semvec/pypi-packages huggingface dataset:
zhenglin 3.0.2
A deep-learning package contains a set of well-organized deep-neural networks&tools.
Requires Python: >=3.8

# pip install zhenglin
*@ Author: zhenglin*
*@version: 3.0.2*

This package contains some off-the-shelves deep-learning networks implemented with [![](https://img.shields.io/badge/Pytorch-ee4c2c?style=flat-square&logo=pytorch&logoColor=white)](https://pytorch.org/).

use
```bash
pip install zhenglin
```

to install this package.

## Major Updates
+ Aug 7 2025: `Version: 3.0.1`. Add `patchify`, vr and cv function updates.
+ May 31 2025: `Version: 3.0.0`. Introduce `FileSystem` class under `filesystem`, use a pre-defined 1 or 2-layer folder structure to rapidly manage batch image or video datasets.
+ Apr 30 2025: `Version: 2.1`. Add a new class wrapper `vd` class under `supercv`, supports video io now.
+ Apr 18 2025: `Version: 2.0`. Add init version of `supercv(cv)` class, a universal wrapper for cv2, PIL.Image and Torch.tensor.
+ Nov 13 2024: `Version: 1.21`. Package overhaul. Add `huggingface accelerate`, `pytorch-lightning`, and `pytorch-ddp` support.
+ Oct 26 2023: `Version: 1.20.19` Add `TransUNet` and `SwinUNet`
+ Aug 13 2023: `Version: 1.18.15` Add basic quantization support.


## Introduction

[zhenglin](https://pypi.org/project/zhenglin/) package was motivated by eriklindernoren and his [repo](https://github.com/eriklindernoren/PyTorch-GAN) which provides many **grab-and-run** implementation of DL variants. It is the level ground for zhenglin's deep learning projects.

Specifically, this package provides
+ universal structure-template:
    - `zhenglin.accelerate.train`
    - `zhenglin.lightning.train`
    - `zhenglin.pytorch.train`
+ loss functions and metrics
    - `zhenglin.utils.loss`
    - `zhenglin.utils.metrics`
+ utils
    - `zhenglin.utils.utils`
+ 20 modular deep-learning networks
    - `zhenglin.networks.*`

### Networks(Alphabetically):
- [cycleGAN](https://github.com/aitorzip/PyTorch-CycleGAN)
- [DDPM](https://github.com/dome272/Diffusion-Models-pytorch)
- [DeblurGAN](https://github.com/fourson/DeblurGAN-pytorch/tree/master)
- [EDSR](https://github.com/twtygqyy/pytorch-edsr/blob/master/edsr.py)
- [ESRGAN](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/esrgan/esrgan.py)
- [Noise2Void](https://github.com/JohnYKiyo/Noise2Void/blob/master/02_training_test_Noise2Void.ipynb)
- [Pix2Pix](https://github.com/mrzhu-cool/pix2pix-pytorch)
- [RCAN](https://github.com/yjn870/RCAN-pytorch)
- ResNet
- [Restormer](https://github.com/leftthomas/Restormer)
- RRDBNet
- [SqueezeNet](https://github.com/gsp-27/pytorch_Squeezenet/tree/master)
- [SRDRM](https://github.com/xahidbuffon/SRDRM/tree/master)
- SRGAN
- [SwinIR](https://github.com/JingyunLiang/SwinIR)
- [SwinUNet](https://github.com/HuCaoFighting/Swin-Unet/tree/main)
- [TransUNet](https://github.com/Beckschen/TransUNet)
- U2Net
- UNet
- [Attention-UNet](https://github.com/Andy-zhujunwen/UNET-ZOO/blob/master)
PySide6Plot 0.0.3
Scientific/Financial plot utilities with PySide6+pyqtgraph.
Requires Python: >=3.11
Keywords: visualization, plotting, pyside6, pyqtgraph, financial, scientific

<h1 align="center">
  <br>PySide6Plot<br>
</h1>

-----

A PySide6-based data visualization tool.

### Special Thanks
* Initial version borrowed from [QStockPlotter](https://github.com/qiauil/QStockPlotter) by qiauil.
* Beautiful UI design inspired by [QFluentWidgets](https://qfluentwidgets.com/).
turtlewave-hdEEG 2.1.0
High-density EEG processing for sleep event detection
Requires Python: >=3.8

# TurtleWave-hdEEG

High-density EEG processing for sleep research, extended from Wonambi for large datasets.

