A refcounting issue which leads to potential memory leak was discovered in scipy commit 8627df31ab in Py_FindObjects() function.
Release Notes
scipy/scipy (scipy)
### [`v1.10.0`](https://togithub.com/scipy/scipy/releases/tag/v1.10.0): SciPy 1.10.0
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.9.3...v1.10.0)
# SciPy 1.10.0 Release Notes
SciPy `1.10.0` is the culmination of `6` months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with `python -Wd` and check for `DeprecationWarning` s).
Our development attention will now shift to bug-fix releases on the
1.10.x branch, and on adding new features on the main branch.
This release requires Python `3.8+` and NumPy `1.19.5` or greater.
For running on PyPy, PyPy3 `6.0+` is required.
# Highlights of this release
- A new dedicated datasets submodule (`scipy.datasets`) has been added, and is
now preferred over usage of `scipy.misc` for dataset retrieval.
- A new `scipy.interpolate.make_smoothing_spline` function was added. This
function constructs a smoothing cubic spline from noisy data, using the
generalized cross-validation (GCV) criterion to find the tradeoff between
smoothness and proximity to data points.
- `scipy.stats` has three new distributions, two new hypothesis tests, three
new sample statistics, a class for greater control over calculations
involving covariance matrices, and many other enhancements.
# New features
# `scipy.datasets` introduction
- A new dedicated `datasets` submodule has been added. The submodules
is meant for datasets that are relevant to other SciPy submodules ands
content (tutorials, examples, tests), as well as contain a curated
set of datasets that are of wider interest. As of this release, all
the datasets from `scipy.misc` have been added to `scipy.datasets`
(and deprecated in `scipy.misc`).
- The submodule is based on [Pooch](https://www.fatiando.org/pooch/latest/)
(a new optional dependency for SciPy), a Python package to simplify fetching
data files. This move will, in a subsequent release, facilitate SciPy
to trim down the sdist/wheel sizes, by decoupling the data files and
moving them out of the SciPy repository, hosting them externally and
downloading them when requested. After downloading the datasets once,
the files are cached to avoid network dependence and repeated usage.
- Added datasets from `scipy.misc`: `scipy.datasets.face`,
`scipy.datasets.ascent`, `scipy.datasets.electrocardiogram`
- Added download and caching functionality:
- `scipy.datasets.download_all`: a function to download all the `scipy.datasets`
associated files at once.
- `scipy.datasets.clear_cache`: a simple utility function to clear cached dataset
files from the file system.
- `scipy/datasets/_download_all.py` can be run as a standalone script for
packaging purposes to avoid any external dependency at build or test time.
This can be used by SciPy packagers (e.g., for Linux distros) which may
have to adhere to rules that forbid downloading sources from external
repositories at package build time.
# `scipy.integrate` improvements
- Added parameter `complex_func` to `scipy.integrate.quad`, which can be set
`True` to integrate a complex integrand.
# `scipy.interpolate` improvements
- `scipy.interpolate.interpn` now supports tensor-product interpolation methods
(`slinear`, `cubic`, `quintic` and `pchip`)
- Tensor-product interpolation methods (`slinear`, `cubic`, `quintic` and
`pchip`) in `scipy.interpolate.interpn` and
`scipy.interpolate.RegularGridInterpolator` now allow values with trailing
dimensions.
- `scipy.interpolate.RegularGridInterpolator` has a new fast path for
`method="linear"` with 2D data, and `RegularGridInterpolator` is now
easier to subclass
- `scipy.interpolate.interp1d` now can take a single value for non-spline
methods.
- A new `extrapolate` argument is available to `scipy.interpolate.BSpline.design_matrix`,
allowing extrapolation based on the first and last intervals.
- A new function `scipy.interpolate.make_smoothing_spline` has been added. It is an
implementation of the generalized cross-validation spline smoothing
algorithm. The `lam=None` (default) mode of this function is a clean-room
reimplementation of the classic `gcvspl.f` Fortran algorithm for
constructing GCV splines.
- A new `method="pchip"` mode was aded to
`scipy.interpolate.RegularGridInterpolator`. This mode constructs an
interpolator using tensor products of C1-continuous monotone splines
(essentially, a `scipy.interpolate.PchipInterpolator` instance per
dimension).
# `scipy.sparse.linalg` improvements
- The spectral 2-norm is now available in `scipy.sparse.linalg.norm`.
- The performance of `scipy.sparse.linalg.norm` for the default case (Frobenius
norm) has been improved.
- LAPACK wrappers were added for `trexc` and `trsen`.
- The `scipy.sparse.linalg.lobpcg` algorithm was rewritten, yielding
the following improvements:
- a simple tunable restart potentially increases the attainable
accuracy for edge cases,
- internal postprocessing runs one final exact Rayleigh-Ritz method
giving more accurate and orthonormal eigenvectors,
- output the computed iterate with the smallest max norm of the residual
and drop the history of subsequent iterations,
- remove the check for `LinearOperator` format input and thus allow
a simple function handle of a callable object as an input,
- better handling of common user errors with input data, rather
than letting the algorithm fail.
# `scipy.linalg` improvements
- `scipy.linalg.lu_factor` now accepts rectangular arrays instead of being restricted
to square arrays.
# `scipy.ndimage` improvements
- The new `scipy.ndimage.value_indices` function provides a time-efficient method to
search for the locations of individual values with an array of image data.
- A new `radius` argument is supported by `scipy.ndimage.gaussian_filter1d` and
`scipy.ndimage.gaussian_filter` for adjusting the kernel size of the filter.
# `scipy.optimize` improvements
- `scipy.optimize.brute` now coerces non-iterable/single-value `args` into a
tuple.
- `scipy.optimize.least_squares` and `scipy.optimize.curve_fit` now accept
`scipy.optimize.Bounds` for bounds constraints.
- Added a tutorial for `scipy.optimize.milp`.
- Improved the pretty-printing of `scipy.optimize.OptimizeResult` objects.
- Additional options (`parallel`, `threads`, `mip_rel_gap`) can now
be passed to `scipy.optimize.linprog` with `method='highs'`.
# `scipy.signal` improvements
- The new window function `scipy.signal.windows.lanczos` was added to compute a
Lanczos window, also known as a sinc window.
# `scipy.sparse.csgraph` improvements
- the performance of `scipy.sparse.csgraph.dijkstra` has been improved, and
star graphs in particular see a marked performance improvement
# `scipy.special` improvements
- The new function `scipy.special.powm1`, a ufunc with signature
`powm1(x, y)`, computes `x**y - 1`. The function avoids the loss of
precision that can result when `y` is close to 0 or when `x` is close to
1\.
- `scipy.special.erfinv` is now more accurate as it leverages the Boost equivalent under
the hood.
# `scipy.stats` improvements
- Added `scipy.stats.goodness_of_fit`, a generalized goodness-of-fit test for
use with any univariate distribution, any combination of known and unknown
parameters, and several choices of test statistic (Kolmogorov-Smirnov,
Cramer-von Mises, and Anderson-Darling).
- Improved `scipy.stats.bootstrap`: Default method `'BCa'` now supports
multi-sample statistics. Also, the bootstrap distribution is returned in the
result object, and the result object can be passed into the function as
parameter `bootstrap_result` to add additional resamples or change the
confidence interval level and type.
- Added maximum spacing estimation to `scipy.stats.fit`.
- Added the Poisson means test ("E-test") as `scipy.stats.poisson_means_test`.
- Added new sample statistics.
- Added `scipy.stats.contingency.odds_ratio` to compute both the conditional
and unconditional odds ratios and corresponding confidence intervals for
2x2 contingency tables.
- Added `scipy.stats.directional_stats` to compute sample statistics of
n-dimensional directional data.
- Added `scipy.stats.expectile`, which generalizes the expected value in the
same way as quantiles are a generalization of the median.
- Added new statistical distributions.
- Added `scipy.stats.uniform_direction`, a multivariate distribution to
sample uniformly from the surface of a hypersphere.
- Added `scipy.stats.random_table`, a multivariate distribution to sample
uniformly from m x n contingency tables with provided marginals.
- Added `scipy.stats.truncpareto`, the truncated Pareto distribution.
- Improved the `fit` method of several distributions.
- `scipy.stats.skewnorm` and `scipy.stats.weibull_min` now use an analytical
solution when `method='mm'`, which also serves a starting guess to
improve the performance of `method='mle'`.
