I have a 2D array which describes index ranges for a 1D array like
z = np.array([[0,4],[4,9]])
The 1D array
a = np.array([1,1,1,1,0,0,0,0,0,1,1,1,1])
I want to have a view on the 1D array with the index range defined by z. So, for only the first range
a[z[0][0]:z[0][1]]
How to get it for all ranges? Is it possible to use as_strided with unequal lengths defined by z as shape? I want to avoid to copy data, actually I only want a different view on a for further computation.
In [66]: a = np.array([1,1,1,1,0,0,0,0,0,1,1,1,1])
In [67]: z = np.array([[0,4],[4,9]])
So generating the slices from the rows of z we get 2 arrays:
In [68]: [a[x[0]:x[1]] for x in z]
Out[68]: [array([1, 1, 1, 1]), array([0, 0, 0, 0, 0])]
Individually those arrays are views. But together they aren't an array. The lengths diff, so they can't be vstacked into a (2,?) array. They can be hstacked but that won't be a view.
The calculation core of np.array_split is:
sub_arys = []
sary = _nx.swapaxes(ary, axis, 0)
for i in range(Nsections):
st = div_points[i]
end = div_points[i + 1]
sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))
Ignoring the swapaxes bit, this is doing the same thing as my list comprehension.
b = np.split(a, [4, 9]) and a[1] = -1, then b[0][1] == -1for x, y in z:
array_view = a[x:y]
# do something with array_view
a[z[0][0]:z[0][1]],a[z[1][0]:z[1][1]]and so on?