Hello,
Thanks for the answer. I quite get the drift of your explanations. But my question was looking for a specific practical answer. As my codes involve a lot of mathematical formulae and some of them require ultra-high precision, hence I was looking for an answer that either Fortran is better suited or Python is better at it. One of the example, I'm thinking about is the commoving distance of redshift in the universe and hence to calculate the radius of the observable universe.see Hogg's paper on it: http://arxiv.org/abs/astro-ph/9905116 This is a mathematical integration of several parameters in cosmology. This particular calculation requires the best precision you can get because by reducing it, you get different answers for the age of the universe and hence the radius of the universe. Example: S for integration S 0 to z ..> dz'/SQRT(omega_radiation (1+z)^4 + omega_matter (1+z)^3 + omega_curvature (1+z)^2 + Omega_dark energy) S 0 to 1e+31....> 1/sqrt(.000086 *(1+z)^4 + .282 * (1+z)^3 - .000086 *(1+z)^2 + .718) if you try this integration you will get completely wrong numbers on computing devices that do not possess ultra-high precision and accuracy. I had to use mpmath with precision of 250 and integration method called Tanh Sinh Quadrature with accuracy 120 to get the desired results. I checked the answer with Mathematica and they concur. But the calculation is done in about 3 seconds on my laptop with intel core i5 at 2.5 GHz with 4 GB RAM. So can Fortran crunch 250 digits numbers in an integration formula under 3 seconds with the same computing parameters as above? Or is Python better at it? Thanks Vick -----Original Message----- From: Laura Creighton [mailto:l...@openend.se] Sent: Saturday, 11 April, 2015 20:57 To: William Ray Wing Cc: Vick; webmas...@python.org; tutor@python.org; l...@openend.se Subject: Re: [Tutor] On learning Fortran and C++ for scientific computing These days, most important scientific libraries are wrapped so that you call call them directly from python. Google for "python bindings <name of the library you want>" and see if you get any hits. If you have a library that doesn't have python bindings, you can probably make them. Start reading here: <http://intermediate-and-advanced-software-carpentry.readthedocs.org/en/late st/c++-wrapping.html> http://intermediate-and-advanced-software-carpentry.readthedocs.org/en/lates t/c++-wrapping.html <http://scicomp.stackexchange.com/questions/2283/which-package-should-i-use- to-wrap-modern-fortran-code-with-python> http://scicomp.stackexchange.com/questions/2283/which-package-should-i-use-t o-wrap-modern-fortran-code-with-python to see about wrapping C++ and Fortran code. But chances are somebody has already done it. And for astronomical calculations, I just use pyephem. <http://rhodesmill.org/pyephem/index.html> http://rhodesmill.org/pyephem/index.html It is difficult, and usually pointless to argue with somebody who prefers to use Fortran or C++ about using some other language. People like what they are used to. Moreover, in the case of Fortran and C++, these languages are significantly harder to use than Python, where harder, in this case, means -- the code you write is more prone to errors. Also, it is very easy to write inefficient programs in C++ or Fortran, and scientists, who are not professional programmers are extremely prone to doing this. So while a Fortran or C++ library may be fast (if the library author knew what he or she was doing, or the community has fixed inefficiencies over the years) that doesn't mean that your own C++ or Fortran code is guaranteed to be fast. And, of course, if it takes you 5 times as long in your personal time to write the code, do you actually care how fast the result runs? For very long running calculations, where time is money, the answer may be yes. But if all you want to know is where Uranus is going to be next Thursday so you can point your telescope at it, not a bit. If you need something that runs faster than the standard version of python, you may be able to get it with numpy and the other scientific tools here: <http://www.scipy.org/> http://www.scipy.org/ or with pypy <http://speed.pypy.org/> http://speed.pypy.org/ . The number of problems where you can legitimately claim that you badly need the speed that C++ and Fortran provide is shrinking all the time, as machines grow faster. Plus, in modern computing the new challenge is to use multiple cores. It's a very hard problem, and all the old C++ and Fortran libraries were created with the assumption that there is only one core. Right now we don't actually know how to use multiple cores efficiently -- it is hot research in computer science right now, with no particularly good answers. But once we figure out how to do this, all the old C++ and Fortran libraries are probably going to have to be rewritten with this in mind. (A solution that doesn't require such re-writing would, of course be highly desirable, but things are not looking very good on that front.) If your real question is 'would it be desirable to learn C++ and Fortran from a historical perspective, to get an understanding of how important computational libraries in my field are structured', then I am the wrong person to ask. I knew both of these languages before there was a Python. I think learning these langauges will indeed make understanding the old libraries easier, but _using_ these languages to do real work isn't needed. Or rather, if you are one of the people who needs to use them, then you would already know that you are, and why, and wouldn't be asking the question. The world is full of things to learn, so you need to do your own prioritisation of Fortran vs everything else you haven't learned yet. :) Best of luck, Laura Creighton p.s. I gave this topic a more descriptive subject line than 'Hi' but it should still thread with the original. _______________________________________________ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: https://mail.python.org/mailman/listinfo/tutor
