semvec/pypi-packages huggingface dataset:
py-wsl-scraper 0.1.1
A Python package for scraping Women's Super League (WSL) football data
License: MIT
Requires Python: >=3.9
Keywords: football, wsl, data, scraping, sports

# py_wsl_scraper

[![PyPI version](https://img.shields.io/pypi/v/py-wsl-scraper.svg)](https://pypi.org/project/py-wsl-scraper/)

<p align="center">
  <img src="images/pywsl-banner.jpg" alt="py-wsl-scraper logo" width="300"/>
</p>

**py_wsl_scraper** is a Python package for scraping Women's Super League (WSL) football data. It provides tools to retrieve league tables, fixtures, top scorers, squad market valuations, and venue attendances.

## Why This Matters

While there are several Python packages for scraping football data, most are focused exclusively on men's leagues. Access to structured, reliable data for women's football is still limited and fragmented.

**py-wsl-scraper** helps fill this gap by targeting the [Women's Super League (WSL)](https://www.soccerdonna.de/) using Soccerdonna — a specialist data source affiliated with Transfermarkt that provides deeper coverage of the women’s game.

This package supports researchers, analysts, and developers who want to build inclusive tools, run analysis, and advocate for visibility and representation in football data.

## Installation


Install via pip:

```bash
pip install py-wsl-scraper
```

Alternatively, clone and install manually:

```bash
git clone https://github.com/yourusername/pywsl.git
cd pywsl
pip install -r requirements.txt

```


## Usage

```python
from pywsl import (
    get_league_table,
    get_fixtures,
    get_top_scorers,
    get_squad_valuations,
    get_venue_attendance
)

# League table
league_df = get_league_table()

# Fixtures
fixtures_df = get_fixtures()

# Top scorers for a season
scorers_df = get_top_scorers("2024")

# Squad market values
squads_df = get_squad_valuations()

# Venue attendance
venues_df = get_venue_attendance("2024")

```

## Modules 

- `league.py` – Scrapes the current league table.
- `fixtures.py` – Scrapes upcoming and past match fixtures.
- `scorers.py` – Scrapes top goal scorers by season.
- `squads.py` – Scrapes squad size, average age, and market values.
- `venues.py` – Scrapes venue data including capacity and attendance.



## Dependencies
- ```pandas```
- ```requests```
- ```beautifulsoup4```
- ```selenium```
- ```webdriver-manager```


## License
MIT License
vlm-inference 0.1.36
Vision language models for robotics
License: MIT

# vlm_inference

This repository contains code for performing inference using a Vision-Language Model (VLM) for robotic navigation tasks.

## Setup Instructions

Follow these steps to use the library, it is already available by PyP:

1.  **Intall library:**

Here you have two options, install twrought the PyP:
```bash
    pip install openai==1.78.1 opencv-python==4.11.0.86 pyyaml dotenv==0.9.9 pillow==9.0.1
```

```bash
    pip install vlm_inference
```
You can try the library in action using this Colab notebook:  
📎 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/11CpTjpGd-fZ2qdRMq8kt_fb5nq7TQD9b?usp=sharing)



Install since the repostitory:

```bash
    git clone ssh://git@gitlab.iri.upc.edu:2202/mobile_robotics/moonshot_project/vlm/vlm_inference.git

    cd vlm_inference
```

```bash
    python3 pip install -r requirements.txt
```
Install by the repository

```bash
    pip install -e .
```




2.  **Create a `.env` file and add your OpenAI API key and the VLM configuration path:**

    Create a file named `.env` in the root directory of the repository and add the following content, replacing `sk-proj-_HFJE2I64...........` with your actual OpenAI API key and ensuring the `VLM_CONFIG_PATH` points to your configuration file:

```
    # .env
    OPENAI_API_KEY=sk-proj-_HFJE2I64...........
    VLM_CONFIG_PATH=vlm_inference/config.yaml
```

If you have can´t not modify the python version use the following command:
```bash
    python3 pip install -r requirements.txt
```


5.  **Set the navigation goal (optional):**

    If you want to specify a navigation goal as an object or person with a description, open the following file:

```
    vlm_navigation/prompt_manager/navigation_prompt.txt
```

    Locate line 7, which defines the `navigation_goal` variable, and modify it according to your desired goal. For example:

```
    navigation_goal = "a red chair near the window"
```

6.  **Run the inference script:**

    Execute the main inference script using Python 3:

```bash
    python3 vlm_navigation/inference.py
```

This script will load the VLM, potentially process images (depending on the script's functionality), and output the inference results based on the configuration and any specified navigation goal.