python38-scipy_1_10_0-gnu-hpc-1.10.0-1.1 RPM for armv6hl

From OpenSuSE Ports Tumbleweed for armv6hl

Scipy is open-source software for mathematics, science, and
engineering. The core library is NumPy which provides convenient and
fast N-dimensional array manipulation. The SciPy library is built to
work with NumPy arrays, and provides many numerical routines such as
for numerical integration and optimization.

Provides

• python38-scipy_1_10_0-gnu-hpc
• python3.8dist(scipy)
• python38-scipy_1_10_0-gnu-hpc(armv6hl-32)
• python3dist(scipy)

Requires

• /bin/sh
• ld-linux-armhf.so.3
• ld-linux-armhf.so.3(GLIBC_2.4)
• libc.so.6
• libc.so.6(GLIBC_2.11)
• libc.so.6(GLIBC_2.32)
• libc.so.6(GLIBC_2.34)
• libc.so.6(GLIBC_2.4)
• libc.so.6(GLIBC_2.7)
• libgcc_s.so.1
• libgcc_s.so.1(GCC_3.5)
• libgcc_s.so.1(GCC_4.0.0)
• libgfortran.so.5
• libgfortran.so.5(GFORTRAN_8)
• libm.so.6
• libm.so.6(GLIBC_2.23)
• libm.so.6(GLIBC_2.27)
• libm.so.6(GLIBC_2.29)
• libm.so.6(GLIBC_2.35)
• libm.so.6(GLIBC_2.4)
• libopenblas-gnu-hpc
• libstdc++.so.6
• libstdc++.so.6(CXXABI_1.3)
• libstdc++.so.6(CXXABI_1.3.13)
• libstdc++.so.6(CXXABI_1.3.2)
• libstdc++.so.6(CXXABI_1.3.3)
• libstdc++.so.6(CXXABI_1.3.5)
• libstdc++.so.6(CXXABI_1.3.7)
• libstdc++.so.6(CXXABI_1.3.8)
• libstdc++.so.6(CXXABI_1.3.9)
• libstdc++.so.6(CXXABI_ARM_1.3.3)
• libstdc++.so.6(GLIBCXX_3.4)
• libstdc++.so.6(GLIBCXX_3.4.11)
• libstdc++.so.6(GLIBCXX_3.4.14)
• libstdc++.so.6(GLIBCXX_3.4.15)
• libstdc++.so.6(GLIBCXX_3.4.17)
• libstdc++.so.6(GLIBCXX_3.4.18)
• libstdc++.so.6(GLIBCXX_3.4.19)
• libstdc++.so.6(GLIBCXX_3.4.20)
• libstdc++.so.6(GLIBCXX_3.4.21)
• libstdc++.so.6(GLIBCXX_3.4.22)
• libstdc++.so.6(GLIBCXX_3.4.29)
• libstdc++.so.6(GLIBCXX_3.4.30)
• libstdc++.so.6(GLIBCXX_3.4.9)
• python38-numpy-gnu-hpc >= 1.18.5
• rpmlib(CompressedFileNames) <= 3.0.4-1
• rpmlib(FileDigests) <= 4.6.0-1
• rpmlib(PayloadFilesHavePrefix) <= 4.0-1
• rpmlib(PayloadIsZstd) <= 5.4.18-1

License

BSD-3-Clause AND LGPL-2.0-or-later AND BSL-1.0

Changelog

* Mon Jan 16 2023 Ben Greiner <code@bnavigator.de>
  - Highlights of the 1.10.0 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.
  - Add scipy-pr17717-ro-interpn.patch gh#scipy/scipy#17717
    * Fixes gh#spacetelescope/gwcs#433
  - Provide scipy-datasets.tar.gz for pooch cache and tests without
    needing to download during test time.
* Wed Jan 11 2023 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Update to version 1.10.0
  - Drop upstream pacthes:
    * fix-tests.patch
    * fix-tests-pytest72.patch
    * scipy-pr17467-no-np.int0.patch
* Fri Dec 23 2022 Ben Greiner <code@bnavigator.de>
  - Add scipy-pr17467-no-np.int0.patch gh#scipy/scipy#17467
  - Move the flavored packaganame definition so that quilt does not
    fail
* Thu Dec 08 2022 Dominique Leuenberger <dimstar@opensuse.org>
  - Ensure the test flavor has a different source name than the main
    flavor: OBS uses the source names to create the dep-chain. With
    the test package having the same name as the mani flavor, all
    builds behind python-scipy are blocked until the test suite
    passed.
* Wed Dec 07 2022 Daniel Garcia <daniel.garcia@suse.com>
  - Add fix-tests-pytest72.patch to fix the tests that fails with pytest 7
    gh#scipy/scipy#17296
* Fri Dec 02 2022 Daniel Garcia <daniel.garcia@suse.com>
  - Add fix-tests.patch gh#scipy/scipy#16926#issuecomment-1287507634
* Thu Oct 20 2022 Ben Greiner <code@bnavigator.de>
  - Update to version 1.9.3
    * SciPy 1.9.3 is a bug-fix release with no new features compared
      to 1.9.2.
