overlap-integral 0.1.2

This package aims to compute the overlap integral between two probability density functions.

Overlap Integral Project

This project focuses on calculating the overlap integral between two probability density functions (PDFs). The overlap integral is a measure of similarity between two distributions and is used in various fields such as statistics, data science, etc. The code and data files in this project are designed to perform these calculations efficiently and accurately.

Project Structure

• src/overlap_integral/: Contains the core Python code for calculating the overlap integral.
• tests/: Includes the test scripts to validate the functionality of the code.
• README.md: Provides an overview and instructions for the project.
• pyproject.toml: Configuration file for the project dependencies and metadata.

Installation

To install the package using pip, run the following command:

pip install overlap-integral

Importing the Class: Import the OverlapIntegral class in your Python script.

```python
from overlap_integral.overlap_integral import OverlapIntegral
```

Usage Example: Provide a simple example to demonstrate how to use the OverlapIntegral class.

```python

        import numpy as np
        from overlap_integral.overlap_integral import OverlapIntegral

        import plotly.io as pio
        pio.kaleido.scope.default_format = "png"


        def main():
            np.random.seed(3)  # Set random seed for reproducibility

            metrics = OverlapIntegral()

            # Generate or load data
            data1 = np.random.normal(loc=30, scale=1, size=1000)
            data2 = np.random.normal(loc=30, scale=1.2, size=1000)

            # Choose PDF method: 'kde' or 'gaussian'
            pdf_method = 'gaussian'

            # Get PDFs
            pdf_1 = metrics.get_pdf(data1, method=pdf_method)
            pdf_2 = metrics.get_pdf(data2, method=pdf_method)

            # Calculate overlap integral
            lower_limit = min(np.min(data1), np.min(data2)) - 12 * max(np.std(data1), np.std(data2))
            upper_limit = max(np.max(data1), np.max(data2)) + 12 * max(np.std(data1), np.std(data2))
            integral, error = metrics.overlap_integral(pdf_1, pdf_2, lower_limit, upper_limit)

            print(f"Overlap integral: {integral}")
            print(f"Estimated error: {error}")

            # Plot distributions
            fig = metrics.plot_distributions(pdf_1, pdf_2, integral, error, x_range=(lower_limit, upper_limit))
            fig.write_image("overlap_plot.png")
            ##fig.show()

        if __name__ == '__main__':
            main()
```

Requirements

• Python 3.11 or higher
• NumPy
• SciPy
• Plotly
• Kaleido

License

This project is licensed under the MIT License.