- `scipy.stats.gumbel_r` and `scipy.stats.gumbel_l`: analytical maximum
likelihood estimates have been extended to the cases in which location or
scale are fixed by the user.
- Analytical maximum likelihood estimates have been added for
`scipy.stats.powerlaw`.
- Improved random variate sampling of several distributions.
- Drawing multiple samples from `scipy.stats.matrix_normal`,
`scipy.stats.ortho_group`, `scipy.stats.special_ortho_group`, and
`scipy.stats.unitary_group` is faster.
- The `rvs` method of `scipy.stats.vonmises` now wraps to the interval
`[-np.pi, np.pi]`.
- Improved the reliability of `scipy.stats.loggamma` `rvs` method for small
values of the shape parameter.
- Improved the speed and/or accuracy of functions of several statistical
distributions.
- Added `scipy.stats.Covariance` for better speed, accuracy, and user control
in multivariate normal calculations.
- `scipy.stats.skewnorm` methods `cdf`, `sf`, `ppf`, and `isf`
methods now use the implementations from Boost, improving speed while
maintaining accuracy. The calculation of higher-order moments is also
faster and more accurate.
- `scipy.stats.invgauss` methods `ppf` and `isf` methods now use the
implementations from Boost, improving speed and accuracy.
- `scipy.stats.invweibull` methods `sf` and `isf` are more accurate for
small probability masses.
- `scipy.stats.nct` and `scipy.stats.ncx2` now rely on the implementations
from Boost, improving speed and accuracy.
- Implemented the `logpdf` method of `scipy.stats.vonmises` for reliability
in extreme tails.
- Implemented the `isf` method of `scipy.stats.levy` for speed and
accuracy.
- Improved the robustness of `scipy.stats.studentized_range` for large `df`
by adding an infinite degree-of-freedom approximation.
- Added a parameter `lower_limit` to `scipy.stats.multivariate_normal`,
allowing the user to change the integration limit from -inf to a desired
value.
- Improved the robustness of `entropy` of `scipy.stats.vonmises` for large
concentration values.
- Enhanced `scipy.stats.gaussian_kde`.
- Added `scipy.stats.gaussian_kde.marginal`, which returns the desired
marginal distribution of the original kernel density estimate distribution.
- The `cdf` method of `scipy.stats.gaussian_kde` now accepts a
`lower_limit` parameter for integrating the PDF over a rectangular region.
- Moved calculations for `scipy.stats.gaussian_kde.logpdf` to Cython,
improving speed.
- The global interpreter lock is released by the `pdf` method of
`scipy.stats.gaussian_kde` for improved multithreading performance.
- Replaced explicit matrix inversion with Cholesky decomposition for speed
and accuracy.
- Enhanced the result objects returned by many `scipy.stats` functions
- Added a `confidence_interval` method to the result object returned by
`scipy.stats.ttest_1samp` and `scipy.stats.ttest_rel`.
- The `scipy.stats` functions `combine_pvalues`, `fisher_exact`,
`chi2_contingency`, `median_test` and `mood` now return
bunch objects rather than plain tuples, allowing attributes to be
accessed by name.
- Attributes of the result objects returned by `multiscale_graphcorr`,
`anderson_ksamp`, `binomtest`, `crosstab`, `pointbiserialr`,
`spearmanr`, `kendalltau`, and `weightedtau` have been renamed to
`statistic` and `pvalue` for consistency throughout `scipy.stats`.
Old attribute names are still allowed for backward compatibility.
- `scipy.stats.anderson` now returns the parameters of the fitted
distribution in a `scipy.stats._result_classes.FitResult` object.
- The `plot` method of `scipy.stats._result_classes.FitResult` now accepts
a `plot_type` parameter; the options are `'hist'` (histogram, default),
`'qq'` (Q-Q plot), `'pp'` (P-P plot), and `'cdf'` (empirical CDF
plot).
- Kolmogorov-Smirnov tests (e.g. `scipy.stats.kstest`) now return the
location (argmax) at which the statistic is calculated and the variant
of the statistic used.
- Improved the performance of several `scipy.stats` functions.
- Improved the performance of `scipy.stats.cramervonmises_2samp` and
`scipy.stats.ks_2samp` with `method='exact'`.
- Improved the performance of `scipy.stats.siegelslopes`.
- Improved the performance of `scipy.stats.mstats.hdquantile_sd`.
- Improved the performance of `scipy.stats.binned_statistic_dd` for several
NumPy statistics, and binned statistics methods now support complex data.
- Added the `scramble` optional argument to `scipy.stats.qmc.LatinHypercube`.
It replaces `centered`, which is now deprecated.
- Added a parameter `optimization` to all `scipy.stats.qmc.QMCEngine`
subclasses to improve characteristics of the quasi-random variates.
- Added tie correction to `scipy.stats.mood`.
- Added tutorials for resampling methods in `scipy.stats`.
- `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and
`scipy.stats.monte_carlo_test` now automatically detect whether the provided
`statistic` is vectorized, so passing the `vectorized` argument
explicitly is no longer required to take advantage of vectorized statistics.
- Improved the speed of `scipy.stats.permutation_test` for permutation types
`'samples'` and `'pairings'`.
- Added `axis`, `nan_policy`, and masked array support to
`scipy.stats.jarque_bera`.
- Added the `nan_policy` optional argument to `scipy.stats.rankdata`.
# Deprecated features
- `scipy.misc` module and all the methods in `misc` are deprecated in v1.10
and will be completely removed in SciPy v2.0.0. Users are suggested to
utilize the `scipy.datasets` module instead for the dataset methods.
- `scipy.stats.qmc.LatinHypercube` parameter `centered` has been deprecated.
It is replaced by the `scramble` argument for more consistency with other
QMC engines.
- `scipy.interpolate.interp2d` class has been deprecated. The docstring of the
deprecated routine lists recommended replacements.
# Expired Deprecations
- There is an ongoing effort to follow through on long-standing deprecations.
- The following previously deprecated features are affected:
- Removed `cond` & `rcond` kwargs in `linalg.pinv`
- Removed wrappers `scipy.linalg.blas.{clapack, flapack}`
- Removed `scipy.stats.NumericalInverseHermite` and removed `tol` & `max_intervals` kwargs from `scipy.stats.sampling.NumericalInverseHermite`
- Removed `local_search_options` kwarg frrom `scipy.optimize.dual_annealing`.
# Other changes
- `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and
`scipy.stats.monte_carlo_test` now automatically detect whether the provided
`statistic` is vectorized by looking for an `axis` parameter in the
signature of `statistic`. If an `axis` parameter is present in
`statistic` but should not be relied on for vectorized calls, users must
pass option `vectorized==False` explicitly.
- `scipy.stats.multivariate_normal` will now raise a `ValueError` when the
covariance matrix is not positive semidefinite, regardless of which method
is called.
# Authors
- Name (commits)
- h-vetinari (10)
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A total of 184 people contributed to this release.
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This list of names is automatically generated, and may not be fully complete.
### [`v1.9.3`](https://togithub.com/scipy/scipy/releases/tag/v1.9.3): SciPy 1.9.3
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.9.2...v1.9.3)
# SciPy 1.9.3 Release Notes
SciPy `1.9.3` is a bug-fix release with no new features
compared to `1.9.2`.
# Authors
- Jelle Aalbers (1)
- Peter Bell (1)
- Jake Bowhay (3)
- Matthew Brett (3)
- Evgeni Burovski (5)
- drpeteb (1) +
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- Tyler Reddy (51)
- Martin Reinecke (1)
- Marie Roald (1) +
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- Eli Schwartz (1)
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- Gavin Zhang (1) +
A total of 31 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.
### [`v1.9.2`](https://togithub.com/scipy/scipy/releases/tag/v1.9.2): SciPy 1.9.2
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.9.1...v1.9.2)
# SciPy 1.9.2 Release Notes
SciPy `1.9.2` is a bug-fix release with no new features
compared to `1.9.1`. It also provides wheels for Python `3.11`
on several platforms.
# Authors
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- Meekail Zain (1) +
A total of 14 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.
### [`v1.9.1`](https://togithub.com/scipy/scipy/releases/tag/v1.9.1): SciPy 1.9.1
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.9.0...v1.9.1)
# SciPy 1.9.1 Release Notes
SciPy `1.9.1` is a bug-fix release with no new features
compared to `1.9.0`. Notably, some important meson build
fixes are included.
# Authors
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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.
