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Update dependency scipy to ~=1.12.0 #45

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profitroll merged 1 commits from renovate/scipy-1.x into dev 2024-01-21 13:54:37 +02:00
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This PR contains the following updates:

Package Update Change
scipy (source) minor ~=1.11.0 -> ~=1.12.0

Release Notes

scipy/scipy (scipy)

v1.12.0: SciPy 1.12.0

Compare Source

SciPy 1.12.0 Release Notes

SciPy 1.12.0 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
1.12.x branch, and on adding new features on the main branch.

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

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

  • Experimental support for the array API standard has been added to part of
    scipy.special, and to all of scipy.fft and scipy.cluster. There are
    likely to be bugs and early feedback for usage with CuPy arrays, PyTorch
    tensors, and other array API compatible libraries is appreciated. Use the
    SCIPY_ARRAY_API environment variable for testing.
  • A new class, ShortTimeFFT, provides a more versatile implementation of the
    short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
    spectrogram. It utilizes an improved algorithm for calculating the ISTFT.
  • Several new constructors have been added for sparse arrays, and many operations
    now additionally support sparse arrays, further facilitating the migration
    from sparse matrices.
  • A large portion of the scipy.stats API now has improved support for handling
    NaN values, masked arrays, and more fine-grained shape-handling. The
    accuracy and performance of a number of stats methods have been improved,
    and a number of new statistical tests and distributions have been added.

New features

scipy.cluster improvements

  • Experimental support added for the array API standard; PyTorch tensors,
    CuPy arrays and array API compatible array libraries are now accepted
    (GPU support is limited to functions with pure Python implementations).
    CPU arrays which can be converted to and from NumPy are supported
    module-wide and returned arrays will match the input type.
    This behaviour is enabled by setting the SCIPY_ARRAY_API environment
    variable before importing scipy. This experimental support is still
    under development and likely to contain bugs - testing is very welcome.

scipy.fft improvements

  • Experimental support added for the array API standard; functions which are
    part of the fft array API standard extension module, as well as the
    Fast Hankel Transforms and the basic FFTs which are not in the extension
    module, now accept PyTorch tensors, CuPy arrays and array API compatible
    array libraries. CPU arrays which can be converted to and from NumPy arrays
    are supported module-wide and returned arrays will match the input type.
    This behaviour is enabled by setting the SCIPY_ARRAY_API environment
    variable before importing scipy. This experimental support is still under
    development and likely to contain bugs - testing is very welcome.

scipy.integrate improvements

  • Added scipy.integrate.cumulative_simpson for cumulative quadrature
    from sampled data using Simpson's 1/3 rule.

scipy.interpolate improvements

  • New class NdBSpline represents tensor-product splines in N dimensions.
    This class only knows how to evaluate a tensor product given coefficients
    and knot vectors. This way it generalizes BSpline for 1D data to N-D, and
    parallels NdPPoly (which represents N-D tensor product polynomials).
    Evaluations exploit the localized nature of b-splines.
  • NearestNDInterpolator.__call__ accepts **query_options, which are
    passed through to the KDTree.query call to find nearest neighbors. This
    allows, for instance, to limit the neighbor search distance and parallelize
    the query using the workers keyword.
  • BarycentricInterpolator now allows computing the derivatives.
  • It is now possible to change interpolation values in an existing
    CloughTocher2DInterpolator instance, while also saving the barycentric
    coordinates of interpolation points.

scipy.linalg improvements

  • Access to new low-level LAPACK functions is provided via dtgsyl and
    stgsyl.

scipy.optimize improvements

  • scipy.optimize.isotonic_regression has been added to allow nonparametric isotonic
    regression.
  • scipy.optimize.nnls is rewritten in Python and now implements the so-called
    fnnls or fast nnls, making it more efficient for high-dimensional problems.
  • The result object of scipy.optimize.root and scipy.optimize.root_scalar
    now reports the method used.
  • The callback method of scipy.optimize.differential_evolution can now be
    passed more detailed information via the intermediate_results keyword
    parameter. Also, the evolution strategy now accepts a callable for
    additional customization. The performance of differential_evolution has
    also been improved.
  • scipy.optimize.minimize method Newton-CG now supports functions that
    return sparse Hessian matrices/arrays for the hess parameter and is slightly
    more efficient.
  • scipy.optimize.minimize method BFGS now accepts an initial estimate for the
    inverse of the Hessian, which allows for more efficient workflows in some
    circumstances. The new parameter is hess_inv0.
  • scipy.optimize.minimize methods CG, Newton-CG, and BFGS now accept
    parameters c1 and c2, allowing specification of the Armijo and curvature rule
    parameters, respectively.
  • scipy.optimize.curve_fit performance has improved due to more efficient memoization
    of the callable function.

