semvec/pypi-packages huggingface dataset:
ARAB64 0.1
MT FIRST LIB TEST IT
Requires Python: >=3.6

# ArabicBase64Encoder

مكتبة لتشفير النصوص وفك التشفير باستخدام Base64 بنظام الحروف العربي.

## التثبيت

لتثبيت المكتبة:

```bash
pip install git+https://github.com/yourusername/ArabicBase64Encoder.git
omniscale 0.2b0
A Python package for various unit conversions
Requires Python: >=3.6

# рџ“¦ Omniscale

**Omniscale** is a Python package that provides various unit conversion functions, including temperature, distance, weight, speed, and time conversions.

## рџљЂ Installation

You can install the package directly from PyPI using:

```sh
pip install omniscale==0.1
```

## рџ›  Usage

Import the package and use the available functions:

```python
import omniscale

# Convert Celsius to Fahrenheit
print(omniscale.celsius_to_fahrenheit(0))  # Output: 32.0
print(omniscale.celsius_to_fahrenheit(100))  # Output: 212.0

# Convert Fahrenheit to Celsius
print(omniscale.fahrenheit_to_celsius(98.6))  # Output: 37.0

# Convert meters to feet
print(omniscale.meters_to_feet(1))  # Output: 3.28084

# Convert feet to meters
print(omniscale.feet_to_meters(3.28084))  # Output: 1.0

# Convert kilograms to pounds
print(omniscale.kg_to_pounds(10))  # Output: 22.05

# Convert pounds to kilograms
print(omniscale.pounds_to_kg(22.05))  # Output: 10.0

# Convert kilometers per hour to miles per hour
print(omniscale.kmh_to_mph(100))  # Output: 62.14

# Convert miles per hour to kilometers per hour
print(omniscale.mph_to_kmh(62.14))  # Output: 100.0

# Convert minutes to seconds
print(omniscale.minutes_to_seconds(5))  # Output: 300

# Convert seconds to minutes
print(omniscale.seconds_to_minutes(300))  # Output: 5.0
```

## рџ“Њ Available Conversions

- `celsius_to_fahrenheit(celsius)`: Converts Celsius to Fahrenheit.
- `fahrenheit_to_celsius(fahrenheit)`: Converts Fahrenheit to Celsius.
- `meters_to_feet(meters)`: Converts meters to feet.
- `feet_to_meters(feet)`: Converts feet to meters.
- `kg_to_pounds(kg)`: Converts kilograms to pounds.
- `pounds_to_kg(pounds)`: Converts pounds to kilograms.
- `kmh_to_mph(kmh)`: Converts kilometers per hour to miles per hour.
- `mph_to_kmh(mph)`: Converts miles per hour to kilometers per hour.
- `minutes_to_seconds(minutes)`: Converts minutes to seconds.
- `seconds_to_minutes(seconds)`: Converts seconds to minutes.

## рџ¤ќ Contributing

Feel free to fork this repository, make improvements, but don't forget to say my name!

## рџ“„ License

This project is licensed under the **MIT License**.
pastit-cli 1.0.7
A command-line tool for quickly sharing files and text snippets using a Zipline server
Requires Python: >=3.6

# pastit-cli

A command-line tool for quickly sharing files and text snippets using a Zipline server.

## Features
- Upload files directly from command line
- Upload text from stdin
- Progress bar with upload status
- Configurable file extensions
- Supports custom Zipline servers
- Automatic first-time setup

## Installation

```bash
pip install pastit-cli
```

## Usage

Upload a file:
```bash
pasta filename.txt
```

Upload from stdin:
```bash
echo "Hello World" | pastit
```

Or pipe any command output:
```bash
ls -la | pastit
```

On first run, you'll be prompted to enter your:
- Zipline server URL
- Authorization token

Your configuration will be saved in `~/.config/pastit/.env` and will be used for future uploads.

## Manual Configuration

If you need to change your configuration, you can either:

1. Edit `~/.config/pastit/.env` directly:
```ini
URL=https://your.server/api/upload
AUTHORIZATION_TOKEN=your_token
DEFAULT_EXTENSION=sh
CONSIDER_FILES_STARTING_WITH_DOT_EXTENSIONLESS=true
```

2. Or run the setup command:
```bash
pastit-setup
```
augmentimg 1.2.4
A simple no-code tool to augment Images and Annotations
Requires Python: >=3.7

# AugmentImg 📚

[![PyPI](https://img.shields.io/pypi/v/augmentimg)](https://pypi.org/project/augmentimg/)
[![Language](https://img.shields.io/badge/lang-en-blue.svg)](#)
[![PyPI Downloads](https://static.pepy.tech/badge/augmentimg)](https://pepy.tech/projects/augmentimg)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![GitHub](https://img.shields.io/badge/GitHub-Repository-green)](https://github.com/zamalali/augmentimg)


AugmentImg is a user-friendly image augmentation tool for computer vision tasks. It offers a no-code graphical interface to apply various augmentations to images and their annotations. Simply input the desired count and select the types of augmentations. It supports both YOLO (txt) and COCO (json) annotation formats.

Simple User Interface-

<div align="center">
  <img src="https://raw.githubusercontent.com/zamalali/AugmentImg/main/images/gui.png" alt="User Interface">
</div>



## Features

- Supports image augmentation with YOLO and COCO annotation formats.
- Apply multiple augmentations like Rotation, Flip, Blur, Saturation, Contrast, Sharpness, Gamma correction, CLAHE, HSV adjustment, shift-scale-rotate, and Brightness.
- Easy to use GUI.
- Allows selection of the desired number of augmented images.
- Options to augment images with or without annotations.

## Installation

You can install the package via pip:

