semvec/pypi-packages huggingface dataset:
qCodePlot3D 0.1.0
Create 3D visualizations of 3D quantum color codes.
Requires Python: >=3.9
Keywords: color code, 3D, visualization, 2D

# qCodePlot3D

qCodePlot3D provides 3D visualizations of 2D and 3D quantum color codes, making it easy to inspect the code and visualize syndromes of error configurations.

## Available Quantum Codes

The following quantum codes are currently implemented:
- 2D square color code
- 2D triangular color code
- 3D cubic color code
- 3D tetrahedral color code

The package provides an interface to extend it to further quantum codes.
Contributions of such implementations to the package are appreciated.


## Usage

The package comes with a minimal GUI to construct and plot the predefined quantum codes.
Start the GUI by calling
```
python3 -m qcodeplot3d
```
After `Build`ing a available code with a given distance, you can hit `Plot Primary Graph` to show it.
If you press `Build Dual Graph` in addition, you can also plot the dual graph or both graphs at once.

Alternatively, the package can be used to script the creation of figures.
A small example snipped might look as following:

```
import pathlib
from qcodeplot3d.cc_3d import TetrahedronPlotter, tetrahedron_3d_dual_graph

THIS_DIR = pathlib.Path(__file__).parent

distance = 5
dual_graph = tetrahedron_3d_dual_graph(distance)
plotter = TetrahedronPlotter(dual_graph, distance=distance)
# describes the position of the camera in the 3D visualization
CPOS = [(333.44920594948127, -92.62132121918515, 221.35429214703458),
        (-1.365808884682137, 1.7594735760392801, -12.072404824078472),
        (0.5292985009453346, 0.7030498941163704, -0.4749357254197479)]
plotter.plot_primary_mesh(
    camera_position=CPOS,
    filename= THIS_DIR / "tetrahedron-d=5-1-primary"
)
dual_graph_mesh = plotter.construct_dual_mesh(
    dual_graph,
    use_edges_colors=True,
    highlighted_edges=dual_graph.edges(),
    highlighted_nodes=dual_graph.nodes()
)
plotter.plot_debug_primary_mesh(
    dual_graph_mesh,
    camera_position=CPOS,
    transparent_faces=True,
    filename= THIS_DIR / "tetrahedron-d=5-2-both"
)
```
metaspread 2024.0.6
A cancer and metastasis simulation package
Requires Python: >=3.10

# MetaSpread: a cancer and metastasis simulation package

MetaSpread is a cancer and metastasis simulator written in Python, based on the work of Franssen et al [[1]](#1) and the MESA framework [[2]](#2). With this package it is possible to simulate how cancer cells reproduce and diffuse locally, and generate metastases by spreading to other parts of the body.

The underlying model is a reaction-diffusion type model with deterministic and stochastic aspects, tracking the spatiotemporal evolution of: i) numbers of epithelial and mesenchymal cancer cells (modelled as discrete agents); and ii) the densities of the extracellular matrix and the diffusible matrix metalloproteinase-2 (MMP-2), in both the primary site and all secondary sites. Other processes represented in the simulator are the circulation of cancer cell clusters and single cancer cells in the vasculature, including cell death and disaggregation of clusters, and the extravasation of clusters and single cells to distant metastatic sites.

# Requirements

matplotlib==3.8.0
Mesa==2.1.2
numpy==1.26.0
pandas==2.1.1
pynput==1.7.6
opencv_python==4.8.1.78

# Installing the package

MetaSpread is available as an official PyPi package. To install, simply run:

```
pip install metaspread
```

If you want to install manually, you can also download the source code as a zip file, unzip in an appropiate directory, and run

```
python -m metaspread
```
# Documentation

For further details on how to use the program please refer to the [Documentation.](https://metaspread.readthedocs.io/en/stable/)


# Contributing

Everyone is welcome to contribute! To check more details about how to help the project, please go to [Contributing](https://github.com/alfredohernandezinostroza/MetaSpread/blob/main/CONTRIBUTING.md).

# References

><a id="1">[1]</a>  Franssen, L. C., Lorenzi, T., Burgess, A. E., & Chaplain, M. A. (2019). A mathematical framework for modelling the metastatic spread of cancer. Bulletin of Mathematical Biology, 81, 1965–2010.

><a id="2">[2]</a>  Masad, D., & Kazil, J. (2015). MESA: An agent-based modeling framework. 14th PYTHON in Science Conference, 2015, 53–60.

><a id="3">[3]</a>  Hernández-Inostroza, A. and Gjini, E., MetaSpread: A cancer growth and metastatic spread simulation program in Python, 2024 (submitted).
hack4uAll 0.1.0
Una biblioteca para consultar cursos de hack4u.