    * #3691: scipy.interpolate.UnivariateSpline segfault
    * #5286: BUG: multivariate_normal returns a pdf for values
      outside its…
    * #6551: BUG: stats: inconsistency in docs and behavior of gmean
      and hmean
    * #9245: running
      scipy.interpolate.tests.test_fitpack::test_bisplev_integer_overflow…
    * #12471: test_bisplev_integer_overflow: Segmentation fault (core
      dumped)
    * #13321: Bug: setting iprint=0 hides all output from
      fmin_l_bfgs_b, but…
    * #13730: `scipy.stats.mood` does not correct for ties
    * #14019: ks_2samp throws `RuntimeWarning: overflow encountered
      in double_scalars`
    * #14589: `shgo` error since scipy 1.8.0.dev0+1529.803e52d
    * #14591: Input data validation for RectSphereBivariateSpline
    * #15101: BUG: binom.pmf - RuntimeWarning: divide by zero
    * #15342: BUG: scipy.optimize.minimize: Powell’s method function
      evaluated…
    * #15964: BUG: lombscargle fails if argument is a view
    * #16211: BUG: Possible bug when using winsorize on pandas data
      instead…
    * #16459: BUG: stats.ttest_ind returns wrong p-values with
      permutations
    * #16500: odr.Model default meta value fails with __getattr__
    * #16519: BUG: Error in error message for incorrect sample
      dimension in…
    * #16527: BUG: dimension of isuppz in syevr is mistranslated
    * #16600: BUG: `KDTree`’s optional argument `eps` seems to have
      no…
    * #16656: dtype not preserved with operations on sparse arrays
    * #16751: BUG: `stats.fit` on `boltzmann` expects `bound` for
      `lambda`,…
    * #17012: BUG: Small oversight in sparse.linalg.lsmr?
    * #17020: BUG: Build failure due to problems with shebang line in
      cythoner.py
    * #17088: BUG: stats.rayleigh.fit: returns `loc` that is
      inconsistent…
    * #17104: BUG? Incorrect branch in `LAMV` / `_specfunc.lamv`
    * #17196: DOC: keepdims in stats.mode is incorrectly documented
  - Move multibuild flavor ":standard" to unflavored build
  - Test in parallel (pytest-xdist)
* Tue Oct 11 2022 Ben Greiner <code@bnavigator.de>
  - Update to version 1.9.2
    * SciPy 1.9.2 is a bug-fix release with no new features compared
      to 1.9.1.
* Sat Sep 10 2022 Ben Greiner <code@bnavigator.de>
  - Update to version 1.9.1
    * 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.
  - Release 1.9.0
    * Full changelog at
      https://docs.scipy.org/doc/scipy/release.1.9.0.html
  - Highlights of the 1.9.0 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.
  - Switch to meson-python PEP517 build
* Mon Jul 18 2022 Ben Greiner <code@bnavigator.de>
  - Keep lowercase egg-info despite setuptools 60+
* Sat May 21 2022 andy great <andythe_great@pm.me>
  - Update to version 1.8.1.
    * Bug-fix release with no new features.
* Tue Apr 12 2022 Martin Liška <mliska@suse.cz>
  - With the previously added -ffloat-store, some tests that fail on i586.
    Disable them.
* Tue Apr 12 2022 Martin Liška <mliska@suse.cz>
  - Limit double floating point precision for x87, triggered by GCC 12.
    Fixes test_kolmogorov.py Fatal Python error: Floating point exception
    which is a double floating-point test.
* Mon Mar 28 2022 Ben Greiner <code@bnavigator.de>
  - Update to version 1.8.0
    * https://scipy.github.io/devdocs/release.1.8.0.html
    * 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. 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).
    * 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.
    * 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.
* Sat Jan 29 2022 Ben Greiner <code@bnavigator.de>
  - Provide empty debuginfo extraction for :test flavor
* Sun Jan 23 2022 Ben Greiner <code@bnavigator.de>
  - Update to version 1.7.3
    * 3rd bugfix release since 1.7.0
  - Highlights from the 1.7.0 release
    * A new submodule for quasi-Monte Carlo, scipy.stats.qmc, was
      added
    * The documentation design was updated to use the same
      PyData-Sphinx theme as NumPy and other ecosystem libraries.
    * We now vendor and leverage the Boost C++ library to enable
      numerous improvements for long-standing weaknesses in
      scipy.stats
    * scipy.stats has six new distributions, eight new (or
      overhauled) hypothesis tests, a new function for bootstrapping,
      a class that enables fast random variate sampling and
      percentile point function evaluation, and many other
      enhancements.
    * cdist and pdist distance calculations are faster for several
      metrics, especially weighted cases, thanks to a rewrite to a
      new C++ backend framework
    * A new class for radial basis function interpolation,
      RBFInterpolator, was added to address issues with the Rbf
      class.
  - Enable fast part of the test suite
* Mon Jul 26 2021 Andreas Schwab <schwab@suse.de>
  - Enable openblas on riscv64
* Mon May 03 2021 Arun Persaud <arun@gmx.de>
  - update to version 1.6.3:
    * Issues closed
      + #13772: Divide by zero in distance.yule
      + #13796: CI: prerelease_deps failures
      + #13890: TST: spatial rotation failure in (1.6.3) wheels repo
      (ARM64)
    * Pull requests
      + #13755: CI: fix the matplotlib warning emitted during builing
      docs
      + #13773: BUG: Divide by zero in yule dissimilarity of constant
      vectors
      + #13799: CI/MAINT: deprecated np.typeDict
      + #13819: substitute np.math.factorial with math.factorial
      + #13895: TST: add random seeds in Rotation module
* Sun Apr 04 2021 Arun Persaud <arun@gmx.de>
  - update to version 1.6.2:
    * Issues closed for 1.6.2
      + #13512: `stats.gaussian_kde.evaluate` broken on S390X
      + #13584: rotation._compute_euler_from_matrix() creates an array
      with negative...
      + #13585: Behavior change in coo_matrix when dtype=None
      + #13686: delta0 argument of scipy.odr.ODR() ignored
    * Pull requests for 1.6.2
      + #12862: REL: put upper bounds on versions of dependencies
      + #13575: BUG: fix `gaussian_kernel_estimate` on S390X
      + #13586: BUG: sparse: Create a utility function `getdata`
      + #13598: MAINT, BUG: enforce contiguous layout for output array
      in Rotation.as_euler
      + #13687: BUG: fix scipy.odr to consider given delta0 argument
* Wed Mar 03 2021 Arun Persaud <arun@gmx.de>
  - update to version 1.6.1:
    * Issues closed
      + #13072: BLD: Quadpack undefined references
      + #13241: Not enough values to unpack when passing tuple to
      `blocksize`...
      + #13329: Large sparse matrices of big integers lose information
      + #13342: fftn crashes if shape arguments are supplied as ndarrays
      + #13356: LSQBivariateSpline segmentation fault when quitting the
      Python...
