[Scipy-svn] r3245 - in trunk/Lib/sandbox/maskedarray: . tests

scipy-svn@scip... scipy-svn@scip...
Wed Aug 15 08:38:23 CDT 2007


Author: pierregm
Date: 2007-08-15 08:38:19 -0500 (Wed, 15 Aug 2007)
New Revision: 3245

Modified:
   trunk/Lib/sandbox/maskedarray/core.py
   trunk/Lib/sandbox/maskedarray/mrecords.py
   trunk/Lib/sandbox/maskedarray/tests/test_core.py
Log:
mrecords : * fixed a pb w/ numpy.void
           * returns 'masked' when accessing a masked attribute from a unique record.
core     : * modified .tolist() so that fill_value=None now outputs None for masked values

Modified: trunk/Lib/sandbox/maskedarray/core.py
===================================================================
--- trunk/Lib/sandbox/maskedarray/core.py	2007-08-15 06:04:30 UTC (rev 3244)
+++ trunk/Lib/sandbox/maskedarray/core.py	2007-08-15 13:38:19 UTC (rev 3245)
@@ -0,0 +1,2698 @@
+# pylint: disable-msg=E1002
+"""MA: a facility for dealing with missing observations
+MA is generally used as a numpy.array look-alike.
+by Paul F. Dubois.
+
+Copyright 1999, 2000, 2001 Regents of the University of California.
+Released for unlimited redistribution.
+Adapted for numpy_core 2005 by Travis Oliphant and
+(mainly) Paul Dubois.
+
+Subclassing of the base ndarray 2006 by Pierre Gerard-Marchant.
+pgmdevlist_AT_gmail_DOT_com
+Improvements suggested by Reggie Dugard (reggie_AT_merfinllc_DOT_com)
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id$
+"""
+__author__ = "Pierre GF Gerard-Marchant ($Author$)"
+__version__ = '1.0'
+__revision__ = "$Revision$"
+__date__     = '$Date$'
+
+__all__ = ['MAError', 'MaskType', 'MaskedArray',
+           'bool_', 'complex_', 'float_', 'int_', 'object_',
+           'abs', 'absolute', 'add', 'all', 'allclose', 'allequal', 'alltrue',
+               'amax', 'amin', 'anom', 'anomalies', 'any', 'arange',
+               'arccos', 'arccosh', 'arcsin', 'arcsinh', 'arctan', 'arctan2',
+               'arctanh', 'argmax', 'argmin', 'argsort', 'around',
+               'array', 'asarray',
+           'bitwise_and', 'bitwise_or', 'bitwise_xor',
+           'ceil', 'choose', 'compressed', 'concatenate', 'conjugate',
+               'cos', 'cosh', 'count',
+           'diagonal', 'divide', 'dump', 'dumps',
+           'empty', 'empty_like', 'equal', 'exp',
+           'fabs', 'fmod', 'filled', 'floor', 'floor_divide',
+           'getmask', 'getmaskarray', 'greater', 'greater_equal', 'hypot',
+           'ids', 'inner', 'innerproduct',
+               'isMA', 'isMaskedArray', 'is_mask', 'is_masked', 'isarray',
+           'left_shift', 'less', 'less_equal', 'load', 'loads', 'log', 'log10',
+               'logical_and', 'logical_not', 'logical_or', 'logical_xor',
+           'make_mask', 'make_mask_none', 'mask_or', 'masked',
+               'masked_array', 'masked_equal', 'masked_greater',
+               'masked_greater_equal', 'masked_inside', 'masked_less',
+               'masked_less_equal', 'masked_not_equal', 'masked_object',
+               'masked_outside', 'masked_print_option', 'masked_singleton',
+               'masked_values', 'masked_where', 'max', 'maximum', 'mean', 'min',
+               'minimum', 'multiply',
+           'negative', 'nomask', 'nonzero', 'not_equal',
+           'ones', 'outer', 'outerproduct',
+           'power', 'product', 'ptp', 'put', 'putmask',
+           'rank', 'ravel', 'remainder', 'repeat', 'reshape', 'resize',
+               'right_shift', 'round_',
+           'shape', 'sin', 'sinh', 'size', 'sometrue', 'sort', 'sqrt', 'std',
+               'subtract', 'sum', 'swapaxes',
+           'take', 'tan', 'tanh', 'transpose', 'true_divide',
+           'var', 'where',
+           'zeros']
+
+import sys
+import types
+import cPickle
+import operator
+#
+import numpy
+from numpy import bool_, complex_, float_, int_, object_, str_
+
+import numpy.core.umath as umath
+import numpy.core.fromnumeric  as fromnumeric
+import numpy.core.numeric as numeric
+import numpy.core.numerictypes as ntypes
+from numpy import bool_, dtype, typecodes, amax, amin, ndarray
+from numpy import expand_dims as n_expand_dims
+import warnings
+
+
+MaskType = bool_
+nomask = MaskType(0)
+
+divide_tolerance = 1.e-35
+numpy.seterr(all='ignore')
+
+# TODO: There's still a problem with N.add.reduce not working...
+# TODO: ...neither does N.add.accumulate
+
+#####--------------------------------------------------------------------------
+#---- --- Exceptions ---
+#####--------------------------------------------------------------------------
+class MAError(Exception):
+    "Class for MA related errors."
+    def __init__ (self, args=None):
+        "Creates an exception."
+        Exception.__init__(self,args)
+        self.args = args
+    def __str__(self):
+        "Calculates the string representation."
+        return str(self.args)
+    __repr__ = __str__
+
+#####--------------------------------------------------------------------------
+#---- --- Filling options ---
+#####--------------------------------------------------------------------------
+# b: boolean - c: complex - f: floats - i: integer - O: object - S: string
+default_filler = {'b': True,
+                  'c' : 1.e20 + 0.0j,
+                  'f' : 1.e20,
+                  'i' : 999999,
+                  'O' : '?',
+                  'S' : 'N/A',
+                  'u' : 999999,
+                  'V' : '???',
+                  }
+max_filler = ntypes._minvals
+max_filler.update([(k,-numeric.inf) for k in [numpy.float32, numpy.float64]])
+min_filler = ntypes._maxvals
+min_filler.update([(k,numeric.inf) for k in [numpy.float32, numpy.float64]])
+if 'float128' in ntypes.typeDict:
+    max_filler.update([(numpy.float128,-numeric.inf)])
+    min_filler.update([(numpy.float128, numeric.inf)])
+
+
+def default_fill_value(obj):
+    "Calculates the default fill value for an object `obj`."
+    if hasattr(obj,'dtype'):
+        defval = default_filler[obj.dtype.kind]
+    elif isinstance(obj, numeric.dtype):
+        defval = default_filler[obj.kind]
+    elif isinstance(obj, float):
+        defval = default_filler['f']
+    elif isinstance(obj, int) or isinstance(obj, long):
+        defval = default_filler['i']
+    elif isinstance(obj, str):
+        defval = default_filler['S']
+    elif isinstance(obj, complex):
+        defval = default_filler['c']
+    else:
+        defval = default_filler['O']
+    return defval
+
+def minimum_fill_value(obj):
+    "Calculates the default fill value suitable for taking the minimum of `obj`."
+    if hasattr(obj, 'dtype'):
+        objtype = obj.dtype
+        filler = min_filler[objtype]
+        if filler is None:
+            raise TypeError, 'Unsuitable type for calculating minimum.'
+        return filler
+    elif isinstance(obj, float):
+        return min_filler[ntypes.typeDict['float_']]
+    elif isinstance(obj, int):
+        return min_filler[ntypes.typeDict['int_']]
+    elif isinstance(obj, long):
+        return min_filler[ntypes.typeDict['uint']]
+    elif isinstance(obj, numeric.dtype):
+        return min_filler[obj]
+    else:
+        raise TypeError, 'Unsuitable type for calculating minimum.'
+
+def maximum_fill_value(obj):
+    "Calculates the default fill value suitable for taking the maximum of `obj`."
+    if hasattr(obj, 'dtype'):
+        objtype = obj.dtype
+        filler = max_filler[objtype]
+        if filler is None:
+            raise TypeError, 'Unsuitable type for calculating minimum.'
+        return filler
+    elif isinstance(obj, float):
+        return max_filler[ntypes.typeDict['float_']]
+    elif isinstance(obj, int):
+        return max_filler[ntypes.typeDict['int_']]
+    elif isinstance(obj, long):
+        return max_filler[ntypes.typeDict['uint']]
+    elif isinstance(obj, numeric.dtype):
+        return max_filler[obj]
+    else:
+        raise TypeError, 'Unsuitable type for calculating minimum.'
+
+def set_fill_value(a, fill_value):
+    "Sets the fill value of `a` if it is a masked array."
+    if isinstance(a, MaskedArray):
+        a.set_fill_value(fill_value)
+
+def get_fill_value(a):
+    """Returns the fill value of `a`, if any.
+    Otherwise, returns the default fill value for that type.
+    """
+    if isinstance(a, MaskedArray):
+        result = a.fill_value
+    else:
+        result = default_fill_value(a)
+    return result
+
+def common_fill_value(a, b):
+    "Returns the common fill_value of `a` and `b`, if any, or `None`."
+    t1 = get_fill_value(a)
+    t2 = get_fill_value(b)
+    if t1 == t2:
+        return t1
+    return None
+
+#................................................
+def filled(a, value = None):
+    """Returns `a` as an array with masked data replaced by `value`.
+If `value` is `None` 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 numpy itself.
+    """
+    if hasattr(a, 'filled'):
+        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_masked_subclass(*arrays):
+    """Returns the youngest subclass of MaskedArray from a list of arrays,
+ or MaskedArray. In case of siblings, the first takes over."""
+    if len(arrays) == 1:
+        arr = arrays[0]
+        if isinstance(arr, MaskedArray):
+            rcls = type(arr)
+        else:
+            rcls = MaskedArray
+    else:
+        arrcls = [type(a) for a in arrays]
+        rcls = arrcls[0]
+        if not issubclass(rcls, MaskedArray):
+            rcls = MaskedArray
+        for cls in arrcls[1:]:
+            if issubclass(cls, rcls):
+                rcls = cls
+    return rcls
+
+#####--------------------------------------------------------------------------
+#---- --- Ufuncs ---
+#####--------------------------------------------------------------------------
+ufunc_domain = {}
+ufunc_fills = {}
+
+class domain_check_interval:
+    """Defines a valid interval,
+so that `domain_check_interval(a,b)(x) = true` where `x < a` or `x > b`."""
+    def __init__(self, a, b):
+        "domain_check_interval(a,b)(x) = true where x < a or y > b"
+        if (a > b):
+            (a, b) = (b, a)
+        self.a = a
+        self.b = b
+
+    def __call__ (self, x):
+        "Execute the call behavior."
+        return umath.logical_or(umath.greater (x, self.b),
+                                umath.less(x, self.a))
+#............................
+class domain_tan:
+    """Defines a valid interval for the `tan` function,
+so that `domain_tan(eps) = True where `abs(cos(x)) < eps`"""
+    def __init__(self, eps):
+        "domain_tan(eps) = true where abs(cos(x)) < eps)"
+        self.eps = eps
+    def __call__ (self, x):
+        "Execute the call behavior."
+        return umath.less(umath.absolute(umath.cos(x)), self.eps)
+#............................
+class domain_safe_divide:
+    """defines a domain for safe division."""
+    def __init__ (self, tolerance=divide_tolerance):
+        self.tolerance = tolerance
+    def __call__ (self, a, b):
+        return umath.absolute(a) * self.tolerance >= umath.absolute(b)
+#............................
+class 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:
+    """Defines masked version of unary operations,
+where invalid values are pre-masked.
+
+:IVariables:
+    - `f` : function.
+    - `fill` : Default filling value *[0]*.
+    - `domain` : Default domain *[None]*.
+    """
+    def __init__ (self, mufunc, fill=0, domain=None):
+        """ 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 = mufunc
+        self.fill = fill
+        self.domain = domain
+        self.__doc__ = getattr(mufunc, "__doc__", str(mufunc))
+        self.__name__ = getattr(mufunc, "__name__", str(mufunc))
+        ufunc_domain[mufunc] = domain
+        ufunc_fills[mufunc] = fill
+    #
+    def __call__ (self, a, *args, **kwargs):
+        "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, numeric.asarray(self.domain(d1)))
+        # Take care of the masked singletong first ...
+        if m.ndim == 0 and m:
+            return masked
+        # Get the result....
+        if isinstance(a, MaskedArray):
+            result = self.f(d1, *args, **kwargs).view(type(a))
+        else:
+            result = self.f(d1, *args, **kwargs).view(MaskedArray)
+        # Fix the mask if we don't have a scalar
+        if result.ndim > 0:
+            result._mask = m
+        return result
+    #
+    def __str__ (self):
+        return "Masked version of %s. [Invalid values are masked]" % str(self.f)
+#..............................................................................
+class masked_binary_operation:
+    """Defines masked version of binary operations,
+where invalid values are pre-masked.
+
+:IVariables:
+    - `f` : function.
+    - `fillx` : Default filling value for first array*[0]*.
+    - `filly` : Default filling value for second array*[0]*.
+    - `domain` : Default domain *[None]*.
+    """
+    def __init__ (self, mbfunc, fillx=0, filly=0):
+        """abfunc(fillx, filly) must be defined.
+           abfunc(x, filly) = x for all x to enable reduce.
+        """
+        self.f = mbfunc
+        self.fillx = fillx
+        self.filly = filly
+        self.__doc__ = getattr(mbfunc, "__doc__", str(mbfunc))
+        self.__name__ = getattr(mbfunc, "__name__", str(mbfunc))
+        ufunc_domain[mbfunc] = None
+        ufunc_fills[mbfunc] = (fillx, filly)
+    #
+    def __call__ (self, a, b, *args, **kwargs):
+        "Execute the call behavior."
+        m = mask_or(getmask(a), getmask(b))
+        if (not m.ndim) and m:
+            return masked
+        d1 = filled(a, self.fillx)
+        d2 = filled(b, self.filly)
+# CHECK : Do we really need to fill the arguments ? Pro'ly not        
+#        result = self.f(a, b, *args, **kwargs).view(get_masked_subclass(a,b))
+        result = self.f(d1, d2, *args, **kwargs).view(get_masked_subclass(a,b))
+        if result.ndim > 0:
+            result._mask = m
+        return result
+    #
+    def reduce (self, target, axis=0, dtype=None):
+        """Reduces `target` along the given `axis`."""
+        if isinstance(target, MaskedArray):
+            tclass = type(target)
+        else:
+            tclass = MaskedArray
+        m = getmask(target)
+        t = filled(target, self.filly)
+        if t.shape == ():
+            t = t.reshape(1)
+            if m is not nomask:
+                m = make_mask(m, copy=1)
+                m.shape = (1,)
+        if m is nomask:
+            return self.f.reduce(t, axis).view(tclass)
+        t = t.view(tclass)
+        t._mask = m
+        # XXX: "or t.dtype" below is a workaround for what appears
+        # XXX: to be a bug in reduce.
+        tr = self.f.reduce(filled(t, self.filly), axis, dtype=dtype or t.dtype)
+        mr = umath.logical_and.reduce(m, axis)
+        tr = tr.view(tclass)
+        if mr.ndim > 0:
+            tr._mask = mr
+            return tr
+        elif mr:
+            return masked
+        return tr
+
+    def outer (self, a, b):
+        "Returns the function applied to the outer product of a and b."
+        ma = getmask(a)
+        mb = getmask(b)
+        if ma is nomask and mb is nomask:
+            m = nomask
+        else:
+            ma = getmaskarray(a)
+            mb = getmaskarray(b)
+            m = umath.logical_or.outer(ma, mb)
+        if (not m.ndim) and m:
+            return masked
+        rcls = get_masked_subclass(a,b)
+        d = self.f.outer(filled(a, self.fillx), filled(b, self.filly)).view(rcls)
+        if d.ndim > 0:
+            d._mask = m
+        return d
+
+    def accumulate (self, target, axis=0):
+        """Accumulates `target` along `axis` after filling with y fill value."""
+        if isinstance(target, MaskedArray):
+            tclass = type(target)
+        else:
+            tclass = masked_array
+        t = filled(target, self.filly)
+        return self.f.accumulate(t, axis).view(tclass)
+
+    def __str__ (self):
+        return "Masked version of " + str(self.f)
+#..............................................................................
+class domained_binary_operation:
+    """Defines binary operations that have a domain, like divide.
+
+These are complicated so they are a separate class.
+They have no reduce, outer or accumulate.
+
+:IVariables:
+    - `f` : function.
+    - `fillx` : Default filling value for first array*[0]*.
+    - `filly` : Default filling value for second array*[0]*.
+    - `domain` : Default domain *[None]*.
+    """
+    def __init__ (self, dbfunc, domain, fillx=0, filly=0):
+        """abfunc(fillx, filly) must be defined.
+           abfunc(x, filly) = x for all x to enable reduce.
+        """
+        self.f = dbfunc
+        self.domain = domain
+        self.fillx = fillx
+        self.filly = filly
+        self.__doc__ = getattr(dbfunc, "__doc__", str(dbfunc))
+        self.__name__ = getattr(dbfunc, "__name__", str(dbfunc))
+        ufunc_domain[dbfunc] = domain
+        ufunc_fills[dbfunc] = (fillx, filly)
+
+    def __call__(self, a, b):
+        "Execute the call behavior."
+        ma = getmask(a)
+        mb = getmask(b)
+        d1 = filled(a, self.fillx)
+        d2 = filled(b, self.filly)
+        t = numeric.asarray(self.domain(d1, d2))
+
+        if fromnumeric.sometrue(t, None):
+            d2 = numeric.where(t, self.filly, d2)
+            mb = mask_or(mb, t)
+        m = mask_or(ma, mb)
+        if (not m.ndim) and m:
+            return masked       
+        result =  self.f(d1, d2).view(get_masked_subclass(a,b))
+        if result.ndim > 0:
+            result._mask = m
+        return result
+
+    def __str__ (self):
+        return "Masked version of " + str(self.f)
+
+#..............................................................................
+# Unary ufuncs
+exp = 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)
+arctan = masked_unary_operation(umath.arctan)
+arcsinh = masked_unary_operation(umath.arcsinh)
+sinh = masked_unary_operation(umath.sinh)
+cosh = masked_unary_operation(umath.cosh)
+tanh = masked_unary_operation(umath.tanh)
+abs = absolute = masked_unary_operation(umath.absolute)
+fabs = masked_unary_operation(umath.fabs)
+negative = masked_unary_operation(umath.negative)
+floor = masked_unary_operation(umath.floor)
+ceil = masked_unary_operation(umath.ceil)
+around = masked_unary_operation(fromnumeric.round_)
+logical_not = masked_unary_operation(umath.logical_not)
+# Domained unary ufuncs
+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))
+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))
+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))
+# Binary ufuncs
+add = masked_binary_operation(umath.add)
+subtract = masked_binary_operation(umath.subtract)
+multiply = masked_binary_operation(umath.multiply, 1, 1)
+arctan2 = masked_binary_operation(umath.arctan2, 0.0, 1.0)
+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)
+hypot = masked_binary_operation(umath.hypot)
+# Domained binary ufuncs
+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)
+
+
+#####--------------------------------------------------------------------------
+#---- --- Mask creation functions ---
+#####--------------------------------------------------------------------------
+def getmask(a):
+    """Returns the mask of `a`, if any, or `nomask`.
+Returns `nomask` if `a` is not a masked array.
+To get an array for sure use getmaskarray."""
+    if hasattr(a, "_mask"):
+        return a._mask
+    else:
+        return nomask
+
+def getmaskarray(a):
+    """Returns the mask of `a`, if any.
+Otherwise, returns an array of `False`, with the same shape as `a`.
+    """
+    m = getmask(a)
+    if m is nomask:
+        return make_mask_none(fromnumeric.shape(a))
+    else:
+        return m
+
+def is_mask(m):
+    """Returns `True` if `m` is a legal mask.
+Does not check contents, only type.
+    """
+    try:
+        return m.dtype.type is MaskType
+    except AttributeError:
+        return False
+#
+def make_mask(m, copy=False, small_mask=True, flag=None):
