overlap-integral 0.1.8

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

Overlap Integral Package

This project focuses on calculating the overlap integral between two probability density functions (PDFs):

The overlap integral, also known as coefficient of overlap, is a measure of similarity between two PDFs (f(x) and g(x)) and is used in various fields such as statistics, data science, etc. Additionally, it corresponds to the common area under the two PDFs.

• Kernel Density Estimation (KDE): Estimate the PDFs from data using KDE, a non-parametric approach that builds a smooth probability density function based on observed data.

• Gaussian Distribution: Alternatively, use the parameters (mean mu and standard deviation sigma) of an analytical Gaussian distribution to generate a PDF. This method is ideal when you know or assume that the data follows a normal distribution.

• Overlap Integral Calculation: The package computes the overlap integral, which measures the common area under two PDFs. This can be used to assess the similarity between two distributions.

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

            overlap_integral_instance = OverlapIntegral()

            # Generate or load data
            data1 = np.random.normal(loc=10, scale=1, size=1000)
            data2 = np.random.normal(loc=10, scale=2, size=1000)

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

            # Get PDFs
            pdf_1 = overlap_integral_instance.get_pdf(data1, pdf_type=function_type)
            pdf_2 = overlap_integral_instance.get_pdf(data2, pdf_type=function_type)

            # 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 = overlap_integral_instance.overlap_integral(pdf_1, pdf_2, lower_limit, upper_limit)

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

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

            print(f"Everything worked!")

        if __name__ == '__main__':
            main()

```

Requirements

• python >=3.12.3

Dependencies

• numpy
• scipy
• plotly
• kaleido
• pytest

License

This project is licensed under the MIT License.

Contribution

Feel free to submit issues or pull requests. Your contributions are welcome!

Contact

For questions or suggestions, please contact [e-mail]