Python dataclasses.dataclass方法代碼示例

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def __init__(self, config_class, time, **kwargs):
        """Specify an intervention in the dynamical system.

        Parameters
        ----------
            time: int
                Time of the intervention.
            config_class:
                The Config class, a child class of dataclass.
            kwargs: dict
                Only valid keyword arguments are parameters of the config class.

        """
        self.time = time
        config_args = set(f.name for f in dataclasses.fields(config_class))
        for arg in kwargs:
            if arg not in config_args:
                raise TypeError(f"__init__() got an unexpected keyword argument {arg}!")
        self.updates = kwargs 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def test_baseconfig():
    """Test the basic configuration object."""

    @dataclasses.dataclass
    class Config(wn.dynamics.BaseConfig):
        param1: float = 0
        param2: float = 23

    config = Config()

    assert config.parameter_names() == ["param1", "param2"]

    intervention = wn.dynamics.BaseIntervention(Config, 1970, param1=19)

    assert config.param1 == 0
    updated = config.update(intervention)
    assert config.param1 == 0
    assert config.param2 == 23
    assert updated.param1 == 19
    assert updated.param2 == 23 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def test_basestate():
    """Test the basic state object."""

    @dataclasses.dataclass
    class State(wn.dynamics.BaseState):
        state1: float = 0
        state2: float = 1
        state3: float = 3

    assert State.num_variables() == 3
    assert State.variable_names() == ["state1", "state2", "state3"]

    state2 = [2]
    state = State(state2=state2)
    assert state.num_variables() == 3

    values = state.values()
    assert values[0] == 0
    assert values[1] == [2]
    assert values[2] == 3

    # Ensure values are shallow copied
    assert values[1] is state2 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def to_dict(self, omit_none: bool = True, validate: bool = False, validate_enums: bool = True) -> JsonDict:
        """Converts the dataclass instance to a JSON encodable dict, with optional JSON schema validation.

        If omit_none (default True) is specified, any items with value None are removed
        """
        data = {}
        for f in self._get_fields():
            value = getattr(self, f.field.name)
            try:
                value = self._encode_field(f.field.type, value, omit_none)
            except UnknownEnumValueError as e:
                warnings.warn(str(e))

            if omit_none and value is None:
                continue
            if value is NULL:
                value = None
            data[f.mapped_name] = value

        if self.__discriminator_name is not None:
            data[self.__discriminator_name] = self.__class__.__name__

        if validate:
            self._validate(data, validate_enums)
        return data 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def test_variablesize(self):
        @dataclass(frozen=True)
        @streamable
        class TestClass2(Streamable):
            a: uint32
            b: uint32
            c: bytes

        a = TestClass2(uint32(1), uint32(2), b"3")
        bytes(a)

        @dataclass(frozen=True)
        @streamable
        class TestClass3(Streamable):
            a: int

        b = TestClass3(1)
        try:
            bytes(b)
            assert False
        except NotImplementedError:
            pass 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def test_recursive_json(self):
        @dataclass(frozen=True)
        @streamable
        class TestClass1(Streamable):
            a: List[uint32]

        @dataclass(frozen=True)
        @streamable
        class TestClass2(Streamable):
            a: uint32
            b: List[Optional[List[TestClass1]]]
            c: bytes32

        tc1_a = TestClass1([uint32(1), uint32(2)])
        tc1_b = TestClass1([uint32(4), uint32(5)])
        tc1_c = TestClass1([uint32(7), uint32(8)])

        tc2 = TestClass2(
            uint32(5), [[tc1_a], [tc1_b, tc1_c], None], bytes32(bytes([1] * 32))
        )
        assert TestClass2.from_json_dict(tc2.to_json_dict()) == tc2 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def __init__(self, config, step, timestamp, client=None, delete=False):

        # Init dataclass fields from SLO config and Error Budget Policy
        self.__set_fields(**config,
                          **step,
                          lambdas={
                              'slo_target': float,
                              'alerting_burn_rate_threshold': int
                          })

        # Set other fields
        self.window = int(step['measurement_window_seconds'])
        self.timestamp = int(timestamp)
        self.timestamp_human = utils.get_human_time(timestamp)

        # Get backend results
        result = self.run_backend(config, client=client, delete=delete)
        if result:
            self.build(step, result) 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def __set_fields(self, lambdas={}, **kwargs):
        """Set all fields in dataclasses from configs passed and apply function
        on values whose key match one in the dictionaries.