+    """make_mask(m, copy=0, small_mask=0)
+Returns `m` as a mask, creating a copy if necessary or requested.
+The function can accept any sequence of integers or `nomask`.
+Does not check that contents must be 0s and 1s.
+If `small_mask=True`, returns `nomask` if `m` contains no true elements.
+
+:Parameters:
+    - `m` (ndarray) : Mask.
+    - `copy` (boolean, *[False]*) : Returns a copy of `m` if true.
+    - `small_mask` (boolean, *[False]*): Flattens mask to `nomask` if `m` is all false.
+    """
+    if flag is not None:
+        warnings.warn("The flag 'flag' is now called 'small_mask'!",
+                      DeprecationWarning)
+        small_mask = flag
+    if m is nomask:
+        return nomask
+    elif isinstance(m, ndarray):
+        m = filled(m, True)
+        if m.dtype.type is MaskType:
+            if copy:
+                result = numeric.array(m, dtype=MaskType, copy=copy)
+            else:
+                result = m
+        else:
+            result = numeric.array(m, dtype=MaskType)
+    else:
+        result = numeric.array(filled(m, True), dtype=MaskType)
+    # Bas les masques !
+    if small_mask and not result.any():
+        return nomask
+    else:
+        return result
+
+def make_mask_none(s):
+    "Returns a mask of shape `s`, filled with `False`."
+    result = numeric.zeros(s, dtype=MaskType)
+    return result
+
+def mask_or (m1, m2, copy=False, small_mask=True):
+    """Returns the combination of two masks `m1` and `m2`.
+The masks are combined with the `logical_or` operator, treating `nomask` as false.
+The result may equal m1 or m2 if the other is nomask.
+
+:Parameters:
+    - `m` (ndarray) : Mask.
+    - `copy` (boolean, *[False]*) : Returns a copy of `m` if true.
+    - `small_mask` (boolean, *[False]*): Flattens mask to `nomask` if `m` is all false.
+     """
+    if m1 is nomask:
+        return make_mask(m2, copy=copy, small_mask=small_mask)
+    if m2 is nomask:
+        return make_mask(m1, copy=copy, small_mask=small_mask)
+    if m1 is m2 and is_mask(m1):
+        return m1
+    return make_mask(umath.logical_or(m1, m2), copy=copy, small_mask=small_mask)
+
+#####--------------------------------------------------------------------------
+#--- --- Masking functions ---
+#####--------------------------------------------------------------------------
+def masked_where(condition, a, copy=True):
+    """Returns `x` as an array masked where `condition` is true.
+Masked values of `x` or `condition` are kept.
+
+:Parameters:
+    - `condition` (ndarray) : Masking condition.
+    - `x` (ndarray) : Array to mask.
+    - `copy` (boolean, *[False]*) : Returns a copy of `m` if true.
+    """
+    cond = filled(condition,1)
+    a = numeric.array(a, copy=copy, subok=True)
+    if hasattr(a, '_mask'):
+        cond = mask_or(cond, a._mask)
+        cls = type(a)
+    else:
+        cls = MaskedArray
+    result = a.view(cls)
+    result._mask = cond
+    return result
+
+def masked_greater(x, value, copy=1):
+    "Shortcut to `masked_where`, with ``condition = (x > value)``."
+    return masked_where(greater(x, value), x, copy=copy)
+
+def masked_greater_equal(x, value, copy=1):
+    "Shortcut to `masked_where`, with ``condition = (x >= value)``."
+    return masked_where(greater_equal(x, value), x, copy=copy)
+
+def masked_less(x, value, copy=True):
+    "Shortcut to `masked_where`, with ``condition = (x < value)``."
+    return masked_where(less(x, value), x, copy=copy)
+
+def masked_less_equal(x, value, copy=True):
+    "Shortcut to `masked_where`, with ``condition = (x <= value)``."
+    return masked_where(less_equal(x, value), x, copy=copy)
+
+def masked_not_equal(x, value, copy=True):
+    "Shortcut to `masked_where`, with ``condition = (x != value)``."
+    return masked_where((x != value), x, copy=copy)
+
+#
+def masked_equal(x, value, copy=True):
+    """Shortcut to `masked_where`, with ``condition = (x == value)``.
+For floating point, consider `masked_values(x, value)` instead.
+    """
+    return masked_where((x == value), x, copy=copy)
+#    d = filled(x, 0)
+#    c = umath.equal(d, value)
+#    m = mask_or(c, getmask(x))
+#    return array(d, mask=m, copy=copy)
+
+def masked_inside(x, v1, v2, copy=True):
+    """Shortcut to `masked_where`, where `condition` is True for x inside
+the interval `[v1,v2]` ``(v1 <= x <= v2)``.
+The boundaries `v1` and `v2` can be given in either order.
+    """
+    if v2 < v1:
+        (v1, v2) = (v2, v1)
+    xf = filled(x)
+    condition = (xf >= v1) & (xf <= v2)
+    return masked_where(condition, x, copy=copy)
+
+def masked_outside(x, v1, v2, copy=True):
+    """Shortcut to `masked_where`, where `condition` is True for x outside
+the interval `[v1,v2]` ``(x < v1)|(x > v2)``.
+The boundaries `v1` and `v2` can be given in either order.
+    """
+    if v2 < v1:
+        (v1, v2) = (v2, v1)
+    xf = filled(x)
+    condition = (xf < v1) | (xf > v2)
+    return masked_where(condition, x, copy=copy)
+
+#
+def masked_object(x, value, copy=True):
+    """Masks the array `x` where the data are exactly equal to `value`.
+This function is suitable only for `object` arrays: for floating point,
+please use `masked_values` instead.
+The mask is set to `nomask` if posible.
+
+:parameter copy (Boolean, *[True]*):  Returns a copy of `x` if true. """
+    if isMaskedArray(x):
+        condition = umath.equal(x._data, value)
+        mask = x._mask
+    else:
+        condition = umath.equal(fromnumeric.asarray(x), value)
+        mask = nomask
+    mask = mask_or(mask, make_mask(condition, small_mask=True))
+    return masked_array(x, mask=mask, copy=copy, fill_value=value)
+
+def masked_values(x, value, rtol=1.e-5, atol=1.e-8, copy=True):
+    """Masks the array `x` where the data are approximately equal to `value`
+(that is, ``abs(x - value) <= atol+rtol*abs(value)``).
+Suitable only for floating points. For integers, please use `masked_equal`.
+The mask is set to `nomask` if posible.
+
+:Parameters:
+    - `rtol` (Float, *[1e-5]*): Tolerance parameter.
+    - `atol` (Float, *[1e-8]*): Tolerance parameter.
+    - `copy` (boolean, *[False]*) : Returns a copy of `x` if True.
+    """
+    abs = umath.absolute
+    xnew = filled(x, value)
+    if issubclass(xnew.dtype.type, numeric.floating):
+        condition = umath.less_equal(abs(xnew-value), atol+rtol*abs(value))
+        try:
+            mask = x._mask
+        except AttributeError:
+            mask = nomask
+    else:
+        condition = umath.equal(xnew, value)
+        mask = nomask
+    mask = mask_or(mask, make_mask(condition, small_mask=True))
+    return masked_array(xnew, mask=mask, copy=copy, fill_value=value)
+
+#####--------------------------------------------------------------------------
+#---- --- Printing options ---
+#####--------------------------------------------------------------------------
+class _MaskedPrintOption:
+    """Handles the string used to represent missing data in a masked array."""
+    def __init__ (self, display):
+        "Creates the masked_print_option object."
+        self._display = display
+        self._enabled = True
+
+    def display(self):
+        "Displays the string to print for masked values."
+        return self._display
+
+    def set_display (self, s):
+        "Sets the string to print for masked values."
+        self._display = s
+
+    def enabled(self):
+        "Is the use of the display value enabled?"
+        return self._enabled
+
+    def enable(self, small_mask=1):
+        "Set the enabling small_mask to `small_mask`."
+        self._enabled = small_mask
+
+    def __str__ (self):
+        return str(self._display)
+
+    __repr__ = __str__
+
+#if you single index into a masked location you get this object.
+masked_print_option = _MaskedPrintOption('--')
+
+#####--------------------------------------------------------------------------
+#---- --- MaskedArray class ---
+#####--------------------------------------------------------------------------
+##def _getoptions(a_out, a_in):
+##    "Copies standards options of a_in to a_out."
+##    for att in [']
+#class _mathmethod(object):
+#    """Defines a wrapper for arithmetic methods.
+#Instead of directly calling a ufunc, the corresponding method of  the `array._data`
+#object is called instead.
+#    """
+#    def __init__ (self, methodname, fill_self=0, fill_other=0, domain=None):
+#        """
+#:Parameters:
+#    - `methodname` (String) : Method name.
+#    - `fill_self` (Float *[0]*) : Fill value for the instance.
+#    - `fill_other` (Float *[0]*) : Fill value for the target.
+#    - `domain` (Domain object *[None]*) : Domain of non-validity.
+#        """
+#        self.methodname = methodname
+#        self.fill_self = fill_self
+#        self.fill_other = fill_other
+#        self.domain = domain
+#        self.obj = None
+#        self.__doc__ = self.getdoc()
+#    #
+#    def getdoc(self):
+#        "Returns the doc of the function (from the doc of the method)."
+#        try:
+#            return getattr(MaskedArray, self.methodname).__doc__
+#        except:
+#            return getattr(ndarray, self.methodname).__doc__
+#    #
+#    def __get__(self, obj, objtype=None):
+#        self.obj = obj
+#        return self
+#    #
+#    def __call__ (self, other, *args):
+#        "Execute the call behavior."
+#        instance = self.obj
+#        m_self = instance._mask
+#        m_other = getmask(other)
+#        base = instance.filled(self.fill_self)
+#        target = filled(other, self.fill_other)
+#        if self.domain is not None:
+#            # We need to force the domain to a ndarray only.
+#            if self.fill_other > self.fill_self:
+#                domain = self.domain(base, target)
+#            else:
+#                domain = self.domain(target, base)
+#            if domain.any():
+#                #If `other` is a subclass of ndarray, `filled` must have the
+#                # same subclass, else we'll lose some info.
+#                #The easiest then is to fill `target` instead of creating
+#                # a pure ndarray.
+#                #Oh, and we better make a copy!
+#                if isinstance(other, ndarray):
+#                    # We don't want to modify other: let's copy target, then
+#                    target = target.copy()
+#                    target[fromnumeric.asarray(domain)] = self.fill_other
+#                else:
+#                    target = numeric.where(fromnumeric.asarray(domain),
+#                                           self.fill_other, target)
+#                m_other = mask_or(m_other, domain)
+#        m = mask_or(m_self, m_other)
+#        method = getattr(base, self.methodname)
+#        result = method(target, *args).view(type(instance))
+#        try:
+#            result._mask = m
+#        except AttributeError:
+#            if m:
+#                result = masked
+#        return result
+#...............................................................................
+class _arraymethod(object):
+    """Defines a wrapper for basic array methods.
+Upon call, returns a masked array, where the new `_data` array is the output
+of the corresponding method called on the original `_data`.
+
+If `onmask` is True, the new mask is the output of the method calld on the initial mask.
+If `onmask` is False, the new mask is just a reference to the initial mask.
+
+:Parameters:
+    `funcname` : String
+        Name of the function to apply on data.
+    `onmask` : Boolean *[True]*
+        Whether the mask must be processed also (True) or left alone (False).
+    """
+    def __init__(self, funcname, onmask=True):
+        self._name = funcname
+        self._onmask = onmask
+        self.obj = None
+        self.__doc__ = self.getdoc()
+    #
+    def getdoc(self):
+        "Returns the doc of the function (from the doc of the method)."
+        methdoc = getattr(ndarray, self._name, None)
+        methdoc = getattr(numpy, self._name, methdoc)
+#        methdoc = getattr(MaskedArray, self._name, methdoc)
+        if methdoc is not None:
+            return methdoc.__doc__
+#        try:
+#            return getattr(MaskedArray, self._name).__doc__
+#        except:
+#            try:
+#                return getattr(numpy, self._name).__doc__
+#            except:
+#                return getattr(ndarray, self._name).__doc
+    #
+    def __get__(self, obj, objtype=None):
+        self.obj = obj
+        return self
+    #
+    def __call__(self, *args, **params):
+        methodname = self._name
+        data = self.obj._data
+        mask = self.obj._mask
+        cls = type(self.obj)
+        result = getattr(data, methodname)(*args, **params).view(cls)
+        result._smallmask = self.obj._smallmask
+        if result.ndim:
+            if not self._onmask:
+                result._mask = mask
+            elif mask is not nomask:
+                result.__setmask__(getattr(mask, methodname)(*args, **params))
+        return result
+#..........................................................
+
+class flatiter(object):
+    "Defines an interator."
+    def __init__(self, ma):
+        self.ma = ma
+        self.ma_iter = numpy.asarray(ma).flat
+
+        if ma._mask is nomask:
+            self.maskiter = None
+        else:
+            self.maskiter = ma._mask.flat
+
+    def __iter__(self):
+        return self
+
+    ### This won't work is ravel makes a copy
+    def __setitem__(self, index, value):
+        a = self.ma.ravel()
+        a[index] = value
+
+    def next(self):
+        d = self.ma_iter.next()
+        if self.maskiter is not None and self.maskiter.next():
+            d = masked
+        return d
+
+
+class MaskedArray(numeric.ndarray):
+    """Arrays with possibly masked values.
+Masked values of True exclude the corresponding element from any computation.
+
+Construction:
+    x = array(data, dtype=None, copy=True, order=False,
+              mask = nomask, fill_value=None, small_mask=True)
+
+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 different from data.dtype.char then a data copy is required.
+Otherwise, the candidate is used.
+
+If a data copy is required, the raw (unmasked) 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.
+If `small_mask` is True, a mask consisting of zeros (False) only is compressed to `nomask`.
+Otherwise, the mask is not compressed.
+
+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
+    _defaultmask = nomask
+    _defaulthardmask = False
+    _baseclass =  numeric.ndarray
+    def __new__(cls, data=None, mask=nomask, dtype=None, copy=False, fill_value=None,
+                keep_mask=True, small_mask=True, hard_mask=False, flag=None,
+                subok=True, **options):
+        """array(data, dtype=None, copy=True, mask=nomask, fill_value=None)
+
+If `data` is already a ndarray, its dtype becomes the default value of dtype.
+        """
+        if flag is not None:
+            warnings.warn("The flag 'flag' is now called 'small_mask'!",
+                          DeprecationWarning)
+            small_mask = flag
+        # Process data............
+        _data = numeric.array(data, dtype=dtype, copy=copy, subok=subok)
+        _baseclass = getattr(data, '_baseclass', type(_data))
+        _basedict = getattr(data, '_basedict', getattr(data, '__dict__', None))
+        if not isinstance(data, MaskedArray): 
+            _data = _data.view(cls)
+        elif not subok:
+            _data = data.view(cls)
+        else:
+            _data = _data.view(type(data))
+        # Backwards compat .......
+        if hasattr(data,'_mask') and not isinstance(data, ndarray):
+            _data._mask = data._mask
+            _sharedmask = True
+        # Process mask ...........
+        if mask is nomask:
+            if not keep_mask:
+                _data._mask = nomask
+            if copy:
+                _data._mask = _data._mask.copy()
+        else:
+            mask = numeric.array(mask, dtype=MaskType, copy=copy)
+            if mask.shape != _data.shape:
+                (nd, nm) = (_data.size, mask.size) 
+                if nm == 1:
+                    mask = numeric.resize(mask, _data.shape)
+                elif nm == nd:
+                    mask = fromnumeric.reshape(mask, _data.shape)
+                else:
+                    msg = "Mask and data not compatible: data size is %i, "+\
+                          "mask size is %i."
+                    raise MAError, msg % (nd, nm)
+            if _data._mask is nomask:
+                _data._mask = mask
+                _data._sharedmask = True
+            else:
+                # Make a copy of the mask to avoid propagation
+                _data._sharedmask = False
+                if not keep_mask:
+                    _data._mask = mask
+                else:
+                    _data._mask = umath.logical_or(mask, _data._mask) 
+                    
+                    
+        # Update fill_value.......
+        _data._fill_value = getattr(data, '_fill_value', fill_value)
+        if _data._fill_value is None:
+            _data._fill_value = default_fill_value(_data)
+        # Process extra options ..
+        _data._hardmask = hard_mask
+        _data._smallmask = small_mask
+        _data._baseclass = _baseclass
+        _data._basedict = _basedict
+        return _data
+    #........................
+    def __array_finalize__(self,obj):
+        """Finalizes the masked array.
+        """
+        # Finalize mask ...............
+        self._mask = getattr(obj, '_mask', nomask)
+        if self._mask is not nomask:
+            self._mask.shape = self.shape
+        # Get the remaining options ...
+        self._hardmask = getattr(obj, '_hardmask', self._defaulthardmask)
+        self._smallmask = getattr(obj, '_smallmask', True)
+        self._sharedmask = True
+        self._baseclass = getattr(obj, '_baseclass', type(obj))
+        self._fill_value = getattr(obj, '_fill_value', None)
+        # Update special attributes ...
+        self._basedict = getattr(obj, '_basedict', getattr(obj, '__dict__', None))
+        if self._basedict is not None:
+            self.__dict__.update(self._basedict)
+        return
+    #..................................
+    def __array_wrap__(self, obj, context=None):
+        """Special hook for ufuncs.
+Wraps the numpy array and sets the mask according to context.
+        """
+        #TODO : Should we check for type result 
+        result = obj.view(type(self))
+        #..........
+        if context is not None:
+            result._mask = result._mask.copy()
+            (func, args, _) = context
+            m = reduce(mask_or, [getmask(arg) for arg in args])
+            # Get domain mask
+            domain = ufunc_domain.get(func, None)
+            if domain is not None:
+                if len(args) > 2:
+                    d = reduce(domain, args)
+                else:
+                    d = domain(*args)
+                if m is nomask:
+                    if d is not nomask:
+                        m = d
+                else:
+                    m |= d
+            if not m.ndim and m:
+                if m:
+                    if result.shape == ():
+                        return masked
+                    result._mask = numeric.ones(result.shape, bool_)
+            else:
+                result._mask = m
+        #....
+#        result._mask = m
+        result._fill_value = self._fill_value
+        result._hardmask = self._hardmask
+        result._smallmask = self._smallmask
+        result._baseclass = self._baseclass
+        return result
+    #.............................................
+    def __getitem__(self, indx):
+        """x.__getitem__(y) <==> x[y]
+Returns the item described by i. Not a copy as in previous versions.
+        """
+        # This test is useful, but we should keep things light...
+#        if getmask(indx) is not nomask:
