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)
SeqTR: A Simple yet Universal Network for Visual Grounding

SeqTR This is the official implementation of SeqTR: A Simple yet Universal Network for Visual Grounding, which simplifies and unifies the modelling fo

seanZhuh 76 Dec 24, 2022
Code for a seq2seq architecture with Bahdanau attention designed to map stereotactic EEG data from human brains to spectrograms, using the PyTorch Lightning.

stereoEEG2speech We provide code for a seq2seq architecture with Bahdanau attention designed to map stereotactic EEG data from human brains to spectro

15 Nov 11, 2022
Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection Main requirements torch = 1.0 torchvision = 0.2.0 Python 3 Environm

15 Apr 04, 2022
ScriptProfilerPy - Module to visualize where your python script is slow

ScriptProfiler helps you track where your code is slow It provides: Code lines t

Lucas BLP 3 Jun 02, 2022
Codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks

DominoSearch This is repository for codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense n

11 Sep 10, 2022
The repository forked from NVlabs uses our data. (Differentiable rasterization applied to 3D model simplification tasks)

nvdiffmodeling [origin_code] Differentiable rasterization applied to 3D model simplification tasks, as described in the paper: Appearance-Driven Autom

Qiujie (Jay) Dong 2 Oct 31, 2022
Style transfer, deep learning, feature transform

FastPhotoStyle License Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons

NVIDIA Corporation 10.9k Jan 02, 2023
Implementation for Homogeneous Unbalanced Regularized Optimal Transport

HUROT: An Homogeneous formulation of Unbalanced Regularized Optimal Transport. This repository provides code related to this preprint. This is an alph

Théo Lacombe 1 Feb 17, 2022
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (ICCV, 2021) (PyTorch) - We released the training code!

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution Kai Zhang, Jingyun Liang, Luc Van Gool, Radu Timofte Computer Vision Lab

Kai Zhang 804 Jan 08, 2023
GNN-based Recommendation Benchmark

GRecX A Fair Benchmark for GNN-based Recommendation Homepage and Documentation Homepage: Documentation: Paper: GRecX: An Efficient and Unified Benchma

73 Oct 17, 2022
PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)

PointCNN: Convolution On X-Transformed Points Created by Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. Introduction PointCNN

Yangyan Li 1.3k Dec 21, 2022
Various operations like path tracking, counting, etc by using yolov5

Object-tracing-with-YOLOv5 Various operations like path tracking, counting, etc by using yolov5

Pawan Valluri 5 Nov 28, 2022
Improving Contrastive Learning by Visualizing Feature Transformation, ICCV 2021 Oral

Improving Contrastive Learning by Visualizing Feature Transformation This project hosts the codes, models and visualization tools for the paper: Impro

Bingchen Zhao 83 Dec 15, 2022
I explore rock vs. mine prediction using a SONAR dataset

I explore rock vs. mine prediction using a SONAR dataset. Using a Logistic Regression Model for my prediction algorithm, I intend on predicting what an object is based on supervised learning.

Jeff Shen 1 Jan 11, 2022
Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling"

Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling" Pipeline of Tip-Adapter Tip-Adapter can provid

peng gao 187 Dec 28, 2022
Code for the paper "Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness"

DU-VAE This is the pytorch implementation of the paper "Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness" Acknowledgement

Dazhong Shen 4 Oct 19, 2022
Image Lowpoly based on Centroid Voronoi Diagram via python-opencv and taichi

CVTLowpoly: Image Lowpoly via Centroid Voronoi Diagram Image Sharp Feature Extraction using Guide Filter's Local Linear Theory via opencv-python. The

Pupa 4 Jul 29, 2022
Code in conjunction with the publication 'Contrastive Representation Learning for Hand Shape Estimation'

HanCo Dataset & Contrastive Representation Learning for Hand Shape Estimation Code in conjunction with the publication: Contrastive Representation Lea

Computer Vision Group, Albert-Ludwigs-Universität Freiburg 38 Dec 13, 2022
Galaxy images labelled by morphology (shape). Aimed at ML development and teaching

Galaxy images labelled by morphology (shape). Aimed at ML debugging and teaching.

Mike Walmsley 14 Nov 28, 2022
Lipschitz-constrained Unsupervised Skill Discovery

Lipschitz-constrained Unsupervised Skill Discovery This repository is the official implementation of Seohong Park, Jongwook Choi*, Jaekyeom Kim*, Hong

Seohong Park 17 Dec 18, 2022