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I have some data represented in a 1300x1341 matrix. I would like to split this matrix in several pieces (e.g. 9) so that I can loop over and process them. The data needs to stay ordered in the sense that x[0,1] stays below (or above if you like) x[0,0] and besides x[1,1].
Just like if you had imaged the data, you could draw 2 vertical and 2 horizontal lines over the image to illustrate the 9 parts.
If I use numpys reshape (eg. matrix.reshape(9,260,745) or any other combination of 9,260,745) it doesn't yield the required structure since the above mentioned ordering is lost...
Did I misunderstand the reshape method or can it be done this way?
What other pythonic/numpy way is there to do this?
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Splitting NumPy ArraysWe use array_split() for splitting arrays, we pass it the array we want to split and the number of splits.
divide(arr1, arr2, out = None, where = True, casting = 'same_kind', order = 'K', dtype = None) : Array element from first array is divided by elements from second element (all happens element-wise). Both arr1 and arr2 must have same shape and element in arr2 must not be zero; otherwise it will raise an error.
Python's numpy. divide() computes the element-wise division of array elements. The elements in the first array are divided by the elements in the second array.
Sounds like you need to use numpy.split() which has its documentation here ... or perhaps its sibling numpy.array_split() here. They are for splitting an array into equal subsections without re-arranging the numbers like reshape does,
I haven't tested this but something like:
numpy.array_split(numpy.zeros((1300,1341)), 9)
should do the trick.
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