scipy/scipy
### [`v1.6.0`](https://togithub.com/scipy/scipy/releases/v1.6.0)
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.5.4...v1.6.0)
# SciPy 1.6.0 Release Notes
SciPy `1.6.0` is the culmination of 6 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with `python -Wd` and check for `DeprecationWarning` s).
Our development attention will now shift to bug-fix releases on the
`1.6.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
() 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](https://togithub.com/scipy/scipy/issues/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](https://togithub.com/scipy/scipy/issues/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](https://togithub.com/endolith)
- [@vkk800](https://togithub.com/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 +
- Matti Picus
- 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 122 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.
### [`v1.5.4`](https://togithub.com/scipy/scipy/releases/v1.5.4)
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.5.3...v1.5.4)
# SciPy 1.5.4 Release Notes
SciPy `1.5.4` is a bug-fix release with no new features
compared to `1.5.3`. Importantly, wheels are now available
for Python `3.9` and a more complete fix has been applied for
issues building with XCode `12`.
# Authors
- Peter Bell
- CJ Carey
- Andrew McCluskey +
- Andrew Nelson
- Tyler Reddy
- Eli Rykoff +
- Ian Thomas +
A total of 7 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.
### [`v1.5.3`](https://togithub.com/scipy/scipy/releases/v1.5.3)
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.5.2...v1.5.3)
# SciPy 1.5.3 Release Notes
SciPy `1.5.3` is a bug-fix release with no new features
compared to `1.5.2`. In particular, Linux ARM64 wheels are now
available and a compatibility issue with XCode 12 has
been fixed.
# Authors
- Peter Bell
- CJ Carey
- Thomas Duvernay +
- Gregory Lee
- Eric Moore
- odidev
- Dima Pasechnik
- Tyler Reddy
- Simon Segerblom Rex +
- Daniel B. Smith
- Will Tirone +
- Warren Weckesser
A total of 12 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.
### [`v1.5.2`](https://togithub.com/scipy/scipy/releases/v1.5.2)
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.5.1...v1.5.2)
# SciPy 1.5.2 Release Notes
SciPy `1.5.2` is a bug-fix release with no new features
compared to `1.5.1`.
# Authors
- Peter Bell
- Tobias Biester +
- Evgeni Burovski
- Thomas A Caswell
- Ralf Gommers
- Sturla Molden
- Andrew Nelson
- ofirr +
- Sambit Panda
- Ilhan Polat
- Tyler Reddy
- Atsushi Sakai
- Pauli Virtanen
A total of 13 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.
### [`v1.5.1`](https://togithub.com/scipy/scipy/releases/v1.5.1)
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.5.0...v1.5.1)
# SciPy 1.5.1 Release Notes
SciPy `1.5.1` is a bug-fix release with no new features
compared to `1.5.0`. In particular, an issue where DLL loading
can fail for SciPy wheels on Windows with Python `3.6` has been
fixed.
# Authors
- Peter Bell
- Loïc Estève
- Philipp Thölke +
- Tyler Reddy
- Paul van Mulbregt
- Pauli Virtanen
- Warren Weckesser
A total of 7 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.
### [`v1.5.0`](https://togithub.com/scipy/scipy/releases/v1.5.0)
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.4.1...v1.5.0)
# SciPy 1.5.0 Release Notes
SciPy `1.5.0` is the culmination of 6 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with `python -Wd` and check for `DeprecationWarning` s).
Our development attention will now shift to bug-fix releases on the
1.5.x branch, and on adding new features on the master branch.
This release requires Python `3.6+` and NumPy `1.14.5` or greater.
For running on PyPy, PyPy3 `6.0+` and NumPy `1.15.0` are required.
## Highlights of this release
- wrappers for more than a dozen new `LAPACK` routines are now available
in `scipy.linalg.lapack`
- Improved support for leveraging 64-bit integer size from linear algebra
backends
- addition of the probability distribution for two-sided one-sample
Kolmogorov-Smirnov tests
## New features
## `scipy.cluster` improvements
Initialization of `scipy.cluster.vq.kmeans2` using `minit="++"` had a
quadratic complexity in the number of samples. It has been improved, resulting
in a much faster initialization with quasi-linear complexity.
`scipy.cluster.hierarchy.dendrogram` now respects the `matplotlib` color
palette
## `scipy.fft` improvements
A new keyword-only argument `plan` is added to all FFT functions in this
module. It is reserved for passing in a precomputed plan from libraries
providing a FFT backend (such as `PyFFTW` and `mkl-fft`), and it is
currently not used in SciPy.
## `scipy.integrate` improvements
## `scipy.interpolate` improvements
## `scipy.io` improvements
`scipy.io.wavfile` error messages are more explicit about what's wrong, and
extraneous bytes at the ends of files are ignored instead of raising an error
when the data has successfully been read.