scipy.signal improvements

  • freqz, freqz_zpk, and group_delay are now more accurate
    when fs has a default value.
  • The new class ShortTimeFFT provides a more versatile implementation of the
    short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
    spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on
    dual windows and provides more fine-grained control of the parametrization especially
    in regard to scaling and phase-shift. Functionality was implemented to ease
    working with signal and STFT chunks. A section has been added to the "SciPy User Guide"
    providing algorithmic details. The functions stft, istft and spectrogram
    have been marked as legacy.

scipy.sparse improvements

  • sparse.linalg iterative solvers sparse.linalg.cg,
    sparse.linalg.cgs, sparse.linalg.bicg, sparse.linalg.bicgstab,
    sparse.linalg.gmres, and sparse.linalg.qmr are rewritten in Python.
  • Updated vendored SuperLU version to 6.0.1, along with a few additional
    fixes.
  • Sparse arrays have gained additional constructors: eye_array,
    random_array, block_array, and identity. kron and kronsum
    have been adjusted to additionally support operation on sparse arrays.
  • Sparse matrices now support a transpose with axes=(1, 0), to mirror
    the .T method.
  • LaplacianNd now allows selection of the largest subset of eigenvalues,
    and additionally now supports retrieval of the corresponding eigenvectors.
    The performance of LaplacianNd has also been improved.
  • The performance of dok_matrix and dok_array has been improved,
    and their inheritance behavior should be more robust.
  • hstack, vstack, and block_diag now work with sparse arrays, and
    preserve the input sparse type.
  • A new function, scipy.sparse.linalg.matrix_power, has been added, allowing
    for exponentiation of sparse arrays.

scipy.spatial improvements

  • Two new methods were implemented for spatial.transform.Rotation:
    __pow__ to raise a rotation to integer or fractional power and
    approx_equal to check if two rotations are approximately equal.
  • The method Rotation.align_vectors was extended to solve a constrained
    alignment problem where two vectors are required to be aligned precisely.
    Also when given a single pair of vectors, the algorithm now returns the
    rotation with minimal magnitude, which can be considered as a minor
    backward incompatible change.
  • A new representation for spatial.transform.Rotation called Davenport
    angles is available through from_davenport and as_davenport methods.
  • Performance improvements have been added to distance.hamming and
    distance.correlation.
  • Improved performance of SphericalVoronoi sort_vertices_of_regions
    and two dimensional area calculations.

scipy.special improvements

  • Added scipy.special.stirling2 for computation of Stirling numbers of the
    second kind. Both exact calculation and an asymptotic approximation
    (the default) are supported via exact=True and exact=False (the
    default) respectively.
  • Added scipy.special.betaincc for computation of the complementary
    incomplete Beta function and scipy.special.betainccinv for computation of
    its inverse.
  • Improved precision of scipy.special.betainc and scipy.special.betaincinv.
  • Experimental support added for alternative backends: functions
    scipy.special.log_ndtr, scipy.special.ndtr, scipy.special.ndtri,
    scipy.special.erf, scipy.special.erfc, scipy.special.i0,
    scipy.special.i0e, scipy.special.i1, scipy.special.i1e,
    scipy.special.gammaln, scipy.special.gammainc, scipy.special.gammaincc,
    scipy.special.logit, and scipy.special.expit now accept PyTorch tensors
    and CuPy arrays. These features are still under development and likely to
    contain bugs, so they are disabled by default; enable them by setting a
    SCIPY_ARRAY_API environment variable to 1 before importing scipy.
    Testing is appreciated!

