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svn+ssh://pythondev@svn.python.org/python/trunk ........ r61098 | jeffrey.yasskin | 2008-02-28 05:45:36 +0100 (Thu, 28 Feb 2008) | 7 lines Move abc._Abstract into object by adding a new flag Py_TPFLAGS_IS_ABSTRACT, which forbids constructing types that have it set. The effect is to speed ./python.exe -m timeit -s 'import abc' -s 'class Foo(object): __metaclass__ = abc.ABCMeta' 'Foo()' up from 2.5us to 0.201us. This fixes issue 1762. ........ r61099 | jeffrey.yasskin | 2008-02-28 06:53:18 +0100 (Thu, 28 Feb 2008) | 3 lines Speed test_socketserver up from 28.739s to 0.226s, simplify the logic, and make sure all tests run even if some fail. ........ r61100 | jeffrey.yasskin | 2008-02-28 07:09:19 +0100 (Thu, 28 Feb 2008) | 21 lines Thread.start() used sleep(0.000001) to make sure it didn't return before the new thread had started. At least on my MacBook Pro, that wound up sleeping for a full 10ms (probably 1 jiffy). By using an Event instead, we can be absolutely certain that the thread has started, and return more quickly (217us). Before: $ ./python.exe -m timeit -s 'from threading import Thread' 't = Thread(); t.start(); t.join()' 100 loops, best of 3: 10.3 msec per loop $ ./python.exe -m timeit -s 'from threading import Thread; t = Thread()' 't.isAlive()' 1000000 loops, best of 3: 0.47 usec per loop After: $ ./python.exe -m timeit -s 'from threading import Thread' 't = Thread(); t.start(); t.join()' 1000 loops, best of 3: 217 usec per loop $ ./python.exe -m timeit -s 'from threading import Thread; t = Thread()' 't.isAlive()' 1000000 loops, best of 3: 0.86 usec per loop To be fair, the 10ms isn't CPU time, and other threads including the spawned one get to run during it. There are also some slightly more complicated ways to get back the .4us in isAlive() if we want. ........ r61101 | raymond.hettinger | 2008-02-28 10:23:48 +0100 (Thu, 28 Feb 2008) | 1 line Add repeat keyword argument to itertools.product(). ........ r61102 | christian.heimes | 2008-02-28 12:18:49 +0100 (Thu, 28 Feb 2008) | 1 line The empty tuple is usually a singleton with a much higher refcnt than 1 ........
481 lines
12 KiB
Python
481 lines
12 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.test_support import sortdict
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import pprint
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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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<type '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-existant 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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<type 'list'>
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>>> [].__class__
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<type 'list'>
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>>> list
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<type '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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'__contains__',
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'__delattr__',
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'__delitem__',
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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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'__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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'__str__',
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'__subclasshook__',
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'append',
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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 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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<type '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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# Magic test name that regrtest.py invokes *after* importing this module.
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# This worms around a bootstrap problem.
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# Note that doctest and regrtest both look in sys.argv for a "-v" argument,
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# so this works as expected in both ways of running regrtest.
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def test_main(verbose=None):
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# Obscure: import this module as test.test_descrtut instead of as
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# plain test_descrtut because the name of this module works its way
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# into the doctest examples, and unless the full test.test_descrtut
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# business is used the name can change depending on how the test is
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# invoked.
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from test import test_support, test_descrtut
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test_support.run_doctest(test_descrtut, verbose)
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# This part isn't needed for regrtest, but for running the test directly.
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if __name__ == "__main__":
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test_main(1)
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