[Numpy-svn] r4576 - in branches/maskedarray/numpy/core: . ma ma/tests
numpy-svn at scipy.org
numpy-svn at scipy.org
Fri Dec 14 19:39:42 EST 2007
Author: stefan
Date: 2007-12-14 18:38:47 -0600 (Fri, 14 Dec 2007)
New Revision: 4576
Added:
branches/maskedarray/numpy/core/ma/
branches/maskedarray/numpy/core/ma/LICENSE
branches/maskedarray/numpy/core/ma/__init__.py
branches/maskedarray/numpy/core/ma/bench.py
branches/maskedarray/numpy/core/ma/core.py
branches/maskedarray/numpy/core/ma/extras.py
branches/maskedarray/numpy/core/ma/morestats.py
branches/maskedarray/numpy/core/ma/mrecords.py
branches/maskedarray/numpy/core/ma/mstats.py
branches/maskedarray/numpy/core/ma/setup.py
branches/maskedarray/numpy/core/ma/tests/
branches/maskedarray/numpy/core/ma/tests/test_core.py
branches/maskedarray/numpy/core/ma/tests/test_extras.py
branches/maskedarray/numpy/core/ma/tests/test_morestats.py
branches/maskedarray/numpy/core/ma/tests/test_mrecords.py
branches/maskedarray/numpy/core/ma/tests/test_mstats.py
branches/maskedarray/numpy/core/ma/tests/test_subclassing.py
branches/maskedarray/numpy/core/ma/testutils.py
branches/maskedarray/numpy/core/ma/timer_comparison.py
branches/maskedarray/numpy/core/ma/version.py
Removed:
branches/maskedarray/numpy/core/ma.py
Log:
Merge Pierre's implementation of MaskedArray.
Added: branches/maskedarray/numpy/core/ma/LICENSE
===================================================================
--- branches/maskedarray/numpy/core/ma/LICENSE 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/LICENSE 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,24 @@
+* Copyright (c) 2006, University of Georgia and Pierre G.F. Gerard-Marchant
+* All rights reserved.
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions are met:
+*
+* * Redistributions of source code must retain the above copyright
+* notice, this list of conditions and the following disclaimer.
+* * Redistributions in binary form must reproduce the above copyright
+* notice, this list of conditions and the following disclaimer in the
+* documentation and/or other materials provided with the distribution.
+* * Neither the name of the University of Georgia nor the
+* names of its contributors may be used to endorse or promote products
+* derived from this software without specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND ANY
+* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+* DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY
+* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
Added: branches/maskedarray/numpy/core/ma/__init__.py
===================================================================
--- branches/maskedarray/numpy/core/ma/__init__.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/__init__.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,22 @@
+"""Masked arrays add-ons.
+
+A collection of utilities for maskedarray
+
+:author: Pierre GF Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: __init__.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3473 $"
+__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
+
+import core
+from core import *
+
+import extras
+from extras import *
+
+__all__ = ['core', 'extras']
+__all__ += core.__all__
+__all__ += extras.__all__
Added: branches/maskedarray/numpy/core/ma/bench.py
===================================================================
--- branches/maskedarray/numpy/core/ma/bench.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/bench.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,198 @@
+#! python
+
+import timeit
+#import IPython.ipapi
+#ip = IPython.ipapi.get()
+#from IPython import ipmagic
+import numpy
+import maskedarray
+from maskedarray import filled
+from maskedarray.testutils import assert_equal
+
+
+#####---------------------------------------------------------------------------
+#---- --- Global variables ---
+#####---------------------------------------------------------------------------
+
+# Small arrays ..................................
+xs = numpy.random.uniform(-1,1,6).reshape(2,3)
+ys = numpy.random.uniform(-1,1,6).reshape(2,3)
+zs = xs + 1j * ys
+m1 = [[True, False, False], [False, False, True]]
+m2 = [[True, False, True], [False, False, True]]
+nmxs = numpy.ma.array(xs, mask=m1)
+nmys = numpy.ma.array(ys, mask=m2)
+nmzs = numpy.ma.array(zs, mask=m1)
+mmxs = maskedarray.array(xs, mask=m1)
+mmys = maskedarray.array(ys, mask=m2)
+mmzs = maskedarray.array(zs, mask=m1)
+# Big arrays ....................................
+xl = numpy.random.uniform(-1,1,100*100).reshape(100,100)
+yl = numpy.random.uniform(-1,1,100*100).reshape(100,100)
+zl = xl + 1j * yl
+maskx = xl > 0.8
+masky = yl < -0.8
+nmxl = numpy.ma.array(xl, mask=maskx)
+nmyl = numpy.ma.array(yl, mask=masky)
+nmzl = numpy.ma.array(zl, mask=maskx)
+mmxl = maskedarray.array(xl, mask=maskx, shrink=True)
+mmyl = maskedarray.array(yl, mask=masky, shrink=True)
+mmzl = maskedarray.array(zl, mask=maskx, shrink=True)
+
+#####---------------------------------------------------------------------------
+#---- --- Functions ---
+#####---------------------------------------------------------------------------
+
+def timer(s, v='', nloop=500, nrep=3):
+ units = ["s", "ms", "\xb5s", "ns"]
+ scaling = [1, 1e3, 1e6, 1e9]
+ print "%s : %-50s : " % (v,s),
+ varnames = ["%ss,nm%ss,mm%ss,%sl,nm%sl,mm%sl" % tuple(x*6) for x in 'xyz']
+ setup = 'from __main__ import numpy, maskedarray, %s' % ','.join(varnames)
+ Timer = timeit.Timer(stmt=s, setup=setup)
+ best = min(Timer.repeat(nrep, nloop)) / nloop
+ if best > 0.0:
+ order = min(-int(numpy.floor(numpy.log10(best)) // 3), 3)
+ else:
+ order = 3
+ print "%d loops, best of %d: %.*g %s per loop" % (nloop, nrep,
+ 3,
+ best * scaling[order],
+ units[order])
+# ip.magic('timeit -n%i %s' % (nloop,s))
+
+
+
+def compare_functions_1v(func, nloop=500, test=True,
+ xs=xs, nmxs=nmxs, mmxs=mmxs,
+ xl=xl, nmxl=nmxl, mmxl=mmxl):
+ funcname = func.__name__
+ print "-"*50
+ print "%s on small arrays" % funcname
+ if test:
+ assert_equal(filled(eval("numpy.ma.%s(nmxs)" % funcname),0),
+ filled(eval("maskedarray.%s(mmxs)" % funcname),0))
+ for (module, data) in zip(("numpy", "numpy.ma","maskedarray"),
+ ("xs","nmxs","mmxs")):
+ timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
+ #
+ print "%s on large arrays" % funcname
+ if test:
+ assert_equal(filled(eval("numpy.ma.%s(nmxl)" % funcname),0),
+ filled(eval("maskedarray.%s(mmxl)" % funcname),0))
+ for (module, data) in zip(("numpy", "numpy.ma","maskedarray"),
+ ("xl","nmxl","mmxl")):
+ timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
+ return
+
+def compare_methods(methodname, args, vars='x', nloop=500, test=True,
+ xs=xs, nmxs=nmxs, mmxs=mmxs,
+ xl=xl, nmxl=nmxl, mmxl=mmxl):
+ print "-"*50
+ print "%s on small arrays" % methodname
+ if test:
+ assert_equal(filled(eval("nm%ss.%s(%s)" % (vars,methodname,args)),0),
+ filled(eval("mm%ss.%s(%s)" % (vars,methodname,args)),0))
+ for (data, ver) in zip(["nm%ss" % vars, "mm%ss" % vars], ('numpy.ma ','maskedarray')):
+ timer("%(data)s.%(methodname)s(%(args)s)" % locals(), v=ver, nloop=nloop)
+ #
+ print "%s on large arrays" % methodname
+ if test:
+ assert_equal(filled(eval("nm%sl.%s(%s)" % (vars,methodname,args)),0),
+ filled(eval("mm%sl.%s(%s)" % (vars,methodname,args)),0))
+ for (data, ver) in zip(["nm%sl" % vars, "mm%sl" % vars], ('numpy.ma ','maskedarray')):
+ timer("%(data)s.%(methodname)s(%(args)s)" % locals(), v=ver, nloop=nloop)
+ return
+
+def compare_functions_2v(func, nloop=500, test=True,
+ xs=xs, nmxs=nmxs, mmxs=mmxs,
+ ys=ys, nmys=nmys, mmys=mmys,
+ xl=xl, nmxl=nmxl, mmxl=mmxl,
+ yl=yl, nmyl=nmyl, mmyl=mmyl):
+ funcname = func.__name__
+ print "-"*50
+ print "%s on small arrays" % funcname
+ if test:
+ assert_equal(filled(eval("numpy.ma.%s(nmxs,nmys)" % funcname),0),
+ filled(eval("maskedarray.%s(mmxs,mmys)" % funcname),0))
+ for (module, data) in zip(("numpy", "numpy.ma","maskedarray"),
+ ("xs,ys","nmxs,nmys","mmxs,mmys")):
+ timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
+ #
+ print "%s on large arrays" % funcname
+ if test:
+ assert_equal(filled(eval("numpy.ma.%s(nmxl, nmyl)" % funcname),0),
+ filled(eval("maskedarray.%s(mmxl, mmyl)" % funcname),0))
+ for (module, data) in zip(("numpy", "numpy.ma","maskedarray"),
+ ("xl,yl","nmxl,nmyl","mmxl,mmyl")):
+ timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
+ return
+
+
+###############################################################################
+
+
+################################################################################
+if __name__ == '__main__':
+# # Small arrays ..................................
+# xs = numpy.random.uniform(-1,1,6).reshape(2,3)
+# ys = numpy.random.uniform(-1,1,6).reshape(2,3)
+# zs = xs + 1j * ys
+# m1 = [[True, False, False], [False, False, True]]
+# m2 = [[True, False, True], [False, False, True]]
+# nmxs = numpy.ma.array(xs, mask=m1)
+# nmys = numpy.ma.array(ys, mask=m2)
+# nmzs = numpy.ma.array(zs, mask=m1)
+# mmxs = maskedarray.array(xs, mask=m1)
+# mmys = maskedarray.array(ys, mask=m2)
+# mmzs = maskedarray.array(zs, mask=m1)
+# # Big arrays ....................................
+# xl = numpy.random.uniform(-1,1,100*100).reshape(100,100)
+# yl = numpy.random.uniform(-1,1,100*100).reshape(100,100)
+# zl = xl + 1j * yl
+# maskx = xl > 0.8
+# masky = yl < -0.8
+# nmxl = numpy.ma.array(xl, mask=maskx)
+# nmyl = numpy.ma.array(yl, mask=masky)
+# nmzl = numpy.ma.array(zl, mask=maskx)
+# mmxl = maskedarray.array(xl, mask=maskx, shrink=True)
+# mmyl = maskedarray.array(yl, mask=masky, shrink=True)
+# mmzl = maskedarray.array(zl, mask=maskx, shrink=True)
+#
+ compare_functions_1v(numpy.sin)
+ compare_functions_1v(numpy.log)
+ compare_functions_1v(numpy.sqrt)
+ #....................................................................
+ compare_functions_2v(numpy.multiply)
+ compare_functions_2v(numpy.divide)
+ compare_functions_2v(numpy.power)
+ #....................................................................
+ compare_methods('ravel','', nloop=1000)
+ compare_methods('conjugate','','z', nloop=1000)
+ compare_methods('transpose','', nloop=1000)
+ compare_methods('compressed','', nloop=1000)
+ compare_methods('__getitem__','0', nloop=1000)
+ compare_methods('__getitem__','(0,0)', nloop=1000)
+ compare_methods('__getitem__','[0,-1]', nloop=1000)
+ compare_methods('__setitem__','0, 17', nloop=1000, test=False)
+ compare_methods('__setitem__','(0,0), 17', nloop=1000, test=False)
+ #....................................................................
+ print "-"*50
+ print "__setitem__ on small arrays"
+ timer('nmxs.__setitem__((-1,0),numpy.ma.masked)', 'numpy.ma ',nloop=10000)
+ timer('mmxs.__setitem__((-1,0),maskedarray.masked)', 'maskedarray',nloop=10000)
+ print "-"*50
+ print "__setitem__ on large arrays"
+ timer('nmxl.__setitem__((-1,0),numpy.ma.masked)', 'numpy.ma ',nloop=10000)
+ timer('mmxl.__setitem__((-1,0),maskedarray.masked)', 'maskedarray',nloop=10000)
+ #....................................................................
+ print "-"*50
+ print "where on small arrays"
+ assert_equal(eval("numpy.ma.where(nmxs>2,nmxs,nmys)"),
+ eval("maskedarray.where(mmxs>2, mmxs,mmys)"))
+ timer('numpy.ma.where(nmxs>2,nmxs,nmys)', 'numpy.ma ',nloop=1000)
+ timer('maskedarray.where(mmxs>2, mmxs,mmys)', 'maskedarray',nloop=1000)
+ print "-"*50
+ print "where on large arrays"
+ timer('numpy.ma.where(nmxl>2,nmxl,nmyl)', 'numpy.ma ',nloop=100)
+ timer('maskedarray.where(mmxl>2, mmxl,mmyl)', 'maskedarray',nloop=100)
Added: branches/maskedarray/numpy/core/ma/core.py
===================================================================
--- branches/maskedarray/numpy/core/ma/core.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/core.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,2979 @@
+# pylint: disable-msg=E1002
+"""MA: a facility for dealing with missing observations
+MA is generally used as a numpy.array look-alike.
+by Paul F. Dubois.
+
+Copyright 1999, 2000, 2001 Regents of the University of California.
+Released for unlimited redistribution.
+Adapted for numpy_core 2005 by Travis Oliphant and
+(mainly) Paul Dubois.
+
+Subclassing of the base ndarray 2006 by Pierre Gerard-Marchant.
+pgmdevlist_AT_gmail_DOT_com
+Improvements suggested by Reggie Dugard (reggie_AT_merfinllc_DOT_com)
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: core.py 3639 2007-12-13 03:39:17Z pierregm $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: pierregm $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3639 $"
+__date__ = '$Date: 2007-12-13 05:39:17 +0200 (Thu, 13 Dec 2007) $'
+
+__docformat__ = "restructuredtext en"
+
+__all__ = ['MAError', 'MaskType', 'MaskedArray',
+ 'bool_', 'complex_', 'float_', 'int_', 'object_',
+ 'abs', 'absolute', 'add', 'all', 'allclose', 'allequal', 'alltrue',
+ 'amax', 'amin', 'anom', 'anomalies', 'any', 'arange',
+ 'arccos', 'arccosh', 'arcsin', 'arcsinh', 'arctan', 'arctan2',
+ 'arctanh', 'argmax', 'argmin', 'argsort', 'around',
+ 'array', 'asarray','asanyarray',
+ 'bitwise_and', 'bitwise_or', 'bitwise_xor',
+ 'ceil', 'choose', 'compressed', 'concatenate', 'conjugate',
+ 'cos', 'cosh', 'count',
+ 'default_fill_value', 'diagonal', 'divide', 'dump', 'dumps',
+ 'empty', 'empty_like', 'equal', 'exp',
+ 'fabs', 'fmod', 'filled', 'floor', 'floor_divide','fix_invalid',
+ 'getmask', 'getmaskarray', 'greater', 'greater_equal', 'hypot',
+ 'ids', 'inner', 'innerproduct',
+ 'isMA', 'isMaskedArray', 'is_mask', 'is_masked', 'isarray',
+ 'left_shift', 'less', 'less_equal', 'load', 'loads', 'log', 'log10',
+ 'logical_and', 'logical_not', 'logical_or', 'logical_xor',
+ 'make_mask', 'make_mask_none', 'mask_or', 'masked',
+ 'masked_array', 'masked_equal', 'masked_greater',
+ 'masked_greater_equal', 'masked_inside', 'masked_less',
+ 'masked_less_equal', 'masked_not_equal', 'masked_object',
+ 'masked_outside', 'masked_print_option', 'masked_singleton',
+ 'masked_values', 'masked_where', 'max', 'maximum', 'mean', 'min',
+ 'minimum', 'multiply',
+ 'negative', 'nomask', 'nonzero', 'not_equal',
+ 'ones', 'outer', 'outerproduct',
+ 'power', 'product', 'ptp', 'put', 'putmask',
+ 'rank', 'ravel', 'remainder', 'repeat', 'reshape', 'resize',
+ 'right_shift', 'round_',
+ 'shape', 'sin', 'sinh', 'size', 'sometrue', 'sort', 'sqrt', 'std',
+ 'subtract', 'sum', 'swapaxes',
+ 'take', 'tan', 'tanh', 'transpose', 'true_divide',
+ 'var', 'where',
+ 'zeros']
+
+import sys
+import types
+import cPickle
+import operator
+#
+import numpy
+from numpy import bool_, complex_, float_, int_, object_, str_
+
+import numpy.core.umath as umath
+import numpy.core.fromnumeric as fromnumeric
+import numpy.core.numeric as numeric
+import numpy.core.numerictypes as ntypes
+from numpy import bool_, dtype, typecodes, amax, amin, ndarray
+from numpy import expand_dims as n_expand_dims
+from numpy import array as narray
+import warnings
+
+
+MaskType = bool_
+nomask = MaskType(0)
+
+divide_tolerance = 1.e-35
+numpy.seterr(all='ignore')
+
+
+
+#####--------------------------------------------------------------------------
+#---- --- Exceptions ---
+#####--------------------------------------------------------------------------
+class MAError(Exception):
+ "Class for MA related errors."
+ def __init__ (self, args=None):
+ "Creates an exception."
+ Exception.__init__(self,args)
+ self.args = args
+ def __str__(self):
+ "Calculates the string representation."
+ return str(self.args)
+ __repr__ = __str__
+
+#####--------------------------------------------------------------------------
+#---- --- Filling options ---
+#####--------------------------------------------------------------------------
+# b: boolean - c: complex - f: floats - i: integer - O: object - S: string
+default_filler = {'b': True,
+ 'c' : 1.e20 + 0.0j,
+ 'f' : 1.e20,
+ 'i' : 999999,
+ 'O' : '?',
+ 'S' : 'N/A',
+ 'u' : 999999,
+ 'V' : '???',
+ }
+max_filler = ntypes._minvals
+max_filler.update([(k,-numpy.inf) for k in [numpy.float32, numpy.float64]])
+min_filler = ntypes._maxvals
+min_filler.update([(k,numpy.inf) for k in [numpy.float32, numpy.float64]])
+if 'float128' in ntypes.typeDict:
+ max_filler.update([(numpy.float128,-numpy.inf)])
+ min_filler.update([(numpy.float128, numpy.inf)])
+
+def default_fill_value(obj):
+ """Calculates the default fill value for the argument object.
+ """
+ if hasattr(obj,'dtype'):
+ defval = default_filler[obj.dtype.kind]
+ elif isinstance(obj, numeric.dtype):
+ defval = default_filler[obj.kind]
+ elif isinstance(obj, float):
+ defval = default_filler['f']
+ elif isinstance(obj, int) or isinstance(obj, long):
+ defval = default_filler['i']
+ elif isinstance(obj, str):
+ defval = default_filler['S']
+ elif isinstance(obj, complex):
+ defval = default_filler['c']
+ else:
+ defval = default_filler['O']
+ return defval
+
+def minimum_fill_value(obj):
+ "Calculates the default fill value suitable for taking the minimum of ``obj``."
+ if hasattr(obj, 'dtype'):
+ objtype = obj.dtype
+ filler = min_filler[objtype]
+ if filler is None:
+ raise TypeError, 'Unsuitable type for calculating minimum.'
+ return filler
+ elif isinstance(obj, float):
+ return min_filler[ntypes.typeDict['float_']]
+ elif isinstance(obj, int):
+ return min_filler[ntypes.typeDict['int_']]
+ elif isinstance(obj, long):
+ return min_filler[ntypes.typeDict['uint']]
+ elif isinstance(obj, numeric.dtype):
+ return min_filler[obj]
+ else:
+ raise TypeError, 'Unsuitable type for calculating minimum.'
+
+def maximum_fill_value(obj):
+ "Calculates the default fill value suitable for taking the maximum of ``obj``."
+ if hasattr(obj, 'dtype'):
+ objtype = obj.dtype
+ filler = max_filler[objtype]
+ if filler is None:
+ raise TypeError, 'Unsuitable type for calculating minimum.'
+ return filler
+ elif isinstance(obj, float):
+ return max_filler[ntypes.typeDict['float_']]
+ elif isinstance(obj, int):
+ return max_filler[ntypes.typeDict['int_']]
+ elif isinstance(obj, long):
+ return max_filler[ntypes.typeDict['uint']]
+ elif isinstance(obj, numeric.dtype):
+ return max_filler[obj]
+ else:
+ raise TypeError, 'Unsuitable type for calculating minimum.'
+
+def set_fill_value(a, fill_value):
+ """Sets the filling value of a, if a is a masked array.
+Otherwise, does nothing.
+
+*Returns*:
+ None
+ """
+ if isinstance(a, MaskedArray):
+ a.set_fill_value(fill_value)
+ return
+
+def get_fill_value(a):
+ """Returns the filling value of a, if any.
+Otherwise, returns the default filling value for that type.
+ """
+ if isinstance(a, MaskedArray):
+ result = a.fill_value
+ else:
+ result = default_fill_value(a)
+ return result
+
+def common_fill_value(a, b):
+ """Returns the common filling value of a and b, if any.
+ If a and b have different filling values, returns None."""
+ t1 = get_fill_value(a)
+ t2 = get_fill_value(b)
+ if t1 == t2:
+ return t1
+ return None
+
+#####--------------------------------------------------------------------------
+def filled(a, value = None):
+ """Returns a as an array with masked data replaced by value.
+If value is None, get_fill_value(a) is used instead.
+If a is already a ndarray, a itself is returned.
+
+*Parameters*:
+ a : {var}
+ An input object.
+ value : {var}, optional
+ Filling value. If not given, the output of get_fill_value(a) is used instead.
+
+*Returns*:
+ A ndarray.
+
+ """
+ if hasattr(a, 'filled'):
+ return a.filled(value)
+ elif isinstance(a, ndarray): # and a.flags['CONTIGUOUS']:
+ return a
+ elif isinstance(a, dict):
+ return narray(a, 'O')
+ else:
+ return narray(a)
+
+#####--------------------------------------------------------------------------
+def get_masked_subclass(*arrays):
+ """Returns the youngest subclass of MaskedArray from a list of (masked) arrays.
+In case of siblings, the first takes over."""
+ if len(arrays) == 1:
+ arr = arrays[0]
+ if isinstance(arr, MaskedArray):
+ rcls = type(arr)
+ else:
+ rcls = MaskedArray
+ else:
+ arrcls = [type(a) for a in arrays]
+ rcls = arrcls[0]
+ if not issubclass(rcls, MaskedArray):
+ rcls = MaskedArray
+ for cls in arrcls[1:]:
+ if issubclass(cls, rcls):
+ rcls = cls
+ return rcls
+
+#####--------------------------------------------------------------------------
+def get_data(a, subok=True):
+ """Returns the _data part of a (if any), or a as a ndarray.
+
+*Parameters* :
+ a : {ndarray}
+ A ndarray or a subclass of.
+ subok : {boolean}
+ Whether to force the output to a 'pure' ndarray (False) or to return a subclass
+ of ndarray if approriate (True).
+ """
+ data = getattr(a, '_data', numpy.array(a, subok=subok))
+ if not subok:
+ return data.view(ndarray)
+ return data
+getdata = get_data
+
+def fix_invalid(a, copy=True, fill_value=None):
+ """Returns (a copy of) a where invalid data (nan/inf) are masked and replaced
+by fill_value.
+Note that a copy is performed by default (just in case...).
+
+*Parameters*:
+ a : {ndarray}
+ A (subclass of) ndarray.
+ copy : {boolean}
+ Whether to use a copy of a (True) or to fix a in place (False).
+ fill_value : {var}, optional
+ Value used for fixing invalid data.
+ If not given, the output of get_fill_value(a) is used instead.
+
+*Returns* :
+ MaskedArray object
+ """
+ a = masked_array(a, copy=copy, subok=True)
+ invalid = (numpy.isnan(a._data) | numpy.isinf(a._data))
+ a._mask |= invalid
+ if fill_value is None:
+ fill_value = a.fill_value
+ a._data[invalid] = fill_value
+ return a
+
+
+
+#####--------------------------------------------------------------------------
+#---- --- Ufuncs ---
+#####--------------------------------------------------------------------------
+ufunc_domain = {}
+ufunc_fills = {}
+
+class _DomainCheckInterval:
+ """Defines a valid interval, so that :
+``domain_check_interval(a,b)(x) = true`` where ``x < a`` or ``x > b``."""
+ def __init__(self, a, b):
+ "domain_check_interval(a,b)(x) = true where x < a or y > b"
+ if (a > b):
+ (a, b) = (b, a)
+ self.a = a
+ self.b = b
+
+ def __call__ (self, x):
+ "Executes the call behavior."
+ return umath.logical_or(umath.greater (x, self.b),
+ umath.less(x, self.a))
+#............................
+class _DomainTan:
+ """Defines a valid interval for the `tan` function, so that:
+``domain_tan(eps) = True`` where ``abs(cos(x)) < eps``"""
+ def __init__(self, eps):
+ "domain_tan(eps) = true where abs(cos(x)) < eps)"
+ self.eps = eps
+ def __call__ (self, x):
+ "Executes the call behavior."
+ return umath.less(umath.absolute(umath.cos(x)), self.eps)
+#............................
+class _DomainSafeDivide:
+ """Defines a domain for safe division."""
+ def __init__ (self, tolerance=divide_tolerance):
+ self.tolerance = tolerance
+ def __call__ (self, a, b):
+ return umath.absolute(a) * self.tolerance >= umath.absolute(b)
+#............................
+class _DomainGreater:
+ "DomainGreater(v)(x) = true where x <= v"
+ def __init__(self, critical_value):
+ "DomainGreater(v)(x) = true where x <= v"
+ self.critical_value = critical_value
+
+ def __call__ (self, x):
+ "Executes the call behavior."
+ return umath.less_equal(x, self.critical_value)
+#............................
+class _DomainGreaterEqual:
+ "DomainGreaterEqual(v)(x) = true where x < v"
+ def __init__(self, critical_value):
+ "DomainGreaterEqual(v)(x) = true where x < v"
+ self.critical_value = critical_value
+
+ def __call__ (self, x):
+ "Executes the call behavior."
+ return umath.less(x, self.critical_value)
+
+#..............................................................................
+class _MaskedUnaryOperation:
+ """Defines masked version of unary operations, where invalid values are pre-masked.
+
+:IVariables:
+ f : function.
+ fill : Default filling value *[0]*.
+ domain : Default domain *[None]*.
+ """
+ def __init__ (self, mufunc, fill=0, domain=None):
+ """ _MaskedUnaryOperation(aufunc, fill=0, domain=None)
+ aufunc(fill) must be defined
+ self(x) returns aufunc(x)
+ with masked values where domain(x) is true or getmask(x) is true.
+ """
+ self.f = mufunc
+ self.fill = fill
+ self.domain = domain
+ self.__doc__ = getattr(mufunc, "__doc__", str(mufunc))
+ self.__name__ = getattr(mufunc, "__name__", str(mufunc))
+ ufunc_domain[mufunc] = domain
+ ufunc_fills[mufunc] = fill
+ #
+ def __call__ (self, a, *args, **kwargs):
+ "Executes the call behavior."
+ m = getmask(a)
+ d1 = get_data(a)
+ if self.domain is not None:
+ dm = narray(self.domain(d1), copy=False)
+ m = numpy.logical_or(m, dm)
+ # The following two lines control the domain filling methods.
+ d1 = d1.copy()
+# d1[dm] = self.fill
+ numpy.putmask(d1, dm, self.fill)
+ # Take care of the masked singletong first ...
+ if not m.ndim and m:
+ return masked
+ # Get the result..............................
+ if isinstance(a, MaskedArray):
+ result = self.f(d1, *args, **kwargs).view(type(a))
+ else:
+ result = self.f(d1, *args, **kwargs).view(MaskedArray)
+ # Fix the mask if we don't have a scalar
+ if result.ndim > 0:
+ result._mask = m
+ return result
+ #
+ def __str__ (self):
+ return "Masked version of %s. [Invalid values are masked]" % str(self.f)
+
+#..............................................................................
+class _MaskedBinaryOperation:
+ """Defines masked version of binary operations,
+where invalid values are pre-masked.
+
+:IVariables:
+ f : function.
+ fillx : Default filling value for the first argument *[0]*.
+ filly : Default filling value for the second argument *[0]*.
+ domain : Default domain *[None]*.
+ """
+ def __init__ (self, mbfunc, fillx=0, filly=0):
+ """abfunc(fillx, filly) must be defined.
+ abfunc(x, filly) = x for all x to enable reduce.
+ """
+ self.f = mbfunc
+ self.fillx = fillx
+ self.filly = filly
+ self.__doc__ = getattr(mbfunc, "__doc__", str(mbfunc))
+ self.__name__ = getattr(mbfunc, "__name__", str(mbfunc))
+ ufunc_domain[mbfunc] = None
+ ufunc_fills[mbfunc] = (fillx, filly)
+ #
+ def __call__ (self, a, b, *args, **kwargs):
+ "Execute the call behavior."
+ m = mask_or(getmask(a), getmask(b))
+ (d1, d2) = (get_data(a), get_data(b))
+ result = self.f(d1, d2, *args, **kwargs).view(get_masked_subclass(a,b))
+ if result.size > 1:
+ if m is not nomask:
+ result._mask = make_mask_none(result.shape)
+ result._mask.flat = m
+ elif m:
+ return masked
+ return result
+ #
+ def reduce (self, target, axis=0, dtype=None):
+ """Reduces `target` along the given `axis`."""
+ if isinstance(target, MaskedArray):
+ tclass = type(target)
+ else:
+ tclass = MaskedArray
+ m = getmask(target)
+ t = filled(target, self.filly)
+ if t.shape == ():
+ t = t.reshape(1)
+ if m is not nomask:
+ m = make_mask(m, copy=1)
+ m.shape = (1,)
+ if m is nomask:
+ return self.f.reduce(t, axis).view(tclass)
+ t = t.view(tclass)
+ t._mask = m
+ tr = self.f.reduce(getdata(t), axis, dtype=dtype or t.dtype)
+ mr = umath.logical_and.reduce(m, axis)
+ tr = tr.view(tclass)
+ if mr.ndim > 0:
+ tr._mask = mr
+ return tr
+ elif mr:
+ return masked
+ return tr
+
+ def outer (self, a, b):
+ "Returns the function applied to the outer product of a and b."
+ ma = getmask(a)
+ mb = getmask(b)
+ if ma is nomask and mb is nomask:
+ m = nomask
+ else:
+ ma = getmaskarray(a)
+ mb = getmaskarray(b)
+ m = umath.logical_or.outer(ma, mb)
+ if (not m.ndim) and m:
+ return masked
+ rcls = get_masked_subclass(a,b)
+# d = self.f.outer(filled(a, self.fillx), filled(b, self.filly)).view(rcls)
+ d = self.f.outer(getdata(a), getdata(b)).view(rcls)
+ if d.ndim > 0:
+ d._mask = m
+ return d
+
+ def accumulate (self, target, axis=0):
+ """Accumulates `target` along `axis` after filling with y fill value."""
+ if isinstance(target, MaskedArray):
+ tclass = type(target)
+ else:
+ tclass = masked_array
+ t = filled(target, self.filly)
+ return self.f.accumulate(t, axis).view(tclass)
+
+ def __str__ (self):
+ return "Masked version of " + str(self.f)
+
+#..............................................................................
+class _DomainedBinaryOperation:
+ """Defines binary operations that have a domain, like divide.
+
+They have no reduce, outer or accumulate.
+
+:IVariables:
+ f : function.
+ domain : Default domain.
+ fillx : Default filling value for the first argument *[0]*.
+ filly : Default filling value for the second argument *[0]*.
+ """
+ def __init__ (self, dbfunc, domain, fillx=0, filly=0):
+ """abfunc(fillx, filly) must be defined.
+ abfunc(x, filly) = x for all x to enable reduce.
+ """
+ self.f = dbfunc
+ self.domain = domain
+ self.fillx = fillx
+ self.filly = filly
+ self.__doc__ = getattr(dbfunc, "__doc__", str(dbfunc))
+ self.__name__ = getattr(dbfunc, "__name__", str(dbfunc))
+ ufunc_domain[dbfunc] = domain
+ ufunc_fills[dbfunc] = (fillx, filly)
+
+ def __call__(self, a, b):
+ "Execute the call behavior."
+ ma = getmask(a)
+ mb = getmask(b)
+ d1 = getdata(a)
+ d2 = get_data(b)
+ t = narray(self.domain(d1, d2), copy=False)
+ if t.any(None):
+ mb = mask_or(mb, t)
+ # The following two lines control the domain filling
+ d2 = d2.copy()
+ numpy.putmask(d2, t, self.filly)
+ m = mask_or(ma, mb)
+ if (not m.ndim) and m:
+ return masked
+ result = self.f(d1, d2).view(get_masked_subclass(a,b))
+ if result.ndim > 0:
+ result._mask = m
+ return result
+
+ def __str__ (self):
+ return "Masked version of " + str(self.f)
+
+#..............................................................................
+# Unary ufuncs
+exp = _MaskedUnaryOperation(umath.exp)
+conjugate = _MaskedUnaryOperation(umath.conjugate)
+sin = _MaskedUnaryOperation(umath.sin)
+cos = _MaskedUnaryOperation(umath.cos)
+tan = _MaskedUnaryOperation(umath.tan)
+arctan = _MaskedUnaryOperation(umath.arctan)
+arcsinh = _MaskedUnaryOperation(umath.arcsinh)
+sinh = _MaskedUnaryOperation(umath.sinh)
+cosh = _MaskedUnaryOperation(umath.cosh)
+tanh = _MaskedUnaryOperation(umath.tanh)
+abs = absolute = _MaskedUnaryOperation(umath.absolute)
+fabs = _MaskedUnaryOperation(umath.fabs)
+negative = _MaskedUnaryOperation(umath.negative)
+floor = _MaskedUnaryOperation(umath.floor)
+ceil = _MaskedUnaryOperation(umath.ceil)
+around = _MaskedUnaryOperation(fromnumeric.round_)
+logical_not = _MaskedUnaryOperation(umath.logical_not)
+# Domained unary ufuncs .......................................................
+sqrt = _MaskedUnaryOperation(umath.sqrt, 0.0,
+ _DomainGreaterEqual(0.0))
+log = _MaskedUnaryOperation(umath.log, 1.0,
+ _DomainGreater(0.0))
+log10 = _MaskedUnaryOperation(umath.log10, 1.0,
+ _DomainGreater(0.0))
+tan = _MaskedUnaryOperation(umath.tan, 0.0,
+ _DomainTan(1.e-35))
+arcsin = _MaskedUnaryOperation(umath.arcsin, 0.0,
+ _DomainCheckInterval(-1.0, 1.0))
+arccos = _MaskedUnaryOperation(umath.arccos, 0.0,
+ _DomainCheckInterval(-1.0, 1.0))
+arccosh = _MaskedUnaryOperation(umath.arccosh, 1.0,
+ _DomainGreaterEqual(1.0))
+arctanh = _MaskedUnaryOperation(umath.arctanh, 0.0,
+ _DomainCheckInterval(-1.0+1e-15, 1.0-1e-15))
+# Binary ufuncs ...............................................................
+add = _MaskedBinaryOperation(umath.add)
+subtract = _MaskedBinaryOperation(umath.subtract)
+multiply = _MaskedBinaryOperation(umath.multiply, 1, 1)
+arctan2 = _MaskedBinaryOperation(umath.arctan2, 0.0, 1.0)
+equal = _MaskedBinaryOperation(umath.equal)
+equal.reduce = None
+not_equal = _MaskedBinaryOperation(umath.not_equal)
+not_equal.reduce = None
+less_equal = _MaskedBinaryOperation(umath.less_equal)
+less_equal.reduce = None
+greater_equal = _MaskedBinaryOperation(umath.greater_equal)
+greater_equal.reduce = None
+less = _MaskedBinaryOperation(umath.less)
+less.reduce = None
+greater = _MaskedBinaryOperation(umath.greater)
+greater.reduce = None
+logical_and = _MaskedBinaryOperation(umath.logical_and)
+alltrue = _MaskedBinaryOperation(umath.logical_and, 1, 1).reduce
+logical_or = _MaskedBinaryOperation(umath.logical_or)
+sometrue = logical_or.reduce
+logical_xor = _MaskedBinaryOperation(umath.logical_xor)
+bitwise_and = _MaskedBinaryOperation(umath.bitwise_and)
+bitwise_or = _MaskedBinaryOperation(umath.bitwise_or)
+bitwise_xor = _MaskedBinaryOperation(umath.bitwise_xor)
+hypot = _MaskedBinaryOperation(umath.hypot)
+# Domained binary ufuncs ......................................................
+divide = _DomainedBinaryOperation(umath.divide, _DomainSafeDivide(), 0, 1)
+true_divide = _DomainedBinaryOperation(umath.true_divide,
+ _DomainSafeDivide(), 0, 1)
+floor_divide = _DomainedBinaryOperation(umath.floor_divide,
+ _DomainSafeDivide(), 0, 1)
+remainder = _DomainedBinaryOperation(umath.remainder,
+ _DomainSafeDivide(), 0, 1)
+fmod = _DomainedBinaryOperation(umath.fmod, _DomainSafeDivide(), 0, 1)
+
+
+#####--------------------------------------------------------------------------
+#---- --- Mask creation functions ---
+#####--------------------------------------------------------------------------
+def get_mask(a):
+ """Returns the mask of a, if any, or nomask.
+To get a full array of booleans of the same shape as a, use getmaskarray.
+ """
+ return getattr(a, '_mask', nomask)
+getmask = get_mask
+
+def getmaskarray(a):
+ """Returns the mask of a, if any, or a boolean array of the shape of a, full of False.
+ """
+ m = getmask(a)
+ if m is nomask:
+ m = make_mask_none(fromnumeric.shape(a))
+ return m
+
+def is_mask(m):
+ """Returns True if m is a legal mask.
+Does not check contents, only type.
+ """
+ try:
+ return m.dtype.type is MaskType
+ except AttributeError:
+ return False
+#
+def make_mask(m, copy=False, shrink=True, flag=None):
+ """Returns m as a mask, creating a copy if necessary or requested.
+The function can accept any sequence of integers or nomask.
+Does not check that contents must be 0s and 1s.
+
+*Parameters*:
+ m : {ndarray}
+ Potential mask.
+ copy : {boolean}
+ Whether to return a copy of m (True) or m itself (False).
+ shrink : {boolean}
+ Whether to shrink m to nomask if all its values are False.
+ """
+ if flag is not None:
+ warnings.warn("The flag 'flag' is now called 'shrink'!",
+ DeprecationWarning)
+ shrink = flag
+ if m is nomask:
+ return nomask
+ elif isinstance(m, ndarray):
+ m = filled(m, True)
+ if m.dtype.type is MaskType:
+ if copy:
+ result = narray(m, dtype=MaskType, copy=copy)
+ else:
+ result = m
+ else:
+ result = narray(m, dtype=MaskType)
+ else:
+ result = narray(filled(m, True), dtype=MaskType)
+ # Bas les masques !
+ if shrink and not result.any():
+ return nomask
+ else:
+ return result
+
+def make_mask_none(s):
+ """Returns a mask of shape s, filled with False.
+
+*Parameters*:
+ s : {tuple}
+ A tuple indicating the shape of the final mask.
+ """
+ result = numeric.zeros(s, dtype=MaskType)
+ return result
+
+def mask_or (m1, m2, copy=False, shrink=True):
+ """Returns the combination of two masks m1 and m2.
+The masks are combined with the *logical_or* operator, treating nomask as False.
+The result may equal m1 or m2 if the other is nomask.
+
+*Parameters*:
+ m1 : {ndarray}
+ First mask.
+ m2 : {ndarray}
+ Second mask
+ copy : {boolean}
+ Whether to return a copy.
+ shrink : {boolean}
+ Whether to shrink m to nomask if all its values are False.
+ """
+ if m1 is nomask:
+ return make_mask(m2, copy=copy, shrink=shrink)
+ if m2 is nomask:
+ return make_mask(m1, copy=copy, shrink=shrink)
+ if m1 is m2 and is_mask(m1):
+ return m1
+ return make_mask(umath.logical_or(m1, m2), copy=copy, shrink=shrink)
+
+#####--------------------------------------------------------------------------
+#--- --- Masking functions ---
+#####--------------------------------------------------------------------------
+def masked_where(condition, a, copy=True):
+ """Returns a as an array masked where condition is true.
+Masked values of a or condition are kept.
+
+*Parameters*:
+ condition : {ndarray}
+ Masking condition.
+ a : {ndarray}
+ Array to mask.
+ copy : {boolean}
+ Whether to return a copy of a (True) or modify a in place.
+ """
+ cond = filled(condition,1)
+ a = narray(a, copy=copy, subok=True)
+ if hasattr(a, '_mask'):
+ cond = mask_or(cond, a._mask)
+ cls = type(a)
+ else:
+ cls = MaskedArray
+ result = a.view(cls)
+ result._mask = cond
+ return result
+
+def masked_greater(x, value, copy=True):
+ "Shortcut to masked_where, with condition = (x > value)."
+ return masked_where(greater(x, value), x, copy=copy)
+
+def masked_greater_equal(x, value, copy=True):
+ "Shortcut to masked_where, with condition = (x >= value)."
+ return masked_where(greater_equal(x, value), x, copy=copy)
+
+def masked_less(x, value, copy=True):
+ "Shortcut to masked_where, with condition = (x < value)."
+ return masked_where(less(x, value), x, copy=copy)
+
+def masked_less_equal(x, value, copy=True):
+ "Shortcut to masked_where, with condition = (x <= value)."
+ return masked_where(less_equal(x, value), x, copy=copy)
+
+def masked_not_equal(x, value, copy=True):
+ "Shortcut to masked_where, with condition = (x != value)."
+ return masked_where((x != value), x, copy=copy)
+
+#
+def masked_equal(x, value, copy=True):
+ """Shortcut to masked_where, with condition = (x == value).
+For floating point, consider `masked_values(x, value)` instead.
+ """
+ return masked_where((x == value), x, copy=copy)
+# d = filled(x, 0)
+# c = umath.equal(d, value)
+# m = mask_or(c, getmask(x))
+# return array(d, mask=m, copy=copy)
+
+def masked_inside(x, v1, v2, copy=True):
+ """Shortcut to masked_where, where condition is True for x inside the interval
+[v1,v2] (v1 <= x <= v2).
+The boundaries v1 and v2 can be given in either order.
+
+*Note*:
+ The array x is prefilled with its filling value.
+ """
+ if v2 < v1:
+ (v1, v2) = (v2, v1)
+ xf = filled(x)
+ condition = (xf >= v1) & (xf <= v2)
+ return masked_where(condition, x, copy=copy)
+
+def masked_outside(x, v1, v2, copy=True):
+ """Shortcut to masked_where, where condition is True for x outside the interval
+[v1,v2] (x < v1)|(x > v2).
+The boundaries v1 and v2 can be given in either order.
+
+*Note*:
+ The array x is prefilled with its filling value.
+ """
+ if v2 < v1:
+ (v1, v2) = (v2, v1)
+ xf = filled(x)
+ condition = (xf < v1) | (xf > v2)
+ return masked_where(condition, x, copy=copy)
+
+#
+def masked_object(x, value, copy=True):
+ """Masks the array x where the data are exactly equal to value.
+
+This function is suitable only for object arrays: for floating point, please use
+``masked_values`` instead.
+
+*Notes*:
+ The mask is set to `nomask` if posible.
+ """
+ if isMaskedArray(x):
+ condition = umath.equal(x._data, value)
+ mask = x._mask
+ else:
+ condition = umath.equal(fromnumeric.asarray(x), value)
+ mask = nomask
+ mask = mask_or(mask, make_mask(condition, shrink=True))
+ return masked_array(x, mask=mask, copy=copy, fill_value=value)
+
+def masked_values(x, value, rtol=1.e-5, atol=1.e-8, copy=True):
+ """Masks the array x where the data are approximately equal to value
+(abs(x - value) <= atol+rtol*abs(value)).
+Suitable only for floating points. For integers, please use ``masked_equal``.
+The mask is set to nomask if posible.
+
+*Parameters*:
+ x : {ndarray}
+ Array to fill.
+ value : {float}
+ Masking value.
+ rtol : {float}
+ Tolerance parameter.
+ atol : {float}, *[1e-8]*
+ Tolerance parameter.
+ copy : {boolean}
+ Whether to return a copy of x.
+ """
+ abs = umath.absolute
+ xnew = filled(x, value)
+ if issubclass(xnew.dtype.type, numeric.floating):
+ condition = umath.less_equal(abs(xnew-value), atol+rtol*abs(value))
+ mask = getattr(x, '_mask', nomask)
+ else:
+ condition = umath.equal(xnew, value)
+ mask = nomask
+ mask = mask_or(mask, make_mask(condition, shrink=True))
+ return masked_array(xnew, mask=mask, copy=copy, fill_value=value)
+
+def masked_invalid(a, copy=True):
+ """Masks the array for invalid values (nans or infs).
+ Any preexisting mask is conserved."""
+ a = narray(a, copy=copy, subok=True)
+ condition = (numpy.isnan(a) | numpy.isinf(a))
+ if hasattr(a, '_mask'):
+ condition = mask_or(condition, a._mask)
+ cls = type(a)
+ else:
+ cls = MaskedArray
+ result = a.view(cls)
+ result._mask = cond
+ return result
+
+
+#####--------------------------------------------------------------------------
+#---- --- Printing options ---
+#####--------------------------------------------------------------------------
+class _MaskedPrintOption:
+ """Handles the string used to represent missing data in a masked array."""
+ def __init__ (self, display):
+ "Creates the masked_print_option object."
+ self._display = display
+ self._enabled = True
+
+ def display(self):
+ "Displays the string to print for masked values."
+ return self._display
+
+ def set_display (self, s):
+ "Sets the string to print for masked values."
+ self._display = s
+
+ def enabled(self):
+ "Is the use of the display value enabled?"
+ return self._enabled
+
+ def enable(self, shrink=1):
+ "Set the enabling shrink to `shrink`."
+ self._enabled = shrink
+
+ def __str__ (self):
+ return str(self._display)
+
+ __repr__ = __str__
+
+#if you single index into a masked location you get this object.
+masked_print_option = _MaskedPrintOption('--')
+
+#####--------------------------------------------------------------------------
+#---- --- MaskedArray class ---
+#####--------------------------------------------------------------------------
+
+#...............................................................................
+class _arraymethod(object):
+ """Defines a wrapper for basic array methods.
+Upon call, returns a masked array, where the new _data array is the output of
+the corresponding method called on the original _data.
+
+If onmask is True, the new mask is the output of the method called on the initial
+mask. Otherwise, the new mask is just a reference to the initial mask.
+
+:IVariables:
+ _name : String
+ Name of the function to apply on data.
+ _onmask : {boolean} *[True]*
+ Whether the mask must be processed also (True) or left alone (False).
+ obj : Object
+ The object calling the arraymethod
+ """
+ def __init__(self, funcname, onmask=True):
+ self._name = funcname
+ self._onmask = onmask
+ self.obj = None
+ self.__doc__ = self.getdoc()
+ #
+ def getdoc(self):
+ "Returns the doc of the function (from the doc of the method)."
+ methdoc = getattr(ndarray, self._name, None)
+ methdoc = getattr(numpy, self._name, methdoc)
+ if methdoc is not None:
+ return methdoc.__doc__
+ #
+ def __get__(self, obj, objtype=None):
+ self.obj = obj
+ return self
+ #
+ def __call__(self, *args, **params):
+ methodname = self._name
+ data = self.obj._data
+ mask = self.obj._mask
+ cls = type(self.obj)
+ result = getattr(data, methodname)(*args, **params).view(cls)
+ result._update_from(self.obj)
+ #result._shrinkmask = self.obj._shrinkmask
+ if result.ndim:
+ if not self._onmask:
+ result.__setmask__(mask)
+ elif mask is not nomask:
+ result.__setmask__(getattr(mask, methodname)(*args, **params))
+ else:
+ if mask.ndim and mask.all():
+ return masked
+ return result
+#..........................................................
+
+class FlatIter(object):
+ "Defines an interator."
+ def __init__(self, ma):
+ self.ma = ma
+ self.ma_iter = numpy.asarray(ma).flat
+
+ if ma._mask is nomask:
+ self.maskiter = None
+ else:
+ self.maskiter = ma._mask.flat
+
+ def __iter__(self):
+ return self
+
+ ### This won't work is ravel makes a copy
+ def __setitem__(self, index, value):
+ a = self.ma.ravel()
+ a[index] = value
+
+ def next(self):
+ d = self.ma_iter.next()
+ if self.maskiter is not None and self.maskiter.next():
+ d = masked
+ return d
+
+
+class MaskedArray(numeric.ndarray):
+ """Arrays with possibly masked values.
+Masked values of True exclude the corresponding element from any computation.
+
+Construction:
+ x = MaskedArray(data, mask=nomask, dtype=None, copy=True, fill_value=None,
+ mask = nomask, fill_value=None, shrink=True)
+
+*Parameters*:
+ data : {var}
+ Input data.
+ mask : {nomask, sequence}
+ Mask.
+ Must be convertible to an array of booleans with the same shape as data:
+ True indicates a masked (eg., invalid) data.
+ dtype : {dtype}
+ Data type of the output. If None, the type of the data argument is used.
+ If dtype is not None and different from data.dtype, a copy is performed.
+ copy : {boolean}
+ Whether to copy the input data (True), or to use a reference instead.
+ Note: data are NOT copied by default.
+ fill_value : {var}
+ Value used to fill in the masked values when necessary. If None, a default
+ based on the datatype is used.
+ keep_mask : {True, boolean}
+ Whether to combine mask with the mask of the input data, if any (True),
+ or to use only mask for the output (False).
+ hard_mask : {False, boolean}
+ Whether to use a hard mask or not. With a hard mask, masked values cannot
+ be unmasked.
+ subok : {True, boolean}
+ Whether to return a subclass of MaskedArray (if possible) or a plain
+ MaskedArray.
