7 ms·

This is great news for Julia and the scientific computing community in general. MATLAB doesn't scale, and python solutions are too scatter brained for me.

• Nrpf 11y ago
  What python solutions are scatterbrained? Have you tried Numba?
  • oiuswv 11y ago
    Here, I'll use logical indexing to pull out all the positive even numbers from this array of random integers: Numpy: a = np.random.random_integers(low = -100, high = 100, size = (100,)) a[np.logical_and(a % 2 == 0, a > 0) (or a[(a % 2 == 0) * (a > 0)]) Logical indexing is used everywhere in numerical computing. Using functions like logical_and() or boolean multiplication or addition is more difficult to follow than Matlab or Julia. Julia: a = rand(-100:100, 100) a[(a % 2 .== 0) & (a .> 0)] Well, that's beautiful. Element-wise operations are prefixed with "."
    • jdreaver 11y ago
      You have made a very verbose numpy version :) Here is a slightly better one: a = np.random.randint(-100, 100, size=100) a[(a & 2 == 0) & (a > 0)] Python does indeed have logical boolean operators. I think this is clearer than your Julia example because: - The numpy version makes it clear that you are using random integers, not floating point values. - The keyword argument for "size" makes it clear what that second 100 is for. (The use of the first two numbers, -100 and 100, is pretty clear from context.) - In numpy, most operations are element-wise by default, because the result would be ambiguous or not useful otherwise. This removes the line noise of the extra "." before operations. Don't get me wrong, I think Julia is awesome. I just think you've constructed a very poor example for numpy.
      • timholy 11y ago
        > The numpy version makes it clear that you are using random integers, not floating point values. That's also completely clear in the Julia version, if you learn a little Julia. > In numpy, most operations are element-wise by default, because the result would be ambiguous or not useful otherwise. This is why I think the Julia approach is better. If I write `a == 7`, am I testing whether `a` is 7 or whether any of the elements of `a` are 7?
        • jdreaver 11y ago
          > That's also completely clear in the Julia version, if you learn a little Julia. In every language I've used, the default is for a "rand" function to return random floats between 0 and 1, and given arguments it returns floats between the arguments. I don't think it has to do with learning Julia, it is just that including "integer" in the function name makes it clear the function returns integers. > This is why I think the Julia approach is better. If I write `a == 7`, am I testing whether `a` is 7 or whether any of the elements of `a` are 7? I think this is more of a comment about mixing arrays and scalars in a dynamic language. I made my comment assuming you are performing operations on arrays. If you are comparing two arrays, I think the default of element-wise operations makes more sense.
          • KenoFischer 11y ago
            In julia the rand function is more general in that it samples from a distribution, which you can pass as the first argument (defaulting to uniform on [0,1]). Since the first argument is an integer range, you get an integer value.
  • Tarrosion 11y ago
    I'm not the parent author, but why I haven't tried the Python ecosystem: I've heard of NumPy, PyPy, SciPy, Pandas, matplotlib, and now Numba. I don't particularly know what these do or how they overlap or interact. Which is kind of the point: to a complete outsider, the world of Python scientific computing feels like a wild west where everybody is happily proclaiming that their setup is just right. Additionally, Python is slow; Julia is fast. I've heard things like "well, Python is only slow at certain things, and makes it easy to write C code when needed." I don't want to write any C code. Same goes for "typically only a small portion of your Python code is a bottleneck, and it's easy to port that to C." A huge part of the appeal of Julia is that you don't have to worry about language interoperability, calling C, etc. Everything can be written efficiently and readably in Julia, and because the base language is designed around scientific computing, there's little worry about add-on scientific computing packages not playing nice together. Now, I fully believe that I could get the right Python environment and set of packages set up be productive. But to get going with Julia, I just download Julia and go. Also, JuMP [1] is just amazing. [1] http://www.juliaopt.org http://www.juliaopt.org
    • ngoldbaum 11y ago
      I usually use cython for optimizing python, which is a lot easier than working directly with the CPython C API. You do need to know C to get the most out of it, but you can go a long way just adding type declarations to python to speed it up.
    • Redoubts 11y ago
      > NumPy, PyPy, SciPy, Pandas, matplotlib, and now Numba I'm not sure I buy this argument. Except for pypy, these are all complementary packages and work together. If you breakup anything into its constituent parts, you can make it seem complicated if you want to.
    • stdbrouw 11y ago
      > But to get going with Julia, I just download Julia and go. Until you need to do something very straightforward like web scraping or interacting with AWS and find out that nobody's released a package for that yet, so you're going to have to reinvent the wheel before you can get to the scientific question that is of actual interest. Julia looks very interesting as a language, let's just hope it doesn't end up a ghetto like R (lots of awesome statistical tools, a dearth of libraries and tools for everything else).
• jbssm 11y ago
  I actually, more and more think that python is an excellent language for science. The only issue is that, when it comes to numerical computation, there is only "one right way to do it", which goes against Python philosophy. Meaning, either you do it while using vectorial operations mostly everywhere, or it's just too slow to work. The only real advantage I see in Julia, is that it doesn't constrain you to a specific way to think about calculations. You can think about them in a vectorial way (which is not always possible) or you can just use an iterative approach. Both work fine. This is what I mainly expect to get from Julia, but we must admit that the inertia behind python and it's excellent libraries for numerical computation, make it hard to change to Julia.
  • r0muald 11y ago
    > The only issue is that, when it comes to numerical computation, there is only "one right way to do it", which goes against Python philosophy. But ... This is actually one of the pillars of Python: There should be one-- and preferably only one --obvious way to do it <https://www.python.org/dev/PEPs/pep-0020/> https://www.python.org/dev/PEPs/pep-0020/>
    • jbssm 11y ago
      My bad... I sometimes mix Perl and Python philosophy.
      • jkyle 11y ago
        > I sometimes mix Perl and Python philosophy. That's like mixing up Vulcans and Romulans. Very dangerous indeed.
  • nsomaru 11y ago
    >...there is only "one right way to do it", which goes against Python philosophy There should be one-- and preferably only one --obvious way to do it.[0] [0] https://www.python.org/dev/peps/pep-0020/ https://www.python.org/dev/peps/pep-0020/
• pavanky 11y ago
  If you don't mind trying yet another library, care to have a look at arrayfire-python ? https://github.com/arrayfire/arrayfire-python https://github.com/arrayfire/arrayfire-python We started off as a C/C++ library but are constantly adding support for new languages. The one benefit of ArrayFire would be the ability to use the same codebase across CPU, CUDA and OpenCL devices.
  • ViralBShah 11y ago
    There is a start for Julia bindings, but not currently maintained. https://github.com/JuliaComputing/ArrayFire.jl https://github.com/JuliaComputing/ArrayFire.jl
• aswanson 11y ago
  MATLAB is also rather expensive if you are not a student.
  • frik 11y ago
    there is octave, an open source alternative
    • jbssm 11y ago
      Octave is quite slow tough.
      • frozenport 11y ago
        And doesn't have the Matlab GUI.
        • frik 11y ago
          all true, but it is 99% syntax compatible with Matlab. As a student I had saved a lot of money using Octave