# coding=utf-8
#
# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis-python
#
# Most of this work is copyright (C) 2013-2017 David R. MacIver
# (david@drmaciver.com), but it contains contributions by others. See
# CONTRIBUTING.rst for a full list of people who may hold copyright, and
# consult the git log if you need to determine who owns an individual
# contribution.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at http://mozilla.org/MPL/2.0/.
#
# END HEADER
from __future__ import division, print_function, absolute_import
import math
import datetime as dt
import operator
from decimal import Decimal, InvalidOperation
from numbers import Rational
from fractions import Fraction
from hypothesis.errors import InvalidArgument
from hypothesis.control import assume
from hypothesis.searchstrategy import SearchStrategy
from hypothesis.internal.compat import hrange, text_type, integer_types, \
getfullargspec, implements_iterator
from hypothesis.internal.floats import is_negative, float_to_int, \
int_to_float, count_between_floats
from hypothesis.utils.conventions import not_set
from hypothesis.internal.reflection import proxies
from hypothesis.searchstrategy.reprwrapper import ReprWrapperStrategy
__all__ = [
'nothing',
'just', 'one_of',
'none',
'choices', 'streaming',
'booleans', 'integers', 'floats', 'complex_numbers', 'fractions',
'decimals',
'characters', 'text', 'binary', 'uuids',
'tuples', 'lists', 'sets', 'frozensets', 'iterables',
'dictionaries', 'fixed_dictionaries',
'sampled_from', 'permutations',
'datetimes', 'dates', 'times', 'timedeltas',
'builds',
'randoms', 'random_module',
'recursive', 'composite',
'shared', 'runner', 'data',
]
_strategies = set()
class FloatKey(object):
def __init__(self, f):
self.value = float_to_int(f)
def __eq__(self, other):
return isinstance(other, FloatKey) and (
other.value == self.value
)
def __ne__(self, other):
return not self.__eq__(other)
def __hash__(self):
return hash(self.value)
def convert_value(v):
if isinstance(v, float):
return FloatKey(v)
return (type(v), v)
def cacheable(fn):
cache = {}
@proxies(fn)
def cached_strategy(*args, **kwargs):
kwargs_cache_key = set()
try:
for k, v in kwargs.items():
kwargs_cache_key.add((k, convert_value(v)))
except TypeError:
return fn(*args, **kwargs)
cache_key = (
tuple(map(convert_value, args)), frozenset(kwargs_cache_key))
try:
return cache[cache_key]
except TypeError:
return fn(*args, **kwargs)
except KeyError:
result = fn(*args, **kwargs)
cache[cache_key] = result
return result
return cached_strategy
def defines_strategy(strategy_definition):
from hypothesis.searchstrategy.deferred import DeferredStrategy
_strategies.add(strategy_definition.__name__)
@proxies(strategy_definition)
def accept(*args, **kwargs):
return DeferredStrategy(strategy_definition, args, kwargs)
return accept
class Nothing(SearchStrategy):
is_empty = True
def do_draw(self, data):
data.mark_invalid()
def __repr__(self):
return 'nothing()'
def map(self, f):
return self
def filter(self, f):
return self
def flatmap(self, f):
return self
NOTHING = Nothing()
@cacheable
[docs]def nothing():
"""This strategy never successfully draws a value and will always reject on
an attempt to draw."""
return NOTHING
[docs]def just(value):
"""Return a strategy which only generates value.
Note: value is not copied. Be wary of using mutable values.
"""
from hypothesis.searchstrategy.misc import JustStrategy
def calc_repr():
return 'just(%s)' % (repr(value),)
return ReprWrapperStrategy(JustStrategy(value), calc_repr)
@defines_strategy
[docs]def none():
"""Return a strategy which only generates None."""
return just(None)
[docs]def one_of(*args):
"""Return a strategy which generates values from any of the argument
strategies.
This may be called with one iterable argument instead of multiple
strategy arguments. In which case one_of(x) and one_of(\*x) are
equivalent.
"""
if len(args) == 1 and not isinstance(args[0], SearchStrategy):
try:
args = tuple(args[0])
except TypeError:
pass
strategies = []
for arg in args:
check_strategy(arg)
if not arg.is_empty:
strategies.extend([s for s in arg.branches if not s.is_empty])
if not strategies:
return nothing()
if len(strategies) == 1:
return strategies[0]
from hypothesis.searchstrategy.strategies import OneOfStrategy
return OneOfStrategy(strategies)
@cacheable
@defines_strategy
[docs]def integers(min_value=None, max_value=None):
"""Returns a strategy which generates integers (in Python 2 these may be
ints or longs).
