Multidimensional Slicing
I want to slice out parts of my array foo multiple times. Currently I am using a for loop which I want to substitute through matrix computation to get a better performance in terms of speed.
foo = np.arange(6000).reshape(6,10,10,10)
target = np.zeros((100,6,3,4,5))
startIndices = np.random.randint(5, size=(100))
This is my current approach.
for i in range(len(target)):
startIdx=startIndices[i]
target[i, :]=foo[:, startIdx:startIdx+3,
startIdx:startIdx+4,
startIdx:startIdx+5]
I tried to represent the slices as arrays, but I couldn't find the proper representation.
arrays python-2.7 performance numpy slice
edited Nov 20 '18 at 2:20
Cœur
18k9108147
asked Mar 26 '18 at 5:09
KoanashiKoanashi
32018
add a comment |
I want to slice out parts of my array foo multiple times. Currently I am using a for loop which I want to substitute through matrix computation to get a better performance in terms of speed.
foo = np.arange(6000).reshape(6,10,10,10)
target = np.zeros((100,6,3,4,5))
startIndices = np.random.randint(5, size=(100))
This is my current approach.
for i in range(len(target)):
startIdx=startIndices[i]
target[i, :]=foo[:, startIdx:startIdx+3,
startIdx:startIdx+4,
startIdx:startIdx+5]
I tried to represent the slices as arrays, but I couldn't find the proper representation.
arrays python-2.7 performance numpy slice
edited Nov 20 '18 at 2:20
Cœur
18k9108147
asked Mar 26 '18 at 5:09
KoanashiKoanashi
32018
add a comment |
I want to slice out parts of my array foo multiple times. Currently I am using a for loop which I want to substitute through matrix computation to get a better performance in terms of speed.
foo = np.arange(6000).reshape(6,10,10,10)
target = np.zeros((100,6,3,4,5))
startIndices = np.random.randint(5, size=(100))
This is my current approach.
for i in range(len(target)):
startIdx=startIndices[i]
target[i, :]=foo[:, startIdx:startIdx+3,
startIdx:startIdx+4,
startIdx:startIdx+5]
I tried to represent the slices as arrays, but I couldn't find the proper representation.
arrays python-2.7 performance numpy slice
edited Nov 20 '18 at 2:20
Cœur
18k9108147
asked Mar 26 '18 at 5:09
KoanashiKoanashi
32018
I want to slice out parts of my array foo multiple times. Currently I am using a for loop which I want to substitute through matrix computation to get a better performance in terms of speed.
foo = np.arange(6000).reshape(6,10,10,10)
target = np.zeros((100,6,3,4,5))
startIndices = np.random.randint(5, size=(100))
This is my current approach.
for i in range(len(target)):
startIdx=startIndices[i]
target[i, :]=foo[:, startIdx:startIdx+3,
startIdx:startIdx+4,
startIdx:startIdx+5]
I tried to represent the slices as arrays, but I couldn't find the proper representation.
