Testing

Codecov coverage standard for Python

Python-Standard This is a Python application, with basic unit tests, for which coverage is uploaded to Codecov on a daily basis. It can also serve as an example for how to integrate Codecov into your Python project. If the build is passing for this project, then Codecov’s Python report processing is functional and correct on codecov.io. Configuration This project is written in Python 3.9. Unit tests are written with the pytest framework and coverage reports are generated using the pytest-cov […]

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API

An API wrapper around the pythonanywhere’s API

pyaww An API wrapper around the pythonanywhere’s API. The name stands for pythonanywherewrapper. 100% API coverage Most of the codebase is documented & typehinted Maintained # import the module from pyaww.user import User # construct the user class client = User(auth=’…’, username=’…’) for console in client.consoles(): print(console.name) Please look at the documentations: https://ammarsys.github.io/pyaww-docs/ PyPi: https://pypi.org/project/pyaww # Linux/MacOS python3 -m pip install pyaww # Windows py -m pip install pyaww For the dev version, do: git clone https://github.com/ammarsys/pyaww cd pyaww GitHub […]

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Automation

A Python package to facilitate research on building and evaluating automated scoring models

Automated scoring of written and spoken test responses is a growing field in educational natural language processing. Automated scoring engines employ machine learning models to predict scores for such responses based on features extracted from the text/audio of these responses. Examples of automated scoring engines include Project Essay Grade for written responses and SpeechRater for spoken responses. Rater Scoring Modeling Tool (RSMTool) is a python package which automates and combines in a single pipeline multiple analyses that are commonly conducted […]

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Face recognition

A lightweight face-recognition toolbox and pipeline based on tensorflow-lite

FaceIDLight A lightweight face-recognition toolbox and pipeline based on tensorflow-lite with MTCNN-Face-Detection and ArcFace-Face-Recognition. No need to install complete tensorflow, tflite-runtime is enough. All tools are using CPU only. 📘 Description A lightweight face-recognition toolbox and pipeline based on tensorflow-lite with MTCNN-Face-Detectionand ArcFace-Face-Recognition. No need to install complete tensorflow, tflite-runtime is enough. All tools areusing CPU only. Pull request are welcome! ⚡️ Features Online Face-Recognition Running completely on CPU Multi Faces ~4 FPS on a MacBookPro2015 Tools for Face-Detection, -Verification […]

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Excel

Accelerated Excel XLSX Writing Library for Python 2/3

PyExcelerate Accelerated Excel XLSX writing library for Python PyExcelerate is a Python for writing Excel-compatible XLSX spreadsheet files, with an emphasis on speed. Benchmarks Benchmark code located in pyexcelerate/tests/benchmark.pyUbuntu 12.04 LTS, Core i5-3450, 8GB DDR3, Python 2.7.3 | TEST_NAME | NUM_ROWS | NUM_COLS | TIME_IN_SECONDS | |—————————–|———-|———-|—————–| | pyexcelerate value fastest | 1000 | 100 | 0.47 | | pyexcelerate value faster | 1000 | 100 | 0.51 | | pyexcelerate value fast | 1000 | 100 | 1.53 | […]

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Deep Learning

A library for Deep Learning Implementations and utils

deeply A library for Deep Learning Implementations and utils. Features Installation $ pip install git+https://github.com/achillesrasquinha/deeply.git Usage Application Interface >>> import deeply Command-Line Interface $ deeply Usage: deeply [OPTIONS] COMMAND [ARGS]… A Deep Learning library Options: –version Show the version and exit. -h, –help Show this message and exit. Commands: help Show this message and exit. version Show version and exit. GitHub https://github.com/achillesrasquinha/deeply    

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Server

A web GUI to manage and monitorize your Wireguard server

Linguard Linguard aims to provide an easy way to manage your WireGuard server, and it’s written in Python3 and powered by Flask. Screenshots Installation Git Install dependencies: sudo apt update sudo apt install wireguard iptables uwsgi uwsgi-plugin-python3 libpcre3 libpcre3-dev Download any release (or clone the repository) and put the files somewhere you will remember later, such as /var/www/linguard. Edit the configuration files to fit your needs. Add a linguard user and group to your computer: groupadd linguard useradd -g linguard […]

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Wrapper

Wrapper for the undocumented CodinGame API

codingame API wrapper Pythonic wrapper for the undocumented CodinGame API. Installation Python 3.6 or higher is required. Install codingame with pip: pip install codingame Quickstart Create an application, in example.py: import codingame client = codingame.Client() # if you want to log in client.login(“[email protected]”, “password”) # get a codingamer codingamer = client.get_codingamer(“username”) print(codingamer.pseudo) # get the global leaderboard global_leaderboard = client.get_global_leaderboard() # print the pseudo of the top codingamer print(global_leaderboard.users[0].pseudo) GitHub https://github.com/takos22/codingame    

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Dataset

A python library that generates random facts

Randfacts Randfacts is a python library that generates random facts. You can use randfacts.get_fact() to return a random fun fact. Disclaimer: Facts are not guaranteed to be true. randfacts can either be installed via pip or via the AUR, whichever way you prefer. Installation via pip: $ pip3 install randfacts Installation via AUR: $ git clone https://aur.archlinux.org/python-randfacts.git && cd python-randfacts $ makepkg -si import randfacts x = randfacts.get_fact() print(x) will print a random fact like:Penguins can’t taste sweet or savory […]

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