semvec/pypi-packages huggingface dataset:
Datasis 0.1
A short description of your package
Requires Python: >=3.6

# Datasis - A Python Library for Data Analysis

## Introduction
`Datasis` is a data analysis library built using `pandas`, `numpy`, `matplotlib`, and `seaborn`. It provides tools for loading, cleaning, analyzing data, handling outliers and duplicates, as well as filtering, grouping, and sorting data.

## Requirements
Make sure you have the following libraries installed before using `Datasis`:
```bash
pip install Datasis
```
IF you want a new version :
```bash
pip install update Datasis

```

## How to Use
### 1. Import the Class and Create an Object
```python
from Datasis import Datasis as da

```


### 2. Load Data
```python
da.load_data("data.csv")  # Or any Excel or JSON file
```

### 3. Display Data
```python
print(da.show_data())  # Display the first 5 rows
print(da.tail())       # Display the last 5 rows
```

### 4. Get Data Info and Statistics
```python
print(da.get_info())
print(da.get_statistics())
```

### 5. Handle Missing Values
```python
print(da.missing_values())  # Show count of missing values per column
print(da.fill_missing(strategy='mean'))  # Replace missing values with the mean
```

### 6. Detect and Handle Outliers
```python
print(da.outlier_detection("column_name"))  # Detect outliers
print(da.drop_outliers("column_name"))  # Remove outliers
```

### 7. Remove Duplicates
```python
print(da.drop_duplicates())
```

### 8. Filter, Group, and Sort Data
```python
print(da.filter_data("column_name > 50"))  # Filter data based on a condition
print(da.group_by("category"))  # Group data by a specific column
print(da.sort_data("column_name", ascending=False))  # Sort data in descending order
```

### 9. Save Data After Modifications
```python
da.save_data("cleaned_data.csv")
```

## Contribution
If you'd like to improve the code or add new features, feel free to submit a pull request on GitHub.

## License
This library is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.
## Contributing
Contributions are welcome! Feel free to submit issues or pull requests to improve the library.
## Author
Created by Ibrahem abo kila. For any inquiries, please contact me at ibrahemabokila@gmail.com.
vezor 1.0.1
A collection of algorithm designs for easy application to assist mathematical problems.
Requires Python: >=3.6

# Vezor Package 
---

[![License](https://img.shields.io/badge/License-MIT-green.svg?logo=open-source-initiative)](https://github.com/avezoor/vezor/blob/main/LICENSE)
![Author](https://img.shields.io/badge/Author-Izzar%20Suly%20Nashrudin-blue) [![Email](https://img.shields.io/badge/Email-izzarsuly@proton.me-orange?style=flat-square&logo=gmail)](mailto:izzarsuly@proton.me) [![Repository](https://img.shields.io/badge/Repository-vezor-black?logo=github)](https://github.com/avezoor/vezor)  [![GitHub](https://img.shields.io/badge/GitHub-avezoor-black?logo=github)](https://github.com/avezoor) 

 ## Information

![Version](https://img.shields.io/badge/version-1.0.1-black) ![Features](https://img.shields.io/badge/features-3-blue) ![Modules](https://img.shields.io/badge/modules-23-black) ![File](https://img.shields.io/badge/file-33-orange) 

 ## Documentation

 [![Website](https://img.shields.io/badge/Website-vezor.vercel.app-blue?logo=google-chrome)](https://vezor.vercel.app/)

---
## Description
**Vezor** is a Python package designed to enhance computational accuracy and usability across various mathematical domains. It provides an efficient and well-structured implementation for mathematical computations, making it easier for users to perform complex calculations with minimal effort.

