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I have a 4 dimensional array, i.e., data.shape = (20,30,33,288). I am finding the index of the closest array to n using
index = abs(data - n).argmin(axis = 1), so
index.shape = (20,33,288) with the indices varying.
I would like to use data[index] = "values" with values.shape = (20,33,288), but data[index] returns the error "index (8) out of range (0<=index<1) in dimension 0" or this operation takes a relatively long time to compute and returns a matrix with a shape that doesn't seem to make sense.
How do I return a array of correct values? i.e.,
data[index] = "values" with values.shape = (20,33,288)
This seems like a simple problem, is there a simple answer?
I would eventually like to find index2 = abs(data - n2).argmin(axis = 1), so I can perform an operation, say sum data at index to data at index2 without looping through the variables. Is this possible?
I am using python2.7 and numpy version 1.5.1.
236
In general numpy arrays can have more than one dimension. One way to create such array is to start with a 1-dimensional array and use the numpy reshape() function that rearranges elements of that array into a new shape.
Argmax is a mathematical function. It is typically applied to another function that takes an argument. For example, given a function g() that takes the argument x, the argmax operation of that function would be described as follows: result = argmax(g(x))
You should be able to access the maximum values indexed by index using numpy.indices():
x, z, t = numpy.indices(index.shape)
data[x, index, z, t]
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