Changelog
### 1.9.0
```
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+` and NumPy `1.18.5` or greater.
For running on PyPy, PyPy3 `6.0+` is required.
Highlights of this release
===================
- We have modernized our build system to use ``meson``, substantially reducing
our source build times
- Added `scipy.optimize.milp`, new function for mixed-integer linear
programming.
- Added `scipy.stats.fit` for fitting discrete and continuous distributions
to data.
- Tensor-product spline interpolation modes were added to
`scipy.interpolate.RegularGridInterpolator`.
- A new global optimizer (DIviding RECTangles algorithm)
`scipy.optimize.direct`
New features
===========
`scipy.interpolate` improvements
================================
- Speed up the ``RBFInterpolator`` evaluation with high dimensional
interpolants.
- Added new spline based interpolation methods for
`scipy.interpolate.RegularGridInterpolator` and its tutorial.
- `scipy.interpolate.RegularGridInterpolator` and `scipy.interpolate.interpn`
now accept descending ordered points.
- ``RegularGridInterpolator`` now handles length-1 grid axes.
- The ``BivariateSpline`` subclasses have a new method ``partial_derivative``
which constructs a new spline object representing a derivative of an
original spline. This mirrors the corresponding functionality for univariate
splines, ``splder`` and ``BSpline.derivative``, and can substantially speed
up repeated evaluation of derivatives.
`scipy.linalg` improvements
===========================
- `scipy.linalg.expm` now accepts nD arrays. Its speed is also improved.
- Minimum required LAPACK version is bumped to ``3.7.1``.
`scipy.fft` improvements
========================
- Added ``uarray`` multimethods for `scipy.fft.fht` and `scipy.fft.ifht`
to allow provision of third party backend implementations such as those
recently added to CuPy.
`scipy.optimize` improvements
=============================
- A new global optimizer, `scipy.optimize.direct` (DIviding RECTangles algorithm)
was added. For problems with inexpensive function evaluations, like the ones
in the SciPy benchmark suite, ``direct`` is competitive with the best other
solvers in SciPy (``dual_annealing`` and ``differential_evolution``) in terms
of execution time. See
`gh-14300 <https://github.com/scipy/scipy/pull/14300>`__ for more details.
- Add a ``full_output`` parameter to `scipy.optimize.curve_fit` to output
additional solution information.
- Add a ``integrality`` parameter to `scipy.optimize.differential_evolution`,
enabling integer constraints on parameters.
- Add a ``vectorized`` parameter to call a vectorized objective function only
once per iteration. This can improve minimization speed by reducing
interpreter overhead from the multiple objective function calls.
- The default method of `scipy.optimize.linprog` is now ``'highs'``.
- Added `scipy.optimize.milp`, new function for mixed-integer linear
programming.
- Added Newton-TFQMR method to ``newton_krylov``.
- Added support for the ``Bounds`` class in ``shgo`` and ``dual_annealing`` for
a more uniform API across `scipy.optimize`.
- Added the ``vectorized`` keyword to ``differential_evolution``.
- ``approx_fprime`` now works with vector-valued functions.
`scipy.signal` improvements
===========================
- The new window function `scipy.signal.windows.kaiser_bessel_derived` was
added to compute the Kaiser-Bessel derived window.
- Single-precision ``hilbert`` operations are now faster as a result of more
consistent ``dtype`` handling.
`scipy.sparse` improvements
===========================
- Add a ``copy`` parameter to `scipy.sparce.csgraph.laplacian`. Using inplace
computation with ``copy=False`` reduces the memory footprint.
- Add a ``dtype`` parameter to `scipy.sparce.csgraph.laplacian` for type casting.
- Add a ``symmetrized`` parameter to `scipy.sparce.csgraph.laplacian` to produce
symmetric Laplacian for directed graphs.
- Add a ``form`` parameter to `scipy.sparce.csgraph.laplacian` taking one of the
three values: ``array``, or ``function``, or ``lo`` determining the format of
the output Laplacian:
* ``array`` is a numpy array (backward compatible default);
* ``function`` is a pointer to a lambda-function evaluating the
Laplacian-vector or Laplacian-matrix product;
* ``lo`` results in the format of the ``LinearOperator``.
`scipy.sparse.linalg` improvements
==================================
- ``lobpcg`` performance improvements for small input cases.
`scipy.spatial` improvements
============================
- Add an ``order`` parameter to `scipy.spatial.transform.Rotation.from_quat`
and `scipy.spatial.transform.Rotation.as_quat` to specify quaternion format.
`scipy.stats` improvements
==========================
- `scipy.stats.monte_carlo_test` performs one-sample Monte Carlo hypothesis
tests to assess whether a sample was drawn from a given distribution. Besides
reproducing the results of hypothesis tests like `scipy.stats.ks_1samp`,
`scipy.stats.normaltest`, and `scipy.stats.cramervonmises` without small sample
size limitations, it makes it possible to perform similar tests using arbitrary
statistics and distributions.
- Several `scipy.stats` functions support new ``axis`` (integer or tuple of
integers) and ``nan_policy`` ('raise', 'omit', or 'propagate'), and
``keepdims`` arguments.