## Installation
Install **vezor** using pip:

```sh
pip install vezor
```

## Supported Platforms
**vezor** is compatible with the following platforms:

![Platforms](https://img.shields.io/badge/platforms-Windows%20%7C%20macOS%20%7C%20Linux%20%7C%20Unix-blue)

Additionally, it supports execution in environments such as:

![Frameworks](https://img.shields.io/badge/frrameworks-Jupyter%20Notebook%20-blue)

## Features
- Tool prints algorithm and writes algorithm code from class calls
- Added some additional modules and files 
- Fixing bugs
---
## Contribution
Thanks to all contributors! ❤️

[![Contributors](https://contrib.rocks/image?repo=avezoor/vezor)](https://github.com/avezoor/vezor/graphs/contributors)
rcdtool 2.1.1
Script that downloads telegram restricted content.
Requires Python: >=3.9
Keywords: library, telegram, restricted, download

# rcdtool

Script that downloads telegram restricted content.

## Usage

For help:

```bash
./rcdtool --help
```

Normally, the script will take the channel and message IDs from the CLI argument, but if you did not provide them, then the script will prompt your for each missing ID. You can also customize the output filename.

If you have channel and message IDs you can do:

```bash
./rcdtool -c /path/to/config.ini -C <channel ID here> -M <message ID here> -O interesting-video.mp4
```

Then the script starts the downloading.

Well, if you use the Python script, then change `rcdtool` by `python rcdtool` et voilà.

### Where I find these IDs?

In desktop: right click > copy message/post link.
In mobile: touch the message > copy link

This link will have this format:

```
https://t.me/c/<channel ID>/<message ID>
```

Then, extract the IDs from the link. Or you can pass that link to the tool with `--link` as follows:

```bash
./rcdtool --link https://t.me/c/106942033f/123 -O stuff.png
```

## Dist

In this repository we release the source code (Python) and a binary option for GNU/Linux. You can build a binary for any other operating system using tool as [PyInstaller](https://pyinstaller.org/en/).

## Telegram session

You MUST have an API ID provided by Telegram at https://my.telegram.org/ (I think). This is as follows:

```
api_id: 32767
api_hash: ed855a59bbe4a3360dbf7a0538842142
```

Then rename `config.ini.sample` to `config.ini`, edit it and save wherever you want. If the file is in the same directory as `rcdtool` and its name is exactly "config.ini", then `rcdtool` will load it automatically.

The first time, **rcdtool** will ask you for your phone number, and will start a login process. When this is done, a `.session` file will be created. With this `.session` file, the tool could access to your Telegram account to read messages and download medias. The name of the .session file is set in `config.ini`.

This tool answers to the question of how to bypass content forwarding/download restriction on telegram channels suck as restricted videos.

### Advanced usage

You can define multiple message IDs separated by commas and define a range of message IDs using '..'.

```bash
# message id 34
rcdtool -c config.ini -C qwert -M 34

# message id 34 and 50
rcdtool -c config.ini -C qwert -M 34,50 -O download/photo

# message id range from 34 to 60
rcdtool -c config.ini -C -100200200 -M 34..60

# two message id and a message id range
rcdtool -c config.ini -C 200200 -M 13,15,20..30

# passing a simple link
rcdtool -c config.ini --link https://t.me/c/200200/503

# passing a link where the <message id> section define multiple message ids
rcdtool -c config.ini --link https://t.me/c/200200/13,15,20..30
```

You can request that the script infer the file extension.

```bash
rcdtool -c config.ini -C qwert -M 34 -O download/base --infer-extension
```

---

If you want to find a media in a comment on a channel post, use `--discussion-message-id` to set the message id of the comment.
mcp-lightpdf 0.1.9
LightPDF文档转换工具
Requires Python: >=3.12

# MCP-LightPDF

MCP-LightPDF 是一个基于 MCP (Model Control Protocol) 的文档格式转换工具，支持多种文档格式之间的相互转换。该工具需要在支持 MCP 功能的软件（如 Cursor）中配置使用。

## 配置说明

在 Cursor 中配置 MCP-LightPDF 工具需要修改 `~/.cursor/mcp.json` 文件。添加以下配置：

```json
{
  "mcpServers": {
    "lightpdf": {
      "command": "uvx",
      "args": [
        "-n",
        "/path/to/mcp_lightpdf-x.x.x-py3-none-any.whl"
      ],
      "env": {
        "API_KEY": "your_api_key_here"
      }
    }
  }
}
```