+#            msg = "Masked arrays must be filled before they can be used as indices!"
+#            raise IndexError, msg
+        # super() can't work here if the underlying data is a matrix...
+        dout = (self._data).__getitem__(indx)
+        m = self._mask
+        if hasattr(dout, 'shape') and len(dout.shape) > 0:
+            # Not a scalar: make sure that dout is a MA
+            dout = dout.view(type(self))
+            dout._smallmask = self._smallmask
+            if m is not nomask:
+                # use _set_mask to take care of the shape
+                dout.__setmask__(m[indx])
+        elif m is not nomask and m[indx]:
+            return masked
+        return dout
+    #........................
+    def __setitem__(self, indx, value):
+        """x.__setitem__(i, y) <==> x[i]=y
+Sets item described by index. If value is masked, masks those locations.
+        """
+        if self is masked:
+            raise MAError, 'Cannot alter the masked element.'
+#        if getmask(indx) is not nomask:
+#            msg = "Masked arrays must be filled before they can be used as indices!"
+#            raise IndexError, msg
+        #....
+        if value is masked:
+            m = self._mask
+            if m is nomask:
+                m = make_mask_none(self.shape)
+#            else:
+#                m = m.copy()
+            m[indx] = True
+            self.__setmask__(m)
+            return
+        #....
+        dval = numeric.asarray(value).astype(self.dtype)
+        valmask = getmask(value)
+        if self._mask is nomask:
+            if valmask is not nomask:
+                self._mask = make_mask_none(self.shape)
+                self._mask[indx] = valmask
+        elif not self._hardmask:
+            _mask = self._mask.copy()
+            if valmask is nomask:
+                _mask[indx] = False
+            else:
+                _mask[indx] = valmask
+            self._set_mask(_mask)
+        elif hasattr(indx, 'dtype') and (indx.dtype==bool_):
+            indx = indx * umath.logical_not(self._mask)
+        else:
+            mindx = mask_or(self._mask[indx], valmask, copy=True)
+            dindx = self._data[indx]
+            if dindx.size > 1:
+                dindx[~mindx] = dval
+            elif mindx is nomask:
+                dindx = dval
+            dval = dindx
+            self._mask[indx] = mindx
+        # Set data ..........
+        #dval = filled(value).astype(self.dtype)
+        ndarray.__setitem__(self._data,indx,dval)
+    #............................................
+    def __getslice__(self, i, j):
+        """x.__getslice__(i, j) <==> x[i:j]
+Returns the slice described by i, j.
+The use of negative indices is not supported."""
+        return self.__getitem__(slice(i,j))
+    #........................
+    def __setslice__(self, i, j, value):
+        """x.__setslice__(i, j, value) <==> x[i:j]=value
+Sets a slice i:j to `value`.
+If `value` is masked, masks those locations."""
+        self.__setitem__(slice(i,j), value)
+    #............................................
+    def __setmask__(self, mask, copy=False):
+        newmask = make_mask(mask, copy=copy, small_mask=self._smallmask)
+#        self.unshare_mask()
+        if self._mask is nomask:
+            self._mask = newmask
+        elif self._hardmask:
+            if newmask is not nomask:
+                self._mask.__ior__(newmask)
+        else:
+            # This one is tricky: if we set the mask that way, we may break the
+            # propagation. But if we don't, we end up with a mask full of False
+            # and a test on nomask fails...
+            if newmask is nomask:
+                self._mask = nomask
+            else:
+                self._mask.flat = newmask
+        if self._mask.shape:
+            self._mask = numeric.reshape(self._mask, self.shape)
+    _set_mask = __setmask__
+    
+    def _get_mask(self):
+        """Returns the current mask."""
+        return self._mask
+
+    mask = property(fget=_get_mask, fset=__setmask__, doc="Mask")
+    #............................................
+    def harden_mask(self):
+        "Forces the mask to hard."
+        self._hardmask = True
+        
+    def soften_mask(self):
+        "Forces the mask to soft."
+        self._hardmask = False     
+        
+    def unshare_mask(self):
+        "Copies the mask and set the sharedmask flag to False."
+        if self._sharedmask:
+            self._mask = self._mask.copy()
+            self._sharedmask = False
+        
+    #............................................
+    def _get_data(self):
+        "Returns the current data (as a view of the original underlying data)>"
+        return self.view(self._baseclass)
+    _data = property(fget=_get_data)        
+    #............................................
+    def _get_flat(self):
+        """Calculates the flat value.
+        """
+        return flatiter(self)
+    #
+    def _set_flat (self, value):
+        "x.flat = value"
+        y = self.ravel()
+        y[:] = value
+    #
+    flat = property(fget=_get_flat, fset=_set_flat, doc="Flat version")
+    #............................................
+    def get_fill_value(self):
+        "Returns the filling value."
+        if self._fill_value is None:
+            self._fill_value = default_fill_value(self)
+        return self._fill_value
+
+    def set_fill_value(self, value=None):
+        """Sets the filling value to `value`.
+If None, uses the default, based on the data type."""
+        if value is None:
+            value = default_fill_value(self)
+        self._fill_value = value
+
+    fill_value = property(fget=get_fill_value, fset=set_fill_value,
+                          doc="Filling value")
+
+    def filled(self, fill_value=None):
+        """Returns an array of the same class as `_data`,
+ with masked values filled with `fill_value`.
+Subclassing is preserved.
+
+If `fill_value` is None, uses self.fill_value.
+        """
+        m = self._mask
+        if m is nomask or not m.any():
+            return self._data
+        #
+        if fill_value is None:
+            fill_value = self.fill_value
+        #
+        if self is masked_singleton:
+            result = numeric.asanyarray(fill_value)
+        else:
+            result = self._data.copy()
+            try:
+                result[m] = fill_value
+            except (TypeError, AttributeError):
+                fill_value = numeric.array(fill_value, dtype=object)
+                d = result.astype(object)
+                result = fromnumeric.choose(m, (d, fill_value))
+            except IndexError:
+                #ok, if scalar
+                if self._data.shape:
+                    raise
+                elif m:
+                    result = numeric.array(fill_value, dtype=self.dtype)
+                else:
+                    result = self._data
+        return result
+
+    def compressed(self):
+        "A 1-D array of all the non-masked data."
+        d = self.ravel()
+        if self._mask is nomask:
+            return d
+        elif not self._smallmask and not self._mask.any():
+            return d
+        else:
+            return d[numeric.logical_not(d._mask)]
+    #............................................
+    def __str__(self):
+        """x.__str__() <==> str(x)
+Calculates the string representation, using masked for fill if it is enabled.
+Otherwise, fills with fill value.
+        """
+        if masked_print_option.enabled():
+            f = masked_print_option
+            if self is masked:
+                return str(f)
+            m = self._mask
+            if m is nomask:
+                res = self._data
+            else:
+                if m.shape == ():
+                    if m:
+                        return str(f)
+                    else:
+                        return str(self._data)
+                # convert to object array to make filled work
+#CHECK: the two lines below seem more robust than the self._data.astype
+#                res = numeric.empty(self._data.shape, object_)
+#                numeric.putmask(res,~m,self._data)
+                res = self._data.astype("|O8")
+                res[m] = f
+        else:
+            res = self.filled(self.fill_value)
+        return str(res)
+
+    def __repr__(self):
+        """x.__repr__() <==> repr(x)
+Calculates the repr representation, using masked for fill if it is enabled.
+Otherwise fill with fill value.
+        """
+        with_mask = """\
+masked_%(name)s(data =
+ %(data)s,
+      mask =
+ %(mask)s,
+      fill_value=%(fill)s)
+"""
+        with_mask1 = """\
+masked_%(name)s(data = %(data)s,
+      mask = %(mask)s,
+      fill_value=%(fill)s)
+"""
+        n = len(self.shape)
+        name = repr(self._data).split('(')[0]
+        if n <= 1:
+            return with_mask1 % {
+                'name': name,
+                'data': str(self),
+                'mask': str(self._mask),
+                'fill': str(self.fill_value),
+                }
+        return with_mask % {
+            'name': name,
+            'data': str(self),
+            'mask': str(self._mask),
+            'fill': str(self.fill_value),
+            }
+    #............................................
+    def __iadd__(self, other):
+        "Adds other to self in place."
+        ndarray.__iadd__(self._data,other)
+        m = getmask(other)
+        if self._mask is nomask:
+            self._mask = m
+        elif m is not nomask:
+            self._mask += m
+        return self
+    #....
+    def __isub__(self, other):
+        "Subtracts other from self in place."
+        ndarray.__isub__(self._data,other)
+        m = getmask(other)
+        if self._mask is nomask:
+            self._mask = m
+        elif m is not nomask:
+            self._mask += m
+        return self
+    #....
+    def __imul__(self, other):
+        "Multiplies self by other in place."
+        ndarray.__imul__(self._data,other)
+        m = getmask(other)
+        if self._mask is nomask:
+            self._mask = m
+        elif m is not nomask:
+            self._mask += m
+        return self
+    #....
+    def __idiv__(self, other):
+        "Divides self by other in place."
+        dom_mask = domain_safe_divide().__call__(self, filled(other,1))
+        other_mask = getmask(other)
+        new_mask = mask_or(other_mask, dom_mask)
+        ndarray.__idiv__(self._data, other)
+        self._mask = mask_or(self._mask, new_mask)
+        return self
+    #............................................
+    def __float__(self):
+        "Converts self to float."
+        if self._mask is not nomask:
+            warnings.warn("Warning: converting a masked element to nan.")
+            return numpy.nan
+            #raise MAError, 'Cannot convert masked element to a Python float.'
+        return float(self.item())
+
+    def __int__(self):
+        "Converts self to int."
+        if self._mask is not nomask:
+            raise MAError, 'Cannot convert masked element to a Python int.'
+        return int(self.item())
+    #............................................
+    def count(self, axis=None):
+        """Counts the non-masked elements of the array along a given axis,
+and returns a masked array where the mask is True where all data are masked.
+If `axis` is None, counts all the non-masked elements, and returns either a
+scalar or the masked singleton."""
+        m = self._mask
+        s = self.shape
+        ls = len(s)
+        if m is nomask:
+            if ls == 0:
+                return 1
+            if ls == 1:
+                return s[0]
+            if axis is None:
+                return self.size
+            else:
+                n = s[axis]
+                t = list(s)
+                del t[axis]
+                return numeric.ones(t) * n
+        n1 = fromnumeric.size(m, axis)
+        n2 = m.astype(int_).sum(axis)
+        if axis is None:
+            return (n1-n2)
+        else:
+            return masked_array(n1 - n2)
+    #............................................
+    def reshape (self, *s):
+        """Reshapes the array to shape s.
+Returns a new masked array.
+If you want to modify the shape in place, please use `a.shape = s`"""
+        result = self._data.reshape(*s).view(type(self))
+        result.__dict__.update(self.__dict__)
+        if result._mask is not nomask:
+            result._mask = self._mask.copy()
+            result._mask.shape = result.shape
+        return result
+    #
+    repeat = _arraymethod('repeat')
+    #
+    def resize(self, newshape, refcheck=True, order=False):
+        """Attempts to modify size and shape of self inplace.
+        The array must own its own memory and not be referenced by other arrays.
+        Returns None.
+        """
+        try:
+            self._data.resize(newshape, refcheck, order)
+            if self.mask is not nomask:
+                self._mask.resize(newshape, refcheck, order)
+        except ValueError:
+            raise ValueError("Cannot resize an array that has been referenced "
+                             "or is referencing another array in this way.\n"
+                             "Use the resize function.")
+        return None
+    #
+    flatten = _arraymethod('flatten')
+    #
+    def put(self, indices, values, mode='raise'):
+        """Sets storage-indexed locations to corresponding values.
+a.put(values, indices, mode) sets a.flat[n] = values[n] for each n in indices.
+`values` can be scalar or an array shorter than indices, and it will be repeated,
+if necessary.
+If `values` has some masked values, the initial mask is updated in consequence,
+else the corresponding values are unmasked.
+        """
+        m = self._mask
+        # Hard mask: Get rid of the values/indices that fall on masked data
+        if self._hardmask and self._mask is not nomask:
+            mask = self._mask[indices]
+            indices = numeric.asarray(indices)
+            values = numeric.asanyarray(values)
+            values.resize(indices.shape)
+            indices = indices[~mask]
+            values = values[~mask]
+        #....
+        self._data.put(indices, values, mode=mode)
+        #....
+        if m is nomask:
+            m = getmask(values)
+        else:
+            m = m.copy()
+            if getmask(values) is nomask:
+                m.put(indices, False, mode=mode)
+            else:
+                m.put(indices, values._mask, mode=mode)
+            m = make_mask(m, copy=False, small_mask=True)
+        self._mask = m
+    #............................................
+    def ids (self):
+        """Return the address of the data and mask areas."""
+        return (self.ctypes.data, self._mask.ctypes.data)    
+    #............................................
+    def all(self, axis=None, out=None):
+        """a.all(axis) returns True if all entries along the axis are True.
+    Returns False otherwise. If axis is None, uses the flatten array.
+    Masked data are considered as True during computation.
+    Outputs a masked array, where the mask is True if all data are masked along the axis.
+    Note: the out argument is not really operational...
+        """
+        d = self.filled(True).all(axis=axis, out=out).view(type(self))
+        if d.ndim > 0:
+            d.__setmask__(self._mask.all(axis))
+        return d
+
+    def any(self, axis=None, out=None):
+        """a.any(axis) returns True if some or all entries along the axis are True.
+    Returns False otherwise. If axis is None, uses the flatten array.
+    Masked data are considered as False during computation.
+    Outputs a masked array, where the mask is True if all data are masked along the axis.
+    Note: the out argument is not really operational...
+        """
+        d = self.filled(False).any(axis=axis, out=out).view(type(self))
+        if d.ndim > 0:
+            d.__setmask__(self._mask.all(axis))
+        return d
+    
+    def nonzero(self):
+        """a.nonzero() returns a tuple of arrays
+
+    Returns a tuple of arrays, one for each dimension of a,
+    containing the indices of the non-zero elements in that
+    dimension.  The corresponding non-zero values can be obtained
+    with
+        a[a.nonzero()].
+
+    To group the indices by element, rather than dimension, use
+        transpose(a.nonzero())
+    instead. The result of this is always a 2d array, with a row for
+    each non-zero element."""
+        return numeric.asarray(self.filled(0)).nonzero()
+    #............................................
+    def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):
+        """a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
+Returns the sum along the offset diagonal of the array's indicated `axis1` and `axis2`.
+        """
+        # TODO: What are we doing with `out`?
+        m = self._mask
+        if m is nomask:
+            result = super(MaskedArray, self).trace(offset=offset, axis1=axis1,
+                                                    axis2=axis2, out=out)
+            return result.astype(dtype)
+        else:
+            D = self.diagonal(offset=offset, axis1=axis1, axis2=axis2)
+            return D.astype(dtype).sum(axis=None)
+    #............................................
+    def sum(self, axis=None, dtype=None):
+        """a.sum(axis=None, dtype=None)
+Sums the array `a` over the given axis `axis`.
+Masked values are set to 0.
+If `axis` is None, applies to a flattened version of the array.
+    """
+        if self._mask is nomask:
+            mask = nomask
+        else:
+            mask = self._mask.all(axis)
+            if (not mask.ndim) and mask:
+                return masked
+        result = self.filled(0).sum(axis, dtype=dtype).view(type(self))
+        if result.ndim > 0:
+            result.__setmask__(mask)
+        return result
+
+    def cumsum(self, axis=None, dtype=None):
+        """a.cumprod(axis=None, dtype=None)
+Returns the cumulative sum of the elements of array `a` along the given axis `axis`.
+Masked values are set to 0.
+If `axis` is None, applies to a flattened version of the array.
+        """
+        result = self.filled(0).cumsum(axis=axis, dtype=dtype).view(type(self))
+        result.__setmask__(self.mask)
+        return result
+
+    def prod(self, axis=None, dtype=None):
+        """a.prod(axis=None, dtype=None)
+Returns the product of the elements of array `a` along the given axis `axis`.
+Masked elements are set to 1.
+If `axis` is None, applies to a flattened version of the array.
+        """
+        if self._mask is nomask:
+            mask = nomask
+        else:
+            mask = self._mask.all(axis)
+            if (not mask.ndim) and mask:
+                return masked
+        result = self.filled(1).prod(axis=axis, dtype=dtype).view(type(self))
+        if result.ndim:
+            result.__setmask__(mask)
+        return result
+    product = prod
+
+    def cumprod(self, axis=None, dtype=None):
+        """a.cumprod(axis=None, dtype=None)
+Returns the cumulative product of ethe lements of array `a` along the given axis `axis`.
+Masked values are set to 1.
+If `axis` is None, applies to a flattened version of the array.
+        """
+        result = self.filled(1).cumprod(axis=axis, dtype=dtype).view(type(self))
+        result.__setmask__(self.mask)
+        return result
+
+    def mean(self, axis=None, dtype=None):
+        """a.mean(axis=None, dtype=None)
+
+    Averages the array over the given axis.  If the axis is None,
+    averages over all dimensions of the array.  Equivalent to
+
+      a.sum(axis, dtype) / size(a, axis).
+
+    The optional dtype argument is the data type for intermediate
+    calculations in the sum.
+
+    Returns a masked array, of the same class as a.
+        """
+        if self._mask is nomask:
