Source code for monty.functools

functools, especially backported from Python 3.

from __future__ import absolute_import

__author__ = 'Shyue Ping Ong'
__copyright__ = 'Copyright 2013, The Materials Virtual Lab'
__version__ = '0.1'
__maintainer__ = 'Shyue Ping Ong'
__email__ = ''
__date__ = '8/29/14'

from collections import namedtuple
from functools import update_wrapper, wraps, partial

    from threading import RLock
    class RLock:
        """Dummy reentrant lock for builds without threads"""

        def __enter__(self):

        def __exit__(self, exctype, excinst, exctb):

_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"])

class _HashedSeq(list):
    This class guarantees that hash() will be called no more than once
    per element.  This is important because the lru_cache() will hash
    the key multiple times on a cache miss.

    __slots__ = 'hashvalue'

    def __init__(self, tup, hash=hash):
        self[:] = tup
        self.hashvalue = hash(tup)

    def __hash__(self):
        return self.hashvalue

def _make_key(args, kwds, typed,
              fasttypes={int, str, frozenset, type(None)},
              sorted=sorted, tuple=tuple, type=type, len=len):
    Make a cache key from optionally typed positional and keyword arguments

    The key is constructed in a way that is flat as possible rather than
    as a nested structure that would take more memory.

    If there is only a single argument and its data type is known to cache
    its hash value, then that argument is returned without a wrapper.  This
    saves space and improves lookup speed.

    key = args
    if kwds:
        sorted_items = sorted(kwds.items())
        key += kwd_mark
        for item in sorted_items:
            key += item
    if typed:
        key += tuple(type(v) for v in args)
        if kwds:
            key += tuple(type(v) for k, v in sorted_items)
    elif len(key) == 1 and type(key[0]) in fasttypes:
        return key[0]
    return _HashedSeq(key)

