mirror of
https://github.com/boostorg/histogram.git
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154 lines
4.3 KiB
Python
Executable File
154 lines
4.3 KiB
Python
Executable File
#!@PYTHON_EXECUTABLE@
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##
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## Copyright 2015-2016 Hans Dembinski
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##
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## Distributed under the Boost Software License, Version 1.0.
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## (See accompanying file LICENSE_1_0.txt
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## or copy at http://www.boost.org/LICENSE_1_0.txt)
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import numpy as np
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from timeit import default_timer as timer
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from histogram import histogram
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from histogram.axis import regular
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def compare_1d(n, distrib):
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if distrib == 0:
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r = np.random.rand(n)
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else:
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r = 0.5 + 0.3 * np.random.randn(n)
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best_numpy = float("infinity")
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best_boost = float("infinity")
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for k in xrange(50):
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t = timer()
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w, xe = np.histogram(r, bins=100, range=(0.0, 1.0))
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t = timer() - t
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best_numpy = min(t, best_numpy)
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h = histogram(regular(100, 0, 1))
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t = timer()
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h.fill(r)
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t = timer() - t
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best_boost = min(t, best_boost)
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assert(np.all(w == np.array(h)[:-2]))
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print "py:numpy %.3f" % best_numpy
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print "py:hd_sd %.3f" % best_boost
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def compare_2d(n, distrib):
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if distrib == 0:
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r = np.random.rand(n)
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else:
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r = 0.5 + 0.3 * np.random.randn(n)
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r = r.reshape(2, n/2)
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best_numpy = float("infinity")
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best_boost = float("infinity")
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for k in xrange(50):
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t = timer()
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w, xe, ye = np.histogram2d(r[0], r[1], bins=(100, 100),
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range=((0.0, 1.0), (0.0, 1.0)))
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t = timer() - t
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best_numpy = min(t, best_numpy)
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h = histogram(regular(100, 0, 1), regular(100, 0, 1))
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t = timer()
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h.fill(r[0], r[1])
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t = timer() - t
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best_boost = min(t, best_boost)
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assert(np.all(w == np.array(h)[:-2,:-2]))
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print "py:numpy %.3f" % best_numpy
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print "py:hd_sd %.3f" % best_boost
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def compare_3d(n, distrib):
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if distrib == 0:
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r = np.random.rand(n)
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else:
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r = 0.3 * np.random.randn(n)
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r = r.reshape(3, n/3)
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best_numpy = float("infinity")
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best_boost = float("infinity")
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for k in xrange(50):
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t = timer()
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w, xe = np.histogramdd(r.T, bins=(100, 100, 100),
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range=((0.0, 1.0),
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(0.0, 1.0),
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(0.0, 1.0)))
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t = timer() - t
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best_numpy = min(t, best_numpy)
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h = histogram(regular(100, 0, 1),
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regular(100, 0, 1),
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regular(100, 0, 1))
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t = timer()
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h.fill(r[0], r[1], r[2])
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t = timer() - t
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best_boost = min(t, best_boost)
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assert(np.all(w == np.array(h)[:-2,:-2,:-2]))
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print "py:numpy %.3f" % best_numpy
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print "py:hd_sd %.3f" % best_boost
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def compare_6d(n, distrib):
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if distrib == 0:
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r = np.random.rand(n)
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else:
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r = 0.3 * np.random.randn(n)
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r = r.reshape(6, n/6)
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best_numpy = float("infinity")
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best_boost = float("infinity")
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for k in xrange(50):
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t = timer()
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w, xe = np.histogramdd(r.T, bins=(10, 10, 10,
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10, 10, 10),
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range=((0.0, 1.0),
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(0.0, 1.0),
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(0.0, 1.0),
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(0.0, 1.0),
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(0.0, 1.0),
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(0.0, 1.0)))
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t = timer() - t
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best_numpy = min(t, best_numpy)
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h = histogram(regular(10, 0, 1),
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regular(10, 0, 1),
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regular(10, 0, 1),
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regular(10, 0, 1),
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regular(10, 0, 1),
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regular(10, 0, 1))
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t = timer()
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h.fill(r[0], r[1], r[2], r[3], r[4], r[5])
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t = timer() - t
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best_boost = min(t, best_boost)
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assert(np.all(w == np.array(h)[:-2,:-2,:-2,:-2,:-2,:-2]))
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print "py:numpy %.3f" % best_numpy
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print "py:hd_sd %.3f" % best_boost
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print "1D"
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print "uniform distribution"
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compare_1d(6000000, 0)
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print "normal distribution"
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compare_1d(6000000, 1)
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print "2D"
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print "uniform distribution"
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compare_2d(6000000, 0)
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print "normal distribution"
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compare_2d(6000000, 1)
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print "3D"
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print "uniform distribution"
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compare_3d(6000000, 0)
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print "normal distribution"
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compare_3d(6000000, 1)
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print "6D"
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print "uniform distribution"
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compare_6d(6000000, 0)
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print "normal distribution"
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compare_6d(6000000, 1)
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