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How to split array by indices where the splitted sub-arrays include the split point
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I have a 2D array containing values and a 1D array with index values where I would like to split the 2D matrix, where the splitted sub-arrays include the 'split-point'.
I know I can use the numpy.split function to split by indices and I know I can use stride_tricks to split an array for creating consecutive overlapping subset-views.
But it seems the stride_ticks only applies if we want to split an array into equal sized sub-arrays.
Minimal example, I can do the following:
>>> import numpy as np
>>> array = np.random.randint(0,10, (10,2))
>>> indices = np.array([2,3,8])
>>> array
array([[8, 1],
[1, 0],
[2, 0],
[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7],
[6, 4]])
>>> split_array = np.split(array, indices, axis=0)
>>> split_array
[array([[8, 1],
[1, 0]]),
array([[2, 0]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4]]),
array([[6, 7],
[6, 4]])]
But I'm merely looking for an option within the split function where I could define include_split_point=True, which would give me a result as such:
[array([[8, 1],
[1, 0],
[2, 0]]),
array([[2, 0],
[8, 8]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7]]),
array([[6, 7],
[6, 4]])]
python arrays numpy split stride
asked Jan 3 at 10:30
MattijnMattijn
4,07463042
add a comment |
I have a 2D array containing values and a 1D array with index values where I would like to split the 2D matrix, where the splitted sub-arrays include the 'split-point'.
I know I can use the numpy.split function to split by indices and I know I can use stride_tricks to split an array for creating consecutive overlapping subset-views.
But it seems the stride_ticks only applies if we want to split an array into equal sized sub-arrays.
Minimal example, I can do the following:
>>> import numpy as np
>>> array = np.random.randint(0,10, (10,2))
>>> indices = np.array([2,3,8])
>>> array
array([[8, 1],
[1, 0],
[2, 0],
[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7],
[6, 4]])
>>> split_array = np.split(array, indices, axis=0)
>>> split_array
[array([[8, 1],
[1, 0]]),
array([[2, 0]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4]]),
array([[6, 7],
[6, 4]])]
But I'm merely looking for an option within the split function where I could define include_split_point=True, which would give me a result as such:
[array([[8, 1],
[1, 0],
[2, 0]]),
array([[2, 0],
[8, 8]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7]]),
array([[6, 7],
[6, 4]])]
python arrays numpy split stride
asked Jan 3 at 10:30
MattijnMattijn
4,07463042
add a comment |
I have a 2D array containing values and a 1D array with index values where I would like to split the 2D matrix, where the splitted sub-arrays include the 'split-point'.
I know I can use the numpy.split function to split by indices and I know I can use stride_tricks to split an array for creating consecutive overlapping subset-views.
But it seems the stride_ticks only applies if we want to split an array into equal sized sub-arrays.
Minimal example, I can do the following:
>>> import numpy as np
>>> array = np.random.randint(0,10, (10,2))
>>> indices = np.array([2,3,8])
>>> array
array([[8, 1],
[1, 0],
[2, 0],
[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7],
[6, 4]])
>>> split_array = np.split(array, indices, axis=0)
>>> split_array
[array([[8, 1],
[1, 0]]),
array([[2, 0]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4]]),
array([[6, 7],
[6, 4]])]
But I'm merely looking for an option within the split function where I could define include_split_point=True, which would give me a result as such:
[array([[8, 1],
[1, 0],
[2, 0]]),
array([[2, 0],
[8, 8]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7]]),
array([[6, 7],
[6, 4]])]
python arrays numpy split stride
asked Jan 3 at 10:30
MattijnMattijn
4,07463042
I have a 2D array containing values and a 1D array with index values where I would like to split the 2D matrix, where the splitted sub-arrays include the 'split-point'.
I know I can use the numpy.split function to split by indices and I know I can use stride_tricks to split an array for creating consecutive overlapping subset-views.
But it seems the stride_ticks only applies if we want to split an array into equal sized sub-arrays.
Minimal example, I can do the following:
>>> import numpy as np
>>> array = np.random.randint(0,10, (10,2))
>>> indices = np.array([2,3,8])
>>> array
array([[8, 1],
[1, 0],
[2, 0],
[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7],
[6, 4]])
>>> split_array = np.split(array, indices, axis=0)
>>> split_array
[array([[8, 1],
[1, 0]]),
array([[2, 0]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4]]),
array([[6, 7],
[6, 4]])]
But I'm merely looking for an option within the split function where I could define include_split_point=True, which would give me a result as such:
[array([[8, 1],
[1, 0],
[2, 0]]),
array([[2, 0],
[8, 8]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7]]),
array([[6, 7],
[6, 4]])]
python arrays numpy split stride
python arrays numpy split stride
asked Jan 3 at 10:30
MattijnMattijn
4,07463042
asked Jan 3 at 10:30
MattijnMattijn
4,07463042
asked Jan 3 at 10:30
MattijnMattijn
4,07463042
asked Jan 3 at 10:30
MattijnMattijn
4,07463042
asked Jan 3 at 10:30
MattijnMattijn
4,07463042
4,07463042
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Create a new array with the index elements repeated
new_indices = np.zeros(array.shape[0], dtype = int)
new_indices[indices] = 1
new_indices += 1
new_array = np.repeat(array, new_indices, axis = 0)
Update indices to account for the changed array
indices = indices + np.arange(1, len(indices)+1)
Split using the indices as usual
np.split(new_array, indices, axis = 0)
output:
[array([[8, 1],
[1, 0],
[2, 0]]),
array([[2, 0],
[8, 8]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7]]),
array([[6, 7],
[6, 4]])]
answered Jan 3 at 10:58
Ernest S KirubakaranErnest S Kirubakaran
911510
1
Thanks @Ernest S Kirubakaran. It works as expected. A pity I've to make a modified duplicate of thearray, but this is worthwhile since I still can make use of the fast splitting functionality ofnumpy. Thanks again!
