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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.
####################################################################
# Benchmark for checking if numexpr leaks memory when evaluating
# expressions that changes continously. It also serves for computing
# the latency of numexpr when working with small arrays.
from __future__ import print_function
import sys
from time import time
import numpy as np
import numexpr as ne
N = 100
M = 10
def timed_eval(eval_func, expr_func):
t1 = time()
for i in range(N):
r = eval_func(expr_func(i))
if i % 10 == 0:
sys.stdout.write('.')
print(" done in %s seconds" % round(time() - t1, 3))
print("Number of iterations %s. Length of the array: %s " % (N, M))
a = np.arange(M)
# lots of duplicates to collapse
#expr = '+'.join('(a + 1) * %d' % i for i in range(50))
# no duplicate to collapse
expr = '+'.join('(a + %d) * %d' % (i, i) for i in range(50))
def non_cacheable(i):
return expr + '+ %d' % i
def cacheable(i):
return expr + '+ i'
print("* Numexpr with non-cacheable expressions: ", end=" ")
timed_eval(ne.evaluate, non_cacheable)
print("* Numexpr with cacheable expressions: ", end=" ")
timed_eval(ne.evaluate, cacheable)
print("* Numpy with non-cacheable expressions: ", end=" ")
timed_eval(eval, non_cacheable)
print("* Numpy with cacheable expressions: ", end=" ")
timed_eval(eval, cacheable)
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