semvec/pypi-packages huggingface dataset:
nitslog 1.0.0
A simple task logger application
Requires Python: >=3.6

# TaskLogger

TaskLogger is a simple command-line application for managing tasks. It allows you to add, view, edit, and delete tasks, and it automatically generates an HTML file to display the tasks.

## Features

- Add new tasks
- View existing tasks
- Edit tasks
- Delete tasks
- Generate HTML file for tasks
- Push changes to GitHub

## Installation

You can install TaskLogger from PyPI:

```bash
pip install tasklogger
```

## Usage
After installing, you can run the TaskLogger application using the following command:
```bash
nitslog
```

### Commands
1. n - Add a new task
2. v - View a task
3. e - Edit a task
4. d - Delete a task
5. q - Quit the application
gameforged 0.1.1
A comprehensive Python framework for game theory modeling, analysis, and simulation.

# **GameForge**  

*A comprehensive, modular framework for advanced game theory applications, integrating classical, computational, behavioral, and AI-driven game theory into a unified, scalable ecosystem.*  

---

## **Overview**  

GameForge is a powerful, extensible library designed to facilitate game theory research, simulation, and application across diverse domains. Whether you are a researcher, developer, or strategist, GameForge provides tools to model, analyze, and refine strategic interactions using a wide range of theoretical and computational approaches.  

GameForge supports:  

- **Classical Game Theory** – Nash equilibria, mixed strategies, extensive and strategic forms.  
- **Computational Game Theory** – Algorithmic analysis, AI-driven game strategies, and equilibrium computation.  
- **Behavioral Game Theory** – Human decision-making biases, prospect theory, and bounded rationality.  
- **Multi-Agent Systems & AI** – Reinforcement learning, adaptive strategies, and agent-based modeling.  
- **Metagaming & Dynamic Environments** – Iterated play, evolving incentives, and external factors.  

---

## **Features**  

### **1. Game Representation & Modeling**  
- **Extensive Form** (game trees, sequential decisions, perfect/imperfect information)  
- **Strategic Form** (payoff matrices, simultaneous play)  
- **Graph-Based & Hybrid Representations**  

### **2. Computational Analysis**  
- **Equilibrium Computation** (Nash, correlated, evolutionary)  
- **Algorithmic Strategy Optimization**  
- **Monte Carlo & Minimax Simulations**  

### **3. Behavioral & Psychological Modeling**  
- **Prospect Theory & Risk Preferences**  
- **Social Preferences & Fairness**  
- **Framing Effects & Decision Heuristics**  

### **4. Multi-Agent Systems & AI Integration**  
- **Reinforcement Learning Agents**  
- **Adaptive Strategy Learning**  
- **Agent-Based Modeling for Dynamic Environments**  

### **5. Game Length & Complexity Refinements**  
- **Finite vs. Infinite Horizon Adjustments**  
- **Endgame Scenarios & Tipping Points**  
- **Computational Complexity & Algorithmic Playability**  

### **6. Metagaming & External Incentives**  
- **Iterated Play & Reputation Systems**  
- **Policy & Regulatory Game Theory**  
- **Real-World Incentive Modeling**  

---

## **Installation**  

GameForge is in active development. Once released, it will be available via PyPI:  

```bash
pip install gameforge
footap 1.6.1
Package d'analyse des touches de balle au football
Requires Python: >=3.7

# FootAP

FootAP (FOOTball Analysis Package) is a Python package for analyzing ball touches in football videos.

## Installation

```bash
pip install footap
```

## Usage

You can use FootAP in two ways:

### 1. Command Line Interface

The simplest way to use FootAP is through the command line:

```bash
# Basic analysis (generates only results file)
footap video.mp4

# Analysis with real-time display
footap video.mp4 --display

# Analysis with video output
footap video.mp4 --save-video

# Full analysis with all options
footap video.mp4 --save-video -o output.mp4 -r results.txt --orientation 90 --display

# Display version
footap -v
# or
footap --version
```

Available options:
- `--save-video`: Generate an annotated output video
- `--display`: Show real-time processing
- `-o, --output`: Specify output video name (only with --save-video)
- `-r, --results`: Specify results file name
- `--orientation`: Video orientation in degrees (0, 90, 180, 270)
- `-v, --version`: Display version

### 2. Python API

```python
from footap import analyze_ball_touch

# Basic analysis
analyze_ball_touch("video.mp4")

# Full analysis with all options
analyze_ball_touch(
    input_video_path="video.mp4",
    display_processing=True,    # Show real-time processing
    generate_video=True,        # Generate output video
    video_orientation=90        # Rotate video if needed
)

# Get version
from footap import __version__
print(__version__)
```

## Output

The program generates two files:

1. Results file (always generated):
   ```
   Left Foot Touches: 5
   Right Foot Touches: 12
   ```

2. Details file (CSV format):
   ```csv
   Time,Touch
   00:00:01.233,Left Foot
   00:00:02.567,Right Foot
   00:00:03.100,Right Foot
   ```

3. Annotated video (optional, with --save-video):
   - Ball detection
   - Feet tracking
   - Real-time touch counter
  
## Dependencies

- OpenCV (opencv-contrib-python)
- MediaPipe
- NumPy
- Ultralytics (YOLO)
- Pillow

## License

This project is licensed under the MIT License.