+            return super(MaskedArray, self).mean(axis=axis, dtype=dtype)
+        else:
+            dsum = self.sum(axis=axis, dtype=dtype)
+            cnt = self.count(axis=axis)
+            return dsum*1./cnt
+
+    def anom(self, axis=None, dtype=None):
+        """a.anom(axis=None, dtype=None)
+    Returns the anomalies, or deviation from the average.
+            """
+        m = self.mean(axis, dtype)
+        if not axis:
+            return (self - m)
+        else:
+            return (self - expand_dims(m,axis))
+
+    def var(self, axis=None, dtype=None):
+        """a.var(axis=None, dtype=None)
+Returns the variance, a measure of the spread of a distribution.
+
+The variance is the average of the squared deviations from the mean,
+i.e. var = mean((x - x.mean())**2).
+        """
+        if self._mask is nomask:
+            # TODO: Do we keep super, or var _data and take a view ?
+            return super(MaskedArray, self).var(axis=axis, dtype=dtype)
+        else:
+            cnt = self.count(axis=axis)
+            danom = self.anom(axis=axis, dtype=dtype)
+            danom *= danom
+            dvar = numeric.array(danom.sum(axis) / cnt).view(type(self))
+            if axis is not None:
+                dvar._mask = mask_or(self._mask.all(axis), (cnt==1))
+            return dvar
+
+    def std(self, axis=None, dtype=None):
+        """a.std(axis=None, dtype=None)
+Returns the standard deviation, a measure of the spread of a distribution.
+
+The standard deviation is the square root of the average of the squared
+deviations from the mean, i.e. std = sqrt(mean((x - x.mean())**2)).
+        """
+        dvar = self.var(axis,dtype)
+        if axis is not None or dvar is not masked:
+            dvar = sqrt(dvar)
+        return dvar
+    #............................................
+    def argsort(self, axis=None, fill_value=None, kind='quicksort',
+                order=None):
+        """Returns an array of indices that sort 'a' along the specified axis.
+    Masked values are filled beforehand to `fill_value`.
+    If `fill_value` is None, uses the default for the data type.
+    Returns a numpy array.
+
+:Keywords:
+    `axis` : Integer *[None]*
+        Axis to be indirectly sorted (default -1)
+    `kind` : String *['quicksort']*
+        Sorting algorithm (default 'quicksort')
+        Possible values: 'quicksort', 'mergesort', or 'heapsort'
+
+    Returns: array of indices that sort 'a' along the specified axis.
+
+    This method executes an indirect sort along the given axis using the
+    algorithm specified by the kind keyword. It returns an array of indices of
+    the same shape as 'a' that index data along the given axis in sorted order.
+
+    The various sorts are characterized by average speed, worst case
+    performance, need for work space, and whether they are stable. A stable
+    sort keeps items with the same key in the same relative order. The three
+    available algorithms have the following properties:
+
+    |------------------------------------------------------|
+    |    kind   | speed |  worst case | work space | stable|
+    |------------------------------------------------------|
+    |'quicksort'|   1   | O(n^2)      |     0      |   no  |
+    |'mergesort'|   2   | O(n*log(n)) |    ~n/2    |   yes |
+    |'heapsort' |   3   | O(n*log(n)) |     0      |   no  |
+    |------------------------------------------------------|
+
+    All the sort algorithms make temporary copies of the data when the sort is not
+    along the last axis. Consequently, sorts along the last axis are faster and use
+    less space than sorts along other axis.
+        """
+        if fill_value is None:
+            fill_value = default_fill_value(self)
+        d = self.filled(fill_value).view(ndarray)
+        return d.argsort(axis=axis, kind=kind, order=order)
+    #........................
+    def argmin(self, axis=None, fill_value=None):
+        """Returns a ndarray of indices for the minimum values of `a` along the
+    specified axis.
+    Masked values are treated as if they had the value `fill_value`.
+    If `fill_value` is None, the default for the data type is used.
+    Returns a numpy array.
+
+:Keywords:
+    `axis` : Integer *[None]*
+        Axis to be indirectly sorted (default -1)
+    `fill_value` : var *[None]*
+        Default filling value. If None, uses the minimum default for the data type.
+        """
+        if fill_value is None:
+            fill_value = minimum_fill_value(self)
+        d = self.filled(fill_value).view(ndarray)
+        return d.argmin(axis)
+    #........................
+    def argmax(self, axis=None, fill_value=None):
+        """Returns the array of indices for the maximum values of `a` along the
+    specified axis.
+    Masked values are treated as if they had the value `fill_value`.
+    If `fill_value` is None, the maximum default for the data type is used.
+    Returns a numpy array.
+
+:Keywords:
+    `axis` : Integer *[None]*
+        Axis to be indirectly sorted (default -1)
+    `fill_value` : var *[None]*
+        Default filling value. If None, uses the data type default.
+        """
+        if fill_value is None:
+            fill_value = maximum_fill_value(self._data)
+        d = self.filled(fill_value).view(ndarray)
+        return d.argmax(axis)
+
+    def sort(self, axis=-1, kind='quicksort', order=None, 
+             endwith=True, fill_value=None):
+        """
+        Sort a along the given axis.
+
+    Keyword arguments:
+
+    axis  -- axis to be sorted (default -1)
+    kind  -- sorting algorithm (default 'quicksort')
+             Possible values: 'quicksort', 'mergesort', or 'heapsort'.
+    order -- If a has fields defined, then the order keyword can be the
+             field name to sort on or a list (or tuple) of field names
+             to indicate the order that fields should be used to define
+             the sort.
+    endwith--Boolean flag indicating whether missing values (if any) should
+             be forced in the upper indices (at the end of the array) or
+             lower indices (at the beginning).
+
+    Returns: None.
+
+    This method sorts 'a' in place along the given axis using the algorithm
+    specified by the kind keyword.
+
+    The various sorts may characterized by average speed, worst case
+    performance, need for work space, and whether they are stable. A stable
+    sort keeps items with the same key in the same relative order and is most
+    useful when used with argsort where the key might differ from the items
+    being sorted. The three available algorithms have the following properties:
+
+    |------------------------------------------------------|
+    |    kind   | speed |  worst case | work space | stable|
+    |------------------------------------------------------|
+    |'quicksort'|   1   | O(n^2)      |     0      |   no  |
+    |'mergesort'|   2   | O(n*log(n)) |    ~n/2    |   yes |
+    |'heapsort' |   3   | O(n*log(n)) |     0      |   no  |
+    |------------------------------------------------------|
+
+    """
+        if self._mask is nomask:
+            ndarray.sort(self,axis=axis, kind=kind, order=order)
+        else:
+            if fill_value is None:
+                if endwith:
+                    filler = minimum_fill_value(self)
+                else:
+                    filler = maximum_fill_value(self)
+            else:
+                filler = fill_value
+            idx = numpy.indices(self.shape)
+            idx[axis] = self.filled(filler).argsort(axis=axis,kind=kind,order=order)
+            idx_l = idx.tolist()
+            tmp_mask = self._mask[idx_l].flat
+            tmp_data = self._data[idx_l].flat
+            self.flat = tmp_data
+            self._mask.flat = tmp_mask
+        return
+    #............................................
+    def min(self, axis=None, fill_value=None):
+        """Returns the minimum/a along the given axis.
+If `axis` is None, applies to the flattened array. Masked values are filled 
+with `fill_value` during processing. If `fill_value is None, it is set to the
+maximum_fill_value corresponding to the data type."""
+        mask = self._mask
+        # Check all/nothing case ......
+        if mask is nomask:
+            return super(MaskedArray, self).min(axis=axis)
+        elif (not mask.ndim) and mask:
+            return masked
+        # Get the mask ................
+        if axis is None:
+            mask = umath.logical_and.reduce(mask.flat)
+        else:
+            mask = umath.logical_and.reduce(mask, axis=axis)
+        # Get the fil value ...........
+        if fill_value is None:
+            fill_value = minimum_fill_value(self)
+        # Get the data ................
+        result = self.filled(fill_value).min(axis=axis).view(type(self))
+        if result.ndim > 0:
+            result._mask = mask
+        return result
+    #........................
+    def max(self, axis=None, fill_value=None):
+        """Returns the maximum/a along the given axis.
+If `axis` is None, applies to the flattened array. Masked values are filled 
+with `fill_value` during processing. If `fill_value is None, it is set to the
+maximum_fill_value corresponding to the data type."""
+        mask = self._mask
+        # Check all/nothing case ......
+        if mask is nomask:
+            return super(MaskedArray, self).max(axis=axis)
+        elif (not mask.ndim) and mask:
+            return masked
+        # Check the mask ..............
+        if axis is None:
+            mask = umath.logical_and.reduce(mask.flat)
+        else:
+            mask = umath.logical_and.reduce(mask, axis=axis)
+        # Get the fill value ..........
+        if fill_value is None:
+            fill_value = maximum_fill_value(self)
+        # Get the data ................
+        result = self.filled(fill_value).max(axis=axis).view(type(self))
+        if result.ndim > 0:
+            result._mask = mask
+        return result
+    #........................
+    def ptp(self, axis=None, fill_value=None):
+        """Returns the visible data range (max-min) along the given axis.
+If the axis is `None`, applies on a flattened array. Masked values are filled
+with `fill_value` for processing. If `fill_value` is None, the maximum is uses
+the maximum default, the minimum uses the minimum default."""
+        return self.max(axis, fill_value) - self.min(axis, fill_value)
+
+    # Array methods ---------------------------------------
+    conj = conjugate = _arraymethod('conjugate')
+    copy = _arraymethod('copy')
+    diagonal = _arraymethod('diagonal')
+    take = _arraymethod('take')
+    ravel = _arraymethod('ravel')
+    transpose = _arraymethod('transpose')
+    T = property(fget=lambda self:self.transpose())
+    swapaxes = _arraymethod('swapaxes')
+    clip = _arraymethod('clip', onmask=False)
+    compress = _arraymethod('compress')
+    copy = _arraymethod('copy')
+    squeeze = _arraymethod('squeeze')
+    #--------------------------------------------
+    def tolist(self, fill_value=None):
+        """Copies the data portion of the array to a hierarchical python list and
+    returns that list. Data items are converted to the nearest compatible Python 
+    type. 
+    Masked values are converted to `fill_value`. If `fill_value` is None, the
+    corresponding entries in the output list will be None.
+    """
+        if fill_value is not None:
+            return self.filled(fill_value).tolist()
+        result = self.filled().tolist()
+        if self._mask is nomask:
+            return result
+        if self.ndim == 0:
+            return [None]
+        elif self.ndim == 1:
+            maskedidx = self._mask.nonzero()[0].tolist()
+            [operator.setitem(result,i,None) for i in maskedidx]
+        else:
+            for idx in zip(*[i.tolist() for i in self._mask.nonzero()]):
+                tmp = result
+                for i in idx[:-1]:
+                    tmp = tmp[i]
+                tmp[idx[-1]] = None
+        return result
+            
+            
+    #........................
+    def tostring(self, fill_value=None):
+        """a.tostring(order='C', fill_value=None) -> raw copy of array data as a Python string.
+
+    Keyword arguments:
+        order      : order of the data item in the copy {"C","F","A"} (default "C")
+        fill_value : value used in lieu of missing data 
+
+    Construct a Python string containing the raw bytes in the array. The order
+    of the data in arrays with ndim > 1 is specified by the 'order' keyword and
+    this keyword overrides the order of the array. The
+    choices are:
+
+        "C"       -- C order (row major)
+        "Fortran" -- Fortran order (column major)
+        "Any"     -- Current order of array.
+        None      -- Same as "Any"
+    
+    Masked data are filled with fill_value. If fill_value is None, the data-type-
+    dependent default is used."""
+        return self.filled(fill_value).tostring()   
+    #--------------------------------------------
+    # Backwards Compatibility. Heck...
+    @property
+    def data(self):
+        """Returns the `_data` part of the MaskedArray."""
+        return self._data
+    def raw_data(self):
+        """Returns the `_data` part of the MaskedArray.
+You should really use `data` instead..."""
+        return self._data
+    #--------------------------------------------
+    # Pickling
+    def __getstate__(self):
+        "Returns the internal state of the masked array, for pickling purposes."
+        state = (1,
+                 self.shape,
+                 self.dtype,
+                 self.flags.fnc,
+                 self._data.tostring(),
+                 getmaskarray(self).tostring(),
+                 self._fill_value,
+                 )
+        return state    
+    #
+    def __setstate__(self, state):
+        """Restores the internal state of the masked array, for pickling purposes.
+    `state` is typically the output of the ``__getstate__`` output, and is a 5-tuple:
+    
+        - class name
+        - a tuple giving the shape of the data
+        - a typecode for the data
+        - a binary string for the data
+        - a binary string for the mask.
+            """
+        (ver, shp, typ, isf, raw, msk, flv) = state
+        ndarray.__setstate__(self, (shp, typ, isf, raw))
+        self._mask.__setstate__((shp, dtype(bool), isf, msk))
+        self.fill_value = flv
+    #
+    def __reduce__(self):
+        """Returns a 3-tuple for pickling a MaskedArray."""
+        return (_mareconstruct,
+                (self.__class__, self._baseclass, (0,), 'b', ),
+                self.__getstate__())
+    
+    
+def _mareconstruct(subtype, baseclass, baseshape, basetype,):
+    """Internal function that builds a new MaskedArray from the information stored
+in a pickle."""
+    _data = ndarray.__new__(baseclass, baseshape, basetype)
+    _mask = ndarray.__new__(ndarray, baseshape, 'b1')
+    return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype, small_mask=False)
+#MaskedArray.__dump__ = dump
+#MaskedArray.__dumps__ = dumps
+    
+    
+
+#####--------------------------------------------------------------------------
+#---- --- Shortcuts ---
+#####---------------------------------------------------------------------------
+def isMaskedArray(x):
+    "Is x a masked array, that is, an instance of MaskedArray?"
+    return isinstance(x, MaskedArray)
+isarray = isMaskedArray
+isMA = isMaskedArray  #backward compatibility
+#masked = MaskedArray(0, int, mask=1)
+masked_singleton = MaskedArray(0, dtype=int_, mask=True)
+masked = masked_singleton
+
+masked_array = MaskedArray
+def array(data, dtype=None, copy=False, order=False, mask=nomask, subok=True,
+          keep_mask=True, small_mask=True, hard_mask=None, fill_value=None):
+    """array(data, dtype=None, copy=True, order=False, mask=nomask,
+             keep_mask=True, small_mask=True, fill_value=None)
+Acts as shortcut to MaskedArray, with options in a different order for convenience.
+And backwards compatibility...
+    """
+    #TODO: we should try to put 'order' somwehere
+    return MaskedArray(data, mask=mask, dtype=dtype, copy=copy, subok=subok,
+                       keep_mask=keep_mask, small_mask=small_mask,
+                       hard_mask=hard_mask, fill_value=fill_value)
+
+def is_masked(x):
+    """Returns whether x has some masked values."""
+    m = getmask(x)
+    if m is nomask:
+        return False
+    elif m.any():
+        return True
+    return False
+
+
+#####---------------------------------------------------------------------------
+#---- --- Extrema functions ---
+#####---------------------------------------------------------------------------
+class _extrema_operation(object):
+    "Generic class for maximum/minimum functions."
+    def __call__(self, a, b=None):
+        "Executes the call behavior."
+        if b is None:
+            return self.reduce(a)
+        return where(self.compare(a, b), a, b)
+    #.........
+    def reduce(self, target, axis=None):
+        """Reduces target along the given axis."""
+        m = getmask(target)
+        if axis is not None:
+            kargs = { 'axis' : axis }
+        else:
+            kargs = {}
+            target = target.ravel()
+            if not (m is nomask):
+                m = m.ravel()
+        if m is nomask:
+            t = self.ufunc.reduce(target, **kargs)
+        else:
+            target = target.filled(self.fill_value_func(target)).view(type(target))
+            t = self.ufunc.reduce(target, **kargs)
+            m = umath.logical_and.reduce(m, **kargs)
+            if hasattr(t, '_mask'):
+                t._mask = m
+            elif m:
+                t = masked
+        return t
+    #.........
+    def outer (self, a, b):
+        "Returns the function applied to the outer product of a and b."
+        ma = getmask(a)
+        mb = getmask(b)
+        if ma is nomask and mb is nomask:
+            m = nomask
+        else:
+            ma = getmaskarray(a)
+            mb = getmaskarray(b)
+            m = logical_or.outer(ma, mb)
+        result = self.ufunc.outer(filled(a), filled(b))
+        result._mask = m
+        return result
+#............................
+class _minimum_operation(_extrema_operation):
+    "Object to calculate minima"
+    def __init__ (self):
+        """minimum(a, b) or minimum(a)
+In one argument case, returns the scalar minimum.
+        """
+        self.ufunc = umath.minimum
+        self.afunc = amin
+        self.compare = less
+        self.fill_value_func = minimum_fill_value
+#............................
+class _maximum_operation(_extrema_operation):
+    "Object to calculate maxima"
+    def __init__ (self):
+        """maximum(a, b) or maximum(a)
+           In one argument case returns the scalar maximum.
+        """
+        self.ufunc = umath.maximum
+        self.afunc = amax
+        self.compare = greater
+        self.fill_value_func = maximum_fill_value
+#..........................................................
+def min(array, axis=None, out=None):
+    """Returns the minima along the given axis.
+If `axis` is None, applies to the flattened array."""
+    if out is not None:
+        raise TypeError("Output arrays Unsupported for masked arrays")
+    if axis is None:
+        return minimum(array)
+    else:
+        return minimum.reduce(array, axis)
+#............................
+def max(obj, axis=None, out=None):
+    """Returns the maxima along the given axis.
+If `axis` is None, applies to the flattened array."""
+    if out is not None:
+        raise TypeError("Output arrays Unsupported for masked arrays")
+    if axis is None:
+        return maximum(obj)
+    else:
+        return maximum.reduce(obj, axis)
+#.............................
+def ptp(obj, axis=None):
