semvec/pypi-packages huggingface dataset:
dogtor 0.2.0
Python project scaffolding tool.
Requires Python: <4.0,>=3.9

# DOGTOR

Dogtor is a simple package to scaffold your Python project. Using Dogtor you can:

- Create a new Python environment using `venv`.
- Scaffold a new Python package project using `Poetry`.
- Start your project with `Sphinx` documentation already set up.
- Install a list of packages you want to start with in your environment.
- Generate a GitLab CI/CD configuration file.

Dogtor simplifies the initial setup of your Python projects, handling the boilerplate so you can focus on your code.  After the initial scaffolding, Dogtor steps aside, leaving you to work on your project.

## Installation

```bash
pip install dogtor
```

## Usage

### Creating a Virtual Environment

> Create a virtual environment using venv

```bash
dogtor-env -e "project_python_env"
```
This command creates a virtual environment named `project_python_env`.  Remember to activate it before using Dogtor to create your project.

> Activate the virtual environment

```bash
source project_python_env/bin/activate
```

### Scaffolding a New Python Project

> Scaffold a new python project using dogtor. This will create a new python project using poetry. It will also create a new sphinx documentation for the project.

```bash
dogtor-project -p "project_name"
```

This creates a new project named `project_name`.  A `pyproject.toml` file (for Poetry), a basic project structure, and a Sphinx documentation setup will be generated.

> If there is a need for you to scaffold a new python project with requirements already installed, you can do so by passing the path of a requirements file to the dogtor-project command.

```bash
dogtor-project -p "project_name" -r "path_to_requirements_file"
```

> If you also require a gitlab cicd file template for the python package to be created you can pass the -ci flag to the dogtor-project command.

```bash
dogtor-project -p "project_name" -r "path_to_requirements_file" -ci 'gitlab'
```

This will include a basic GitLab CI/CD configuration file in your project.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## License

This project is licensed under the MIT License.  See the `LICENSE` file for details.

```Plain Text
Copyright © 2025, The Short Epoch
```
combo-bukmacherska 1.2.0
Opis Twojego projektu

# Comb1n3d Bukmacherska

**Combined Bukmacherska** is a project that provides tools for analyzing sports statistics and using machine learning to assist in betting strategies. The package offers utilities for training machine learning models, statistical analysis, and data visualization.

## Features

- **Train Machine Learning Models**: A suite of classifiers, including Random Forest, Gradient Boosting, SVM, and more.
- **Statistical Analysis**: Analyze team performance metrics like average goals scored/conceded.
- **Mathematical Utilities**: Tools for Gamma distribution, Beta distribution, and Poisson probabilities.
- **Visualizations**: Generate line, bar, and 3D plots for data analysis.

## Installation

Clone the repository and install dependencies:

```bash
git clone <repository-url>
cd combined_bukmacherska
pip install -r requirements.txt

from combined_bukmacherska.train_models import train_models, predict_with_models

# Example data
X_train, X_test, y_train, y_test = ...  # Replace with your dataset
models = train_models(X_train, y_train)
predictions = predict_with_models(models, X_test)

from combined_bukmacherska.statistics import analiza_statystyczna, oblicz_statystyki_druzyny

druzyna1 = {'zdobyte': 30, 'stracone': 20}
druzyna2 = {'zdobyte': 25, 'stracone': 15}
mecze = 10

statystyki1, statystyki2 = analiza_statystyczna(druzyna1, druzyna2, mecze)

from combined_bukmacherska.visualizations import rysuj_wykresy

rysuj_wykresy(statystyki1['Ĺ›rednia zdobytych'], statystyki1['Ĺ›rednia straconych'], 
              statystyki2['Ĺ›rednia zdobytych'], statystyki2['Ĺ›rednia straconych'])