```bash
pip install augmentimg
```
After the installation just run:
```bash
augment-img
```

## Contributing 🙋‍♂️
We are an open-source project in a fast-changing field and love getting help. Whether it's new features, better setup, or clearer docs, we'd appreciate your input. Check out our [Contributing](https://github.com/zamalali/AugmentImg/blob/main/CONTRIBUTING.rst)
 Guide for more info!
ZenX 1.0.0
ZenX: Text data Optimizer
Requires Python: >=3.6

# ZenX: Text Data Optimizer

## Introduction

ZenX is an advanced optimizer designed to enhance text data processing with efficiency, balance, and innovation. Inspired by the principles of Zen, it achieves peak performance with minimal effort, while the 'X' signifies acceleration, transformation, and cutting-edge capabilities. ZenX streamlines text processing workflows, making them more efficient, accurate, and adaptable to various applications.

## Installation

To install ZenX, run the following command:

```bash
pip install zenx
```

## Example Usage

You can use ZenX in your text optimization workflows as shown below:

```python
import tensorflow as tf
from loglu import LogLU
from zenx import ZenX  # ZenX Text Optimizer

# Define an RNN model
model = tf.keras.models.Sequential([
    tf.keras.layers.Embedding(input_dim=5000, output_dim=128, input_length=100),
    tf.keras.layers.SimpleRNN(128, activation=LogLU()),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Compile the model with ZenX optimizer
model.compile(
    optimizer=ZenX(
        learning_rate=0.001,
        beta_1=0.9,
        beta_2=0.999,
        epsilon=1e-7
    ),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)
model.summary()
```

## Feedback, Bugs, & Contributions

If you have feedback, encounter any bugs, or would like to contribute, please feel free to open an issue or contribute on GitHub.

Email: [poorni.murumuri05@gmail.com](mailto:poorni.murumuri05@gmail.com), [rishichaitanya888@gmail.com](mailto:rishichaitanya888@gmail.com)

Let’s collaborate to enhance text optimization efficiency!
flowinn 1.2.2
fl0wINN: Multi-Scale Turbulent Flow Investigation using Neural Networks
Requires Python: >=3.8

# fl0wINN

fl0wINN: Multi-Scale Turbulent Flow Investigation using Neural Networks

## Description

fl0wINN is a Python package for investigating multi-scale turbulent flow using neural networks. It leverages Physics-Informed Neural Networks (PINNs) to solve complex fluid dynamics problems.

## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)

## Installation

You can install fl0wINN using pip:

```bash
pip install flowinn
```

## Usage

```python
from flowinn import PINN

# Example usage
pinn = PINN()
# ... your code ...
```

### Running Examples

You can run the provided examples to see how the package works:

```bash
python examples/MinimalChannelFlow.py
python examples/LidDrivenCavity.py
python examples/FlowOverAirfoil.py
```

### Project Structure

The project is organized as follows:

```
flowINN/
├── examples/                # Example scripts for different simulations
├── scripts/                 # Utility scripts for cleaning dependencies
├── flowinn/                 # Source code for the package
│   ├── config.py            # Configuration settings
│   ├── mesh/                # Mesh generation and handling
│   ├── models/              # Neural network models
│   ├── physics/             # Physics-based loss functions and boundary conditions
│   ├── plot/                # Plotting and post-processing
│   ├── tests/               # Test cases for different flow problems
│   └── training/            # Training routines and loss functions
├── requirements.txt         # List of dependencies
├── setup.py                 # Setup script for packaging
└── README.md                # Project documentation
```

### Examples

#### Minimal Channel Flow

This example demonstrates a 3D channel flow simulation.