If min_value is not None then all values will be >= min_value. If
max_value is not None then all values will be <= max_value
"""
check_valid_bound(min_value, 'min_value')
check_valid_bound(max_value, 'max_value')
check_valid_interval(min_value, max_value, 'min_value', 'max_value')
from hypothesis.searchstrategy.numbers import IntegersFromStrategy, \
BoundedIntStrategy, WideRangeIntStrategy
min_int_value = None
if min_value is not None:
min_int_value = int(min_value)
if min_int_value != min_value and min_value > 0:
min_int_value += 1
max_int_value = None
if max_value is not None:
max_int_value = int(max_value)
if max_int_value != max_value and max_value < 0:
max_int_value -= 1
if min_int_value is None:
if max_int_value is None:
return (
WideRangeIntStrategy()
)
else:
return IntegersFromStrategy(0).map(lambda x: max_int_value - x)
else:
if max_int_value is None:
return IntegersFromStrategy(min_int_value)
else:
assert min_int_value <= max_int_value
if min_int_value == max_int_value:
return just(min_int_value)
elif min_int_value >= 0:
return BoundedIntStrategy(min_int_value, max_int_value)
elif max_int_value <= 0:
return BoundedIntStrategy(
-max_int_value, -min_int_value
).map(lambda t: -t)
else:
return integers(min_value=0, max_value=max_int_value) | \
integers(min_value=min_int_value, max_value=0)
@cacheable
@defines_strategy
[docs]def booleans():
"""Returns a strategy which generates instances of bool."""
from hypothesis.searchstrategy.misc import BoolStrategy
return BoolStrategy()
@cacheable
@defines_strategy
[docs]def floats(
min_value=None, max_value=None, allow_nan=None, allow_infinity=None
):
"""Returns a strategy which generates floats.
- If min_value is not None, all values will be >= min_value.
- If max_value is not None, all values will be <= max_value.
- If min_value or max_value is not None, it is an error to enable
allow_nan.
- If both min_value and max_value are not None, it is an error to enable
allow_infinity.
Where not explicitly ruled out by the bounds, all of infinity, -infinity
and NaN are possible values generated by this strategy.
"""
if allow_nan is None:
allow_nan = bool(min_value is None and max_value is None)
elif allow_nan:
if min_value is not None or max_value is not None:
raise InvalidArgument(
'Cannot have allow_nan=%r, with min_value or max_value' % (
allow_nan
))
check_valid_bound(min_value, 'min_value')
check_valid_bound(max_value, 'max_value')
check_valid_interval(min_value, max_value, 'min_value', 'max_value')
if min_value is not None:
min_value = float(min_value)
if max_value is not None:
max_value = float(max_value)
if min_value == float(u'-inf'):
min_value = None
if max_value == float(u'inf'):
max_value = None
if allow_infinity is None:
allow_infinity = bool(min_value is None or max_value is None)
elif allow_infinity:
if min_value is not None and max_value is not None:
raise InvalidArgument(
'Cannot have allow_infinity=%r, with both min_value and '
'max_value' % (
allow_infinity
))
from hypothesis.searchstrategy.numbers import FloatStrategy, \
FixedBoundedFloatStrategy
if min_value is None and max_value is None:
return FloatStrategy(
allow_infinity=allow_infinity, allow_nan=allow_nan,
)
elif min_value is not None and max_value is not None:
if min_value == max_value:
return just(min_value)
elif is_negative(min_value):
if is_negative(max_value):
return floats(min_value=-max_value, max_value=-min_value).map(
operator.neg
)
else:
return floats(min_value=0.0, max_value=max_value) | floats(
min_value=0.0, max_value=-min_value).map(operator.neg)
elif count_between_floats(min_value, max_value) > 1000:
return FixedBoundedFloatStrategy(
lower_bound=min_value, upper_bound=max_value
)
else:
ub_int = float_to_int(max_value)
lb_int = float_to_int(min_value)
assert lb_int <= ub_int
return integers(min_value=lb_int, max_value=ub_int).map(
int_to_float
)
elif min_value is not None:
if min_value < 0:
result = floats(
min_value=0.0
) | floats(min_value=min_value, max_value=-0.0)
else:
result = (
floats(allow_infinity=allow_infinity, allow_nan=False).map(
lambda x: assume(not math.isnan(x)) and min_value + abs(x)
)
)
if min_value == 0 and not is_negative(min_value):
result = result.filter(lambda x: math.copysign(1.0, x) == 1)
return result
else:
assert max_value is not None
if max_value > 0:
result = floats(
min_value=0.0,
max_value=max_value,
) | floats(max_value=-0.0)
else:
result = (
floats(allow_infinity=allow_infinity, allow_nan=False).map(
lambda x: assume(not math.isnan(x)) and max_value - abs(x)
)
)
if max_value == 0 and is_negative(max_value):
result = result.filter(is_negative)
return result
@cacheable
@defines_strategy
[docs]def complex_numbers():
"""Returns a strategy that generates complex numbers."""
from hypothesis.searchstrategy.numbers import ComplexStrategy
return ComplexStrategy(
tuples(floats(), floats())
)
@cacheable
@defines_strategy
[docs]def tuples(*args):
"""Return a strategy which generates a tuple of the same length as args by
generating the value at index i from args[i].
e.g. tuples(integers(), integers()) would generate a tuple of length
two with both values an integer.
