This adds recipe-maintainers section in meta.yaml, inspired by conda-forge.
There is no automated usage for this section yet, but it suggests who we can ping if
something is wrong with a package. In the future, we may make a tool that automatically
notifies recipe maintainers.
Upgrade 75 packages. The following packages have not been upgraded
because of test failures:
* matplotlib
* packaging
* Pillow
* rebound
* reboundx
* xgboost
* yt
* zarr
This resolves#2189.
> build isolation would be a bit difficult to use in our case, as for instance
> when building scipy we need the patched numpy on the host and not the numpy
> version specified in pyproject.toml (which would be unpatched)
This is indeed the case, certain packages cannot be isolated. My strategy is to
make a list of packages that shouldn't be isolated and add symlinks from the
isolated build environment into the `.artifacts` directory to "unisolate" them.
Then we remove the unisolated package requirements from the list of packages to
install, in case pesky constraints aren't satisfied. In particular, packages
that expect to be used with `pypa/build` often feel free to put very specific
constraints on their build dependencies (often asking them to be == to a
particular version). Specific version constraints is good for build
reproducibility and with build isolation doesn't cost anything. So we just
ignore the constraints. Hopefully nothing goes wrong.
In particular, any package that does stuff both at build time and at runtime and
requires synchronization between the build time and run time environments needs
the unisolation. This includes cffi with `_cffi_backend.so`, and of course numpy
and scipy. pycparser needs to be unisolated because it is a dependency of cffi.
Currently I have also unisolated pythran and cython, though these are build time
only tools and do not really need to be unisolated. Cython I unisolated
specifically because numcodecs needs it but it isn't in the numcodecs build
dependencies. Pythran I unisolated because of a problem with the scipy build
which I don't fully understand (some problem with long double feature
detection).
Hood Chatham