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rS rS rS rS r " S S5 rS rS S S4S jrS rS r S r S r S r SSS .S jr g) N)CallableTypeVar CallableT.)boundc 4 S n[ R " X5 $ )a
Compose any number of unary functions into a single unary function.
>>> import textwrap
>>> expected = str.strip(textwrap.dedent(compose.__doc__))
>>> strip_and_dedent = compose(str.strip, textwrap.dedent)
>>> strip_and_dedent(compose.__doc__) == expected
True
Compose also allows the innermost function to take arbitrary arguments.
>>> round_three = lambda x: round(x, ndigits=3)
>>> f = compose(round_three, int.__truediv__)
>>> [f(3*x, x+1) for x in range(1,10)]
[1.5, 2.0, 2.25, 2.4, 2.5, 2.571, 2.625, 2.667, 2.7]
c ^ ^ U U4S j$ )Nc > T" T" U 0 UD65 $ N )argskwargsf1f2s ڟ/builddir/build/BUILDROOT/alt-python313-setuptools-69.0.2-3.el8.x86_64/opt/alt/python313/lib/python3.13/site-packages/pkg_resources/_vendor/jaraco/functools.py.compose..compose_two..$ s r"d*=f*='> r )r r s ``r compose_two compose..compose_two# s >>r ) functoolsreduce)funcsr s r composer s $? K //r c ^ ^^ UUU 4S jnU$ )z
Return a function that will call a named method on the
target object with optional positional and keyword
arguments.
>>> lower = method_caller('lower')
>>> lower('MyString')
'mystring'
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method_names r call_method"method_caller..call_method4 s v{+T$V$$r r )r r r
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Decorate func so it's only ever called the first time.
This decorator can ensure that an expensive or non-idempotent function
will not be expensive on subsequent calls and is idempotent.
>>> add_three = once(lambda a: a+3)
>>> add_three(3)
6
>>> add_three(9)
6
>>> add_three('12')
6
To reset the stored value, simply clear the property ``saved_result``.
>>> del add_three.saved_result
>>> add_three(9)
12
>>> add_three(8)
12
Or invoke 'reset()' on it.
>>> add_three.reset()
>>> add_three(-3)
0
>>> add_three(0)
0
c X > [ TS5 ( d
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r wrappers r r( once..wrapper[ s. w//#'#8#8G ###r c 8 > [ T 5 R S5 $ r% )vars __delitem__)r( s r r once..a s DM 5 5n Er )r wrapsresetr r( s `@r oncer1 ; s0 @ __T$ $
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cache_wrapperreturnc ^ ^ S[ S[ S[ S[ 4UU 4S jjnS Ul [ T T5 =( d U$ )a
Wrap lru_cache to support storing the cache data in the object instances.
Abstracts the common paradigm where the method explicitly saves an
underscore-prefixed protected property on first call and returns that
subsequently.
>>> class MyClass:
... calls = 0
...
... @method_cache
... def method(self, value):
... self.calls += 1
... return value
>>> a = MyClass()
>>> a.method(3)
3
>>> for x in range(75):
... res = a.method(x)
>>> a.calls
75
Note that the apparent behavior will be exactly like that of lru_cache
except that the cache is stored on each instance, so values in one
instance will not flush values from another, and when an instance is
deleted, so are the cached values for that instance.
>>> b = MyClass()
>>> for x in range(35):
... res = b.method(x)
>>> b.calls
35
>>> a.method(0)
0
>>> a.calls
75
Note that if method had been decorated with ``functools.lru_cache()``,
a.calls would have been 76 (due to the cached value of 0 having been
flushed by the 'b' instance).
Clear the cache with ``.cache_clear()``
>>> a.method.cache_clear()
Same for a method that hasn't yet been called.
