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

Last updated: May 18,2024

1. Install "python-lmfit-doc" package

Learn how to install python-lmfit-doc on Ubuntu 21.10 (Impish Indri)


    
        
            $
        

        
            
                sudo apt update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt install
            
                        
                python-lmfit-doc
            
                    
    
    Copied

2. Uninstall "python-lmfit-doc" package

This guide covers the steps necessary to uninstall python-lmfit-doc on Ubuntu 21.10 (Impish Indri):


    
        
            $
        

        
            
                sudo apt remove
            
                        
                python-lmfit-doc
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the python-lmfit-doc package on Ubuntu 21.10 (Impish Indri)

Package: python-lmfit-doc
Architecture: all
Version: 1.0.1-6
Built-Using: sphinx (= 3.5.4-1)
Priority: extra
Section: universe/doc
Source: lmfit-py
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Science Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 3315
Depends: libjs-mathjax, libjs-sphinxdoc (>= 2.4.3-5~)
Filename: pool/universe/l/lmfit-py/python-lmfit-doc_1.0.1-6_all.deb
Size: 1507160
MD5sum: 0c1db3c79a9f2be2176d057acf8f00f1
SHA1: fce4c0e4fc0b7bcb703713aaf96af9a7791bf0a3
SHA256: 20d2d1c94f615918c5a4dd7b0725ba1bc24ba2a1239cdccbcda42528b6959814
SHA512: 64fe99142440ec9f97a68797764572470d022db22892733c991e885b0c3c8021e0863dc1c6c3f01098f6e3451f5b3b63b504c3953eadf1cf8e88f6481020c7d7
Homepage: https://lmfit.github.io/lmfit-py/
Description-en: Least-Squares Minimization with Constraints (Documentation)
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 common documentation package.
Description-md5: d2b76cd612dbe4e7b83254d034b16642