## Installation

```bash
pip install turtlewave-hdEEG

```
## For development installation:

``` bash
git clone https://github.com/TancyKao/turtlewave-hdEEG.git
cd turtlewave-hdEEG
pip install -e .

```
## Features
Generate annotation, detection slow wave and spindle events
JSON to XML conversion for EEG evnet data files

## Usage
Basic usage example:
```bash
turtlewave_gui

```
goutool 0.1.1
🐶 Python工具类库 更加快速的开发项目 便捷优雅的使用类库
Requires Python: >=3.6

# GouTool

#### 介绍
🐶 Python工具类库 更加快速的开发项目 便捷优雅的使用类库

#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request
pripy 0.1.8
Phase Retrieval algorithms in Python
Requires Python: >=3.7

# pripy
<!--- These are examples. See https://shields.io for others or to customize this set of shields. You might want to include dependencies, project status and licence info here --->
![GitHub contributors](https://img.shields.io/github/contributors/jcranney/pripy) ![License](https://img.shields.io/github/license/jcranney/pripy) 
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pripy) ![PyPI version](https://img.shields.io/pypi/v/pripy) 

pripy is a python package aimed at providing phase retrieval algorithms, primarily for use in adaptive optics systems and simulators. These algorithms must have a common and minimal API, in order to accelerate the testing of various algorithms on a given optical system.


## Prerequisites

* A recent version of Python 3 installed on any Windows/Linux/MacOS machine,

## Installing pripy

To install the most recent stable version of pripy, simply:

```bash
pip install pripy
```

To install the latest development version from this git repo, instead do:

```bash
git clone https://github.com/jcranney/pripy
cd pripy
pip install -e .
```

## Using pripy

To use pripy, follow the provided examples, e.g., using Gerchberg-Saxton in the sandbox AO environment (no external simulator required):

```bash
cd examples
ipython -i sandbox_gs.py   # run the sandbox Gerchberg-Saxton example
```

## Contributing to pripy
pripy is in its infancy and welcomes collaborative input. To contribute to pripy, follow these steps:

1. Fork this repository.
2. Create a branch: `git checkout -b <branch_name>`.
3. Make your changes and commit them: `git commit -m '<commit_message>'`
4. Push to the branch: `git push`
5. Create the pull request.

Alternatively see the GitHub documentation on [creating a pull request](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request).

## Contributors

* [@jcranney](https://github.com/jcranney) 
* [@mvandam](https://github.com/mvandam)
* [@ndoucet](https://github.com/ndoucet)

## Contact

If you want to contact me you can reach me at jesse.cranney@anu.edu.au.

## License

This project uses the following license: [MIT License](https://github.com/jcranney/pripy/blob/main/LICENSE).
StarChecker 1.3
A project that sends you SMS notifications via ntfy.sh when someone stars one of your devlogs!
Requires Python: >=3.9

# Star Checker

###### A web scraper that lets you know when someone stars or unstars one of your Summer of Making devlogs!

## Features:
- Uses ntfy.sh to send notifications
- Uses Selenium and Beautiful Soup for web scraping
- Gets cookies from the user by asking for them

## Usage:
### pip (Recommended)
- Before installing via pip log in or create an account at ntfy.sh and subscribe to the topic "Devlog_Stars"
- Before installing via pip, log in or create an account at ntfy.sh and subscribe to the topic "Devlog_Stars"
- Then run `pip install StarChecker` in the terminal
- You might need to use `pip3 install StarChecker` on some systems
- Finally, type in starchecker in the terminal!
- Then when someone stars your project you should see which devlog is starred in which project in that topic in ntfy.sh

- Finally, type in `starchecker` in the terminal!
- Then when someone stars your project you should see which devlog is starred in which project in that topic in ntfy.sh
cosmic-ray-to-mutation-elements-adapter 1.0.1
Adapter to show cosmic-ray python mutation report in stryker's mutation testing elements format
Requires Python: >=3.10



## Usage

```
usage: cr-to-stryker-report [-h] [-f {html,json}] [-o OUTPUT] [-w WORKSPACE]
                            sqlite_db_path

Convert Cosmic Ray mutation results to Stryker JSON report.

positional arguments:
  sqlite_db_path        Path to the Cosmic Ray mutation session SQLite database.

options:
  -h, --help            show this help message and exit
  -f {html,json}, --format {html,json}
                        Output format for the report.
  -o OUTPUT, --output OUTPUT
                        Path to output Stryker report file.
  -w WORKSPACE, --workspace WORKSPACE
                        Workspace root directory for resolving module paths.
```