[Numpy-svn] r4952 - trunk/numpy/oldnumeric

numpy-svn@scip... numpy-svn@scip...
Tue Apr 1 09:23:49 CDT 2008


Author: oliphant
Date: 2008-04-01 09:23:48 -0500 (Tue, 01 Apr 2008)
New Revision: 4952

Modified:
   trunk/numpy/oldnumeric/ma.py
Log:
Add old ma.py interface to oldnumeric compatibility layer so that it stays the same.

Modified: trunk/numpy/oldnumeric/ma.py
===================================================================
--- trunk/numpy/oldnumeric/ma.py	2008-03-30 03:04:26 UTC (rev 4951)
+++ trunk/numpy/oldnumeric/ma.py	2008-04-01 14:23:48 UTC (rev 4952)
@@ -1,14 +1,2269 @@
-# Incompatibility in that getmask and a.mask returns nomask
-#  instead of None
+"""MA: a facility for dealing with missing observations
+MA is generally used as a numpy.array look-alike.
+by Paul F. Dubois.
 
-from numpy.ma import *
-import numpy.ma as nca
+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 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, 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 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.
+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)
+
 def repeat(a, repeats, axis=0):
-    return nca.repeat(a, repeats, axis)
+    return new_repeat(a, repeats, axis)
 
 def average(a, axis=0, weights=None, returned=0):
-    return nca.average(a, axis, weights, returned)
+    return new_average(a, axis, weights, returned)
 
 def take(a, indices, axis=0):
-    return nca.average(a, indices, axis=0)
+    return new_take(a, indices, axis=0)
+
+



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