Changelog
### 1.7.1
```
compared to `1.7.0`.
Authors
=======
* Peter Bell
* Evgeni Burovski
* Justin Charlong +
* Ralf Gommers
* Matti Picus
* Tyler Reddy
* Pamphile Roy
* Sebastian Wallkötter
* Arthur Volant
A total of 9 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.
```
### 1.7.0
```
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
1.7.x branch, and on adding new features on the master branch.
This release requires Python `3.7+` and NumPy `1.16.5` or greater.
For running on PyPy, PyPy3 `6.0+` is required.
Highlights of this release
- 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
other NumFOCUS packages like NumPy.
- 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.
*We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source
Software for Science program for supporting many of the improvements to*
`scipy.stats`.
New features
`scipy.cluster` improvements
An optional argument, ``seed``, has been added to ``kmeans`` and ``kmeans2`` to
set the random generator and random state.
`scipy.interpolate` improvements
Improved input validation and error messages for ``fitpack.bispev`` and
``fitpack.parder`` for scenarios that previously caused substantial confusion
for users.
The class `RBFInterpolator` was added to supersede the `Rbf` class. The new
class has usage that more closely follows other interpolator classes, corrects
sign errors that caused unexpected smoothing behavior, includes polynomial
terms in the interpolant (which are necessary for some RBF choices), and
supports interpolation using only the k-nearest neighbors for memory
efficiency.
`scipy.linalg` improvements
An LAPACK wrapper was added for access to the ``tgexc`` subroutine.
`scipy.ndimage` improvements
`scipy.ndimage.affine_transform` is now able to infer the ``output_shape`` from
the ``out`` array.
`scipy.optimize` improvements
The optional parameter ``bounds`` was added to
``_minimize_neldermead`` to support bounds constraints
for the Nelder-Mead solver.
``trustregion`` methods ``trust-krylov``, ``dogleg`` and ``trust-ncg`` can now
estimate ``hess`` by finite difference using one of
``["2-point", "3-point", "cs"]``.
``halton`` was added as a ``sampling_method`` in `scipy.optimize.shgo`.
``sobol`` was fixed and is now using `scipy.stats.qmc.Sobol`.
``halton`` and ``sobol`` were added as ``init`` methods in
`scipy.optimize.differential_evolution.`
``differential_evolution`` now accepts an ``x0`` parameter to provide an
initial guess for the minimization.
``least_squares`` has a modest performance improvement when SciPy is built
with Pythran transpiler enabled.
When ``linprog`` is used with ``method`` ``'highs'``, ``'highs-ipm'``, or
``'highs-ds'``, the result object now reports the marginals (AKA shadow
prices, dual values) and residuals associated with each constraint.
`scipy.signal` improvements
``get_window`` supports ``general_cosine`` and ``general_hamming`` window
functions.
`scipy.signal.medfilt2d` now releases the GIL where appropriate to enable
performance gains via multithreaded calculations.
`scipy.sparse` improvements
Addition of ``dia_matrix`` sparse matrices is now faster.
`scipy.spatial` improvements
``distance.cdist`` and ``distance.pdist`` performance has greatly improved for
certain weighted metrics. Namely: ``minkowski``, ``euclidean``, ``chebyshev``,
``canberra``, and ``cityblock``.
Modest performance improvements for many of the unweighted ``cdist`` and
``pdist`` metrics noted above.
The parameter ``seed`` was added to `scipy.spatial.vq.kmeans` and
`scipy.spatial.vq.kmeans2`.
The parameters ``axis`` and ``keepdims`` where added to
`scipy.spatial.distance.jensenshannon`.
The ``rotation`` methods ``from_rotvec`` and ``as_rotvec`` now accept a
``degrees`` argument to specify usage of degrees instead of radians.
`scipy.special` improvements
Wright's generalized Bessel function for positive arguments was added as
`scipy.special.wright_bessel.`
An implementation of the inverse of the Log CDF of the Normal Distribution is
now available via `scipy.special.ndtri_exp`.
`scipy.stats` improvements
Hypothesis Tests
The Mann-Whitney-Wilcoxon test, ``mannwhitneyu``, has been rewritten. It now
supports n-dimensional input, an exact test method when there are no ties,
and improved documentation. Please see "Other changes" for adjustments to
default behavior.
The new function `scipy.stats.binomtest` replaces `scipy.stats.binom_test`. The
new function returns an object that calculates a confidence intervals of the
proportion parameter. Also, performance was improved from O(n) to O(log(n)) by
using binary search.
The two-sample version of the Cramer-von Mises test is implemented in
`scipy.stats.cramervonmises_2samp`.
The Alexander-Govern test is implemented in the new function
`scipy.stats.alexandergovern`.
The new functions `scipy.stats.barnard_exact` and `scipy.stats. boschloo_exact`
respectively perform Barnard's exact test and Boschloo's exact test
for 2x2 contingency tables.
The new function `scipy.stats.page_trend_test` performs Page's test for ordered
alternatives.
The new function `scipy.stats.somersd` performs Somers' D test for ordinal
association between two variables.
An option, ``permutations``, has been added in `scipy.stats.ttest_ind` to
perform permutation t-tests. A ``trim`` option was also added to perform
a trimmed (Yuen's) t-test.
The ``alternative`` parameter was added to the ``skewtest``, ``kurtosistest``,
``ranksums``, ``mood``, ``ansari``, ``linregress``, and ``spearmanr`` functions
to allow one-sided hypothesis testing.
Sample statistics
The new function `scipy.stats.differential_entropy` estimates the differential
entropy of a continuous distribution from a sample.
The ``boxcox`` and ``boxcox_normmax`` now allow the user to control the
optimizer used to minimize the negative log-likelihood function.
A new function `scipy.stats.contingency.relative_risk` calculates the
relative risk, or risk ratio, of a 2x2 contingency table. The object
returned has a method to compute the confidence interval of the relative risk.
Performance improvements in the ``skew`` and ``kurtosis`` functions achieved
by removal of repeated/redundant calculations.
Substantial performance improvements in `scipy.stats.mstats.hdquantiles_sd`.
The new function `scipy.stats.contingency.association` computes several
measures of association for a contingency table: Pearsons contingency
coefficient, Cramer's V, and Tschuprow's T.
The parameter ``nan_policy`` was added to `scipy.stats.zmap` to provide options
for handling the occurrence of ``nan`` in the input data.
The parameter ``ddof`` was added to `scipy.stats.variation` and
`scipy.stats.mstats.variation`.
The parameter ``weights`` was added to `scipy.stats.gmean`.
Statistical Distributions
We now vendor and leverage the Boost C++ library to address a number of
previously reported issues in ``stats``. Notably, ``beta``, ``binom``,
``nbinom`` now have Boost backends, and it is straightforward to leverage
the backend for additional functions.
The skew Cauchy probability distribution has been implemented as
`scipy.stats.skewcauchy`.
The Zipfian probability distribution has been implemented as
`scipy.stats.zipfian`.
The new distributions ``nchypergeom_fisher`` and ``nchypergeom_wallenius``
implement the Fisher and Wallenius versions of the noncentral hypergeometric
distribution, respectively.
The generalized hyperbolic distribution was added in
`scipy.stats.genhyperbolic`.
The studentized range distribution was added in `scipy.stats.studentized_range`.
`scipy.stats.argus` now has improved handling for small parameter values.
Better argument handling/preparation has resulted in performance improvements
for many distributions.
The ``cosine`` distribution has added ufuncs for ``ppf``, ``cdf``, ``sf``, and
``isf`` methods including numerical precision improvements at the edges of the
support of the distribution.
An option to fit the distribution to data by the method of moments has been
added to the ``fit`` method of the univariate continuous distributions.
Other
`scipy.stats.bootstrap` has been added to allow estimation of the confidence
interval and standard error of a statistic.
The new function `scipy.stats.contingency.crosstab` computes a contingency
table (i.e. a table of counts of unique entries) for the given data.
`scipy.stats.NumericalInverseHermite` enables fast random variate sampling
and percentile point function evaluation of an arbitrary univariate statistical
distribution.
New `scipy.stats.qmc` module
This new module provides Quasi-Monte Carlo (QMC) generators and associated
helper functions.
It provides a generic class `scipy.stats.qmc.QMCEngine` which defines a QMC
engine/sampler. An engine is state aware: it can be continued, advanced and
reset. 3 base samplers are available:
- `scipy.stats.qmc.Sobol` the well known Sobol low discrepancy sequence.
Several warnings have been added to guide the user into properly using this
sampler. The sequence is scrambled by default.
- `scipy.stats.qmc.Halton`: Halton low discrepancy sequence. The sequence is
scrambled by default.
- `scipy.stats.qmc.LatinHypercube`: plain LHS design.
And 2 special samplers are available:
- `scipy.stats.qmc.MultinomialQMC`: sampling from a multinomial distribution
using any of the base `scipy.stats.qmc.QMCEngine`.
- `scipy.stats.qmc.MultivariateNormalQMC`: sampling from a multivariate Normal
using any of the base `scipy.stats.qmc.QMCEngine`.
The module also provide the following helpers:
- `scipy.stats.qmc.discrepancy`: assess the quality of a set of points in terms
of space coverage.
