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.. -*- rest -*-
==================================================
API changes in the new masked array implementation
==================================================
Masked arrays are subclasses of ndarray
---------------------------------------
Contrary to the original implementation, masked arrays are now regular
ndarrays::
>>> x = masked_array([1,2,3],mask=[0,0,1])
>>> print isinstance(x, numpy.ndarray)
True
``_data`` returns a view of the masked array
--------------------------------------------
Masked arrays are composed of a ``_data`` part and a ``_mask``. Accessing the
``_data`` part will return a regular ndarray or any of its subclass, depending
on the initial data::
>>> x = masked_array(numpy.matrix([[1,2],[3,4]]),mask=[[0,0],[0,1]])
>>> print x._data
[[1 2]
[3 4]]
>>> print type(x._data)
<class 'numpy.matrixlib.defmatrix.matrix'>
In practice, ``_data`` is implemented as a property, not as an attribute.
Therefore, you cannot access it directly, and some simple tests such as the
following one will fail::
>>>x._data is x._data
False
``filled(x)`` can return a subclass of ndarray
----------------------------------------------
The function ``filled(a)`` returns an array of the same type as ``a._data``::
>>> x = masked_array(numpy.matrix([[1,2],[3,4]]),mask=[[0,0],[0,1]])
>>> y = filled(x)
>>> print type(y)
<class 'numpy.matrixlib.defmatrix.matrix'>
>>> print y
matrix([[ 1, 2],
[ 3, 999999]])
``put``, ``putmask`` behave like their ndarray counterparts
-----------------------------------------------------------
Previously, ``putmask`` was used like this::
mask = [False,True,True]
x = array([1,4,7],mask=mask)
putmask(x,mask,[3])
which translated to::
x[~mask] = [3]
(Note that a ``True``-value in a mask suppresses a value.)
In other words, the mask had the same length as ``x``, whereas
``values`` had ``sum(~mask)`` elements.
Now, the behaviour is similar to that of ``ndarray.putmask``, where
the mask and the values are both the same length as ``x``, i.e.
::
putmask(x,mask,[3,0,0])
``fill_value`` is a property
----------------------------
``fill_value`` is no longer a method, but a property::
>>> print x.fill_value
999999
``cumsum`` and ``cumprod`` ignore missing values
------------------------------------------------
Missing values are assumed to be the identity element, i.e. 0 for
``cumsum`` and 1 for ``cumprod``::
>>> x = N.ma.array([1,2,3,4],mask=[False,True,False,False])
>>> print x
[1 -- 3 4]
>>> print x.cumsum()
[1 -- 4 8]
>> print x.cumprod()
[1 -- 3 12]
``bool(x)`` raises a ValueError
-------------------------------
Masked arrays now behave like regular ``ndarrays``, in that they cannot be
converted to booleans:
::
>>> x = N.ma.array([1,2,3])
>>> bool(x)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
==================================
New features (non exhaustive list)
==================================
``mr_``
-------
``mr_`` mimics the behavior of ``r_`` for masked arrays::
>>> np.ma.mr_[3,4,5]
masked_array(data = [3 4 5],
mask = False,
fill_value=999999)
``anom``
--------
The ``anom`` method returns the deviations from the average (anomalies).
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