– Mattijn
Jan 3 at 12:43
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Create a new array with the index elements repeated
new_indices = np.zeros(array.shape[0], dtype = int)
new_indices[indices] = 1
new_indices += 1
new_array = np.repeat(array, new_indices, axis = 0)
Update indices to account for the changed array
indices = indices + np.arange(1, len(indices)+1)
Split using the indices as usual
np.split(new_array, indices, axis = 0)
output:
[array([[8, 1],
[1, 0],
[2, 0]]),
array([[2, 0],
[8, 8]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7]]),
array([[6, 7],
[6, 4]])]
answered Jan 3 at 10:58
Ernest S KirubakaranErnest S Kirubakaran
911510
1
Thanks @Ernest S Kirubakaran. It works as expected. A pity I've to make a modified duplicate of thearray, but this is worthwhile since I still can make use of the fast splitting functionality ofnumpy. Thanks again!
– Mattijn
Jan 3 at 12:43
add a comment |
Create a new array with the index elements repeated
new_indices = np.zeros(array.shape[0], dtype = int)
new_indices[indices] = 1
new_indices += 1
new_array = np.repeat(array, new_indices, axis = 0)
Update indices to account for the changed array
indices = indices + np.arange(1, len(indices)+1)
Split using the indices as usual
np.split(new_array, indices, axis = 0)
output:
[array([[8, 1],
[1, 0],
[2, 0]]),
array([[2, 0],
[8, 8]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7]]),
array([[6, 7],
[6, 4]])]
answered Jan 3 at 10:58
Ernest S KirubakaranErnest S Kirubakaran
911510
1
Thanks @Ernest S Kirubakaran. It works as expected. A pity I've to make a modified duplicate of thearray, but this is worthwhile since I still can make use of the fast splitting functionality ofnumpy. Thanks again!
– Mattijn
Jan 3 at 12:43
add a comment |
Create a new array with the index elements repeated
new_indices = np.zeros(array.shape[0], dtype = int)
new_indices[indices] = 1
new_indices += 1
new_array = np.repeat(array, new_indices, axis = 0)
Update indices to account for the changed array
indices = indices + np.arange(1, len(indices)+1)
Split using the indices as usual
np.split(new_array, indices, axis = 0)
output:
[array([[8, 1],
[1, 0],
[2, 0]]),
array([[2, 0],
[8, 8]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7]]),
array([[6, 7],
[6, 4]])]
answered Jan 3 at 10:58
Ernest S KirubakaranErnest S Kirubakaran
911510
Create a new array with the index elements repeated
new_indices = np.zeros(array.shape[0], dtype = int)
new_indices[indices] = 1
new_indices += 1
new_array = np.repeat(array, new_indices, axis = 0)
Update indices to account for the changed array
indices = indices + np.arange(1, len(indices)+1)
Split using the indices as usual
np.split(new_array, indices, axis = 0)
output:
[array([[8, 1],
[1, 0],
[2, 0]]),
array([[2, 0],
[8, 8]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7]]),
array([[6, 7],
[6, 4]])]
answered Jan 3 at 10:58
Ernest S KirubakaranErnest S Kirubakaran
911510
answered Jan 3 at 10:58
Ernest S KirubakaranErnest S Kirubakaran
911510
answered Jan 3 at 10:58
Ernest S KirubakaranErnest S Kirubakaran
911510
answered Jan 3 at 10:58
Ernest S KirubakaranErnest S Kirubakaran
911510
911510
1
Thanks @Ernest S Kirubakaran. It works as expected. A pity I've to make a modified duplicate of thearray, but this is worthwhile since I still can make use of the fast splitting functionality ofnumpy. Thanks again!
– Mattijn
Jan 3 at 12:43
add a comment |
1
Thanks @Ernest S Kirubakaran. It works as expected. A pity I've to make a modified duplicate of thearray, but this is worthwhile since I still can make use of the fast splitting functionality ofnumpy. Thanks again!
– Mattijn
Jan 3 at 12:43
1
1
Thanks @Ernest S Kirubakaran. It works as expected. A pity I've to make a modified duplicate of the
array, but this is worthwhile since I still can make use of the fast splitting functionality of numpy. Thanks again!– Mattijn
Jan 3 at 12:43
Thanks @Ernest S Kirubakaran. It works as expected. A pity I've to make a modified duplicate of the
array, but this is worthwhile since I still can make use of the fast splitting functionality of numpy. Thanks again!– Mattijn
Jan 3 at 12:43
add a comment |
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