Tipragot
628be439b8
Cela permet de ne pas avoir de problèmes de compatibilité car python est dans le git.
557 lines
15 KiB
Python
557 lines
15 KiB
Python
import functools
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import time
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import inspect
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import collections
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import types
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import itertools
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import warnings
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import setuptools.extern.more_itertools
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from typing import Callable, TypeVar
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CallableT = TypeVar("CallableT", bound=Callable[..., object])
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def compose(*funcs):
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"""
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Compose any number of unary functions into a single unary function.
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>>> import textwrap
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>>> expected = str.strip(textwrap.dedent(compose.__doc__))
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>>> strip_and_dedent = compose(str.strip, textwrap.dedent)
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>>> strip_and_dedent(compose.__doc__) == expected
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True
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Compose also allows the innermost function to take arbitrary arguments.
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>>> round_three = lambda x: round(x, ndigits=3)
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>>> f = compose(round_three, int.__truediv__)
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>>> [f(3*x, x+1) for x in range(1,10)]
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[1.5, 2.0, 2.25, 2.4, 2.5, 2.571, 2.625, 2.667, 2.7]
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"""
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def compose_two(f1, f2):
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return lambda *args, **kwargs: f1(f2(*args, **kwargs))
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return functools.reduce(compose_two, funcs)
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def method_caller(method_name, *args, **kwargs):
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"""
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Return a function that will call a named method on the
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target object with optional positional and keyword
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arguments.
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>>> lower = method_caller('lower')
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>>> lower('MyString')
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'mystring'
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"""
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def call_method(target):
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func = getattr(target, method_name)
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return func(*args, **kwargs)
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return call_method
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def once(func):
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"""
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Decorate func so it's only ever called the first time.
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This decorator can ensure that an expensive or non-idempotent function
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will not be expensive on subsequent calls and is idempotent.
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>>> add_three = once(lambda a: a+3)
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>>> add_three(3)
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6
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>>> add_three(9)
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6
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>>> add_three('12')
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6
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To reset the stored value, simply clear the property ``saved_result``.
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>>> del add_three.saved_result
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>>> add_three(9)
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12
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>>> add_three(8)
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12
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Or invoke 'reset()' on it.
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>>> add_three.reset()
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>>> add_three(-3)
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0
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>>> add_three(0)
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0
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"""
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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if not hasattr(wrapper, 'saved_result'):
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wrapper.saved_result = func(*args, **kwargs)
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return wrapper.saved_result
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wrapper.reset = lambda: vars(wrapper).__delitem__('saved_result')
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return wrapper
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def method_cache(
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method: CallableT,
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cache_wrapper: Callable[
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[CallableT], CallableT
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] = functools.lru_cache(), # type: ignore[assignment]
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) -> CallableT:
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"""
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Wrap lru_cache to support storing the cache data in the object instances.
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Abstracts the common paradigm where the method explicitly saves an
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underscore-prefixed protected property on first call and returns that
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subsequently.
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>>> class MyClass:
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... calls = 0
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...
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... @method_cache
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... def method(self, value):
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... self.calls += 1
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... return value
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>>> a = MyClass()
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>>> a.method(3)
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3
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>>> for x in range(75):
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... res = a.method(x)
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>>> a.calls
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75
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Note that the apparent behavior will be exactly like that of lru_cache
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except that the cache is stored on each instance, so values in one
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instance will not flush values from another, and when an instance is
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deleted, so are the cached values for that instance.
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>>> b = MyClass()
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>>> for x in range(35):
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... res = b.method(x)
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>>> b.calls
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35
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>>> a.method(0)
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0
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>>> a.calls
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75
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Note that if method had been decorated with ``functools.lru_cache()``,
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a.calls would have been 76 (due to the cached value of 0 having been
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flushed by the 'b' instance).
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Clear the cache with ``.cache_clear()``
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>>> a.method.cache_clear()
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Same for a method that hasn't yet been called.
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>>> c = MyClass()
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>>> c.method.cache_clear()
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Another cache wrapper may be supplied:
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>>> cache = functools.lru_cache(maxsize=2)
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>>> MyClass.method2 = method_cache(lambda self: 3, cache_wrapper=cache)
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>>> a = MyClass()
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>>> a.method2()
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3
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Caution - do not subsequently wrap the method with another decorator, such
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as ``@property``, which changes the semantics of the function.
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See also
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http://code.activestate.com/recipes/577452-a-memoize-decorator-for-instance-methods/
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for another implementation and additional justification.
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"""
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def wrapper(self: object, *args: object, **kwargs: object) -> object:
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# it's the first call, replace the method with a cached, bound method
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bound_method: CallableT = types.MethodType( # type: ignore[assignment]
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method, self
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)
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cached_method = cache_wrapper(bound_method)
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setattr(self, method.__name__, cached_method)
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return cached_method(*args, **kwargs)
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# Support cache clear even before cache has been created.