      + #13358: scipy.spatial.transform.Rotation object can not be
      deepcopied...
      + #13408: Type of `has_sorted_indices` property
      + #13412: Sorting spherical Voronoi vertices leads to crash in
      area calculation
      + #13421: linear_sum_assignment - support for matrices with more
      than 2^31...
      + #13428: `stats.exponnorm.cdf` returns `nan` for small values of
      `K`...
      + #13465: KDTree.count_neighbors : 0xC0000005 error for tuple of
      different...
      + #13468: directed_hausdorff issue with shuffle
      + #13472: Failures on FutureWarnings with numpy 1.20.0 for
      lfilter, sosfilt...
      + #13565: BUG: 32-bit wheels repo test failure in optimize
    * Pull requests
      + #13318: REL: prepare for SciPy 1.6.1
      + #13344: BUG: fftpack doesn't work with ndarray shape argument
      + #13345: MAINT: Replace scipy.take with numpy.take in FFT
      function docstrings.
      + #13354: BUG: optimize: rename private functions to include
      leading underscore
      + #13387: BUG: Support big-endian platforms and big-endian WAVs
      + #13394: BUG: Fix Python crash by allocating larger array in
      LSQBivariateSpline
      + #13400: BUG: sparse: Better validation for BSR ctor
      + #13403: BUG: sparse: Propagate dtype through CSR/CSC
      constructors
      + #13414: BUG: maintain dtype of SphericalVoronoi regions
      + #13422: FIX: optimize: use npy_intp to store array dims for lsap
      + #13425: BUG: spatial: make Rotation picklable
      + #13426: BUG: `has_sorted_indices` and `has_canonical_format`
      should...
      + #13430: BUG: stats: Fix exponnorm.cdf and exponnorm.sf for small
      K
      + #13470: MAINT: silence warning generated by
      `spatial.directed_hausdorff`
      + #13473: TST: fix failures due to new FutureWarnings in NumPy
      1.21.dev0
      + #13479: MAINT: update directed_hausdorff Cython code
      + #13485: BUG: KDTree weighted count_neighbors doesn't work
      between two...
      + #13503: TST: fix `test_fortranfile_read_mixed_record` on
      big-endian...
      + #13518: DOC: document that pip >= 20.3.3 is needed for macOS 11
      + #13520: BLD: update reqs based on oldest-supported-numpy in
      pyproject.toml
      + #13567: TST, BUG: adjust tol on test_equivalence
* Sat Jan 16 2021 Benjamin Greiner <code@bnavigator.de>
  - NEP 29: Last minorversion bump deprecated Python 3.6
    https://numpy.org/neps/nep-0029-deprecation_policy.html
  - Fix hpc setup for coinstallable python3 flavors, needs
    gh#openSUSE/hpc#3
* Tue Jan 05 2021 Paolo Stivanin <info@paolostivanin.com>
  - Update to 1.6.0:
    * scipy.ndimage improvements: Fixes and ehancements to boundary extension
      modes for interpolation functions. Support for complex-valued inputs
      in many filtering and interpolation functions. New grid_mode option
      for scipy.ndimage.zoom to enable results consistent with scikit-image’s rescale.
    * scipy.optimize.linprog has fast, new methods for large, sparse
      problems from the HiGHS library.
    * scipy.stats improvements including new distributions, a new test,
      and enhancements to existing distributions and tests
    * scipy.special now has improved support for 64-bit LAPACK backend
    * scipy.odr now has support for 64-bit integer BLAS
    * scipy.odr.ODR has gained an optional overwrite argument so that
      existing files may be overwritten.
    * scipy.cluster.hierarchy.DisjointSet has been added for incremental
      connectivity queries.
    * scipy.cluster.hierarchy.dendrogram return value now also includes
      leaf color information in leaves_color_list.
    * scipy.interpolate.interp1d has a new method nearest-up, similar to
      the existing method nearest but rounds half-integers up instead of down.
    * scipy.ndimage.convolve, scipy.ndimage.correlate and their 1d counterparts
      now accept both complex-valued images and/or complex-valued filter kernels.
      All convolution-based filters also now accept complex-valued inputs
    * scipy.optimize.linprog has fast, new methods for large, sparse problems
      from the HiGHS C++ library
    * scipy.optimize.quadratic_assignment has been added for approximate solution of
      the quadratic assignment problem.
    * scipy.optimize.linear_sum_assignment now has a substantially reduced
      overhead for small cost matrix sizes
    * scipy.optimize.least_squares has improved performance when the user
      provides the jacobian as a sparse jacobian already in csr_matrix format
    * scipy.signal.gammatone has been added to design FIR or IIR filters that
      model the human auditory system.
    * scipy.signal.iircomb has been added to design IIR peaking/notching
      comb filters that can boost/attenuate a frequency from a signal.
    * scipy.signal.sosfilt performance has been improved to avoid some
      previously- observed slowdowns
    * scipy.signal.windows.taylor has been added–the Taylor window function
      is commonly used in radar digital signal processing
    * scipy.signal.gauss_spline now supports list type input for consistency
      with other related SciPy functions
    * scipy.signal.correlation_lags has been added to allow calculation of
      the lag/ displacement indices array for 1D cross-correlation.
* Fri Dec 18 2020 andy great <andythe_great@pm.me>
  - Update to version 1.5.4.
    * Bug fix release with no new feature.
  - Updates for 1.5.3.
    * Bug fix release with no new feature.
* Thu Aug 13 2020 Marketa Calabkova <mcalabkova@suse.com>
  - Update to 1.5.2
    * wrappers for more than a dozen new LAPACK routines are now available in scipy.linalg.lapack
    * Improved support for leveraging 64-bit integer size from linear algebra backends
    * addition of the probability distribution for two-sided one-sample Kolmogorov-Smirnov tests
    * see upstream changelog for more detailed info
  - Drop breaking patch no_implicit_decl.patch
    * the problem is with lapacke
* Thu Mar 19 2020 Martin Liška <mliska@suse.cz>
  - Add -std=legacy in order to build with GCC10:
    https://gcc.gnu.org/gcc-10/porting_to.html#argument-mismatch
* Mon Mar 16 2020 Egbert Eich <eich@suse.com>
  - 'umpfack' is a runtime dependency of scipy. No build time
    dependency to suitesparse is required (jsc#SLE-11732).