- `scipy.stats.qmc.update_discrepancy`: can be used in an optimization loop to
construct a good set of points.
- `scipy.stats.qmc.scale`: easily scale a set of points from (to) the unit
interval to (from) a given range.
Deprecated features
`scipy.linalg` deprecations
- `scipy.linalg.pinv2` is deprecated and its functionality is completely
subsumed into `scipy.linalg.pinv`
- Both ``rcond``, ``cond`` keywords of `scipy.linalg.pinv` and
`scipy.linalg.pinvh` were not working and now are deprecated. They are now
replaced with functioning ``atol`` and ``rtol`` keywords with clear usage.
`scipy.spatial` deprecations
- `scipy.spatial.distance` metrics expect 1d input vectors but will call
``np.squeeze`` on their inputs to accept any extra length-1 dimensions. That
behaviour is now deprecated.
Backwards incompatible changes
Other changes
We now accept and leverage performance improvements from the ahead-of-time
Python-to-C++ transpiler, Pythran, which can be optionally disabled (via
``export SCIPY_USE_PYTHRAN=0``) but is enabled by default at build time.
There are two changes to the default behavior of `scipy.stats.mannwhitenyu`:
- For years, use of the default ``alternative=None`` was deprecated; explicit
``alternative`` specification was required. Use of the new default value of
``alternative``, "two-sided", is now permitted.
- Previously, all p-values were based on an asymptotic approximation. Now, for
small samples without ties, the p-values returned are exact by default.
Support has been added for PEP 621 (project metadata in ``pyproject.toml``)
We now support a Gitpod environment to reduce the barrier to entry for SciPy
development; for more details see `quickstart-gitpod`.
Authors
* endolith
* Jelle Aalbers +
* Adam +
* Tania Allard +
* Sven Baars +
* Max Balandat +
* baumgarc +
* Christoph Baumgarten
* Peter Bell
* Lilian Besson
* Robinson Besson +
* Max Bolingbroke
* Blair Bonnett +
* Jordão Bragantini
* Harm Buisman +
* Evgeni Burovski
* Matthias Bussonnier
* Dominic C
* CJ Carey
* Ramón Casero +
* Chachay +
* charlotte12l +
* Benjamin Curtice Corbett +
* Falcon Dai +
* Ian Dall +
* Terry Davis
* droussea2001 +
* DWesl +
* dwight200 +
* Thomas J. Fan +
* Joseph Fox-Rabinovitz
* Max Frei +
* Laura Gutierrez Funderburk +
* gbonomib +
* Matthias Geier +
* Pradipta Ghosh +
* Ralf Gommers
* Evan H +
* h-vetinari
* Matt Haberland
* Anselm Hahn +
* Alex Henrie
* Piet Hessenius +
* Trever Hines +
* Elisha Hollander +
* Stephan Hoyer
* Tom Hu +
* Kei Ishikawa +
* Julien Jerphanion
* Robert Kern
* Shashank KS +
* Peter Mahler Larsen
* Eric Larson
* Cheng H. Lee +
* Gregory R. Lee
* Jean-Benoist Leger +
* lgfunderburk +
* liam-o-marsh +
* Xingyu Liu +
* Alex Loftus +
* Christian Lorentzen +
* Cong Ma
* Marc +
* MarkPundurs +
* Markus Löning +
* Liam Marsh +
* Nicholas McKibben
* melissawm +
* Jamie Morton
* Andrew Nelson
* Nikola Forró
* Tor Nordam +
* Olivier Gauthé +
* Rohit Pandey +
* Avanindra Kumar Pandeya +
* Tirth Patel
* paugier +
* Alex H. Wagner, PhD +
* Jeff Plourde +
* Ilhan Polat
* pranavrajpal +
* Vladyslav Rachek
* Bharat Raghunathan
* Recursing +
* Tyler Reddy
* Lucas Roberts
* Gregor Robinson +
* Pamphile Roy +
* Atsushi Sakai
* Benjamin Santos
* Martin K. Scherer +
* Thomas Schmelzer +
* Daniel Scott +
* Sebastian Wallkötter +
* serge-sans-paille +
* Namami Shanker +
* Masashi Shibata +
* Alexandre de Siqueira +
* Albert Steppi +
* Adam J. Stewart +
* Kai Striega
* Diana Sukhoverkhova
* Søren Fuglede Jørgensen
* Mike Taves
* Dan Temkin +
* Nicolas Tessore +
* tsubota20 +
* Robert Uhl
* christos val +
* Bas van Beek +
* Ashutosh Varma +
* Jose Vazquez +
* Sebastiano Vigna
* Aditya Vijaykumar
* VNMabus
* Arthur Volant +
* Samuel Wallan
* Stefan van der Walt
* Warren Weckesser
* Anreas Weh
* Josh Wilson
* Rory Yorke
* Egor Zemlyanoy
* Marc Zoeller +
* zoj613 +
* 秋纫 +
A total of 126 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.
```
### 1.6.3
```
compared to `1.6.2`.
Authors
======
* Peter Bell
* Ralf Gommers
* Matt Haberland
* Peter Mahler Larsen
* Tirth Patel
* Tyler Reddy
* Pamphile ROY +
* Xingyu Liu +
A total of 8 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.
```
### 1.6.2
```
compared to `1.6.1`. This is also the first SciPy release
to place upper bounds on some dependencies to improve
the long-term repeatability of source builds.
Authors
=======
* Pradipta Ghosh +
* Tyler Reddy
* Ralf Gommers
* Martin K. Scherer +
* Robert Uhl
* Warren Weckesser
A total of 6 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.
```
### 1.6.1
```
compared to `1.6.0`.
Please note that for SciPy wheels to correctly install with pip on
macOS 11, pip `>= 20.3.3` is needed.
Authors
=======
* Peter Bell
* Evgeni Burovski
* CJ Carey
* Ralf Gommers
* Peter Mahler Larsen
* Cheng H. Lee +
* Cong Ma
* Nicholas McKibben
* Nikola Forró
* Tyler Reddy
* Warren Weckesser
A total of 11 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.
```
### 1.6.0
```
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
1.6.x branch, and on adding new features on the master branch.
This release requires Python `3.7`+ and NumPy `1.16.5` or greater.
For running on PyPy, PyPy3 `6.0`+ is required.
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
New features
============
`scipy.special` improvements
-----------------------------
`scipy.special` now has improved support for 64-bit ``LAPACK`` backend
`scipy.odr` improvements
-------------------------
`scipy.odr` now has support for 64-bit integer ``BLAS``
`scipy.odr.ODR` has gained an optional ``overwrite`` argument so that existing
files may be overwritten.
`scipy.integrate` improvements
-------------------------------
Some renames of functions with poor names were done, with the old names
retained without being in the reference guide for backwards compatibility
reasons:
- ``integrate.simps`` was renamed to ``integrate.simpson``
- ``integrate.trapz`` was renamed to ``integrate.trapezoid``
- ``integrate.cumtrapz`` was renamed to ``integrate.cumulative_trapezoid``
`scipy.cluster` improvements
-------------------------------
`scipy.cluster.hierarchy.DisjointSet` has been added for incremental
connectivity queries.
`scipy.cluster.hierarchy.dendrogram` return value now also includes leaf color
information in `leaves_color_list`.
`scipy.interpolate` improvements
---------------------------------
`scipy.interpolate.interp1d` has a new method ``nearest-up``, similar to the
existing method ``nearest`` but rounds half-integers up instead of down.
`scipy.io` improvements
------------------------
Support has been added for reading arbitrary bit depth integer PCM WAV files
from 1- to 32-bit, including the commonly-requested 24-bit depth.
`scipy.linalg` improvements
----------------------------
The new function `scipy.linalg.matmul_toeplitz` uses the FFT to compute the
product of a Toeplitz matrix with another matrix.
`scipy.linalg.sqrtm` and `scipy.linalg.logm` have performance improvements
thanks to additional Cython code.
Python ``LAPACK`` wrappers have been added for ``pptrf``, ``pptrs``, ``ppsv``,
``pptri``, and ``ppcon``.
`scipy.linalg.norm` and the ``svd`` family of functions will now use 64-bit
integer backends when available.
`scipy.ndimage` improvements
-----------------------------
`scipy.ndimage.convolve`, `scipy.ndimage.correlate` and their 1d counterparts
now accept both complex-valued images and/or complex-valued filter kernels. All
convolution-based filters also now accept complex-valued inputs
(e.g. ``gaussian_filter``, ``uniform_filter``, etc.).
Multiple fixes and enhancements to boundary handling were introduced to
`scipy.ndimage` interpolation functions (i.e. ``affine_transform``,
``geometric_transform``, ``map_coordinates``, ``rotate``, ``shift``, ``zoom``).
A new boundary mode, ``grid-wrap`` was added which wraps images periodically,
using a period equal to the shape of the input image grid. This is in contrast
to the existing ``wrap`` mode which uses a period that is one sample smaller
than the original signal extent along each dimension.
A long-standing bug in the ``reflect`` boundary condition has been fixed and
the mode ``grid-mirror`` was introduced as a synonym for ``reflect``.
