[Numpy-svn] r4952 - trunk/numpy/oldnumeric
numpy-svn at scipy.org
numpy-svn at scipy.org
Tue Apr 1 10:23:49 EDT 2008
Author: oliphant
Date: 2008-04-01 09:23:48 -0500 (Tue, 01 Apr 2008)
New Revision: 4952
Modified:
trunk/numpy/oldnumeric/ma.py
Log:
Add old ma.py interface to oldnumeric compatibility layer so that it stays the same.
Modified: trunk/numpy/oldnumeric/ma.py
===================================================================
--- trunk/numpy/oldnumeric/ma.py 2008-03-30 03:04:26 UTC (rev 4951)
+++ trunk/numpy/oldnumeric/ma.py 2008-04-01 14:23:48 UTC (rev 4952)
@@ -1,14 +1,2269 @@
-# Incompatibility in that getmask and a.mask returns nomask
-# instead of None
+"""MA: a facility for dealing with missing observations
+MA is generally used as a numpy.array look-alike.
+by Paul F. Dubois.
-from numpy.ma import *
-import numpy.ma as nca
+Copyright 1999, 2000, 2001 Regents of the University of California.
+Released for unlimited redistribution.
+Adapted for numpy_core 2005 by Travis Oliphant and
+(mainly) Paul Dubois.
+"""
+import types, sys
+
+import umath
+import fromnumeric
+from numeric import newaxis, ndarray, inf
+from fromnumeric import amax, amin
+from numerictypes import bool_, typecodes
+import numeric
+import warnings
+
+# Ufunc domain lookup for __array_wrap__
+ufunc_domain = {}
+# Ufunc fills lookup for __array__
+ufunc_fills = {}
+
+MaskType = bool_
+nomask = MaskType(0)
+divide_tolerance = 1.e-35
+
+class MAError (Exception):
+ def __init__ (self, args=None):
+ "Create an exception"
+
+ # The .args attribute must be a tuple.
+ if not isinstance(args, tuple):
+ args = (args,)
+ self.args = args
+ def __str__(self):
+ "Calculate the string representation"
+ return str(self.args[0])
+ __repr__ = __str__
+
+class _MaskedPrintOption:
+ "One instance of this class, masked_print_option, is created."
+ def __init__ (self, display):
+ "Create the masked print option object."
+ self.set_display(display)
+ self._enabled = 1
+
+ def display (self):
+ "Show what prints for masked values."
+ return self._display
+
+ def set_display (self, s):
+ "set_display(s) sets what prints for masked values."
+ self._display = s
+
+ def enabled (self):
+ "Is the use of the display value enabled?"
+ return self._enabled
+
+ def enable(self, flag=1):
+ "Set the enabling flag to flag."
+ self._enabled = flag
+
+ def __str__ (self):
+ return str(self._display)
+
+ __repr__ = __str__
+
+#if you single index into a masked location you get this object.
+masked_print_option = _MaskedPrintOption('--')
+
+# Use single element arrays or scalars.
+default_real_fill_value = 1.e20
+default_complex_fill_value = 1.e20 + 0.0j
+default_character_fill_value = '-'
+default_integer_fill_value = 999999
+default_object_fill_value = '?'
+
+def default_fill_value (obj):
+ "Function to calculate default fill value for an object."
+ if isinstance(obj, types.FloatType):
+ return default_real_fill_value
+ elif isinstance(obj, types.IntType) or isinstance(obj, types.LongType):
+ return default_integer_fill_value
+ elif isinstance(obj, types.StringType):
+ return default_character_fill_value
+ elif isinstance(obj, types.ComplexType):
+ return default_complex_fill_value
+ elif isinstance(obj, MaskedArray) or isinstance(obj, ndarray):
+ x = obj.dtype.char
+ if x in typecodes['Float']:
+ return default_real_fill_value
+ if x in typecodes['Integer']:
+ return default_integer_fill_value
+ if x in typecodes['Complex']:
+ return default_complex_fill_value
+ if x in typecodes['Character']:
+ return default_character_fill_value
+ if x in typecodes['UnsignedInteger']:
+ return umath.absolute(default_integer_fill_value)
+ return default_object_fill_value
+ else:
+ return default_object_fill_value
+
+def minimum_fill_value (obj):
+ "Function to calculate default fill value suitable for taking minima."
+ if isinstance(obj, types.FloatType):
+ return numeric.inf
+ elif isinstance(obj, types.IntType) or isinstance(obj, types.LongType):
+ return sys.maxint
+ elif isinstance(obj, MaskedArray) or isinstance(obj, ndarray):
+ x = obj.dtype.char
+ if x in typecodes['Float']:
+ return numeric.inf
+ if x in typecodes['Integer']:
+ return sys.maxint
+ if x in typecodes['UnsignedInteger']:
+ return sys.maxint
+ else:
+ raise TypeError, 'Unsuitable type for calculating minimum.'
+
+def maximum_fill_value (obj):
+ "Function to calculate default fill value suitable for taking maxima."
+ if isinstance(obj, types.FloatType):
+ return -inf
+ elif isinstance(obj, types.IntType) or isinstance(obj, types.LongType):
+ return -sys.maxint
+ elif isinstance(obj, MaskedArray) or isinstance(obj, ndarray):
+ x = obj.dtype.char
+ if x in typecodes['Float']:
+ return -inf
+ if x in typecodes['Integer']:
+ return -sys.maxint
+ if x in typecodes['UnsignedInteger']:
+ return 0
+ else:
+ raise TypeError, 'Unsuitable type for calculating maximum.'
+
+def set_fill_value (a, fill_value):
+ "Set fill value of a if it is a masked array."
+ if isMaskedArray(a):
+ a.set_fill_value (fill_value)
+
+def getmask (a):
+ """Mask of values in a; could be nomask.
+ Returns nomask if a is not a masked array.
+ To get an array for sure use getmaskarray."""
+ if isinstance(a, MaskedArray):
+ return a.raw_mask()
+ else:
+ return nomask
+
+def getmaskarray (a):
+ """Mask of values in a; an array of zeros if mask is nomask
+ or not a masked array, and is a byte-sized integer.
+ Do not try to add up entries, for example.
+ """
+ m = getmask(a)
+ if m is nomask:
+ return make_mask_none(shape(a))
+ else:
+ return m
+
+def is_mask (m):
+ """Is m a legal mask? Does not check contents, only type.
+ """
+ try:
+ return m.dtype.type is MaskType
+ except AttributeError:
+ return False
+
+def make_mask (m, copy=0, flag=0):
+ """make_mask(m, copy=0, flag=0)
+ return m as a mask, creating a copy if necessary or requested.
+ Can accept any sequence of integers or nomask. Does not check
+ that contents must be 0s and 1s.
+ if flag, return nomask if m contains no true elements.
+ """
+ if m is nomask:
+ return nomask
+ elif isinstance(m, ndarray):
+ if m.dtype.type is MaskType:
+ if copy:
+ result = numeric.array(m, dtype=MaskType, copy=copy)
+ else:
+ result = m
+ else:
+ result = m.astype(MaskType)
+ else:
+ result = filled(m, True).astype(MaskType)
+
+ if flag and not fromnumeric.sometrue(fromnumeric.ravel(result)):
+ return nomask
+ else:
+ return result
+
+def make_mask_none (s):
+ "Return a mask of all zeros of shape s."
+ result = numeric.zeros(s, dtype=MaskType)
+ result.shape = s
+ return result
+
+def mask_or (m1, m2):
+ """Logical or of the mask candidates m1 and m2, treating nomask as false.
+ Result may equal m1 or m2 if the other is nomask.
+ """
+ if m1 is nomask: return make_mask(m2)
+ if m2 is nomask: return make_mask(m1)
+ if m1 is m2 and is_mask(m1): return m1
+ return make_mask(umath.logical_or(m1, m2))
+
+def filled (a, value = None):
+ """a as a contiguous numeric array with any masked areas replaced by value
+ if value is None or the special element "masked", get_fill_value(a)
+ is used instead.
+
+ If a is already a contiguous numeric array, a itself is returned.
+
+ filled(a) can be used to be sure that the result is numeric when
+ passing an object a to other software ignorant of MA, in particular to
+ numeric itself.
+ """
+ if isinstance(a, MaskedArray):
+ return a.filled(value)
+ elif isinstance(a, ndarray) and a.flags['CONTIGUOUS']:
+ return a
+ elif isinstance(a, types.DictType):
+ return numeric.array(a, 'O')
+ else:
+ return numeric.array(a)
+
+def get_fill_value (a):
+ """
+ The fill value of a, if it has one; otherwise, the default fill value
+ for that type.
+ """
+ if isMaskedArray(a):
+ result = a.fill_value()
+ else:
+ result = default_fill_value(a)
+ return result
+
+def common_fill_value (a, b):
+ "The common fill_value of a and b, if there is one, or None"
+ t1 = get_fill_value(a)
+ t2 = get_fill_value(b)
+ if t1 == t2: return t1
+ return None
+
+# Domain functions return 1 where the argument(s) are not in the domain.
+class domain_check_interval:
+ "domain_check_interval(a,b)(x) = true where x < a or y > b"
+ def __init__(self, y1, y2):
+ "domain_check_interval(a,b)(x) = true where x < a or y > b"
+ self.y1 = y1
+ self.y2 = y2
+
+ def __call__ (self, x):
+ "Execute the call behavior."