+    """a.ptp(axis=None) =  a.max(axis)-a.min(axis)"""
+    try:
+        return obj.max(axis)-obj.min(axis)
+    except AttributeError:
+        return max(obj, axis=axis) - min(obj, axis=axis)
+
+
+#####---------------------------------------------------------------------------
+#---- --- Definition of functions from the corresponding methods ---
+#####---------------------------------------------------------------------------
+class _frommethod:
+    """Defines functions from existing MaskedArray methods.
+:ivar _methodname (String): Name of the method to transform.
+    """
+    def __init__(self, methodname):
+        self._methodname = methodname
+        self.__doc__ = self.getdoc()
+    def getdoc(self):
+        "Returns the doc of the function (from the doc of the method)."
+        try:
+            return getattr(MaskedArray, self._methodname).__doc__
+        except:
+            return getattr(numpy, self._methodname).__doc__
+    def __call__(self, a, *args, **params):
+        if isinstance(a, MaskedArray):
+            return getattr(a, self._methodname).__call__(*args, **params)
+        #FIXME ----
+        #As x is not a MaskedArray, we transform it to a ndarray with asarray
+        #... and call the corresponding method.
+        #Except that sometimes it doesn't work (try reshape([1,2,3,4],(2,2)))
+        #we end up with a "SystemError: NULL result without error in PyObject_Call"
+        #A dirty trick is then to call the initial numpy function...
+        method = getattr(fromnumeric.asarray(a), self._methodname)
+        try:
+            return method(*args, **params)
+        except SystemError:
+            return getattr(numpy,self._methodname).__call__(a, *args, **params)
+
+all = _frommethod('all')
+anomalies = anom = _frommethod('anom')
+any = _frommethod('any')
+conjugate = _frommethod('conjugate')
+ids = _frommethod('ids')
+nonzero = _frommethod('nonzero')
+diagonal = _frommethod('diagonal')
+maximum = _maximum_operation()
+mean = _frommethod('mean')
+minimum = _minimum_operation ()
+product = _frommethod('prod')
+ptp = _frommethod('ptp')
+ravel = _frommethod('ravel')
+repeat = _frommethod('repeat')
+std = _frommethod('std')
+sum = _frommethod('sum')
+swapaxes = _frommethod('swapaxes')
+take = _frommethod('take')
+var = _frommethod('var')
+
+#..............................................................................
+def power(a, b, third=None):
+    """Computes a**b elementwise.
+    Masked values are set to 1."""
+    if third is not None:
+        raise MAError, "3-argument power not supported."
+    ma = getmask(a)
+    mb = getmask(b)
+    m = mask_or(ma, mb)
+    fa = 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((fa < 0), small_mask=1)
+    m = mask_or(m, md)
+    if m is nomask:
+        return masked_array(umath.power(fa, fb))
+    else:
+        fa[m] = 1
+        return masked_array(umath.power(fa, fb), m)
+
+#..............................................................................
+def argsort(a, axis=None, kind='quicksort', order=None, fill_value=None):
+    """Returns an array of indices that sort 'a' along the specified axis.
+    Masked values are filled beforehand to `fill_value`.
+    If `fill_value` is None, uses the default for the data type.
+    Returns a numpy array.
+
+:Keywords:
+    `axis` : Integer *[None]*
+        Axis to be indirectly sorted (default -1)
+    `kind` : String *['quicksort']*
+        Sorting algorithm (default 'quicksort')
+        Possible values: 'quicksort', 'mergesort', or 'heapsort'
+
+    Returns: array of indices that sort 'a' along the specified axis.
+
+    This method executes an indirect sort along the given axis using the
+    algorithm specified by the kind keyword. It returns an array of indices of
+    the same shape as 'a' that index data along the given axis in sorted order.
+
+    The various sorts are characterized by average speed, worst case
+    performance, need for work space, and whether they are stable. A stable
+    sort keeps items with the same key in the same relative order. The three
+    available algorithms have the following properties:
+
+    |------------------------------------------------------|
+    |    kind   | speed |  worst case | work space | stable|
+    |------------------------------------------------------|
+    |'quicksort'|   1   | O(n^2)      |     0      |   no  |
+    |'mergesort'|   2   | O(n*log(n)) |    ~n/2    |   yes |
+    |'heapsort' |   3   | O(n*log(n)) |     0      |   no  |
+    |------------------------------------------------------|
+
+    All the sort algorithms make temporary copies of the data when the sort is not
+    along the last axis. Consequently, sorts along the last axis are faster and use
+    less space than sorts along other axis.
+    """
+    if fill_value is None:
+        fill_value = default_fill_value(a)
+    d = filled(a, fill_value)
+    if axis is None:
+        return d.argsort(kind=kind, order=order)
+    return d.argsort(axis, kind=kind, order=order)
+
+def argmin(a, axis=None, fill_value=None):
+    """Returns the array of indices for the minimum values of `a` along the
+    specified axis.
+    Masked values are treated as if they had the value `fill_value`.
+    If `fill_value` is None, the default for the data type is used.
+    Returns a numpy array.
+
+:Keywords:
+    `axis` : Integer *[None]*
+        Axis to be indirectly sorted (default -1)
+    `fill_value` : var *[None]*
+        Default filling value. If None, uses the data type default.
+    """
+    if fill_value is None:
+        fill_value = default_fill_value(a)
+    d = filled(a, fill_value)
+    return d.argmin(axis=axis)
+
+def argmax(a, axis=None, fill_value=None):
+    """Returns the array of indices for the maximum values of `a` along the
+    specified axis.
+    Masked values are treated as if they had the value `fill_value`.
+    If `fill_value` is None, the default for the data type is used.
+    Returns a numpy array.
+
+:Keywords:
+    `axis` : Integer *[None]*
+        Axis to be indirectly sorted (default -1)
+    `fill_value` : var *[None]*
+        Default filling value. If None, uses the data type default.
+    """
+    if fill_value is None:
+        fill_value = default_fill_value(a)
+        try:
+            fill_value = - fill_value
+        except:
+            pass
+    d = filled(a, fill_value)
+    return d.argmax(axis=axis)
+
+def sort(a, axis=-1, kind='quicksort', order=None, endwith=True, fill_value=None):
+    """
+    Sort a along the given axis.
+
+Keyword arguments:
+
+axis  -- axis to be sorted (default -1)
+kind  -- sorting algorithm (default 'quicksort')
+         Possible values: 'quicksort', 'mergesort', or 'heapsort'.
+order -- If a has fields defined, then the order keyword can be the
+         field name to sort on or a list (or tuple) of field names
+         to indicate the order that fields should be used to define
+         the sort.
+endwith--Boolean flag indicating whether missing values (if any) should
+         be forced in the upper indices (at the end of the array) or
+         lower indices (at the beginning).
+
+Returns: None.
+
+This method sorts 'a' in place along the given axis using the algorithm
+specified by the kind keyword.
+
+The various sorts may characterized by average speed, worst case
+performance, need for work space, and whether they are stable. A stable
+sort keeps items with the same key in the same relative order and is most
+useful when used with argsort where the key might differ from the items
+being sorted. The three available algorithms have the following properties:
+
+|------------------------------------------------------|
+|    kind   | speed |  worst case | work space | stable|
+|------------------------------------------------------|
+|'quicksort'|   1   | O(n^2)      |     0      |   no  |
+|'mergesort'|   2   | O(n*log(n)) |    ~n/2    |   yes |
+|'heapsort' |   3   | O(n*log(n)) |     0      |   no  |
+|------------------------------------------------------|
+
+All the sort algorithms make temporary copies of the data when the sort is
+not along the last axis. Consequently, sorts along the last axis are faster
+and use less space than sorts along other axis.
+
+"""
+    a = numeric.asanyarray(a)
+    if fill_value is None:
+        if endwith:
+            filler = minimum_fill_value(a)
+        else:
+            filler = maximum_fill_value(a)
+    else:
+        filler = fill_value
+#    return
+    indx = numpy.indices(a.shape).tolist()
+    indx[axis] = filled(a,filler).argsort(axis=axis,kind=kind,order=order)
+    return a[indx]
+
+def compressed(x):
+    """Returns a compressed version of a masked array (or just the array if it
+    wasn't masked first)."""
+    if getmask(x) is None:
+        return x
+    else:
+        return x.compressed()
+
+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 concatenate(arrays, axis=0):
+    "Concatenates the arrays along the given axis"
+    d = numeric.concatenate([filled(a) for a in arrays], axis)
+    rcls = get_masked_subclass(*arrays)
+    data = d.view(rcls)
+    for x in arrays:
+        if getmask(x) is not nomask:
+            break
+    else:
+        return data
+    dm = numeric.concatenate([getmaskarray(a) for a in arrays], axis)
+    dm = make_mask(dm, copy=False, small_mask=True)
+    data._mask = dm
+    return data
+
+def expand_dims(x,axis):
+    """Expand the shape of a by including newaxis before given axis."""
+    result = n_expand_dims(x,axis)
+    if isinstance(x, MaskedArray):
+        new_shape = result.shape
+        result = x.view()
+        result.shape = new_shape
+        if result._mask is not nomask:
+            result._mask.shape = new_shape
+    return result
+
+#......................................
+def left_shift (a, n):
+    "Left shift n bits"
+    m = getmask(a)
+    if m is nomask:
+        d = umath.left_shift(filled(a), n)
+        return masked_array(d)
+    else:
+        d = umath.left_shift(filled(a, 0), n)
+        return masked_array(d, mask=m)
+
+def right_shift (a, n):
+    "Right shift n bits"
+    m = getmask(a)
+    if m is nomask:
+        d = umath.right_shift(filled(a), n)
+        return masked_array(d)
+    else:
+        d = umath.right_shift(filled(a, 0), n)
+        return masked_array(d, mask=m)
+#......................................
+def put(a, indices, values, mode='raise'):
+    """Sets storage-indexed locations to corresponding values.
+    Values and indices are filled if necessary."""
+    # We can't use 'frommethod', the order of arguments is different
+    try:
+        return a.put(indices, values, mode=mode)
+    except AttributeError:
+        return fromnumeric.asarray(a).put(indices, values, mode=mode)
+
+def putmask(a, mask, values): #, mode='raise'):
+    """`putmask(a, mask, v)` results in `a = v` for all places where `mask` is true.
+If `v` is shorter than `mask`, it will be repeated as necessary.
+In particular `v` can be a scalar or length 1 array."""
+    # We can't use 'frommethod', the order of arguments is different
+    try:
+        return a.putmask(values, mask)
+    except AttributeError:
+        return fromnumeric.asarray(a).putmask(values, mask)
+
+def transpose(a,axes=None):
+    """Returns a view of the array with dimensions permuted according to axes.
+If `axes` is None (default), returns array with dimensions reversed.
+    """
+    #We can't use 'frommethod', as 'transpose' doesn't take keywords
+    try:
+        return a.transpose(axes)
+    except AttributeError:
+        return fromnumeric.asarray(a).transpose(axes)
+
+def reshape(a, new_shape):
+    """Changes the shape of the array `a` to `new_shape`."""
+    #We can't use 'frommethod', it whine about some parameters. Dmmit.
+    try:
+        return a.reshape(new_shape)
+    except AttributeError:
+        return fromnumeric.asarray(a).reshape(new_shape)
+
+def resize(x, new_shape):
+    """resize(a,new_shape) returns a new array with the specified shape.
+    The total size of the original array can be any size.
+    The new array is filled with repeated copies of a. If a was masked, the new
+    array will be masked, and the new mask will be a repetition of the old one.
+    """
+    # We can't use _frommethods here, as N.resize is notoriously whiny.
+    m = getmask(x)
+    if m is not nomask:
+        m = fromnumeric.resize(m, new_shape)
+    result = fromnumeric.resize(x, new_shape).view(get_masked_subclass(x))
+    if result.ndim:
+        result._mask = m
+    return result
+
+
+#................................................
+def rank(obj):
+    """Gets the rank of sequence a (the number of dimensions, not a matrix rank)
+The rank of a scalar is zero."""
+    return fromnumeric.rank(filled(obj))
+#
+def shape(obj):
+    """Returns the shape of `a` (as a function call which also works on nested sequences).
+    """
+    return fromnumeric.shape(filled(obj))
+#
+def size(obj, axis=None):
+    """Returns the number of elements in the array along the given axis,
+or in the sequence if `axis` is None.
+    """
+    return fromnumeric.size(filled(obj), axis)
+#................................................
+
+#####--------------------------------------------------------------------------
+#---- --- Extra functions ---
+#####--------------------------------------------------------------------------
+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=False, small_mask=True)
+    return masked_array(d, mask=m)
+
+def choose (indices, t, out=None, mode='raise'):
+    "Returns array shaped like indices with elements chosen from t"
+    #TODO: implement options `out` and `mode`, if possible.
+    def fmask (x):
+        "Returns the filled array, or True if ``masked``."
+        if x is masked:
+            return 1
+        return filled(x)
+    def nmask (x):
+        "Returns the mask, True if ``masked``, False if ``nomask``."
+        if x is masked:
+            return 1
+        m = getmask(x)
+        if m is nomask:
+            return 0
+        return m
+    c = filled(indices, 0)
+    masks = [nmask(x) for x in t]
+    a = [fmask(x) for x in t]
+    d = numeric.choose(c, a)
+    m = numeric.choose(c, masks)
+    m = make_mask(mask_or(m, getmask(indices)), copy=0, small_mask=1)
+    return masked_array(d, mask=m)
+
+def round_(a, decimals=0, out=None):
+    """Returns reference to result. Copies a and rounds to 'decimals' places.
+
+    Keyword arguments:
+        decimals -- number of decimals to round to (default 0). May be negative.
+        out -- existing array to use for output (default copy of a).
+
+    Return:
+        Reference to out, where None specifies a copy of the original array a.
+
+    Round to the specified number of decimals. When 'decimals' is negative it
+    specifies the number of positions to the left of the decimal point. The
+    real and imaginary parts of complex numbers are rounded separately.
+    Nothing is done if the array is not of float type and 'decimals' is greater
+    than or equal to 0."""
+    result = fromnumeric.round_(filled(a), decimals, out)
+    if isinstance(a,MaskedArray):
+        result = result.view(type(a))
+        result._mask = a._mask
+    else:
+        result = result.view(MaskedArray)
+    return result
+
+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),mask=nomask)
+
+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, mask=m)
+outerproduct = outer
+
+def allequal (a, b, fill_value=True):
+    """
+Returns `True` if all entries of  a and b are equal, using
+fill_value as a truth value where either or both are masked.
+    """
+    m = mask_or(getmask(a), getmask(b))
+    if m is nomask:
+        x = filled(a)
+        y = filled(b)
+        d = umath.equal(x, y)
+        return d.all()
+    elif fill_value:
+        x = filled(a)
+        y = filled(b)
+        d = umath.equal(x, y)
+        dm = array(d, mask=m, copy=False)
+        return dm.filled(True).all(None)
+    else:
+        return False
+
+def allclose (a, b, fill_value=True, rtol=1.e-5, atol=1.e-8):
+    """ Returns `True` if all elements of `a` and `b` are equal subject to given tolerances.
+If `fill_value` is True, masked values are considered equal.
+If `fill_value` is False, masked values considered unequal.
+The relative error rtol should be positive and << 1.0
+The absolute error `atol` comes into play for those elements of `b`
+ that are very small or zero; it says how small `a` must be also.
+    """
+    m = mask_or(getmask(a), getmask(b))
+    d1 = 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 asarray(a, dtype=None):
+    """asarray(data, dtype) = array(data, dtype, copy=0)
+Returns `a` as an masked array.
+No copy is performed if `a` is already an array.
+Subclasses are converted to base class MaskedArray.
+    """
+    return masked_array(a, dtype=dtype, copy=False, keep_mask=True)
+
+def empty(new_shape, dtype=float):
+    """empty((d1,...,dn),dtype=float,order='C')
+Returns a new array of shape (d1,...,dn) and given type with all its
+entries uninitialized. This can be faster than zeros."""
+    return numeric.empty(new_shape, dtype).view(MaskedArray)
+
+def empty_like(a):
+    """empty_like(a)
+Returns an empty (uninitialized) array of the shape and typecode of a.
+Note that this does NOT initialize the returned array.
+If you require your array to be initialized, you should use zeros_like()."""
+    return numeric.empty_like(a).view(MaskedArray)
+
+def ones(new_shape, dtype=float):
+    """ones(shape, dtype=None)
+Returns an array of the given dimensions, initialized to all ones."""
+    return numeric.ones(new_shape, dtype).view(MaskedArray)
+
+def zeros(new_shape, dtype=float):
+    """zeros(new_shape, dtype=None)
+Returns an array of the given dimensions, initialized to all zeros."""
+    return numeric.zeros(new_shape, dtype).view(MaskedArray)
+
+#####--------------------------------------------------------------------------
+#---- --- Pickling ---
+#####--------------------------------------------------------------------------
+def dump(a,F):
+    """Pickles the MaskedArray `a` to the file `F`.
+`F` can either be the handle of an exiting file, or a string representing a file name.
+    """
+    if not hasattr(F,'readline'):
+        F = open(F,'w')
+    return cPickle.dump(a,F)
+
+def dumps(a):
+    """Returns a string corresponding to the pickling of the MaskedArray."""
+    return cPickle.dumps(a)
+
+def load(F):
+    """Wrapper around ``cPickle.load`` which accepts either a file-like object or
+ a filename."""
+    if not hasattr(F, 'readline'):
+        F = open(F,'r')
+    return cPickle.load(F)
+
+def loads(strg):
+    "Loads a pickle from the current string."""
+    return cPickle.loads(strg)
+
+
+################################################################################
+
+if __name__ == '__main__':
+    from testutils import assert_equal, assert_almost_equal
+    if 1:
+        x = arange(10)
+        assert(x.ctypes.data == x.filled().ctypes.data)
+    if 0:
+        a = array([1,2,3,4],mask=[0,0,0,0],small_mask=True)
+        a[1] = masked
+        a[1] = 1
+        assert(a.ravel()._mask, [0,0,0,0])
+        assert(a.compressed(), a)
+        a[0] = masked
+        assert(a.compressed()._mask, [0,0,0])
+    if 0:
+        x = array(0, mask=0)
+        I = x.ctypes.data
+        J = x.filled().ctypes.data
+        print (I,J)
+        x = array([0,0], mask=0)
+        (I,J) = (x.ctypes.data, x.filled().ctypes.data)
+        print (I,J)
+    if 1:
+        x = array(numpy.arange(12))
+        x[[1,-2]] = masked
+        xlist = x.tolist()
+        assert(xlist[1] is None)
+        assert(xlist[-2] is None)
+        #
+        x.shape = (3,4) 
+        xlist = x.tolist()
+        #
+        assert_equal(xlist[0],[0,None,2,3])
+        assert_equal(xlist[1],[4,5,6,7])
+        assert_equal(xlist[2],[8,9,None,11])
+        
+        
+        
\ No newline at end of file

