uumpy is a 'micro' implementation of parts of numpy for Micropython. It aims to provide an efficient and compact implementation of arithmetic on matricies and multi-dimensional data types, as well as basic linear algebra. In due course the aim is also to support FFT functions.
Why on Earth would anyone want to put a huge package like numpy onto a microcontroller? In general you would not. That's why this is not a port of numpy but instead a new implementation of a subset of the functionality. That subset is geared towards making it easy and efficient to implement the sorts of algorithms that people do want to put on a microcontroller. For instance, a microcontroller that has to make decisions based on noisy sensor data from a variety of sensors might need to implement a Kalman filter to get a best guess as to the its state. Implementing this sort of filter is vastly easier when matrix operations can be expressed cleanly. Similarly performing Fourier transforms and convolutions can be important for extracting the relevant parts of signals coming from sensors in control systems.
One of the goals of uumpy is to allow developers to tune the size of the implementation to their needs. Large but self-contained blocks of functionality should be optional, so that they don't need to be included if they are not needed. Where there are speed/size trade-offs to be made it should be possible for these to be adjusted at compile-time. If math on multi-dimensional arrays is all that is needed then uumpy can be pretty compact; when data-type specific math code makes use of fully unrolled loops it can be fast. You choose.
As far as possible uumpy should work just like numpy. As a result you should just be able
to use import uumpy as np and use the np module as you would with numpy.
This code forms an 'external module' for Micropython. Documentation about how to make use of external modules can be found in the Micropython documentation.
This code is currently still in a pretty early state. It supports core matrix and multi-dimensional array math operations a some basic linear algebra but is currently missing most of the more esoteric functions (although the foundations for them are mostly laid). As it stands it is useful for making code that implements matrix maths more readable and for solving simple linear systems but is otherwise incomplete.