```python
python examples/MinimalChannelFlow.py
```

#### Lid-Driven Cavity

This example demonstrates a 2D lid-driven cavity simulation.

```python
python examples/LidDrivenCavity.py
```

#### Flow Over Airfoil

This example demonstrates a 2D flow over an airfoil simulation.

```python
python examples/FlowOverAirfoil.py
```

### Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes.

### License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
sark 9.0.0
IDA Scripting Library

====
Sark
====


General
-------

IDA Plugins & IDAPython Scripting Library.

For documentation, see `sark.rtfd.io <http://sark.rtfd.io/>`_.


Installation (Python 3 & IDA 7.4)
---------------------------------

For latest version (IDA7.4 & Python3):

.. code:: bash

    pip3 install -U git+https://github.com/tmr232/Sark.git#egg=Sark

Or from PyPI:

.. code:: bash

    pip3 install sark



For more info see `here <http://sark.readthedocs.org/en/latest/Installation.html>`_.


Python 2 & IDA < 7.4
~~~~~~~~~~~~~~~~~~~~

As of the release of IDA 7.4, Sark is only actively developed for IDA7.4 or
newer, and Python 3.

Python2 and older IDA is still supported for bugfixes & community contributions and
is maintained on the `IDA-6.x branch <https://github.com/tmr232/Sark/tree/IDA-6.x>`_.

To install Sark for older IDA use:

.. code:: bash

    pip2 install -U git+https://github.com/tmr232/Sark.git@IDA-6.x#egg=Sark

Or from PyPI:

.. code:: bash

    pip2 install "sark<7.4"


Dependencies
------------

1. `NetworkX <https://networkx.github.io/>`_
2. `wrapt <https://pypi.python.org/pypi/wrapt>`_

Plugins
-------

Plugin `documentation <http://sark.readthedocs.org/en/latest/plugins/index.html>`_
and `installation instructions <http://sark.readthedocs.org/en/latest/plugins/installation.html>`_.
geometric-sampling 0.4.3
Python package for Geometric Sampling Method
Requires Python: >=3.10


# Geometric Sampling Method - Python Package

The Geometric Sampling Method, introduced by Panahbehagh (2024), presents an innovative approach to finite population sampling based on a unique geometric framework. This method allows researchers to visually depict first-order inclusion probabilities (FIP) as bars on a two-dimensional graph. By adjusting the positions of these bars, users can explore a wide range of sampling designs while controlling second-order inclusion probabilities (SIP).

This package, `geometric_sampling`, provides tools for implementing the Geometric Sampling Method and is available on PyPI.

---

## Features
- Create various unequal probability sampling designs with a fixed FIP.
- Control and explore second-order inclusion probabilities (SIP) through visual manipulation.
- Incorporate search algorithms, such as A* and Genetic Algorithm, to optimize sampling designs for specific needs (e.g., well-spread or optimal sampling).

## Installation

Install the package via pip:
```bash
pip install geometric_sampling
```

## How to Use

The package includes core classes and methods to facilitate sampling design creation and optimization. Below is a basic example demonstrating the API.

### Example Usage

```python
import geometric_sampling as gm
import numpy as np

# Set up random generator and sample data
rng = np.random.default_rng()
N = 50  # Population size
x = rng.random(size=N)  # Auxiliary variable
n = 5  # Sample size

# Generate initial inclusion probabilities
inclusion = rng.random(N)
inclusion *= n / inclusion.sum()

# Define initial sampling design and evaluation criteria
initial_design = gm.Design(inclusion)
nht = gm.criteria.VarNHT(x, inclusion)
astar = gm.search.AStar(initial_design, nht, switch_coefficient=1)

# Display initial criteria and design
print("Initial criteria value:", astar.initial_criteria_value)
astar.initial_design.show()

# Run the A* search algorithm to optimize the design
astar.run(max_iterations=2000, num_new_nodes=10, max_open_set_size=10000, num_changes=1)

# Display results after optimization
print("Best criteria value:", astar.best_criteria_value)
astar.best_design.show()
```


## Maintainers

- Bardia Panahbehagh - [bardia.panah@gmail.com](mailto:bardia.panah@gmail.com)
- Mehdi Mohebbi - [mehdi.mohebbi23@gmail.com](mailto:mehdi.mohebbi23@gmail.com)
- AmirMohammad HosseiniNasab - [awmirhn@gmail.com](mailto:awmirhn@gmail.com)
- Mehdi Hosseini Moghadam - [m.h.moghadam1996@gmail.com](mailto:m.h.moghadam1996@gmail.com)

---

For more details, consult the official paper: Panahbehagh, B. (2024). Geometric Sampling Method.
nonebot-plugin-template 0.1.0
插件描述
Requires Python: <3.13,>=3.10

