gaussian-theorem 1.1
Gaussian and Binomial distributions
Requires Python: >=3.6

# Gaussian Theorem

**Gaussian Theorem: A Python Package for Gaussian and Binomial Distributions**

## Table of Contents

- [Introduction](#introduction)
- [Features](#features)
- [Getting Started](#getting-started)
  - [Prerequisites](#prerequisites)
  - [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Examples](#examples)
- [Testing](#testing)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgments](#acknowledgments)
- [Contact](#contact)

## Introduction

Gaussian Theorem is a Python package that provides classes for working with Gaussian and Binomial distributions. This package is designed to make it easy for developers and data scientists to perform calculations, visualize distributions, and integrate statistical functionality into their projects.

## Features

- **Gaussian Distribution:**
  - Calculate mean and standard deviation.
  - Generate probability density function (PDF) values.
  - Perform distribution addition.

- **Binomial Distribution:**
  - Calculate mean and standard deviation from given probabilities and sample sizes.
  - Visualize distributions with histograms.
  - Plot PDFs for Binomial distributions.

## Getting Started

### Prerequisites

Ensure you have Python 3.x installed on your machine. This project requires the following dependencies:

- `numpy`
- `matplotlib`

Install the dependencies using the following command:

```bash
pip install numpy matplotlib
Installation
Clone the repository and navigate to the project directory:
```

```bash
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git clone https://github.com/your-username/Gaussian_Theorem.git
cd Gaussian_Theorem
```

```python
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# Example code demonstrating how to use the Gaussian class
from Gaussiandistributions.gaussiandistribution import Gaussian

# Create a Gaussian distribution instance
gaussian = Gaussian(25, 4)

# Read data from a file (replace 'data.txt' with your data file)
gaussian.read_data_file('datasets/numbers.txt')

# Calculate mean and standard deviation
mean = gaussian.calculate_mean()
stdev = gaussian.calculate_stdev()

# Print the Gaussian distribution characteristics
print(gaussian)

# Plot histogram and PDF
gaussian.plot_histogram_pdf()

Project Structure
The project is structured as follows:

```
GAUSSIAN_THEOREM/
|-- Gaussian_Theorem/
|   |-- __init__.py
|   |-- gaussiandistribution.py
|   |-- generaldistribution.py
|-- Binomialdistributions/
|   |-- __init__.py
|   |-- binomialdistributions.py
|-- tests/
|   |-- __init__.py
|   |-- tests.py
|-- setup.py
|-- License.txt
|-- setup.cfg
|-- datasets/
|   |-- numbers.txt
|   |-- numbers_binomial.txt
|-- build


Examples
Check the examples/ directory for detailed usage examples and Jupyter notebooks.

Testing
To run tests, execute the following command:
```

```bash
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python -m unittest tests.tests
```

## Contributing
We welcome contributions! If you find a bug or have an enhancement idea, please open an issue or submit a pull request.

## License
This project is licensed under the MIT License - see the LICENSE file for details.

## Acknowledgments
The project is inspired by the need for a simple yet powerful Python package for working with statistical distributions.
Special thanks to the NumPy and Matplotlib teams for their excellent libraries.

## Contact
For any questions or feedback, feel free to reach out:

Email: dharrenpius@outlook.com
Twitter: @iamdevdharrenzug
GitHub: https://www.github.com/Dharren09