mirror of https://github.com/python/cpython
482 lines
11 KiB
Python
482 lines
11 KiB
Python
# This contains most of the executable examples from Guido's descr
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# tutorial, once at
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#
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# http://www.python.org/2.2/descrintro.html
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#
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# A few examples left implicit in the writeup were fleshed out, a few were
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# skipped due to lack of interest (e.g., faking super() by hand isn't
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# of much interest anymore), and a few were fiddled to make the output
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# deterministic.
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from test.support import sortdict
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import pprint
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import doctest
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import unittest
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class defaultdict(dict):
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def __init__(self, default=None):
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dict.__init__(self)
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self.default = default
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def __getitem__(self, key):
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try:
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return dict.__getitem__(self, key)
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except KeyError:
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return self.default
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def get(self, key, *args):
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if not args:
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args = (self.default,)
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return dict.get(self, key, *args)
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def merge(self, other):
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for key in other:
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if key not in self:
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self[key] = other[key]
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test_1 = """
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Here's the new type at work:
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>>> print(defaultdict) # show our type
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<class 'test.test_descrtut.defaultdict'>
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>>> print(type(defaultdict)) # its metatype
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<class 'type'>
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>>> a = defaultdict(default=0.0) # create an instance
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>>> print(a) # show the instance
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{}
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>>> print(type(a)) # show its type
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<class 'test.test_descrtut.defaultdict'>
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>>> print(a.__class__) # show its class
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<class 'test.test_descrtut.defaultdict'>
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>>> print(type(a) is a.__class__) # its type is its class
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True
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>>> a[1] = 3.25 # modify the instance
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>>> print(a) # show the new value
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{1: 3.25}
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>>> print(a[1]) # show the new item
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3.25
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>>> print(a[0]) # a non-existent item
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0.0
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>>> a.merge({1:100, 2:200}) # use a dict method
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>>> print(sortdict(a)) # show the result
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{1: 3.25, 2: 200}
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>>>
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We can also use the new type in contexts where classic only allows "real"
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dictionaries, such as the locals/globals dictionaries for the exec
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statement or the built-in function eval():
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>>> print(sorted(a.keys()))
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[1, 2]
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>>> a['print'] = print # need the print function here
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>>> exec("x = 3; print(x)", a)
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3
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>>> print(sorted(a.keys(), key=lambda x: (str(type(x)), x)))
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[1, 2, '__builtins__', 'print', 'x']
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>>> print(a['x'])
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3
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>>>
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Now I'll show that defaultdict instances have dynamic instance variables,
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just like classic classes:
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>>> a.default = -1
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>>> print(a["noway"])
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-1
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>>> a.default = -1000
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>>> print(a["noway"])
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-1000
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>>> 'default' in dir(a)
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True
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>>> a.x1 = 100
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>>> a.x2 = 200
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>>> print(a.x1)
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100
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>>> d = dir(a)
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>>> 'default' in d and 'x1' in d and 'x2' in d
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True
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>>> print(sortdict(a.__dict__))
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{'default': -1000, 'x1': 100, 'x2': 200}
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>>>
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"""
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class defaultdict2(dict):
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__slots__ = ['default']
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def __init__(self, default=None):
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dict.__init__(self)
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self.default = default
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def __getitem__(self, key):
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try:
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return dict.__getitem__(self, key)
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except KeyError:
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return self.default
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def get(self, key, *args):
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if not args:
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args = (self.default,)
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return dict.get(self, key, *args)
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def merge(self, other):
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for key in other:
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if key not in self:
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self[key] = other[key]
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test_2 = """
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The __slots__ declaration takes a list of instance variables, and reserves
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space for exactly these in the instance. When __slots__ is used, other
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instance variables cannot be assigned to:
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>>> a = defaultdict2(default=0.0)
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>>> a[1]
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0.0
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>>> a.default = -1
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>>> a[1]
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-1
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>>> a.x1 = 1
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Traceback (most recent call last):
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File "<stdin>", line 1, in ?
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AttributeError: 'defaultdict2' object has no attribute 'x1'
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>>>
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"""
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test_3 = """
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Introspecting instances of built-in types
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For instance of built-in types, x.__class__ is now the same as type(x):
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>>> type([])
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<class 'list'>
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>>> [].__class__
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<class 'list'>
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>>> list
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<class 'list'>
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>>> isinstance([], list)
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True
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>>> isinstance([], dict)
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False
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>>> isinstance([], object)
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True
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>>>
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You can get the information from the list type:
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>>> pprint.pprint(dir(list)) # like list.__dict__.keys(), but sorted
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['__add__',
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'__class__',
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'__class_getitem__',
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'__contains__',
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'__delattr__',
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'__delitem__',
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'__dir__',
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'__doc__',
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'__eq__',
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'__format__',
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'__ge__',
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'__getattribute__',
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'__getitem__',
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'__gt__',
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'__hash__',
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'__iadd__',
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'__imul__',
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'__init__',
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'__init_subclass__',
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'__iter__',
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'__le__',
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'__len__',
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'__lt__',
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'__mul__',
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'__ne__',
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'__new__',
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'__reduce__',
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'__reduce_ex__',
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'__repr__',
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'__reversed__',
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'__rmul__',
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'__setattr__',
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'__setitem__',
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'__sizeof__',
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'__str__',
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'__subclasshook__',
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'append',
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'clear',
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'copy',
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'count',
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'extend',
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'index',
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'insert',
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'pop',
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'remove',
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'reverse',
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'sort']
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The new introspection API gives more information than the old one: in
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addition to the regular methods, it also shows the methods that are
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normally invoked through special notations, e.g. __iadd__ (+=), __len__
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(len), __ne__ (!=). You can invoke any method from this list directly:
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>>> a = ['tic', 'tac']
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>>> list.__len__(a) # same as len(a)
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2
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>>> a.__len__() # ditto
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2
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>>> list.append(a, 'toe') # same as a.append('toe')
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>>> a
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['tic', 'tac', 'toe']
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>>>
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This is just like it is for user-defined classes.
