## What is ulab

ulab (pronounced "micro lab") lets you perform number crunching tasks in CircuitPython more quickly, often around 10x as fast. This can be very handy when dealing with sensor data, as we'll see below.

Make sure you have CircuitPython 5.1.0 or newer, and any Adafruit CircuitPython board with an M4 or higher processor, including all SAMD51 and nRF boards.

ulab is modeled after `numpy`, but is not entirely compatible; so after the examples there are guidelines to help you move between numpy and ulab.

ulab is not available in Blinka, Adafruit's Single Board Computer layer for CircuitPython - for those boards we recommend using plain numpy since it's available! If your code needs to run on both CircuitPython and Blinka, you'll probably either need to use conditional code or forego the use of ulab altogether.

Starting with CircuitPython 7 the ulab APIs were changed to move the functions into ulab.numpy and ulab.scipy respectively based on where they exist in their CPython library counterparts. If you have projects using ulab functions you'll likely need to update the code to import and call the functions using the new names.

## The ulab API

ulab makes things faster by operating on entire arrays of values in one operation.  For example, when you have two numbers `a` and `b`, `a+b` adds them together, returning a new number.  When you have two ulab arrays `a` and `b`, `a+b` adds the corresponding numbers in `a` to the corresponding numbers in `b`, returning a new array.  Want to double every number in an array?  That's `a*2`.  Compute its sine?  `ulab.numpy.sin(a)`.  It also has special versions of functions like `sum` that act on a whole array and return a single number.  Documentation for all functions in ulab are on readthedocs.

These examples only cover a portion of the functions available in ulab.  The items below are beyond the scope of this gude:

• Matrix functions in `ulab.numpy.linalg`, such as determinant, inverse, and eigenvectors of a matrix
• Creating vectors with `ulab.numpy.linspace`, which is sort of like `range()` but for arrays
• Statistical functions such as standard deviation, `ulab.numpy.std` and others
• Functions for working with polynomials in `ulab.numpy.polyfit` and `ulab.numpy.polyval`
• Slicing arrays with `arr[lo:hi:step]`

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This guide was first published on Mar 06, 2020. It was last updated on Mar 06, 2020.