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https://lmfit.github.io/lmfit-py/

Non-Linear Least Squares Minimization, with flexible Parameter settings, based on scipy.optimize, and with many additional classes and methods for curve fitting.
https://lmfit.github.io/lmfit-py/

curve-fitting least-squares python scipy

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Non-Linear Least Squares Minimization, with flexible Parameter settings, based on scipy.optimize, and with many additional classes and methods for curve fitting.

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README 

        
LMfit-py

========



.. image:: https://dev.azure.com/lmfit/lmfit-py/_apis/build/status/lmfit.lmfit-py?branchName=master

    :target: https://dev.azure.com/lmfit/lmfit-py/_build/latest?definitionId=1&branchName=master



.. image:: https://codecov.io/gh/lmfit/lmfit-py/branch/master/graph/badge.svg

  :target: https://codecov.io/gh/lmfit/lmfit-py



.. image:: https://img.shields.io/pypi/v/lmfit.svg

   :target: https://pypi.org/project/lmfit



.. image:: https://img.shields.io/pypi/dm/lmfit.svg

   :target: https://pypi.org/project/lmfit



.. image:: https://img.shields.io/badge/docs-read-brightgreen

   :target: https://lmfit.github.io/lmfit-py/



.. image:: https://zenodo.org/badge/4185/lmfit/lmfit-py.svg

   :target: https://doi.org/10.5281/zenodo.598352



.. _LMfit google mailing list: https://groups.google.com/group/lmfit-py

.. _Github Discussions: https://github.com/lmfit/lmfit-py/discussions

.. _Github Issues: https://github.com/lmfit/lmfit-py/issues



..

   Note: the Zenodo target should be

   https://zenodo.org/badge/latestdoi/4185/lmfit/lmfit-py

   but see https://github.com/lmfit/lmfit-py/discussions/862



Overview

---------



The lmfit Python library supports provides tools for non-linear least-squares

minimization and curve fitting.  The goal is to make these optimization

algorithms more flexible, more comprehensible, and easier to use well, with the

key feature of casting variables in minimization and fitting routines as named

parameters that can have many attributes beside just a current value.



LMfit is a pure Python package, built on top of Scipy and Numpy, and so easy to

install with ``pip install lmfit``.



For questions, comments, and suggestions, please use the `LMfit google mailing

list`_ or `Github discussions`_.  For software issues and bugs, use `Github

Issues`_, but please read `Contributing.md <.github/CONTRIBUTING.md>`_ before

creating an Issue.



Parameters and Minimization

------------------------------



LMfit provides optimization routines similar to (and based on) those from

``scipy.optimize``, but with a simple, flexible approach to parameterizing a

model for fitting to data using named parameters. These named Parameters can be

held fixed or freely adjusted in the fit, or held between lower and upper

bounds. Parameters can also be constrained as a simple mathematical expression

of other Parameters.



A Parameters object (which acts like a Python dictionary) contains named

parameters, and can be built as with::



    import lmfit

    fit_params = lmfit.Parameters()

    fit_params['amp'] = lmfit.Parameter(value=1.2)

    fit_params['cen'] = lmfit.Parameter(value=40.0, vary=False)

    fit_params['wid'] = lmfit.Parameter(value=4, min=0)

    fit_params['fwhm'] = lmfit.Parameter(expr='wid*2.355')



or using the equivalent::



    fit_params = lmfit.create_params(amp=1.2,

                                     cen={'value':40, 'vary':False},

                                     wid={'value': 4, 'min':0},

                                     fwhm={'expr': 'wid*2.355'})



In the general minimization case (see below for Curve-fitting), the user will

also write an objective function to be minimized (in the least-squares sense)

with its first argument being this Parameters object, and additional positional

and keyword arguments as desired::



    def myfunc(params, x, data, someflag=True):

        amp = params['amp'].value

        cen = params['cen'].value

        wid = params['wid'].value

        ...

        return residual_array



For each call of this function, the values for the ``params`` may have changed,

subject to the bounds and constraint settings for each Parameter. The function

should return the residual (i.e., ``data-model``) array to be minimized.



The advantage here is that the function to be minimized does not have to be

changed if different bounds or constraints are placed on the fitting Parameters.

The fitting model (as described in myfunc) is instead written in terms of

physical parameters of the system, and remains remains independent of what is

actually varied in the fit. In addition, which parameters are adjusted and which

are fixed happens at run-time, so that changing what is varied and what

constraints are placed on the parameters can easily be modified by the user in

real-time data analysis.



To perform the fit, the user calls::



    result = lmfit.minimize(myfunc, fit_params, args=(x, data), kws={'someflag':True}, ....)



After the fit, a ``MinimizerResult`` class is returned that holds the results

the fit (e.g., fitting statistics and optimized parameters). The dictionary

``result.params`` contains the best-fit values, estimated standard deviations,

and correlations with other variables in the fit.



By default, the underlying fit algorithm is the Levenberg-Marquardt algorithm

with numerically-calculated derivatives from MINPACK's lmdif function, as used

by ``scipy.optimize.leastsq``. Most other solvers that are present in ``scipy``

(e.g., Nelder-Mead, differential_evolution, basin-hopping, and more) are also

supported.



Curve-Fitting with lmfit.Model

----------------------------------



One of the most common use of least-squares minimization is for curve fitting,

where minimization of ``data-model``, or ``(data-model)*weights``.  Using

``lmfit.minimize`` as above, the objective function would take ``data`` and

``weights`` and effectively calculated the model and then return the value of

``(data-model)*weights``.



To simplify this, and make curve-fitting more flexible, lmfit provides a Model

class that wraps a *model function* that represents the model (without the data

or weights).  Parameters are then automatically found from the named arguments

of the model function.  In addition, simple model functions can be readily

combined and reused, and several common model functions are included in lmfit.



Exploration of Confidence Intervals

-------------------------------------



Lmfit tries to always estimate uncertainties in fitting parameters and

correlations between them.  It does this even for those methods where the

corresponding ``scipy.optimize`` routines do not estimate uncertainties.  Lmfit

also provides methods to explicitly explore and evaluate the confidence

intervals in fit results.