"""
for arg in args:
check_strategy(arg)
for arg in args:
if arg.is_empty:
return nothing()
from hypothesis.searchstrategy.collections import TupleStrategy
return TupleStrategy(args, tuple)
@defines_strategy
[docs]def sampled_from(elements):
"""Returns a strategy which generates any value present in the iterable
elements.
Note that as with just, values will not be copied and thus you
should be careful of using mutable data.
"""
from hypothesis.searchstrategy.misc import SampledFromStrategy, \
JustStrategy
from hypothesis.internal.conjecture.utils import check_sample
elements = check_sample(elements)
if not elements:
return nothing()
if len(elements) == 1:
return JustStrategy(elements[0])
return SampledFromStrategy(elements)
@cacheable
@defines_strategy
[docs]def lists(
elements=None, min_size=None, average_size=None, max_size=None,
unique_by=None, unique=False,
):
"""Returns a list containing values drawn from elements length in the
interval [min_size, max_size] (no bounds in that direction if these are
None). If max_size is 0 then elements may be None and only the empty list
will be drawn.
average_size may be used as a size hint to roughly control the size
of list but it may not be the actual average of sizes you get, due
to a variety of factors.
If unique is True (or something that evaluates to True), we compare direct
object equality, as if unique_by was `lambda x: x`. This comparison only
works for hashable types.
if unique_by is not None it must be a function returning a hashable type
when given a value drawn from elements. The resulting list will satisfy the
condition that for i != j, unique_by(result[i]) != unique_by(result[j]).
"""
check_valid_sizes(min_size, average_size, max_size)
if elements is None or (max_size is not None and max_size <= 0):
if max_size is None or max_size > 0:
raise InvalidArgument(
u'Cannot create non-empty lists without an element type'
)
else:
return builds(list)
check_strategy(elements)
if elements.is_empty:
if (min_size or 0) > 0:
raise InvalidArgument((
'Cannot create non-empty lists with elements drawn from '
'strategy %r because it has no values.') % (elements,))
else:
return builds(list)
if unique:
if unique_by is not None:
raise InvalidArgument((
'cannot specify both unique and unique_by (you probably only '
'want to set unique_by)'
))
else:
def unique_by(x):
return x
if unique_by is not None:
from hypothesis.searchstrategy.collections import UniqueListStrategy
check_strategy(elements)
min_size = min_size or 0
max_size = max_size or float(u'inf')
if average_size is None:
if max_size < float(u'inf'):
if max_size <= 5:
average_size = min_size + 0.75 * (max_size - min_size)
else:
average_size = (max_size + min_size) / 2
else:
average_size = max(
_AVERAGE_LIST_LENGTH,
min_size * 2
)
check_valid_sizes(min_size, average_size, max_size)
result = UniqueListStrategy(
elements=elements,
average_size=average_size,
max_size=max_size,
min_size=min_size,
key=unique_by
)
return result
check_valid_sizes(min_size, average_size, max_size)
from hypothesis.searchstrategy.collections import ListStrategy
if min_size is None:
min_size = 0
if average_size is None:
if max_size is None:
average_size = _AVERAGE_LIST_LENGTH
else:
average_size = (min_size + max_size) * 0.5
check_strategy(elements)
return ListStrategy(
(elements,), average_length=average_size,
min_size=min_size, max_size=max_size,
)
@cacheable
@defines_strategy
[docs]def sets(elements=None, min_size=None, average_size=None, max_size=None):
"""This has the same behaviour as lists, but returns sets instead.
Note that Hypothesis cannot tell if values are drawn from elements
are hashable until running the test, so you can define a strategy
for sets of an unhashable type but it will fail at test time.
"""
return lists(
elements=elements, min_size=min_size, average_size=average_size,
max_size=max_size, unique=True
).map(set)
@cacheable
@defines_strategy
[docs]def frozensets(elements=None, min_size=None, average_size=None, max_size=None):
"""This is identical to the sets function but instead returns
frozensets."""
return lists(
elements=elements, min_size=min_size, average_size=average_size,
max_size=max_size, unique=True
).map(frozenset)
@defines_strategy
[docs]def iterables(elements=None, min_size=None, average_size=None, max_size=None,
unique_by=None, unique=False):
"""This has the same behaviour as lists, but returns iterables instead.
Some iterables cannot be indexed (e.g. sets) and some do not have a
fixed length (e.g. generators). This strategy produces iterators,
which cannot be indexed and do not have a fixed length. This ensures
that you do not accidentally depend on sequence behaviour.