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"""
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test_4 = """
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Static methods and class methods
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The new introspection API makes it possible to add static methods and class
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methods. Static methods are easy to describe: they behave pretty much like
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static methods in C++ or Java. Here's an example:
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>>> class C:
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...
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... @staticmethod
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... def foo(x, y):
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... print("staticmethod", x, y)
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>>> C.foo(1, 2)
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staticmethod 1 2
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>>> c = C()
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>>> c.foo(1, 2)
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staticmethod 1 2
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Class methods use a similar pattern to declare methods that receive an
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implicit first argument that is the *class* for which they are invoked.
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>>> class C:
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... @classmethod
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... def foo(cls, y):
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... print("classmethod", cls, y)
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>>> C.foo(1)
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classmethod <class 'test.test_descrtut.C'> 1
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>>> c = C()
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>>> c.foo(1)
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classmethod <class 'test.test_descrtut.C'> 1
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>>> class D(C):
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... pass
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>>> D.foo(1)
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classmethod <class 'test.test_descrtut.D'> 1
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>>> d = D()
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>>> d.foo(1)
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classmethod <class 'test.test_descrtut.D'> 1
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This prints "classmethod __main__.D 1" both times; in other words, the
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class passed as the first argument of foo() is the class involved in the
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call, not the class involved in the definition of foo().
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But notice this:
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>>> class E(C):
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... @classmethod
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... def foo(cls, y): # override C.foo
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... print("E.foo() called")
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... C.foo(y)
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>>> E.foo(1)
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E.foo() called
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classmethod <class 'test.test_descrtut.C'> 1
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>>> e = E()
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>>> e.foo(1)
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E.foo() called
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classmethod <class 'test.test_descrtut.C'> 1
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In this example, the call to C.foo() from E.foo() will see class C as its
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first argument, not class E. This is to be expected, since the call
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specifies the class C. But it stresses the difference between these class
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methods and methods defined in metaclasses (where an upcall to a metamethod
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would pass the target class as an explicit first argument).
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"""
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test_5 = """
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Attributes defined by get/set methods
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>>> class property(object):
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...
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... def __init__(self, get, set=None):
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... self.__get = get
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... self.__set = set
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...
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... def __get__(self, inst, type=None):
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... return self.__get(inst)
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...
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... def __set__(self, inst, value):
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... if self.__set is None:
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... raise AttributeError("this attribute is read-only")
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... return self.__set(inst, value)
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Now let's define a class with an attribute x defined by a pair of methods,
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getx() and setx():
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>>> class C(object):
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...
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... def __init__(self):
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... self.__x = 0
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...
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... def getx(self):
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... return self.__x
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...
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... def setx(self, x):
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... if x < 0: x = 0
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... self.__x = x
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...
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... x = property(getx, setx)
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Here's a small demonstration:
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>>> a = C()
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>>> a.x = 10
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>>> print(a.x)
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10
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>>> a.x = -10
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>>> print(a.x)
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0
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>>>
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Hmm -- property is builtin now, so let's try it that way too.
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>>> del property # unmask the builtin
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>>> property
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<class 'property'>
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>>> class C(object):
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... def __init__(self):
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... self.__x = 0
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... def getx(self):
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... return self.__x
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... def setx(self, x):
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... if x < 0: x = 0
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... self.__x = x
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... x = property(getx, setx)
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>>> a = C()
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>>> a.x = 10
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>>> print(a.x)
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10
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>>> a.x = -10
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>>> print(a.x)
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0
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>>>
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"""
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test_6 = """
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Method resolution order
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This example is implicit in the writeup.
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>>> class A: # implicit new-style class
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... def save(self):
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... print("called A.save()")
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>>> class B(A):
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... pass
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>>> class C(A):
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... def save(self):
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... print("called C.save()")
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>>> class D(B, C):
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... pass
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>>> D().save()
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called C.save()
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>>> class A(object): # explicit new-style class
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... def save(self):
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... print("called A.save()")
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>>> class B(A):
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... pass
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>>> class C(A):
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... def save(self):
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... print("called C.save()")
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>>> class D(B, C):
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... pass
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>>> D().save()
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called C.save()
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"""
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class A(object):
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def m(self):
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return "A"
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class B(A):
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def m(self):
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return "B" + super(B, self).m()
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class C(A):
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def m(self):
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return "C" + super(C, self).m()
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class D(C, B):
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def m(self):
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return "D" + super(D, self).m()
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test_7 = """
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Cooperative methods and "super"
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>>> print(D().m()) # "DCBA"
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DCBA
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"""
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test_8 = """
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Backwards incompatibilities
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>>> class A:
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... def foo(self):
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... print("called A.foo()")
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>>> class B(A):
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... pass
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>>> class C(A):
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... def foo(self):
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... B.foo(self)
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>>> C().foo()
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called A.foo()
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>>> class C(A):
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... def foo(self):
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... A.foo(self)
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>>> C().foo()
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called A.foo()
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"""
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__test__ = {"tut1": test_1,
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"tut2": test_2,
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"tut3": test_3,
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"tut4": test_4,
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"tut5": test_5,
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"tut6": test_6,
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"tut7": test_7,
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"tut8": test_8}
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def load_tests(loader, tests, pattern):
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tests.addTest(doctest.DocTestSuite())
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return tests
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if __name__ == "__main__":
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unittest.main()
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