+ """
+
+ __array_priority__ = 15
+ _defaultmask = nomask
+ _defaulthardmask = False
+ _baseclass = numeric.ndarray
+
+ def __new__(cls, data=None, mask=nomask, dtype=None, copy=False, fill_value=None,
+ keep_mask=True, hard_mask=False, flag=None,
+ subok=True, **options):
+ """Creates a new masked array from scratch.
+ Note: you can also create an array with the .view(MaskedArray) method...
+ """
+ if flag is not None:
+ warnings.warn("The flag 'flag' is now called 'shrink'!",
+ DeprecationWarning)
+ shrink = flag
+ # Process data............
+ _data = narray(data, dtype=dtype, copy=copy, subok=True)
+ _baseclass = getattr(data, '_baseclass', type(_data))
+ _basedict = getattr(data, '_basedict', getattr(data, '__dict__', None))
+ if not isinstance(data, MaskedArray) or not subok:
+ _data = _data.view(cls)
+ else:
+ _data = _data.view(type(data))
+ # Backwards compatibility w/ numpy.core.ma .......
+ if hasattr(data,'_mask') and not isinstance(data, ndarray):
+ _data._mask = data._mask
+ _sharedmask = True
+ # Process mask ...........
+ if mask is nomask:
+ if not keep_mask:
+ _data._mask = nomask
+ if copy:
+ _data._mask = _data._mask.copy()
+ _data._sharedmask = False
+ else:
+ _data._sharedmask = True
+ else:
+ mask = narray(mask, dtype=MaskType, copy=copy)
+ if mask.shape != _data.shape:
+ (nd, nm) = (_data.size, mask.size)
+ if nm == 1:
+ mask = numeric.resize(mask, _data.shape)
+ elif nm == nd:
+ mask = fromnumeric.reshape(mask, _data.shape)
+ else:
+ msg = "Mask and data not compatible: data size is %i, "+\
+ "mask size is %i."
+ raise MAError, msg % (nd, nm)
+ copy = True
+ if _data._mask is nomask:
+ _data._mask = mask
+ _data._sharedmask = not copy
+ else:
+ if not keep_mask:
+ _data._mask = mask
+ _data._sharedmask = not copy
+ else:
+ _data._mask = umath.logical_or(mask, _data._mask)
+ _data._sharedmask = False
+
+ # Update fill_value.......
+ if fill_value is None:
+ _data._fill_value = getattr(data, '_fill_value',
+ default_fill_value(_data))
+ else:
+ _data._fill_value = fill_value
+ # Process extra options ..
+ _data._hardmask = hard_mask
+ _data._baseclass = _baseclass
+ _data._basedict = _basedict
+ return _data
+ #
+ def _update_from(self, obj):
+ """Copies some attributes of obj to self.
+ """
+ self._hardmask = getattr(obj, '_hardmask', self._defaulthardmask)
+ self._sharedmask = getattr(obj, '_sharedmask', False)
+ if obj is not None:
+ self._baseclass = getattr(obj, '_baseclass', type(obj))
+ else:
+ self._baseclass = ndarray
+ self._fill_value = getattr(obj, '_fill_value', None)
+ return
+ #........................
+ def __array_finalize__(self,obj):
+ """Finalizes the masked array.
+ """
+ # Get main attributes .........
+ self._mask = getattr(obj, '_mask', nomask)
+ self._update_from(obj)
+ # Update special attributes ...
+ self._basedict = getattr(obj, '_basedict', getattr(obj, '__dict__', None))
+ if self._basedict is not None:
+ self.__dict__.update(self._basedict)
+ # Finalize the mask ...........
+ if self._mask is not nomask:
+ self._mask.shape = self.shape
+ return
+ #..................................
+ def __array_wrap__(self, obj, context=None):
+ """Special hook for ufuncs.
+Wraps the numpy array and sets the mask according to context.
+ """
+ result = obj.view(type(self))
+ result._update_from(self)
+ #..........
+ if context is not None:
+ result._mask = result._mask.copy()
+ (func, args, _) = context
+ m = reduce(mask_or, [getmask(arg) for arg in args])
+ # Get the domain mask................
+ domain = ufunc_domain.get(func, None)
+ if domain is not None:
+ if len(args) > 2:
+ d = reduce(domain, args)
+ else:
+ d = domain(*args)
+ # Fill the result where the domain is wrong
+ try:
+ # Binary domain: take the last value
+ fill_value = ufunc_fills[func][-1]
+ except TypeError:
+ # Unary domain: just use this one
+ fill_value = ufunc_fills[func]
+ except KeyError:
+ # Domain not recognized, use fill_value instead
+ fill_value = self.fill_value
+ result = result.copy()
+ numpy.putmask(result, d, fill_value)
+ # Update the mask
+ if m is nomask:
+ if d is not nomask:
+ m = d
+ else:
+ m |= d
+ # Make sure the mask has the proper size
+ if result.shape == () and m:
+ return masked
+ else:
+ result._mask = m
+ result._sharedmask = False
+ #....
+ return result
+ #.............................................
+ def __getitem__(self, indx):
+ """x.__getitem__(y) <==> x[y]
+Returns the item described by i, as a masked array.
+ """
+ # This test is useful, but we should keep things light...
+# if getmask(indx) is not nomask:
+# msg = "Masked arrays must be filled before they can be used as indices!"
+# raise IndexError, msg
+ dout = ndarray.__getitem__(self.view(ndarray), indx)
+ m = self._mask
+ if not getattr(dout,'ndim', False):
+ # Just a scalar............
+ if m is not nomask and m[indx]:
+ return masked
+ else:
+ # Force dout to MA ........
+ dout = dout.view(type(self))
+ # Inherit attributes from self
+ dout._update_from(self)
+ # Update the mask if needed
+ if m is not nomask:
+ dout._mask = ndarray.__getitem__(m, indx).reshape(dout.shape)
+# Note: Don't try to check for m.any(), that'll take too long...
+# mask = ndarray.__getitem__(m, indx).reshape(dout.shape)
+# if self._shrinkmask and not m.any():
+# dout._mask = nomask
+# else:
+# dout._mask = mask
+ return dout
+ #........................
+ def __setitem__(self, indx, value):
+ """x.__setitem__(i, y) <==> x[i]=y
+Sets item described by index. If value is masked, masks those locations.
+ """
+ if self is masked:
+ raise MAError, 'Cannot alter the masked element.'
+# if getmask(indx) is not nomask:
+# msg = "Masked arrays must be filled before they can be used as indices!"
+# raise IndexError, msg
+ #....
+ if value is masked:
+ m = self._mask
+ if m is nomask:
+ m = numpy.zeros(self.shape, dtype=MaskType)
+ m[indx] = True
+ self._mask = m
+ self._sharedmask = False
+ return
+ #....
+ dval = getdata(value).astype(self.dtype)
+ valmask = getmask(value)
+ if self._mask is nomask:
+ if valmask is not nomask:
+ self._mask = numpy.zeros(self.shape, dtype=MaskType)
+ self._mask[indx] = valmask
+ elif not self._hardmask:
+ # Unshare the mask if necessary to avoid propagation
+ self.unshare_mask()
+ self._mask[indx] = valmask
+ elif hasattr(indx, 'dtype') and (indx.dtype==bool_):
+ indx = indx * umath.logical_not(self._mask)
+ else:
+ mindx = mask_or(self._mask[indx], valmask, copy=True)
+ dindx = self._data[indx]
+ if dindx.size > 1:
+ dindx[~mindx] = dval
+ elif mindx is nomask:
+ dindx = dval
+ dval = dindx
+ self._mask[indx] = mindx
+ # Set data ..........
+ ndarray.__setitem__(self._data,indx,dval)
+ #............................................
+ def __getslice__(self, i, j):
+ """x.__getslice__(i, j) <==> x[i:j]
+Returns the slice described by (i, j).
+The use of negative indices is not supported."""
+ return self.__getitem__(slice(i,j))
+ #........................
+ def __setslice__(self, i, j, value):
+ """x.__setslice__(i, j, value) <==> x[i:j]=value
+Sets the slice (i,j) of a to value. If value is masked, masks those locations.
+ """
+ self.__setitem__(slice(i,j), value)
+ #............................................
+ def __setmask__(self, mask, copy=False):
+ """Sets the mask."""
+ if mask is not nomask:
+ mask = narray(mask, copy=copy, dtype=MaskType)
+# if self._shrinkmask and not mask.any():
+# mask = nomask
+ if self._mask is nomask:
+ self._mask = mask
+ elif self._hardmask:
+ if mask is not nomask:
+ self._mask.__ior__(mask)
+ else:
+ # This one is tricky: if we set the mask that way, we may break the
+ # propagation. But if we don't, we end up with a mask full of False
+ # and a test on nomask fails...
+ if mask is nomask:
+ self._mask = nomask
+ else:
+ self.unshare_mask()
+ self._mask.flat = mask
+ if self._mask.shape:
+ self._mask = numeric.reshape(self._mask, self.shape)
+ _set_mask = __setmask__
+ #....
+ def _get_mask(self):
+ """Returns the current mask."""
+ return self._mask
+# return self._mask.reshape(self.shape)
+ mask = property(fget=_get_mask, fset=__setmask__, doc="Mask")
+ #............................................
+ def harden_mask(self):
+ "Forces the mask to hard."
+ self._hardmask = True
+
+ def soften_mask(self):
+ "Forces the mask to soft."
+ self._hardmask = False
+
+ def unshare_mask(self):
+ "Copies the mask and set the sharedmask flag to False."
+ if self._sharedmask:
+ self._mask = self._mask.copy()
+ self._sharedmask = False
+
+ def shrink_mask(self):
+ "Reduces a mask to nomask when possible."
+ m = self._mask
+ if m.ndim and not m.any():
+ self._mask = nomask
+
+ #............................................
+ def _get_data(self):
+ "Returns the current data, as a view of the original underlying data."
+ return self.view(self._baseclass)
+ _data = property(fget=_get_data)
+ data = property(fget=_get_data)
+
+ def raw_data(self):
+ """Returns the _data part of the MaskedArray.
+DEPRECATED: You should really use ``.data`` instead..."""
+ return self._data
+ #............................................
+ def _get_flat(self):
+ "Returns a flat iterator."
+ return FlatIter(self)
+ #
+ def _set_flat (self, value):
+ "Sets a flattened version of self to value."
+ y = self.ravel()
+ y[:] = value
+ #
+ flat = property(fget=_get_flat, fset=_set_flat,
+ doc="Flat version of the array.")
+ #............................................
+ def get_fill_value(self):
+ "Returns the filling value."
+ if self._fill_value is None:
+ self._fill_value = default_fill_value(self)
+ return self._fill_value
+
+ def set_fill_value(self, value=None):
+ """Sets the filling value to value.
+If value is None, uses a default based on the data type."""
+ if value is None:
+ value = default_fill_value(self)
+ self._fill_value = value
+
+ fill_value = property(fget=get_fill_value, fset=set_fill_value,
+ doc="Filling value.")
+
+ def filled(self, fill_value=None):
+ """Returns a copy of self._data, where masked values are filled with
+fill_value.
+
+If fill_value is None, self.fill_value is used instead.
+
+*Note*:
+ + Subclassing is preserved
+ + The result is NOT a MaskedArray !
+
+*Examples*:
+ >>> x = array([1,2,3,4,5], mask=[0,0,1,0,1], fill_value=-999)
+ >>> x.filled()
+ array([1,2,-999,4,-999])
+ >>> type(x.filled())
+ <type 'numpy.ndarray'>
+ """
+ m = self._mask
+ if m is nomask or not m.any():
+ return self._data
+ #
+ if fill_value is None:
+ fill_value = self.fill_value
+ #
+ if self is masked_singleton:
+ result = numeric.asanyarray(fill_value)
+ else:
+ result = self._data.copy()
+ try:
+ numpy.putmask(result, m, fill_value)
+ except (TypeError, AttributeError):
+ fill_value = narray(fill_value, dtype=object)
+ d = result.astype(object)
+ result = fromnumeric.choose(m, (d, fill_value))
+ except IndexError:
+ #ok, if scalar
+ if self._data.shape:
+ raise
+ elif m:
+ result = narray(fill_value, dtype=self.dtype)
+ else:
+ result = self._data
+ return result
+
+ def compressed(self):
+ """Returns a 1-D array of all the non-masked data."""
+ data = ndarray.ravel(self._data).view(type(self))
+ data._update_from(self)
+ if self._mask is not nomask:
+ data = data[numpy.logical_not(ndarray.ravel(self._mask))]
+# if not self._shrinkmask:
+# data._mask = numpy.zeros(data.shape, dtype=MaskType)
+ return data
+
+ #............................................
+ def __str__(self):
+ """Calculates the string representation.
+ """
+ if masked_print_option.enabled():
+ f = masked_print_option
+ if self is masked:
+ return str(f)
+ m = self._mask
+ if m is nomask:
+ res = self._data
+ else:
+ if m.shape == ():
+ if m:
+ return str(f)
+ else:
+ return str(self._data)
+ # convert to object array to make filled work
+#CHECK: the two lines below seem more robust than the self._data.astype
+# res = numeric.empty(self._data.shape, object_)
+# numeric.putmask(res,~m,self._data)
+ res = self._data.astype("|O8")
+ res[m] = f
+ else:
+ res = self.filled(self.fill_value)
+ return str(res)
+
+ def __repr__(self):
+ """Calculates the repr representation.
+ """
+ with_mask = """\
+masked_%(name)s(data =
+ %(data)s,
+ mask =
+ %(mask)s,
+ fill_value=%(fill)s)
+"""
+ with_mask1 = """\
+masked_%(name)s(data = %(data)s,
+ mask = %(mask)s,
+ fill_value=%(fill)s)
+"""
+ n = len(self.shape)
+ name = repr(self._data).split('(')[0]
+ if n <= 1:
+ return with_mask1 % {
+ 'name': name,
+ 'data': str(self),
+ 'mask': str(self._mask),
+ 'fill': str(self.fill_value),
+ }
+ return with_mask % {
+ 'name': name,
+ 'data': str(self),
+ 'mask': str(self._mask),
+ 'fill': str(self.fill_value),
+ }
+ #............................................
+ def __add__(self, other):
+ "Adds other to self, and returns a new masked array."
+ return add(self, other)
+ #
+ def __sub__(self, other):
+ "Subtracts other to self, and returns a new masked array."
+ return subtract(self, other)
+ #
+ def __mul__(self, other):
+ "Multiplies other to self, and returns a new masked array."
+ return multiply(self, other)
+ #
+ def __div__(self, other):
+ "Divides other to self, and returns a new masked array."
+ return divide(self, other)
+ #
+ def __truediv__(self, other):
+ "Divides other to self, and returns a new masked array."
+ return true_divide(self, other)
+ #
+ def __floordiv__(self, other):
+ "Divides other to self, and returns a new masked array."
+ return floor_divide(self, other)
+
+ #............................................
+ def __iadd__(self, other):
+ "Adds other to self in place."
+ ndarray.__iadd__(self._data, getdata(other))
+ m = getmask(other)
+ if self._mask is nomask:
+ self._mask = m
+ elif m is not nomask:
+ self._mask += m
+ return self
+ #....
+ def __isub__(self, other):
+ "Subtracts other from self in place."
+ ndarray.__isub__(self._data, getdata(other))
+ m = getmask(other)
+ if self._mask is nomask:
+ self._mask = m
+ elif m is not nomask:
+ self._mask += m
+ return self
+ #....
+ def __imul__(self, other):
+ "Multiplies self by other in place."
+ ndarray.__imul__(self._data, getdata(other))
+ m = getmask(other)
+ if self._mask is nomask:
+ self._mask = m
+ elif m is not nomask:
+ self._mask += m
+ return self
+ #....
+ def __idiv__(self, other):
+ "Divides self by other in place."
+ other_data = getdata(other)
+ dom_mask = _DomainSafeDivide().__call__(self._data, other_data)
+ other_mask = getmask(other)
+ new_mask = mask_or(other_mask, dom_mask)
+ # The following 3 lines control the domain filling
+ if dom_mask.any():
+ other_data = other_data.copy()
+ numpy.putmask(other_data, dom_mask, 1)
+ ndarray.__idiv__(self._data, other_data)
+ self._mask = mask_or(self._mask, new_mask)
+ return self
+ #............................................
+ def __float__(self):
+ "Converts self to float."
+ if self._mask is not nomask:
+ warnings.warn("Warning: converting a masked element to nan.")
+ return numpy.nan
+ #raise MAError, 'Cannot convert masked element to a Python float.'
+ return float(self.item())
+
+ def __int__(self):
+ "Converts self to int."
+ if self._mask is not nomask:
+ raise MAError, 'Cannot convert masked element to a Python int.'
+ return int(self.item())
+ #............................................
+ def get_imag(self):
+ result = self._data.imag.view(type(self))
+ result.__setmask__(self._mask)
+ return result
+ imag = property(fget=get_imag,doc="Imaginary part")
+
+ def get_real(self):
+ result = self._data.real.view(type(self))
+ result.__setmask__(self._mask)
+ return result
+ real = property(fget=get_real,doc="Real part")
+
+
+ #............................................
+ def count(self, axis=None):
+ """Counts the non-masked elements of the array along the given axis.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to count the non-masked elements. If not given, all the
+ non masked elements are counted.
+
+*Returns*:
+ A masked array where the mask is True where all data are masked.
+ If axis is None, returns either a scalar ot the masked singleton if all values
+ are masked.
+ """
+ m = self._mask
+ s = self.shape
+ ls = len(s)
+ if m is nomask:
+ if ls == 0:
+ return 1
+ if ls == 1:
+ return s[0]
+ if axis is None:
+ return self.size
+ else:
+ n = s[axis]
+ t = list(s)
+ del t[axis]
+ return numeric.ones(t) * n
+ n1 = numpy.size(m, axis)
+ n2 = m.astype(int_).sum(axis)
+ if axis is None:
+ return (n1-n2)
+ else:
+ return masked_array(n1 - n2)
+ #............................................
+ flatten = _arraymethod('flatten')
+# ravel = _arraymethod('ravel')
+ def ravel(self):
+ """Returns a 1D version of self, as a view."""
+ r = ndarray.ravel(self._data).view(type(self))
+ r._update_from(self)
+ if self._mask is not nomask:
+ r._mask = ndarray.ravel(self._mask).reshape(r.shape)
+ else:
+ r._mask = nomask
+ return r
+ repeat = _arraymethod('repeat')
+ #
+ def reshape (self, *s):
+ """Reshapes the array to shape s.
+
+*Returns*:
+ A new masked array.
+
+*Notes:
+ If you want to modify the shape in place, please use ``a.shape = s``
+ """
+ result = self._data.reshape(*s).view(type(self))
+ result.__dict__.update(self.__dict__)
+ if result._mask is not nomask:
+ result._mask = self._mask.copy()
+ result._mask.shape = result.shape
+ return result
+ #
+ def resize(self, newshape, refcheck=True, order=False):
+ """Attempts to modify the size and the shape of the array in place.
+
+The array must own its own memory and not be referenced by other arrays.
+
+*Returns*:
+ None.
+ """
+ try:
+ self._data.resize(newshape, refcheck, order)
+ if self.mask is not nomask:
+ self._mask.resize(newshape, refcheck, order)
+ except ValueError:
+ raise ValueError("Cannot resize an array that has been referenced "
+ "or is referencing another array in this way.\n"
+ "Use the resize function.")
+ return None
+ #
+ def put(self, indices, values, mode='raise'):
+ """Sets storage-indexed locations to corresponding values.
+
+a.put(values, indices, mode) sets a.flat[n] = values[n] for each n in indices.
+If values is shorter than indices then it will repeat.
+If values has some masked values, the initial mask is updated in consequence,
+else the corresponding values are unmasked.
+ """
+ m = self._mask
+ # Hard mask: Get rid of the values/indices that fall on masked data
+ if self._hardmask and self._mask is not nomask:
+ mask = self._mask[indices]
+ indices = narray(indices, copy=False)
+ values = narray(values, copy=False, subok=True)
+ values.resize(indices.shape)
+ indices = indices[~mask]
+ values = values[~mask]
+ #....
+ self._data.put(indices, values, mode=mode)
+ #....
+ if m is nomask:
+ m = getmask(values)
+ else:
+ m = m.copy()
+ if getmask(values) is nomask:
+ m.put(indices, False, mode=mode)
+ else:
+ m.put(indices, values._mask, mode=mode)
+ m = make_mask(m, copy=False, shrink=True)
+ self._mask = m
+ #............................................
+ def ids (self):
+ """Returns the addresses of the data and mask areas."""
+ return (self.ctypes.data, self._mask.ctypes.data)
+ #............................................
+ def all(self, axis=None, out=None):
+ """Returns True if all entries along the given axis are True, False otherwise.
+Masked values are considered as True during computation.
+
+*Parameters*
+ axis : {integer}, optional
+ Axis along which the operation is performed.
+ If None, the operation is performed on a flatten array
+ out : {MaskedArray}, optional
+ Alternate optional output.
+ If not None, out should be a valid MaskedArray of the same shape as the
+ output of self._data.all(axis).
+
+*Returns*
+ A masked array, where the mask is True if all data along the axis are masked.
+
+*Notes*
+ An exception is raised if ``out`` is not None and not of the same type as self.
+ """
+ if out is None:
+ d = self.filled(True).all(axis=axis).view(type(self))
+ if d.ndim > 0:
+ d.__setmask__(self._mask.all(axis))
+ return d
+ elif type(out) is not type(self):
+ raise TypeError("The external array should have a type %s (got %s instead)" %\
+ (type(self), type(out)))
+ self.filled(True).all(axis=axis, out=out)
+ if out.ndim:
+ out.__setmask__(self._mask.all(axis))
+ return out
+
+
+ def any(self, axis=None, out=None):
+ """Returns True if at least one entry along the given axis is True.
+
+Returns False if all entries are False.
+Masked values are considered as True during computation.
+
+*Parameters*
+ axis : {integer}, optional
+ Axis along which the operation is performed.
+ If None, the operation is performed on a flatten array
+ out : {MaskedArray}, optional
+ Alternate optional output.
+ If not None, out should be a valid MaskedArray of the same shape as the
+ output of self._data.all(axis).
+
+*Returns*
+ A masked array, where the mask is True if all data along the axis are masked.
+
+*Notes*
+ An exception is raised if ``out`` is not None and not of the same type as self.
+ """
+ if out is None:
+ d = self.filled(False).any(axis=axis).view(type(self))
+ if d.ndim > 0:
+ d.__setmask__(self._mask.all(axis))
+ return d
+ elif type(out) is not type(self):
+ raise TypeError("The external array should have a type %s (got %s instead)" %\
+ (type(self), type(out)))
+ self.filled(False).any(axis=axis, out=out)
+ if out.ndim:
+ out.__setmask__(self._mask.all(axis))
+ return out
+
+
+ def nonzero(self):
+ """Returns the indices of the elements of a that are not zero nor masked,
+as a tuple of arrays.
+
+There are as many tuples as dimensions of a, each tuple contains the indices of
+the non-zero elements in that dimension. The corresponding non-zero values can
+be obtained with ``a[a.nonzero()]``.
+
+To group the indices by element, rather than dimension, use instead:
+``transpose(a.nonzero())``.
+
+The result of this is always a 2d array, with a row for each non-zero element.
+ """
+ return narray(self.filled(0), copy=False).nonzero()
+ #............................................
+ def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):
+ """a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
+Returns the sum along the offset diagonal of the array's indicated `axis1` and `axis2`.
+ """
+ # TODO: What are we doing with `out`?
+ m = self._mask
+ if m is nomask:
+ result = super(MaskedArray, self).trace(offset=offset, axis1=axis1,
+ axis2=axis2, out=out)
+ return result.astype(dtype)
+ else:
+ D = self.diagonal(offset=offset, axis1=axis1, axis2=axis2)
+ return D.astype(dtype).filled(0).sum(axis=None)
+ #............................................
+ def sum(self, axis=None, dtype=None):
+ """Sums the array over the given axis.
+
+Masked elements are set to 0 internally.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ dtype : {dtype}, optional
+ Datatype for the intermediary computation. If not given, the current dtype
+ is used instead.
+ """
+ if self._mask is nomask:
+ mask = nomask
+ else:
+ mask = self._mask.all(axis)
+ if (not mask.ndim) and mask:
+ return masked
+ result = self.filled(0).sum(axis, dtype=dtype).view(type(self))
+ if result.ndim > 0:
+ result.__setmask__(mask)
+ return result
+
+ def cumsum(self, axis=None, dtype=None):
+ """Returns the cumulative sum of the elements of the array along the given axis.
+
+Masked values are set to 0 internally.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ dtype : {dtype}, optional
+ Datatype for the intermediary computation. If not given, the current dtype
+ is used instead.
+ """
+ result = self.filled(0).cumsum(axis=axis, dtype=dtype).view(type(self))
+ result.__setmask__(self.mask)
+ return result
+
+ def prod(self, axis=None, dtype=None):
+ """Returns the product of the elements of the array along the given axis.
+
+Masked elements are set to 1 internally.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ dtype : {dtype}, optional
+ Datatype for the intermediary computation. If not given, the current dtype
+ is used instead.
+ """
+ if self._mask is nomask:
+ mask = nomask
+ else:
+ mask = self._mask.all(axis)
+ if (not mask.ndim) and mask:
+ return masked
+ result = self.filled(1).prod(axis=axis, dtype=dtype).view(type(self))
+ if result.ndim:
+ result.__setmask__(mask)
+ return result
+ product = prod
+
+ def cumprod(self, axis=None, dtype=None):
+ """Returns the cumulative product of the elements of the array along the given axis.
+
+Masked values are set to 1 internally.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ dtype : {dtype}, optional
+ Datatype for the intermediary computation. If not given, the current dtype
+ is used instead.
+ """
+ result = self.filled(1).cumprod(axis=axis, dtype=dtype).view(type(self))
+ result.__setmask__(self.mask)
+ return result
+
+ def mean(self, axis=None, dtype=None):
+ """Averages the array over the given axis. Equivalent to
+
+ a.sum(axis, dtype) / a.size(axis).
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ dtype : {dtype}, optional
+ Datatype for the intermediary computation. If not given, the current dtype
+ is used instead.
+ """
+ if self._mask is nomask:
+ return super(MaskedArray, self).mean(axis=axis, dtype=dtype)
+ else:
+ dsum = self.sum(axis=axis, dtype=dtype)
+ cnt = self.count(axis=axis)
+ return dsum*1./cnt
+
+ def anom(self, axis=None, dtype=None):
+ """Returns the anomalies (deviations from the average) along the given axis.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ dtype : {dtype}, optional
+ Datatype for the intermediary computation. If not given, the current dtype
+ is used instead.
+ """
+ m = self.mean(axis, dtype)
+ if not axis:
+ return (self - m)
+ else:
+ return (self - expand_dims(m,axis))
+
+ def var(self, axis=None, dtype=None):
+ """Returns the variance, a measure of the spread of a distribution.
+
+The variance is the average of the squared deviations from the mean,
+i.e. var = mean((x - x.mean())**2).
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ dtype : {dtype}, optional
+ Datatype for the intermediary computation. If not given, the current dtype
+ is used instead.
+
+*Notes*:
+ The value returned is a biased estimate of the true variance.
+ For the (more standard) unbiased estimate, use varu.
+ """
+ if self._mask is nomask:
+ # TODO: Do we keep super, or var _data and take a view ?
+ return super(MaskedArray, self).var(axis=axis, dtype=dtype)
+ else:
+ cnt = self.count(axis=axis)
+ danom = self.anom(axis=axis, dtype=dtype)
+ danom *= danom
+ dvar = narray(danom.sum(axis) / cnt).view(type(self))
+ if axis is not None:
+ dvar._mask = mask_or(self._mask.all(axis), (cnt==1))
+ dvar._update_from(self)
+ return dvar
+
+ def std(self, axis=None, dtype=None):
+ """Returns the standard deviation, a measure of the spread of a distribution.
+
+The standard deviation is the square root of the average of the squared
+deviations from the mean, i.e. std = sqrt(mean((x - x.mean())**2)).
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ dtype : {dtype}, optional
+ Datatype for the intermediary computation.
+ If not given, the current dtype is used instead.
+
+*Notes*:
+ The value returned is a biased estimate of the true standard deviation.
+ For the more standard unbiased estimate, use stdu.
+ """
+ dvar = self.var(axis,dtype)
+ if axis is not None or dvar is not masked:
+ dvar = sqrt(dvar)
+ return dvar
+
+ #............................................
+ def argsort(self, axis=None, fill_value=None, kind='quicksort',
+ order=None):
+ """Returns a ndarray of indices that sort the array along the specified axis.
+ Masked values are filled beforehand to fill_value.
+ Returns a numpy array.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis to be indirectly sorted.
+ If not given, uses a flatten version of the array.
+ fill_value : {var}
+ Value used to fill in the masked values.
+ If not given, self.fill_value is used instead.
+ kind : {string}
+ Sorting algorithm (default 'quicksort')
+ Possible values: 'quicksort', 'mergesort', or 'heapsort'
+
+*Notes*:
+ This method executes an indirect sort along the given axis using the algorithm
+ specified by the kind keyword. It returns an array of indices of the same shape
+ as 'a' that index data along the given axis in sorted order.
+
+ The various sorts are characterized by average speed, worst case performance
+ need for work space, and whether they are stable. A stable sort keeps items
+ with the same key in the same relative order. The three available algorithms
+ have the following properties:
+
+ |------------------------------------------------------|
+ | kind | speed | worst case | work space | stable|
+ |------------------------------------------------------|
+ |'quicksort'| 1 | O(n^2) | 0 | no |
+ |'mergesort'| 2 | O(n*log(n)) | ~n/2 | yes |
+ |'heapsort' | 3 | O(n*log(n)) | 0 | no |
+ |------------------------------------------------------|
+
+ All the sort algorithms make temporary copies of the data when the sort is not
+ along the last axis. Consequently, sorts along the last axis are faster and use
+ less space than sorts along other axis.
+ """
+ if fill_value is None:
+ fill_value = default_fill_value(self)
+ d = self.filled(fill_value).view(ndarray)
+ return d.argsort(axis=axis, kind=kind, order=order)
+ #........................
+ def argmin(self, axis=None, fill_value=None):
+ """Returns a ndarray of indices for the minimum values of a along the
+specified axis.
+
+Masked values are treated as if they had the value fill_value.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ fill_value : {var}, optional
+ Value used to fill in the masked values.
+ If None, the output of minimum_fill_value(self._data) is used.
+ """
+ if fill_value is None:
+ fill_value = minimum_fill_value(self)
+ d = self.filled(fill_value).view(ndarray)
+ return d.argmin(axis)
+ #........................
+ def argmax(self, axis=None, fill_value=None):
+ """Returns the array of indices for the maximum values of `a` along the
+specified axis.
+
+Masked values are treated as if they had the value fill_value.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ fill_value : {var}, optional
+ Value used to fill in the masked values.
+ If None, the output of maximum_fill_value(self._data) is used.
+ """
+ if fill_value is None:
+ fill_value = maximum_fill_value(self._data)
+ d = self.filled(fill_value).view(ndarray)
+ return d.argmax(axis)
+
+ def sort(self, axis=-1, kind='quicksort', order=None,
+ endwith=True, fill_value=None):
+ """Sort a along the given axis.
+
+*Parameters*:
+ axis : {integer}
+ Axis to be indirectly sorted.
+ kind : {string}
+ Sorting algorithm (default 'quicksort')
+ Possible values: 'quicksort', 'mergesort', or 'heapsort'.
+ order : {var}
+ If a has fields defined, then the order keyword can be the field
+ name to sort on or a list (or tuple) of field names to indicate
+ the order that fields should be used to define the sort.
+ fill_value : {var}
+ Value used to fill in the masked values.
+ If None, use the the output of minimum_fill_value().
+ endwith : {boolean}
+ Whether missing values (if any) should be forced in the upper indices
+ (at the end of the array) (True) or lower indices (at the beginning).
+
+*Returns*:
+ When used as method, returns None.
+ When used as a function, returns an array.
+
+*Notes*:
+ This method sorts 'a' in place along the given axis using the algorithm
+ specified by the kind keyword.
+
+ The various sorts may characterized by average speed, worst case performance
+ need for work space, and whether they are stable. A stable sort keeps items
+ with the same key in the same relative order and is most useful when used w/
+ argsort where the key might differ from the items being sorted.
+ The three available algorithms have the following properties:
+
+ |------------------------------------------------------|
+ | kind | speed | worst case | work space | stable|
+ |------------------------------------------------------|
+ |'quicksort'| 1 | O(n^2) | 0 | no |
+ |'mergesort'| 2 | O(n*log(n)) | ~n/2 | yes |
+ |'heapsort' | 3 | O(n*log(n)) | 0 | no |
+ |------------------------------------------------------|
+
+ """
+ if self._mask is nomask:
+ ndarray.sort(self,axis=axis, kind=kind, order=order)
+ else:
+ if fill_value is None:
+ if endwith:
+ filler = minimum_fill_value(self)
+ else:
+ filler = maximum_fill_value(self)
+ else:
+ filler = fill_value
+ idx = numpy.indices(self.shape)
+ idx[axis] = self.filled(filler).argsort(axis=axis,kind=kind,order=order)
+ idx_l = idx.tolist()
+ tmp_mask = self._mask[idx_l].flat
+ tmp_data = self._data[idx_l].flat
+ self.flat = tmp_data
+ self._mask.flat = tmp_mask
+ return
+
+ #............................................
+ def min(self, axis=None, fill_value=None):
+ """Returns the minimum of a along the given axis.
+
+Masked values are filled with fill_value.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ fill_value : {var}, optional
+ Value used to fill in the masked values.
+ If None, use the the output of minimum_fill_value().
+ """
+ mask = self._mask
+ # Check all/nothing case ......
+ if mask is nomask:
+ return super(MaskedArray, self).min(axis=axis)
+ elif (not mask.ndim) and mask:
+ return masked
+ # Get the mask ................
+ if axis is None:
+ mask = umath.logical_and.reduce(mask.flat)
+ else:
+ mask = umath.logical_and.reduce(mask, axis=axis)
+ # Get the fil value ...........
+ if fill_value is None:
+ fill_value = minimum_fill_value(self)
+ # Get the data ................
+ result = self.filled(fill_value).min(axis=axis).view(type(self))
+ if result.ndim > 0:
+ result._mask = mask
+ return result
+ #........................
+ def max(self, axis=None, fill_value=None):
+ """Returns the maximum/a along the given axis.
+
+Masked values are filled with fill_value.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ fill_value : {var}, optional
+ Value used to fill in the masked values.
+ If None, use the the output of maximum_fill_value().
+ """
+ mask = self._mask
+ # Check all/nothing case ......
+ if mask is nomask:
+ return super(MaskedArray, self).max(axis=axis)
+ elif (not mask.ndim) and mask:
+ return masked
+ # Check the mask ..............
+ if axis is None:
+ mask = umath.logical_and.reduce(mask.flat)
+ else:
+ mask = umath.logical_and.reduce(mask, axis=axis)
+ # Get the fill value ..........
+ if fill_value is None:
+ fill_value = maximum_fill_value(self)
+ # Get the data ................
+ result = self.filled(fill_value).max(axis=axis).view(type(self))
+ if result.ndim > 0:
+ result._mask = mask
+ return result
+ #........................
+ def ptp(self, axis=None, fill_value=None):
+ """Returns the visible data range (max-min) along the given axis.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ fill_value : {var}, optional
+ Value used to fill in the masked values.
+ If None, the maximum uses the maximum default, the minimum uses
+ the minimum default.
+ """
+ return self.max(axis, fill_value) - self.min(axis, fill_value)
+
+
+ # Array methods ---------------------------------------
+# conj = conjugate = _arraymethod('conjugate')
+ copy = _arraymethod('copy')
+ diagonal = _arraymethod('diagonal')
+ take = _arraymethod('take')
+# ravel = _arraymethod('ravel')
+ transpose = _arraymethod('transpose')
+ T = property(fget=lambda self:self.transpose())
+ swapaxes = _arraymethod('swapaxes')
+ clip = _arraymethod('clip', onmask=False)
+ compress = _arraymethod('compress')
+ copy = _arraymethod('copy')
+ squeeze = _arraymethod('squeeze')
+ #--------------------------------------------
+ def tolist(self, fill_value=None):
+ """Copies the data portion of the array to a hierarchical python list and
+returns that list.
+
+Data items are converted to the nearest compatible Python type.
+Masked values are converted to fill_value. If fill_value is None, the corresponding
+entries in the output list will be ``None``.
+ """
+ if fill_value is not None:
+ return self.filled(fill_value).tolist()
+ result = self.filled().tolist()
+ if self._mask is nomask:
+ return result
+ if self.ndim == 0:
+ return [None]
+ elif self.ndim == 1:
+ maskedidx = self._mask.nonzero()[0].tolist()
+ [operator.setitem(result,i,None) for i in maskedidx]
+ else:
+ for idx in zip(*[i.tolist() for i in self._mask.nonzero()]):
+ tmp = result
+ for i in idx[:-1]:
+ tmp = tmp[i]
+ tmp[idx[-1]] = None
+ return result
+ #........................
+ def tostring(self, fill_value=None, order='C'):
+ """Returns a copy of array data as a Python string containing the raw
+bytes in the array.
+
+*Parameters*:
+ fill_value : {var}, optional
+ Value used to fill in the masked values.
+ If None, uses self.fill_value instead.
+ order : {string}
+ Order of the data item in the copy {"C","F","A"}.
+ "C" -- C order (row major)
+ "Fortran" -- Fortran order (column major)
+ "Any" -- Current order of array.
+ None -- Same as "Any"
+ """
+ return self.filled(fill_value).tostring(order=order)
+ #--------------------------------------------
+ # Pickling
+ def __getstate__(self):
+ "Returns the internal state of the masked array, for pickling purposes."
+ state = (1,
+ self.shape,
+ self.dtype,
+ self.flags.fnc,
+ self._data.tostring(),
+ getmaskarray(self).tostring(),
+ self._fill_value,
+ )
+ return state
+ #
+ def __setstate__(self, state):
+ """Restores the internal state of the masked array, for pickling purposes.
+``state`` is typically the output of the ``__getstate__`` output, and is a 5-tuple:
+
+ - class name
+ - a tuple giving the shape of the data
+ - a typecode for the data
+ - a binary string for the data
+ - a binary string for the mask.
+ """
+ (ver, shp, typ, isf, raw, msk, flv) = state
+ ndarray.__setstate__(self, (shp, typ, isf, raw))
+ self._mask.__setstate__((shp, dtype(bool), isf, msk))
+ self.fill_value = flv
+ #
+ def __reduce__(self):
+ """Returns a 3-tuple for pickling a MaskedArray."""
+ return (_mareconstruct,
+ (self.__class__, self._baseclass, (0,), 'b', ),
+ self.__getstate__())
+
+
+def _mareconstruct(subtype, baseclass, baseshape, basetype,):
+ """Internal function that builds a new MaskedArray from the information stored
+in a pickle."""
+ _data = ndarray.__new__(baseclass, baseshape, basetype)
+ _mask = ndarray.__new__(ndarray, baseshape, 'b1')
+ return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype, shrink=False)
+#MaskedArray.__dump__ = dump
+#MaskedArray.__dumps__ = dumps
+
+
+
+#####--------------------------------------------------------------------------
+#---- --- Shortcuts ---
+#####---------------------------------------------------------------------------
+def isMaskedArray(x):
+ "Is x a masked array, that is, an instance of MaskedArray?"
+ return isinstance(x, MaskedArray)
+isarray = isMaskedArray
+isMA = isMaskedArray #backward compatibility
+# We define the masked singleton as a float for higher precedence...
+masked_singleton = MaskedArray(0, dtype=float_, mask=True)
+masked = masked_singleton
+
+masked_array = MaskedArray
+
+def array(data, dtype=None, copy=False, order=False, mask=nomask, subok=True,
+ keep_mask=True, hard_mask=False, fill_value=None, shrink=True):
+ """array(data, dtype=None, copy=True, order=False, mask=nomask,
+ keep_mask=True, shrink=True, fill_value=None)
+Acts as shortcut to MaskedArray, with options in a different order for convenience.
+And backwards compatibility...
+ """
+ #TODO: we should try to put 'order' somwehere
+ return MaskedArray(data, mask=mask, dtype=dtype, copy=copy, subok=subok,
+ keep_mask=keep_mask, hard_mask=hard_mask,
+ fill_value=fill_value)
+array.__doc__ = masked_array.__doc__
+
+def is_masked(x):
+ """Returns whether x has some masked values."""
+ m = getmask(x)
+ if m is nomask:
+ return False
+ elif m.any():
+ return True
+ return False
+
+
+#####---------------------------------------------------------------------------
+#---- --- Extrema functions ---
+#####---------------------------------------------------------------------------
+class _extrema_operation(object):
+ "Generic class for maximum/minimum functions."
+ def __call__(self, a, b=None):
+ "Executes the call behavior."
+ if b is None:
+ return self.reduce(a)
+ return where(self.compare(a, b), a, b)
+ #.........
+ def reduce(self, target, axis=None):
+ "Reduces target along the given axis."
+ m = getmask(target)
+ if axis is not None:
+ kargs = { 'axis' : axis }
+ else:
+ kargs = {}
+ target = target.ravel()
+ if not (m is nomask):
+ m = m.ravel()
+ if m is nomask:
+ t = self.ufunc.reduce(target, **kargs)
+ else:
+ target = target.filled(self.fill_value_func(target)).view(type(target))
+ t = self.ufunc.reduce(target, **kargs)
+ m = umath.logical_and.reduce(m, **kargs)
+ if hasattr(t, '_mask'):
+ t._mask = m
+ elif m:
+ t = masked
+ return t
+ #.........
+ def outer (self, a, b):
+ "Returns the function applied to the outer product of a and b."
+ ma = getmask(a)
+ mb = getmask(b)
+ if ma is nomask and mb is nomask:
+ m = nomask
+ else:
+ ma = getmaskarray(a)
+ mb = getmaskarray(b)
+ m = logical_or.outer(ma, mb)
+ result = self.ufunc.outer(filled(a), filled(b))
+ result._mask = m
+ return result
+
+#............................
+class _minimum_operation(_extrema_operation):
+ "Object to calculate minima"
+ def __init__ (self):
+ """minimum(a, b) or minimum(a)
+In one argument case, returns the scalar minimum.
+ """
+ self.ufunc = umath.minimum
+ self.afunc = amin
+ self.compare = less
+ self.fill_value_func = minimum_fill_value
+
+#............................
+class _maximum_operation(_extrema_operation):
+ "Object to calculate maxima"
+ def __init__ (self):
+ """maximum(a, b) or maximum(a)
+ In one argument case returns the scalar maximum.
+ """
+ self.ufunc = umath.maximum
+ self.afunc = amax
+ self.compare = greater
+ self.fill_value_func = maximum_fill_value
+
+#..........................................................
+def min(array, axis=None, out=None):
+ """Returns the minima along the given axis.
+If `axis` is None, applies to the flattened array."""
+ if out is not None:
+ raise TypeError("Output arrays Unsupported for masked arrays")
+ if axis is None:
+ return minimum(array)
+ else:
+ return minimum.reduce(array, axis)
+min.__doc__ = MaskedArray.min.__doc__
+#............................
+def max(obj, axis=None, out=None):
+ if out is not None:
+ raise TypeError("Output arrays Unsupported for masked arrays")
+ if axis is None:
+ return maximum(obj)
+ else:
+ return maximum.reduce(obj, axis)
+max.__doc__ = MaskedArray.max.__doc__
+#.............................
+def ptp(obj, axis=None):
+ """a.ptp(axis=None) = a.max(axis)-a.min(axis)"""
+ try:
+ return obj.max(axis)-obj.min(axis)
+ except AttributeError:
+ return max(obj, axis=axis) - min(obj, axis=axis)
+ptp.__doc__ = MaskedArray.ptp.__doc__
+
+
+#####---------------------------------------------------------------------------
+#---- --- Definition of functions from the corresponding methods ---
+#####---------------------------------------------------------------------------
+class _frommethod:
+ """Defines functions from existing MaskedArray methods.
+:ivar _methodname (String): Name of the method to transform.
+ """
+ def __init__(self, methodname):
+ self._methodname = methodname
+ self.__doc__ = self.getdoc()
+ def getdoc(self):
+ "Returns the doc of the function (from the doc of the method)."
+ try:
+ return getattr(MaskedArray, self._methodname).__doc__
+ except:
+ return getattr(numpy, self._methodname).__doc__
+ def __call__(self, a, *args, **params):
+ if isinstance(a, MaskedArray):
+ return getattr(a, self._methodname).__call__(*args, **params)
+ #FIXME ----
+ #As x is not a MaskedArray, we transform it to a ndarray with asarray
+ #... and call the corresponding method.
+ #Except that sometimes it doesn't work (try reshape([1,2,3,4],(2,2)))
+ #we end up with a "SystemError: NULL result without error in PyObject_Call"
+ #A dirty trick is then to call the initial numpy function...
+ method = getattr(narray(a, copy=False), self._methodname)
+ try:
+ return method(*args, **params)
+ except SystemError:
+ return getattr(numpy,self._methodname).__call__(a, *args, **params)
+
+all = _frommethod('all')
+anomalies = anom = _frommethod('anom')
+any = _frommethod('any')
+conjugate = _frommethod('conjugate')
+ids = _frommethod('ids')
+nonzero = _frommethod('nonzero')
+diagonal = _frommethod('diagonal')
+maximum = _maximum_operation()
+mean = _frommethod('mean')
+minimum = _minimum_operation ()
+product = _frommethod('prod')
+ptp = _frommethod('ptp')
+ravel = _frommethod('ravel')
+repeat = _frommethod('repeat')
+std = _frommethod('std')
+sum = _frommethod('sum')
+swapaxes = _frommethod('swapaxes')
+take = _frommethod('take')
+var = _frommethod('var')
+
+#..............................................................................
+def power(a, b, third=None):
+ """Computes a**b elementwise.
+ Masked values are set to 1."""
+ if third is not None:
+ raise MAError, "3-argument power not supported."
+ ma = getmask(a)
+ mb = getmask(b)
+ m = mask_or(ma, mb)
+ fa = getdata(a)
+ fb = getdata(b)
+ if fb.dtype.char in typecodes["Integer"]:
+ return masked_array(umath.power(fa, fb), m)
+ md = make_mask((fa < 0), shrink=True)
+ m = mask_or(m, md)
+ if m is nomask:
+ return masked_array(umath.power(fa, fb))
+ else:
+ fa = fa.copy()
+ fa[(fa < 0)] = 1
+ return masked_array(umath.power(fa, fb), m)
+
+#..............................................................................
+def argsort(a, axis=None, kind='quicksort', order=None, fill_value=None):
+ "Function version of the eponymous method."
+ if fill_value is None:
+ fill_value = default_fill_value(a)
+ d = filled(a, fill_value)
+ if axis is None:
+ return d.argsort(kind=kind, order=order)
+ return d.argsort(axis, kind=kind, order=order)
+argsort.__doc__ = MaskedArray.argsort.__doc__
+
+def argmin(a, axis=None, fill_value=None):
+ "Function version of the eponymous method."
+ if fill_value is None:
+ fill_value = default_fill_value(a)
+ d = filled(a, fill_value)
+ return d.argmin(axis=axis)
+argmin.__doc__ = MaskedArray.argmin.__doc__
+
+def argmax(a, axis=None, fill_value=None):
+ "Function version of the eponymous method."
+ if fill_value is None:
+ fill_value = default_fill_value(a)
+ try:
+ fill_value = - fill_value
+ except:
+ pass
+ d = filled(a, fill_value)
+ return d.argmax(axis=axis)
+argmin.__doc__ = MaskedArray.argmax.__doc__
+
+def sort(a, axis=-1, kind='quicksort', order=None, endwith=True, fill_value=None):
+ "Function version of the eponymous method."
+ a = narray(a, copy=False, subok=True)
+ if fill_value is None:
+ if endwith:
+ filler = minimum_fill_value(a)
+ else:
+ filler = maximum_fill_value(a)
+ else:
+ filler = fill_value
+# return
+ indx = numpy.indices(a.shape).tolist()
+ indx[axis] = filled(a,filler).argsort(axis=axis,kind=kind,order=order)
+ return a[indx]
+sort.__doc__ = MaskedArray.sort.__doc__
+
+
+def compressed(x):
+ """Returns a 1-D array of all the non-masked data."""
+ if getmask(x) is nomask:
+ return x
+ else:
+ return x.compressed()
+
+def concatenate(arrays, axis=0):
+ "Concatenates the arrays along the given axis."
+ d = numpy.concatenate([getdata(a) for a in arrays], axis)
+ rcls = get_masked_subclass(*arrays)
+ data = d.view(rcls)
+ # Check whether one of the arrays has a non-empty mask...
+ for x in arrays:
+ if getmask(x) is not nomask:
+ break
+ return data
+ # OK, so we have to concatenate the masks
+ dm = numpy.concatenate([getmaskarray(a) for a in arrays], axis)
+ shrink = numpy.logical_or.reduce([getattr(a,'_shrinkmask',True) for a in arrays])
+ if shrink and not dm.any():
+ data._mask = nomask
+ else:
+ data._mask = dm.reshape(d.shape)
+ return data
+
+def count(a, axis = None):
+ return masked_array(a, copy=False).count(axis)
+count.__doc__ = MaskedArray.count.__doc__
+
+
+def expand_dims(x,axis):
+ "Expands the shape of the array by including a new axis before the given one."
+ result = n_expand_dims(x,axis)
+ if isinstance(x, MaskedArray):
+ new_shape = result.shape
+ result = x.view()
+ result.shape = new_shape
+ if result._mask is not nomask:
+ result._mask.shape = new_shape
+ return result
+
+#......................................
+def left_shift (a, n):
+ "Left shift n bits."
+ m = getmask(a)
+ if m is nomask:
+ d = umath.left_shift(filled(a), n)
+ return masked_array(d)
+ else:
+ d = umath.left_shift(filled(a, 0), n)
+ return masked_array(d, mask=m)
+
+def right_shift (a, n):
+ "Right shift n bits."