A new boundary mode, ``grid-constant`` is now available. This is similar to
the existing ndimage ``constant`` mode, but interpolation will still performed
at coordinate values outside of the original image extent. This
``grid-constant`` mode is consistent with OpenCV's ``BORDER_CONSTANT`` mode
and scikit-image's ``constant`` mode.
Spline pre-filtering (used internally by ``ndimage`` interpolation functions
when ``order >= 2``), now supports all boundary modes rather than always
defaulting to mirror boundary conditions. The standalone functions
``spline_filter`` and ``spline_filter1d`` have analytical boundary conditions
that match modes ``mirror``, ``grid-wrap`` and ``reflect``.
`scipy.ndimage` interpolation functions now accept complex-valued inputs. In
this case, the interpolation is applied independently to the real and
imaginary components.
The ``ndimage`` tutorials
(https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been
updated with new figures to better clarify the exact behavior of all of the
interpolation boundary modes.
`scipy.ndimage.zoom` now has a ``grid_mode`` option that changes the coordinate
of the center of the first pixel along an axis from 0 to 0.5. This allows
resizing in a manner that is consistent with the behavior of scikit-image's
``resize`` and ``rescale`` functions (and OpenCV's ``cv2.resize``).
`scipy.optimize` improvements
------------------------------
`scipy.optimize.linprog` has fast, new methods for large, sparse problems from
the ``HiGHS`` C++ library. ``method='highs-ds'`` uses a high performance dual
revised simplex implementation (HSOL), ``method='highs-ipm'`` uses an
interior-point method with crossover, and ``method='highs'`` chooses between
the two automatically. These methods are typically much faster and often exceed
the accuracy of other ``linprog`` methods, so we recommend explicitly
specifying one of these three method values when using ``linprog``.
`scipy.optimize.quadratic_assignment` has been added for approximate solution
of the quadratic assignment problem.
`scipy.optimize.linear_sum_assignment` now has a substantially reduced overhead
for small cost matrix sizes
`scipy.optimize.least_squares` has improved performance when the user provides
the jacobian as a sparse jacobian already in ``csr_matrix`` format
`scipy.optimize.linprog` now has an ``rr_method`` argument for specification
of the method used for redundancy handling, and a new method for this purpose
is available based on the interpolative decomposition approach.
`scipy.signal` improvements
----------------------------
`scipy.signal.gammatone` has been added to design FIR or IIR filters that
model the human auditory system.
`scipy.signal.iircomb` has been added to design IIR peaking/notching comb
filters that can boost/attenuate a frequency from a signal.
`scipy.signal.sosfilt` performance has been improved to avoid some previously-
observed slowdowns
`scipy.signal.windows.taylor` has been added--the Taylor window function is
commonly used in radar digital signal processing
`scipy.signal.gauss_spline` now supports ``list`` type input for consistency
with other related SciPy functions
`scipy.signal.correlation_lags` has been added to allow calculation of the lag/
displacement indices array for 1D cross-correlation.
`scipy.sparse` improvements
----------------------------
A solver for the minimum weight full matching problem for bipartite graphs,
also known as the linear assignment problem, has been added in
`scipy.sparse.csgraph.min_weight_full_bipartite_matching`. In particular, this
provides functionality analogous to that of
`scipy.optimize.linear_sum_assignment`, but with improved performance for sparse
inputs, and the ability to handle inputs whose dense representations would not
fit in memory.
The time complexity of `scipy.sparse.block_diag` has been improved dramatically
from quadratic to linear.
`scipy.sparse.linalg` improvements
-----------------------------------
The vendored version of ``SuperLU`` has been updated
`scipy.fft` improvements
-------------------------
The vendored ``pocketfft`` library now supports compiling with ARM neon vector
extensions and has improved thread pool behavior.
`scipy.spatial` improvements
-----------------------------
The python implementation of ``KDTree`` has been dropped and ``KDTree`` is now
implemented in terms of ``cKDTree``. You can now expect ``cKDTree``-like
performance by default. This also means ``sys.setrecursionlimit`` no longer
needs to be increased for querying large trees.
``transform.Rotation`` has been updated with support for Modified Rodrigues
Parameters alongside the existing rotation representations (PR gh-12667).
`scipy.spatial.transform.Rotation` has been partially cythonized, with some
performance improvements observed
`scipy.spatial.distance.cdist` has improved performance with the ``minkowski``
metric, especially for p-norm values of 1 or 2.
`scipy.stats` improvements
---------------------------
New distributions have been added to `scipy.stats`:
- The asymmetric Laplace continuous distribution has been added as
`scipy.stats.laplace_asymmetric`.
- The negative hypergeometric distribution has been added as `scipy.stats.nhypergeom`.
- The multivariate t distribution has been added as `scipy.stats.multivariate_t`.
- The multivariate hypergeometric distribution has been added as `scipy.stats.multivariate_hypergeom`.
The ``fit`` method has been overridden for several distributions (``laplace``,
``pareto``, ``rayleigh``, ``invgauss``, ``logistic``, ``gumbel_l``,
``gumbel_r``); they now use analytical, distribution-specific maximum
likelihood estimation results for greater speed and accuracy than the generic
(numerical optimization) implementation.
The one-sample Cramér-von Mises test has been added as
`scipy.stats.cramervonmises`.
An option to compute one-sided p-values was added to `scipy.stats.ttest_1samp`,
`scipy.stats.ttest_ind_from_stats`, `scipy.stats.ttest_ind` and
`scipy.stats.ttest_rel`.
The function `scipy.stats.kendalltau` now has an option to compute Kendall's
tau-c (also known as Stuart's tau-c), and support has been added for exact
p-value calculations for sample sizes ``> 171``.
`stats.trapz` was renamed to `stats.trapezoid`, with the former name retained
as an alias for backwards compatibility reasons.
The function `scipy.stats.linregress` now includes the standard error of the
intercept in its return value.
The ``_logpdf``, ``_sf``, and ``_isf`` methods have been added to
`scipy.stats.nakagami`; ``_sf`` and ``_isf`` methods also added to
`scipy.stats.gumbel_r`
The ``sf`` method has been added to `scipy.stats.levy` and `scipy.stats.levy_l`
for improved precision.
`scipy.stats.binned_statistic_dd` performance improvements for the following
computed statistics: ``max``, ``min``, ``median``, and ``std``.
We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source
Software for Science program for supporting many of these improvements to
`scipy.stats`.
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
==============================
`scipy` 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. It had been deprecated since SciPy
``1.1``.
`scipy.spatial` changes
------------------------
``cKDTree.query`` now returns 64-bit rather than 32-bit integers on Windows,
making behaviour consistent between platforms (PR gh-12673).
`scipy.stats` changes
----------------------
The ``frechet_l`` and ``frechet_r`` distributions were removed. They were
deprecated since SciPy ``1.0``.
Other changes
=============
``setup_requires`` was removed from ``setup.py``. This means that users
invoking ``python setup.py install`` without having numpy already installed
will now get an error, rather than having numpy installed for them via
``easy_install``. This install method was always fragile and problematic, users
are encouraged to use ``pip`` when installing from source.
- Fixed a bug in `scipy.optimize.dual_annealing` ``accept_reject`` calculation
that caused uphill jumps to be accepted less frequently.
- The time required for (un)pickling of `scipy.stats.rv_continuous`,
`scipy.stats.rv_discrete`, and `scipy.stats.rv_frozen` has been significantly
reduced (gh12550). Inheriting subclasses should note that ``__setstate__`` no
longer calls ``__init__`` upon unpickling.
Authors
=======
* endolith
* vkk800
* aditya +
* George Bateman +
* Christoph Baumgarten
* Peter Bell
* Tobias Biester +
* Keaton J. Burns +
* Evgeni Burovski
* Rüdiger Busche +
* Matthias Bussonnier
* Dominic C +
* Corallus Caninus +
* CJ Carey
* Thomas A Caswell
* chapochn +
* Lucía Cheung
* Zach Colbert +
* Coloquinte +
* Yannick Copin +
* Devin Crowley +
* Terry Davis +
* Michaël Defferrard +
* devonwp +
* Didier +
* divenex +
* Thomas Duvernay +
* Eoghan O'Connell +
* Gökçen Eraslan
* Kristian Eschenburg +
* Ralf Gommers
* Thomas Grainger +
* GreatV +
* Gregory Gundersen +
* h-vetinari +
* Matt Haberland
* Mark Harfouche +
* He He +
* Alex Henrie
* Chun-Ming Huang +
* Martin James McHugh III +
* Alex Izvorski +
* Joey +
* ST John +
* Jonas Jonker +
* Julius Bier Kirkegaard
* Marcin Konowalczyk +
* Konrad0
* Sam Van Kooten +
* Sergey Koposov +
* Peter Mahler Larsen
* Eric Larson
* Antony Lee
* Gregory R. Lee
* Loïc Estève
* Jean-Luc Margot +
* MarkusKoebis +
* Nikolay Mayorov
* G. D. McBain
* Andrew McCluskey +
* Nicholas McKibben
* Sturla Molden
* Denali Molitor +
* Eric Moore
* Shashaank N +
* Prashanth Nadukandi +
* nbelakovski +
* Andrew Nelson
* Nick +
* Nikola Forró +
* odidev
* ofirr +
* Sambit Panda
* Dima Pasechnik
* Tirth Patel +
* Paweł Redzyński +
* Vladimir Philipenko +
* Philipp Thölke +
* Ilhan Polat
* Eugene Prilepin +
* Vladyslav Rachek
* Ram Rachum +
* Tyler Reddy
* Martin Reinecke +
* Simon Segerblom Rex +
* Lucas Roberts
* Benjamin Rowell +
* Eli Rykoff +
* Atsushi Sakai
* Moritz Schulte +
* Daniel B. Smith
* Steve Smith +
* Jan Soedingrekso +
* Victor Stinner +
* Jose Storopoli +
* Diana Sukhoverkhova +
* Søren Fuglede Jørgensen
* taoky +
* Mike Taves +
* Ian Thomas +
* Will Tirone +
* Frank Torres +
* Seth Troisi
* Ronald van Elburg +
* Hugo van Kemenade
* Paul van Mulbregt
* Saul Ivan Rivas Vega +
* Pauli Virtanen
* Jan Vleeshouwers
* Samuel Wallan
* Warren Weckesser
* Ben West +
* Eric Wieser
* WillTirone +
* Levi John Wolf +
* Zhiqing Xiao
* Rory Yorke +
* Yun Wang (Maigo) +
* Egor Zemlyanoy +
* ZhihuiChen0903 +
* Jacob Zhong +
A total of 121 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
Changelog
### 0.29.24
```
====================
Bugs fixed
----------
* Inline functions in pxd files that used memory views could lead to invalid
C code if the module that imported from them does not use memory views.