"""
@implements_iterator
class PrettyIter(object):
def __init__(self, values):
self._values = values
self._iter = iter(self._values)
def __iter__(self):
return self._iter
def __next__(self):
return next(self._iter)
def __repr__(self):
return 'iter({!r})'.format(self._values)
return lists(
elements=elements, min_size=min_size, average_size=average_size,
max_size=max_size, unique_by=unique_by, unique=unique
).map(PrettyIter)
@defines_strategy
[docs]def fixed_dictionaries(mapping):
"""Generate a dictionary of the same type as mapping with a fixed set of
keys mapping to strategies. mapping must be a dict subclass.
Generated values have all keys present in mapping, with the
corresponding values drawn from mapping[key]. If mapping is an
instance of OrderedDict the keys will also be in the same order,
otherwise the order is arbitrary.
"""
from hypothesis.searchstrategy.collections import FixedKeysDictStrategy
check_type(dict, mapping, 'mapping')
for v in mapping.values():
check_strategy(v)
for v in mapping.values():
if v.is_empty:
return nothing()
return FixedKeysDictStrategy(mapping)
@cacheable
@defines_strategy
[docs]def dictionaries(
keys, values, dict_class=dict,
min_size=None, average_size=None, max_size=None
):
"""Generates dictionaries of type dict_class with keys drawn from the keys
argument and values drawn from the values argument.
The size parameters have the same interpretation as for lists.
"""
check_valid_sizes(min_size, average_size, max_size)
if max_size == 0:
return fixed_dictionaries(dict_class())
check_strategy(keys)
check_strategy(values)
return lists(
tuples(keys, values),
min_size=min_size, average_size=average_size, max_size=max_size,
unique_by=lambda x: x[0]
).map(dict_class)
@cacheable
@defines_strategy
[docs]def streaming(elements):
"""Generates an infinite stream of values where each value is drawn from
elements.
The result is iterable (the iterator will never terminate) and
indexable.
"""
check_strategy(elements)
from hypothesis.searchstrategy.streams import StreamStrategy
return StreamStrategy(elements)
@cacheable
@defines_strategy
[docs]def characters(whitelist_categories=None, blacklist_categories=None,
blacklist_characters=None, min_codepoint=None,
max_codepoint=None):
"""Generates unicode text type (unicode on python 2, str on python 3)
characters following specified filtering rules.
This strategy accepts lists of Unicode categories, characters of which
should (`whitelist_categories`) or should not (`blacklist_categories`)
be produced.
Also there could be applied limitation by minimal and maximal produced
code point of the characters.
If you know what exactly characters you don't want to be produced,
pass them with `blacklist_characters` argument.
"""
if (
min_codepoint is not None and max_codepoint is not None and
min_codepoint > max_codepoint
):
raise InvalidArgument(
'Cannot have min_codepoint=%d > max_codepoint=%d ' % (
min_codepoint, max_codepoint
)
)
from hypothesis.searchstrategy.strings import OneCharStringStrategy
return OneCharStringStrategy(whitelist_categories=whitelist_categories,
blacklist_categories=blacklist_categories,
blacklist_characters=blacklist_characters,
min_codepoint=min_codepoint,
max_codepoint=max_codepoint)
@cacheable
@defines_strategy
[docs]def text(
alphabet=None,
min_size=None, average_size=None, max_size=None
):
"""Generates values of a unicode text type (unicode on python 2, str on
python 3) with values drawn from alphabet, which should be an iterable of
length one strings or a strategy generating such. If it is None it will
default to generating the full unicode range. If it is an empty collection
this will only generate empty strings.
min_size, max_size and average_size have the usual interpretations.
"""
from hypothesis.searchstrategy.strings import StringStrategy
if alphabet is None:
char_strategy = characters(blacklist_categories=('Cs',))
elif not alphabet:
if (min_size or 0) > 0:
raise InvalidArgument(
'Invalid min_size %r > 0 for empty alphabet' % (
min_size,
)
)
return just(u'')
elif isinstance(alphabet, SearchStrategy):
char_strategy = alphabet
else:
char_strategy = sampled_from(list(map(text_type, alphabet)))
return StringStrategy(lists(
char_strategy, average_size=average_size, min_size=min_size,
max_size=max_size
))
@cacheable
@defines_strategy
[docs]def binary(
min_size=None, average_size=None, max_size=None
):
"""Generates the appropriate binary type (str in python 2, bytes in python
3).
min_size, average_size and max_size have the usual interpretations.
"""
from hypothesis.searchstrategy.strings import BinaryStringStrategy, \
FixedSizeBytes
check_valid_sizes(min_size, average_size, max_size)
if min_size == max_size is not None:
return FixedSizeBytes(min_size)
return BinaryStringStrategy(
lists(
integers(min_value=0, max_value=255),
average_size=average_size, min_size=min_size, max_size=max_size
)
)
@cacheable
@defines_strategy
[docs]def randoms():
"""Generates instances of Random (actually a Hypothesis specific
RandomWithSeed class which displays what it was initially seeded with)"""
from hypothesis.searchstrategy.misc import RandomStrategy
return RandomStrategy(integers())
class RandomSeeder(object):
def __init__(self, seed):
self.seed = seed
def __repr__(self):
return 'random.seed(%r)' % (self.seed,)
@cacheable
@defines_strategy
[docs]def random_module():
"""If your code depends on the global random module then you need to use
this.