+ m = getmask(a)
+ if m is nomask:
+ d = umath.right_shift(filled(a), n)
+ return masked_array(d)
+ else:
+ d = umath.right_shift(filled(a, 0), n)
+ return masked_array(d, mask=m)
+
+#......................................
+def put(a, indices, values, mode='raise'):
+ """Sets storage-indexed locations to corresponding values.
+Values and indices are filled if necessary."""
+ # We can't use 'frommethod', the order of arguments is different
+ try:
+ return a.put(indices, values, mode=mode)
+ except AttributeError:
+ return narray(a, copy=False).put(indices, values, mode=mode)
+
+def putmask(a, mask, values): #, mode='raise'):
+ """Sets a.flat[n] = values[n] for each n where mask.flat[n] is true.
+
+If values is not the same size of a and mask then it will repeat as necessary.
+This gives different behavior than a[mask] = values."""
+ # We can't use 'frommethod', the order of arguments is different
+ try:
+ return a.putmask(values, mask)
+ except AttributeError:
+ return numpy.putmask(narray(a, copy=False), mask, values)
+
+def transpose(a,axes=None):
+ """Returns a view of the array with dimensions permuted according to axes,
+as a masked array.
+
+If ``axes`` is None (default), the output view has reversed dimensions compared
+to the original.
+ """
+ #We can't use 'frommethod', as 'transpose' doesn't take keywords
+ try:
+ return a.transpose(axes)
+ except AttributeError:
+ return narray(a, copy=False).transpose(axes).view(MaskedArray)
+
+def reshape(a, new_shape):
+ """Changes the shape of the array a to new_shape."""
+ #We can't use 'frommethod', it whine about some parameters. Dmmit.
+ try:
+ return a.reshape(new_shape)
+ except AttributeError:
+ return narray(a, copy=False).reshape(new_shape).view(MaskedArray)
+
+def resize(x, new_shape):
+ """Returns a new array with the specified shape.
+
+The total size of the original array can be any size.
+The new array is filled with repeated copies of a. If a was masked, the new array
+will be masked, and the new mask will be a repetition of the old one.
+ """
+ # We can't use _frommethods here, as N.resize is notoriously whiny.
+ m = getmask(x)
+ if m is not nomask:
+ m = numpy.resize(m, new_shape)
+ result = numpy.resize(x, new_shape).view(get_masked_subclass(x))
+ if result.ndim:
+ result._mask = m
+ return result
+
+
+#................................................
+def rank(obj):
+ "maskedarray version of the numpy function."
+ return fromnumeric.rank(getdata(obj))
+rank.__doc__ = numpy.rank.__doc__
+#
+def shape(obj):
+ "maskedarray version of the numpy function."
+ return fromnumeric.shape(getdata(obj))
+shape.__doc__ = numpy.shape.__doc__
+#
+def size(obj, axis=None):
+ "maskedarray version of the numpy function."
+ return fromnumeric.size(getdata(obj), axis)
+size.__doc__ = numpy.size.__doc__
+#................................................
+
+#####--------------------------------------------------------------------------
+#---- --- Extra functions ---
+#####--------------------------------------------------------------------------
+def where (condition, x=None, y=None):
+ """where(condition | x, y)
+
+Returns a (subclass of) masked array, shaped like condition, where the elements
+are x when condition is True, and y otherwise. If neither x nor y are given,
+returns a tuple of indices where condition is True (a la condition.nonzero()).
+
+*Parameters*:
+ condition : {var}
+ The condition to meet. Must be convertible to an integer array.
+ x : {var}, optional
+ Values of the output when the condition is met
+ y : {var}, optional
+ Values of the output when the condition is not met.
+ """
+ if x is None and y is None:
+ return filled(condition, 0).nonzero()
+ elif x is None or y is None:
+ raise ValueError, "Either both or neither x and y should be given."
+ # Get the condition ...............
+ fc = filled(condition, 0).astype(bool_)
+ notfc = numpy.logical_not(fc)
+ # Get the data ......................................
+ xv = getdata(x)
+ yv = getdata(y)
+ if x is masked:
+ ndtype = yv.dtype
+ xm = numpy.ones(fc.shape, dtype=MaskType)
+ elif y is masked:
+ ndtype = xv.dtype
+ ym = numpy.ones(fc.shape, dtype=MaskType)
+ else:
+ ndtype = numpy.max([xv.dtype, yv.dtype])
+ xm = getmask(x)
+ d = numpy.empty(fc.shape, dtype=ndtype).view(MaskedArray)
+ numpy.putmask(d._data, fc, xv.astype(ndtype))
+ numpy.putmask(d._data, notfc, yv.astype(ndtype))
+ d._mask = numpy.zeros(fc.shape, dtype=MaskType)
+ numpy.putmask(d._mask, fc, getmask(x))
+ numpy.putmask(d._mask, notfc, getmask(y))
+ d._mask |= getmaskarray(condition)
+ if not d._mask.any():
+ d._mask = nomask
+ return d
+# # Get the data as a (subclass of) MaskedArray
+# xv = getdata(x)
+# yv = getdata(y)
+# d = numpy.choose(fc, (yv, xv)).view(MaskedArray)
+# # Get the mask ....................
+# xm = getmask(x)
+# ym = getmask(y)
+# d.mask = numpy.choose(fc, (ym, xm)) | getmask(condition)
+# # Fix the dtype if one of the values was masked, to prevent an upload to float
+# if y is masked:
+# ndtype = xv.dtype
+# elif x is masked:
+# ndtype = yv.dtype
+# else:
+# ndtype = d.dtype
+# return d.astype(ndtype)
+
+def choose (indices, t, out=None, mode='raise'):
+ "Returns array shaped like indices with elements chosen from t"
+ #TODO: implement options `out` and `mode`, if possible.
+ def fmask (x):
+ "Returns the filled array, or True if masked."
+ if x is masked:
+ return 1
+ return filled(x)
+ def nmask (x):
+ "Returns the mask, True if ``masked``, False if ``nomask``."
+ if x is masked:
+ return 1
+ m = getmask(x)
+ if m is nomask:
+ return 0
+ return m
+ c = filled(indices, 0)
+ masks = [nmask(x) for x in t]
+ a = [fmask(x) for x in t]
+ d = numpy.choose(c, a)
+ m = numpy.choose(c, masks)
+ m = make_mask(mask_or(m, getmask(indices)), copy=0, shrink=True)
+ return masked_array(d, mask=m)
+
+def round_(a, decimals=0, out=None):
+ """Returns a copy of a, rounded to 'decimals' places.
+
+When 'decimals' is negative, it specifies the number of positions to the left of
+the decimal point. The real and imaginary parts of complex numbers are rounded
+separately. Nothing is done if the array is not of float type and 'decimals' is
+greater than or equal to 0.
+
+*Parameters*:
+ decimals : {integer}
+ Number of decimals to round to. May be negative.
+ out : {ndarray}
+ Existing array to use for output.
+ If not given, returns a default copy of a.
+
+*Notes*:
+ If out is given and does not have a mask attribute, the mask of a is lost!
+ """
+ if out is None:
+ result = fromnumeric.round_(getdata(a), decimals, out)
+ if isinstance(a,MaskedArray):
+ result = result.view(type(a))
+ result._mask = a._mask
+ else:
+ result = result.view(MaskedArray)
+ return result
+ else:
+ fromnumeric.round_(getdata(a), decimals, out)
+ if hasattr(out, '_mask'):
+ out._mask = getmask(a)
+ return out
+
+def arange(stop, start=None, step=1, dtype=None):
+ "maskedarray version of the numpy function."
+ return numpy.arange(stop, start, step, dtype).view(MaskedArray)
+arange.__doc__ = numpy.arange.__doc__
+
+def inner(a, b):
+ "maskedarray version of the numpy function."
+ fa = filled(a, 0)
+ fb = filled(b, 0)
+ if len(fa.shape) == 0:
+ fa.shape = (1,)
+ if len(fb.shape) == 0:
+ fb.shape = (1,)
+ return numpy.inner(fa, fb).view(MaskedArray)
+inner.__doc__ = numpy.inner.__doc__
+inner.__doc__ += "\n*Notes*:\n Masked values are replaced by 0."
+innerproduct = inner
+
+def outer(a, b):
+ "maskedarray version of the numpy function."
+ fa = filled(a, 0).ravel()
+ fb = filled(b, 0).ravel()
+ d = numeric.outer(fa, fb)
+ ma = getmask(a)
+ mb = getmask(b)
+ if ma is nomask and mb is nomask:
+ return masked_array(d)
+ ma = getmaskarray(a)
+ mb = getmaskarray(b)
+ m = make_mask(1-numeric.outer(1-ma, 1-mb), copy=0)
+ return masked_array(d, mask=m)
+outer.__doc__ = numpy.outer.__doc__
+outer.__doc__ += "\n*Notes*:\n Masked values are replaced by 0."
+outerproduct = outer
+
+def allequal (a, b, fill_value=True):
+ """Returns True if all entries of a and b are equal, using fill_value
+as a truth value where either or both are masked.
+ """
+ m = mask_or(getmask(a), getmask(b))
+ if m is nomask:
+ x = getdata(a)
+ y = getdata(b)
+ d = umath.equal(x, y)
+ return d.all()
+ elif fill_value:
+ x = getdata(a)
+ y = getdata(b)
+ d = umath.equal(x, y)
+ dm = array(d, mask=m, copy=False)
+ return dm.filled(True).all(None)
+ else:
+ return False
+
+def allclose (a, b, fill_value=True, rtol=1.e-5, atol=1.e-8):
+ """ Returns True if all elements of a and b are equal subject to given tolerances.
+If fill_value is True, masked values are considered equal.
+If fill_value is False, masked values considered unequal.
+The relative error rtol should be positive and << 1.0
+The absolute error atol comes into play for those elements of b that are very small
+or zero; it says how small `a` must be also.
+ """
+ m = mask_or(getmask(a), getmask(b))
+ d1 = getdata(a)
+ d2 = getdata(b)
+ x = filled(array(d1, copy=0, mask=m), fill_value).astype(float)
+ y = filled(array(d2, copy=0, mask=m), 1).astype(float)
+ d = umath.less_equal(umath.absolute(x-y), atol + rtol * umath.absolute(y))
+ return fromnumeric.alltrue(fromnumeric.ravel(d))
+
+#..............................................................................
+def asarray(a, dtype=None):
+ """asarray(data, dtype) = array(data, dtype, copy=0, subok=0)
+Returns a as a MaskedArray object of the given dtype.
+If dtype is not given or None, is is set to the dtype of a.
+No copy is performed if a is already an array.
+Subclasses are converted to the base class MaskedArray.
+ """
+ return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=False)
+
+def asanyarray(a, dtype=None):
+ """asanyarray(data, dtype) = array(data, dtype, copy=0, subok=1)
+Returns a as an masked array.
+If dtype is not given or None, is is set to the dtype of a.
+No copy is performed if a is already an array.
+Subclasses are conserved.
+ """
+ return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=True)
+
+
+def empty(new_shape, dtype=float):
+ "maskedarray version of the numpy function."
+ return numpy.empty(new_shape, dtype).view(MaskedArray)
+empty.__doc__ = numpy.empty.__doc__
+
+def empty_like(a):
+ "maskedarray version of the numpy function."
+ return numpy.empty_like(a).view(MaskedArray)
+empty_like.__doc__ = numpy.empty_like.__doc__
+
+def ones(new_shape, dtype=float):
+ "maskedarray version of the numpy function."
+ return numpy.ones(new_shape, dtype).view(MaskedArray)
+ones.__doc__ = numpy.ones.__doc__
+
+def zeros(new_shape, dtype=float):
+ "maskedarray version of the numpy function."
+ return numpy.zeros(new_shape, dtype).view(MaskedArray)
+zeros.__doc__ = numpy.zeros.__doc__
+
+#####--------------------------------------------------------------------------
+#---- --- Pickling ---
+#####--------------------------------------------------------------------------
+def dump(a,F):
+ """Pickles the MaskedArray `a` to the file `F`.
+`F` can either be the handle of an exiting file, or a string representing a file name.
+ """
+ if not hasattr(F,'readline'):
+ F = open(F,'w')
+ return cPickle.dump(a,F)
+
+def dumps(a):
+ """Returns a string corresponding to the pickling of the MaskedArray."""
+ return cPickle.dumps(a)
+
+def load(F):
+ """Wrapper around ``cPickle.load`` which accepts either a file-like object or
+ a filename."""
+ if not hasattr(F, 'readline'):
+ F = open(F,'r')
+ return cPickle.load(F)
+
+def loads(strg):
+ "Loads a pickle from the current string."""
+ return cPickle.loads(strg)
+
+
+###############################################################################
+
+if __name__ == '__main__':
+ from maskedarray.testutils import assert_equal, assert_almost_equal
+
+ # Small arrays ..................................
+ xs = numpy.random.uniform(-1,1,6).reshape(2,3)
+ ys = numpy.random.uniform(-1,1,6).reshape(2,3)
+ zs = xs + 1j * ys
+ m1 = [[True, False, False], [False, False, True]]
+ m2 = [[True, False, True], [False, False, True]]
+ nmxs = numpy.ma.array(xs, mask=m1)
+ nmys = numpy.ma.array(ys, mask=m2)
+ nmzs = numpy.ma.array(zs, mask=m1)
+ mmxs = array(xs, mask=m1)
+ mmys = array(ys, mask=m2)
+ mmzs = array(zs, mask=m1)
+ # Big arrays ....................................
+ xl = numpy.random.uniform(-1,1,100*100).reshape(100,100)
+ yl = numpy.random.uniform(-1,1,100*100).reshape(100,100)
+ zl = xl + 1j * yl
+ maskx = xl > 0.8
+ masky = yl < -0.8
+ nmxl = numpy.ma.array(xl, mask=maskx)
+ nmyl = numpy.ma.array(yl, mask=masky)
+ nmzl = numpy.ma.array(zl, mask=maskx)
+ mmxl = array(xl, mask=maskx, shrink=True)
+ mmyl = array(yl, mask=masky, shrink=True)
+ mmzl = array(zl, mask=maskx, shrink=True)
+ #
+ z = empty(3,)
+ mmys.all(0, out=z)
+
+ if 1:
+ x = numpy.array([[ 0.13, 0.26, 0.90],
+ [ 0.28, 0.33, 0.63],
+ [ 0.31, 0.87, 0.70]])
+ m = numpy.array([[ True, False, False],
+ [False, False, False],
+ [True, True, False]], dtype=numpy.bool_)
+ mx = masked_array(x, mask=m)
+ xbig = numpy.array([[False, False, True],
+ [False, False, True],
+ [False, True, True]], dtype=numpy.bool_)
+ mxbig = (mx > 0.5)
+ mxsmall = (mx < 0.5)
+ #
+ assert (mxbig.all()==False)
+ assert (mxbig.any()==True)
+ assert_equal(mxbig.all(0),[False, False, True])
+ assert_equal(mxbig.all(1), [False, False, True])
+ assert_equal(mxbig.any(0),[False, False, True])
+ assert_equal(mxbig.any(1), [True, True, True])
+
+ if 1:
+ xx = array([1+10j,20+2j], mask=[1,0])
+ assert_equal(xx.imag,[10,2])
+ assert_equal(xx.imag.filled(), [1e+20,2])
+ assert_equal(xx.real,[1,20])
+ assert_equal(xx.real.filled(), [1e+20,20])
Added: branches/maskedarray/numpy/core/ma/extras.py
===================================================================
--- branches/maskedarray/numpy/core/ma/extras.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/extras.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,722 @@
+"""Masked arrays add-ons.
+
+A collection of utilities for maskedarray
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: extras.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3473 $"
+__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
+
+__all__ = [
+'apply_along_axis', 'atleast_1d', 'atleast_2d', 'atleast_3d', 'average',
+'vstack', 'hstack', 'dstack', 'row_stack', 'column_stack',
+'compress_rowcols', 'compress_rows', 'compress_cols', 'count_masked',
+'dot', 'hsplit',
+'mask_rowcols','mask_rows','mask_cols','masked_all','masked_all_like',
+'mediff1d', 'mr_',
+'notmasked_edges','notmasked_contiguous',
+'stdu', 'varu',
+ ]
+
+from itertools import groupby
+
+import core
+from core import *
+
+import numpy
+from numpy import float_
+import numpy.core.umath as umath
+import numpy.core.numeric as numeric
+from numpy.core.numeric import ndarray
+from numpy.core.numeric import array as nxarray
+from numpy.core.fromnumeric import asarray as nxasarray
+
+from numpy.lib.index_tricks import concatenator
+import numpy.lib.function_base as function_base
+
+#...............................................................................
+def issequence(seq):
+ """Returns True if the argument is a sequence (ndarray, list or tuple)."""
+ if isinstance(seq, ndarray):
+ return True
+ elif isinstance(seq, tuple):
+ return True
+ elif isinstance(seq, list):
+ return True
+ return False
+
+def count_masked(arr, axis=None):
+ """Counts the number of masked elements along the given axis.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to count.
+ If None (default), a flattened version of the array is used.
+ """
+ m = getmaskarray(arr)
+ return m.sum(axis)
+
+def masked_all(shape, dtype=float_):
+ """Returns an empty masked array of the given shape and dtype,
+ where all the data are masked.
+
+*Parameters*:
+ dtype : {dtype}, optional
+ Data type of the output.
+ """
+ a = masked_array(numeric.empty(shape, dtype),
+ mask=numeric.ones(shape, bool_))
+ return a
+
+def masked_all_like(arr):
+ """Returns an empty masked array of the same shape and dtype as the array `a`,
+ where all the data are masked."""
+ a = masked_array(numeric.empty_like(arr),
+ mask=numeric.ones(arr.shape, bool_))
+ return a
+
+#####--------------------------------------------------------------------------
+#---- --- New methods ---
+#####--------------------------------------------------------------------------
+def varu(a, axis=None, dtype=None):
+ """Returns an unbiased estimate of the variance.
+ i.e. var = sum((x - x.mean())**2)/(size(x,axis)-1)
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ dtype : {dtype}, optional
+ Datatype for the intermediary computation. If not given, the current dtype
+ is used instead.
+
+*Notes*:
+ The value returned is an unbiased estimate of the true variance.
+ For the (less standard) biased estimate, use var.
+ """
+ a = asarray(a)
+ cnt = a.count(axis=axis)
+ anom = a.anom(axis=axis, dtype=dtype)
+ anom *= anom
+ dvar = anom.sum(axis) / (cnt-1)
+ if axis is None:
+ return dvar
+ dvar.__setmask__(mask_or(a._mask.all(axis), (cnt==1)))
+ return dvar
+# return a.__class__(dvar,
+# mask=mask_or(a._mask.all(axis), (cnt==1)),
+# fill_value=a._fill_value)
+
+def stdu(a, axis=None, dtype=None):
+ """Returns an unbiased estimate of the standard deviation.
+ The standard deviation is the square root of the average of the squared
+ deviations from the mean, i.e. stdu = sqrt(varu(x)).
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ dtype : {dtype}, optional
+ Datatype for the intermediary computation.
+ If not given, the current dtype is used instead.
+
+*Notes*:
+ The value returned is an unbiased estimate of the true standard deviation.
+ For the (less standard) biased estimate, use std.
+ """
+ a = asarray(a)
+ dvar = a.varu(axis,dtype)
+ if axis is None:
+ if dvar is masked:
+ return masked
+ else:
+ # Should we use umath.sqrt instead ?
+ return sqrt(dvar)
+ return sqrt(dvar)
+# return a.__class__(sqrt(dvar._data), mask=dvar._mask,
+# fill_value=a._fill_value)
+
+MaskedArray.stdu = stdu
+MaskedArray.varu = varu
+
+#####--------------------------------------------------------------------------
+#---- --- Standard functions ---
+#####--------------------------------------------------------------------------
+class _fromnxfunction:
+ """Defines a wrapper to adapt numpy functions to masked arrays."""
+ def __init__(self, funcname):
+ self._function = funcname
+ self.__doc__ = self.getdoc()
+ def getdoc(self):
+ "Retrieves the __doc__ string from the function."
+ return getattr(numpy, self._function).__doc__ +\
+ "*Notes*:\n (The function is applied to both the _data and the _mask, if any.)"
+ def __call__(self, *args, **params):
+ func = getattr(numpy, self._function)
+ if len(args)==1:
+ x = args[0]
+ if isinstance(x,ndarray):
+ _d = func(nxasarray(x), **params)
+ _m = func(getmaskarray(x), **params)
+ return masked_array(_d, mask=_m)
+ elif isinstance(x, tuple) or isinstance(x, list):
+ _d = func(tuple([nxasarray(a) for a in x]), **params)
+ _m = func(tuple([getmaskarray(a) for a in x]), **params)
+ return masked_array(_d, mask=_m)
+ else:
+ arrays = []
+ args = list(args)
+ while len(args)>0 and issequence(args[0]):
+ arrays.append(args.pop(0))
+ res = []
+ for x in arrays:
+ _d = func(nxasarray(x), *args, **params)
+ _m = func(getmaskarray(x), *args, **params)
+ res.append(masked_array(_d, mask=_m))
+ return res
+
+atleast_1d = _fromnxfunction('atleast_1d')
+atleast_2d = _fromnxfunction('atleast_2d')
+atleast_3d = _fromnxfunction('atleast_3d')
+
+vstack = row_stack = _fromnxfunction('vstack')
+hstack = _fromnxfunction('hstack')
+column_stack = _fromnxfunction('column_stack')
+dstack = _fromnxfunction('dstack')
+
+hsplit = _fromnxfunction('hsplit')
+
+#####--------------------------------------------------------------------------
+#----
+#####--------------------------------------------------------------------------
+def flatten_inplace(seq):
+ """Flattens a sequence in place."""
+ k = 0
+ while (k != len(seq)):
+ while hasattr(seq[k],'__iter__'):
+ seq[k:(k+1)] = seq[k]
+ k += 1
+ return seq
+
+
+def apply_along_axis(func1d,axis,arr,*args,**kwargs):
+ """ Execute func1d(arr[i],*args) where func1d takes 1-D arrays
+ and arr is an N-d array. i varies so as to apply the function
+ along the given axis for each 1-d subarray in arr.
+ """
+ arr = core.array(arr, copy=False, subok=True)
+ nd = arr.ndim
+ if axis < 0:
+ axis += nd
+ if (axis >= nd):
+ raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d."
+ % (axis,nd))
+ ind = [0]*(nd-1)
+ i = numeric.zeros(nd,'O')
+ indlist = range(nd)
+ indlist.remove(axis)
+ i[axis] = slice(None,None)
+ outshape = numeric.asarray(arr.shape).take(indlist)
+ i.put(indlist, ind)
+ j = i.copy()
+ res = func1d(arr[tuple(i.tolist())],*args,**kwargs)
+ # if res is a number, then we have a smaller output array
+ asscalar = numeric.isscalar(res)
+ if not asscalar:
+ try:
+ len(res)
+ except TypeError:
+ asscalar = True
+ # Note: we shouldn't set the dtype of the output from the first result...
+ #...so we force the type to object, and build a list of dtypes
+ #...we'll just take the largest, to avoid some downcasting
+ dtypes = []
+ if asscalar:
+ dtypes.append(numeric.asarray(res).dtype)
+ outarr = zeros(outshape, object_)
+ outarr[tuple(ind)] = res
+ Ntot = numeric.product(outshape)
+ k = 1
+ while k < Ntot:
+ # increment the index
+ ind[-1] += 1
+ n = -1
+ while (ind[n] >= outshape[n]) and (n > (1-nd)):
+ ind[n-1] += 1
+ ind[n] = 0
+ n -= 1
+ i.put(indlist,ind)
+ res = func1d(arr[tuple(i.tolist())],*args,**kwargs)
+ outarr[tuple(ind)] = res
+ dtypes.append(asarray(res).dtype)
+ k += 1
+ else:
+ res = core.array(res, copy=False, subok=True)
+ j = i.copy()
+ j[axis] = ([slice(None,None)] * res.ndim)
+ j.put(indlist, ind)
+ Ntot = numeric.product(outshape)
+ holdshape = outshape
+ outshape = list(arr.shape)
+ outshape[axis] = res.shape
+ dtypes.append(asarray(res).dtype)
+ outshape = flatten_inplace(outshape)
+ outarr = zeros(outshape, object_)
+ outarr[tuple(flatten_inplace(j.tolist()))] = res
+ k = 1
+ while k < Ntot:
+ # increment the index
+ ind[-1] += 1
+ n = -1
+ while (ind[n] >= holdshape[n]) and (n > (1-nd)):
+ ind[n-1] += 1
+ ind[n] = 0
+ n -= 1
+ i.put(indlist, ind)
+ j.put(indlist, ind)
+ res = func1d(arr[tuple(i.tolist())],*args,**kwargs)
+ outarr[tuple(flatten_inplace(j.tolist()))] = res
+ dtypes.append(asarray(res).dtype)
+ k += 1
+ max_dtypes = numeric.dtype(numeric.asarray(dtypes).max())
+ if not hasattr(arr, '_mask'):
+ result = numeric.asarray(outarr, dtype=max_dtypes)
+ else:
+ result = core.asarray(outarr, dtype=max_dtypes)
+ result.fill_value = core.default_fill_value(result)
+ return result
+
+def average (a, axis=None, weights=None, returned=False):
+ """Averages the array over the given axis.
+
+*Parameters*:
+ axis : {integer}, optional
+ Axis along which to perform the operation.
+ If None, applies to a flattened version of the array.
+ weights : {sequence}, optional
+ Sequence of weights.
+ The weights must have the shape of a, or be 1D with length the size of a
+ along the given axis.
+ If no weights are given, weights are assumed to be 1.
+ returned : {boolean}
+ Flag indicating whether a tuple (result, sum of weights/counts) should be
+ returned as output (True), or just the result (False).
+ """
+ a = asarray(a)
+ mask = a.mask
+ ash = a.shape
+ if ash == ():
+ ash = (1,)
+ if axis is None:
+ if mask is nomask:
+ if weights is None:
+ n = a.sum(axis=None)
+ d = float(a.size)
+ else:
+ w = filled(weights, 0.0).ravel()
+ n = umath.add.reduce(a._data.ravel() * w)
+ d = umath.add.reduce(w)
+ del w
+ else:
+ if weights is None:
+ n = a.filled(0).sum(axis=None)
+ d = umath.add.reduce((-mask).ravel().astype(int_))
+ else:
+ w = array(filled(weights, 0.0), float, mask=mask).ravel()
+ n = add.reduce(a.ravel() * w)
+ d = add.reduce(w)
+ del w
+ else:
+ if mask is nomask:
+ if weights is None:
+ d = ash[axis] * 1.0
+ n = add.reduce(a._data, axis, dtype=float_)
+ else:
+ w = filled(weights, 0.0)
+ wsh = w.shape
+ if wsh == ():
+ wsh = (1,)
+ if wsh == ash:
+ w = numeric.array(w, float_, copy=0)
+ n = add.reduce(a*w, axis)
+ d = add.reduce(w, axis)
+ del w
+ elif wsh == (ash[axis],):
+ ni = ash[axis]
+ r = [None]*len(ash)
+ r[axis] = slice(None, None, 1)
+ w = eval ("w["+ repr(tuple(r)) + "] * ones(ash, float)")
+ n = add.reduce(a*w, axis, dtype=float_)
+ d = add.reduce(w, axis, dtype=float_)
+ del w, r
+ else:
+ raise ValueError, 'average: weights wrong shape.'
+ else:
+ if weights is None:
+ n = add.reduce(a, axis, dtype=float_)
+ d = umath.add.reduce((-mask), axis=axis, dtype=float_)
+ else:
+ w = filled(weights, 0.0)
+ wsh = w.shape
+ if wsh == ():
+ wsh = (1,)
+ if wsh == ash:
+ w = array(w, dtype=float_, mask=mask, copy=0)
+ n = add.reduce(a*w, axis, dtype=float_)
+ d = add.reduce(w, axis, dtype=float_)
+ elif wsh == (ash[axis],):
+ ni = ash[axis]
+ r = [None]*len(ash)
+ r[axis] = slice(None, None, 1)
+ w = eval ("w["+ repr(tuple(r)) + "] * masked_array(ones(ash, float), mask)")
+ n = add.reduce(a*w, axis, dtype=float_)
+ d = add.reduce(w, axis, dtype=float_)
+ else:
+ raise ValueError, 'average: weights wrong shape.'
+ del w
+ if n is masked or d is masked:
+ return masked
+ result = n/d
+ del n
+
+ if isMaskedArray(result):
+ if ((axis is None) or (axis==0 and a.ndim == 1)) and \
+ (result.mask is nomask):
+ result = result._data
+ if returned:
+ if not isMaskedArray(d):
+ d = masked_array(d)
+ if isinstance(d, ndarray) and (not d.shape == result.shape):
+ d = ones(result.shape, dtype=float_) * d
+ if returned:
+ return result, d
+ else:
+ return result
+
+#..............................................................................
+def compress_rowcols(x, axis=None):
+ """Suppresses the rows and/or columns of a 2D array that contains masked values.
+
+ The suppression behavior is selected with the `axis`parameter.
+ - If axis is None, rows and columns are suppressed.
+ - If axis is 0, only rows are suppressed.
+ - If axis is 1 or -1, only columns are suppressed.
+
+*Returns*:
+ compressed_array : a ndarray.
+ """
+ x = asarray(x)
+ if x.ndim != 2:
+ raise NotImplementedError, "compress2d works for 2D arrays only."
+ m = getmask(x)
+ # Nothing is masked: return x
+ if m is nomask or not m.any():
+ return x._data
+ # All is masked: return empty
+ if m.all():
+ return nxarray([])
+ # Builds a list of rows/columns indices
+ (idxr, idxc) = (range(len(x)), range(x.shape[1]))
+ masked = m.nonzero()
+ if not axis:
+ for i in function_base.unique(masked[0]):
+ idxr.remove(i)
+ if axis in [None, 1, -1]:
+ for j in function_base.unique(masked[1]):
+ idxc.remove(j)
+ return x._data[idxr][:,idxc]
+
+def compress_rows(a):
+ """Suppresses whole rows of a 2D array that contain masked values."""
+ return compress_rowcols(a,0)
+
+def compress_cols(a):
+ """Suppresses whole columnss of a 2D array that contain masked values."""
+ return compress_rowcols(a,1)
+
+def mask_rowcols(a, axis=None):
+ """Masks whole rows and/or columns of a 2D array that contain masked values.
+ The masking behavior is selected with the `axis`parameter.
+ - If axis is None, rows and columns are masked.
+ - If axis is 0, only rows are masked.
+ - If axis is 1 or -1, only columns are masked.
+ Returns a *pure* ndarray.
+ """
+ a = asarray(a)
+ if a.ndim != 2:
+ raise NotImplementedError, "compress2d works for 2D arrays only."
+ m = getmask(a)
+ # Nothing is masked: return a
+ if m is nomask or not m.any():
+ return a
+ maskedval = m.nonzero()
+ a._mask = a._mask.copy()
+ if not axis:
+ a[function_base.unique(maskedval[0])] = masked
+ if axis in [None, 1, -1]:
+ a[:,function_base.unique(maskedval[1])] = masked
+ return a
+
+def mask_rows(a, axis=None):
+ """Masks whole rows of a 2D array that contain masked values."""
+ return mask_rowcols(a, 0)
+
+def mask_cols(a, axis=None):
+ """Masks whole columns of a 2D array that contain masked values."""
+ return mask_rowcols(a, 1)
+
+
+def dot(a,b, strict=False):
+ """Returns the dot product of two 2D masked arrays a and b.
+
+ Like the generic numpy equivalent, the product sum is over the last dimension
+ of a and the second-to-last dimension of b.
+ If strict is True, masked values are propagated: if a masked value appears
+ in a row or column, the whole row or column is considered masked.
+
+*Parameters*:
+ strict : {boolean}
+ Whether masked data are propagated (True) or set to 0 for the computation.
+
+*Note*:
+ The first argument is not conjugated.
+ """
+ #TODO: Works only with 2D arrays. There should be a way to get it to run with higher dimension
+ if strict and (a.ndim == 2) and (b.ndim == 2):
+ a = mask_rows(a)
+ b = mask_cols(b)
+ #
+ d = numpy.dot(filled(a, 0), filled(b, 0))
+ #
+ am = (~getmaskarray(a))
+ bm = (~getmaskarray(b))
+ m = ~numpy.dot(am,bm)
+ return masked_array(d, mask=m)
+
+#...............................................................................
+def mediff1d(array, to_end=None, to_begin=None):
+ """Returns the differences between consecutive elements of an array, possibly with
+ prefixed and/or appended values.
+
+*Parameters*:
+ array : {array}
+ Input array, will be flattened before the difference is taken.
+ to_end : {number}, optional
+ If provided, this number will be tacked onto the end of the returned
+ differences.
+ to_begin : {number}, optional
+ If provided, this number will be taked onto the beginning of the
+ returned differences.
+
+*Returns*:
+ ed : {array}
+ The differences. Loosely, this will be (ary[1:] - ary[:-1]).
+ """
+ a = masked_array(array, copy=True)
+ if a.ndim > 1:
+ a.reshape((a.size,))
+ (d, m, n) = (a._data, a._mask, a.size-1)
+ dd = d[1:]-d[:-1]
+ if m is nomask:
+ dm = nomask
+ else:
+ dm = m[1:]-m[:-1]
+ #
+ if to_end is not None:
+ to_end = asarray(to_end)
+ nend = to_end.size
+ if to_begin is not None:
+ to_begin = asarray(to_begin)
+ nbegin = to_begin.size
+ r_data = numeric.empty((n+nend+nbegin,), dtype=a.dtype)
+ r_mask = numeric.zeros((n+nend+nbegin,), dtype=bool_)
+ r_data[:nbegin] = to_begin._data
+ r_mask[:nbegin] = to_begin._mask
+ r_data[nbegin:-nend] = dd
+ r_mask[nbegin:-nend] = dm
+ else:
+ r_data = numeric.empty((n+nend,), dtype=a.dtype)
+ r_mask = numeric.zeros((n+nend,), dtype=bool_)
+ r_data[:-nend] = dd
+ r_mask[:-nend] = dm
+ r_data[-nend:] = to_end._data
+ r_mask[-nend:] = to_end._mask
+ #
+ elif to_begin is not None:
+ to_begin = asarray(to_begin)
+ nbegin = to_begin.size
+ r_data = numeric.empty((n+nbegin,), dtype=a.dtype)
+ r_mask = numeric.zeros((n+nbegin,), dtype=bool_)
+ r_data[:nbegin] = to_begin._data
+ r_mask[:nbegin] = to_begin._mask
+ r_data[nbegin:] = dd
+ r_mask[nbegin:] = dm
+ #
+ else:
+ r_data = dd
+ r_mask = dm
+ return masked_array(r_data, mask=r_mask)
+
+
+
+
+#####--------------------------------------------------------------------------
+#---- --- Concatenation helpers ---
+#####--------------------------------------------------------------------------
+
+class mconcatenator(concatenator):
+ """Translates slice objects to concatenation along an axis."""
+
+ def __init__(self, axis=0):
+ concatenator.__init__(self, axis, matrix=False)
+
+ def __getitem__(self,key):
+ if isinstance(key, str):
+ raise MAError, "Unavailable for masked array."
+ if type(key) is not tuple:
+ key = (key,)
+ objs = []
+ scalars = []
+ final_dtypedescr = None
+ for k in range(len(key)):
+ scalar = False
+ if type(key[k]) is slice:
+ step = key[k].step
+ start = key[k].start
+ stop = key[k].stop
+ if start is None:
+ start = 0
+ if step is None:
+ step = 1
+ if type(step) is type(1j):
+ size = int(abs(step))
+ newobj = function_base.linspace(start, stop, num=size)
+ else:
+ newobj = numeric.arange(start, stop, step)
+ elif type(key[k]) is str:
+ if (key[k] in 'rc'):
+ self.matrix = True
+ self.col = (key[k] == 'c')
+ continue
+ try:
+ self.axis = int(key[k])
+ continue
+ except (ValueError, TypeError):
+ raise ValueError, "Unknown special directive"
+ elif type(key[k]) in numeric.ScalarType:
+ newobj = asarray([key[k]])
+ scalars.append(k)
+ scalar = True
+ else:
+ newobj = key[k]
+ objs.append(newobj)
+ if isinstance(newobj, numeric.ndarray) and not scalar:
+ if final_dtypedescr is None:
+ final_dtypedescr = newobj.dtype
+ elif newobj.dtype > final_dtypedescr:
+ final_dtypedescr = newobj.dtype
+ if final_dtypedescr is not None:
+ for k in scalars:
+ objs[k] = objs[k].astype(final_dtypedescr)
+ res = concatenate(tuple(objs),axis=self.axis)
+ return self._retval(res)
+
+class mr_class(mconcatenator):
+ """Translates slice objects to concatenation along the first axis.
+
+ For example:
+ >>> mr_[array([1,2,3]), 0, 0, array([4,5,6])]
+ array([1, 2, 3, 0, 0, 4, 5, 6])
+ """
+ def __init__(self):
+ mconcatenator.__init__(self, 0)
+
+mr_ = mr_class()
+
+#####--------------------------------------------------------------------------
+#---- ---
+#####--------------------------------------------------------------------------
+
+def flatnotmasked_edges(a):
+ """Finds the indices of the first and last not masked values in a 1D masked array.
+ If all values are masked, returns None.
+ """
+ m = getmask(a)
+ if m is nomask or not numpy.any(m):
+ return [0,-1]
+ unmasked = numeric.flatnonzero(~m)
+ if len(unmasked) > 0:
+ return unmasked[[0,-1]]
+ else:
+ return None
+
+def notmasked_edges(a, axis=None):
+ """Finds the indices of the first and last not masked values along the given
+ axis in a masked array.
+ If all values are masked, returns None.
+ Otherwise, returns a list of 2 tuples, corresponding to the indices of the
+ first and last unmasked values respectively.
+ """
+ a = asarray(a)
+ if axis is None or a.ndim == 1:
+ return flatnotmasked_edges(a)
+ m = getmask(a)
+ idx = array(numpy.indices(a.shape), mask=nxasarray([m]*a.ndim))
+ return [tuple([idx[i].min(axis).compressed() for i in range(a.ndim)]),
+ tuple([idx[i].max(axis).compressed() for i in range(a.ndim)]),]
+
+def flatnotmasked_contiguous(a):
+ """Finds contiguous unmasked data in a flattened masked array.
+ Returns a sorted sequence of slices (start index, end index).
+ """
+ m = getmask(a)
+ if m is nomask:
+ return (a.size, [0,-1])
+ unmasked = numeric.flatnonzero(~m)
+ if len(unmasked) == 0:
+ return None
+ result = []
+ for k, group in groupby(enumerate(unmasked), lambda (i,x):i-x):
+ tmp = numpy.fromiter((g[1] for g in group), int_)
+# result.append((tmp.size, tuple(tmp[[0,-1]])))
+ result.append( slice(tmp[0],tmp[-1]) )
+ result.sort()
+ return result
+
+def notmasked_contiguous(a, axis=None):
+ """Finds contiguous unmasked data in a masked array along the given axis.
+ Returns a sorted sequence of slices (start index, end index).
+ Note: Only accepts 2D arrays at most.
+ """
+ a = asarray(a)
+ nd = a.ndim
+ if nd > 2:
+ raise NotImplementedError,"Currently limited to atmost 2D array."
+ if axis is None or nd == 1:
+ return flatnotmasked_contiguous(a)
+ #
+ result = []
+ #
+ other = (axis+1)%2
+ idx = [0,0]
+ idx[axis] = slice(None,None)
+ #
+ for i in range(a.shape[other]):
+ idx[other] = i
+ result.append( flatnotmasked_contiguous(a[idx]) )
+ return result
+
+################################################################################
+if __name__ == '__main__':
+ #
+ import numpy as N
+ from maskedarray.testutils import assert_equal
+ if 1:
+ b = ones(5)
+ m = [1,0,0,0,0]
+ d = masked_array(b,mask=m)
+ c = mr_[d,0,0,d]
Added: branches/maskedarray/numpy/core/ma/morestats.py
===================================================================
--- branches/maskedarray/numpy/core/ma/morestats.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/morestats.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,406 @@
+"""
+Generic statistics functions, with support to MA.
+
+:author: Pierre GF Gerard-Marchant
+:contact: pierregm_at_uga_edu
+:date: $Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $
+:version: $Id: morestats.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3473 $"
+__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
+
+
+import numpy
+from numpy import bool_, float_, int_, ndarray, \
+ sqrt,\
+ arange, empty,\
+ r_
+from numpy import array as narray
+import numpy.core.numeric as numeric
+from numpy.core.numeric import concatenate
+
+import maskedarray as MA
+from maskedarray.core import masked, nomask, MaskedArray, masked_array
+from maskedarray.extras import apply_along_axis, dot
+from maskedarray.mstats import trim_both, trimmed_stde, mquantiles, mmedian, stde_median
+
+from scipy.stats.distributions import norm, beta, t, binom
+from scipy.stats.morestats import find_repeats
+
+__all__ = ['hdquantiles', 'hdmedian', 'hdquantiles_sd',
+ 'trimmed_mean_ci', 'mjci', 'rank_data']
+
+
+#####--------------------------------------------------------------------------
+#---- --- Quantiles ---
+#####--------------------------------------------------------------------------
+def hdquantiles(data, prob=list([.25,.5,.75]), axis=None, var=False,):
+ """Computes quantile estimates with the Harrell-Davis method, where the estimates
+are calculated as a weighted linear combination of order statistics.
+
+*Parameters* :
+ data: {ndarray}
+ Data array.
+ prob: {sequence}
+ Sequence of quantiles to compute.
+ axis : {integer}
+ Axis along which to compute the quantiles. If None, use a flattened array.
+ var : {boolean}
+ Whether to return the variance of the estimate.
+
+*Returns*
+ A (p,) array of quantiles (if ``var`` is False), or a (2,p) array of quantiles
+ and variances (if ``var`` is True), where ``p`` is the number of quantiles.
+
+:Note:
+ The function is restricted to 2D arrays.
+ """
+ def _hd_1D(data,prob,var):
+ "Computes the HD quantiles for a 1D array. Returns nan for invalid data."
+ xsorted = numpy.squeeze(numpy.sort(data.compressed().view(ndarray)))
+ # Don't use length here, in case we have a numpy scalar
+ n = xsorted.size
+ #.........
+ hd = empty((2,len(prob)), float_)
+ if n < 2:
+ hd.flat = numpy.nan
+ if var:
+ return hd
+ return hd[0]
+ #.........
+ v = arange(n+1) / float(n)
+ betacdf = beta.cdf
+ for (i,p) in enumerate(prob):
+ _w = betacdf(v, (n+1)*p, (n+1)*(1-p))
+ w = _w[1:] - _w[:-1]
+ hd_mean = dot(w, xsorted)
+ hd[0,i] = hd_mean
+ #
+ hd[1,i] = dot(w, (xsorted-hd_mean)**2)
+ #
+ hd[0, prob == 0] = xsorted[0]
+ hd[0, prob == 1] = xsorted[-1]
+ if var:
+ hd[1, prob == 0] = hd[1, prob == 1] = numpy.nan
+ return hd
+ return hd[0]
+ # Initialization & checks ---------
+ data = masked_array(data, copy=False, dtype=float_)
+ p = numpy.array(prob, copy=False, ndmin=1)
+ # Computes quantiles along axis (or globally)
+ if (axis is None) or (data.ndim == 1):
+ result = _hd_1D(data, p, var)
+ else:
+ assert data.ndim <= 2, "Array should be 2D at most !"
+ result = apply_along_axis(_hd_1D, axis, data, p, var)
+ #
+ return masked_array(result, mask=numpy.isnan(result))
+
+#..............................................................................
+def hdmedian(data, axis=-1, var=False):
+ """Returns the Harrell-Davis estimate of the median along the given axis.
+
+*Parameters* :
+ data: {ndarray}
+ Data array.
+ axis : {integer}
+ Axis along which to compute the quantiles. If None, use a flattened array.
+ var : {boolean}
+ Whether to return the variance of the estimate.
+ """
+ result = hdquantiles(data,[0.5], axis=axis, var=var)
+ return result.squeeze()
+
+
+#..............................................................................
+def hdquantiles_sd(data, prob=list([.25,.5,.75]), axis=None):
+ """Computes the standard error of the Harrell-Davis quantile estimates by jackknife.
+
+
+*Parameters* :
+ data: {ndarray}
+ Data array.
+ prob: {sequence}
+ Sequence of quantiles to compute.
+ axis : {integer}
+ Axis along which to compute the quantiles. If None, use a flattened array.
+
+*Note*:
+ The function is restricted to 2D arrays.
+ """
+ def _hdsd_1D(data,prob):
+ "Computes the std error for 1D arrays."
+ xsorted = numpy.sort(data.compressed())
+ n = len(xsorted)
+ #.........
+ hdsd = empty(len(prob), float_)
+ if n < 2:
+ hdsd.flat = numpy.nan
+ #.........
+ vv = arange(n) / float(n-1)
+ betacdf = beta.cdf
+ #
+ for (i,p) in enumerate(prob):
+ _w = betacdf(vv, (n+1)*p, (n+1)*(1-p))
+ w = _w[1:] - _w[:-1]
+ mx_ = numpy.fromiter([dot(w,xsorted[r_[range(0,k),
+ range(k+1,n)].astype(int_)])
+ for k in range(n)], dtype=float_)
+ mx_var = numpy.array(mx_.var(), copy=False, ndmin=1) * n / float(n-1)
+ hdsd[i] = float(n-1) * sqrt(numpy.diag(mx_var).diagonal() / float(n))
+ return hdsd
+ # Initialization & checks ---------
+ data = masked_array(data, copy=False, dtype=float_)
+ p = numpy.array(prob, copy=False, ndmin=1)
+ # Computes quantiles along axis (or globally)
+ if (axis is None):
+ result = _hdsd_1D(data.compressed(), p)
+ else:
+ assert data.ndim <= 2, "Array should be 2D at most !"
+ result = apply_along_axis(_hdsd_1D, axis, data, p)
+ #
+ return masked_array(result, mask=numpy.isnan(result)).ravel()
+
+
+#####--------------------------------------------------------------------------
+#---- --- Confidence intervals ---
+#####--------------------------------------------------------------------------
+
+def trimmed_mean_ci(data, proportiontocut=0.2, alpha=0.05, axis=None):
+ """Returns the selected confidence interval of the trimmed mean along the
+given axis.
+
+*Parameters* :
+ data : {sequence}
+ Input data. The data is transformed to a masked array
+ proportiontocut : {float}
+ Proportion of the data to cut from each side of the data .
+ As a result, (2*proportiontocut*n) values are actually trimmed.
+ alpha : {float}
+ Confidence level of the intervals.
+ axis : {integer}
+ Axis along which to cut. If None, uses a flattened version of the input.
+ """
+ data = masked_array(data, copy=False)
+ trimmed = trim_both(data, proportiontocut=proportiontocut, axis=axis)
+ tmean = trimmed.mean(axis)
+ tstde = trimmed_stde(data, proportiontocut=proportiontocut, axis=axis)
+ df = trimmed.count(axis) - 1
+ tppf = t.ppf(1-alpha/2.,df)
+ return numpy.array((tmean - tppf*tstde, tmean+tppf*tstde))
+
+#..............................................................................
+def mjci(data, prob=[0.25,0.5,0.75], axis=None):
+ """Returns the Maritz-Jarrett estimators of the standard error of selected
+experimental quantiles of the data.
+
+*Parameters* :
+ data: {ndarray}
+ Data array.
+ prob: {sequence}
+ Sequence of quantiles to compute.
+ axis : {integer}
+ Axis along which to compute the quantiles. If None, use a flattened array.
+ """
+ def _mjci_1D(data, p):
+ data = data.compressed()
+ sorted = numpy.sort(data)
+ n = data.size
+ prob = (numpy.array(p) * n + 0.5).astype(int_)
+ betacdf = beta.cdf
+ #
+ mj = empty(len(prob), float_)
+ x = arange(1,n+1, dtype=float_) / n
+ y = x - 1./n
+ for (i,m) in enumerate(prob):
+ (m1,m2) = (m-1, n-m)
+ W = betacdf(x,m-1,n-m) - betacdf(y,m-1,n-m)
+ C1 = numpy.dot(W,sorted)
+ C2 = numpy.dot(W,sorted**2)
+ mj[i] = sqrt(C2 - C1**2)
+ return mj
+ #
+ data = masked_array(data, copy=False)
+ assert data.ndim <= 2, "Array should be 2D at most !"
+ p = numpy.array(prob, copy=False, ndmin=1)
+ # Computes quantiles along axis (or globally)
+ if (axis is None):
+ return _mjci_1D(data, p)
+ else:
+ return apply_along_axis(_mjci_1D, axis, data, p)
+
+#..............................................................................
+def mquantiles_cimj(data, prob=[0.25,0.50,0.75], alpha=0.05, axis=None):
+ """Computes the alpha confidence interval for the selected quantiles of the
+data, with Maritz-Jarrett estimators.
+
+*Parameters* :
+ data: {ndarray}
+ Data array.
+ prob: {sequence}
+ Sequence of quantiles to compute.
+ alpha : {float}
+ Confidence level of the intervals.
+ axis : {integer}
+ Axis along which to compute the quantiles. If None, use a flattened array.
+ """
+ alpha = min(alpha, 1-alpha)
+ z = norm.ppf(1-alpha/2.)
+ xq = mquantiles(data, prob, alphap=0, betap=0, axis=axis)
+ smj = mjci(data, prob, axis=axis)
+ return (xq - z * smj, xq + z * smj)
+
+
+#.............................................................................
+def median_cihs(data, alpha=0.05, axis=None):
+ """Computes the alpha-level confidence interval for the median of the data,
+following the Hettmasperger-Sheather method.
+
+*Parameters* :
+ data : {sequence}
+ Input data. Masked values are discarded. The input should be 1D only, or
+ axis should be set to None.
+ alpha : {float}
+ Confidence level of the intervals.
+ axis : {integer}
+ Axis along which to compute the quantiles. If None, use a flattened array.