+ return umath.logical_or(umath.greater (x, self.y2),
+ umath.less(x, self.y1)
+ )
+
+class domain_tan:
+ "domain_tan(eps) = true where abs(cos(x)) < eps)"
+ def __init__(self, eps):
+ "domain_tan(eps) = true where abs(cos(x)) < eps)"
+ self.eps = eps
+
+ def __call__ (self, x):
+ "Execute the call behavior."
+ return umath.less(umath.absolute(umath.cos(x)), self.eps)
+
+class domain_greater:
+ "domain_greater(v)(x) = true where x <= v"
+ def __init__(self, critical_value):
+ "domain_greater(v)(x) = true where x <= v"
+ self.critical_value = critical_value
+
+ def __call__ (self, x):
+ "Execute the call behavior."
+ return umath.less_equal (x, self.critical_value)
+
+class domain_greater_equal:
+ "domain_greater_equal(v)(x) = true where x < v"
+ def __init__(self, critical_value):
+ "domain_greater_equal(v)(x) = true where x < v"
+ self.critical_value = critical_value
+
+ def __call__ (self, x):
+ "Execute the call behavior."
+ return umath.less (x, self.critical_value)
+
+class masked_unary_operation:
+ def __init__ (self, aufunc, fill=0, domain=None):
+ """ masked_unary_operation(aufunc, fill=0, domain=None)
+ aufunc(fill) must be defined
+ self(x) returns aufunc(x)
+ with masked values where domain(x) is true or getmask(x) is true.
+ """
+ self.f = aufunc
+ self.fill = fill
+ self.domain = domain
+ self.__doc__ = getattr(aufunc, "__doc__", str(aufunc))
+ self.__name__ = getattr(aufunc, "__name__", str(aufunc))
+ ufunc_domain[aufunc] = domain
+ ufunc_fills[aufunc] = fill,
+
+ def __call__ (self, a, *args, **kwargs):
+ "Execute the call behavior."
+# numeric tries to return scalars rather than arrays when given scalars.
+ m = getmask(a)
+ d1 = filled(a, self.fill)
+ if self.domain is not None:
+ m = mask_or(m, self.domain(d1))
+ result = self.f(d1, *args, **kwargs)
+ return masked_array(result, m)
+
+ def __str__ (self):
+ return "Masked version of " + str(self.f)
+
+
+class domain_safe_divide:
+ def __init__ (self, tolerance=divide_tolerance):
+ self.tolerance = tolerance
+ def __call__ (self, a, b):
+ return umath.absolute(a) * self.tolerance >= umath.absolute(b)
+
+class domained_binary_operation:
+ """Binary operations that have a domain, like divide. These are complicated
+ so they are a separate class. They have no reduce, outer or accumulate.
+ """
+ def __init__ (self, abfunc, domain, fillx=0, filly=0):
+ """abfunc(fillx, filly) must be defined.
+ abfunc(x, filly) = x for all x to enable reduce.
+ """
+ self.f = abfunc
+ self.domain = domain
+ self.fillx = fillx
+ self.filly = filly
+ self.__doc__ = getattr(abfunc, "__doc__", str(abfunc))
+ self.__name__ = getattr(abfunc, "__name__", str(abfunc))
+ ufunc_domain[abfunc] = domain
+ ufunc_fills[abfunc] = fillx, filly
+
+ def __call__(self, a, b):
+ "Execute the call behavior."
+ ma = getmask(a)
+ mb = getmask(b)
+ d1 = filled(a, self.fillx)
+ d2 = filled(b, self.filly)
+ t = self.domain(d1, d2)
+
+ if fromnumeric.sometrue(t, None):
+ d2 = where(t, self.filly, d2)
+ mb = mask_or(mb, t)
+ m = mask_or(ma, mb)
+ result = self.f(d1, d2)
+ return masked_array(result, m)
+
+ def __str__ (self):
+ return "Masked version of " + str(self.f)
+
+class masked_binary_operation:
+ def __init__ (self, abfunc, fillx=0, filly=0):
+ """abfunc(fillx, filly) must be defined.
+ abfunc(x, filly) = x for all x to enable reduce.
+ """
+ self.f = abfunc
+ self.fillx = fillx
+ self.filly = filly
+ self.__doc__ = getattr(abfunc, "__doc__", str(abfunc))
+ ufunc_domain[abfunc] = None
+ ufunc_fills[abfunc] = fillx, filly
+
+ def __call__ (self, a, b, *args, **kwargs):
+ "Execute the call behavior."
+ m = mask_or(getmask(a), getmask(b))
+ d1 = filled(a, self.fillx)
+ d2 = filled(b, self.filly)
+ result = self.f(d1, d2, *args, **kwargs)
+ if isinstance(result, ndarray) \
+ and m.ndim != 0 \
+ and m.shape != result.shape:
+ m = mask_or(getmaskarray(a), getmaskarray(b))
+ return masked_array(result, m)
+
+ def reduce (self, target, axis=0, dtype=None):
+ """Reduce target along the given axis with this function."""
+ m = getmask(target)
+ t = filled(target, self.filly)
+ if t.shape == ():
+ t = t.reshape(1)
+ if m is not nomask:
+ m = make_mask(m, copy=1)
+ m.shape = (1,)
+ if m is nomask:
+ t = self.f.reduce(t, axis)
+ else:
+ t = masked_array (t, m)
+ # XXX: "or t.dtype" below is a workaround for what appears
+ # XXX: to be a bug in reduce.
+ t = self.f.reduce(filled(t, self.filly), axis,
+ dtype=dtype or t.dtype)
+ m = umath.logical_and.reduce(m, axis)
+ if isinstance(t, ndarray):
+ return masked_array(t, m, get_fill_value(target))
+ elif m:
+ return masked
+ else:
+ return t
+
+ def outer (self, a, b):
+ "Return the function applied to the outer product of a and b."
+ ma = getmask(a)
+ mb = getmask(b)
+ if ma is nomask and mb is nomask:
+ m = nomask
+ else:
+ ma = getmaskarray(a)
+ mb = getmaskarray(b)
+ m = logical_or.outer(ma, mb)
+ d = self.f.outer(filled(a, self.fillx), filled(b, self.filly))
+ return masked_array(d, m)
+
+ def accumulate (self, target, axis=0):
+ """Accumulate target along axis after filling with y fill value."""
+ t = filled(target, self.filly)
+ return masked_array (self.f.accumulate (t, axis))
+ def __str__ (self):
+ return "Masked version of " + str(self.f)
+
+sqrt = masked_unary_operation(umath.sqrt, 0.0, domain_greater_equal(0.0))
+log = masked_unary_operation(umath.log, 1.0, domain_greater(0.0))
+log10 = masked_unary_operation(umath.log10, 1.0, domain_greater(0.0))
+exp = masked_unary_operation(umath.exp)
+conjugate = masked_unary_operation(umath.conjugate)
+sin = masked_unary_operation(umath.sin)
+cos = masked_unary_operation(umath.cos)
+tan = masked_unary_operation(umath.tan, 0.0, domain_tan(1.e-35))
+arcsin = masked_unary_operation(umath.arcsin, 0.0, domain_check_interval(-1.0, 1.0))
+arccos = masked_unary_operation(umath.arccos, 0.0, domain_check_interval(-1.0, 1.0))
+arctan = masked_unary_operation(umath.arctan)
+# Missing from numeric
+arcsinh = masked_unary_operation(umath.arcsinh)
+arccosh = masked_unary_operation(umath.arccosh, 1.0, domain_greater_equal(1.0))
+arctanh = masked_unary_operation(umath.arctanh, 0.0, domain_check_interval(-1.0+1e-15, 1.0-1e-15))
+sinh = masked_unary_operation(umath.sinh)
+cosh = masked_unary_operation(umath.cosh)
+tanh = masked_unary_operation(umath.tanh)
+absolute = masked_unary_operation(umath.absolute)
+fabs = masked_unary_operation(umath.fabs)
+negative = masked_unary_operation(umath.negative)
+
+def nonzero(a):
+ """returns the indices of the elements of a which are not zero
+ and not masked
+ """
+ return numeric.asarray(filled(a, 0).nonzero())
+
+around = masked_unary_operation(fromnumeric.round_)
+floor = masked_unary_operation(umath.floor)
+ceil = masked_unary_operation(umath.ceil)
+logical_not = masked_unary_operation(umath.logical_not)
+
+add = masked_binary_operation(umath.add)
+subtract = masked_binary_operation(umath.subtract)
+subtract.reduce = None
+multiply = masked_binary_operation(umath.multiply, 1, 1)
+divide = domained_binary_operation(umath.divide, domain_safe_divide(), 0, 1)
+true_divide = domained_binary_operation(umath.true_divide, domain_safe_divide(), 0, 1)
+floor_divide = domained_binary_operation(umath.floor_divide, domain_safe_divide(), 0, 1)
+remainder = domained_binary_operation(umath.remainder, domain_safe_divide(), 0, 1)
+fmod = domained_binary_operation(umath.fmod, domain_safe_divide(), 0, 1)
+hypot = masked_binary_operation(umath.hypot)
+arctan2 = masked_binary_operation(umath.arctan2, 0.0, 1.0)
+arctan2.reduce = None
+equal = masked_binary_operation(umath.equal)
+equal.reduce = None
+not_equal = masked_binary_operation(umath.not_equal)
+not_equal.reduce = None
+less_equal = masked_binary_operation(umath.less_equal)
+less_equal.reduce = None
+greater_equal = masked_binary_operation(umath.greater_equal)
+greater_equal.reduce = None
+less = masked_binary_operation(umath.less)
+less.reduce = None
+greater = masked_binary_operation(umath.greater)
+greater.reduce = None
+logical_and = masked_binary_operation(umath.logical_and)
+alltrue = masked_binary_operation(umath.logical_and, 1, 1).reduce
+logical_or = masked_binary_operation(umath.logical_or)
+sometrue = logical_or.reduce
+logical_xor = masked_binary_operation(umath.logical_xor)
+bitwise_and = masked_binary_operation(umath.bitwise_and)
+bitwise_or = masked_binary_operation(umath.bitwise_or)
+bitwise_xor = masked_binary_operation(umath.bitwise_xor)
+
+def rank (object):
+ return fromnumeric.rank(filled(object))
+
+def shape (object):
+ return fromnumeric.shape(filled(object))
+
+def size (object, axis=None):
+ return fromnumeric.size(filled(object), axis)
+
+class MaskedArray (object):
+ """Arrays with possibly masked values.
