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# Source: https://github.com/python/pyperformance
# License: MIT
# Artificial, floating point-heavy benchmark originally used by Factor.
from math import sin, cos, sqrt
class Point(object):
__slots__ = ("x", "y", "z")
def __init__(self, i):
self.x = x = sin(i)
self.y = cos(i) * 3
self.z = (x * x) / 2
def __repr__(self):
return "<Point: x=%s, y=%s, z=%s>" % (self.x, self.y, self.z)
def normalize(self):
x = self.x
y = self.y
z = self.z
norm = sqrt(x * x + y * y + z * z)
self.x /= norm
self.y /= norm
self.z /= norm
def maximize(self, other):
self.x = self.x if self.x > other.x else other.x
self.y = self.y if self.y > other.y else other.y
self.z = self.z if self.z > other.z else other.z
return self
def maximize(points):
next = points[0]
for p in points[1:]:
next = next.maximize(p)
return next
def benchmark(n):
points = [None] * n
for i in range(n):
points[i] = Point(i)
for p in points:
p.normalize()
return maximize(points)
###########################################################################
# Benchmark interface
bm_params = {
(50, 25): (1, 150),
(100, 100): (1, 250),
(1000, 1000): (10, 1500),
(5000, 1000): (20, 3000),
}
def bm_setup(params):
state = None
def run():
nonlocal state
for _ in range(params[0]):
state = benchmark(params[1])
def result():
return params[0] * params[1], "Point(%.4f, %.4f, %.4f)" % (state.x, state.y, state.z)
return run, result
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