diff options
Diffstat (limited to 'numpy/doc/subclassing.py')
| -rw-r--r-- | numpy/doc/subclassing.py | 82 |
1 files changed, 41 insertions, 41 deletions
diff --git a/numpy/doc/subclassing.py b/numpy/doc/subclassing.py index f23cf6652..859ab32f9 100644 --- a/numpy/doc/subclassing.py +++ b/numpy/doc/subclassing.py @@ -7,7 +7,7 @@ Credits ------- This page is based with thanks on the wiki page on subclassing by Pierre -Gerard-Marchant - http://www.scipy.org/Subclasses. +Gerard-Marchant - http://www.scipy.org/Subclasses. Introduction ------------ @@ -40,21 +40,21 @@ this machinery that makes subclassing slightly non-standard. To allow subclassing, and views of subclasses, ndarray uses the ndarray ``__new__`` method for the main work of object initialization, -rather then the more usual ``__init__`` method. +rather then the more usual ``__init__`` method. ``__new__`` and ``__init__`` ============================ ``__new__`` is a standard python method, and, if present, is called before ``__init__`` when we create a class instance. Consider the -following:: +following:: class C(object): def __new__(cls, *args): - print 'Args in __new__:', args - return object.__new__(cls, *args) + print 'Args in __new__:', args + return object.__new__(cls, *args) def __init__(self, *args): - print 'Args in __init__:', args + print 'Args in __init__:', args C('hello') @@ -75,7 +75,7 @@ following. As you can see, the object can be initialized in the ``__new__`` method or the ``__init__`` method, or both, and in fact ndarray does not have an ``__init__`` method, because all the initialization is -done in the ``__new__`` method. +done in the ``__new__`` method. Why use ``__new__`` rather than just the usual ``__init__``? Because in some cases, as for ndarray, we want to be able to return an object @@ -83,21 +83,21 @@ of some other class. Consider the following:: class C(object): def __new__(cls, *args): - print 'cls is:', cls - print 'Args in __new__:', args - return object.__new__(cls, *args) + print 'cls is:', cls + print 'Args in __new__:', args + return object.__new__(cls, *args) def __init__(self, *args): - print 'self is :', self - print 'Args in __init__:', args + print 'self is :', self + print 'Args in __init__:', args class D(C): def __new__(cls, *args): - print 'D cls is:', cls - print 'D args in __new__:', args - return C.__new__(C, *args) + print 'D cls is:', cls + print 'D args in __new__:', args + return C.__new__(C, *args) def __init__(self, *args): - print 'D self is :', self - print 'D args in __init__:', args + print 'D self is :', self + print 'D args in __init__:', args D('hello') @@ -131,7 +131,7 @@ this way, in its standard methods for taking views, but the ndarray ``__new__`` method knows nothing of what we have done in our own ``__new__`` method in order to set attributes, and so on. (Aside - why not call ``obj = subdtype.__new__(...`` then? Because we may not -have a ``__new__`` method with the same call signature). +have a ``__new__`` method with the same call signature). So, when creating a new view object of our subclass, we need to be able to set any extra attributes from the original object of our @@ -153,21 +153,21 @@ Simple example - adding an extra attribute to ndarray class InfoArray(np.ndarray): def __new__(subtype, shape, dtype=float, buffer=None, offset=0, - strides=None, order=None, info=None): - # Create the ndarray instance of our type, given the usual - # input arguments. This will call the standard ndarray - # constructor, but return an object of our type - obj = np.ndarray.__new__(subtype, shape, dtype, buffer, offset, strides, - order) - # add the new attribute to the created instance - obj.info = info - # Finally, we must return the newly created object: - return obj + strides=None, order=None, info=None): + # Create the ndarray instance of our type, given the usual + # input arguments. This will call the standard ndarray + # constructor, but return an object of our type + obj = np.ndarray.__new__(subtype, shape, dtype, buffer, offset, strides, + order) + # add the new attribute to the created instance + obj.info = info + # Finally, we must return the newly created object: + return obj def __array_finalize__(self,obj): - # reset the attribute from passed original object - self.info = getattr(obj, 'info', None) - # We do not need to return anything + # reset the attribute from passed original object + self.info = getattr(obj, 'info', None) + # We do not need to return anything obj = InfoArray(shape=(3,), info='information') print type(obj) @@ -200,18 +200,18 @@ extra attribute:: class RealisticInfoArray(np.ndarray): def __new__(cls, input_array, info=None): - # Input array is an already formed ndarray instance - # We first cast to be our class type - obj = np.asarray(input_array).view(cls) - # add the new attribute to the created instance - obj.info = info - # Finally, we must return the newly created object: - return obj + # Input array is an already formed ndarray instance + # We first cast to be our class type + obj = np.asarray(input_array).view(cls) + # add the new attribute to the created instance + obj.info = info + # Finally, we must return the newly created object: + return obj def __array_finalize__(self,obj): - # reset the attribute from passed original object - self.info = getattr(obj, 'info', None) - # We do not need to return anything + # reset the attribute from passed original object + self.info = getattr(obj, 'info', None) + # We do not need to return anything arr = np.arange(5) obj = RealisticInfoArray(arr, info='information') |