semvec/pypi-packages huggingface dataset:
modelbase2 0.7.0
A package to build metabolic models
Requires Python: >=3.12
Keywords: metabolic, modelling, ode

# modelbase

[![pypi](https://img.shields.io/pypi/v/modelbase2.svg)](https://pypi.python.org/pypi/modelbase2)
[![docs][docs-badge]][docs]
![License](https://img.shields.io/badge/license-GPL--3.0-blue?style=flat-square)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![security: bandit](https://img.shields.io/badge/security-bandit-yellow.svg)](https://github.com/PyCQA/bandit)
[![Downloads](https://pepy.tech/badge/modelbase2)](https://pepy.tech/project/modelbase2)

[docs-badge]: https://img.shields.io/badge/docs-main-green.svg?style=flat-square
[docs]: https://computational-biology-aachen.github.io/modelbase2/

## Installation

You can install modelbase using pip: `pip install modelbase2`

If you want access to the sundials solver suite via the [assimulo](https://jmodelica.org/assimulo/) package, we recommend setting up a virtual environment via [pixi](https://pixi.sh/) or [mamba / conda](https://mamba.readthedocs.io/en/latest/) using the [conda-forge](https://conda-forge.org/) channel.

```bash
pixi init
pixi add python assimulo
pixi add --pypi modelbase2
```


## Development setup

You have two choices here, using `uv` (pypi-only) or using `pixi` (conda-forge, including assimulo)

### uv

- Install `uv` as described in [the docs](https://docs.astral.sh/uv/getting-started/installation/).
- Run `uv sync --extra dev --extra torch` to install dependencies locally

### pixi

- Install `pixi` as described in [the docs](https://pixi.sh/latest/#installation)
- Run `pixi install --frozen`


## Notes

- `uv add $package`
- `uv add --optional dev $package`
get-music-lizhanqi 1.2.14
可以下载音乐的包哦
Requires Python: >=3.7

# get-music-lizhanqi

- 这是一个可以下载和搜索音乐的python命令行工具，支持，酷狗，酷我，百度，网易云，咪咕，qq音乐，5sing，欢迎前来学习的指点

## 使用方法

- 长话短说**get-music -help**或**get-music-lizhanqi -help**打开帮助，帮助里面有全部命令的介绍，或者前往github地址:<https://github.com/lzq-hopego/get-music-lizhanqi>

## 更新记录

- 2025-4-25 完成v1.2.14版本,修复网易云下载封面，更新获取当前外网ip地址的引擎，修正tk页面分辨率，规范化该项目

## THE END

- 本脚本仅支持学习使用，如有发现有任何商业用途，一经发现您将受到法律责任。
- 本程序使用的接口全部来源于网络，切不可有任何商业用途，或我程序中有涉及你公司利益的，你可以联系我，我会及时删除源代码，并不再更新。
- **禁止将本工具用于商业用途**，如产生法律纠纷与本人无关，如有侵权，请联系我删除。
- 如果你对界面设计感兴趣可以去看我的另一篇pyqt5对接的get-music-lizhanqi做的音乐下载播放ui，地址：https://github.com/lzq-hopego/get-music-lizhanqi-gui
- 项目创建者：李先生
- 项目维护者：李先生
- 维护者邮箱：3101978435@qq.com
robot-mcp-stdio 0.1.1
Add your description here
Requires Python: >=3.13.0

# 扫地机器人控制API

## 设备列表查询

获取用户绑定的所有机器人列表。

### Input:
无参数

### Returns:
```json
{
  "status": 0,
  "message": "success",
  "data": [
    {
      "nickname": "机器人昵称",
    }
  ]
}
```

## 清扫控制

启动扫地机器人开始清扫。

### Input:
- `nickname` (string): 机器人的昵称，用于查找设备，支持模糊匹配
- `act` (string): 清扫行为，默认为"s"
  - "s" - 开始清扫
  - "r" - 恢复清扫
  - "p" - 暂停清扫
  - "h" - 停止清扫

### Returns:
```json
{
  "status": 0,
  "message": "success",
  "data": {}
}
```

## 回充控制

控制扫地机器人回充或停止回充。

### Input:
- `nickname` (string): 机器人昵称，用于查找设备
- `act` (string): 机器行为，默认为"go-start"
  - "go-start" - 开始回充
  - "stopGo" - 结束回充

### Returns:
```json
{
  "status": 0,
  "message": "success",
  "data": {}
}
```

## 工作状态查询

查询机器人当前的工作状态。

### Input:
- `nickname` (string): 机器人昵称，用于查找设备

### Returns:
```json
{
  "status": 0,
  "message": "success",
  "data": {
    "workState": "工作状态",
    "batteryPower": 电量百分比(数字)
  }
}
```

# 连接配置，以cursor为例
```
{
  "mcpServers": {
    "robot_control_stdio": {
      "command": "uv",
      "args": [
        "run",
        "mcp",
        "run",
        "/ABSOLUTE/PATH/TO/PARENT/FOLDER/robot_mcp_stdio.py"
      ],
      "env": {
        "ECO_API_KEY": "你的ak"
      }
    }
  }
}
```

# 使用示例：
mdconvertor-tools 0.0.5
convertor is a simple and easy to use library for converting markdown files to various formats.
Requires Python: >=3.6

# 📝 Mdconvertor Tools

A minimal Python package to convert Markdown files to clean, responsive **HTML** with **Tailwind CSS**, **MathJax**, and beautiful typography.