scipy.stats improvements

  • Added scipy.stats.quantile_test, a nonparametric test of whether a
    hypothesized value is the quantile associated with a specified probability.
    The confidence_interval method of the result object gives a confidence
    interval of the quantile.
  • scipy.stats.sampling.FastGeneratorInversion provides a convenient
    interface to fast random sampling via numerical inversion of distribution
    CDFs.
  • scipy.stats.geometric_discrepancy adds geometric/topological discrepancy
    metrics for random samples.
  • scipy.stats.multivariate_normal now has a fit method for fitting
    distribution parameters to data via maximum likelihood estimation.
  • scipy.stats.bws_test performs the Baumgartner-Weiss-Schindler test of
    whether two-samples were drawn from the same distribution.
  • scipy.stats.jf_skew_t implements the Jones and Faddy skew-t distribution.
  • scipy.stats.anderson_ksamp now supports a permutation version of the test
    using the method parameter.
  • The fit methods of scipy.stats.halfcauchy, scipy.stats.halflogistic, and
    scipy.stats.halfnorm are faster and more accurate.
  • scipy.stats.beta entropy accuracy has been improved for extreme values of
    distribution parameters.
  • The accuracy of sf and/or isf methods have been improved for
    several distributions: scipy.stats.burr, scipy.stats.hypsecant,
    scipy.stats.kappa3, scipy.stats.loglaplace, scipy.stats.lognorm,
    scipy.stats.lomax, scipy.stats.pearson3, scipy.stats.rdist, and
    scipy.stats.pareto.
  • The following functions now support parameters axis, nan_policy, and
    keep_dims: scipy.stats.entropy, scipy.stats.differential_entropy,
    scipy.stats.variation, scipy.stats.ansari, scipy.stats.bartlett,
    scipy.stats.levene, scipy.stats.fligner, scipy.stats.circmean,
    scipy.stats.circvar, scipy.stats.circstd, scipy.stats.tmean,
    scipy.stats.tvar, scipy.stats.tstd, scipy.stats.tmin, scipy.stats.tmax,
    and scipy.stats.tsem.
  • The logpdf and fit methods of scipy.stats.skewnorm have been improved.
  • The beta negative binomial distribution is implemented as scipy.stats.betanbinom.
  • Improved performance of scipy.stats.invwishart rvs and logpdf.
  • A source of intermediate overflow in scipy.stats.boxcox_normmax with
    method='mle' has been eliminated, and the returned value of lmbda is
    constrained such that the transformed data will not overflow.
  • scipy.stats.nakagami stats is more accurate and reliable.
  • A source of intermediate overflow in scipy.norminvgauss.pdf has been eliminated.
  • Added support for masked arrays to scipy.stats.circmean, scipy.stats.circvar,
    scipy.stats.circstd, and scipy.stats.entropy.
  • scipy.stats.dirichlet has gained a new covariance (cov) method.
  • Improved accuracy of entropy method of scipy.stats.multivariate_t for large
    degrees of freedom.
  • scipy.stats.loggamma has an improved entropy method.

Deprecated features

  • Error messages have been made clearer for objects that don't exist in the
    public namespace and warnings sharpened for private attributes that are not
    supposed to be imported at all.

  • scipy.signal.cmplx_sort has been deprecated and will be removed in
    SciPy 1.15. A replacement you can use is provided in the deprecation message.

  • Values the the argument initial of scipy.integrate.cumulative_trapezoid
    other than 0 and None are now deprecated.

  • scipy.stats.rvs_ratio_uniforms is deprecated in favour of
    scipy.stats.sampling.RatioUniforms

  • scipy.integrate.quadrature and scipy.integrate.romberg have been
    deprecated due to accuracy issues and interface shortcomings. They will
    be removed in SciPy 1.15. Please use scipy.integrate.quad instead.

  • Coinciding with upcoming changes to function signatures (e.g. removal of a
    deprecated keyword), we are deprecating positional use of keyword arguments
    for the affected functions, which will raise an error starting with
    SciPy 1.14. In some cases, this has delayed the originally announced
    removal date, to give time to respond to the second part of the deprecation.
    Affected functions are:

    • linalg.{eigh, eigvalsh, pinv}
    • integrate.simpson
    • signal.{firls, firwin, firwin2, remez}
    • sparse.linalg.{bicg, bicgstab, cg, cgs, gcrotmk, gmres, lgmres, minres, qmr, tfqmr}
    • special.comb
    • stats.kendalltau

  • All wavelet functions have been deprecated, as PyWavelets provides suitable
    implementations; affected functions are: signal.{daub, qmf, cascade, morlet, morlet2, ricker, cwt}

  • scipy.integrate.trapz, scipy.integrate.cumtrapz, and scipy.integrate.simps have
    been deprecated in favour of scipy.integrate.trapezoid, scipy.integrate.cumulative_trapezoid,
    and scipy.integrate.simpson respectively and will be removed in SciPy 1.14.

