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angle_list = list(numpy.ndarray.flatten(angles)) |
Hi 👋 I noticed that the code uses numpy.ndarray.flatten(...) to flatten arrays, for example:
angle_list = list(numpy.ndarray.flatten(angles))
While this is functionally correct, it would be more efficient to use numpy.ndarray.ravel(...) instead:
angle_list = list(numpy.ndarray.ravel(angles))
The difference is that flatten() always returns a copy of the array, which introduces unnecessary memory overhead when a copy is not needed. On the other hand, ravel() returns a view of the array when possible, making it faster and more memory-efficient, especially with large arrays.
Since the flattened array is only being used to construct a Python list and not modified in-place, ravel() is a more optimal choice here.
This change keeps the structure and style (numpy.ndarray.method(...)) consistent, while improving runtime performance and reducing memory usage.
pycoal/examples/example_sam.py
Line 71 in dd461f6
Hi 👋 I noticed that the code uses numpy.ndarray.flatten(...) to flatten arrays, for example:
angle_list = list(numpy.ndarray.flatten(angles))While this is functionally correct, it would be more efficient to use numpy.ndarray.ravel(...) instead:
angle_list = list(numpy.ndarray.ravel(angles))The difference is that flatten() always returns a copy of the array, which introduces unnecessary memory overhead when a copy is not needed. On the other hand, ravel() returns a view of the array when possible, making it faster and more memory-efficient, especially with large arrays.
Since the flattened array is only being used to construct a Python list and not modified in-place, ravel() is a more optimal choice here.
This change keeps the structure and style (numpy.ndarray.method(...)) consistent, while improving runtime performance and reducing memory usage.