lmfit

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

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

Keywords

curve-fitting least-squares python scipy

Keywords from Contributors

closember gtk qt tk wx conda package-management optimizing-compiler astronomy robust-estimation

Last synced: 4 months ago · JSON representation ·

Repository

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

Basic Info

Host: GitHub
Owner: lmfit
License: other
Language: Python
Default Branch: master
Homepage: https://lmfit.github.io/lmfit-py/
Size: 358 MB

Statistics

Stars: 1,140
Watchers: 30
Forks: 286
Open Issues: 4
Releases: 0

Topics

curve-fitting least-squares python scipy

Created over 14 years ago · Last pushed 6 months ago

Metadata Files

Readme Contributing License Citation Authors

README.rst

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 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, 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 is 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 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
of 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``, effectively calculate the model, and 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.

Owner

Name: lmfit
Login: lmfit
Kind: organization

Repositories: 4
Profile: https://github.com/lmfit

High-level Curve Fitting in Python

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software and want to cite it, please use the DOI citation below."
title: "LMFIT: Non-Linear Least-Squares Minimization and Curve-Fitting for Python"
version: 1.3.3
date-released: 2025-03-09
identifiers:
  - type: doi
    value: 10.5281/zenodo.12785036
authors:
  - family-names: Newville
    given-names: Matthew
    orcid: https://orcid.org/0000-0001-6938-1014
  - family-names: Otten
    given-names: Renee
    orcid: https://orcid.org/0000-0001-7342-6131
  - family-names: Nelson
    given-names: Andrew
    orcid: https://orcid.org/0000-0002-4548-3558
  - family-names: Stensitzki
    given-names: Till
    orcid: https://orcid.org/0000-0003-2317-4874
  - family-names: Ingargiola
    given-names: Antonino
    orcid: https://orcid.org/0000-0002-9348-1397
  - family-names: Allan
    given-names: Daniel
    orcid: https://orcid.org/0000-0002-5947-6017
  - family-names: Fox
    given-names: Austin
    orcid: https://orcid.org/0000-0002-8926-0524
  - family-names: Carter
    given-names: Faustin
    orcid: https://orcid.org/0000-0002-1236-3517
  - family-names: Rawlik
    given-names: Michal
    orcid: https://orcid.org/0000-0002-1232-4497

GitHub Events

Total

Create event: 9
Release event: 2
Issues event: 13
Watch event: 91
Delete event: 4
Issue comment event: 69
Push event: 31
Pull request review comment event: 10
Pull request review event: 10
Pull request event: 29
Fork event: 14

Last Year

Create event: 9
Release event: 2
Issues event: 13
Watch event: 91
Delete event: 4
Issue comment event: 69
Push event: 31
Pull request review comment event: 10
Pull request review event: 10
Pull request event: 29
Fork event: 14

Committers

Last synced: 8 months ago

All Time

Total Commits: 2,436
Total Committers: 96
Avg Commits per committer: 25.375
Development Distribution Score (DDS): 0.484

Past Year

Commits: 50
Committers: 7
Avg Commits per committer: 7.143
Development Distribution Score (DDS): 0.48

Top Committers

Name	Email	Commits
Matt Newville	n**e@c**u	1,258
reneeotten	r****n	685
Andrew Nelson	a**f@g**m	79
Antonino Ingargiola	t**o@g**m	58
Till Stensitzki	m**l@g**e	54
danielballan	d**n@g**m	35
Austin Fox	o**x@g**m	26
Faustin Carter	f**r@g**m	23
Michał Rawlik	m**k@p**h	22
François Boulogne	f****g	12
Ray Osborn	r**n@m**m	8
dpustakhod	d**d@t**l	8
s-weigand	s**y@g**m	6
François Boulogne	f****g	6
licode	l**i@b**v	6
Yoav Ram	y**b@g**m	6
gitj	g**h@g**m	5
Leonhard	l**s@g**m	5
aaristov	a**v@p**r	5
Colin-N. Brosseau	c**u@g**m	4
Merlin	m**n@n**k	4
Christoph Deil	D**h@g**m	4
mgunyho	m**o@g**m	4
Mark	m**n@g**m	4
Allan L. R. Hansen	a**h@g**m	3
Leon Foks	n**s@c**v	3
Gustavo Pasquevich	g**v@f**r	3
Nicholas Zobrist	n**t@p**u	3
julian stuermer	j**r@o**e	3
Oliver Frost	o**t@g**t	3
and 66 more...

