How to optimize a for loop to fill a 3-D np array using values from a 2-D array?
I have A 2-d numpy array 'points' of shape (N,3) which consists of N point coordinates. The point coordinates in this array are ordered in such a way that there exist unique contiguous groups of points such that 'indices' contains the indices where each group starts from and 'counts' contains the number of elements in each group. Now, I want to create a 3-D array 'output' with dimensions (K,T,3) where K is the number of unique groups, T is a fixed number. The 'output' array has to contain a maximum of T point coordinates for each unique group.
The functionality I want can be achieved using the following for loop but it's a time-consuming process. I want to optimize this for loop. I have been trying to use multiprocessing pool but am not able to use it correctly in this context. It would be great if someone could help. Thanks!
output = np.zeros(shape=(K, T, 3), dtype=np.float32)
counts = np.clip(counts, 0, T)
for i in range(K):
output[i, :counts[i], :3] = points[indices[i]:indices[i]+counts[i]]
python for-loop multidimensional-array multiprocessing point-clouds
asked Dec 28 '18 at 19:38
Sumit SaxenaSumit Saxena
62
add a comment |
I have A 2-d numpy array 'points' of shape (N,3) which consists of N point coordinates. The point coordinates in this array are ordered in such a way that there exist unique contiguous groups of points such that 'indices' contains the indices where each group starts from and 'counts' contains the number of elements in each group. Now, I want to create a 3-D array 'output' with dimensions (K,T,3) where K is the number of unique groups, T is a fixed number. The 'output' array has to contain a maximum of T point coordinates for each unique group.
The functionality I want can be achieved using the following for loop but it's a time-consuming process. I want to optimize this for loop. I have been trying to use multiprocessing pool but am not able to use it correctly in this context. It would be great if someone could help. Thanks!
output = np.zeros(shape=(K, T, 3), dtype=np.float32)
counts = np.clip(counts, 0, T)
for i in range(K):
output[i, :counts[i], :3] = points[indices[i]:indices[i]+counts[i]]
python for-loop multidimensional-array multiprocessing point-clouds
asked Dec 28 '18 at 19:38
Sumit SaxenaSumit Saxena
62
add a comment |
I have A 2-d numpy array 'points' of shape (N,3) which consists of N point coordinates. The point coordinates in this array are ordered in such a way that there exist unique contiguous groups of points such that 'indices' contains the indices where each group starts from and 'counts' contains the number of elements in each group. Now, I want to create a 3-D array 'output' with dimensions (K,T,3) where K is the number of unique groups, T is a fixed number. The 'output' array has to contain a maximum of T point coordinates for each unique group.
The functionality I want can be achieved using the following for loop but it's a time-consuming process. I want to optimize this for loop. I have been trying to use multiprocessing pool but am not able to use it correctly in this context. It would be great if someone could help. Thanks!
output = np.zeros(shape=(K, T, 3), dtype=np.float32)
counts = np.clip(counts, 0, T)
for i in range(K):
output[i, :counts[i], :3] = points[indices[i]:indices[i]+counts[i]]
python for-loop multidimensional-array multiprocessing point-clouds
asked Dec 28 '18 at 19:38
Sumit SaxenaSumit Saxena
62
I have A 2-d numpy array 'points' of shape (N,3) which consists of N point coordinates. The point coordinates in this array are ordered in such a way that there exist unique contiguous groups of points such that 'indices' contains the indices where each group starts from and 'counts' contains the number of elements in each group. Now, I want to create a 3-D array 'output' with dimensions (K,T,3) where K is the number of unique groups, T is a fixed number. The 'output' array has to contain a maximum of T point coordinates for each unique group.
The functionality I want can be achieved using the following for loop but it's a time-consuming process. I want to optimize this for loop. I have been trying to use multiprocessing pool but am not able to use it correctly in this context. It would be great if someone could help. Thanks!
output = np.zeros(shape=(K, T, 3), dtype=np.float32)
counts = np.clip(counts, 0, T)
for i in range(K):
output[i, :counts[i], :3] = points[indices[i]:indices[i]+counts[i]]
python for-loop multidimensional-array multiprocessing point-clouds
python for-loop multidimensional-array multiprocessing point-clouds
asked Dec 28 '18 at 19:38
Sumit SaxenaSumit Saxena
62
asked Dec 28 '18 at 19:38
Sumit SaxenaSumit Saxena
62
edited Dec 28 '18 at 21:04
Sumit Saxena
asked Dec 28 '18 at 19:38
Sumit SaxenaSumit Saxena
62
asked Dec 28 '18 at 19:38
Sumit SaxenaSumit Saxena
62
asked Dec 28 '18 at 19:38
Sumit SaxenaSumit Saxena
62
62
add a comment |
add a comment |
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