Package

The Python3 import playground

I have been confused about python modules and packages, this text tries to clear the topic up a bit. Sources: https://chrisyeh96.github.io/2017/08/08/definitive-guide-python-imports.html https://abarker.github.io/understanding_python_imports/ https://stackoverflow.com/questions/44834/can-someone-explain-all-in-python https://docs.python.org/3/reference/import.html https://stackoverflow.com/questions/43059267/how-to-do-from-module-import-using-importlib Modules Each file with extension .py can be used as a module. The module is in source file module_source.py, while it is imported as import module_source. In this example, both the module source file and the importing file are in the same directory. Lets import the module. (the module source includes a print statement print(“module_foo […]

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Tracker

Pytorch-Lightning implementation of the Box-Aware Tracker

Pytorch-Lightning implementation of the Box-Aware Tracker. Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds. ICCV 2021 Chaoda Zheng, Xu Yan, Jiaotao Gao, Weibing Zhao, Wei Zhang, Zhen Li*, Shuguang Cui Citation @InProceedings{zheng2021box, title={Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds}, author={Chaoda Zheng, Xu Yan, Jiaotao Gao, Weibing Zhao, Wei Zhang, Zhen Li, Shuguang Cui}, journal={ICCV}, year={2021} }   To finish reading, please visit source site

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Shortener URL

Shortens the website URL With Python

It shortens the website URL. How to run the script -Download the library using command pip install pyshorteners in command prompt. How it works It takes the website URL as input. It shortens the given URL and returns the shortened URL. Long URLs are not easy to handle so URLs should be short and sweet so that they are easy to share. pyshorteners is a Python lib to help you short and expand urls using URL Shorteners available. GitHub https://github.com/bindaldhara/URL-shortener […]

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Django

Simple application TodoList django with ReactJS

Django We basically follow the Django REST framework quickstart guide here. Create backend folder with a virtual Python environment: mkdir backend cd backend pipenv install; pipenv shell Install Django and Django REST framework: pipenv install django djangorestframework Create Django project structure: django-admin startproject backend . cd backend django-admin startapp todo cd .. Create Django super user: ./manage.py migrate ./manage.py createsuperuser –email    

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Codemirror

Codemirror syntax definition for the wren language

A JupyterLab extension. Requirements Install To install the extension, execute: pip install jupyter_wren_syntax Uninstall To remove the extension, execute: pip uninstall jupyter_wren_syntax Contributing Development install Note: You will need NodeJS to build the extension package. The jlpm command is JupyterLab’s pinned version ofyarn that is installed with JupyterLab. You may useyarn or npm in lieu of jlpm below.

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Telegram

Advanced telegram link in a message attach bot

An advanced telegram attach bot git clone https://github.com/FayasNoushad/Attach-Bot-V2/tree/main cd Attach-Bot-V2 pip3 install -r requirements.txt # python3 main.py Variables API_HASH Your API Hash from my.telegram.org API_ID Your API ID from my.telegram.org BOT_TOKEN Your bot token from @BotFather DATABASE_URL MongoDB URL BOT_OWNER Bot owner ID Credits GitHub https://github.com/FayasNoushad/Attach-Bot-V2    

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Parser

A small project to provide machine parseable BSIMM version 12 framework data

This is a small project to provide machine parseable BSIMM (Building Security in Maturity Model) version 12 framework data (in JSON format). Here is the tool I used to parse BSIMMv12 SSF data from bsimm.com. Here is the BSIMM12 foundations document that contains the vertical tables. These are annoying to copy & paste (at least via my PDF reader). I double checked my work including running the two tests outlined in vert-check.py. Things seem to line up currently. file descr […]

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Segmentation

SIIM-ACR Pneumothorax Segmentation With Python

Model segmentation classification Augmentations Used following transforms from [albumentations] RESIZE_SIZE = 1024 # or 768 train_transform = albumentations.Compose([ albumentations.Resize(RESIZE_SIZE, RESIZE_SIZE), albumentations.OneOf([ albumentations.RandomGamma(gamma_limit=(60, 120), p=0.9), albumentations.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2,    

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