Hi ! Thanks a lot for your fast/detailed reply. A very good point for Numpy ;-)
I spent all my time trying to prepare my testcase to better share with you, that's why I didn't reply fast. I understand the weakness of the missing JITcompiler in Python vs Matlab, that's why I invistigated numpy vectorization/broadcast. (hoping to find a cool way to write our code in fast Numpy) I used the page http://www.scipy.org/PerformancePython to write my code efficiently in Numpy. While doing it I found one issue. To have pretty code, I created p0 and p1 arrays of indexes. In "test8" I wished to see the commented line working, which is not the case. Having to use "ix_" is not pretty enough, and seems to not work with further dimensions. Why the comment line is not working ? ############################################ def test8(): m = 1024 n = 512 Out = numpy.zeros((m,n)) In = numpy.zeros((m,n)) p0 = numpy.ogrid[0:m] p1 = numpy.ogrid[0:n] Out[0:m,0:n] = In[0:m,0:n] #Out[p0,p1] = In[p0,p1] #This doesn't work Out[numpy.ix_(p0,p1)] = In[numpy.ix_(p0,p1)] ############################################ What is maybe not clear in the above code, is that I don't want to predefine all possible ogrid/vector. The number of possible ogrid/vector is big if in need to define all. ... And this vector definition become more paintful. So Numpy vector style is fine if i can write something like: Out[p0,p1] = In[p0,p1] #2 dimensions case And Out[p0,p1,1] = In[p0,p1,1] #3 dimensions case But is not fine if i have to add ".ix_()" or to multiply the number of vector definitions. Below example with 3 dimensions instead of 2. ############################################ def test9(): m = 1024 n = 512 Out = numpy.zeros((m,n,3)) In = numpy.zeros((m,n,3)) p0 = numpy.ogrid[0:m] p1 = numpy.ogrid[0:n] Out[0:m,0:n,2] = In[0:m,0:n,2] #Out[p0,p1,2] = In[p0,p1,2] Out[numpy.ix_(p0,p1,2)] = In[numpy.ix_(p0,p1,2)] ############################################ Tanks again for your support ;-) Cheers, Nicolas. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
