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I have a 3d matrix (n-by-m-by-t) in MATLAB representing n-by-m measurements in a grid over a period of time. I would like to have a 2d matrix, where the spatial information is gone and only n*m measurements over time t are left (ie: n*m-by-t)
How can I do this?
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reshape() function to convert a 3D array with dimensions (4, 2, 2) to a 2D array with dimensions (4, 4) in Python. In the above code, we first initialize a 3D array arr using numpy. array() function and then convert it into a 2D array newarr with numpy. reshape() function.
B = reshape( A , sz ) reshapes A using the size vector, sz , to define size(B) . For example, reshape(A,[2,3]) reshapes A into a 2-by-3 matrix. sz must contain at least 2 elements, and prod(sz) must be the same as numel(A) . B = reshape( A , sz1,...,szN ) reshapes A into a sz1 -by- ...
A 3D matrix is nothing but a collection (or a stack) of many 2D matrices, just like how a 2D matrix is a collection/stack of many 1D vectors. So, matrix multiplication of 3D matrices involves multiple multiplications of 2D matrices, which eventually boils down to a dot product between their row/column vectors.
You need the command reshape:
Say your initial matrix is (just for me to get some data):
a=rand(4,6,8); Then, if the last two coordinates are spatial (time is 4, m is 6, n is 8) you use:
a=reshape(a,[4 48]); and you end up with a 4x48 array.
If the first two are spatial and the last is time (m is 4, n is 6, time is 8) you use:
a=reshape(a,[24 8]); and you end up with a 24x8 array.
This is a fast, O(1) operation (it just adjusts it header of what the shape of the data is). There are other ways of doing it, e.g. a=a(:,:) to condense the last two dimensions, but reshape is faster.
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