These functions also support masked arrays as inputs, even if they do not have
a `scipy.stats.mstats` counterpart. Edge cases for multidimensional arrays,
such as when axis-slices have no unmasked elements or entire inputs are of
size zero, are handled consistently.
- Add a ``weight`` parameter to `scipy.stats.hmean`.
- Several improvements have been made to `scipy.stats.levy_stable`. Substantial
improvement has been made for numerical evaluation of the pdf and cdf,
resolving [12658](https://github.com/scipy/scipy/issues/12658) and
[14944](https://github.com/scipy/scipy/issues/14994). The improvement is
particularly dramatic for stability parameter ``alpha`` close to or equal to 1
and for ``alpha`` below but approaching its maximum value of 2. The alternative
fast Fourier transform based method for pdf calculation has also been updated
to use the approach of Wang and Zhang from their 2008 conference paper
*Simpson’s rule based FFT method to compute densities of stable distribution*,
making this method more competitive with the default method. In addition,
users now have the option to change the parametrization of the Levy Stable
distribution to Nolan's "S0" parametrization which is used internally by
SciPy's pdf and cdf implementations. The "S0" parametrization is described in
Nolan's paper [*Numerical calculation of stable densities and distribution
functions*](https://doi.org/10.1080/15326349708807450) upon which SciPy's
implementation is based. "S0" has the advantage that ``delta`` and ``gamma``
are proper location and scale parameters. With ``delta`` and ``gamma`` fixed,
the location and scale of the resulting distribution remain unchanged as
``alpha`` and ``beta`` change. This is not the case for the default "S1"
parametrization. Finally, more options have been exposed to allow users to
trade off between runtime and accuracy for both the default and FFT methods of
pdf and cdf calculation. More information can be found in the documentation
here (to be linked).
- Added `scipy.stats.fit` for fitting discrete and continuous distributions to
data.
- The methods ``"pearson"`` and ``"tippet"`` from `scipy.stats.combine_pvalues`
have been fixed to return the correct p-values, resolving
[15373](https://github.com/scipy/scipy/issues/15373). In addition, the
documentation for `scipy.stats.combine_pvalues` has been expanded and improved.
- Unlike other reduction functions, ``stats.mode`` didn't consume the axis
being operated on and failed for negative axis inputs. Both the bugs have been
fixed. Note that ``stats.mode`` will now consume the input axis and return an
ndarray with the ``axis`` dimension removed.
- Replaced implementation of `scipy.stats.ncf` with the implementation from
Boost for improved reliability.
- Add a `bits` parameter to `scipy.stats.qmc.Sobol`. It allows to use from 0
to 64 bits to compute the sequence. Default is ``None`` which corresponds to
30 for backward compatibility. Using a higher value allow to sample more
points. Note: ``bits`` does not affect the output dtype.
- Add a `integers` method to `scipy.stats.qmc.QMCEngine`. It allows sampling
integers using any QMC sampler.
- Improved the fit speed and accuracy of ``stats.pareto``.
- Added ``qrvs`` method to ``NumericalInversePolynomial`` to match the
situation for ``NumericalInverseHermite``.
- Faster random variate generation for ``gennorm`` and ``nakagami``.
- ``lloyd_centroidal_voronoi_tessellation`` has been added to allow improved
sample distributions via iterative application of Voronoi diagrams and
centering operations
- Add `scipy.stats.qmc.PoissonDisk` to sample using the Poisson disk sampling
method. It guarantees that samples are separated from each other by a
given ``radius``.
- Add `scipy.stats.pmean` to calculate the weighted power mean also called
generalized mean.
Deprecated features
================
- Due to collision with the shape parameter ``n`` of several distributions,
use of the distribution ``moment`` method with keyword argument ``n`` is
deprecated. Keyword ``n`` is replaced with keyword ``order``.
- Similarly, use of the distribution ``interval`` method with keyword arguments
``alpha`` is deprecated. Keyword ``alpha`` is replaced with keyword
``confidence``.
- The ``'simplex'``, ``'revised simplex'``, and ``'interior-point'`` methods
of `scipy.optimize.linprog` are deprecated. Methods ``highs``, ``highs-ds``,
or ``highs-ipm`` should be used in new code.
- Support for non-numeric arrays has been deprecated from ``stats.mode``.
``pandas.DataFrame.mode`` can be used instead.
- The function `spatial.distance.kulsinski` has been deprecated in favor
of `spatial.distance.kulczynski1`.
- The ``maxiter`` keyword of the truncated Newton (TNC) algorithm has been
deprecated in favour of ``maxfun``.
- The ``vertices`` keyword of ``Delauney.qhull`` now raises a
DeprecationWarning, after having been deprecated in documentation only
for a long time.
- The ``extradoc`` keyword of ``rv_continuous``, ``rv_discrete`` and
``rv_sample`` now raises a DeprecationWarning, after having been deprecated in
documentation only for a long time.
Expired Deprecations
=================
There is an ongoing effort to follow through on long-standing deprecations.
The following previously deprecated features are affected:
- Object arrays in sparse matrices now raise an error.
- Inexact indices into sparse matrices now raise an error.
- Passing ``radius=None`` to `scipy.spatial.SphericalVoronoi` now raises an
error (not adding ``radius`` defaults to 1, as before).