### [`v1.9.0`](https://togithub.com/scipy/scipy/releases/tag/v1.9.0): SciPy 1.9.0
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.8.1...v1.9.0)
# SciPy 1.9.0 Release Notes
SciPy `1.9.0` is the culmination of 6 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with `python -Wd` and check for `DeprecationWarning` s).
Our development attention will now shift to bug-fix releases on the
1.9.x branch, and on adding new features on the main branch.
This release requires Python `3.8-3.11` and NumPy `1.18.5` or greater.
For running on PyPy, PyPy3 `6.0+` is required.
# Highlights of this release
- We have modernized our build system to use `meson`, substantially improving
our build performance, and providing better build-time configuration and
cross-compilation support,
- Added `scipy.optimize.milp`, new function for mixed-integer linear
programming,
- Added `scipy.stats.fit` for fitting discrete and continuous distributions
to data,
- Tensor-product spline interpolation modes were added to
`scipy.interpolate.RegularGridInterpolator`,
- A new global optimizer (DIviding RECTangles algorithm)
`scipy.optimize.direct`.
# New features
# `scipy.interpolate` improvements
- Speed up the `RBFInterpolator` evaluation with high dimensional
interpolants.
- Added new spline based interpolation methods for
`scipy.interpolate.RegularGridInterpolator` and its tutorial.
- `scipy.interpolate.RegularGridInterpolator` and `scipy.interpolate.interpn`
now accept descending ordered points.
- `RegularGridInterpolator` now handles length-1 grid axes.
- The `BivariateSpline` subclasses have a new method `partial_derivative`
which constructs a new spline object representing a derivative of an
original spline. This mirrors the corresponding functionality for univariate
splines, `splder` and `BSpline.derivative`, and can substantially speed
up repeated evaluation of derivatives.
# `scipy.linalg` improvements
- `scipy.linalg.expm` now accepts nD arrays. Its speed is also improved.
- Minimum required LAPACK version is bumped to `3.7.1`.
# `scipy.fft` improvements
- Added `uarray` multimethods for `scipy.fft.fht` and `scipy.fft.ifht`
to allow provision of third party backend implementations such as those
recently added to CuPy.
# `scipy.optimize` improvements
- A new global optimizer, `scipy.optimize.direct` (DIviding RECTangles algorithm)
was added. For problems with inexpensive function evaluations, like the ones
in the SciPy benchmark suite, `direct` is competitive with the best other
solvers in SciPy (`dual_annealing` and `differential_evolution`) in terms
of execution time. See
`gh-14300 `\__ for more details.
- Add a `full_output` parameter to `scipy.optimize.curve_fit` to output
additional solution information.
- Add a `integrality` parameter to `scipy.optimize.differential_evolution`,
enabling integer constraints on parameters.
- Add a `vectorized` parameter to call a vectorized objective function only
once per iteration. This can improve minimization speed by reducing
interpreter overhead from the multiple objective function calls.
- The default method of `scipy.optimize.linprog` is now `'highs'`.
- Added `scipy.optimize.milp`, new function for mixed-integer linear
programming.
- Added Newton-TFQMR method to `newton_krylov`.
- Added support for the `Bounds` class in `shgo` and `dual_annealing` for
a more uniform API across `scipy.optimize`.
- Added the `vectorized` keyword to `differential_evolution`.
- `approx_fprime` now works with vector-valued functions.
# `scipy.signal` improvements
- The new window function `scipy.signal.windows.kaiser_bessel_derived` was
added to compute the Kaiser-Bessel derived window.
- Single-precision `hilbert` operations are now faster as a result of more
consistent `dtype` handling.
# `scipy.sparse` improvements
- Add a `copy` parameter to `scipy.sparce.csgraph.laplacian`. Using inplace
computation with `copy=False` reduces the memory footprint.
- Add a `dtype` parameter to `scipy.sparce.csgraph.laplacian` for type casting.
- Add a `symmetrized` parameter to `scipy.sparce.csgraph.laplacian` to produce
symmetric Laplacian for directed graphs.
- Add a `form` parameter to `scipy.sparce.csgraph.laplacian` taking one of the
three values: `array`, or `function`, or `lo` determining the format of
the output Laplacian:
- `array` is a numpy array (backward compatible default);
- `function` is a pointer to a lambda-function evaluating the
Laplacian-vector or Laplacian-matrix product;
- `lo` results in the format of the `LinearOperator`.
# `scipy.sparse.linalg` improvements
- `lobpcg` performance improvements for small input cases.
# `scipy.spatial` improvements
- Add an `order` parameter to `scipy.spatial.transform.Rotation.from_quat`
and `scipy.spatial.transform.Rotation.as_quat` to specify quaternion format.
# `scipy.stats` improvements
- `scipy.stats.monte_carlo_test` performs one-sample Monte Carlo hypothesis
tests to assess whether a sample was drawn from a given distribution. Besides
reproducing the results of hypothesis tests like `scipy.stats.ks_1samp`,
`scipy.stats.normaltest`, and `scipy.stats.cramervonmises` without small sample
size limitations, it makes it possible to perform similar tests using arbitrary
statistics and distributions.
- Several `scipy.stats` functions support new `axis` (integer or tuple of
integers) and `nan_policy` ('raise', 'omit', or 'propagate'), and
`keepdims` arguments.
These functions also support masked arrays as inputs, even if they do not have
a `scipy.stats.mstats` counterpart. Edge cases for multidimensional arrays,
such as when axis-slices have no unmasked elements or entire inputs are of
size zero, are handled consistently.
- Add a `weight` parameter to `scipy.stats.hmean`.
- Several improvements have been made to `scipy.stats.levy_stable`. Substantial
improvement has been made for numerical evaluation of the pdf and cdf,
resolving [#12658](https://togithub.com/scipy/scipy/issues/12658) and
[#14944](https://togithub.com/scipy/scipy/issues/14994). The improvement is
particularly dramatic for stability parameter `alpha` close to or equal to 1
and for `alpha` below but approaching its maximum value of 2. The alternative
fast Fourier transform based method for pdf calculation has also been updated
to use the approach of Wang and Zhang from their 2008 conference paper
*Simpson’s rule based FFT method to compute densities of stable distribution*,
making this method more competitive with the default method. In addition,
users now have the option to change the parametrization of the Levy Stable
distribution to Nolan's "S0" parametrization which is used internally by
SciPy's pdf and cdf implementations. The "S0" parametrization is described in
Nolan's paper [*Numerical calculation of stable densities and distribution
functions*](https://doi.org/10.1080/15326349708807450) upon which SciPy's
implementation is based. "S0" has the advantage that `delta` and `gamma`
are proper location and scale parameters. With `delta` and `gamma` fixed,
the location and scale of the resulting distribution remain unchanged as
`alpha` and `beta` change. This is not the case for the default "S1"
parametrization. Finally, more options have been exposed to allow users to
trade off between runtime and accuracy for both the default and FFT methods of
pdf and cdf calculation. More information can be found in the documentation
here (to be linked).
- Added `scipy.stats.fit` for fitting discrete and continuous distributions to
data.
- The methods `"pearson"` and `"tippet"` from `scipy.stats.combine_pvalues`
have been fixed to return the correct p-values, resolving
[#15373](https://togithub.com/scipy/scipy/issues/15373). In addition, the
documentation for `scipy.stats.combine_pvalues` has been expanded and improved.
- Unlike other reduction functions, `stats.mode` didn't consume the axis
being operated on and failed for negative axis inputs. Both the bugs have been
fixed. Note that `stats.mode` will now consume the input axis and return an
ndarray with the `axis` dimension removed.
- Replaced implementation of `scipy.stats.ncf` with the implementation from
Boost for improved reliability.
- Add a `bits` parameter to `scipy.stats.qmc.Sobol`. It allows to use from 0
to 64 bits to compute the sequence. Default is `None` which corresponds to
30 for backward compatibility. Using a higher value allow to sample more
points. Note: `bits` does not affect the output dtype.
- Add a `integers` method to `scipy.stats.qmc.QMCEngine`. It allows sampling
integers using any QMC sampler.
- Improved the fit speed and accuracy of `stats.pareto`.
- Added `qrvs` method to `NumericalInversePolynomial` to match the
situation for `NumericalInverseHermite`.
- Faster random variate generation for `gennorm` and `nakagami`.
- `lloyd_centroidal_voronoi_tessellation` has been added to allow improved
sample distributions via iterative application of Voronoi diagrams and
centering operations
- Add `scipy.stats.qmc.PoissonDisk` to sample using the Poisson disk sampling
method. It guarantees that samples are separated from each other by a
given `radius`.
