2010/9/15 Mark Fenner :

One method of using indices seems to be as follows:

In [19]: a = N.array(range(6)).reshape(3,2) In [20]: i = N.indices([3,2]) In [21]: r,c = i In [22]: a[r,c] Out[22]: array([[0, 1],       [2, 3],       [4, 5]])

In [23]: a[tuple(i)] Out[23]: array([[0, 1],       [2, 3],       [4, 5]])

*****Question 1:*****

For using the results of argmax, how would one "pad" the indices returned by argmax to be usable as follows (note, I resorted to method I found on numpy-discussion ... google "take_axis" ... that revolves around rolling the desired axis to the first position and then using the indices ... I'm curious how to make something like what I have below work).  Incidentally, the number of  dimensions can be arbitrary (for my needs):

x = N.array([[2, 3],                 [1, 5]])

for a in [0, 1]:     indices = N.argmax(x, axis=a)

    # perhaps something similar to the following?     # grid = N.indices(x.shape)     # grid[a] = indices # <--- this is the idea, but it fails to get the necessary result     # usable = tuple(grid)

    assert x[usableIndices] == x.max(axis=a) # or similar

I first will try to understand what you want. I go to the simplest case, 1-dimensional. There we have only one axis. x is sth like [1, 42, 3, 4]. numpy.argmax(x, axis=0) delivers 1 in this case (scalar). This is because there are no dimensions left besides the zeroth. So grid would be [[0, 1, 2, 3]]. Assigning 1 to this grid in the zeroth component of this grid would work in this case, and would set all the indexing elements to 1. Thus, when indexing with this grid, you would spread out the 42 on the whole axis (axis=0 in this case). Is that what you want? I for now assume that I got your point. Would be a good idea to post the desired output too. When going to higher dimensions, say x.shape = (10, 11, 12), then grid.shape = (3, 10, 11, 12). Meaning all elements of grid have again shape (10, 11, 12). Your argmax indices array, say in axis=0, has shape (11, 12). Indexed with some tuple (j, k) it gives the coordinate of the corresponing maximum element as (indices[j, k], j, k). Further grid[i, j, k] gives the index where to take from in the zeroth axis for element (i, j, k) of the result array. You want to spread on the zeroth axis in that case. Meaning you want to say grid[0][i, j, k] = indices[j, k] for all i. For the zeroth axis, this will work via broadcasting directly: grid[0] = indices. For the say 1st axis, axis=1, indices.shape = (10, 12). You want to say in this case grid[1][i, j, k] = indices[i, k]. For this to get it working, you would probably have to reshape with the a shape containing the 1 in the appropriate place, in this case grid[0] = indices.reshape((10, 1, 12)). This shouldn't be super-hard. Just take the original shape, and substitute the axis'th value by a one, and put this into .reshape(). With this reshaped indices, broadcasting will work as expected. I guess you want to do some stuff with the "corrected" indices, otherwise x.max(axis=a) would do the job? x = numpy.random.random((10, 11, 12)) for axis in xrange(0, 3): maximum_indices = x.argmax(axis=axis) full_shape = list(x.shape) full_shape[axis] = 1 grid = numpy.indices(x.shape) grid[axis] = maximum_indices.reshape(full_shape) There is some room for optimisation because we create the same grid all the time. I did not do this now.

*****Question 2:*****

A separate question.  Suppose I have a slice for indexing that looks like:

[:, :, 2, :, 5]

How can I get an indexing slice for all OTHER dimension values besides those specified.  Conceptually, something like:

[:, :, all but 2, :, all but 5]

Incidentally, the goal is to construct a new array with all those "other" spots filled in with zero and the specified spots with their original values.  Would it be easier to construct a 0-1 indicator array with 1s in the [:,:,2,:,5] positions and multiply it out?  Humm, I may have just answered my own question.

For argument sake, how would you do it with indexing/slicing?  I suppose one develops some intuition as one gains experience with numpy with regards to when to (1) use clever matrix ops and when to (2) use clever slicing and when to (3) use a combination of both.

I really think the multiplication approach is a good one, and you can speed it up using broadcastring. Assuming x.shape = (10, 11, 12, 13, 14), for your example, you would say then: factor = numpy.zeros(1, 1, 12, 1, 14, dtype=numpy.bool) factor[:, :, 2] = True factor[:, :, :, :, 5] = True x_masked = x * factor Notice that as I understand you, you want to have 1s in those cells, which have either 2 or 5 or both in their indices. This means, that a cell is zero iff. it has indices not containing the 2 and not containing the 5. On the contrary, when you want to keep only those which have 2 and 5 in their indices, you may use two separate factors as above. Friedrich