CODEWITHSUNDEEP
pythonarraysnumpy
user8991539
user8991539

Reputation:

Numpy view on 1D array via 2D array as indices range

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.

Upvotes: 1

Views: 197

Answers (2)

hpaulj
hpaulj

Reputation: 231335

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.

Upvotes: 1

dkato
dkato

Reputation: 895

for x, y in z:
    array_view = a[x:y]
    # do something with array_view

Upvotes: 1

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