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 <charlesr.harris@gmail.com> wrote:

Hi All,

There is a PR 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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