Octave vs. SciLab vs. Python

John W. Eaton continues to be heavily involved with GNU Octave development as seen in the commit log. The GNU Octave developer community has been making approximately yearly major releases. Octave is useful to:

  • run Matlab code to determine if it’s worth porting a function to Python
  • use Matlab function from Python with oct2py

Octave allows running Matlab “.m” code without changes for many tasks. “.m” code that calls proprietary toolboxes or advanced functions may not work in Octave.

I generally recommend learning and using Python unless one already has significant experience and a lot of code in Matlab. Practically what happens is that we choose a “good enough” language. What’s important is having a language that most other people are using so we can share results. The team might be building a radar or robot or satellite imager, and what’s being used in those domains is C, C++, Matlab, and Python.

I want a data analysis language that can scale from Cortex-M0 to Raspberry Pi to supercomputer. Yes, Matlab can use the Raspberry Pi as a target, works with software defined radio, etc. Will my collaborators have the “right” version of Matlab and the toolbox licenses to replicate my results? How can I debug 100 Raspberry Pi’s sitting out in a field? I need to use the GPIO, SDR, do machine learning processing and forward packets, perhaps using coroutines.

Since 2014, MicroPython has been rapidly growing in the number of MPU/SoC it supports. For just a few dollars, numerous IoT wireless modules can run an expansive subset of Python 3 including exception handling, coroutines, etc. For rapid prototyping, one can get the prototype SoC running remote sensing code passed to the cloud before the first planning meeting. Consider the higher-level languages ease of development and tools or inherent memory safety.

Like math systems such as Sage, SciLab allows integrating multiple numerical systems together. However, SciLab is its own language–with convenient syntax, and a Matlab to SciLab converter. SciLab, IDL, Mathematica, and Maple suffer from small audience size and limited number of third-party libraries.