- Add `scipy.stats.pmean` to calculate the weighted power mean also called
generalized mean.
# Deprecated features
- Due to collision with the shape parameter `n` of several distributions,
use of the distribution `moment` method with keyword argument `n` is
deprecated. Keyword `n` is replaced with keyword `order`.
- Similarly, use of the distribution `interval` method with keyword arguments
`alpha` is deprecated. Keyword `alpha` is replaced with keyword
`confidence`.
- The `'simplex'`, `'revised simplex'`, and `'interior-point'` methods
of `scipy.optimize.linprog` are deprecated. Methods `highs`, `highs-ds`,
or `highs-ipm` should be used in new code.
- Support for non-numeric arrays has been deprecated from `stats.mode`.
`pandas.DataFrame.mode` can be used instead.
- The function `spatial.distance.kulsinski` has been deprecated in favor
of `spatial.distance.kulczynski1`.
- The `maxiter` keyword of the truncated Newton (TNC) algorithm has been
deprecated in favour of `maxfun`.
- The `vertices` keyword of `Delauney.qhull` now raises a
DeprecationWarning, after having been deprecated in documentation only
for a long time.
- The `extradoc` keyword of `rv_continuous`, `rv_discrete` and
`rv_sample` now raises a DeprecationWarning, after having been deprecated in
documentation only for a long time.
# Expired Deprecations
There is an ongoing effort to follow through on long-standing deprecations.
The following previously deprecated features are affected:
- Object arrays in sparse matrices now raise an error.
- Inexact indices into sparse matrices now raise an error.
- Passing `radius=None` to `scipy.spatial.SphericalVoronoi` now raises an
error (not adding `radius` defaults to 1, as before).
- Several BSpline methods now raise an error if inputs have `ndim > 1`.
- The `_rvs` method of statistical distributions now requires a `size`
parameter.
- Passing a `fillvalue` that cannot be cast to the output type in
`scipy.signal.convolve2d` now raises an error.
- `scipy.spatial.distance` now enforces that the input vectors are
one-dimensional.
- Removed `stats.itemfreq`.
- Removed `stats.median_absolute_deviation`.
- Removed `n_jobs` keyword argument and use of `k=None` from
`kdtree.query`.
- Removed `right` keyword from `interpolate.PPoly.extend`.
- Removed `debug` keyword from `scipy.linalg.solve_*`.
- Removed class `_ppform` `scipy.interpolate`.
- Removed BSR methods `matvec` and `matmat`.
- Removed `mlab` truncation mode from `cluster.dendrogram`.
- Removed `cluster.vq.py_vq2`.
- Removed keyword arguments `ftol` and `xtol` from
`optimize.minimize(method='Nelder-Mead')`.
- Removed `signal.windows.hanning`.
- Removed LAPACK `gegv` functions from `linalg`; this raises the minimally
required LAPACK version to 3.7.1.
- Removed `spatial.distance.matching`.
- Removed the alias `scipy.random` for `numpy.random`.
- Removed docstring related functions from `scipy.misc` (`docformat`,
`inherit_docstring_from`, `extend_notes_in_docstring`,
`replace_notes_in_docstring`, `indentcount_lines`, `filldoc`,
`unindent_dict`, `unindent_string`).
- Removed `linalg.pinv2`.
# Backwards incompatible changes
- Several `scipy.stats` functions now convert `np.matrix` to `np.ndarray`s
before the calculation is performed. In this case, the output will be a scalar
or `np.ndarray` of appropriate shape rather than a 2D `np.matrix`.
Similarly, while masked elements of masked arrays are still ignored, the
output will be a scalar or `np.ndarray` rather than a masked array with
`mask=False`.
- The default method of `scipy.optimize.linprog` is now `'highs'`, not
`'interior-point'` (which is now deprecated), so callback functions and
some options are no longer supported with the default method. With the
default method, the `x` attribute of the returned `OptimizeResult` is
now `None` (instead of a non-optimal array) when an optimal solution
cannot be found (e.g. infeasible problem).
- For `scipy.stats.combine_pvalues`, the sign of the test statistic returned
for the method `"pearson"` has been flipped so that higher values of the
statistic now correspond to lower p-values, making the statistic more
consistent with those of the other methods and with the majority of the
literature.
- `scipy.linalg.expm` due to historical reasons was using the sparse
implementation and thus was accepting sparse arrays. Now it only works with
nDarrays. For sparse usage, `scipy.sparse.linalg.expm` needs to be used
explicitly.
- The definition of `scipy.stats.circvar` has reverted to the one that is
standard in the literature; note that this is not the same as the square of
`scipy.stats.circstd`.
- Remove inheritance to `QMCEngine` in `MultinomialQMC` and
`MultivariateNormalQMC`. It removes the methods `fast_forward` and `reset`.
- Init of `MultinomialQMC` now require the number of trials with `n_trials`.
Hence, `MultinomialQMC.random` output has now the correct shape `(n, pvals)`.
- Several function-specific warnings (`F_onewayConstantInputWarning`,
`F_onewayBadInputSizesWarning`, `PearsonRConstantInputWarning`,
`PearsonRNearConstantInputWarning`, `SpearmanRConstantInputWarning`, and
`BootstrapDegenerateDistributionWarning`) have been replaced with more
general warnings.
# Other changes
- A draft developer CLI is available for SciPy, leveraging the `doit`,
`click` and `rich-click` tools. For more details, see
[gh-15959](https://togithub.com/scipy/scipy/pull/15959).
- The SciPy contributor guide has been reorganized and updated
(see [#15947](https://togithub.com/scipy/scipy/pull/15947) for details).
- QUADPACK Fortran routines in `scipy.integrate`, which power
`scipy.integrate.quad`, have been marked as `recursive`. This should fix rare
issues in multivariate integration (`nquad` and friends) and obviate the need
for compiler-specific compile flags (`/recursive` for ifort etc). Please file
an issue if this change turns out problematic for you. This is also true for
`FITPACK` routines in `scipy.interpolate`, which power `splrep`,
`splev` etc., and `*UnivariateSpline` and `*BivariateSpline` classes.
- the `USE_PROPACK` environment variable has been renamed to
`SCIPY_USE_PROPACK`; setting to a non-zero value will enable
the usage of the `PROPACK` library as before
- Building SciPy on windows with MSVC now requires at least the vc142
toolset (available in Visual Studio 2019 and higher).
# Lazy access to subpackages
Before this release, all subpackages of SciPy (`cluster`, `fft`, `ndimage`,
etc.) had to be explicitly imported. Now, these subpackages are lazily loaded
as soon as they are accessed, so that the following is possible (if desired
for interactive use, it's not actually recommended for code,
see :ref:`scipy-api`):
`import scipy as sp; sp.fft.dct([1, 2, 3])`. Advantages include: making it
easier to navigate SciPy in interactive terminals, reducing subpackage import
conflicts (which before required
`import networkx.linalg as nla; import scipy.linalg as sla`),
and avoiding repeatedly having to update imports during teaching &
experimentation. Also see
[the related community specification document](https://scientific-python.org/specs/spec-0001/).
# SciPy switched to Meson as its build system
This is the first release that ships with [Meson](https://mesonbuild.com) as
the build system. When installing with `pip` or `pypa/build`, Meson will be
used (invoked via the `meson-python` build hook). This change brings
significant benefits - most importantly much faster build times, but also
better support for cross-compilation and cleaner build logs.
Note:
This release still ships with support for `numpy.distutils`-based builds
as well. Those can be invoked through the `setup.py` command-line
interface (e.g., `python setup.py install`). It is planned to remove
`numpy.distutils` support before the 1.10.0 release.
When building from source, a number of things have changed compared to building
with `numpy.distutils`:
- New build dependencies: `meson`, `ninja`, and `pkg-config`.
`setuptools` and `wheel` are no longer needed.
- BLAS and LAPACK libraries that are supported haven't changed, however the
discovery mechanism has: that is now using `pkg-config` instead of hardcoded
paths or a `site.cfg` file.
- The build defaults to using OpenBLAS. See :ref:`blas-lapack-selection` for
details.
The two CLIs that can be used to build wheels are `pip` and `build`. In
addition, the SciPy repo contains a `python dev.py` CLI for any kind of
development task (see its `--help` for details). For a comparison between old
(`distutils`) and new (`meson`) build commands, see :ref:`meson-faq`.
For more information on the introduction of Meson support in SciPy, see
`gh-13615 `\__ and
`this blog post `\__.