Patch by David Woods. (Github issue :issue:`1415`)
* Several declarations in ``libcpp.string`` were added and corrected.
Patch by Janek Bevendorff. (Github issue :issue:`4268`)
* Pickling unbound Cython compiled methods failed.
Patch by Pierre Glaser. (Github issue :issue:`2972`)
* The tracing code was adapted to work with CPython 3.10.
* The optimised ``in`` operator failed on unicode strings in Py3.9 and later
that were constructed from an external ``wchar_t`` source.
Also, related C compiler warnings about deprecated C-API usage were resolved.
(Github issue :issue:`3925`)
* Some compiler crashes were resolved.
Patch by David Woods. (Github issues :issue:`4214`, :issue:`2811`)
* An incorrect warning about 'unused' generator expressions was removed.
(GIthub issue :issue:`1699`)
* The attributes ``gen.gi_frame`` and ``coro.cr_frame`` of Cython compiled
generators and coroutines now return an actual frame object for introspection,
instead of ``None``.
(Github issue :issue:`2306`)
```
### 0.29.23
```
====================
Bugs fixed
----------
* Some problems with Python 3.10 were resolved.
Patches by Victor Stinner and David Woods. (Github issues :issue:`4046`, :issue:`4100`)
* An incorrect "optimisation" was removed that allowed changes to a keyword
dict to leak into keyword arguments passed into a function.
Patch by Peng Weikang. (Github issue :issue:`3227`)
* Multiplied str constants could end up as bytes constants with language_level=2.
Patch by Alphadelta14 and David Woods. (Github issue :issue:`3951`)
* ``PY_SSIZE_T_CLEAN`` does not get defined any more if it is already defined.
Patch by Andrew Jones. (Github issue :issue:`4104`)
```
Links
- PyPI: https://pypi.org/project/cython
- Changelog: https://pyup.io/changelogs/cython/
- Homepage: http://cython.org/
Changelog
### 3.1.1
```
==========================
.. warning:: This is the last release supporting Python 2.
* Fix typo in Requires-Python package metadata.
* Regenerate C sources with Cython 0.29.24.
```
### 3.1.0
```
==========================
.. warning:: This is the last release supporting Python 2.
* New features:
+ `mpi4py.util`: New package collecting miscellaneous utilities.
* Enhancements:
+ Add pickle-based ``Request.waitsome()`` and ``Request.testsome()``.
+ Add lowercase methods ``Request.get_status()`` and ``Request.cancel()``.
+ Support for passing Python GPU arrays compliant with the `DLPack`_ data
interchange mechanism (`link <DIM_>`_) and the ``__cuda_array_interface__``
(CAI) standard (`link <CAI_>`_) to uppercase methods. This support requires
that mpi4py is built against `CUDA-aware MPI <CAM_>`_ implementations. This
feature is currently experimental and subject to future changes.
+ `mpi4py.futures`: Add support for initializers and canceling futures at shutdown.
Environment variables names now follow the pattern ``MPI4PY_FUTURES_*``, the
previous ``MPI4PY_*`` names are deprecated.
+ Add type annotations to Cython code. The first line of the docstring of functions
and methods displays a signature including type annotations.
+ Add companion stub files to support type checkers.
+ Support for weak references.
* Miscellaneous:
+ Add a new mpi4py publication (`link <DOI_>`_) to the citation listing.
.. _DLPack: https://github.com/dmlc/dlpack
.. _DIM: https://data-apis.org/array-api/latest/design_topics/data_interchange.html
.. _CAI: https://numba.readthedocs.io/en/stable/cuda/cuda_array_interface.html
.. _CAM: https://developer.nvidia.com/blog/introduction-cuda-aware-mpi/
.. _DOI: https://doi.org/10.1109/MCSE.2021.3083216
```
### 3.0.3
```
==========================
* Regenerate Cython wrappers to support Python 3.8.
```
### 3.0.2
```
==========================
* Bug fixes:
+ Fix handling of readonly buffers in support for Python 2 legacy
buffer interface. The issue triggers only when using a buffer-like
object that is readonly and does not export the new Python 3
buffer interface.
+ Fix build issues with Open MPI 4.0.x series related to removal of
many MPI-1 symbols deprecated in MPI-2 and removed in MPI-3.
+ Minor documentation fixes.
```
### 3.0.1
```
==========================
* Bug fixes:
+ Fix ``Comm.scatter()`` and other collectives corrupting input send
list. Add safety measures to prevent related issues in global
reduction operations.
+ Fix error-checking code for counts in ``Op.Reduce_local()``.
* Enhancements:
+ Map size-specific Python/NumPy typecodes to MPI datatypes.
+ Allow partial specification of target list/tuple arguments in the
various ``Win`` RMA methods.
+ Workaround for removal of ``MPI_{LB|UB}`` in Open MPI 4.0.
+ Support for Microsoft MPI v10.0.
```
Links
- PyPI: https://pypi.org/project/mpi4py
- Changelog: https://pyup.io/changelogs/mpi4py/
- Repo: https://github.com/mpi4py/mpi4py/releases/download/3.1.2/mpi4py-3.1.2.tar.gz
Changelog
### 5.0.0
```
- meshio now only provides one command-line tool, `meshio`, with subcommands like
`info`, `convert`, etc. This replaces the former `meshio-info`, `meshio-convert` etc.
```
### 4.4.0
```
- Polygons are now stored as `"polygon"` cell blocks, not `"polygonN"` (where `N` is the
number of nodes per polygon). One can simply retrieve the number of points via
`cellblock.data.shape[1]`.
```
### 4.0.0
```
- `mesh.cells` used to be a dictionary of the form
python
{
"triangle": [[0, 1, 2], [0, 2, 3]],
"quad": [[0, 7, 1, 10], ...]
}
From 4.0.0 on, `mesh.cells` is a list of tuples,
python
[
("triangle", [[0, 1, 2], [0, 2, 3]]),
("quad", [[0, 7, 1, 10], ...])
]
This has the advantage that multiple blocks of the same cell type can be accounted
for. Also, cell ordering can be preserved.
You can now use the method `mesh.get_cells_type("triangle")` to get all cells of
`"triangle"` type, or use `mesh.cells_dict` to build the old dictionary structure.
- `mesh.cell_data` used to be a dictionary of the form
python
{
"triangle": {"a": [0.5, 1.3], "b": [2.17, 41.3]},
"quad": {"a": [1.1, -0.3, ...], "b": [3.14, 1.61, ...]},
}
From 4.0.0 on, `mesh.cell_data` is a dictionary of lists,
python
{
"a": [[0.5, 1.3], [1.1, -0.3, ...]],
"b": [[2.17, 41.3], [3.14, 1.61, ...]],
}
Each data list, e.g., `mesh.cell_data["a"]`, can be `zip`ped with `mesh.cells`.
An old-style `cell_data` dictionary can be retrieved via `mesh.cell_data_dict`.
```
Links
- PyPI: https://pypi.org/project/meshio
- Changelog: https://pyup.io/changelogs/meshio/
- Repo: https://github.com/nschloe/meshio
Changelog
### 0.16.3
```
Changed
- Fixed computation of `genus` and `euler_characteristic`
```
### 0.16.0
```
Changed
- `mesh.cells` is now a function; e.g., `mesh.cells["points"]` is now
`mesh.cells("points")`
- `mesh.idx_hierarchy` is deprecated in favor of `mesh.idx[-1]` (the new `idx` list
contains more index magic)
```
### 0.14.0
```
Changed
- `node_coords` is now `points`
- `mesh_tri`: fixed inconsistent state after setting the points
```
Links
- PyPI: https://pypi.org/project/meshplex
- Changelog: https://pyup.io/changelogs/meshplex/
- Repo: https://github.com/nschloe/meshplex
Update numpy from 1.19.5 to 1.21.4.