  • The tol argument of scipy.sparse.linalg.{bcg,bicstab,cg,cgs,gcrotmk,gmres,lgmres,minres,qmr,tfqmr}
    is now deprecated in favour of rtol and will be removed in SciPy 1.14.
    Furthermore, the default value of atol for these functions is due
    to change to 0.0 in SciPy 1.14.

Expired Deprecations

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

  • The centered keyword of scipy.stats.qmc.LatinHypercube has been removed.
    Use scrambled=False instead of centered=True.
  • scipy.stats.binom_test has been removed in favour of scipy.stats.binomtest.
  • In scipy.stats.iqr, the use of scale='raw' has been removed in favour
    of scale=1.

Backwards incompatible changes

Other changes

  • The arguments used to compile and link SciPy are now available via
    show_config.

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This PR contains the following updates: | Package | Update | Change | |---|---|---| | [scipy](https://scipy.org/) ([source](https://github.com/scipy/scipy)) | minor | `~=1.11.0` -> `~=1.12.0` | --- ### Release Notes <details> <summary>scipy/scipy (scipy)</summary> ### [`v1.12.0`](https://github.com/scipy/scipy/releases/tag/v1.12.0): SciPy 1.12.0 [Compare Source](https://github.com/scipy/scipy/compare/v1.11.4...v1.12.0) # SciPy 1.12.0 Release Notes SciPy `1.12.0` 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 1.12.x branch, and on adding new features on the main branch. This release requires Python `3.9+` and NumPy `1.22.4` or greater. For running on PyPy, PyPy3 `6.0+` is required. # Highlights of this release - Experimental support for the array API standard has been added to part of `scipy.special`, and to all of `scipy.fft` and `scipy.cluster`. There are likely to be bugs and early feedback for usage with CuPy arrays, PyTorch tensors, and other array API compatible libraries is appreciated. Use the `SCIPY_ARRAY_API` environment variable for testing. - A new class, `ShortTimeFFT`, provides a more versatile implementation of the short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-) spectrogram. It utilizes an improved algorithm for calculating the ISTFT. - Several new constructors have been added for sparse arrays, and many operations now additionally support sparse arrays, further facilitating the migration from sparse matrices. - A large portion of the `scipy.stats` API now has improved support for handling `NaN` values, masked arrays, and more fine-grained shape-handling. The accuracy and performance of a number of `stats` methods have been improved, and a number of new statistical tests and distributions have been added. # New features # `scipy.cluster` improvements - Experimental support added for the array API standard; PyTorch tensors, CuPy arrays and array API compatible array libraries are now accepted (GPU support is limited to functions with pure Python implementations). CPU arrays which can be converted to and from NumPy are supported module-wide and returned arrays will match the input type. This behaviour is enabled by setting the `SCIPY_ARRAY_API` environment variable before importing `scipy`. This experimental support is still under development and likely to contain bugs - testing is very welcome. # `scipy.fft` improvements - Experimental support added for the array API standard; functions which are part of the `fft` array API standard extension module, as well as the Fast Hankel Transforms and the basic FFTs which are not in the extension module, now accept PyTorch tensors, CuPy arrays and array API compatible array libraries. CPU arrays which can be converted to and from NumPy arrays are supported module-wide and returned arrays will match the input type. This behaviour is enabled by setting the `SCIPY_ARRAY_API` environment variable before importing `scipy`. This experimental support is still under development and likely to contain bugs - testing is very welcome. # `scipy.integrate` improvements - Added `scipy.integrate.cumulative_simpson` for cumulative quadrature from sampled data using Simpson's 1/3 rule. # `scipy.interpolate` improvements - New class `NdBSpline` represents tensor-product splines in N dimensions. This class only knows how to evaluate a tensor product given coefficients and knot vectors. This way it generalizes `BSpline` for 1D data to N-D, and parallels `NdPPoly` (which represents N-D tensor product polynomials). Evaluations exploit the localized nature of b-splines. - `NearestNDInterpolator.