Pyeventbus: a publish/subscribe event bus

Overview

pyeventbus

https://travis-ci.org/n89nanda/pyeventbus.svg?branch=master

pyeventbus is a publish/subscribe event bus for Python 2.7.

  • simplifies the communication between python classes
  • decouples event senders and receivers
  • performs well threads, greenlets, queues and concurrent processes
  • avoids complex and error-prone dependencies and life cycle issues
  • makes code simpler
  • has advanced features like delivery threads, workers and spawning different processes, etc.
  • is tiny (3KB archive)

pyeventbus in 3 steps:

  1. Define events:

    class MessageEvent:
        # Additional fields and methods if needed
        def __init__(self):
            pass
    
  2. Prepare subscribers: Declare and annotate your subscribing method, optionally specify a thread mode:

    from pyeventbus import *
    
    @subscribe(onEvent=MessageEvent)
    def func(self, event):
        # Do something
        pass
    

    Register your subscriber. For example, if you want to register a class in Python:

    from pyeventbus import *
    
    class MyClass:
        def __init__(self):
            pass
    
        def register(self, myclass):
            PyBus.Instance().register(myclass, self.__class__.__name__)
    
    # then during initilization
    
    myclass = MyClass()
    myclass.register(myclass)
    
  3. Post events:

    from pyeventbus import *
    
    class MyClass:
        def __init__(self):
            pass
    
        def register(self, myclass):
            PyBus.Instance().register(myclass, self.__class__.__name__)
    
        def postingAnEvent(self):
            PyBus.Instance().post(MessageEvent())
    
     myclass = MyClass()
     myclass.register(myclass)
     myclass.postingAnEvent()
    

Modes: pyeventbus can run the subscribing methods in 5 different modes

  1. POSTING:

    Runs the method in the same thread as posted. For example, if an event is posted from main thread, the subscribing method also runs in the main thread. If an event is posted in a seperate thread, the subscribing method runs in the same seperate method
    
    This is the default mode, if no mode has been provided::
    
    @subscribe(threadMode = Mode.POSTING, onEvent=MessageEvent)
    def func(self, event):
        # Do something
        pass
    
  2. PARALLEL:

    Runs the method in a seperate python thread::
    
    @subscribe(threadMode = Mode.PARALLEL, onEvent=MessageEvent)
    def func(self, event):
        # Do something
        pass
    
  3. GREENLET:

    Runs the method in a greenlet using gevent library::
    
    @subscribe(threadMode = Mode.GREENLET, onEvent=MessageEvent)
    def func(self, event):
        # Do something
        pass
    
  4. BACKGROUND:

    Adds the subscribing methods to a queue which is executed by workers::
    
    @subscribe(threadMode = Mode.BACKGROUND, onEvent=MessageEvent)
    def func(self, event):
        # Do something
        pass
    
  1. CONCURRENT:

    Runs the method in a seperate python process::
    
    @subscribe(threadMode = Mode.CONCURRENT, onEvent=MessageEvent)
    def func(self, event):
        # Do something
        pass
    

Adding pyeventbus to your project:

pip install pyeventbus

Example:

git clone https://github.com/n89nanda/pyeventbus.git

cd pyeventbus

virtualenv venv

source venv/bin/activate

pip install pyeventbus

python example.py

Benchmarks and Performance:

Refer /pyeventbus/tests/benchmarks.txt for performance benchmarks on CPU, I/O and networks heavy tasks.

Run /pyeventbus/tests/test.sh to generate the same benchmarks.

Performance comparison between all the modes with Python and Cython

alternate text

Inspiration

Inspired by Eventbus from greenrobot: https://github.com/greenrobot/EventBus
You might also like...
Code for the paper
Code for the paper "Unsupervised Contrastive Learning of Sound Event Representations", ICASSP 2021.

Unsupervised Contrastive Learning of Sound Event Representations This repository contains the code for the following paper. If you use this code or pa

Repo for "Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks"

Summary This is the code for the paper Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks by Yanxiang Wang, Xian Zh

Cross-media Structured Common Space for Multimedia Event Extraction (ACL2020)
Cross-media Structured Common Space for Multimedia Event Extraction (ACL2020)

Cross-media Structured Common Space for Multimedia Event Extraction Table of Contents Overview Requirements Data Quickstart Citation Overview The code

CVPRW 2021: How to calibrate your event camera
CVPRW 2021: How to calibrate your event camera

E2Calib: How to Calibrate Your Event Camera This repository contains code that implements video reconstruction from event data for calibration as desc

