[Python-checkins] benchmarks: Port PyPy's chaos benchmark.
brett.cannon
python-checkins at python.org
Sat Sep 15 00:12:07 CEST 2012
http://hg.python.org/benchmarks/rev/853737d363da
changeset: 165:853737d363da
user: Brett Cannon <brett at python.org>
date: Fri Sep 14 10:23:19 2012 -0400
summary:
Port PyPy's chaos benchmark.
files:
perf.py | 12 +-
performance/bm_chaos.py | 264 ++++++++++++++++++++++++++++
2 files changed, 274 insertions(+), 2 deletions(-)
diff --git a/perf.py b/perf.py
--- a/perf.py
+++ b/perf.py
@@ -1614,10 +1614,18 @@
bm_path = Relative("performance/bm_nqueens.py")
return MeasureGeneric(python, options, bm_path)
-
def BM_NQueens(*args, **kwargs):
return SimpleBenchmark(MeasureNQueens, *args, **kwargs)
+
+def MeasureChaos(python, options):
+ bm_path = Relative("performance/bm_chaos.py")
+ return MeasureGeneric(python, options, bm_path, iteration_scaling=1)
+
+def BM_Chaos(*args, **kwargs):
+ return SimpleBenchmark(MeasureChaos, *args, **kwargs)
+
+
def MeasureLogging(python, options, extra_args):
"""Test the performance of Python's logging module.
@@ -2004,7 +2012,7 @@
"template" : ["slowspitfire", "django", "mako"],
"logging": ["silent_logging", "simple_logging", "formatted_logging"],
# Benchmarks natively 2.x- and 3.x-compatible
- "2n3": ["calls", "math", "fastpickle", "fastunpickle",
+ "2n3": ["calls", "chaos", "math", "fastpickle", "fastunpickle",
"json_dump", "json_load", "regex", "threading",
"nqueens", "unpack_sequence", "richards",
"logging", "normal_startup", "startup_nosite",
diff --git a/performance/bm_chaos.py b/performance/bm_chaos.py
new file mode 100644
--- /dev/null
+++ b/performance/bm_chaos.py
@@ -0,0 +1,264 @@
+# Copyright (C) 2005 Carl Friedrich Bolz
+
+"""create chaosgame-like fractals
+"""
+
+from __future__ import division
+
+import operator
+import optparse
+import random
+random.seed(1234)
+import math
+import os
+import sys
+import time
+
+from compat import print_, reduce, xrange
+
+class GVector(object):
+ def __init__(self, x = 0, y = 0, z = 0):
+ self.x = x
+ self.y = y
+ self.z = z
+
+ def Mag(self):
+ return math.sqrt(self.x ** 2 + self.y ** 2 + self.z ** 2)
+
+ def dist(self, other):
+ return math.sqrt((self.x - other.x) ** 2 +
+ (self.y - other.y) ** 2 +
+ (self.z - other.z) ** 2)
+
+ def __add__(self, other):
+ if not isinstance(other, GVector):
+ raise ValueError("Can't add GVector to " + str(type(other)))
+ v = GVector(self.x + other.x, self.y + other.y, self.z + other.z)
+ return v
+
+ def __sub__(self, other):
+ return self + other * -1
+
+ def __mul__(self, other):
+ v = GVector(self.x * other, self.y * other, self.z * other)
+ return v
+ __rmul__ = __mul__
+
+ def linear_combination(self, other, l1, l2=None):
+ if l2 is None:
+ l2 = 1 - l1
+ v = GVector(self.x * l1 + other.x * l2,
+ self.y * l1 + other.y * l2,
+ self.z * l1 + other.z * l2)
+ return v
+
+
+ def __str__(self):
+ return "<%f, %f, %f>" % (self.x, self.y, self.z)
+
+ def __repr__(self):
+ return "GVector(%f, %f, %f)" % (self.x, self.y, self.z)
+
+def GetKnots(points, degree):
+ knots = [0] * degree + range(1, len(points) - degree)
+ knots += [len(points) - degree] * degree
+ return knots
+
+class Spline(object):
+ """Class for representing B-Splines and NURBS of arbitrary degree"""
+ def __init__(self, points, degree = 3, knots = None):
+ """Creates a Spline. points is a list of GVector, degree is the
+degree of the Spline."""
