Thanks, I'll try your solution. Data (L) is not so big actually, it represents pixels on screen and (I) represents line position (for grids). I need to compute this quantity everytime the user zoom in or out.
Nicolas On 22 Jun 2014, at 19:05, Eelco Hoogendoorn <[email protected]> wrote: > Well, if the spacing is truly uniform, then of course you don't really need > the search, and you can do away with the extra log-n, and there is a purely > linear solution: > > def find_closest_direct(start, end, count, A): > Q = (A-start)/(end-start)*count > mid = ((Q[1:]+Q[:-1]+1)/2).astype(np.int) > boundary = np.zeros(count, np.int) > boundary[mid] = 1 > return np.add.accumulate(boundary) > > I expect this to be a bit faster, but nothing dramatic, unless your datasets > are huge. It isn't really more or less elegant either, id say. Note that the > output isn't 100% identical; youd need to do a little tinkering to figure out > the correct/desired rounding behavior. > > > On Sun, Jun 22, 2014 at 5:16 PM, Nicolas P. Rougier > <[email protected]> wrote: > > Thanks for the answer. > I was secretly hoping for some kind of hardly-known numpy function that would > make things faster auto-magically... > > > Nicolas > > > On 22 Jun 2014, at 10:30, Eelco Hoogendoorn <[email protected]> > wrote: > > > 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 > > > > _______________________________________________ > > NumPy-Discussion mailing list > > [email protected] > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