It will explicitly seed the random module at the start of your test
so that tests are reproducible. The value it passes you is an opaque
object whose only useful feature is that its repr displays the
random seed. It is not itself a random number generator. If you want
a random number generator you should use the randoms() strategy
which will give you one.
"""
from hypothesis.control import cleanup
import random
def seed_random(seed):
state = random.getstate()
random.seed(seed)
cleanup(lambda: random.setstate(state))
return RandomSeeder(seed)
return shared(
integers().map(seed_random),
'hypothesis.strategies.random_module()',
)
@cacheable
@defines_strategy
[docs]def builds(target, *args, **kwargs):
"""Generates values by drawing from args and kwargs and passing them to
target in the appropriate argument position.
e.g. builds(target, integers(), flag=booleans()) would draw an
integer i and a boolean b and call target(i, flag=b).
"""
return tuples(tuples(*args), fixed_dictionaries(kwargs)).map(
lambda value: target(*value[0], **value[1])
)
@cacheable
@defines_strategy
[docs]def fractions(min_value=None, max_value=None, max_denominator=None):
"""Returns a strategy which generates Fractions.
If min_value is not None then all generated values are no less than
min_value.
If max_value is not None then all generated values are no greater than
max_value.
If max_denominator is not None then the absolute value of the denominator
of any generated values is no greater than max_denominator. Note that
max_denominator must be at least 1.
"""
check_valid_bound(min_value, 'min_value')
check_valid_bound(max_value, 'max_value')
check_valid_interval(min_value, max_value, 'min_value', 'max_value')
check_valid_integer(max_denominator)
if max_denominator is not None and max_denominator < 1:
raise InvalidArgument(
u'Invalid denominator bound %s' % max_denominator
)
denominator_strategy = integers(min_value=1, max_value=max_denominator)
def dm_func(denom):
max_num = max_value * denom if max_value is not None else None
min_num = min_value * denom if min_value is not None else None
return builds(
Fraction,
integers(min_value=min_num, max_value=max_num),
just(denom)
)
return denominator_strategy.flatmap(dm_func)
@cacheable
@defines_strategy
[docs]def decimals(min_value=None, max_value=None,
allow_nan=None, allow_infinity=None, places=None):
"""Generates instances of decimals.Decimal, which may be:
- A finite rational number, between ``min_value`` and ``max_value``.
- Not a Number, if ``allow_nan`` is True. None means "allow NaN, unless
``min__value`` and ``max_value`` are not None".
- Positive or negative infinity, if ``max_value`` and ``min_value``
respectively are None, and ``allow_infinity`` is not False. None means
"allow infinity, unless excluded by the min and max values".
Note that where floats have one `NaN` value, Decimals have four: signed,
and either *quiet* or *signalling*. See `the decimal module docs
<https://docs.python.org/3/library/decimal.html#special-values>`_ for
more information on special values.
If ``places`` is not None, all finite values drawn from the strategy will
have that number of digits after the decimal place.
"""
# Convert min_value and max_value to Decimal values, and validate args
check_valid_integer(places)
if places is not None and places < 0:
raise InvalidArgument('places=%r may not be negative' % places)
if min_value is not None:
min_value = Decimal(min_value)
if min_value.is_infinite() and min_value < 0:
if not (allow_infinity or allow_infinity is None):
raise InvalidArgument('allow_infinity=%r, but min_value=%r'
% (allow_infinity, min_value))
min_value = None
elif not min_value.is_finite():
# This could be positive infinity, quiet NaN, or signalling NaN
raise InvalidArgument(u'Invalid min_value=%r' % min_value)
if max_value is not None:
max_value = Decimal(max_value)
if max_value.is_infinite() and max_value > 0:
if not (allow_infinity or allow_infinity is None):
raise InvalidArgument('allow_infinity=%r, but max_value=%r'
% (allow_infinity, max_value))
max_value = None
elif not max_value.is_finite():
raise InvalidArgument(u'Invalid max_value=%r' % max_value)
check_valid_bound(min_value, 'min_value')
check_valid_bound(max_value, 'max_value')
check_valid_interval(min_value, max_value, 'min_value', 'max_value')
if allow_infinity and (None not in (min_value, max_value)):
raise InvalidArgument('Cannot allow infinity between finite bounds')
# Set up a strategy for finite decimals
if places is not None:
# Fixed-point decimals are basically integers with a scale factor
def try_quantize(d):
try:
return d.quantize(factor)
except InvalidOperation: # pragma: no cover
return None
factor = Decimal(10) ** -places
max_num = max_value / factor if max_value is not None else None
min_num = min_value / factor if min_value is not None else None
strat = integers(min_value=min_num, max_value=max_num)\
.map(lambda d: try_quantize(d * factor))\
.filter(lambda d: d is not None)
else:
# Otherwise, they're like fractions featuring a power of ten
strat = fractions(
min_value=min_value, max_value=max_value
).map(lambda f: Decimal(f.numerator) / f.denominator)
# Compose with sampled_from for infinities and NaNs as appropriate
special = []
if allow_nan or (allow_nan is None and (None in (min_value, max_value))):
special.extend(map(Decimal, ('NaN', '-NaN', 'sNaN', '-sNaN')))
if allow_infinity or (allow_infinity is max_value is None):
special.append(Decimal('Infinity'))
if allow_infinity or (allow_infinity is min_value is None):
special.append(Decimal('-Infinity'))
return strat | sampled_from(special)
[docs]def recursive(base, extend, max_leaves=100):
"""base: A strategy to start from.
