Mercurial > hg > vamp-build-and-test
comparison DEPENDENCIES/mingw32/Python27/Lib/site-packages/numpy/ma/bench.py @ 87:2a2c65a20a8b
Add Python libs and headers
| author | Chris Cannam |
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| date | Wed, 25 Feb 2015 14:05:22 +0000 |
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| children |
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| 86:413a9d26189e | 87:2a2c65a20a8b |
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| 1 #! python | |
| 2 # encoding: utf-8 | |
| 3 from __future__ import division, absolute_import, print_function | |
| 4 | |
| 5 import timeit | |
| 6 #import IPython.ipapi | |
| 7 #ip = IPython.ipapi.get() | |
| 8 #from IPython import ipmagic | |
| 9 import numpy | |
| 10 #from numpy import ma | |
| 11 #from numpy.ma import filled | |
| 12 #from numpy.ma.testutils import assert_equal | |
| 13 | |
| 14 | |
| 15 #####--------------------------------------------------------------------------- | |
| 16 #---- --- Global variables --- | |
| 17 #####--------------------------------------------------------------------------- | |
| 18 | |
| 19 # Small arrays .................................. | |
| 20 xs = numpy.random.uniform(-1, 1, 6).reshape(2, 3) | |
| 21 ys = numpy.random.uniform(-1, 1, 6).reshape(2, 3) | |
| 22 zs = xs + 1j * ys | |
| 23 m1 = [[True, False, False], [False, False, True]] | |
| 24 m2 = [[True, False, True], [False, False, True]] | |
| 25 nmxs = numpy.ma.array(xs, mask=m1) | |
| 26 nmys = numpy.ma.array(ys, mask=m2) | |
| 27 nmzs = numpy.ma.array(zs, mask=m1) | |
| 28 # Big arrays .................................... | |
| 29 xl = numpy.random.uniform(-1, 1, 100*100).reshape(100, 100) | |
| 30 yl = numpy.random.uniform(-1, 1, 100*100).reshape(100, 100) | |
| 31 zl = xl + 1j * yl | |
| 32 maskx = xl > 0.8 | |
| 33 masky = yl < -0.8 | |
| 34 nmxl = numpy.ma.array(xl, mask=maskx) | |
| 35 nmyl = numpy.ma.array(yl, mask=masky) | |
| 36 nmzl = numpy.ma.array(zl, mask=maskx) | |
| 37 | |
| 38 #####--------------------------------------------------------------------------- | |
| 39 #---- --- Functions --- | |
| 40 #####--------------------------------------------------------------------------- | |
| 41 | |
| 42 def timer(s, v='', nloop=500, nrep=3): | |
| 43 units = ["s", "ms", "µs", "ns"] | |
| 44 scaling = [1, 1e3, 1e6, 1e9] | |
| 45 print("%s : %-50s : " % (v, s), end=' ') | |
| 46 varnames = ["%ss,nm%ss,%sl,nm%sl" % tuple(x*4) for x in 'xyz'] | |
| 47 setup = 'from __main__ import numpy, ma, %s' % ','.join(varnames) | |
| 48 Timer = timeit.Timer(stmt=s, setup=setup) | |
| 49 best = min(Timer.repeat(nrep, nloop)) / nloop | |
| 50 if best > 0.0: | |
| 51 order = min(-int(numpy.floor(numpy.log10(best)) // 3), 3) | |
| 52 else: | |
| 53 order = 3 | |
| 54 print("%d loops, best of %d: %.*g %s per loop" % (nloop, nrep, | |
| 55 3, | |
| 56 best * scaling[order], | |
| 57 units[order])) | |
| 58 # ip.magic('timeit -n%i %s' % (nloop,s)) | |
| 59 | |
| 60 | |
| 61 | |
| 62 def compare_functions_1v(func, nloop=500, | |
| 63 xs=xs, nmxs=nmxs, xl=xl, nmxl=nmxl): | |
| 64 funcname = func.__name__ | |
| 65 print("-"*50) | |
| 66 print("%s on small arrays" % funcname) | |
| 67 module, data = "numpy.ma", "nmxs" | |
| 68 timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop) | |
| 69 # | |
| 70 print("%s on large arrays" % funcname) | |
| 71 module, data = "numpy.ma", "nmxl" | |
| 72 timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop) | |
| 73 return | |
| 74 | |
| 75 def compare_methods(methodname, args, vars='x', nloop=500, test=True, | |
| 76 xs=xs, nmxs=nmxs, xl=xl, nmxl=nmxl): | |
| 77 print("-"*50) | |
| 78 print("%s on small arrays" % methodname) | |
| 79 data, ver = "nm%ss" % vars, 'numpy.ma' | |
| 80 timer("%(data)s.%(methodname)s(%(args)s)" % locals(), v=ver, nloop=nloop) | |
| 81 # | |
| 82 print("%s on large arrays" % methodname) | |
| 83 data, ver = "nm%sl" % vars, 'numpy.ma' | |
