On 1/7/2009 4:16 PM, David Cournapeau wrote: > I think on recent versions of matlab, there is nothing you can do > without modifying the code: matlab has some JIT compilation for loops, > which is supposed to speed up those cases - at least, that's what is > claimed by matlab.
Yes it does. After using both for more than 10 years, my impression is this: - Matlab slicing creates new arrays. NumPy slicing creates views. NumPy is faster and more memory efficient. - Matlab JIT compiles loops. NumPy does not. Matlab is faster for stupid programmers that don't know how use slices. But neither Matlab nor Python/NumPy is meant to be used like Java. - Python has psyco. It is about as good as Matlab's JIT. But psyco has no knowledge of NumPy ndarrays. - Using Cython is easier than writing Matlab MEX files. - Python has better support for data structures, better built-in structures (tuple, lists, dics, sets), and general purpose libraries. Matlab has extensive numerical toolboxes that you can buy. - Matlab pass function arguments by value (albeit COW optimized). Python pass references. This makes NumPy more efficient if you need to pass large arrays or array slices. - Matlab tends to fragment the heap (hence the pack command). Python/NumPy does not. This makes long-running processes notoriously unstable on Matlab. - Matlab has some numerical libraries that are better. - I like the Matlab command prompt and IDE better. But its not enough to make me want to use it. - Python is a proper programming language. Matlab is a numerical scripting language - good for small scripts but not complex software systems. Sturla Molden _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