# Authors
- endolith (12)
- h-vetinari (11)
- Caio Agiani (2) +
- Emmy Albert (1) +
- Joseph Albert (1)
- Tania Allard (3)
- Carsten Allefeld (1) +
- Kartik Anand (1) +
- Virgile Andreani (2) +
- Weh Andreas (1) +
- Francesco Andreuzzi (5) +
- Kian-Meng Ang (2) +
- Gerrit Ansmann (1)
- Ar-Kareem (1) +
- Shehan Atukorala (1) +
- avishai231 (1) +
- Blair Azzopardi (1)
- Sayantika Banik (2) +
- Ross Barnowski (9)
- Christoph Baumgarten (3)
- Nickolai Belakovski (1)
- Peter Bell (9)
- Sebastian Berg (3)
- Bharath (1) +
- bobcatCA (2) +
- boussoffara (2) +
- Islem BOUZENIA (1) +
- Jake Bowhay (41) +
- Matthew Brett (11)
- Dietrich Brunn (2) +
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- Evgeni Burovski (96)
- Matthias Bussonnier (20)
- Dominic C (1)
- Cameron (1) +
- CJ Carey (3)
- Thomas A Caswell (2)
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- Brandon David (1)
- deepakdinesh1123 (1) +
- Denton DeLoss (1) +
- derbuihan (2) +
- Sameer Deshmukh (13) +
- Niels Doucet (1) +
- DWesl (8)
- eytanadler (30) +
- Thomas J. Fan (5)
- Isuru Fernando (3)
- Joseph Fox-Rabinovitz (1)
- Ryan Gibson (4) +
- Ralf Gommers (327)
- Srinivas Gorur-Shandilya (1) +
- Alex Griffing (2)
- Matt Haberland (461)
- Tristan Hearn (1) +
- Jonathan Helgert (1) +
- Samuel Hinton (1) +
- Jake (1) +
- Stewart Jamieson (1) +
- Jan-Hendrik Müller (1)
- Yikun Jiang (1) +
- JuliaMelle01 (1) +
- jyuv (12) +
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- Chris Keefe (1) +
- Robert Kern (4)
- Andrew Knyazev (11)
- Matthias Koeppe (4) +
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- Volodymyr Kozachynskyi (1) +
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- Peter Mahler Larsen (8)
- Eric Larson (4)
- Laurynas Mikšys (1) +
- Antony Lee (1)
- Gregory R. Lee (2)
- lerichi (1) +
- Tim Leslie (2)
- P. L. Lim (1)
- Smit Lunagariya (43)
- lutefiskhotdish (1) +
- Cong Ma (12)
- Syrtis Major (1)
- Nicholas McKibben (18)
- Melissa Weber Mendonça (10)
- Mark Mikofski (1)
- Jarrod Millman (13)
- Harsh Mishra (6)
- ML-Nielsen (3) +
- Matthew Murray (1) +
- Andrew Nelson (50)
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- sabonerune (1) +
- Niyas Sait (2) +
- Atsushi Sakai (25)
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- Soph (1) +
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- Albert Steppi (7)
- Christopher Strickland (1) +
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- Aleksandr Tagilov (1) +
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- yuanx749 (2) +
- Gang Zhao (23)
- ZhihuiChen0903 (1)
- Pavel Zun (1) +
- David Zwicker (1) +
A total of 154 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.
### [`v1.8.1`](https://togithub.com/scipy/scipy/releases/tag/v1.8.1): SciPy 1.8.1
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.8.0...v1.8.1)
# SciPy 1.8.1 Release Notes
SciPy `1.8.1` is a bug-fix release with no new features
compared to `1.8.0`. Notably, usage of Pythran has been
restored for Windows builds/binaries.
# Authors
- Henry Schreiner
- Maximilian Nöthe
- Sebastian Berg (1)
- Sameer Deshmukh (1) +
- Niels Doucet (1) +
- DWesl (4)
- Isuru Fernando (1)
- Ralf Gommers (4)
- Matt Haberland (1)
- Andrew Nelson (1)
- Dimitri Papadopoulos Orfanos (1) +
- Tirth Patel (3)
- Tyler Reddy (46)
- Pamphile Roy (7)
- Niyas Sait (1) +
- H. Vetinari (2)
- Warren Weckesser (1)
A total of 17 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.
### [`v1.8.0`](https://togithub.com/scipy/scipy/releases/tag/v1.8.0): SciPy 1.8.0
[Compare Source](https://togithub.com/scipy/scipy/compare/v1.7.3...v1.8.0)
# SciPy 1.8.0 Release Notes
SciPy `1.8.0` is the culmination of `6` months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with `python -Wd` and check for `DeprecationWarning` s).
Our development attention will now shift to bug-fix releases on the
1.8.x branch, and on adding new features on the master branch.
This release requires Python `3.8+` and `NumPy 1.17.3` or greater.
For running on PyPy, PyPy3 `6.0+` is required.
# Highlights of this release
- A sparse array API has been added for early testing and feedback; this
work is ongoing, and users should expect minor API refinements over
the next few releases.
- The sparse SVD library PROPACK is now vendored with SciPy, and an interface
is exposed via `scipy.sparse.svds` with `solver='PROPACK'`. It is currently
default-off due to potential issues on Windows that we aim to
resolve in the next release, but can be optionally enabled at runtime for
friendly testing with an environment variable setting of `USE_PROPACK=1`.
- A new `scipy.stats.sampling` submodule that leverages the `UNU.RAN` C
library to sample from arbitrary univariate non-uniform continuous and
discrete distributions
- All namespaces that were private but happened to miss underscores in
their names have been deprecated.
# New features
# `scipy.fft` improvements
Added an `orthogonalize=None` parameter to the real transforms in `scipy.fft`
which controls whether the modified definition of DCT/DST is used without
changing the overall scaling.
`scipy.fft` backend registration is now smoother, operating with a single
registration call and no longer requiring a context manager.
# `scipy.integrate` improvements
`scipy.integrate.quad_vec` introduces a new optional keyword-only argument,
`args`. `args` takes in a tuple of extra arguments if any (default is
`args=()`), which is then internally used to pass into the callable function
(needing these extra arguments) which we wish to integrate.
# `scipy.interpolate` improvements
`scipy.interpolate.BSpline` has a new method, `design_matrix`, which
constructs a design matrix of b-splines in the sparse CSR format.
A new method `from_cubic` in `BSpline` class allows to convert a
`CubicSpline` object to `BSpline` object.
# `scipy.linalg` improvements
`scipy.linalg` gained three new public array structure investigation functions.
`scipy.linalg.bandwidth` returns information about the bandedness of an array
and can be used to test for triangular structure discovery, while
`scipy.linalg.issymmetric` and `scipy.linalg.ishermitian` test the array for
exact and approximate symmetric/Hermitian structure.
# `scipy.optimize` improvements
`scipy.optimize.check_grad` introduces two new optional keyword only arguments,
`direction` and `seed`. `direction` can take values, `'all'` (default),
in which case all the one hot direction vectors will be used for verifying
the input analytical gradient function and `'random'`, in which case a
random direction vector will be used for the same purpose. `seed`
(default is `None`) can be used for reproducing the return value of
`check_grad` function. It will be used only when `direction='random'`.
The `scipy.optimize.minimize` `TNC` method has been rewritten to use Cython
bindings. This also fixes an issue with the callback altering the state of the
optimization.
Added optional parameters `target_accept_rate` and `stepwise_factor` for
adapative step size adjustment in `basinhopping`.
The `epsilon` argument to `approx_fprime` is now optional so that it may
have a default value consistent with most other functions in `scipy.optimize`.
# `scipy.signal` improvements
Add `analog` argument, default `False`, to `zpk2sos`, and add new pairing
option `'minimal'` to construct analog and minimal discrete SOS arrays.
`tf2sos` uses zpk2sos; add `analog` argument here as well, and pass it on
to `zpk2sos`.
`savgol_coeffs` and `savgol_filter` now work for even window lengths.
Added the Chirp Z-transform and Zoom FFT available as `scipy.signal.CZT` and
`scipy.signal.ZoomFFT`.
# `scipy.sparse` improvements
An array API has been added for early testing and feedback; this
work is ongoing, and users should expect minor API refinements over
the next few releases. Please refer to the `scipy.sparse`
docstring for more information.
