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- Update to version 1.9.2
  * SciPy 1.9.2 is a bug-fix release with no new features compared
    to 1.9.1.

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- 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

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- Keep lowercase egg-info despite setuptools 60+

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baserev update by copy to link target

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- Update to version 1.8.1.
  * Bug-fix release with no new features.

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baserev update by copy to link target

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- With the previously added -ffloat-store, some tests that fail on i586.
  Disable them.

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- 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.

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- 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.

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- Provide empty debuginfo extraction for :test flavor

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- 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

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- Enable openblas on riscv64

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baserev update by copy to link target

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update to latest version

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baserev update by copy to link target

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update to latest version

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