SciPy v1.5.0.rc1 Release Notes

Release Date: 2020-05-30 // over 2 years ago
  • πŸš€ SciPy 1.5.0 Release Notes

    Note : Scipy 1.5.0 is not released yet!

    SciPy 1.5.0 is the culmination of 6 months of hard work. It contains
    βœ… many new features, numerous bug-fixes, improved test coverage and better
    πŸ“š documentation. There have been a number of deprecations and API changes
    πŸš€ in this release, which are documented below. All users are encouraged to
    πŸš€ upgrade to this release, as there are a large number of bug-fixes and
    ⬆️ optimizations. Before upgrading, we recommend that users check that
    πŸ—„ their own code does not use deprecated SciPy functionality (to do so,
    πŸ—„ run your code with python -Wd and check for DeprecationWarning s).
    πŸš€ Our development attention will now shift to bug-fix releases on the
    1.5.x branch, and on adding new features on the master branch.

    πŸš€ This release requires Python 3.6+ and NumPy 1.14.5 or greater.

    For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

    πŸš€ Highlights of this release

    • wrappers for more than a dozen new LAPACK routines are now available
      in scipy.linalg.lapack
    • πŸ‘Œ Improved support for leveraging 64-bit integer size from linear algebra
    • βž• addition of the probability distribution for two-sided one-sample
      βœ… Kolmogorov-Smirnov tests

    πŸ†• New features

    scipy.cluster improvements

    πŸŽ‰ Initialization of scipy.cluster.vq.kmeans2 using minit="++" had a
    quadratic complexity in the number of samples. It has been improved, resulting
    in a much faster initialization with quasi-linear complexity.

    scipy.cluster.hierarchy.dendrogram now respects the matplotlib color

    scipy.fft improvements

    A new keyword-only argument plan is added to all FFT functions in this
    module. It is reserved for passing in a precomputed plan from libraries
    providing a FFT backend (such as PyFFTW and mkl-fft), and it is
    currently not used in SciPy.

    scipy.integrate improvements

    scipy.interpolate improvements improvements error messages are more explicit about what's wrong, and
    extraneous bytes at the ends of files are ignored instead of raising an error
    when the data has successfully been read. gained a simplify_cells parameter, which if set to
    True simplifies the structure of the return value if the .mat file
    contains cell arrays.

    πŸ‘ pathlib.Path objects are now supported in Matrix Market I/O

    scipy.linalg improvements

    scipy.linalg.eigh has been improved. Now various LAPACK drivers can be
    selected at will and also subsets of eigenvalues can be requested via
    subset_by_value keyword. Another keyword subset_by_index is introduced.
    πŸ—„ Keywords turbo and eigvals are deprecated.

    Similarly, standard and generalized Hermitian eigenvalue LAPACK routines
    ?<sy/he>evx are added and existing ones now have full _lwork

    Wrappers for the following LAPACK routines have been added to

    • ?getc2: computes the LU factorization of a general matrix with complete
    • ?gesc2: solves a linear system given an LU factorization from ?getc2
    • ?gejsv: computes the singular value decomposition of a general matrix
      with higher accuracy calculation of tiny singular values and their
      corresponding singular vectors
    • ?geqrfp: computes the QR factorization of a general matrix with
      non-negative elements on the diagonal of R
    • ?gtsvx: solves a linear system with general tridiagonal matrix
    • ?gttrf: computes the LU factorization of a tridiagonal matrix
    • ?gttrs: solves a linear system given an LU factorization from ?gttrf
    • ?ptsvx: solves a linear system with symmetric positive definite
      tridiagonal matrix
    • ?pttrf: computes the LU factorization of a symmetric positive definite
      tridiagonal matrix
    • ?pttrs: solves a linear system given an LU factorization from ?pttrf
    • ?pteqr: computes the eigenvectors and eigenvalues of a positive definite
      tridiagonal matrix
    • ?tbtrs: solves a linear system with a triangular banded matrix
    • ?csd: computes the Cosine Sine decomposition of an orthogonal/unitary

    Generalized QR factorization routines (?geqrf) now have full _lwork

    scipy.linalg.cossin Cosine Sine decomposition of unitary matrices has been
    βž• added.

