def __init__(self, fn, callback=None):
warnings.warn("deprecated", DeprecationWarning)
precondition(hasattr(fn, 'im_self'),
"fn is required to be a bound method.")
self._cleanupcallback = callback
self._obj = ref(fn.im_self, self.call_cleanup_cb)
self._meth = fn.im_func
def next(self):
if self.i is self.c.ts:
raise StopIteration
precondition(self.c.d.has_key(self.i), "The iterated LRUCache doesn't have the next key. Most likely this is because someone altered the contents of the LRUCache while the iteration was in progress.", self.i, self.c)
(v, p, n,) = self.c.d[self.i]
self.i = n
return v
def measure_ref_leakage(f, numsamples=2**7, iterspersample=2**4, *args, **kwargs):
"""
The idea is we are going to use sys.gettotalrefcount() to see how many
references are extant, and keep track of that number with respect to how
many times we've invoked f(), and return the slope of the best linear
fit.
@param numsamples: recommended: 2**7
@param iterspersample: how many times f() should be invoked per sample;
Basically, choose iterspersample such that
iterspersample * numsamples *
how-long-it-takes-to-compute-f() is slightly less
than how long you are willing to wait for this
leak test.
@return: the slope of the best linear fit, which can be interpreted as 'the
approximate number of Python references created and not
nullified per invocation of f()'
"""
precondition(numsamples > 0, "numsamples is required to be positive.", numsamples)
precondition(iterspersample > 0, "iterspersample is required to be positive.", iterspersample)
try:
sys.gettotalrefcount()
except AttributeError, le:
raise AttributeError(le, "Probably this is not a debug build of Python, so it doesn't have a sys.gettotalrefcount function.")
def next(self):
precondition(self.i <= len(self.c._lru), "The iterated SmallLRUCache doesn't have this many elements. Most likely this is because someone altered the contents of the LRUCache while the iteration was in progress.", self.i, self.c)
precondition(dict.has_key(self.c, self.c._lru[self.i]), "The iterated SmallLRUCache doesn't have this key. Most likely this is because someone altered the contents of the LRUCache while the iteration was in progress.", self.i, self.c._lru[self.i], self.c)
if self.i == len(self.c._lru):
raise StopIteration
k = self.i
self.i += 1
return dict.__getitem__(self.c, k)
def next(self):
if self.i is self.c.hs:
raise StopIteration
k = self.i
precondition(self.c.d.has_key(k), "The iterated OrderedDict doesn't have the next key. Most likely this is because someone altered the contents of the OrderedDict while the iteration was in progress.", k, self.c)
(v, p, n,) = self.c.d[k]
self.i = p
return k
def next(self):
if self.i is self.c.hs:
raise StopIteration
k = self.i
precondition(self.c.d.has_key(k), "The iterated OrderedDict doesn't have the next key. Most likely this is because someone altered the contents of the OrderedDict while the iteration was in progress.", k, self.c)
(v, p, n,) = self.c.d[k]
self.i = p
return k
def __init__(self, initialdata={}, maxsize=128):
precondition(maxsize > 0)
self.m = maxsize+2 # The +2 is for the head and tail nodes.
self.d = {} # k: k, v: [v, prev, next,] # the dict
self.hs = LRUCache.Sentinel("hs")
self.ts = LRUCache.Sentinel("ts")
self.d[self.hs] = [None, self.hs, self.ts,] # This allows us to use sentinels as normal nodes.
self.d[self.ts] = [None, self.hs, self.ts,] # This allows us to use sentinels as normal nodes.
self.update(initialdata)
assert self._assert_invariants()
def measure_mem_leakage(f,
numsamples=2**7,
iterspersample=2**4,
*args,
**kwargs):
"""
This does the same thing as measure_obj_leakage() but instead of using
count_all_objects() it uses get_mem_usage(), which is currently
implemented for Linux and barely implemented for Mac OS X.
@param numsamples: recommended: 2**7
@param iterspersample: how many times `f()' should be invoked per sample;
Basically, choose `iterspersample' such that
(iterspersample * numsamples *
how-long-it-takes-to-compute-`f()') is slightly
less than how long you are willing to wait for
this leak test.
@return: the slope of the best linear fit, which can be interpreted as
'the approximate number of system bytes allocated and not freed
per invocation of f()'
"""
precondition(numsamples > 0, "numsamples is required to be positive.",
numsamples)
precondition(iterspersample > 0,
"iterspersample is required to be positive.", iterspersample)
resiters = [None] * numsamples # values: iters
resmemusage = [None] * numsamples # values: memusage
totaliters = 0
for i in range(numsamples):
for j in range(iterspersample):
f(*args, **kwargs)
totaliters = totaliters + iterspersample
resiters[i] = totaliters
gc.collect()
resmemusage[i] = get_mem_used_res()
# print "totaliters: %s, numobjs: %s" % (resiters[-1], resmemusage[-1],)
avex = float(reduce(operator.__add__, resiters)) / len(resiters)
avey = float(reduce(operator.__add__, resmemusage)) / len(resmemusage)
sxy = reduce(
operator.__add__,
map(lambda a, avex=avex, avey=avey: (a[0] - avex) * (a[1] - avey),
zip(resiters, resmemusage)))
sxx = reduce(operator.__add__,
map(lambda a, avex=avex: (a - avex)**2, resiters))
if sxx == 0:
return None
return sxy / sxx
def measure_obj_leakage(f,
numsamples=2**7,
iterspersample=2**4,
*args,
**kwargs):
"""
The idea is we are going to use count_all_objects() to see how many
objects are in use, and keep track of that number with respect to how
many times we've invoked f(), and return the slope of the best linear
fit.
