Perhaps you could simplify some statements, but at least the algorithmic complexity is fine, and everything is vectorized, so I doubt you will get huge gains.
You could take a look at the functions in scipy.spatial, and see how they perform for your problem parameters. On Sun, Jun 22, 2014 at 10:22 AM, Nicolas P. Rougier < [email protected]> wrote: > > > Hi, > > I have an array L with regular spaced values between 0 and width. > I have a (sorted) array I with irregular spaced values between 0 and width. > > I would like to find the closest value in I for any value in L. > > Currently, I'm using the following script but I wonder if I missed an > obvious (and faster) solution: > > > import numpy as np > > def find_closest(A, target): > idx = A.searchsorted(target) > idx = np.clip(idx, 1, len(A) - 1) > left = A[idx - 1] > right = A[idx] > idx -= target - left < right - target > return idx > > n, width = 256, 100.0 > > # 10 random sorted values in [0,width] > I = np.sort(np.random.randint(0,width,10)) > > # n regular spaced values in [0,width] > L = np.linspace(0, width, n) > > print I[find_closest(I,L)] > > > > Nicolas > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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