8000 Merge Pierre's implementation of MaskedArray. · numpy/numpy@61f9f6d · GitHub
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Merge Pierre's implementation of MaskedArray.
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‎numpy/core/ma.py

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‎numpy/core/ma/LICENSE

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* Copyright (c) 2006, University of Georgia and Pierre G.F. Gerard-Marchant
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* All rights reserved.
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of the University of Georgia nor the
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* names of its contributors may be used to endorse or promote products
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* derived from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY
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* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

‎numpy/core/ma/__init__.py

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"""Masked arrays add-ons.
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A collection of utilities for maskedarray
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:author: Pierre GF Gerard-Marchant
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:contact: pierregm_at_uga_dot_edu
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:version: $Id: __init__.py 3473 2007-10-29 15:18:13Z jarrod.millman $
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"""
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__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
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__version__ = '1.0'
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__revision__ = "$Revision: 3473 $"
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__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
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import core
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from core import *
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import extras
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from extras import *
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__all__ = ['core', 'extras']
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__all__ += core.__all__
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__all__ += extras.__all__

‎numpy/core/ma/bench.py

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#! python
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import timeit
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#import IPython.ipapi
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#ip = IPython.ipapi.get()
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#from IPython import ipmagic
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import numpy
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import maskedarray
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from maskedarray import filled
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from maskedarray.testutils import assert_equal
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#####---------------------------------------------------------------------------
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#---- --- Global variables ---
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#####---------------------------------------------------------------------------
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# Small arrays ..................................
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xs = numpy.random.uniform(-1,1,6).reshape(2,3)
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ys = numpy.random.uniform(-1,1,6).reshape(2,3)
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zs = xs + 1j * ys
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m1 = [[True, False, False], [False, False, True]]
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m2 = [[True, False, True], [False, False, True]]
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nmxs = numpy.ma.array(xs, mask=m1)
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nmys = numpy.ma.array(ys, mask=m2)
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nmzs = numpy.ma.array(zs, mask=m1)
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mmxs = maskedarray.array(xs, mask=m1)
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mmys = maskedarray.array(ys, mask=m2)
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mmzs = maskedarray.array(zs, mask=m1)
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# Big arrays ....................................
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xl = numpy.random.uniform(-1,1,100*100).reshape(100,100)
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yl = numpy.random.uniform(-1,1,100*100).reshape(100,100)
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zl = xl + 1j * yl
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maskx = xl > 0.8
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masky = yl < -0.8
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nmxl = numpy.ma.array(xl, mask=maskx)
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nmyl = numpy.ma.array(yl, mask=masky)
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nmzl = numpy.ma.array(zl, mask=maskx)
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mmxl = maskedarray.array(xl, mask=maskx, shrink=True)
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mmyl = maskedarray.array(yl, mask=masky, shrink=True)
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mmzl = maskedarray.array(zl, mask=maskx, shrink=True)
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#####---------------------------------------------------------------------------
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#---- --- Functions ---
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#####---------------------------------------------------------------------------
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def timer(s, v='', nloop=500, nrep=3):
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units = ["s", "ms", "\xb5s", "ns"]
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scaling = [1, 1e3, 1e6, 1e9]
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print "%s : %-50s : " % (v,s),
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varnames = ["%ss,nm%ss,mm%ss,%sl,nm%sl,mm%sl" % tuple(x*6) for x in 'xyz']
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setup = 'from __main__ import numpy, maskedarray, %s' % ','.join(varnames)
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Timer = timeit.Timer(stmt=s, setup=setup)
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best = min(Timer.repeat(nrep, nloop)) / nloop
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if best > 0.0:
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order = min(-int(numpy.floor(numpy.log10(best)) // 3), 3)
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else:
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order = 3
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print "%d loops, best of %d: %.*g %s per loop" % (nloop, nrep,
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3,
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best * scaling[order],
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units[order])
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# ip.magic('timeit -n%i %s' % (nloop,s))
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def compare_functions_1v(func, nloop=500, test=True,
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xs=xs, nmxs=nmxs, mmxs=mmxs,
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xl=xl, nmxl=nmxl, mmxl=mmxl):
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funcname = func.__name__
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print "-"*50
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print "%s on small arrays" % funcname
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if test:
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assert_equal(filled(eval("numpy.ma.%s(nmxs)" % funcname),0),
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filled(eval("maskedarray.%s(mmxs)" % funcname),0))
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for (module, data) in zip(("numpy", "numpy.ma","maskedarray"),
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("xs","nmxs","mmxs")):
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timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
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#
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print "%s on large arrays" % funcname
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if test:
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assert_equal(filled(eval("numpy.ma.%s(nmxl)" % funcname),0),
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filled(eval("maskedarray.%s(mmxl)" % funcname),0))
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for (module, data) in zip(("numpy", "numpy.ma","maskedarray"),
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("xl","nmxl","mmxl")):
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timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
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return
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def compare_methods(methodname, args, vars='x', nloop=500, test=True,
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xs=xs, nmxs=nmxs, mmxs=mmxs,
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xl=xl, nmxl=nmxl, mmxl=mmxl):
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print "-"*50
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print "%s on small arrays" % methodname
