Update dependency scipy to ~1.9.0

[renovate[bot]]

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
scipy (source)	~1.7.0 -> ~1.9.0

---

Release Notes

scipy/scipy

### [`v1.9.1`](https://togithub.com/scipy/scipy/releases/tag/v1.9.1) [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) [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) + - Michael Burkhart (2) + - Evgeni Burovski (96) - Matthias Bussonnier (20) - Dominic C (1) - Cameron (1) + - CJ Carey (3) - Thomas A Caswell (2) - Ali Cetin (2) + - Hood Chatham (5) + - Klesk Chonkin (1) - Craig Citro (1) + - Dan Cogswell (1) + - Luigi Cruz (1) + - Anirudh Dagar (5) - Brandon David (1) - deepakdinesh1123 (1) + - Denton DeLoss (1) + - derbuihan (2) + - Sameer Deshmukh (13) + - Niels Doucet (1) + - DWesl (8) - eytanadler (30) + - Thomas J. Fan (5) - Isuru Fernando (3) - Joseph Fox-Rabinovitz (1) - Ryan Gibson (4) + - Ralf Gommers (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) + - Toshiki Kataoka (1) - Chris Keefe (1) + - Robert Kern (4) - Andrew Knyazev (11) - Matthias Koeppe (4) + - Sergey Koposov (1) - Volodymyr Kozachynskyi (1) + - Yotaro Kubo (2) + - Jacob Lapenna (1) + - Peter Mahler Larsen (8) - Eric Larson (4) - Laurynas Mikšys (1) + - Antony Lee (1) - Gregory R. Lee (2) - lerichi (1) + - Tim Leslie (2) - P. L. Lim (1) - Smit Lunagariya (43) - lutefiskhotdish (1) + - Cong Ma (12) - Syrtis Major (1) - Nicholas McKibben (18) - Melissa Weber Mendonça (10) - Mark Mikofski (1) - Jarrod Millman (13) - Harsh Mishra (6) - ML-Nielsen (3) + - Matthew Murray (1) + - Andrew Nelson (50) - Dimitri Papadopoulos Orfanos (1) + - Evgueni Ovtchinnikov (2) + - Sambit Panda (1) - Nick Papior (2) - Tirth Patel (43) - Petar Mlinarić (1) - petroselo (1) + - Ilhan Polat (64) - Anthony Polloreno (1) - Amit Portnoy (1) + - Quentin Barthélemy (9) - Patrick N. Raanes (1) + - Tyler Reddy (185) - Pamphile Roy (199) - Vivek Roy (2) + - sabonerune (1) + - Niyas Sait (2) + - Atsushi Sakai (25) - Mazen Sayed (1) + - Eduardo Schettino (5) + - Daniel Schmitz (6) + - Eli Schwartz (4) + - SELEE (2) + - Namami Shanker (4) - siddhantwahal (1) + - Gagandeep Singh (8) - Soph (1) + - Shivnaren Srinivasan (1) + - Scott Staniewicz (1) + - Leo C. Stein (4) - Albert Steppi (7) - Christopher Strickland (1) + - Kai Striega (4) - Søren Fuglede Jørgensen (1) - Aleksandr Tagilov (1) + - Masayuki Takagi (1) + - Sai Teja (1) + - Ewout ter Hoeven (2) + - Will Tirone (2) - Bas van Beek (7) - Dhruv Vats (1) - Arthur Volant (1) - Samuel Wallan (5) - Stefan van der Walt (8) - Warren Weckesser (84) - Anreas Weh (1) - Nils Werner (1) - Aviv Yaish (1) + - Dowon Yi (1) - Rory Yorke (1) - Yosshi999 (1) + - yuanx749 (2) + - Gang Zhao (23) - ZhihuiChen0903 (1) - Pavel Zun (1) + - David Zwicker (1) + A total of 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) [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) [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 generators added are: - TransformedDensityRejection - DiscreteAliasUrn - NumericalInversePolynomial - DiscreteGuideTable - SimpleRatioUniforms The `binned_statistic` set of functions now have improved performance for the `std`, `min`, `max`, and `median` statistic calculations. `somersd` and `_tau_b` now have faster Pythran-based implementations. Some general efficiency improvements to handling of `nan` values in several `stats` functions. Added the Tukey-Kramer test as `scipy.stats.tukey_hsd`. Improved performance of `scipy.stats.argus` `rvs` method. Added the parameter `keepdims` to `scipy.stats.variation` and prevent the undesirable return of a masked array from the function in some cases. `permutation_test` performs an exact or randomized permutation test of a given statistic on provided data. # Deprecated features # Clear split between public and private API SciPy has always documented what its public API consisted of in :ref:`its API reference docs `, however there never was a clear split between public and private namespaces in the code base. In this release, all namespaces that were private but happened to miss underscores in their names have been deprecated. These include (as examples, there are many more): - `scipy.signal.spline` - `scipy.ndimage.filters` - `scipy.ndimage.fourier` - `scipy.ndimage.measurements` - `scipy.ndimage.morphology` - `scipy.ndimage.interpolation` - `scipy.sparse.linalg.solve` - `scipy.sparse.linalg.eigen` - `scipy.sparse.linalg.isolve` All