Jupyter Notebook and GNU Octave.

To install using pip:

pip install octave_kernel

To install using conda:

conda config --add channels conda-forge
conda install octave_kernel
conda install texinfo # For the inline documentation (shift-tab) to appear.

We require the octave-cli or octave executable to run the kernel. Add that executable's directory to the PATH environment variable or create the environment variable OCTAVE_EXECUTABLE to point to the executable itself. Note that on Octave 5+ on Windows, the executable is in "Octave-x.x.x.x\mingw64\bin".

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, remove the directory for the octave kernel, and install using python -m octave_kernel install. See python -m octave_kernel install --help for available options.

To use the kernel, run one of:

jupyter notebook  # or ``jupyter lab``, if available
# In the notebook interface, select Octave from the 'New' menu
jupyter qtconsole --kernel octave
jupyter console --kernel octave

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

A sample notebook is available online.

The kernel can be configured by adding an octave_kernel_config.py file to the jupyter config path. The OctaveKernel class offers plot_settings, inline_toolkit, kernel_json, and cli_options as configurable traits. The available plot settings are: 'format', 'backend', 'width', 'height', 'resolution', and 'plot_dir'.

cat ~/.jupyter/octave_kernel_config.py
# use Qt as the default backend for plots
c.OctaveKernel.plot_settings = dict(backend='qt')

The path to the Octave kernel JSON file can also be specified by creating an OCTAVE_KERNEL_JSON environment variable.

The command line options to Octave can also be specified with an OCTAVE_CLI_OPTIONS environment variable. The cli options be appended to the default options of --interactive --quiet --no-init-file. Note that the init file is explicitly called after the kernel has set more off to prevent a lockup when the pager is invoked in ~/.octaverc.

The inline toolkit is the graphics_toolkit used to generate plots for the inline backend. It defaults to qt. The different backend can be used for inline plotting either by using this configuration or by using the plot magic and putting the backend name after inline:, e.g. plot -b inline:fltk.

The octave_kernel supports running on Linux, MacOS, or Windows. On Linux, it supports Octave installed using apt-get, flatpak, or snap. There is no additional configuration required to use flatpak or snap.

If the kernel does not start, run the following command from a terminal:

python -m octave_kernel.check

This can help diagnose problems with setting up integration with Octave. If in doubt, create an issue with the output of that command.

If the kernel is not listed as an available kernel, first try the following command:

python -m octave_kernel install --user

If the kernel is still not listed, verify that the following point to the same version of python:

which python  # use "where" if using cmd.exe
which jupyter

On newer versions of Octave, the qt graphics toolkit is only available when running with a display enabled. By default, this kernel launches octave-cli, which supports only gnuplot (or fltk in some cases) and has limited inline plotting support.

To use the qt backend for inline plots, you must run the full octave executable instead. Set the OCTAVE_EXECUTABLE environment variable (assuming octave is on the PATH, otherwise use the full path to the executable):

export OCTAVE_EXECUTABLE=octave

Or configure it permanently in ~/.jupyter/octave_kernel_config.py:

c.OctaveKernel.executable = "octave"

On a remote system without a display, you can use xvfb-run to provide a virtual framebuffer:

export OCTAVE_EXECUTABLE="xvfb-run octave"

Or in the config file:

c.OctaveKernel.executable = "xvfb-run octave"

Specify a different format using the %plot -f <backend> magic or using a configuration setting. On some systems, the default 'png' produces a black plot. On other systems 'svg' produces a black plot.

To install from a git checkout run:

pip install -e .