`scipy.io.loadmat` gained a `simplify_cells` parameter, which if set to
`True` simplifies the structure of the return value if the `.mat` file
contains cell arrays.
`pathlib.Path` objects are now supported in `scipy.io` Matrix Market I/O
functions
## `scipy.linalg` improvements
`scipy.linalg.eigh` has been improved. Now various `LAPACK` drivers can be
selected at will and also subsets of eigenvalues can be requested via
`subset_by_value` keyword. Another keyword `subset_by_index` is introduced.
Keywords `turbo` and `eigvals` are deprecated.
Similarly, standard and generalized Hermitian eigenvalue `LAPACK` routines
`?evx` are added and existing ones now have full `_lwork`
counterparts.
Wrappers for the following `LAPACK` routines have been added to
`scipy.linalg.lapack`:
- `?getc2`: computes the LU factorization of a general matrix with complete
pivoting
- `?gesc2`: solves a linear system given an LU factorization from `?getc2`
- `?gejsv`: computes the singular value decomposition of a general matrix
with higher accuracy calculation of tiny singular values and their
corresponding singular vectors
- `?geqrfp`: computes the QR factorization of a general matrix with
non-negative elements on the diagonal of R
- `?gtsvx`: solves a linear system with general tridiagonal matrix
- `?gttrf`: computes the LU factorization of a tridiagonal matrix
- `?gttrs`: solves a linear system given an LU factorization from `?gttrf`
- `?ptsvx`: solves a linear system with symmetric positive definite
tridiagonal matrix
- `?pttrf`: computes the LU factorization of a symmetric positive definite
tridiagonal matrix
- `?pttrs`: solves a linear system given an LU factorization from `?pttrf`
- `?pteqr`: computes the eigenvectors and eigenvalues of a positive definite
tridiagonal matrix
- `?tbtrs`: solves a linear system with a triangular banded matrix
- `?csd`: computes the Cosine Sine decomposition of an orthogonal/unitary
matrix
Generalized QR factorization routines (`?geqrf`) now have full `_lwork`
counterparts.
`scipy.linalg.cossin` Cosine Sine decomposition of unitary matrices has been
added.
The function `scipy.linalg.khatri_rao`, which computes the Khatri-Rao product,
was added.
The new function `scipy.linalg.convolution_matrix` constructs the Toeplitz
matrix representing one-dimensional convolution.
## `scipy.ndimage` improvements
## `scipy.optimize` improvements
The finite difference numerical differentiation used in various `minimize`
methods that use gradients has several new features:
- 2-point, 3-point, or complex step finite differences can be used. Previously
only a 2-step finite difference was available.
- There is now the possibility to use a relative step size, previously only an
absolute step size was available.
- If the `minimize` method uses bounds the numerical differentiation strictly
obeys those limits.
- The numerical differentiation machinery now makes use of a simple cache,
which in some cases can reduce the number of function evaluations.
- `minimize`'s `method= 'powell'` now supports simple bound constraints
There have been several improvements to `scipy.optimize.linprog`:
- The `linprog` benchmark suite has been expanded considerably.
- `linprog`'s dense pivot-based redundancy removal routine and sparse
presolve are faster
- When `scikit-sparse` is available, solving sparse problems with
`method='interior-point'` is faster
The caching of values when optimizing a function returning both value and
gradient together has been improved, avoiding repeated function evaluations
when using a `HessianApproximation` such as `BFGS`.
`differential_evolution` can now use the modern `np.random.Generator` as
well as the legacy `np.random.RandomState` as a seed.
## `scipy.signal` improvements
A new optional argument `include_nyquist` is added to `freqz` functions in
this module. It is used for including the last frequency (Nyquist frequency).
`scipy.signal.find_peaks_cwt` now accepts a `window_size` parameter for the
size of the window used to calculate the noise floor.
## `scipy.sparse` improvements
Outer indexing is now faster when using a 2d column vector to select column
indices.
`scipy.sparse.lil.tocsr` is faster
Fixed/improved comparisons between pydata sparse arrays and sparse matrices
BSR format sparse multiplication performance has been improved.
`scipy.sparse.linalg.LinearOperator` has gained the new `ndim` class
attribute
## `scipy.spatial` improvements
`scipy.spatial.geometric_slerp` has been added to enable geometric
spherical linear interpolation on an n-sphere
`scipy.spatial.SphericalVoronoi` now supports calculation of region areas in 2D
and 3D cases
The tree building algorithm used by `cKDTree` has improved from quadratic
worst case time complexity to loglinear. Benchmarks are also now available for
building and querying of balanced/unbalanced kd-trees.
