How to Install and Uninstall libiir-devel Package on openSuSE Tumbleweed
Last updated: December 25,2024
1. Install "libiir-devel" package
Please follow the step by step instructions below to install libiir-devel on openSuSE Tumbleweed
$
sudo zypper refresh
Copied
$
sudo zypper install
libiir-devel
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2. Uninstall "libiir-devel" package
This guide covers the steps necessary to uninstall libiir-devel on openSuSE Tumbleweed:
$
sudo zypper remove
libiir-devel
Copied
3. Information about the libiir-devel package on openSuSE Tumbleweed
Information for package libiir-devel:
-------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : libiir-devel
Version : 1.9.4-1.4
Arch : x86_64
Vendor : openSUSE
Installed Size : 115.7 KiB
Installed : No
Status : not installed
Source package : iir-1.9.4-1.4.src
Upstream URL : https://github.com/berndporr/iir1
Summary : DSP infinite impulse response realtime C++ filter library
Description :
An infinite impulse response (IIR) filter library for Linux, Mac OSX and Windows
which implements Butterworth, RBJ, Chebychev filters and can easily import coefficients generated by Python (scipy).
The filter processes the data sample by sample for realtime processing.
It uses templates to allocate the required memory so that it can run without any malloc / new commands.
Memory is allocated at compile time so that there is never the risk of memory leaks.
All realtime filter code is in the header files which guarantees efficient integration into the main program
and the compiler can optimise both filter code and main program at the same time.
-------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : libiir-devel
Version : 1.9.4-1.4
Arch : x86_64
Vendor : openSUSE
Installed Size : 115.7 KiB
Installed : No
Status : not installed
Source package : iir-1.9.4-1.4.src
Upstream URL : https://github.com/berndporr/iir1
Summary : DSP infinite impulse response realtime C++ filter library
Description :
An infinite impulse response (IIR) filter library for Linux, Mac OSX and Windows
which implements Butterworth, RBJ, Chebychev filters and can easily import coefficients generated by Python (scipy).
The filter processes the data sample by sample for realtime processing.
It uses templates to allocate the required memory so that it can run without any malloc / new commands.
Memory is allocated at compile time so that there is never the risk of memory leaks.
All realtime filter code is in the header files which guarantees efficient integration into the main program
and the compiler can optimise both filter code and main program at the same time.