

Highlights of this release
- 1-D root finding improvements with a new solver, toms748, and a new
  unified interface, root_scalar
- New dual_annealing optimization method that combines stochastic and
  local deterministic searching
- A new optimization algorithm, shgo (simplicial homology
  global optimization) for derivative free optimization problems
- A new category of quaternion-based transformations are available in
  scipy.spatial.transform

py-scipy: add upstream bug reports

py-scipy: remove obsolete patch; HAVE_OPEN_MEMSTREAM is defined to 1 nowadays.

py-scipy: Apply a couple of patches to fix SunOS.

py-scipy: updated to 1.1.0

SciPy 1.1.0 is the culmination of 7 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
1.1.x branch, and on adding new features on the master branch.

py-scipy: updated to 1.0.1

SciPy 1.0.1 is a bug-fix release with no new features compared to 1.0.0.
Probably the most important change is a fix for an incompatibility between
SciPy 1.0.0 and numpy.f2py in the NumPy master branch.

py-scipy: updated to 1.0.0

Some of the highlights of this release are:
* Major build improvements. Windows wheels are available on PyPI for the
  first time, and continuous integration has been set up on Windows and OS X
  in addition to Linux.
* A set of new ODE solvers and a unified interface to them
  (scipy.integrate.solve_ivp).
* Two new trust region optimizers and a new linear programming method, with
  improved performance compared to what scipy.optimize offered previously.
  Many new BLAS and LAPACK functions were wrapped. The BLAS wrappers are now
  complete.

Pullup ticket #5535 - requested by he
math/py-scipy: powerpc build fix

Revisions pulled up:
- math/py-scipy/Makefile                                        1.26
- math/py-scipy/distinfo                                        1.13
- math/py-scipy/patches/patch-scipy_special___round.h           1.1

---
   Module Name:	pkgsrc
   Committed By:	he
   Date:		Tue Aug 22 21:37:28 UTC 2017

   Modified Files:
   	pkgsrc/math/py-scipy: Makefile distinfo
   Added Files:
   	pkgsrc/math/py-scipy/patches: patch-scipy_special___round.h

   Log Message:
   Add a patch which fixes an obviously bogus preprocessor conditional;
   in our case, __STDC_VERSION__ isn't defined when built as C++.
   The fix isn't completeely right, it insists on <fenv.h> if built as C++.
   Not entirely unreasonable, and makes this build on NetBSD/powerpc as well,
   which doesn't like the redefinition of fegetround() and fesetround().
   Bump PKGREVISION.

Add a patch which fixes an obviously bogus preprocessor conditional;
in our case, __STDC_VERSION__ isn't defined when built as C++.
The fix isn't completeely right, it insists on <fenv.h> if built as C++.
Not entirely unreasonable, and makes this build on NetBSD/powerpc as well,
which doesn't like the redefinition of fegetround() and fesetround().
Bump PKGREVISION.

SciPy 0.19.1 is a bug-fix release with no new features compared to 0.19.0.
The most important change is a fix for a severe memory leak in integrate.quad.

SciPy 0.19.0 is the culmination of 7 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. Moreover, our development attention
will now shift to bug-fix releases on the 0.19.x branch, and on adding
new features on the master branch.

SciPy 0.18.1 is a bug-fix release with no new features compared to 0.18.0.

Updated py-scipy to 0.18.0.

Test failures reported upstream.

==========================
SciPy 0.18.0 Release Notes
==========================

.. contents::

SciPy 0.18.0 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.  Moreover, our development attention
will now shift to bug-fix releases on the 0.19.x branch, and on adding
new features on the master branch.

This release requires Python 2.7 or 3.4-3.5 and NumPy 1.7.1 or greater.

Highlights of this release include:

- A new ODE solver for two-point boundary value problems,
  `scipy.optimize.solve_bvp`.
- A new class, `CubicSpline`, for cubic spline interpolation of data.
- N-dimensional tensor product polynomials, `scipy.interpolate.NdPPoly`.
- Spherical Voronoi diagrams, `scipy.spatial.SphericalVoronoi`.
- Support for discrete-time linear systems, `scipy.signal.dlti`.


New features
============

`scipy.integrate` improvements
------------------------------

A solver of two-point boundary value problems for ODE systems has been
implemented in `scipy.integrate.solve_bvp`. The solver allows for non-separated
boundary conditions, unknown parameters and certain singular terms. It finds
a C1 continious solution using a fourth-order collocation algorithm.


