"""Very simple test that compares the speed of operating with
aligned vs unaligned arrays.
"""
from timeit import Timer
import numpy as np
import numexpr as ne
niter = 10
# shape = (1000*10000) # unidimensional test
shape = (1000, 10000) # multidimensional test
ne.print_versions()
Z_fast = np.zeros(shape, dtype=[("x", np.float64), ("y", np.int64)])
Z_slow = np.zeros(shape, dtype=[("x", np.float64), ("y", np.bool)])
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"
expr9 = 'where(a%2 != 0.0, 2, b+5)'
expr10 = 'a**2 + (b+1)**-2.5'
expr11 = '(a+1)**50'
expr12 = 'sqrt(a**2 + b**2)'
def compare(check_only=False):
experiments = [(setup1, expr1), (setup2, expr2), (setup3, expr3),
(setup4, expr4), (setup5, expr5), (setup5, expr6),
(setup5, expr7), (setup5, expr8), (setup5, expr9),
(setup5, expr10), (setup5, expr11), (setup5, expr12),
]
total = 0
for params in experiments:
total += compare_times(*params)
print
average = total / len(experiments)
print "Average =", round(average, 2)
return average
if __name__ == '__main__':
import numexpr
numexpr.print_versions()
averages = []
for i in range(iterations):
averages.append(compare())
print "Averages:", ', '.join("%.2f" % x for x in averages)
(setup1, expr1),
(setup2, expr2),
(setup3, expr3),
(setup4, expr4),
(setup5, expr5),
(setup5, expr6),
(setup5, expr7),
(setup5, expr8),
(setup5, expr9),
(setup5, expr10),
(setup5, expr11),
(setup5, expr12),
]
total = 0
for params in experiments:
total += compare_times(*params)
print
average = total / len(experiments)
print "Average =", round(average, 2)
return average
if __name__ == '__main__':
import numexpr
numexpr.print_versions()
averages = []
for i in range(iterations):
averages.append(compare())
print "Averages:", ', '.join("%.2f" % x for x in averages)
# 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 timeit import Timer
import numpy as np
import numexpr as ne
niter = 10
#shape = (1000*10000) # unidimensional test
shape = (1000, 10000) # multidimensional test
ne.print_versions()
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")