- Several BSpline methods now raise an error if inputs have ``ndim > 1``.
- The ``_rvs`` method of statistical distributions now requires a ``size``
parameter.
- Passing a ``fillvalue`` that cannot be cast to the output type in
`scipy.signal.convolve2d` now raises an error.
- `scipy.spatial.distance` now enforces that the input vectors are
one-dimensional.
- Removed ``stats.itemfreq``.
- Removed ``stats.median_absolute_deviation``.
- Removed ``n_jobs`` keyword argument and use of ``k=None`` from
``kdtree.query``.
- Removed ``right`` keyword from ``interpolate.PPoly.extend``.
- Removed ``debug`` keyword from ``scipy.linalg.solve_*``.
- Removed class ``_ppform`` ``scipy.interpolate``.
- Removed BSR methods ``matvec`` and ``matmat``.
- Removed ``mlab`` truncation mode from ``cluster.dendrogram``.
- Removed ``cluster.vq.py_vq2``.
- Removed keyword arguments ``ftol`` and ``xtol`` from
``optimize.minimize(method='Nelder-Mead')``.
- Removed ``signal.windows.hanning``.
- Removed LAPACK ``gegv`` functions from ``linalg``; this raises the minimally
required LAPACK version to 3.7.1.
- Removed ``spatial.distance.matching``.
- Removed the alias ``scipy.random`` for ``numpy.random``.
- Removed docstring related functions from ``scipy.misc`` (``docformat``,
``inherit_docstring_from``, ``extend_notes_in_docstring``,
``replace_notes_in_docstring``, ``indentcount_lines``, ``filldoc``,
``unindent_dict``, ``unindent_string``).
- Removed ``linalg.pinv2``.
Backwards incompatible changes
==========================
- Several `scipy.stats` functions now convert ``np.matrix`` to ``np.ndarray``s
before the calculation is performed. In this case, the output will be a scalar
or ``np.ndarray`` of appropriate shape rather than a 2D ``np.matrix``.
Similarly, while masked elements of masked arrays are still ignored, the
output will be a scalar or ``np.ndarray`` rather than a masked array with
``mask=False``.
- The default method of `scipy.optimize.linprog` is now ``'highs'``, not
``'interior-point'`` (which is now deprecated), so callback functions and some
options are no longer supported with the default method.
- For `scipy.stats.combine_pvalues`, the sign of the test statistic returned
for the method ``"pearson"`` has been flipped so that higher values of the
statistic now correspond to lower p-values, making the statistic more
consistent with those of the other methods and with the majority of the
literature.
- `scipy.linalg.expm` due to historical reasons was using the sparse
implementation and thus was accepting sparse arrays. Now it only works with
nDarrays. For sparse usage, `scipy.sparse.linalg.expm` needs to be used
explicitly.
- The definition of `scipy.stats.circvar` has reverted to the one that is
standard in the literature; note that this is not the same as the square of
`scipy.stats.circstd`.
- Remove inheritance to `QMCEngine` in `MultinomialQMC` and
`MultivariateNormalQMC`. It removes the methods `fast_forward` and `reset`.
- Init of `MultinomialQMC` now require the number of trials with `n_trials`.
Hence, `MultinomialQMC.random` output has now the correct shape ``(n, pvals)``.
- Several function-specific warnings (``F_onewayConstantInputWarning``,
``F_onewayBadInputSizesWarning``, ``PearsonRConstantInputWarning``,
``PearsonRNearConstantInputWarning``, ``SpearmanRConstantInputWarning``, and
``BootstrapDegenerateDistributionWarning``) have been replaced with more
general warnings.
Other changes
============
- A draft developer CLI is available for SciPy, leveraging the ``doit``,
``click`` and ``rich-click`` tools. For more details, see
[gh-15959](https://github.com/scipy/scipy/pull/15959).
- The SciPy contributor guide has been reorganized and updated
(see [15947](https://github.com/scipy/scipy/pull/15947) for details).
- QUADPACK Fortran routines in `scipy.integrate`, which power
`scipy.integrate.quad`, have been marked as `recursive`. This should fix rare
issues in multivariate integration (`nquad` and friends) and obviate the need
for compiler-specific compile flags (`/recursive` for ifort etc). Please file
an issue if this change turns out problematic for you. This is also true for
``FITPACK`` routines in `scipy.interpolate`, which power ``splrep``,
``splev`` etc., and ``*UnivariateSpline`` and ``*BivariateSpline`` classes.
- the ``USE_PROPACK`` environment variable has been renamed to
``SCIPY_USE_PROPACK``; setting to a non-zero value will enable
the usage of the ``PROPACK`` library as before
Lazy access to subpackages
======================
Before this release, all subpackages of SciPy (`cluster`, `fft`, `ndimage`,
etc.) had to be explicitly imported. Now, these subpackages are lazily loaded
as soon as they are accessed, so that the following is possible (if desired
for interactive use, it's not actually recommended for code,
see :ref:`scipy-api`):
``import scipy as sp; sp.fft.dct([1, 2, 3])``. Advantages include: making it
easier to navigate SciPy in interactive terminals, reducing subpackage import
conflicts (which before required
``import networkx.linalg as nla; import scipy.linalg as sla``),
and avoiding repeatedly having to update imports during teaching &
experimentation. Also see
[the related community specification document](https://scientific-python.org/specs/spec-0001/).
