Metadata-Version: 2.4 Name: adaptive-curvefitting Version: 0.1.7 Summary: A tool for adaptive selection of curve-fitting models. Home-page: https://github.com/longavailable/adaptive-curvefitting Author: Xiaolong "Bruce" Liu, Meixiu Yu Author-email: liuxiaolong125@gmail.com, meixiuyu@hhu.edu.cn Classifier: Programming Language :: Python :: 3 Classifier: License :: OSI Approved :: MIT License Classifier: Operating System :: OS Independent Requires-Python: >=3.6 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: pandas Requires-Dist: matplotlib Requires-Dist: scipy Dynamic: author Dynamic: author-email Dynamic: classifier Dynamic: description Dynamic: description-content-type Dynamic: home-page Dynamic: license-file Dynamic: requires-dist Dynamic: requires-python Dynamic: summary # Adaptive Curvefitting Tool [![PyPI version](https://badge.fury.io/py/adaptive-curvefitting.svg)](https://badge.fury.io/py/adaptive-curvefitting) ![PyPI - Downloads](https://img.shields.io/pypi/dm/adaptive-curvefitting) [![Downloads](https://pepy.tech/badge/adaptive-curvefitting)](https://pepy.tech/project/adaptive-curvefitting) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3893596.svg)](https://doi.org/10.5281/zenodo.3893596) Adaptive curvefitting is a tool to find potentially optimal models for your research data. It's based on [scipy], [numpy], and [matplotlib].

## Table of contents - [Why is this tool?](#why-is-this-tool) - [Installation, update and uninstallation](#installation--update-and-uninstallation) * [To install](#to-install) * [To update](#to-update) * [To uninstall](#to-uninstall) - [Usage](#usage) * [Import the required module](#import-the-required-module) * [Do the curvefitting](#do-the-curvefitting) * [Generate a expected model](#generate-a-expected-model) * [Re-use the fitted curve](#re-use-the-fitted-curve) - [Shortages](#shortages) - [How to cite?](#how-to-cite) - [Changelog](#changelog) ## Why is this tool The very difference of adaptive-curvefitting with [numpy.polyfit], [scipy.optimize.curve_fit] or [scipy.optimize.least_squares] is ***the hypothesis you don’t know which model to fit***. If you already have the expected model, the methods in [scipy] and [numpy] are fantastic tools and better than this one. ***When you explore something unknown, this will be a maybe***. ## Installation, update and uninstallation ### To install Quick installation with [pip]: ```bash pip install adaptive-curvefitting ``` Or from github: ```bash pip install git+https://github.com/longavailable/adaptive-curvefitting ``` ### To update ```bash pip install --upgrade adaptive-curvefitting ``` ### To uninstall ```bash pip uninstall adaptive-curvefitting ``` ## Usage ### Import the required module In general, ```python import longscurvefitting ``` or import the specified function: ```python from longscurvefitting import oneClickCurveFitting from longscurvefitting import generateFunction from longscurvefitting import generateModels ``` ### Do the curvefitting ```python oneClickCurveFitting(xdata, ydata) ``` There are some optional arguments of `oneClickCurveFitting`. - functions: specified or all (default) basic models(name of models) to fit. - Type: list of string - Default: basicModels_nameList - piecewise: if consider custom a piecewise function. It is mandatory not to 'piecewise' when the data size is less than 20. - Type: bool - Default: False - operator: operatation between basic models. - Type: string - Default: '+' - maxCombination: max number of combination of basic models. - Type: integer - Default: 2 - plot_opt: the number of plot for optimal models. - Type: integer - Default: 10 - xscale: one of {"linear", "log", "symlog", "logit", ...} - Type: string - Default: None - yscale: one of {"linear", "log", "symlog", "logit", ...} - Type: string - Default: None - filename_startwith: a custom string mark as part of output filename - Type: string - Default: 'curvefit' - silent: minimal output to monitor - Type: boolean - Default: False - feedback: if True, return the optimal model(function object), parameters - Type: boolean - Default: False - kwargs: keyword arguments passed to `curve_fit_m`. Note that `bounds` and `p0` will take no effect when multi-models. - Type: dict See the complete example "[/tests/curvefitting.py]". ### Generate a expected model Create a model composited by gaussian and erf function: ```python funcs = ['gaussian','erf'] myfunc = generateFunction(funcs, functionName='myfunc', operator='+')['model'] ``` See the complete example "[/tests/custom_a_model.py]". ### Re-use the fitted curve See the complete example "[/tests/reuse_the_fitted_model.py]". ## Shortages - Based on [scipy.optimize.least_squares], it cannot enhance the estimate of specified model. Evenmore, it has more limit than [scipy.optimize.least_squares]. For example, arguments of `bounds`, `x0` or `p0` were not supported due to the ***basic hypothesis***. ## How to cite If this tool is useful to your research, star and cite it as below: ``` Xiaolong Liu, & Meixiu Yu. (2020, June 14). longavailable/adaptive-curvefitting. Zenodo. http://doi.org/10.5281/zenodo.3893596 ``` Easily, you can import it to Mendeley. ## Changelog ### v0.1.3 - First release. ### v0.1.4 - Add `queryModel()` to simplify the reuse of a fitted model. - Replace `from scipy._lib._util import getargspec_no_self as _getargspec` with `from ._helpers import funcArgsNr` ### v0.1.5 - Updated the outdated module of [scipy]. ### v0.1.7 - This bug occurs because Python 3.13 no longer exposes exec-defined local functions to `locals()` or `eval()` due to **PEP 667**. We resolve it by explicitly passing a namespace dictionary to `exec()` and retrieving the generated function from that dictionary instead. [scipy]: https://scipy.org/ [numpy]: https://numpy.org/ [matplotlib]: https://matplotlib.org/ [scipy.optimize.curve_fit]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html [numpy.polyfit]: https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html?highlight=fit#numpy-polyfit [scipy.optimize.least_squares]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html [pip]: https://pip.pypa.io/en/stable/ [/tests/curvefitting.py]: https://github.com/longavailable/adaptive-curvefitting/raw/master/tests/curvefitting.py [/tests/custom_a_model.py]: https://github.com/longavailable/adaptive-curvefitting/raw/master/tests/custom_a_model.py [/tests/reuse_the_fitted_model.py]: https://github.com/longavailable/adaptive-curvefitting/raw/master/tests/reuse_the_fitted_model.py