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45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
###################################################################
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# Numexpr - Fast numerical array expression evaluator for NumPy.
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#
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# License: MIT
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# Author: See AUTHORS.txt
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#
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# See LICENSE.txt and LICENSES/*.txt for details about copyright and
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# rights to use.
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####################################################################
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"""Very simple test that compares the speed of operating with
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aligned vs unaligned arrays.
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"""
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from __future__ import print_function
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from timeit import Timer
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import numpy as np
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import numexpr as ne
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niter = 10
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#shape = (1000*10000) # unidimensional test
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shape = (1000, 10000) # multidimensional test
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print("Numexpr version: ", ne.__version__)
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Z_fast = np.zeros(shape, dtype=[('x',np.float64),('y',np.int64)])
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Z_slow = np.zeros(shape, dtype=[('y1',np.int8),('x',np.float64),('y2',np.int8,(7,))])
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x_fast = Z_fast['x']
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t = Timer("x_fast * x_fast", "from __main__ import x_fast")
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print("NumPy aligned: \t", round(min(t.repeat(3, niter)), 3), "s")
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x_slow = Z_slow['x']
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t = Timer("x_slow * x_slow", "from __main__ import x_slow")
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print("NumPy unaligned:\t", round(min(t.repeat(3, niter)), 3), "s")
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t = Timer("ne.evaluate('x_fast * x_fast')", "from __main__ import ne, x_fast")
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print("Numexpr aligned:\t", round(min(t.repeat(3, niter)), 3), "s")
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t = Timer("ne.evaluate('x_slow * x_slow')", "from __main__ import ne, x_slow")
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print("Numexpr unaligned:\t", round(min(t.repeat(3, niter)), 3), "s")
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