+ if knots == None:
+ self.knots = GetKnots(points, degree)
+ else:
+ if len(points) > len(knots) - degree + 1:
+ raise ValueError("too many control points")
+ elif len(points) < len(knots) - degree + 1:
+ raise ValueError("not enough control points")
+ last = knots[0]
+ for cur in knots[1:]:
+ if cur < last:
+ raise ValueError("knots not strictly increasing")
+ last = cur
+ self.knots = knots
+ self.points = points
+ self.degree = degree
+
+ def GetDomain(self):
+ """Returns the domain of the B-Spline"""
+ return (self.knots[self.degree - 1],
+ self.knots[len(self.knots) - self.degree])
+
+ def __call__(self, u):
+ """Calculates a point of the B-Spline using de Boors Algorithm"""
+ dom = self.GetDomain()
+ if u < dom[0] or u > dom[1]:
+ raise ValueError("Function value not in domain")
+ if u == dom[0]:
+ return self.points[0]
+ if u == dom[1]:
+ return self.points[-1]
+ I = self.GetIndex(u)
+ d = [self.points[I - self.degree + 1 + ii]
+ for ii in range(self.degree + 1)]
+ U = self.knots
+ for ik in range(1, self.degree + 1):
+ for ii in range(I - self.degree + ik + 1, I + 2):
+ ua = U[ii + self.degree - ik]
+ ub = U[ii - 1]
+ co1 = (ua - u) / (ua - ub)
+ co2 = (u - ub) / (ua - ub)
+ index = ii - I + self.degree - ik - 1
+ d[index] = d[index].linear_combination(d[index + 1], co1, co2)
+ return d[0]
+
+ def GetIndex(self, u):
+ dom = self.GetDomain()
+ for ii in range(self.degree - 1, len(self.knots) - self.degree):
+ if u >= self.knots[ii] and u < self.knots[ii + 1]:
+ I = ii
+ break
+ else:
+ I = dom[1] - 1
+ return I
+
+ def __len__(self):
+ return len(self.points)
+
+ def __repr__(self):
+ return "Spline(%r, %r, %r)" % (self.points, self.degree, self.knots)
+
+
+class Chaosgame(object):
+ def __init__(self, splines, thickness=0.1):
+ self.splines = splines
+ self.thickness = thickness
+ self.minx = min([p.x for spl in splines for p in spl.points])
+ self.miny = min([p.y for spl in splines for p in spl.points])
+ self.maxx = max([p.x for spl in splines for p in spl.points])
+ self.maxy = max([p.y for spl in splines for p in spl.points])
+ self.height = self.maxy - self.miny
+ self.width = self.maxx - self.minx
+ self.num_trafos = []
+ maxlength = thickness * self.width / self.height
+ for spl in splines:
+ length = 0
+ curr = spl(0)
+ for i in range(1, 1000):
+ last = curr
+ t = 1 / 999 * i
+ curr = spl(t)
+ length += curr.dist(last)
+ self.num_trafos.append(max(1, int(length / maxlength * 1.5)))
+ self.num_total = reduce(operator.add, self.num_trafos, 0)
+
+
+ def get_random_trafo(self):
+ r = random.randrange(int(self.num_total) + 1)
+ l = 0
+ for i in range(len(self.num_trafos)):
+ if r >= l and r < l + self.num_trafos[i]:
+ return i, random.randrange(self.num_trafos[i])
+ l += self.num_trafos[i]
+ return len(self.num_trafos) - 1, random.randrange(self.num_trafos[-1])
+
+ def transform_point(self, point, trafo=None):