配置说明：
- `command`: 使用 `uvx` 命令运行工具
- `args`: 命令行参数
  - `-n`: 直接运行wheel包
  - wheel包路径：指向下载的 MCP-LightPDF wheel 包
- `env`: 环境变量配置
  - `API_KEY`: 你的API密钥
  - `API_BASE_URL`: （可选）API基础URL，默认使用官方地址

配置完成后，重启 Cursor 使配置生效。

## 功能特性

- 支持多种文档格式的转换
- 支持本地文件和网络URL
- 支持批量转换
- 并发处理，提高转换效率
- 详细的转换进度和结果报告
- 灵活的输出目录配置

## 支持的格式

### PDF转换为其他格式
- Word (DOCX)
- Excel (XLSX)
- PowerPoint (PPTX)
- 图片 (JPG, JPEG, PNG, BMP, GIF)
- HTML
- 文本 (TXT)

### 其他格式转换为PDF
- Word (DOCX)
- Excel (XLSX)
- PowerPoint (PPTX)
- 图片 (JPG, JPEG, PNG, BMP, GIF)
- CAD (DWG)
- CAJ
- OFD

## 使用方法

1. 确保你使用的软件支持 MCP 功能（如 Cursor）
2. 在软件中完成 MCP-LightPDF 工具的配置
3. 调用工具时，指定：
   - 输入文件路径或URL
   - 目标转换格式
   - 输出目录（可选）

## 参数说明

- `file_paths`: 要转换的文件路径或URL列表
- `format`: 目标格式，支持：
  - `pdf`: 转换为PDF格式
  - `docx`: 转换为Word格式
  - `xlsx`: 转换为Excel格式
  - `pptx`: 转换为PowerPoint格式
  - `jpg`/`jpeg`: 转换为JPG格式
  - `png`: 转换为PNG格式
  - `bmp`: 转换为BMP格式
  - `gif`: 转换为GIF格式
  - `html`: 转换为HTML格式
  - `txt`: 转换为文本格式
- `output_dir`: 输出目录路径（可选）

## 注意事项

1. 确保有足够的磁盘空间用于文件转换
2. 对于网络URL，确保URL可访问且文件大小在合理范围内
3. 批量转换时会自动控制并发数，避免过度占用系统资源
4. 某些格式转换可能需要较长时间，请耐心等待

## 错误处理

工具会提供详细的错误信息，常见的错误包括：

- API密钥未配置或无效
- 文件格式不支持
- 文件不存在或无法访问
- 网络连接问题
- 输出目录无写入权限

## 许可证

MIT License
joow-image-processing 0.0.1
Image processing using skimage 'Fork da Karina'
Requires Python: >=3.5

# image_processing

Description. 
The package joow_image_processing is used to:
	Processing:
	- Histogram matching
	- Structural Similarity
	- Resize image
	Utils:
	- Read Image
	- Save Image
	- Plot Image
	- Plot Result
	- Plot Histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install image_processing
```

## Usage
<!-- 
```python
from package_name.module1_name import file1_name
file1_name.my_function()
``` -->

## Author
Jonatas

## License
[MIT](https://choosealicense.com/licenses/mit/)
mc10-tools 0.9
Set of MC-10 tools
Requires Python: >=3.9.19
Keywords: coco, image, conversion, trs-80, tandy

# mc10-tools

This is a simple collection of tools to assist with developing software for
the [TRS-80 Microcolor Computer MC-10](https://en.wikipedia.org/wiki/TRS-80_MC-10).


## Installation
```bash
git clone https://github.com/jamieleecho/mc10-tools.git
cd mc10-tools
make install-pre-commit
make install
```

## Tools
### c10tobas
```
Usage: c10tobas [OPTIONS] INPUT_FILE OUTPUT_FILE

  Extract and detokenizes the *.bas file in INPUT_FILE and stores it in
  OUTPUT_FILE

Options:
  --version  Show the version and exit.
  --help  Show this message and exit.
```

### bastoc10
```
Usage: bastoc10 [OPTIONS] INPUT_FILE OUTPUT_FILE

  Tokenizes the given *.bas file specified byt INPUT_FILE, outputing the
  result into a *.c10 file specified by OUTPUT_FILE.