__call__` accepts `**query_options`, which are passed through to the `KDTree.query` call to find nearest neighbors. This allows, for instance, to limit the neighbor search distance and parallelize the query using the `workers` keyword. - `BarycentricInterpolator` now allows computing the derivatives. - It is now possible to change interpolation values in an existing `CloughTocher2DInterpolator` instance, while also saving the barycentric coordinates of interpolation points. # `scipy.linalg` improvements - Access to new low-level LAPACK functions is provided via `dtgsyl` and `stgsyl`. # `scipy.optimize` improvements - `scipy.optimize.isotonic_regression` has been added to allow nonparametric isotonic regression. - `scipy.optimize.nnls` is rewritten in Python and now implements the so-called fnnls or fast nnls, making it more efficient for high-dimensional problems. - The result object of `scipy.optimize.root` and `scipy.optimize.root_scalar` now reports the method used. - The `callback` method of `scipy.optimize.differential_evolution` can now be passed more detailed information via the `intermediate_results` keyword parameter. Also, the evolution `strategy` now accepts a callable for additional customization. The performance of `differential_evolution` has also been improved. - `scipy.optimize.minimize` method `Newton-CG` now supports functions that return sparse Hessian matrices/arrays for the `hess` parameter and is slightly more efficient. - `scipy.optimize.minimize` method `BFGS` now accepts an initial estimate for the inverse of the Hessian, which allows for more efficient workflows in some circumstances. The new parameter is `hess_inv0`. - `scipy.optimize.minimize` methods `CG`, `Newton-CG`, and `BFGS` now accept parameters `c1` and `c2`, allowing specification of the Armijo and curvature rule parameters, respectively. - `scipy.optimize.curve_fit` performance has improved due to more efficient memoization of the callable function. # `scipy.signal` improvements - `freqz`, `freqz_zpk`, and `group_delay` are now more accurate when `fs` has a default value. - The new class `ShortTimeFFT` provides a more versatile implementation of the short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-) spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on dual windows and provides more fine-grained control of the parametrization especially in regard to scaling and phase-shift. Functionality was implemented to ease working with signal and STFT chunks. A section has been added to the "SciPy User Guide" providing algorithmic details. The functions `stft`, `istft` and `spectrogram` have been marked as legacy. # `scipy.sparse` improvements - `sparse.linalg` iterative solvers `sparse.linalg.cg`, `sparse.linalg.cgs`, `sparse.linalg.bicg`, `sparse.linalg.bicgstab`, `sparse.linalg.gmres`, and `sparse.linalg.qmr` are rewritten in Python. - Updated vendored SuperLU version to `6.0.1`, along with a few additional fixes. - Sparse arrays have gained additional constructors: `eye_array`, `random_array`, `block_array`, and `identity`. `kron` and `kronsum` have been adjusted to additionally support operation on sparse arrays. - Sparse matrices now support a transpose with `axes=(1, 0)`, to mirror the `.T` method. - `LaplacianNd` now allows selection of the largest subset of eigenvalues, and additionally now supports retrieval of the corresponding eigenvectors. The performance of `LaplacianNd` has also been improved. - The performance of `dok_matrix` and `dok_array` has been improved, and their inheritance behavior should be more robust. - `hstack`, `vstack`, and `block_diag` now work with sparse arrays, and preserve the input sparse type. - A new