Modified: trunk/Lib/sandbox/maskedarray/mrecords.py
===================================================================
--- trunk/Lib/sandbox/maskedarray/mrecords.py	2007-08-15 06:04:30 UTC (rev 3244)
+++ trunk/Lib/sandbox/maskedarray/mrecords.py	2007-08-15 13:38:19 UTC (rev 3245)
@@ -117,4 +117,615 @@
         _names = descr.names    
         mdescr = [(n,'|b1') for n in _names]
         # get the shape .........................
-   
\ No newline at end of file
+        try:
+            shape = numeric.asarray(data[0]).shape
+        except IndexError:
+            shape = len(data.dtype)            
+        if isinstance(shape, int):
+            shape = (shape,)
+        # Construct the _data recarray ..........
+        if isinstance(data, record):
+            _data = numeric.asarray(data).view(recarray)
+            _fieldmask = mask
+        elif isinstance(data, MaskedRecords):
+            _data = data._data
+            _fieldmask = data._fieldmask
+        elif isinstance(data, recarray):
+            _data = data
+            if mask is nomask:
+                _fieldmask = data.astype(mdescr)
+                _fieldmask.flat = tuple([False]*len(mdescr))
+            else:
+                _fieldmask = mask
+        elif (isinstance(data, (tuple, numpy.void)) or\
+              hasattr(data,'__len__') and isinstance(data[0], (tuple, numpy.void))):
+            data = numeric.array(data, dtype=descr).view(recarray)
+            _data = data
+            if mask is nomask:
+                _fieldmask = data.astype(mdescr)
+                _fieldmask.flat = tuple([False]*len(mdescr))
+            else:
+                _fieldmask = mask
+        else:
+            _data = recarray(shape, dtype=descr)
+            _fieldmask = recarray(shape, dtype=mdescr)
+            for (n,v) in zip(_names, data):
+                _data[n] = numeric.asarray(v).view(ndarray)
+                _fieldmask[n] = getmaskarray(v)
+        #........................................
+        _data = _data.view(cls)
+        _data._fieldmask = _fieldmask
+        _data._hardmask = hard_mask
+        if fill_value is None:
+            _data._fill_value = [default_fill_value(numeric.dtype(d[1]))
+                                 for d in descr.descr]
+        else:
+            _data._fill_value = fill_value
+        return _data
+        
+    def __array_finalize__(self,obj):
+        if isinstance(obj, MaskedRecords):
+            self.__dict__.update(_fieldmask=obj._fieldmask,
+                                 _hardmask=obj._hardmask,
+                                 _fill_value=obj._fill_value                                 
+                                 )
+        else:     
+            self.__dict__.update(_fieldmask = nomask,
+                                 _hardmask = False,
+                                 fill_value = None
+                                )
+        return
+    
+    def _getdata(self):
+        "Returns the data as a recarray."
+        return self.view(recarray)
+    _data = property(fget=_getdata)
+    
+    #......................................................
+    def __getattribute__(self, attr):
+        try:
+            # Returns a generic attribute
+            return object.__getattribute__(self,attr)
+        except AttributeError: 
+            # OK, so attr must be a field name
+            pass
+        # Get the list of fields ......
+        _names = self.dtype.names
+        if attr in _names:
+            _data = self._data
+            _mask = self._fieldmask
+            obj = numeric.asarray(_data.__getattribute__(attr)).view(MaskedArray)
+            obj.__setmask__(_mask.__getattribute__(attr))
+            if (obj.ndim == 0) and obj._mask:
+                return masked
+            return obj
+        raise AttributeError,"No attribute '%s' !" % attr
+            
+    def __setattr__(self, attr, val):
+        newattr = attr not in self.__dict__
+        try:
+            # Is attr a generic attribute ?
+            ret = object.__setattr__(self, attr, val)
+        except:
+            # Not a generic attribute: exit if it's not a valid field
+            fielddict = self.dtype.names or {}
+            if attr not in fielddict:
+                exctype, value = sys.exc_info()[:2]
+                raise exctype, value
+        else:
+            if attr not in list(self.dtype.names) + ['_mask']:
+                return ret
+            if newattr:         # We just added this one
+                try:            #  or this setattr worked on an internal
+                                #  attribute. 
+                    object.__delattr__(self, attr)
+                except:
+                    return ret
+        # Case #1.: Basic field ............
+        base_fmask = self._fieldmask
+        _names = self.dtype.names
+        if attr in _names:
+            fval = filled(val)
+            mval = getmaskarray(val)
+            if self._hardmask:
+                mval = mask_or(mval, base_fmask.__getattr__(attr))
+            self._data.__setattr__(attr, fval)
+            base_fmask.__setattr__(attr, mval)
+            return
+        elif attr == '_mask':
+            self.__setmask__(val)
+            return
+    #............................................
+    def __getitem__(self, indx):
+        """Returns all the fields sharing the same fieldname base.
+    The fieldname base is either `_data` or `_mask`."""
+        _localdict = self.__dict__
+        _data = self._data
+        # We want a field ........
+        if isinstance(indx, str):           
+            obj = _data[indx].view(MaskedArray)
+            obj._set_mask(_localdict['_fieldmask'][indx])
+            return obj
+        # We want some elements ..
+        # First, the data ........
+        obj = ndarray.__getitem__(self, indx)
+        if isinstance(obj, numpy.void):
+            obj = self.__class__(obj, dtype=self.dtype)
+        else:
+            obj = obj.view(type(self))
+        obj._fieldmask = numpy.asarray(_localdict['_fieldmask'][indx]).view(recarray)
+        return obj
+    #............................................
+    def __setitem__(self, indx, value):
+        """Sets the given record to value."""
+        MaskedArray.__setitem__(self, indx, value)
+        
+#    def __getslice__(self, i, j):
+#        """Returns the slice described by [i,j]."""
+#        _localdict = self.__dict__
+#        return MaskedRecords(_localdict['_data'][i:j], 
+#                        mask=_localdict['_fieldmask'][i:j],
+#                       dtype=self.dtype)      
+#        
+    def __setslice__(self, i, j, value):
+        """Sets the slice described by [i,j] to `value`."""
+        _localdict = self.__dict__
+        d = self._data
+        m = _localdict['_fieldmask']
+        names = self.dtype.names
+        if value is masked:
+            for n in names:
+                m[i:j][n] = True
+        elif not self._hardmask:
+            fval = filled(value)
+            mval = getmaskarray(value)
+            for n in names:
+                d[n][i:j] = fval
+                m[n][i:j] = mval
+        else:
+            mindx = getmaskarray(self)[i:j]
+            dval = numeric.asarray(value)
+            valmask = getmask(value)
+            if valmask is nomask:
+                for n in names:
+                    mval = mask_or(m[n][i:j], valmask)
+                    d[n][i:j][~mval] = value
+            elif valmask.size > 1:
+                for n in names:
+                    mval = mask_or(m[n][i:j], valmask)
+                    d[n][i:j][~mval] = dval[~mval]
+                    m[n][i:j] = mask_or(m[n][i:j], mval) 
+        self._fieldmask = m
+        
+    #.....................................................           
+    def __setmask__(self, mask):
+        names = self.dtype.names
+        fmask = self.__dict__['_fieldmask']
+        newmask = make_mask(mask, copy=False)
+#        self.unshare_mask()
+        if self._hardmask:
+            for n in names:
+                fmask[n].__ior__(newmask)
+        else:
+            for n in names:
+                fmask[n].flat = newmask
+                
+    def _getmask(self):
+        """Returns the mask of the mrecord: a record is masked when all the fields
+are masked."""
+        if self.size > 1:
+            return self._fieldmask.view((bool_, len(self.dtype))).all(1)
+                
+    _setmask = __setmask__    
+    _mask = property(fget=_getmask, fset=_setmask)
+        
+    #......................................................
+    def __str__(self):
+        """x.__str__() <==> str(x)
+Calculates the string representation, using masked for fill if it is enabled. 
+Otherwise, fills with fill value.
+        """
+        if self.size > 1:
+            mstr = ["(%s)" % ",".join([str(i) for i in s])  
+                    for s in zip(*[getattr(self,f) for f in self.dtype.names])]
+            return "[%s]" % ", ".join(mstr)
+        else:
+            mstr = numeric.asarray(self._data.item(), dtype=object_)
+            mstr[list(self._fieldmask)] = masked_print_option
+            return str(mstr)
+    
+    def __repr__(self):
+        """x.__repr__() <==> repr(x)
+Calculates the repr representation, using masked for fill if it is enabled. 
+Otherwise fill with fill value.
+        """
+        _names = self.dtype.names
+        fmt = "%%%is : %%s" % (max([len(n) for n in _names])+4,)
+        reprstr = [fmt % (f,getattr(self,f)) for f in self.dtype.names]
+        reprstr.insert(0,'masked_records(')
+        reprstr.extend([fmt % ('    fill_value', self._fill_value), 
+                         '              )'])
+        return str("\n".join(reprstr))
+    #......................................................
+    def view(self, obj):
+        """Returns a view of the mrecarray."""
+        try:
+            if issubclass(obj, ndarray):
+                return ndarray.view(self, obj)
+        except TypeError:
+            pass
+        dtype = numeric.dtype(obj)
+        if dtype.fields is None:
+            return self.__array__().view(dtype)
+        return ndarray.view(self, obj)            
+    #......................................................
+    def filled(self, fill_value=None):
+        """Returns an array of the same class as `_data`,
+ with masked values filled with `fill_value`.
+Subclassing is preserved.
+        
+If `fill_value` is None, uses self.fill_value.
+        """
+        _localdict = self.__dict__
+        d = self._data
+        fm = _localdict['_fieldmask']
+        if not numeric.asarray(fm, dtype=bool_).any():
+            return d
+        #
+        if fill_value is None:
+            value = _localdict['_fill_value']
+        else:
+            value = fill_value
+            if numeric.size(value) == 1:
+                value = [value,] * len(self.dtype)
+        #
+        if self is masked:
+            result = numeric.asanyarray(value)
+        else:
+            result = d.copy()
+            for (n, v) in zip(d.dtype.names, value):
+                numpy.putmask(numeric.asarray(result[n]), 
+                              numeric.asarray(fm[n]), v)
+        return result
+    #............................................
+    def harden_mask(self):
+        "Forces the mask to hard"
+        self._hardmask = True
+    def soften_mask(self):
+        "Forces the mask to soft"
+        self._hardmask = False
+    #.............................................
+    def copy(self):
+        """Returns a copy of the masked record."""
+        _localdict = self.__dict__
+        return MaskedRecords(self._data.copy(),
+                        mask=_localdict['_fieldmask'].copy(),
+                       dtype=self.dtype)
+    #.............................................
+
+
+#####---------------------------------------------------------------------------
+#---- --- Constructors ---
+#####---------------------------------------------------------------------------
+
+def fromarrays(arraylist, dtype=None, shape=None, formats=None,
+               names=None, titles=None, aligned=False, byteorder=None):
+    """Creates a mrecarray from a (flat) list of masked arrays.
+
+:Parameters:
+    - `arraylist` : Sequence
+      A list of (masked) arrays. Each element of the sequence is first converted
+      to a masked array if needed. If a 2D array is passed as argument, it is
+      processed line by line
+    - `dtype` : numeric.dtype
+      Data type descriptor.
+    - `shape` : Integer *[None]*
