One way to do this is to move to vendorized dependencies into an submodule
of numpy itself (e.g., sklearn.externals.joblib, though maybe even a little
more indirection than that would be valuable to make it clear that it isn't
part of NumPy public API). This would avoid further enlarging the set of
namespaces we use.
In any case, I'm perfectly OK with using something like npy_tempita
internally, too, as long as we can be sure that we're using NumPy's
vendorized version, not whatever version is installed locally. We're not
planning to actually install "npy_tempita" when installing numpy (even for
dev installs), right?
On Fri, Sep 30, 2016 at 7:30 AM, Charles R Harris wrote: Hi All, There is a PR https://github.com/numpy/numpy/pull/8096 to vendorize
tempita. This removes tempita as a dependency and simplifies some things.
Feedback on this step is welcome. One question is whether the package
should be renamed to something like `npy_tempita`, as otherwise installed
tempita, if any has priority. Thoughts? Chuck _______________________________________________
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