Overview

pyMultiFit is an open-source Python library designed to simplify fitting multiple models or a mixture of models to data with ease. It is particularly useful for researchers working with signals, spectra, and experimental datasets.

Why pyMultiFit?

Data fitting is the backbone of scientific analysis, serving as the bread-and-butter for any researcher dealing with experimental or simulated data. While popular libraries like NumPy and SciPy offer functions such as polyfit and curve_fit for polynomial and generic curve fittings, extending these tools for multi-model fitting is often cumbersome and repetitive.

This is where pyMultiFit steps in. It provides out-of-the-box support for common multi-fitters and allows seamless integration of user-defined fitters with minimal effort.

Key Features

1. Traditional Multi-Fitters Built-in support for common fitting models such as:

   • GaussianDistribution

   • LaplaceDistribution

2. User-Defined Fitters A BaseFitter class serves as an anchor for users to create custom fitters with minimal boilerplate code.

3. N-Modal Data Generation Generate synthetic datasets with N Gaussian components or other statistical distributions, perfect for:

   • Testing fitters.

   • Simulating realistic multi-modal data for research.

4. Statistical Distributions Provides built-in statistical distributions that can be easily incorporated into your workflows.

Benefits

• Ease of Use: Simplifies the process of fitting multiple models without requiring extensive redefinitions.

• Customizable: Offers flexibility through the BaseFitter class for domain-specific models.

• Synthetic Data Support: Makes it easy to generate complex, multi-modal datasets for testing and validation.

• Research Focused: Tailored to meet the needs of researchers dealing with signals, spectra, and experimental data.

Get Started

With pyMultiFit, you can focus more on analyzing your data and less on redefining model fitters. Whether you’re fitting a spectrum with five Gaussian peaks or creating your custom statistical models, pyMultiFit has got you covered.