Committer Domains (Top 20 + Academic)

mac.com: 2 gmaill.com: 1 columbia.edu: 1 ias.u-psud.fr: 1 torrinos.com: 1 trialphaenergy.com: 1 anazazi.imp.fu-berlin.de: 1 uni-ulm.de: 1 gmx.net: 1 online.de: 1 physics.ucsb.edu: 1 fisica.unlp.edu.ar: 1 contractor.usgs.gov: 1 nbi.dk: 1 pasteur.fr: 1 bnl.gov: 1 tue.nl: 1 psi.ch: 1 gmx.de: 1 cars.uchicago.edu: 1 inserm.fr: 1 tudelft.nl: 1 lbl.gov: 1 tu-dortmund.de: 1 fu-berlin.de: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 77
Total pull requests: 121
Average time to close issues: about 2 months
Average time to close pull requests: 16 days
Total issue authors: 50
Total pull request authors: 41
Average comments per issue: 3.94
Average comments per pull request: 4.4
Merged pull requests: 92
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 11
Pull requests: 28
Average time to close issues: about 1 month
Average time to close pull requests: 7 days
Issue authors: 10
Pull request authors: 12
Average comments per issue: 2.45
Average comments per pull request: 2.0
Merged pull requests: 17
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

newville (13)
nomeata (4)
jcjaskula-aws (4)
schtandard (4)
yurivict (2)
paulmueller (2)
Julian-Hochhaus (2)
edr-choi (1)
earthshrink (1)
GuillermoAbadLopez (1)
tillea (1)
bennyrowland (1)
parejkoj (1)
SilentStorming (1)
Emily-Linden (1)

Pull Request Authors

newville (55)
reneeotten (15)
jcjaskula-aws (4)
Anselmoo (3)
schtandard (3)
eendebakpt (2)
Timothy-J-Warner (2)
lellid (2)
matpompili (2)
stuart-cls (2)
paulmueller (2)
imnotgonnasithereandtryallday (2)
vyrjana (2)
mstekiel (2)
peendebak (2)

Top Labels

Issue Labels

rude user - did not follow instructions (1)

Pull Request Labels

Packages

Total packages: 2
Total downloads:
- pypi 945,719 last-month
Total docker downloads: 82,030

Total dependent packages: 289
(may contain duplicates)
Total dependent repositories: 694
(may contain duplicates)
Total versions: 59
Total maintainers: 1

pypi.org: lmfit

Least-Squares Minimization with Bounds and Constraints

Homepage: https://lmfit.github.io//lmfit-py/
Documentation: https://lmfit.github.io/lmfit-py/
License: other
Latest release: 1.3.4
published 6 months ago

Versions: 42
Dependent Packages: 256
Dependent Repositories: 661
Downloads: 945,719 Last month
Docker Downloads: 82,030

Rankings

Dependent packages count: 0.1%

Dependent repos count: 0.5%

Downloads: 0.7%

Docker downloads count: 1.1%

Average: 1.3%

Stargazers count: 2.1%

Forks count: 3.2%

Maintainers (1)

newville

Last synced: 4 months ago

conda-forge.org: lmfit

Homepage: http://lmfit.github.io/lmfit-py/
License: BSD-3-Clause
Latest release: 1.0.3
published about 4 years ago

Versions: 17
Dependent Packages: 33
Dependent Repositories: 33

Rankings

Dependent packages count: 2.1%

Dependent repos count: 6.4%

Average: 8.3%

Forks count: 11.3%

Stargazers count: 13.6%

Last synced: 5 months ago

lmfit

Science Score: 77.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.rst

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

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Past Year

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Issues and Pull Requests

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Packages

pypi.org: lmfit

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Maintainers (1)

conda-forge.org: lmfit

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