How to Install and Uninstall python3-catch22.x86_64 Package on Fedora 39
Last updated: November 25,2024
1. Install "python3-catch22.x86_64" package
This is a short guide on how to install python3-catch22.x86_64 on Fedora 39
$
sudo dnf update
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$
sudo dnf install
python3-catch22.x86_64
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2. Uninstall "python3-catch22.x86_64" package
This tutorial shows how to uninstall python3-catch22.x86_64 on Fedora 39:
$
sudo dnf remove
python3-catch22.x86_64
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$
sudo dnf autoremove
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3. Information about the python3-catch22.x86_64 package on Fedora 39
Last metadata expiration check: 4:21:22 ago on Thu Mar 7 17:44:52 2024.
Available Packages
Name : python3-catch22
Version : 0.4.0
Release : 13.fc39
Architecture : x86_64
Size : 55 k
Source : catch22-0.4.0-13.fc39.src.rpm
Repository : updates
Summary : CAnonical Time-series CHaracteristics
URL : https://github.com/chlubba/catch22
License : GPL-3.0-or-later
Description : catch22 is a collection of 22 time-series features coded in C that can be run
: from Python, R, Matlab, and Julia. The catch22 features are a high-performing
: subset of the over 7000 features in hctsa.
:
: Features were selected based on their classification performance across a
: collection of 93 real-world time-series classification problems, as described
: in our open-access paper:
:
: - Lubba et al. (2019). catch22: CAnonical Time-series CHaracteristics
: (https://doi.org/10.1007/s10618-019-00647-x)
:
: The computational pipeline used to generate the catch22 feature set is in the
: op_importance (https://github.com/chlubba/op_importance) repository.
:
: For catch22-related information and resources, including a list of publications
: using catch22, see the catch22 wiki (https://github.com/chlubba/catch22/wiki).
Available Packages
Name : python3-catch22
Version : 0.4.0
Release : 13.fc39
Architecture : x86_64
Size : 55 k
Source : catch22-0.4.0-13.fc39.src.rpm
Repository : updates
Summary : CAnonical Time-series CHaracteristics
URL : https://github.com/chlubba/catch22
License : GPL-3.0-or-later
Description : catch22 is a collection of 22 time-series features coded in C that can be run
: from Python, R, Matlab, and Julia. The catch22 features are a high-performing
: subset of the over 7000 features in hctsa.
:
: Features were selected based on their classification performance across a
: collection of 93 real-world time-series classification problems, as described
: in our open-access paper:
:
: - Lubba et al. (2019). catch22: CAnonical Time-series CHaracteristics
: (https://doi.org/10.1007/s10618-019-00647-x)
:
: The computational pipeline used to generate the catch22 feature set is in the
: op_importance (https://github.com/chlubba/op_importance) repository.
:
: For catch22-related information and resources, including a list of publications
: using catch22, see the catch22 wiki (https://github.com/chlubba/catch22/wiki).