  - Get rid of site.cfg entirely as it is used nowhwere in scipy.
* Wed Jan 15 2020 Tomáš Chvátal <tchvatal@suse.com>
  - Fix pybind11 devel dependency to match real name
* Fri Dec 20 2019 Todd R <toddrme2178@gmail.com>
  - Update to 1.4.1
    * SciPy 1.4.1 is a bug-fix release with no new features compared
      to 1.4.0. Importantly, it aims to fix a problem where an older
      version of pybind11 may cause a segmentation fault when
      imported alongside incompatible libraries.
* Tue Dec 17 2019 Todd R <toddrme2178@gmail.com>
  - Update to 1.4.0
    + Highlights of this release
    * a new submodule, `scipy.fft`, now supersedes `scipy.fftpack`; this
      means support for ``long double`` transforms, faster multi-dimensional
      transforms, improved algorithm time complexity, release of the global
      intepreter lock, and control over threading behavior
    * support for ``pydata/sparse`` arrays in `scipy.sparse.linalg`
    * substantial improvement to the documentation and functionality of
      several `scipy.special` functions, and some new additions
    * the generalized inverse Gaussian distribution has been added to
      `scipy.stats`
    * an implementation of the Edmonds-Karp algorithm in
      `scipy.sparse.csgraph.maximum_flow`
    * `scipy.spatial.SphericalVoronoi` now supports n-dimensional input,
      has linear memory complexity, improved performance, and
      supports single-hemisphere generators
    + New features
      > Infrastructure
    * Documentation can now be built with ``runtests.py --doc``
    * A ``Dockerfile`` is now available in the ``scipy/scipy-dev`` repository to
      acilitate getting started with SciPy development.
      > `scipy.constants` improvements
    * `scipy.constants` has been updated with the CODATA 2018 constants.
      > `scipy.fft` added
    * `scipy.fft` is a new submodule that supersedes the `scipy.fftpack` submodule.
      or the most part, this is a drop-in replacement for ``numpy.fft`` and
      scipy.fftpack` alike. With some important differences, `scipy.fft`:
    * uses NumPy's conventions for real transforms (``rfft``). This means the
      eturn value is a complex array, half the size of the full ``fft`` output.
      his is different from the output of ``fftpack`` which returned a real array
      epresenting complex components packed together.
    * the inverse real to real transforms (``idct`` and ``idst``) are normalized
      or ``norm=None`` in thesame way as ``ifft``. This means the identity
      `idct(dct(x)) == x`` is now ``True`` for all norm modes.
    * does not include the convolutions or pseudo-differential operators
      rom ``fftpack``.
    * This submodule is based on the ``pypocketfft`` library, developed by the
      uthor of ``pocketfft`` which was recently adopted by NumPy as well.
      `pypocketfft`` offers a number of advantages over fortran ``FFTPACK``:
    * support for long double (``np.longfloat``) precision transforms.
    * faster multi-dimensional transforms using vectorisation
    * Bluestein’s algorithm removes the worst-case ``O(n^2)`` complexity of
      `FFTPACK``
    * the global interpreter lock (``GIL``) is released during transforms
    * optional multithreading of multi-dimensional transforms via the ``workers``
      rgument
    * Note that `scipy.fftpack` has not been deprecated and will continue to be
      aintained but is now considered legacy. New code is recommended to use
      scipy.fft` instead, where possible.
      > `scipy.fftpack` improvements
    * `scipy.fftpack` now uses pypocketfft to perform its FFTs, offering the same
      peed and accuracy benefits listed for scipy.fft above but without the
      mproved API.
      > `scipy.integrate` improvements
    * The function `scipy.integrate.solve_ivp` now has an ``args`` argument.
      his allows the user-defined functions passed to the function to have
      dditional parameters without having to create wrapper functions or
      ambda expressions for them.
    * `scipy.integrate.solve_ivp` can now return a ``y_events`` attribute
      epresenting the solution of the ODE at event times
    * New ``OdeSolver`` is implemented --- ``DOP853``. This is a high-order explicit
      unge-Kutta method originally implemented in Fortran. Now we provide a pure
      ython implementation usable through ``solve_ivp`` with all its features.
    * `scipy.integrate.quad` provides better user feedback when break points are
      pecified with a weighted integrand.
    * `scipy.integrate.quad_vec` is now available for general purpose integration
      f vector-valued functions
      > `scipy.interpolate` improvements
    * `scipy.interpolate.pade` now handles complex input data gracefully
    * `scipy.interpolate.Rbf` can now interpolate multi-dimensional functions
      > `scipy.io` improvements
    * `scipy.io.wavfile.read` can now read data from a `WAV` file that has a
      alformed header, similar to other modern `WAV` file parsers
    * `scipy.io.FortranFile` now has an expanded set of available ``Exception``
      lasses for handling poorly-formatted files
      > `scipy.linalg` improvements
    * The function ``scipy.linalg.subspace_angles(A, B)`` now gives correct
      esults for complex-valued matrices. Before this, the function only returned
      orrect values for real-valued matrices.
    * New boolean keyword argument ``check_finite`` for `scipy.linalg.norm`; whether
      o check that the input matrix contains only finite numbers. Disabling may
      ive a performance gain, but may result in problems (crashes, non-termination)
      f the inputs do contain infinities or NaNs.
    * `scipy.linalg.solve_triangular` has improved performance for a C-ordered
      riangular matrix
    * ``LAPACK`` wrappers have been added for ``?geequ``, ``?geequb``, ``?syequb``,
      nd ``?heequb``
    * Some performance improvements may be observed due to an internal optimization
      n operations involving LAPACK routines via ``_compute_lwork``. This is
      articularly true for operations on small arrays.
