• Multiple input and multiple output (as opposed to functions where inputs and outputs are always synchronised)

• Less memory (because of streaming)

MiMo is a multi-input multi-output Python streaming library. It allows users to define a stream with multiple inputs and multiple outputs and run them completely from beginning to end. Back-pressure has also been implemented to prevent too much memory from being used.

There are two core components in MiMo; the Stream and the Workflow. Streams to the computational processing but do not handle how the data is passed between streams. Workflows pass the data around streams but do no processing of their own. To create a workflow, the user needs to implement streams and pipe them together.

Implementing a stream can be done through inheriting a sub-class from the Stream class or creating a Stream class with a custom function as the fn parameter. The following code shows two implementations of a stream that will produce the numbers from 0 to 99.

from mimo import Stream


# Method 1 (inheritance)

class MyStream(Stream):

    IN = []
    OUT = ['entity']
    
    async def run(self, ins, outs):
        for item in iter(range(100)):
            await outs.entity.push(item)


# Method 2 (constructor)

my_stream = Stream(outs=['entity], fn=my_stream_fn)

async def my_stream_fn(ins, outs, state):
    for item in iter(range(100)):
        await outs.entity.push(item)

There are a few things to note about the run function.

1. It must be asynchronous, ie. it must be defined wth the async def keywords.
2. It takes two parameters, ins and outs, that contain the input streams and the output streams. The names of the input and output streams are defined by the IN and OUT member variables or overridden using the ins and outs of the initialisation function. Accessing the input and output streams can be done through the attributes. From the example above, accessing the entity output stream can be done with outs.entity.
3. Input streams can be popped and peeked and this must be done using the await keyword. Input streams haven't been used in the above example, but the entities in the stream can be accessed one at a time with the functions pop and peek. Popping an entity will remove it from the input stream, and peeking will look at the top-most entity without removing it from the stream. Input streams can also be iterated using the async for looping construct.
4. Output streams can be pushed and must also use the await keyword. Pushing an entity to an output stream will make it available to any connected downstream streams.

Workflows are created by piping streams together. First a workflow must be instantiated and populated with the desired streams. The steps returned by populating a workflow can then be used to make the connections between the streams using the pipe function. The function returns the stream being piped to, so pipe calls can be chained.

from mimo import Stream, Workflow


def main():
    workflow = Workflow()
    step1 = workflow.add_stream(Stream(outs=['a']))
    step2 = workflow.add_stream(Stream(['b'], ['c']))
    step3 = workflow.add_stream(Stream(['d']))
    
    step1.pipe(step2).pipe(step3)
    
    print(str(workflow))
    workflow.run()

if __name__ == '__main__':
    import sys
    sys.exit(main())