| 84 timer("%(data)s.%(methodname)s(%(args)s)" % locals(), v=ver, nloop=nloop) | |
| 85 return | |
| 86 | |
| 87 def compare_functions_2v(func, nloop=500, test=True, | |
| 88 xs=xs, nmxs=nmxs, | |
| 89 ys=ys, nmys=nmys, | |
| 90 xl=xl, nmxl=nmxl, | |
| 91 yl=yl, nmyl=nmyl): | |
| 92 funcname = func.__name__ | |
| 93 print("-"*50) | |
| 94 print("%s on small arrays" % funcname) | |
| 95 module, data = "numpy.ma", "nmxs,nmys" | |
| 96 timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop) | |
| 97 # | |
| 98 print("%s on large arrays" % funcname) | |
| 99 module, data = "numpy.ma", "nmxl,nmyl" | |
| 100 timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop) | |
| 101 return | |
| 102 | |
| 103 | |
| 104 ############################################################################### | |
| 105 | |
| 106 | |
| 107 ################################################################################ | |
| 108 if __name__ == '__main__': | |
| 109 # # Small arrays .................................. | |
| 110 # xs = numpy.random.uniform(-1,1,6).reshape(2,3) | |
| 111 # ys = numpy.random.uniform(-1,1,6).reshape(2,3) | |
| 112 # zs = xs + 1j * ys | |
| 113 # m1 = [[True, False, False], [False, False, True]] | |
| 114 # m2 = [[True, False, True], [False, False, True]] | |
| 115 # nmxs = numpy.ma.array(xs, mask=m1) | |
| 116 # nmys = numpy.ma.array(ys, mask=m2) | |
| 117 # nmzs = numpy.ma.array(zs, mask=m1) | |
| 118 # mmxs = maskedarray.array(xs, mask=m1) | |
| 119 # mmys = maskedarray.array(ys, mask=m2) | |
| 120 # mmzs = maskedarray.array(zs, mask=m1) | |
| 121 # # Big arrays .................................... | |
| 122 # xl = numpy.random.uniform(-1,1,100*100).reshape(100,100) | |
| 123 # yl = numpy.random.uniform(-1,1,100*100).reshape(100,100) | |
| 124 # zl = xl + 1j * yl | |
| 125 # maskx = xl > 0.8 | |
| 126 # masky = yl < -0.8 | |
| 127 # nmxl = numpy.ma.array(xl, mask=maskx) | |
| 128 # nmyl = numpy.ma.array(yl, mask=masky) | |
| 129 # nmzl = numpy.ma.array(zl, mask=maskx) | |
| 130 # mmxl = maskedarray.array(xl, mask=maskx, shrink=True) | |
| 131 # mmyl = maskedarray.array(yl, mask=masky, shrink=True) | |
| 132 # mmzl = maskedarray.array(zl, mask=maskx, shrink=True) | |
| 133 # | |
| 134 compare_functions_1v(numpy.sin) | |
| 135 compare_functions_1v(numpy.log) | |
| 136 compare_functions_1v(numpy.sqrt) | |
| 137 #.................................................................... | |
| 138 compare_functions_2v(numpy.multiply) | |
| 139 compare_functions_2v(numpy.divide) | |
| 140 compare_functions_2v(numpy.power) | |
| 141 #.................................................................... | |
| 142 compare_methods('ravel', '', nloop=1000) | |
| 143 compare_methods('conjugate', '', 'z', nloop=1000) | |
| 144 compare_methods('transpose', '', nloop=1000) | |
| 145 compare_methods('compressed', '', nloop=1000) | |
| 146 compare_methods('__getitem__', '0', nloop=1000) | |
| 147 compare_methods('__getitem__', '(0,0)', nloop=1000) | |
| 148 compare_methods('__getitem__', '[0,-1]', nloop=1000) | |
| 149 compare_methods('__setitem__', '0, 17', nloop=1000, test=False) | |
| 150 compare_methods('__setitem__', '(0,0), 17', nloop=1000, test=False) | |
| 151 #.................................................................... | |
| 152 print("-"*50) | |
| 153 print("__setitem__ on small arrays") | |
| 154 timer('nmxs.__setitem__((-1,0),numpy.ma.masked)', 'numpy.ma ', nloop=10000) | |
| 155 | |
| 156 print("-"*50) | |
| 157 print("__setitem__ on large arrays") | |
| 158 timer('nmxl.__setitem__((-1,0),numpy.ma.masked)', 'numpy.ma ', nloop=10000) | |
| 159 | |
| 160 #.................................................................... | |
| 161 print("-"*50) | |
| 162 print("where on small arrays") | |
| 163 timer('numpy.ma.where(nmxs>2,nmxs,nmys)', 'numpy.ma ', nloop=1000) | |
| 164 print("-"*50) | |
| 165 print("where on large arrays") | |
| 166 timer('numpy.ma.where(nmxl>2,nmxl,nmyl)', 'numpy.ma ', nloop=100) |