functions and other objects in these namespaces that were meant to be public are accessible from their respective public namespace (e.g. `scipy.signal`). The design principle is that any public object must be accessible from a single namespace only; there are a few exceptions, mostly for historical reasons (e.g., `stats` and `stats.distributions` overlap). For other libraries aiming to provide a SciPy-compatible API, it is now unambiguous what namespace structure to follow. See `gh-14360 `\_ for more details. # Other deprecations `NumericalInverseHermite` has been deprecated from `scipy.stats` and moved to the `scipy.stats.sampling` submodule. It now uses the C implementation of the UNU.RAN library so the result of methods like `ppf` may vary slightly. Parameter `tol` has been deprecated and renamed to `u_resolution`. The parameter `max_intervals` has also been deprecated and will be removed in a future release of SciPy. # Backwards incompatible changes - SciPy has raised the minimum compiler versions to GCC 6.3 on linux and VS2019 on windows. In particular, this means that SciPy may now use C99 and C++14 features. For more details see `here `\_. - The result for empty bins for `scipy.stats.binned_statistic` with the builtin `'std'` metric is now `nan`, for consistency with `np.std`. - The function `scipy.spatial.distance.wminkowski` has been removed. To achieve the same results as before, please use the `minkowski` distance function with the (optional) `w=` keyword-argument for the given weight. # Other changes Some Fortran 77 code was modernized to be compatible with NAG's nagfor Fortran compiler (see, e.g., `PR 13229 `\_). `threadpoolctl` may now be used by our test suite to substantially improve the efficiency of parallel test suite runs. # Authors - [@​endolith](https://togithub.com/endolith) - adamadanandy + - akeemlh + - Anton Akhmerov - Marvin Albert + - alegresor + - Andrew Annex + - Pantelis Antonoudiou + - Ross Barnowski + - Christoph Baumgarten - Stephen Becker + - Nickolai Belakovski - Peter Bell - berberto + - Georgii Bocharov + - Evgeni Burovski - Matthias Bussonnier - CJ Carey - Justin Charlong + - Hood Chatham + - Dennis Collaris + - David Cottrell + - cruyffturn + - da-woods + - Anirudh Dagar - Tiger Du + - Thomas Duvernay - Dani El-Ayyass + - Castedo Ellerman + - Donnie Erb + - Andreas Esders-Kopecky + - Livio F + - Isuru Fernando - Evelyn Fitzgerald + - Sara Fridovich-Keil + - Mark E Fuller + - Ralf Gommers - Kevin Richard Green + - guiweber + - Nitish Gupta + - h-vetinari - Matt Haberland - J. Hariharan + - Charles Harris - Jonathan Helgert + - Trever Hines - Nadav Horesh - Ian Hunt-Isaak + - ich + - Itrimel + - Jan-Hendrik Müller + - Jebby993 + - Yikun Jiang + - Evan W Jones + - Nathaniel Jones + - Jeffrey Kelling + - Malik Idrees Hasan Khan + - Paul Kienzle - Sergey B Kirpichev - Kadatatlu Kishore + - Andrew Knyazev - Ravin Kumar + - Peter Mahler Larsen - Eric Larson - Antony Lee - Gregory R. Lee - Tim Leslie - lezcano + - Xingyu Liu - Christian Lorentzen - Lorenzo + - Smit Lunagariya + - Lv101Magikarp + - Yair M + - Cong Ma - Lorenzo Maffioli + - majiang + - Brian McFee + - Nicholas McKibben - John Speed Meyers + - millivolt9 + - Jarrod Millman - Harsh Mishra + - Boaz Mohar + - naelsondouglas + - Andrew Nelson - Nico Schlömer - Thomas Nowotny + - nullptr + - Teddy Ort + - Nick Papior - ParticularMiner + - Dima Pasechnik - Tirth Patel - Matti Picus - Ilhan Polat - Adrian Price-Whelan + - Quentin Barthélemy + - Sundar R + - Judah Rand + - Tyler Reddy - Renal-Of-Loon + - Frederic Renner + - Pamphile Roy - Bharath Saiguhan + - Atsushi Sakai - Eric Schanet + - Sebastian Wallkötter - serge-sans-paille - Reshama Shaikh + - Namami Shanker - siddhantwahal + - Walter Simson + - Gagandeep Singh + - Leo C. Stein + - Albert Steppi - Kai Striega - Diana Sukhoverkhova - Søren Fuglede Jørgensen - Masayuki Takagi + - Mike Taves - Ben Thompson + - Bas van Beek - Jacob Vanderplas - Dhruv Vats + - H. Vetinari + - Thomas Viehmann + - Pauli Virtanen - Vlad + - Arthur Volant - Samuel Wallan - Stefan van der Walt - Warren Weckesser - Josh Wilson - Haoyin Xu + - Rory Yorke - Egor Zemlyanoy - Gang Zhao + - 赵丰 (Zhao Feng) + A total of 139 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete.