+ """
+ def _cihs_1D(data, alpha):
+ data = numpy.sort(data.compressed())
+ n = len(data)
+ alpha = min(alpha, 1-alpha)
+ k = int(binom._ppf(alpha/2., n, 0.5))
+ gk = binom.cdf(n-k,n,0.5) - binom.cdf(k-1,n,0.5)
+ if gk < 1-alpha:
+ k -= 1
+ gk = binom.cdf(n-k,n,0.5) - binom.cdf(k-1,n,0.5)
+ gkk = binom.cdf(n-k-1,n,0.5) - binom.cdf(k,n,0.5)
+ I = (gk - 1 + alpha)/(gk - gkk)
+ lambd = (n-k) * I / float(k + (n-2*k)*I)
+ lims = (lambd*data[k] + (1-lambd)*data[k-1],
+ lambd*data[n-k-1] + (1-lambd)*data[n-k])
+ return lims
+ data = masked_array(data, copy=False)
+ # Computes quantiles along axis (or globally)
+ if (axis is None):
+ result = _cihs_1D(data.compressed(), p, var)
+ else:
+ assert data.ndim <= 2, "Array should be 2D at most !"
+ result = apply_along_axis(_cihs_1D, axis, data, alpha)
+ #
+ return result
+
+#..............................................................................
+def compare_medians_ms(group_1, group_2, axis=None):
+ """Compares the medians from two independent groups along the given axis.
+
+The comparison is performed using the McKean-Schrader estimate of the standard
+error of the medians.
+
+*Parameters* :
+ group_1 : {sequence}
+ First dataset.
+ group_2 : {sequence}
+ Second dataset.
+ axis : {integer}
+ Axis along which the medians are estimated. If None, the arrays are flattened.
+
+*Returns* :
+ A (p,) array of comparison values.
+
+ """
+ (med_1, med_2) = (mmedian(group_1, axis=axis), mmedian(group_2, axis=axis))
+ (std_1, std_2) = (stde_median(group_1, axis=axis),
+ stde_median(group_2, axis=axis))
+ W = abs(med_1 - med_2) / sqrt(std_1**2 + std_2**2)
+ return 1 - norm.cdf(W)
+
+
+#####--------------------------------------------------------------------------
+#---- --- Ranking ---
+#####--------------------------------------------------------------------------
+
+#..............................................................................
+def rank_data(data, axis=None, use_missing=False):
+ """Returns the rank (also known as order statistics) of each data point
+along the given axis.
+
+If some values are tied, their rank is averaged.
+If some values are masked, their rank is set to 0 if use_missing is False, or
+set to the average rank of the unmasked values if use_missing is True.
+
+*Parameters* :
+ data : {sequence}
+ Input data. The data is transformed to a masked array
+ axis : {integer}
+ Axis along which to perform the ranking. If None, the array is first
+ flattened. An exception is raised if the axis is specified for arrays
+ with a dimension larger than 2
+ use_missing : {boolean}
+ Whether the masked values have a rank of 0 (False) or equal to the
+ average rank of the unmasked values (True).
+ """
+ #
+ def _rank1d(data, use_missing=False):
+ n = data.count()
+ rk = numpy.empty(data.size, dtype=float_)
+ idx = data.argsort()
+ rk[idx[:n]] = numpy.arange(1,n+1)
+ #
+ if use_missing:
+ rk[idx[n:]] = (n+1)/2.
+ else:
+ rk[idx[n:]] = 0
+ #
+ repeats = find_repeats(data)
+ for r in repeats[0]:
+ condition = (data==r).filled(False)
+ rk[condition] = rk[condition].mean()
+ return rk
+ #
+ data = masked_array(data, copy=False)
+ if axis is None:
+ if data.ndim > 1:
+ return _rank1d(data.ravel(), use_missing).reshape(data.shape)
+ else:
+ return _rank1d(data, use_missing)
+ else:
+ return apply_along_axis(_rank1d, axis, data, use_missing)
+
+###############################################################################
+if __name__ == '__main__':
+
+ if 0:
+ from maskedarray.testutils import assert_almost_equal
+ data = [0.706560797,0.727229578,0.990399276,0.927065621,0.158953014,
+ 0.887764025,0.239407086,0.349638551,0.972791145,0.149789972,
+ 0.936947700,0.132359948,0.046041972,0.641675031,0.945530547,
+ 0.224218684,0.771450991,0.820257774,0.336458052,0.589113496,
+ 0.509736129,0.696838829,0.491323573,0.622767425,0.775189248,
+ 0.641461450,0.118455200,0.773029450,0.319280007,0.752229111,
+ 0.047841438,0.466295911,0.583850781,0.840581845,0.550086491,
+ 0.466470062,0.504765074,0.226855960,0.362641207,0.891620942,
+ 0.127898691,0.490094097,0.044882048,0.041441695,0.317976349,
+ 0.504135618,0.567353033,0.434617473,0.636243375,0.231803616,
+ 0.230154113,0.160011327,0.819464108,0.854706985,0.438809221,
+ 0.487427267,0.786907310,0.408367937,0.405534192,0.250444460,
+ 0.995309248,0.144389588,0.739947527,0.953543606,0.680051621,
+ 0.388382017,0.863530727,0.006514031,0.118007779,0.924024803,
+ 0.384236354,0.893687694,0.626534881,0.473051932,0.750134705,
+ 0.241843555,0.432947602,0.689538104,0.136934797,0.150206859,
+ 0.474335206,0.907775349,0.525869295,0.189184225,0.854284286,
+ 0.831089744,0.251637345,0.587038213,0.254475554,0.237781276,
+ 0.827928620,0.480283781,0.594514455,0.213641488,0.024194386,
+ 0.536668589,0.699497811,0.892804071,0.093835427,0.731107772]
+ #
+ assert_almost_equal(hdquantiles(data,[0., 1.]),
+ [0.006514031, 0.995309248])
+ hdq = hdquantiles(data,[0.25, 0.5, 0.75])
+ assert_almost_equal(hdq, [0.253210762, 0.512847491, 0.762232442,])
+ hdq = hdquantiles_sd(data,[0.25, 0.5, 0.75])
+ assert_almost_equal(hdq, [0.03786954, 0.03805389, 0.03800152,], 4)
+ #
+ data = numpy.array(data).reshape(10,10)
+ hdq = hdquantiles(data,[0.25,0.5,0.75],axis=0)
Added: branches/maskedarray/numpy/core/ma/mrecords.py
===================================================================
--- branches/maskedarray/numpy/core/ma/mrecords.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/mrecords.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,717 @@
+"""mrecords
+Defines a class of record arrays supporting masked arrays.
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: mrecords.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3473 $"
+__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
+
+import sys
+import types
+
+import numpy
+from numpy import bool_, complex_, float_, int_, str_, object_
+from numpy import array as narray
+import numpy.core.numeric as numeric
+import numpy.core.numerictypes as ntypes
+from numpy.core.defchararray import chararray
+from numpy.core.records import find_duplicate
+
+from numpy.core.records import format_parser, record, recarray
+from numpy.core.records import fromarrays as recfromarrays
+
+ndarray = numeric.ndarray
+_byteorderconv = numpy.core.records._byteorderconv
+_typestr = ntypes._typestr
+
+import maskedarray
+from maskedarray import MaskedArray, masked, nomask, masked_array,\
+ make_mask, mask_or, getmask, getmaskarray, filled
+from maskedarray.core import default_fill_value, masked_print_option
+
+import warnings
+
+reserved_fields = ['_data','_mask','_fieldmask', 'dtype']
+
+def _getformats(data):
+ "Returns the formats of each array of arraylist as a comma-separated string."
+ if hasattr(data,'dtype'):
+ return ",".join([desc[1] for desc in data.dtype.descr])
+
+ formats = ''
+ for obj in data:
+ obj = numeric.asarray(obj)
+# if not isinstance(obj, ndarray):
+## if not isinstance(obj, ndarray):
+# raise ValueError, "item in the array list must be an ndarray."
+ formats += _typestr[obj.dtype.type]
+ if issubclass(obj.dtype.type, ntypes.flexible):
+ formats += `obj.itemsize`
+ formats += ','
+ return formats[:-1]
+
+def _checknames(descr, names=None):
+ """Checks that the field names of the descriptor ``descr`` are not some
+reserved keywords. If this is the case, a default 'f%i' is substituted.
+If the argument `names` is not None, updates the field names to valid names.
+ """
+ ndescr = len(descr)
+ default_names = ['f%i' % i for i in range(ndescr)]
+ if names is None:
+ new_names = default_names
+ else:
+ if isinstance(names, (tuple, list)):
+ new_names = names
+ elif isinstance(names, str):
+ new_names = names.split(',')
+ else:
+ raise NameError, "illegal input names %s" % `names`
+ nnames = len(new_names)
+ if nnames < ndescr:
+ new_names += default_names[nnames:]
+ ndescr = []
+ for (n, d, t) in zip(new_names, default_names, descr.descr):
+ if n in reserved_fields:
+ if t[0] in reserved_fields:
+ ndescr.append((d,t[1]))
+ else:
+ ndescr.append(t)
+ else:
+ ndescr.append((n,t[1]))
+ return numeric.dtype(ndescr)
+
+
+
+class MaskedRecords(MaskedArray, object):
+ """
+
+*IVariables*:
+ _data : {recarray}
+ Underlying data, as a record array.
+ _mask : {boolean array}
+ Mask of the records. A record is masked when all its fields are masked.
+ _fieldmask : {boolean recarray}
+ Record array of booleans, setting the mask of each individual field of each record.
+ _fill_value : {record}
+ Filling values for each field.
+ """
+ _defaultfieldmask = nomask
+ _defaulthardmask = False
+ def __new__(cls, data, mask=nomask, dtype=None,
+ hard_mask=False, fill_value=None,
+# offset=0, strides=None,
+ formats=None, names=None, titles=None,
+ byteorder=None, aligned=False):
+ # Get the new descriptor ................
+ if dtype is not None:
+ descr = numeric.dtype(dtype)
+ else:
+ if formats is None:
+ formats = _getformats(data)
+ parsed = format_parser(formats, names, titles, aligned, byteorder)
+ descr = parsed._descr
+ if names is not None:
+ descr = _checknames(descr,names)
+ _names = descr.names
+ mdescr = [(n,'|b1') for n in _names]
+ # get the shape .........................
+ try:
+ shape = numeric.asarray(data[0]).shape
+ except IndexError:
+ shape = len(data.dtype)
+ if isinstance(shape, int):
+ shape = (shape,)
+ # Construct the _data recarray ..........
+ if isinstance(data, record):
+ _data = numeric.asarray(data).view(recarray)
+ _fieldmask = mask
+ elif isinstance(data, MaskedRecords):
+ _data = data._data
+ _fieldmask = data._fieldmask
+ elif isinstance(data, recarray):
+ _data = data
+ if mask is nomask:
+ _fieldmask = data.astype(mdescr)
+ _fieldmask.flat = tuple([False]*len(mdescr))
+ else:
+ _fieldmask = mask
+ elif (isinstance(data, (tuple, numpy.void)) or\
+ hasattr(data,'__len__') and isinstance(data[0], (tuple, numpy.void))):
+ data = numeric.array(data, dtype=descr).view(recarray)
+ _data = data
+ if mask is nomask:
+ _fieldmask = data.astype(mdescr)
+ _fieldmask.flat = tuple([False]*len(mdescr))
+ else:
+ _fieldmask = mask
+ else:
+ _data = recarray(shape, dtype=descr)
+ _fieldmask = recarray(shape, dtype=mdescr)
+ for (n,v) in zip(_names, data):
+ _data[n] = numeric.asarray(v).view(ndarray)
+ _fieldmask[n] = getmaskarray(v)
+ #........................................
+ _data = _data.view(cls)
+ _data._fieldmask = _fieldmask
+ _data._hardmask = hard_mask
+ if fill_value is None:
+ _data._fill_value = [default_fill_value(numeric.dtype(d[1]))
+ for d in descr.descr]
+ else:
+ _data._fill_value = fill_value
+ return _data
+
+ def __array_finalize__(self,obj):
+ if isinstance(obj, MaskedRecords):
+ self.__dict__.update(_fieldmask=obj._fieldmask,
+ _hardmask=obj._hardmask,
+ _fill_value=obj._fill_value
+ )
+ else:
+ self.__dict__.update(_fieldmask = nomask,
+ _hardmask = False,
+ fill_value = None
+ )
+ return
+
+ def _getdata(self):
+ "Returns the data as a recarray."
+ return self.view(recarray)
+ _data = property(fget=_getdata)
+
+ #......................................................
+ def __getattribute__(self, attr):
+ "Returns the given attribute."
+ try:
+ # Returns a generic attribute
+ return object.__getattribute__(self,attr)
+ except AttributeError:
+ # OK, so attr must be a field name
+ pass
+ # Get the list of fields ......
+ _names = self.dtype.names
+ if attr in _names:
+ _data = self._data
+ _mask = self._fieldmask
+# obj = masked_array(_data.__getattribute__(attr), copy=False,
+# mask=_mask.__getattribute__(attr))
+ # Use a view in order to avoid the copy of the mask in MaskedArray.__new__
+ obj = narray(_data.__getattribute__(attr), copy=False).view(MaskedArray)
+ obj._mask = _mask.__getattribute__(attr)
+ if not obj.ndim and obj._mask:
+ return masked
+ return obj
+ raise AttributeError,"No attribute '%s' !" % attr
+
+ def __setattr__(self, attr, val):
+ "Sets the attribute attr to the value val."
+ newattr = attr not in self.__dict__
+ try:
+ # Is attr a generic attribute ?
+ ret = object.__setattr__(self, attr, val)
+ except:
+ # Not a generic attribute: exit if it's not a valid field
+ fielddict = self.dtype.names or {}
+ if attr not in fielddict:
+ exctype, value = sys.exc_info()[:2]
+ raise exctype, value
+ else:
+ if attr not in list(self.dtype.names) + ['_mask','mask']:
+ return ret
+ if newattr: # We just added this one
+ try: # or this setattr worked on an internal
+ # attribute.
+ object.__delattr__(self, attr)
+ except:
+ return ret
+ # Case #1.: Basic field ............
+ base_fmask = self._fieldmask
+ _names = self.dtype.names
+ if attr in _names:
+ fval = filled(val)
+ mval = getmaskarray(val)
+ if self._hardmask:
+ mval = mask_or(mval, base_fmask.__getattr__(attr))
+ self._data.__setattr__(attr, fval)
+ base_fmask.__setattr__(attr, mval)
+ return
+ elif attr == '_mask':
+ self.__setmask__(val)
+ return
+ #............................................
+ def __getitem__(self, indx):
+ """Returns all the fields sharing the same fieldname base.
+The fieldname base is either `_data` or `_mask`."""
+ _localdict = self.__dict__
+ _data = self._data
+ # We want a field ........
+ if isinstance(indx, str):
+ obj = _data[indx].view(MaskedArray)
+ obj._set_mask(_localdict['_fieldmask'][indx])
+ # Force to nomask if the mask is empty
+ if not obj._mask.any():
+ obj._mask = nomask
+ return obj
+ # We want some elements ..
+ # First, the data ........
+ obj = ndarray.__getitem__(self, indx)
+ if isinstance(obj, numpy.void):
+ obj = self.__class__(obj, dtype=self.dtype)
+ else:
+ obj = obj.view(type(self))
+ obj._fieldmask = numpy.asarray(_localdict['_fieldmask'][indx]).view(recarray)
+ return obj
+ #............................................
+ def __setitem__(self, indx, value):
+ "Sets the given record to value."
+ MaskedArray.__setitem__(self, indx, value)
+
+
+ def __setslice__(self, i, j, value):
+ "Sets the slice described by [i,j] to `value`."
+ _localdict = self.__dict__
+ d = self._data
+ m = _localdict['_fieldmask']
+ names = self.dtype.names
+ if value is masked:
+ for n in names:
+ m[i:j][n] = True
+ elif not self._hardmask:
+ fval = filled(value)
+ mval = getmaskarray(value)
+ for n in names:
+ d[n][i:j] = fval
+ m[n][i:j] = mval
+ else:
+ mindx = getmaskarray(self)[i:j]
+ dval = numeric.asarray(value)
+ valmask = getmask(value)
+ if valmask is nomask:
+ for n in names:
+ mval = mask_or(m[n][i:j], valmask)
+ d[n][i:j][~mval] = value
+ elif valmask.size > 1:
+ for n in names:
+ mval = mask_or(m[n][i:j], valmask)
+ d[n][i:j][~mval] = dval[~mval]
+ m[n][i:j] = mask_or(m[n][i:j], mval)
+ self._fieldmask = m
+
+ #.....................................................
+ def __setmask__(self, mask):
+ "Sets the mask."
+ names = self.dtype.names
+ fmask = self.__dict__['_fieldmask']
+ newmask = make_mask(mask, copy=False)
+# self.unshare_mask()
+ if self._hardmask:
+ for n in names:
+ fmask[n].__ior__(newmask)
+ else:
+ for n in names:
+ fmask[n].flat = newmask
+ return
+
+ def _getmask(self):
+ """Returns the mask of the mrecord: a record is masked when all the fields
+are masked."""
+ if self.size > 1:
+ return self._fieldmask.view((bool_, len(self.dtype))).all(1)
+
+ _setmask = __setmask__
+ _mask = property(fget=_getmask, fset=_setmask)
+
+ #......................................................
+ def __str__(self):
+ "Calculates the string representation."
+ if self.size > 1:
+ mstr = ["(%s)" % ",".join([str(i) for i in s])
+ for s in zip(*[getattr(self,f) for f in self.dtype.names])]
+ return "[%s]" % ", ".join(mstr)
+ else:
+ mstr = ["%s" % ",".join([str(i) for i in s])
+ for s in zip([getattr(self,f) for f in self.dtype.names])]
+ return "(%s)" % ", ".join(mstr)
+
+ def __repr__(self):
+ "Calculates the repr representation."
+ _names = self.dtype.names
+ fmt = "%%%is : %%s" % (max([len(n) for n in _names])+4,)
+ reprstr = [fmt % (f,getattr(self,f)) for f in self.dtype.names]
+ reprstr.insert(0,'masked_records(')
+ reprstr.extend([fmt % (' fill_value', self._fill_value),
+ ' )'])
+ return str("\n".join(reprstr))
+ #......................................................
+ def view(self, obj):
+ """Returns a view of the mrecarray."""
+ try:
+ if issubclass(obj, ndarray):
+ return ndarray.view(self, obj)
+ except TypeError:
+ pass
+ dtype = numeric.dtype(obj)
+ if dtype.fields is None:
+ return self.__array__().view(dtype)
+ return ndarray.view(self, obj)
+ #......................................................
+ def filled(self, fill_value=None):
+ """Returns an array of the same class as ``_data``, with masked values
+filled with ``fill_value``. If ``fill_value`` is None, ``self.fill_value`` is
+used instead.
+
+Subclassing is preserved.
+
+ """
+ _localdict = self.__dict__
+ d = self._data
+ fm = _localdict['_fieldmask']
+ if not numeric.asarray(fm, dtype=bool_).any():
+ return d
+ #
+ if fill_value is None:
+ value = _localdict['_fill_value']
+ else:
+ value = fill_value
+ if numeric.size(value) == 1:
+ value = [value,] * len(self.dtype)
+ #
+ if self is masked:
+ result = numeric.asanyarray(value)
+ else:
+ result = d.copy()
+ for (n, v) in zip(d.dtype.names, value):
+ numpy.putmask(numeric.asarray(result[n]),
+ numeric.asarray(fm[n]), v)
+ return result
+ #............................................
+ def harden_mask(self):
+ "Forces the mask to hard"
+ self._hardmask = True
+ def soften_mask(self):
+ "Forces the mask to soft"
+ self._hardmask = False
+ #.............................................
+ def copy(self):
+ """Returns a copy of the masked record."""
+ _localdict = self.__dict__
+ return MaskedRecords(self._data.copy(),
+ mask=_localdict['_fieldmask'].copy(),
+ dtype=self.dtype)
+ #.............................................
+
+
+#####---------------------------------------------------------------------------
+#---- --- Constructors ---
+#####---------------------------------------------------------------------------
+
+def fromarrays(arraylist, dtype=None, shape=None, formats=None,
+ names=None, titles=None, aligned=False, byteorder=None):
+ """Creates a mrecarray from a (flat) list of masked arrays.
+
+*Parameters*:
+ arraylist : {sequence}
+ A list of (masked) arrays. Each element of the sequence is first converted
+ to a masked array if needed. If a 2D array is passed as argument, it is
+ processed line by line
+ dtype : {numeric.dtype}
+ Data type descriptor.
+ {shape} : {integer}
+ Number of records. If None, ``shape`` is defined from the shape of the
+ first array in the list.
+ formats : {sequence}
+ Sequence of formats for each individual field. If None, the formats will
+ be autodetected by inspecting the fields and selecting the highest dtype
+ possible.
+ names : {sequence}
+ Sequence of the names of each field.
+ -titles : {sequence}
+ (Description to write)
+ aligned : {boolean}
+ (Description to write, not used anyway)
+ byteorder: {boolean}
+ (Description to write, not used anyway)
+
+*Notes*:
+ Lists of tuples should be preferred over lists of lists for faster processing.
+ """
+ arraylist = [masked_array(x) for x in arraylist]
+ # Define/check the shape.....................
+ if shape is None or shape == 0:
+ shape = arraylist[0].shape
+ if isinstance(shape, int):
+ shape = (shape,)
+ # Define formats from scratch ...............
+ if formats is None and dtype is None:
+ formats = _getformats(arraylist)
+ # Define the dtype ..........................
+ if dtype is not None:
+ descr = numeric.dtype(dtype)
+ _names = descr.names
+ else:
+ parsed = format_parser(formats, names, titles, aligned, byteorder)
+ _names = parsed._names
+ descr = parsed._descr
+ # Determine shape from data-type.............
+ if len(descr) != len(arraylist):
+ msg = "Mismatch between the number of fields (%i) and the number of "\
+ "arrays (%i)"
+ raise ValueError, msg % (len(descr), len(arraylist))
+ d0 = descr[0].shape
+ nn = len(d0)
+ if nn > 0:
+ shape = shape[:-nn]
+ # Make sure the shape is the correct one ....
+ for k, obj in enumerate(arraylist):
+ nn = len(descr[k].shape)
+ testshape = obj.shape[:len(obj.shape)-nn]
+ if testshape != shape:
+ raise ValueError, "Array-shape mismatch in array %d" % k
+ # Reconstruct the descriptor, by creating a _data and _mask version
+ return MaskedRecords(arraylist, dtype=descr)
+#..............................................................................
+def fromrecords(reclist, dtype=None, shape=None, formats=None, names=None,
+ titles=None, aligned=False, byteorder=None):
+ """Creates a MaskedRecords from a list of records.
+
+*Parameters*:
+ arraylist : {sequence}
+ A list of (masked) arrays. Each element of the sequence is first converted
+ to a masked array if needed. If a 2D array is passed as argument, it is
+ processed line by line
+ dtype : {numeric.dtype}
+ Data type descriptor.
+ {shape} : {integer}
+ Number of records. If None, ``shape`` is defined from the shape of the
+ first array in the list.
+ formats : {sequence}
+ Sequence of formats for each individual field. If None, the formats will
+ be autodetected by inspecting the fields and selecting the highest dtype
+ possible.
+ names : {sequence}
+ Sequence of the names of each field.
+ -titles : {sequence}
+ (Description to write)
+ aligned : {boolean}
+ (Description to write, not used anyway)
+ byteorder: {boolean}
+ (Description to write, not used anyway)
+
+*Notes*:
+ Lists of tuples should be preferred over lists of lists for faster processing.
+ """
+ # reclist is in fact a mrecarray .................
+ if isinstance(reclist, MaskedRecords):
+ mdescr = reclist.dtype
+ shape = reclist.shape
+ return MaskedRecords(reclist, dtype=mdescr)
+ # No format, no dtype: create from to arrays .....
+ nfields = len(reclist[0])
+ if formats is None and dtype is None: # slower
+ if isinstance(reclist, recarray):
+ arrlist = [reclist.field(i) for i in range(len(reclist.dtype))]
+ if names is None:
+ names = reclist.dtype.names
+ else:
+ obj = numeric.array(reclist,dtype=object)
+ arrlist = [numeric.array(obj[...,i].tolist())
+ for i in xrange(nfields)]
+ return MaskedRecords(arrlist, formats=formats, names=names,
+ titles=titles, aligned=aligned, byteorder=byteorder)
+ # Construct the descriptor .......................
+ if dtype is not None:
+ descr = numeric.dtype(dtype)
+ _names = descr.names
+ else:
+ parsed = format_parser(formats, names, titles, aligned, byteorder)
+ _names = parsed._names
+ descr = parsed._descr
+
+ try:
+ retval = numeric.array(reclist, dtype = descr).view(recarray)
+ except TypeError: # list of lists instead of list of tuples
+ if (shape is None or shape == 0):
+ shape = len(reclist)*2
+ if isinstance(shape, (int, long)):
+ shape = (shape*2,)
+ if len(shape) > 1:
+ raise ValueError, "Can only deal with 1-d array."
+ retval = recarray(shape, mdescr)
+ for k in xrange(retval.size):
+ retval[k] = tuple(reclist[k])
+ return MaskedRecords(retval, dtype=descr)
+ else:
+ if shape is not None and retval.shape != shape:
+ retval.shape = shape
+ #
+ return MaskedRecords(retval, dtype=descr)
+
+def _guessvartypes(arr):
+ """Tries to guess the dtypes of the str_ ndarray `arr`, by testing element-wise
+conversion. Returns a list of dtypes.
+The array is first converted to ndarray. If the array is 2D, the test is performed
+on the first line. An exception is raised if the file is 3D or more.
+ """
+ vartypes = []
+ arr = numeric.asarray(arr)
+ if len(arr.shape) == 2 :
+ arr = arr[0]
+ elif len(arr.shape) > 2:
+ raise ValueError, "The array should be 2D at most!"
+ # Start the conversion loop .......
+ for f in arr:
+ try:
+ val = int(f)
+ except ValueError:
+ try:
+ val = float(f)
+ except ValueError:
+ try:
+ val = complex(f)
+ except ValueError:
+ vartypes.append(arr.dtype)
+ else:
+ vartypes.append(complex_)
+ else:
+ vartypes.append(float_)
+ else:
+ vartypes.append(int_)
+ return vartypes
+
+def openfile(fname):
+ "Opens the file handle of file `fname`"
+ # A file handle ...................
+ if hasattr(fname, 'readline'):
+ return fname
+ # Try to open the file and guess its type
+ try:
+ f = open(fname)
+ except IOError:
+ raise IOError, "No such file: '%s'" % fname
+ if f.readline()[:2] != "\\x":
+ f.seek(0,0)
+ return f
+ raise NotImplementedError, "Wow, binary file"
+
+
+def fromtextfile(fname, delimitor=None, commentchar='#', missingchar='',
+ varnames=None, vartypes=None):
+ """Creates a mrecarray from data stored in the file `filename`.
+
+*Parameters* :
+ filename : {file name/handle}
+ Handle of an opened file.
+ delimitor : {string}
+ Alphanumeric character used to separate columns in the file.
+ If None, any (group of) white spacestring(s) will be used.
+ commentchar : {string}
+ Alphanumeric character used to mark the start of a comment.
+ missingchar` : {string}
+ String indicating missing data, and used to create the masks.
+ varnames : {sequence}
+ Sequence of the variable names. If None, a list will be created from
+ the first non empty line of the file.
+ vartypes : {sequence}
+ Sequence of the variables dtypes. If None, it will be estimated from
+ the first non-commented line.
+
+
+ Ultra simple: the varnames are in the header, one line"""
+ # Try to open the file ......................
+ f = openfile(fname)
+ # Get the first non-empty line as the varnames
+ while True:
+ line = f.readline()
+ firstline = line[:line.find(commentchar)].strip()
+ _varnames = firstline.split(delimitor)
+ if len(_varnames) > 1:
+ break
+ if varnames is None:
+ varnames = _varnames
+ # Get the data ..............................
+ _variables = masked_array([line.strip().split(delimitor) for line in f
+ if line[0] != commentchar and len(line) > 1])
+ (_, nfields) = _variables.shape
+ # Try to guess the dtype ....................
+ if vartypes is None:
+ vartypes = _guessvartypes(_variables[0])
+ else:
+ vartypes = [numeric.dtype(v) for v in vartypes]
+ if len(vartypes) != nfields:
+ msg = "Attempting to %i dtypes for %i fields!"
+ msg += " Reverting to default."
+ warnings.warn(msg % (len(vartypes), nfields))
+ vartypes = _guessvartypes(_variables[0])
+ # Construct the descriptor ..................
+ mdescr = [(n,f) for (n,f) in zip(varnames, vartypes)]
+ # Get the data and the mask .................
+ # We just need a list of masked_arrays. It's easier to create it like that:
+ _mask = (_variables.T == missingchar)
+ _datalist = [masked_array(a,mask=m,dtype=t)
+ for (a,m,t) in zip(_variables.T, _mask, vartypes)]
+ return MaskedRecords(_datalist, dtype=mdescr)
+
+#....................................................................
+def addfield(mrecord, newfield, newfieldname=None):
+ """Adds a new field to the masked record array, using `newfield` as data
+and `newfieldname` as name. If `newfieldname` is None, the new field name is
+set to 'fi', where `i` is the number of existing fields.
+ """
+ _data = mrecord._data
+ _mask = mrecord._fieldmask
+ if newfieldname is None or newfieldname in reserved_fields:
+ newfieldname = 'f%i' % len(_data.dtype)
+ newfield = masked_array(newfield)
+ # Get the new data ............
+ # Create a new empty recarray
+ newdtype = numeric.dtype(_data.dtype.descr + \
+ [(newfieldname, newfield.dtype)])
+ newdata = recarray(_data.shape, newdtype)
+ # Add the exisintg field
+ [newdata.setfield(_data.getfield(*f),*f)
+ for f in _data.dtype.fields.values()]
+ # Add the new field
+ newdata.setfield(newfield._data, *newdata.dtype.fields[newfieldname])
+ newdata = newdata.view(MaskedRecords)
+ # Get the new mask .............
+ # Create a new empty recarray
+ newmdtype = numeric.dtype([(n,bool_) for n in newdtype.names])
+ newmask = recarray(_data.shape, newmdtype)
+ # Add the old masks
+ [newmask.setfield(_mask.getfield(*f),*f)
+ for f in _mask.dtype.fields.values()]
+ # Add the mask of the new field
+ newmask.setfield(getmaskarray(newfield),
+ *newmask.dtype.fields[newfieldname])
+ newdata._fieldmask = newmask
+ return newdata
+
+################################################################################
+if __name__ == '__main__':
+ import numpy as N
+ from maskedarray.testutils import assert_equal
+ if 1:
+ d = N.arange(5)
+ m = maskedarray.make_mask([1,0,0,1,1])
+ base_d = N.r_[d,d[::-1]].reshape(2,-1).T
+ base_m = N.r_[[m, m[::-1]]].T
+ base = masked_array(base_d, mask=base_m).T
+ mrecord = fromarrays(base,dtype=[('a',N.float_),('b',N.float_)])
+ mrec = MaskedRecords(mrecord.copy())
+ #
+ if 1:
+ mrec = mrec.copy()
+ mrec.harden_mask()
+ assert(mrec._hardmask)
+ mrec._mask = nomask
+ assert_equal(mrec._mask, N.r_[[m,m[::-1]]].all(0))
+ mrec.soften_mask()
+ assert(not mrec._hardmask)
+ mrec.mask = nomask
+ tmp = mrec['b']._mask
+ assert(mrec['b']._mask is nomask)
+ assert_equal(mrec['a']._mask,mrec['b']._mask)
Added: branches/maskedarray/numpy/core/ma/mstats.py
===================================================================
--- branches/maskedarray/numpy/core/ma/mstats.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/mstats.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,433 @@
+"""
+Generic statistics functions, with support to MA.
+
+:author: Pierre GF Gerard-Marchant
+:contact: pierregm_at_uga_edu
+:date: $Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $
+:version: $Id: mstats.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3473 $"
+__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
+
+
+import numpy
+from numpy import bool_, float_, int_, \
+ sqrt
+from numpy import array as narray
+import numpy.core.numeric as numeric
+from numpy.core.numeric import concatenate
+
+import maskedarray
+from maskedarray.core import masked, nomask, MaskedArray, masked_array
+from maskedarray.extras import apply_along_axis, dot
+
+__all__ = ['cov','meppf','plotting_positions','meppf','mmedian','mquantiles',
+ 'stde_median','trim_tail','trim_both','trimmed_mean','trimmed_stde',
+ 'winsorize']
+
+#####--------------------------------------------------------------------------
+#---- -- Trimming ---
+#####--------------------------------------------------------------------------
+
+def winsorize(data, alpha=0.2):
+ """Returns a Winsorized version of the input array.
+
+The (alpha/2.) lowest values are set to the (alpha/2.)th percentile, and
+the (alpha/2.) highest values are set to the (1-alpha/2.)th percentile
+Masked values are skipped.
+
+*Parameters*:
+ data : {ndarray}
+ Input data to Winsorize. The data is first flattened.
+ alpha : {float}, optional
+ Percentage of total Winsorization : alpha/2. on the left, alpha/2. on the right
+ """
+ data = masked_array(data, copy=False).ravel()
+ idxsort = data.argsort()
+ (nsize, ncounts) = (data.size, data.count())
+ ntrim = int(alpha * ncounts)
+ (xmin,xmax) = data[idxsort[[ntrim, ncounts-nsize-ntrim-1]]]
+ return masked_array(numpy.clip(data, xmin, xmax), mask=data._mask)
+
+#..............................................................................
+def trim_both(data, proportiontocut=0.2, axis=None):
+ """Trims the data by masking the int(trim*n) smallest and int(trim*n) largest
+values of data along the given axis, where n is the number of unmasked values.
+
+*Parameters*:
+ data : {ndarray}
+ Data to trim.
+ proportiontocut : {float}
+ Percentage of trimming. If n is the number of unmasked values before trimming,
+ the number of values after trimming is (1-2*trim)*n.
+ axis : {integer}
+ Axis along which to perform the trimming. If None, the input array is first
+ flattened.
+ """
+ #...................
+ def _trim_1D(data, trim):
+ "Private function: return a trimmed 1D array."
+ nsize = data.size
+ ncounts = data.count()
+ ntrim = int(trim * ncounts)
+ idxsort = data.argsort()
+ data[idxsort[:ntrim]] = masked
+ data[idxsort[ncounts-nsize-ntrim:]] = masked
+ return data
+ #...................
+ data = masked_array(data, copy=False, subok=True)
+ data.unshare_mask()
+ if (axis is None):
+ return _trim_1D(data.ravel(), proportiontocut)
+ else:
+ assert data.ndim <= 2, "Array should be 2D at most !"
+ return apply_along_axis(_trim_1D, axis, data, proportiontocut)
+
+#..............................................................................
+def trim_tail(data, proportiontocut=0.2, tail='left', axis=None):
+ """Trims the data by masking int(trim*n) values from ONE tail of the data
+along the given axis, where n is the number of unmasked values.
+
+*Parameters*:
+ data : {ndarray}
+ Data to trim.
+ proportiontocut : {float}
+ Percentage of trimming. If n is the number of unmasked values before trimming,
+ the number of values after trimming is (1-trim)*n.
+ tail : {string}
+ Trimming direction, in ('left', 'right'). If left, the proportiontocut
+ lowest values are set to the corresponding percentile. If right, the
+ proportiontocut highest values are used instead.
+ axis : {integer}
+ Axis along which to perform the trimming. If None, the input array is first
+ flattened.
+ """
+ #...................
+ def _trim_1D(data, trim, left):
+ "Private function: return a trimmed 1D array."
+ nsize = data.size
+ ncounts = data.count()
+ ntrim = int(trim * ncounts)
+ idxsort = data.argsort()
+ if left:
+ data[idxsort[:ntrim]] = masked
+ else:
+ data[idxsort[ncounts-nsize-ntrim:]] = masked
+ return data
+ #...................
+ data = masked_array(data, copy=False, subok=True)
+ data.unshare_mask()
+ #
+ if not isinstance(tail, str):
+ raise TypeError("The tail argument should be in ('left','right')")
+ tail = tail.lower()[0]
+ if tail == 'l':
+ left = True
+ elif tail == 'r':
+ left=False
+ else:
+ raise ValueError("The tail argument should be in ('left','right')")
+ #
+ if (axis is None):
+ return _trim_1D(data.ravel(), proportiontocut, left)
+ else:
+ assert data.ndim <= 2, "Array should be 2D at most !"
+ return apply_along_axis(_trim_1D, axis, data, proportiontocut, left)
+
+#..............................................................................
+def trimmed_mean(data, proportiontocut=0.2, axis=None):
+ """Returns the trimmed mean of the data along the given axis. Trimming is
+performed on both ends of the distribution.
+
+*Parameters*:
+ data : {ndarray}
+ Data to trim.
+ proportiontocut : {float}
+ Proportion of the data to cut from each side of the data .
+ As a result, (2*proportiontocut*n) values are actually trimmed.
+ axis : {integer}
+ Axis along which to perform the trimming. If None, the input array is first
+ flattened.
+ """
+ return trim_both(data, proportiontocut=proportiontocut, axis=axis).mean(axis=axis)
+
+#..............................................................................
+def trimmed_stde(data, proportiontocut=0.2, axis=None):
+ """Returns the standard error of the trimmed mean for the input data,
+along the given axis. Trimming is performed on both ends of the distribution.
+
+*Parameters*:
+ data : {ndarray}
+ Data to trim.
+ proportiontocut : {float}
+ Proportion of the data to cut from each side of the data .
+ As a result, (2*proportiontocut*n) values are actually trimmed.
+ axis : {integer}
+ Axis along which to perform the trimming. If None, the input array is first
+ flattened.
+ """
+ #........................
+ def _trimmed_stde_1D(data, trim=0.2):
+ "Returns the standard error of the trimmed mean for a 1D input data."
+ winsorized = winsorize(data)
+ nsize = winsorized.count()
+ winstd = winsorized.stdu()
+ return winstd / ((1-2*trim) * numpy.sqrt(nsize))
+ #........................
+ data = masked_array(data, copy=False, subok=True)
+ data.unshare_mask()
+ if (axis is None):
+ return _trimmed_stde_1D(data.ravel(), proportiontocut)
+ else:
+ assert data.ndim <= 2, "Array should be 2D at most !"
+ return apply_along_axis(_trimmed_stde_1D, axis, data, proportiontocut)
+
+#.............................................................................
+def stde_median(data, axis=None):
+ """Returns the McKean-Schrader estimate of the standard error of the sample
+median along the given axis.
+
+
+*Parameters*:
+ data : {ndarray}
+ Data to trim.
+ axis : {integer}
+ Axis along which to perform the trimming. If None, the input array is first
+ flattened.
+ """
+ def _stdemed_1D(data):
+ sorted = numpy.sort(data.compressed())
+ n = len(sorted)
+ z = 2.5758293035489004
+ k = int(round((n+1)/2. - z * sqrt(n/4.),0))
+ return ((sorted[n-k] - sorted[k-1])/(2.*z))
+ #
+ data = masked_array(data, copy=False, subok=True)
+ if (axis is None):
+ return _stdemed_1D(data)
+ else:
+ assert data.ndim <= 2, "Array should be 2D at most !"
+ return apply_along_axis(_stdemed_1D, axis, data)
+
+
+#####--------------------------------------------------------------------------
+#---- --- Quantiles ---
+#####--------------------------------------------------------------------------
+
+
+def mquantiles(data, prob=list([.25,.5,.75]), alphap=.4, betap=.4, axis=None):
+ """Computes empirical quantiles for a *1xN* data array.
+Samples quantile are defined by:
+*Q(p) = (1-g).x[i] +g.x[i+1]*
+where *x[j]* is the jth order statistic,
+with *i = (floor(n*p+m))*, *m=alpha+p*(1-alpha-beta)* and *g = n*p + m - i)*.
+
+Typical values of (alpha,beta) are:
+
+ - (0,1) : *p(k) = k/n* : linear interpolation of cdf (R, type 4)
+ - (.5,.5) : *p(k) = (k+1/2.)/n* : piecewise linear function (R, type 5)
+ - (0,0) : *p(k) = k/(n+1)* : (R type 6)
+ - (1,1) : *p(k) = (k-1)/(n-1)*. In this case, p(k) = mode[F(x[k])].
+ That's R default (R type 7)
+ - (1/3,1/3): *p(k) = (k-1/3)/(n+1/3)*. Then p(k) ~ median[F(x[k])].
+ The resulting quantile estimates are approximately median-unbiased
+ regardless of the distribution of x. (R type 8)
+ - (3/8,3/8): *p(k) = (k-3/8)/(n+1/4)*. Blom.
+ The resulting quantile estimates are approximately unbiased
+ if x is normally distributed (R type 9)
+ - (.4,.4) : approximately quantile unbiased (Cunnane)
+ - (.35,.35): APL, used with PWM
+
+*Parameters*:
+ x : {sequence}
+ Input data, as a sequence or array of dimension at most 2.
+ prob : {sequence}
+ List of quantiles to compute.
+ alpha : {float}
+ Plotting positions parameter.
+ beta : {float}
+ Plotting positions parameter.
+ axis : {integer}
+ Axis along which to perform the trimming. If None, the input array is first
+ flattened.
+ """
+ def _quantiles1D(data,m,p):
+ x = numpy.sort(data.compressed())
+ n = len(x)
+ if n == 0:
+ return masked_array(numpy.empty(len(p), dtype=float_), mask=True)
+ elif n == 1:
+ return masked_array(numpy.resize(x, p.shape), mask=nomask)
+ aleph = (n*p + m)
+ k = numpy.floor(aleph.clip(1, n-1)).astype(int_)
+ gamma = (aleph-k).clip(0,1)
+ return (1.-gamma)*x[(k-1).tolist()] + gamma*x[k.tolist()]
+
+ # Initialization & checks ---------
+ data = masked_array(data, copy=False)
+ p = narray(prob, copy=False, ndmin=1)
+ m = alphap + p*(1.-alphap-betap)
+ # Computes quantiles along axis (or globally)
+ if (axis is None):
+ return _quantiles1D(data, m, p)
+ else:
+ assert data.ndim <= 2, "Array should be 2D at most !"
+ return apply_along_axis(_quantiles1D, axis, data, m, p)
+
+
+def plotting_positions(data, alpha=0.4, beta=0.4):
+ """Returns the plotting positions (or empirical percentile points) for the
+ data.
+ Plotting positions are defined as (i-alpha)/(n-alpha-beta), where:
+ - i is the rank order statistics
+ - n is the number of unmasked values along the given axis
+ - alpha and beta are two parameters.
+
+ Typical values for alpha and beta are:
+ - (0,1) : *p(k) = k/n* : linear interpolation of cdf (R, type 4)
+ - (.5,.5) : *p(k) = (k-1/2.)/n* : piecewise linear function (R, type 5)
+ - (0,0) : *p(k) = k/(n+1)* : Weibull (R type 6)
+ - (1,1) : *p(k) = (k-1)/(n-1)*. In this case, p(k) = mode[F(x[k])].
+ That's R default (R type 7)
+ - (1/3,1/3): *p(k) = (k-1/3)/(n+1/3)*. Then p(k) ~ median[F(x[k])].
+ The resulting quantile estimates are approximately median-unbiased
+ regardless of the distribution of x. (R type 8)
+ - (3/8,3/8): *p(k) = (k-3/8)/(n+1/4)*. Blom.
+ The resulting quantile estimates are approximately unbiased
+ if x is normally distributed (R type 9)
+ - (.4,.4) : approximately quantile unbiased (Cunnane)
+ - (.35,.35): APL, used with PWM
+ """
+ data = masked_array(data, copy=False).reshape(1,-1)
+ n = data.count()
+ plpos = numpy.empty(data.size, dtype=float_)
+ plpos[n:] = 0
+ plpos[data.argsort()[:n]] = (numpy.arange(1,n+1) - alpha)/(n+1-alpha-beta)
+ return masked_array(plpos, mask=data._mask)
+
+meppf = plotting_positions
+
+
+def mmedian(data, axis=None):
+ """Returns the median of data along the given axis. Missing data are discarded."""
+ def _median1D(data):
+ x = numpy.sort(data.compressed())
+ if x.size == 0:
+ return masked
+ return numpy.median(x)
+ data = masked_array(data, subok=True, copy=True)
+ if axis is None:
+ return _median1D(data)
+ else:
+ return apply_along_axis(_median1D, axis, data)
+
+
+def cov(x, y=None, rowvar=True, bias=False, strict=False):
+ """Estimates the covariance matrix.
+
+
+Normalization is by (N-1) where N is the number of observations (unbiased
+estimate). If bias is True then normalization is by N.
+
+*Parameters*:
+ x : {ndarray}
+ Input data. If x is a 1D array, returns the variance. If x is a 2D array,
+ returns the covariance matrix.
+ y : {ndarray}, optional
+ Optional set of variables.
+ rowvar : {boolean}
+ If rowvar is true, then each row is a variable with obersvations in columns.
+ If rowvar is False, each column is a variable and the observations are in
+ the rows.
+ bias : {boolean}
+ Whether to use a biased or unbiased estimate of the covariance.
+ If bias is True, then the normalization is by N, the number of observations.
+ Otherwise, the normalization is by (N-1)
+ strict : {boolean}
+ If strict is True, masked values are propagated: if a masked value appears in
+ a row or column, the whole row or column is considered masked.
+ """
+ X = narray(x, ndmin=2, subok=True, dtype=float)
+ if X.shape[0] == 1:
+ rowvar = True
+ if rowvar:
+ axis = 0
+ tup = (slice(None),None)
+ else:
+ axis = 1
+ tup = (None, slice(None))
+ #
+ if y is not None:
+ y = narray(y, copy=False, ndmin=2, subok=True, dtype=float)
+ X = concatenate((X,y),axis)
+ #
+ X -= X.mean(axis=1-axis)[tup]
+ n = X.count(1-axis)
+ #
+ if bias:
+ fact = n*1.0
+ else:
+ fact = n-1.0
+ #
+ if not rowvar:
+ return (dot(X.T, X.conj(), strict=False) / fact).squeeze()
+ else:
+ return (dot(X, X.T.conj(), strict=False) / fact).squeeze()
+
+
+def idealfourths(data, axis=None):
+ """Returns an estimate of the interquartile range of the data along the given
+axis, as computed with the ideal fourths.
+ """
+ def _idf(data):
+ x = numpy.sort(data.compressed())
+ n = len(x)
+ (j,h) = divmod(n/4. + 5/12.,1)
+ qlo = (1-h)*x[j] + h*x[j+1]
+ k = n - j
+ qup = (1-h)*x[k] + h*x[k-1]
+ return qup - qlo
+ data = masked_array(data, copy=False)
+ if (axis is None):
+ return _idf(data)
+ else:
+ return apply_along_axis(_idf, axis, data)
+
+
+def rsh(data, points=None):
+ """Evalutates Rosenblatt's shifted histogram estimators for each point
+on the dataset 'data'.
+
+*Parameters* :
+ data : {sequence}
+ Input data. Masked values are ignored.
+ points : {sequence}
+ Sequence of points where to evaluate Rosenblatt shifted histogram.
+ If None, use the data.
+ """
+ data = masked_array(data, copy=False)
+ if points is None:
+ points = data
+ else:
+ points = numpy.array(points, copy=False, ndmin=1)
+ if data.ndim != 1:
+ raise AttributeError("The input array should be 1D only !")
+ n = data.count()
+ h = 1.2 * idealfourths(data) / n**(1./5)
+ nhi = (data[:,None] <= points[None,:] + h).sum(0)
+ nlo = (data[:,None] < points[None,:] - h).sum(0)
+ return (nhi-nlo) / (2.*n*h)
+
+################################################################################
+if __name__ == '__main__':
+ from maskedarray.testutils import assert_almost_equal
+ if 1:
+ a = maskedarray.arange(1,101)
+ a[1::2] = masked
+ b = maskedarray.resize(a, (100,100))
+ assert_almost_equal(mquantiles(b), [25., 50., 75.])
+ assert_almost_equal(mquantiles(b, axis=0), maskedarray.resize(a,(3,100)))
+ assert_almost_equal(mquantiles(b, axis=1),
+ maskedarray.resize([24.9, 50., 75.1], (100,3)))
Added: branches/maskedarray/numpy/core/ma/setup.py
===================================================================
--- branches/maskedarray/numpy/core/ma/setup.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/setup.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,19 @@
+#!/usr/bin/env python
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3473 $"
+__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
+
+import os
+
+def configuration(parent_package='',top_path=None):
+ from numpy.distutils.misc_util import Configuration
+ config = Configuration('maskedarray',parent_package,top_path)
+ config.add_data_dir('tests')
+ return config
+
+if __name__ == "__main__":
+ from numpy.distutils.core import setup
+ #setup.update(nmasetup)
+ config = configuration(top_path='').todict()
+ setup(**config)
Added: branches/maskedarray/numpy/core/ma/tests/test_core.py
===================================================================
--- branches/maskedarray/numpy/core/ma/tests/test_core.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/tests/test_core.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,1318 @@
+# pylint: disable-msg=W0611, W0612, W0511,R0201
+"""Tests suite for MaskedArray & subclassing.
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: test_core.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3473 $"
+__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
+
+import types
+
+import numpy
+import numpy.core.fromnumeric as fromnumeric
+from numpy.testing import NumpyTest, NumpyTestCase
+from numpy.testing.utils import build_err_msg
+from numpy import array as narray
+
+import maskedarray.testutils
+from maskedarray.testutils import *
+
+import maskedarray.core as coremodule
+from maskedarray.core import *
+
+pi = numpy.pi
+
+#..............................................................................
+class TestMA(NumpyTestCase):
+ "Base test class for MaskedArrays."
+ def __init__(self, *args, **kwds):
+ NumpyTestCase.__init__(self, *args, **kwds)
+ self.setUp()
+
+ def setUp (self):
+ "Base data definition."
+ x = narray([1.,1.,1.,-2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
+ y = narray([5.,0.,3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
+ a10 = 10.
+ m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
+ m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0 ,0, 1]
+ xm = masked_array(x, mask=m1)
+ ym = masked_array(y, mask=m2)
+ z = narray([-.5, 0., .5, .8])
+ zm = masked_array(z, mask=[0,1,0,0])
+ xf = numpy.where(m1, 1.e+20, x)
+ xm.set_fill_value(1.e+20)
+ self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
+ #........................
+ def check_basic1d(self):
+ "Test of basic array creation and properties in 1 dimension."