+ Masked values of 1 exclude the corresponding element from
+ any computation.
+
+ Construction:
+ x = array(data, dtype=None, copy=True, order=False,
+ mask = nomask, fill_value=None)
+
+ If copy=False, every effort is made not to copy the data:
+ If data is a MaskedArray, and argument mask=nomask,
+ then the candidate data is data.data and the
+ mask used is data.mask. If data is a numeric array,
+ it is used as the candidate raw data.
+ If dtype is not None and
+ is != data.dtype.char then a data copy is required.
+ Otherwise, the candidate is used.
+
+ If a data copy is required, raw data stored is the result of:
+ numeric.array(data, dtype=dtype.char, copy=copy)
+
+ If mask is nomask there are no masked values. Otherwise mask must
+ be convertible to an array of booleans with the same shape as x.
+
+ fill_value is used to fill in masked values when necessary,
+ such as when printing and in method/function filled().
+ The fill_value is not used for computation within this module.
+ """
+ __array_priority__ = 10.1
+ def __init__(self, data, dtype=None, copy=True, order=False,
+ mask=nomask, fill_value=None):
+ """array(data, dtype=None, copy=True, order=False, mask=nomask, fill_value=None)
+ If data already a numeric array, its dtype becomes the default value of dtype.
+ """
+ if dtype is None:
+ tc = None
+ else:
+ tc = numeric.dtype(dtype)
+ need_data_copied = copy
+ if isinstance(data, MaskedArray):
+ c = data.data
+ if tc is None:
+ tc = c.dtype
+ elif tc != c.dtype:
+ need_data_copied = True
+ if mask is nomask:
+ mask = data.mask
+ elif mask is not nomask: #attempting to change the mask
+ need_data_copied = True
+
+ elif isinstance(data, ndarray):
+ c = data
+ if tc is None:
+ tc = c.dtype
+ elif tc != c.dtype:
+ need_data_copied = True
+ else:
+ need_data_copied = False #because I'll do it now
+ c = numeric.array(data, dtype=tc, copy=True, order=order)
+ tc = c.dtype
+
+ if need_data_copied:
+ if tc == c.dtype:
+ self._data = numeric.array(c, dtype=tc, copy=True, order=order)
+ else:
+ self._data = c.astype(tc)
+ else:
+ self._data = c
+
+ if mask is nomask:
+ self._mask = nomask
+ self._shared_mask = 0
+ else:
+ self._mask = make_mask (mask)
+ if self._mask is nomask:
+ self._shared_mask = 0
+ else:
+ self._shared_mask = (self._mask is mask)
+ nm = size(self._mask)
+ nd = size(self._data)
+ if nm != nd:
+ if nm == 1:
+ self._mask = fromnumeric.resize(self._mask, self._data.shape)
+ self._shared_mask = 0
+ elif nd == 1:
+ self._data = fromnumeric.resize(self._data, self._mask.shape)
+ self._data.shape = self._mask.shape
+ else:
+ raise MAError, "Mask and data not compatible."
+ elif nm == 1 and shape(self._mask) != shape(self._data):
+ self.unshare_mask()
+ self._mask.shape = self._data.shape
+
+ self.set_fill_value(fill_value)
+
+ def __array__ (self, t=None, context=None):
+ "Special hook for numeric. Converts to numeric if possible."
+ if self._mask is not nomask:
+ if fromnumeric.ravel(self._mask).any():
+ if context is None:
+ warnings.warn("Cannot automatically convert masked array to "\
+ "numeric because data\n is masked in one or "\
+ "more locations.");
+ return self._data
+ #raise MAError, \
+ # """Cannot automatically convert masked array to numeric because data
+ # is masked in one or more locations.
+ # """
+ else:
+ func, args, i = context
+ fills = ufunc_fills.get(func)
+ if fills is None:
+ raise MAError, "%s not known to ma" % func
+ return self.filled(fills[i])
+ else: # Mask is all false
+ # Optimize to avoid future invocations of this section.
+ self._mask = nomask
+ self._shared_mask = 0
+ if t:
+ return self._data.astype(t)
+ else:
+ return self._data
+
+ def __array_wrap__ (self, array, context=None):
+ """Special hook for ufuncs.
+
+ Wraps the numpy array and sets the mask according to
+ context.
+ """
+ if context is None:
+ return MaskedArray(array, copy=False, mask=nomask)
+ func, args = context[:2]
+ domain = ufunc_domain[func]
+ m = reduce(mask_or, [getmask(a) for a in args])
+ if domain is not None:
+ m = mask_or(m, domain(*[getattr(a, '_data', a)
+ for a in args]))
+ if m is not nomask:
+ try:
+ shape = array.shape
+ except AttributeError:
+ pass
+ else:
+ if m.shape != shape:
+ m = reduce(mask_or, [getmaskarray(a) for a in args])
+
+ return MaskedArray(array, copy=False, mask=m)
+
+ def _get_shape(self):
+ "Return the current shape."
+ return self._data.shape
+
+ def _set_shape (self, newshape):
+ "Set the array's shape."
+ self._data.shape = newshape
+ if self._mask is not nomask:
+ self._mask = self._mask.copy()
+ self._mask.shape = newshape
+
+ def _get_flat(self):
+ """Calculate the flat value.
+ """
+ if self._mask is nomask:
+ return masked_array(self._data.ravel(), mask=nomask,
+ fill_value = self.fill_value())
+ else:
+ return masked_array(self._data.ravel(),
+ mask=self._mask.ravel(),
+ fill_value = self.fill_value())
+
+ def _set_flat (self, value):
+ "x.flat = value"
+ y = self.ravel()
+ y[:] = value
+
+ def _get_real(self):
+ "Get the real part of a complex array."
+ if self._mask is nomask:
+ return masked_array(self._data.real, mask=nomask,
+ fill_value = self.fill_value())
+ else:
+ return masked_array(self._data.real, mask=self._mask,
+ fill_value = self.fill_value())
+
+ def _set_real (self, value):
+ "x.real = value"
+ y = self.real
+ y[...] = value
+
+ def _get_imaginary(self):
+ "Get the imaginary part of a complex array."
+ if self._mask is nomask:
+ return masked_array(self._data.imag, mask=nomask,
+ fill_value = self.fill_value())
+ else:
+ return masked_array(self._data.imag, mask=self._mask,
+ fill_value = self.fill_value())
+
+ def _set_imaginary (self, value):
+ "x.imaginary = value"
+ y = self.imaginary
+ y[...] = value
+
+ def __str__(self):
+ """Calculate the str representation, using masked for fill if
+ it is enabled. Otherwise fill with fill value.
+ """
+ if masked_print_option.enabled():
+ f = masked_print_option
+ # XXX: Without the following special case masked
+ # XXX: would print as "[--]", not "--". Can we avoid
+ # XXX: checks for masked by choosing a different value
+ # XXX: for the masked singleton? 2005-01-05 -- sasha
+ if self is masked:
+ return str(f)
+ m = self._mask
+ if m is not nomask and m.shape == () and m:
+ return str(f)
+ # convert to object array to make filled work
+ self = self.astype(object)
+ else:
+ f = self.fill_value()
+ res = self.filled(f)
+ return str(res)
+
+ def __repr__(self):
+ """Calculate the repr representation, using masked for fill if
+ it is enabled. Otherwise fill with fill value.
+ """
+ with_mask = """\
+array(data =
+ %(data)s,
+ mask =
+ %(mask)s,
+ fill_value=%(fill)s)
+"""
+ with_mask1 = """\
+array(data = %(data)s,
+ mask = %(mask)s,
+ fill_value=%(fill)s)
+"""
+ without_mask = """array(
+ %(data)s)"""
+ without_mask1 = """array(%(data)s)"""
+
+ n = len(self.shape)
+ if self._mask is nomask:
+ if n <= 1:
+ return without_mask1 % {'data':str(self.filled())}
+ return without_mask % {'data':str(self.filled())}
+ else:
+ if n <= 1:
+ return with_mask % {
+ 'data': str(self.filled()),
+ 'mask': str(self._mask),
+ 'fill': str(self.fill_value())
+ }
+ return with_mask % {
+ 'data': str(self.filled()),
+ 'mask': str(self._mask),
+ 'fill': str(self.fill_value())
+ }
+ without_mask1 = """array(%(data)s)"""
+ if self._mask is nomask:
+ return without_mask % {'data':str(self.filled())}
+ else:
+ return with_mask % {
+ 'data': str(self.filled()),
+ 'mask': str(self._mask),
+ 'fill': str(self.fill_value())
+ }
+
+ def __float__(self):
+ "Convert self to float."
+ self.unmask()
+ if self._mask is not nomask:
+ raise MAError, 'Cannot convert masked element to a Python float.'
+ return float(self.data.item())
+
+ def __int__(self):
+ "Convert self to int."