>>> c = MyClass()
>>> c.method.cache_clear()
Another cache wrapper may be supplied:
>>> cache = functools.lru_cache(maxsize=2)
>>> MyClass.method2 = method_cache(lambda self: 3, cache_wrapper=cache)
>>> a = MyClass()
>>> a.method2()
3
Caution - do not subsequently wrap the method with another decorator, such
as ``@property``, which changes the semantics of the function.
See also
http://code.activestate.com/recipes/577452-a-memoize-decorator-for-instance-methods/
for another implementation and additional justification.
selfr r
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Because Python treats special methods differently, it's not
possible to use instance attributes to implement the cached
methods.
Instead, install the wrapper method under a different name
and return a simple proxy to that wrapper.
https://github.com/jaraco/jaraco.functools/issues/5
) __getattr__ __getitem__N__cachedc > T[ U 5 ; a- [ R " TU 5 nT" U5 n[ U TU5 O [ U T5 nU" U0 UD6$ r
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r cacher3 r2 wrapper_names r proxy$_special_method_cache..proxy sS tDz )$$VT2E!%(E D, .D,/Ed%f%%r )r; )r2 r3 name
special_namesrL rK s `` @r rC rC s2 ??D0M $L& Lr c ^ U 4S jnU$ )a6
Decorate a function with a transform function that is
invoked on results returned from the decorated function.
>>> @apply(reversed)
... def get_numbers(start):
... "doc for get_numbers"
... return range(start, start+3)
>>> list(get_numbers(4))
[6, 5, 4]
>>> get_numbers.__doc__
'doc for get_numbers'
c P > [ R " U 5 " [ TU 5 5 $ r
)r r. r )r transforms r wrapapply..wrap s t$WY%=>>r r )rR rS s ` r applyrU s ? Kr c ^ U 4S jnU$ )a
Decorate a function with an action function that is
invoked on the results returned from the decorated
function (for its side-effect), then return the original
result.
>>> @result_invoke(print)
... def add_two(a, b):
... return a + b
>>> x = add_two(2, 3)
5
>>> x
5
c J >^ [ R " T 5 UU 4S j5 nU$ )Nc ( > T" U 0 UD6nT" U5 U$ r
r )r r
resultactionr s r r( ,result_invoke..wrap..wrapper s 4*6*F 6NMr r r. )r r( rZ s ` r rS result_invoke..wrap s%
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Call a function for its side effect after initialization.
The benefit of using the decorator instead of simply invoking a function
after defining it is that it makes explicit the author's intent for the
function to be called immediately. Whereas if one simply calls the
function immediately, it's less obvious if that was intentional or
incidental. It also avoids repeating the name - the two actions, defining
the function and calling it immediately are modeled separately, but linked
by the decorator construct.
The benefit of having a function construct (opposed to just invoking some
behavior inline) is to serve as a scope in which the behavior occurs. It
avoids polluting the global namespace with local variables, provides an
anchor on which to attach documentation (docstring), keeps the behavior
logically separated (instead of conceptually separated or not separated at
all), and provides potential to re-use the behavior for testing or other
purposes.
This function is named as a pithy way to communicate, "call this function
primarily for its side effect", or "while defining this function, also
take it aside and call it". It exists because there's no Python construct
for "define and call" (nor should there be, as decorators serve this need
just fine). The behavior happens immediately and synchronously.
>>> @invoke
... def func(): print("called")
called
>>> func()
called
Use functools.partial to pass parameters to the initial call
>>> @functools.partial(invoke, name='bingo')
... def func(name): print("called with", name)
called with bingo
r )fr r
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Deprecated name for invoke.
z$call_aside is deprecated, use invoke)warningswarnDeprecationWarningra )r r
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call_asiderf 8 s$
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Rate-limit a function (or other callable)
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__module__ __qualname____firstlineno____doc__floatrm r/ rt rs r __static_attributes__r r r rh rh @ s* ',El *'Kr rh c ^ ^ U U4S jnU$ )z
Return a function that when invoked will invoke func1 without
any parameters (for its side-effect) and then invoke func2
with whatever parameters were passed, returning its result.
