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|Build| |PyVersion| |Status| |PyPiVersion| |License| |Docs|
The primary use cases of eventkit are
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.
::
pip3 install eventkit
Python_ version 3.6 or higher is required.
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>
_
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()
* `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>`_
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
.. |Build| image:: https://github.com/erdewit/eventkit/actions/workflows/test.yml/badge.svg?branch=master :alt: Build :target: https://github.com/erdewit/eventkit/actions
.. |PyPiVersion| image:: https://img.shields.io/pypi/v/eventkit.svg :alt: PyPi :target: https://pypi.python.org/pypi/eventkit
.. |PyVersion| image:: https://img.shields.io/badge/python-3.6+-blue.svg :alt:
.. |Status| image:: https://img.shields.io/badge/status-stable-green.svg :alt:
.. |License| image:: https://img.shields.io/badge/license-BSD-blue.svg :alt:
.. |Docs| image:: https://readthedocs.org/projects/eventkit/badge/?version=latest :alt: Documentation :target: https://eventkit.readthedocs.io
FAQs
Event-driven data pipelines
We found that eventkit demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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