    The function scipy.linalg.khatri_rao, which computes the Khatri-Rao product,
    was added.

    The new function scipy.linalg.convolution_matrix constructs the Toeplitz
    matrix representing one-dimensional convolution.

    scipy.ndimage improvements

    ⚑️ scipy.optimize improvements

    The finite difference numerical differentiation used in various minimize
    methods that use gradients has several new features:

    • 2-point, 3-point, or complex step finite differences can be used. Previously
      only a 2-step finite difference was available.
    • There is now the possibility to use a relative step size, previously only an
      absolute step size was available.
    • If the minimize method uses bounds the numerical differentiation strictly
      obeys those limits.
    • The numerical differentiation machinery now makes use of a simple cache,
      which in some cases can reduce the number of function evaluations.
    • πŸ‘ minimize's method= 'powell' now supports simple bound constraints

    ⚑️ There have been several improvements to scipy.optimize.linprog:

    • The linprog benchmark suite has been expanded considerably.
    • πŸ“œ linprog's dense pivot-based redundancy removal routine and sparse
      presolve are faster
    • πŸ“œ When scikit-sparse is available, solving sparse problems with
      method='interior-point' is faster

    ⚑️ The caching of values when optimizing a function returning both value and
    gradient together has been improved, avoiding repeated function evaluations
    when using a HessianApproximation such as BFGS.

    differential_evolution can now use the modern np.random.Generator as
    πŸ‘€ well as the legacy np.random.RandomState as a seed.

    🚦 scipy.signal improvements

    A new optional argument include_nyquist is added to freqz functions in
    this module. It is used for including the last frequency (Nyquist frequency).

    scipy.signal.find_peaks_cwt now accepts a window_size parameter for the
    size of the window used to calculate the noise floor.

    πŸ“œ scipy.sparse improvements

    Outer indexing is now faster when using a 2d column vector to select column

    πŸ“œ scipy.sparse.lil.tocsr is faster

    πŸ›  Fixed/improved comparisons between pydata sparse arrays and sparse matrices

    🐎 BSR format sparse multiplication performance has been improved.

    πŸ“œ scipy.sparse.linalg.LinearOperator has gained the new ndim class

    scipy.spatial improvements

    scipy.spatial.geometric_slerp has been added to enable geometric
    spherical linear interpolation on an n-sphere

    πŸ‘ scipy.spatial.SphericalVoronoi now supports calculation of region areas in 2D
    and 3D cases

    πŸ— The tree building algorithm used by cKDTree has improved from quadratic
    worst case time complexity to loglinear. Benchmarks are also now available for
    πŸ— building and querying of balanced/unbalanced kd-trees.

    scipy.special improvements

    The following functions now have Cython interfaces in cython_special:

    • scipy.special.erfinv
    • scipy.special.erfcinv
    • scipy.special.spherical_jn
    • scipy.special.spherical_yn
    • scipy.special.spherical_in
    • scipy.special.spherical_kn

    scipy.special.log_softmax has been added to calculate the logarithm of softmax
    🌲 function. It provides better accuracy than log(scipy.special.softmax(x)) for
    inputs that make softmax saturate.

    scipy.stats improvements

    The function for generating random samples in scipy.stats.dlaplace has been
    πŸ‘Œ improved. The new function is approximately twice as fast with a memory
    πŸ‘€ footprint reduction between 25 % and 60 % (see gh-11069).

    πŸ‘€ scipy.stats functions that accept a seed for reproducible calculations using
    random number generation (e.g. random variates from distributions) can now use
    the modern np.random.Generator as well as the legacy
    πŸ‘€ np.random.RandomState as a seed.

    The axis parameter was added to scipy.stats.rankdata. This allows slices
    of an array along the given axis to be ranked independently.

    The axis parameter was added to scipy.stats.f_oneway, allowing it to
    βœ… compute multiple one-way ANOVA tests for data stored in n-dimensional
    🐎 arrays. The performance of f_oneway was also improved for some cases.

    The PDF and CDF methods for stats.geninvgauss are now significantly faster
    as the numerical integration to calculate the CDF uses a Cython based

    Moments of the normal distribution (scipy.stats.norm) are now calculated using
    analytical formulas instead of numerical integration for greater speed and

    Moments and entropy trapezoidal distribution (scipy.stats.trapz) are now
    calculated using analytical formulas instead of numerical integration for
    greater speed and accuracy

    Methods of the truncated normal distribution (scipy.stats.truncnorm),
    especially _rvs, are significantly faster after a complete rewrite.