extend: A function which takes a strategy and returns a new strategy.
max_leaves: The maximum number of elements to be drawn from base on a given
run.
This returns a strategy S such that S = extend(base | S). That is, values
maybe drawn from base, or from any strategy reachable by mixing
applications of | and extend.
An example may clarify: recursive(booleans(), lists) would return a
strategy that may return arbitrarily nested and mixed lists of booleans.
So e.g. False, [True], [False, []], [[[[True]]]], are all valid values to
be drawn from that strategy.
"""
from hypothesis.searchstrategy.recursive import RecursiveStrategy
return RecursiveStrategy(base, extend, max_leaves)
@defines_strategy
[docs]def permutations(values):
"""Return a strategy which returns permutations of the collection
"values"."""
from hypothesis.internal.conjecture.utils import integer_range
values = list(values)
if not values:
return builds(list)
class PermutationStrategy(SearchStrategy):
def do_draw(self, data):
# Reversed Fisher-Yates shuffle. Reverse order so that it shrinks
# propertly: This way we prefer things that are lexicographically
# closer to the identity.
result = list(values)
for i in hrange(len(result)):
j = integer_range(data, i, len(result) - 1)
result[i], result[j] = result[j], result[i]
return result
return PermutationStrategy()
@defines_strategy
[docs]def datetimes(min_datetime=dt.datetime.min, max_datetime=dt.datetime.max,
timezones=none()):
"""A strategy for generating datetimes, which may be timezone-aware.
This strategy works by drawing a naive datetime between ``min_datetime``
and ``max_datetime``, which must both be naive (have no timezone).
``timezones`` must be a strategy that generates tzinfo objects (or None,
which is valid for naive datetimes). A value drawn from this strategy
will be added to a naive datetime, and the resulting tz-aware datetime
returned.
.. note::
tz-aware datetimes from this strategy may be ambiguous or non-existent
due to daylight savings, leap seconds, timezone and calendar
adjustments, etc. This is intentional, as malformed timestamps are a
common source of bugs.
:py:func:`hypothesis.extra.timezones` requires the ``pytz`` package, but
provides all timezones in the Olsen database. If you also want to allow
naive datetimes, combine strategies like ``none() | timezones()``.
Alternatively, you can create a list of the timezones you wish to allow
(e.g. from the standard library, ``datetutil``, or ``pytz``) and use
:py:func:`sampled_from`. Ensure that simple values such as None or UTC
are at the beginning of the list for proper minimisation.
"""
# Why must bounds be naive? In principle, we could also write a strategy
# that took aware bounds, but the API and validation is much harder.
# If you want to generate datetimes between two particular momements in
# time I suggest (a) just filtering out-of-bounds values; (b) if bounds
# are very close, draw a value and subtract it's UTC offset, handling
# overflows and nonexistent times; or (c) do something customised to
# handle datetimes in e.g. a four-microsecond span which is not
# representable in UTC. Handling (d), all of the above, leads to a much
# more complex API for all users and a useful feature for very few.
from hypothesis.searchstrategy.datetime import DatetimeStrategy
check_type(dt.datetime, min_datetime, 'min_datetime')
check_type(dt.datetime, max_datetime, 'max_datetime')
if min_datetime.tzinfo is not None:
raise InvalidArgument('min_datetime=%r must not have tzinfo'
% (min_datetime,))
if max_datetime.tzinfo is not None:
raise InvalidArgument('max_datetime=%r must not have tzinfo'
% (max_datetime,))
check_valid_interval(min_datetime, max_datetime,
'min_datetime', 'max_datetime')
if not isinstance(timezones, SearchStrategy):
raise InvalidArgument(
'timezones=%r must be a SearchStrategy that can provide tzinfo '
'for datetimes (either None or dt.tzinfo objects)' % (timezones,))
return DatetimeStrategy(min_datetime, max_datetime, timezones)
@defines_strategy
[docs]def dates(min_date=dt.date.min, max_date=dt.date.max):
"""A strategy for dates between ``min_date`` and ``max_date``."""
from hypothesis.searchstrategy.datetime import DateStrategy
check_type(dt.date, min_date, 'min_date')
check_type(dt.date, max_date, 'max_date')
check_valid_interval(min_date, max_date, 'min_date', 'max_date')
if min_date == max_date:
return just(min_date)
return DateStrategy(min_date, max_date)
@defines_strategy
[docs]def times(min_time=dt.time.min, max_time=dt.time.max, timezones=none()):
"""A strategy for times between ``min_time`` and ``max_time``.
