On Thu, Jan 21, 2010 at 1:03 PM, Emmanuelle Gouillart <emmanuelle.gouill...@normalesup.org> wrote: > > Hi Thomas, > > broadcasting rules are only for ufuncs (and by extension, some numpy > functions using ufuncs). Indexing obeys different rules and always starts > by the first dimension.
Just a clarification: If there are several index arrays, then standard broadcasting rules apply for them. It's a bit messier when arrays and slice objects are mixed. An informative explanation was in the thread March 2009 about "Is this a bug?" and lots of examples are on the mailing list Josef > > However, you don't have to use broadcasting for such indexing operations: >>>> a[:, c] = 0 > zeroes columns indexed by c. > > If you want to index along the 3rd dimension, you can use a[:, :, c], > etc. If the dimension along which you index is a variable, you can also > use the function np.rollaxis that allows to change the order of the > dimensions of an array. You may then index along the first dimension > (a[c]), then change back the order of the dimensions. Here is an example: >>>> a = np.ones((3,4,5,6)) >>>> c = np.array([1,0,1,0,1], dtype=bool) >>>> tmp_a = np.rollaxis(a, 2, 0) >>>> tmp_a.shape > (5, 3, 4, 6) >>>> tmp_a[c] = 0 >>>> a = np.rollaxis(tmp_a, 0, 3) >>>> a.shape > (3, 4, 5, 6) > > Hope this helps. > > Cheers, > > Emmanuelle > > On Thu, Jan 21, 2010 at 11:37:09AM -0500, Thomas Robitaille wrote: >> Hello, > >> I'm trying to understand how array broadcasting can be used for indexing. In >> the following, I use the term 'row' to refer to the first dimension of a 2D >> array, and 'column' to the second, just because that's how numpy prints them >> out. > >> If I consider the following example: > >> >>> a = np.random.random((4,5)) >> >>> b = np.random.random((5,)) >> >>> a + b >> array([[ 1.45499556, 0.60633959, 0.48236157, 1.55357393, 1.4339261 ], >> [ 1.28614593, 1.11265001, 0.63308615, 1.28904227, 1.34070499], >> [ 1.26988279, 0.84683018, 0.98959466, 0.76388223, 0.79273084], >> [ 1.27859505, 0.9721984 , 1.02725009, 1.38852061, 1.56065028]]) > >> I understand how this works, because it works as expected as described in > >> http://docs.scipy.org/doc/numpy/reference/ufuncs.html#broadcasting > >> So b gets broadcast to shape (1,5), then because the first dimension is 1, >> the operation is applied to all rows. > >> Now I am trying to apply this to array indexing. So for example, I want to >> set specific columns, indicated by a boolean array, to zero, but the >> following fails: > >> >>> c = np.array([1,0,1,0,1], dtype=bool) >> >>> a[c] = 0 >> Traceback (most recent call last): >> File "<stdin>", line 1, in <module> >> IndexError: index (4) out of range (0<=index<3) in dimension 0 > >> However, if I try reducing the size of c to 4, then it works, and sets rows, >> not columns, equal to zero > >> >>> c = np.array([1,0,1,0], dtype=bool) >> >>> a[c] = 0 >> >>> a >> array([[ 0. , 0. , 0. , 0. , 0. ], >> [ 0.41526315, 0.7425491 , 0.39872546, 0.56141914, 0.69795153], >> [ 0. , 0. , 0. , 0. , 0. ], >> [ 0.40771227, 0.60209749, 0.7928894 , 0.66089748, 0.91789682]]) > >> But I would have thought that the indexing array would have been broadcast >> in the same way as for a sum, i.e. c would be broadcast to have dimensions >> (1,5) and then would have been able to set certain columns in all rows to >> zero. > >> Why is it that for indexing, the broadcasting seems to happen in a different >> way than when performing operations like additions or multiplications? For >> background info, I'm trying to write a routine which performs a set of >> operations on an n-d array, where n is not known in advance, with a 1D >> array, so I can use broadcasting rules for most operations without knowing >> the dimensionality of the n-d array, but now that I need to perform >> indexing, and the convention seems to change, this is a real issue. > >> Thanks in advance for any advice, > >> Thomas >> _______________________________________________ >> 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 > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