SciPy switched to Meson as its build system
===========================================
This is the first release that ships with [Meson](https://mesonbuild.com) as
the build system. When installing with ``pip`` or ``pypa/build``, Meson will be
used (invoked via the ``meson-python`` build hook). This change brings
significant benefits - most importantly much faster build times, but also
better support for cross-compilation and cleaner build logs.
*Note*:
This release still ships with support for ``numpy.distutils``-based builds
as well. Those can be invoked through the ``setup.py`` command-line
interface (e.g., ``python setup.py install``). It is planned to remove
``numpy.distutils`` support before the 1.10.0 release.
When building from source, a number of things have changed compared to building
with ``numpy.distutils``:
- New build dependencies: ``meson``, ``ninja``, and ``pkg-config``.
``setuptools`` and ``wheel`` are no longer needed.
- BLAS and LAPACK libraries that are supported haven't changed, however the
discovery mechanism has: that is now using ``pkg-config`` instead of hardcoded
paths or a ``site.cfg`` file.
- The build defaults to using OpenBLAS. See :ref:`blas-lapack-selection` for
details.
The two CLIs that can be used to build wheels are ``pip`` and ``build``. In
addition, the SciPy repo contains a ``python dev.py`` CLI for any kind of
development task (see its ``--help`` for details). For a comparison between old
(``distutils``) and new (``meson``) build commands, see :ref:`meson-faq`.
For more information on the introduction of Meson support in SciPy, see
`gh-13615 <https://github.com/scipy/scipy/issues/13615>`__ and
`this blog post <https://labs.quansight.org/blog/2021/07/moving-scipy-to-meson/>`__.
Authors
=======
* endolith (12)
* Caio Agiani (2) +
* Emmy Albert (1) +
* Joseph Albert (1)
* Tania Allard (3)
* Carsten Allefeld (1) +
* Kartik Anand (1) +
* Virgile Andreani (2) +
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* Bharath (1) +
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* Islem BOUZENIA (1) +
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* Matthew Brett (11)
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* Michael Burkhart (2) +
* Evgeni Burovski (96)
* Matthias Bussonnier (20)
* Dominic C (1)
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* derbuihan (2) +
* Sameer Deshmukh (13) +
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* DWesl (8)
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* Thomas J. Fan (5)
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* David Zwicker (1) +
A total of 153 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.
```
### 1.8.1
```
compared to `1.8.0`. Notably, usage of Pythran has been
restored for Windows builds/binaries.
Authors
=======
* Henry Schreiner
* Maximilian Nöthe
* Sebastian Berg (1)
* Sameer Deshmukh (1) +
* Niels Doucet (1) +
* DWesl (4)
* Isuru Fernando (1)
* Ralf Gommers (4)
* Matt Haberland (1)
* Andrew Nelson (1)
* Dimitri Papadopoulos Orfanos (1) +
* Tirth Patel (3)
* Tyler Reddy (46)
* Pamphile Roy (7)
* Niyas Sait (1) +
* H. Vetinari (2)
* Warren Weckesser (1)
A total of 17 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.
```
Links
- PyPI: https://pypi.org/project/scipy
- Changelog: https://pyup.io/changelogs/scipy/
- Repo: https://github.com/scipy/scipy/releases
- Homepage: https://www.scipy.org
pyup-bot
commented
2 years ago
This PR updates scipy from 1.8.0 to 1.9.0.
Changelog
### 1.9.0 ``` 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+` and NumPy `1.18.5` or greater. For running on PyPy, PyPy3 `6.0+` is required. Highlights of this release =================== - We have modernized our build system to use ``meson``, substantially reducing our source build times - Added `scipy.optimize.milp`, new function for mixed-integer linear programming. - Added `scipy.stats.fit` for fitting discrete and continuous distributions to data. - Tensor-product spline interpolation modes were added to `scipy.interpolate.RegularGridInterpolator`. - A new global optimizer (DIviding RECTangles algorithm) `scipy.optimize.direct` New features =========== `scipy.interpolate` improvements ================================ - Speed up the ``RBFInterpolator`` evaluation with high dimensional interpolants. - Added new spline based interpolation methods for `scipy.interpolate.RegularGridInterpolator` and its tutorial. - `scipy.interpolate.RegularGridInterpolator` and `scipy.interpolate.interpn` now accept descending ordered points. - ``RegularGridInterpolator`` now handles length-1 grid axes. - The ``BivariateSpline`` subclasses have a new method ``partial_derivative`` which constructs a new spline object representing a derivative of an original spline. This mirrors the corresponding functionality for univariate splines, ``splder`` and ``BSpline.derivative``, and can substantially speed up repeated evaluation of derivatives. `scipy.linalg` improvements =========================== - `scipy.linalg.expm` now accepts nD arrays. Its speed is also