Python functools32.lru_cache方法代碼示例

# 需要導入模塊: import functools32 [as 別名]
# 或者: from functools32 import lru_cache [as 別名]
def in_stdlib(module_name, version=None):
    """
    Return a ``bool`` indicating if module ``module_name`` is in the list of stdlib
    symbols for python version ``version``. If ``version`` is ``None`` (default), the
    version of current python interpreter is used.

    Note that ``True`` will be returned for built-in modules too, since this project
    considers they are part of stdlib. See :issue:21.

    It relies on ``@lru_cache`` to cache the stdlib list and query results for similar
    calls. Therefore it is much more efficient than ``module_name in stdlib_list()``
    especially if you wish to perform multiple checks.

    :param str|None module_name: The module name (as a string) to query for.
    :param str|None version: The version (as a string) whose list of libraries you want
    (one of ``"2.6"``, ``"2.7"``, ``"3.2"``, ``"3.3"``, ``"3.4"``, or ``"3.5"``).
    If not specified, the current version of Python will be used.

    :return: A bool indicating if the given module name is part of standard libraries
    for the specified version of Python.
    :rtype: list
    """
    ref_list = _stdlib_list_with_cache(version=version)
    return module_name in ref_list 

# 需要導入模塊: import functools32 [as 別名]
# 或者: from functools32 import lru_cache [as 別名]
def memoized_method(*lru_args, **lru_kwargs):
    def decorator(func):
        @functools.wraps(func)
        def wrapped_func(self, *args, **kwargs):
            # We're storing the wrapped method inside the instance. If we had
            # a strong reference to self the instance would never die.
            self_weak = weakref.ref(self)

            @functools.wraps(func)
            @functools.lru_cache(*lru_args, **lru_kwargs)
            def cached_method(*args, **kwargs):
                return func(self_weak(), *args, **kwargs)

            setattr(self, func.__name__, cached_method)
            return cached_method(*args, **kwargs)

        return wrapped_func

    return decorator 

# 需要導入模塊: import functools32 [as 別名]
# 或者: from functools32 import lru_cache [as 別名]
def cases_generator(names=None,       # type: Union[str, Callable[[Any], str], Iterable[str]]
                    lru_cache=False,  # type: bool,
                    case_func=DECORATED,
                    **param_ranges    # type: Iterable[Any]
                    ):
    """
    Decorator to declare a case function as being a cases generator. `param_ranges` should be a named list of parameter
    ranges to explore to generate the cases.

    The decorator will use `itertools.product` to create a cartesian product of the named parameter ranges, and create
    a case for each combination. When the case function will be called for a given combination, the corresponding
    parameters will be passed to the decorated function.

    >>> @cases_generator("test with i={i}", i=range(10))
    >>> def case_10_times(i):
    >>>     ''' Generates 10 cases '''
    >>>     ins = dict(a=i, b=i+1)
    >>>     outs = i+1, i+2
    >>>     return ins, outs, None

    :param names: a name template, that will be transformed into the case name using
        `names.format(**params)` for each case, where `params` is the dictionary of parameter values for this
        generated case. Alternately a callable returning a string can be provided, in which case
        `names(**params)` will be used. Finally an explicit list of names can be provided, in which case it should have
        the correct length (an error will be raised otherwise).
    :param lru_cache: a boolean (default False) indicating if the generated cases should be cached. This is identical
        to decorating the function with an additional `@lru_cache(maxsize=n)` where n is the total number of generated
        cases.
    :param param_ranges: named parameters and for each of them the list of values to be used to generate cases. For
        each combination of values (a cartesian product is made) the parameters will be passed to the underlying
        function so they should have names the underlying function can handle.
    :return:
    """
    kwarg_values = list(product(*param_ranges.values()))
    setattr(case_func, _GENERATOR_FIELD, (names, param_ranges.keys(), kwarg_values))
    if lru_cache:
        nb_cases = len(kwarg_values)
        # decorate the function with the appropriate lru cache size
        case_func = lru(maxsize=nb_cases)(case_func)

    return case_func 

# 需要導入模塊: import functools32 [as 別名]
# 或者: from functools32 import lru_cache [as 別名]
def setup_dataset(db):
    # this is run once per db thanks to the lru_cache decorator
    print("setup for %s" % db) 

# 需要導入模塊: import functools32 [as 別名]
# 或者: from functools32 import lru_cache [as 別名]
def finalize_dataset(db):
    # this is run once per db thanks to the lru_cache decorator
    print("teardown for %s" % db) 

# 需要導入模塊: import functools32 [as 別名]
# 或者: from functools32 import lru_cache [as 別名]
def LruCache(maxsize=None):
  """LRU Cache decorator.

  Args:
    maxsize: the maximum cache size, or None for unlimited size.
  """
  def wrapper(fn):
    return functools32.lru_cache(maxsize=maxsize)(fn)

  return wrapper


# Cache management 

# 需要導入模塊: import functools32 [as 別名]
# 或者: from functools32 import lru_cache [as 別名]
def __enter__(self):
    self._wrapper_fn = functools32.lru_cache(maxsize=self._maxsize)(self._fn)
    self._overrideModuleFunctionWith(self._wrapper_fn)
    return self 

# 需要導入模塊: import functools32 [as 別名]
# 或者: from functools32 import lru_cache [as 別名]
def _inverse_phi(self, u_loc, theta, order):
        @lru_cache(None)
        def iphi(n):
            if n:
                return sum(special.comb(n, i-1)*iphi(n-i)*sigma(i)
                           for i in range(1, n+1))
            return numpy.e**(-u_loc**(1/theta))
        @lru_cache(None)
        def sigma(n):
            return self._sigma(u_loc, theta, n)
        return iphi(order) 

# 需要導入模塊: import functools32 [as 別名]
# 或者: from functools32 import lru_cache [as 別名]
def _inverse_phi(self, u_loc, theta, order):
        if not order:
            return 1-(1-numpy.e**-u_loc)**(1/theta)
        @lru_cache(None)
        def rho(n, m=1):
            if n == m:
                return self._sigma(1-numpy.e**-u_loc, theta, n)*numpy.e**(-n*theta)
            return rho(n, m+1)-m*rho(n-1, m)
        return rho(order) 

# 需要導入模塊: import functools32 [as 別名]
# 或者: from functools32 import lru_cache [as 別名]
def lru_cache(maxsize=100):
        def decorate(func):
            @functools.wraps(func)
            def wrapper(*args, **kwargs):
                return func(*args, **kwargs)

            return wrapper

        return decorate