`maximum_flow` introduces optional keyword only argument, `method`
which accepts either, `'edmonds-karp'` (Edmonds Karp algorithm) or
`'dinic'` (Dinic's algorithm). Moreover, `'dinic'` is used as default
value for `method` which means that Dinic's algorithm is used for computing
maximum flow unless specified. See, the comparison between the supported
algorithms in
`this comment `\_.
Parameters `atol`, `btol` now default to 1e-6 in
`scipy.sparse.linalg.lsmr` to match with default values in
`scipy.sparse.linalg.lsqr`.
Add the Transpose-Free Quasi-Minimal Residual algorithm (TFQMR) for general
nonsingular non-Hermitian linear systems in `scipy.sparse.linalg.tfqmr`.
The sparse SVD library PROPACK is now vendored with SciPy, and an interface is
exposed via `scipy.sparse.svds` with `solver='PROPACK'`. For some problems,
this may be faster and/or more accurate than the default, ARPACK. PROPACK
functionality is currently opt-in--you must specify `USE_PROPACK=1` at
runtime to use it due to potential issues on Windows
that we aim to resolve in the next release.
`sparse.linalg` iterative solvers now have a nonzero initial guess option,
which may be specified as `x0 = 'Mb'`.
The `trace` method has been added for sparse matrices.
# `scipy.spatial` improvements
`scipy.spatial.transform.Rotation` now supports item assignment and has a new
`concatenate` method.
Add `scipy.spatial.distance.kulczynski1` in favour of
`scipy.spatial.distance.kulsinski` which will be deprecated in the next
release.
`scipy.spatial.distance.minkowski` now also supports `0`\_, which
have computational advantages over the classical Legendre integrals. Previous
versions included some elliptic integrals from the Cephes library
(`scipy.special.ellip{k,km1,kinc,e,einc}`) but was missing the integral of
third kind (Legendre's Pi), which can be evaluated using the new Carlson
functions. The new Carlson elliptic integral functions can be evaluated in the
complex plane, whereas the Cephes library's functions are only defined for
real inputs.
Several defects in `scipy.special.hyp2f1` have been corrected. Approximately
correct values are now returned for `z` near `exp(+-i*pi/3)`, fixing
`#8054 `*. Evaluation for such `z`
is now calculated through a series derived by
`López and Temme (2013) `* that converges in
these regions. In addition, degenerate cases with one or more of `a`, `b`,
and/or `c` a non-positive integer are now handled in a manner consistent with
`mpmath's hyp2f1 implementation `*,
which fixes `#7340 `*. These fixes
were made as part of an effort to rewrite the Fortran 77 implementation of
hyp2f1 in Cython piece by piece. This rewriting is now roughly 50% complete.
# `scipy.stats` improvements
`scipy.stats.qmc.LatinHypercube` introduces two new optional keyword-only
arguments, `optimization` and `strength`. `optimization` is either
`None` or `random-cd`. In the latter, random permutations are performed to
improve the centered discrepancy. `strength` is either 1 or 2. 1 corresponds
to the classical LHS while 2 has better sub-projection properties. This
construction is referred to as an orthogonal array based LHS of strength 2.
In both cases, the output is still a LHS.
`scipy.stats.qmc.Halton` is faster as the underlying Van der Corput sequence
was ported to Cython.
The `alternative` parameter was added to the `kendalltau` and `somersd`
functions to allow one-sided hypothesis testing. Similarly, the masked
versions of `skewtest`, `kurtosistest`, `ttest_1samp`, `ttest_ind`,
and `ttest_rel` now also have an `alternative` parameter.
Add `scipy.stats.gzscore` to calculate the geometrical z score.
Random variate generators to sample from arbitrary univariate non-uniform
continuous and discrete distributions have been added to the new
`scipy.stats.sampling` submodule. Implementations of a C library
`UNU.RAN `\_ are used for
performance. The
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==1.7.2->==1.10.0GitHub Vulnerability Alerts
CVE-2023-25399
A refcounting issue which leads to potential memory leak was discovered in scipy commit 8627df31ab in
Py_FindObjects()function.Release Notes
scipy/scipy (scipy)
### [`v1.10.0`](https://togithub.com/scipy/scipy/releases/tag/v1.10.0): SciPy 1.10.0 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.9.3...v1.10.0) # SciPy 1.10.0 Release Notes SciPy `1.10.0` is the culmination of `6` months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with `python -Wd` and check for `DeprecationWarning` s). Our development attention will now shift to bug-fix releases on the 1.10.x branch, and on adding new features on the main branch. This release requires Python `3.8+` and NumPy `1.19.5` or greater. For running on PyPy, PyPy3 `6.0+` is required. # Highlights of this release - A new dedicated datasets submodule (`scipy.datasets`) has been added, and is now preferred over usage of `scipy.misc` for dataset retrieval. - A new `scipy.interpolate.make_smoothing_spline` function was added. This function constructs a smoothing cubic spline from noisy data, using the generalized cross-validation (GCV) criterion to find the tradeoff between smoothness and proximity to data points. - `scipy.stats` has three new distributions, two new hypothesis tests, three new sample statistics, a class for greater control over calculations involving covariance matrices, and many other enhancements. # New features # `scipy.datasets` introduction - A new dedicated `datasets` submodule has been added. The submodules is meant for datasets that are relevant to other SciPy submodules ands content (tutorials, examples, tests), as well as contain a curated set of datasets that are of wider interest. As of this release, all the datasets from `scipy.misc` have been added to `scipy.datasets` (and deprecated in `scipy.misc`). - The submodule is based on [Pooch](https://www.fatiando.org/pooch/latest/) (a new optional dependency for SciPy), a Python package to simplify fetching data files. This move will, in a subsequent release, facilitate SciPy to trim down the sdist/wheel sizes, by decoupling the data files and moving them out of the SciPy repository, hosting them externally and downloading them when requested. After downloading the datasets once, the files are cached to avoid network dependence and repeated usage. - Added datasets from `scipy.misc`: `scipy.datasets.face`, `scipy.datasets.ascent`, `scipy.datasets.electrocardiogram` - Added download and caching functionality: - `scipy.datasets.download_all`: a function to download all the `scipy.datasets` associated files at once. - `scipy.datasets.clear_cache`: a simple utility function to clear cached dataset files from the file system. - `scipy/datasets/_download_all.py` can be run as a standalone script for packaging purposes to avoid any external dependency at build or test time. This can be used by SciPy packagers (e.g., for Linux distros) which may have to adhere to rules that forbid downloading sources from external repositories at package build time. # `scipy.integrate` improvements - Added parameter `complex_func` to `scipy.integrate.quad`, which can be set `True` to integrate a complex integrand. # `scipy.interpolate` improvements - `scipy.interpolate.interpn` now supports tensor-product interpolation methods (`slinear`, `cubic`, `quintic` and `pchip`) - Tensor-product interpolation methods (`slinear`, `cubic`, `quintic` and `pchip`) in `scipy.interpolate.interpn` and `scipy.interpolate.RegularGridInterpolator` now allow values with trailing dimensions. - `scipy.interpolate.RegularGridInterpolator` has a new fast path for `method="linear"` with 2D data, and `RegularGridInterpolator` is now easier to subclass - `scipy.interpolate.interp1d` now can take a single value for non-spline methods. - A new `extrapolate` argument is available to `scipy.interpolate.BSpline.design_matrix`, allowing extrapolation based on the first and last intervals. - A new function `scipy.interpolate.make_smoothing_spline` has been added. It is an implementation of the generalized cross-validation spline smoothing algorithm. The `lam=None` (default) mode of this function is a clean-room reimplementation of the classic `gcvspl.f` Fortran algorithm for constructing GCV splines. - A new `method="pchip"` mode was aded to `scipy.interpolate.RegularGridInterpolator`. This mode constructs an interpolator using tensor products of C1-continuous monotone splines (essentially, a `scipy.interpolate.PchipInterpolator` instance per dimension). # `scipy.sparse.linalg` improvements - The spectral 2-norm is now available in `scipy.sparse.linalg.norm`. - The performance of `scipy.sparse.linalg.norm` for the default case (Frobenius norm) has been improved. - LAPACK wrappers were