improved. - Minimum required LAPACK version is bumped to ``3.7.1``. `scipy.fft` improvements ======================== - Added ``uarray`` multimethods for `scipy.fft.fht` and `scipy.fft.ifht` to allow provision of third party backend implementations such as those recently added to CuPy. `scipy.optimize` improvements ============================= - A new global optimizer, `scipy.optimize.direct` (DIviding RECTangles algorithm) was added. For problems with inexpensive function evaluations, like the ones in the SciPy benchmark suite, ``direct`` is competitive with the best other solvers in SciPy (``dual_annealing`` and ``differential_evolution``) in terms of execution time. See `gh-14300 <https://github.com/scipy/scipy/pull/14300>`__ for more details. - Add a ``full_output`` parameter to `scipy.optimize.curve_fit` to output additional solution information. - Add a ``integrality`` parameter to `scipy.optimize.differential_evolution`, enabling integer constraints on parameters. - Add a ``vectorized`` parameter to call a vectorized objective function only once per iteration. This can improve minimization speed by reducing interpreter overhead from the multiple objective function calls. - The default method of `scipy.optimize.linprog` is now ``'highs'``. - Added `scipy.optimize.milp`, new function for mixed-integer linear programming. - Added Newton-TFQMR method to ``newton_krylov``. - Added support for the ``Bounds`` class in ``shgo`` and ``dual_annealing`` for a more uniform API across `scipy.optimize`. - Added the ``vectorized`` keyword to ``differential_evolution``. - ``approx_fprime`` now works with vector-valued functions. `scipy.signal` improvements =========================== - The new window function `scipy.signal.windows.kaiser_bessel_derived` was added to compute the Kaiser-Bessel derived window. - Single-precision ``hilbert`` operations are now faster as a result of more consistent ``dtype`` handling. `scipy.sparse` improvements =========================== - Add a ``copy`` parameter to `scipy.sparce.csgraph.laplacian`. Using inplace computation with ``copy=False`` reduces the memory footprint. - Add a ``dtype`` parameter to `scipy.sparce.csgraph.laplacian` for type casting. - Add a ``symmetrized`` parameter to `scipy.sparce.csgraph.laplacian` to produce symmetric Laplacian for directed graphs. - Add a ``form`` parameter to `scipy.sparce.csgraph.laplacian` taking one of the three values: ``array``, or ``function``, or ``lo`` determining the format of the output Laplacian: * ``array`` is a numpy array (backward compatible default); * ``function`` is a pointer to a lambda-function evaluating the Laplacian-vector or Laplacian-matrix product; * ``lo`` results in the format of the ``LinearOperator``. `scipy.sparse.linalg` improvements ================================== - ``lobpcg`` performance improvements for small input cases. `scipy.spatial` improvements ============================ - Add an ``order`` parameter to `scipy.spatial.transform.Rotation.from_quat` and `scipy.spatial.transform.Rotation.as_quat` to specify quaternion format. `scipy.stats` improvements ========================== - `scipy.stats.monte_carlo_test` performs one-sample Monte Carlo hypothesis tests to assess whether a sample was drawn from a given distribution. Besides reproducing the results of hypothesis tests like `scipy.stats.ks_1samp`, `scipy.stats.normaltest`, and `scipy.stats.cramervonmises` without small sample size limitations, it makes it possible to perform similar tests using arbitrary statistics and distributions. - Several `scipy.stats` functions support new ``axis`` (integer or tuple of integers) and ``nan_policy`` ('raise', 'omit', or 'propagate'), and ``keepdims`` arguments. These functions also support masked arrays as inputs, even if they do not have a `scipy.stats.mstats` counterpart. Edge cases for multidimensional arrays, such as when axis-slices have no unmasked elements or entire inputs are of size zero, are handled consistently. - Add a ``weight`` parameter to `scipy.stats.hmean`. - Several improvements have been made to `scipy.stats.levy_stable`. Substantial improvement has been made for numerical evaluation of the pdf and cdf, resolving [12658](https://github.com/scipy/scipy/issues/12658) and [14944](https://github.com/scipy/scipy/issues/14994). The improvement is particularly dramatic for stability parameter ``alpha`` close to or equal to 1 and for ``alpha`` below but approaching its maximum value of 2. The alternative fast Fourier transform based method for pdf calculation has also been updated to use the approach of Wang and Zhang from their 2008 conference paper *Simpson’s rule based FFT method to compute densities of stable distribution*, making this method more competitive with the default method. In addition, users now have the option to change the parametrization of the Levy Stable distribution to Nolan's "S0" parametrization which is used internally by SciPy's pdf and cdf implementations. The "S0" parametrization is described in Nolan's paper [*Numerical calculation