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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.
####################################################################
"""Very simple test that compares the speed of operating with
aligned vs unaligned arrays.
"""
from __future__ import print_function
from timeit import Timer
import numpy as np
import numexpr as ne
niter = 10
#shape = (1000*10000) # unidimensional test
shape = (1000, 10000) # multidimensional test
print("Numexpr version: ", ne.__version__)
Z_fast = np.zeros(shape, dtype=[('x',np.float64),('y',np.int64)])
Z_slow = np.zeros(shape, dtype=[('y1',np.int8),('x',np.float64),('y2',np.int8,(7,))])
x_fast = Z_fast['x']
t = Timer("x_fast * x_fast", "from __main__ import x_fast")
print("NumPy aligned: \t", round(min(t.repeat(3, niter)), 3), "s")
x_slow = Z_slow['x']
t = Timer("x_slow * x_slow", "from __main__ import x_slow")
print("NumPy unaligned:\t", round(min(t.repeat(3, niter)), 3), "s")
t = Timer("ne.evaluate('x_fast * x_fast')", "from __main__ import ne, x_fast")
print("Numexpr aligned:\t", round(min(t.repeat(3, niter)), 3), "s")
t = Timer("ne.evaluate('x_slow * x_slow')", "from __main__ import ne, x_slow")
print("Numexpr unaligned:\t", round(min(t.repeat(3, niter)), 3), "s")
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