kernel-scilab 1.0.3

Important

This project is a fork from the original scilab_kernel. The goal of forking was to perform an important refactor and improve PEP 8 compliance, but also to fix a few bugs related to executable path and figure display. Since the original kernel has not been updated for a few years and it did not work out-of-the-box with the latest version of Scilab, This was also a personal opportunity to learn a bit more about kernel implementation. There are still some open ends, specially in command parsing (Jupyter does not have a mimetype JavaScript file for Scilab language and here we continue using the one from Octave) and the criteria to display figures. Any help is welcomed!

Prerequisites

• Jupyter Notebook

• Scilab

Installation

pip install kernel_scilab

To use it, run one of:

ipython notebook
# In the notebook interface, select Scilab from the 'New' menu
ipython qtconsole --kernel scilab
ipython console --kernel scilab

This kernel is based on MetaKernel, which means it features a standard set of magics. For a full list of magics, run %lsmagic in a cell.

If Scilab is not added to the PATH environmental variable, then you need to specify the path to your Scilab executable by creating a SCILAB_EXECUTABLE environmental variable. Use the scilab-adv-cli executable if using a Posix-like OS, and WScilex-cli.exe if using Windows.

Configuration

The kernel can be configured by adding an kernel_scilab_config.py file to the jupyter config path. The ScilabKernel class offers plot_settings as a configurable traits. The available plot settings are shown in the following snippet.

cat ~/.jupyter/kernel_scilab_config.py
c.ScilabKernel.plot_settings = {
    "format": "svg",
    "backend": "inline",
    "size": "600,500",
}

Troubleshooting

If the kernel is not starting, try running the following from a terminal.

python -m kernel_scilab.check

Please include that output if opening an issue.

Advanced Installation Notes

We automatically install a Jupyter kernelspec when installing the python package. This location can be found using jupyter kernelspec list. If the default location is not desired, you can remove the directory for the kernel_scilab kernel, and install using python -m kernel_scilab install. See python -m kernel_scilab install --help for available options.