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:
...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:
Documentation
The complete API documentation.