Test 3D feature-finding

In response to an email gently pointing out that our N-dimensional capabilities are completely undocumented aside from "encouraging statements about how trackpy can be used for arbitrarily high dimensional data" (Ha! Guilty as charged!)

I added two simple 3D tests and refactored all the feature-finding tests to make it easier to extend more tests in time. Happily, everything works as advertised: no changes to `feature.py` were necessary.

**Key Point:**
It will be up to the user to keep track of what x, y, and z mean for their data. Trackpy flips x and y internally because images are conventionally displayed column-major (as in `imshow` or any image viewer) but indexed row-major by numpy. But for higher-dimensional data, trackpy leaves ordering to the user. Different experiments would seem to call for different conventions here. By doing nothing, trackpy minimizes confusion.

 There should certainly be more tests, but this at least proves that trackpy can find a spot in a 3D image.