    * Block ``QR`` wrappers are now available in `scipy.linalg.lapack`
      > `scipy.optimize` improvements
    * It is now possible to use linear and non-linear constraints with
      scipy.optimize.differential_evolution`.
    * `scipy.optimize.linear_sum_assignment` has been re-written in C++ to improve
      erformance, and now allows input costs to be infinite.
    * A ``ScalarFunction.fun_and_grad`` method was added for convenient simultaneous
      etrieval of a function and gradient evaluation
    * `scipy.optimize.minimize` ``BFGS`` method has improved performance by avoiding
      uplicate evaluations in some cases
    * Better user feedback is provided when an objective function returns an array
      nstead of a scalar.
      > `scipy.signal` improvements
    * Added a new function to calculate convolution using the overlap-add method,
      amed `scipy.signal.oaconvolve`. Like `scipy.signal.fftconvolve`, this
      unction supports specifying dimensions along which to do the convolution.
    * `scipy.signal.cwt` now supports complex wavelets.
    * The implementation of ``choose_conv_method`` has been updated to reflect the
      ew FFT implementation. In addition, the performance has been significantly
      mproved (with rather drastic improvements in edge cases).
    * The function ``upfirdn`` now has a ``mode`` keyword argument that can be used
      o select the signal extension mode used at the signal boundaries. These modes
      re also available for use in ``resample_poly`` via a newly added ``padtype``
      rgument.
    * `scipy.signal.sosfilt` now benefits from Cython code for improved performance
    * `scipy.signal.resample` should be more efficient by leveraging ``rfft`` when
      ossible
      > `scipy.sparse` improvements
    * It is now possible to use the LOBPCG method in `scipy.sparse.linalg.svds`.
    * `scipy.sparse.linalg.LinearOperator` now supports the operation ``rmatmat``
      or adjoint matrix-matrix multiplication, in addition to ``rmatvec``.
    * Multiple stability updates enable float32 support in the LOBPCG eigenvalue
      olver for symmetric and Hermitian eigenvalues problems in
      `scipy.sparse.linalg.lobpcg``.
    * A solver for the maximum flow problem has been added as
      scipy.sparse.csgraph.maximum_flow`.
    * `scipy.sparse.csgraph.maximum_bipartite_matching` now allows non-square inputs,
      o longer requires a perfect matching to exist, and has improved performance.
    * `scipy.sparse.lil_matrix` conversions now perform better in some scenarios
    * Basic support is available for ``pydata/sparse`` arrays in
      scipy.sparse.linalg`
    * `scipy.sparse.linalg.spsolve_triangular` now supports the ``unit_diagonal``
      rgument to improve call signature similarity with its dense counterpart,
      scipy.linalg.solve_triangular`
    * ``assertAlmostEqual`` may now be used with sparse matrices, which have added
      upport for ``__round__``
      > `scipy.spatial` improvements
    * The bundled Qhull library was upgraded to version 2019.1, fixing several
      ssues. Scipy-specific patches are no longer applied to it.
    * `scipy.spatial.SphericalVoronoi` now has linear memory complexity, improved
      erformance, and supports single-hemisphere generators. Support has also been
      dded for handling generators that lie on a great circle arc (geodesic input)
      nd for generators in n-dimensions.
    * `scipy.spatial.transform.Rotation` now includes functions for calculation of a
      ean rotation, generation of the 3D rotation groups, and reduction of rotations
      ith rotational symmetries.
    * `scipy.spatial.transform.Slerp` is now callable with a scalar argument
    * `scipy.spatial.voronoi_plot_2d` now supports furthest site Voronoi diagrams
    * `scipy.spatial.Delaunay` and `scipy.spatial.Voronoi` now have attributes
      or tracking whether they are furthest site diagrams
      > `scipy.special` improvements
    * The Voigt profile has been added as `scipy.special.voigt_profile`.
    * A real dispatch has been added for the Wright Omega function
      `scipy.special.wrightomega`).
    * The analytic continuation of the Riemann zeta function has been added. (The
      iemann zeta function is the one-argument variant of `scipy.special.zeta`.)
    * The complete elliptic integral of the first kind (`scipy.special.ellipk`) is
      ow available in `scipy.special.cython_special`.
    * The accuracy of `scipy.special.hyp1f1` for real arguments has been improved.
    * The documentation of many functions has been improved.
      > `scipy.stats` improvements
    * `scipy.stats.multiscale_graphcorr` added as an independence test that
      perates on high dimensional and nonlinear data sets. It has higher statistical
      ower than other `scipy.stats` tests while being the only one that operates on
      ultivariate data.
    * The generalized inverse Gaussian distribution (`scipy.stats.geninvgauss`) has
      een added.
    * It is now possible to efficiently reuse `scipy.stats.binned_statistic_dd`
      ith new values by providing the result of a previous call to the function.
    * `scipy.stats.hmean` now handles input with zeros more gracefully.
    * The beta-binomial distribution is now available in `scipy.stats.betabinom`.
    * `scipy.stats.zscore`, `scipy.stats.circmean`, `scipy.stats.circstd`, and
      scipy.stats.circvar` now support the ``nan_policy`` argument for enhanced
      andling of ``NaN`` values
    * `scipy.stats.entropy` now accepts an ``axis`` argument
    * `scipy.stats.gaussian_kde.resample` now accepts a ``seed`` argument to empower
      eproducibility
    * `scipy.stats.kendalltau` performance has improved, especially for large inputs,
      ue to improved cache usage
    * `scipy.stats.truncnorm` distribution has been rewritten to support much wider
      ails
    + Deprecated features
      > `scipy` deprecations
    * Support for NumPy functions exposed via the root SciPy namespace is deprecated
      nd will be removed in 2.0.0. For example, if you use ``scipy.rand`` or
      `scipy.diag``, you should change your code to directly use
      `numpy.random.default_rng`` or ``numpy.diag``, respectively.
      hey remain available in the currently continuing Scipy 1.x release series.
