Python hypothesis.strategies.dictionaries() Examples

def merge_dicts_max_size_strategy(dict1, dict2, max_size):
    """Combine dict strategies into one to produce a dict up to a max size.

    Assumes both dicts have distinct keys.

    :param max_size: Maximum number of keys in dicts generated by the strategy.
    :type max_size: int
    """
    # This is grim, but combine both dictionaries after creating a copy of the
    # second containing a reduced number of keys if that would take us over the
    # max size.
    result = hy_st.builds(
        lambda x, y: dict((list(x.items()) + list(y.items()))[:max_size]),
        dict1,
        dict2)
    return result 

def simple_attrs_with_metadata(draw):
    """
    Create a simple attribute with arbitrary metadata.
    """
    c_attr = draw(simple_attrs)
    keys = st.booleans() | st.binary() | st.integers() | st.text()
    vals = st.booleans() | st.binary() | st.integers() | st.text()
    metadata = draw(
        st.dictionaries(keys=keys, values=vals, min_size=1, max_size=3)
    )

    return attr.ib(
        default=c_attr._default,
        validator=c_attr._validator,
        repr=c_attr.repr,
        eq=c_attr.eq,
        order=c_attr.order,
        hash=c_attr.hash,
        init=c_attr.init,
        metadata=metadata,
        type=None,
        converter=c_attr.converter,
    ) 

def generate_dictionary_with_fixed_tokens(draw):
    """
    Builds random nested dictionary structure which is then used as JSON to
    mask two fixed "token" keys.

    Structure is based on TEST_JSON sample fixture defined above.
    """
    base = draw(
        st.fixed_dictionaries({'token': st.text(printable, min_size=10)})
    )

    optional = draw(
        st.nothing() | st.dictionaries(
            st.text(ascii_letters, min_size=1),
            st.floats() | st.integers() | st.text(printable) | st.booleans()
            | st.nothing(),
            min_size=10,
            max_size=50
        )
    )

    return {**base, **optional} 

def ds_vars_dims_mixed():
    def build_it(vars_to_dims_):
        all_dims = list(set(sum((list(dd) for dd in vars_to_dims_.values()), [])))

        ds = fixed_datasets(vars_to_dims_)

        dims = subset_lists(all_dims)

        vars_ = sampled_from(list(vars_to_dims_.keys()))
        vars_dict = dictionaries(vars_, dims, dict_class=OrderedDict)
        vars_dict = vars_dict.map(OrderedDict.items).map(list)

        return tuples(ds, vars_dict, just(all_dims))

    vars_to_dims_st = vars_to_dims_dicts(min_vars=0, min_dims=0)

    S = vars_to_dims_st.flatmap(build_it)
    return S 

def sig_pair():
    def separate(D):
        signatures, signatures_ref = {}, {}
        for kk in D:
            if len(D[kk]) == 1:
                v_ref, = D[kk]
                signatures_ref[kk] = np.asarray(v_ref)
            elif len(D[kk]) == 2:
                v, v_ref = D[kk]
                signatures[kk] = np.asarray(v)
                signatures_ref[kk] = np.asarray(v_ref)
            else:
                assert False
        return signatures, signatures_ref

    sig_dict = dictionaries(text(), tuples(bsigs()) | tuples(bsigs(), bsigs()))
    S = sig_dict.map(separate)
    return S 

def homogeneous_dictionary(**kwargs):
    """Return a strategy which generates a dictionary of uniform key:value type."""
    return index_types.flatmap(lambda s: hs.dictionaries(s(), s(),  **kwargs)) 

def dict_attrs(draw, defaults=None):
    """
    Generate a tuple of an attribute and a strategy that yields dictionaries
    for that attribute. The dictionaries map strings to integers.
    """
    default = NOTHING
    val_strat = st.dictionaries(keys=st.text(), values=st.integers())
    if defaults is True or (defaults is None and draw(st.booleans())):
        default_val = draw(val_strat)
        default = attr.Factory(lambda: default_val)
    return (attr.ib(default=default), val_strat) 

def create_dict_and_type(tuple_of_strats):
    """Map two primitive strategies into a strategy for dict and type."""
    (prim_strat_1, type_1), (prim_strat_2, type_2) = tuple_of_strats

    return st.tuples(
        st.dictionaries(prim_strat_1, prim_strat_2),
        create_generic_dict_type(type_1, type_2),
    ) 

def json(value_limit=5):
    """Hypothesis strategy for generating values that can be passed to
    `json.dumps` to produce valid JSON data.

