Introduction

The primary use cases of eventkit are

• to send events between loosely coupled components;
• to compose all kinds of event-driven data pipelines.

The interface is kept as Pythonic as possible, with familiar names from Python and its libraries where possible. For scheduling asyncio is used and there is seamless integration with it.

See the examples and the introduction notebook to get a true feel for the possibilities.

Installation

pip3 install eventkit

Python version 3.6 or higher is required.

Examples

Create an event and connect two listeners

import eventkit as ev

def f(a, b):
    print(a * b)

def g(a, b):
    print(a / b)

event = ev.Event()
event += f
event += g
event.emit(10, 5)

Create a simple pipeline

import eventkit as ev

event = (
    ev.Sequence('abcde')
    .map(str.upper)
    .enumerate()
)

print(event.run())  # in Jupyter: await event.list()

Output:

[(0, 'A'), (1, 'B'), (2, 'C'), (3, 'D'), (4, 'E')]

Create a pipeline to get a running average and standard deviation

import random
import eventkit as ev

source = ev.Range(1000).map(lambda i: random.gauss(0, 1))

event = source.array(500)[ev.ArrayMean, ev.ArrayStd].zip()

print(event.last().run())  # in Jupyter: await event.last()

Output:

[(0.00790957852672618, 1.0345673260655333)]

Combine async iterators together

import asyncio
import eventkit as ev

async def ait(r):
    for i in r:
        await asyncio.sleep(0.1)
        yield i

async def main():
    async for t in ev.Zip(ait('XYZ'), ait('123')):
        print(t)

asyncio.get_event_loop().run_until_complete(main())  # in Jupyter: await main()

Output:

('X', '1')
('Y', '2')
('Z', '3')

Real-time video analysis pipeline

self.video = VideoStream(conf.CAM_ID)
scene = self.video | FaceTracker | SceneAnalyzer
lastScene = scene.aiter(skip_to_last=True)
async for frame, persons in lastScene:
    ...

Full source code

Distributed computing

The distex library provides a poolmap extension method to put multiple cores or machines to use:

from distex import Pool
import eventkit as ev
import bz2

pool = Pool()
# await pool  # un-comment in Jupyter
data = [b'A' * 1000000] * 1000

pipe = ev.Sequence(data).poolmap(pool, bz2.compress).map(len).mean().last()

print(pipe.run())  # in Jupyter: print(await pipe)
pool.shutdown()

Inspired by:

• Qt Signals & Slots
• itertools
• aiostream
• Bacon
• aioreactive
• Reactive extensions
• underscore.js
• .NET Events

Documentation

The complete API documentation.