How to Install and Uninstall python38-lmfit Package on openSuSE Tumbleweed

Last updated: May 05,2024

Deprecated! Installation of this package may no longer be supported.

1. Install "python38-lmfit" package

This tutorial shows how to install python38-lmfit on openSuSE Tumbleweed


    
        
            $
        

        
            
                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                python38-lmfit
            
                    
    
    Copied

2. Uninstall "python38-lmfit" package

Here is a brief guide to show you how to uninstall python38-lmfit on openSuSE Tumbleweed:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                python38-lmfit
            
                    
    
    Copied

3. Information about the python38-lmfit package on openSuSE Tumbleweed

Information for package python38-lmfit:
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Repository : openSUSE-Tumbleweed-Oss
Name : python38-lmfit
Version : 1.0.2-1.5
Arch : noarch
Vendor : openSUSE
Installed Size : 921,1 KiB
Installed : No
Status : not installed
Source package : python-lmfit-1.0.2-1.5.src
Summary : Least-Squares Minimization with Bounds and Constraints
Description :
A library for least-squares minimization and data fitting in
Python. Built on top of scipy.optimize, lmfit provides a Parameter object
which can be set as fixed or free, can have upper and/or lower bounds, or
can be written in terms of algebraic constraints of other Parameters. The
user writes a function to be minimized as a function of these Parameters,
and the scipy.optimize methods are used to find the optimal values for the
Parameters. The Levenberg-Marquardt (leastsq) is the default minimization
algorithm, and provides estimated standard errors and correlations between
varied Parameters. Other minimization methods, including Nelder-Mead's
downhill simplex, Powell's method, BFGS, Sequential Least Squares, and
others are also supported. Bounds and constraints can be placed on
Parameters for all of these methods.
In addition, methods for explicitly calculating confidence intervals are
provided for exploring minmization problems where the approximation of
estimating Parameter uncertainties from the covariance matrix is
questionable.