"""
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__ = 'ongsp@ucsd.edu'
__date__ = '8/29/14'
from collections import namedtuple
from functools import update_wrapper, wraps, partial
try:
from threading import RLock
except:
class RLock:
"""Dummy reentrant lock for builds without threads"""
def __enter__(self):
pass
def __exit__(self, exctype, excinst, exctb):
pass
_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,
kwd_mark=(object(),),
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: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
"""
# 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:
$ script.py arg --foo=1
becomes
$ script.py 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