+      Number of records. If None, `shape` is defined from the shape of the first
+      array in the list.
+    - `formats` :
+      (Description to write)
+    - `names` : 
+      (description to write)
+    - `titles`:
+      (Description to write)
+    - `aligned`: Boolen *[False]*
+      (Description to write, not used anyway)   
+    - `byteorder`: Boolen *[None]*
+      (Description to write, not used anyway)
+       
+
+    """
+    arraylist = [MA.asarray(x) for x in arraylist]
+    # Define/check the shape.....................
+    if shape is None or shape == 0:
+        shape = arraylist[0].shape
+    if isinstance(shape, int):
+        shape = (shape,)
+    # Define formats from scratch ...............
+    if formats is None and dtype is None:
+        formats = _getformats(arraylist)
+    # Define the dtype ..........................
+    if dtype is not None:
+        descr = numeric.dtype(dtype)
+        _names = descr.names
+    else:
+        parsed = format_parser(formats, names, titles, aligned, byteorder)
+        _names = parsed._names
+        descr = parsed._descr
+    # Determine shape from data-type.............
+    if len(descr) != len(arraylist):
+        msg = "Mismatch between the number of fields (%i) and the number of "\
+              "arrays (%i)"
+        raise ValueError, msg % (len(descr), len(arraylist))
+    d0 = descr[0].shape
+    nn = len(d0)
+    if nn > 0:
+        shape = shape[:-nn]
+    # Make sure the shape is the correct one ....
+    for k, obj in enumerate(arraylist):
+        nn = len(descr[k].shape)
+        testshape = obj.shape[:len(obj.shape)-nn]
+        if testshape != shape:
+            raise ValueError, "Array-shape mismatch in array %d" % k
+    # Reconstruct the descriptor, by creating a _data and _mask version
+    return MaskedRecords(arraylist, dtype=descr)
+#..............................................................................
+def fromrecords(reclist, dtype=None, shape=None, formats=None, names=None,
+                titles=None, aligned=False, byteorder=None):
+    """Creates a MaskedRecords from a list of records.
+
+    The data in the same field can be heterogeneous, they will be promoted
+    to the highest data type.  This method is intended for creating
+    smaller record arrays.  If used to create large array without formats
+    defined, it can be slow.
+
+    If formats is None, then this will auto-detect formats. Use a list of
+    tuples rather than a list of lists for faster processing.
+    """    
+    # reclist is in fact a mrecarray .................
+    if isinstance(reclist, MaskedRecords):
+        mdescr = reclist.dtype
+        shape = reclist.shape
+        return MaskedRecords(reclist, dtype=mdescr)
+    # No format, no dtype: create from to arrays .....
+    nfields = len(reclist[0])
+    if formats is None and dtype is None:  # slower
+        if isinstance(reclist, recarray):
+            arrlist = [reclist.field(i) for i in range(len(reclist.dtype))]
+            if names is None:
+                names = reclist.dtype.names
+        else:
+            obj = numeric.array(reclist,dtype=object)
+            arrlist = [numeric.array(obj[...,i].tolist()) 
+                               for i in xrange(nfields)]
+        return MaskedRecords(arrlist, formats=formats, names=names, 
+                             titles=titles, aligned=aligned, byteorder=byteorder)
+    # Construct the descriptor .......................
+    if dtype is not None:
+        descr = numeric.dtype(dtype)
+        _names = descr.names
+    else:
+        parsed = format_parser(formats, names, titles, aligned, byteorder)
+        _names = parsed._names
+        descr = parsed._descr
+
+    try:
+        retval = numeric.array(reclist, dtype = descr).view(recarray)
+    except TypeError:  # list of lists instead of list of tuples
+        if (shape is None or shape == 0):
+            shape = len(reclist)*2
+        if isinstance(shape, (int, long)):
+            shape = (shape*2,)
+        if len(shape) > 1:
+            raise ValueError, "Can only deal with 1-d array."
+        retval = recarray(shape, mdescr)
+        for k in xrange(retval.size):
+            retval[k] = tuple(reclist[k])
+        return MaskedRecords(retval, dtype=descr)
+    else:
+        if shape is not None and retval.shape != shape:
+            retval.shape = shape
+    #
+    return MaskedRecords(retval, dtype=descr)
+
+def _guessvartypes(arr):        
+    """Tries to guess the dtypes of the str_ ndarray `arr`, by testing element-wise
+    conversion. Returns a list of dtypes.
+    The array is first converted to ndarray. If the array is 2D, the test is 
+    performed on the first line. An exception is raised if the file is 3D or more.
+    """
+    vartypes = []
+    arr = numeric.asarray(arr)
+    if len(arr.shape) == 2 :
+        arr = arr[0]
+    elif len(arr.shape) > 2:
+        raise ValueError, "The array should be 2D at most!"
+    # Start the conversion loop .......
+    for f in arr:
+        try:
+            val = int(f)
+        except ValueError:
+            try:
+                val = float(f)
+            except ValueError:
+                try: 
+                    val = complex(f)
+                except ValueError:
+                    vartypes.append(arr.dtype)
+                else:
+                    vartypes.append(complex_)
+            else:
+                vartypes.append(float_)
+        else:
+            vartypes.append(int_)
+    return vartypes
+
+def openfile(fname):
+    "Opens the file handle of file `fname`"
+    # A file handle ...................
+    if hasattr(fname, 'readline'):
+        return fname
+    # Try to open the file and guess its type
+    try:
+        f = open(fname)
+    except IOError:
+        raise IOError, "No such file: '%s'" % fname
+    if f.readline()[:2] != "\\x":
+        f.seek(0,0)
+        return f
+    raise NotImplementedError, "Wow, binary file" 
+    
+
+def fromtextfile(fname, delimitor=None, commentchar='#', missingchar='',
+                 varnames=None, vartypes=None):
+    """Creates a mrecarray from data stored in the file `filename`.
+
+:Parameters:
+    - `filename` : file name/handle
+      Handle of an opened file.  
+    - `delimitor` : Character *None*
+      Alphanumeric character used to separate columns in the file.
+      If None, any (group of) white spacestring(s) will be used.
+    - `commentchar` : String *['#']*
+      Alphanumeric character used to mark the start of a comment.
+    - `missingchar` : String *['']*
+      String indicating missing data, and used to create the masks.
+    - `varnames` : Sequence *[None]*
+      Sequence of the variable names. If None, a list will be created from
+      the first non empty line of the file.
+    - `vartypes` : Sequence *[None]*
+      Sequence of the variables dtypes. If None, the sequence will be estimated
+      from the first non-commented line.  
+    
+    
+    Ultra simple: the varnames are in the header, one line"""
+    # Try to open the file ......................
+    f = openfile(fname)
+    # Get the first non-empty line as the varnames
+    while True:
+        line = f.readline()
+        firstline = line[:line.find(commentchar)].strip()
+        _varnames = firstline.split(delimitor)
+        if len(_varnames) > 1:
+            break
+    if varnames is None:
+        varnames = _varnames
+    # Get the data ..............................
+    _variables = MA.asarray([line.strip().split(delimitor) for line in f
+                                  if line[0] != commentchar and len(line) > 1])
+    (_, nfields) = _variables.shape
+    # Try to guess the dtype ....................
+    if vartypes is None:
+        vartypes = _guessvartypes(_variables[0])
+    else:
+        vartypes = [numeric.dtype(v) for v in vartypes]
+        if len(vartypes) != nfields:
+            msg = "Attempting to %i dtypes for %i fields!"
+            msg += " Reverting to default."
+            warnings.warn(msg % (len(vartypes), nfields))
+            vartypes = _guessvartypes(_variables[0])
+    # Construct the descriptor ..................
+    mdescr = [(n,f) for (n,f) in zip(varnames, vartypes)]
+    # Get the data and the mask .................
+    # We just need a list of masked_arrays. It's easier to create it like that:
+    _mask = (_variables.T == missingchar)
+    _datalist = [masked_array(a,mask=m,dtype=t)
+                     for (a,m,t) in zip(_variables.T, _mask, vartypes)]
+    return MaskedRecords(_datalist, dtype=mdescr)
+
+#....................................................................
+def addfield(mrecord, newfield, newfieldname=None):
+    """Adds a new field to the masked record array, using `newfield` as data
+and `newfieldname` as name. If `newfieldname` is None, the new field name is
+set to 'fi', where `i` is the number of existing fields.
+    """
+    _data = mrecord._data
+    _mask = mrecord._fieldmask
+    if newfieldname is None or newfieldname in reserved_fields:
+        newfieldname = 'f%i' % len(_data.dtype)
+    newfield = MA.asarray(newfield)
+    # Get the new data ............
+    # Create a new empty recarray
+    newdtype = numeric.dtype(_data.dtype.descr + \
+                             [(newfieldname, newfield.dtype)])
+    newdata = recarray(_data.shape, newdtype)
+    # Add the exisintg field
+    [newdata.setfield(_data.getfield(*f),*f) 
+         for f in _data.dtype.fields.values()]
+    # Add the new field
+    newdata.setfield(newfield._data, *newdata.dtype.fields[newfieldname])
+    newdata = newdata.view(MaskedRecords)
+    # Get the new mask .............
+    # Create a new empty recarray
+    newmdtype = numeric.dtype([(n,bool_) for n in newdtype.names])
+    newmask = recarray(_data.shape, newmdtype)
+    # Add the old masks
+    [newmask.setfield(_mask.getfield(*f),*f)
+         for f in _mask.dtype.fields.values()]
+    # Add the mask of the new field
+    newmask.setfield(getmaskarray(newfield), 
+                     *newmask.dtype.fields[newfieldname])
+    newdata._fieldmask = newmask
+    return newdata
+        
+################################################################################
+if __name__ == '__main__':
+    import numpy as N
+    from maskedarray.testutils import assert_equal
+    if 1:
+        d = N.arange(5)
+        m = MA.make_mask([1,0,0,1,1])
+        base_d = N.r_[d,d[::-1]].reshape(2,-1).T
+        base_m = N.r_[[m, m[::-1]]].T
+        base = MA.array(base_d, mask=base_m).T
+        mrecord = fromarrays(base,dtype=[('a',N.float_),('b',N.float_)])
+        mrec = MaskedRecords(mrecord.copy())
+        #
+        mrec.a[3:] = 5
+        assert_equal(mrec.a, [0,1,2,5,5])
+        assert_equal(mrec.a._mask, [1,0,0,0,0])
+        #
+        mrec.b[3:] = masked
+        assert_equal(mrec.b, [4,3,2,1,0])
+        assert_equal(mrec.b._mask, [1,1,0,1,1])
+        #
+        mrec[:2] = masked
+        assert_equal(mrec._mask, [1,1,0,0,0])
+        mrec[-1] = masked
+        assert_equal(mrec._mask, [1,1,0,0,1])
+    if 1:
+        nrec = N.core.records.fromarrays(N.r_[[d,d[::-1]]],
+                                         dtype=[('a',N.float_),('b',N.float_)])
+        mrec = mrecord
+        #....................
+        mrecfr = fromrecords(nrec)
+        assert_equal(mrecfr.a, mrec.a)
+        assert_equal(mrecfr.dtype, mrec.dtype)
+        #....................
+        tmp = mrec[::-1] #.tolist()
+        mrecfr = fromrecords(tmp)
+        assert_equal(mrecfr.a, mrec.a[::-1])
+        #....................        
+        mrecfr = fromrecords(nrec.tolist(), names=nrec.dtype.names)
+        assert_equal(mrecfr.a, mrec.a)
+        assert_equal(mrecfr.dtype, mrec.dtype)
+    if 1:
+        assert_equal(mrec.a, MA.array(d,mask=m))
+        assert_equal(mrec.b, MA.array(d[::-1],mask=m[::-1]))
+        assert((mrec._fieldmask == N.core.records.fromarrays([m, m[::-1]])).all())
+        assert_equal(mrec._mask, N.r_[[m,m[::-1]]].all(0))
+        assert_equal(mrec.a[1], mrec[1].a)
+
+    if 1:
+        x = [(1.,10.,'a'),(2.,20,'b'),(3.14,30,'c'),(5.55,40,'d')]
+        desc = [('ffloat', N.float_), ('fint', N.int_), ('fstr', 'S10')] 
+        mr = MaskedRecords(x,dtype=desc)
+        mr[0] = masked
+        mr.ffloat[-1] = masked
+        #
+        mrlast = mr[-1]
+        assert(isinstance(mrlast,MaskedRecords))
+        assert(hasattr(mrlast,'ffloat'))
+        assert_equal(mrlast.ffloat, masked)
+        
+        
\ No newline at end of file