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File name: poetry.lock

installing v2 tool python v3.10.7
Using prebuild python
Requirement already satisfied: pip in /opt/buildpack/tools/python/3.10.7/lib/python3.10/site-packages (22.2.2)
Files removed: 85
linking tool python v3.10.7
Python 3.10.7
pip 22.2.2 from /opt/buildpack/tools/python/3.10.7/lib/python3.10/site-packages/pip (python 3.10)
Installed v2 /usr/local/buildpack/tools/v2/python.sh in 16 seconds
skip cleanup, not a docker build: e1a01969c994
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Collecting pycparser
  Downloading pycparser-2.21-py2.py3-none-any.whl (118 kB)
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Installing collected packages: webencodings, pylev, ptyprocess, msgpack, lockfile, distlib, urllib3, tomlkit, six, shellingham, pyrsistent, pyparsing, pycparser, poetry-core, platformdirs, pkginfo, pexpect, more-itertools, jeepney, idna, filelock, crashtest, charset-normalizer, certifi, cachy, attrs, virtualenv, requests, packaging, jsonschema, jaraco.classes, html5lib, dulwich, cleo, cffi, requests-toolbelt, cryptography, cachecontrol, SecretStorage, keyring, poetry-plugin-export, poetry
Successfully installed SecretStorage-3.3.3 attrs-22.1.0 cachecontrol-0.12.11 cachy-0.3.0 certifi-2022.9.24 cffi-1.15.1 charset-normalizer-2.1.1 cleo-1.0.0a5 crashtest-0.3.1 cryptography-38.0.1 distlib-0.3.6 dulwich-0.20.46 filelock-3.8.0 html5lib-1.1 idna-3.4 jaraco.classes-3.2.3 jeepney-0.8.0 jsonschema-4.16.0 keyring-23.9.3 lockfile-0.12.2 more-itertools-8.14.0 msgpack-1.0.4 packaging-21.3 pexpect-4.8.0 pkginfo-1.8.3 platformdirs-2.5.2 poetry-1.2.1 poetry-core-1.2.0 poetry-plugin-export-1.1.1 ptyprocess-0.7.0 pycparser-2.21 pylev-1.4.0 pyparsing-3.0.9 pyrsistent-0.18.1 requests-2.28.1 requests-toolbelt-0.9.1 shellingham-1.5.0 six-1.16.0 tomlkit-0.11.5 urllib3-1.26.12 virtualenv-20.16.5 webencodings-0.5.1
Updating dependencies
Resolving dependencies...

The current project's Python requirement (>=3.7.1,<3.11) is not compatible with some of the required packages Python requirement:
  - scipy requires Python >=3.8,<3.12, so it will not be satisfied for Python >=3.7.1,<3.8
  - scipy requires Python >=3.8,<3.12, so it will not be satisfied for Python >=3.7.1,<3.8

Because no versions of scipy match >1.9.0,<1.9.1 || >1.9.1,<1.10.0
 and scipy (1.9.0) requires Python >=3.8,<3.12, scipy is forbidden.
So, because scipy (1.9.1) requires Python >=3.8,<3.12
 and skqulacs depends on scipy (~1.9.0), version solving failed.

  • Check your dependencies Python requirement: The Python requirement can be specified via the `python` or `markers` properties

    For scipy, a possible solution would be to set the `python` property to ">=3.8,<3.11"
    For scipy, a possible solution would be to set the `python` property to ">=3.8,<3.11"

    https://python-poetry.org/docs/dependency-specification/#python-restricted-dependencies,
    https://python-poetry.org/docs/dependency-specification/#using-environment-markers

[renovate[bot]]

Renovate Ignore Notification

As this PR has been closed unmerged, Renovate will now ignore this update (~1.9.0). You will still receive a PR once a newer version is released, so if you wish to permanently ignore this dependency, please add it to the ignoreDeps array of your renovate config.

If this PR was closed by mistake or you changed your mind, you can simply rename this PR and you will soon get a fresh replacement PR opened.