.venv/Lib/site-packages/numpy/ma/API_CHANGES.txt
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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). |