How to Install and Uninstall python3-lmfit Package on Ubuntu 21.10 (Impish Indri)

Last updated: May 13,2024

1. Install "python3-lmfit" package

This guide covers the steps necessary to install python3-lmfit on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                python3-lmfit
            
                    
    
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2. Uninstall "python3-lmfit" package

Please follow the steps below to uninstall python3-lmfit on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                python3-lmfit
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the python3-lmfit package on Ubuntu 21.10 (Impish Indri)

Package: python3-lmfit
Architecture: all
Version: 1.0.1-6
Priority: extra
Section: universe/python
Source: lmfit-py
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Science Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 344
Depends: python3-numpy, python3-asteval, python3-scipy, python3-uncertainties (>= 3.0.2.github~), python3:any
Suggests: python-lmfit-doc
Filename: pool/universe/l/lmfit-py/python3-lmfit_1.0.1-6_all.deb
Size: 68488
MD5sum: 2ed7043de9c2147b2f266a9b507fef35
SHA1: 043dda3a88644863cc707f5b98049704b9c1869e
SHA256: e155e7de741ed1ea31681811aff253e6139e81990dbc540435b363babf7e9664
SHA512: da4c36a0720fd8a2d14855216165e394f5ee053e9d2807bd8f4899b707eea61be3a6a320339c2061e14db5f54d377c710ed7e0dae3acd8ff37d91ea68231d2d0
Homepage: https://lmfit.github.io/lmfit-py/
Description-en: Least-Squares Minimization with Constraints (Python 3)
The lmfit Python package provides a simple, flexible interface to
non-linear optimization or curve fitting problems. The package
extends the optimization capabilities of scipy.optimize by replacing
floating pointing values for the variables to be optimized with
Parameter objects. These Parameters can be fixed or varied, have
upper and/or lower bounds placed on its value, or written as an
algebraic expression of other Parameters.
.
The principal advantage of using Parameters instead of simple
variables is that the objective function does not have to be
rewritten to reflect every change of what is varied in the fit, or
what relationships or constraints are placed on the Parameters. This
means a scientific programmer can write a general model that
encapsulates the phenomenon to be optimized, and then allow user of
that model to change what is varied and fixed, what range of values
is acceptable for Parameters, and what constraints are placed on the
model. The ease with which the model can be changed also allows one
to easily test the significance of certain Parameters in a fitting
model.
.
The lmfit package allows a choice of several optimization methods
available from scipy.optimize. The default, and by far best tested
optimization method used is the Levenberg-Marquardt algorithm from
MINPACK-1 as implemented in scipy.optimize.leastsq. This method
is by far the most tested and best support method in lmfit, and much
of this document assumes this algorithm is used unless explicitly
stated. An important point for many scientific analysis is that this
is only method that automatically estimates uncertainties and
correlations between fitted variables from the covariance matrix
calculated during the fit.
.
A few other optimization routines are also supported, including
Nelder-Mead simplex downhill, Powell's method, COBYLA, Sequential
Least Squares methods as implemented in scipy.optimize.fmin, and
several others from scipy.optimize. In their native form, some of
these methods setting allow upper or lower bounds on parameter
variables, or adding constraints on fitted variables. By using
Parameter objects, lmfit allows bounds and constraints for all of
these methods, and makes it easy to swap between methods without
hanging the objective function or set of Parameters.
.
Finally, because the approach derived from MINPACK-1 usin the
covariance matrix to determine uncertainties is sometimes questioned
(and sometimes rightly so), lmfit supports methods to do a brute
force search of the confidence intervals and correlations for sets of
parameters.
.
This is the Python 3 version of the package.
Description-md5: b173ac402654d4372a51c519aaac81c5