of stable densities and distribution functions*](https://doi.org/10.1080/15326349708807450) upon which SciPy's implementation is based. "S0" has the advantage that ``delta`` and ``gamma`` are proper location and scale parameters. With ``delta`` and ``gamma`` fixed, the location and scale of the resulting distribution remain unchanged as ``alpha`` and ``beta`` change. This is not the case for the default "S1" parametrization. Finally, more options have been exposed to allow users to trade off between runtime and accuracy for both the default and FFT methods of pdf and cdf calculation. More information can be found in the documentation here (to be linked). - Added `scipy.stats.fit` for fitting discrete and continuous distributions to data. - The methods ``"pearson"`` and ``"tippet"`` from `scipy.stats.combine_pvalues` have been fixed to return the correct p-values, resolving [15373](https://github.com/scipy/scipy/issues/15373). In addition, the documentation for `scipy.stats.combine_pvalues` has been expanded and improved. - Unlike other reduction functions, ``stats.mode`` didn't consume the axis being operated on and failed for negative axis inputs. Both the bugs have been fixed. Note that ``stats.mode`` will now consume the input axis and return an ndarray with the ``axis`` dimension removed. - Replaced implementation of `scipy.stats.ncf` with the implementation from Boost for improved reliability. - Add a `bits` parameter to `scipy.stats.qmc.Sobol`. It allows to use from 0 to 64 bits to compute the sequence. Default is ``None`` which corresponds to 30 for backward compatibility. Using a higher value allow to sample more points. Note: ``bits`` does not affect the output dtype. - Add a `integers` method to `scipy.stats.qmc.QMCEngine`. It allows sampling integers using any QMC sampler. - Improved the fit speed and accuracy of ``stats.pareto``. - Added ``qrvs`` method to ``NumericalInversePolynomial`` to match the situation for ``NumericalInverseHermite``. - Faster random variate generation for ``gennorm`` and ``nakagami``. - ``lloyd_centroidal_voronoi_tessellation`` has been added to allow improved sample distributions via iterative application of Voronoi diagrams and centering operations - Add `scipy.stats.qmc.PoissonDisk` to sample using the Poisson disk sampling method. It guarantees that samples are separated from each other by a given ``radius``. - Add `scipy.stats.pmean` to calculate the weighted power mean also called generalized mean. Deprecated features ================ - Due to collision with the shape parameter ``n`` of several distributions, use of the distribution ``moment`` method with keyword argument ``n`` is deprecated. Keyword ``n`` is replaced with keyword ``order``. - Similarly, use of the distribution ``interval`` method with keyword arguments ``alpha`` is deprecated. Keyword ``alpha`` is replaced with keyword ``confidence``. - The ``'simplex'``, ``'revised simplex'``, and ``'interior-point'`` methods of `scipy.optimize.linprog` are deprecated. Methods ``highs``, ``highs-ds``, or ``highs-ipm`` should be used in new code. - Support for non-numeric arrays has been deprecated from ``stats.mode``. ``pandas.DataFrame.mode`` can be used instead. - The function `spatial.distance.kulsinski` has been deprecated in favor of `spatial.distance.kulczynski1`. - The ``maxiter`` keyword of the truncated Newton (TNC) algorithm has been deprecated in favour of ``maxfun``. - The ``vertices`` keyword of ``Delauney.qhull`` now raises a DeprecationWarning, after having been deprecated in documentation only for a long time. - The ``extradoc`` keyword of ``rv_continuous``, ``rv_discrete`` and ``rv_sample`` now raises a DeprecationWarning, after having been deprecated in documentation only for a long time. Expired Deprecations ================= There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected: - Object arrays in sparse matrices now raise an error. - Inexact indices into sparse matrices now raise an error. - Passing ``radius=None`` to `scipy.spatial.SphericalVoronoi` now raises an error (not adding ``radius`` defaults to 1, as before). - Several BSpline methods now raise an error if inputs have ``ndim > 1``. - The ``_rvs`` method of statistical distributions now requires a ``size`` parameter. - Passing a ``fillvalue`` that cannot be cast to the output type in `scipy.signal.convolve2d` now raises an error. - `scipy.spatial.distance` now enforces that the input vectors are one-dimensional. - Removed ``stats.itemfreq``. - Removed ``stats.median_absolute_deviation``. - Removed ``n_jobs`` keyword argument and use of ``k=None`` from ``kdtree.query``. - Removed ``right`` keyword from ``interpolate.PPoly.extend``. - Removed ``debug`` keyword from ``scipy.linalg.solve_*``. - Removed class ``_ppform`` ``scipy.interpolate``. - Removed BSR methods ``matvec`` and ``matmat``. - Removed ``mlab`` truncation mode from ``cluster.dendrogram``. - Removed ``cluster.vq.py_vq2``. - Removed keyword arguments ``ftol`` and ``xtol`` from ``optimize.minimize(method='Nelder-Mead')``. - Removed ``signal.windows.hanning``. - Removed LAPACK ``gegv`` functions from ``linalg``; this raises the minimally required LAPACK version to 3.7.1. - Removed ``spatial.distance.matching``. - Removed the alias ``scipy.random`` for ``numpy.random``. - Removed docstring related functions from ``scipy.misc`` (``docformat``, ``inherit_docstring_from``, ``extend_notes_in_docstring``, ``replace_notes_in_docstring``, ``indentcount_lines``, ``filldoc``, ``unindent_dict``, ``unindent_string``). - Removed ``linalg.pinv2``. Backwards incompatible changes ========================== - Several `scipy.stats` functions now convert ``np.matrix`` to ``np.ndarray``s before the calculation is performed. In this case, the output will be a scalar or ``np.ndarray`` of appropriate shape rather than a 2D ``np.matrix``. Similarly, while masked elements of masked arrays are still ignored, the output will be a scalar or ``np.ndarray`` rather than a masked array with ``mask=False``. - The default method of `scipy.optimize.linprog` is now ``'highs'``, not ``'interior-point'`` (which is now deprecated), so callback functions and some options are no longer supported with the default method. - For `scipy.stats.combine_pvalues`, the sign of the test statistic returned for the method ``"pearson"`` has been flipped so that higher values of the statistic now correspond to lower p-values, making the statistic more consistent with those of the other methods and with the majority of the literature. - `scipy.linalg.expm` due to historical reasons was using the sparse implementation and thus was accepting sparse arrays. Now it only works with nDarrays. For sparse usage, `scipy.sparse.linalg.expm` needs to be used explicitly. - The definition of `scipy.stats.circvar` has reverted to the one that is standard in the literature; note that this is not the same as the square of `scipy.stats.circstd`. - Remove inheritance to `QMCEngine` in `MultinomialQMC` and `MultivariateNormalQMC`. It removes the methods `fast_forward` and `reset`. - Init of `MultinomialQMC` now require the number of trials with `n_trials`. Hence, `MultinomialQMC.random` output has now the correct shape ``(n, pvals)``. - Several function-specific warnings (``F_onewayConstantInputWarning``, ``F_onewayBadInputSizesWarning``, ``PearsonRConstantInputWarning``, ``PearsonRNearConstantInputWarning``, ``SpearmanRConstantInputWarning``, and ``BootstrapDegenerateDistributionWarning``) have been replaced with more general warnings. Other changes ============ - A draft developer CLI is available for SciPy, leveraging the ``doit``, ``click`` and ``rich-click`` tools. For more details, see [gh-15959](https://github.com/scipy/scipy/pull/15959). - The SciPy contributor guide has been reorganized and updated (see [15947](https://github.com/scipy/scipy/pull/15947) for details). - QUADPACK Fortran routines in `scipy.integrate`, which power `scipy.integrate.quad`, have been marked as `recursive`. This should fix rare issues in multivariate integration (`nquad` and friends) and obviate the need for compiler-specific compile flags (`/recursive` for ifort etc). Please file an issue if this change turns out problematic for you. This is also true for ``FITPACK`` routines in `scipy.interpolate`, which power ``splrep``, ``splev`` etc., and ``*UnivariateSpline`` and ``*BivariateSpline`` classes. - the ``USE_PROPACK`` environment variable has been renamed to ``SCIPY_USE_PROPACK``; setting to a non-zero value will enable the usage of the ``PROPACK`` library as before Lazy access to subpackages ====================== Before this release, all subpackages of SciPy (`cluster`, `fft`, `ndimage`, etc.) had to be explicitly imported. Now, these subpackages are lazily loaded as soon as they are accessed, so that the following is possible (if desired for interactive use, it's not actually recommended for code, see :ref:`scipy-api`): ``import scipy as sp; sp.fft.dct([1, 2, 3])``. Advantages include: making it easier to navigate SciPy in interactive terminals, reducing subpackage import conflicts (which before required ``import networkx.linalg as nla; import scipy.linalg as sla``), and avoiding repeatedly having to update imports during teaching & experimentation. Also see [the related community specification document](https://scientific-python.org/specs/spec-0001/). SciPy switched to Meson as its build system =========================================== This is the first release that ships with [Meson](https://mesonbuild.com) as the build system. When installing with ``pip`` or ``pypa/build``, Meson will be used (invoked via the ``meson-python`` build hook). This change brings significant benefits - most importantly much faster build times, but also better support for cross-compilation and cleaner build logs. *Note*: This release still ships with support for ``numpy.distutils``-based builds as well. Those can be invoked through the ``setup.py`` command-line interface (e.g., ``python setup.py install``). It is planned to remove ``numpy.distutils`` support before the 1.10.0 