scientific-computing

I was doing the mathematical calculations with python or octave because of availability of really nice functions and libraries at hand. But recently I gained interest in ruby and I wonder if there is an equivalent in Ruby to the numpy, scipy in Python for scientific programming. Specifically, I'm looking something that I can draw plots as in matplotlib and do mathematical, algebraic calculations quickly as in numpy and scipy. Linear algebra is at the heart of most large-scale scientific computing. LAPACK is the gold standard for linear algebra libraries, first written in FORTRAN. There's a

C# seems to show some promise for scientific computing, but I found very little about one plotting 2D graphs, which is very important both for science student and scientists. Is there a reliable, free, way to create publication quality 2D plot with C# ? And the capacity to save to several formats (png, eps, ...). Something similar to Python's matplotlib ? There is Oxyplot which I recommend and also there is Live-Charts (see update2). Oxyplot has packages for WPF, Metro, Silverlight, Windows Forms, Avalonia UI, XWT. Besides graphics it can export to Svg, Pdf, OpenXml, etc. And it even supports

Is there an open-source alternative to MATLAB's fmincon function for constrained linear optimization? I'm rewriting a MATLAB program to use Python / NumPy / SciPy and this is the only function I haven't found an equivalent to. A NumPy-based solution would be ideal, but any language will do. Is your problem convex? Linear? Non-linear? I agree that SciPy.optimize will probably do the job, but fmincon is a sort of bazooka for solving optimization problems, and you'll be better off if you can confine it to one of the categories below (in increasing level of difficulty to solve efficiently) Linear

I am curious as to how F# performance compares to C++ performance? I asked a similar question with regards to Java, and the impression I got was that Java is not suitable for heavy numbercrunching. I have read that F# is supposed to be more scalable and more performant, but how is this real-world performance compares to C++? specific questions about current implementation are: How well does it do floating-point? Does it allow vector instructions how friendly is it towards optimizing compilers? How big a memory foot print does it have? Does it allow fine-grained control over memory locality?

In some scientific environments, you often cannot go without FORTRAN as most of the developers only know that idiom, and there is lot of legacy code and related experience. And frankly, there are not many other cross-platform options for high performance programming (C++ would do the task, but the syntax, zero-starting arrays, and pointers are not compatible with some people). So, let's assume a new project must use Fortran 90, but I want to build the most modern software architecture out of it, while being compatible with most recent compilers (Intel ifort, but also including the Sun/HP/IBM

I'm currently implementing a complex microbial food-web in Python using SciPy.integrate.ode . I need the ability to easily add species and reactions to the system, so I have to code up something quite general. My scheme looks something like this: class Reaction(object): def __init__(self): #stuff common to all reactions def __getReactionRate(self, **kwargs): raise NotImplementedError ... Reaction subclasses that ... implement specific types of reactions class Species(object): def __init__(self, reactionsDict): self.reactionsDict = reactionsDict #reactionsDict looks like {'ReactionName'