eventkit

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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 <https://github.com/erdewit/eventkit/tree/master/notebooks/eventkit_introduction.ipynb>_ 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

.. code-block:: python

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

.. code-block:: python

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

.. code-block:: python

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

.. code-block:: python

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

.. code-block:: python

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 <https://github.com/erdewit/heartwave/blob/100e1a89d18756e141f9dcfbb73c55a1009debf4/heartwave/app.py#L88>_

Distributed computing

The distex <https://github.com/erdewit/distex>_ library provides a poolmap extension method to put multiple cores or machines to use:

.. code-block:: python

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 <https://doc.qt.io/qt-5/signalsandslots.html>`_
* `itertools <https://docs.python.org/3/library/itertools.html>`_
* `aiostream <https://github.com/vxgmichel/aiostream>`_
* `Bacon <https://baconjs.github.io/index.html>`_
* `aioreactive <https://github.com/dbrattli/aioreactive>`_
* `Reactive extensions <http://reactivex.io/documentation/operators.html>`_
* `underscore.js <https://underscorejs.org>`_
* `.NET Events <https://docs.microsoft.com/en-us/dotnet/standard/events>`_

Documentation

The complete API documentation <https://eventkit.readthedocs.io/en/latest/api.html>_.

.. _Python: http://www.python.org .. _Interactive Brokers Python API: http://interactivebrokers.github.io

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