@param numsamples: recommended: 2**7
@param iterspersample: how many times f() should be invoked per sample;
Basically, choose iterspersample such that
iterspersample * numsamples *
how-long-it-takes-to-compute-f() is slightly less
than how long you are willing to wait for this
leak test.
@return: the slope of the best linear fit, which can be interpreted as 'the
approximate number of Python objects created and not destroyed
per invocation of f()'
"""
precondition(numsamples > 0, "numsamples is required to be positive.",
numsamples)
precondition(iterspersample > 0,
"iterspersample is required to be positive.", iterspersample)
resiters = [None] * numsamples # values: iters
resnumobjs = [None] * numsamples # values: numobjs
totaliters = 0
for i in range(numsamples):
for j in range(iterspersample):
f(*args, **kwargs)
totaliters = totaliters + iterspersample
resiters[i] = totaliters
gc.collect()
resnumobjs[i] = count_all_objects()
# print "totaliters: %s, numobjs: %s" % (resiters[-1], resnumobjs[-1],)
avex = float(reduce(operator.__add__, resiters)) / len(resiters)
avey = float(reduce(operator.__add__, resnumobjs)) / len(resnumobjs)
sxy = reduce(
operator.__add__,
map(lambda a, avex=avex, avey=avey: (a[0] - avex) * (a[1] - avey),
zip(resiters, resnumobjs)))
sxx = reduce(operator.__add__,
map(lambda a, avex=avex: (a - avex)**2, resiters))
return sxy / sxx
def measure_mem_leakage(f, numsamples=2**7, iterspersample=2**4, *args, **kwargs):
"""
This does the same thing as measure_obj_leakage() but instead of using
count_all_objects() it uses get_mem_usage(), which is currently
implemented for Linux and barely implemented for Mac OS X.
@param numsamples: recommended: 2**7
@param iterspersample: how many times `f()' should be invoked per sample;
Basically, choose `iterspersample' such that
(iterspersample * numsamples *
how-long-it-takes-to-compute-`f()') is slightly
less than how long you are willing to wait for
this leak test.
@return: the slope of the best linear fit, which can be interpreted as
'the approximate number of system bytes allocated and not freed
per invocation of f()'
"""
precondition(numsamples > 0, "numsamples is required to be positive.", numsamples)
precondition(iterspersample > 0, "iterspersample is required to be positive.", iterspersample)
resiters = [None]*numsamples # values: iters
resmemusage = [None]*numsamples # values: memusage
totaliters = 0
for i in range(numsamples):
for j in range(iterspersample):
f(*args, **kwargs)
totaliters = totaliters + iterspersample
resiters[i] = totaliters
gc.collect()
resmemusage[i] = get_mem_used_res()
# print "totaliters: %s, numobjs: %s" % (resiters[-1], resmemusage[-1],)
avex = float(reduce(operator.__add__, resiters)) / len(resiters)
avey = float(reduce(operator.__add__, resmemusage)) / len(resmemusage)
sxy = reduce(operator.__add__, map(lambda a, avex=avex, avey=avey: (a[0] - avex) * (a[1] - avey), zip(resiters, resmemusage)))
sxx = reduce(operator.__add__, map(lambda a, avex=avex: (a - avex) ** 2, resiters))
if sxx == 0:
return None
return sxy / sxx
def measure_obj_leakage(f, numsamples=2**7, iterspersample=2**4, *args, **kwargs):
"""
The idea is we are going to use count_all_objects() to see how many
objects are in use, and keep track of that number with respect to how
many times we've invoked f(), and return the slope of the best linear
fit.
@param numsamples: recommended: 2**7
@param iterspersample: how many times f() should be invoked per sample;
Basically, choose iterspersample such that
iterspersample * numsamples *
how-long-it-takes-to-compute-f() is slightly less
than how long you are willing to wait for this
leak test.
@return: the slope of the best linear fit, which can be interpreted as 'the
approximate number of Python objects created and not destroyed
per invocation of f()'
"""
precondition(numsamples > 0, "numsamples is required to be positive.", numsamples)
precondition(iterspersample > 0, "iterspersample is required to be positive.", iterspersample)
resiters = [None]*numsamples # values: iters
resnumobjs = [None]*numsamples # values: numobjs
totaliters = 0
for i in range(numsamples):
for j in range(iterspersample):
f(*args, **kwargs)
totaliters = totaliters + iterspersample
resiters[i] = totaliters
gc.collect()
resnumobjs[i] = count_all_objects()
# print "totaliters: %s, numobjs: %s" % (resiters[-1], resnumobjs[-1],)
avex = float(reduce(operator.__add__, resiters)) / len(resiters)
avey = float(reduce(operator.__add__, resnumobjs)) / len(resnumobjs)
sxy = reduce(operator.__add__, map(lambda a, avex=avex, avey=avey: (a[0] - avex) * (a[1] - avey), zip(resiters, resnumobjs)))
sxx = reduce(operator.__add__, map(lambda a, avex=avex: (a - avex) ** 2, resiters))
return sxy / sxx
def __init__(self, fn, callback=None):
warnings.warn("deprecated", DeprecationWarning)
precondition(hasattr(fn, "im_self"), "fn is required to be a bound method.")
self._cleanupcallback = callback
self._obj = ref(fn.im_self, self.call_cleanup_cb)
self._meth = fn.im_func
