Software & AppsOperating SystemLinux

How To Fix Scilab 6.0.1 Launch Error on Ubuntu 18.04 LTS

Ubuntu 10

Scilab is a popular open-source software for numerical computation that provides a powerful computing environment for engineering and scientific applications. However, users may encounter issues while launching Scilab 6.0.1 on Ubuntu 18.04 LTS. This article will guide you through different methods to fix these issues.

Method 1: Repository-based Method

This method involves installing Scilab directly from the Ubuntu repositories using the apt-get command. However, due to a bug, this method may not work properly. Here are the steps:

sudo apt-get update
sudo apt-get install scilab

The sudo apt-get update command updates the package lists for upgrades and new package installations. The sudo apt-get install scilab command installs Scilab. If Scilab fails to start or gives an error related to the missing libjava.so file, you can report the bug to the Ubuntu community.

Method 2: Binary Download Method

This method involves removing the deb-packaged version of Scilab and installing the latest binary archive. Here are the steps:

First, remove the deb-packaged version of Scilab:

sudo apt-get purge scilab scilab scilab-cli scilab-data scilab-doc scilab-full-bin scilab-include scilab-minimal-bin scilab-sivp scilab-test
sudo apt-get autoremove

The sudo apt-get purge command removes the packages and their configuration files. The sudo apt-get autoremove command removes packages that were automatically installed to satisfy dependencies for other packages and are now no longer needed.

Next, download the latest binary archive from the official Scilab website and install it:

mkdir ~/Software
cd ~/Software
wget https://www.scilab.org/download/6.1.1/scilab-6.1.1.bin.linux-x86_64.tar.gz
tar -xzf scilab-6.1.1.bin.linux-x86_64.tar.gz
cd scilab-6.1.1
echo "PATH=$PATH:/home/$USER/Software/scilab-6.1.1/bin" >> ~/.bashrc
echo "PATH=$PATH:/home/$USER/Software/scilab-6.1.1/bin" >> ~/.profile
mkdir -p ~/.local/share/applications
cp -a ~/Software/scilab-6.1.1/share/{icons,applications,mime} ~/.local/share/
update-mime-database ~/.local/share/mime/
update-menus

The wget command downloads the Scilab binary archive. The tar -xzf command extracts the archive. The echo commands add the Scilab binary directory to the PATH environment variable in the bashrc and profile files. The cp command copies the Scilab icons, applications, and mime files to the local share directory. The update-mime-database command updates the MIME database, and the update-menus command updates the system menus.

Please note that you should install the build-essential package if you plan to run Xcos Modelica simulations.

Method 3: Flatpak Method

Scilab 6.1.1 is available on Flathub, and you can install it using Flatpak. Here are the steps:

flatpak install flathub org.scilab.Scilab

The flatpak install command installs the Scilab Flatpak from Flathub. To run Scilab, use the following command:

flatpak run org.scilab.Scilab

Method 4: AppImage Download Method

This method involves downloading the latest AppImage release of Scilab. Here are the steps:

mkdir ~/Software
cd ~/Software
wget https://github.com/davidcl/Scilab.AppDir/releases/download/6.1.0-1/Scilab-x86_64.AppImage
chmod +x Scilab-x86_64.AppImage
ln -s Scilab-x86_64.AppImage scilab

The wget command downloads the Scilab AppImage. The chmod +x command makes the AppImage executable. The ln -s command creates a symbolic link to the AppImage.

Next, add the AppImage to your PATH:

echo "PATH=$PATH:/home/$USER/Software/" >> ~/.bashrc
echo "PATH=$PATH:/home/$USER/Software/" >> ~/.profile

The echo commands add the directory containing the Scilab AppImage to the PATH environment variable in the bashrc and profile files.

This method will make Scilab fully functional, but note that there may be no MIME associations yet.

These solutions are based on the provided context and may not work in all cases. It is recommended to try them one by one and see which one works best for your specific setup. Remember to always backup your data before making any significant changes to your system.

Can I install Scilab on other Linux distributions?

Yes, Scilab can be installed on various Linux distributions. However, the installation process may vary. It is recommended to refer to the official Scilab documentation or the package manager of your specific distribution for installation instructions.

How can I uninstall Scilab?

To uninstall Scilab, you can use the package manager of your Linux distribution. For example, on Ubuntu, you can run the command sudo apt-get remove scilab to remove Scilab and its associated packages. Additionally, you can run sudo apt-get autoremove to remove any remaining dependencies that are no longer needed.

Can I use Scilab for commercial purposes?

Yes, Scilab can be used for commercial purposes. It is released under the terms of the CeCILL license, which allows free use, modification, and distribution of the software, even for commercial purposes. However, it is always recommended to review and comply with the specific license terms and conditions.

How can I update Scilab to a newer version?

To update Scilab to a newer version, you can follow the installation method mentioned in this article for the specific version you want to update to. Make sure to remove the older version of Scilab before installing the new version. Alternatively, you can check the official Scilab website or the package manager of your Linux distribution for any available updates or instructions on updating Scilab.

Can I use Scilab on Windows or macOS?

Yes, Scilab is available for Windows and macOS as well. You can visit the official Scilab website and download the appropriate installer for your operating system. The installation process may differ from the Linux installation methods mentioned in this article.

Does Scilab support parallel computing?

Yes, Scilab has built-in support for parallel computing. It provides parallel programming constructs and functions that allow you to distribute computations across multiple CPU cores or even across multiple machines in a cluster. You can refer to the Scilab documentation for more information on how to utilize parallel computing capabilities in Scilab.

Can I use Scilab for machine learning or deep learning tasks?

While Scilab is primarily focused on numerical computation and scientific computing, it may not have the same extensive machine learning or deep learning libraries and frameworks as dedicated platforms like Python with TensorFlow or PyTorch. However, Scilab can still be used for basic machine learning tasks by utilizing its numerical computation capabilities and implementing algorithms from scratch.

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