+ (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+ assert(not isMaskedArray(x))
+ assert(isMaskedArray(xm))
+ assert((xm-ym).filled(0).any())
+ fail_if_equal(xm.mask.astype(int_), ym.mask.astype(int_))
+ s = x.shape
+ assert_equal(numpy.shape(xm), s)
+ assert_equal(xm.shape, s)
+ assert_equal(xm.dtype, x.dtype)
+ assert_equal(zm.dtype, z.dtype)
+ assert_equal(xm.size , reduce(lambda x,y:x*y, s))
+ assert_equal(count(xm) , len(m1) - reduce(lambda x,y:x+y, m1))
+ assert_array_equal(xm, xf)
+ assert_array_equal(filled(xm, 1.e20), xf)
+ assert_array_equal(x, xm)
+ #........................
+ def check_basic2d(self):
+ "Test of basic array creation and properties in 2 dimensions."
+ (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+ for s in [(4,3), (6,2)]:
+ x.shape = s
+ y.shape = s
+ xm.shape = s
+ ym.shape = s
+ xf.shape = s
+
+ assert(not isMaskedArray(x))
+ assert(isMaskedArray(xm))
+ assert_equal(shape(xm), s)
+ assert_equal(xm.shape, s)
+ assert_equal( xm.size , reduce(lambda x,y:x*y, s))
+ assert_equal( count(xm) , len(m1) - reduce(lambda x,y:x+y, m1))
+ assert_equal(xm, xf)
+ assert_equal(filled(xm, 1.e20), xf)
+ assert_equal(x, xm)
+ #........................
+ def check_basic_arithmetic (self):
+ "Test of basic arithmetic."
+ (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+ a2d = array([[1,2],[0,4]])
+ a2dm = masked_array(a2d, [[0,0],[1,0]])
+ assert_equal(a2d * a2d, a2d * a2dm)
+ assert_equal(a2d + a2d, a2d + a2dm)
+ assert_equal(a2d - a2d, a2d - a2dm)
+ for s in [(12,), (4,3), (2,6)]:
+ x = x.reshape(s)
+ y = y.reshape(s)
+ xm = xm.reshape(s)
+ ym = ym.reshape(s)
+ xf = xf.reshape(s)
+ assert_equal(-x, -xm)
+ assert_equal(x + y, xm + ym)
+ assert_equal(x - y, xm - ym)
+ assert_equal(x * y, xm * ym)
+ assert_equal(x / y, xm / ym)
+ assert_equal(a10 + y, a10 + ym)
+ assert_equal(a10 - y, a10 - ym)
+ assert_equal(a10 * y, a10 * ym)
+ assert_equal(a10 / y, a10 / ym)
+ assert_equal(x + a10, xm + a10)
+ assert_equal(x - a10, xm - a10)
+ assert_equal(x * a10, xm * a10)
+ assert_equal(x / a10, xm / a10)
+ assert_equal(x**2, xm**2)
+ assert_equal(abs(x)**2.5, abs(xm) **2.5)
+ assert_equal(x**y, xm**ym)
+ assert_equal(numpy.add(x,y), add(xm, ym))
+ assert_equal(numpy.subtract(x,y), subtract(xm, ym))
+ assert_equal(numpy.multiply(x,y), multiply(xm, ym))
+ assert_equal(numpy.divide(x,y), divide(xm, ym))
+ #........................
+ def check_mixed_arithmetic(self):
+ "Tests mixed arithmetics."
+ na = narray([1])
+ ma = array([1])
+ self.failUnless(isinstance(na + ma, MaskedArray))
+ self.failUnless(isinstance(ma + na, MaskedArray))
+ #........................
+ def check_inplace_arithmetic(self):
+ """Test of inplace operations and rich comparisons"""
+ # addition
+ x = arange(10)
+ y = arange(10)
+ xm = arange(10)
+ xm[2] = masked
+ x += 1
+ assert_equal(x, y+1)
+ xm += 1
+ assert_equal(xm, y+1)
+ # subtraction
+ x = arange(10)
+ xm = arange(10)
+ xm[2] = masked
+ x -= 1
+ assert_equal(x, y-1)
+ xm -= 1
+ assert_equal(xm, y-1)
+ # multiplication
+ x = arange(10)*1.0
+ xm = arange(10)*1.0
+ xm[2] = masked
+ x *= 2.0
+ assert_equal(x, y*2)
+ xm *= 2.0
+ assert_equal(xm, y*2)
+ # division
+ x = arange(10)*2
+ xm = arange(10)*2
+ xm[2] = masked
+ x /= 2
+ assert_equal(x, y)
+ xm /= 2
+ assert_equal(xm, y)
+ # division, pt 2
+ x = arange(10)*1.0
+ xm = arange(10)*1.0
+ xm[2] = masked
+ x /= 2.0
+ assert_equal(x, y/2.0)
+ xm /= arange(10)
+ assert_equal(xm, ones((10,)))
+
+ x = arange(10).astype(float_)
+ xm = arange(10)
+ xm[2] = masked
+# id1 = id(x.raw_data())
+ id1 = x.raw_data().ctypes.data
+ x += 1.
+# assert id1 == id(x.raw_data())
+ assert (id1 == x.raw_data().ctypes.data)
+ assert_equal(x, y+1.)
+ # addition w/ array
+ x = arange(10, dtype=float_)
+ xm = arange(10, dtype=float_)
+ xm[2] = masked
+ m = xm.mask
+ a = arange(10, dtype=float_)
+ a[-1] = masked
+ x += a
+ xm += a
+ assert_equal(x,y+a)
+ assert_equal(xm,y+a)
+ assert_equal(xm.mask, mask_or(m,a.mask))
+ # subtraction w/ array
+ x = arange(10, dtype=float_)
+ xm = arange(10, dtype=float_)
+ xm[2] = masked
+ m = xm.mask
+ a = arange(10, dtype=float_)
+ a[-1] = masked
+ x -= a
+ xm -= a
+ assert_equal(x,y-a)
+ assert_equal(xm,y-a)
+ assert_equal(xm.mask, mask_or(m,a.mask))
+ # multiplication w/ array
+ x = arange(10, dtype=float_)
+ xm = arange(10, dtype=float_)
+ xm[2] = masked
+ m = xm.mask
+ a = arange(10, dtype=float_)
+ a[-1] = masked
+ x *= a
+ xm *= a
+ assert_equal(x,y*a)
+ assert_equal(xm,y*a)
+ assert_equal(xm.mask, mask_or(m,a.mask))
+ # division w/ array
+ x = arange(10, dtype=float_)
+ xm = arange(10, dtype=float_)
+ xm[2] = masked
+ m = xm.mask
+ a = arange(10, dtype=float_)
+ a[-1] = masked
+ x /= a
+ xm /= a
+ assert_equal(x,y/a)
+ assert_equal(xm,y/a)
+ assert_equal(xm.mask, mask_or(mask_or(m,a.mask), (a==0)))
+ #
+ (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+ z = xm/ym
+ assert_equal(z._mask, [1,1,1,0,0,1,1,0,0,0,1,1])
+ assert_equal(z._data, [0.2,1.,1./3.,-1.,-pi/2.,-1.,5.,1.,1.,1.,2.,1.])
+ xm = xm.copy()
+ xm /= ym
+ assert_equal(xm._mask, [1,1,1,0,0,1,1,0,0,0,1,1])
+ assert_equal(xm._data, [1/5.,1.,1./3.,-1.,-pi/2.,-1.,5.,1.,1.,1.,2.,1.])
+
+
+ #..........................
+ def check_scalararithmetic(self):
+ "Tests some scalar arithmetics on MaskedArrays."
+ xm = array(0, mask=1)
+ assert((1/array(0)).mask)
+ assert((1 + xm).mask)
+ assert((-xm).mask)
+ assert((-xm).mask)
+ assert(maximum(xm, xm).mask)
+ assert(minimum(xm, xm).mask)
+ assert(xm.filled().dtype is xm.data.dtype)
+ x = array(0, mask=0)
+ assert_equal(x.filled().ctypes.data, x.ctypes.data)
+ assert_equal(str(xm), str(masked_print_option))
+ #.........................
+ def check_basic_ufuncs (self):
+ "Test various functions such as sin, cos."
+ (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+ assert_equal(numpy.cos(x), cos(xm))
+ assert_equal(numpy.cosh(x), cosh(xm))
+ assert_equal(numpy.sin(x), sin(xm))
+ assert_equal(numpy.sinh(x), sinh(xm))
+ assert_equal(numpy.tan(x), tan(xm))
+ assert_equal(numpy.tanh(x), tanh(xm))
+ assert_equal(numpy.sqrt(abs(x)), sqrt(xm))
+ assert_equal(numpy.log(abs(x)), log(xm))
+ assert_equal(numpy.log10(abs(x)), log10(xm))
+ assert_equal(numpy.exp(x), exp(xm))
+ assert_equal(numpy.arcsin(z), arcsin(zm))
+ assert_equal(numpy.arccos(z), arccos(zm))
+ assert_equal(numpy.arctan(z), arctan(zm))
+ assert_equal(numpy.arctan2(x, y), arctan2(xm, ym))
+ assert_equal(numpy.absolute(x), absolute(xm))
+ assert_equal(numpy.equal(x,y), equal(xm, ym))
+ assert_equal(numpy.not_equal(x,y), not_equal(xm, ym))
+ assert_equal(numpy.less(x,y), less(xm, ym))
+ assert_equal(numpy.greater(x,y), greater(xm, ym))
+ assert_equal(numpy.less_equal(x,y), less_equal(xm, ym))
+ assert_equal(numpy.greater_equal(x,y), greater_equal(xm, ym))
+ assert_equal(numpy.conjugate(x), conjugate(xm))
+ #........................
+ def check_count_func (self):
+ "Tests count"
+ ott = array([0.,1.,2.,3.], mask=[1,0,0,0])
+ assert( isinstance(count(ott), int))
+ assert_equal(3, count(ott))
+ assert_equal(1, count(1))
+ assert_equal(0, array(1,mask=[1]))
+ ott = ott.reshape((2,2))
+ assert isMaskedArray(count(ott,0))
+ assert isinstance(count(ott), types.IntType)
+ assert_equal(3, count(ott))
+ assert getmask(count(ott,0)) is nomask
+ assert_equal([1,2],count(ott,0))
+ #........................
+ def check_minmax_func (self):
+ "Tests minimum and maximum."
+ (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+ xr = numpy.ravel(x) #max doesn't work if shaped
+ xmr = ravel(xm)
+ assert_equal(max(xr), maximum(xmr)) #true because of careful selection of data
+ assert_equal(min(xr), minimum(xmr)) #true because of careful selection of data
+ #
+ assert_equal(minimum([1,2,3],[4,0,9]), [1,0,3])
+ assert_equal(maximum([1,2,3],[4,0,9]), [4,2,9])
+ x = arange(5)
+ y = arange(5) - 2
+ x[3] = masked
+ y[0] = masked
+ assert_equal(minimum(x,y), where(less(x,y), x, y))
+ assert_equal(maximum(x,y), where(greater(x,y), x, y))
+ assert minimum(x) == 0
+ assert maximum(x) == 4
+ #
+ x = arange(4).reshape(2,2)
+ x[-1,-1] = masked
+ assert_equal(maximum(x), 2)
+
+ def check_minmax_methods(self):
+ "Additional tests on max/min"
+ (_, _, _, _, _, xm, _, _, _, _) = self.d
+ xm.shape = (xm.size,)
+ assert_equal(xm.max(), 10)
+ assert(xm[0].max() is masked)
+ assert(xm[0].max(0) is masked)
+ assert(xm[0].max(-1) is masked)
+ assert_equal(xm.min(), -10.)
+ assert(xm[0].min() is masked)
+ assert(xm[0].min(0) is masked)
+ assert(xm[0].min(-1) is masked)
+ assert_equal(xm.ptp(), 20.)
+ assert(xm[0].ptp() is masked)
+ assert(xm[0].ptp(0) is masked)
+ assert(xm[0].ptp(-1) is masked)
+ #........................
+ def check_addsumprod (self):
+ "Tests add, sum, product."
+ (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+ assert_equal(numpy.add.reduce(x), add.reduce(x))
+ assert_equal(numpy.add.accumulate(x), add.accumulate(x))
+ assert_equal(4, sum(array(4),axis=0))
+ assert_equal(4, sum(array(4), axis=0))
+ assert_equal(numpy.sum(x,axis=0), sum(x,axis=0))
+ assert_equal(numpy.sum(filled(xm,0),axis=0), sum(xm,axis=0))
+ assert_equal(numpy.sum(x,0), sum(x,0))
+ assert_equal(numpy.product(x,axis=0), product(x,axis=0))
+ assert_equal(numpy.product(x,0), product(x,0))
+ assert_equal(numpy.product(filled(xm,1),axis=0), product(xm,axis=0))
+ s = (3,4)
+ x.shape = y.shape = xm.shape = ym.shape = s
+ if len(s) > 1:
+ assert_equal(numpy.concatenate((x,y),1), concatenate((xm,ym),1))
+ assert_equal(numpy.add.reduce(x,1), add.reduce(x,1))
+ assert_equal(numpy.sum(x,1), sum(x,1))
+ assert_equal(numpy.product(x,1), product(x,1))
+ #.........................
+ def check_concat(self):
+ "Tests concatenations."
+ (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+ # basic concatenation
+ assert_equal(numpy.concatenate((x,y)), concatenate((xm,ym)))
+ assert_equal(numpy.concatenate((x,y)), concatenate((x,y)))
+ assert_equal(numpy.concatenate((x,y)), concatenate((xm,y)))
+ assert_equal(numpy.concatenate((x,y,x)), concatenate((x,ym,x)))
+ # Concatenation along an axis
+ s = (3,4)
+ x.shape = y.shape = xm.shape = ym.shape = s
+ assert_equal(xm.mask, numpy.reshape(m1, s))
+ assert_equal(ym.mask, numpy.reshape(m2, s))
+ xmym = concatenate((xm,ym),1)
+ assert_equal(numpy.concatenate((x,y),1), xmym)
+ assert_equal(numpy.concatenate((xm.mask,ym.mask),1), xmym._mask)
+ #........................
+ def check_indexing(self):
+ "Tests conversions and indexing"
+ x1 = numpy.array([1,2,4,3])
+ x2 = array(x1, mask=[1,0,0,0])
+ x3 = array(x1, mask=[0,1,0,1])
+ x4 = array(x1)
+ # test conversion to strings
+ junk, garbage = str(x2), repr(x2)
+ assert_equal(numpy.sort(x1),sort(x2,endwith=False))
+ # tests of indexing
+ assert type(x2[1]) is type(x1[1])
+ assert x1[1] == x2[1]
+ assert x2[0] is masked
+ assert_equal(x1[2],x2[2])
+ assert_equal(x1[2:5],x2[2:5])
+ assert_equal(x1[:],x2[:])
+ assert_equal(x1[1:], x3[1:])
+ x1[2] = 9
+ x2[2] = 9
+ assert_equal(x1,x2)
+ x1[1:3] = 99
+ x2[1:3] = 99
+ assert_equal(x1,x2)
+ x2[1] = masked
+ assert_equal(x1,x2)
+ x2[1:3] = masked
+ assert_equal(x1,x2)
+ x2[:] = x1
+ x2[1] = masked
+ assert allequal(getmask(x2),array([0,1,0,0]))
+ x3[:] = masked_array([1,2,3,4],[0,1,1,0])
+ assert allequal(getmask(x3), array([0,1,1,0]))
+ x4[:] = masked_array([1,2,3,4],[0,1,1,0])
+ assert allequal(getmask(x4), array([0,1,1,0]))
+ assert allequal(x4, array([1,2,3,4]))
+ x1 = numpy.arange(5)*1.0
+ x2 = masked_values(x1, 3.0)
+ assert_equal(x1,x2)
+ assert allequal(array([0,0,0,1,0],MaskType), x2.mask)
+#FIXME: Well, eh, fill_value is now a property assert_equal(3.0, x2.fill_value())
+ assert_equal(3.0, x2.fill_value)
+ x1 = array([1,'hello',2,3],object)
+ x2 = numpy.array([1,'hello',2,3],object)
+ s1 = x1[1]
+ s2 = x2[1]
+ assert_equal(type(s2), str)
+ assert_equal(type(s1), str)
+ assert_equal(s1, s2)
+ assert x1[1:1].shape == (0,)
+ #........................
+ def check_copy(self):
+ "Tests of some subtle points of copying and sizing."
+ n = [0,0,1,0,0]
+ m = make_mask(n)
+ m2 = make_mask(m)
+ assert(m is m2)
+ m3 = make_mask(m, copy=1)
+ assert(m is not m3)
+
+ x1 = numpy.arange(5)
+ y1 = array(x1, mask=m)
+ #assert( y1._data is x1)
+ assert_equal(y1._data.__array_interface__, x1.__array_interface__)
+ assert( allequal(x1,y1.raw_data()))
+ #assert( y1.mask is m)
+ assert_equal(y1._mask.__array_interface__, m.__array_interface__)
+
+ y1a = array(y1)
+ #assert( y1a.raw_data() is y1.raw_data())
+ assert( y1a._data.__array_interface__ == y1._data.__array_interface__)
+ assert( y1a.mask is y1.mask)
+
+ y2 = array(x1, mask=m)
+ #assert( y2.raw_data() is x1)
+ assert (y2._data.__array_interface__ == x1.__array_interface__)
+ #assert( y2.mask is m)
+ assert (y2._mask.__array_interface__ == m.__array_interface__)
+ assert( y2[2] is masked)
+ y2[2] = 9
+ assert( y2[2] is not masked)
+ #assert( y2.mask is not m)
+ assert (y2._mask.__array_interface__ != m.__array_interface__)
+ assert( allequal(y2.mask, 0))
+
+ y3 = array(x1*1.0, mask=m)
+ assert(filled(y3).dtype is (x1*1.0).dtype)
+
+ x4 = arange(4)
+ x4[2] = masked
+ y4 = resize(x4, (8,))
+ assert_equal(concatenate([x4,x4]), y4)
+ assert_equal(getmask(y4),[0,0,1,0,0,0,1,0])
+ y5 = repeat(x4, (2,2,2,2), axis=0)
+ assert_equal(y5, [0,0,1,1,2,2,3,3])
+ y6 = repeat(x4, 2, axis=0)
+ assert_equal(y5, y6)
+ y7 = x4.repeat((2,2,2,2), axis=0)
+ assert_equal(y5,y7)
+ y8 = x4.repeat(2,0)
+ assert_equal(y5,y8)
+
+ y9 = x4.copy()
+ assert_equal(y9._data, x4._data)
+ assert_equal(y9._mask, x4._mask)
+ #
+ x = masked_array([1,2,3], mask=[0,1,0])
+ # Copy is False by default
+ y = masked_array(x)
+# assert_equal(id(y._data), id(x._data))
+# assert_equal(id(y._mask), id(x._mask))
+ assert_equal(y._data.ctypes.data, x._data.ctypes.data)
+ assert_equal(y._mask.ctypes.data, x._mask.ctypes.data)
+ y = masked_array(x, copy=True)
+# assert_not_equal(id(y._data), id(x._data))
+# assert_not_equal(id(y._mask), id(x._mask))
+ assert_not_equal(y._data.ctypes.data, x._data.ctypes.data)
+ assert_not_equal(y._mask.ctypes.data, x._mask.ctypes.data)
+ #........................
+ def check_where(self):
+ "Test the where function"
+ (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+ d = where(xm>2,xm,-9)
+ assert_equal(d, [-9.,-9.,-9.,-9., -9., 4., -9., -9., 10., -9., -9., 3.])
+ assert_equal(d._mask, xm._mask)
+ d = where(xm>2,-9,ym)
+ assert_equal(d, [5.,0.,3., 2., -1.,-9.,-9., -10., -9., 1., 0., -9.])
+ assert_equal(d._mask, [1,0,1,0,0,0,1,0,0,0,0,0])
+ d = where(xm>2, xm, masked)
+ assert_equal(d, [-9.,-9.,-9.,-9., -9., 4., -9., -9., 10., -9., -9., 3.])
+ tmp = xm._mask.copy()
+ tmp[(xm<=2).filled(True)] = True
+ assert_equal(d._mask, tmp)
+ #
+ ixm = xm.astype(int_)
+ d = where(ixm>2, ixm, masked)
+ assert_equal(d, [-9,-9,-9,-9, -9, 4, -9, -9, 10, -9, -9, 3])
+ assert_equal(d.dtype, ixm.dtype)
+ #
+ x = arange(10)
+ x[3] = masked
+ c = x >= 8
+ z = where(c , x, masked)
+ assert z.dtype is x.dtype
+ assert z[3] is masked
+ assert z[4] is masked
+ assert z[7] is masked
+ assert z[8] is not masked
+ assert z[9] is not masked
+ assert_equal(x,z)
+ #
+ z = where(c , masked, x)
+ assert z.dtype is x.dtype
+ assert z[3] is masked
+ assert z[4] is not masked
+ assert z[7] is not masked
+ assert z[8] is masked
+ assert z[9] is masked
+
+ #........................
+ def check_oddfeatures_1(self):
+ "Test of other odd features"
+ x = arange(20)
+ x = x.reshape(4,5)
+ x.flat[5] = 12
+ assert x[1,0] == 12
+ z = x + 10j * x
+ assert_equal(z.real, x)
+ assert_equal(z.imag, 10*x)
+ assert_equal((z*conjugate(z)).real, 101*x*x)
+ z.imag[...] = 0.0
+
+ x = arange(10)
+ x[3] = masked
+ assert str(x[3]) == str(masked)
+ c = x >= 8
+ assert count(where(c,masked,masked)) == 0
+ assert shape(where(c,masked,masked)) == c.shape
+ #
+ z = masked_where(c, x)
+ assert z.dtype is x.dtype
+ assert z[3] is masked
+ assert z[4] is not masked
+ assert z[7] is not masked
+ assert z[8] is masked
+ assert z[9] is masked
+ assert_equal(x,z)
+ #
+ #........................
+ def check_oddfeatures_2(self):
+ "Tests some more features."
+ x = array([1.,2.,3.,4.,5.])
+ c = array([1,1,1,0,0])
+ x[2] = masked
+ z = where(c, x, -x)
+ assert_equal(z, [1.,2.,0., -4., -5])
+ c[0] = masked
+ z = where(c, x, -x)
+ assert_equal(z, [1.,2.,0., -4., -5])
+ assert z[0] is masked
+ assert z[1] is not masked
+ assert z[2] is masked
+ #
+ x = arange(6)
+ x[5] = masked
+ y = arange(6)*10
+ y[2] = masked
+ c = array([1,1,1,0,0,0], mask=[1,0,0,0,0,0])
+ cm = c.filled(1)
+ z = where(c,x,y)
+ zm = where(cm,x,y)
+ assert_equal(z, zm)
+ assert getmask(zm) is nomask
+ assert_equal(zm, [0,1,2,30,40,50])
+ z = where(c, masked, 1)
+ assert_equal(z, [99,99,99,1,1,1])
+ z = where(c, 1, masked)
+ assert_equal(z, [99, 1, 1, 99, 99, 99])
+ #........................
+ def check_oddfeatures_3(self):
+ """Tests some generic features."""
+ atest = ones((10,10,10), dtype=float_)
+ btest = zeros(atest.shape, MaskType)
+ ctest = masked_where(btest,atest)
+ assert_equal(atest,ctest)
+ #........................
+ def check_maskingfunctions(self):
+ "Tests masking functions."
+ x = array([1.,2.,3.,4.,5.])
+ x[2] = masked
+ assert_equal(masked_where(greater(x, 2), x), masked_greater(x,2))
+ assert_equal(masked_where(greater_equal(x, 2), x), masked_greater_equal(x,2))
+ assert_equal(masked_where(less(x, 2), x), masked_less(x,2))
+ assert_equal(masked_where(less_equal(x, 2), x), masked_less_equal(x,2))
+ assert_equal(masked_where(not_equal(x, 2), x), masked_not_equal(x,2))
+ assert_equal(masked_where(equal(x, 2), x), masked_equal(x,2))
+ assert_equal(masked_where(not_equal(x,2), x), masked_not_equal(x,2))
+ assert_equal(masked_inside(range(5), 1, 3), [0, 199, 199, 199, 4])
+ assert_equal(masked_outside(range(5), 1, 3),[199,1,2,3,199])
+ assert_equal(masked_inside(array(range(5), mask=[1,0,0,0,0]), 1, 3).mask, [1,1,1,1,0])
+ assert_equal(masked_outside(array(range(5), mask=[0,1,0,0,0]), 1, 3).mask, [1,1,0,0,1])
+ assert_equal(masked_equal(array(range(5), mask=[1,0,0,0,0]), 2).mask, [1,0,1,0,0])
+ assert_equal(masked_not_equal(array([2,2,1,2,1], mask=[1,0,0,0,0]), 2).mask, [1,0,1,0,1])
+ assert_equal(masked_where([1,1,0,0,0], [1,2,3,4,5]), [99,99,3,4,5])
+ #........................
+ def check_TakeTransposeInnerOuter(self):
+ "Test of take, transpose, inner, outer products"
+ x = arange(24)
+ y = numpy.arange(24)
+ x[5:6] = masked
+ x = x.reshape(2,3,4)
+ y = y.reshape(2,3,4)
+ assert_equal(numpy.transpose(y,(2,0,1)), transpose(x,(2,0,1)))
+ assert_equal(numpy.take(y, (2,0,1), 1), take(x, (2,0,1), 1))
+ assert_equal(numpy.inner(filled(x,0),filled(y,0)),
+ inner(x, y))
+ assert_equal(numpy.outer(filled(x,0),filled(y,0)),
+ outer(x, y))
+ y = array(['abc', 1, 'def', 2, 3], object)
+ y[2] = masked
+ t = take(y,[0,3,4])
+ assert t[0] == 'abc'
+ assert t[1] == 2
+ assert t[2] == 3
+ #.......................
+ def check_maskedelement(self):
+ "Test of masked element"
+ x = arange(6)
+ x[1] = masked
+ assert(str(masked) == '--')
+ assert(x[1] is masked)
+ assert_equal(filled(x[1], 0), 0)
+ # don't know why these should raise an exception...
+ #self.failUnlessRaises(Exception, lambda x,y: x+y, masked, masked)
+ #self.failUnlessRaises(Exception, lambda x,y: x+y, masked, 2)
+ #self.failUnlessRaises(Exception, lambda x,y: x+y, masked, xx)
+ #self.failUnlessRaises(Exception, lambda x,y: x+y, xx, masked)
+ #........................
+ def check_scalar(self):
+ "Checks masking a scalar"
+ x = masked_array(0)
+ assert_equal(str(x), '0')
+ x = masked_array(0,mask=True)
+ assert_equal(str(x), str(masked_print_option))
+ x = masked_array(0, mask=False)
+ assert_equal(str(x), '0')
+ #........................
+ def check_usingmasked(self):
+ "Checks that there's no collapsing to masked"
+ x = masked_array([1,2])
+ y = x * masked
+ assert_equal(y.shape, x.shape)
+ assert_equal(y._mask, [True, True])
+ y = x[0] * masked
+ assert y is masked
+ y = x + masked
+ assert_equal(y.shape, x.shape)
+ assert_equal(y._mask, [True, True])
+
+ #........................
+ def check_topython(self):
+ "Tests some communication issues with Python."
+ assert_equal(1, int(array(1)))
+ assert_equal(1.0, float(array(1)))
+ assert_equal(1, int(array([[[1]]])))
+ assert_equal(1.0, float(array([[1]])))
+ self.failUnlessRaises(ValueError, float, array([1,1]))
+ assert numpy.isnan(float(array([1],mask=[1])))
+#TODO: Check how bool works...
+#TODO: self.failUnless(bool(array([0,1])))
+#TODO: self.failUnless(bool(array([0,0],mask=[0,1])))
+#TODO: self.failIf(bool(array([0,0])))
+#TODO: self.failIf(bool(array([0,0],mask=[0,0])))
+ #........................
+ def check_arraymethods(self):
+ "Tests some MaskedArray methods."
+ a = array([1,3,2])
+ b = array([1,3,2], mask=[1,0,1])
+ assert_equal(a.any(), a.data.any())
+ assert_equal(a.all(), a.data.all())
+ assert_equal(a.argmax(), a.data.argmax())
+ assert_equal(a.argmin(), a.data.argmin())
+ assert_equal(a.choose(0,1,2,3,4), a.data.choose(0,1,2,3,4))
+ assert_equal(a.compress([1,0,1]), a.data.compress([1,0,1]))
+ assert_equal(a.conj(), a.data.conj())
+ assert_equal(a.conjugate(), a.data.conjugate())
+ #
+ m = array([[1,2],[3,4]])
+ assert_equal(m.diagonal(), m.data.diagonal())
+ assert_equal(a.sum(), a.data.sum())
+ assert_equal(a.take([1,2]), a.data.take([1,2]))
+ assert_equal(m.transpose(), m.data.transpose())
+ #........................
+ def check_basicattributes(self):
+ "Tests some basic array attributes."
+ a = array([1,3,2])
+ b = array([1,3,2], mask=[1,0,1])
+ assert_equal(a.ndim, 1)
+ assert_equal(b.ndim, 1)
+ assert_equal(a.size, 3)
+ assert_equal(b.size, 3)
+ assert_equal(a.shape, (3,))
+ assert_equal(b.shape, (3,))
+ #........................
+ def check_single_element_subscript(self):
+ "Tests single element subscripts of Maskedarrays."
+ a = array([1,3,2])
+ b = array([1,3,2], mask=[1,0,1])
+ assert_equal(a[0].shape, ())
+ assert_equal(b[0].shape, ())
+ assert_equal(b[1].shape, ())
+ #........................
+ def check_maskcreation(self):
+ "Tests how masks are initialized at the creation of Maskedarrays."
+ data = arange(24, dtype=float_)
+ data[[3,6,15]] = masked
+ dma_1 = MaskedArray(data)
+ assert_equal(dma_1.mask, data.mask)
+ dma_2 = MaskedArray(dma_1)
+ assert_equal(dma_2.mask, dma_1.mask)
+ dma_3 = MaskedArray(dma_1, mask=[1,0,0,0]*6)
+ fail_if_equal(dma_3.mask, dma_1.mask)
+
+ def check_backwards(self):
+ "Tests backward compatibility with numpy.core.ma"
+ import numpy.core.ma as nma
+ x = nma.arange(5)
+ x[2] = nma.masked
+ X = masked_array(x, mask=x._mask)
+ assert_equal(X._mask, x.mask)
+ assert_equal(X._data, x._data)
+ X = masked_array(x)
+ assert_equal(X._data, x._data)
+ assert_equal(X._mask, x.mask)
+ assert_equal(getmask(x), [0,0,1,0,0])
+
+ def check_pickling(self):
+ "Tests pickling"
+ import cPickle
+ a = arange(10)
+ a[::3] = masked
+ a.fill_value = 999
+ a_pickled = cPickle.loads(a.dumps())
+ assert_equal(a_pickled._mask, a._mask)
+ assert_equal(a_pickled._data, a._data)
+ assert_equal(a_pickled.fill_value, 999)
+ #
+ a = array(numpy.matrix(range(10)), mask=[1,0,1,0,0]*2)
+ a_pickled = cPickle.loads(a.dumps())
+ assert_equal(a_pickled._mask, a._mask)
+ assert_equal(a_pickled, a)
+ assert(isinstance(a_pickled._data,numpy.matrix))
+ #
+ def check_fillvalue(self):
+ "Check that we don't lose the fill_value"
+ data = masked_array([1,2,3],fill_value=-999)
+ series = data[[0,2,1]]
+ assert_equal(series._fill_value, data._fill_value)
+ #
+ def check_asarray(self):
+ (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+ xmm = asarray(xm)
+ assert_equal(xmm._data, xm._data)
+ assert_equal(xmm._mask, xm._mask)
+ #
+ def check_fix_invalid(self):
+ "Checks fix_invalid."
+ data = masked_array(numpy.sqrt([-1., 0., 1.]), mask=[0,0,1])
+ data_fixed = fix_invalid(data)
+ assert_equal(data_fixed._data, [data.fill_value, 0., 1.])
+ assert_equal(data_fixed._mask, [1., 0., 1.])
+ #
+ def check_imag_real(self):
+ xx = array([1+10j,20+2j], mask=[1,0])
+ assert_equal(xx.imag,[10,2])
+ assert_equal(xx.imag.filled(), [1e+20,2])
+ assert_equal(xx.imag.dtype, xx._data.imag.dtype)
+ assert_equal(xx.real,[1,20])
+ assert_equal(xx.real.filled(), [1e+20,20])
+ assert_equal(xx.real.dtype, xx._data.real.dtype)
+
+#...............................................................................
+
+class TestUfuncs(NumpyTestCase):
+ "Test class for the application of ufuncs on MaskedArrays."
+ def setUp(self):
+ "Base data definition."
+ self.d = (array([1.0, 0, -1, pi/2]*2, mask=[0,1]+[0]*6),
+ array([1.0, 0, -1, pi/2]*2, mask=[1,0]+[0]*6),)
+
+ def check_testUfuncRegression(self):
+ "Tests new ufuncs on MaskedArrays."
+ for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
+ 'sin', 'cos', 'tan',
+ 'arcsin', 'arccos', 'arctan',
+ 'sinh', 'cosh', 'tanh',
+ 'arcsinh',
+ 'arccosh',
+ 'arctanh',
+ 'absolute', 'fabs', 'negative',
+ # 'nonzero', 'around',
+ 'floor', 'ceil',
+ # 'sometrue', 'alltrue',
+ 'logical_not',
+ 'add', 'subtract', 'multiply',
+ 'divide', 'true_divide', 'floor_divide',
+ 'remainder', 'fmod', 'hypot', 'arctan2',
+ 'equal', 'not_equal', 'less_equal', 'greater_equal',
+ 'less', 'greater',
+ 'logical_and', 'logical_or', 'logical_xor',
+ ]:
+ #print f
+ try:
+ uf = getattr(umath, f)
+ except AttributeError:
+ uf = getattr(fromnumeric, f)
+ mf = getattr(coremodule, f)
+ args = self.d[:uf.nin]
+ ur = uf(*args)
+ mr = mf(*args)
+ assert_equal(ur.filled(0), mr.filled(0), f)
+ assert_mask_equal(ur.mask, mr.mask)
+ #........................
+ def test_reduce(self):
+ "Tests reduce on MaskedArrays."
+ a = self.d[0]
+ assert(not alltrue(a,axis=0))
+ assert(sometrue(a,axis=0))
+ assert_equal(sum(a[:3],axis=0), 0)
+ assert_equal(product(a,axis=0), 0)
+ assert_equal(add.reduce(a), pi)
+ #........................
+ def test_minmax(self):
+ "Tests extrema on MaskedArrays."
+ a = arange(1,13).reshape(3,4)
+ amask = masked_where(a < 5,a)
+ assert_equal(amask.max(), a.max())
+ assert_equal(amask.min(), 5)
+ assert_equal(amask.max(0), a.max(0))
+ assert_equal(amask.min(0), [5,6,7,8])
+ assert(amask.max(1)[0].mask)
+ assert(amask.min(1)[0].mask)
+
+#...............................................................................
+
+class TestArrayMethods(NumpyTestCase):
+ "Test class for miscellaneous MaskedArrays methods."
+ def setUp(self):
+ "Base data definition."
+ x = numpy.array([ 8.375, 7.545, 8.828, 8.5 , 1.757, 5.928,
+ 8.43 , 7.78 , 9.865, 5.878, 8.979, 4.732,
+ 3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
+ 6.04 , 9.63 , 7.712, 3.382, 4.489, 6.479,
+ 7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
+ 7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
+ X = x.reshape(6,6)
+ XX = x.reshape(3,2,2,3)
+
+ m = numpy.array([0, 1, 0, 1, 0, 0,
+ 1, 0, 1, 1, 0, 1,
+ 0, 0, 0, 1, 0, 1,
+ 0, 0, 0, 1, 1, 1,
+ 1, 0, 0, 1, 0, 0,
+ 0, 0, 1, 0, 1, 0])
+ mx = array(data=x,mask=m)
+ mX = array(data=X,mask=m.reshape(X.shape))
+ mXX = array(data=XX,mask=m.reshape(XX.shape))
+
+ m2 = numpy.array([1, 1, 0, 1, 0, 0,
+ 1, 1, 1, 1, 0, 1,
+ 0, 0, 1, 1, 0, 1,
+ 0, 0, 0, 1, 1, 1,
+ 1, 0, 0, 1, 1, 0,
+ 0, 0, 1, 0, 1, 1])
+ m2x = array(data=x,mask=m2)
+ m2X = array(data=X,mask=m2.reshape(X.shape))
+ m2XX = array(data=XX,mask=m2.reshape(XX.shape))
+ self.d = (x,X,XX,m,mx,mX,mXX,m2x,m2X,m2XX)
+
+ #------------------------------------------------------
+ def check_trace(self):
+ "Tests trace on MaskedArrays."
+ (x,X,XX,m,mx,mX,mXX,m2x,m2X,m2XX) = self.d
+ mXdiag = mX.diagonal()
+ assert_equal(mX.trace(), mX.diagonal().compressed().sum())
+ assert_almost_equal(mX.trace(),
+ X.trace() - sum(mXdiag.mask*X.diagonal(),axis=0))
+
+ def check_clip(self):
+ "Tests clip on MaskedArrays."
+ (x,X,XX,m,mx,mX,mXX,m2x,m2X,m2XX) = self.d
+ clipped = mx.clip(2,8)
+ assert_equal(clipped.mask,mx.mask)
+ assert_equal(clipped.data,x.clip(2,8))
+ assert_equal(clipped.data,mx.data.clip(2,8))
+
+ def check_ptp(self):
+ "Tests ptp on MaskedArrays."
+ (x,X,XX,m,mx,mX,mXX,m2x,m2X,m2XX) = self.d
+ (n,m) = X.shape
+ assert_equal(mx.ptp(),mx.compressed().ptp())
+ rows = numpy.zeros(n,numpy.float_)
+ cols = numpy.zeros(m,numpy.float_)
+ for k in range(m):
+ cols[k] = mX[:,k].compressed().ptp()
+ for k in range(n):
+ rows[k] = mX[k].compressed().ptp()
+ assert_equal(mX.ptp(0),cols)
+ assert_equal(mX.ptp(1),rows)
+
+ def check_swapaxes(self):
+ "Tests swapaxes on MaskedArrays."
+ (x,X,XX,m,mx,mX,mXX,m2x,m2X,m2XX) = self.d
+ mXswapped = mX.swapaxes(0,1)
+ assert_equal(mXswapped[-1],mX[:,-1])
+ mXXswapped = mXX.swapaxes(0,2)
+ assert_equal(mXXswapped.shape,(2,2,3,3))
+
+ def check_cumsumprod(self):
+ "Tests cumsum & cumprod on MaskedArrays."
+ (x,X,XX,m,mx,mX,mXX,m2x,m2X,m2XX) = self.d
+ mXcp = mX.cumsum(0)
+ assert_equal(mXcp.data,mX.filled(0).cumsum(0))
+ mXcp = mX.cumsum(1)
+ assert_equal(mXcp.data,mX.filled(0).cumsum(1))
+ #
+ mXcp = mX.cumprod(0)
+ assert_equal(mXcp.data,mX.filled(1).cumprod(0))
+ mXcp = mX.cumprod(1)
+ assert_equal(mXcp.data,mX.filled(1).cumprod(1))
+
+ def check_varstd(self):
+ "Tests var & std on MaskedArrays."
+ (x,X,XX,m,mx,mX,mXX,m2x,m2X,m2XX) = self.d
+ assert_almost_equal(mX.var(axis=None),mX.compressed().var())
+ assert_almost_equal(mX.std(axis=None),mX.compressed().std())
+ assert_equal(mXX.var(axis=3).shape,XX.var(axis=3).shape)
+ assert_equal(mX.var().shape,X.var().shape)
+ (mXvar0,mXvar1) = (mX.var(axis=0), mX.var(axis=1))
+ for k in range(6):
+ assert_almost_equal(mXvar1[k],mX[k].compressed().var())
+ assert_almost_equal(mXvar0[k],mX[:,k].compressed().var())
+ assert_almost_equal(numpy.sqrt(mXvar0[k]), mX[:,k].compressed().std())
+
+ def check_argmin(self):
+ "Tests argmin & argmax on MaskedArrays."
+ (x,X,XX,m,mx,mX,mXX,m2x,m2X,m2XX) = self.d
+ #
+ assert_equal(mx.argmin(),35)
+ assert_equal(mX.argmin(),35)
+ assert_equal(m2x.argmin(),4)
+ assert_equal(m2X.argmin(),4)
+ assert_equal(mx.argmax(),28)
+ assert_equal(mX.argmax(),28)
+ assert_equal(m2x.argmax(),31)
+ assert_equal(m2X.argmax(),31)
+ #
+ assert_equal(mX.argmin(0), [2,2,2,5,0,5])
+ assert_equal(m2X.argmin(0), [2,2,4,5,0,4])
+ assert_equal(mX.argmax(0), [0,5,0,5,4,0])
+ assert_equal(m2X.argmax(0), [5,5,0,5,1,0])
+ #
+ assert_equal(mX.argmin(1), [4,1,0,0,5,5,])
+ assert_equal(m2X.argmin(1), [4,4,0,0,5,3])
+ assert_equal(mX.argmax(1), [2,4,1,1,4,1])
+ assert_equal(m2X.argmax(1), [2,4,1,1,1,1])
+
+ def check_put(self):
+ "Tests put."
+ d = arange(5)
+ n = [0,0,0,1,1]
+ m = make_mask(n)
+ x = array(d, mask = m)
+ assert( x[3] is masked)
+ assert( x[4] is masked)
+ x[[1,4]] = [10,40]
+# assert( x.mask is not m)
+ assert( x[3] is masked)
+ assert( x[4] is not masked)
+ assert_equal(x, [0,10,2,-1,40])
+ #
+ x = masked_array(arange(10), mask=[1,0,0,0,0]*2)
+ i = [0,2,4,6]
+ x.put(i, [6,4,2,0])
+ assert_equal(x, asarray([6,1,4,3,2,5,0,7,8,9,]))
+ assert_equal(x.mask, [0,0,0,0,0,1,0,0,0,0])
+ x.put(i, masked_array([0,2,4,6],[1,0,1,0]))
+ assert_array_equal(x, [0,1,2,3,4,5,6,7,8,9,])
+ assert_equal(x.mask, [1,0,0,0,1,1,0,0,0,0])
+ #
+ x = masked_array(arange(10), mask=[1,0,0,0,0]*2)
+ put(x, i, [6,4,2,0])
+ assert_equal(x, asarray([6,1,4,3,2,5,0,7,8,9,]))
+ assert_equal(x.mask, [0,0,0,0,0,1,0,0,0,0])
+ put(x, i, masked_array([0,2,4,6],[1,0,1,0]))
+ assert_array_equal(x, [0,1,2,3,4,5,6,7,8,9,])
+ assert_equal(x.mask, [1,0,0,0,1,1,0,0,0,0])
+
+ def check_put_hardmask(self):
+ "Tests put on hardmask"
+ d = arange(5)
+ n = [0,0,0,1,1]
+ m = make_mask(n)
+ xh = array(d+1, mask = m, hard_mask=True, copy=True)
+ xh.put([4,2,0,1,3],[1,2,3,4,5])
+ assert_equal(xh._data, [3,4,2,4,5])
+
+ def check_take(self):
+ "Tests take"
+ x = masked_array([10,20,30,40],[0,1,0,1])
+ assert_equal(x.take([0,0,3]), masked_array([10, 10, 40], [0,0,1]) )
+ assert_equal(x.take([0,0,3]), x[[0,0,3]])
+ assert_equal(x.take([[0,1],[0,1]]),
+ masked_array([[10,20],[10,20]], [[0,1],[0,1]]) )
+ #
+ x = array([[10,20,30],[40,50,60]], mask=[[0,0,1],[1,0,0,]])
+ assert_equal(x.take([0,2], axis=1),
+ array([[10,30],[40,60]], mask=[[0,1],[1,0]]))
+ assert_equal(take(x, [0,2], axis=1),
+ array([[10,30],[40,60]], mask=[[0,1],[1,0]]))
+ #........................
+ def check_anyall(self):
+ """Checks the any/all methods/functions."""
+ x = numpy.array([[ 0.13, 0.26, 0.90],
+ [ 0.28, 0.33, 0.63],
+ [ 0.31, 0.87, 0.70]])
+ m = numpy.array([[ True, False, False],
+ [False, False, False],
+ [True, True, False]], dtype=numpy.bool_)
+ mx = masked_array(x, mask=m)
+ xbig = numpy.array([[False, False, True],
+ [False, False, True],
+ [False, True, True]], dtype=numpy.bool_)
+ mxbig = (mx > 0.5)
+ mxsmall = (mx < 0.5)
+ #
+ assert (mxbig.all()==False)
+ assert (mxbig.any()==True)
+ assert_equal(mxbig.all(0),[False, False, True])
+ assert_equal(mxbig.all(1), [False, False, True])
+ assert_equal(mxbig.any(0),[False, False, True])
+ assert_equal(mxbig.any(1), [True, True, True])
+ #
+ assert (mxsmall.all()==False)
+ assert (mxsmall.any()==True)
+ assert_equal(mxsmall.all(0), [True, True, False])
+ assert_equal(mxsmall.all(1), [False, False, False])
+ assert_equal(mxsmall.any(0), [True, True, False])
+ assert_equal(mxsmall.any(1), [True, True, False])
+ #
+ X = numpy.matrix(x)
+ mX = masked_array(X, mask=m)
+ mXbig = (mX > 0.5)
+ mXsmall = (mX < 0.5)
+ #
+ assert (mXbig.all()==False)
+ assert (mXbig.any()==True)
+ assert_equal(mXbig.all(0), numpy.matrix([False, False, True]))
+ assert_equal(mXbig.all(1), numpy.matrix([False, False, True]).T)
+ assert_equal(mXbig.any(0), numpy.matrix([False, False, True]))
+ assert_equal(mXbig.any(1), numpy.matrix([ True, True, True]).T)
+ #
+ assert (mXsmall.all()==False)
+ assert (mXsmall.any()==True)
+ assert_equal(mXsmall.all(0), numpy.matrix([True, True, False]))
+ assert_equal(mXsmall.all(1), numpy.matrix([False, False, False]).T)
+ assert_equal(mXsmall.any(0), numpy.matrix([True, True, False]))
+ assert_equal(mXsmall.any(1), numpy.matrix([True, True, False]).T)
+
+ def check_keepmask(self):
+ "Tests the keep mask flag"
+ x = masked_array([1,2,3], mask=[1,0,0])
+ mx = masked_array(x)
+ assert_equal(mx.mask, x.mask)
+ mx = masked_array(x, mask=[0,1,0], keep_mask=False)
+ assert_equal(mx.mask, [0,1,0])
+ mx = masked_array(x, mask=[0,1,0], keep_mask=True)
+ assert_equal(mx.mask, [1,1,0])
+ # We default to true
+ mx = masked_array(x, mask=[0,1,0])
+ assert_equal(mx.mask, [1,1,0])
+
+ def check_hardmask(self):
+ "Test hard_mask"
+ d = arange(5)
+ n = [0,0,0,1,1]
+ m = make_mask(n)
+ xh = array(d, mask = m, hard_mask=True)
+ # We need to copy, to avoid updating d in xh!
+ xs = array(d, mask = m, hard_mask=False, copy=True)
+ xh[[1,4]] = [10,40]
+ xs[[1,4]] = [10,40]
+ assert_equal(xh._data, [0,10,2,3,4])
+ assert_equal(xs._data, [0,10,2,3,40])
+ #assert_equal(xh.mask.ctypes.data, m.ctypes.data)
+ assert_equal(xs.mask, [0,0,0,1,0])
+ assert(xh._hardmask)
+ assert(not xs._hardmask)
+ xh[1:4] = [10,20,30]
+ xs[1:4] = [10,20,30]
+ assert_equal(xh._data, [0,10,20,3,4])
+ assert_equal(xs._data, [0,10,20,30,40])
+ #assert_equal(xh.mask.ctypes.data, m.ctypes.data)
+ assert_equal(xs.mask, nomask)
+ xh[0] = masked
+ xs[0] = masked
+ assert_equal(xh.mask, [1,0,0,1,1])
+ assert_equal(xs.mask, [1,0,0,0,0])
+ xh[:] = 1
+ xs[:] = 1
+ assert_equal(xh._data, [0,1,1,3,4])
+ assert_equal(xs._data, [1,1,1,1,1])
+ assert_equal(xh.mask, [1,0,0,1,1])
+ assert_equal(xs.mask, nomask)
+ # Switch to soft mask
+ xh.soften_mask()
+ xh[:] = arange(5)
+ assert_equal(xh._data, [0,1,2,3,4])
+ assert_equal(xh.mask, nomask)
+ # Switch back to hard mask
+ xh.harden_mask()
+ xh[xh<3] = masked
+ assert_equal(xh._data, [0,1,2,3,4])
+ assert_equal(xh._mask, [1,1,1,0,0])
+ xh[filled(xh>1,False)] = 5
+ assert_equal(xh._data, [0,1,2,5,5])
+ assert_equal(xh._mask, [1,1,1,0,0])
+ #
+ xh = array([[1,2],[3,4]], mask = [[1,0],[0,0]], hard_mask=True)
+ xh[0] = 0
+ assert_equal(xh._data, [[1,0],[3,4]])
+ assert_equal(xh._mask, [[1,0],[0,0]])
+ xh[-1,-1] = 5
+ assert_equal(xh._data, [[1,0],[3,5]])
+ assert_equal(xh._mask, [[1,0],[0,0]])
+ xh[filled(xh<5,False)] = 2
+ assert_equal(xh._data, [[1,2],[2,5]])
+ assert_equal(xh._mask, [[1,0],[0,0]])
+ #
+ "Another test of hardmask"
+ d = arange(5)
+ n = [0,0,0,1,1]
+ m = make_mask(n)
+ xh = array(d, mask = m, hard_mask=True)
+ xh[4:5] = 999
+ #assert_equal(xh.mask.ctypes.data, m.ctypes.data)
+ xh[0:1] = 999
+ assert_equal(xh._data,[999,1,2,3,4])
+
+ def check_smallmask(self):
+ "Checks the behaviour of _smallmask"
+ a = arange(10)
+ a[1] = masked
+ a[1] = 1
+ assert_equal(a._mask, nomask)
+ a = arange(10)
+ a._smallmask = False
+ a[1] = masked
+ a[1] = 1
+ assert_equal(a._mask, zeros(10))
+
+
+ def check_sort(self):
+ "Test sort"
+ x = array([1,4,2,3],mask=[0,1,0,0],dtype=numpy.uint8)
+ #
+ sortedx = sort(x)
+ assert_equal(sortedx._data,[1,2,3,4])
+ assert_equal(sortedx._mask,[0,0,0,1])
+ #
+ sortedx = sort(x, endwith=False)
+ assert_equal(sortedx._data, [4,1,2,3])
+ assert_equal(sortedx._mask, [1,0,0,0])
+ #
+ x.sort()
+ assert_equal(x._data,[1,2,3,4])
+ assert_equal(x._mask,[0,0,0,1])
+ #
+ x = array([1,4,2,3],mask=[0,1,0,0],dtype=numpy.uint8)
+ x.sort(endwith=False)
+ assert_equal(x._data, [4,1,2,3])
+ assert_equal(x._mask, [1,0,0,0])
+ #
+ x = [1,4,2,3]
+ sortedx = sort(x)
+ assert(not isinstance(sorted, MaskedArray))
+ #
+ x = array([0,1,-1,-2,2], mask=nomask, dtype=numpy.int8)
+ sortedx = sort(x, endwith=False)
+ assert_equal(sortedx._data, [-2,-1,0,1,2])
+ x = array([0,1,-1,-2,2], mask=[0,1,0,0,1], dtype=numpy.int8)
+ sortedx = sort(x, endwith=False)
+ assert_equal(sortedx._data, [1,2,-2,-1,0])
+ assert_equal(sortedx._mask, [1,1,0,0,0])
+
+ def check_sort_2d(self):
+ "Check sort of 2D array."