Options:
  --version  Show the version and exit.
  --help  Show this message and exit.
```


## Developing and Testing

You will need a fairly modern python environment with [uv](https://github.com/astral-sh/uv) installed.

You can begin by entering:
```bash
make install-pre-commit
make sync
make run-tests
```

The `Makefile` makes it easy to perform the most common operations:
* `make check-all` runs linting and `uv.lock` checks
* `make check-lint` checks for linting issues
* `make check-lock` verifies the `uv.lock` is aligned to `pyproject.toml`
* `make clean` cleans the virtual environment and caches
* `make default` runs a default set of checks on the code
* `make fix-all` formats the code, fixes lint errors and runs locks `uv.lock` to `pyproject.toml`
* `make fix-format` formats the code
* `make fix-lint` fixes linting issues
* `make fix-lint-unsafe` fixes linting issues potentially adding inadvertant bugs
* `make help` outputs the different make options
* `make install` build install the distribution
* `make install-pre-commit` installs pre-commit hooks
* `make lock` locks `uv.lock` to `pyproject.toml`
* `make install-pre-commit` installs pre-commit hooks
* `make run-tests` runs the unit tests
* `make sync` syncs the python environment with `uv.lock`

`.vscode/settings.json` is set so that unit tests can be run without further configuration.
types-polib 1.2.0.20250401
Typing stubs for polib
Requires Python: >=3.9

## Typing stubs for polib

This is a [PEP 561](https://peps.python.org/pep-0561/)
type stub package for the [`polib`](https://github.com/izimobil/polib) package.
It can be used by type-checking tools like
[mypy](https://github.com/python/mypy/),
[pyright](https://github.com/microsoft/pyright),
[pytype](https://github.com/google/pytype/),
[Pyre](https://pyre-check.org/),
PyCharm, etc. to check code that uses `polib`. This version of
`types-polib` aims to provide accurate annotations for
`polib==1.2.*`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/polib`](https://github.com/python/typeshed/tree/main/stubs/polib)
directory.

This package was tested with
mypy 1.15.0,
pyright 1.1.398,
and pytype 2024.10.11.
It was generated from typeshed commit
[`bfd032156c59bbf851f62174014f24f4f89b96af`](https://github.com/python/typeshed/commit/bfd032156c59bbf851f62174014f24f4f89b96af).
types-editdistance 0.8.0.20250401
Typing stubs for editdistance
Requires Python: >=3.9

## Typing stubs for editdistance

This is a [PEP 561](https://peps.python.org/pep-0561/)
type stub package for the [`editdistance`](https://github.com/roy-ht/editdistance) package.
It can be used by type-checking tools like
[mypy](https://github.com/python/mypy/),
[pyright](https://github.com/microsoft/pyright),
[pytype](https://github.com/google/pytype/),
[Pyre](https://pyre-check.org/),
PyCharm, etc. to check code that uses `editdistance`. This version of
`types-editdistance` aims to provide accurate annotations for
`editdistance==0.8.*`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/editdistance`](https://github.com/python/typeshed/tree/main/stubs/editdistance)
directory.

This package was tested with
mypy 1.15.0,
pyright 1.1.398,
and pytype 2024.10.11.
It was generated from typeshed commit
[`bfd032156c59bbf851f62174014f24f4f89b96af`](https://github.com/python/typeshed/commit/bfd032156c59bbf851f62174014f24f4f89b96af).
types-whatthepatch 1.0.2.20250401
Typing stubs for whatthepatch
Requires Python: >=3.9

## Typing stubs for whatthepatch

This is a [PEP 561](https://peps.python.org/pep-0561/)
type stub package for the [`whatthepatch`](https://github.com/cscorley/whatthepatch) package.
It can be used by type-checking tools like
[mypy](https://github.com/python/mypy/),
[pyright](https://github.com/microsoft/pyright),
[pytype](https://github.com/google/pytype/),
[Pyre](https://pyre-check.org/),
PyCharm, etc. to check code that uses `whatthepatch`. This version of
`types-whatthepatch` aims to provide accurate annotations for
`whatthepatch==1.0.*`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/whatthepatch`](https://github.com/python/typeshed/tree/main/stubs/whatthepatch)
directory.

This package was tested with
mypy 1.15.0,
pyright 1.1.398,
and pytype 2024.10.11.
It was generated from typeshed commit
[`bfd032156c59bbf851f62174014f24f4f89b96af`](https://github.com/python/typeshed/commit/bfd032156c59bbf851f62174014f24f4f89b96af).
datasis-lib 0.1.0
A short description of your package
Requires Python: >=3.6

# Datasis - A Python Library for Data Analysis

## Introduction
`Datasis` is a data analysis library built using `pandas`, `numpy`, `matplotlib`, and `seaborn`. It provides tools for loading, cleaning, analyzing data, handling outliers and duplicates, as well as filtering, grouping, and sorting data.

## Requirements
Make sure you have the following libraries installed before using `Datasis`:
```bash
pip install Datasis
```

## How to Use
### 1. Import the Class and Create an Object
```python
from data_analysis import Datasis

