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