How to Install and Uninstall gdl-mpfit Package on Ubuntu 21.04 (Hirsute Hippo)

Last updated: November 26,2024

1. Install "gdl-mpfit" package

Please follow the step by step instructions below to install gdl-mpfit on Ubuntu 21.04 (Hirsute Hippo)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                gdl-mpfit
            
                    
    
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2. Uninstall "gdl-mpfit" package

In this section, we are going to explain the necessary steps to uninstall gdl-mpfit on Ubuntu 21.04 (Hirsute Hippo):


    
        
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                sudo apt remove
            
                        
                gdl-mpfit
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the gdl-mpfit package on Ubuntu 21.04 (Hirsute Hippo)

Package: gdl-mpfit
Architecture: all
Version: 1.85+2017.01.03-4
Priority: optional
Section: universe/science
Source: mpfit
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Astro Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 773
Depends: gnudatalanguage (>= 0.9.5)
Filename: pool/universe/m/mpfit/gdl-mpfit_1.85+2017.01.03-4_all.deb
Size: 117988
MD5sum: 9657e996b60dc846f637c1d9c9d44436
SHA1: 3bb341a121da59311c414a629c6cc276ac0614e3
SHA256: 920bfc174f10c5ee35c3617dc6750c78d95369d7a774a21848ae88dcd5177c9f
SHA512: 80d24ba8d40ef93686e5d319cdc1494b034224b8f7b954bae4eff1f8844258be278069d92bb6798bd2c266f0dced659e4dc4b4b1fd4df60a0b3fc9e3a1192501
Homepage: https://cow.physics.wisc.edu/~craigm/idl/fitting.html
Description-en: Robust non-linear least squares curve fitting for GDL
These GDL routines provide a robust and relatively fast way to perform
least-squares curve and surface fitting. The algorithms are translated from
MINPACK-1, which is a rugged minimization routine found on Netlib, and
distributed with permission. This algorithm is more desirable than CURVEFIT
because it is generally more stable and less likely to crash than the
brute-force approach taken by CURVEFIT, which is based upon Numerical
Recipes.
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MPFIT has additional capabilities not found in CURVEFIT. Model parameters can
be "frozen" (that is, held constant during the fitting process). Simple
boundary constraints can be imposed on parameter values, which can be helpful
to keep parameters from becoming negative, for example. Please see the
documentation for the PARINFO keyword if you wish to use this facility.
Description-md5: ee8f9e8d972a6b3b9c388076df0e0b39