How to Install and Uninstall python311-lmfit Package on openSuSE Tumbleweed
Last updated: December 27,2024
1. Install "python311-lmfit" package
This guide let you learn how to install python311-lmfit on openSuSE Tumbleweed
$
sudo zypper refresh
Copied
$
sudo zypper install
python311-lmfit
Copied
2. Uninstall "python311-lmfit" package
In this section, we are going to explain the necessary steps to uninstall python311-lmfit on openSuSE Tumbleweed:
$
sudo zypper remove
python311-lmfit
Copied
3. Information about the python311-lmfit package on openSuSE Tumbleweed
Information for package python311-lmfit:
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python311-lmfit
Version : 1.2.2-1.3
Arch : noarch
Vendor : openSUSE
Installed Size : 884.9 KiB
Installed : No
Status : not installed
Source package : python-lmfit-1.2.2-1.3.src
Upstream URL : https://lmfit.github.io/lmfit-py/
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.
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python311-lmfit
Version : 1.2.2-1.3
Arch : noarch
Vendor : openSUSE
Installed Size : 884.9 KiB
Installed : No
Status : not installed
Source package : python-lmfit-1.2.2-1.3.src
Upstream URL : https://lmfit.github.io/lmfit-py/
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.