Native Scilab Kernel for Jupyter

Jupyter project provides a way to perform scientific/mathematical computations with several different programming languages using the same user interface. It achieves that by distributing the processing between a front-end (Jupyter clients, like the web based Jupyter Notebook) and a back-end (Jupyter kernels, written in Python, R, Java, etc.), that communicate among themselves with standardized format messages, transmitted through ZeroMQ connection.

Besides the ability to switch across different kernels from the same common interface, another advantage of this approach is the possibility to run the two software components on different machines, e.g., offloading the heavier backend processing to a remote server while sending commands and visualizing data from a mobile device running the frontend.

For Scilab, there is currently a kernel that uses a compatibility layer with file access data exchange and numpy calls to communicate with clients through the Python reference implementation.

This Google Summer of Code proposal aims to adapt or rewrite the current suboptimal solution, to make it a truly native kernel, linking ZeroMQ messages directly to Scilab API.