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A streaming multi-input, multi-output Python library

Project Description
[![Build Status](](


+ 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.

### Streams

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

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. The workflow can be run by calling the `start` function.

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']))



if __name__ == '__main__':
import sys
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mimo-1.2.4-py3.5.egg (36.5 kB) Copy SHA256 Checksum SHA256 3.5 Egg Oct 13, 2016
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