The bot wasn't able to find a changelog for this release. Got an idea?
Links
- PyPI: https://pypi.org/project/numpy - Homepage: https://www.numpy.orgUpdate scipy from 1.5.4 to 1.7.2.
Changelog
### 1.7.1 ``` compared to `1.7.0`. Authors ======= * Peter Bell * Evgeni Burovski * Justin Charlong + * Ralf Gommers * Matti Picus * Tyler Reddy * Pamphile Roy * Sebastian Wallkötter * Arthur Volant A total of 9 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. ``` ### 1.7.0 ``` 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 1.7.x branch, and on adding new features on the master branch. This release requires Python `3.7+` and NumPy `1.16.5` or greater. For running on PyPy, PyPy3 `6.0+` is required. Highlights of this release - 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 other NumFOCUS packages like NumPy. - 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. *We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source Software for Science program for supporting many of the improvements to* `scipy.stats`. New features `scipy.cluster` improvements An optional argument, ``seed``, has been added to ``kmeans`` and ``kmeans2`` to set the random generator and random state. `scipy.interpolate` improvements Improved input validation and error messages for ``fitpack.bispev`` and ``fitpack.parder`` for scenarios that previously caused substantial confusion for users. The class `RBFInterpolator` was added to supersede the `Rbf` class. The new class has usage that more closely follows other interpolator classes, corrects sign errors that caused unexpected smoothing behavior, includes polynomial terms in the interpolant (which are necessary for some RBF choices), and supports interpolation using only the k-nearest neighbors for memory efficiency. `scipy.linalg` improvements An LAPACK wrapper was added for access to the ``tgexc`` subroutine. `scipy.ndimage` improvements `scipy.ndimage.affine_transform` is now able to infer the ``output_shape`` from the ``out`` array. `scipy.optimize` improvements The optional parameter ``bounds`` was added to ``_minimize_neldermead`` to support bounds constraints for the Nelder-Mead solver. ``trustregion`` methods ``trust-krylov``, ``dogleg`` and ``trust-ncg`` can now estimate ``hess`` by finite difference using one of ``["2-point", "3-point", "cs"]``. ``halton`` was added as a ``sampling_method`` in `scipy.optimize.shgo`. ``sobol`` was fixed and is now using `scipy.stats.qmc.Sobol`. ``halton`` and ``sobol`` were added as ``init`` methods in `scipy.optimize.differential_evolution.` ``differential_evolution`` now accepts an ``x0`` parameter to provide an initial guess for the minimization. ``least_squares`` has a modest performance improvement when SciPy is built with Pythran transpiler enabled. When ``linprog`` is used with ``method`` ``'highs'``, ``'highs-ipm'``, or ``'highs-ds'``, the result object now reports the marginals (AKA shadow prices, dual values) and residuals associated with each constraint. `scipy.signal` improvements ``get_window`` supports ``general_cosine`` and ``general_hamming`` window functions. `scipy.signal.medfilt2d` now releases the GIL where appropriate to enable performance gains via multithreaded calculations. `scipy.sparse` improvements Addition of ``dia_matrix`` sparse matrices is now faster. `scipy.spatial` improvements ``distance.cdist`` and ``distance.pdist`` performance has greatly improved for certain weighted metrics. Namely: ``minkowski``, ``euclidean``, ``chebyshev``, ``canberra``, and ``cityblock``. Modest performance improvements for many of the unweighted ``cdist`` and ``pdist`` metrics noted above. The parameter ``seed`` was added to `scipy.spatial.vq.kmeans` and `scipy.spatial.vq.kmeans2`. The parameters ``axis`` and ``keepdims`` where added to `scipy.spatial.distance.jensenshannon`. The ``rotation`` methods ``from_rotvec`` and ``as_rotvec`` now accept a ``degrees`` argument to specify usage of degrees instead of radians. `scipy.special` improvements Wright's generalized Bessel function for positive arguments was added as `scipy.special.wright_bessel.` An implementation of the inverse of the Log CDF of the Normal Distribution is now available via `scipy.special.ndtri_exp`. `scipy.stats` improvements Hypothesis Tests The Mann-Whitney-Wilcoxon test, ``mannwhitneyu``, has been rewritten. It now supports n-dimensional input, an exact test method when there are no ties, and improved documentation. Please see "Other changes" for adjustments to default behavior. The new function `scipy.stats.binomtest` replaces `scipy.stats.binom_test`. The new function returns an object that calculates a confidence intervals of the proportion parameter. Also, performance was improved from O(n) to O(log(n)) by using binary search. The two-sample version of the Cramer-von Mises test is implemented in `scipy.stats.cramervonmises_2samp`. The Alexander-Govern test is implemented in the new function `scipy.stats.alexandergovern`. The new functions `scipy.stats.barnard_exact` and `scipy.stats. boschloo_exact` respectively perform Barnard's exact test and Boschloo's exact test for 2x2 contingency tables. The new function `scipy.stats.page_trend_test` performs Page's test for ordered alternatives. The new function `scipy.stats.somersd` performs Somers' D test for ordinal association between two variables. An option, ``permutations``, has been added in `scipy.stats.ttest_ind` to perform permutation t-tests. A ``trim`` option was also added to perform a trimmed (Yuen's) t-test. The ``alternative`` parameter was added to the ``skewtest``, ``kurtosistest``, ``ranksums``, ``mood``, ``ansari``, ``linregress``, and ``spearmanr`` functions to allow one-sided hypothesis testing. Sample statistics The new function `scipy.stats.differential_entropy` estimates the differential entropy of a continuous distribution from a sample. The ``boxcox`` and ``boxcox_normmax`` now allow the user to control the optimizer used to minimize the negative log-likelihood function. A new function `scipy.stats.contingency.relative_risk` calculates the relative risk, or risk ratio, of a 2x2 contingency table. The object returned has a method to compute the confidence interval of the relative risk. Performance improvements in the ``skew`` and ``kurtosis`` functions achieved by removal of repeated/redundant calculations. Substantial performance improvements in `scipy.stats.mstats.hdquantiles_sd`. The new function `scipy.stats.contingency.association` computes several measures of association for a contingency table: Pearsons contingency coefficient, Cramer's V, and Tschuprow's T. The parameter ``nan_policy`` was added to `scipy.stats.zmap` to provide options for handling the occurrence of ``nan`` in the input data. The parameter ``ddof`` was added to `scipy.stats.variation` and `scipy.stats.mstats.variation`. The parameter ``weights`` was added to `scipy.stats.gmean`. Statistical Distributions We now vendor and leverage the Boost C++ library to address a number of previously reported issues in ``stats``. Notably, ``beta``, ``binom``, ``nbinom`` now have Boost backends, and it is straightforward to leverage the backend for additional functions. The skew Cauchy probability distribution has been implemented as `scipy.stats.skewcauchy`. The Zipfian probability distribution has been implemented as `scipy.stats.zipfian`. The new distributions ``nchypergeom_fisher`` and ``nchypergeom_wallenius`` implement the Fisher and Wallenius versions of the noncentral hypergeometric distribution, respectively. The generalized hyperbolic distribution was added in `scipy.stats.genhyperbolic`. The studentized range distribution was added in `scipy.stats.studentized_range`. `scipy.stats.argus` now has improved handling for small parameter values. Better argument handling/preparation has resulted in performance improvements for many distributions. The ``cosine`` distribution has added ufuncs for ``ppf``, ``cdf``, ``sf``, and ``isf`` methods including numerical precision improvements at the edges of the support of the distribution. An option to fit the distribution to data by the method of moments has been added to the ``fit`` method of the univariate continuous distributions. Other `scipy.stats.bootstrap` has been added to allow estimation of the confidence interval and standard error of a statistic. The new function `scipy.stats.contingency.crosstab` computes a contingency table (i.e. a table of counts of unique entries) for the given data. `scipy.stats.NumericalInverseHermite` enables fast random variate sampling and percentile point function evaluation of an arbitrary univariate statistical distribution. New `scipy.stats.qmc` module This new module provides Quasi-Monte Carlo (QMC) generators and associated helper functions. It provides a generic class `scipy.stats.qmc.QMCEngine` which defines a QMC engine/sampler. An engine is state aware: it can be continued, advanced and reset. 