    :param value_limit: A limit on the number of values in the JSON data -
                        setting this too high can cause value generation to
                        time out.
    :type value_limit: int
    """
    return hy_st.recursive(
        hy_st.floats() | hy_st.booleans() | hy_st.text() | hy_st.none(),
        lambda children: hy_st.dictionaries(hy_st.text(), children),
        max_leaves=value_limit) 

def nested_dictionaries():
    simple_strings_alphabet = 'abcdefghijklmnopqrstuvwxyz\'"\r\n '
    simple_text = st.text(alphabet=simple_strings_alphabet, min_size=5)

    def extend(base):
        return st.one_of(
            st.lists(base, min_size=5),
            st.dictionaries(keys=simple_text, values=base, min_size=1)
        )

    return st.recursive(simple_text, extend, max_leaves=50) 

def containers(primitives):
    def extend(base):
        return st.one_of(
            st.lists(base, max_size=50),
            st.lists(base, max_size=50).map(tuple),
            st.dictionaries(
                keys=hashable_containers(primitives),
                values=base,
                max_size=10
            ),
        )

    return st.recursive(primitives, extend, max_leaves=50) 

def field_to_strategy(field, env):
    """Generate strategy for field."""
    if SCALAR_MAPPINGS.get(field.type) is not None:
        return apply_modifier(
            strategy=SCALAR_MAPPINGS[field.type],
            field=field
        )

    if field.type is FieldDescriptor.TYPE_ENUM:
        return apply_modifier(
            strategy=find_strategy_in_env(field.enum_type, env),
            field=field
        )

    if field.type is FieldDescriptor.TYPE_MESSAGE:
        field_options = field.message_type.GetOptions()

        if field_options.deprecated:
            return st.none()

        if field_options.map_entry:
            k, v = field.message_type.fields
            return st.dictionaries(
                field_to_strategy(k, env).filter(non_null),
                field_to_strategy(v, env).filter(non_null)
            )

        return apply_modifier(
            strategy=find_strategy_in_env(field.message_type, env),
            field=field
        )

    raise Exception("Unhandled field {}.".format(field)) 

def chromeHeaders ():
    # token as defined by https://tools.ietf.org/html/rfc7230#section-3.2.6
    token = st.sampled_from('abcdefghijklmnopqrstuvwxyz0123456789!#$%&\'*+-.^_`|~')
    # XXX: the value should be asciiText without leading/trailing spaces
    return st.dictionaries (token, token) 

def jsonObject ():
    """ JSON-encodable objects """
    return st.dictionaries (st.text (), st.one_of (st.integers (), st.text ())) 

def dict_node(draw, key=const_node(), value=const_node(), **kwargs):
    items = draw(hs.dictionaries(key, value, **kwargs)).items()
    node = astroid.Dict()
    node.postinit(items)
    return node 

def random_dict_variable_homogeneous_value(**kwargs):
    """Return a strategy which generates a random dictionary of variable name and value"""
    return primitive_types.flatmap(lambda s: hs.dictionaries(valid_identifier(), s(), **kwargs)) 

def random_dictionary(**kwargs):
    """Return a strategy which generates a random list."""
    return hs.dictionaries(primitive_values, primitive_values, **kwargs) 

def dict_str(draw):
    return draw(st.dictionaries(st.text(min_size=1), st.text(min_size=1), min_size=1)) 

def strategy(self):
        """Return a hypothesis strategy defining this collection, including
        random additional properties if the object supports them.