added for `trexc` and `trsen`. - The `scipy.sparse.linalg.lobpcg` algorithm was rewritten, yielding the following improvements: - a simple tunable restart potentially increases the attainable accuracy for edge cases, - internal postprocessing runs one final exact Rayleigh-Ritz method giving more accurate and orthonormal eigenvectors, - output the computed iterate with the smallest max norm of the residual and drop the history of subsequent iterations, - remove the check for `LinearOperator` format input and thus allow a simple function handle of a callable object as an input, - better handling of common user errors with input data, rather than letting the algorithm fail. # `scipy.linalg` improvements - `scipy.linalg.lu_factor` now accepts rectangular arrays instead of being restricted to square arrays. # `scipy.ndimage` improvements - The new `scipy.ndimage.value_indices` function provides a time-efficient method to search for the locations of individual values with an array of image data. - A new `radius` argument is supported by `scipy.ndimage.gaussian_filter1d` and `scipy.ndimage.gaussian_filter` for adjusting the kernel size of the filter. # `scipy.optimize` improvements - `scipy.optimize.brute` now coerces non-iterable/single-value `args` into a tuple. - `scipy.optimize.least_squares` and `scipy.optimize.curve_fit` now accept `scipy.optimize.Bounds` for bounds constraints. - Added a tutorial for `scipy.optimize.milp`. - Improved the pretty-printing of `scipy.optimize.OptimizeResult` objects. - Additional options (`parallel`, `threads`, `mip_rel_gap`) can now be passed to `scipy.optimize.linprog` with `method='highs'`. # `scipy.signal` improvements - The new window function `scipy.signal.windows.lanczos` was added to compute a Lanczos window, also known as a sinc window. # `scipy.sparse.csgraph` improvements - the performance of `scipy.sparse.csgraph.dijkstra` has been improved, and star graphs in particular see a marked performance improvement # `scipy.special` improvements - The new function `scipy.special.powm1`, a ufunc with signature `powm1(x, y)`, computes `x**y - 1`. The function avoids the loss of precision that can result when `y` is close to 0 or when `x` is close to 1\. - `scipy.special.erfinv` is now more accurate as it leverages the Boost equivalent under the hood. # `scipy.stats` improvements - Added `scipy.stats.goodness_of_fit`, a generalized goodness-of-fit test for use with any univariate distribution, any combination of known and unknown parameters, and several choices of test statistic (Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling). - Improved `scipy.stats.bootstrap`: Default method `'BCa'` now supports multi-sample statistics. Also, the bootstrap distribution is returned in the result object, and the result object can be passed into the function as parameter `bootstrap_result` to add additional resamples or change the confidence interval level and type. - Added maximum spacing estimation to `scipy.stats.fit`. - Added the Poisson means test ("E-test") as `scipy.stats.poisson_means_test`. - Added new sample statistics. - Added `scipy.stats.contingency.odds_ratio` to compute both the conditional and unconditional odds ratios and corresponding confidence intervals for 2x2 contingency tables. - Added `scipy.stats.directional_stats` to compute sample statistics of n-dimensional directional data. - Added `scipy.stats.expectile`, which generalizes the expected value in the same way as quantiles are a generalization of the median. - Added new statistical distributions. - Added `scipy.stats.uniform_direction`, a multivariate distribution to sample uniformly from the surface of a hypersphere. - Added `scipy.stats.random_table`, a multivariate distribution to sample uniformly from m x n contingency tables with provided marginals. - Added `scipy.stats.truncpareto`, the truncated Pareto distribution. - Improved the `fit` method of several distributions. - `scipy.stats.skewnorm` and `scipy.stats.weibull_min` now use an analytical solution when `method='mm'`, which also serves a starting guess to improve the performance of `method='mle'`. - `scipy.stats.gumbel_r` and `scipy.stats.gumbel_l`: analytical maximum likelihood estimates have been extended to the cases in which location or scale are fixed by the user. - Analytical maximum likelihood estimates have been added for `scipy.stats.powerlaw`. - Improved random variate sampling of several distributions. - Drawing multiple samples from `scipy.stats.matrix_normal`, `scipy.stats.ortho_group`, `scipy.stats.special_ortho_group`, and `scipy.stats.unitary_group` is faster. - The `rvs` method of `scipy.stats.vonmises` now wraps to the interval `[-np.pi, np.pi]`. - Improved the reliability of `scipy.stats.loggamma` `rvs` method for small values of the shape parameter. - Improved the speed and/or accuracy of functions of several statistical distributions. - Added `scipy.stats.Covariance` for better speed, accuracy, and user control in multivariate normal calculations. - `scipy.stats.skewnorm` methods `cdf`, `sf`, `ppf`, and `isf` methods now use the implementations from Boost, improving speed while maintaining accuracy. The calculation of higher-order moments is also faster and more accurate. - `scipy.stats.invgauss` methods `ppf` and `isf` methods now use the implementations from Boost, improving speed and accuracy. - `scipy.stats.invweibull` methods `sf` and `isf` are more accurate for small probability masses. - `scipy.stats.nct` and `scipy.stats.ncx2` now rely on the implementations from Boost, improving speed and accuracy. - Implemented the `logpdf` method of `scipy.stats.vonmises` for reliability in extreme tails. - Implemented the `isf` method of `scipy.stats.levy` for speed and accuracy. - Improved the robustness of `scipy.stats.studentized_range` for large `df` by adding an infinite degree-of-freedom approximation. - Added a parameter `lower_limit` to `scipy.stats.multivariate_normal`, allowing the user to change the integration limit from -inf to a desired value. - Improved the robustness of `entropy` of `scipy.stats.vonmises` for large concentration values. - Enhanced `scipy.stats.gaussian_kde`. - Added `scipy.stats.gaussian_kde.marginal`, which returns the desired marginal distribution of the original kernel density estimate distribution. - The `cdf` method of `scipy.stats.gaussian_kde` now accepts a `lower_limit` parameter for integrating the PDF over a rectangular region. - Moved calculations for `scipy.stats.gaussian_kde.logpdf` to Cython, improving speed. - The global interpreter lock is released by the `pdf` method of `scipy.stats.gaussian_kde` for improved multithreading performance. - Replaced explicit matrix inversion with Cholesky decomposition for speed and accuracy. - Enhanced the result objects returned by many `scipy.stats` functions - Added a `confidence_interval` method to the result object returned by `scipy.stats.ttest_1samp` and `scipy.stats.ttest_rel`. - The `scipy.stats` functions `combine_pvalues`, `fisher_exact`, `chi2_contingency`, `median_test` and `mood` now return bunch objects rather than plain tuples, allowing attributes to be accessed by name. - Attributes of the result objects returned by `multiscale_graphcorr`, `anderson_ksamp`, `binomtest`, `crosstab`, `pointbiserialr`, `spearmanr`, `kendalltau`, and `weightedtau` have been renamed to `statistic` and `pvalue` for consistency throughout `scipy.stats`. Old attribute names are still allowed for backward compatibility. - `scipy.stats.anderson` now returns the parameters of the fitted distribution in a `scipy.stats._result_classes.FitResult` object. - The `plot` method of `scipy.stats._result_classes.FitResult` now accepts a `plot_type` parameter; the options are `'hist'` (histogram, default), `'qq'` (Q-Q plot), `'pp'` (P-P plot), and `'cdf'` (empirical CDF plot). - Kolmogorov-Smirnov tests (e.g. `scipy.stats.kstest`) now return the location (argmax) at which the statistic is calculated and the variant of the statistic used. - Improved the performance of several `scipy.stats` functions. - Improved the performance of `scipy.stats.cramervonmises_2samp` and `scipy.stats.ks_2samp` with `method='exact'`. - Improved the performance of `scipy.stats.siegelslopes`. - Improved the performance of `scipy.stats.mstats.hdquantile_sd`. - Improved the performance of `scipy.stats.binned_statistic_dd` for several NumPy statistics, and binned statistics methods now support complex data. - Added the `scramble` optional argument to `scipy.stats.qmc.LatinHypercube`. It replaces `centered`, which is now deprecated. - Added a parameter `optimization` to all `scipy.stats.qmc.QMCEngine` subclasses to improve characteristics