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###################################################################
# Numexpr - Fast numerical array expression evaluator for NumPy.
#
# License: MIT
# Author: See AUTHORS.txt
#
# See LICENSE.txt and LICENSES/*.txt for details about copyright and
# rights to use.
####################################################################
from __future__ import print_function
import sys
import timeit
import numpy
array_size = 5_000_000
iterations = 10
numpy_ttime = []
numpy_sttime = []
numpy_nttime = []
numexpr_ttime = []
numexpr_sttime = []
numexpr_nttime = []
def compare_times(expr, nexpr):
global numpy_ttime
global numpy_sttime
global numpy_nttime
global numexpr_ttime
global numexpr_sttime
global numexpr_nttime
print("******************* Expression:", expr)
setup_contiguous = setupNP_contiguous
setup_strided = setupNP_strided
setup_unaligned = setupNP_unaligned
numpy_timer = timeit.Timer(expr, setup_contiguous)
numpy_time = round(numpy_timer.timeit(number=iterations), 4)
numpy_ttime.append(numpy_time)
print('numpy:', numpy_time / iterations)
numpy_timer = timeit.Timer(expr, setup_strided)
numpy_stime = round(numpy_timer.timeit(number=iterations), 4)
numpy_sttime.append(numpy_stime)
print('numpy strided:', numpy_stime / iterations)
numpy_timer = timeit.Timer(expr, setup_unaligned)
numpy_ntime = round(numpy_timer.timeit(number=iterations), 4)
numpy_nttime.append(numpy_ntime)
print('numpy unaligned:', numpy_ntime / iterations)
evalexpr = 'evaluate("%s", optimization="aggressive")' % expr
numexpr_timer = timeit.Timer(evalexpr, setup_contiguous)
numexpr_time = round(numexpr_timer.timeit(number=iterations), 4)
numexpr_ttime.append(numexpr_time)
print("numexpr:", numexpr_time/iterations, end=" ")
print("Speed-up of numexpr over numpy:", round(numpy_time/numexpr_time, 4))
evalexpr = 'evaluate("%s", optimization="aggressive")' % expr
numexpr_timer = timeit.Timer(evalexpr, setup_strided)
numexpr_stime = round(numexpr_timer.timeit(number=iterations), 4)
numexpr_sttime.append(numexpr_stime)
print("numexpr strided:", numexpr_stime/iterations, end=" ")
print("Speed-up of numexpr strided over numpy:",
round(numpy_stime/numexpr_stime, 4))
evalexpr = 'evaluate("%s", optimization="aggressive")' % expr
numexpr_timer = timeit.Timer(evalexpr, setup_unaligned)
numexpr_ntime = round(numexpr_timer.timeit(number=iterations), 4)
numexpr_nttime.append(numexpr_ntime)
print("numexpr unaligned:", numexpr_ntime/iterations, end=" ")
print("Speed-up of numexpr unaligned over numpy:",
round(numpy_ntime/numexpr_ntime, 4))
setupNP = """\
from numpy import arange, where, arctan2, sqrt
from numpy import rec as records
from numexpr import evaluate
# Initialize a recarray of 16 MB in size
r=records.array(None, formats='a%s,i4,f8', shape=%s)
c1 = r.field('f0')%s
i2 = r.field('f1')%s
f3 = r.field('f2')%s
c1[:] = "a"
i2[:] = arange(%s)/1000
f3[:] = i2/2.
"""
setupNP_contiguous = setupNP % (4, array_size,
".copy()", ".copy()", ".copy()",
array_size)
setupNP_strided = setupNP % (4, array_size, "", "", "", array_size)
setupNP_unaligned = setupNP % (1, array_size, "", "", "", array_size)
expressions = []
expressions.append('i2 > 0')
expressions.append('i2 < 0')
expressions.append('i2 < f3')
expressions.append('i2-10 < f3')
expressions.append('i2*f3+f3*f3 > i2')
expressions.append('0.1*i2 > arctan2(i2, f3)')
expressions.append('i2%2 > 3')
expressions.append('i2%10 < 4')
expressions.append('i2**2 + (f3+1)**-2.5 < 3')
expressions.append('(f3+1)**50 > i2')
expressions.append('sqrt(i2**2 + f3**2) > 1')
expressions.append('(i2>2) | ((f3**2>3) & ~(i2*f3<2))')
def compare(expression=None):
if expression:
compare_times(expression, 1)
sys.exit(0)
nexpr = 0
for expr in expressions:
nexpr += 1
compare_times(expr, nexpr)
print()
if __name__ == '__main__':
import numexpr
numexpr.print_versions()
if len(sys.argv) > 1:
expression = sys.argv[1]
print("expression-->", expression)
compare(expression)
else:
compare()
tratios = numpy.array(numpy_ttime) / numpy.array(numexpr_ttime)
stratios = numpy.array(numpy_sttime) / numpy.array(numexpr_sttime)
ntratios = numpy.array(numpy_nttime) / numpy.array(numexpr_nttime)
print("*************** Numexpr vs NumPy speed-ups *******************")
# print "numpy total:", sum(numpy_ttime)/iterations
# print "numpy strided total:", sum(numpy_sttime)/iterations
# print "numpy unaligned total:", sum(numpy_nttime)/iterations
# print "numexpr total:", sum(numexpr_ttime)/iterations
print("Contiguous case:\t %s (mean), %s (min), %s (max)" % \
(round(tratios.mean(), 2),
round(tratios.min(), 2),
round(tratios.max(), 2)))
# print "numexpr strided total:", sum(numexpr_sttime)/iterations
print("Strided case:\t\t %s (mean), %s (min), %s (max)" % \
(round(stratios.mean(), 2),
round(stratios.min(), 2),
round(stratios.max(), 2)))
# print "numexpr unaligned total:", sum(numexpr_nttime)/iterations
print("Unaligned case:\t\t %s (mean), %s (min), %s (max)" % \
(round(ntratios.mean(), 2),
round(ntratios.min(), 2),
round(ntratios.max(), 2)))
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