# Hack4U Academy Courses Library
Una biblioteca Python para consultar cursos de la academia Hack4U.

## Cursos disponibles:
- Introducción a Linux [15 horas]
- Personalización de Linux [3 horas]
- Introducción al Hacking [53 horas]

---

## Instalación
Instala el paquete usando `pip3`:
```bash
pip3 install hack4u
```
## Uso Básico

## Listar todos los cursos

```python
from hack4u import list_courses

for course in list_courses():
    print(course)
```
## Obetener curso por nombre

```python
from hack4u import get_course_by_name

course = get_course_by_name("Introducción a Linux")
print(course)

```
## Calcular duración de los cursos

```python
from hack4u.utils import total_duration

print(f"Duración total: {total_duration()} horas")
```
mcp-server-proj 1.0.0
MCP Server PROJ - 坐标系统转换服务器
Requires Python: >=3.10
Keywords: GIS, MCP, coordinate, projection, transformation

# MCP Server PROJ

A Model Context Protocol (MCP) server for coordinate system transformations and map projections.

## Features

- Multiple coordinate system format support:
  - EPSG codes
  - WKT (Well-Known Text)
  - Proj string format
- Batch coordinate transformation
- Simple and intuitive API
- Dual-mode operation: server or library

## Installation

```bash
pip install mcp-server-proj
```

## Usage

### Server Mode

```bash
mcp-server-proj
```

### Library Mode

```python
from mcp_server_proj import CoordinateTransformer

# Create transformer instance
transformer = CoordinateTransformer()

# Set source and target coordinate systems
transformer.set_source_crs("EPSG:4326")  # WGS84
transformer.set_target_crs("EPSG:3857")  # Web Mercator

# Initialize transformer
transformer.initialize_transformer()

# Transform coordinates
x, y = transformer.transform_point(116.3, 39.9)
print(f"Transformed coordinates: ({x}, {y})")
```

## API Documentation

### CoordinateTransformer

The main coordinate transformation class provides the following methods:

- `set_source_crs(crs: str)`: Set source coordinate system
- `set_target_crs(crs: str)`: Set target coordinate system
- `initialize_transformer()`: Initialize the transformer
- `transform_point(x: float, y: float) -> tuple[float, float]`: Transform a single point

### Server

The MCP protocol server provides two tools:

- `transform-coordinates`: Transform coordinates between different systems
  ```json
  {
    "source_crs": "EPSG:4326",
    "target_crs": "EPSG:3857",
    "coordinates": [
      {"x": 116.3, "y": 39.9}
    ]
  }
  ```

- `list-supported-crs`: List all supported coordinate systems

## Supported Coordinate Systems

### 1. EPSG Codes
Standard identifiers for coordinate reference systems:
- EPSG:4326 - WGS84 Geographic
- EPSG:3857 - Web Mercator
- And many more...

### 2. WKT Format
Example of a geographic coordinate system:
```
GEOGCS["WGS 84",
  DATUM["WGS_1984",
    SPHEROID["WGS 84",6378137,298.257223563]],
  PRIMEM["Greenwich",0],
  UNIT["degree",0.0174532925199433]]
```

### 3. Proj Format
Example of WGS84:
```
+proj=longlat +datum=WGS84 +no_defs +type=crs
```

## Development

### Debugging
For the best debugging experience, use the [MCP Inspector](https://github.com/modelcontextprotocol/inspector):

```bash
npx @modelcontextprotocol/inspector mcp-server-proj
```

## Dependencies

- Python >= 3.12
- mcp >= 1.3.0
- pyproj >= 3.0.0

## License

MIT

## Author

radial-hks (radialjiajie@gmail.com)

## Contributing

Issues and Pull Requests are welcome!
zenbroker 1.5.2
Zenbroker client

# Zenbroker Client Example

This project demonstrates how to use the Zenbroker client library to publish messages to channels.

## Installation

```bash
pip install zenbroker
```

## Configuration

You need to configure your Zenbroker client with your application ID:

```python
from zenbroker import ZenbrokerClient

client = ZenbrokerClient(
    application_id="YOUR_APPLICATION_ID",
    base_url="https://broker.oraczen.xyz"
)
```

## Usage

### Publishing a single message

```python
resp = client.publish(
    channel="CHANNEL-ID",
    data={
        "key": "value",
        "message": "hello"
    }
)
```

## Notes

- Replace `YOUR_APPLICATION_ID` with your actual application ID
- Replace `CHANNEL-ID` with your target channel ID when publishing messages