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posted by janrinok on Monday June 01 2015, @07:28PM   Printer-friendly
from the plot-this dept.

I wasn't aware of the GNU Octave project until I saw a post on Reddit that it had hit version 4.0.0. If you're not familiar with it either, here's a brief overview:

GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.

So why is this exciting? Aside from a Windows installer for all you people too lazy to switch to GNU/Linux, it apparently finally got a GUI (kind of a must for "modern" software):

Octave 4.0 is a major new release with many new features, including a graphical user interface, support for classdef object-oriented programming, better compatibility with Matlab, and many new and improved functions.

You can also get the full list of user-visible changes here.

Share and enjoy!

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  • (Score: 0) by Anonymous Coward on Monday June 01 2015, @09:21PM

    by Anonymous Coward on Monday June 01 2015, @09:21PM (#190882)

    I know you Julia guys have a huge R chip on your shoulder (pretty much every web page has something to the effect: "See, we really are better than R!"), but geez, why not grow up and grow a pair instead of modding me down?

    I know. How about you just STFU until you've stopped fucking around with basic functionality and you actually are something useful to use? Want to do some curve fitting? Well, you need to call "curve_fit" from the Optim package. Oops! My bad! It is in the LsqFit package, and we've decided to change what the function returns. I hope that didn't fuck up any of your code. It's easier for me to just slap it together in Octave than to try to sort out your dicking around. What, is Julia written in PHP or something, that it has to fundamentally break shit on version releases?

  • (Score: 2) by physicsmajor on Monday June 01 2015, @10:00PM

    by physicsmajor (1471) on Monday June 01 2015, @10:00PM (#190905)

    If you like Julia but want more maturity/stability, check out the Scientific Python Stack (SciPy, NumPy, Pandas, Simpy, Matplotlib, and Nose) plus scikit-* for more specialized tasks like machine learning or image analysis.

    • (Score: 3, Funny) by kaszz on Monday June 01 2015, @11:42PM

      by kaszz (4211) on Monday June 01 2015, @11:42PM (#190954) Journal

      And if you don't like Python tabulation nazism? ;-)

      • (Score: 4, Interesting) by physicsmajor on Monday June 01 2015, @11:58PM

        by physicsmajor (1471) on Monday June 01 2015, @11:58PM (#190961)

        For a language that deliberately decided to do away with braces or other markings of scope, using tabulation is just... prudent in Python. PEP8 isn't always a perfect standard, but simply having a standard makes the majority of code more readable/accessible/maintainable to the rest of the community.

        I will admit I have a soft spot for standards that tell you to ignore the standard if you think it makes things worse ;)

        I do understand those who don't like it, but personally I find the result incredibly clean and readable. The only valid criticism I've heard is that one can get "lost" with multiple tab levels, but with a quality editor you get a subtle mark at each stop. I know I'd much rather track indentation than attempt to hunt through and verify brace closures...

    • (Score: 3, Informative) by Anonymous Coward on Tuesday June 02 2015, @03:00PM

      by Anonymous Coward on Tuesday June 02 2015, @03:00PM (#191158)

      Scipy/Numpy/etc is really useful. However, the Julia syntax is much slicker for numerical tasks, since that was the original purpose. I've been using Julia for quite a bit of my own work. Since it can be nearly as fast as C, development is sped up by not having to write things in Fortran/C.

      Critically, there is a package, PyCall, to call Python code. To do a simple plot with matplotlib:

      using PyCall
      @pyimport matplotlib.pyplot as plt

      You can barely tell the matplotlib parts are calling out to python. Lisp-style homoiconicity really allows for some pretty cool stuff.