semvec/pypi-packages huggingface dataset:
pyjacket 0.3.0
Lorem ipsum

# PyJacket
A collection of various broadly applicable tools.
 Written by Kasper Arfman

## Arrtools:
Matrix tools

## Core
Broadest, most useful tools

## CVtools
Image/Video analysis toolbox

## Filetools
File reading and writing

## Graphs
Useful tools for Graph theory

## ntheory
Useful tools for Number theory

## stheory
String operations
mieshah 0.0.1
A Python package to calculate light scattering properties/parameters of spherical particles by using Mie theory.
Requires Python: >=3.6

# This Python package calculates light scattering properties/parameters of spheres by using Mie theory. 
This has been developed from a mie theory program originally written in FORTRAN and published in the paper: Ghanshyam A. Shah, "Numerical Methods for Mie Theory of Scattering by a Sphere",Kodaikanal Obs. Bull. Soc.(1977) 2, 42-63.  

- Python form developed by Dwaipayan Deb (2025)

Install command: 
---------------
`pip install mieshah`


Use example:
------------
```
import mieshah as ms
from matplotlib import pyplot as plt

# ps-> particle radius(microns), wl-> wavelength (microns), m-> complex refractive index (real,imaginary), 
# f-> size distribution function, incr-> increment for size distribution function
mymie = ms.miescatter(ps=[0.1,1], wl=6.283185307,m=(1.5,0),f="x**-2",incr=0.01)  # With size distribution function
#mymie = ms.miescatter(ps=100, wl=6.283185307,m=(1.5,0.0)) # Without size distribution function (single particle)

print(mymie.ps)
print(mymie.wl)
print(mymie.m)
print(mymie.f)
print(mymie.X) # Size parameter
print(mymie.ALBED) # Albedo
print(mymie.QSCA) # Scattering efficiency
print(mymie.QEXT) # Extinction efficiency
print(mymie.QBAK) # Backscattering efficiency
print(mymie.QABS) # Absorption efficiency
print(mymie.QPR) # Radiation pressure efficiency
print(mymie.I_perp) # Intensity perpendicular to the plane of incidence
print(mymie.I_parl) # Intensity parallel to the plane of incidence
print(mymie.Polar) # Degree of linear polarization
print(mymie.p_theta) # Phase function
print(mymie.theta) # Scattering angle in degrees

plt.plot(mymie.theta[::2], mymie.Polar[::2])
plt.xlabel('Theta (degrees)')
plt.ylabel('Polarization')
plt.title('Degree of Linear Polarization')
plt.show()
plt.plot(mymie.theta[::2], mymie.p_theta[::2])
plt.yscale('log')    
plt.xlabel('Theta (degrees)')
plt.ylabel('Phase Function')
plt.title('Phase Function')
plt.show()
```
flametree 0.2.1
Python file and zip operations made easy
Keywords: file system, zip, archive, file, directory

Flametree
=========


Flametree is a Python library which provides a simple syntax for handling files and folders
(no ``os.path.join``, ``os.listdir`` etc.), and works the same way for different file systems.

Write a Flametree program to read/write files in disk folders, and your code will also be
able to read/write in zip archives and virtual (in-memory) archives - which is particularly
useful on web servers.

As an illustration, here is how to use Flametree to read a file ``texts/poems/the_raven.txt``, replace all
occurences of the word "raven" by "seagull" in the text, and write the result to a new
file ``the_seagull.txt`` in the same folder:

.. code:: python

     from flametree import file_tree

     with file_tree("texts") as root:
         poem_text = root.poems.the_raven_txt.read()
         new_text = poem_text.replace("raven", "seagull")
         root.poems._file("the_seagull.txt").write(new_text)

Even in this very simple use case, the syntax is clearer than the ``os`` way,
which would write as follows:

.. code:: python

    import os

    with open(os.path.join("poems", "the_raven.txt"), "r") as f:
        poem_text = f.read()
    new_text = poem_text.replace("raven", "seagull")
    with open(os.path.join("poems", "the_seagull.txt"), "w") as f:
        content = f.write(new_text)

Moreover, the same Flametree code also works for files inside a zip archive:

.. code:: python

     with file_tree("my_archive.zip") as root:
         poem_text = root.poems.the_raven_txt.read()
         new_text = poem_text.replace("raven", "seagull")
         root.poems._file("the_seagull.txt").write(new_text)

Now in hard mode: suppose that your server receives binary zip data of an
archive containing ``poems/the_raven.txt``, and must return back a new zip
containing a file ``poems/the_seagull.txt``. Here again, the syntax of the core
operations is the same:

.. code:: python

     destination_zip = file_tree("@memory") # Create a new virtual zip
     with file_tree(the_raven_zip_data) as root:
         poem_text = root.poems.the_raven_txt.read()
         new_text = poem_text.replace("raven", "seagull")
         destination_zip._dir("poems")._file("the_seagull.txt").write(new_text)
     destination_zip_data = destination_zip._close()
     # Now send the data to the client

See section *Usage* below for more examples and features.