+ x = (point.x - self.minx) / self.width
+ y = (point.y - self.miny) / self.height
+ if trafo is None:
+ trafo = self.get_random_trafo()
+ start, end = self.splines[trafo[0]].GetDomain()
+ length = end - start
+ seg_length = length / self.num_trafos[trafo[0]]
+ t = start + seg_length * trafo[1] + seg_length * x
+ basepoint = self.splines[trafo[0]](t)
+ if t + 1/50000 > end:
+ neighbour = self.splines[trafo[0]](t - 1/50000)
+ derivative = neighbour - basepoint
+ else:
+ neighbour = self.splines[trafo[0]](t + 1/50000)
+ derivative = basepoint - neighbour
+ if derivative.Mag() != 0:
+ basepoint.x += derivative.y / derivative.Mag() * (y - 0.5) * \
+ self.thickness
+ basepoint.y += -derivative.x / derivative.Mag() * (y - 0.5) * \
+ self.thickness
+ else:
+ print_("r", end='')
+ self.truncate(basepoint)
+ return basepoint
+
+ def truncate(self, point):
+ if point.x >= self.maxx:
+ point.x = self.maxx
+ if point.y >= self.maxy:
+ point.y = self.maxy
+ if point.x < self.minx:
+ point.x = self.minx
+ if point.y < self.miny:
+ point.y = self.miny
+
+ def create_image_chaos(self, w, h, n):
+ im = [[1] * h for i in range(w)]
+ point = GVector((self.maxx + self.minx) / 2,
+ (self.maxy + self.miny) / 2, 0)
+ colored = 0
+ times = []
+ for _ in range(n):
+ t1 = time.time()
+ for i in xrange(5000):
+ point = self.transform_point(point)
+ x = (point.x - self.minx) / self.width * w
+ y = (point.y - self.miny) / self.height * h
+ x = int(x)
+ y = int(y)
+ if x == w:
+ x -= 1
+ if y == h:
+ y -= 1
+ im[x][h - y - 1] = 0
+ t2 = time.time()
+ times.append(t2 - t1)
+ return times
+
+
+def main(n):
+ splines = [
+ Spline([
+ GVector(1.597350, 3.304460, 0.000000),
+ GVector(1.575810, 4.123260, 0.000000),
+ GVector(1.313210, 5.288350, 0.000000),
+ GVector(1.618900, 5.329910, 0.000000),
+ GVector(2.889940, 5.502700, 0.000000),
+ GVector(2.373060, 4.381830, 0.000000),
+ GVector(1.662000, 4.360280, 0.000000)],
+ 3, [0, 0, 0, 1, 1, 1, 2, 2, 2]),
+ Spline([
+ GVector(2.804500, 4.017350, 0.000000),
+ GVector(2.550500, 3.525230, 0.000000),
+ GVector(1.979010, 2.620360, 0.000000),
+ GVector(1.979010, 2.620360, 0.000000)],
+ 3, [0, 0, 0, 1, 1, 1]),
+ Spline([
+ GVector(2.001670, 4.011320, 0.000000),
+ GVector(2.335040, 3.312830, 0.000000),
+ GVector(2.366800, 3.233460, 0.000000),
+ GVector(2.366800, 3.233460, 0.000000)],
+ 3, [0, 0, 0, 1, 1, 1])
+ ]
+ c = Chaosgame(splines, 0.25)
+ return c.create_image_chaos(1000, 1200, n)
+
+
+
+if __name__ == "__main__":
+ import util
+ parser = optparse.OptionParser(
+ usage="%prog [options]",
+ description="Test the performance of the Chaos benchmark")
+ util.add_standard_options_to(parser)
+ options, args = parser.parse_args()
+
+ util.run_benchmark(options, options.num_runs, main)
+
--
Repository URL: http://hg.python.org/benchmarks
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