+ # 2D array w/o mask
+ a = masked_array([[8,4,1],[2,0,9]])
+ a.sort(0)
+ assert_equal(a, [[2,0,1],[8,4,9]])
+ a = masked_array([[8,4,1],[2,0,9]])
+ a.sort(1)
+ assert_equal(a, [[1,4,8],[0,2,9]])
+ # 2D array w/mask
+ a = masked_array([[8,4,1],[2,0,9]], mask=[[1,0,0],[0,0,1]])
+ a.sort(0)
+ assert_equal(a, [[2,0,1],[8,4,9]])
+ assert_equal(a._mask, [[0,0,0],[1,0,1]])
+ a = masked_array([[8,4,1],[2,0,9]], mask=[[1,0,0],[0,0,1]])
+ a.sort(1)
+ assert_equal(a, [[1,4,8],[0,2,9]])
+ assert_equal(a._mask, [[0,0,1],[0,0,1]])
+ # 3D
+ a = masked_array([[[7, 8, 9],[4, 5, 6],[1, 2, 3]],
+ [[1, 2, 3],[7, 8, 9],[4, 5, 6]],
+ [[7, 8, 9],[1, 2, 3],[4, 5, 6]],
+ [[4, 5, 6],[1, 2, 3],[7, 8, 9]]])
+ a[a%4==0] = masked
+ am = a.copy()
+ an = a.filled(99)
+ am.sort(0)
+ an.sort(0)
+ assert_equal(am, an)
+ am = a.copy()
+ an = a.filled(99)
+ am.sort(1)
+ an.sort(1)
+ assert_equal(am, an)
+ am = a.copy()
+ an = a.filled(99)
+ am.sort(2)
+ an.sort(2)
+ assert_equal(am, an)
+
+
+ def check_ravel(self):
+ "Tests ravel"
+ a = array([[1,2,3,4,5]], mask=[[0,1,0,0,0]])
+ aravel = a.ravel()
+ assert_equal(a._mask.shape, a.shape)
+ a = array([0,0], mask=[1,1])
+ aravel = a.ravel()
+ assert_equal(a._mask.shape, a.shape)
+ a = array(numpy.matrix([1,2,3,4,5]), mask=[[0,1,0,0,0]])
+ aravel = a.ravel()
+ assert_equal(a.shape,(1,5))
+ assert_equal(a._mask.shape, a.shape)
+ # Checs that small_mask is preserved
+ a = array([1,2,3,4],mask=[0,0,0,0],shrink=False)
+ assert_equal(a.ravel()._mask, [0,0,0,0])
+
+ def check_reshape(self):
+ "Tests reshape"
+ x = arange(4)
+ x[0] = masked
+ y = x.reshape(2,2)
+ assert_equal(y.shape, (2,2,))
+ assert_equal(y._mask.shape, (2,2,))
+ assert_equal(x.shape, (4,))
+ assert_equal(x._mask.shape, (4,))
+
+ def check_compressed(self):
+ "Tests compressed"
+ a = array([1,2,3,4],mask=[0,0,0,0])
+ b = a.compressed()
+ assert_equal(b, a)
+ assert_equal(b._mask, nomask)
+ a[0] = masked
+ b = a.compressed()
+ assert_equal(b._data, [2,3,4])
+ assert_equal(b._mask, nomask)
+
+ def check_tolist(self):
+ "Tests to list"
+ x = array(numpy.arange(12))
+ x[[1,-2]] = masked
+ xlist = x.tolist()
+ assert(xlist[1] is None)
+ assert(xlist[-2] is None)
+ #
+ x.shape = (3,4)
+ xlist = x.tolist()
+ #
+ assert_equal(xlist[0],[0,None,2,3])
+ assert_equal(xlist[1],[4,5,6,7])
+ assert_equal(xlist[2],[8,9,None,11])
+
+ def check_squeeze(self):
+ "Check squeeze"
+ data = masked_array([[1,2,3]])
+ assert_equal(data.squeeze(), [1,2,3])
+ data = masked_array([[1,2,3]], mask=[[1,1,1]])
+ assert_equal(data.squeeze(), [1,2,3])
+ assert_equal(data.squeeze()._mask, [1,1,1])
+ data = masked_array([[1]], mask=True)
+ assert(data.squeeze() is masked)
+
+#..............................................................................
+
+###############################################################################
+#------------------------------------------------------------------------------
+if __name__ == "__main__":
+ NumpyTest().run()
Added: branches/maskedarray/numpy/core/ma/tests/test_extras.py
===================================================================
--- branches/maskedarray/numpy/core/ma/tests/test_extras.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/tests/test_extras.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,331 @@
+# pylint: disable-msg=W0611, W0612, W0511
+"""Tests suite for MaskedArray.
+Adapted from the original test_ma by Pierre Gerard-Marchant
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: test_extras.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3473 $"
+__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
+
+import numpy as N
+from numpy.testing import NumpyTest, NumpyTestCase
+from numpy.testing.utils import build_err_msg
+
+import maskedarray.testutils
+from maskedarray.testutils import *
+
+import maskedarray.core
+from maskedarray.core import *
+import maskedarray.extras
+from maskedarray.extras import *
+
+class TestAverage(NumpyTestCase):
+ "Several tests of average. Why so many ? Good point..."
+ def check_testAverage1(self):
+ "Test of average."
+ ott = array([0.,1.,2.,3.], mask=[1,0,0,0])
+ assert_equal(2.0, average(ott,axis=0))
+ assert_equal(2.0, average(ott, weights=[1., 1., 2., 1.]))
+ result, wts = average(ott, weights=[1.,1.,2.,1.], returned=1)
+ assert_equal(2.0, result)
+ assert(wts == 4.0)
+ ott[:] = masked
+ assert_equal(average(ott,axis=0).mask, [True])
+ ott = array([0.,1.,2.,3.], mask=[1,0,0,0])
+ ott = ott.reshape(2,2)
+ ott[:,1] = masked
+ assert_equal(average(ott,axis=0), [2.0, 0.0])
+ assert_equal(average(ott,axis=1).mask[0], [True])
+ assert_equal([2.,0.], average(ott, axis=0))
+ result, wts = average(ott, axis=0, returned=1)
+ assert_equal(wts, [1., 0.])
+
+ def check_testAverage2(self):
+ "More tests of average."
+ w1 = [0,1,1,1,1,0]
+ w2 = [[0,1,1,1,1,0],[1,0,0,0,0,1]]
+ x = arange(6, dtype=float_)
+ assert_equal(average(x, axis=0), 2.5)
+ assert_equal(average(x, axis=0, weights=w1), 2.5)
+ y = array([arange(6, dtype=float_), 2.0*arange(6)])
+ assert_equal(average(y, None), N.add.reduce(N.arange(6))*3./12.)
+ assert_equal(average(y, axis=0), N.arange(6) * 3./2.)
+ assert_equal(average(y, axis=1), [average(x,axis=0), average(x,axis=0) * 2.0])
+ assert_equal(average(y, None, weights=w2), 20./6.)
+ assert_equal(average(y, axis=0, weights=w2), [0.,1.,2.,3.,4.,10.])
+ assert_equal(average(y, axis=1), [average(x,axis=0), average(x,axis=0) * 2.0])
+ m1 = zeros(6)
+ m2 = [0,0,1,1,0,0]
+ m3 = [[0,0,1,1,0,0],[0,1,1,1,1,0]]
+ m4 = ones(6)
+ m5 = [0, 1, 1, 1, 1, 1]
+ assert_equal(average(masked_array(x, m1),axis=0), 2.5)
+ assert_equal(average(masked_array(x, m2),axis=0), 2.5)
+ assert_equal(average(masked_array(x, m4),axis=0).mask, [True])
+ assert_equal(average(masked_array(x, m5),axis=0), 0.0)
+ assert_equal(count(average(masked_array(x, m4),axis=0)), 0)
+ z = masked_array(y, m3)
+ assert_equal(average(z, None), 20./6.)
+ assert_equal(average(z, axis=0), [0.,1.,99.,99.,4.0, 7.5])
+ assert_equal(average(z, axis=1), [2.5, 5.0])
+ assert_equal(average(z,axis=0, weights=w2), [0.,1., 99., 99., 4.0, 10.0])
+
+ def check_testAverage3(self):
+ "Yet more tests of average!"
+ a = arange(6)
+ b = arange(6) * 3
+ r1, w1 = average([[a,b],[b,a]], axis=1, returned=1)
+ assert_equal(shape(r1) , shape(w1))
+ assert_equal(r1.shape , w1.shape)
+ r2, w2 = average(ones((2,2,3)), axis=0, weights=[3,1], returned=1)
+ assert_equal(shape(w2) , shape(r2))
+ r2, w2 = average(ones((2,2,3)), returned=1)
+ assert_equal(shape(w2) , shape(r2))
+ r2, w2 = average(ones((2,2,3)), weights=ones((2,2,3)), returned=1)
+ assert_equal(shape(w2), shape(r2))
+ a2d = array([[1,2],[0,4]], float)
+ a2dm = masked_array(a2d, [[0,0],[1,0]])
+ a2da = average(a2d, axis=0)
+ assert_equal(a2da, [0.5, 3.0])
+ a2dma = average(a2dm, axis=0)
+ assert_equal(a2dma, [1.0, 3.0])
+ a2dma = average(a2dm, axis=None)
+ assert_equal(a2dma, 7./3.)
+ a2dma = average(a2dm, axis=1)
+ assert_equal(a2dma, [1.5, 4.0])
+
+class TestConcatenator(NumpyTestCase):
+ "Tests for mr_, the equivalent of r_ for masked arrays."
+ def check_1d(self):
+ "Tests mr_ on 1D arrays."
+ assert_array_equal(mr_[1,2,3,4,5,6],array([1,2,3,4,5,6]))
+ b = ones(5)
+ m = [1,0,0,0,0]
+ d = masked_array(b,mask=m)
+ c = mr_[d,0,0,d]
+ assert(isinstance(c,MaskedArray) or isinstance(c,core.MaskedArray))
+ assert_array_equal(c,[1,1,1,1,1,0,0,1,1,1,1,1])
+ assert_array_equal(c.mask, mr_[m,0,0,m])
+
+ def check_2d(self):
+ "Tests mr_ on 2D arrays."
+ a_1 = rand(5,5)
+ a_2 = rand(5,5)
+ m_1 = N.round_(rand(5,5),0)
+ m_2 = N.round_(rand(5,5),0)
+ b_1 = masked_array(a_1,mask=m_1)
+ b_2 = masked_array(a_2,mask=m_2)
+ d = mr_['1',b_1,b_2] # append columns
+ assert(d.shape == (5,10))
+ assert_array_equal(d[:,:5],b_1)
+ assert_array_equal(d[:,5:],b_2)
+ assert_array_equal(d.mask, N.r_['1',m_1,m_2])
+ d = mr_[b_1,b_2]
+ assert(d.shape == (10,5))
+ assert_array_equal(d[:5,:],b_1)
+ assert_array_equal(d[5:,:],b_2)
+ assert_array_equal(d.mask, N.r_[m_1,m_2])
+
+class TestNotMasked(NumpyTestCase):
+ "Tests notmasked_edges and notmasked_contiguous."
+ def check_edges(self):
+ "Tests unmasked_edges"
+ a = masked_array(N.arange(24).reshape(3,8),
+ mask=[[0,0,0,0,1,1,1,0],
+ [1,1,1,1,1,1,1,1],
+ [0,0,0,0,0,0,1,0],])
+ #
+ assert_equal(notmasked_edges(a, None), [0,23])
+ #
+ tmp = notmasked_edges(a, 0)
+ assert_equal(tmp[0], (array([0,0,0,0,2,2,0]), array([0,1,2,3,4,5,7])))
+ assert_equal(tmp[1], (array([2,2,2,2,2,2,2]), array([0,1,2,3,4,5,7])))
+ #
+ tmp = notmasked_edges(a, 1)
+ assert_equal(tmp[0], (array([0,2,]), array([0,0])))
+ assert_equal(tmp[1], (array([0,2,]), array([7,7])))
+
+ def check_contiguous(self):
+ "Tests notmasked_contiguous"
+ a = masked_array(N.arange(24).reshape(3,8),
+ mask=[[0,0,0,0,1,1,1,1],
+ [1,1,1,1,1,1,1,1],
+ [0,0,0,0,0,0,1,0],])
+ tmp = notmasked_contiguous(a, None)
+ assert_equal(tmp[-1], slice(23,23,None))
+ assert_equal(tmp[-2], slice(16,21,None))
+ assert_equal(tmp[-3], slice(0,3,None))
+ #
+ tmp = notmasked_contiguous(a, 0)
+ assert(len(tmp[-1]) == 1)
+ assert(tmp[-2] is None)
+ assert_equal(tmp[-3],tmp[-1])
+ assert(len(tmp[0]) == 2)
+ #
+ tmp = notmasked_contiguous(a, 1)
+ assert_equal(tmp[0][-1], slice(0,3,None))
+ assert(tmp[1] is None)
+ assert_equal(tmp[2][-1], slice(7,7,None))
+ assert_equal(tmp[2][-2], slice(0,5,None))
+
+class Test2DFunctions(NumpyTestCase):
+ "Tests 2D functions"
+ def check_compress2d(self):
+ "Tests compress2d"
+ x = array(N.arange(9).reshape(3,3), mask=[[1,0,0],[0,0,0],[0,0,0]])
+ assert_equal(compress_rowcols(x), [[4,5],[7,8]] )
+ assert_equal(compress_rowcols(x,0), [[3,4,5],[6,7,8]] )
+ assert_equal(compress_rowcols(x,1), [[1,2],[4,5],[7,8]] )
+ x = array(x._data, mask=[[0,0,0],[0,1,0],[0,0,0]])
+ assert_equal(compress_rowcols(x), [[0,2],[6,8]] )
+ assert_equal(compress_rowcols(x,0), [[0,1,2],[6,7,8]] )
+ assert_equal(compress_rowcols(x,1), [[0,2],[3,5],[6,8]] )
+ x = array(x._data, mask=[[1,0,0],[0,1,0],[0,0,0]])
+ assert_equal(compress_rowcols(x), [[8]] )
+ assert_equal(compress_rowcols(x,0), [[6,7,8]] )
+ assert_equal(compress_rowcols(x,1,), [[2],[5],[8]] )
+ x = array(x._data, mask=[[1,0,0],[0,1,0],[0,0,1]])
+ assert_equal(compress_rowcols(x).size, 0 )
+ assert_equal(compress_rowcols(x,0).size, 0 )
+ assert_equal(compress_rowcols(x,1).size, 0 )
+ #
+ def check_mask_rowcols(self):
+ "Tests mask_rowcols."
+ x = array(N.arange(9).reshape(3,3), mask=[[1,0,0],[0,0,0],[0,0,0]])
+ assert_equal(mask_rowcols(x).mask, [[1,1,1],[1,0,0],[1,0,0]] )
+ assert_equal(mask_rowcols(x,0).mask, [[1,1,1],[0,0,0],[0,0,0]] )
+ assert_equal(mask_rowcols(x,1).mask, [[1,0,0],[1,0,0],[1,0,0]] )
+ x = array(x._data, mask=[[0,0,0],[0,1,0],[0,0,0]])
+ assert_equal(mask_rowcols(x).mask, [[0,1,0],[1,1,1],[0,1,0]] )
+ assert_equal(mask_rowcols(x,0).mask, [[0,0,0],[1,1,1],[0,0,0]] )
+ assert_equal(mask_rowcols(x,1).mask, [[0,1,0],[0,1,0],[0,1,0]] )
+ x = array(x._data, mask=[[1,0,0],[0,1,0],[0,0,0]])
+ assert_equal(mask_rowcols(x).mask, [[1,1,1],[1,1,1],[1,1,0]] )
+ assert_equal(mask_rowcols(x,0).mask, [[1,1,1],[1,1,1],[0,0,0]] )
+ assert_equal(mask_rowcols(x,1,).mask, [[1,1,0],[1,1,0],[1,1,0]] )
+ x = array(x._data, mask=[[1,0,0],[0,1,0],[0,0,1]])
+ assert(mask_rowcols(x).all())
+ assert(mask_rowcols(x,0).all())
+ assert(mask_rowcols(x,1).all())
+ #
+ def test_dot(self):
+ "Tests dot product"
+ n = N.arange(1,7)
+ #
+ m = [1,0,0,0,0,0]
+ a = masked_array(n, mask=m).reshape(2,3)
+ b = masked_array(n, mask=m).reshape(3,2)
+ c = dot(a,b,True)
+ assert_equal(c.mask, [[1,1],[1,0]])
+ c = dot(b,a,True)
+ assert_equal(c.mask, [[1,1,1],[1,0,0],[1,0,0]])
+ c = dot(a,b,False)
+ assert_equal(c, N.dot(a.filled(0), b.filled(0)))
+ c = dot(b,a,False)
+ assert_equal(c, N.dot(b.filled(0), a.filled(0)))
+ #
+ m = [0,0,0,0,0,1]
+ a = masked_array(n, mask=m).reshape(2,3)
+ b = masked_array(n, mask=m).reshape(3,2)
+ c = dot(a,b,True)
+ assert_equal(c.mask,[[0,1],[1,1]])
+ c = dot(b,a,True)
+ assert_equal(c.mask, [[0,0,1],[0,0,1],[1,1,1]])
+ c = dot(a,b,False)
+ assert_equal(c, N.dot(a.filled(0), b.filled(0)))
+ assert_equal(c, dot(a,b))
+ c = dot(b,a,False)
+ assert_equal(c, N.dot(b.filled(0), a.filled(0)))
+ #
+ m = [0,0,0,0,0,0]
+ a = masked_array(n, mask=m).reshape(2,3)
+ b = masked_array(n, mask=m).reshape(3,2)
+ c = dot(a,b)
+ assert_equal(c.mask,nomask)
+ c = dot(b,a)
+ assert_equal(c.mask,nomask)
+ #
+ a = masked_array(n, mask=[1,0,0,0,0,0]).reshape(2,3)
+ b = masked_array(n, mask=[0,0,0,0,0,0]).reshape(3,2)
+ c = dot(a,b,True)
+ assert_equal(c.mask,[[1,1],[0,0]])
+ c = dot(a,b,False)
+ assert_equal(c, N.dot(a.filled(0),b.filled(0)))
+ c = dot(b,a,True)
+ assert_equal(c.mask,[[1,0,0],[1,0,0],[1,0,0]])
+ c = dot(b,a,False)
+ assert_equal(c, N.dot(b.filled(0),a.filled(0)))
+ #
+ a = masked_array(n, mask=[0,0,0,0,0,1]).reshape(2,3)
+ b = masked_array(n, mask=[0,0,0,0,0,0]).reshape(3,2)
+ c = dot(a,b,True)
+ assert_equal(c.mask,[[0,0],[1,1]])
+ c = dot(a,b)
+ assert_equal(c, N.dot(a.filled(0),b.filled(0)))
+ c = dot(b,a,True)
+ assert_equal(c.mask,[[0,0,1],[0,0,1],[0,0,1]])
+ c = dot(b,a,False)
+ assert_equal(c, N.dot(b.filled(0), a.filled(0)))
+ #
+ a = masked_array(n, mask=[0,0,0,0,0,1]).reshape(2,3)
+ b = masked_array(n, mask=[0,0,1,0,0,0]).reshape(3,2)
+ c = dot(a,b,True)
+ assert_equal(c.mask,[[1,0],[1,1]])
+ c = dot(a,b,False)
+ assert_equal(c, N.dot(a.filled(0),b.filled(0)))
+ c = dot(b,a,True)
+ assert_equal(c.mask,[[0,0,1],[1,1,1],[0,0,1]])
+ c = dot(b,a,False)
+ assert_equal(c, N.dot(b.filled(0),a.filled(0)))
+
+ def test_mediff1d(self):
+ "Tests mediff1d"
+ x = masked_array(N.arange(5), mask=[1,0,0,0,1])
+ difx_d = (x._data[1:]-x._data[:-1])
+ difx_m = (x._mask[1:]-x._mask[:-1])
+ dx = mediff1d(x)
+ assert_equal(dx._data, difx_d)
+ assert_equal(dx._mask, difx_m)
+ #
+ dx = mediff1d(x, to_begin=masked)
+ assert_equal(dx._data, N.r_[0,difx_d])
+ assert_equal(dx._mask, N.r_[1,difx_m])
+ dx = mediff1d(x, to_begin=[1,2,3])
+ assert_equal(dx._data, N.r_[[1,2,3],difx_d])
+ assert_equal(dx._mask, N.r_[[0,0,0],difx_m])
+ #
+ dx = mediff1d(x, to_end=masked)
+ assert_equal(dx._data, N.r_[difx_d,0])
+ assert_equal(dx._mask, N.r_[difx_m,1])
+ dx = mediff1d(x, to_end=[1,2,3])
+ assert_equal(dx._data, N.r_[difx_d,[1,2,3]])
+ assert_equal(dx._mask, N.r_[difx_m,[0,0,0]])
+ #
+ dx = mediff1d(x, to_end=masked, to_begin=masked)
+ assert_equal(dx._data, N.r_[0,difx_d,0])
+ assert_equal(dx._mask, N.r_[1,difx_m,1])
+ dx = mediff1d(x, to_end=[1,2,3], to_begin=masked)
+ assert_equal(dx._data, N.r_[0,difx_d,[1,2,3]])
+ assert_equal(dx._mask, N.r_[1,difx_m,[0,0,0]])
+ #
+ dx = mediff1d(x._data, to_end=masked, to_begin=masked)
+ assert_equal(dx._data, N.r_[0,difx_d,0])
+ assert_equal(dx._mask, N.r_[1,0,0,0,0,1])
+
+class TestApplyAlongAxis(NumpyTestCase):
+ "Tests 2D functions"
+ def check_3d(self):
+ a = arange(12.).reshape(2,2,3)
+ def myfunc(b):
+ return b[1]
+ xa = apply_along_axis(myfunc,2,a)
+ assert_equal(xa,[[1,4],[7,10]])
+
+###############################################################################
+#------------------------------------------------------------------------------
+if __name__ == "__main__":
+ NumpyTest().run()
Added: branches/maskedarray/numpy/core/ma/tests/test_morestats.py
===================================================================
--- branches/maskedarray/numpy/core/ma/tests/test_morestats.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/tests/test_morestats.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,114 @@
+# pylint: disable-msg=W0611, W0612, W0511,R0201
+"""Tests suite for maskedArray statistics.
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: test_morestats.py 317 2007-10-04 19:31:14Z backtopop $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: backtopop $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 317 $"
+__date__ = '$Date: 2007-10-04 15:31:14 -0400 (Thu, 04 Oct 2007) $'
+
+import numpy
+
+import maskedarray
+from maskedarray import masked, masked_array
+
+import maskedarray.mstats
+from maskedarray.mstats import *
+import maskedarray.morestats
+from maskedarray.morestats import *
+
+import maskedarray.testutils
+from maskedarray.testutils import *
+
+
+class TestMisc(NumpyTestCase):
+ #
+ def __init__(self, *args, **kwargs):
+ NumpyTestCase.__init__(self, *args, **kwargs)
+ #
+ def test_mjci(self):
+ "Tests the Marits-Jarrett estimator"
+ data = masked_array([ 77, 87, 88,114,151,210,219,246,253,262,
+ 296,299,306,376,428,515,666,1310,2611])
+ assert_almost_equal(mjci(data),[55.76819,45.84028,198.8788],5)
+ #
+ def test_trimmedmeanci(self):
+ "Tests the confidence intervals of the trimmed mean."
+ data = masked_array([545,555,558,572,575,576,578,580,
+ 594,605,635,651,653,661,666])
+ assert_almost_equal(trimmed_mean(data,0.2), 596.2, 1)
+ assert_equal(numpy.round(trimmed_mean_ci(data,0.2),1), [561.8, 630.6])
+
+#..............................................................................
+class TestRanking(NumpyTestCase):
+ #
+ def __init__(self, *args, **kwargs):
+ NumpyTestCase.__init__(self, *args, **kwargs)
+ #
+ def test_ranking(self):
+ x = masked_array([0,1,1,1,2,3,4,5,5,6,])
+ assert_almost_equal(rank_data(x),[1,3,3,3,5,6,7,8.5,8.5,10])
+ x[[3,4]] = masked
+ assert_almost_equal(rank_data(x),[1,2.5,2.5,0,0,4,5,6.5,6.5,8])
+ assert_almost_equal(rank_data(x,use_missing=True),
+ [1,2.5,2.5,4.5,4.5,4,5,6.5,6.5,8])
+ x = masked_array([0,1,5,1,2,4,3,5,1,6,])
+ assert_almost_equal(rank_data(x),[1,3,8.5,3,5,7,6,8.5,3,10])
+ x = masked_array([[0,1,1,1,2], [3,4,5,5,6,]])
+ assert_almost_equal(rank_data(x),[[1,3,3,3,5],[6,7,8.5,8.5,10]])
+ assert_almost_equal(rank_data(x,axis=1),[[1,3,3,3,5],[1,2,3.5,3.5,5]])
+ assert_almost_equal(rank_data(x,axis=0),[[1,1,1,1,1],[2,2,2,2,2,]])
+
+#..............................................................................
+class TestQuantiles(NumpyTestCase):
+ #
+ def __init__(self, *args, **kwargs):
+ NumpyTestCase.__init__(self, *args, **kwargs)
+ #
+ def test_hdquantiles(self):
+ data = [0.706560797,0.727229578,0.990399276,0.927065621,0.158953014,
+ 0.887764025,0.239407086,0.349638551,0.972791145,0.149789972,
+ 0.936947700,0.132359948,0.046041972,0.641675031,0.945530547,
+ 0.224218684,0.771450991,0.820257774,0.336458052,0.589113496,
+ 0.509736129,0.696838829,0.491323573,0.622767425,0.775189248,
+ 0.641461450,0.118455200,0.773029450,0.319280007,0.752229111,
+ 0.047841438,0.466295911,0.583850781,0.840581845,0.550086491,
+ 0.466470062,0.504765074,0.226855960,0.362641207,0.891620942,
+ 0.127898691,0.490094097,0.044882048,0.041441695,0.317976349,
+ 0.504135618,0.567353033,0.434617473,0.636243375,0.231803616,
+ 0.230154113,0.160011327,0.819464108,0.854706985,0.438809221,
+ 0.487427267,0.786907310,0.408367937,0.405534192,0.250444460,
+ 0.995309248,0.144389588,0.739947527,0.953543606,0.680051621,
+ 0.388382017,0.863530727,0.006514031,0.118007779,0.924024803,
+ 0.384236354,0.893687694,0.626534881,0.473051932,0.750134705,
+ 0.241843555,0.432947602,0.689538104,0.136934797,0.150206859,
+ 0.474335206,0.907775349,0.525869295,0.189184225,0.854284286,
+ 0.831089744,0.251637345,0.587038213,0.254475554,0.237781276,
+ 0.827928620,0.480283781,0.594514455,0.213641488,0.024194386,
+ 0.536668589,0.699497811,0.892804071,0.093835427,0.731107772]
+ #
+ assert_almost_equal(hdquantiles(data,[0., 1.]),
+ [0.006514031, 0.995309248])
+ hdq = hdquantiles(data,[0.25, 0.5, 0.75])
+ assert_almost_equal(hdq, [0.253210762, 0.512847491, 0.762232442,])
+ hdq = hdquantiles_sd(data,[0.25, 0.5, 0.75])
+ assert_almost_equal(hdq, [0.03786954, 0.03805389, 0.03800152,], 4)
+ #
+ data = numpy.array(data).reshape(10,10)
+ hdq = hdquantiles(data,[0.25,0.5,0.75],axis=0)
+ assert_almost_equal(hdq[:,0], hdquantiles(data[:,0],[0.25,0.5,0.75]))
+ assert_almost_equal(hdq[:,-1], hdquantiles(data[:,-1],[0.25,0.5,0.75]))
+ hdq = hdquantiles(data,[0.25,0.5,0.75],axis=0,var=True)
+ assert_almost_equal(hdq[...,0],
+ hdquantiles(data[:,0],[0.25,0.5,0.75],var=True))
+ assert_almost_equal(hdq[...,-1],
+ hdquantiles(data[:,-1],[0.25,0.5,0.75], var=True))
+
+
+###############################################################################
+#------------------------------------------------------------------------------
+if __name__ == "__main__":
+ NumpyTest().run()
Added: branches/maskedarray/numpy/core/ma/tests/test_mrecords.py
===================================================================
--- branches/maskedarray/numpy/core/ma/tests/test_mrecords.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/tests/test_mrecords.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,180 @@
+# pylint: disable-msg=W0611, W0612, W0511,R0201
+"""Tests suite for mrecarray.
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: test_mrecords.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3473 $"
+__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
+
+import types
+
+import numpy as N
+import numpy.core.fromnumeric as fromnumeric
+from numpy.testing import NumpyTest, NumpyTestCase
+from numpy.testing.utils import build_err_msg
+
+import maskedarray.testutils
+from maskedarray.testutils import *
+
+import maskedarray
+from maskedarray import masked_array, masked, nomask
+
+#import maskedarray.mrecords
+#from maskedarray.mrecords import mrecarray, fromarrays, fromtextfile, fromrecords
+import maskedarray.mrecords
+from maskedarray.mrecords import MaskedRecords, \
+ fromarrays, fromtextfile, fromrecords, addfield
+
+#..............................................................................
+class TestMRecords(NumpyTestCase):
+ "Base test class for MaskedArrays."
+ def __init__(self, *args, **kwds):
+ NumpyTestCase.__init__(self, *args, **kwds)
+ self.setup()
+
+ def setup(self):
+ "Generic setup"
+ d = N.arange(5)
+ m = maskedarray.make_mask([1,0,0,1,1])
+ base_d = N.r_[d,d[::-1]].reshape(2,-1).T
+ base_m = N.r_[[m, m[::-1]]].T
+ base = masked_array(base_d, mask=base_m)
+ mrecord = fromarrays(base.T, dtype=[('a',N.float_),('b',N.float_)])
+ self.data = [d, m, mrecord]
+
+ def test_get(self):
+ "Tests fields retrieval"
+ [d, m, mrec] = self.data
+ mrec = mrec.copy()
+ assert_equal(mrec.a, masked_array(d,mask=m))
+ assert_equal(mrec.b, masked_array(d[::-1],mask=m[::-1]))
+ assert((mrec._fieldmask == N.core.records.fromarrays([m, m[::-1]], dtype=mrec._fieldmask.dtype)).all())
+ assert_equal(mrec._mask, N.r_[[m,m[::-1]]].all(0))
+ assert_equal(mrec.a[1], mrec[1].a)
+ #
+ assert(isinstance(mrec[:2], MaskedRecords))
+ assert_equal(mrec[:2]['a'], d[:2])
+
+ def test_set(self):
+ "Tests setting fields/attributes."
+ [d, m, mrecord] = self.data
+ mrecord.a._data[:] = 5
+ assert_equal(mrecord['a']._data, [5,5,5,5,5])
+ mrecord.a = 1
+ assert_equal(mrecord['a']._data, [1]*5)
+ assert_equal(getmaskarray(mrecord['a']), [0]*5)
+ mrecord.b = masked
+ assert_equal(mrecord.b.mask, [1]*5)
+ assert_equal(getmaskarray(mrecord['b']), [1]*5)
+ mrecord._mask = masked
+ assert_equal(getmaskarray(mrecord['b']), [1]*5)
+ assert_equal(mrecord['a']._mask, mrecord['b']._mask)
+ mrecord._mask = nomask
+ assert_equal(getmaskarray(mrecord['b']), [0]*5)
+ assert_equal(mrecord['a']._mask, mrecord['b']._mask)
+ #
+ def test_setfields(self):
+ "Tests setting fields."
+ [d, m, mrecord] = self.data
+ mrecord.a[3:] = 5
+ assert_equal(mrecord.a, [0,1,2,5,5])
+ assert_equal(mrecord.a._mask, [1,0,0,0,0])
+ #
+ mrecord.b[3:] = masked
+ assert_equal(mrecord.b, [4,3,2,1,0])
+ assert_equal(mrecord.b._mask, [1,1,0,1,1])
+
+ def test_setslices(self):
+ "Tests setting slices."
+ [d, m, mrec] = self.data
+ mrec[:2] = 5
+ assert_equal(mrec.a._data, [5,5,2,3,4])
+ assert_equal(mrec.b._data, [5,5,2,1,0])
+ assert_equal(mrec.a._mask, [0,0,0,1,1])
+ assert_equal(mrec.b._mask, [0,0,0,0,1])
+ #
+ mrec[:2] = masked
+ assert_equal(mrec._mask, [1,1,0,0,1])
+ mrec[-2] = masked
+ assert_equal(mrec._mask, [1,1,0,1,1])
+ #
+ def test_setslices_hardmask(self):
+ "Tests setting slices w/ hardmask."
+ [d, m, mrec] = self.data
+ mrec.harden_mask()
+ mrec[-2:] = 5
+ assert_equal(mrec.a._data, [0,1,2,3,4])
+ assert_equal(mrec.b._data, [4,3,2,5,0])
+ assert_equal(mrec.a._mask, [1,0,0,1,1])
+ assert_equal(mrec.b._mask, [1,1,0,0,1])
+
+ def test_hardmask(self):
+ "Test hardmask"
+ [d, m, mrec] = self.data
+ mrec = mrec.copy()
+ mrec.harden_mask()
+ assert(mrec._hardmask)
+ mrec._mask = nomask
+ assert_equal(mrec._mask, N.r_[[m,m[::-1]]].all(0))
+ mrec.soften_mask()
+ assert(not mrec._hardmask)
+ mrec._mask = nomask
+ assert(mrec['b']._mask is nomask)
+ assert_equal(mrec['a']._mask,mrec['b']._mask)
+
+ def test_fromrecords(self):
+ "Test from recarray."
+ [d, m, mrec] = self.data
+ nrec = N.core.records.fromarrays(N.r_[[d,d[::-1]]],
+ dtype=[('a',N.float_),('b',N.float_)])
+ #....................
+ mrecfr = fromrecords(nrec)
+ assert_equal(mrecfr.a, mrec.a)
+ assert_equal(mrecfr.dtype, mrec.dtype)
+ #....................
+ tmp = mrec[::-1] #.tolist()
+ mrecfr = fromrecords(tmp)
+ assert_equal(mrecfr.a, mrec.a[::-1])
+ #....................
+ mrecfr = fromrecords(nrec.tolist(), names=nrec.dtype.names)
+ assert_equal(mrecfr.a, mrec.a)
+ assert_equal(mrecfr.dtype, mrec.dtype)
+
+ def test_fromtextfile(self):
+ "Tests reading from a text file."
+ fcontent = """#
+'One (S)','Two (I)','Three (F)','Four (M)','Five (-)','Six (C)'
+'strings',1,1.0,'mixed column',,1
+'with embedded "double quotes"',2,2.0,1.0,,1
+'strings',3,3.0E5,3,,1
+'strings',4,-1e-10,,,1
+"""
+ import os
+ from datetime import datetime
+ fname = 'tmp%s' % datetime.now().strftime("%y%m%d%H%M%S%s")
+ f = open(fname, 'w')
+ f.write(fcontent)
+ f.close()
+ mrectxt = fromtextfile(fname,delimitor=',',varnames='ABCDEFG')
+ os.unlink(fname)
+ #
+ assert(isinstance(mrectxt, MaskedRecords))
+ assert_equal(mrectxt.F, [1,1,1,1])
+ assert_equal(mrectxt.E._mask, [1,1,1,1])
+ assert_equal(mrectxt.C, [1,2,3.e+5,-1e-10])
+
+ def test_addfield(self):
+ "Tests addfield"
+ [d, m, mrec] = self.data
+ mrec = addfield(mrec, masked_array(d+10, mask=m[::-1]))
+ assert_equal(mrec.f2, d+10)
+ assert_equal(mrec.f2._mask, m[::-1])
+
+###############################################################################
+#------------------------------------------------------------------------------
+if __name__ == "__main__":
+ NumpyTest().run()
Added: branches/maskedarray/numpy/core/ma/tests/test_mstats.py
===================================================================
--- branches/maskedarray/numpy/core/ma/tests/test_mstats.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/tests/test_mstats.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,174 @@
+# pylint: disable-msg=W0611, W0612, W0511,R0201
+"""Tests suite for maskedArray statistics.
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: test_mstats.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3473 $"
+__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
+
+import numpy
+
+import maskedarray
+from maskedarray import masked, masked_array
+
+import maskedarray.testutils
+from maskedarray.testutils import *
+
+from maskedarray.mstats import *
+
+#..............................................................................
+class TestQuantiles(NumpyTestCase):
+ "Base test class for MaskedArrays."
+ def __init__(self, *args, **kwds):
+ NumpyTestCase.__init__(self, *args, **kwds)
+ self.a = maskedarray.arange(1,101)
+ #
+ def test_1d_nomask(self):
+ "Test quantiles 1D - w/o mask."
+ a = self.a
+ assert_almost_equal(mquantiles(a, alphap=1., betap=1.),
+ [25.75, 50.5, 75.25])
+ assert_almost_equal(mquantiles(a, alphap=0, betap=1.),
+ [25., 50., 75.])
+ assert_almost_equal(mquantiles(a, alphap=0.5, betap=0.5),
+ [25.5, 50.5, 75.5])
+ assert_almost_equal(mquantiles(a, alphap=0., betap=0.),
+ [25.25, 50.5, 75.75])
+ assert_almost_equal(mquantiles(a, alphap=1./3, betap=1./3),
+ [25.41666667, 50.5, 75.5833333])
+ assert_almost_equal(mquantiles(a, alphap=3./8, betap=3./8),
+ [25.4375, 50.5, 75.5625])
+ assert_almost_equal(mquantiles(a), [25.45, 50.5, 75.55])#
+ #
+ def test_1d_mask(self):
+ "Test quantiles 1D - w/ mask."
+ a = self.a
+ a[1::2] = masked
+ assert_almost_equal(mquantiles(a, alphap=1., betap=1.),
+ [25.5, 50.0, 74.5])
+ assert_almost_equal(mquantiles(a, alphap=0, betap=1.),
+ [24., 49., 74.])
+ assert_almost_equal(mquantiles(a, alphap=0.5, betap=0.5),
+ [25., 50., 75.])
+ assert_almost_equal(mquantiles(a, alphap=0., betap=0.),
+ [24.5, 50.0, 75.5])
+ assert_almost_equal(mquantiles(a, alphap=1./3, betap=1./3),
+ [24.833333, 50.0, 75.166666])
+ assert_almost_equal(mquantiles(a, alphap=3./8, betap=3./8),
+ [24.875, 50., 75.125])
+ assert_almost_equal(mquantiles(a), [24.9, 50., 75.1])
+ #
+ def test_2d_nomask(self):
+ "Test quantiles 2D - w/o mask."
+ a = self.a
+ b = maskedarray.resize(a, (100,100))
+ assert_almost_equal(mquantiles(b), [25.45, 50.5, 75.55])
+ assert_almost_equal(mquantiles(b, axis=0), maskedarray.resize(a,(3,100)))
+ assert_almost_equal(mquantiles(b, axis=1),
+ maskedarray.resize([25.45, 50.5, 75.55], (100,3)))
+ #
+ def test_2d_mask(self):
+ "Test quantiles 2D - w/ mask."
+ a = self.a
+ a[1::2] = masked
+ b = maskedarray.resize(a, (100,100))
+ assert_almost_equal(mquantiles(b), [25., 50., 75.])
+ assert_almost_equal(mquantiles(b, axis=0), maskedarray.resize(a,(3,100)))
+ assert_almost_equal(mquantiles(b, axis=1),
+ maskedarray.resize([24.9, 50., 75.1], (100,3)))
+
+class TestMedian(NumpyTestCase):
+ def __init__(self, *args, **kwds):
+ NumpyTestCase.__init__(self, *args, **kwds)
+
+ def test_2d(self):
+ "Tests median w/ 2D"
+ (n,p) = (101,30)
+ x = masked_array(numpy.linspace(-1.,1.,n),)
+ x[:10] = x[-10:] = masked
+ z = masked_array(numpy.empty((n,p), dtype=numpy.float_))
+ z[:,0] = x[:]
+ idx = numpy.arange(len(x))
+ for i in range(1,p):
+ numpy.random.shuffle(idx)
+ z[:,i] = x[idx]
+ assert_equal(mmedian(z[:,0]), 0)
+ assert_equal(mmedian(z), numpy.zeros((p,)))
+
+ def test_3d(self):
+ "Tests median w/ 3D"
+ x = maskedarray.arange(24).reshape(3,4,2)
+ x[x%3==0] = masked
+ assert_equal(mmedian(x,0), [[12,9],[6,15],[12,9],[18,15]])
+ x.shape = (4,3,2)
+ assert_equal(mmedian(x,0),[[99,10],[11,99],[13,14]])
+ x = maskedarray.arange(24).reshape(4,3,2)
+ x[x%5==0] = masked
+ assert_equal(mmedian(x,0), [[12,10],[8,9],[16,17]])
+
+#..............................................................................
+class TestTrimming(NumpyTestCase):
+ #
+ def __init__(self, *args, **kwds):
+ NumpyTestCase.__init__(self, *args, **kwds)
+ #
+ def test_trim(self):
+ "Tests trimming."
+ x = maskedarray.arange(100)
+ assert_equal(trim_both(x).count(), 60)
+ assert_equal(trim_tail(x,tail='r').count(), 80)
+ x[50:70] = masked
+ trimx = trim_both(x)
+ assert_equal(trimx.count(), 48)
+ assert_equal(trimx._mask, [1]*16 + [0]*34 + [1]*20 + [0]*14 + [1]*16)
+ x._mask = nomask
+ x.shape = (10,10)
+ assert_equal(trim_both(x).count(), 60)
+ assert_equal(trim_tail(x).count(), 80)
+ #
+ def test_trimmedmean(self):
+ "Tests the trimmed mean."
+ data = masked_array([ 77, 87, 88,114,151,210,219,246,253,262,
+ 296,299,306,376,428,515,666,1310,2611])
+ assert_almost_equal(trimmed_mean(data,0.1), 343, 0)
+ assert_almost_equal(trimmed_mean(data,0.2), 283, 0)
+ #
+ def test_trimmed_stde(self):
+ "Tests the trimmed mean standard error."
+ data = masked_array([ 77, 87, 88,114,151,210,219,246,253,262,
+ 296,299,306,376,428,515,666,1310,2611])
+ assert_almost_equal(trimmed_stde(data,0.2), 56.1, 1)
+ #
+ def test_winsorization(self):
+ "Tests the Winsorization of the data."
+ data = masked_array([ 77, 87, 88,114,151,210,219,246,253,262,
+ 296,299,306,376,428,515,666,1310,2611])
+ assert_almost_equal(winsorize(data).varu(), 21551.4, 1)
+ data[5] = masked
+ winsorized = winsorize(data)
+ assert_equal(winsorized.mask, data.mask)
+#..............................................................................
+
+class TestMisc(NumpyTestCase):
+ def __init__(self, *args, **kwds):
+ NumpyTestCase.__init__(self, *args, **kwds)
+
+ def check_cov(self):
+ "Tests the cov function."
+ x = masked_array([[1,2,3],[4,5,6]], mask=[[1,0,0],[0,0,0]])
+ c = cov(x[0])
+ assert_equal(c, (x[0].anom()**2).sum())
+ c = cov(x[1])
+ assert_equal(c, (x[1].anom()**2).sum()/2.)
+ c = cov(x)
+ assert_equal(c[1,0], (x[0].anom()*x[1].anom()).sum())
+
+
+###############################################################################
+#------------------------------------------------------------------------------
+if __name__ == "__main__":
+ NumpyTest().run()
Added: branches/maskedarray/numpy/core/ma/tests/test_subclassing.py
===================================================================
--- branches/maskedarray/numpy/core/ma/tests/test_subclassing.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/tests/test_subclassing.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,183 @@
+# pylint: disable-msg=W0611, W0612, W0511,R0201
+"""Tests suite for MaskedArray & subclassing.
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: test_subclassing.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3473 $"
+__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
+
+import numpy as N
+import numpy.core.numeric as numeric
+
+from numpy.testing import NumpyTest, NumpyTestCase
+
+import maskedarray.testutils
+from maskedarray.testutils import *
+
+import maskedarray.core as coremodule
+from maskedarray.core import *
+
+
+class SubArray(N.ndarray):
+ """Defines a generic N.ndarray subclass, that stores some metadata
+ in the dictionary `info`."""
+ def __new__(cls,arr,info={}):
+ x = N.asanyarray(arr).view(cls)
+ x.info = info
+ return x
+ def __array_finalize__(self, obj):
+ self.info = getattr(obj,'info',{})
+ return
+ def __add__(self, other):
+ result = N.ndarray.__add__(self, other)
+ result.info.update({'added':result.info.pop('added',0)+1})
+ return result
+subarray = SubArray
+
+class MSubArray(SubArray,MaskedArray):
+ def __new__(cls, data, info=None, mask=nomask):
+ subarr = SubArray(data, info)
+ _data = MaskedArray.__new__(cls, data=subarr, mask=mask)
+ _data.info = subarr.info
+ return _data
+ def __array_finalize__(self,obj):
+ MaskedArray.__array_finalize__(self,obj)
+ SubArray.__array_finalize__(self, obj)
+ return
+ def _get_series(self):
+ _view = self.view(MaskedArray)
+ _view._sharedmask = False
+ return _view
+ _series = property(fget=_get_series)
+msubarray = MSubArray
+
+class MMatrix(MaskedArray, N.matrix,):
+ def __new__(cls, data, mask=nomask):
+ mat = N.matrix(data)
+ _data = MaskedArray.__new__(cls, data=mat, mask=mask)
+ return _data
+ def __array_finalize__(self,obj):
+ N.matrix.__array_finalize__(self, obj)
+ MaskedArray.__array_finalize__(self,obj)
+ return
+ def _get_series(self):
+ _view = self.view(MaskedArray)
+ _view._sharedmask = False
+ return _view
+ _series = property(fget=_get_series)
+mmatrix = MMatrix
+
+
+
+class TestSubclassing(NumpyTestCase):
+ """Test suite for masked subclasses of ndarray."""
+
+ def check_data_subclassing(self):
+ "Tests whether the subclass is kept."
+ x = N.arange(5)
+ m = [0,0,1,0,0]
+ xsub = SubArray(x)
+ xmsub = masked_array(xsub, mask=m)
+ assert isinstance(xmsub, MaskedArray)
+ assert_equal(xmsub._data, xsub)
+ assert isinstance(xmsub._data, SubArray)
+
+ def check_maskedarray_subclassing(self):
+ "Tests subclassing MaskedArray"
+ x = N.arange(5)
+ mx = mmatrix(x,mask=[0,1,0,0,0])
+ assert isinstance(mx._data, N.matrix)
+ "Tests masked_unary_operation"
+ assert isinstance(add(mx,mx), mmatrix)
+ assert isinstance(add(mx,x), mmatrix)
+ assert_equal(add(mx,x), mx+x)
+ assert isinstance(add(mx,mx)._data, N.matrix)
+ assert isinstance(add.outer(mx,mx), mmatrix)
+ "Tests masked_binary_operation"
+ assert isinstance(hypot(mx,mx), mmatrix)
+ assert isinstance(hypot(mx,x), mmatrix)
+
+ def check_attributepropagation(self):
+ x = array(arange(5), mask=[0]+[1]*4)
+ my = masked_array(subarray(x))
+ ym = msubarray(x)
+ #
+ z = (my+1)
+ assert isinstance(z,MaskedArray)
+ assert not isinstance(z, MSubArray)
+ assert isinstance(z._data, SubArray)
+ assert_equal(z._data.info, {})
+ #
+ z = (ym+1)
+ assert isinstance(z, MaskedArray)
+ assert isinstance(z, MSubArray)
+ assert isinstance(z._data, SubArray)
+ assert z._data.info['added'] > 0
+ #
+ ym._set_mask([1,0,0,0,1])
+ assert_equal(ym._mask, [1,0,0,0,1])
+ ym._series._set_mask([0,0,0,0,1])
+ assert_equal(ym._mask, [0,0,0,0,1])
+ #
+ xsub = subarray(x, info={'name':'x'})
+ mxsub = masked_array(xsub)
+ assert hasattr(mxsub, 'info')
+ assert_equal(mxsub.info, xsub.info)
+
+ def check_subclasspreservation(self):
+ "Checks that masked_array(...,subok=True) preserves the class."
