chore: import upstream snapshot with attribution

bench/unaligned-simple.py

@@ -0,0 +1,44 @@
+###################################################################
+#  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")