https://github.com/mgjho/xrfit

Model Based Fitting on Xarray
https://github.com/mgjho/xrfit

fitting regression regression-analysis

Last synced: 2 months ago
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Model Based Fitting on Xarray

• Host: GitHub
• URL: https://github.com/mgjho/xrfit
• Owner: mgjho
• License: gpl-3.0
• Created: 2025-01-29T18:19:10.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2026-04-06T19:32:24.000Z (2 months ago)
• Last Synced: 2026-04-06T21:23:03.987Z (2 months ago)
• Topics: fitting, regression, regression-analysis
• Language: Python
• Homepage:
• Size: 283 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

          
# xrfit

[![Supported Python Versions](https://img.shields.io/pypi/pyversions/xrfit?logo=python&logoColor=white)](https://pypi.org/project/xrfit/)

[![PyPi](https://img.shields.io/pypi/v/xrfit?logo=pypi&logoColor=white)](https://pypi.org/project/xrfit/)

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[![Codecov Coverage](https://img.shields.io/codecov/c/github/mgjho/xrfit?logo=codecov&logoColor=white)](https://codecov.io/gh/mgjho/xrfit)

[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

## Overview 🎯

[*xrfit*](https://github.com/mgjho/xrfit) provides Model-based, Multidimensional regression/analysis methods. By Integrating,

1. [*xarray*](https://xarray.pydata.org/) to handle multivariate data by utilizing coordinate-aware structure of DataArray.

2. [*lmfit*](https://lmfit.github.io/lmfit-py/) to deal complex regression problems using the composite-model based approach.

## Features ✨

1. __Model based regression__

    ```python

    model = Model_1() + Model_2()

    guess = data.fit.guess(model=model)

    result = data.fit(model=model, params=guess)

    ```

2. __Parameter handling__ (e.g. Sorting and Smoothing parameters)

    ```python

    sorted_result = result.params.sort("params_name")

    smoothened_result = sorted_result.params.smoothen("params_name")

    new_result = data.fit(model=model, params=smoothened_result.parmams)

    ```

3. __Interactive visualization__

    ```python

    result.display()

    ```