## `scipy.special` improvements
The following functions now have Cython interfaces in `cython_special`:
- `scipy.special.erfinv`
- `scipy.special.erfcinv`
- `scipy.special.spherical_jn`
- `scipy.special.spherical_yn`
- `scipy.special.spherical_in`
- `scipy.special.spherical_kn`
`scipy.special.log_softmax` has been added to calculate the logarithm of softmax
function. It provides better accuracy than `log(scipy.special.softmax(x))` for
inputs that make softmax saturate.
## `scipy.stats` improvements
The function for generating random samples in `scipy.stats.dlaplace` has been
improved. The new function is approximately twice as fast with a memory
footprint reduction between 25 % and 60 % (see [gh-11069](https://togithub.com/scipy/scipy/issues/11069)).
`scipy.stats` functions that accept a seed for reproducible calculations using
random number generation (e.g. random variates from distributions) can now use
the modern `np.random.Generator` as well as the legacy
`np.random.RandomState` as a seed.
The `axis` parameter was added to `scipy.stats.rankdata`. This allows slices
of an array along the given axis to be ranked independently.
The `axis` parameter was added to `scipy.stats.f_oneway`, allowing it to
compute multiple one-way ANOVA tests for data stored in n-dimensional
arrays. The performance of `f_oneway` was also improved for some cases.
The PDF and CDF methods for `stats.geninvgauss` are now significantly faster
as the numerical integration to calculate the CDF uses a Cython based
`LowLevelCallable`.
Moments of the normal distribution (`scipy.stats.norm`) are now calculated using
analytical formulas instead of numerical integration for greater speed and
accuracy
Moments and entropy trapezoidal distribution (`scipy.stats.trapz`) are now
calculated using analytical formulas instead of numerical integration for
greater speed and accuracy
Methods of the truncated normal distribution (`scipy.stats.truncnorm`),
especially `_rvs`, are significantly faster after a complete rewrite.
The `fit` method of the Laplace distribution, `scipy.stats.laplace`, now uses
the analytical formulas for the maximum likelihood estimates of the parameters.
Generation of random variates is now thread safe for all SciPy distributions.
3rd-party distributions may need to modify the signature of the `_rvs()`
method to conform to `_rvs(self, ..., size=None, random_state=None)`. (A
one-time VisibleDeprecationWarning is emitted when using non-conformant
distributions.)
The Kolmogorov-Smirnov two-sided test statistic distribution
(`scipy.stats.kstwo`) was added. Calculates the distribution of the K-S
two-sided statistic `D_n` for a sample of size n, using a mixture of exact
and asymptotic algorithms.
The new function `median_abs_deviation` replaces the deprecated
`median_absolute_deviation`.
The `wilcoxon` function now computes the p-value for Wilcoxon's signed rank
test using the exact distribution for inputs up to length 25. The function has
a new `mode` parameter to specify how the p-value is to be computed. The
default is `"auto"`, which uses the exact distribution for inputs up to length
25 and the normal approximation for larger inputs.
Added a new Cython-based implementation to evaluate guassian kernel estimates,
which should improve the performance of `gaussian_kde`
The `winsorize` function now has a `nan_policy` argument for refined
handling of `nan` input values.
The `binned_statistic_dd` function with `statistic="std"` performance was
improved by ~4x.
`scipy.stats.kstest(rvs, cdf,...)` now handles both one-sample and
two-sample testing. The one-sample variation uses `scipy.stats.ksone`
(or `scipy.stats.kstwo` with back off to `scipy.stats.kstwobign`) to calculate
the p-value. The two-sample variation, invoked if `cdf` is array_like, uses
an algorithm described by Hodges to compute the probability directly, only
backing off to `scipy.stats.kstwo` in case of overflow. The result in both
cases is more accurate p-values, especially for two-sample testing with
smaller (or quite different) sizes.
`scipy.stats.maxwell` performance improvements include a 20 % speed up for
\`fit()`and 5 % for`pdf()``
`scipy.stats.shapiro` and `scipy.stats.jarque_bera` now return a named tuple
for greater consistency with other `stats` functions
# Deprecated features
## `scipy` deprecations
## `scipy.special` changes
The `bdtr`, `bdtrc`, and `bdtri` functions are deprecating non-negative
non-integral `n` arguments.
## `scipy.stats` changes
The function `median_absolute_deviation` is deprecated. Use
`median_abs_deviation` instead.
The use of the string `"raw"` with the `scale` parameter of `iqr` is
deprecated. Use `scale=1` instead.
# Backwards incompatible changes
## `scipy.interpolate` changes
## `scipy.linalg` changes
The output signatures of `?syevr`, `?heevr` have been changed from
`w, v, info` to `w, v, m, isuppz, info`
The order of output arguments `w`, `v` of `{gv, gvd, gvx}` is
swapped.
