My question is similar to this: subsampling every nth entry in a numpy array
Let's say I have an array as given below: a = [1,2,2,2,3,4,1,2,2,2,3,4,1,2,2,2,3,4....]
How can I extend the slice such that I slice three elements at specific intervals? I.e. how can I slice 2s from the array? I believe basic slicing does not work in this case.
You can do this through individual indexing.
We want to start from the element at index 1, take 3 elements and then skip 3 elements:
a = np.array([1, 2, 2, 2, 3, 4, 1, 2, 2, 2, 3, 4, 1, 2, 2, 2, 3, 4])
start = 1
take = 3
skip = 3
indices = np.concatenate([np.arange(i, i + take) for i in range(start, len(a), take + skip)])
print(indices)
print(a[indices])
Output:
[ 1 2 3 7 8 9 13 14 15]
[2 2 2 2 2 2 2 2 2]
The simplest here seems:
a = np.array([1,2,2,2,3,4,1,2,2,2,3,4,1,2,2,2,3,4])
a.reshape(-1,6)[1:4].ravel()
or if a doesn't chunk well :
period = 6
a.resize(np.math.ceil(a.size/period),period)
a[:,1:4].ravel()
Here's a vectorized one with masking -
def take_sliced_regions(a, start, take, skip):
r = np.arange(len(a))-start
return a[r%(take+skip)<take]
Sample run -
In [90]: a = np.array([1,2,2,2,3,4,1,2,2,2,3,4,1,2,2,2,3,4,1,2])
In [91]: take_sliced_regions(a, start=1, take=3, skip=3)
Out[91]: array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
