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factcheck.py
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factcheck.py
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"""A simple but extensible implementation of QuickCheck 1 for Python 3.
Copyright (c) 2012 Nat Pryce.
"""
import sys
import random
from itertools import product, cycle, repeat, islice, chain
import inspect
if sys.version_info[0] > 2:
imap = map
izip = zip
else:
from itertools import imap, izip
def _random_values(_generator_fn, *args, **kwargs):
while True:
yield _generator_fn(*args, **kwargs)
def _defaulted(value, default_value):
return value if value is not None else default_value
def _actual(xs):
return [x for x in xs if x is not None]
def always(v):
"""always returns 'v'"""
return repeat(v)
def choices(seq):
"""random element from non-empty sequence 'seq'"""
return _random_values(random.choice, seq)
def bitseqs(lengths):
"""long ints with 'n' random bits"""
return (random.getrandbits(length) for length in lengths)
default_min_int = -1000
"""Default min value for ints"""
default_max_int = +1000
"""Default max value for ints"""
def ints(min=None, max=None):
"""random integers between min and max, inclusive"""
return chain(_actual([min,max]),
_random_values(random.randint,
_defaulted(min, default_min_int),
_defaulted(max, default_max_int)))
def from_range(start, stop=None, step=1):
"""random integers taken from range(start, stop[, step])"""
return chain(_actual(set([start,stop-step])),
_random_values(random.randrange, start, stop, step))
default_min_float = -1000
"""Default min value for floats"""
default_max_float = 1000
"""Default max value for floats"""
def floats(lower=None, upper=None):
"""random floating-point numbers selected uniformly from [a,b) or [a,b] depending on rounding."""
return chain(_actual([lower]),
_random_values(random.uniform,
_defaulted(lower, default_min_float),
_defaulted(upper, default_max_float)))
default_sequence_lengths = ints(min=0, max=32)
"""Default lengths for sequences and lists"""
default_sequence_elements = ints()
"""Default elements for sequences and lists"""
def sequences(lengths=None, elements=None):
"""random length sequences of random elements"""
elements = _defaulted(elements, default_sequence_elements)
lengths = _defaulted(lengths, default_sequence_lengths)
return (islice(elements, length) for length in lengths)
def lists(lengths=None, elements=None):
"""random length lists of random elements"""
return imap(list, sequences(lengths, elements))
def tuples(*elementses):
"""fixed-size tuples of random elements."""
return izip(*elementses)
def dicts(d):
"""dicts with fixed keys and random values.
Parameters:
d : a dictionary mapping keys to generators
Returns: a generator of dictionaries, mapping each key, k, of d to
the next element of d[k].
"""
if d:
keys, value_iters = izip(*d.items())
return (dict(izip(keys,values)) for values in izip(*value_iters))
else:
return always({})
def mapping(f, *args_gens, **kwargs_gens):
return (f(*args,**kwargs) for (args, kwargs) in izip(tuples(*args_gens), dicts(kwargs_gens)))
def unique(elements, key=(lambda x:x)):
"""Yield unique elements, preserving order."""
seen = set()
for element in elements:
k = key(element)
if k not in seen:
seen.add(k)
yield element
def _annotations(f):
return f.__annotations__ if hasattr(f, "__annotations__") else {}
def _always(*args, **kwargs):
return True
def _params(param_bindings, f):
argspec = inspect.getargspec(f)
if argspec.keywords is not None:
return param_bindings
else:
return dict((k,param_bindings[k]) for k in argspec.args)
def forall(_test_fn=None, samples=1000, where=_always, **parameter_generators):
where_argspec = inspect.getargspec(where)
if where_argspec.keywords is None:
where_bindings = lambda d: {k:d[k] for k in where_argspec.args}
else:
where_bindings = lambda d: d
def bind_parameters(test_fn):
parameter_generators.update(_annotations(test_fn))
# Note: should be decorated by @functools.wraps(test_fn) but that confuses pytest
def bound_test_fn(*args):
param_names, param_value_iters = zip(*parameter_generators.items())
# Shuffle the values so that fixed boundary values are not always applied together
param_value_populations = [list(islice(cycle(i),0,samples)) for i in param_value_iters]
for l in param_value_populations:
random.shuffle(l)
for param_values in zip(*param_value_populations):
param_bindings = dict(zip(param_names, param_values))
if where(**where_bindings(param_bindings)):
test_fn(*args, **param_bindings)
return bound_test_fn
# Allow @forall, @forall(samples=100) or @forall(param1=generator1, param2=generator2, ...)
return bind_parameters if _test_fn is None else bind_parameters(_test_fn)