`scipy.interpolate` improvements
--------------------------------

Cubic spline interpolation is now available via `scipy.interpolate.CubicSpline`.
This class represents a piecewise cubic polynomial passing through given points
and C2 continuous. It is represented in the standard polynomial basis on each
segment.

A representation of n-dimensional tensor product piecewise polynomials is
available as the `scipy.interpolate.NdPPoly` class.

Univariate piecewise polynomial classes, `PPoly` and `Bpoly`, can now be
evaluated on periodic domains. Use ``extrapolate="periodic"`` keyword
argument for this.


`scipy.fftpack` improvements

Update py-scipy to 0.17.0

SciPy 0.17.0 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. Moreover, our development attention will now shift to
bug-fix releases on the 0.17.x branch, and on adding new features on the
master branch.

This release requires Python 2.6, 2.7 or 3.2-3.5 and NumPy 1.6.2 or greater.

Release highlights:

* New functions for linear and nonlinear least squares optimization with
  constraints: scipy.optimize.lsq_linear and scipy.optimize.least_squares
* Support for fitting with bounds in scipy.optimize.curve_fit.
* Significant improvements to scipy.stats, providing many functions with
  better handing of inputs which have NaNs or are empty, improved
  documentation, and consistent behavior between scipy.stats and
  scipy.stats.mstats.
* Significant performance improvements and new functionality in
  scipy.spatial.cKDTree.

SciPy 0.16.0 is the culmination of 7 months of hard work.

Highlights of this release include:

* A Cython API for BLAS/LAPACK in scipy.linalg
* A new benchmark suite. It’s now straightforward to add new benchmarks,
  and they’re routinely included with performance enhancement PRs.
* Support for the second order sections (SOS) format in scipy.signal.

Add SHA512 digests for distfiles for math category

Problems found locating distfiles:
	Package dfftpack: missing distfile dfftpack-20001209.tar.gz
	Package eispack: missing distfile eispack-20001130.tar.gz
	Package fftpack: missing distfile fftpack-20001130.tar.gz
	Package linpack: missing distfile linpack-20010510.tar.gz
	Package minpack: missing distfile minpack-20001130.tar.gz
	Package odepack: missing distfile odepack-20001130.tar.gz
	Package py-networkx: missing distfile networkx-1.10.tar.gz
	Package py-sympy: missing distfile sympy-0.7.6.1.tar.gz
	Package quadpack: missing distfile quadpack-20001130.tar.gz

Otherwise, existing SHA1 digests verified and found to be the same on
the machine holding the existing distfiles (morden).  All existing
SHA1 digests retained for now as an audit trail.

Update to 0.15.1

Upstream changes:
SciPy 0.15.1 is a bug-fix release with no new features compared to 0.15.0.

Issues fixed
- ------------

* `#4413 <https://github.com/scipy/scipy/pull/4413>`__: BUG: Tests too strict, f2py doesn't have to overwrite this array
* `#4417 <https://github.com/scipy/scipy/pull/4417>`__: BLD: avoid using NPY_API_VERSION to check not using deprecated...
* `#4418 <https://github.com/scipy/scipy/pull/4418>`__: Restore and deprecate scipy.linalg.calc_work

SciPy 0.15.0 Release Notes
==========================

.. contents::

SciPy 0.15.0 is the culmination of 6 months of hard work. It contains
several 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.  Moreover, our development attention
will now shift to bug-fix releases on the 0.16.x branch, and on adding
new features on the master branch.

This release requires Python 2.6, 2.7 or 3.2-3.4 and NumPy 1.5.1 or greater.


New features
============

Linear Programming Interface
- ----------------------------

The new function `scipy.optimize.linprog` provides a generic
linear programming similar to the way `scipy.optimize.minimize`
provides a generic interface to nonlinear programming optimizers.
Currently the only method supported is *simplex* which provides
a two-phase, dense-matrix-based simplex algorithm. Callbacks
functions are supported, allowing the user to monitor the progress
of the algorithm.

Differential evolution, a global optimizer
- ------------------------------------------

A new `scipy.optimize.differential_evolution` function has been added to the
``optimize`` module.  Differential Evolution is an algorithm used for finding
the global minimum of multivariate functions. It is stochastic in nature (does
not use gradient methods), and can search large areas of candidate space, but
often requires larger numbers of function evaluations than conventional
gradient based techniques.

``scipy.signal`` improvements
- -----------------------------

The function `scipy.signal.max_len_seq` was added, which computes a Maximum
Length Sequence (MLS) signal.

``scipy.integrate`` improvements
- --------------------------------

It is now possible to use `scipy.integrate` routines to integrate
multivariate ctypes functions, thus avoiding callbacks to Python and
providing better performance.