+ self.unmask()
+ if self._mask is not nomask:
+ raise MAError, 'Cannot convert masked element to a Python int.'
+ return int(self.data.item())
+
+ def __getitem__(self, i):
+ "Get item described by i. Not a copy as in previous versions."
+ self.unshare_mask()
+ m = self._mask
+ dout = self._data[i]
+ if m is nomask:
+ try:
+ if dout.size == 1:
+ return dout
+ else:
+ return masked_array(dout, fill_value=self._fill_value)
+ except AttributeError:
+ return dout
+ mi = m[i]
+ if mi.size == 1:
+ if mi:
+ return masked
+ else:
+ return dout
+ else:
+ return masked_array(dout, mi, fill_value=self._fill_value)
+
+# --------
+# setitem and setslice notes
+# note that if value is masked, it means to mask those locations.
+# setting a value changes the mask to match the value in those locations.
+
+ def __setitem__(self, index, value):
+ "Set item described by index. If value is masked, mask those locations."
+ d = self._data
+ if self is masked:
+ raise MAError, 'Cannot alter masked elements.'
+ if value is masked:
+ if self._mask is nomask:
+ self._mask = make_mask_none(d.shape)
+ self._shared_mask = False
+ else:
+ self.unshare_mask()
+ self._mask[index] = True
+ return
+ m = getmask(value)
+ value = filled(value).astype(d.dtype)
+ d[index] = value
+ if m is nomask:
+ if self._mask is not nomask:
+ self.unshare_mask()
+ self._mask[index] = False
+ else:
+ if self._mask is nomask:
+ self._mask = make_mask_none(d.shape)
+ self._shared_mask = True
+ else:
+ self.unshare_mask()
+ self._mask[index] = m
+
+ def __nonzero__(self):
+ """returns true if any element is non-zero or masked
+
+ """
+ # XXX: This changes bool conversion logic from MA.
+ # XXX: In MA bool(a) == len(a) != 0, but in numpy
+ # XXX: scalars do not have len
+ m = self._mask
+ d = self._data
+ return bool(m is not nomask and m.any()
+ or d is not nomask and d.any())
+
+ def __len__ (self):
+ """Return length of first dimension. This is weird but Python's
+ slicing behavior depends on it."""
+ return len(self._data)
+
+ def __and__(self, other):
+ "Return bitwise_and"
+ return bitwise_and(self, other)
+
+ def __or__(self, other):
+ "Return bitwise_or"
+ return bitwise_or(self, other)
+
+ def __xor__(self, other):
+ "Return bitwise_xor"
+ return bitwise_xor(self, other)
+
+ __rand__ = __and__
+ __ror__ = __or__
+ __rxor__ = __xor__
+
+ def __abs__(self):
+ "Return absolute(self)"
+ return absolute(self)
+
+ def __neg__(self):
+ "Return negative(self)"
+ return negative(self)
+
+ def __pos__(self):
+ "Return array(self)"
+ return array(self)
+
+ def __add__(self, other):
+ "Return add(self, other)"
+ return add(self, other)
+
+ __radd__ = __add__
+
+ def __mod__ (self, other):
+ "Return remainder(self, other)"
+ return remainder(self, other)
+
+ def __rmod__ (self, other):
+ "Return remainder(other, self)"
+ return remainder(other, self)
+
+ def __lshift__ (self, n):
+ return left_shift(self, n)
+
+ def __rshift__ (self, n):
+ return right_shift(self, n)
+
+ def __sub__(self, other):
+ "Return subtract(self, other)"
+ return subtract(self, other)
+
+ def __rsub__(self, other):
+ "Return subtract(other, self)"
+ return subtract(other, self)
+
+ def __mul__(self, other):
+ "Return multiply(self, other)"
+ return multiply(self, other)
+
+ __rmul__ = __mul__
+
+ def __div__(self, other):
+ "Return divide(self, other)"
+ return divide(self, other)
+
+ def __rdiv__(self, other):
+ "Return divide(other, self)"
+ return divide(other, self)
+
+ def __truediv__(self, other):
+ "Return divide(self, other)"
+ return true_divide(self, other)
+
+ def __rtruediv__(self, other):
+ "Return divide(other, self)"
+ return true_divide(other, self)
+
+ def __floordiv__(self, other):
+ "Return divide(self, other)"
+ return floor_divide(self, other)
+
+ def __rfloordiv__(self, other):
+ "Return divide(other, self)"
+ return floor_divide(other, self)
+
+ def __pow__(self, other, third=None):
+ "Return power(self, other, third)"
+ return power(self, other, third)
+
+ def __sqrt__(self):
+ "Return sqrt(self)"
+ return sqrt(self)
+
+ def __iadd__(self, other):
+ "Add other to self in place."
+ t = self._data.dtype.char
+ f = filled(other, 0)
+ t1 = f.dtype.char
+ if t == t1:
+ pass
+ elif t in typecodes['Integer']:
+ if t1 in typecodes['Integer']:
+ f = f.astype(t)
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ elif t in typecodes['Float']:
+ if t1 in typecodes['Integer']:
+ f = f.astype(t)
+ elif t1 in typecodes['Float']:
+ f = f.astype(t)
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ elif t in typecodes['Complex']:
+ if t1 in typecodes['Integer']:
+ f = f.astype(t)
+ elif t1 in typecodes['Float']:
+ f = f.astype(t)
+ elif t1 in typecodes['Complex']:
+ f = f.astype(t)
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+
+ if self._mask is nomask:
+ self._data += f
+ m = getmask(other)
+ self._mask = m
+ self._shared_mask = m is not nomask
+ else:
+ result = add(self, masked_array(f, mask=getmask(other)))
+ self._data = result.data
+ self._mask = result.mask
+ self._shared_mask = 1
+ return self
+
+ def __imul__(self, other):
+ "Add other to self in place."
+ t = self._data.dtype.char
+ f = filled(other, 0)
+ t1 = f.dtype.char
+ if t == t1:
+ pass
+ elif t in typecodes['Integer']:
+ if t1 in typecodes['Integer']:
+ f = f.astype(t)
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ elif t in typecodes['Float']:
+ if t1 in typecodes['Integer']:
+ f = f.astype(t)
+ elif t1 in typecodes['Float']:
+ f = f.astype(t)
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ elif t in typecodes['Complex']:
+ if t1 in typecodes['Integer']:
+ f = f.astype(t)
+ elif t1 in typecodes['Float']:
+ f = f.astype(t)
+ elif t1 in typecodes['Complex']:
+ f = f.astype(t)
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+
+ if self._mask is nomask:
+ self._data *= f
+ m = getmask(other)
+ self._mask = m
+ self._shared_mask = m is not nomask
+ else:
+ result = multiply(self, masked_array(f, mask=getmask(other)))
+ self._data = result.data
+ self._mask = result.mask
+ self._shared_mask = 1
+ return self
+
+ def __isub__(self, other):
+ "Subtract other from self in place."
+ t = self._data.dtype.char
+ f = filled(other, 0)
+ t1 = f.dtype.char
+ if t == t1:
+ pass
+ elif t in typecodes['Integer']:
+ if t1 in typecodes['Integer']:
+ f = f.astype(t)
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ elif t in typecodes['Float']:
+ if t1 in typecodes['Integer']:
+ f = f.astype(t)
+ elif t1 in typecodes['Float']:
+ f = f.astype(t)
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ elif t in typecodes['Complex']:
+ if t1 in typecodes['Integer']:
+ f = f.astype(t)
+ elif t1 in typecodes['Float']:
+ f = f.astype(t)
+ elif t1 in typecodes['Complex']:
+ f = f.astype(t)
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+
+ if self._mask is nomask:
+ self._data -= f
+ m = getmask(other)
+ self._mask = m
+ self._shared_mask = m is not nomask
+ else:
+ result = subtract(self, masked_array(f, mask=getmask(other)))
+ self._data = result.data
+ self._mask = result.mask
+ self._shared_mask = 1
+ return self
+
+
+
+ def __idiv__(self, other):
+ "Divide self by other in place."
+ t = self._data.dtype.char
+ f = filled(other, 0)
+ t1 = f.dtype.char
+ if t == t1:
+ pass
+ elif t in typecodes['Integer']:
+ if t1 in typecodes['Integer']:
+ f = f.astype(t)
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ elif t in typecodes['Float']:
+ if t1 in typecodes['Integer']:
+ f = f.astype(t)
+ elif t1 in typecodes['Float']:
+ f = f.astype(t)
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ elif t in typecodes['Complex']:
+ if t1 in typecodes['Integer']:
+ f = f.astype(t)
+ elif t1 in typecodes['Float']:
+ f = f.astype(t)
+ elif t1 in typecodes['Complex']:
+ f = f.astype(t)
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ else:
+ raise TypeError, 'Incorrect type for in-place operation.'
+ mo = getmask(other)
+ result = divide(self, masked_array(f, mask=mo))
+ self._data = result.data
+ dm = result.raw_mask()
+ if dm is not self._mask:
+ self._mask = dm
+ self._shared_mask = 1
+ return self
+
+ def __eq__(self, other):
+ return equal(self,other)
+
+ def __ne__(self, other):
+ return not_equal(self,other)
+
+ def __lt__(self, other):
+ return less(self,other)
+
+ def __le__(self, other):
+ return less_equal(self,other)
+
+ def __gt__(self, other):
+ return greater(self,other)
+
+ def __ge__(self, other):
+ return greater_equal(self,other)
+
+ def astype (self, tc):
+ "return self as array of given type."