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wrapper.cache_clear = lambda: None # type: ignore[attr-defined]
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return ( # type: ignore[return-value]
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_special_method_cache(method, cache_wrapper) or wrapper
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)
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def _special_method_cache(method, cache_wrapper):
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"""
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Because Python treats special methods differently, it's not
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possible to use instance attributes to implement the cached
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methods.
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Instead, install the wrapper method under a different name
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and return a simple proxy to that wrapper.
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https://github.com/jaraco/jaraco.functools/issues/5
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"""
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name = method.__name__
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special_names = '__getattr__', '__getitem__'
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if name not in special_names:
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return
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wrapper_name = '__cached' + name
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def proxy(self, *args, **kwargs):
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if wrapper_name not in vars(self):
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bound = types.MethodType(method, self)
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cache = cache_wrapper(bound)
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setattr(self, wrapper_name, cache)
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else:
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cache = getattr(self, wrapper_name)
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return cache(*args, **kwargs)
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return proxy
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def apply(transform):
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"""
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Decorate a function with a transform function that is
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invoked on results returned from the decorated function.
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>>> @apply(reversed)
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... def get_numbers(start):
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... "doc for get_numbers"
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... return range(start, start+3)
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>>> list(get_numbers(4))
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[6, 5, 4]
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>>> get_numbers.__doc__
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'doc for get_numbers'
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"""
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def wrap(func):
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return functools.wraps(func)(compose(transform, func))
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return wrap
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def result_invoke(action):
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r"""
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Decorate a function with an action function that is
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invoked on the results returned from the decorated
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function (for its side-effect), then return the original
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result.
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>>> @result_invoke(print)
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... def add_two(a, b):
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... return a + b
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>>> x = add_two(2, 3)
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5
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>>> x
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5
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"""
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def wrap(func):
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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result = func(*args, **kwargs)
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action(result)
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return result
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return wrapper
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return wrap
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def invoke(f, *args, **kwargs):
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"""
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Call a function for its side effect after initialization.
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The benefit of using the decorator instead of simply invoking a function
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after defining it is that it makes explicit the author's intent for the
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function to be called immediately. Whereas if one simply calls the
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function immediately, it's less obvious if that was intentional or
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incidental. It also avoids repeating the name - the two actions, defining
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the function and calling it immediately are modeled separately, but linked
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by the decorator construct.
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The benefit of having a function construct (opposed to just invoking some
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behavior inline) is to serve as a scope in which the behavior occurs. It
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avoids polluting the global namespace with local variables, provides an
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anchor on which to attach documentation (docstring), keeps the behavior
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logically separated (instead of conceptually separated or not separated at
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all), and provides potential to re-use the behavior for testing or other
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purposes.
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This function is named as a pithy way to communicate, "call this function
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primarily for its side effect", or "while defining this function, also
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take it aside and call it". It exists because there's no Python construct
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for "define and call" (nor should there be, as decorators serve this need
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just fine). The behavior happens immediately and synchronously.
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>>> @invoke
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... def func(): print("called")
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called
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>>> func()
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called
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Use functools.partial to pass parameters to the initial call
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>>> @functools.partial(invoke, name='bingo')
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... def func(name): print("called with", name)
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called with bingo
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"""
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f(*args, **kwargs)
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return f
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def call_aside(*args, **kwargs):
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"""
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Deprecated name for invoke.
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"""
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warnings.warn("call_aside is deprecated, use invoke", DeprecationWarning)
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return invoke(*args, **kwargs)
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class Throttler:
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"""
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Rate-limit a function (or other callable)
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"""
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def __init__(self, func, max_rate=float('Inf')):
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if isinstance(func, Throttler):
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func = func.func
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self.func = func
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self.max_rate = max_rate
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self.reset()
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def reset(self):
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self.last_called = 0
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def __call__(self, *args, **kwargs):
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self._wait()
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return self.func(*args, **kwargs)
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def _wait(self):
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"ensure at least 1/max_rate seconds from last call"
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elapsed = time.time() - self.last_called
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must_wait = 1 / self.max_rate - elapsed
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time.sleep(max(0, must_wait))
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self.last_called = time.time()
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def __get__(self, obj, type=None):
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return first_invoke(self._wait, functools.partial(self.func, obj))
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def first_invoke(func1, func2):
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"""
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Return a function that when invoked will invoke func1 without
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any parameters (for its side-effect) and then invoke func2
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with whatever parameters were passed, returning its result.
