Add explicit MIT license to repo

[Huite]

Very minor PR, mostly an excuse to get in touch!

I was looking at some KDTree implementations, especially one that works well with numba. I found your comment in this issue: numba/numba-scipy#36
... which obviously brought me here!

Some background:
I'm coming from a geospatial use case, I want to do some (2D for now) unstructured mesh regridding which involves computing some areal overlaps from overlapping cells. Most (vector-based) GIS stuff seems awfully slow or some existing solutions come with massive binary dependencies, so I figured I'd have a look at it myself. A KDTree with a radius query seems like a decent starting point to create some sort of short list of possibly overlapping cells. I greatly prefer numba since it's basically seamless to Python, and it's much nicer to distribute than e.g. Cython; the JIT'ing also allows arbritary area weighting functions at runtime, so it's great all around.

Anyway, numba-neighbors is looking pretty spiffy! I've been looking at these:

• https://github.com/storpipfugl/pykdtree
• https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.cKDTree.html
• https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html

And for my ad hoc benchmark (but fairly realistic for the use case), numba-neighbors beats them by a fairly wide margin (> 30% on cKTree, more so on the others). Might be interesting to redo this analysis at some point: https://jakevdp.github.io/blog/2013/04/29/benchmarking-nearest-neighbor-searches-in-python/

Anyway, my main issue with the other methods is how they return the indices. I don't really want to be stuck with a fixed number of neighbors, so sklearn's radius_query is what I want... except it returns an array of arrays which probably isn't going to be great for further numba functions to work on. I could copy and edit some Cython stuff, but as mentioned numba is just much nicer to distribute. So with numba-neighbors it should be pretty easy to write a custom query function for my goals!

Wrapping up: numba-neigbors seems useful, I'd like to use it. I saw you did put license='MIT' in the setup.py -- but I had to look for it (if only briefly).

[jackd]

Thanks for getting in touch / fixing this up - glad to hear you're finding it useful. This was very much a "I have code I developed for another project, might as well open source it" rather than "let's build a general-purpose KDTree library", and I opened it up in a bit of a rush. There'll no doubt be other things like this, and happy to hear thoughts on the interface. I'm a little tied up with other things at the moment to give this a thorough cleaning, but if you notice anything else dodgy/confusing I'll keep an eye on PRs :).

So with numba-neighbors it should be pretty easy to write a custom query function for my goals!

Note the BinaryTree class has a query_radius method. It's fairly similar to those other interfaces, except
(a) you need to specify a max_count for pre-allocation purposes (and if you under-estimate the returned indices are not necessarily the closest); and
(b) it doesn't sort the returned indices by distance.

I originally wrote wrappers that solved each of these issues, but they slowed things down and weren't necessary for the use case I developed it for.