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func1func2s r r( first_invoke..wrappere s
d%f%%r r )r r r( s `` r r r ^ s & Nr c g r
r r r r r r l s Tr r c U[ S5 :X a [ R " 5 O
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Given a callable func, trap the indicated exceptions
for up to 'retries' times, invoking cleanup on the
exception. On the final attempt, allow any exceptions
to propagate.
inf)r itertoolscountrange)r cleanupretriestrapattemptsattempts r
retry_callr l sV %,uU|$;y w H 6M 6M I s A
AAc ^ ^ U U4S jnU$ )a
Decorator wrapper for retry_call. Accepts arguments to retry_call
except func and then returns a decorator for the decorated function.
Ex:
>>> @retry(retries=3)
... def my_func(a, b):
... "this is my funk"
... print(a, b)
>>> my_func.__doc__
'this is my funk'
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Convert a generator into a function that prints all yielded elements
>>> @print_yielded
... def x():
... yield 3; yield None
>>> x()
3
None
)r r mapprintr more_itertoolsconsumer. )r print_all
print_resultss r
print_yieldedr s< !!#u-IN 2 2IDM ??4 //r c F ^ [ R " T 5 U 4S j5 nU$ )z
Wrap func so it's not called if its first param is None
>>> print_text = pass_none(print)
>>> print_text('text')
text
>>> print_text(None)
c $ > U b T" U /UQ70 UD6$ g r
r )paramr r
r s r r( pass_none..wrapper s# /// / r r\ r0 s ` r pass_noner s' __T0 0 Nr c [ R " U 5 nUR R 5 nU Vs0 s H oDU; d M
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Assign parameters from namespace where func solicits.
>>> def func(x, y=3):
... print(x, y)
>>> assigned = assign_params(func, dict(x=2, z=4))
>>> assigned()
2 3
The usual errors are raised if a function doesn't receive
its required parameters:
>>> assigned = assign_params(func, dict(y=3, z=4))
>>> assigned()
Traceback (most recent call last):
TypeError: func() ...argument...
It even works on methods:
>>> class Handler:
... def meth(self, arg):
... print(arg)
>>> assign_params(Handler().meth, dict(arg='crystal', foo='clear'))()
crystal
)inspect signature
parameterskeysr r )r namespacesigparamskcall_nss r
assign_paramsr sb 4
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Wrap a method such that when it is called, the args and kwargs are
saved on the method.
>>> class MyClass:
... @save_method_args
... def method(self, a, b):
... print(a, b)
>>> my_ob = MyClass()
>>> my_ob.method(1, 2)
1 2
>>> my_ob._saved_method.args
(1, 2)
>>> my_ob._saved_method.kwargs
{}
>>> my_ob.method(a=3, b='foo')
3 foo
>>> my_ob._saved_method.args
()
>>> my_ob._saved_method.kwargs == dict(a=3, b='foo')
True
The arguments are stored on the instance, allowing for
different instance to save different args.
>>> your_ob = MyClass()
>>> your_ob.method({str('x'): 3}, b=[4])
{'x': 3} [4]
>>> your_ob._saved_method.args
({'x': 3},)
>>> my_ob._saved_method.args
()
args_and_kwargsz args kwargsc b > STR - nT" X5 n[ XU5 T" U /UQ70 UD6$ )N_saved_)r; r: )r6 r r
attr_nameattrr r2 s r r( !save_method_args..wrapper s: / t,&d,T,V,,r ) collections
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Replace the indicated exceptions, if raised, with the indicated
literal replacement or evaluated expression (if present).
>>> safe_int = except_(ValueError)(int)
>>> safe_int('five')
>>> safe_int('5')
5
Specify a literal replacement with ``replace``.
>>> safe_int_r = except_(ValueError, replace=0)(int)
>>> safe_int_r('five')
0
Provide an expression to ``use`` to pass through particular parameters.
>>> safe_int_pt = except_(ValueError, use='args[0]')(int)
>>> safe_int_pt('five')
'five'
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