On Sat, Nov 17, 2012 at 8:28 AM, Chao YUE <[email protected]> wrote: > Dear all, > > I need to make a linear contrast of the 2D numpy array "data" from an > interval to another, the approach is: > I have another two list: "base" & "target", then I check for each ndarray > element "data[i,j]", > if base[m] =< data[i,j] <= base[m+1], then it will be linearly converted > to be in the interval of (target[m], target[m+1]), > using another function called "lintrans". > > > #The way I do is to loop each row and column of the 2D array, and finally > loop the intervals constituted by base list: > > for row in range(data.shape[0]): > for col in range(data.shape[1]): > for i in range(len(base)-1): > if data[row,col]>=base[i] and data[row,col]<=base[i+1]: > > data[row,col]=lintrans(data[row,col],(base[i],base[i+1]),(target[i],target[i+1])) > break #use break to jump out of loop as the data have to be > ONLY transferred ONCE. > > > Now the profiling result shows that most of the time has been used in this > loop over the array ("plot_array_transg"), > and less time in calling the linear transformation fuction "lintrans": > > ncalls tottime percall cumtime percall > filename:lineno(function) > 18047 0.110 0.000 0.110 0.000 > mathex.py:132(lintrans) > 1 12.495 12.495 19.061 19.061 > mathex.py:196(plot_array_transg) > > > so is there anyway I can speed up this loop? Thanks for any suggestions!! > > best, > > Chao
If lintrans() is a linear interpolation, could you use interp? http://docs.scipy.org/doc/numpy/reference/generated/numpy.interp.html _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