arrays python-2.7 performance numpy slice
arrays python-2.7 performance numpy slice
edited Nov 20 '18 at 2:20
Cœur
18k9108147
asked Mar 26 '18 at 5:09
KoanashiKoanashi
32018
edited Nov 20 '18 at 2:20
Cœur
18k9108147
asked Mar 26 '18 at 5:09
KoanashiKoanashi
32018
edited Nov 20 '18 at 2:20
Cœur
18k9108147
edited Nov 20 '18 at 2:20
Cœur
18k9108147
edited Nov 20 '18 at 2:20
Cœur
18k9108147
18k9108147
asked Mar 26 '18 at 5:09
KoanashiKoanashi
32018
asked Mar 26 '18 at 5:09
KoanashiKoanashi
32018
asked Mar 26 '18 at 5:09
KoanashiKoanashi
32018
32018
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
We can leverage np.lib.stride_tricks.as_strided based scikit-image's view_as_windows for efficient patch extraction, like so -
from skimage.util.shape import view_as_windows
# Get sliding windows (these are simply views)
WSZ = (1,3,4,5) # window sizes along the axes
w = view_as_windows(foo,WSZ)[...,0,:,:,:]
# Index with startIndices along the appropriate axes for desired output
out = w[:,startIndices, startIndices, startIndices].swapaxes(0,1)
Related :
NumPy Fancy Indexing - Crop different ROIs from different channels
Take N first values from every row in NumPy matrix that fulfill condition
Selecting Random Windows from Multidimensional Numpy Array Rows
how can I extract multiple random sub-sequences from a numpy array
answered Mar 26 '18 at 5:30
DivakarDivakar
156k1485176
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
We can leverage np.lib.stride_tricks.as_strided based scikit-image's view_as_windows for efficient patch extraction, like so -
from skimage.util.shape import view_as_windows
# Get sliding windows (these are simply views)
WSZ = (1,3,4,5) # window sizes along the axes
w = view_as_windows(foo,WSZ)[...,0,:,:,:]
# Index with startIndices along the appropriate axes for desired output
out = w[:,startIndices, startIndices, startIndices].swapaxes(0,1)
Related :
NumPy Fancy Indexing - Crop different ROIs from different channels
Take N first values from every row in NumPy matrix that fulfill condition
Selecting Random Windows from Multidimensional Numpy Array Rows
how can I extract multiple random sub-sequences from a numpy array
answered Mar 26 '18 at 5:30
DivakarDivakar
156k1485176
add a comment |
We can leverage np.lib.stride_tricks.as_strided based scikit-image's view_as_windows for efficient patch extraction, like so -
from skimage.util.shape import view_as_windows
# Get sliding windows (these are simply views)
WSZ = (1,3,4,5) # window sizes along the axes
w = view_as_windows(foo,WSZ)[...,0,:,:,:]
# Index with startIndices along the appropriate axes for desired output
out = w[:,startIndices, startIndices, startIndices].swapaxes(0,1)
Related :
NumPy Fancy Indexing - Crop different ROIs from different channels
Take N first values from every row in NumPy matrix that fulfill condition
Selecting Random Windows from Multidimensional Numpy Array Rows
how can I extract multiple random sub-sequences from a numpy array
answered Mar 26 '18 at 5:30
DivakarDivakar
156k1485176
add a comment |
We can leverage np.lib.stride_tricks.as_strided based scikit-image's view_as_windows for efficient patch extraction, like so -
from skimage.util.shape import view_as_windows
# Get sliding windows (these are simply views)
WSZ = (1,3,4,5) # window sizes along the axes
w = view_as_windows(foo,WSZ)[...,0,:,:,:]
# Index with startIndices along the appropriate axes for desired output
out = w[:,startIndices, startIndices, startIndices].swapaxes(0,1)
Related :
NumPy Fancy Indexing - Crop different ROIs from different channels
Take N first values from every row in NumPy matrix that fulfill condition
Selecting Random Windows from Multidimensional Numpy Array Rows
how can I extract multiple random sub-sequences from a numpy array
answered Mar 26 '18 at 5:30
DivakarDivakar
156k1485176
We can leverage np.lib.stride_tricks.as_strided based scikit-image's view_as_windows for efficient patch extraction, like so -
from skimage.util.shape import view_as_windows
# Get sliding windows (these are simply views)
WSZ = (1,3,4,5) # window sizes along the axes
w = view_as_windows(foo,WSZ)[...,0,:,:,:]
# Index with startIndices along the appropriate axes for desired output
out = w[:,startIndices, startIndices, startIndices].swapaxes(0,1)
Related :
NumPy Fancy Indexing - Crop different ROIs from different channels
Take N first values from every row in NumPy matrix that fulfill condition
Selecting Random Windows from Multidimensional Numpy Array Rows
how can I extract multiple random sub-sequences from a numpy array
answered Mar 26 '18 at 5:30
DivakarDivakar
156k1485176
answered Mar 26 '18 at 5:30
DivakarDivakar
156k1485176
answered Mar 26 '18 at 5:30
DivakarDivakar
156k1485176
answered Mar 26 '18 at 5:30
DivakarDivakar
156k1485176
156k1485176
add a comment |
add a comment |
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