The ``timezones`` argument is handled as for :py:func:`datetimes`.
"""
check_type(dt.time, min_time, 'min_time')
check_type(dt.time, max_time, 'max_time')
if min_time.tzinfo is not None:
raise InvalidArgument('min_time=%r must not have tzinfo' % min_time)
if max_time.tzinfo is not None:
raise InvalidArgument('max_time=%r must not have tzinfo' % max_time)
check_valid_interval(min_time, max_time, 'min_time', 'max_time')
day = dt.date(2000, 1, 1)
return datetimes(min_datetime=dt.datetime.combine(day, min_time),
max_datetime=dt.datetime.combine(day, max_time),
timezones=timezones).map(lambda t: t.timetz())
@defines_strategy
[docs]def timedeltas(min_delta=dt.timedelta.min, max_delta=dt.timedelta.max):
"""A strategy for timedeltas between ``min_delta`` and ``max_delta``."""
from hypothesis.searchstrategy.datetime import TimedeltaStrategy
check_type(dt.timedelta, min_delta, 'min_delta')
check_type(dt.timedelta, max_delta, 'max_delta')
check_valid_interval(min_delta, max_delta, 'min_delta', 'max_delta')
if min_delta == max_delta:
return just(min_delta)
return TimedeltaStrategy(min_delta=min_delta, max_delta=max_delta)
@cacheable
[docs]def composite(f):
"""Defines a strategy that is built out of potentially arbitrarily many
other strategies.
This is intended to be used as a decorator. See
:ref:`the full documentation for more details <composite-strategies>`
about how to use this function.
"""
from hypothesis.internal.reflection import define_function_signature
argspec = getfullargspec(f)
if (
argspec.defaults is not None and
len(argspec.defaults) == len(argspec.args)
):
raise InvalidArgument(
'A default value for initial argument will never be used')
if len(argspec.args) == 0 and not argspec.varargs:
raise InvalidArgument(
'Functions wrapped with composite must take at least one '
'positional argument.'
)
annots = {k: v for k, v in argspec.annotations.items()
if k in (argspec.args + argspec.kwonlyargs + ['return'])}
new_argspec = argspec._replace(args=argspec.args[1:], annotations=annots)
@defines_strategy
@define_function_signature(f.__name__, f.__doc__, new_argspec)
def accept(*args, **kwargs):
class CompositeStrategy(SearchStrategy):
def do_draw(self, data):
first_draw = [True]
def draw(strategy):
if not first_draw[0]:
data.mark_bind()
first_draw[0] = False
return data.draw(strategy)
return f(draw, *args, **kwargs)
return CompositeStrategy()
accept.__module__ = f.__module__
return accept
[docs]def shared(base, key=None):
"""Returns a strategy that draws a single shared value per run, drawn from
base. Any two shared instances with the same key will share the same value,
otherwise the identity of this strategy will be used. That is:
>>> s = integers() # or any other strategy
>>> x = shared(s)
>>> y = shared(s)
In the above x and y may draw different (or potentially the same) values.
In the following they will always draw the same:
>>> x = shared(s, key="hi")
>>> y = shared(s, key="hi")
"""
from hypothesis.searchstrategy.shared import SharedStrategy
return SharedStrategy(base, key)
@cacheable
[docs]def choices():
"""Strategy that generates a function that behaves like random.choice.
Will note choices made for reproducibility.
"""
from hypothesis.control import note, current_build_context
from hypothesis.internal.conjecture.utils import choice, check_sample
class Chooser(object):
def __init__(self, build_context, data):
self.build_context = build_context
self.data = data
self.choice_count = 0
def __call__(self, values):
if not values:
raise IndexError('Cannot choose from empty sequence')
result = choice(self.data, check_sample(values))
with self.build_context.local():
self.choice_count += 1
note('Choice #%d: %r' % (self.choice_count, result))
return result
def __repr__(self):
return 'choice'
class ChoiceStrategy(SearchStrategy):
supports_find = False
def do_draw(self, data):
return Chooser(current_build_context(), data)
return ReprWrapperStrategy(
shared(
ChoiceStrategy(),
key='hypothesis.strategies.chooser.choice_function'
), 'choices()')
@cacheable
[docs]def uuids():
"""Returns a strategy that generates UUIDs.
All returned values from this will be unique, so e.g. if you do
lists(uuids()) the resulting list will never contain duplicates.
"""
from uuid import UUID
return ReprWrapperStrategy(
shared(randoms(), key='hypothesis.strategies.uuids.generator').map(
lambda r: UUID(int=r.getrandbits(128))
), 'uuids()')
@defines_strategy
[docs]def runner(default=not_set):
"""A strategy for getting "the current test runner", whatever that may be.