da = Datasis()
```

### 2. Load Data
```python
da.load_data("data.csv")  # Or any Excel or JSON file
```

### 3. Display Data
```python
print(da.show_data())  # Display the first 5 rows
print(da.tail())       # Display the last 5 rows
```

### 4. Get Data Info and Statistics
```python
print(da.get_info())
print(da.get_statistics())
```

### 5. Handle Missing Values
```python
print(da.missing_values())  # Show count of missing values per column
print(da.fill_missing(strategy='mean'))  # Replace missing values with the mean
```

### 6. Detect and Handle Outliers
```python
print(da.outlier_detection("column_name"))  # Detect outliers
print(da.drop_outliers("column_name"))  # Remove outliers
```

### 7. Remove Duplicates
```python
print(da.drop_duplicates())
```

### 8. Filter, Group, and Sort Data
```python
print(da.filter_data("column_name > 50"))  # Filter data based on a condition
print(da.group_by("category"))  # Group data by a specific column
print(da.sort_data("column_name", ascending=False))  # Sort data in descending order
```

### 9. Save Data After Modifications
```python
da.save_data("cleaned_data.csv")
```

## Contribution
If you'd like to improve the code or add new features, feel free to submit a pull request on GitHub.

## License
This library is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.
## Contributing
Contributions are welcome! Feel free to submit issues or pull requests to improve the library.
## Author
Created by Ibrahem abo kila. For any inquiries, please contact me at ibrahemabokila@gmail.com.
autoservice 0.1.3
一个简单高效的 systemd 服务管理工具，支持自动创建配置文件并智能适配不同类型的服务。
Requires Python: >=3.8
Keywords: systemd, service, linux, daemon, process-manager

# AutoService

一个简单高效的 systemd 服务管理工具。

## 痛点

- systemd 服务创建流程繁琐，需要手动编写配置文件
- 不同类型程序（Python、Shell等）的服务配置方式不同
- 服务管理命令分散，使用不便

## 解决方案

- 自动生成标准化的服务配置文件
- 智能识别命令类型，自动适配不同运行环境
- 统一的命令行接口管理全部服务

## 使用效果

```bash
# 创建服务（自动处理配置文件）
sudo autoservice create my-app "python app.py"

# 支持 Python 虚拟环境
sudo autoservice create venv-app "poetry run python app.py"

# 查看服务状态
sudo autoservice list
my-app: 🟢 运行中
venv-app: ⚫ 已停止

# 实时查看日志
sudo autoservice logs my-app -f

# 服务管理
sudo autoservice start my-app
sudo autoservice stop my-app
sudo autoservice restart my-app
sudo autoservice remove my-app
```

## 安装

