Eli Bressert wrote: > Hi, > > I'm using masked arrays to compute large-scale standard deviation, > multiplication, gaussian, and weighted averages. At first I thought > using the masked arrays would be a great way to sidestep looping > (which it is), but it's still slower than expected. Here's a snippet > of the code that I'm using it for. > > # Computing nearest neighbor distances. > # Output will be about 270,000 rows long for the index > # and 270,000x50 for the dist array. > tree = ann.kd_tree(np.column_stack([l,b])) > index, dist = tree.search(np.column_stack([l,b]),k=nth) > > # Clipping bad values by replacing them acceptable values > av[np.where(av<=-10)] = -10 > av[np.where(av>=50)] = 50 > > # Distance clipping and creating mask > dist_arcsec = np.sqrt(dist)*3600 > mask = dist_arcsec <= d_thresh > > # Creating masked array > av_good = ma.array(av[index],mask=mask) > dist_good = ma.array(dist_arcsec,mask=mask) > > # Reason why I'm using masked arrays. If these were > # ndarrays with nan's, then the output would be nan. > Std = np.array(np.std(av_good,axis=1)) > Var = Std*Std > > Rho = np.zeros( (len(av), nth) ) > Rho2 = np.zeros( (len(av), nth) ) > > dist_std = np.std(dist_good,axis=1) > > for j in range(nth): > Rho[:,j] = dist_std > Rho2[:,j] = Var > > # This part takes about 20 seconds to compute for a 270,000x50 masked array. > # Using ndarrays of the same size takes about 2 second > spatial_weight = 1.0 / (Rho*np.sqrt(2*np.pi)) * np.exp( - dist_good / > (2*Rho**2)) > > # Like the spatial_weight section, this takes about 20 seconds > W = spatial_weight / Rho2
The short answer to your subject line is "yes". A simple illustration of division: In [11]:x = np.ones((270000,50), float) In [12]:y = np.ones((270000,50), float) In [13]:timeit x/y 10 loops, best of 3: 199 ms per loop In [14]:x = np.ma.ones((270000,50), float) In [15]:y = np.ma.ones((270000,50), float) In [16]:x[1,1] = np.ma.masked In [17]:y[1,2] = np.ma.masked In [18]:timeit x/y 10 loops, best of 3: 2.45 s per loop So it is slower by more than a factor of 10. That's much worse than I expected for division (and multiplication is similar). It makes me suspect there is might be a simple way to improve it greatly, but I haven't looked. > > # Takes less than one second. > Ave = np.average(av_good,axis=1,weights=W) > > Any ideas on why it would take such a long time for processing? > Especially the spatial_weight and W variables? Would there be a faster > way to do this? Or is there a way that numpy.std can process ignore > nan's when processing? There is a numpy.nansum; and see the following thread: http://www.mail-archive.com/numpy-discussion@scipy.org/msg09407.html Eric > > Thanks, > > Eli Bressert > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
