Mailman 3 January 2011 - NumPy-Discussion

Re: [Numpy-discussion] numpy installation
by Paul Ivanov Jan. 3, 2011

Jan. 3, 2011

Waqar Rashid, on 2011-01-02 00:38, wrote: > Hi, > > trying to install numpy on MacOS with python 3.1 > > Having installation issues. Has anyone managed to install this on the Mac? > > regards > Waqar - you sent this to the IPython-User list, but I think you probably meant to send it to the numpy-discussion list, since your question does not pertain to IPython itself, so I'm forwarding your email there. Also, can you be more specific about what issues you are having? best, -- Paul Ivanov 314 address only used for lists, off-list direct email at: http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7

1 0

Arrays with aliased elements?
by Enzo Michelangeli Jan. 2, 2011

Jan. 2, 2011

Is there any way, not involving compilation of C code, to define ndarrays where some rows or columns share the same data buffers? For example, something built by a hypothetical variant of the np.repeat() function, such that, if a = array([2,3]), calling: b = np.aliasedrepeat(x, [1, 2], axis=0) would return in b: array([[2, 3], [2, 3], [2, 3]]) ...with the understanding that the three rows would actually share the same data, so setting e.g.: b[0,1] = 5 ...would change b into: array([[2, 5], [2, 5], [2, 5]]) In other words, something with a behaviour similar to a list of lists: >>> a = [2,3] >>> b = [a,a,a] >>> b [[2, 3], [2, 3], [2, 3]] >>> b[0][1] = 5 >>> b [[2, 5], [2, 5], [2, 5]] This would save memory (and time spent in unnecessary copying) in some applications with large arrays, and would allow to cope with the current inability of weave.blitz to understand broadcasting rules, e.g. for calculating outer products (I mentioned this in a previous thread). Enzo

3 3

OS X binaries.
by Gideon Jan. 1, 2011

Jan. 1, 2011

I noticed that 1.5.1 was released, and sourceforge is suggesting I use the package numpy-1.5.1-py2.6-python.org-macosx10.3.dmg. However, I have an OS X 10.6 machine. Can/should I use this binary? Should I just compile from source?

3 2

Optimization suggestion sought
by Enzo Michelangeli Jan. 1, 2011

Jan. 1, 2011

For a pivoted algorithm, I have to perform an operation that in fully vectorized form can be expressed as: pivot = tableau[locat,:]/tableau[locat,cand] tableau -= tableau[:,cand:cand+1]*pivot tableau[locat,:] = pivot tableau is a rather large bidimensional array, and I'd like to avoid the allocation of a temporary array of the same size holding the result of the right-hand side expression in the second line of code (the outer product of tableau[:,cand] and pivot). On the other hand, if I replace that line with: for i in xrange(tableau.shape[0]): tableau[i] -= tableau[i,cand]*pivot ...I incur some CPU overhead for the "for" loop -- and this part of code is the botteneck of the whole algorithm. Is there any smarter (i.e., more time-efficient) way of achieving my goal? TIA -- Enzo

4 5