    * The exception to this rule is using ``scipy.fft`` as a function --
      mod:`scipy.fft` is now meant to be used only as a module, so the ability to
      all ``scipy.fft(...)`` will be removed in SciPy 1.5.0.
    * In `scipy.spatial.Rotation` methods ``from_dcm``, ``as_dcm`` were renamed to
      `from_matrix``, ``as_matrix`` respectively. The old names will be removed in
      ciPy 1.6.0.
    * Method ``Rotation.match_vectors`` was deprecated in favor of
      `Rotation.align_vectors``, which provides a more logical and
      eneral API to the same functionality. The old method
      ill be removed in SciPy 1.6.0.
    + Backwards incompatible changes
      > `scipy.special` changes
    * The deprecated functions ``hyp2f0``, ``hyp1f2``, and ``hyp3f0`` have been
      emoved.
    * The deprecated function ``bessel_diff_formula`` has been removed.
    * The function ``i0`` is no longer registered with ``numpy.dual``, so that
      `numpy.dual.i0`` will unconditionally refer to the NumPy version regardless
      f whether `scipy.special` is imported.
    * The function ``expn`` has been changed to return ``nan`` outside of its
      omain of definition (``x, n < 0``) instead of ``inf``.
      > `scipy.sparse` changes
    * Sparse matrix reshape now raises an error if shape is not two-dimensional,
      rather than guessing what was meant. The behavior is now the same as before
      ciPy 1.1.0.
    * ``CSR`` and ``CSC`` sparse matrix classes should now return empty matrices
      f the same type when indexed out of bounds. Previously, for some versions
      f SciPy, this would raise an ``IndexError``. The change is largely motivated
      y greater consistency with ``ndarray`` and ``numpy.matrix`` semantics.
      > `scipy.signal` changes
    * `scipy.signal.resample` behavior for length-1 signal inputs has been
      ixed to output a constant (DC) value rather than an impulse, consistent with
      he assumption of signal periodicity in the FFT method.
    * `scipy.signal.cwt` now performs complex conjugation and time-reversal of
      avelet data, which is a backwards-incompatible bugfix for
      ime-asymmetric wavelets.
      > `scipy.stats` changes
    * `scipy.stats.loguniform` added with better documentation as (an alias for
      `scipy.stats.reciprocal``). ``loguniform`` generates random variables
      hat are equally likely in the log space; e.g., ``1``, ``10`` and ``100``
      re all equally likely if ``loguniform(10 ** 0, 10 ** 2).rvs()`` is used.
    + Other changes
    * The ``LSODA`` method of `scipy.integrate.solve_ivp` now correctly detects stiff
      roblems.
    * `scipy.spatial.cKDTree` now accepts and correctly handles empty input data
    * `scipy.stats.binned_statistic_dd` now calculates the standard deviation
      tatistic in a numerically stable way.
    * `scipy.stats.binned_statistic_dd` now throws an error if the input data
      ontains either ``np.nan`` or ``np.inf``. Similarly, in `scipy.stats` now all
      ontinuous distributions' ``.fit()`` methods throw an error if the input data
      ontain any instance of either ``np.nan`` or ``np.inf``.
  - Rebase no_implicit_decl.patch
* Tue Dec 10 2019 Todd R <toddrme2178@gmail.com>
  - Update to 1.3.3
    * Fix deadlock on osx for python 3.8
    * MAINT: TST: Skip tests with multiprocessing that use "spawn" start method
* Tue Nov 19 2019 Todd R <toddrme2178@gmail.com>
  - Update to 1.3.2
    * Bug in unique_roots in scipy.signal.signaltools.py for roots...
    * Optimizers reporting success when the minimum is NaN
    * ValueError raised if scipy.sparse.linalg.expm recieves array...
    * linprog(method='revised simplex') doctest bug
    * Graph shortest path with Floyd-Warshall removes explicit zeros.
    * BUG: stats: Formula for the variance of the noncentral F distribution...
    * BUG: Assignation issues in csr_matrix with fancy indexing
    * root_scalar fails when passed a function wrapped with functools.lru_cache
    * CI: travis osx build failure
    * macOS build failure in SuperLU on maintenance/1.3.x
    * Typo in sp.stats.wilcoxon docstring
* Fri Aug 16 2019 Todd R <toddrme2178@gmail.com>
  - Update to 1.3.1
    * BUG: Empty data handling of (c)KDTrees
    * lsoda fails to detect stiff problem when called from solve_ivp
    * sparse matrices indexing with scipy 1.3
    * Exception in loadarff with quoted nominal attributes in scipy...
    * DOC/REL: Some sections of the release notes are not nested correctly.
    * BUG: optimize: `linprog` failing TestLinprogSimplexBland::test_unbounded_below_no_presolve_corrected
    * TST: Travis CI fails (with pytest 5.0 ?)
    * CircleCI doc build failing on new warnings
    * Scipy 1.3.0 build broken in AIX
    * BUG: scipy.spatial.HalfspaceIntersection works incorrectly
    * BUG: cKDTree GIL handling is incorrect
    * TST: master branch CI failures
    * BUG: ckdtree query_ball_point errors on discontiguous input
    * BUG: No warning on PchipInterpolator changing from bernstein base to local power base
* Sun May 19 2019 Todd R <toddrme2178@gmail.com>
  - Update to 1.3.0
    + Highlights of this release
    * Three new ``stats`` functions, a rewrite of ``pearsonr``, and an exact
      computation of the Kolmogorov-Smirnov two-sample test
    * A new Cython API for bounded scalar-function root-finders in `scipy.optimize`
    * Substantial ``CSR`` and ``CSC`` sparse matrix indexing performance
      improvements
    * Added support for interpolation of rotations with continuous angular
      rate and acceleration in ``RotationSpline``
    + New features
      > `scipy.interpolate` improvements
    * A new class ``CubicHermiteSpline`` is introduced. It is a piecewise-cubic
      interpolator which matches observed values and first derivatives. Existing
      cubic interpolators ``CubicSpline``, ``PchipInterpolator`` and
      ``Akima1DInterpolator`` were made subclasses of ``CubicHermiteSpline``.