3 base samplers are available: - `scipy.stats.qmc.Sobol` the well known Sobol low discrepancy sequence. Several warnings have been added to guide the user into properly using this sampler. The sequence is scrambled by default. - `scipy.stats.qmc.Halton`: Halton low discrepancy sequence. The sequence is scrambled by default. - `scipy.stats.qmc.LatinHypercube`: plain LHS design. And 2 special samplers are available: - `scipy.stats.qmc.MultinomialQMC`: sampling from a multinomial distribution using any of the base `scipy.stats.qmc.QMCEngine`. - `scipy.stats.qmc.MultivariateNormalQMC`: sampling from a multivariate Normal using any of the base `scipy.stats.qmc.QMCEngine`. The module also provide the following helpers: - `scipy.stats.qmc.discrepancy`: assess the quality of a set of points in terms of space coverage. - `scipy.stats.qmc.update_discrepancy`: can be used in an optimization loop to construct a good set of points. - `scipy.stats.qmc.scale`: easily scale a set of points from (to) the unit interval to (from) a given range. Deprecated features `scipy.linalg` deprecations - `scipy.linalg.pinv2` is deprecated and its functionality is completely subsumed into `scipy.linalg.pinv` - Both ``rcond``, ``cond`` keywords of `scipy.linalg.pinv` and `scipy.linalg.pinvh` were not working and now are deprecated. They are now replaced with functioning ``atol`` and ``rtol`` keywords with clear usage. `scipy.spatial` deprecations - `scipy.spatial.distance` metrics expect 1d input vectors but will call ``np.squeeze`` on their inputs to accept any extra length-1 dimensions. That behaviour is now deprecated. Backwards incompatible changes Other changes We now accept and leverage performance improvements from the ahead-of-time Python-to-C++ transpiler, Pythran, which can be optionally disabled (via ``export SCIPY_USE_PYTHRAN=0``) but is enabled by default at build time. There are two changes to the default behavior of `scipy.stats.mannwhitenyu`: - For years, use of the default ``alternative=None`` was deprecated; explicit ``alternative`` specification was required. Use of the new default value of ``alternative``, "two-sided", is now permitted. - Previously, all p-values were based on an asymptotic approximation. Now, for small samples without ties, the p-values returned are exact by default. Support has been added for PEP 621 (project metadata in ``pyproject.toml``) We now support a Gitpod environment to reduce the barrier to entry for SciPy development; for more details see `quickstart-gitpod`. Authors * endolith * Jelle Aalbers + * Adam + * Tania Allard + * Sven Baars + * Max Balandat + * baumgarc + * Christoph Baumgarten * Peter Bell * Lilian Besson * Robinson Besson + * Max Bolingbroke * Blair Bonnett + * Jordão Bragantini * Harm Buisman + * Evgeni Burovski * Matthias Bussonnier * Dominic C * CJ Carey * Ramón Casero + * Chachay + * charlotte12l + * Benjamin Curtice Corbett + * Falcon Dai + * Ian Dall + * Terry Davis * droussea2001 + * DWesl + * dwight200 + * Thomas J. Fan + * Joseph Fox-Rabinovitz * Max Frei + * Laura Gutierrez Funderburk + * gbonomib + * Matthias Geier + * Pradipta Ghosh + * Ralf Gommers * Evan H + * h-vetinari * Matt Haberland * Anselm Hahn + * Alex Henrie * Piet Hessenius + * Trever Hines + * Elisha Hollander + * Stephan Hoyer * Tom Hu + * Kei Ishikawa + * Julien Jerphanion * Robert Kern * Shashank KS + * Peter Mahler Larsen * Eric Larson * Cheng H. Lee + * Gregory R. Lee * Jean-Benoist Leger + * lgfunderburk + * liam-o-marsh + * Xingyu Liu + * Alex Loftus + * Christian Lorentzen + * Cong Ma * Marc + * MarkPundurs + * Markus Löning + * Liam Marsh + * Nicholas McKibben * melissawm + * Jamie Morton * Andrew Nelson * Nikola Forró * Tor Nordam + * Olivier Gauthé + * Rohit Pandey + * Avanindra Kumar Pandeya + * Tirth Patel * paugier + * Alex H. Wagner, PhD + * Jeff Plourde + * Ilhan Polat * pranavrajpal + * Vladyslav Rachek * Bharat Raghunathan * Recursing + * Tyler Reddy * Lucas Roberts * Gregor Robinson + * Pamphile Roy + * Atsushi Sakai * Benjamin Santos * Martin K. Scherer + * Thomas Schmelzer + * Daniel Scott + * Sebastian Wallkötter + * serge-sans-paille + * Namami Shanker + * Masashi Shibata + * Alexandre de Siqueira + * Albert Steppi + * Adam J. Stewart + * Kai Striega * Diana Sukhoverkhova * Søren Fuglede Jørgensen * Mike Taves * Dan Temkin + * Nicolas Tessore + * tsubota20 + * Robert Uhl * christos val + * Bas van Beek + * Ashutosh Varma + * Jose Vazquez + * Sebastiano Vigna * Aditya Vijaykumar * VNMabus * Arthur Volant + * Samuel Wallan * Stefan van der Walt * Warren Weckesser * Anreas Weh * Josh Wilson * Rory Yorke * Egor Zemlyanoy * Marc Zoeller + * zoj613 + * 秋纫 + A total of 126 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. ``` ### 1.6.3 ``` compared to `1.6.2`. Authors ====== * Peter Bell * Ralf Gommers * Matt Haberland * Peter Mahler Larsen * Tirth Patel * Tyler Reddy * Pamphile ROY + * Xingyu Liu + A total of 8 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. ``` ### 1.6.2 ``` compared to `1.6.1`. This is also the first SciPy release to place upper bounds on some dependencies to improve the long-term repeatability of source builds. Authors ======= * Pradipta Ghosh + * Tyler Reddy * Ralf Gommers * Martin K. Scherer + * Robert Uhl * Warren Weckesser A total of 6 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. ``` ### 1.6.1 ``` compared to `1.6.0`. Please note that for SciPy wheels to correctly install with pip on macOS 11, pip `>= 20.3.3` is needed. Authors ======= * Peter Bell * Evgeni Burovski * CJ Carey * Ralf Gommers * Peter Mahler Larsen * Cheng H. Lee + * Cong Ma * Nicholas McKibben * Nikola Forró * Tyler Reddy * Warren Weckesser A total of 11 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. ``` ### 1.6.0 ``` 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 1.6.x branch, and on adding new features on the master branch. This release requires Python `3.7`+ and NumPy `1.16.5` or greater. For running on PyPy, PyPy3 `6.0`+ is required. 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 New features ============ `scipy.special` improvements ----------------------------- `scipy.special` now has improved support for 64-bit ``LAPACK`` backend `scipy.odr` improvements ------------------------- `scipy.odr` now has support for 64-bit integer ``BLAS`` `scipy.odr.ODR` has gained an optional ``overwrite`` argument so that existing files may be overwritten. `scipy.integrate` improvements ------------------------------- Some renames of functions with poor names were done, with the old names retained without being in the reference guide for backwards compatibility reasons: - ``integrate.simps`` was renamed to ``integrate.simpson`` - ``integrate.trapz`` was renamed to ``integrate.trapezoid`` - ``integrate.cumtrapz`` was renamed to ``integrate.cumulative_trapezoid`` `scipy.cluster` improvements ------------------------------- `scipy.cluster.hierarchy.DisjointSet` has been added for incremental connectivity queries. `scipy.cluster.hierarchy.dendrogram` return value now also includes leaf color information in `leaves_color_list`. `scipy.interpolate` improvements --------------------------------- `scipy.interpolate.interp1d` has a new method ``nearest-up``, similar to the existing method ``nearest`` but rounds half-integers up instead of down. `scipy.io` improvements ------------------------ Support has been added for reading arbitrary bit depth integer PCM WAV files from 1- to 32-bit, including the commonly-requested 24-bit depth. `scipy.linalg` improvements ---------------------------- The new function `scipy.linalg.matmul_toeplitz` uses the FFT to compute the product of a Toeplitz matrix with another matrix. `scipy.linalg.sqrtm` and `scipy.linalg.logm` have performance improvements thanks to additional Cython code. Python ``LAPACK`` wrappers have been added for ``pptrf``, ``pptrs``, ``ppsv``, ``pptri``, and ``ppcon``. `scipy.linalg.norm` and the ``svd`` family of functions will now use 64-bit integer backends when available. `scipy.ndimage` improvements ----------------------------- `scipy.ndimage.convolve`, `scipy.ndimage.correlate` and their 1d counterparts now accept both complex-valued images and/or complex-valued filter kernels. All convolution-based filters also now accept complex-valued inputs (e.g. ``gaussian_filter``, ``uniform_filter``, etc.). Multiple fixes and enhancements to boundary handling were introduced to `scipy.ndimage` interpolation functions (i.e. ``affine_transform``, ``geometric_transform``, ``map_coordinates``, ``rotate``, ``shift``, ``zoom``). A new boundary mode, ``grid-wrap`` was added which wraps images periodically, using a period equal to the shape of the input image grid. This is in contrast to the existing ``wrap`` mode which uses a period that is one sample smaller than the original signal extent along each dimension. A long-standing bug in the ``reflect`` boundary condition has been fixed and the mode ``grid-mirror`` was introduced as a synonym for ``reflect``. A new boundary mode, ``grid-constant`` is now available. This is similar to the existing ndimage ``constant`` mode, but interpolation will still performed at coordinate values outside of the original image extent. This ``grid-constant`` mode is consistent with OpenCV's ``BORDER_CONSTANT`` mode and scikit-image's ``constant`` mode. Spline pre-filtering (used internally by ``ndimage`` interpolation functions when ``order >= 2``), now supports all boundary modes rather than always defaulting to mirror boundary conditions. The standalone functions ``spline_filter`` and ``spline_filter1d`` have analytical boundary conditions that match modes ``mirror``, ``grid-wrap`` and ``reflect``. `scipy.ndimage` interpolation functions now accept complex-valued inputs. In this case, the interpolation is applied independently to the real and imaginary components. The ``ndimage`` tutorials (https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been updated with new figures to better clarify the exact behavior of all