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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