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if test:
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assert_equal(filled(eval("nm%ss.%s(%s)" % (vars,methodname,args)),0),
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filled(eval("mm%ss.%s(%s)" % (vars,methodname,args)),0))
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for (data, ver) in zip(["nm%ss" % vars, "mm%ss" % vars], ('numpy.ma ','maskedarray')):
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timer("%(data)s.%(methodname)s(%(args)s)" % locals(), v=ver, nloop=nloop)
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#
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print "%s on large arrays" % methodname
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if test:
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assert_equal(filled(eval("nm%sl.%s(%s)" % (vars,methodname,args)),0),
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filled(eval("mm%sl.%s(%s)" % (vars,methodname,args)),0))
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for (data, ver) in zip(["nm%sl" % vars, "mm%sl" % vars], ('numpy.ma ','maskedarray')):
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timer("%(data)s.%(methodname)s(%(args)s)" % locals(), v=ver, nloop=nloop)
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return
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def compare_functions_2v(func, nloop=500, test=True,
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xs=xs, nmxs=nmxs, mmxs=mmxs,
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ys=ys, nmys=nmys, mmys=mmys,
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xl=xl, nmxl=nmxl, mmxl=mmxl,
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yl=yl, nmyl=nmyl, mmyl=mmyl):
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funcname = func.__name__
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print "-"*50
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print "%s on small arrays" % funcname
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if test:
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assert_equal(filled(eval("numpy.ma.%s(nmxs,nmys)" % funcname),0),
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filled(eval("maskedarray.%s(mmxs,mmys)" % funcname),0))
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for (module, data) in zip(("numpy", "numpy.ma","maskedarray"),
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("xs,ys","nmxs,nmys","mmxs,mmys")):
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timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
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#
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print "%s on large arrays" % funcname
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if test:
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assert_equal(filled(eval("numpy.ma.%s(nmxl, nmyl)" % funcname),0),
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filled(eval("maskedarray.%s(mmxl, mmyl)" % funcname),0))
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for (module, data) in zip(("numpy", "numpy.ma","maskedarray"),
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("xl,yl","nmxl,nmyl","mmxl,mmyl")):
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timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
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return
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###############################################################################
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################################################################################
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if __name__ == '__main__':
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# # Small arrays ..................................
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# xs = numpy.random.uniform(-1,1,6).reshape(2,3)
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# ys = numpy.random.uniform(-1,1,6).reshape(2,3)
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# zs = xs + 1j * ys
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# m1 = [[True, False, False], [False, False, True]]
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# m2 = [[True, False, True], [False, False, True]]
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# nmxs = numpy.ma.array(xs, mask=m1)
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# nmys = numpy.ma.array(ys, mask=m2)
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# nmzs = numpy.ma.array(zs, mask=m1)
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# mmxs = maskedarray.array(xs, mask=m1)
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# mmys = maskedarray.array(ys, mask=m2)
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# mmzs = maskedarray.array(zs, mask=m1)
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# # Big arrays ....................................
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# xl = numpy.random.uniform(-1,1,100*100).reshape(100,100)
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# yl = numpy.random.uniform(-1,1,100*100).reshape(100,100)
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# zl = xl + 1j * yl
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# maskx = xl > 0.8
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# masky = yl < -0.8
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# nmxl = numpy.ma.array(xl, mask=maskx)
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# nmyl = numpy.ma.array(yl, mask=masky)
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# nmzl = numpy.ma.array(zl, mask=maskx)
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# mmxl = maskedarray.array(xl, mask=maskx, shrink=True)
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# mmyl = maskedarray.array(yl, mask=masky, shrink=True)
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# mmzl = maskedarray.array(zl, mask=maskx, shrink=True)
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#
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compare_functions_1v(numpy.sin)
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compare_functions_1v(numpy.log)
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compare_functions_1v(numpy.sqrt)
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#....................................................................
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compare_functions_2v(numpy.multiply)
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compare_functions_2v(numpy.divide)
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compare_functions_2v(numpy.power)
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#....................................................................
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compare_methods('ravel','', nloop=1000)
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compare_methods('conjugate','','z', nloop=1000)
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compare_methods('transpose','', nloop=1000)
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compare_methods('compressed','', nloop=1000)
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compare_methods('__getitem__','0', nloop=1000)
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compare_methods('__getitem__','(0,0)', nloop=1000)
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compare_methods('__getitem__','[0,-1]', nloop=1000)
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compare_methods('__setitem__','0, 17', nloop=1000, test=False)
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compare_methods('__setitem__','(0,0), 17', nloop=1000, test=False)
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#....................................................................
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print "-"*50
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print "__setitem__ on small arrays"
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timer('nmxs.__setitem__((-1,0),numpy.ma.masked)', 'numpy.ma ',nloop=10000)
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timer('mmxs.__setitem__((-1,0),maskedarray.masked)', 'maskedarray',nloop=10000)
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print "-"*50
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print "__setitem__ on large arrays"
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timer('nmxl.__setitem__((-1,0),numpy.ma.masked)', 'numpy.ma ',nloop=10000)
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timer('mmxl.__setitem__((-1,0),maskedarray.masked)', 'maskedarray',nloop=10000)
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#....................................................................
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print "-"*50
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print "where on small arrays"
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assert_equal(eval("numpy.ma.where(nmxs>2,nmxs,nmys)"),
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eval("maskedarray.where(mmxs>2, mmxs,mmys)"))
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timer('numpy.ma.where(nmxs>2,nmxs,nmys)', 'numpy.ma ',nloop=1000)
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timer('maskedarray.where(mmxs>2, mmxs,mmys)', 'maskedarray',nloop=1000)
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print "-"*50
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print "where on large arrays"
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timer('numpy.ma.where(nmxl>2,nmxl,nmyl)', 'numpy.ma ',nloop=100)
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timer('maskedarray.where(mmxl>2, mmxl,mmyl)', 'maskedarray',nloop=100)

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