## `scipy.signal` changes
The output length of `scipy.signal.upfirdn` has been corrected, resulting
outputs may now be shorter for some combinations of up/down ratios and input
signal and filter lengths.
`scipy.signal.resample` now supports a `domain` keyword argument for
specification of time or frequency domain input.
## `scipy.stats` changes
# Other changes
Improved support for leveraging 64-bit integer size from linear algebra backends
in several parts of the SciPy codebase.
Shims designed to ensure the compatibility of SciPy with Python 2.7 have now
been removed.
Many warnings due to unused imports and unused assignments have been addressed.
Many usage examples were added to function docstrings, and many input
validations and intuitive exception messages have been added throughout the
codebase.
Early stage adoption of type annotations in a few parts of the codebase
# Authors
- [@endolith](https://togithub.com/endolith)
- Hameer Abbasi
- ADmitri +
- Wesley Alves +
- Berkay Antmen +
- Sylwester Arabas +
- Arne Küderle +
- Christoph Baumgarten
- Peter Bell
- Felix Berkenkamp
- Jordão Bragantini +
- Clemens Brunner +
- Evgeni Burovski
- Matthias Bussonnier +
- CJ Carey
- Derrick Chambers +
- Leander Claes +
- Christian Clauss
- Luigi F. Cruz +
- dankleeman
- Andras Deak
- Milad Sadeghi DM +
- jeremie du boisberranger +
- Stefan Endres
- Malte Esders +
- Leo Fang +
- felixhekhorn +
- Isuru Fernando
- Andrew Fowlie
- Lakshay Garg +
- Gaurav Gijare +
- Ralf Gommers
- Emmanuelle Gouillart +
- Kevin Green +
- Martin Grignard +
- Maja Gwozdz
- Sturla Molden
- gyu-don +
- Matt Haberland
- hakeemo +
- Charles Harris
- Alex Henrie
- Santi Hernandez +
- William Hickman +
- Till Hoffmann +
- Joseph T. Iosue +
- Anany Shrey Jain
- Jakob Jakobson
- Charles Jekel +
- Julien Jerphanion +
- Jiacheng-Liu +
- Christoph Kecht +
- Paul Kienzle +
- Reidar Kind +
- Dmitry E. Kislov +
- Konrad +
- Konrad0
- Takuya KOUMURA +
- Krzysztof Pióro
- Peter Mahler Larsen
- Eric Larson
- Antony Lee
- Gregory Lee +
- Gregory R. Lee
- Chelsea Liu
- Cong Ma +
- Kevin Mader +
- Maja Gwóźdź +
- Alex Marvin +
- Matthias Kümmerer
- Nikolay Mayorov
- Mazay0 +
- G. D. McBain
- Nicholas McKibben +
- Sabrina J. Mielke +
- Sebastian J. Mielke +
- Miloš Komarčević +
- Shubham Mishra +
- Santiago M. Mola +
- Grzegorz Mrukwa +
- Peyton Murray
- Andrew Nelson
- Nico Schlömer
- nwjenkins +
- odidev +
- Sambit Panda
- Vikas Pandey +
- Rick Paris +
- Harshal Prakash Patankar +
- Balint Pato +
- Matti Picus
- Ilhan Polat
- poom +
- Siddhesh Poyarekar
- Vladyslav Rachek +
- Bharat Raghunathan
- Manu Rajput +
- Tyler Reddy
- Andrew Reed +
- Lucas Roberts
- Ariel Rokem
- Heshy Roskes
- Matt Ruffalo
- Atsushi Sakai +
- Benjamin Santos +
- Christoph Schock +
- Lisa Schwetlick +
- Chris Simpson +
- Leo Singer
- Kai Striega
- Søren Fuglede Jørgensen
- Kale-ab Tessera +
- Seth Troisi +
- Robert Uhl +
- Paul van Mulbregt
- Vasiliy +
- Isaac Virshup +
- Pauli Virtanen
- Shakthi Visagan +
- Jan Vleeshouwers +
- Sam Wallan +
- Lijun Wang +
- Warren Weckesser
- Richard Weiss +
- wenhui-prudencemed +
- Eric Wieser
- Josh Wilson
- James Wright +
- Ruslan Yevdokymov +
- Ziyao Zhang +
A total of 129 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.
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This PR contains the following updates:
==1.4.1->==1.6.0Release Notes
scipy/scipy
### [`v1.6.0`](https://togithub.com/scipy/scipy/releases/v1.6.0) [Compare Source](https://togithub.com/scipy/scipy/compare/v1.5.4...v1.6.0) # SciPy 1.6.0 Release Notes SciPy `1.6.0` is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with `python -Wd` and check for `DeprecationWarning` s). Our development attention will now shift to bug-fix releases on the `1.6.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 (Renovate configuration
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