+ x = N.arange(5)
+ m = [0,0,1,0,0]
+ xinfo = [(i,j) for (i,j) in zip(x,m)]
+ xsub = MSubArray(x, mask=m, info={'xsub':xinfo})
+ #
+ mxsub = masked_array(xsub, subok=False)
+ assert not isinstance(mxsub, MSubArray)
+ assert isinstance(mxsub, MaskedArray)
+ assert_equal(mxsub._mask, m)
+ #
+ mxsub = asarray(xsub)
+ assert not isinstance(mxsub, MSubArray)
+ assert isinstance(mxsub, MaskedArray)
+ assert_equal(mxsub._mask, m)
+ #
+ mxsub = masked_array(xsub, subok=True)
+ assert isinstance(mxsub, MSubArray)
+ assert_equal(mxsub.info, xsub.info)
+ assert_equal(mxsub._mask, xsub._mask)
+ #
+ mxsub = asanyarray(xsub)
+ assert isinstance(mxsub, MSubArray)
+ assert_equal(mxsub.info, xsub.info)
+ assert_equal(mxsub._mask, m)
+
+
+################################################################################
+if __name__ == '__main__':
+ NumpyTest().run()
+ #
+ if 0:
+ x = array(arange(5), mask=[0]+[1]*4)
+ my = masked_array(subarray(x))
+ ym = msubarray(x)
+ #
+ z = (my+1)
+ assert isinstance(z,MaskedArray)
+ assert not isinstance(z, MSubArray)
+ assert isinstance(z._data, SubArray)
+ assert_equal(z._data.info, {})
+ #
+ z = (ym+1)
+ assert isinstance(z, MaskedArray)
+ assert isinstance(z, MSubArray)
+ assert isinstance(z._data, SubArray)
+ assert z._data.info['added'] > 0
+ #
+ ym._set_mask([1,0,0,0,1])
+ assert_equal(ym._mask, [1,0,0,0,1])
+ ym._series._set_mask([0,0,0,0,1])
+ assert_equal(ym._mask, [0,0,0,0,1])
Added: branches/maskedarray/numpy/core/ma/testutils.py
===================================================================
--- branches/maskedarray/numpy/core/ma/testutils.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/testutils.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,220 @@
+"""Miscellaneous functions for testing masked arrays and subclasses
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: testutils.py 3529 2007-11-13 08:01:14Z jarrod.millman $
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = "1.0"
+__revision__ = "$Revision: 3529 $"
+__date__ = "$Date: 2007-11-13 10:01:14 +0200 (Tue, 13 Nov 2007) $"
+
+
+import numpy as N
+from numpy.core import ndarray
+from numpy.core.numerictypes import float_
+import numpy.core.umath as umath
+from numpy.testing import NumpyTest, NumpyTestCase
+from numpy.testing.utils import build_err_msg, rand
+
+import core
+from core import mask_or, getmask, getmaskarray, masked_array, nomask, masked
+from core import filled, equal, less
+
+#------------------------------------------------------------------------------
+def approx (a, b, fill_value=True, rtol=1.e-5, atol=1.e-8):
+ """Returns true if all components of a and b are equal subject to given tolerances.
+
+If fill_value is True, masked values considered equal. Otherwise, masked values
+are considered unequal.
+The relative error rtol should be positive and << 1.0
+The absolute error atol comes into play for those elements of b that are very
+small or zero; it says how small a must be also.
+ """
+ m = mask_or(getmask(a), getmask(b))
+ d1 = filled(a)
+ d2 = filled(b)
+ if d1.dtype.char == "O" or d2.dtype.char == "O":
+ return N.equal(d1,d2).ravel()
+ x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
+ y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
+ d = N.less_equal(umath.absolute(x-y), atol + rtol * umath.absolute(y))
+ return d.ravel()
+#................................................
+def _assert_equal_on_sequences(actual, desired, err_msg=''):
+ "Asserts the equality of two non-array sequences."
+ assert_equal(len(actual),len(desired),err_msg)
+ for k in range(len(desired)):
+ assert_equal(actual[k], desired[k], 'item=%r\n%s' % (k,err_msg))
+ return
+
+def assert_equal_records(a,b):
+ """Asserts that two records are equal. Pretty crude for now."""
+ assert_equal(a.dtype, b.dtype)
+ for f in a.dtype.names:
+ (af, bf) = (getattr(a,f), getattr(b,f))
+ if not (af is masked) and not (bf is masked):
+ assert_equal(getattr(a,f), getattr(b,f))
+ return
+
+def assert_equal(actual,desired,err_msg=''):
+ """Asserts that two items are equal.
+ """
+ # Case #1: dictionary .....
+ if isinstance(desired, dict):
+ assert isinstance(actual, dict), repr(type(actual))
+ assert_equal(len(actual),len(desired),err_msg)
+ for k,i in desired.items():
+ assert k in actual, repr(k)
+ assert_equal(actual[k], desired[k], 'key=%r\n%s' % (k,err_msg))
+ return
+ # Case #2: lists .....
+ if isinstance(desired, (list,tuple)) and isinstance(actual, (list,tuple)):
+ return _assert_equal_on_sequences(actual, desired, err_msg='')
+ if not (isinstance(actual, ndarray) or isinstance(desired, ndarray)):
+ msg = build_err_msg([actual, desired], err_msg,)
+ assert desired == actual, msg
+ return
+ # Case #4. arrays or equivalent
+ if ((actual is masked) and not (desired is masked)) or \
+ ((desired is masked) and not (actual is masked)):
+ msg = build_err_msg([actual, desired], err_msg, header='', names=('x', 'y'))
+ raise ValueError(msg)
+ actual = N.array(actual, copy=False, subok=True)
+ desired = N.array(desired, copy=False, subok=True)
+ if actual.dtype.char in "OS" and desired.dtype.char in "OS":
+ return _assert_equal_on_sequences(actual.tolist(),
+ desired.tolist(),
+ err_msg='')
+ return assert_array_equal(actual, desired, err_msg)
+#.............................
+def fail_if_equal(actual,desired,err_msg='',):
+ """Raises an assertion error if two items are equal.
+ """
+ if isinstance(desired, dict):
+ assert isinstance(actual, dict), repr(type(actual))
+ fail_if_equal(len(actual),len(desired),err_msg)
+ for k,i in desired.items():
+ assert k in actual, repr(k)
+ fail_if_equal(actual[k], desired[k], 'key=%r\n%s' % (k,err_msg))
+ return
+ if isinstance(desired, (list,tuple)) and isinstance(actual, (list,tuple)):
+ fail_if_equal(len(actual),len(desired),err_msg)
+ for k in range(len(desired)):
+ fail_if_equal(actual[k], desired[k], 'item=%r\n%s' % (k,err_msg))
+ return
+ if isinstance(actual, N.ndarray) or isinstance(desired, N.ndarray):
+ return fail_if_array_equal(actual, desired, err_msg)
+ msg = build_err_msg([actual, desired], err_msg)
+ assert desired != actual, msg
+assert_not_equal = fail_if_equal
+#............................
+def assert_almost_equal(actual,desired,decimal=7,err_msg=''):
+ """Asserts that two items are almost equal.
+ The test is equivalent to abs(desired-actual) < 0.5 * 10**(-decimal)
+ """
+ if isinstance(actual, N.ndarray) or isinstance(desired, N.ndarray):
+ return assert_array_almost_equal(actual, desired, decimal, err_msg)
+ msg = build_err_msg([actual, desired], err_msg)
+ assert round(abs(desired - actual),decimal) == 0, msg
+#............................
+def assert_array_compare(comparison, x, y, err_msg='', header='',
+ fill_value=True):
+ """Asserts that a comparison relation between two masked arrays is satisfied
+ elementwise."""
+ xf = filled(x)
+ yf = filled(y)
+ m = mask_or(getmask(x), getmask(y))
+
+ x = masked_array(xf, copy=False, subok=False, mask=m).filled(fill_value)
+ y = masked_array(yf, copy=False, subok=False, mask=m).filled(fill_value)
+
+ if ((x is masked) and not (y is masked)) or \
+ ((y is masked) and not (x is masked)):
+ msg = build_err_msg([x, y], err_msg, header=header, names=('x', 'y'))
+ raise ValueError(msg)
+
+ if (x.dtype.char != "O") and (x.dtype.char != "S"):
+ x = x.astype(float_)
+ if isinstance(x, N.ndarray) and x.size > 1:
+ x[N.isnan(x)] = 0
+ elif N.isnan(x):
+ x = 0
+ if (y.dtype.char != "O") and (y.dtype.char != "S"):
+ y = y.astype(float_)
+ if isinstance(y, N.ndarray) and y.size > 1:
+ y[N.isnan(y)] = 0
+ elif N.isnan(y):
+ y = 0
+ try:
+ cond = (x.shape==() or y.shape==()) or x.shape == y.shape
+ if not cond:
+ msg = build_err_msg([x, y],
+ err_msg
+ + '\n(shapes %s, %s mismatch)' % (x.shape,
+ y.shape),
+ header=header,
+ names=('x', 'y'))
+ assert cond, msg
+ val = comparison(x,y)
+ if m is not nomask and fill_value:
+ val = masked_array(val, mask=m, copy=False)
+ if isinstance(val, bool):
+ cond = val
+ reduced = [0]
+ else:
+ reduced = val.ravel()
+ cond = reduced.all()
+ reduced = reduced.tolist()
+ if not cond:
+ match = 100-100.0*reduced.count(1)/len(reduced)
+ msg = build_err_msg([x, y],
+ err_msg
+ + '\n(mismatch %s%%)' % (match,),
+ header=header,
+ names=('x', 'y'))
+ assert cond, msg
+ except ValueError:
+ msg = build_err_msg([x, y], err_msg, header=header, names=('x', 'y'))
+ raise ValueError(msg)
+#............................
+def assert_array_equal(x, y, err_msg=''):
+ """Checks the elementwise equality of two masked arrays."""
+ assert_array_compare(equal, x, y, err_msg=err_msg,
+ header='Arrays are not equal')
+##............................
+def fail_if_array_equal(x, y, err_msg=''):
+ "Raises an assertion error if two masked arrays are not equal (elementwise)."
+ def compare(x,y):
+
+ return (not N.alltrue(approx(x, y)))
+ assert_array_compare(compare, x, y, err_msg=err_msg,
+ header='Arrays are not equal')
+#............................
+def assert_array_almost_equal(x, y, decimal=6, err_msg=''):
+ """Checks the elementwise equality of two masked arrays, up to a given
+ number of decimals."""
+ def compare(x, y):
+ "Returns the result of the loose comparison between x and y)."
+ return approx(x,y, rtol=10.**-decimal)
+ assert_array_compare(compare, x, y, err_msg=err_msg,
+ header='Arrays are not almost equal')
+#............................
+def assert_array_less(x, y, err_msg=''):
+ "Checks that x is smaller than y elementwise."
+ assert_array_compare(less, x, y, err_msg=err_msg,
+ header='Arrays are not less-ordered')
+#............................
+assert_close = assert_almost_equal
+#............................
+def assert_mask_equal(m1, m2):
+ """Asserts the equality of two masks."""
+ if m1 is nomask:
+ assert(m2 is nomask)
+ if m2 is nomask:
+ assert(m1 is nomask)
+ assert_array_equal(m1, m2)
+
+if __name__ == '__main__':
+ a = 12
+ assert_equal(a, masked)
Added: branches/maskedarray/numpy/core/ma/timer_comparison.py
===================================================================
--- branches/maskedarray/numpy/core/ma/timer_comparison.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/timer_comparison.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,461 @@
+
+import timeit
+
+import numpy
+from numpy import int_, float_, bool_
+import numpy.core.fromnumeric as fromnumeric
+
+from numpy.testing.utils import build_err_msg, rand
+
+
+numpy.seterr(all='ignore')
+
+pi = numpy.pi
+
+class moduletester:
+ #-----------------------------------
+ def __init__(self, module):
+ self.module = module
+ self.allequal = module.allequal
+ self.arange = module.arange
+ self.array = module.array
+# self.average = module.average
+ self.concatenate = module.concatenate
+ self.count = module.count
+ self.equal = module.equal
+ self.filled = module.filled
+ self.getmask = module.getmask
+ self.getmaskarray = module.getmaskarray
+ self.id = id
+ self.inner = module.inner
+ self.make_mask = module.make_mask
+ self.masked = module.masked
+ self.masked_array = module.masked_array
+ self.masked_values = module.masked_values
+ self.mask_or = module.mask_or
+ self.nomask = module.nomask
+ self.ones = module.ones
+ self.outer = module.outer
+ self.repeat = module.repeat
+ self.resize = module.resize
+ self.sort = module.sort
+ self.take = module.take
+ self.transpose = module.transpose
+ self.zeros = module.zeros
+ self.MaskType = module.MaskType
+ try:
+ self.umath = module.umath
+ except AttributeError:
+ self.umath = module.core.umath
+ self.testnames = []
+ #........................
+ def assert_array_compare(self, comparison, x, y, err_msg='', header='',
+ fill_value=True):
+ """Asserts that a comparison relation between two masked arrays is satisfied
+ elementwise."""
+ xf = self.filled(x)
+ yf = self.filled(y)
+ m = self.mask_or(self.getmask(x), self.getmask(y))
+
+ x = self.filled(self.masked_array(xf, mask=m), fill_value)
+ y = self.filled(self.masked_array(yf, mask=m), fill_value)
+ if (x.dtype.char != "O"):
+ x = x.astype(float_)
+ if isinstance(x, numpy.ndarray) and x.size > 1:
+ x[numpy.isnan(x)] = 0
+ elif numpy.isnan(x):
+ x = 0
+ if (y.dtype.char != "O"):
+ y = y.astype(float_)
+ if isinstance(y, numpy.ndarray) and y.size > 1:
+ y[numpy.isnan(y)] = 0
+ elif numpy.isnan(y):
+ y = 0
+ try:
+ cond = (x.shape==() or y.shape==()) or x.shape == y.shape
+ if not cond:
+ msg = build_err_msg([x, y],
+ err_msg
+ + '\n(shapes %s, %s mismatch)' % (x.shape,
+ y.shape),
+ header=header,
+ names=('x', 'y'))
+ assert cond, msg
+ val = comparison(x,y)
+ if m is not self.nomask and fill_value:
+ val = self.masked_array(val, mask=m)
+ if isinstance(val, bool):
+ cond = val
+ reduced = [0]
+ else:
+ reduced = val.ravel()
+ cond = reduced.all()
+ reduced = reduced.tolist()
+ if not cond:
+ match = 100-100.0*reduced.count(1)/len(reduced)
+ msg = build_err_msg([x, y],
+ err_msg
+ + '\n(mismatch %s%%)' % (match,),
+ header=header,
+ names=('x', 'y'))
+ assert cond, msg
+ except ValueError:
+ msg = build_err_msg([x, y], err_msg, header=header, names=('x', 'y'))
+ raise ValueError(msg)
+ #............................
+ def assert_array_equal(self, x, y, err_msg=''):
+ """Checks the elementwise equality of two masked arrays."""
+ self.assert_array_compare(self.equal, x, y, err_msg=err_msg,
+ header='Arrays are not equal')
+ #----------------------------------
+ def test_0(self):
+ "Tests creation"
+ x = numpy.array([1.,1.,1.,-2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
+ m = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
+ xm = self.masked_array(x, mask=m)
+ xm[0]
+ #----------------------------------
+ def test_1(self):
+ "Tests creation"
+ x = numpy.array([1.,1.,1.,-2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
+ y = numpy.array([5.,0.,3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
+ a10 = 10.
+ m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
+ m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0 ,0, 1]
+ xm = self.masked_array(x, mask=m1)
+ ym = self.masked_array(y, mask=m2)
+ z = numpy.array([-.5, 0., .5, .8])
+ zm = self.masked_array(z, mask=[0,1,0,0])
+ xf = numpy.where(m1, 1.e+20, x)
+ xm.set_fill_value(1.e+20)
+ #.....
+ assert((xm-ym).filled(0).any())
+ #fail_if_equal(xm.mask.astype(int_), ym.mask.astype(int_))
+ s = x.shape
+ assert(xm.size == reduce(lambda x,y:x*y, s))
+ assert(self.count(xm) == len(m1) - reduce(lambda x,y:x+y, m1))
+ #.....
+ for s in [(4,3), (6,2)]:
+ x.shape = s
+ y.shape = s
+ xm.shape = s
+ ym.shape = s
+ xf.shape = s
+
+ assert(self.count(xm) == len(m1) - reduce(lambda x,y:x+y, m1))
+ #----------------------------------
+ def test_2(self):
+ "Tests conversions and indexing"
+ x1 = numpy.array([1,2,4,3])
+ x2 = self.array(x1, mask=[1,0,0,0])
+ x3 = self.array(x1, mask=[0,1,0,1])
+ x4 = self.array(x1)
+ # test conversion to strings
+ junk, garbage = str(x2), repr(x2)
+# assert_equal(numpy.sort(x1), self.sort(x2, fill_value=0))
+ # tests of indexing
+ assert type(x2[1]) is type(x1[1])
+ assert x1[1] == x2[1]
+# assert self.allequal(x1[2],x2[2])
+# assert self.allequal(x1[2:5],x2[2:5])
+# assert self.allequal(x1[:],x2[:])
+# assert self.allequal(x1[1:], x3[1:])
+ x1[2] = 9
+ x2[2] = 9
+ self.assert_array_equal(x1,x2)
+ x1[1:3] = 99
+ x2[1:3] = 99
+# assert self.allequal(x1,x2)
+ x2[1] = self.masked
+# assert self.allequal(x1,x2)
+ x2[1:3] = self.masked
+# assert self.allequal(x1,x2)
+ x2[:] = x1
+ x2[1] = self.masked
+# assert self.allequal(self.getmask(x2),self.array([0,1,0,0]))
+ x3[:] = self.masked_array([1,2,3,4],[0,1,1,0])
+# assert self.allequal(self.getmask(x3), self.array([0,1,1,0]))
+ x4[:] = self.masked_array([1,2,3,4],[0,1,1,0])
+# assert self.allequal(self.getmask(x4), self.array([0,1,1,0]))
+# assert self.allequal(x4, self.array([1,2,3,4]))
+ x1 = numpy.arange(5)*1.0
+ x2 = self.masked_values(x1, 3.0)
+# assert self.allequal(x1,x2)
+# assert self.allequal(self.array([0,0,0,1,0], self.MaskType), x2.mask)
+ x1 = self.array([1,'hello',2,3],object)
+ x2 = numpy.array([1,'hello',2,3],object)
+ s1 = x1[1]
+ s2 = x2[1]
+ assert x1[1:1].shape == (0,)
+ # Tests copy-size
+ n = [0,0,1,0,0]
+ m = self.make_mask(n)
+ m2 = self.make_mask(m)
+ assert(m is m2)
+ m3 = self.make_mask(m, copy=1)
+ assert(m is not m3)
+
+ #----------------------------------
+ def test_3(self):
+ "Tests resize/repeat"
+ x4 = self.arange(4)
+ x4[2] = self.masked
+ y4 = self.resize(x4, (8,))
+ assert self.allequal(self.concatenate([x4,x4]), y4)
+ assert self.allequal(self.getmask(y4),[0,0,1,0,0,0,1,0])
+ y5 = self.repeat(x4, (2,2,2,2), axis=0)
+ self.assert_array_equal(y5, [0,0,1,1,2,2,3,3])
+ y6 = self.repeat(x4, 2, axis=0)
+ assert self.allequal(y5, y6)
+ y7 = x4.repeat((2,2,2,2), axis=0)
+ assert self.allequal(y5,y7)
+ y8 = x4.repeat(2,0)
+ assert self.allequal(y5,y8)
+
+ #----------------------------------
+ def test_4(self):
+ "Test of take, transpose, inner, outer products"
+ x = self.arange(24)
+ y = numpy.arange(24)
+ x[5:6] = self.masked
+ x = x.reshape(2,3,4)
+ y = y.reshape(2,3,4)
+ assert self.allequal(numpy.transpose(y,(2,0,1)), self.transpose(x,(2,0,1)))
+ assert self.allequal(numpy.take(y, (2,0,1), 1), self.take(x, (2,0,1), 1))
+ assert self.allequal(numpy.inner(self.filled(x,0), self.filled(y,0)),
+ self.inner(x, y))
+ assert self.allequal(numpy.outer(self.filled(x,0), self.filled(y,0)),
+ self.outer(x, y))
+ y = self.array(['abc', 1, 'def', 2, 3], object)
+ y[2] = self.masked
+ t = self.take(y,[0,3,4])
+ assert t[0] == 'abc'
+ assert t[1] == 2
+ assert t[2] == 3
+ #----------------------------------
+ def test_5(self):
+ "Tests inplace w/ scalar"
+
+ x = self.arange(10)
+ y = self.arange(10)
+ xm = self.arange(10)
+ xm[2] = self.masked
+ x += 1
+ assert self.allequal(x, y+1)
+ xm += 1
+ assert self.allequal(xm, y+1)
+
+ x = self.arange(10)
+ xm = self.arange(10)
+ xm[2] = self.masked
+ x -= 1
+ assert self.allequal(x, y-1)
+ xm -= 1
+ assert self.allequal(xm, y-1)
+
+ x = self.arange(10)*1.0
+ xm = self.arange(10)*1.0
+ xm[2] = self.masked
+ x *= 2.0
+ assert self.allequal(x, y*2)
+ xm *= 2.0
+ assert self.allequal(xm, y*2)
+
+ x = self.arange(10)*2
+ xm = self.arange(10)*2
+ xm[2] = self.masked
+ x /= 2
+ assert self.allequal(x, y)
+ xm /= 2
+ assert self.allequal(xm, y)
+
+ x = self.arange(10)*1.0
+ xm = self.arange(10)*1.0
+ xm[2] = self.masked
+ x /= 2.0
+ assert self.allequal(x, y/2.0)
+ xm /= self.arange(10)
+ self.assert_array_equal(xm, self.ones((10,)))
+
+ x = self.arange(10).astype(float_)
+ xm = self.arange(10)
+ xm[2] = self.masked
+ id1 = self.id(x.raw_data())
+ x += 1.
+ #assert id1 == self.id(x.raw_data())
+ assert self.allequal(x, y+1.)
+
+
+ def test_6(self):
+ "Tests inplace w/ array"
+
+ x = self.arange(10, dtype=float_)
+ y = self.arange(10)
+ xm = self.arange(10, dtype=float_)
+ xm[2] = self.masked
+ m = xm.mask
+ a = self.arange(10, dtype=float_)
+ a[-1] = self.masked
+ x += a
+ xm += a
+ assert self.allequal(x,y+a)
+ assert self.allequal(xm,y+a)
+ assert self.allequal(xm.mask, self.mask_or(m,a.mask))
+
+ x = self.arange(10, dtype=float_)
+ xm = self.arange(10, dtype=float_)
+ xm[2] = self.masked
+ m = xm.mask
+ a = self.arange(10, dtype=float_)
+ a[-1] = self.masked
+ x -= a
+ xm -= a
+ assert self.allequal(x,y-a)
+ assert self.allequal(xm,y-a)
+ assert self.allequal(xm.mask, self.mask_or(m,a.mask))
+
+ x = self.arange(10, dtype=float_)
+ xm = self.arange(10, dtype=float_)
+ xm[2] = self.masked
+ m = xm.mask
+ a = self.arange(10, dtype=float_)
+ a[-1] = self.masked
+ x *= a
+ xm *= a
+ assert self.allequal(x,y*a)
+ assert self.allequal(xm,y*a)
+ assert self.allequal(xm.mask, self.mask_or(m,a.mask))
+
+ x = self.arange(10, dtype=float_)
+ xm = self.arange(10, dtype=float_)
+ xm[2] = self.masked
+ m = xm.mask
+ a = self.arange(10, dtype=float_)
+ a[-1] = self.masked
+ x /= a
+ xm /= a
+
+ #----------------------------------
+ def test_7(self):
+ "Tests ufunc"
+ d = (self.array([1.0, 0, -1, pi/2]*2, mask=[0,1]+[0]*6),
+ self.array([1.0, 0, -1, pi/2]*2, mask=[1,0]+[0]*6),)
+ for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
+# 'sin', 'cos', 'tan',
+# 'arcsin', 'arccos', 'arctan',
+# 'sinh', 'cosh', 'tanh',
+# 'arcsinh',
+# 'arccosh',
+# 'arctanh',
+# 'absolute', 'fabs', 'negative',
+# # 'nonzero', 'around',
+# 'floor', 'ceil',
+# # 'sometrue', 'alltrue',
+# 'logical_not',
+# 'add', 'subtract', 'multiply',
+# 'divide', 'true_divide', 'floor_divide',
+# 'remainder', 'fmod', 'hypot', 'arctan2',
+# 'equal', 'not_equal', 'less_equal', 'greater_equal',
+# 'less', 'greater',
+# 'logical_and', 'logical_or', 'logical_xor',
+ ]:
+ #print f
+ try:
+ uf = getattr(self.umath, f)
+ except AttributeError:
+ uf = getattr(fromnumeric, f)
+ mf = getattr(self.module, f)
+ args = d[:uf.nin]
+ ur = uf(*args)
+ mr = mf(*args)
+ self.assert_array_equal(ur.filled(0), mr.filled(0), f)
+ self.assert_array_equal(ur._mask, mr._mask)
+
+ #----------------------------------
+ def test_99(self):
+ # test average
+ ott = self.array([0.,1.,2.,3.], mask=[1,0,0,0])
+ self.assert_array_equal(2.0, self.average(ott,axis=0))
+ self.assert_array_equal(2.0, self.average(ott, weights=[1., 1., 2., 1.]))
+ result, wts = self.average(ott, weights=[1.,1.,2.,1.], returned=1)
+ self.assert_array_equal(2.0, result)
+ assert(wts == 4.0)
+ ott[:] = self.masked
+ assert(self.average(ott,axis=0) is self.masked)
+ ott = self.array([0.,1.,2.,3.], mask=[1,0,0,0])
+ ott = ott.reshape(2,2)
+ ott[:,1] = self.masked
+ self.assert_array_equal(self.average(ott,axis=0), [2.0, 0.0])
+ assert(self.average(ott,axis=1)[0] is self.masked)
+ self.assert_array_equal([2.,0.], self.average(ott, axis=0))
+ result, wts = self.average(ott, axis=0, returned=1)
+ self.assert_array_equal(wts, [1., 0.])
+ w1 = [0,1,1,1,1,0]
+ w2 = [[0,1,1,1,1,0],[1,0,0,0,0,1]]
+ x = self.arange(6)
+ self.assert_array_equal(self.average(x, axis=0), 2.5)
+ self.assert_array_equal(self.average(x, axis=0, weights=w1), 2.5)
+ y = self.array([self.arange(6), 2.0*self.arange(6)])
+ self.assert_array_equal(self.average(y, None), numpy.add.reduce(numpy.arange(6))*3./12.)
+ self.assert_array_equal(self.average(y, axis=0), numpy.arange(6) * 3./2.)
+ self.assert_array_equal(self.average(y, axis=1), [self.average(x,axis=0), self.average(x,axis=0) * 2.0])
+ self.assert_array_equal(self.average(y, None, weights=w2), 20./6.)
+ self.assert_array_equal(self.average(y, axis=0, weights=w2), [0.,1.,2.,3.,4.,10.])
+ self.assert_array_equal(self.average(y, axis=1), [self.average(x,axis=0), self.average(x,axis=0) * 2.0])
+ m1 = self.zeros(6)
+ m2 = [0,0,1,1,0,0]
+ m3 = [[0,0,1,1,0,0],[0,1,1,1,1,0]]
+ m4 = self.ones(6)
+ m5 = [0, 1, 1, 1, 1, 1]
+ self.assert_array_equal(self.average(self.masked_array(x, m1),axis=0), 2.5)
+ self.assert_array_equal(self.average(self.masked_array(x, m2),axis=0), 2.5)
+ # assert(self.average(masked_array(x, m4),axis=0) is masked)
+ self.assert_array_equal(self.average(self.masked_array(x, m5),axis=0), 0.0)
+ self.assert_array_equal(self.count(self.average(self.masked_array(x, m4),axis=0)), 0)
+ z = self.masked_array(y, m3)
+ self.assert_array_equal(self.average(z, None), 20./6.)
+ self.assert_array_equal(self.average(z, axis=0), [0.,1.,99.,99.,4.0, 7.5])
+ self.assert_array_equal(self.average(z, axis=1), [2.5, 5.0])
+ self.assert_array_equal(self.average(z,axis=0, weights=w2), [0.,1., 99., 99., 4.0, 10.0])
+ #------------------------
+ def test_A(self):
+ x = self.arange(24)
+ y = numpy.arange(24)
+ x[5:6] = self.masked
+ x = x.reshape(2,3,4)
+
+
+################################################################################
+if __name__ == '__main__':
+
+ setup_base = "from __main__ import moduletester \n"\
+ "import numpy\n" \
+ "tester = moduletester(module)\n"
+ setup_old = "import numpy.core.ma as module\n"+setup_base
+ setup_new = "import maskedarray.core_ini as module\n"+setup_base
+ setup_cur = "import maskedarray.core as module\n"+setup_base
+# setup_alt = "import maskedarray.core_alt as module\n"+setup_base
+# setup_tmp = "import maskedarray.core_tmp as module\n"+setup_base
+
+ (nrepeat, nloop) = (10, 10)
+
+ if 1:
+ for i in range(1,8):
+ func = 'tester.test_%i()' % i
+ old = timeit.Timer(func, setup_old).repeat(nrepeat, nloop*10)
+ new = timeit.Timer(func, setup_new).repeat(nrepeat, nloop*10)
+ cur = timeit.Timer(func, setup_cur).repeat(nrepeat, nloop*10)
+# alt = timeit.Timer(func, setup_alt).repeat(nrepeat, nloop*10)
+# tmp = timeit.Timer(func, setup_tmp).repeat(nrepeat, nloop*10)
+ old = numpy.sort(old)
+ new = numpy.sort(new)
+ cur = numpy.sort(cur)
+# alt = numpy.sort(alt)
+# tmp = numpy.sort(tmp)
+ print "#%i" % i +50*'.'
+ print eval("moduletester.test_%i.__doc__" % i)
+ print "numpy.core.ma: %.3f - %.3f" % (old[0], old[1])
+ print "core_ini : %.3f - %.3f" % (new[0], new[1])
+ print "core_current : %.3f - %.3f" % (cur[0], cur[1])
+# print "core_alt : %.3f - %.3f" % (alt[0], alt[1])
+# print "core_tmp : %.3f - %.3f" % (tmp[0], tmp[1])
Added: branches/maskedarray/numpy/core/ma/version.py
===================================================================
--- branches/maskedarray/numpy/core/ma/version.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma/version.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -0,0 +1,11 @@
+"""Version number"""
+
+version = '1.00'
+release = False
+
+if not release:
+ import core
+ import extras
+ revision = [core.__revision__.split(':')[-1][:-1].strip(),
+ extras.__revision__.split(':')[-1][:-1].strip(),]
+ version += '.dev%04i' % max([int(rev) for rev in revision])
Deleted: branches/maskedarray/numpy/core/ma.py
===================================================================
--- branches/maskedarray/numpy/core/ma.py 2007-12-14 22:19:54 UTC (rev 4575)
+++ branches/maskedarray/numpy/core/ma.py 2007-12-15 00:38:47 UTC (rev 4576)
@@ -1,2255 +0,0 @@
-"""MA: a facility for dealing with missing observations
-MA is generally used as a numpy.array look-alike.
-by Paul F. Dubois.
-
-Copyright 1999, 2000, 2001 Regents of the University of California.
-Released for unlimited redistribution.
-Adapted for numpy_core 2005 by Travis Oliphant and
-(mainly) Paul Dubois.
-"""
-import types, sys
-
-import umath
-import fromnumeric
-from numeric import newaxis, ndarray, inf
-from fromnumeric import amax, amin
-from numerictypes import bool_, typecodes
-import numeric
-import warnings
-
-# Ufunc domain lookup for __array_wrap__
-ufunc_domain = {}
-# Ufunc fills lookup for __array__
-ufunc_fills = {}
-
-MaskType = bool_
-nomask = MaskType(0)
-divide_tolerance = 1.e-35
-
-class MAError (Exception):
- def __init__ (self, args=None):
- "Create an exception"
-
- # The .args attribute must be a tuple.
- if not isinstance(args, tuple):
- args = (args,)
- self.args = args
- def __str__(self):
- "Calculate the string representation"
- return str(self.args[0])
- __repr__ = __str__
-
-class _MaskedPrintOption:
- "One instance of this class, masked_print_option, is created."
- def __init__ (self, display):
- "Create the masked print option object."
- self.set_display(display)
- self._enabled = 1
-
- def display (self):
- "Show what prints for masked values."
- return self._display
-
- def set_display (self, s):
- "set_display(s) sets what prints for masked values."
- self._display = s
-
- def enabled (self):
- "Is the use of the display value enabled?"
- return self._enabled
-
- def enable(self, flag=1):
- "Set the enabling flag to flag."
- self._enabled = flag
-
- def __str__ (self):
- return str(self._display)
-
- __repr__ = __str__
-
-#if you single index into a masked location you get this object.
-masked_print_option = _MaskedPrintOption('--')
-
-# Use single element arrays or scalars.
-default_real_fill_value = 1.e20
-default_complex_fill_value = 1.e20 + 0.0j
-default_character_fill_value = '-'
-default_integer_fill_value = 999999
-default_object_fill_value = '?'
-
-def default_fill_value (obj):
- "Function to calculate default fill value for an object."
- if isinstance(obj, types.FloatType):
- return default_real_fill_value
- elif isinstance(obj, types.IntType) or isinstance(obj, types.LongType):
- return default_integer_fill_value
- elif isinstance(obj, types.StringType):
- return default_character_fill_value
- elif isinstance(obj, types.ComplexType):
- return default_complex_fill_value
- elif isinstance(obj, MaskedArray) or isinstance(obj, ndarray):
- x = obj.dtype.char
- if x in typecodes['Float']:
- return default_real_fill_value
- if x in typecodes['Integer']:
- return default_integer_fill_value
- if x in typecodes['Complex']:
- return default_complex_fill_value
- if x in typecodes['Character']:
- return default_character_fill_value
- if x in typecodes['UnsignedInteger']:
- return umath.absolute(default_integer_fill_value)
- return default_object_fill_value
- else:
- return default_object_fill_value
-
-def minimum_fill_value (obj):
- "Function to calculate default fill value suitable for taking minima."
- if isinstance(obj, types.FloatType):
- return numeric.inf
- elif isinstance(obj, types.IntType) or isinstance(obj, types.LongType):
- return sys.maxint
- elif isinstance(obj, MaskedArray) or isinstance(obj, ndarray):
- x = obj.dtype.char
- if x in typecodes['Float']:
- return numeric.inf
- if x in typecodes['Integer']:
- return sys.maxint
- if x in typecodes['UnsignedInteger']:
- return sys.maxint
- else:
- raise TypeError, 'Unsuitable type for calculating minimum.'
-
-def maximum_fill_value (obj):
- "Function to calculate default fill value suitable for taking maxima."
- if isinstance(obj, types.FloatType):
- return -inf
- elif isinstance(obj, types.IntType) or isinstance(obj, types.LongType):
- return -sys.maxint
- elif isinstance(obj, MaskedArray) or isinstance(obj, ndarray):
- x = obj.dtype.char
- if x in typecodes['Float']:
- return -inf
- if x in typecodes['Integer']:
- return -sys.maxint
- if x in typecodes['UnsignedInteger']:
- return 0
- else:
- raise TypeError, 'Unsuitable type for calculating maximum.'
-
-def set_fill_value (a, fill_value):
- "Set fill value of a if it is a masked array."
- if isMaskedArray(a):
- a.set_fill_value (fill_value)
-
-def getmask (a):
- """Mask of values in a; could be nomask.
- Returns nomask if a is not a masked array.
- To get an array for sure use getmaskarray."""
- if isinstance(a, MaskedArray):
- return a.raw_mask()
- else:
- return nomask
-
-def getmaskarray (a):
- """Mask of values in a; an array of zeros if mask is nomask
- or not a masked array, and is a byte-sized integer.
- Do not try to add up entries, for example.
- """
- m = getmask(a)
- if m is nomask:
- return make_mask_none(shape(a))
- else:
- return m
-
-def is_mask (m):
- """Is m a legal mask? Does not check contents, only type.
- """
- try:
- return m.dtype.type is MaskType
- except AttributeError:
- return False
-
-def make_mask (m, copy=0, flag=0):
- """make_mask(m, copy=0, flag=0)
- return m as a mask, creating a copy if necessary or requested.
- Can accept any sequence of integers or nomask. Does not check
- that contents must be 0s and 1s.
- if flag, return nomask if m contains no true elements.
- """
- if m is nomask:
- return nomask
- elif isinstance(m, ndarray):
- if m.dtype.type is MaskType:
- if copy:
- result = numeric.array(m, dtype=MaskType, copy=copy)
- else:
- result = m
- else:
- result = m.astype(MaskType)
- else:
- result = filled(m, True).astype(MaskType)
-
- if flag and not fromnumeric.sometrue(fromnumeric.ravel(result)):
- return nomask
- else:
- return result
-
-def make_mask_none (s):
- "Return a mask of all zeros of shape s."
- result = numeric.zeros(s, dtype=MaskType)
- result.shape = s
- return result
-
-def mask_or (m1, m2):
- """Logical or of the mask candidates m1 and m2, treating nomask as false.
- Result may equal m1 or m2 if the other is nomask.
- """
- if m1 is nomask: return make_mask(m2)
- if m2 is nomask: return make_mask(m1)
- if m1 is m2 and is_mask(m1): return m1
- return make_mask(umath.logical_or(m1, m2))
-
-def filled (a, value = None):
- """a as a contiguous numeric array with any masked areas replaced by value
- if value is None or the special element "masked", get_fill_value(a)
- is used instead.
-
- If a is already a contiguous numeric array, a itself is returned.
-
- filled(a) can be used to be sure that the result is numeric when
- passing an object a to other software ignorant of MA, in particular to
- numeric itself.
- """
- if isinstance(a, MaskedArray):
- return a.filled(value)
- elif isinstance(a, ndarray) and a.flags['CONTIGUOUS']:
- return a
- elif isinstance(a, types.DictType):
- return numeric.array(a, 'O')
- else:
- return numeric.array(a)
-
-def get_fill_value (a):
- """
- The fill value of a, if it has one; otherwise, the default fill value
- for that type.
- """
- if isMaskedArray(a):
- result = a.fill_value()
- else:
- result = default_fill_value(a)
- return result
-
-def common_fill_value (a, b):
- "The common fill_value of a and b, if there is one, or None"
- t1 = get_fill_value(a)
- t2 = get_fill_value(b)
- if t1 == t2: return t1
- return None
-
-# Domain functions return 1 where the argument(s) are not in the domain.
-class domain_check_interval:
- "domain_check_interval(a,b)(x) = true where x < a or y > b"
- def __init__(self, y1, y2):
- "domain_check_interval(a,b)(x) = true where x < a or y > b"
- self.y1 = y1
- self.y2 = y2
-
- def __call__ (self, x):
- "Execute the call behavior."
- return umath.logical_or(umath.greater (x, self.y2),
- umath.less(x, self.y1)
- )
-
-class domain_tan:
- "domain_tan(eps) = true where abs(cos(x)) < eps)"
- def __init__(self, eps):
- "domain_tan(eps) = true where abs(cos(x)) < eps)"
- self.eps = eps
-
- def __call__ (self, x):
- "Execute the call behavior."
- return umath.less(umath.absolute(umath.cos(x)), self.eps)
-
-class domain_greater:
- "domain_greater(v)(x) = true where x <= v"
- def __init__(self, critical_value):
- "domain_greater(v)(x) = true where x <= v"
- self.critical_value = critical_value
-
- def __call__ (self, x):
- "Execute the call behavior."
- return umath.less_equal (x, self.critical_value)
-
-class domain_greater_equal:
- "domain_greater_equal(v)(x) = true where x < v"
- def __init__(self, critical_value):
- "domain_greater_equal(v)(x) = true where x < v"
- self.critical_value = critical_value
-
- def __call__ (self, x):
- "Execute the call behavior."
- return umath.less (x, self.critical_value)
-
-class masked_unary_operation:
- def __init__ (self, aufunc, fill=0, domain=None):
- """ masked_unary_operation(aufunc, fill=0, domain=None)
- aufunc(fill) must be defined
- self(x) returns aufunc(x)
- with masked values where domain(x) is true or getmask(x) is true.
- """
- self.f = aufunc
- self.fill = fill
- self.domain = domain
- self.__doc__ = getattr(aufunc, "__doc__", str(aufunc))
- self.__name__ = getattr(aufunc, "__name__", str(aufunc))
- ufunc_domain[aufunc] = domain
- ufunc_fills[aufunc] = fill,
-
- def __call__ (self, a, *args, **kwargs):
- "Execute the call behavior."
-# numeric tries to return scalars rather than arrays when given scalars.
- m = getmask(a)
- d1 = filled(a, self.fill)
- if self.domain is not None:
- m = mask_or(m, self.domain(d1))
- result = self.f(d1, *args, **kwargs)
- return masked_array(result, m)
-
- def __str__ (self):
- return "Masked version of " + str(self.f)
-
-
-class domain_safe_divide:
- def __init__ (self, tolerance=divide_tolerance):
- self.tolerance = tolerance
- def __call__ (self, a, b):
- return umath.absolute(a) * self.tolerance >= umath.absolute(b)
-
-class domained_binary_operation:
- """Binary operations that have a domain, like divide. These are complicated
- so they are a separate class. They have no reduce, outer or accumulate.
- """
- def __init__ (self, abfunc, domain, fillx=0, filly=0):
- """abfunc(fillx, filly) must be defined.
- abfunc(x, filly) = x for all x to enable reduce.
- """
- self.f = abfunc
- self.domain = domain
- self.fillx = fillx
- self.filly = filly
- self.__doc__ = getattr(abfunc, "__doc__", str(abfunc))
- self.__name__ = getattr(abfunc, "__name__", str(abfunc))
- ufunc_domain[abfunc] = domain
- ufunc_fills[abfunc] = fillx, filly
-
- def __call__(self, a, b):
- "Execute the call behavior."
- ma = getmask(a)
- mb = getmask(b)
- d1 = filled(a, self.fillx)
- d2 = filled(b, self.filly)
- t = self.domain(d1, d2)
-
- if fromnumeric.sometrue(t, None):
- d2 = where(t, self.filly, d2)
- mb = mask_or(mb, t)
- m = mask_or(ma, mb)
- result = self.f(d1, d2)
- return masked_array(result, m)
-
- def __str__ (self):
- return "Masked version of " + str(self.f)
-
-class masked_binary_operation:
- def __init__ (self, abfunc, fillx=0, filly=0):
- """abfunc(fillx, filly) must be defined.
- abfunc(x, filly) = x for all x to enable reduce.
- """
- self.f = abfunc
- self.fillx = fillx
- self.filly = filly
- self.__doc__ = getattr(abfunc, "__doc__", str(abfunc))
- ufunc_domain[abfunc] = None
- ufunc_fills[abfunc] = fillx, filly
-
- def __call__ (self, a, b, *args, **kwargs):
- "Execute the call behavior."
- m = mask_or(getmask(a), getmask(b))
- d1 = filled(a, self.fillx)
- d2 = filled(b, self.filly)
- result = self.f(d1, d2, *args, **kwargs)
- if isinstance(result, ndarray) \
- and m.ndim != 0 \
- and m.shape != result.shape:
- m = mask_or(getmaskarray(a), getmaskarray(b))
- return masked_array(result, m)
-
- def reduce (self, target, axis=0, dtype=None):
- """Reduce target along the given axis with this function."""
- m = getmask(target)
- t = filled(target, self.filly)
- if t.shape == ():
- t = t.reshape(1)
- if m is not nomask:
- m = make_mask(m, copy=1)
- m.shape = (1,)
- if m is nomask:
- t = self.f.reduce(t, axis)
- else:
- t = masked_array (t, m)
- # XXX: "or t.dtype" below is a workaround for what appears
- # XXX: to be a bug in reduce.
- t = self.f.reduce(filled(t, self.filly), axis,
- dtype=dtype or t.dtype)
- m = umath.logical_and.reduce(m, axis)
- if isinstance(t, ndarray):
- return masked_array(t, m, get_fill_value(target))
- elif m:
- return masked
- else:
- return t
-
- def outer (self, a, b):
- "Return the function applied to the outer product of a and b."
- ma = getmask(a)
- mb = getmask(b)
- if ma is nomask and mb is nomask:
- m = nomask
- else:
- ma = getmaskarray(a)
- mb = getmaskarray(b)
- m = logical_or.outer(ma, mb)
- d = self.f.outer(filled(a, self.fillx), filled(b, self.filly))
- return masked_array(d, m)
-
- def accumulate (self, target, axis=0):
- """Accumulate target along axis after filling with y fill value."""
- t = filled(target, self.filly)
- return masked_array (self.f.accumulate (t, axis))
- def __str__ (self):
- return "Masked version of " + str(self.f)
-
-sqrt = masked_unary_operation(umath.sqrt, 0.0, domain_greater_equal(0.0))
-log = masked_unary_operation(umath.log, 1.0, domain_greater(0.0))
-log10 = masked_unary_operation(umath.log10, 1.0, domain_greater(0.0))
-exp = masked_unary_operation(umath.exp)
-conjugate = masked_unary_operation(umath.conjugate)
-sin = masked_unary_operation(umath.sin)
-cos = masked_unary_operation(umath.cos)
-tan = masked_unary_operation(umath.tan, 0.0, domain_tan(1.e-35))
-arcsin = masked_unary_operation(umath.arcsin, 0.0, domain_check_interval(-1.0, 1.0))
-arccos = masked_unary_operation(umath.arccos, 0.0, domain_check_interval(-1.0, 1.0))
-arctan = masked_unary_operation(umath.arctan)
-# Missing from numeric
-arcsinh = masked_unary_operation(umath.arcsinh)
-arccosh = masked_unary_operation(umath.arccosh, 1.0, domain_greater_equal(1.0))
-arctanh = masked_unary_operation(umath.arctanh, 0.0, domain_check_interval(-1.0+1e-15, 1.0-1e-15))
-sinh = masked_unary_operation(umath.sinh)
-cosh = masked_unary_operation(umath.cosh)
-tanh = masked_unary_operation(umath.tanh)
-absolute = masked_unary_operation(umath.absolute)
-fabs = masked_unary_operation(umath.fabs)
-negative = masked_unary_operation(umath.negative)
-
-def nonzero(a):
- """returns the indices of the elements of a which are not zero
- and not masked
- """
- return numeric.asarray(filled(a, 0).nonzero())
-
-around = masked_unary_operation(fromnumeric.round_)
-floor = masked_unary_operation(umath.floor)
-ceil = masked_unary_operation(umath.ceil)
-logical_not = masked_unary_operation(umath.logical_not)
-
-add = masked_binary_operation(umath.add)
-subtract = masked_binary_operation(umath.subtract)
-subtract.reduce = None
-multiply = masked_binary_operation(umath.multiply, 1, 1)
-divide = domained_binary_operation(umath.divide, domain_safe_divide(), 0, 1)
-true_divide = domained_binary_operation(umath.true_divide, domain_safe_divide(), 0, 1)
-floor_divide = domained_binary_operation(umath.floor_divide, domain_safe_divide(), 0, 1)
-remainder = domained_binary_operation(umath.remainder, domain_safe_divide(), 0, 1)
-fmod = domained_binary_operation(umath.fmod, domain_safe_divide(), 0, 1)
-hypot = masked_binary_operation(umath.hypot)
-arctan2 = masked_binary_operation(umath.arctan2, 0.0, 1.0)
-arctan2.reduce = None
-equal = masked_binary_operation(umath.equal)
-equal.reduce = None
-not_equal = masked_binary_operation(umath.not_equal)
-not_equal.reduce = None
-less_equal = masked_binary_operation(umath.less_equal)
-less_equal.reduce = None
-greater_equal = masked_binary_operation(umath.greater_equal)
-greater_equal.reduce = None
-less = masked_binary_operation(umath.less)
-less.reduce = None
-greater = masked_binary_operation(umath.greater)
-greater.reduce = None
-logical_and = masked_binary_operation(umath.logical_and)
-alltrue = masked_binary_operation(umath.logical_and, 1, 1).reduce
-logical_or = masked_binary_operation(umath.logical_or)
-sometrue = logical_or.reduce
-logical_xor = masked_binary_operation(umath.logical_xor)
-bitwise_and = masked_binary_operation(umath.bitwise_and)
-bitwise_or = masked_binary_operation(umath.bitwise_or)
-bitwise_xor = masked_binary_operation(umath.bitwise_xor)
-
-def rank (object):
- return fromnumeric.rank(filled(object))
-
-def shape (object):
- return fromnumeric.shape(filled(object))
-
-def size (object, axis=None):
- return fromnumeric.size(filled(object), axis)
-
-class MaskedArray (object):
- """Arrays with possibly masked values.
- Masked values of 1 exclude the corresponding element from
- any computation.
-
- Construction:
- x = array(data, dtype=None, copy=True, order=False,
- mask = nomask, fill_value=None)
-
- If copy=False, every effort is made not to copy the data:
- If data is a MaskedArray, and argument mask=nomask,
- then the candidate data is data.data and the
- mask used is data.mask. If data is a numeric array,
- it is used as the candidate raw data.
- If dtype is not None and
- is != data.dtype.char then a data copy is required.
- Otherwise, the candidate is used.
-
- If a data copy is required, raw data stored is the result of:
- numeric.array(data, dtype=dtype.char, copy=copy)
-
- If mask is nomask there are no masked values. Otherwise mask must
- be convertible to an array of booleans with the same shape as x.
-
- fill_value is used to fill in masked values when necessary,
- such as when printing and in method/function filled().
- The fill_value is not used for computation within this module.
- """
- __array_priority__ = 10.1
- def __init__(self, data, dtype=None, copy=True, order=False,
- mask=nomask, fill_value=None):
- """array(data, dtype=None, copy=True, order=False, mask=nomask, fill_value=None)
- If data already a numeric array, its dtype becomes the default value of dtype.
