Python hypothesis.strategies方法代码示例

# 需要导入模块: import hypothesis [as 别名]
# 或者: from hypothesis import strategies [as 别名]
def a_composite_strategy(draw):
    """Generates a (List[int], index) pair.  The index points to a random positive
    element (>= 1); if there are no positive elements index is None.

    `draw` is used within a composite strategy as, e.g.::

        >>> draw(st.booleans()) # can draw True or False
        True

    Note that `draw` is a reserved parameter that will be used by the
    `st.composite` decorator to interactively draw values from the
    strategies that you invoke within this function. That is, you need
    not pass a value to `draw` when calling this strategy::

       >>> a_composite_strategy().example()
       ([-1, -2, -3, 4], 3)
    """
    # TODO: draw a list, determine the allowed indices, and choose one to return
    lst = []  # TODO: draw a list of integers here
    index = None
    # TODO: determine the list of allowed indices, and choose one if non-empty
    return (lst, index) 

# 需要导入模块: import hypothesis [as 别名]
# 或者: from hypothesis import strategies [as 别名]
def test_map_odd_numbers(x):
    assert x[-1] in "13579"


# Takeaway
# --------
# `.map` permits us to extend Hypothesis' core strategies in powerful
# ways. See that it can be used to affect the individual values being
# produced by a strategy (e.g. mapping integers to even-valued
# integers), as well as to cast the values to a different type (e.g.
# mapping an integer to a string.
#
# If it seems like a data-type is missing from Hypothesis'
# strategies, then it is likely that a simple application of `.map`
# will suffice. E.g. suppose you want a strategy that generate deques,
# then
#     `deque_strat = st.lists(...).map(deque)`
# will serve nicely - we don't even need a lambda!


##############################################################################
# Defining recursive data.

# There are a couple of ways to define recursive data with Hypothesis,
# leaning on the fact that strategies are lazily instantiated.
#
# `st.recursive` takes a base strategy, and a function that takes a strategy
# and returns an extended strategy.  All good if we want that structure!
# If you want mutual recursion though, or have a complicated kind of data
# (or just limited time in a tutorial), `st.deferred` is the way to go.
#
# The `Record` exercise in pbt-101.py defined JSON using `st.recursive`,
# if you want to compare them, and has some extension problems that you
# could write as tests here instead.


# JSON values are defined as one of null, false, true, a finite number,
# a string, an array of json values, or a dict of string to json values. 

# 需要导入模块: import hypothesis [as 别名]
# 或者: from hypothesis import strategies [as 别名]
def test_a_composite_strategy(value):
    lst, index = value
    assert all(isinstance(n, int) for n in lst)
    if index is None:
        assert all(n < 1 for n in lst)
    else:
        assert lst[index] >= 1


# Takeaway
# --------
# Why generate a tuple with a `@composite` strategy instead of using two
# separate strategies?  This way we can ensure certain relationships between
# the `lst` and `index` values!  (You can get a similar effect with st.data(),
# but the reporting and reproducibility isn't as nice.) 

# 需要导入模块: import hypothesis [as 别名]
# 或者: from hypothesis import strategies [as 别名]
def get_examples(func: typing.Callable, kwargs: typing.Dict[str, typing.Any],
                 count: int) -> typing.List[ArgsKwargsType]:
    kwargs = kwargs.copy()
    for name, value in kwargs.items():
        if isinstance(value, hypothesis.strategies.SearchStrategy):
            continue
        kwargs[name] = hypothesis.strategies.just(value)

    def pass_along_variables(*args, **kwargs) -> ArgsKwargsType:
        return args, kwargs

    pass_along_variables.__signature__ = signature(func)    # type: ignore
    pass_along_variables.__annotations__ = getattr(func, '__annotations__', {})
    strategy = hypothesis.strategies.builds(pass_along_variables, **kwargs)
    examples = []

    @hypothesis.given(strategy)
    @hypothesis.settings(
        database=None,
        max_examples=count,
        deadline=None,
        verbosity=hypothesis.Verbosity.quiet,
        phases=(hypothesis.Phase.generate,),
        suppress_health_check=hypothesis.HealthCheck.all(),
    )
    def example_generator(ex: ArgsKwargsType) -> None:
        examples.append(ex)

    example_generator()  # pylint: disable=no-value-for-parameter
    return examples 

# 需要导入模块: import hypothesis [as 别名]
# 或者: from hypothesis import strategies [as 别名]
def test_colour_type_reg_for_fmt(self):
        """Test registering a template for a specific type/format works."""
        value_factory = swaggerconformance.strategies.StrategyFactory()
        value_factory.register("string", "hexcolour", HexColourStrTemplate)
        self._run_test_colour_type(value_factory) 

# 需要导入模块: import hypothesis [as 别名]
# 或者: from hypothesis import strategies [as 别名]
def test_colour_type_default_fmt(self):
        """Test registering a default template for a type works."""
        value_factory = swaggerconformance.strategies.StrategyFactory()
        value_factory.register_type_default("string", HexColourStrTemplate)
        self._run_test_colour_type(value_factory) 

# 需要导入模块: import hypothesis [as 别名]
# 或者: from hypothesis import strategies [as 别名]
def test_colour_int_codec(self):
        """Generate `Colour` objects and translate them to `int` at the point
        of building the JSON body, so the test only accesses `Colour` objects.
        """
        value_factory = swaggerconformance.strategies.StrategyFactory()
        value_factory.register("integer", "intcolour", ColourObjTemplate)

        codec = swaggerconformance.codec.CodecFactory()
        codec.register("integer", "intcolour", ColourIntCodec)

        self._run_test_colour_type(codec, value_factory) 

# 需要导入模块: import hypothesis [as 别名]
# 或者: from hypothesis import strategies [as 别名]
def test_colour_int_codec_with_hex(self):
        """Generating hex strings for '`intcolour`' fields still means they are
        converted to `Colour` objects and then to `int` inside the JSON.
        """
        value_factory = swaggerconformance.strategies.StrategyFactory()
        value_factory.register("integer", "intcolour", HexColourStrTemplate)

        codec = swaggerconformance.codec.CodecFactory()
        codec.register("integer", "intcolour", ColourIntCodec)

        self._run_test_colour_type(codec, value_factory) 

# 需要导入模块: import hypothesis [as 别名]
# 或者: from hypothesis import strategies [as 别名]
def test_scene_codec(self):
        """Test that JSON objects can be converted at both test edges."""
        value_factory = swaggerconformance.strategies.StrategyFactory()
        value_factory.register("integer", "intcolour", ColourObjTemplate)
        value_factory.register("object", "scene", SceneTemplate)

        codec = swaggerconformance.codec.CodecFactory()
        codec.register("integer", "intcolour", ColourIntCodec)
        codec.register("object", "scene", SceneCodec)

        self._run_test_colour_type(codec, value_factory)