Log message:
py-scipy: work around undefined PLT symbol "log1pl" on NetBSD. Bump.

Log message:
cipy: fix build on SunOS (system header conflict)

Log message:
py-scipy: updated to 1.8.0

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.

Log message:
*: bump PKGREVISION for egg.mk users

They now have a tool dependency on py-setuptools instead of a DEPENDS

Log message:
Forget about Python 3.6

Log message:
py-scipy: remove stale patch

it was added in 1.7.1 but there is no checksum, so patch is skipped.
Package apparently builds fine without it.

Log message:
py-scipy: updated to 1.7.3

SciPy 1.7.3 is a bug-fix release that provides binary wheels
for MacOS arm64 with Python 3.8, 3.9, and 3.10. The MacOS arm64 wheels
are only available for MacOS version 12.0 and greater, as explained
in Issue 14688, linked below.

Issues closed for 1.7.3
-----------------------
* Segmentation fault on import of scipy.integrate on Apple M1 ARM...
* BUG: ARPACK's eigsh & OpenBLAS from Apple Silicon M1 (arm64)...
* four CI failures on pre-release job
* Remaining test failures for macOS arm64 wheel
* BUG: Segmentation fault caused by scipy.stats.qmc.qmc.update_discrepancy

Pull requests for 1.7.3
-----------------------
* BLD: update pyproject.toml for Python 3.10 changes
* BUG: out of bounds indexing in stats.qmc.update_discrepancy
* MAINT: skip a few failing tests in \`1.7.x\` for macOS arm64

Log message:
py-scipy: updated to 1.7.2

SciPy 1.7.2 is a bug-fix release with no new features
compared to 1.7.1. Notably, the release includes wheels
for Python 3.10, and wheels are now built with a newer
version of OpenBLAS, 0.3.17. Python 3.10 wheels are provided
for MacOS x86_64 (thin, not universal2 or arm64 at this time),
and Windows/Linux 64-bit. Many wheels are now built with newer
versions of manylinux, which may require newer versions of pip.

Log message:
py-scipy: updated to 1.7.1

SciPy 1.7.1 is a bug-fix release with no new features compared to 1.7.0.

1.7.0:

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.

Log message:
math: Replace RMD160 checksums with BLAKE2s checksums

All checksums have been double-checked against existing RMD160 and
SHA512 hashes