```bash
sudo pip install autoservice
```

## 功能特点

- 支持创建、启动、停止、重启和删除 systemd 服务
- 自动适配不同类型的命令（Python、Shell 等）
- 自动检测 Python 虚拟环境
- 简单易用的命令行界面

## 使用方法

### 创建服务

```bash
sudo autoservice create <服务名> <命令>
```
agno-yaml-builder 0.1.3
A Python utility library for defining and constructing Agno Agents, Teams, and Workflows using declarative YAML configuration files
Requires Python: >=3.12

# Agno YAML Builder

A Python utility library for defining and constructing [Agno](https://github.com/agno-agi/agno) Agents, Teams, and Workflows using declarative YAML configuration files.

## Status

[![PyPI version](https://badge.fury.io/py/agno-yaml-builder.svg)](https://badge.fury.io/py/agno-yaml-builder) [![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/MIT) 

## Overview

The Agno library provides powerful primitives for building sophisticated AI agent systems. However, defining complex Agents, Teams with multiple agents, and multi-step Workflows directly in Python code can become verbose and harder to manage.

`agno-yaml-builder` bridges this gap by allowing you to define your Agno components in human-readable YAML files. This promotes a declarative approach, making it easier to configure, share, and version control your agent structures.

## Key Features

* **Declarative Configuration:** Define Agno Agents, Teams, and Workflows using intuitive YAML syntax.
* **Simplified Instantiation:** Load complex Agno structures from YAML files with a simple Python function call.
* **Component Referencing:** Easily reference defined Agents within Teams or Workflows by name.
* **Human-Readable:** YAML format makes configurations easy to read, write, and maintain.
* **Seamless Integration:** Works directly with the core `agno` library objects.

## Prerequisites

* Python 3.8+ (or your supported versions)
* `pip` package manager

## Installation

You can install `agno-yaml-builder` using pip:

```bash
pip install agno-yaml-builder
```

## Quickstart

You can clone this repo using:

```bash
git clone git@github.com:zilin/agno-yaml.git
```

Try some examples.
```bash
python -m agno_yaml.main
```
osstorchconnector 1.1.0
OSS connector for AI/ML
Requires Python: <3.13,>=3.8

# OSS Connector for AI/ML

[ossconnector.github.io](https://ossconnector.github.io/)

## Overview

OSS Connector for AI/ML contains some high-performance Python libraries specifically designed for AI and ML scenariosis, tailored to work with [Alibaba Cloud OSS (Object Storage Service)](https://www.alibabacloud.com/en/product/object-storage-service).

Currently, the OSS connector is composed of two libraries: OSS Model Connector and OSS Torch Connector.

- [OSS Torch Connector](https://aliyun.github.io/oss-connector-for-ai-ml/#/torchconnector/introduction) is dedicated to AI training scenarios, including loading [datasets](https://pytorch.org/docs/stable/data.html#dataset-types) from OSS and loading/saving checkpoints from/to OSS.

- [OSS Model Connector](https://aliyun.github.io/oss-connector-for-ai-ml/#/modelconnector/introduction) focuses on AI inference scenarios, loading large model files from OSS into local AI inference frameworks.

The core component of the OSS Connector for AI/ML is implemented in C++ using [PhotonLibOS](https://github.com/alibaba/PhotonLibOS) and is provided as dynamic link libraries within wheel packages. This repository only contains the code of Python.

For details, please refer to [ossconnector.github.io](https://ossconnector.github.io/) or [aliyun.github.io/oss-connector-for-ai-ml](https://aliyun.github.io/oss-connector-for-ai-ml).


## Related

[OSS Connector for AI/ML 中文文档](https://help.aliyun.com/zh/oss/developer-reference/oss-connector-for-ai-ml)

## License

This project is licensed under the terms of the [MIT License](LICENSE).