https://github.com/sebsikora/curve_fitting
A short guide to using Python tools to perform arbitrary curve-fitting of research data via constrained minimisation.
https://github.com/sebsikora/curve_fitting
curve-fitting minimisation numpy optimisation python scipy
Last synced: about 2 months ago
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A short guide to using Python tools to perform arbitrary curve-fitting of research data via constrained minimisation.
- Host: GitHub
- URL: https://github.com/sebsikora/curve_fitting
- Owner: sebsikora
- Created: 2016-04-27T10:04:59.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2021-09-30T16:02:04.000Z (over 3 years ago)
- Last Synced: 2025-02-03T12:17:12.461Z (4 months ago)
- Topics: curve-fitting, minimisation, numpy, optimisation, python, scipy
- Language: Jupyter Notebook
- Homepage:
- Size: 346 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Piece-wise curve-fitting with Python tools - A tutorial
© 2021 Dr Sebastien Sikora.
[[email protected]](mailto:[email protected])
Updated 29/09/2021.
What is it?
-------------------------
This IPython notebook forms a tutorial that I wrote some years ago while working as a research fellow in the Department of Mechanical Engineering at Leeds University.
It is a guide to using Python tools including numpy and scipy to work with research data, in this case to perform arbitrary curve-fitting, for the benefit of scientist colleagues comfortable with the algebra but uncomfortable moving beyond Microsoft Excel or the odd bit of Matlab to perform their analyses.
More broadly, the same approach can be used to solve for unknowns in any multivariate scalar function, via constrained minimisation.
[The tutorial](tutorial_ipython_notebook.ipynb)