Hacker News Reader

Comment by ptero | Hacker News Reader

It probably is, but new vs old is not a big decision driver for me. The things that matter to me for scientific computing languages are visualization tools (most critical to me, by far), libraries for standard functionality like optimization and signal processing (not critical as I can use another language for back-end processing) and price.

I will try Julia's visualization tools sometime, but if MATLAB is significantly faster will stick to it for now. Just a single user's data point, not claiming that this is a universal view.

View on HN

> Just a single user's data point, not claiming that this is a universal view.

Definitely not for me. As a matter of principle, I try to keep my computation and visualization tools strictly separate. Even, "artificially" separate, if you may. I would actually like octave more if it didn't provide a plot function. It would be closer to the spirit of the unix philosophy.

Your point is perfectly valid. I do separate my computation and visualization (sometimes both are MATLAB; just separate). For me the MATLAB is primarily an interactive visualization tool, although the main use case for most engineers I am sure is to use MATLAB for its scientific computation functionality.

ggplot?

If your benchmark is plotting 2 million points, it may also be slow. However, I find its ability to rapidly prototype tons of different visualizations useful. I try to avoid needing to plot 2 million points anyway.

I often need to visualize data and unfortunately, plotting and working with many millions of points is a key capability. For example, we might have an expensive piece of hardware that we are using and need to understand if an issue we are seeing is a problem in our user software, a firmware we wrote to interface with it, or comes from the hardware itself.

Having technical representatives from each team (each sure that his team has nothing to do with it; can they please go back to their offices now) in a room looking at the same instrumentation data with ability to quickly dig into various features and subsets is a huge help for such problem isolation. At least in my experience it depends on fast interactive plotting and filtering of large datasets.

Reply on news.ycombinator.com