      > `scipy.io` improvements
    * For the Attribute-Relation File Format (ARFF) `scipy.io.arff.loadarff`
      now supports relational attributes.
    * `scipy.io.mmread` can now parse Matrix Market format files with empty lines.
      > `scipy.linalg` improvements
    * Added wrappers for ``?syconv`` routines, which convert a symmetric matrix
      given by a triangular matrix factorization into two matrices and vice versa.
    * `scipy.linalg.clarkson_woodruff_transform` now uses an algorithm that leverages
      sparsity. This may provide a 60-90 percent speedup for dense input matrices.
      Truly sparse input matrices should also benefit from the improved sketch
      algorithm, which now correctly runs in ``O(nnz(A))`` time.
    * Added new functions to calculate symmetric Fiedler matrices and
      Fiedler companion matrices, named `scipy.linalg.fiedler` and
      `scipy.linalg.fiedler_companion`, respectively. These may be used
      for root finding.
      > `scipy.ndimage` improvements
    * Gaussian filter performances may improve by an order of magnitude in
      some cases, thanks to removal of a dependence on ``np.polynomial``. This
      may impact `scipy.ndimage.gaussian_filter` for example.
      > `scipy.optimize` improvements
    * The `scipy.optimize.brute` minimizer obtained a new keyword ``workers``, which
      can be used to parallelize computation.
    * A Cython API for bounded scalar-function root-finders in `scipy.optimize`
      is available in a new module `scipy.optimize.cython_optimize` via ``cimport``.
      This API may be used with ``nogil`` and ``prange`` to loop
      over an array of function arguments to solve for an array of roots more
      quickly than with pure Python.
    * ``'interior-point'`` is now the default method for ``linprog``, and
      ``'interior-point'`` now uses SuiteSparse for sparse problems when the
      required scikits  (scikit-umfpack and scikit-sparse) are available.
      On benchmark problems (gh-10026), execution time reductions by factors of 2-3
      were typical. Also, a new ``method='revised simplex'`` has been added.
      It is not as fast or robust as ``method='interior-point'``, but it is a faster,
      more robust, and equally accurate substitute for the legacy
      ``method='simplex'``.
    * ``differential_evolution`` can now use a ``Bounds`` class to specify the
      bounds for the optimizing argument of a function.
    * `scipy.optimize.dual_annealing` performance improvements related to
      vectorisation of some internal code.
      > `scipy.signal` improvements
    * Two additional methods of discretization are now supported by
      `scipy.signal.cont2discrete`: ``impulse`` and ``foh``.
    * `scipy.signal.firls` now uses faster solvers
    * `scipy.signal.detrend` now has a lower physical memory footprint in some
      cases, which may be leveraged using the new ``overwrite_data`` keyword argument
    * `scipy.signal.firwin` ``pass_zero`` argument now accepts new string arguments
      that allow specification of the desired filter type: ``'bandpass'``,
      ``'lowpass'``, ``'highpass'``, and ``'bandstop'``
    * `scipy.signal.sosfilt` may have improved performance due to lower retention
      of the global interpreter lock (GIL) in algorithm
      > `scipy.sparse` improvements
    * A new keyword was added to ``csgraph.dijsktra`` that
      allows users to query the shortest path to ANY of the passed in indices,
      as opposed to the shortest path to EVERY passed index.
    * `scipy.sparse.linalg.lsmr` performance has been improved by roughly 10 percent
      on large problems
    * Improved performance and reduced physical memory footprint of the algorithm
      used by `scipy.sparse.linalg.lobpcg`
    * ``CSR`` and ``CSC`` sparse matrix fancy indexing performance has been
      improved substantially
      > `scipy.spatial` improvements
    * `scipy.spatial.ConvexHull` now has a ``good`` attribute that can be used
      alongsize the ``QGn`` Qhull options to determine which external facets of a
      convex hull are visible from an external query point.
    * `scipy.spatial.cKDTree.query_ball_point` has been modernized to use some newer
      Cython features, including GIL handling and exception translation. An issue
      with ``return_sorted=True`` and scalar queries was fixed, and a new mode named
      ``return_length`` was added. ``return_length`` only computes the length of the
      returned indices list instead of allocating the array every time.
    * `scipy.spatial.transform.RotationSpline` has been added to enable interpolation
      of rotations with continuous angular rates and acceleration
      > `scipy.stats` improvements
    * Added a new function to compute the Epps-Singleton test statistic,
      `scipy.stats.epps_singleton_2samp`, which can be applied to continuous and
      discrete distributions.
    * New functions `scipy.stats.median_absolute_deviation` and `scipy.stats.gstd`
      (geometric standard deviation) were added. The `scipy.stats.combine_pvalues`
      method now supports ``pearson``,  ``tippett`` and ``mudholkar_george`` pvalue
      combination methods.
    * The `scipy.stats.ortho_group` and `scipy.stats.special_ortho_group`
      ``rvs(dim)`` functions' algorithms were updated from a ``O(dim^4)``
      implementation to a ``O(dim^3)`` which gives large speed improvements
      for ``dim>100``.
    * A rewrite of `scipy.stats.pearsonr` to use a more robust algorithm,
      provide meaningful exceptions and warnings on potentially pathological input,
      and fix at least five separate reported issues in the original implementation.
    * Improved the precision of ``hypergeom.logcdf`` and ``hypergeom.logsf``.
    * Added exact computation for Kolmogorov-Smirnov (KS) two-sample test, replacing
      the previously approximate computation for the two-sided test `stats.ks_2samp`.
      Also added a one-sided, two-sample KS test, and a keyword ``alternative`` to
      `stats.ks_2samp`.