    The fit method of the Laplace distribution, scipy.stats.laplace, now uses
    the analytical formulas for the maximum likelihood estimates of the parameters.

    Generation of random variates is now thread safe for all SciPy distributions.
    3rd-party distributions may need to modify the signature of the _rvs()
    method to conform to _rvs(self, ..., size=None, random_state=None). (A
    πŸ—„ one-time VisibleDeprecationWarning is emitted when using non-conformant

    βœ… The Kolmogorov-Smirnov two-sided test statistic distribution
    (scipy.stats.kstwo) was added. Calculates the distribution of the K-S
    two-sided statistic D_n for a sample of size n, using a mixture of exact
    and asymptotic algorithms.

    The new function median_abs_deviation replaces the deprecated

    The wilcoxon function now computes the p-value for Wilcoxon's signed rank
    βœ… test using the exact distribution for inputs up to length 25. The function has
    a new mode parameter to specify how the p-value is to be computed. The
    0️⃣ default is "auto", which uses the exact distribution for inputs up to length
    25 and the normal approximation for larger inputs.

    βž• Added a new Cython-based implementation to evaluate guassian kernel estimates,
    🐎 which should improve the performance of gaussian_kde

    The winsorize function now has a nan_policy argument for refined
    handling of nan input values.

    The binned_statistic_dd function with statistic="std" performance was
    πŸ‘Œ improved by ~4x.

    βœ… scipy.stats.kstest(rvs, cdf,...) now handles both one-sample and
    βœ… two-sample testing. The one-sample variation uses scipy.stats.ksone
    (or scipy.stats.kstwo with back off to scipy.stats.kstwobign) to calculate
    the p-value. The two-sample variation, invoked if cdf is array_like, uses
    an algorithm described by Hodges to compute the probability directly, only
    backing off to scipy.stats.kstwo in case of overflow. The result in both
    βœ… cases is more accurate p-values, especially for two-sample testing with
    smaller (or quite different) sizes.

    🐎 scipy.stats.maxwell performance improvements include a 20 % speed up for
    fit()and 5 % forpdf()`

    scipy.stats.shapiro and scipy.stats.jarque_bera now return a named tuple
    for greater consistency with other stats functions

    πŸ—„ Deprecated features

    πŸ—„ scipy deprecations

    scipy.special changes

    πŸ—„ The bdtr, bdtrc, and bdtri functions are deprecating non-negative
    non-integral n arguments.

    scipy.stats changes

    The function median_absolute_deviation is deprecated. Use
    median_abs_deviation instead.

    The use of the string "raw" with the scale parameter of iqr is
    πŸ—„ deprecated. Use scale=1 instead.

    Backwards incompatible changes

    scipy.interpolate changes

    scipy.linalg changes

    The output signatures of ?syevr, ?heevr have been changed from
    w, v, info to w, v, m, isuppz, info

    The order of output arguments w, v of <sy/he>{gv, gvd, gvx} is

    🚦 scipy.signal changes

    🚦 The output length of scipy.signal.upfirdn has been corrected, resulting
    outputs may now be shorter for some combinations of up/down ratios and input
    🚦 signal and filter lengths.

    🚦 scipy.signal.resample now supports a domain keyword argument for
    specification of time or frequency domain input.

    scipy.stats changes

    Other changes

    πŸ‘Œ Improved support for leveraging 64-bit integer size from linear algebra backends
    in several parts of the SciPy codebase.

    Shims designed to ensure the compatibility of SciPy with Python 2.7 have now
    🚚 been removed.

    ⚠ Many warnings due to unused imports and unused assignments have been addressed.

    πŸ“„ Many usage examples were added to function docstrings, and many input
    πŸ‘» validations and intuitive exception messages have been added throughout the

    Early stage adoption of type annotations in a few parts of the codebase


    • @endolith
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    πŸš€ A total of 129 people contributed to this release.
    People with a "+" by their names contributed a patch for the first time.
    This list of names is automatically generated, and may not be fully complete.