- """
- if dtype is None:
- tc = None
- else:
- tc = numeric.dtype(dtype)
- need_data_copied = copy
- if isinstance(data, MaskedArray):
- c = data.data
- if tc is None:
- tc = c.dtype
- elif tc != c.dtype:
- need_data_copied = True
- if mask is nomask:
- mask = data.mask
- elif mask is not nomask: #attempting to change the mask
- need_data_copied = True
-
- elif isinstance(data, ndarray):
- c = data
- if tc is None:
- tc = c.dtype
- elif tc != c.dtype:
- need_data_copied = True
- else:
- need_data_copied = False #because I'll do it now
- c = numeric.array(data, dtype=tc, copy=True, order=order)
- tc = c.dtype
-
- if need_data_copied:
- if tc == c.dtype:
- self._data = numeric.array(c, dtype=tc, copy=True, order=order)
- else:
- self._data = c.astype(tc)
- else:
- self._data = c
-
- if mask is nomask:
- self._mask = nomask
- self._shared_mask = 0
- else:
- self._mask = make_mask (mask)
- if self._mask is nomask:
- self._shared_mask = 0
- else:
- self._shared_mask = (self._mask is mask)
- nm = size(self._mask)
- nd = size(self._data)
- if nm != nd:
- if nm == 1:
- self._mask = fromnumeric.resize(self._mask, self._data.shape)
- self._shared_mask = 0
- elif nd == 1:
- self._data = fromnumeric.resize(self._data, self._mask.shape)
- self._data.shape = self._mask.shape
- else:
- raise MAError, "Mask and data not compatible."
- elif nm == 1 and shape(self._mask) != shape(self._data):
- self.unshare_mask()
- self._mask.shape = self._data.shape
-
- self.set_fill_value(fill_value)
-
- def __array__ (self, t=None, context=None):
- "Special hook for numeric. Converts to numeric if possible."
- if self._mask is not nomask:
- if fromnumeric.ravel(self._mask).any():
- if context is None:
- warnings.warn("Cannot automatically convert masked array to "\
- "numeric because data\n is masked in one or "\
- "more locations.");
- return self._data
- #raise MAError, \
- # """Cannot automatically convert masked array to numeric because data
- # is masked in one or more locations.
- # """
- else:
- func, args, i = context
- fills = ufunc_fills.get(func)
- if fills is None:
- raise MAError, "%s not known to ma" % func
- return self.filled(fills[i])
- else: # Mask is all false
- # Optimize to avoid future invocations of this section.
- self._mask = nomask
- self._shared_mask = 0
- if t:
- return self._data.astype(t)
- else:
- return self._data
-
- def __array_wrap__ (self, array, context=None):
- """Special hook for ufuncs.
-
- Wraps the numpy array and sets the mask according to
- context.
- """
- if context is None:
- return MaskedArray(array, copy=False, mask=nomask)
- func, args = context[:2]
- domain = ufunc_domain[func]
- m = reduce(mask_or, [getmask(a) for a in args])
- if domain is not None:
- m = mask_or(m, domain(*[getattr(a, '_data', a)
- for a in args]))
- if m is not nomask:
- try:
- shape = array.shape
- except AttributeError:
- pass
- else:
- if m.shape != shape:
- m = reduce(mask_or, [getmaskarray(a) for a in args])
-
- return MaskedArray(array, copy=False, mask=m)
-
- def _get_shape(self):
- "Return the current shape."
- return self._data.shape
-
- def _set_shape (self, newshape):
- "Set the array's shape."
- self._data.shape = newshape
- if self._mask is not nomask:
- self._mask = self._mask.copy()
- self._mask.shape = newshape
-
- def _get_flat(self):
- """Calculate the flat value.
- """
- if self._mask is nomask:
- return masked_array(self._data.ravel(), mask=nomask,
- fill_value = self.fill_value())
- else:
- return masked_array(self._data.ravel(),
- mask=self._mask.ravel(),
- fill_value = self.fill_value())
-
- def _set_flat (self, value):
- "x.flat = value"
- y = self.ravel()
- y[:] = value
-
- def _get_real(self):
- "Get the real part of a complex array."
- if self._mask is nomask:
- return masked_array(self._data.real, mask=nomask,
- fill_value = self.fill_value())
- else:
- return masked_array(self._data.real, mask=self._mask,
- fill_value = self.fill_value())
-
- def _set_real (self, value):
- "x.real = value"
- y = self.real
- y[...] = value
-
- def _get_imaginary(self):
- "Get the imaginary part of a complex array."
- if self._mask is nomask:
- return masked_array(self._data.imag, mask=nomask,
- fill_value = self.fill_value())
- else:
- return masked_array(self._data.imag, mask=self._mask,
- fill_value = self.fill_value())
-
- def _set_imaginary (self, value):
- "x.imaginary = value"
- y = self.imaginary
- y[...] = value
-
- def __str__(self):
- """Calculate the str representation, using masked for fill if
- it is enabled. Otherwise fill with fill value.
- """
- if masked_print_option.enabled():
- f = masked_print_option
- # XXX: Without the following special case masked
- # XXX: would print as "[--]", not "--". Can we avoid
- # XXX: checks for masked by choosing a different value
- # XXX: for the masked singleton? 2005-01-05 -- sasha
- if self is masked:
- return str(f)
- m = self._mask
- if m is not nomask and m.shape == () and m:
- return str(f)
- # convert to object array to make filled work
- self = self.astype(object)
- else:
- f = self.fill_value()
- res = self.filled(f)
- return str(res)
-
- def __repr__(self):
- """Calculate the repr representation, using masked for fill if
- it is enabled. Otherwise fill with fill value.
- """
- with_mask = """\
-array(data =
- %(data)s,
- mask =
- %(mask)s,
- fill_value=%(fill)s)
-"""
- with_mask1 = """\
-array(data = %(data)s,
- mask = %(mask)s,
- fill_value=%(fill)s)
-"""
- without_mask = """array(
- %(data)s)"""
- without_mask1 = """array(%(data)s)"""
-
- n = len(self.shape)
- if self._mask is nomask:
- if n <= 1:
- return without_mask1 % {'data':str(self.filled())}
- return without_mask % {'data':str(self.filled())}
- else:
- if n <= 1:
- return with_mask % {
- 'data': str(self.filled()),
- 'mask': str(self._mask),
- 'fill': str(self.fill_value())
- }
- return with_mask % {
- 'data': str(self.filled()),
- 'mask': str(self._mask),
- 'fill': str(self.fill_value())
- }
- without_mask1 = """array(%(data)s)"""
- if self._mask is nomask:
- return without_mask % {'data':str(self.filled())}
- else:
- return with_mask % {
- 'data': str(self.filled()),
- 'mask': str(self._mask),
- 'fill': str(self.fill_value())
- }
-
- def __float__(self):
- "Convert self to float."
- self.unmask()
- if self._mask is not nomask:
- raise MAError, 'Cannot convert masked element to a Python float.'
- return float(self.data.item())
-
- def __int__(self):
- "Convert self to int."
- self.unmask()
- if self._mask is not nomask:
- raise MAError, 'Cannot convert masked element to a Python int.'
- return int(self.data.item())
-
- def __getitem__(self, i):
- "Get item described by i. Not a copy as in previous versions."
- self.unshare_mask()
- m = self._mask
- dout = self._data[i]
- if m is nomask:
- try:
- if dout.size == 1:
- return dout
- else:
- return masked_array(dout, fill_value=self._fill_value)
- except AttributeError:
- return dout
- mi = m[i]
- if mi.size == 1:
- if mi:
- return masked
- else:
- return dout
- else:
- return masked_array(dout, mi, fill_value=self._fill_value)
-
-# --------
-# setitem and setslice notes
-# note that if value is masked, it means to mask those locations.
-# setting a value changes the mask to match the value in those locations.
-
- def __setitem__(self, index, value):
- "Set item described by index. If value is masked, mask those locations."
- d = self._data
- if self is masked:
- raise MAError, 'Cannot alter masked elements.'
- if value is masked:
- if self._mask is nomask:
- self._mask = make_mask_none(d.shape)
- self._shared_mask = False
- else:
- self.unshare_mask()
- self._mask[index] = True
- return
- m = getmask(value)
- value = filled(value).astype(d.dtype)
- d[index] = value
- if m is nomask:
- if self._mask is not nomask:
- self.unshare_mask()
- self._mask[index] = False
- else:
- if self._mask is nomask:
- self._mask = make_mask_none(d.shape)
- self._shared_mask = True
- else:
- self.unshare_mask()
- self._mask[index] = m
-
- def __nonzero__(self):
- """returns true if any element is non-zero or masked
-
- """
- # XXX: This changes bool conversion logic from MA.
- # XXX: In MA bool(a) == len(a) != 0, but in numpy
- # XXX: scalars do not have len
- m = self._mask
- d = self._data
- return bool(m is not nomask and m.any()
- or d is not nomask and d.any())
-
- def __len__ (self):
- """Return length of first dimension. This is weird but Python's
- slicing behavior depends on it."""
- return len(self._data)
-
- def __and__(self, other):
- "Return bitwise_and"
- return bitwise_and(self, other)
-
- def __or__(self, other):
- "Return bitwise_or"
- return bitwise_or(self, other)
-
- def __xor__(self, other):
- "Return bitwise_xor"
- return bitwise_xor(self, other)
-
- __rand__ = __and__
- __ror__ = __or__
- __rxor__ = __xor__
-
- def __abs__(self):
- "Return absolute(self)"
- return absolute(self)
-
- def __neg__(self):
- "Return negative(self)"
- return negative(self)
-
- def __pos__(self):
- "Return array(self)"
- return array(self)
-
- def __add__(self, other):
- "Return add(self, other)"
- return add(self, other)
-
- __radd__ = __add__
-
- def __mod__ (self, other):
- "Return remainder(self, other)"
- return remainder(self, other)
-
- def __rmod__ (self, other):
- "Return remainder(other, self)"
- return remainder(other, self)
-
- def __lshift__ (self, n):
- return left_shift(self, n)
-
- def __rshift__ (self, n):
- return right_shift(self, n)
-
- def __sub__(self, other):
- "Return subtract(self, other)"
- return subtract(self, other)
-
- def __rsub__(self, other):
- "Return subtract(other, self)"
- return subtract(other, self)
-
- def __mul__(self, other):
- "Return multiply(self, other)"
- return multiply(self, other)
-
- __rmul__ = __mul__
-
- def __div__(self, other):
- "Return divide(self, other)"
- return divide(self, other)
-
- def __rdiv__(self, other):
- "Return divide(other, self)"
- return divide(other, self)
-
- def __truediv__(self, other):
- "Return divide(self, other)"
- return true_divide(self, other)
-
- def __rtruediv__(self, other):
- "Return divide(other, self)"
- return true_divide(other, self)
-
- def __floordiv__(self, other):
- "Return divide(self, other)"
- return floor_divide(self, other)
-
- def __rfloordiv__(self, other):
- "Return divide(other, self)"
- return floor_divide(other, self)
-
- def __pow__(self, other, third=None):
- "Return power(self, other, third)"
- return power(self, other, third)
-
- def __sqrt__(self):
- "Return sqrt(self)"
- return sqrt(self)
-
- def __iadd__(self, other):
- "Add other to self in place."
- t = self._data.dtype.char
- f = filled(other, 0)
- t1 = f.dtype.char
- if t == t1:
- pass
- elif t in typecodes['Integer']:
- if t1 in typecodes['Integer']:
- f = f.astype(t)
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- elif t in typecodes['Float']:
- if t1 in typecodes['Integer']:
- f = f.astype(t)
- elif t1 in typecodes['Float']:
- f = f.astype(t)
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- elif t in typecodes['Complex']:
- if t1 in typecodes['Integer']:
- f = f.astype(t)
- elif t1 in typecodes['Float']:
- f = f.astype(t)
- elif t1 in typecodes['Complex']:
- f = f.astype(t)
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
-
- if self._mask is nomask:
- self._data += f
- m = getmask(other)
- self._mask = m
- self._shared_mask = m is not nomask
- else:
- result = add(self, masked_array(f, mask=getmask(other)))
- self._data = result.data
- self._mask = result.mask
- self._shared_mask = 1
- return self
-
- def __imul__(self, other):
- "Add other to self in place."
- t = self._data.dtype.char
- f = filled(other, 0)
- t1 = f.dtype.char
- if t == t1:
- pass
- elif t in typecodes['Integer']:
- if t1 in typecodes['Integer']:
- f = f.astype(t)
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- elif t in typecodes['Float']:
- if t1 in typecodes['Integer']:
- f = f.astype(t)
- elif t1 in typecodes['Float']:
- f = f.astype(t)
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- elif t in typecodes['Complex']:
- if t1 in typecodes['Integer']:
- f = f.astype(t)
- elif t1 in typecodes['Float']:
- f = f.astype(t)
- elif t1 in typecodes['Complex']:
- f = f.astype(t)
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
-
- if self._mask is nomask:
- self._data *= f
- m = getmask(other)
- self._mask = m
- self._shared_mask = m is not nomask
- else:
- result = multiply(self, masked_array(f, mask=getmask(other)))
- self._data = result.data
- self._mask = result.mask
- self._shared_mask = 1
- return self
-
- def __isub__(self, other):
- "Subtract other from self in place."
- t = self._data.dtype.char
- f = filled(other, 0)
- t1 = f.dtype.char
- if t == t1:
- pass
- elif t in typecodes['Integer']:
- if t1 in typecodes['Integer']:
- f = f.astype(t)
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- elif t in typecodes['Float']:
- if t1 in typecodes['Integer']:
- f = f.astype(t)
- elif t1 in typecodes['Float']:
- f = f.astype(t)
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- elif t in typecodes['Complex']:
- if t1 in typecodes['Integer']:
- f = f.astype(t)
- elif t1 in typecodes['Float']:
- f = f.astype(t)
- elif t1 in typecodes['Complex']:
- f = f.astype(t)
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
-
- if self._mask is nomask:
- self._data -= f
- m = getmask(other)
- self._mask = m
- self._shared_mask = m is not nomask
- else:
- result = subtract(self, masked_array(f, mask=getmask(other)))
- self._data = result.data
- self._mask = result.mask
- self._shared_mask = 1
- return self
-
-
-
- def __idiv__(self, other):
- "Divide self by other in place."
- t = self._data.dtype.char
- f = filled(other, 0)
- t1 = f.dtype.char
- if t == t1:
- pass
- elif t in typecodes['Integer']:
- if t1 in typecodes['Integer']:
- f = f.astype(t)
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- elif t in typecodes['Float']:
- if t1 in typecodes['Integer']:
- f = f.astype(t)
- elif t1 in typecodes['Float']:
- f = f.astype(t)
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- elif t in typecodes['Complex']:
- if t1 in typecodes['Integer']:
- f = f.astype(t)
- elif t1 in typecodes['Float']:
- f = f.astype(t)
- elif t1 in typecodes['Complex']:
- f = f.astype(t)
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- else:
- raise TypeError, 'Incorrect type for in-place operation.'
- mo = getmask(other)
- result = divide(self, masked_array(f, mask=mo))
- self._data = result.data
- dm = result.raw_mask()
- if dm is not self._mask:
- self._mask = dm
- self._shared_mask = 1
- return self
-
- def __eq__(self, other):
- return equal(self,other)
-
- def __ne__(self, other):
- return not_equal(self,other)
-
- def __lt__(self, other):
- return less(self,other)
-
- def __le__(self, other):
- return less_equal(self,other)
-
- def __gt__(self, other):
- return greater(self,other)
-
- def __ge__(self, other):
- return greater_equal(self,other)
-
- def astype (self, tc):
- "return self as array of given type."
- d = self._data.astype(tc)
- return array(d, mask=self._mask)
-
- def byte_swapped(self):
- """Returns the raw data field, byte_swapped. Included for consistency
- with numeric but doesn't make sense in this context.
- """
- return self._data.byte_swapped()
-
- def compressed (self):
- "A 1-D array of all the non-masked data."
- d = fromnumeric.ravel(self._data)
- if self._mask is nomask:
- return array(d)
- else:
- m = 1 - fromnumeric.ravel(self._mask)
- c = fromnumeric.compress(m, d)
- return array(c, copy=0)
-
- def count (self, axis = None):
- "Count of the non-masked elements in a, or along a certain axis."
- m = self._mask
- s = self._data.shape
- ls = len(s)
- if m is nomask:
- if ls == 0:
- return 1
- if ls == 1:
- return s[0]
- if axis is None:
- return reduce(lambda x, y:x*y, s)
- else:
- n = s[axis]
- t = list(s)
- del t[axis]
- return ones(t) * n
- if axis is None:
- w = fromnumeric.ravel(m).astype(int)
- n1 = size(w)
- if n1 == 1:
- n2 = w[0]
- else:
- n2 = umath.add.reduce(w)
- return n1 - n2
- else:
- n1 = size(m, axis)
- n2 = sum(m.astype(int), axis)
- return n1 - n2
-
- def dot (self, other):
- "s.dot(other) = innerproduct(s, other)"
- return innerproduct(self, other)
-
- def fill_value(self):
- "Get the current fill value."
- return self._fill_value
-
- def filled (self, fill_value=None):
- """A numeric array with masked values filled. If fill_value is None,
- use self.fill_value().
-
- If mask is nomask, copy data only if not contiguous.
- Result is always a contiguous, numeric array.
-# Is contiguous really necessary now?
- """
- d = self._data
- m = self._mask
- if m is nomask:
- if d.flags['CONTIGUOUS']:
- return d
- else:
- return d.copy()
- else:
- if fill_value is None:
- value = self._fill_value
- else:
- value = fill_value
-
- if self is masked:
- result = numeric.array(value)
- else:
- try:
- result = numeric.array(d, dtype=d.dtype, copy=1)
- result[m] = value
- except (TypeError, AttributeError):
- #ok, can't put that value in here
- value = numeric.array(value, dtype=object)
- d = d.astype(object)
- result = fromnumeric.choose(m, (d, value))
- return result
-
- def ids (self):
- """Return the ids of the data and mask areas"""
- return (id(self._data), id(self._mask))
-
- def iscontiguous (self):
- "Is the data contiguous?"
- return self._data.flags['CONTIGUOUS']
-
- def itemsize(self):
- "Item size of each data item."
- return self._data.itemsize
-
-
- def outer(self, other):
- "s.outer(other) = outerproduct(s, other)"
- return outerproduct(self, other)
-
- def put (self, values):
- """Set the non-masked entries of self to filled(values).
- No change to mask
- """
- iota = numeric.arange(self.size)
- d = self._data
- if self._mask is nomask:
- ind = iota
- else:
- ind = fromnumeric.compress(1 - self._mask, iota)
- d[ind] = filled(values).astype(d.dtype)
-
- def putmask (self, values):
- """Set the masked entries of self to filled(values).
- Mask changed to nomask.
- """
- d = self._data
- if self._mask is not nomask:
- d[self._mask] = filled(values).astype(d.dtype)
- self._shared_mask = 0
- self._mask = nomask
-
- def ravel (self):
- """Return a 1-D view of self."""
- if self._mask is nomask:
- return masked_array(self._data.ravel())
- else:
- return masked_array(self._data.ravel(), self._mask.ravel())
-
- def raw_data (self):
- """ Obsolete; use data property instead.
- The raw data; portions may be meaningless.
- May be noncontiguous. Expert use only."""
- return self._data
- data = property(fget=raw_data,
- doc="The data, but values at masked locations are meaningless.")
-
- def raw_mask (self):
- """ Obsolete; use mask property instead.
- May be noncontiguous. Expert use only.
- """
- return self._mask
- mask = property(fget=raw_mask,
- doc="The mask, may be nomask. Values where mask true are meaningless.")
-
- def reshape (self, *s):
- """This array reshaped to shape s"""
- d = self._data.reshape(*s)
- if self._mask is nomask:
- return masked_array(d)
- else:
- m = self._mask.reshape(*s)
- return masked_array(d, m)
-
- def set_fill_value (self, v=None):
- "Set the fill value to v. Omit v to restore default."
- if v is None:
- v = default_fill_value (self.raw_data())
- self._fill_value = v
-
- def _get_ndim(self):
- return self._data.ndim
- ndim = property(_get_ndim, doc=numeric.ndarray.ndim.__doc__)
-
- def _get_size (self):
- return self._data.size
- size = property(fget=_get_size, doc="Number of elements in the array.")
-## CHECK THIS: signature of numeric.array.size?
-
- def _get_dtype(self):
- return self._data.dtype
- dtype = property(fget=_get_dtype, doc="type of the array elements.")
-
- def item(self, *args):
- "Return Python scalar if possible"
- if self._mask is not nomask:
- m = self._mask.item(*args)
- try:
- if m[0]:
- return masked
- except IndexError:
- return masked
- return self._data.item(*args)
-
- def itemset(self, *args):
- "Set Python scalar into array"
- item = args[-1]
- args = args[:-1]
- self[args] = item
-
- def tolist(self, fill_value=None):
- "Convert to list"
- return self.filled(fill_value).tolist()
-
- def tostring(self, fill_value=None):
- "Convert to string"
- return self.filled(fill_value).tostring()
-
- def unmask (self):
- "Replace the mask by nomask if possible."
- if self._mask is nomask: return
- m = make_mask(self._mask, flag=1)
- if m is nomask:
- self._mask = nomask
- self._shared_mask = 0
-
- def unshare_mask (self):
- "If currently sharing mask, make a copy."
- if self._shared_mask:
- self._mask = make_mask (self._mask, copy=1, flag=0)
- self._shared_mask = 0
-
- def _get_ctypes(self):
- return self._data.ctypes
-
- def _get_T(self):
- if (self.ndim < 2):
- return self
- return self.transpose()
-
- shape = property(_get_shape, _set_shape,
- doc = 'tuple giving the shape of the array')
-
- flat = property(_get_flat, _set_flat,
- doc = 'Access array in flat form.')
-
- real = property(_get_real, _set_real,
- doc = 'Access the real part of the array')
-
- imaginary = property(_get_imaginary, _set_imaginary,
- doc = 'Access the imaginary part of the array')
-
- imag = imaginary
-
- ctypes = property(_get_ctypes, None, doc="ctypes")
-
- T = property(_get_T, None, doc="get transpose")
-
-#end class MaskedArray
-
-array = MaskedArray
-
-def isMaskedArray (x):
- "Is x a masked array, that is, an instance of MaskedArray?"
- return isinstance(x, MaskedArray)
-
-isarray = isMaskedArray
-isMA = isMaskedArray #backward compatibility
-
-def allclose (a, b, fill_value=1, rtol=1.e-5, atol=1.e-8):
- """ Returns true if all components of a and b are equal
- subject to given tolerances.
- If fill_value is 1, masked values considered equal.
- If fill_value is 0, masked values considered unequal.
- The relative error rtol should be positive and << 1.0
- The absolute error atol comes into play for those elements
- of b that are very small or zero; it says how small a must be also.
- """
- m = mask_or(getmask(a), getmask(b))
- d1 = filled(a)
- d2 = filled(b)
- x = filled(array(d1, copy=0, mask=m), fill_value).astype(float)
- y = filled(array(d2, copy=0, mask=m), 1).astype(float)
- d = umath.less_equal(umath.absolute(x-y), atol + rtol * umath.absolute(y))
- return fromnumeric.alltrue(fromnumeric.ravel(d))
-
-def allequal (a, b, fill_value=1):
- """
- True if all entries of a and b are equal, using
- fill_value as a truth value where either or both are masked.
- """
- m = mask_or(getmask(a), getmask(b))
- if m is nomask:
- x = filled(a)
- y = filled(b)
- d = umath.equal(x, y)
- return fromnumeric.alltrue(fromnumeric.ravel(d))
- elif fill_value:
- x = filled(a)
- y = filled(b)
- d = umath.equal(x, y)
- dm = array(d, mask=m, copy=0)
- return fromnumeric.alltrue(fromnumeric.ravel(filled(dm, 1)))
- else:
- return 0
-
-def masked_values (data, value, rtol=1.e-5, atol=1.e-8, copy=1):
- """
- masked_values(data, value, rtol=1.e-5, atol=1.e-8)
- Create a masked array; mask is nomask if possible.
- If copy==0, and otherwise possible, result
- may share data values with original array.
- Let d = filled(data, value). Returns d
- masked where abs(data-value)<= atol + rtol * abs(value)
- if d is of a floating point type. Otherwise returns
- masked_object(d, value, copy)
- """
- abs = umath.absolute
- d = filled(data, value)
- if issubclass(d.dtype.type, numeric.floating):
- m = umath.less_equal(abs(d-value), atol+rtol*abs(value))
- m = make_mask(m, flag=1)
- return array(d, mask = m, copy=copy,
- fill_value=value)
- else:
- return masked_object(d, value, copy=copy)
-
-def masked_object (data, value, copy=1):
- "Create array masked where exactly data equal to value"
- d = filled(data, value)
- dm = make_mask(umath.equal(d, value), flag=1)
- return array(d, mask=dm, copy=copy, fill_value=value)
-
-def arange(start, stop=None, step=1, dtype=None):
- """Just like range() except it returns a array whose type can be specified
- by the keyword argument dtype.
- """
- return array(numeric.arange(start, stop, step, dtype))
-
-arrayrange = arange
-
-def fromstring (s, t):
- "Construct a masked array from a string. Result will have no mask."
- return masked_array(numeric.fromstring(s, t))
-
-def left_shift (a, n):
- "Left shift n bits"
- m = getmask(a)
- if m is nomask:
- d = umath.left_shift(filled(a), n)
- return masked_array(d)
- else:
- d = umath.left_shift(filled(a, 0), n)
- return masked_array(d, m)
-
-def right_shift (a, n):
- "Right shift n bits"
- m = getmask(a)
- if m is nomask:
- d = umath.right_shift(filled(a), n)
- return masked_array(d)
- else:
- d = umath.right_shift(filled(a, 0), n)
- return masked_array(d, m)
-
-def resize (a, new_shape):
- """resize(a, new_shape) returns a new array with the specified shape.
- The original array's total size can be any size."""
- m = getmask(a)
- if m is not nomask:
- m = fromnumeric.resize(m, new_shape)
- result = array(fromnumeric.resize(filled(a), new_shape), mask=m)
- result.set_fill_value(get_fill_value(a))
- return result
-
-def repeat(a, repeats, axis=None):
- """repeat elements of a repeats times along axis
- repeats is a sequence of length a.shape[axis]
- telling how many times to repeat each element.
- """
- af = filled(a)
- if isinstance(repeats, types.IntType):
- if axis is None:
- num = af.size
- else:
- num = af.shape[axis]
- repeats = tuple([repeats]*num)
-
- m = getmask(a)
- if m is not nomask:
- m = fromnumeric.repeat(m, repeats, axis)
- d = fromnumeric.repeat(af, repeats, axis)
- result = masked_array(d, m)
- result.set_fill_value(get_fill_value(a))
- return result
-
-def identity(n):
- """identity(n) returns the identity matrix of shape n x n.
- """
- return array(numeric.identity(n))
-
-def indices (dimensions, dtype=None):
- """indices(dimensions,dtype=None) returns an array representing a grid
- of indices with row-only, and column-only variation.
- """
- return array(numeric.indices(dimensions, dtype))
-
-def zeros (shape, dtype=float):
- """zeros(n, dtype=float) =
- an array of all zeros of the given length or shape."""
- return array(numeric.zeros(shape, dtype))
-
-def ones (shape, dtype=float):
- """ones(n, dtype=float) =
- an array of all ones of the given length or shape."""
- return array(numeric.ones(shape, dtype))
-
-def count (a, axis = None):
- "Count of the non-masked elements in a, or along a certain axis."
- a = masked_array(a)
- return a.count(axis)
-
-def power (a, b, third=None):
- "a**b"
- if third is not None:
- raise MAError, "3-argument power not supported."
- ma = getmask(a)
- mb = getmask(b)
- m = mask_or(ma, mb)
- fa = filled(a, 1)
- fb = filled(b, 1)
- if fb.dtype.char in typecodes["Integer"]:
- return masked_array(umath.power(fa, fb), m)
- md = make_mask(umath.less(fa, 0), flag=1)
- m = mask_or(m, md)
- if m is nomask:
- return masked_array(umath.power(fa, fb))
- else:
- fa = numeric.where(m, 1, fa)
- return masked_array(umath.power(fa, fb), m)
-
-def masked_array (a, mask=nomask, fill_value=None):
- """masked_array(a, mask=nomask) =
- array(a, mask=mask, copy=0, fill_value=fill_value)
- """
- return array(a, mask=mask, copy=0, fill_value=fill_value)
-
-def sum (target, axis=None, dtype=None):
- if axis is None:
- target = ravel(target)
- axis = 0
- return add.reduce(target, axis, dtype)
-
-def product (target, axis=None, dtype=None):
- if axis is None:
- target = ravel(target)
- axis = 0
- return multiply.reduce(target, axis, dtype)
-
-def average (a, axis=None, weights=None, returned = 0):
- """average(a, axis=None, weights=None)
- Computes average along indicated axis.
- If axis is None, average over the entire array
- Inputs can be integer or floating types; result is of type float.
-
- If weights are given, result is sum(a*weights,axis=0)/(sum(weights,axis=0)*1.0)
- weights must have a's shape or be the 1-d with length the size
- of a in the given axis.
-
- If returned, return a tuple: the result and the sum of the weights
- or count of values. Results will have the same shape.
-
- masked values in the weights will be set to 0.0
- """
- a = masked_array(a)
- mask = a.mask
- ash = a.shape
- if ash == ():
- ash = (1,)
- if axis is None:
- if mask is nomask:
- if weights is None:
- n = add.reduce(a.raw_data().ravel())
- d = reduce(lambda x, y: x * y, ash, 1.0)
- else:
- w = filled(weights, 0.0).ravel()
- n = umath.add.reduce(a.raw_data().ravel() * w)
- d = umath.add.reduce(w)
- del w
- else:
- if weights is None:
- n = add.reduce(a.ravel())
- w = fromnumeric.choose(mask, (1.0, 0.0)).ravel()
- d = umath.add.reduce(w)
- del w
- else:
- w = array(filled(weights, 0.0), float, mask=mask).ravel()
- n = add.reduce(a.ravel() * w)
- d = add.reduce(w)
- del w
- else:
- if mask is nomask:
- if weights is None:
- d = ash[axis] * 1.0
- n = umath.add.reduce(a.raw_data(), axis)
- else:
- w = filled(weights, 0.0)
- wsh = w.shape
- if wsh == ():
- wsh = (1,)
- if wsh == ash:
- w = numeric.array(w, float, copy=0)
- n = add.reduce(a*w, axis)
- d = add.reduce(w, axis)
- del w
- elif wsh == (ash[axis],):
- r = [newaxis]*len(ash)
- r[axis] = slice(None, None, 1)
- w = eval ("w["+ repr(tuple(r)) + "] * ones(ash, float)")
- n = add.reduce(a*w, axis)
- d = add.reduce(w, axis)
- del w, r
- else:
- raise ValueError, 'average: weights wrong shape.'
- else:
- if weights is None:
- n = add.reduce(a, axis)
- w = numeric.choose(mask, (1.0, 0.0))
- d = umath.add.reduce(w, axis)
- del w
- else:
- w = filled(weights, 0.0)
- wsh = w.shape
- if wsh == ():
- wsh = (1,)
- if wsh == ash:
- w = array(w, float, mask=mask, copy=0)
- n = add.reduce(a*w, axis)
- d = add.reduce(w, axis)
- elif wsh == (ash[axis],):
- r = [newaxis]*len(ash)
- r[axis] = slice(None, None, 1)
- w = eval ("w["+ repr(tuple(r)) + "] * masked_array(ones(ash, float), mask)")
- n = add.reduce(a*w, axis)
- d = add.reduce(w, axis)
- else:
- raise ValueError, 'average: weights wrong shape.'
- del w
- #print n, d, repr(mask), repr(weights)
- if n is masked or d is masked: return masked
- result = divide (n, d)
- del n
-
- if isinstance(result, MaskedArray):
- result.unmask()
- if returned:
- if not isinstance(d, MaskedArray):
- d = masked_array(d)
- if not d.shape == result.shape:
- d = ones(result.shape, float) * d
- d.unmask()
- if returned:
- return result, d
- else:
- return result
-
-def where (condition, x, y):
- """where(condition, x, y) is x where condition is nonzero, y otherwise.
- condition must be convertible to an integer array.
- Answer is always the shape of condition.
- The type depends on x and y. It is integer if both x and y are
- the value masked.
- """
- fc = filled(not_equal(condition, 0), 0)
- xv = filled(x)
- xm = getmask(x)
- yv = filled(y)
- ym = getmask(y)
- d = numeric.choose(fc, (yv, xv))
- md = numeric.choose(fc, (ym, xm))
- m = getmask(condition)
- m = make_mask(mask_or(m, md), copy=0, flag=1)
- return masked_array(d, m)
-
-def choose (indices, t, out=None, mode='raise'):
- "Returns array shaped like indices with elements chosen from t"
- def fmask (x):
- if x is masked: return 1
- return filled(x)
- def nmask (x):
- if x is masked: return 1
- m = getmask(x)
- if m is nomask: return 0
- return m
- c = filled(indices, 0)
- masks = [nmask(x) for x in t]
- a = [fmask(x) for x in t]
- d = numeric.choose(c, a)
- m = numeric.choose(c, masks)
- m = make_mask(mask_or(m, getmask(indices)), copy=0, flag=1)
- return masked_array(d, m)
-
-def masked_where(condition, x, copy=1):
- """Return x as an array masked where condition is true.
- Also masked where x or condition masked.
- """
- cm = filled(condition,1)
- m = mask_or(getmask(x), cm)
- return array(filled(x), copy=copy, mask=m)
-
-def masked_greater(x, value, copy=1):
- "masked_greater(x, value) = x masked where x > value"
- return masked_where(greater(x, value), x, copy)
-
-def masked_greater_equal(x, value, copy=1):
- "masked_greater_equal(x, value) = x masked where x >= value"
- return masked_where(greater_equal(x, value), x, copy)
-
-def masked_less(x, value, copy=1):
- "masked_less(x, value) = x masked where x < value"
- return masked_where(less(x, value), x, copy)
-
-def masked_less_equal(x, value, copy=1):
- "masked_less_equal(x, value) = x masked where x <= value"
- return masked_where(less_equal(x, value), x, copy)
-
-def masked_not_equal(x, value, copy=1):
- "masked_not_equal(x, value) = x masked where x != value"
- d = filled(x, 0)
- c = umath.not_equal(d, value)
- m = mask_or(c, getmask(x))
- return array(d, mask=m, copy=copy)
-
-def masked_equal(x, value, copy=1):
- """masked_equal(x, value) = x masked where x == value
- For floating point consider masked_values(x, value) instead.
- """
- d = filled(x, 0)
- c = umath.equal(d, value)
- m = mask_or(c, getmask(x))
- return array(d, mask=m, copy=copy)
-
-def masked_inside(x, v1, v2, copy=1):
- """x with mask of all values of x that are inside [v1,v2]
- v1 and v2 can be given in either order.
- """
- if v2 < v1:
- t = v2
- v2 = v1
- v1 = t
- d = filled(x, 0)
- c = umath.logical_and(umath.less_equal(d, v2), umath.greater_equal(d, v1))
- m = mask_or(c, getmask(x))
- return array(d, mask = m, copy=copy)
-
-def masked_outside(x, v1, v2, copy=1):
- """x with mask of all values of x that are outside [v1,v2]
- v1 and v2 can be given in either order.
- """
- if v2 < v1:
- t = v2
- v2 = v1
- v1 = t
- d = filled(x, 0)
- c = umath.logical_or(umath.less(d, v1), umath.greater(d, v2))
- m = mask_or(c, getmask(x))
- return array(d, mask = m, copy=copy)
-
-def reshape (a, *newshape):
- "Copy of a with a new shape."
- m = getmask(a)
- d = filled(a).reshape(*newshape)
- if m is nomask:
- return masked_array(d)
- else:
- return masked_array(d, mask=numeric.reshape(m, *newshape))
-
-def ravel (a):
- "a as one-dimensional, may share data and mask"
- m = getmask(a)
- d = fromnumeric.ravel(filled(a))
- if m is nomask:
- return masked_array(d)
- else:
- return masked_array(d, mask=numeric.ravel(m))
-
-def concatenate (arrays, axis=0):
- "Concatenate the arrays along the given axis"
- d = []
- for x in arrays:
- d.append(filled(x))
- d = numeric.concatenate(d, axis)
- for x in arrays:
- if getmask(x) is not nomask: break
- else:
- return masked_array(d)
- dm = []
- for x in arrays:
- dm.append(getmaskarray(x))
- dm = numeric.concatenate(dm, axis)
- return masked_array(d, mask=dm)
-
-def swapaxes (a, axis1, axis2):
- m = getmask(a)
- d = masked_array(a).data
- if m is nomask:
- return masked_array(data=numeric.swapaxes(d, axis1, axis2))
- else:
- return masked_array(data=numeric.swapaxes(d, axis1, axis2),
- mask=numeric.swapaxes(m, axis1, axis2),)
-
-
-def take (a, indices, axis=None, out=None, mode='raise'):
- "returns selection of items from a."
- m = getmask(a)
- # d = masked_array(a).raw_data()
- d = masked_array(a).data
- if m is nomask:
- return masked_array(numeric.take(d, indices, axis))
- else:
- return masked_array(numeric.take(d, indices, axis),
- mask = numeric.take(m, indices, axis))
-
-def transpose(a, axes=None):
- "reorder dimensions per tuple axes"
- m = getmask(a)
- d = filled(a)
- if m is nomask:
- return masked_array(numeric.transpose(d, axes))
- else:
- return masked_array(numeric.transpose(d, axes),
- mask = numeric.transpose(m, axes))
-
-
-def put(a, indices, values, mode='raise'):
- """sets storage-indexed locations to corresponding values.
-
- Values and indices are filled if necessary.
-
- """
- d = a.raw_data()
- ind = filled(indices)
- v = filled(values)
- numeric.put (d, ind, v)
- m = getmask(a)
- if m is not nomask:
- a.unshare_mask()
- numeric.put(a.raw_mask(), ind, 0)
-
-def putmask(a, mask, values):
- "putmask(a, mask, values) sets a where mask is true."
- if mask is nomask:
- return
- numeric.putmask(a.raw_data(), mask, values)
- m = getmask(a)
- if m is nomask: return
- a.unshare_mask()
- numeric.putmask(a.raw_mask(), mask, 0)
-
-def inner(a, b):
- """inner(a,b) returns the dot product of two arrays, which has
- shape a.shape[:-1] + b.shape[:-1] with elements computed by summing the
- product of the elements from the last dimensions of a and b.
- Masked elements are replace by zeros.
- """
- fa = filled(a, 0)
- fb = filled(b, 0)
- if len(fa.shape) == 0: fa.shape = (1,)
- if len(fb.shape) == 0: fb.shape = (1,)
- return masked_array(numeric.inner(fa, fb))
-
-innerproduct = inner
-
-def outer(a, b):
- """outer(a,b) = {a[i]*b[j]}, has shape (len(a),len(b))"""
- fa = filled(a, 0).ravel()
- fb = filled(b, 0).ravel()
- d = numeric.outer(fa, fb)
- ma = getmask(a)
- mb = getmask(b)
- if ma is nomask and mb is nomask:
- return masked_array(d)
- ma = getmaskarray(a)
- mb = getmaskarray(b)
- m = make_mask(1-numeric.outer(1-ma, 1-mb), copy=0)
- return masked_array(d, m)
-
-outerproduct = outer
-
-def dot(a, b):
- """dot(a,b) returns matrix-multiplication between a and b. The product-sum
- is over the last dimension of a and the second-to-last dimension of b.
- Masked values are replaced by zeros. See also innerproduct.
- """
- return innerproduct(filled(a, 0), numeric.swapaxes(filled(b, 0), -1, -2))
-
-def compress(condition, x, dimension=-1, out=None):
- """Select those parts of x for which condition is true.
- Masked values in condition are considered false.
- """
- c = filled(condition, 0)
- m = getmask(x)
- if m is not nomask:
- m = numeric.compress(c, m, dimension)
- d = numeric.compress(c, filled(x), dimension)
- return masked_array(d, m)
-
-class _minimum_operation:
- "Object to calculate minima"
- def __init__ (self):
- """minimum(a, b) or minimum(a)
- In one argument case returns the scalar minimum.
- """
- pass
-
- def __call__ (self, a, b=None):
- "Execute the call behavior."
- if b is None:
- m = getmask(a)
- if m is nomask:
- d = amin(filled(a).ravel())
- return d
- ac = a.compressed()
- if len(ac) == 0:
- return masked
- else:
- return amin(ac.raw_data())
- else:
- return where(less(a, b), a, b)
-
- def reduce (self, target, axis=0):
- """Reduce target along the given axis."""
- m = getmask(target)
- if m is nomask:
- t = filled(target)
- return masked_array (umath.minimum.reduce (t, axis))
- else:
- t = umath.minimum.reduce(filled(target, minimum_fill_value(target)), axis)
- m = umath.logical_and.reduce(m, axis)
- return masked_array(t, m, get_fill_value(target))
-
- def outer (self, a, b):
- "Return the function applied to the outer product of a and b."
- ma = getmask(a)
- mb = getmask(b)
- if ma is nomask and mb is nomask:
- m = nomask
- else:
- ma = getmaskarray(a)
- mb = getmaskarray(b)
- m = logical_or.outer(ma, mb)
- d = umath.minimum.outer(filled(a), filled(b))
- return masked_array(d, m)
-
-minimum = _minimum_operation ()
-
-class _maximum_operation:
- "Object to calculate maxima"
- def __init__ (self):
- """maximum(a, b) or maximum(a)
- In one argument case returns the scalar maximum.
- """
- pass
-
- def __call__ (self, a, b=None):
- "Execute the call behavior."
- if b is None:
- m = getmask(a)
- if m is nomask:
- d = amax(filled(a).ravel())
- return d
- ac = a.compressed()
- if len(ac) == 0:
- return masked
- else:
- return amax(ac.raw_data())
- else:
- return where(greater(a, b), a, b)
-
- def reduce (self, target, axis=0):
- """Reduce target along the given axis."""
- m = getmask(target)
- if m is nomask:
- t = filled(target)
- return masked_array (umath.maximum.reduce (t, axis))
- else:
- t = umath.maximum.reduce(filled(target, maximum_fill_value(target)), axis)
- m = umath.logical_and.reduce(m, axis)
- return masked_array(t, m, get_fill_value(target))
-
- def outer (self, a, b):
- "Return the function applied to the outer product of a and b."
- ma = getmask(a)
- mb = getmask(b)
- if ma is nomask and mb is nomask:
- m = nomask
- else:
- ma = getmaskarray(a)
- mb = getmaskarray(b)
- m = logical_or.outer(ma, mb)
- d = umath.maximum.outer(filled(a), filled(b))
- return masked_array(d, m)
-
-maximum = _maximum_operation ()
-
-def sort (x, axis = -1, fill_value=None):
- """If x does not have a mask, return a masked array formed from the
- result of numeric.sort(x, axis).
- Otherwise, fill x with fill_value. Sort it.
- Set a mask where the result is equal to fill_value.
- Note that this may have unintended consequences if the data contains the
- fill value at a non-masked site.
-
- If fill_value is not given the default fill value for x's type will be
- used.
- """
- if fill_value is None:
- fill_value = default_fill_value (x)
- d = filled(x, fill_value)
- s = fromnumeric.sort(d, axis)
- if getmask(x) is nomask:
- return masked_array(s)
- return masked_values(s, fill_value, copy=0)
-
-def diagonal(a, k = 0, axis1=0, axis2=1):
- """diagonal(a,k=0,axis1=0, axis2=1) = the k'th diagonal of a"""
- d = fromnumeric.diagonal(filled(a), k, axis1, axis2)
- m = getmask(a)
- if m is nomask:
- return masked_array(d, m)
- else:
- return masked_array(d, fromnumeric.diagonal(m, k, axis1, axis2))
-
-def trace (a, offset=0, axis1=0, axis2=1, dtype=None, out=None):
- """trace(a,offset=0, axis1=0, axis2=1) returns the sum along diagonals
- (defined by the last two dimenions) of the array.
- """
- return diagonal(a, offset, axis1, axis2).sum(dtype=dtype)
-
-def argsort (x, axis = -1, out=None, fill_value=None):
- """Treating masked values as if they have the value fill_value,
- return sort indices for sorting along given axis.
- if fill_value is None, use get_fill_value(x)
- Returns a numpy array.
- """
- d = filled(x, fill_value)
- return fromnumeric.argsort(d, axis)
-
-def argmin (x, axis = -1, out=None, fill_value=None):
- """Treating masked values as if they have the value fill_value,
- return indices for minimum values along given axis.
- if fill_value is None, use get_fill_value(x).
- Returns a numpy array if x has more than one dimension.
- Otherwise, returns a scalar index.
- """
- d = filled(x, fill_value)
- return fromnumeric.argmin(d, axis)
-
-def argmax (x, axis = -1, out=None, fill_value=None):
- """Treating masked values as if they have the value fill_value,
- return sort indices for maximum along given axis.
- if fill_value is None, use -get_fill_value(x) if it exists.
- Returns a numpy array if x has more than one dimension.
- Otherwise, returns a scalar index.
- """
- if fill_value is None:
- fill_value = default_fill_value (x)
- try:
- fill_value = - fill_value
- except:
- pass
- d = filled(x, fill_value)
- return fromnumeric.argmax(d, axis)
-
-def fromfunction (f, s):
- """apply f to s to create array as in umath."""
- return masked_array(numeric.fromfunction(f, s))
-
-def asarray(data, dtype=None):
- """asarray(data, dtype) = array(data, dtype, copy=0)
- """
- if isinstance(data, MaskedArray) and \
- (dtype is None or dtype == data.dtype):
- return data
- return array(data, dtype=dtype, copy=0)
-
-# Add methods to support ndarray interface
-# XXX: I is better to to change the masked_*_operation adaptors
-# XXX: to wrap ndarray methods directly to create ma.array methods.
-from types import MethodType
-def _m(f):
- return MethodType(f, None, array)
-def not_implemented(*args, **kwds):
- raise NotImplementedError, "not yet implemented for numpy.ma arrays"
-array.all = _m(alltrue)
-array.any = _m(sometrue)
-array.argmax = _m(argmax)
-array.argmin = _m(argmin)
-array.argsort = _m(argsort)
-array.base = property(_m(not_implemented))
-array.byteswap = _m(not_implemented)
-
-def _choose(self, *args, **kwds):
- return choose(self, args)
-array.choose = _m(_choose)
-del _choose
-
-def _clip(self,a_min,a_max,out=None):
- return MaskedArray(data = self.data.clip(asarray(a_min).data,
- asarray(a_max).data),
- mask = mask_or(self.mask,
- mask_or(getmask(a_min),getmask(a_max))))
-array.clip = _m(_clip)
-
-def _compress(self, cond, axis=None, out=None):
- return compress(cond, self, axis)
-array.compress = _m(_compress)
-del _compress
-
-array.conj = array.conjugate = _m(conjugate)
-array.copy = _m(not_implemented)
-
-def _cumprod(self, axis=None, dtype=None, out=None):
- m = self.mask
- if m is not nomask:
- m = umath.logical_or.accumulate(self.mask, axis)
- return MaskedArray(data = self.filled(1).cumprod(axis, dtype), mask=m)
-array.cumprod = _m(_cumprod)
-
-def _cumsum(self, axis=None, dtype=None, out=None):
- m = self.mask
- if m is not nomask:
- m = umath.logical_or.accumulate(self.mask, axis)
- return MaskedArray(data=self.filled(0).cumsum(axis, dtype), mask=m)
-array.cumsum = _m(_cumsum)
-
-array.diagonal = _m(diagonal)
-array.dump = _m(not_implemented)
-array.dumps = _m(not_implemented)
-array.fill = _m(not_implemented)
-array.flags = property(_m(not_implemented))
-array.flatten = _m(ravel)
-array.getfield = _m(not_implemented)
-
-def _max(a, axis=None, out=None):
- if out is not None:
- raise TypeError("Output arrays Unsupported for masked arrays")
- if axis is None:
- return maximum(a)
- else:
- return maximum.reduce(a, axis)
-array.max = _m(_max)
-del _max
-def _min(a, axis=None, out=None):
- if out is not None:
- raise TypeError("Output arrays Unsupported for masked arrays")
- if axis is None:
- return minimum(a)
- else:
- return minimum.reduce(a, axis)
-array.min = _m(_min)
-del _min
-array.mean = _m(average)
-array.nbytes = property(_m(not_implemented))
-array.newbyteorder = _m(not_implemented)
-array.nonzero = _m(nonzero)
-array.prod = _m(product)
-
-def _ptp(a,axis=None,out=None):
- return a.max(axis,out)-a.min(axis)
-array.ptp = _m(_ptp)
-array.repeat = _m(repeat)
-array.resize = _m(resize)
-array.searchsorted = _m(not_implemented)
-array.setfield = _m(not_implemented)
-array.setflags = _m(not_implemented)
-array.sort = _m(not_implemented) # NB: ndarray.sort is inplace
-
-def _squeeze(self):
- try:
- result = MaskedArray(data = self.data.squeeze(),
- mask = self.mask.squeeze())
- except AttributeError:
- result = _wrapit(self, 'squeeze')
- return result
-array.squeeze = _m(_squeeze)
-
-array.strides = property(_m(not_implemented))
-array.sum = _m(sum)
-def _swapaxes(self,axis1,axis2):
- return MaskedArray(data = self.data.swapaxes(axis1, axis2),
- mask = self.mask.swapaxes(axis1, axis2))
-array.swapaxes = _m(_swapaxes)
-array.take = _m(take)
-array.tofile = _m(not_implemented)
-array.trace = _m(trace)
-array.transpose = _m(transpose)
-
-def _var(self,axis=None,dtype=None, out=None):
- if axis is None:
- return numeric.asarray(self.compressed()).var()
- a = self.swapaxes(axis,0)
- a = a - a.mean(axis=0)
- a *= a
- a /= a.count(axis=0)
- return a.swapaxes(0,axis).sum(axis)
-def _std(self,axis=None, dtype=None, out=None):
- return (self.var(axis,dtype))**0.5
-array.var = _m(_var)
-array.std = _m(_std)
-
-array.view = _m(not_implemented)
-array.round = _m(around)
-del _m, MethodType, not_implemented
-
-
-masked = MaskedArray(0, int, mask=1)
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