+ d = self._data.astype(tc)
+ return array(d, mask=self._mask)
+
+ def byte_swapped(self):
+ """Returns the raw data field, byte_swapped. Included for consistency
+ with numeric but doesn't make sense in this context.
+ """
+ return self._data.byte_swapped()
+
+ def compressed (self):
+ "A 1-D array of all the non-masked data."
+ d = fromnumeric.ravel(self._data)
+ if self._mask is nomask:
+ return array(d)
+ else:
+ m = 1 - fromnumeric.ravel(self._mask)
+ c = fromnumeric.compress(m, d)
+ return array(c, copy=0)
+
+ def count (self, axis = None):
+ "Count of the non-masked elements in a, or along a certain axis."
+ m = self._mask
+ s = self._data.shape
+ ls = len(s)
+ if m is nomask:
+ if ls == 0:
+ return 1
+ if ls == 1:
+ return s[0]
+ if axis is None:
+ return reduce(lambda x, y:x*y, s)
+ else:
+ n = s[axis]
+ t = list(s)
+ del t[axis]
+ return ones(t) * n
+ if axis is None:
+ w = fromnumeric.ravel(m).astype(int)
+ n1 = size(w)
+ if n1 == 1:
+ n2 = w[0]
+ else:
+ n2 = umath.add.reduce(w)
+ return n1 - n2
+ else:
+ n1 = size(m, axis)
+ n2 = sum(m.astype(int), axis)
+ return n1 - n2
+
+ def dot (self, other):
+ "s.dot(other) = innerproduct(s, other)"
+ return innerproduct(self, other)
+
+ def fill_value(self):
+ "Get the current fill value."
+ return self._fill_value
+
+ def filled (self, fill_value=None):
+ """A numeric array with masked values filled. If fill_value is None,
+ use self.fill_value().
+
+ If mask is nomask, copy data only if not contiguous.
+ Result is always a contiguous, numeric array.
+# Is contiguous really necessary now?
+ """
+ d = self._data
+ m = self._mask
+ if m is nomask:
+ if d.flags['CONTIGUOUS']:
+ return d
+ else:
+ return d.copy()
+ else:
+ if fill_value is None:
+ value = self._fill_value
+ else:
+ value = fill_value
+
+ if self is masked:
+ result = numeric.array(value)
+ else:
+ try:
+ result = numeric.array(d, dtype=d.dtype, copy=1)
+ result[m] = value
+ except (TypeError, AttributeError):
+ #ok, can't put that value in here
+ value = numeric.array(value, dtype=object)
+ d = d.astype(object)
+ result = fromnumeric.choose(m, (d, value))
+ return result
+
+ def ids (self):
+ """Return the ids of the data and mask areas"""
+ return (id(self._data), id(self._mask))
+
+ def iscontiguous (self):
+ "Is the data contiguous?"
+ return self._data.flags['CONTIGUOUS']
+
+ def itemsize(self):
+ "Item size of each data item."
+ return self._data.itemsize
+
+
+ def outer(self, other):
+ "s.outer(other) = outerproduct(s, other)"
+ return outerproduct(self, other)
+
+ def put (self, values):
+ """Set the non-masked entries of self to filled(values).
+ No change to mask
+ """
+ iota = numeric.arange(self.size)
+ d = self._data
+ if self._mask is nomask:
+ ind = iota
+ else:
+ ind = fromnumeric.compress(1 - self._mask, iota)
+ d[ind] = filled(values).astype(d.dtype)
+
+ def putmask (self, values):
+ """Set the masked entries of self to filled(values).
+ Mask changed to nomask.
+ """
+ d = self._data
+ if self._mask is not nomask:
+ d[self._mask] = filled(values).astype(d.dtype)
+ self._shared_mask = 0
+ self._mask = nomask
+
+ def ravel (self):
+ """Return a 1-D view of self."""
+ if self._mask is nomask:
+ return masked_array(self._data.ravel())
+ else:
+ return masked_array(self._data.ravel(), self._mask.ravel())
+
+ def raw_data (self):
+ """ Obsolete; use data property instead.
+ The raw data; portions may be meaningless.
+ May be noncontiguous. Expert use only."""
+ return self._data
+ data = property(fget=raw_data,
+ doc="The data, but values at masked locations are meaningless.")
+
+ def raw_mask (self):
+ """ Obsolete; use mask property instead.
+ May be noncontiguous. Expert use only.
+ """
+ return self._mask
+ mask = property(fget=raw_mask,
+ doc="The mask, may be nomask. Values where mask true are meaningless.")
+
+ def reshape (self, *s):
+ """This array reshaped to shape s"""
+ d = self._data.reshape(*s)
+ if self._mask is nomask:
+ return masked_array(d)
+ else:
+ m = self._mask.reshape(*s)
+ return masked_array(d, m)
+
+ def set_fill_value (self, v=None):
+ "Set the fill value to v. Omit v to restore default."
+ if v is None:
+ v = default_fill_value (self.raw_data())
+ self._fill_value = v
+
+ def _get_ndim(self):
+ return self._data.ndim
+ ndim = property(_get_ndim, doc=numeric.ndarray.ndim.__doc__)
+
+ def _get_size (self):
+ return self._data.size
+ size = property(fget=_get_size, doc="Number of elements in the array.")
+## CHECK THIS: signature of numeric.array.size?
+
+ def _get_dtype(self):
+ return self._data.dtype
+ dtype = property(fget=_get_dtype, doc="type of the array elements.")
+
+ def item(self, *args):
+ "Return Python scalar if possible"
+ if self._mask is not nomask:
+ m = self._mask.item(*args)
+ try:
+ if m[0]:
+ return masked
+ except IndexError:
+ return masked
+ return self._data.item(*args)
+
+ def itemset(self, *args):
+ "Set Python scalar into array"
+ item = args[-1]
+ args = args[:-1]
+ self[args] = item
+
+ def tolist(self, fill_value=None):
+ "Convert to list"
+ return self.filled(fill_value).tolist()
+
+ def tostring(self, fill_value=None):
+ "Convert to string"
+ return self.filled(fill_value).tostring()
+
+ def unmask (self):
+ "Replace the mask by nomask if possible."
+ if self._mask is nomask: return
+ m = make_mask(self._mask, flag=1)
+ if m is nomask:
+ self._mask = nomask
+ self._shared_mask = 0
+
+ def unshare_mask (self):
+ "If currently sharing mask, make a copy."
+ if self._shared_mask:
+ self._mask = make_mask (self._mask, copy=1, flag=0)
+ self._shared_mask = 0
+
+ def _get_ctypes(self):
+ return self._data.ctypes
+
+ def _get_T(self):
+ if (self.ndim < 2):
+ return self
+ return self.transpose()
+
+ shape = property(_get_shape, _set_shape,
+ doc = 'tuple giving the shape of the array')
+
+ flat = property(_get_flat, _set_flat,
+ doc = 'Access array in flat form.')
+
+ real = property(_get_real, _set_real,
+ doc = 'Access the real part of the array')
+
+ imaginary = property(_get_imaginary, _set_imaginary,
+ doc = 'Access the imaginary part of the array')
+
+ imag = imaginary
+
+ ctypes = property(_get_ctypes, None, doc="ctypes")
+
+ T = property(_get_T, None, doc="get transpose")
+
+#end class MaskedArray
+
+array = MaskedArray
+
+def isMaskedArray (x):
+ "Is x a masked array, that is, an instance of MaskedArray?"
+ return isinstance(x, MaskedArray)
+
+isarray = isMaskedArray
+isMA = isMaskedArray #backward compatibility
+
+def allclose (a, b, fill_value=1, rtol=1.e-5, atol=1.e-8):
+ """ Returns true if all components of a and b are equal
+ subject to given tolerances.
+ If fill_value is 1, masked values considered equal.
+ If fill_value is 0, masked values considered unequal.
+ The relative error rtol should be positive and << 1.0
+ The absolute error atol comes into play for those elements
+ of b that are very small or zero; it says how small a must be also.
+ """
+ m = mask_or(getmask(a), getmask(b))
+ d1 = filled(a)
+ d2 = filled(b)
+ x = filled(array(d1, copy=0, mask=m), fill_value).astype(float)
+ y = filled(array(d2, copy=0, mask=m), 1).astype(float)
+ d = umath.less_equal(umath.absolute(x-y), atol + rtol * umath.absolute(y))
+ return fromnumeric.alltrue(fromnumeric.ravel(d))
+
+def allequal (a, b, fill_value=1):
+ """
+ True if all entries of a and b are equal, using
+ fill_value as a truth value where either or both are masked.
+ """
+ m = mask_or(getmask(a), getmask(b))
+ if m is nomask:
+ x = filled(a)
+ y = filled(b)
+ d = umath.equal(x, y)
+ return fromnumeric.alltrue(fromnumeric.ravel(d))
+ elif fill_value:
+ x = filled(a)
+ y = filled(b)
+ d = umath.equal(x, y)
+ dm = array(d, mask=m, copy=0)
+ return fromnumeric.alltrue(fromnumeric.ravel(filled(dm, 1)))
+ else:
+ return 0
+
+def masked_values (data, value, rtol=1.e-5, atol=1.e-8, copy=1):
+ """
+ masked_values(data, value, rtol=1.e-5, atol=1.e-8)
+ Create a masked array; mask is nomask if possible.
+ If copy==0, and otherwise possible, result
+ may share data values with original array.
