How to Install and Uninstall python311-nbval Package on openSuSE Tumbleweed
Last updated: November 08,2024
1. Install "python311-nbval" package
This guide let you learn how to install python311-nbval on openSuSE Tumbleweed
$
sudo zypper refresh
Copied
$
sudo zypper install
python311-nbval
Copied
2. Uninstall "python311-nbval" package
This is a short guide on how to uninstall python311-nbval on openSuSE Tumbleweed:
$
sudo zypper remove
python311-nbval
Copied
3. Information about the python311-nbval package on openSuSE Tumbleweed
Information for package python311-nbval:
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python311-nbval
Version : 0.11.0-1.1
Arch : noarch
Vendor : openSUSE
Installed Size : 139.8 KiB
Installed : No
Status : not installed
Source package : python-nbval-0.11.0-1.1.src
Upstream URL : https://github.com/computationalmodelling/nbval
Summary : A pytest plugin to validate Jupyter notebooks
Description :
The plugin adds functionality to py.test to recognise and collect
Jupyter notebooks. The intended purpose of the tests is to determine
whether execution of the stored inputs match the stored outputs of
the .ipynb file. Whilst also ensuring that the notebooks are running
without errors.
The tests were designed to ensure that Jupyter notebooks (especially
those for reference and documentation), are executing consistently.
Each cell is taken as a test, a cell that doesn't reproduce the
expected output will fail.
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python311-nbval
Version : 0.11.0-1.1
Arch : noarch
Vendor : openSUSE
Installed Size : 139.8 KiB
Installed : No
Status : not installed
Source package : python-nbval-0.11.0-1.1.src
Upstream URL : https://github.com/computationalmodelling/nbval
Summary : A pytest plugin to validate Jupyter notebooks
Description :
The plugin adds functionality to py.test to recognise and collect
Jupyter notebooks. The intended purpose of the tests is to determine
whether execution of the stored inputs match the stored outputs of
the .ipynb file. Whilst also ensuring that the notebooks are running
without errors.
The tests were designed to ensure that Jupyter notebooks (especially
those for reference and documentation), are executing consistently.
Each cell is taken as a test, a cell that doesn't reproduce the
expected output will fail.