DEP: Remove deprecated modules numarray and oldnumeric.

They were deprecated in 1.8 and scheduled for removal in 1.9.

Closes #3637.

1 files changed, 0 insertions, 2296 deletions

diff --git a/numpy/oldnumeric/ma.py b/numpy/oldnumeric/ma.py
deleted file mode 100644
index d41c68edc..000000000
--- a/numpy/oldnumeric/ma.py
+++ /dev/null
@@ -1,2296 +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.
-
-"""
-from __future__ import division, absolute_import, print_function
-
-import sys
-import types
-import warnings
-from functools import reduce
-
-import numpy.core.umath as umath
-import numpy.core.fromnumeric as fromnumeric
-import numpy.core.numeric as numeric
-from numpy.core.numeric import newaxis, ndarray, inf
-from numpy.core.fromnumeric import amax, amin
-from numpy.core.numerictypes import bool_, typecodes
-import numpy.core.numeric as numeric
-from numpy.compat import bytes, long
-
-if sys.version_info[0] >= 3:
-    _MAXINT = sys.maxsize
-    _MININT = -sys.maxsize - 1
-else:
-    _MAXINT = sys.maxint
-    _MININT = -sys.maxint - 1
-
-
-# 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, float):
-        return default_real_fill_value
-    elif isinstance(obj, int) or isinstance(obj, long):
-        return default_integer_fill_value
-    elif isinstance(obj, bytes):
-        return default_character_fill_value
-    elif isinstance(obj, complex):
-        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, float):
-        return numeric.inf
-    elif isinstance(obj, int) or isinstance(obj, long):
-        return _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 _MAXINT
-        if x in typecodes['UnsignedInteger']:
-            return _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, float):
-        return -inf
-    elif isinstance(obj, int) or isinstance(obj, long):
-        return -_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 -_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, dict):
-        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 __bool__(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 new_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, int):
-        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 new_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 new_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.
-
-def _m(f):
-    return types.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(new_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(new_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(new_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, not_implemented
-
-
-masked = MaskedArray(0, int, mask=1)
-
-def repeat(a, repeats, axis=0):
-    return new_repeat(a, repeats, axis)
-
-def average(a, axis=0, weights=None, returned=0):
-    return new_average(a, axis, weights, returned)
-
-def take(a, indices, axis=0):
-    return new_take(a, indices, axis)