of the quasi-random variates. - Added tie correction to `scipy.stats.mood`. - Added tutorials for resampling methods in `scipy.stats`. - `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and `scipy.stats.monte_carlo_test` now automatically detect whether the provided `statistic` is vectorized, so passing the `vectorized` argument explicitly is no longer required to take advantage of vectorized statistics. - Improved the speed of `scipy.stats.permutation_test` for permutation types `'samples'` and `'pairings'`. - Added `axis`, `nan_policy`, and masked array support to `scipy.stats.jarque_bera`. - Added the `nan_policy` optional argument to `scipy.stats.rankdata`. # Deprecated features - `scipy.misc` module and all the methods in `misc` are deprecated in v1.10 and will be completely removed in SciPy v2.0.0. Users are suggested to utilize the `scipy.datasets` module instead for the dataset methods. - `scipy.stats.qmc.LatinHypercube` parameter `centered` has been deprecated. It is replaced by the `scramble` argument for more consistency with other QMC engines. - `scipy.interpolate.interp2d` class has been deprecated. The docstring of the deprecated routine lists recommended replacements. # Expired Deprecations - There is an ongoing effort to follow through on long-standing deprecations. - The following previously deprecated features are affected: - Removed `cond` & `rcond` kwargs in `linalg.pinv` - Removed wrappers `scipy.linalg.blas.{clapack, flapack}` - Removed `scipy.stats.NumericalInverseHermite` and removed `tol` & `max_intervals` kwargs from `scipy.stats.sampling.NumericalInverseHermite` - Removed `local_search_options` kwarg frrom `scipy.optimize.dual_annealing`. # Other changes - `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and `scipy.stats.monte_carlo_test` now automatically detect whether the provided `statistic` is vectorized by looking for an `axis` parameter in the signature of `statistic`. If an `axis` parameter is present in `statistic` but should not be relied on for vectorized calls, users must pass option `vectorized==False` explicitly. - `scipy.stats.multivariate_normal` will now raise a `ValueError` when the covariance matrix is not positive semidefinite, regardless of which method is called. # Authors - Name (commits) - h-vetinari (10) - Jelle Aalbers (1) - Oriol Abril-Pla (1) + - Alan-Hung (1) + - Tania Allard (7) - Oren Amsalem (1) + - Sven Baars (10) - Balthasar (1) + - Ross Barnowski (1) - Christoph Baumgarten (2) - Peter Bell (2) - Sebastian Berg (1) - Aaron Berk (1) + - boatwrong (1) + - boeleman (1) + - Jake Bowhay (50) - Matthew Brett (4) - Evgeni Burovski (93) - Matthias Bussonnier (6) - Dominic C (2) - Mingbo Cai (1) + - James Campbell (2) + - CJ Carey (4) - cesaregarza (1) + - charlie0389 (1) + - Hood Chatham (5) - Andrew Chin (1) + - Daniel Ching (1) + - Leo Chow (1) + - chris (3) + - John Clow (1) + - cm7S (1) + - cmgodwin (1) + - Christopher Cowden (2) + - Henry Cuzco (2) + - Anirudh Dagar (12) - Hans Dembinski (2) + - Jaiden di Lanzo (24) + - Felipe Dias (1) + - Dieter Werthmüller (1) - Giuseppe Dilillo (1) + - dpoerio (1) + - drpeteb (1) + - Christopher Dupuis (1) + - Jordan Edmunds (1) + - Pieter Eendebak (1) + - Jérome Eertmans (1) + - Fabian Egli (2) + - Sebastian Ehlert (2) + - Kian Eliasi (1) + - Tomohiro Endo (1) + - Stefan Endres (1) - Zeb Engberg (4) + - Jonas Eschle (1) + - Thomas J. Fan (9) - fiveseven (1) + - Neil Flood (1) + - Franz Forstmayr (1) - Sara Fridovich-Keil (1) - David Gilbertson (1) + - Ralf Gommers (251) - Marco Gorelli (2) + - Matt Haberland (387) - Andrew Hawryluk (2) + - Christoph Hohnerlein (2) + - Loïc Houpert (2) + - Shamus Husheer (1) + - ideasrule (1) + - imoiwm (1) + - Lakshaya Inani (1) + - Joseph T. 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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. ### [`v1.9.3`](https://togithub.com/scipy/scipy/releases/tag/v1.9.3): SciPy 1.9.3 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.9.2...v1.9.3) # SciPy 1.9.3 Release Notes SciPy `1.9.3` is a bug-fix release with no new features compared to `1.9.2`. # Authors - Jelle Aalbers (1) - Peter Bell (1) - Jake Bowhay (3) - Matthew Brett (3) - Evgeni Burovski (5) - drpeteb (1) + - Sebastian Ehlert (1) + - GavinZhang (1) + - Ralf Gommers (2) - Matt Haberland (15) - Lakshaya Inani (1) + - Joseph T. Iosue (1) - Nathan Jacobi (1) + - jmkuebler (1) + - Nikita Karetnikov (1) + - Lechnio (1) + - Nicholas McKibben (1) - Andrew Nelson (1) - o-alexandre-felipe (1) + - Tirth Patel (1) - Tyler Reddy (51) - Martin Reinecke (1) - Marie Roald (1) + - Pamphile Roy (2) - Eli Schwartz (1) - serge-sans-paille (1) - ehsan shirvanian (1) + - Mamoru TASAKA (1) + - Samuel Wallan (1) - Warren Weckesser (7) - Gavin Zhang (1) + A total of 31 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. ### [`v1.9.2`](https://togithub.com/scipy/scipy/releases/tag/v1.9.2): SciPy 1.9.2 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.9.1...v1.9.2) # SciPy 1.9.2 Release Notes SciPy `1.9.2` is a bug-fix release with no new features compared to `1.9.1`. It also provides wheels for Python `3.11` on several platforms. # Authors - Hood Chatham (1) - Thomas J. Fan (1) - Ralf Gommers (22) - Matt Haberland (5) - Julien Jerphanion (1) - Loïc Estève (1) - Nicholas McKibben (2) - Naoto Mizuno (1) - Andrew Nelson (3) - Tyler Reddy (28) - Pamphile Roy (1) - Ewout ter Hoeven (2) - Warren Weckesser (1) - Meekail Zain (1) + A total of 14 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. ### [`v1.9.1`](https://togithub.com/scipy/scipy/releases/tag/v1.9.1): SciPy 1.9.1 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.9.0...v1.9.1) # SciPy 1.9.1 Release Notes SciPy `1.9.1` is a bug-fix release with no new features compared to `1.9.0`. Notably, some important meson build fixes are included. # Authors - Anirudh Dagar (1) - Ralf Gommers (12) - Matt Haberland (2) - Andrew Nelson (1) - Tyler Reddy (14) - Atsushi Sakai (1) - Eli Schwartz (1) - Warren Weckesser (2) 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. ### [`v1.9.0`](https://togithub.com/scipy/scipy/releases/tag/v1.9.0): SciPy 1.9.0 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.8.1...v1.9.0) # SciPy 1.9.0 Release Notes SciPy `1.9.0` is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with `python -Wd` and check for `DeprecationWarning` s). Our development attention will now shift to bug-fix releases on the 1.9.x branch, and on adding new features on the main branch. This release requires Python `3.8-3.11` and NumPy `1.18.5` or greater. For running on PyPy, PyPy3 `6.0+` is required. # Highlights of this release - We have modernized our build system to use `meson`, substantially improving our build performance, and providing better build-time configuration and cross-compilation support, - Added `scipy.optimize.milp`, new function for mixed-integer linear programming, - Added `scipy.stats.fit` for fitting discrete and continuous distributions to data, - Tensor-product spline interpolation modes were added to `scipy.interpolate.RegularGridInterpolator`, - A new global optimizer (DIviding RECTangles algorithm) `scipy.optimize.direct`. # New features # `scipy.interpolate` improvements - Speed up the `RBFInterpolator` evaluation with high dimensional interpolants. - Added new spline based interpolation methods for `scipy.interpolate.RegularGridInterpolator` and its tutorial. - `scipy.interpolate.RegularGridInterpolator` and `scipy.interpolate.interpn` now accept descending ordered points. - `RegularGridInterpolator` now handles length-1 grid axes. - The `BivariateSpline` subclasses have a new method `partial_derivative` which constructs a new spline object representing a derivative of an original spline. This mirrors the corresponding functionality for univariate splines, `splder` and `BSpline.derivative`, and can substantially speed up repeated evaluation of derivatives. # `scipy.linalg` improvements - `scipy.linalg.expm` now accepts nD arrays. Its speed is also improved. - Minimum required LAPACK version is bumped to `3.7.1`. # `scipy.fft` improvements - Added `uarray` multimethods for `scipy.fft.fht` and `scipy.fft.ifht` to allow provision of third party backend implementations such as those recently added to CuPy. # `scipy.optimize` improvements - A new global optimizer, `scipy.optimize.direct` (DIviding RECTangles algorithm) was added. For problems with inexpensive function evaluations, like the ones in the SciPy benchmark suite, `direct` is competitive with the best other solvers in SciPy (`dual_annealing` and `differential_evolution`) in terms of execution time. See `gh-14300Configuration
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