On 22 Jun 2014, at 22:52, Eelco Hoogendoorn <[email protected]> wrote:
> That's pretty cool; and it makes sense that way. Still, couldn't you fold > this kind of computation into a shader? > > Have you looked at vispy btw? I think its a really nice initiative; having a > high quality vector graphics module in there would make it even better. Would > be nice if those projects could be merged. > I'm part of vispy actually, those are side experiments for this project. Nicolas > > On Sun, Jun 22, 2014 at 9:51 PM, Nicolas P. Rougier > <[email protected]> wrote: > > Actually, it's already working pretty well but it slows down when you're > doing a lot of zoom in/out. > > The trick is that rendering is done using shader (OpenGL) and this > computation is used to give information to the shader to where to draw > antialiased lines. In the end, this shader is able to draw any amiunt of > grids/ticks (as in matplotlib). Some old example are available from here: > https://github.com/rougier/gl-agg > > I tested your solution and it is faster by only a tiny amount but the way you > wrote it might open the door for other improvements. Thanks. > > > Nicolas > > On 22 Jun 2014, at 21:14, Eelco Hoogendoorn <[email protected]> > wrote: > > > Protip: if you are writing your own rasterization code in python, be > > prepared to forget about performance altogether. > > > > Something like numba or other c-like extension will be necessary unless you > > are willing to leave big gobs of performance on the table; and even with > > pure C you will get nowhere close to the performance of super-duper > > optimized library code you are used to. > > > > But before you go down that rabbit hole, its probably worth thinking about > > whether you can get an existing rendering framework to do what you want to > > do. > > > > > > On Sun, Jun 22, 2014 at 8:30 PM, Nicolas P. Rougier > > <[email protected]> wrote: > > > > 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 > > > > _______________________________________________ > > 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
