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Comment by acidburnNSA | Hacker News Reader

Looks like a good collection of important topics. The tutorials feel a bit 2005 to me. GNUplot and svn? In my scientific universe it's all matplotlib and git these days. Maybe I'm unique.

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Gnuplot is still used quite a lot in my area. You need to work more to make something aesthetically pleasing, but it's generally a better fit to plotting large amounts of data coming from another code. I've also been quite badly burnt by Matplotlib scripts no longer producing the same plot with changes in the library. Gnuplot version 5+ is nicer to use and has some pretty powerful features.

This blog has been a good cookbook reference for how to use it in a modern way: http://www.gnuplotting.org/

The nice thing about Ggnuplot is that it's extremely easy to connect it with anything, since it's basically its own language. Python has made a lot of inroads into the scientifc computing field but it's by no means the only one. This is a field where twenty year-old Fortran libraries are still relevant.

SVN also has the important advantage of being relatively easy to pick up by someone who's not a programmer. It would not be my first choice for a version control system now that we have Mercurial and git (frankly, it wasn't not even back when our only affordable alternatives were CVS and RCS), but it's very easy to teach it.

Gnuplot can handle data files with millions of points, that some users have reported to cause Matplotlib to crash. It's actually easy to make the plots look good. It can be controlled through a socket interface from any programming language, and seemlessly integrates with LaTeX.

We're about to start rolling out chapters from my book, which covers the latest version:

https://alogus.com/publishing/gnuplot5/

EDITed to reflect comment below.

Hmmm, I've never had mpl crash and we routinely plot data sets with 10s of millions of points. We do a mixture of 2D scatter plots. 1D/2D histograms, and a few 3D plots. What type of plots are you generating? What type of data?

I never had the problem myself, but have seen a handful of user reports in mailing lists and SO. Perhaps recent releases are better performing. I used to use mpl regularly and found it very capable, but never liked the API.

I've edited my comment to make it more accurate.

> we routinely plot data sets with 10s of millions of points

What is the performance like on that?

Another big surprise to me -- Gnuplot is NOT free/open source software in the GNU sense: "Gnuplot is freeware in the sense that you don’t have to pay for it. However it is not freeware in the sense that you would be allowed to distribute a modified version of your gnuplot freely. Please read and accept the modification and redistribution terms in the Copyright file." (see http://www.gnuplot.info/faq/faq.html) I had to remove Gnuplot from SageMath because of their GPL-incompatible license.

Gnuplot is free enough to be included in Debian. But no, not GPL licensed.

GLE can produce some decent plots and is BSD-licensed, but the DSL used to create the plots can be cumbersome. http://glx.sourceforge.net/examples/3dplots/index.html

Gnuplot is more customizable than matplotlib, more powerful, fastar and handles larger datasets, exports seamlessly to images, LaTeX, PDF, HTML5, animated gifs, among others, etc. It's a better tool than matplotlib, for me at least.

That it takes more effort to produce a "beautiful" output is myth, in my experience. People used to say that in large part because up to gnuplot 4 the default colors were ugly primary colors. On gnuplot 5 and above, it's no longer the case. Plus, you can customize the default colors/styles by editing the .gnuplot file. I've done so: https://ghostbin.com/paste/pvj5m

1. Do you know how much time it takes to keep a 600 page book up to the minute? 2. But yeah. I'm going to roll the tutorials into a volume of their own.

It really depends on the size and the age of the code you're working with. We, for instance, use a code package that was born in 2005 (and continuously developed since), so it lives in SVN. For all the heavy-duty scientific visualization, we use VisIt [1], because the size and dimensionality of the data require a special-purpose visualization package.

To me, "gnuplot or matplotlib" is a little beside the point - if we're using one of those it's for something quick 'n dirty, or for a relatively simple plot of summary data.

[1] https://wci.llnl.gov/simulation/computer-codes/visit

I've tried using VisIt (and Paraview) a few times but have never really dedicated myself to it. It looks so nice and seems so powerful. I just haven't been able to get it working in a way that allows me to quickly and quantitatively (with data labels) explore multiple axial levels of a nuclear reactor state and step through time to see how the shuffling algorithms did and whatnot so I keep sticking to this specialized .NET thing an intern wrote 7 years ago. I'm sure VisIt can do it, I think I just need an expert to teach me or something.

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