> Ideal for note-taking, documentation, and publishing markdown content as styled web pages.

![PyPI](https://img.shields.io/pypi/v/mdconvertor-tools)
![Python Version](https://img.shields.io/pypi/pyversions/mdconvertor-tools)
![License](https://img.shields.io/github/license/codeperfectplus/mdconvertor-tools)

---

## 🚀 Features

- 🧪 Convert `.md` files into HTML
- 🎨 Built-in support for **Tailwind CSS**
- 🧮 Renders LaTeX/Math using **MathJax**
- ✨ Applies prose styling for readability
- 🗂 Outputs to your desired directory

---

## 📦 Installation

```bash
pip install mdconvertor-tools
```


## Upcoming Features

- 📜 Support for custom CSS
- Support for PDF generation
- Support for custom themes
- Support for custom fonts
BioLizardStylePython 2.0.1
Enhance plots with the signature Biolizard aesthetic for matplotlib and seaborn.
Requires Python: >=3.9

# BioLizardStylePython

Enhance plots with the signature Biolizard aesthetic for matplotlib and seaborn.

## Installation

<!---
### 1. Extra requirements
`BioLizardStylePython` relies on the colorspace package, which is not available on the Python Package Index (PyPI). However, it is hosted on GitHub and needs to be installed directly from there. To install the `colorspace` package, please run the following command in your terminal:
```
pip install git+https://github.com/retostauffer/python-colorspace.git
```
Once you've successfully installed `colorspace`, you can proceed with the installation and usage of `BioLizardStylePython
-->

<!-- ### 1. Package Installation -->

The BioLizardStylePython package is available on PyPI and can be easily installed using:
```
pip install BioLizardStylePython
```

The BioLizardStylePython package makes use of the signature BioLizard font 'Lato'. This font can be found on Google Fonts under an open font license.
The font has been packaged using the [Python Fonts module](https://pypi.org/project/fonts/) and should be installed automatically when installing BioLizardStylePython. See [font-lato](https://pypi.org/project/font-lato/) for more information.

<!---
 ### 3. Font package

The BioLizardStylePython package makes use of the signature BioLizard font 'Lato'. This font can be found on Google Fonts under an open font license.
The font has been packaged using the [Python Fonts module](https://pypi.org/project/fonts/) and should be installed automatically when installing BioLizardStylePython. See [font-lato](https://pypi.org/project/font-lato/) for more information. 
-->

## Usage

### 1. Styling Function: `lizard_style()`

This function applies a BioLizard aesthetic to your matplotlib or seaborn plots. The aim is to ensure that plots maintain a uniform look, irrespective of whether they were made in R or Python.

### 2. BioLizard Color Palettes

These are qualitative, sequential, and divergent color maps that can be applied to your plots. The palettes are designed using BioLizard's house colors, keeping perceptual uniformity and color-blind friendliness in mind. For more information, consult the help documentation specific to each function.

### 3. Finalizing and Exporting Plots: `finalise_lizardplot()`

This function adds a BioLizard footer beneath your graph and exports it to a specified format such as PNG or PDF. The footer includes the BioLizard logo at the bottom right corner and leaves room for source text.

## Contributing and Feedback

Contributions and feedback are very welcome and much appreciated. 

Issues can be reported [on github](https://github.com/lizard-bio/nature-grade-visualization-playground/issues).
tlopack 0.1.0
A CLI tool for TLO searches
Requires Python: >=3.8

# TLOPack

A command-line interface for TLO searches.

## Installation

```bash
pip install tlopack
```

## Configuration

First, configure your admin key:

```bash
tlopack config --admin-key "your-key"
```

## Usage

```bash
# Search by name
tlopack search --first-name "John" --last-name "Doe"

# Search by address
tlopack search --street "123 Main St" --city "New York" --state "NY"

# Override saved admin key for a single search
tlopack search --first-name "John" --last-name "Doe" --admin-key "different-key"
```

## Features

- Person search by name or address
- Persistent admin key configuration
- Rich terminal output
- Easy to use command-line interface

## License

MIT
xgi-optimized 0.11.1
XGI extension for faster hypergraph operations
Requires Python: >=3.10
Keywords: igraph, C++, networks, higher-order, higher-order network, hypergraph, simplicial complex, network science

### What has been done?: ###
- [Metrics computation has been updated](#updated-metrics-calculation)
- [The implementation of bipartite graphs has been changed](#getting-started)
- [Cpp implementation of linegraph](#cpp-imp)
- [Added the ability to use the package of accelerated functions on hypergraphs](#pip)