Modified: trunk/Lib/sandbox/maskedarray/tests/test_core.py
===================================================================
--- trunk/Lib/sandbox/maskedarray/tests/test_core.py	2007-08-15 06:04:30 UTC (rev 3244)
+++ trunk/Lib/sandbox/maskedarray/tests/test_core.py	2007-08-15 13:38:19 UTC (rev 3245)
@@ -12,10 +12,11 @@
 
 import types
 
-import numpy as N
+import numpy
 import numpy.core.fromnumeric  as fromnumeric
 from numpy.testing import NumpyTest, NumpyTestCase
 from numpy.testing.utils import build_err_msg
+from numpy import array as narray
 
 import maskedarray.testutils
 from maskedarray.testutils import *
@@ -23,7 +24,7 @@
 import maskedarray.core as coremodule
 from maskedarray.core import *
 
-pi = N.pi
+pi = numpy.pi
 
 #..............................................................................
 class test_ma(NumpyTestCase):
@@ -34,16 +35,16 @@
 
     def setUp (self):
         "Base data definition."
-        x = N.array([1.,1.,1.,-2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
-        y = N.array([5.,0.,3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
+        x = narray([1.,1.,1.,-2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
+        y = narray([5.,0.,3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
         a10 = 10.
         m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
         m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0 ,0, 1]
         xm = masked_array(x, mask=m1)
         ym = masked_array(y, mask=m2)
-        z = N.array([-.5, 0., .5, .8])
+        z = narray([-.5, 0., .5, .8])
         zm = masked_array(z, mask=[0,1,0,0])
-        xf = N.where(m1, 1.e+20, x)
+        xf = numpy.where(m1, 1.e+20, x)
         xm.set_fill_value(1.e+20)  
         self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
     #........................
@@ -55,7 +56,7 @@
         assert((xm-ym).filled(0).any())
         fail_if_equal(xm.mask.astype(int_), ym.mask.astype(int_))
         s = x.shape
-        assert_equal(N.shape(xm), s)
+        assert_equal(numpy.shape(xm), s)
         assert_equal(xm.shape, s)
         assert_equal(xm.dtype, x.dtype)
         assert_equal(zm.dtype, z.dtype)
@@ -115,14 +116,14 @@
             assert_equal(x**2, xm**2)
             assert_equal(abs(x)**2.5, abs(xm) **2.5)
             assert_equal(x**y, xm**ym)
-            assert_equal(N.add(x,y), add(xm, ym))
-            assert_equal(N.subtract(x,y), subtract(xm, ym))
-            assert_equal(N.multiply(x,y), multiply(xm, ym))
-            assert_equal(N.divide(x,y), divide(xm, ym))
+            assert_equal(numpy.add(x,y), add(xm, ym))
+            assert_equal(numpy.subtract(x,y), subtract(xm, ym))
+            assert_equal(numpy.multiply(x,y), multiply(xm, ym))
+            assert_equal(numpy.divide(x,y), divide(xm, ym))
     #........................
     def check_mixed_arithmetic(self):
         "Tests mixed arithmetics."
-        na = N.array([1])
+        na = narray([1])
         ma = array([1])
         self.failUnless(isinstance(na + ma, MaskedArray))
         self.failUnless(isinstance(ma + na, MaskedArray))   
@@ -246,28 +247,28 @@
     def check_basic_ufuncs (self):
         "Test various functions such as sin, cos."
         (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
-        assert_equal(N.cos(x), cos(xm))
-        assert_equal(N.cosh(x), cosh(xm))
-        assert_equal(N.sin(x), sin(xm))
-        assert_equal(N.sinh(x), sinh(xm))
-        assert_equal(N.tan(x), tan(xm))
-        assert_equal(N.tanh(x), tanh(xm))
-        assert_equal(N.sqrt(abs(x)), sqrt(xm))
-        assert_equal(N.log(abs(x)), log(xm))
-        assert_equal(N.log10(abs(x)), log10(xm))
-        assert_equal(N.exp(x), exp(xm))
-        assert_equal(N.arcsin(z), arcsin(zm))
-        assert_equal(N.arccos(z), arccos(zm))
-        assert_equal(N.arctan(z), arctan(zm))
-        assert_equal(N.arctan2(x, y), arctan2(xm, ym))
-        assert_equal(N.absolute(x), absolute(xm))
-        assert_equal(N.equal(x,y), equal(xm, ym))
-        assert_equal(N.not_equal(x,y), not_equal(xm, ym))
-        assert_equal(N.less(x,y), less(xm, ym))
-        assert_equal(N.greater(x,y), greater(xm, ym))
-        assert_equal(N.less_equal(x,y), less_equal(xm, ym))
-        assert_equal(N.greater_equal(x,y), greater_equal(xm, ym))
-        assert_equal(N.conjugate(x), conjugate(xm))
+        assert_equal(numpy.cos(x), cos(xm))
+        assert_equal(numpy.cosh(x), cosh(xm))
+        assert_equal(numpy.sin(x), sin(xm))
+        assert_equal(numpy.sinh(x), sinh(xm))
+        assert_equal(numpy.tan(x), tan(xm))
+        assert_equal(numpy.tanh(x), tanh(xm))
+        assert_equal(numpy.sqrt(abs(x)), sqrt(xm))
+        assert_equal(numpy.log(abs(x)), log(xm))
+        assert_equal(numpy.log10(abs(x)), log10(xm))
+        assert_equal(numpy.exp(x), exp(xm))
+        assert_equal(numpy.arcsin(z), arcsin(zm))
+        assert_equal(numpy.arccos(z), arccos(zm))
+        assert_equal(numpy.arctan(z), arctan(zm))
+        assert_equal(numpy.arctan2(x, y), arctan2(xm, ym))
+        assert_equal(numpy.absolute(x), absolute(xm))
+        assert_equal(numpy.equal(x,y), equal(xm, ym))
+        assert_equal(numpy.not_equal(x,y), not_equal(xm, ym))
+        assert_equal(numpy.less(x,y), less(xm, ym))
+        assert_equal(numpy.greater(x,y), greater(xm, ym))
+        assert_equal(numpy.less_equal(x,y), less_equal(xm, ym))
+        assert_equal(numpy.greater_equal(x,y), greater_equal(xm, ym))
+        assert_equal(numpy.conjugate(x), conjugate(xm))
     #........................
     def check_count_func (self):
         "Tests count"
@@ -286,7 +287,7 @@
     def check_minmax_func (self):
         "Tests minimum and maximum."
         (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
-        xr = N.ravel(x) #max doesn't work if shaped
+        xr = numpy.ravel(x) #max doesn't work if shaped
         xmr = ravel(xm)
         assert_equal(max(xr), maximum(xmr)) #true because of careful selection of data
         assert_equal(min(xr), minimum(xmr)) #true because of careful selection of data
@@ -326,50 +327,50 @@
     def check_addsumprod (self):
         "Tests add, sum, product."
         (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
-        assert_equal(N.add.reduce(x), add.reduce(x))
-        assert_equal(N.add.accumulate(x), add.accumulate(x))
+        assert_equal(numpy.add.reduce(x), add.reduce(x))
+        assert_equal(numpy.add.accumulate(x), add.accumulate(x))
         assert_equal(4, sum(array(4),axis=0))
         assert_equal(4, sum(array(4), axis=0))
-        assert_equal(N.sum(x,axis=0), sum(x,axis=0))
-        assert_equal(N.sum(filled(xm,0),axis=0), sum(xm,axis=0))
-        assert_equal(N.sum(x,0), sum(x,0))
-        assert_equal(N.product(x,axis=0), product(x,axis=0))
-        assert_equal(N.product(x,0), product(x,0))
-        assert_equal(N.product(filled(xm,1),axis=0), product(xm,axis=0))
+        assert_equal(numpy.sum(x,axis=0), sum(x,axis=0))
+        assert_equal(numpy.sum(filled(xm,0),axis=0), sum(xm,axis=0))
+        assert_equal(numpy.sum(x,0), sum(x,0))
+        assert_equal(numpy.product(x,axis=0), product(x,axis=0))
+        assert_equal(numpy.product(x,0), product(x,0))
+        assert_equal(numpy.product(filled(xm,1),axis=0), product(xm,axis=0))
         s = (3,4)
         x.shape = y.shape = xm.shape = ym.shape = s
         if len(s) > 1:
-            assert_equal(N.concatenate((x,y),1), concatenate((xm,ym),1))
-            assert_equal(N.add.reduce(x,1), add.reduce(x,1))
-            assert_equal(N.sum(x,1), sum(x,1))
-            assert_equal(N.product(x,1), product(x,1))
+            assert_equal(numpy.concatenate((x,y),1), concatenate((xm,ym),1))
+            assert_equal(numpy.add.reduce(x,1), add.reduce(x,1))
+            assert_equal(numpy.sum(x,1), sum(x,1))
+            assert_equal(numpy.product(x,1), product(x,1))
     #.........................
     def check_concat(self):
         "Tests concatenations."
         (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
         # basic concatenation
-        assert_equal(N.concatenate((x,y)), concatenate((xm,ym)))
-        assert_equal(N.concatenate((x,y)), concatenate((x,y)))
-        assert_equal(N.concatenate((x,y)), concatenate((xm,y)))
-        assert_equal(N.concatenate((x,y,x)), concatenate((x,ym,x)))
+        assert_equal(numpy.concatenate((x,y)), concatenate((xm,ym)))
+        assert_equal(numpy.concatenate((x,y)), concatenate((x,y)))
+        assert_equal(numpy.concatenate((x,y)), concatenate((xm,y)))
+        assert_equal(numpy.concatenate((x,y,x)), concatenate((x,ym,x)))
         # Concatenation along an axis
         s = (3,4)
         x.shape = y.shape = xm.shape = ym.shape = s
-        assert_equal(xm.mask, N.reshape(m1, s))
-        assert_equal(ym.mask, N.reshape(m2, s))
+        assert_equal(xm.mask, numpy.reshape(m1, s))
+        assert_equal(ym.mask, numpy.reshape(m2, s))
         xmym = concatenate((xm,ym),1)
-        assert_equal(N.concatenate((x,y),1), xmym)
-        assert_equal(N.concatenate((xm.mask,ym.mask),1), xmym._mask)
+        assert_equal(numpy.concatenate((x,y),1), xmym)
+        assert_equal(numpy.concatenate((xm.mask,ym.mask),1), xmym._mask)
     #........................
     def check_indexing(self):
         "Tests conversions and indexing"
-        x1 = N.array([1,2,4,3])
+        x1 = numpy.array([1,2,4,3])
         x2 = array(x1, mask=[1,0,0,0])
         x3 = array(x1, mask=[0,1,0,1])
         x4 = array(x1)
     # test conversion to strings
         junk, garbage = str(x2), repr(x2)
-        assert_equal(N.sort(x1),sort(x2,endwith=False))
+        assert_equal(numpy.sort(x1),sort(x2,endwith=False))
     # tests of indexing
         assert type(x2[1]) is type(x1[1])
         assert x1[1] == x2[1]
@@ -396,14 +397,14 @@
         x4[:] = masked_array([1,2,3,4],[0,1,1,0])
         assert allequal(getmask(x4), array([0,1,1,0]))
         assert allequal(x4, array([1,2,3,4]))
-        x1 = N.arange(5)*1.0
+        x1 = numpy.arange(5)*1.0
         x2 = masked_values(x1, 3.0)
         assert_equal(x1,x2)
         assert allequal(array([0,0,0,1,0],MaskType), x2.mask)
 #FIXME: Well, eh, fill_value is now a property        assert_equal(3.0, x2.fill_value())
         assert_equal(3.0, x2.fill_value)
         x1 = array([1,'hello',2,3],object)
-        x2 = N.array([1,'hello',2,3],object)
+        x2 = numpy.array([1,'hello',2,3],object)
         s1 = x1[1]
         s2 = x2[1]
         assert_equal(type(s2), str)
@@ -420,7 +421,7 @@
         m3 = make_mask(m, copy=1)
         assert(m is not m3)
 
-        x1 = N.arange(5)
+        x1 = numpy.arange(5)
         y1 = array(x1, mask=m)
         #assert( y1._data is x1)
         assert_equal(y1._data.__array_interface__, x1.__array_interface__)
@@ -585,15 +586,15 @@
     def check_TakeTransposeInnerOuter(self):
         "Test of take, transpose, inner, outer products"
         x = arange(24)
-        y = N.arange(24)
+        y = numpy.arange(24)
         x[5:6] = masked
         x = x.reshape(2,3,4)
         y = y.reshape(2,3,4)
-        assert_equal(N.transpose(y,(2,0,1)), transpose(x,(2,0,1)))
-        assert_equal(N.take(y, (2,0,1), 1), take(x, (2,0,1), 1))
-        assert_equal(N.inner(filled(x,0),filled(y,0)),
+        assert_equal(numpy.transpose(y,(2,0,1)), transpose(x,(2,0,1)))
+        assert_equal(numpy.take(y, (2,0,1), 1), take(x, (2,0,1), 1))
+        assert_equal(numpy.inner(filled(x,0),filled(y,0)),
                             inner(x, y))
-        assert_equal(N.outer(filled(x,0),filled(y,0)),
+        assert_equal(numpy.outer(filled(x,0),filled(y,0)),
                             outer(x, y))
         y = array(['abc', 1, 'def', 2, 3], object)
         y[2] = masked
@@ -642,7 +643,7 @@
         assert_equal(1, int(array([[[1]]])))
         assert_equal(1.0, float(array([[1]])))
         self.failUnlessRaises(ValueError, float, array([1,1]))
-        assert N.isnan(float(array([1],mask=[1])))
+        assert numpy.isnan(float(array([1],mask=[1])))
 #TODO: Check how bool works...        
 #TODO:        self.failUnless(bool(array([0,1])))
 #TODO:        self.failUnless(bool(array([0,0],mask=[0,1])))
@@ -722,11 +723,11 @@
         assert_equal(a_pickled._data, a._data)
         assert_equal(a_pickled.fill_value, 999)
         #
-        a = array(N.matrix(range(10)), mask=[1,0,1,0,0]*2)
+        a = array(numpy.matrix(range(10)), mask=[1,0,1,0,0]*2)
         a_pickled = cPickle.loads(a.dumps())
         assert_equal(a_pickled._mask, a._mask)
         assert_equal(a_pickled, a)
-        assert(isinstance(a_pickled._data,N.matrix))
+        assert(isinstance(a_pickled._data,numpy.matrix))
         
 #...............................................................................
         
@@ -795,7 +796,7 @@
     "Test class for miscellaneous MaskedArrays methods."
     def setUp(self):
         "Base data definition."
-        x = N.array([ 8.375,  7.545,  8.828,  8.5  ,  1.757,  5.928,  
+        x = numpy.array([ 8.375,  7.545,  8.828,  8.5  ,  1.757,  5.928,  
                       8.43 ,  7.78 ,  9.865,  5.878,  8.979,  4.732,  
                       3.012,  6.022,  5.095,  3.116,  5.238,  3.957,  
                       6.04 ,  9.63 ,  7.712,  3.382,  4.489,  6.479,
@@ -804,7 +805,7 @@
         X = x.reshape(6,6)
         XX = x.reshape(3,2,2,3)
     
-        m = N.array([0, 1, 0, 1, 0, 0, 
+        m = numpy.array([0, 1, 0, 1, 0, 0, 
                      1, 0, 1, 1, 0, 1, 
                      0, 0, 0, 1, 0, 1, 
                      0, 0, 0, 1, 1, 1, 
@@ -814,7 +815,7 @@
         mX = array(data=X,mask=m.reshape(X.shape))
         mXX = array(data=XX,mask=m.reshape(XX.shape))
     
-        m2 = N.array([1, 1, 0, 1, 0, 0, 
+        m2 = numpy.array([1, 1, 0, 1, 0, 0, 
                       1, 1, 1, 1, 0, 1, 
                       0, 0, 1, 1, 0, 1, 
                       0, 0, 0, 1, 1, 1, 
@@ -847,8 +848,8 @@
         (x,X,XX,m,mx,mX,mXX,m2x,m2X,m2XX) = self.d
         (n,m) = X.shape
         assert_equal(mx.ptp(),mx.compressed().ptp())
-        rows = N.zeros(n,N.float_)
-        cols = N.zeros(m,N.float_)
+        rows = numpy.zeros(n,numpy.float_)
+        cols = numpy.zeros(m,numpy.float_)
         for k in range(m):
             cols[k] = mX[:,k].compressed().ptp()
         for k in range(n):
@@ -888,7 +889,7 @@
         for k in range(6):
             assert_almost_equal(mXvar1[k],mX[k].compressed().var())
             assert_almost_equal(mXvar0[k],mX[:,k].compressed().var())
-            assert_almost_equal(N.sqrt(mXvar0[k]), mX[:,k].compressed().std())
+            assert_almost_equal(numpy.sqrt(mXvar0[k]), mX[:,k].compressed().std())
     
     def check_argmin(self):
         "Tests argmin & argmax on MaskedArrays."
@@ -969,16 +970,16 @@
         #........................
     def check_anyall(self):
         """Checks the any/all methods/functions."""
-        x = N.array([[ 0.13,  0.26,  0.90],
+        x = numpy.array([[ 0.13,  0.26,  0.90],
                      [ 0.28,  0.33,  0.63],
                      [ 0.31,  0.87,  0.70]])
-        m = N.array([[ True, False, False],
+        m = numpy.array([[ True, False, False],
                      [False, False, False],
-                     [True,  True, False]], dtype=N.bool_)
+                     [True,  True, False]], dtype=numpy.bool_)
         mx = masked_array(x, mask=m)
-        xbig = N.array([[False, False,  True],
+        xbig = numpy.array([[False, False,  True],
                         [False, False,  True],
-                        [False,  True,  True]], dtype=N.bool_)
+                        [False,  True,  True]], dtype=numpy.bool_)
         mxbig = (mx > 0.5)
         mxsmall = (mx < 0.5)
         #
@@ -996,24 +997,24 @@
         assert_equal(mxsmall.any(0), [True,   True, False])
         assert_equal(mxsmall.any(1), [True,   True, False])
         #
-        X = N.matrix(x)
+        X = numpy.matrix(x)
         mX = masked_array(X, mask=m)
         mXbig = (mX > 0.5)
         mXsmall = (mX < 0.5)
         #
         assert (mXbig.all()==False)
         assert (mXbig.any()==True)
-        assert_equal(mXbig.all(0), N.matrix([False, False, True]))
-        assert_equal(mXbig.all(1), N.matrix([False, False, True]).T)
-        assert_equal(mXbig.any(0), N.matrix([False, False, True]))
-        assert_equal(mXbig.any(1), N.matrix([ True,  True, True]).T)
+        assert_equal(mXbig.all(0), numpy.matrix([False, False, True]))
+        assert_equal(mXbig.all(1), numpy.matrix([False, False, True]).T)
+        assert_equal(mXbig.any(0), numpy.matrix([False, False, True]))
+        assert_equal(mXbig.any(1), numpy.matrix([ True,  True, True]).T)
         #
         assert (mXsmall.all()==False)
         assert (mXsmall.any()==True)
-        assert_equal(mXsmall.all(0), N.matrix([True,   True, False]))
-        assert_equal(mXsmall.all(1), N.matrix([False, False, False]).T)
-        assert_equal(mXsmall.any(0), N.matrix([True,   True, False]))
-        assert_equal(mXsmall.any(1), N.matrix([True,   True, False]).T)
+        assert_equal(mXsmall.all(0), numpy.matrix([True,   True, False]))
+        assert_equal(mXsmall.all(1), numpy.matrix([False, False, False]).T)
+        assert_equal(mXsmall.any(0), numpy.matrix([True,   True, False]))
+        assert_equal(mXsmall.any(1), numpy.matrix([True,   True, False]).T)
    
     def check_keepmask(self):
         "Tests the keep mask flag"        
@@ -1110,7 +1111,7 @@
         
     def check_sort(self):
         "Test sort"
-        x = array([1,4,2,3],mask=[0,1,0,0],dtype=N.uint8)
+        x = array([1,4,2,3],mask=[0,1,0,0],dtype=numpy.uint8)
         #
         sortedx = sort(x)
         assert_equal(sortedx._data,[1,2,3,4])
@@ -1124,7 +1125,7 @@
         assert_equal(x._data,[1,2,3,4])
         assert_equal(x._mask,[0,0,0,1])
         #
-        x = array([1,4,2,3],mask=[0,1,0,0],dtype=N.uint8)
+        x = array([1,4,2,3],mask=[0,1,0,0],dtype=numpy.uint8)
         x.sort(endwith=False)
         assert_equal(x._data, [4,1,2,3])
         assert_equal(x._mask, [1,0,0,0])
@@ -1133,10 +1134,10 @@
         sortedx = sort(x)
         assert(not isinstance(sorted, MaskedArray))
         #
-        x = array([0,1,-1,-2,2], mask=nomask, dtype=N.int8)
+        x = array([0,1,-1,-2,2], mask=nomask, dtype=numpy.int8)
         sortedx = sort(x, endwith=False)
         assert_equal(sortedx._data, [-2,-1,0,1,2])
-        x = array([0,1,-1,-2,2], mask=[0,1,0,0,1], dtype=N.int8)
+        x = array([0,1,-1,-2,2], mask=[0,1,0,0,1], dtype=numpy.int8)
         sortedx = sort(x, endwith=False)
         assert_equal(sortedx._data, [1,2,-2,-1,0])
         assert_equal(sortedx._mask, [1,1,0,0,0])
@@ -1190,7 +1191,7 @@
         a = array([0,0], mask=[1,1])
         aravel = a.ravel()
         assert_equal(a._mask.shape, a.shape)
-        a = array(N.matrix([1,2,3,4,5]), mask=[[0,1,0,0,0]])
+        a = array(numpy.matrix([1,2,3,4,5]), mask=[[0,1,0,0,0]])
         aravel = a.ravel()
         assert_equal(a.shape,(1,5))
         assert_equal(a._mask.shape, a.shape)
@@ -1223,6 +1224,21 @@
         assert_equal(b._data, [2,3,4])
         assert_equal(b._mask, nomask)
         
+    def check_tolist(self):
+        "Tests to list"
+        x = array(numpy.arange(12))
+        x[[1,-2]] = masked
+        xlist = x.tolist()
+        assert(xlist[1] is None)
+        assert(xlist[-2] is None)
+        #
+        x.shape = (3,4) 
+        xlist = x.tolist()
+        #
+        assert_equal(xlist[0],[0,None,2,3])
+        assert_equal(xlist[1],[4,5,6,7])
+        assert_equal(xlist[2],[8,9,None,11])
+        
 #..............................................................................
 
 ###############################################################################



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