release. When building from source, a number of things have changed compared to building with ``numpy.distutils``: - New build dependencies: ``meson``, ``ninja``, and ``pkg-config``. ``setuptools`` and ``wheel`` are no longer needed. - BLAS and LAPACK libraries that are supported haven't changed, however the discovery mechanism has: that is now using ``pkg-config`` instead of hardcoded paths or a ``site.cfg`` file. - The build defaults to using OpenBLAS. See :ref:`blas-lapack-selection` for details. The two CLIs that can be used to build wheels are ``pip`` and ``build``. In addition, the SciPy repo contains a ``python dev.py`` CLI for any kind of development task (see its ``--help`` for details). For a comparison between old (``distutils``) and new (``meson``) build commands, see :ref:`meson-faq`. For more information on the introduction of Meson support in SciPy, see `gh-13615 <https://github.com/scipy/scipy/issues/13615>`__ and `this blog post <https://labs.quansight.org/blog/2021/07/moving-scipy-to-meson/>`__. Authors ======= * endolith (12) * Caio Agiani (2) + * Emmy Albert (1) + * Joseph Albert (1) * Tania Allard (3) * Carsten Allefeld (1) + * Kartik Anand (1) + * Virgile Andreani (2) + * Weh Andreas (1) + * Francesco Andreuzzi (5) + * Kian-Meng Ang (2) + * Gerrit Ansmann (1) * Ar-Kareem (1) + * Shehan Atukorala (1) + * avishai231 (1) + * Blair Azzopardi (1) * Sayantika Banik (2) + * Ross Barnowski (8) * Christoph Baumgarten (3) * Nickolai Belakovski (1) * Peter Bell (9) * Sebastian Berg (2) * Bharath (1) + * bobcatCA (2) + * boussoffara (2) + * Islem BOUZENIA (1) + * Jake Bowhay (41) + * Matthew Brett (11) * Dietrich Brunn (2) + * Michael Burkhart (2) + * Evgeni Burovski (96) * Matthias Bussonnier (20) * Dominic C (1) * Cameron (1) + * CJ Carey (3) * Thomas A Caswell (2) * Ali Cetin (2) + * Hood Chatham (5) + * Klesk Chonkin (1) * Craig Citro (1) + * Dan Cogswell (1) + * Luigi Cruz (1) + * Anirudh Dagar (5) * Brandon David (1) * deepakdinesh1123 (1) + * Denton DeLoss (1) + * derbuihan (2) + * Sameer Deshmukh (13) + * Niels Doucet (1) + * DWesl (8) * eytanadler (30) + * Thomas J. Fan (5) * Isuru Fernando (3) * Joseph Fox-Rabinovitz (1) * Ryan Gibson (4) + * Ralf Gommers (308) * Srinivas Gorur-Shandilya (1) + * Alex Griffing (2) * h-vetinari (3) * Matt Haberland (442) * Tristan Hearn (1) + * Jonathan Helgert (1) + * Samuel Hinton (1) + * Jake (1) + * Stewart Jamieson (1) + * Jan-Hendrik Müller (1) * Yikun Jiang (1) + * JuliaMelle01 (1) + * jyuv (12) + * Chris Keefe (1) + * Robert Kern (4) * Andrew Knyazev (11) * Matthias Koeppe (4) + * Sergey Koposov (1) * Volodymyr Kozachynskyi (1) + * Yotaro Kubo (2) + * Jacob Lapenna (1) + * Peter Mahler Larsen (8) * Eric Larson (4) * Laurynas Mikšys (1) + * Antony Lee (1) * Gregory R. Lee (2) * lerichi (1) + * Tim Leslie (2) * P. L. Lim (1) * Smit Lunagariya (43) * lutefiskhotdish (1) + * Cong Ma (12) * Syrtis Major (1) * Nicholas McKibben (17) * Melissa Weber Mendonça (10) * Mark Mikofski (1) * Jarrod Millman (13) * Harsh Mishra (6) * ML-Nielsen (3) + * Matthew Murray (1) + * Andrew Nelson (50) * Dimitri Papadopoulos Orfanos (1) + * Evgueni Ovtchinnikov (2) + * Sambit Panda (1) * Nick Papior (2) * Tirth Patel (43) * Petar Mlinarić (1) * petroselo (1) + * Ilhan Polat (64) * Anthony Polloreno (1) * Amit Portnoy (1) + * Quentin Barthélemy (9) * Patrick N. Raanes (1) + * Tyler Reddy (121) * Pamphile Roy (196) * Vivek Roy (2) + * Niyas Sait (2) + * Atsushi Sakai (25) * Mazen Sayed (1) + * Eduardo Schettino (5) + * Daniel Schmitz (6) + * Eli Schwartz (3) + * SELEE (2) + * Namami Shanker (4) * siddhantwahal (1) + * Gagandeep Singh (8) * Soph (1) + * Shivnaren Srinivasan (1) + * Scott Staniewicz (1) + * Leo C. Stein (4) * Albert Steppi (7) * Christopher Strickland (1) + * Kai Striega (4) * Søren Fuglede Jørgensen (1) * Aleksandr Tagilov (1) + * Masayuki Takagi (1) + * Sai Teja (1) + * Ewout ter Hoeven (2) + * Will Tirone (2) * Bas van Beek (7) * Dhruv Vats (1) * H. Vetinari (4) * Arthur Volant (1) * Samuel Wallan (5) * Stefan van der Walt (8) * Warren Weckesser (83) * Anreas Weh (1) * Nils Werner (1) * Aviv Yaish (1) + * Dowon Yi (1) * Rory Yorke (1) * Yosshi999 (1) + * yuanx749 (2) + * Gang Zhao (23) * ZhihuiChen0903 (1) * Pavel Zun (1) + * David Zwicker (1) + A total of 153 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ``` ### 1.8.1 ``` compared to `1.8.0`. Notably, usage of Pythran has been restored for Windows builds/binaries. Authors ======= * Henry Schreiner * Maximilian Nöthe * Sebastian Berg (1) * Sameer Deshmukh (1) + * Niels Doucet (1) + * DWesl (4) * Isuru Fernando (1) * Ralf Gommers (4) * Matt Haberland (1) * Andrew Nelson (1) * Dimitri Papadopoulos Orfanos (1) + * Tirth Patel (3) * Tyler Reddy (46) * Pamphile Roy (7) * Niyas Sait (1) + * H. Vetinari (2) * Warren Weckesser (1) A total of 17 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ```Links
- PyPI: https://pypi.org/project/scipy - Changelog: https://pyup.io/changelogs/scipy/ - Repo: https://github.com/scipy/scipy/releases - Homepage: https://www.scipy.org