This GitHub repository consists of the following parts:

• Drivers for hardware documented here.
• Synchronisation primitives described here.
• An introductory tutorial on asynchronous programming and the use of the uasyncio library is offered here. This is a work in progress, not least because uasyncio is not yet complete.

Firstly install the latest version of micropython-uasyncio. To use queues, also install the micropython-uasyncio.queues module.

Instructions on installing library modules may be found here.

On networked hardware, upip may be run locally.

On non-networked hardware the resultant modules will need to be copied to the target. The above Unix installation will create directories under ~/.micropython/lib which may be copied to the target hardware, either to the root or to a lib subdirectory. Alternatively the device may be mounted; then use the "-p" option to upip to specify the target directory as the mounted filesystem.

For those familiar with asyncio under CPython 3.5, uasyncio supports the following Python 3.5 features:

• async def and await syntax.
• Awaitable classes (using __iter__ rather than __await__).
• Asynchronous context managers.
• Asynchronous iterators.
• Event loop methods call_soon and call_later.
• uasyncio sleep(seconds).

It supports millisecond level timing with the following:

• Event loop method call_later_ms_
• Event loop call_at - time is specified in ms.
• uasyncio sleep_ms(time)

It doesn't support objects of type Future and Task. Routines to run concurrently are defined as coroutines instantiated with async def and yield execution with await <awaitable>.

At the time of writing this was under development. Check the current state on GitHub.

For timing asyncio uses floating point values of seconds. The uasyncio sleep method accepts floats (including sub-second values) or integers. Note that in MicroPython the use of floats implies RAM allocation which incurs a performance penalty. uasyncio is designed to be capable of allocation-free scheduling. In applications where performance is an issue, integers should be used and the millisecond level functions (with integer argumnts) employed where necessary.

The loop.time method returns an integer number of milliseconds whereas CPython returns a floating point number of seconds. call_at follows the same convention.