Infos
-----

**PIP installation:**

.. code:: bash

  pip install flametree

**Github Page:** `<https://github.com/Edinburgh-Genome-Foundry/Flametree>`_

**License:** MIT

Copyright 2017 Edinburgh Genome Foundry, University of Edinburgh
ctfr 0.1.0
Package for efficiently computing combined time-frequency representations of audio signals.
Requires Python: >=3.10
Keywords: audio, music, sound, music information retrieval, time-frequency representations

# ctfr

[![PyPI](https://img.shields.io/pypi/v/ctfr.svg)](https://pypi.python.org/pypi/ctfr) [![Python Versions](https://img.shields.io/pypi/pyversions/ctfr.svg)](https://pypi.python.org/pypi/ctfr) ![Licence](https://img.shields.io/github/license/b-boechat/ctfr) 

#### Efficient toolbox for computing combined time-frequency representations of audio signals.

# Table of contents
- [Documentation](#documentation)
- [Installation](#instalation)
    - [Using PyPI](#using-pypi)
    - [Development mode](#development-mode)
- [Citing](#citing)

---

## Documentation

See the ctfr [documentation](https://ctfr.readthedocs.io/en/latest/) for more information about the package, including usage examples and the API reference.

---

## Instalation

### Using PyPI

The latest stable release is available on PyPI, and can be installed with the following command:

```shell
pip install ctfr
```

Note that this doesn’t install the plotting dependencies. To install with plotting included, run

```shell
pip install ctfr[display]
```

### Development mode

If you want to make changes to ctfr, you can install it in editable mode with development dependencies by cloning or downloading the repository and running:

```shell
make dev
```

or

```shell
pip install -e .[dev]
```

When installing in this mode, Cython is a build dependency. If you have trouble running Cython, see [this guide](https://docs.cython.org/en/stable/src/quickstart/install.html).

Note: When developing, `.pyx` files need to be recompiled in order for changes in them to take place. This can be done by running 

```shell
make ext
```

or

```shell
python setup.py build_ext --inplace
```

---

## Citing

If you use ctfr in your work or research, please cite following paper:

```
> To be added!
```

Also, if you use a speficic combination method, please cite the corresponding paper(s). You can find the references for a specific method by running:

```python
>>> ctfr.cite_method("fls")
M. do V. M. da Costa and L. W. P. Biscainho, “The fast local sparsity method: A low-cost combination of time-frequency representations based on the hoyer sparsity,” Journal of the Audio Engineering Society, vol. 70, no. 9, pp. 698–707, Sep. 2022.
```
torchpal 0.1.1
PyTorch辅助工具包：极度新手友好的 PyTorch 伴侣
Requires Python: >=3.10

<p align="center">
  <b style="font-size: 2.2em;">torchpal</b><br>
  <span style="font-size: 1em;"> 极度新手友好的 PyTorch 伙伴；加速机器学习探索之旅！</span>
</p>

<p align="center">
  <img src="https://img.shields.io/badge/PyTorch-助手-FF6F00?style=for-the-badge&logo=pytorch&logoColor=white" alt="PyTorch 助手"/>
  <img src="https://img.shields.io/badge/Python-3.10+-blue?style=for-the-badge&logo=python&logoColor=white" alt="Python 3.10+"/>
  <a href="LICENSE">
    <img src="https://img.shields.io/badge/许可证-MIT-green?style=for-the-badge" alt="MIT 许可证"/>
  </a>
</p>

## 📖 简介

**TorchPal** 是一个专为新手设计的 PyTorch 辅助工具包，拥有简单易用的 API。  
能极大简化深度学习模型的开发、训练和评估流程中的样板代码，帮助您更专注快速地验证自己的想法。

## 🌟 特色功能

- **自动化训练与评估**: 为常见的回归和分类任务提供自动化管理器，无需手动编写训练循环、验证逻辑（内置 K 折交叉验证）
- **实时可视化**: 实时可视化训练与评估过程，直观展示模型性能；支持自定义指标进行绘制，
- **探索性训练**: 支持在小型数据子集上快速运行训练，帮助初步验证模型架构或超参数设置的合理性
- **实用工具集**: 提供丰富的实用工具，如提供模型保存/加载、脚本备份、图片展示等常用辅助功能

## ⚡ 快速开始

### 安装

```bash
pip install torchpal
```

### 基础使用（以回归任务为例）
[点击查看完整示例代码](example.ipynb)

## 📚 模块概览

- **`tp.train`**: 包含 `RegressionAutoManager` 和 `ClassificationAutoManager`，用于自动化训练和评估流程。
- **`tp.utils`**: 提供实用工具
- **`tp.data`**: 数据处理相关工具
- **`tp.da`**: 简单的数据分析工具 (基于 Pandas)


提示：TorchPal 优化了类型提示，您可以在编码时利用 IDE 的提示与代码补全功能轻松查看各模块的内容、函数的可用参数及说明。

## 🤝 贡献

欢迎各种形式的贡献！

1.  **发现 Bug 或有功能建议？** 请在 [GitHub Issues](https://github.com/2bitbit/torchpal/issues) 提出。
2.  **贡献代码：**
    - Fork 本仓库。
    - 创建特性分支 (`git checkout -b feature/YourAmazingFeature`)。
    - 提交更改 (`git commit -m 'Add some AmazingFeature'`)。
    - 推送到分支 (`git push origin feature/YourAmazingFeature`)。
    - 提交 Pull Request。

## 🙏 致谢

- 感谢 PyTorch 团队。
- 感谢所有开源贡献者。


<p align="center">
  <b>TorchPal - 让 PyTorch 更简单，让想法更快落地！</b><br>
  <i>用 ❤️ 制作</i>
</p>