+ Let d = filled(data, value). Returns d
+ masked where abs(data-value)<= atol + rtol * abs(value)
+ if d is of a floating point type. Otherwise returns
+ masked_object(d, value, copy)
+ """
+ abs = umath.absolute
+ d = filled(data, value)
+ if issubclass(d.dtype.type, numeric.floating):
+ m = umath.less_equal(abs(d-value), atol+rtol*abs(value))
+ m = make_mask(m, flag=1)
+ return array(d, mask = m, copy=copy,
+ fill_value=value)
+ else:
+ return masked_object(d, value, copy=copy)
+
+def masked_object (data, value, copy=1):
+ "Create array masked where exactly data equal to value"
+ d = filled(data, value)
+ dm = make_mask(umath.equal(d, value), flag=1)
+ return array(d, mask=dm, copy=copy, fill_value=value)
+
+def arange(start, stop=None, step=1, dtype=None):
+ """Just like range() except it returns a array whose type can be specified
+ by the keyword argument dtype.
+ """
+ return array(numeric.arange(start, stop, step, dtype))
+
+arrayrange = arange
+
+def fromstring (s, t):
+ "Construct a masked array from a string. Result will have no mask."
+ return masked_array(numeric.fromstring(s, t))
+
+def left_shift (a, n):
+ "Left shift n bits"
+ m = getmask(a)
+ if m is nomask:
+ d = umath.left_shift(filled(a), n)
+ return masked_array(d)
+ else:
+ d = umath.left_shift(filled(a, 0), n)
+ return masked_array(d, m)
+
+def right_shift (a, n):
+ "Right shift n bits"
+ m = getmask(a)
+ if m is nomask:
+ d = umath.right_shift(filled(a), n)
+ return masked_array(d)
+ else:
+ d = umath.right_shift(filled(a, 0), n)
+ return masked_array(d, m)
+
+def resize (a, new_shape):
+ """resize(a, new_shape) returns a new array with the specified shape.
+ The original array's total size can be any size."""
+ m = getmask(a)
+ if m is not nomask:
+ m = fromnumeric.resize(m, new_shape)
+ result = array(fromnumeric.resize(filled(a), new_shape), mask=m)
+ result.set_fill_value(get_fill_value(a))
+ return result
+
+def new_repeat(a, repeats, axis=None):
+ """repeat elements of a repeats times along axis
+ repeats is a sequence of length a.shape[axis]
+ telling how many times to repeat each element.
+ """
+ af = filled(a)
+ if isinstance(repeats, types.IntType):
+ if axis is None:
+ num = af.size
+ else:
+ num = af.shape[axis]
+ repeats = tuple([repeats]*num)
+
+ m = getmask(a)
+ if m is not nomask:
+ m = fromnumeric.repeat(m, repeats, axis)
+ d = fromnumeric.repeat(af, repeats, axis)
+ result = masked_array(d, m)
+ result.set_fill_value(get_fill_value(a))
+ return result
+
+
+
+def identity(n):
+ """identity(n) returns the identity matrix of shape n x n.
+ """
+ return array(numeric.identity(n))
+
+def indices (dimensions, dtype=None):
+ """indices(dimensions,dtype=None) returns an array representing a grid
+ of indices with row-only, and column-only variation.
+ """
+ return array(numeric.indices(dimensions, dtype))
+
+def zeros (shape, dtype=float):
+ """zeros(n, dtype=float) =
+ an array of all zeros of the given length or shape."""
+ return array(numeric.zeros(shape, dtype))
+
+def ones (shape, dtype=float):
+ """ones(n, dtype=float) =
+ an array of all ones of the given length or shape."""
+ return array(numeric.ones(shape, dtype))
+
+def count (a, axis = None):
+ "Count of the non-masked elements in a, or along a certain axis."
+ a = masked_array(a)
+ return a.count(axis)
+
+def power (a, b, third=None):
+ "a**b"
+ if third is not None:
+ raise MAError, "3-argument power not supported."
+ ma = getmask(a)
+ mb = getmask(b)
+ m = mask_or(ma, mb)
+ fa = filled(a, 1)
+ fb = filled(b, 1)
+ if fb.dtype.char in typecodes["Integer"]:
+ return masked_array(umath.power(fa, fb), m)
+ md = make_mask(umath.less(fa, 0), flag=1)
+ m = mask_or(m, md)
+ if m is nomask:
+ return masked_array(umath.power(fa, fb))
+ else:
+ fa = numeric.where(m, 1, fa)
+ return masked_array(umath.power(fa, fb), m)
+
+def masked_array (a, mask=nomask, fill_value=None):
+ """masked_array(a, mask=nomask) =
+ array(a, mask=mask, copy=0, fill_value=fill_value)
+ """
+ return array(a, mask=mask, copy=0, fill_value=fill_value)
+
+def sum (target, axis=None, dtype=None):
+ if axis is None:
+ target = ravel(target)
+ axis = 0
+ return add.reduce(target, axis, dtype)
+
+def product (target, axis=None, dtype=None):
+ if axis is None:
+ target = ravel(target)
+ axis = 0
+ return multiply.reduce(target, axis, dtype)
+
+def new_average (a, axis=None, weights=None, returned = 0):
+ """average(a, axis=None, weights=None)
+ Computes average along indicated axis.
+ If axis is None, average over the entire array
+ Inputs can be integer or floating types; result is of type float.
+
+ If weights are given, result is sum(a*weights,axis=0)/(sum(weights,axis=0)*1.0)
+ weights must have a's shape or be the 1-d with length the size
+ of a in the given axis.
+
+ If returned, return a tuple: the result and the sum of the weights
+ or count of values. Results will have the same shape.
+
+ masked values in the weights will be set to 0.0
+ """
+ a = masked_array(a)
+ mask = a.mask
+ ash = a.shape
+ if ash == ():
+ ash = (1,)
+ if axis is None:
+ if mask is nomask:
+ if weights is None:
+ n = add.reduce(a.raw_data().ravel())
+ d = reduce(lambda x, y: x * y, ash, 1.0)
+ else:
+ w = filled(weights, 0.0).ravel()
+ n = umath.add.reduce(a.raw_data().ravel() * w)
+ d = umath.add.reduce(w)
+ del w
+ else:
+ if weights is None:
+ n = add.reduce(a.ravel())
+ w = fromnumeric.choose(mask, (1.0, 0.0)).ravel()
+ d = umath.add.reduce(w)
+ del w
+ else:
+ w = array(filled(weights, 0.0), float, mask=mask).ravel()
+ n = add.reduce(a.ravel() * w)
+ d = add.reduce(w)
+ del w
+ else:
+ if mask is nomask:
+ if weights is None:
+ d = ash[axis] * 1.0
+ n = umath.add.reduce(a.raw_data(), axis)
+ else:
+ w = filled(weights, 0.0)
+ wsh = w.shape
+ if wsh == ():
+ wsh = (1,)
+ if wsh == ash:
+ w = numeric.array(w, float, copy=0)
+ n = add.reduce(a*w, axis)
+ d = add.reduce(w, axis)
+ del w
+ elif wsh == (ash[axis],):
+ r = [newaxis]*len(ash)
+ r[axis] = slice(None, None, 1)
+ w = eval ("w["+ repr(tuple(r)) + "] * ones(ash, float)")
+ n = add.reduce(a*w, axis)
+ d = add.reduce(w, axis)
+ del w, r
+ else:
+ raise ValueError, 'average: weights wrong shape.'
+ else:
+ if weights is None:
+ n = add.reduce(a, axis)
+ w = numeric.choose(mask, (1.0, 0.0))
+ d = umath.add.reduce(w, axis)
+ del w
+ else:
+ w = filled(weights, 0.0)
+ wsh = w.shape
+ if wsh == ():
+ wsh = (1,)
+ if wsh == ash:
+ w = array(w, float, mask=mask, copy=0)
+ n = add.reduce(a*w, axis)
+ d = add.reduce(w, axis)
+ elif wsh == (ash[axis],):
+ r = [newaxis]*len(ash)
+ r[axis] = slice(None, None, 1)
+ w = eval ("w["+ repr(tuple(r)) + "] * masked_array(ones(ash, float), mask)")
+ n = add.reduce(a*w, axis)
+ d = add.reduce(w, axis)
+ else:
+ raise ValueError, 'average: weights wrong shape.'
+ del w
+ #print n, d, repr(mask), repr(weights)
+ if n is masked or d is masked: return masked
+ result = divide (n, d)
+ del n
+
+ if isinstance(result, MaskedArray):
+ result.unmask()
+ if returned:
+ if not isinstance(d, MaskedArray):
+ d = masked_array(d)
+ if not d.shape == result.shape:
+ d = ones(result.shape, float) * d
+ d.unmask()
+ if returned:
+ return result, d
+ else:
+ return result
+
+def where (condition, x, y):
+ """where(condition, x, y) is x where condition is nonzero, y otherwise.
+ condition must be convertible to an integer array.
+ Answer is always the shape of condition.
+ The type depends on x and y. It is integer if both x and y are
+ the value masked.
+ """
+ fc = filled(not_equal(condition, 0), 0)
+ xv = filled(x)
+ xm = getmask(x)
+ yv = filled(y)
+ ym = getmask(y)
+ d = numeric.choose(fc, (yv, xv))
+ md = numeric.choose(fc, (ym, xm))
+ m = getmask(condition)
+ m = make_mask(mask_or(m, md), copy=0, flag=1)
+ return masked_array(d, m)
+
+def choose (indices, t, out=None, mode='raise'):
+ "Returns array shaped like indices with elements chosen from t"
+ def fmask (x):
+ if x is masked: return 1
+ return filled(x)
+ def nmask (x):
+ if x is masked: return 1
+ m = getmask(x)
+ if m is nomask: return 0
+ return m
+ c = filled(indices, 0)
+ masks = [nmask(x) for x in t]
+ a = [fmask(x) for x in t]
+ d = numeric.choose(c, a)
+ m = numeric.choose(c, masks)
+ m = make_mask(mask_or(m, getmask(indices)), copy=0, flag=1)
+ return masked_array(d, m)
+
+def masked_where(condition, x, copy=1):
+ """Return x as an array masked where condition is true.