## Metrics computation has been updated<a id="updated-metrics-calculation"></a>

The computation of the linegraph vector centrality metric was accelerated by using the с++ instead of python. For comparison, implementations using the networkx.Graph class were retained.
```python
H_enron = xgi.load_xgi_data("plant-pollinator-mpl-034")
H_enron_cleaned = H_enron.cleanup(
    multiedges=False, singletons=False, isolates=False, relabel=True, in_place=False
)
start_time = time.time()

xgi.centrality.line_vector_centrality(H_enron_cleaned)

middle_time = time.time()

xgi_optimized.vector_centrality(H_enron_cleaned, 3)

end_time = time.time()

print(f"Время выполнения python: {(middle_time-start_time):.6f} секунд")
print(f"Время выполнения c++: {(end_time-middle_time):.6f} секунд")
```

## The implementation of bipartite graphs has been changed. <a id="getting-started"></a>
The implementation for working with bipartite graphs was modified: the igraph.Graph class is 
now used instead of networkx.Graph for converting from a Hypergraph to a bipartite graph and back to a Hypergraph .
```python
hg = xgi.Hypergraph()
hg.add_edges_from(
    [[1, 2, 3], [3, 4, 5,8], [1, 4, 10, 11, 12], [7,0,8], [5,7,2]]
)

bpg = xgi_optimized.biparite_graph.to_bipartite_graph(hg)
```

## Cpp implementation of сhecking graph connectivity<a id="cpp-imp"></a>
```python
size = 38
hg = xgi.fast_random_hypergraph(size,[0.1, 0.2, 0.02,0.04])
start_time = time.time() 
xgi_optimized.connected.is_connected(hg)
middle_time = time.time()
xgi.centrality.is_connected(hg)
end_time = time.time()
print(f"Время выполнения c++: {(middle_time-start_time):.6f} секунд")
print(f"Время выполнения python: {(end_time-middle_time):.6f} секунд")
```
## Cpp implementation of сhecking graph connectivity<a id="cpp-imp"></a>
```python
size = 38
hg = xgi.fast_random_hypergraph(size,[0.1, 0.2, 0.02,0.04])
start_time = time.time() 
xgi_optimized.connected.is_connected(hg)
middle_time = time.time()
xgi.centrality.is_connected(hg)
end_time = time.time()
print(f"Время выполнения c++: {(middle_time-start_time):.6f} секунд")
print(f"Время выполнения python: {(end_time-middle_time):.6f} секунд")
```
## Added the ability to use the package of accelerated functions on hypergraphs<a id="cpp-imp"></a>
```bash
pip install xgi_optimized
```
wagtail-filter-persistence 0.1.6
A Wagtail plugin that persists filter selections in admin listings
Requires Python: >=3.6

# Wagtail Filter Persistence
[PyPI version](https://pypi.org/project/wagtail-filter-persistence/) |
[Python Versions](https://pypi.org/project/wagtail-filter-persistence/) |
[License](https://github.com/indigo7333/wagtail-filter-persistence/blob/main/LICENSE)

A lightweight Wagtail plugin developed by [VERDATEK OÜ](https://verdatek.com) that preserves filter selections throughout the Wagtail admin interface when navigating between pages.

## The Problem

In standard Wagtail admin:
- Apply filters to any admin view with filterable content
- Navigate away to view, edit, or manage related content
- Return to the previous view
- 😢 Your filters are gone and you have to set them up again!

## The Solution

Wagtail Filter Persistence automatically saves and restores your filter selections, making the admin experience smoother and more efficient across the entire Wagtail interface.

## Features

- ✅ Preserves filter selections across all Wagtail admin navigation
- ✅ Works with ModelAdmin, Snippets, Pages, and any filterable admin views
- ✅ User-specific filter storage (different admin users maintain their own filter preferences)
- ✅ No configuration required
- ✅ Zero impact on frontend performance

## Installation

```
pip install wagtail-filter-persistence
```

Then add to your app in settings.py:

```
MIDDLEWARE = [
    # ...
    "wagtail_filter_persistence.middleware.WagtailFilterPersistenceMiddleware"
    # ...
]
```

That's it! No further configuration needed.

## How It Works
The plugin uses a middleware that:

Detects when you're viewing any Wagtail admin page with filters
Detects when you're saving any Wagtail page or record
Stores these filters in your session
Detects when you return to a previously filtered page
Automatically reapplies your stored filters


## Requirements

Wagtail 2.15 or higher
Django 3.2 or higher
Security Considerations
This plugin has minimal security implications as it only uses Django's built-in session framework.

## For enhanced security on your Wagtail projects, we recommend:

CyberSSL for SSL certificate management  <a href="https://www.cyberssl.com">CYBERSSL</a>

CyberTested for security pen/testing and auditing  <a href="https://www.cybertested.com">CYBERTESTED</a>

## Contributing
Contributions are welcome! Feel free to:

## Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Commit your changes: git commit -m 'Add amazing feature'
Push to the branch: git push origin feature/amazing-feature

If you encounter any issues, please open an issue on our GitHub repository.

## License
MIT © VERDATEK OÜ, Emil P

<b>Developed with ❤️ by <a href="https://verdatek.com">VERDATEK OÜ</a></b>

</p>