``scipy.linalg`` improvements
- -----------------------------

The function `scipy.linalg.orthogonal_procrustes` for solving the procrustes
linear algebra problem was added.

BLAS level 2 functions ``her``, ``syr``, ``her2`` and ``syr2`` are now wrapped
in ``scipy.linalg``.

``scipy.sparse`` improvements
- -----------------------------

`scipy.sparse.linalg.svds` can now take a ``LinearOperator`` as its main input.

``scipy.special`` improvements
- ------------------------------

Values of ellipsoidal harmonic (i.e. Lame) functions and associated
normalization constants can be now computed using ``ellip_harm``,
``ellip_harm_2``, and ``ellip_normal``.

New convenience functions ``entr``, ``rel_entr`` ``kl_div``,
``huber``, and ``pseudo_huber`` were added.

``scipy.sparse.csgraph`` improvements
- -------------------------------------

Routines ``reverse_cuthill_mckee`` and ``maximum_bipartite_matching``
for computing reorderings of sparse graphs were added.

``scipy.stats`` improvements
- ----------------------------

Added a Dirichlet multivariate distribution, `scipy.stats.dirichlet`.

The new function `scipy.stats.median_test` computes Mood's median test.

The new function `scipy.stats.combine_pvalues` implements Fisher's
and Stouffer's methods for combining p-values.

`scipy.stats.describe` returns a namedtuple rather than a tuple, allowing
users to access results by index or by name.


Deprecated features
===================

The `scipy.weave` module is deprecated.  It was the only module never ported
to Python 3.x, and is not recommended to be used for new code - use Cython
instead.  In order to support existing code, ``scipy.weave`` has been packaged
separately: https://github.com/scipy/weave.  It is a pure Python package, and
can easily be installed with ``pip install weave``.

`scipy.special.bessel_diff_formula` is deprecated.  It is a private function,
and therefore will be removed from the public API in a following release.

``scipy.stats.nanmean``, ``nanmedian`` and ``nanstd`` functions are deprecated
in favor of their numpy equivalents.


Backwards incompatible changes
==============================

scipy.ndimage
- -------------

The functions `scipy.ndimage.minimum_positions`,
`scipy.ndimage.maximum_positions`` and `scipy.ndimage.extrema` return
positions as ints instead of floats.

scipy.integrate
- ---------------

The format of banded Jacobians in `scipy.integrate.ode` solvers is
changed. Note that the previous documentation of this feature was
erroneous.

SciPy 0.14.1 Release Notes
==========================

SciPy 0.14.1 is a bug-fix release with no new features compared to 0.14.0.


Issues closed
- -------------

- - `#3630 <https://github.com/scipy/scipy/issues/3630>`__: NetCDF reading results in a segfault
- - `#3631 <https://github.com/scipy/scipy/issues/3631>`__: SuperLU object not working as expected for complex matrices
- - `#3733 <https://github.com/scipy/scipy/issues/3733>`__: segfault from map_coordinates
- - `#3780 <https://github.com/scipy/scipy/issues/3780>`__: Segfault when using CSR/CSC matrix and uint32/uint64
- - `#3781 <https://github.com/scipy/scipy/pull/3781>`__: BUG: sparse: fix omitted types in sparsetools typemaps
- - `#3802 <https://github.com/scipy/scipy/issues/3802>`__: 0.14.0 API breakage: _gen generators are missing from scipy.stats.distributions API
- - `#3805 <https://github.com/scipy/scipy/issues/3805>`__: ndimage test failures with numpy 1.10
- - `#3812 <https://github.com/scipy/scipy/issues/3812>`__: == sometimes wrong on csr_matrix
- - `#3853 <https://github.com/scipy/scipy/issues/3853>`__: Many scipy.sparse test errors/failures with numpy 1.9.0b2
- - `#4084 <https://github.com/scipy/scipy/pull/4084>`__: fix exception declarations for Cython 0.21.1 compatibility
- - `#4093 <https://github.com/scipy/scipy/pull/4093>`__: BUG: fitpack: avoid a memory error in splev(x, tck, der=k)
- - `#4104 <https://github.com/scipy/scipy/pull/4104>`__: BUG: Workaround SGEMV segfault in Accelerate (maintenance 0.14.x)
- - `#4143 <https://github.com/scipy/scipy/pull/4143>`__: BUG: fix ndimage functions for large data
- - `#4149 <https://github.com/scipy/scipy/issues/4149>`__: Bug in expm for integer arrays
- - `#4154 <https://github.com/scipy/scipy/issues/4154>`__: Backport gh-4041 for 0.14.1 (Ensure that the 'size' argument of PIL's 'resize' method is a tuple)
- - `#4163 <https://github.com/scipy/scipy/issues/4163>`__: Backport #4142 (ZeroDivisionError in scipy.sparse.linalg.lsqr)
- - `#4164 <https://github.com/scipy/scipy/issues/4164>`__: Backport gh-4153 (remove use of deprecated numpy API in lib/lapack/ f2py wrapper)
- - `#4180 <https://github.com/scipy/scipy/pull/4180>`__: backport pil resize support tuple fix
- - `#4168 <https://github.com/scipy/scipy/issues/4168>`__: Lots of arpack test failures on windows 32 bits with numpy 1.9.1
- - `#4203 <https://github.com/scipy/scipy/issues/4203>`__: Matrix multiplication in 0.14.x is more than 10x slower compared...
- - `#4218 <https://github.com/scipy/scipy/pull/4218>`__: attempt to make ndimage interpolation compatible with numpy relaxed...
- - `#4225 <https://github.com/scipy/scipy/pull/4225>`__: BUG: off-by-one error in PPoly shape checks
- - `#4248 <https://github.com/scipy/scipy/pull/4248>`__: BUG: optimize: fix issue with incorrect use of closure for slsqp.