+ Also masked where x or condition masked.
+ """
+ cm = filled(condition,1)
+ m = mask_or(getmask(x), cm)
+ return array(filled(x), copy=copy, mask=m)
+
+def masked_greater(x, value, copy=1):
+ "masked_greater(x, value) = x masked where x > value"
+ return masked_where(greater(x, value), x, copy)
+
+def masked_greater_equal(x, value, copy=1):
+ "masked_greater_equal(x, value) = x masked where x >= value"
+ return masked_where(greater_equal(x, value), x, copy)
+
+def masked_less(x, value, copy=1):
+ "masked_less(x, value) = x masked where x < value"
+ return masked_where(less(x, value), x, copy)
+
+def masked_less_equal(x, value, copy=1):
+ "masked_less_equal(x, value) = x masked where x <= value"
+ return masked_where(less_equal(x, value), x, copy)
+
+def masked_not_equal(x, value, copy=1):
+ "masked_not_equal(x, value) = x masked where x != value"
+ d = filled(x, 0)
+ c = umath.not_equal(d, value)
+ m = mask_or(c, getmask(x))
+ return array(d, mask=m, copy=copy)
+
+def masked_equal(x, value, copy=1):
+ """masked_equal(x, value) = x masked where x == value
+ For floating point consider masked_values(x, value) instead.
+ """
+ d = filled(x, 0)
+ c = umath.equal(d, value)
+ m = mask_or(c, getmask(x))
+ return array(d, mask=m, copy=copy)
+
+def masked_inside(x, v1, v2, copy=1):
+ """x with mask of all values of x that are inside [v1,v2]
+ v1 and v2 can be given in either order.
+ """
+ if v2 < v1:
+ t = v2
+ v2 = v1
+ v1 = t
+ d = filled(x, 0)
+ c = umath.logical_and(umath.less_equal(d, v2), umath.greater_equal(d, v1))
+ m = mask_or(c, getmask(x))
+ return array(d, mask = m, copy=copy)
+
+def masked_outside(x, v1, v2, copy=1):
+ """x with mask of all values of x that are outside [v1,v2]
+ v1 and v2 can be given in either order.
+ """
+ if v2 < v1:
+ t = v2
+ v2 = v1
+ v1 = t
+ d = filled(x, 0)
+ c = umath.logical_or(umath.less(d, v1), umath.greater(d, v2))
+ m = mask_or(c, getmask(x))
+ return array(d, mask = m, copy=copy)
+
+def reshape (a, *newshape):
+ "Copy of a with a new shape."
+ m = getmask(a)
+ d = filled(a).reshape(*newshape)
+ if m is nomask:
+ return masked_array(d)
+ else:
+ return masked_array(d, mask=numeric.reshape(m, *newshape))
+
+def ravel (a):
+ "a as one-dimensional, may share data and mask"
+ m = getmask(a)
+ d = fromnumeric.ravel(filled(a))
+ if m is nomask:
+ return masked_array(d)
+ else:
+ return masked_array(d, mask=numeric.ravel(m))
+
+def concatenate (arrays, axis=0):
+ "Concatenate the arrays along the given axis"
+ d = []
+ for x in arrays:
+ d.append(filled(x))
+ d = numeric.concatenate(d, axis)
+ for x in arrays:
+ if getmask(x) is not nomask: break
+ else:
+ return masked_array(d)
+ dm = []
+ for x in arrays:
+ dm.append(getmaskarray(x))
+ dm = numeric.concatenate(dm, axis)
+ return masked_array(d, mask=dm)
+
+def swapaxes (a, axis1, axis2):
+ m = getmask(a)
+ d = masked_array(a).data
+ if m is nomask:
+ return masked_array(data=numeric.swapaxes(d, axis1, axis2))
+ else:
+ return masked_array(data=numeric.swapaxes(d, axis1, axis2),
+ mask=numeric.swapaxes(m, axis1, axis2),)
+
+
+def new_take (a, indices, axis=None, out=None, mode='raise'):
+ "returns selection of items from a."
+ m = getmask(a)
+ # d = masked_array(a).raw_data()
+ d = masked_array(a).data
+ if m is nomask:
+ return masked_array(numeric.take(d, indices, axis))
+ else:
+ return masked_array(numeric.take(d, indices, axis),
+ mask = numeric.take(m, indices, axis))
+
+def transpose(a, axes=None):
+ "reorder dimensions per tuple axes"
+ m = getmask(a)
+ d = filled(a)
+ if m is nomask:
+ return masked_array(numeric.transpose(d, axes))
+ else:
+ return masked_array(numeric.transpose(d, axes),
+ mask = numeric.transpose(m, axes))
+
+
+def put(a, indices, values, mode='raise'):
+ """sets storage-indexed locations to corresponding values.
+
+ Values and indices are filled if necessary.
+
+ """
+ d = a.raw_data()
+ ind = filled(indices)
+ v = filled(values)
+ numeric.put (d, ind, v)
+ m = getmask(a)
+ if m is not nomask:
+ a.unshare_mask()
+ numeric.put(a.raw_mask(), ind, 0)
+
+def putmask(a, mask, values):
+ "putmask(a, mask, values) sets a where mask is true."
+ if mask is nomask:
+ return
+ numeric.putmask(a.raw_data(), mask, values)
+ m = getmask(a)
+ if m is nomask: return
+ a.unshare_mask()
+ numeric.putmask(a.raw_mask(), mask, 0)
+
+def inner(a, b):
+ """inner(a,b) returns the dot product of two arrays, which has
+ shape a.shape[:-1] + b.shape[:-1] with elements computed by summing the
+ product of the elements from the last dimensions of a and b.
+ Masked elements are replace by zeros.
+ """
+ fa = filled(a, 0)
+ fb = filled(b, 0)
+ if len(fa.shape) == 0: fa.shape = (1,)
+ if len(fb.shape) == 0: fb.shape = (1,)
+ return masked_array(numeric.inner(fa, fb))
+
+innerproduct = inner
+
+def outer(a, b):
+ """outer(a,b) = {a[i]*b[j]}, has shape (len(a),len(b))"""
+ fa = filled(a, 0).ravel()
+ fb = filled(b, 0).ravel()
+ d = numeric.outer(fa, fb)
+ ma = getmask(a)
+ mb = getmask(b)
+ if ma is nomask and mb is nomask:
+ return masked_array(d)
+ ma = getmaskarray(a)
+ mb = getmaskarray(b)
+ m = make_mask(1-numeric.outer(1-ma, 1-mb), copy=0)
+ return masked_array(d, m)
+
+outerproduct = outer
+
+def dot(a, b):
+ """dot(a,b) returns matrix-multiplication between a and b. The product-sum
+ is over the last dimension of a and the second-to-last dimension of b.
+ Masked values are replaced by zeros. See also innerproduct.
+ """
+ return innerproduct(filled(a, 0), numeric.swapaxes(filled(b, 0), -1, -2))
+
+def compress(condition, x, dimension=-1, out=None):
+ """Select those parts of x for which condition is true.
+ Masked values in condition are considered false.
+ """
+ c = filled(condition, 0)
+ m = getmask(x)
+ if m is not nomask:
+ m = numeric.compress(c, m, dimension)
+ d = numeric.compress(c, filled(x), dimension)
+ return masked_array(d, m)
+
+class _minimum_operation:
+ "Object to calculate minima"
+ def __init__ (self):
+ """minimum(a, b) or minimum(a)
+ In one argument case returns the scalar minimum.
+ """
+ pass
+
+ def __call__ (self, a, b=None):
+ "Execute the call behavior."
+ if b is None:
+ m = getmask(a)
+ if m is nomask:
+ d = amin(filled(a).ravel())
+ return d
+ ac = a.compressed()
+ if len(ac) == 0:
+ return masked
+ else:
+ return amin(ac.raw_data())
+ else:
+ return where(less(a, b), a, b)
+
+ def reduce (self, target, axis=0):
+ """Reduce target along the given axis."""
+ m = getmask(target)
+ if m is nomask:
+ t = filled(target)
+ return masked_array (umath.minimum.reduce (t, axis))
+ else:
+ t = umath.minimum.reduce(filled(target, minimum_fill_value(target)), axis)
+ m = umath.logical_and.reduce(m, axis)
+ return masked_array(t, m, get_fill_value(target))
+
+ def outer (self, a, b):
+ "Return the function applied to the outer product of a and b."
+ ma = getmask(a)
+ mb = getmask(b)
+ if ma is nomask and mb is nomask:
+ m = nomask
+ else:
+ ma = getmaskarray(a)
+ mb = getmaskarray(b)
+ m = logical_or.outer(ma, mb)
+ d = umath.minimum.outer(filled(a), filled(b))
+ return masked_array(d, m)
+
+minimum = _minimum_operation ()
+
+class _maximum_operation:
+ "Object to calculate maxima"
+ def __init__ (self):
+ """maximum(a, b) or maximum(a)
+ In one argument case returns the scalar maximum.
+ """
+ pass
+
+ def __call__ (self, a, b=None):
+ "Execute the call behavior."
