Hi George,
what you see is due to the memory layout of numpy arrays. If you switch your
array to F-order you'll see that the two functions have the same timings, i.e.
both are fast (on my machine 25 times faster for the 1_000_000 points case).
Try:
vertices = np.array(np.random.random((n, 2)), order='F')
When your array doesn't fit in L1-cache anymore, either order 'C' or order 'F'
becomes (much) more efficient depending on which dimension you are internally
looping through.
You can read more about it here:
https://numpy.org/doc/stable/dev/internals.html#internal-organization-of-numpy-arrays
and
https://numpy.org/doc/stable/reference/arrays.ndarray.html#internal-memory-layout-of-an-ndarray
Hope that helps,
Tiziano
On Fri 21 Mar, 12:10 +0200, George Tsiamasiotis via NumPy-Discussion
<numpy-discussion@python.org> wrote:
Hello NumPy community!
I was writing a function that calculates the bounding box of a polygon (the
smallest rectangle that fully contains the polygon, and who's sides are
parallel to the x and y axes). The input is a (N,2) array containing the
vertices of the polygon, and the output is a 4-tuple containing the vertices of
the 2 corners of the bounding box.
I found two ways to do that using the np.min() and np.max() methods, and I was
surprised to see a significant speed difference, even though they seemingly do
the same thing.
While for small N the speed is essentially the same, the difference becomes
noticeable for larger N. >From my testing, the difference seems to plateau,
with the one way being around 4-5 times faster than the other.
Is there an explanation for this?
Here is a small benchmark I wrote (must be executed with IPython):
import numpy as np
from IPython import get_ipython
vertices = np.random.random((1000, 2))
def calculate_bbox_normal(vertices: np.ndarray) -> tuple[np.float64]:
xmin, ymin = vertices.min(axis=0)
xmax, ymax = vertices.max(axis=0)
return xmin, ymin, xmax, ymax
def calculate_bbox_transpose(vertices: np.ndarray) -> tuple[np.float64]:
xmin = vertices.T[0].min()
xmax = vertices.T[0].max()
ymin = vertices.T[1].min()
ymax = vertices.T[1].max()
return xmin, ymin, xmax, ymax
bbox_normal = calculate_bbox_normal(vertices)
bbox_transpose = calculate_bbox_transpose(vertices)
print(f"Equality: {bbox_normal == bbox_transpose}")
for n in [10, 100, 1000, 10_000, 100_000, 1_000_000]:
print(f"Number of points: {n}")
vertices = np.random.random((n, 2))
print("Normal: ", end="")
get_ipython().run_line_magic("timeit", "calculate_bbox_normal(vertices)")
print("Transpose: ", end="")
get_ipython().run_line_magic("timeit", "calculate_bbox_transpose(vertices)")
print()
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