Tool

The Python agent for Apache SkyWalking

SkyWalking Python Agent SkyWalking-Python: The Python Agent for Apache SkyWalking, which provides the native tracing abilities for Python project. SkyWalking: an APM(application performance monitor) system, especially designed for microservices, cloud native and container-based (Docker, Kubernetes, Mesos) architectures. Install From Pypi The Python agent module is published to Pypi, from where you can use pip to install: # Install the latest version, using the default gRPC protocol to report data to OAP pip install “apache-skywalking” # Install the latest version, using […]

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Command-line Tools

A script for Git-aware customization of the command prompt in Bash and zsh

ps1.py Here we have yet another script for Git-aware customization of the command prompt in Bash and zsh. Unlike all the other scripts, I wrote this one, so it’s better. Features: lets you know if you have mail in $MAIL shows chroot and virtualenv prompt prefixes automatically truncates the current directory path if it gets too long shows the status of the current Git repository (see below) thoroughly documented and easily customizable supports both Bash and zsh can optionally output […]

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Simulation

A fast open-source simulator for poly-articulated systems

Jiminy Jiminy is a fast and lightweight cross-platform open-source simulator for poly-articulated systems. It was built with two ideas in mind: provide a fast yet physically accurate simulator for robotics research. Jiminy is built around Pinocchio, an open-source fast and efficient kinematics and dynamics library. Jiminy thus uses minimal coordinates and Lagrangian dynamics to simulate an articulated system: this makes Jiminy as close as numerically possible to an analytical solution, without the risk of joint violation. build an efficient and […]

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Tool

An open source toolkit using Blender and Python

zpy Collecting, labeling, and cleaning data for computer vision is a pain. Jump into the future and create your own data instead! Synthetic data is faster to develop with, effectively infinite, and gives you full control to prevent bias and privacy issues from creeping in. We created zpy to make synthetic data easy, by simplifying the simulation (sim) creation process and providing an easy way to generate synthetic data at scale. Install You can install zpy with pip: pip install […]

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Network

Library for building WebSocket servers and clients in Python

websockets websockets is a library for building WebSocket servers and clients in Python with a focus on correctness and simplicity. Built on top of asyncio, Python’s standard asynchronous I/O framework, it provides an elegant coroutine-based API. Here’s how a client sends and receives messages: #!/usr/bin/env python import asyncio from websockets import connect async def hello(uri): async with connect(uri) as websocket: await websocket.send(“Hello world!”) await websocket.recv() asyncio.run(hello(“ws://localhost:8765”)) And here’s an echo server: #!/usr/bin/env python import asyncio from websockets import serve async […]

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GUI

A GUI-based Python code generator

Visual Python Visual Python is a GUI-based Python code generator, developed on the Jupyter Notebook environment as an extension. Key Features Automatically generates Python code based on a graphic user interface Creates code blocks by tasks (such as data processing, visualization) You can save the analysis process and share it (as .vp file) with others Who and What is it for? Visual Python is a data analytics solution for both Programmers & Non-programmers. Programmers can use the tool : to […]

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Editor

A NeoVim plugin for highlighting visual selections like in a normal document editor

HighStr.nvim HighStr.nvim is a NeoVim plugin written in Lua that aims to provide a the simple functionality of highlighting text like one would do in a normal document editor. To use it, install it with your favorite plugin manager, select something in visual mode and then run :HSHighlight. You can also add keybindings to the commands. Highlight visual selection in any given pre-defined color. Remove highlighting from lines in visual selection. Users can set up foreground and background of any […]

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Deep Learning

Introducing Convolutions to Vision Transformers

CvT This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolutional vision Transformers (CvT), that improves Vision Transformers (ViT) in performance and efficienty by introducing convolutions into ViT to yield the best of both disignes. This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) […]

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Search

An AI-based image search engine that includes deep transfer learning features Extraction

Deep Image Search Deep Image Search is an AI-based image search engine that includes deep transfer learning features Extraction and tree-based vectorized search technique. Features Faster Search O(logN) Complexity. High Accurate Output Result. Best for Implementing on python based web application or APIs. Best implementation for College students and freshers for project creation. Applications are Images based E-commerce recommendation, Social media and other image-based platforms that want to implement image recommendation and search. Installation This library is compatible with both […]

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Deep Learning

Break down your CNN and visualize the features from within the model

Rover Reverse engineer your CNNs, in style. Rover will help you break down your CNN and visualize the features from within the model. No need to write weirdly abstract code to visualize your model’s features anymore. :computer: Usage git clone https://github.com/Mayukhdeb/rover.git; cd rover install requirements: pip install -r requirements.txt from rover import core from rover.default_models import models_dict core.run(models_dict = models_dict) and then run the script with streamlit as: $ streamlit run your_script.py if everything goes right, you’ll see something like: […]

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