function, `scipy.sparse.linalg.matrix_power`, has been added, allowing for exponentiation of sparse arrays. # `scipy.spatial` improvements - Two new methods were implemented for `spatial.transform.Rotation`: `__pow__` to raise a rotation to integer or fractional power and `approx_equal` to check if two rotations are approximately equal. - The method `Rotation.align_vectors` was extended to solve a constrained alignment problem where two vectors are required to be aligned precisely. Also when given a single pair of vectors, the algorithm now returns the rotation with minimal magnitude, which can be considered as a minor backward incompatible change. - A new representation for `spatial.transform.Rotation` called Davenport angles is available through `from_davenport` and `as_davenport` methods. - Performance improvements have been added to `distance.hamming` and `distance.correlation`. - Improved performance of `SphericalVoronoi` `sort_vertices_of_regions` and two dimensional area calculations. # `scipy.special` improvements - Added `scipy.special.stirling2` for computation of Stirling numbers of the second kind. Both exact calculation and an asymptotic approximation (the default) are supported via `exact=True` and `exact=False` (the default) respectively. - Added `scipy.special.betaincc` for computation of the complementary incomplete Beta function and `scipy.special.betainccinv` for computation of its inverse. - Improved precision of `scipy.special.betainc` and `scipy.special.betaincinv`. - Experimental support added for alternative backends: functions `scipy.special.log_ndtr`, `scipy.special.ndtr`, `scipy.special.ndtri`, `scipy.special.erf`, `scipy.special.erfc`, `scipy.special.i0`, `scipy.special.i0e`, `scipy.special.i1`, `scipy.special.i1e`, `scipy.special.gammaln`, `scipy.special.gammainc`, `scipy.special.gammaincc`, `scipy.special.logit`, and `scipy.special.expit` now accept PyTorch tensors and CuPy arrays. These features are still under development and likely to contain bugs, so they are disabled by default; enable them by setting a `SCIPY_ARRAY_API` environment variable to `1` before importing `scipy`. Testing is appreciated! # `scipy.stats` improvements - Added `scipy.stats.quantile_test`, a nonparametric test of whether a hypothesized value is the quantile associated with a specified probability. The `confidence_interval` method of the result object gives a confidence interval of the quantile. - `scipy.stats.sampling.FastGeneratorInversion` provides a convenient interface to fast random sampling via numerical inversion of distribution CDFs. - `scipy.stats.geometric_discrepancy` adds geometric/topological discrepancy metrics for random samples. - `scipy.stats.multivariate_normal` now has a `fit` method for fitting distribution parameters to data via maximum likelihood estimation. - `scipy.stats.bws_test` performs the Baumgartner-Weiss-Schindler test of whether two-samples were drawn from the same distribution. - `scipy.stats.jf_skew_t` implements the Jones and Faddy skew-t distribution. - `scipy.stats.anderson_ksamp` now supports a permutation version of the test using the `method` parameter. - The `fit` methods of `scipy.stats.halfcauchy`, `scipy.stats.halflogistic`, and `scipy.stats.halfnorm` are faster and more accurate. - `scipy.stats.beta` `entropy` accuracy has been improved for extreme values of distribution parameters. - The accuracy of `sf` and/or `isf` methods have been improved for several distributions: `scipy.stats.burr`, `scipy.stats.hypsecant`, `scipy.stats.kappa3`, `scipy.stats.loglaplace`, `scipy.stats.lognorm`, `scipy.stats.lomax`, `scipy.stats.pearson3`, `scipy.stats.rdist`, and `scipy.stats.pareto`. - The following functions now support parameters `axis`, `nan_policy`, and `keep_dims`: `scipy.stats.entropy`, `scipy.stats.differential_entropy`, `scipy.stats.variation`, `scipy.stats.ansari`, `scipy.stats.bartlett`, `scipy.stats.levene`, `scipy.stats.fligner`, `scipy.stats.circmean`, `scipy.stats.circvar`, `scipy.stats.circstd`, `scipy.stats.tmean`, `scipy.stats.tvar`, `scipy.stats.tstd`, `scipy.stats.tmin`, `scipy.stats.tmax`, and `scipy.stats.tsem`. - The `logpdf` and `fit` methods of `scipy.stats.skewnorm` have been improved. - The beta negative binomial distribution is implemented as `scipy.stats.betanbinom`. - Improved performance of `scipy.stats.invwishart` `rvs` and `logpdf`. - A source of intermediate overflow in `scipy.stats.boxcox_normmax` with `method='mle'` has been eliminated, and the returned value of `lmbda` is constrained such that the transformed data will not overflow. - `scipy.stats.nakagami` `stats` is more accurate and reliable. - A source of intermediate overflow in `scipy.norminvgauss.pdf` has been eliminated. - Added support for masked arrays to `scipy.stats.circmean`, `scipy.stats.circvar`, `scipy.stats.circstd`, and `scipy.stats.entropy`. - `scipy.stats.dirichlet` has gained a new covariance (`cov`) method. - Improved accuracy of `entropy` method of `scipy.stats.multivariate_t` for large degrees of freedom. - `scipy.stats.loggamma` has an improved `entropy` method. # Deprecated features - Error messages have been made clearer for objects that don't exist in the public namespace and warnings sharpened for private attributes that are not supposed to be imported at all. - `scipy.signal.cmplx_sort` has been deprecated and will be removed in SciPy 1.15. A replacement you can use is provided in the deprecation message. - Values the the argument `initial` of `scipy.integrate.cumulative_trapezoid` other than `0` and `None` are now deprecated. - `scipy.stats.rvs_ratio_uniforms` is deprecated in favour of `scipy.stats.sampling.RatioUniforms` - `scipy.integrate.quadrature` and `scipy.integrate.romberg` have been deprecated due to accuracy issues and interface shortcomings. They will be removed in SciPy 1.15. Please use `scipy.integrate.quad` instead. - Coinciding with upcoming changes to function signatures (e.g. removal of a deprecated keyword), we are deprecating positional use of keyword arguments for the affected functions, which will raise an error starting with SciPy 1.14. In some cases, this has delayed the originally announced removal date, to give time to respond to the second part of the deprecation. Affected functions are: - `linalg.{eigh, eigvalsh, pinv}` - `integrate.simpson` - `signal.{firls, firwin, firwin2, remez}` - `sparse.linalg.{bicg, bicgstab, cg, cgs, gcrotmk, gmres, lgmres, minres, qmr, tfqmr}` - `special.comb` - `stats.kendalltau` - All wavelet functions have been deprecated, as PyWavelets provides suitable implementations; affected functions are: `signal.{daub, qmf, cascade, morlet, morlet2, ricker, cwt}` - `scipy.integrate.trapz`, `scipy.integrate.cumtrapz`, and `scipy.integrate.simps` have been deprecated in favour of `scipy.integrate.trapezoid`, `scipy.integrate.cumulative_trapezoid`, and `scipy.integrate.simpson` respectively and will be removed in SciPy 1.14. - The `tol` argument of `scipy.sparse.linalg.{bcg,bicstab,cg,cgs,gcrotmk,gmres,lgmres,minres,qmr,tfqmr}` is now deprecated in favour of `rtol` and will be removed in SciPy 1.14. Furthermore, the default value of `atol` for these functions is due to change to `0.0` in SciPy 1.14. # Expired Deprecations There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected: - The `centered` keyword of `scipy.stats.qmc.LatinHypercube` has been removed. Use `scrambled=False` instead of `centered=True`. - `scipy.stats.binom_test` has been removed in favour of `scipy.stats.binomtest`. - In `scipy.stats.iqr`, the use of `scale='raw'` has been removed in favour of `scale=1`. # Backwards incompatible changes # Other changes - The arguments used to compile and link SciPy are now available via `show_config`. # Authors - Name (commits) - endolith (1) - h-vetinari (34) - Tom Adamczewski (3) + - Anudeep Adiraju (1) + - akeemlh (1) - Alex Amadori (2) + - Raja Yashwanth Avantsa (2) + - Seth Axen (1) + - Ross Barnowski (1) - Dan Barzilay (1) + - Ashish Bastola (1) + - Christoph Baumgarten (2) - Ben Beasley (3) + - Doron Behar (1) - Peter Bell (1) - Sebastian Berg (1) - Ben Boeckel (1) + - David Boetius (1) + - Matt Borland (1) - Jake Bowhay (103) - Larry Bradley (1) + - Dietrich Brunn (5) - Evgeni Burovski (102) - Matthias Bussonnier (18) - CJ Carey (6) - Colin Carroll (1) + - Aadya Chinubhai (1) + - Luca Citi (1) - Lucas Colley (141) + - com3dian (1) + - Anirudh Dagar (4) - Danni (1) + - Dieter Werthmüller (1) - John Doe (2) + - Philippe DONNAT (2) + - drestebon (1) + - Thomas Duvernay (1) - elbarso (1) + - emilfrost (2) + - Paul Estano (8) + - Evandro (2) - Franz Király (1) + - Nikita Furin (1) + - gabrielthomsen (1) + - Lukas Geiger (9) + - Artem Glebov (22) + - Caden Gobat (1) - Ralf Gommers (127) - Alexander Goscinski (2) + - Rohit Goswami (2) + - Olivier Grisel (1) - Matt Haberland (244) - Charles Harris (1) - harshilkamdar (1) + - Alon Hovav (2) + - Gert-Ludwig Ingold (1) - Romain Jacob (1) + - jcwhitehead (1) + - Julien Jerphanion (13) - He Jia (1) - JohnWT (1) + - jokasimr (1) + - Evan W Jones (1) - Karen Róbertsdóttir (1) + - Ganesh Kathiresan (1) - Robert Kern (11) - Andrew Knyazev (4) - Uwe L. Korn (1) + - Rishi Kulkarni (1) - Kale Kundert (3) + - Jozsef Kutas (2) - Kyle0 (2) + - Robert Langefeld (1) + - Jeffrey Larson (1) + - Jessy Lauer (1) + - lciti (1) + - Hoang Le (1) + - Antony Lee (5) - Thilo Leitzbach (4) + - LemonBoy (2) + - Ellie Litwack (8) + - Thomas Loke (4) + - Malte Londschien (1) + - Christian Lorentzen (6) - Adam Lugowski (10) + - lutefiskhotdish (1) - mainak33 (1) + - Ben Mares (11) + - mart-mihkel (2) + - Mateusz Sokół (24) + - Nikolay Mayorov (4) - Nicholas McKibben (1) - Melissa Weber Mendonça (7) - Michał Górny (1) - Kat Mistberg (2) + - mkiffer (1) + - mocquin (1) + - Nicolas Mokus (2) + - Sturla Molden (1) - Roberto Pastor Muela (3) + - Bijay Nayak (1) + - Andrew Nelson (105) - Praveer Nidamaluri (3) + - Lysandros Nikolaou (2) - Dimitri Papadopoulos Orfanos (7) - Pablo Rodríguez Pérez (1) + - Dimitri Papadopoulos (2) - Tirth Patel (14) - Kyle Paterson (1) + - Paul (4) + - Yann Pellegrini (2) + - Matti Picus (4) - Ilhan Polat (36) - Pranav (1) + - Bharat Raghunathan (1) - Chris Rapson (1) + - Matteo Raso (4) - Tyler Reddy (215) - Martin Reinecke (1) - Tilo Reneau-Cardoso (1) + - resting-dove (2) + - Simon Segerblom Rex (4) - Lucas Roberts (2) - Pamphile Roy (31) - Feras Saad (3) + - Atsushi Sakai (3) - Masahiro Sakai (2) + - Omar Salman (14) - Andrej Savikin (1) + - Daniel Schmitz (55) - Dan Schult (19) - Scott Shambaugh (9) - Sheila-nk (2) + - Mauro Silberberg (3) + - Maciej Skorski (1) + - Laurent Sorber (1) + - Albert Steppi (28) - Kai Striega (1) - Saswat Susmoy (1) + - Alex Szatmary (1) + - Søren Fuglede Jørgensen (3) - othmane tamri (3) + - Ewout ter Hoeven (1) - Will Tirone (1) - TLeitzbach (1) + - Kevin Topolski (1) + - Edgar Andrés Margffoy Tuay (1) - Dipansh Uikey (1) + - Matus Valo (3) - Christian Veenhuis (2) - Nicolas Vetsch (1) + - Isaac Virshup (7) - Hielke Walinga (2) + - Stefan van der Walt (2) - Warren Weckesser (7) - Bernhard M. Wiedemann (4) - Levi John Wolf (1) - Xuefeng Xu (4) + - Rory Yorke (2) - YoussefAli1 (1) + - Irwin Zaid (4) + - Jinzhe Zeng (1) + - JIMMY ZHAO (1) + A total of 163 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. </details> --- ### Configuration 📅 **Schedule**: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined). 🚦 **Automerge**: Enabled. ♻ **Rebasing**: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox. 🔕 **Ignore**: Close this PR and you won't be reminded about this update again. --- - [ ] <!-- rebase-check -->If you want to rebase/retry this PR, check this box --- This PR has been generated by [Renovate Bot](https://github.com/renovatebot/renovate). <!--renovate-debug:eyJjcmVhdGVkSW5WZXIiOiIzNi4zNS4wIiwidXBkYXRlZEluVmVyIjoiMzYuMzUuMCIsInRhcmdldEJyYW5jaCI6ImRldiJ9-->
Renovate added 1 commit 2024-01-21 00:09:15 +02:00
Renovate scheduled this pull request to auto merge when all checks succeed 2024-01-21 00:09:16 +02:00
profitroll merged commit 9d70724d64 into dev 2024-01-21 13:54:37 +02:00
profitroll deleted branch renovate/scipy-1.x 2024-01-21 13:54:37 +02:00
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