<div align="center">
  <a href="https://v2.nonebot.dev/store"><img src="https://github.com/fllesser/nonebot-plugin-template/blob/master/.docs/NoneBotPlugin.svg" width="380" height="177" alt="NoneBotPluginLogo"></a>
</div>

<div align="center">

_✨ nonebot-plugin-vocu ✨_

<a href="./LICENSE">
    <img src="https://img.shields.io/github/license/owner/nonebot-plugin-vocu.svg" alt="license">
</a>
<a href="https://pypi.python.org/pypi/nonebot-plugin-vocu">
    <img src="https://img.shields.io/pypi/v/nonebot-plugin-vocu.svg" alt="pypi">
</a>
<img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="python">

</div>


## 📖 介绍

这里是插件的详细介绍部分

## 💿 安装

<details open>
<summary>使用 nb-cli 安装</summary>
在 nonebot2 项目的根目录下打开命令行, 输入以下指令即可安装

    nb plugin install nonebot-plugin-vocu --upgrade

    nb plugin install nonebot-plugin-vocu --upgrade -i "https://pypi.org/simple"


</details>

<details>
<summary>使用包管理器安装</summary>
在 nonebot2 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令

<details>
<summary>pip</summary>

    pip install nonebot-plugin-vocu --upgrade -i "https://pypi.org/simple"

</details>
<details>
<summary>pdm</summary>

    pdm add nonebot-plugin-vocu
</details>
<details>
<summary>poetry</summary>

    poetry add nonebot-plugin-vocu
</details>
<details>
<summary>conda</summary>

    conda install nonebot-plugin-vocu
</details>

打开 nonebot2 项目根目录下的 `pyproject.toml` 文件, 在 `[tool.nonebot]` 部分追加写入

    plugins = ["nonebot_plugin_vocu"]

</details>

## ⚙️ 配置

在 nonebot2 项目的`.env`文件中添加下表中的必填配置

|      配置项       |  必填  | 默认值 |                        说明                        |
| :---------------: | :----: | :----: | :------------------------------------------------: |
|   vocu_api_key    | **是** |   ""   | 请前往 https://www.vocu.ai/ 注册账号，获取 api key |
| vocu_request_type |   否   | async  |   api 请求方式，默认 async， 氪金用户可选择 sync   |
| vocu_chars_limit  |   否   |  100   |                 生成语音的字符限制                 |

## 🎉 使用
### 指令表
|              指令               | 权限  | 需要@ | 范围  |                 说明                 |
| :-----------------------------: | :---: | :---: | :---: | :----------------------------------: |
|        [角色名]说[内容]         |   -   |  否   |   -   | 例如“雷军说我要开小米苏七，创死你们” |
|       vocu.list/角色列表        |   -   |  否   |   -   |             帐户角色列表             |
| vocu.history/历史生成[条数:int] |   -   |  否   |   -   |    []表示可选，默认 10，最大值20     |
|      vocu[历史生成的序号]       |   -   |  否   |   -   |        发送指定历史生成的语音        |
rcrab 0.1.2
Simpe python project template
Requires Python: >=3.12

# 🦀 Crab - Modern Python Project Manager

Crab is a batteries-included CLI tool that streamlines Python project setup and management by automating boilerplate configuration and integrating modern development tools.

## Features

- 🚀 **Quick Project Setup**: Create new Python projects with best practices using `crab init`
- 🛠 **Modern Tooling**: Pre-configured integration with:
  - [`uv`](https://github.com/astral-sh/uv) for fast package management
  - [`ruff`](https://github.com/astral-sh/ruff) for linting and formatting
  - [`mypy`](https://github.com/python/mypy) for type checking
  - [`pytest`](https://github.com/pytest-dev/pytest) for testing
  - [`pre-commit`](https://github.com/pre-commit/pre-commit) for git hooks
- ⚙️ **PEP-Compliant**: Uses `pyproject.toml` for configuration
- 🔄 **Git Integration**: Automatic git repository initialization
- 📦 **Dependency Management**: Simple commands to add, remove, and update packages

## Installation

```bash
pip install rcrab
```

## Quick Start

1. Create a new project

```bash
crab init my_project
cd my_project
```

2. Add dependencies:

```bash
uv add pandas numpy
```

3. Run code quality tools:

```bash
make lint
make fix
```

4. Run test

```bash
make test
```

### Configuration

Crab uses pyproject.toml for project configuration. Default settings are automatically created during project initialization.

Example configuration:

```toml
[tool.crab]
template = "basic"
venv-directory = ".venv"

[tool.crab.paths]
source = "src"
tests = "tests"
docs = "docs"

[tool.crab.lint]
enabled-tools = ["ruff", "mypy"]
```