[docs]def lru_cache(maxsize=128, typed=False): """ Least-recently-used cache decorator, which is a backport of the same function in Python >= 3.2. If *maxsize* is set to None, the LRU features are disabled and the cache can grow without bound. If *typed* is True, arguments of different types will be cached separately. For example, f(3.0) and f(3) will be treated as distinct calls with distinct results. Arguments to the cached function must be hashable. View the cache statistics named tuple (hits, misses, maxsize, currsize) with f.cache_info(). Clear the cache and statistics with f.cache_clear(). Access the underlying function with f.__wrapped__. See: """ # Users should only access the lru_cache through its public API: # cache_info, cache_clear, and f.__wrapped__ # The internals of the lru_cache are encapsulated for thread safety and # to allow the implementation to change (including a possible C version). # Early detection of an erroneous call to @lru_cache without any arguments # resulting in the inner function being passed to maxsize instead of an # integer or None. if maxsize is not None and not isinstance(maxsize, int): raise TypeError('Expected maxsize to be an integer or None') # Constants shared by all lru cache instances: sentinel = object() # unique object used to signal cache misses make_key = _make_key # build a key from the function arguments PREV, NEXT, KEY, RESULT = 0, 1, 2, 3 # names for the link fields def decorating_function(user_function): cache = {} hits = [0] misses = [0] full = [False] cache_get = cache.get # bound method to lookup a key or return None lock = RLock() # because linkedlist updates aren't threadsafe root = [] # root of the circular doubly linked list root[:] = [root, root, None, None] # initialize by pointing to self r = [root] if maxsize == 0: def wrapper(*args, **kwds): # No caching -- just a statistics update after a successful call result = user_function(*args, **kwds) misses[0] += 1 return result elif maxsize is None: def wrapper(*args, **kwds): # Simple caching without ordering or size limit key = make_key(args, kwds, typed) result = cache_get(key, sentinel) if result is not sentinel: hits[0] += 1 return result result = user_function(*args, **kwds) cache[key] = result misses[0] += 1 return result else: def wrapper(*args, **kwds): # Size limited caching that tracks accesses by recency key = make_key(args, kwds, typed) with lock: link = cache_get(key) if link is not None: # Move the link to the front of the circular queue link_prev, link_next, _key, result = link link_prev[NEXT] = link_next link_next[PREV] = link_prev last = r[0][PREV] last[NEXT] = r[0][PREV] = link link[PREV] = last link[NEXT] = r[0] hits[0] += 1 return result result = user_function(*args, **kwds) with lock: if key in cache: # Getting here means that this same key was added to the # cache while the lock was released. Since the link # update is already done, we need only return the # computed result and update the count of misses. pass elif full[0]: # Use the old root to store the new key and result. oldroot = r[0] oldroot[KEY] = key oldroot[RESULT] = result # Empty the oldest link and make it the new root. # Keep a reference to the old key and old result to # prevent their ref counts from going to zero during the # update. That will prevent potentially arbitrary object # clean-up code (i.e. __del__) from running while we're # still adjusting the links. r[0] = oldroot[NEXT] oldkey = r[0][KEY] oldresult = r[0][RESULT] r[0][KEY] = r[0][RESULT] = None # Now update the cache dictionary. del cache[oldkey] # Save the potentially reentrant cache[key] assignment # for last, after the root and links have been put in # a consistent state. cache[key] = oldroot else: # Put result in a new link at the front of the queue. last = r[0][PREV] link = [last, r[0], key, result] last[NEXT] = r[0][PREV] = cache[key] = link full[0] = (len(cache) >= maxsize) misses[0] += 1 return result def cache_info(): """Report cache statistics""" with lock: return _CacheInfo(hits[0], misses[0], maxsize, len(cache)) def cache_clear(): """Clear the cache and cache statistics""" with lock: cache.clear() root[:] = [root, root, None, None] r[0] = root hits[0] = 0 misses[0] = 0 full[0] = False wrapper.cache_info = cache_info wrapper.cache_clear = cache_clear return update_wrapper(wrapper, user_function) return decorating_function
[docs]class lazy_property(object): """ lazy_property descriptor Used as a decorator to create lazy attributes. Lazy attributes are evaluated on first use. """ def __init__(self, func): self.__func = func wraps(self.__func)(self) def __get__(self, inst, inst_cls): if inst is None: return self if not hasattr(inst, '__dict__'): raise AttributeError("'%s' object has no attribute '__dict__'" % (inst_cls.__name__,)) name = self.__name__ if name.startswith('__') and not name.endswith('__'): name = '_%s%s' % (inst_cls.__name__, name) value = self.__func(inst) inst.__dict__[name] = value return value @classmethod
[docs] def invalidate(cls, inst, name): """Invalidate a lazy attribute. This obviously violates the lazy contract. A subclass of lazy may however have a contract where invalidation is appropriate. """ inst_cls = inst.__class__ if not hasattr(inst, '__dict__'): raise AttributeError("'%s' object has no attribute '__dict__'" % (inst_cls.__name__,)) if name.startswith('__') and not name.endswith('__'): name = '_%s%s' % (inst_cls.__name__, name) if not isinstance(getattr(inst_cls, name), cls): raise AttributeError("'%s.%s' is not a %s attribute" % (inst_cls.__name__, name, cls.__name__)) if name in inst.__dict__: del inst.__dict__[name]
[docs]def return_if_raise(exception_tuple, retval_if_exc, disabled=False): """ Decorator for functions, methods or properties. Execute the callable in a try block, and return retval_if_exc if one of the exceptions listed in exception_tuple is raised (se also ``return_node_if_raise``). Setting disabled to True disables the try except block (useful for debugging purposes). One can use this decorator to define properties. Example:: @return_if_raise(ValueError, None) def return_none_if_value_error(self): pass @return_if_raise((ValueError, KeyError), "hello") def another_method(self): pass @property @return_if_raise(AttributeError, None) def name(self): "Name of the object, None if not set." return self._name """ # we need a tuple of exceptions. if isinstance(exception_tuple, list): exception_tuple = tuple(exception_tuple) elif not isinstance(exception_tuple, tuple): exception_tuple = (exception_tuple,) else: raise TypeError("Wrong exception_tuple %s" % type(exception_tuple)) def decorator(func): @wraps(func) def wrapper(*args, **kwargs): if disabled: return func(*args, **kwargs) try: return func(*args, **kwargs) except exception_tuple: return retval_if_exc else: raise return wrapper return decorator
# One could use None as default value in return_if_raise but this one is # explicit and more readable return_none_if_raise = partial(return_if_raise, retval_if_exc=None) """ This decorator returns None if one of the exceptions is raised. @return_none_if_raise(ValueError) def method(self): """
[docs]class timeout(object): """ Timeout function. Use to limit matching to a certain time limit. Note that this works only on Unix-based systems as it uses signal. Usage: try: with timeout(3): do_stuff() except TimeoutError: do_something_else() """ def __init__(self, seconds=1, error_message='Timeout'): """ Args: seconds (int): Allowed time for function in seconds. error_message (str): An error message. """ self.seconds = seconds self.error_message = error_message
[docs] def handle_timeout(self, signum, frame): raise TimeoutError(self.error_message)
def __enter__(self): import signal signal.signal(signal.SIGALRM, self.handle_timeout) signal.alarm(self.seconds) def __exit__(self, type, value, traceback): import signal signal.alarm(0)
[docs]class TimeoutError(Exception): def __init__(self, message): self.message = message
[docs]def prof_main(main): """ Decorator for profiling main programs. Profiling is activated by prepending the command line options supported by the original main program with the keyword `prof`. Example: $ arg --foo=1 becomes $ prof arg --foo=1 The decorated main accepts two new arguments: prof_file: Name of the output file with profiling data If not given, a temporary file is created. sortby: Profiling data are sorted according to this value. default is "time". See sort_stats. """ @wraps(main) def wrapper(*args, **kwargs): import sys try: do_prof = sys.argv[1] == "prof" if do_prof: sys.argv.pop(1) except Exception: do_prof = False if not do_prof: sys.exit(main()) else: print("Entering profiling mode...") import pstats, cProfile, tempfile prof_file = kwargs.get("prof_file", None) if prof_file is None: _, prof_file = tempfile.mkstemp() print("Profiling data stored in %s" % prof_file) sortby = kwargs.get("sortby", "time") cProfile.runctx("main()", globals(), locals(), prof_file) s = pstats.Stats(prof_file) s.strip_dirs().sort_stats(sortby).print_stats() if "retval" not in kwargs: sys.exit(0) else: return kwargs["retval"] return wrapper