The exact meaning depends on the entry point, but it will usually be the
associated 'self' value for it.
If there is no current test runner and a default is provided, return
that default. If no default is provided, raises InvalidArgument.
"""
class RunnerStrategy(SearchStrategy):
def do_draw(self, data):
runner = getattr(data, 'hypothesis_runner', not_set)
if runner is not_set:
if default is not_set:
raise InvalidArgument(
'Cannot use runner() strategy with no '
'associated runner or explicit default.'
)
else:
return default
else:
return runner
return RunnerStrategy()
@cacheable
[docs]def data():
"""This isn't really a normal strategy, but instead gives you an object
which can be used to draw data interactively from other strategies.
It can only be used within :func:`@given <hypothesis.given>`, not
:func:`find() <hypothesis.find>`. This is because the lifetime
of the object cannot outlast the test body.
See :ref:`the rest of the documentation <interactive-draw>` for more
complete information.
"""
from hypothesis.control import note
class DataObject(object):
def __init__(self, data):
self.count = 0
self.data = data
def __repr__(self):
return 'data(...)'
def draw(self, strategy, label=None):
self.data.mark_bind()
result = self.data.draw(strategy)
self.count += 1
if label is not None:
note('Draw %d (%s): %r' % (self.count, label, result))
else:
note('Draw %d: %r' % (self.count, result))
return result
class DataStrategy(SearchStrategy):
supports_find = False
def do_draw(self, data):
if not hasattr(data, 'hypothesis_shared_data_strategy'):
data.hypothesis_shared_data_strategy = DataObject(data)
return data.hypothesis_shared_data_strategy
def __repr__(self):
return 'data()'
def map(self, f):
self.__not_a_first_class_strategy('map')
def filter(self, f):
self.__not_a_first_class_strategy('filter')
def flatmap(self, f):
self.__not_a_first_class_strategy('flatmap')
def example(self):
self.__not_a_first_class_strategy('example')
def __not_a_first_class_strategy(self, name):
raise InvalidArgument((
'Cannot call %s on a DataStrategy. You should probably be '
"using @composite for whatever it is you're trying to do."
) % (name,))
return DataStrategy()
# Private API below here
def check_type(typ, arg, name=''):
if name:
name += '='
if not isinstance(arg, typ):
if isinstance(typ, type):
typ_string = typ.__name__
else:
typ_string = 'one of %s' % (
', '.join(t.__name__ for t in typ))
raise InvalidArgument('Expected %s but got %s%r (type=%s)'
% (typ_string, name, arg, type(arg).__name__))
def check_strategy(arg):
check_type(SearchStrategy, arg)
def check_valid_integer(value):
"""Checks that value is either unspecified, or a valid integer.
Otherwise raises InvalidArgument.
"""
if value is None:
return
check_type(integer_types, value)
def check_valid_bound(value, name):
"""Checks that value is either unspecified, or a valid interval bound.
Otherwise raises InvalidArgument.
"""
if value is None or isinstance(value, integer_types + (Rational,)):
return
if math.isnan(value):
raise InvalidArgument(u'Invalid end point %s %r' % (value, name))
def check_valid_size(value, name):
"""Checks that value is either unspecified, or a valid non-negative size
expressed as an integer/float.
Otherwise raises InvalidArgument.
"""
if value is None:
return
check_type(integer_types + (float,), value)
if value < 0:
raise InvalidArgument(u'Invalid size %s %r < 0' % (value, name))
if isinstance(value, float) and math.isnan(value):
raise InvalidArgument(u'Invalid size %s %r' % (value, name))
def check_valid_interval(lower_bound, upper_bound, lower_name, upper_name):
"""Checks that lower_bound and upper_bound are either unspecified, or they
define a valid interval on the number line.
Otherwise raises InvalidArgument.
"""
if lower_bound is None or upper_bound is None:
return
if upper_bound < lower_bound:
raise InvalidArgument(
'Cannot have %s=%r < %s=%r' % (
upper_name, upper_bound, lower_name, lower_bound
))
def check_valid_sizes(min_size, average_size, max_size):
check_valid_size(min_size, 'min_size')
check_valid_size(max_size, 'max_size')
check_valid_size(average_size, 'average_size')
check_valid_interval(min_size, max_size, 'min_size', 'max_size')
check_valid_interval(average_size, max_size, 'average_size', 'max_size')
check_valid_interval(min_size, average_size, 'min_size', 'average_size')
if average_size is not None:
if (
(max_size is None or max_size > 0) and
average_size is not None and average_size <= 0.0
):
raise InvalidArgument(
'Cannot have average_size=%r < min_size=%r' % (
average_size, min_size
))
_AVERAGE_LIST_LENGTH = 5.0
assert _strategies.issubset(set(__all__)), _strategies - set(__all__)