Repository relating to the CVPR21 paper TimeLens: Event-based Video Frame Interpolation
Repository relating to the CVPR21 paper TimeLens: Event-based Video Frame Interpolation

TimeLens: Event-based Video Frame Interpolation This repository is about the High Speed Event and RGB (HS-ERGB) dataset, used in the 2021 CVPR paper T

An implementation for `Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction`

Text2Event An implementation for Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction Please contact Yaojie Lu (@

Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras (ICCV 2021)
Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras (ICCV 2021)

N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Gra

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Au

Weakly Supervised Dense Event Captioning in Videos, i.e. generating multiple sentence descriptions for a video in a weakly-supervised manner.
Weakly Supervised Dense Event Captioning in Videos, i.e. generating multiple sentence descriptions for a video in a weakly-supervised manner.

WSDEC This is the official repo for our NeurIPS paper Weakly Supervised Dense Event Captioning in Videos. Description Repo directories ./: global conf

Comments
  • Same method name for multiple subscribers bug

    Same method name for multiple subscribers bug

    Please see the code below. To summarize:

    • Define one event
    • Define two subscriber listening for the event above. Each subscriber has a listener method with the name on_event
    • Each of the subscriber classes above defines an instance field, but with unique name (self.something in the first class, self.something2 in the second class)
    • Define another class that posts an event

    Run this scenario and get the error below:

    Exception in thread thread-on_event:
    Traceback (most recent call last):
      File "C:\Anaconda2\envs\python\lib\threading.py", line 801, in __bootstrap_inner
        self.run()
      File "C:\Anaconda2\envs\python\lib\site-packages\pyeventbus\pyeventbus.py", line 112, in run
        self.method(self.subscriber, self.event)
      File "C:/FractureID/projects/python/ui/spectraqc/PyEventBusBug.py", line 16, in on_event
        print (self.something)
    AttributeError: Subscriber2 instance has no attribute 'something'
    
    Exception in thread thread-on_event:
    Traceback (most recent call last):
      File "C:\Anaconda2\envs\python\lib\threading.py", line 801, in __bootstrap_inner
        self.run()
      File "C:\Anaconda2\envs\python\lib\site-packages\pyeventbus\pyeventbus.py", line 112, in run
        self.method(self.subscriber, self.event)
      File "C:/FractureID/projects/python/ui/spectraqc/PyEventBusBug.py", line 26, in on_event
        print (self.something_else)
    AttributeError: Subscriber1 instance has no attribute 'something_else'
    
    

    It complains about the variable in class two not having the attribute in the first class and the other way around.

    If I change on of the on_event to something else like on_event2 then the issue is gone.

    from pyeventbus import *
    
    
    class SomeEvent:
        def __init__(self):
            pass
    
    
    class Subscriber1:
        def __init__(self):
            self.something = 'First subscriber'
            PyBus.Instance().register(self, self.__class__.__name__)
    
        @subscribe(threadMode=Mode.PARALLEL, onEvent=SomeEvent)
        def on_event(self, event):
            print (self.something)
    
    
    class Subscriber2:
        def __init__(self):
            self.something_else = 'Second subscriber'
            PyBus.Instance().register(self, self.__class__.__name__)
    
        @subscribe(threadMode=Mode.PARALLEL, onEvent=SomeEvent)
        def on_event(self, event):
            print (self.something_else)
    
    
    class PyEventBusBug:
    
        def __init__(self):
            Subscriber1()
            Subscriber2()
            PyBus.Instance().post(SomeEvent())
    
    
    if __name__ == "__main__":
        PyEventBusBug()
    
    
    bug 
    opened by ddanny 0
  • Doesn't even start on Windows because 2000 threads is apparently too much

    Doesn't even start on Windows because 2000 threads is apparently too much

      File "C:\Python27\lib\site-packages\pyeventbus\pyeventbus.py", line 116, in subscribe
        bus = PyBus.Instance()
      File "C:\Python27\lib\site-packages\pyeventbus\Singleton.py", line 30, in Instance
        self._instance = self._decorated()
      File "C:\Python27\lib\site-packages\pyeventbus\pyeventbus.py", line 24, in __init__
        for worker in [lambda: self.startWorkers() for i in range(self.num_threads)]: worker()
      File "C:\Python27\lib\site-packages\pyeventbus\pyeventbus.py", line 24, in <lambda>
        for worker in [lambda: self.startWorkers() for i in range(self.num_threads)]: worker()
      File "C:\Python27\lib\site-packages\pyeventbus\pyeventbus.py", line 30, in startWorkers
        worker.start()
      File "C:\Python27\lib\threading.py", line 736, in start
        _start_new_thread(self.__bootstrap, ())
    thread.error: can't start new thread
    

    See also: https://stackoverflow.com/a/1835043/2583080

    bug 
    opened by PawelTroka 4
Releases(0.2)
Scalable, event-driven, deep-learning-friendly backtesting library

...Minimizing the mean square error on future experience. - Richard S. Sutton BTGym Scalable event-driven RL-friendly backtesting library. Build on

Andrew 922 Dec 27, 2022
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

This repository holds NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pytorch. Some of the code here will be included in upstream Pytorch eventually. The intenti

NVIDIA Corporation 6.9k Jan 03, 2023
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).

