Framework for fitting models to (spectroscopic) data.
FitPy is a framework for the advanced fitting of models to spectroscopic data focussing on reproducibility. Supported are semi-stochastic sampling of starting conditions, global fitting of several datasets at once, and fitting several concurrent models to one dataset. FitPy builds upon and extends the ASpecD framework. At the same time, it relies on the SciPy software stack and on lmfit for its fitting capabilities.
Making use of the concept of recipe-driven data analysis, actual fitting no longer requires programming skills, but is as simple as writing a text file defining both, the model and the fitting parameters in an organised way. Curious? Have a look at the following example:
format: type: ASpecD recipe version: '0.2' datasets: - /path/to/dataset tasks: - kind: model type: Gaussian properties: parameters: position: 1.5 width: 0.5 from_dataset: /path/to/dataset output: model result: gaussian_model - kind: fitpy.singleanalysis type: SimpleFit properties: model: gaussian_model parameters: fit: amplitude: start: 5 range: [3, 7] result: fitted_gaussian
For more general information on the FitPy framework see its homepage, and for how to use it, its documentation.
A list of features, planned for the first public release:
Framework for the advanced fitting of models to spectroscopic data focussing on reproducibility.
Simple user interface requiring no programming skills.
Semi-stochastic sampling of starting conditions (Latin hypercube sampling, LHS)
Global fitting of several datasets at once
Fitting of several concurrent models (i.e., “species”) to one dataset
Install the package by running:
pip install fitpy
This program is free software: you can redistribute it and/or modify it under the terms of the BSD License.
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