How to Install and Uninstall vspline-dev Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: November 22,2024

1. Install "vspline-dev" package

Here is a brief guide to show you how to install vspline-dev on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                vspline-dev
            
                    
    
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2. Uninstall "vspline-dev" package

Please follow the steps below to uninstall vspline-dev on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                vspline-dev
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the vspline-dev package on Ubuntu 20.10 (Groovy Gorilla)

Package: vspline-dev
Architecture: all
Version: 1.0.1-1
Priority: optional
Section: universe/math
Source: vspline
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Science Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1121
Depends: libvigraimpex-dev
Suggests: clang, vc-dev
Filename: pool/universe/v/vspline/vspline-dev_1.0.1-1_all.deb
Size: 228100
MD5sum: ea2f19b6d79b77b3da0fcf669ee43d34
SHA1: 1f09b40a34b020bb6790e277feba1c074b82d078
SHA256: 18479edc4250662801cb0a8318cb82acbda70dc3b730dd67111750c24c4110ad
SHA512: e1724512e45f4716b842050f59064437846a25f3d88744c6ef56a83f25339c9aa1297d914396cc510359a446abaa88ead5deddfe09276d4f3abff0ee221c9e4f
Homepage: https://bitbucket.org/kfj/vspline
Description-en: header-only C++ template library for uniform b-spline processing
vspline aims to be as fast as possible, yet mathematically precise.
It's main focus is processing of bulk raster data like sounds, images and
volumes. vspline can handle
.
- splines over real and integer data types and their aggregates:
- all '*xel' data, arbitrary number of channels (template argument)
- single, double precision and long doubles supported (template argument)
- a reasonable selection of boundary conditions
- spline degree up to 45 (runtime argument)
- arbitrary dimensionality of the spline (template argument)
- specialized code for 1D data
- multithreaded code (pthread)
- using the CPU's vector units if possible (like SSE, AVX/2)
.
On the evaluation side it provides
.
- evaluation of the spline at point locations in the defined range
- evaluation of vectorized arguments
- evaluation of the spline's derivatives
- factory functions to create evaluation functors
- specialized code for degrees 0 and 1 (nearest neighbour and n-linear)
- mapping of arbitrary coordinates into the defined range
- evaluation of nD arrays of coordinates ('remap' function)
- discrete-coordinate-fed remap function ('index_remap')
- generalized functor-based 'apply' and 'transform' functions
- restoration of the original data from the coefficients
.
To produce maximum performance, vspline has a fair amount of collateral code,
and some of this code may be helpful beyond vspline:
.
- multithreading with a thread pool
- efficient processing of nD arrays with multiple threads
- functional constructs using vspline::unary_functor
- nD forward-backward n-pole recursive filtering
- nD separable convolution
- efficient access to the b-spline basis functions
- precise precalculated constants (made with GNU GSL, BLAS and GNU GMP)
- many examples, ample comments
.
vspline uses a three-step approach to splines: the first step is coefficient
generation (including prefilering), the second step is generation of specific
functors providing evaluation, the third is application of these functors
to n-dimensional data.
data handling is done with vigra data types, using vigra::MultiArrayView
for handling strided nD arrays, and vigra::TinyVector for small aggregates.
vigra::MultiArrayView is a thin wrapper combining an array's address,
dimension, data type, stride and shape, it's easy to use it to 'package'
raw data. vspline handles vector formation from interleaved data, peeling
and re-interleaving automatically.
vspline optionally uses Vc. Without Vc present, it triggers the compiler's
autovectorization by producing deliberately vector-friendly inner loops.
vspline has been under development for several years and is extensively
tested for scope coverage, reliability, precision and performance.
Description-md5: 2ff3053e8014d3243506f49210750e8a