pymultifit submission

[syedalimohsinbukhari]

Submitting Author: Syed Ali Mohsin Bukhari (@syedalimohsinbukhari)
All current maintainers: (@syedalimohsinbukhari)
Package Name: pymultifit
One-Line Description of Package: A python library for fitting data with multiple models.
Repository Link: https://github.com/syedalimohsinbukhari/pyMultiFit
Version submitted: v1.0.3
EiC: @coatless
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

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pymultifit is built primarily to solve one problem, to fit multiple models (and mixture models) to a given data. Be it multiple Gaussian, Laplacian, or a mixture of such models, this package aims to deal with multi-model data fitting. The package also provides easy-to-use BaseDistribution and BaseFitter classes for respective user-defined functions.

Scope

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  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
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  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

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  • Who is the target audience and what are scientific applications of this package?

Researchers, data scientists, and statisticians who work with datasets requiring multi-model fitting for robust analysis and modeling.

• Are there other Python packages that accomplish the same thing? If so, how does yours differ?

Apart from scipy, lmfit, and scikit-learn the general purpose scientific packages, there exists PyAutoFit, a Python-based probabilistic programming language built on Bayesian inference. Another notable library is Mixture-Models, which specializes in advanced optimization techniques for fitting various families of mixture models, including Gaussian mixture models and their variants. Both libraries are powerful tools for specific use cases, and I recently came to know about them during my search of existing options.

While these libraries offer robust solutions for hierarchical modeling (PyAutoFit) or a diverse array of pre-defined mixture models (Mixture-Models), pyMultiFit distinguishes itself through its simplicity of use and its focus on simplicity of use. Specifically, it is designed to provide a lightweight and user-friendly framework for fitting multi-model data, including custom mixture models (for example, gaussian + laplace + line). pymultifit also provides easy-to-use base classes that can be modified for any distribution/fitter purposes.

One of the more prominent features of pyMultiFit is the BaseFitter template class that provides custom fitting to any definable function with minimal boilerplate code. All the plotting and boundary functionalities are handled inside the template class so that the user can focus solely on running through multiple models quickly without thinking about how to manage multiple models of the same type or even of different types.

Additionally, the generators template function provides the user with an N-model data generator function with added noise capability to mimic real-life scenarios of whatever distribution the user might want.

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  • pymultifit presubmission #221
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1. Please fill out a pre-submission inquiry before submitting a data visualization package. ↩