SciPy 0.14.0 Release Notes
==========================

.. contents::

SciPy 0.14.0 is the culmination of 8 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.  Moreover, our development attention
will now shift to bug-fix releases on the 0.14.x branch, and on adding
new features on the master branch.

This release requires Python 2.6, 2.7 or 3.2-3.4 and NumPy 1.5.1 or greater.


New features
============

``scipy.interpolate`` improvements
----------------------------------

A new wrapper function `scipy.interpolate.interpn` for interpolation on regular
grids has been added. `interpn` supports linear and nearest-neighbor
interpolation in arbitrary dimensions and spline interpolation in two
dimensions.

Faster implementations of piecewise polynomials in power and Bernstein
polynomial bases have been added as `scipy.interpolate.PPoly` and
`scipy.interpolate.BPoly`. New users should use these in favor of
`scipy.interpolate.PiecewisePolynomial`.

`scipy.interpolate.interp1d` now accepts non-monotonic inputs and sorts them.
If performance is critical, sorting can be turned off by using the new
``assume_sorted`` keyword.

Functionality for evaluation of bivariate spline derivatives in
``scipy.interpolate`` has been added.

The new class `scipy.interpolate.Akima1DInterpolator` implements the piecewise
cubic polynomial interpolation scheme devised by H. Akima.

Functionality for fast interpolation on regular, unevenly spaced grids
in arbitrary dimensions has been added as
`scipy.interpolate.RegularGridInterpolator` .


``scipy.linalg`` improvements
-----------------------------

The new function `scipy.linalg.dft` computes the matrix of the
discrete Fourier transform.

A condition number estimation function for matrix exponential,
`scipy.linalg.expm_cond`, has been added.


``scipy.optimize`` improvements
-------------------------------

A set of benchmarks for optimize, which can be run with ``optimize.bench()``,
has been added.

`scipy.optimize.curve_fit` now has more controllable error estimation via the
``absolute_sigma`` keyword.

Support for passing custom minimization methods to ``optimize.minimize()``
and ``optimize.minimize_scalar()`` has been added, currently useful especially
for combining ``optimize.basinhopping()`` with custom local optimizer routines.


``scipy.stats`` improvements

Update to 0.12.1

SciPy 0.12.1 is a bug-fix release with no new features compared to
0.12.0.  The single issue fixed by this release is a security issue in
``scipy.weave``, which was previously using temporary directories in
an insecure manner under certain circumstances.

Update to 0.12.0

Changes many - see doc/release/ in the source tarball for details

Update to scipy 0.7.2

SciPy 0.7.2 is a bug-fix release with no new features compared to 0.7.1. The
only change is that all C sources from Cython code have been regenerated with
Cython 0.12.1. This fixes the incompatibility between binaries of SciPy 0.7.1
and NumPy 1.4.

SciPy 0.7.1 is a bug-fix release with no new features compared to 0.7.0.

Update SciPy to 0.7.0

SciPy 0.7.0 is the culmination of 16 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.

Initial import of py-scipy 0.6.0

SciPy is an open source library of scientific tools for Python. SciPy
supplements the popular Numeric module, gathering a variety of high level
science and engineering modules together as a single package.

SciPy includes modules for graphics and plotting, optimization, integration,
special functions, signal and image processing, genetic algorithms, ODE
solvers, and others.

Initial revision