CapsGNN ⠀⠀ A PyTorch implementation of Capsule Graph Neural Network (ICLR 2019). Abstract The high-quality node embeddings learned from the Graph Neur

Benedek Rozemberczki 1.2k Jan 02, 2023
Black box hyperparameter optimization made easy.

BBopt BBopt aims to provide the easiest hyperparameter optimization you'll ever do. Think of BBopt like Keras (back when Theano was still a thing) for

Evan Hubinger 70 Nov 03, 2022
Official code for 'Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urban Driving Scenes'

PEBAL This repo contains the Pytorch implementation of our paper: Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urba

Yu Tian 115 Dec 29, 2022
deep learning model with only python and numpy with test accuracy 99 % on mnist dataset and different optimization choices

deep_nn_model_with_only_python_100%_test_accuracy deep learning model with only python and numpy with test accuracy 99 % on mnist dataset and differen

0 Aug 28, 2022
Gluon CV Toolkit

Gluon CV Toolkit | Installation | Documentation | Tutorials | GluonCV provides implementations of the state-of-the-art (SOTA) deep learning models in

Distributed (Deep) Machine Learning Community 5.4k Jan 06, 2023
The official codes for the ICCV2021 Oral presentation "Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework"

P2PNet (ICCV2021 Oral Presentation) This repository contains codes for the official implementation in PyTorch of P2PNet as described in Rethinking Cou

Tencent YouTu Research 208 Dec 26, 2022
Deep Sketch-guided Cartoon Video Inbetweening

Cartoon Video Inbetweening Paper | DOI | Video The source code of Deep Sketch-guided Cartoon Video Inbetweening by Xiaoyu Li, Bo Zhang, Jing Liao, Ped

Xiaoyu Li 37 Dec 22, 2022
Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases.

Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases. Ivy wraps the functional APIs of existing frameworks. Framework-agnostic functions, libraries an

Ivy 8.2k Jan 02, 2023
CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation

CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation We propose a novel approach to translate unpaired contrast computed

Nicolae Catalin Ristea 13 Jan 02, 2023
An implementation of chunked, compressed, N-dimensional arrays for Python.

Zarr Latest Release Package Status License Build Status Coverage Downloads Gitter Citation What is it? Zarr is a Python package providing an implement

Zarr Developers 1.1k Dec 30, 2022
Use unsupervised and supervised learning to predict stocks

AIAlpha: Multilayer neural network architecture for stock return prediction This project is meant to be an advanced implementation of stacked neural n

Vivek Palaniappan 1.5k Jan 06, 2023
AntroPy: entropy and complexity of (EEG) time-series in Python

AntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to e

Raphael Vallat 153 Dec 27, 2022
Adversarial Graph Augmentation to Improve Graph Contrastive Learning

ADGCL : Adversarial Graph Augmentation to Improve Graph Contrastive Learning Introduction This repo contains the Pytorch [1] implementation of Adversa

susheel suresh 62 Nov 19, 2022
A PyTorch implementation of a Factorization Machine module in cython.

fmpytorch A library for factorization machines in pytorch. A factorization machine is like a linear model, except multiplicative interaction terms bet

Jack Hessel 167 Jul 06, 2022
Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

OFA Sys 1.4k Jan 08, 2023
PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning.

neural-combinatorial-rl-pytorch PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning. I have implemented the basic

Patrick E. 454 Jan 06, 2023
An unofficial implementation of "Unpaired Image Super-Resolution using Pseudo-Supervision." CVPR2020

UnpairedSR An unofficial implementation of "Unpaired Image Super-Resolution using Pseudo-Supervision." CVPR2020 turn RCAN(modified) -- xmodel(xilinx

JiaKui Hu 10 Oct 28, 2022
This repository contains the official MATLAB implementation of the TDA method for reverse image filtering

ReverseFilter TDA This repository contains the official MATLAB implementation of the TDA method for reverse image filtering proposed in the paper: "Re

Fergaletto 2 Dec 13, 2021