Introduction to High-Performance Scientific Computing

Re:Introduction to High-Performance Scientific Computing by jarvist on Tuesday January 24, 2017 @03:19AM (#13469667)

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/

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Re:Introduction to High-Performance Scientific Computing by leephillips on Tuesday January 24, 2017 @09:35AM (#13471573)

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.

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Re:Introduction to High-Performance Scientific Computing by metaobject on Tuesday January 24, 2017 @10:40AM (#13472187)

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?

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Re:Introduction to High-Performance Scientific Computing by leephillips on Tuesday January 24, 2017 @10:46AM (#13472240)

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.

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Re:Introduction to High-Performance Scientific Computing by n00b101 on Tuesday January 24, 2017 @06:02PM (#13476615)

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

What is the performance like on that?

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Re:Introduction to High-Performance Scientific Computing by notalaser on Tuesday January 24, 2017 @05:09AM (#13470111)

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.

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