+ if b is None:
+ m = getmask(a)
+ if m is nomask:
+ d = amax(filled(a).ravel())
+ return d
+ ac = a.compressed()
+ if len(ac) == 0:
+ return masked
+ else:
+ return amax(ac.raw_data())
+ else:
+ return where(greater(a, b), a, b)
+
+ def reduce (self, target, axis=0):
+ """Reduce target along the given axis."""
+ m = getmask(target)
+ if m is nomask:
+ t = filled(target)
+ return masked_array (umath.maximum.reduce (t, axis))
+ else:
+ t = umath.maximum.reduce(filled(target, maximum_fill_value(target)), axis)
+ m = umath.logical_and.reduce(m, axis)
+ return masked_array(t, m, get_fill_value(target))
+
+ def outer (self, a, b):
+ "Return the function applied to the outer product of a and b."
+ ma = getmask(a)
+ mb = getmask(b)
+ if ma is nomask and mb is nomask:
+ m = nomask
+ else:
+ ma = getmaskarray(a)
+ mb = getmaskarray(b)
+ m = logical_or.outer(ma, mb)
+ d = umath.maximum.outer(filled(a), filled(b))
+ return masked_array(d, m)
+
+maximum = _maximum_operation ()
+
+def sort (x, axis = -1, fill_value=None):
+ """If x does not have a mask, return a masked array formed from the
+ result of numeric.sort(x, axis).
+ Otherwise, fill x with fill_value. Sort it.
+ Set a mask where the result is equal to fill_value.
+ Note that this may have unintended consequences if the data contains the
+ fill value at a non-masked site.
+
+ If fill_value is not given the default fill value for x's type will be
+ used.
+ """
+ if fill_value is None:
+ fill_value = default_fill_value (x)
+ d = filled(x, fill_value)
+ s = fromnumeric.sort(d, axis)
+ if getmask(x) is nomask:
+ return masked_array(s)
+ return masked_values(s, fill_value, copy=0)
+
+def diagonal(a, k = 0, axis1=0, axis2=1):
+ """diagonal(a,k=0,axis1=0, axis2=1) = the k'th diagonal of a"""
+ d = fromnumeric.diagonal(filled(a), k, axis1, axis2)
+ m = getmask(a)
+ if m is nomask:
+ return masked_array(d, m)
+ else:
+ return masked_array(d, fromnumeric.diagonal(m, k, axis1, axis2))
+
+def trace (a, offset=0, axis1=0, axis2=1, dtype=None, out=None):
+ """trace(a,offset=0, axis1=0, axis2=1) returns the sum along diagonals
+ (defined by the last two dimenions) of the array.
+ """
+ return diagonal(a, offset, axis1, axis2).sum(dtype=dtype)
+
+def argsort (x, axis = -1, out=None, fill_value=None):
+ """Treating masked values as if they have the value fill_value,
+ return sort indices for sorting along given axis.
+ if fill_value is None, use get_fill_value(x)
+ Returns a numpy array.
+ """
+ d = filled(x, fill_value)
+ return fromnumeric.argsort(d, axis)
+
+def argmin (x, axis = -1, out=None, fill_value=None):
+ """Treating masked values as if they have the value fill_value,
+ return indices for minimum values along given axis.
+ if fill_value is None, use get_fill_value(x).
+ Returns a numpy array if x has more than one dimension.
+ Otherwise, returns a scalar index.
+ """
+ d = filled(x, fill_value)
+ return fromnumeric.argmin(d, axis)
+
+def argmax (x, axis = -1, out=None, fill_value=None):
+ """Treating masked values as if they have the value fill_value,
+ return sort indices for maximum along given axis.
+ if fill_value is None, use -get_fill_value(x) if it exists.
+ Returns a numpy array if x has more than one dimension.
+ Otherwise, returns a scalar index.
+ """
+ if fill_value is None:
+ fill_value = default_fill_value (x)
+ try:
+ fill_value = - fill_value
+ except:
+ pass
+ d = filled(x, fill_value)
+ return fromnumeric.argmax(d, axis)
+
+def fromfunction (f, s):
+ """apply f to s to create array as in umath."""
+ return masked_array(numeric.fromfunction(f, s))
+
+def asarray(data, dtype=None):
+ """asarray(data, dtype) = array(data, dtype, copy=0)
+ """
+ if isinstance(data, MaskedArray) and \
+ (dtype is None or dtype == data.dtype):
+ return data
+ return array(data, dtype=dtype, copy=0)
+
+# Add methods to support ndarray interface
+# XXX: I is better to to change the masked_*_operation adaptors
+# XXX: to wrap ndarray methods directly to create ma.array methods.
+from types import MethodType
+def _m(f):
+ return MethodType(f, None, array)
+def not_implemented(*args, **kwds):
+ raise NotImplementedError, "not yet implemented for numpy.ma arrays"
+array.all = _m(alltrue)
+array.any = _m(sometrue)
+array.argmax = _m(argmax)
+array.argmin = _m(argmin)
+array.argsort = _m(argsort)
+array.base = property(_m(not_implemented))
+array.byteswap = _m(not_implemented)
+
+def _choose(self, *args, **kwds):
+ return choose(self, args)
+array.choose = _m(_choose)
+del _choose
+
+def _clip(self,a_min,a_max,out=None):
+ return MaskedArray(data = self.data.clip(asarray(a_min).data,
+ asarray(a_max).data),
+ mask = mask_or(self.mask,
+ mask_or(getmask(a_min),getmask(a_max))))
+array.clip = _m(_clip)
+
+def _compress(self, cond, axis=None, out=None):
+ return compress(cond, self, axis)
+array.compress = _m(_compress)
+del _compress
+
+array.conj = array.conjugate = _m(conjugate)
+array.copy = _m(not_implemented)
+
+def _cumprod(self, axis=None, dtype=None, out=None):
+ m = self.mask
+ if m is not nomask:
+ m = umath.logical_or.accumulate(self.mask, axis)
+ return MaskedArray(data = self.filled(1).cumprod(axis, dtype), mask=m)
+array.cumprod = _m(_cumprod)
+
+def _cumsum(self, axis=None, dtype=None, out=None):
+ m = self.mask
+ if m is not nomask:
+ m = umath.logical_or.accumulate(self.mask, axis)
+ return MaskedArray(data=self.filled(0).cumsum(axis, dtype), mask=m)
+array.cumsum = _m(_cumsum)
+
+array.diagonal = _m(diagonal)
+array.dump = _m(not_implemented)
+array.dumps = _m(not_implemented)
+array.fill = _m(not_implemented)
+array.flags = property(_m(not_implemented))
+array.flatten = _m(ravel)
+array.getfield = _m(not_implemented)
+
+def _max(a, axis=None, out=None):
+ if out is not None:
+ raise TypeError("Output arrays Unsupported for masked arrays")
+ if axis is None:
+ return maximum(a)
+ else:
+ return maximum.reduce(a, axis)
+array.max = _m(_max)
+del _max
+def _min(a, axis=None, out=None):
+ if out is not None:
+ raise TypeError("Output arrays Unsupported for masked arrays")
+ if axis is None:
+ return minimum(a)
+ else:
+ return minimum.reduce(a, axis)
+array.min = _m(_min)
+del _min
+array.mean = _m(average)
+array.nbytes = property(_m(not_implemented))
+array.newbyteorder = _m(not_implemented)
+array.nonzero = _m(nonzero)
+array.prod = _m(product)
+
+def _ptp(a,axis=None,out=None):
+ return a.max(axis,out)-a.min(axis)
+array.ptp = _m(_ptp)
+array.repeat = _m(repeat)
+array.resize = _m(resize)
+array.searchsorted = _m(not_implemented)
+array.setfield = _m(not_implemented)
+array.setflags = _m(not_implemented)
+array.sort = _m(not_implemented) # NB: ndarray.sort is inplace
+
+def _squeeze(self):
+ try:
+ result = MaskedArray(data = self.data.squeeze(),
+ mask = self.mask.squeeze())
+ except AttributeError:
+ result = _wrapit(self, 'squeeze')
+ return result
+array.squeeze = _m(_squeeze)
+
+array.strides = property(_m(not_implemented))
+array.sum = _m(sum)
+def _swapaxes(self,axis1,axis2):
+ return MaskedArray(data = self.data.swapaxes(axis1, axis2),
+ mask = self.mask.swapaxes(axis1, axis2))
+array.swapaxes = _m(_swapaxes)
+array.take = _m(take)
+array.tofile = _m(not_implemented)
+array.trace = _m(trace)
+array.transpose = _m(transpose)
+
+def _var(self,axis=None,dtype=None, out=None):
+ if axis is None:
+ return numeric.asarray(self.compressed()).var()
+ a = self.swapaxes(axis,0)
+ a = a - a.mean(axis=0)
+ a *= a
+ a /= a.count(axis=0)
+ return a.swapaxes(0,axis).sum(axis)
+def _std(self,axis=None, dtype=None, out=None):
+ return (self.var(axis,dtype))**0.5
+array.var = _m(_var)
+array.std = _m(_std)
+
+array.view = _m(not_implemented)
+array.round = _m(around)
+del _m, MethodType, not_implemented
+
+
+masked = MaskedArray(0, int, mask=1)
+
def repeat(a, repeats, axis=0):
- return nca.repeat(a, repeats, axis)
+ return new_repeat(a, repeats, axis)
def average(a, axis=0, weights=None, returned=0):
- return nca.average(a, axis, weights, returned)
+ return new_average(a, axis, weights, returned)
def take(a, indices, axis=0):
- return nca.average(a, indices, axis=0)
+ return new_take(a, indices, axis=0)
+
+
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