        Args:
            lambdas (dict): Dict {key: function} to apply a function on certain
            kwargs (dict): Dict of key / values to set in dataclass.
        """
        names = set(f.name for f in fields(self))
        for name in names:
            if name not in kwargs:
                continue
            value = kwargs[name]
            if name in lambdas.keys():
                value = lambdas[name](value)
            setattr(self, name, value) 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def test_validate_works_with_dataclasses(self):
        """
        Test we can compile @dataclass

        @dataclass inspects `sys.modules`, so the module needs to be in
        `sys.modules` when @dataclass is run.
        """
        mod = _compile(
            "foo",
            textwrap.dedent(
                """
                from __future__ import annotations
                from dataclasses import dataclass

                def render(table, params):
                    return table

                @dataclass
                class A:
                    y: int
                """
            ).encode("utf-8"),
        )
        self.kernel.validate(mod)  # do not raise 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def __init_subclass__(cls, **kwargs):
        return dataclasses.dataclass(cls, init=True, frozen=True, repr=False) 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def __set_name__(self, owner, name):
        super().__set_name__(owner, name)
        annotations = {
            name: owner.__annotations__.get(name, None)
            for name in self._field_names
        }
        assert all(annotations.values()), (
            f"Entity owner has to have all field_names as dataclass "
            f"annotations: {self._field_names}"
        )
        # TODO assert immutability of the fields 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def __init_subclass__(cls, **kwargs):
        super().__init_subclass__(**kwargs)
        id_fields = [(k, v) for k, v in cls.__dict__.items() if isinstance(v, Id)]
        if not getattr(cls, '__id_field_name__', None):
            assert len(id_fields) == 1, (
                f"Exactly one ID field is required for any Entity class. Got: "
                f"{dict(id_fields) if id_fields else None}"
            )
            cls.__id_field_name__ = id_fields[0][0]
        return dataclasses.dataclass(cls, eq=False) 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def __init__(self, **kwargs):
        """
        This initializer will be overridden by dataclass decorator above. It is needed to
        persuade static type checkers that Entities have initializers.
        """ 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def _monkeypatch_to_fix_certificate_asdict() -> None:
    # H4ck: monkeypatch the _Certificate class to add __deepcopy__() so that when we call asdict() on a dataclass
    # that contains a _Certificate, asdict() succeeds. Without this, generating JSON for the certinfo scan command
    # will crash because the asdict() function uses deepcopy(), but certificates returned by cryptography.x509
    # don't support it so SSLyze would crash. This class is a workaround to fix JSON output.
    # I opened an issue about it in the cryptography repo at https://github.com/pyca/cryptography/issues/5129
    def _deepcopy_method_for_x509_certificate(inner_self: _Certificate, memo: str) -> x509.Certificate:
        return x509.load_pem_x509_certificate(inner_self.public_bytes(Encoding.PEM), backend=default_backend())

    _Certificate.__deepcopy__ = _deepcopy_method_for_x509_certificate


# Call it on import... hacky but we don't have a choice 

# 需要導入模塊: import dataclasses [as 別名]
# 或者: from dataclasses import dataclass [as 別名]
def test_long_repr(self):
        @dataclass(repr=False)
        class LongContainer(Model):
            attribute_1: int = 1
            attribute_2: int = 2
            attribute_3: int = 3
            attribute_4: int = 4
            attribute_5: int = 5

        @dataclass(repr=False)
        class LongData(Model):
            data: LongContainer
            data_list: List[LongContainer]
            builtin_list: List[int]
            raw_dict: dict
            string: str
            boolean: bool

        data = LongData(
            LongContainer(),
            [LongContainer() for _ in range(20)],
            list(range(10)),
            {str(k): k for k in range(20)},
            'really long string which will most probably be cut off' * 2,
            True
        )
        repr(data)