    + Backwards incompatible changes
      > `scipy.interpolate` changes
    * Functions from ``scipy.interpolate`` (``spleval``, ``spline``, ``splmake``,
      and ``spltopp``) and functions from ``scipy.misc`` (``bytescale``,
      ``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``,
      ``imsave``, ``imshow``, ``toimage``) have been removed. The former set has
      been deprecated since v0.19.0 and the latter has been deprecated since v1.0.0.
      Similarly, aliases from ``scipy.misc`` (``comb``, ``factorial``,
      ``factorial2``, ``factorialk``, ``logsumexp``, ``pade``, ``info``, ``source``,
      ``who``) which have been deprecated since v1.0.0 are removed.
      `SciPy documentation for
      v1.1.0 <https://docs.scipy.org/doc/scipy-1.1.0/reference/misc.html>`__
      can be used to track the new import locations for the relocated functions.
      > `scipy.linalg` changes
    * For ``pinv``, ``pinv2``, and ``pinvh``, the default cutoff values are changed
      for consistency (see the docs for the actual values).
      > `scipy.optimize` changes
    * The default method for ``linprog`` is now ``'interior-point'``. The method's
      robustness and speed come at a cost: solutions may not be accurate to
      machine precision or correspond with a vertex of the polytope defined
      by the constraints. To revert to the original simplex method,
      include the argument ``method='simplex'``.
      > `scipy.stats` changes
    * Previously, ``ks_2samp(data1, data2)`` would run a two-sided test and return
      the approximated p-value. The new signature, ``ks_2samp(data1, data2,
      alternative="two-sided", method="auto")``, still runs the two-sided test by
      default but returns the exact p-value for small samples and the approximated
      value for large samples. ``method="asymp"`` would be equivalent to the
      old version but ``auto`` is the better choice.
    + Other changes
    * Our tutorial has been expanded with a new section on global optimizers
    * There has been a rework of the ``stats.distributions`` tutorials.
    * `scipy.optimize` now correctly sets the convergence flag of the result to
      ``CONVERR``, a convergence error, for bounded scalar-function root-finders
      if the maximum iterations has been exceeded, ``disp`` is false, and
      ``full_output`` is true.
    * `scipy.optimize.curve_fit` no longer fails if ``xdata`` and ``ydata`` dtypes
      differ; they are both now automatically cast to ``float64``.
    * `scipy.ndimage` functions including ``binary_erosion``, ``binary_closing``, and
      ``binary_dilation`` now require an integer value for the number of iterations,
      which alleviates a number of reported issues.
    * Fixed normal approximation in case ``zero_method == "pratt"`` in
      `scipy.stats.wilcoxon`.
    * Fixes for incorrect probabilities, broadcasting issues and thread-safety
      related to stats distributions setting member variables inside ``_argcheck()``.
    * `scipy.optimize.newton` now correctly raises a ``RuntimeError``, when default
      arguments are used, in the case that a derivative of value zero is obtained,
      which is a special case of failing to converge.
    * A draft toolchain roadmap is now available, laying out a compatibility plan
      including Python versions, C standards, and NumPy versions.
  - Python 2 is no longer supported
* Tue Mar 19 2019 Todd R <toddrme2178@gmail.com>
  - Update to 1.2.1
    * SyntaxError: Non-ASCII character 'xe2' in file scipy/stats/_continuous_distns.py on line 3346, but no encoding declared
    * Version 1.2.0 introduces `too many indices for array` error in `optimize.newton()`
    * scipy.stats.gaussian_kde normalizes the weights keyword argument externally.
    * scipy.linalg.qr_update gives NaN result
    * CI: Is scipy.scipy Windows Python36-32bit-full working?
* Fri Mar 01 2019 Matej Cepl <mcepl@suse.com>
  - Use direct number in the Version tag
* Tue Feb 12 2019 Egbert Eich <eich@suse.com>
  bsc#1130564: Apply update from the openSUSE package
  - Properly create and tear down default version links when the
    HPC master packages are installed/uninstalled.
  - Make use of %hpc_modules_init to make modules also known to
    client.
  - Module file:
    * remove PATH element. Package has no binary,
    * make cosmetic changes.
  - Remove use of %%python_module in dependency.
* Mon Jan 21 2019 Jan Engelhardt <jengelh@inai.de>
  - Trim filler wording from description.
* Fri Jan 18 2019 eich@suse.com
  - Some futher changes:
    * Remove the use of fftw. The code doesn't link against it
      anywhere. For HPC we would have to build things separately
      for different MPI flavors as fftw3 exists only with HPC
      support there.
    * restructure the build process: since the environment for
      the right python version of Numpy needs to be loaded, wrap
      entire build (and install) in %%{python_expand: ..}.
* Thu Jan 17 2019 jjolly@suse.com
  - Add support for HPC builds:
    * Add _multibuild file
    * Add standard and gnu-hpc builds
    * Create initialization for both flavors to set the correct
      target directories in macros and replace install paths
      with these.
    * Restructure the build process.
    * Create 'master' packages for non-HPC builds.
    * Create environment module information,

Files

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/usr/lib/hpc/gnu12/python-scipy/1.10.0
/usr/lib/hpc/gnu12/python-scipy/1.10.0/lib
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/usr/lib/hpc/gnu12/python-scipy/1.10.0/lib/python3.8/site-packages/scipy-1.10.0.dist-info/LICENSE.txt
/usr/lib/hpc/gnu12/python-scipy/1.10.0/lib/python3.8/site-packages/scipy-1.10.0.dist-info/METADATA
/usr/lib/hpc/gnu12/python-scipy/1.10.0/lib/python3.8/site-packages/scipy-1.10.0.dist-info/RECORD
/usr/lib/hpc/gnu12/python-scipy/1.10.0/lib/python3.8/site-packages/scipy-1.10.0.dist-info/REQUESTED
/usr/lib/hpc/gnu12/python-scipy/1.10.0/lib/python3.8/site-packages/scipy-1.10.0.dist-info/WHEEL
/usr/lib/hpc/gnu12/python-scipy/1.10.0/lib/python3.8/site-packages/scipy/__config__.py
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Fabrice Bellet, Thu Nov 14 00:25:17 2024