of the interpolation boundary modes. `scipy.ndimage.zoom` now has a ``grid_mode`` option that changes the coordinate of the center of the first pixel along an axis from 0 to 0.5. This allows resizing in a manner that is consistent with the behavior of scikit-image's ``resize`` and ``rescale`` functions (and OpenCV's ``cv2.resize``). `scipy.optimize` improvements ------------------------------ `scipy.optimize.linprog` has fast, new methods for large, sparse problems from the ``HiGHS`` C++ library. ``method='highs-ds'`` uses a high performance dual revised simplex implementation (HSOL), ``method='highs-ipm'`` uses an interior-point method with crossover, and ``method='highs'`` chooses between the two automatically. These methods are typically much faster and often exceed the accuracy of other ``linprog`` methods, so we recommend explicitly specifying one of these three method values when using ``linprog``. `scipy.optimize.quadratic_assignment` has been added for approximate solution of the quadratic assignment problem. `scipy.optimize.linear_sum_assignment` now has a substantially reduced overhead for small cost matrix sizes `scipy.optimize.least_squares` has improved performance when the user provides the jacobian as a sparse jacobian already in ``csr_matrix`` format `scipy.optimize.linprog` now has an ``rr_method`` argument for specification of the method used for redundancy handling, and a new method for this purpose is available based on the interpolative decomposition approach. `scipy.signal` improvements ---------------------------- `scipy.signal.gammatone` has been added to design FIR or IIR filters that model the human auditory system. `scipy.signal.iircomb` has been added to design IIR peaking/notching comb filters that can boost/attenuate a frequency from a signal. `scipy.signal.sosfilt` performance has been improved to avoid some previously- observed slowdowns `scipy.signal.windows.taylor` has been added--the Taylor window function is commonly used in radar digital signal processing `scipy.signal.gauss_spline` now supports ``list`` type input for consistency with other related SciPy functions `scipy.signal.correlation_lags` has been added to allow calculation of the lag/ displacement indices array for 1D cross-correlation. `scipy.sparse` improvements ---------------------------- A solver for the minimum weight full matching problem for bipartite graphs, also known as the linear assignment problem, has been added in `scipy.sparse.csgraph.min_weight_full_bipartite_matching`. In particular, this provides functionality analogous to that of `scipy.optimize.linear_sum_assignment`, but with improved performance for sparse inputs, and the ability to handle inputs whose dense representations would not fit in memory. The time complexity of `scipy.sparse.block_diag` has been improved dramatically from quadratic to linear. `scipy.sparse.linalg` improvements ----------------------------------- The vendored version of ``SuperLU`` has been updated `scipy.fft` improvements ------------------------- The vendored ``pocketfft`` library now supports compiling with ARM neon vector extensions and has improved thread pool behavior. `scipy.spatial` improvements ----------------------------- The python implementation of ``KDTree`` has been dropped and ``KDTree`` is now implemented in terms of ``cKDTree``. You can now expect ``cKDTree``-like performance by default. This also means ``sys.setrecursionlimit`` no longer needs to be increased for querying large trees. ``transform.Rotation`` has been updated with support for Modified Rodrigues Parameters alongside the existing rotation representations (PR gh-12667). `scipy.spatial.transform.Rotation` has been partially cythonized, with some performance improvements observed `scipy.spatial.distance.cdist` has improved performance with the ``minkowski`` metric, especially for p-norm values of 1 or 2. `scipy.stats` improvements --------------------------- New distributions have been added to `scipy.stats`: - The asymmetric Laplace continuous distribution has been added as `scipy.stats.laplace_asymmetric`. - The negative hypergeometric distribution has been added as `scipy.stats.nhypergeom`. - The multivariate t distribution has been added as `scipy.stats.multivariate_t`. - The multivariate hypergeometric distribution has been added as `scipy.stats.multivariate_hypergeom`. The ``fit`` method has been overridden for several distributions (``laplace``, ``pareto``, ``rayleigh``, ``invgauss``, ``logistic``, ``gumbel_l``, ``gumbel_r``); they now use analytical, distribution-specific maximum likelihood estimation results for greater speed and accuracy than the generic (numerical optimization) implementation. The one-sample Cramér-von Mises test has been added as `scipy.stats.cramervonmises`. An option to compute one-sided p-values was added to `scipy.stats.ttest_1samp`, `scipy.stats.ttest_ind_from_stats`, `scipy.stats.ttest_ind` and `scipy.stats.ttest_rel`. The function `scipy.stats.kendalltau` now has an option to compute Kendall's tau-c (also known as Stuart's tau-c), and support has been added for exact p-value calculations for sample sizes ``> 171``. `stats.trapz` was renamed to `stats.trapezoid`, with the former name retained as an alias for backwards compatibility reasons. The function `scipy.stats.linregress` now includes the standard error of the intercept in its return value. The ``_logpdf``, ``_sf``, and ``_isf`` methods have been added to `scipy.stats.nakagami`; ``_sf`` and ``_isf`` methods also added to `scipy.stats.gumbel_r` The ``sf`` method has been added to `scipy.stats.levy` and `scipy.stats.levy_l` for improved precision. `scipy.stats.binned_statistic_dd` performance improvements for the following computed statistics: ``max``, ``min``, ``median``, and ``std``. We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source Software for Science program for supporting many of these improvements to `scipy.stats`. 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 ============================== `scipy` 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. It had been deprecated since SciPy ``1.1``. `scipy.spatial` changes ------------------------ ``cKDTree.query`` now returns 64-bit rather than 32-bit integers on Windows, making behaviour consistent between platforms (PR gh-12673). `scipy.stats` changes ---------------------- The ``frechet_l`` and ``frechet_r`` distributions were removed. They were deprecated since SciPy ``1.0``. Other changes ============= ``setup_requires`` was removed from ``setup.py``. This means that users invoking ``python setup.py install`` without having numpy already installed will now get an error, rather than having numpy installed for them via ``easy_install``. This install method was always fragile and problematic, users are encouraged to use ``pip`` when installing from source. - Fixed a bug in `scipy.optimize.dual_annealing` ``accept_reject`` calculation that caused uphill jumps to be accepted less frequently. - The time required for (un)pickling of `scipy.stats.rv_continuous`, `scipy.stats.rv_discrete`, and `scipy.stats.rv_frozen` has been significantly reduced (gh12550). Inheriting subclasses should note that ``__setstate__`` no longer calls ``__init__`` upon unpickling. Authors ======= * endolith * vkk800 * aditya + * George Bateman + * Christoph Baumgarten * Peter Bell * Tobias Biester + * Keaton J. Burns + * Evgeni Burovski * Rüdiger Busche + * Matthias Bussonnier * Dominic C + * Corallus Caninus + * CJ Carey * Thomas A Caswell * chapochn + * Lucía Cheung * Zach Colbert + * Coloquinte + * Yannick Copin + * Devin Crowley + * Terry Davis + * Michaël Defferrard + * devonwp + * Didier + * divenex + * Thomas Duvernay + * Eoghan O'Connell + * Gökçen Eraslan * Kristian Eschenburg + * Ralf Gommers * Thomas Grainger + * GreatV + * Gregory Gundersen + * h-vetinari + * Matt Haberland * Mark Harfouche + * He He + * Alex Henrie * Chun-Ming Huang + * Martin James McHugh III + * Alex Izvorski + * Joey + * ST John + * Jonas Jonker + * Julius Bier Kirkegaard * Marcin Konowalczyk + * Konrad0 * Sam Van Kooten + * Sergey Koposov + * Peter Mahler Larsen * Eric Larson * Antony Lee * Gregory R. Lee * Loïc Estève * Jean-Luc Margot + * MarkusKoebis + * Nikolay Mayorov * G. D. McBain * Andrew McCluskey + * Nicholas McKibben * Sturla Molden * Denali Molitor + * Eric Moore * Shashaank N + * Prashanth Nadukandi + * nbelakovski + * Andrew Nelson * Nick + * Nikola Forró + * odidev * ofirr + * Sambit Panda * Dima Pasechnik * Tirth Patel + * Paweł Redzyński + * Vladimir Philipenko + * Philipp Thölke + * Ilhan Polat * Eugene Prilepin + * Vladyslav Rachek * Ram Rachum + * Tyler Reddy * Martin Reinecke + * Simon Segerblom Rex + * Lucas Roberts * Benjamin Rowell + * Eli Rykoff + * Atsushi Sakai * Moritz Schulte + * Daniel B. Smith * Steve Smith + * Jan Soedingrekso + * Victor Stinner + * Jose Storopoli + * Diana Sukhoverkhova + * Søren Fuglede Jørgensen * taoky + * Mike Taves + * Ian Thomas + * Will Tirone + * Frank Torres + * Seth Troisi * Ronald van Elburg + * Hugo van Kemenade * Paul van Mulbregt * Saul Ivan Rivas Vega + * Pauli Virtanen * Jan Vleeshouwers * Samuel Wallan * Warren Weckesser * Ben West + * Eric Wieser * WillTirone + * Levi John Wolf + * Zhiqing Xiao * Rory Yorke + * Yun Wang (Maigo) + * Egor Zemlyanoy + * ZhihuiChen0903 + * Jacob Zhong + A total of 121 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.orgUpdate Cython from 0.29.22 to 0.29.24.