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"""
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def wrapper(*args, **kwargs):
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func1()
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return func2(*args, **kwargs)
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return wrapper
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def retry_call(func, cleanup=lambda: None, retries=0, trap=()):
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"""
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Given a callable func, trap the indicated exceptions
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for up to 'retries' times, invoking cleanup on the
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exception. On the final attempt, allow any exceptions
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to propagate.
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"""
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attempts = itertools.count() if retries == float('inf') else range(retries)
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for attempt in attempts:
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try:
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return func()
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except trap:
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cleanup()
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return func()
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def retry(*r_args, **r_kwargs):
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"""
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Decorator wrapper for retry_call. Accepts arguments to retry_call
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except func and then returns a decorator for the decorated function.
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Ex:
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>>> @retry(retries=3)
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... def my_func(a, b):
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... "this is my funk"
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... print(a, b)
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>>> my_func.__doc__
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'this is my funk'
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"""
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def decorate(func):
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@functools.wraps(func)
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def wrapper(*f_args, **f_kwargs):
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bound = functools.partial(func, *f_args, **f_kwargs)
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return retry_call(bound, *r_args, **r_kwargs)
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return wrapper
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return decorate
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def print_yielded(func):
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"""
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Convert a generator into a function that prints all yielded elements
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>>> @print_yielded
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... def x():
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... yield 3; yield None
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>>> x()
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3
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None
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"""
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print_all = functools.partial(map, print)
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print_results = compose(more_itertools.consume, print_all, func)
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return functools.wraps(func)(print_results)
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def pass_none(func):
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"""
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Wrap func so it's not called if its first param is None
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>>> print_text = pass_none(print)
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>>> print_text('text')
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text
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>>> print_text(None)
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"""
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@functools.wraps(func)
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def wrapper(param, *args, **kwargs):
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if param is not None:
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return func(param, *args, **kwargs)
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return wrapper
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def assign_params(func, namespace):
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"""
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Assign parameters from namespace where func solicits.
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>>> def func(x, y=3):
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... print(x, y)
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>>> assigned = assign_params(func, dict(x=2, z=4))
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>>> assigned()
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2 3
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The usual errors are raised if a function doesn't receive
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its required parameters:
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>>> assigned = assign_params(func, dict(y=3, z=4))
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>>> assigned()
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Traceback (most recent call last):
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TypeError: func() ...argument...
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It even works on methods:
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>>> class Handler:
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... def meth(self, arg):
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... print(arg)
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>>> assign_params(Handler().meth, dict(arg='crystal', foo='clear'))()
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crystal
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"""
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sig = inspect.signature(func)
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params = sig.parameters.keys()
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call_ns = {k: namespace[k] for k in params if k in namespace}
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return functools.partial(func, **call_ns)
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def save_method_args(method):
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"""
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Wrap a method such that when it is called, the args and kwargs are
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saved on the method.
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>>> class MyClass:
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... @save_method_args
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... def method(self, a, b):
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... print(a, b)
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>>> my_ob = MyClass()
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>>> my_ob.method(1, 2)
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1 2
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>>> my_ob._saved_method.args
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(1, 2)
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>>> my_ob._saved_method.kwargs
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{}
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>>> my_ob.method(a=3, b='foo')
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3 foo
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>>> my_ob._saved_method.args
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()
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>>> my_ob._saved_method.kwargs == dict(a=3, b='foo')
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True
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The arguments are stored on the instance, allowing for
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different instance to save different args.
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>>> your_ob = MyClass()
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>>> your_ob.method({str('x'): 3}, b=[4])
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{'x': 3} [4]
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>>> your_ob._saved_method.args
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({'x': 3},)
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>>> my_ob._saved_method.args
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()
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"""
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args_and_kwargs = collections.namedtuple('args_and_kwargs', 'args kwargs')
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@functools.wraps(method)
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def wrapper(self, *args, **kwargs):
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attr_name = '_saved_' + method.__name__
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attr = args_and_kwargs(args, kwargs)
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setattr(self, attr_name, attr)
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return method(self, *args, **kwargs)
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return wrapper
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def except_(*exceptions, replace=None, use=None):
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"""
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Replace the indicated exceptions, if raised, with the indicated
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literal replacement or evaluated expression (if present).
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>>> safe_int = except_(ValueError)(int)
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>>> safe_int('five')
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>>> safe_int('5')
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5
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Specify a literal replacement with ``replace``.
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>>> safe_int_r = except_(ValueError, replace=0)(int)
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>>> safe_int_r('five')
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0
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Provide an expression to ``use`` to pass through particular parameters.
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>>> safe_int_pt = except_(ValueError, use='args[0]')(int)
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>>> safe_int_pt('five')
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'five'
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"""
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def decorate(func):
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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try:
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return func(*args, **kwargs)
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except exceptions:
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try:
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return eval(use)
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except TypeError:
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return replace
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return wrapper
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return decorate
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