How to Install and Uninstall python39-nbval Package on openSuSE Tumbleweed
Last updated: December 25,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python39-nbval" package
Learn how to install python39-nbval on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python39-nbval
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2. Uninstall "python39-nbval" package
Please follow the guidelines below to uninstall python39-nbval on openSuSE Tumbleweed:
$
sudo zypper remove
python39-nbval
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3. Information about the python39-nbval package on openSuSE Tumbleweed
Information for package python39-nbval:
---------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python39-nbval
Version : 0.10.0-1.9
Arch : noarch
Vendor : openSUSE
Installed Size : 112.1 KiB
Installed : No
Status : not installed
Source package : python-nbval-0.10.0-1.9.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 : python39-nbval
Version : 0.10.0-1.9
Arch : noarch
Vendor : openSUSE
Installed Size : 112.1 KiB
Installed : No
Status : not installed
Source package : python-nbval-0.10.0-1.9.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.