Changelog
### 0.29.24 ``` ==================== Bugs fixed ---------- * Inline functions in pxd files that used memory views could lead to invalid C code if the module that imported from them does not use memory views. Patch by David Woods. (Github issue :issue:`1415`) * Several declarations in ``libcpp.string`` were added and corrected. Patch by Janek Bevendorff. (Github issue :issue:`4268`) * Pickling unbound Cython compiled methods failed. Patch by Pierre Glaser. (Github issue :issue:`2972`) * The tracing code was adapted to work with CPython 3.10. * The optimised ``in`` operator failed on unicode strings in Py3.9 and later that were constructed from an external ``wchar_t`` source. Also, related C compiler warnings about deprecated C-API usage were resolved. (Github issue :issue:`3925`) * Some compiler crashes were resolved. Patch by David Woods. (Github issues :issue:`4214`, :issue:`2811`) * An incorrect warning about 'unused' generator expressions was removed. (GIthub issue :issue:`1699`) * The attributes ``gen.gi_frame`` and ``coro.cr_frame`` of Cython compiled generators and coroutines now return an actual frame object for introspection, instead of ``None``. (Github issue :issue:`2306`) ``` ### 0.29.23 ``` ==================== Bugs fixed ---------- * Some problems with Python 3.10 were resolved. Patches by Victor Stinner and David Woods. (Github issues :issue:`4046`, :issue:`4100`) * An incorrect "optimisation" was removed that allowed changes to a keyword dict to leak into keyword arguments passed into a function. Patch by Peng Weikang. (Github issue :issue:`3227`) * Multiplied str constants could end up as bytes constants with language_level=2. Patch by Alphadelta14 and David Woods. (Github issue :issue:`3951`) * ``PY_SSIZE_T_CLEAN`` does not get defined any more if it is already defined. Patch by Andrew Jones. (Github issue :issue:`4104`) ```Links
- PyPI: https://pypi.org/project/cython - Changelog: https://pyup.io/changelogs/cython/ - Homepage: http://cython.org/Update mpi4py from 3.0.0 to 3.1.2.
Changelog
### 3.1.1 ``` ========================== .. warning:: This is the last release supporting Python 2. * Fix typo in Requires-Python package metadata. * Regenerate C sources with Cython 0.29.24. ``` ### 3.1.0 ``` ========================== .. warning:: This is the last release supporting Python 2. * New features: + `mpi4py.util`: New package collecting miscellaneous utilities. * Enhancements: + Add pickle-based ``Request.waitsome()`` and ``Request.testsome()``. + Add lowercase methods ``Request.get_status()`` and ``Request.cancel()``. + Support for passing Python GPU arrays compliant with the `DLPack`_ data interchange mechanism (`link <DIM_>`_) and the ``__cuda_array_interface__`` (CAI) standard (`link <CAI_>`_) to uppercase methods. This support requires that mpi4py is built against `CUDA-aware MPI <CAM_>`_ implementations. This feature is currently experimental and subject to future changes. + `mpi4py.futures`: Add support for initializers and canceling futures at shutdown. Environment variables names now follow the pattern ``MPI4PY_FUTURES_*``, the previous ``MPI4PY_*`` names are deprecated. + Add type annotations to Cython code. The first line of the docstring of functions and methods displays a signature including type annotations. + Add companion stub files to support type checkers. + Support for weak references. * Miscellaneous: + Add a new mpi4py publication (`link <DOI_>`_) to the citation listing. .. _DLPack: https://github.com/dmlc/dlpack .. _DIM: https://data-apis.org/array-api/latest/design_topics/data_interchange.html .. _CAI: https://numba.readthedocs.io/en/stable/cuda/cuda_array_interface.html .. _CAM: https://developer.nvidia.com/blog/introduction-cuda-aware-mpi/ .. _DOI: https://doi.org/10.1109/MCSE.2021.3083216 ``` ### 3.0.3 ``` ========================== * Regenerate Cython wrappers to support Python 3.8. ``` ### 3.0.2 ``` ========================== * Bug fixes: + Fix handling of readonly buffers in support for Python 2 legacy buffer interface. The issue triggers only when using a buffer-like object that is readonly and does not export the new Python 3 buffer interface. + Fix build issues with Open MPI 4.0.x series related to removal of many MPI-1 symbols deprecated in MPI-2 and removed in MPI-3. + Minor documentation fixes. ``` ### 3.0.1 ``` ========================== * Bug fixes: + Fix ``Comm.scatter()`` and other collectives corrupting input send list. Add safety measures to prevent related issues in global reduction operations. + Fix error-checking code for counts in ``Op.Reduce_local()``. * Enhancements: + Map size-specific Python/NumPy typecodes to MPI datatypes. + Allow partial specification of target list/tuple arguments in the various ``Win`` RMA methods. + Workaround for removal of ``MPI_{LB|UB}`` in Open MPI 4.0. + Support for Microsoft MPI v10.0. ```Links
- PyPI: https://pypi.org/project/mpi4py - Changelog: https://pyup.io/changelogs/mpi4py/ - Repo: https://github.com/mpi4py/mpi4py/releases/download/3.1.2/mpi4py-3.1.2.tar.gzUpdate petsc4py from 3.11.0 to 3.16.1.
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- PyPI: https://pypi.org/project/petsc4py - Repo: https://gitlab.com/petsc/petscUpdate h5py from 2.9.0 to 3.6.0.
Changelog
### 3.0.0 ``` https://docs.h5py.org/en/latest/whatsnew/3.0.html ```Links
- PyPI: https://pypi.org/project/h5py - Changelog: https://pyup.io/changelogs/h5py/ - Homepage: http://www.h5py.orgUpdate pandas from 1.0.3 to 1.3.4.
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- PyPI: https://pypi.org/project/pandas - Homepage: https://pandas.pydata.orgUpdate ruamel.yaml from 0.16.5 to 0.17.17.
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- PyPI: https://pypi.org/project/ruamel.yaml - Homepage: https://sourceforge.net/p/ruamel-yaml/code/ci/default/treeUpdate meshio from 3.3.1 to 5.0.3.
Changelog
### 5.0.0 ``` - meshio now only provides one command-line tool, `meshio`, with subcommands like `info`, `convert`, etc. This replaces the former `meshio-info`, `meshio-convert` etc. ``` ### 4.4.0 ``` - Polygons are now stored as `"polygon"` cell blocks, not `"polygonN"` (where `N` is the number of nodes per polygon). One can simply retrieve the number of points via `cellblock.data.shape[1]`. ``` ### 4.0.0 ``` - `mesh.cells` used to be a dictionary of the form python { "triangle": [[0, 1, 2], [0, 2, 3]], "quad": [[0, 7, 1, 10], ...] } From 4.0.0 on, `mesh.cells` is a list of tuples, python [ ("triangle", [[0, 1, 2], [0, 2, 3]]), ("quad", [[0, 7, 1, 10], ...]) ] This has the advantage that multiple blocks of the same cell type can be accounted for. Also, cell ordering can be preserved. You can now use the method `mesh.get_cells_type("triangle")` to get all cells of `"triangle"` type, or use `mesh.cells_dict` to build the old dictionary structure. - `mesh.cell_data` used to be a dictionary of the form python { "triangle": {"a": [0.5, 1.3], "b": [2.17, 41.3]}, "quad": {"a": [1.1, -0.3, ...], "b": [3.14, 1.61, ...]}, } From 4.0.0 on, `mesh.cell_data` is a dictionary of lists, python { "a": [[0.5, 1.3], [1.1, -0.3, ...]], "b": [[2.17, 41.3], [3.14, 1.61, ...]], } Each data list, e.g., `mesh.cell_data["a"]`, can be `zip`ped with `mesh.cells`. An old-style `cell_data` dictionary can be retrieved via `mesh.cell_data_dict`. ```Links
- PyPI: https://pypi.org/project/meshio - Changelog: https://pyup.io/changelogs/meshio/ - Repo: https://github.com/nschloe/meshioUpdate meshplex from 0.11.6 to 0.16.7.
Changelog
### 0.16.3 ``` Changed - Fixed computation of `genus` and `euler_characteristic` ``` ### 0.16.0 ``` Changed - `mesh.cells` is now a function; e.g., `mesh.cells["points"]` is now `mesh.cells("points")` - `mesh.idx_hierarchy` is deprecated in favor of `mesh.idx[-1]` (the new `idx` list contains more index magic) ``` ### 0.14.0 ``` Changed - `node_coords` is now `points` - `mesh_tri`: fixed inconsistent state after setting the points ```Links
- PyPI: https://pypi.org/project/meshplex - Changelog: https://pyup.io/changelogs/meshplex/ - Repo: https://github.com/nschloe/meshplexUpdate vtk from 9.0.1 to 9.1.0.
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Links
- PyPI: https://pypi.org/project/vtk - Homepage: https://vtk.org