        Will add only up to `MAX_ADDITIONAL_PROPERTIES` extra values to
        prevent data generation taking too long and timing out.

        :param required_properties: The required fields in the generated dict.
        :type required_properties: dict
        :param optional_properties: The optional fields in the generated dict.
        :type optional_properties: dict
        """
        required_properties = {
            name: field.strategy()
            for name, field in self._properties.items()
            if name in self._swagger_definition.required_properties}
        optional_properties = {
            name: field.strategy()
            for name, field in self._properties.items()
            if name not in self._swagger_definition.required_properties}

        # The result must contain the specified propereties.
        result = base_st.merge_optional_dict_strategy(required_properties,
                                                      optional_properties)

        # If we allow arbitrary additional properties, create a dict with some
        # then update it with the fixed ones to ensure they are retained.
        if self._additional_properties:
            # Generate enough to stay within the allowed bounds, but don't
            # generate more than a fixed maximum.
            min_properties = (0 if self._min_properties is None else
                              self._min_properties)
            min_properties = max(0, min_properties - len(required_properties))
            max_properties = (self.MAX_ADDITIONAL_PROPERTIES
                              if self._max_properties is None else
                              self._max_properties)
            max_properties = min(self.MAX_ADDITIONAL_PROPERTIES,
                                 max_properties - len(required_properties))
            max_properties = max(max_properties, min_properties)
            log.debug("Determined max, min extra properties: %r, %r",
                      max_properties, min_properties)
            forbidden_prop_names = set(required_properties.keys() &
                                       optional_properties.keys())
            extra = hy_st.dictionaries(
                hy_st.text().filter(lambda x: x not in forbidden_prop_names),
                base_st.json(),
                min_size=min_properties,
                max_size=max_properties)

            if self._max_properties is not None:
                result = base_st.merge_dicts_max_size_strategy(
                    result, extra, self._max_properties)
            else:
                result = base_st.merge_dicts_strategy(result, extra)

        return result 

def gen_object(draw: Any) -> Schema:
    """Draw an object schema."""
    out: Schema = {"type": "object"}
    names = draw(st.sampled_from([None, None, None, draw(gen_string())]))
    name_strat = st.text() if names is None else from_schema(names)
    required = draw(
        st.just(False) | st.lists(name_strat, min_size=1, max_size=5, unique=True)
    )

    # Trying to generate schemata that are consistent would mean dealing with
    # overlapping regex and names, and that would suck.  So instead we ensure that
    # there *are* no overlapping requirements, which is much easier.
    properties = draw(st.dictionaries(name_strat, _json_schemata(recur=False)))
    disjoint = REGEX_PATTERNS.filter(
        lambda r: all(re.search(r, string=name) is None for name in properties)
    )
    patterns = draw(st.dictionaries(disjoint, _json_schemata(recur=False), max_size=1))
    additional = draw(st.none() | _json_schemata(recur=False))

    min_size = draw(st.none() | st.integers(0, 5))
    max_size = draw(st.none() | st.integers(2, 5))
    if min_size is not None and max_size is not None and min_size > max_size:
        min_size, max_size = max_size, min_size

    if names is not None:
        out["propertyNames"] = names
    if required:
        out["required"] = required
        if min_size is not None:
            min_size += len(required)
        if max_size is not None:
            max_size += len(required)
    if min_size is not None:
        out["minProperties"] = min_size
    if max_size is not None:
        out["maxProperties"] = max_size
    if properties:
        out["properties"] = properties

        props = st.sampled_from(sorted(properties))
        if draw(st.integers(0, 3)) == 3:
            out["dependencies"] = draw(
                st.dictionaries(props, st.lists(props, unique=True))
            )
        elif draw(st.integers(0, 3)) == 3:
            out["dependencies"] = draw(st.dictionaries(props, json_schemata()))
    if patterns:
        out["patternProperties"] = patterns
    if additional is not None:
        out["additionalProperties"] = additional

    return out