Class swarmauri_standard.norms.SupremumComplexNorm.SupremumComplexNorm

swarmauri_standard.norms.SupremumComplexNorm.SupremumComplexNorm

Bases: NormBase

Supremum norm implementation for complex-valued functions.

This class computes the maximum absolute value in a complex interval [a, b].

Attributes

type : Literal["SupremumComplexNorm"] The type identifier for this norm implementation. resource : str, optional The resource type, defaults to NORM.

type class-attribute instance-attribute

type = 'SupremumComplexNorm'

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="allow", arbitrary_types_allowed=True
)

id class-attribute instance-attribute

id = Field(default_factory=generate_id)

members class-attribute instance-attribute

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

name class-attribute instance-attribute

name = None

resource class-attribute instance-attribute

resource = Field(default=NORM.value)

version class-attribute instance-attribute

version = '0.1.0'

compute

compute(x)

Compute the supremum norm of the input.

For complex-valued functions, this returns the maximum absolute value.

Parameters

x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType] The input for which to compute the supremum norm.

Returns

complex The computed supremum norm value.

Raises

TypeError If the input type is not supported. ValueError If the norm cannot be computed for the given input.

Source code in swarmauri_standard/norms/SupremumComplexNorm.py

def compute(
    self, x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
) -> complex:
    """
    Compute the supremum norm of the input.

    For complex-valued functions, this returns the maximum absolute value.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
        The input for which to compute the supremum norm.

    Returns
    -------
    complex
        The computed supremum norm value.

    Raises
    ------
    TypeError
        If the input type is not supported.
    ValueError
        If the norm cannot be computed for the given input.
    """
    logger.debug(f"Computing supremum complex norm for {type(x)}")

    try:
        if isinstance(x, (list, tuple, np.ndarray)):
            # For sequence types, find the maximum absolute value
            if len(x) == 0:
                return complex(0)

            if isinstance(x, np.ndarray) and x.ndim > 1:
                x = x.flatten()

            # Convert all values to complex and compute absolute values
            complex_values = [
                complex(val) if not isinstance(val, complex) else val for val in x
            ]
            abs_values = [abs(val) for val in complex_values]
            max_abs = max(abs_values)

            # Find the complex value with the maximum absolute value
            max_index = abs_values.index(max_abs)
            return complex_values[max_index]

        elif hasattr(x, "to_array"):
            # For vector or matrix types with to_array method
            return self.compute(x.to_array())

        elif callable(x):
            # For callable types, we would need a domain to evaluate the function
            # This is a simplified implementation assuming a predefined domain
            domain = np.linspace(-1, 1, 100)  # Example domain
            values = [x(t) for t in domain]
            return self.compute(values)

        elif isinstance(x, str):
            # For string types, convert to complex numbers if possible
            try:
                # Try to interpret the string as a complex number
                return complex(x)
            except ValueError:
                # If not a single complex number, try to parse as a list
                try:
                    values = eval(x)
                    if isinstance(values, (list, tuple)):
                        return self.compute(values)
                except Exception:
                    raise ValueError(
                        f"Cannot interpret string '{x}' as complex value(s)"
                    )

        else:
            # For single values
            return complex(x)

    except Exception as e:
        logger.error(f"Error computing supremum complex norm: {str(e)}")
        raise ValueError(f"Failed to compute supremum complex norm: {str(e)}")

check_non_negativity

check_non_negativity(x)

Check if the norm satisfies the non-negativity property.

For complex norms, this checks if the absolute value of the norm is non-negative.

Parameters

x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType] The input to check.

Returns

bool True if the norm is non-negative, False otherwise.

Source code in swarmauri_standard/norms/SupremumComplexNorm.py

def check_non_negativity(
    self, x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
) -> bool:
    """
    Check if the norm satisfies the non-negativity property.

    For complex norms, this checks if the absolute value of the norm is non-negative.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
        The input to check.

    Returns
    -------
    bool
        True if the norm is non-negative, False otherwise.
    """
    try:
        norm_value = self.compute(x)
        # For complex numbers, we check if the absolute value is non-negative
        # (which is always true for a properly implemented complex norm)
        return abs(norm_value) >= 0
    except Exception as e:
        logger.error(f"Error checking non-negativity: {str(e)}")
        return False

check_definiteness

check_definiteness(x)

Check if the input is the zero vector.

For the definiteness property: norm(x) = 0 if and only if x = 0.

Parameters

x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType] The input to check.

Returns

bool True if the input is the zero vector, False otherwise.

Source code in swarmauri_standard/norms/SupremumComplexNorm.py

def check_definiteness(
    self, x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
) -> bool:
    """
    Check if the input is the zero vector.

    For the definiteness property: norm(x) = 0 if and only if x = 0.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
        The input to check.

    Returns
    -------
    bool
        True if the input is the zero vector, False otherwise.
    """
    try:
        # Check if x is zero
        is_zero = False

        if isinstance(x, (list, tuple, np.ndarray)):
            is_zero = all(abs(complex(val)) < 1e-10 for val in x)
        elif hasattr(x, "to_array"):
            array_form = x.to_array()
            is_zero = all(abs(complex(val)) < 1e-10 for val in array_form)
        elif callable(x):
            # For callable types, sample at several points
            domain = np.linspace(-1, 1, 10)
            is_zero = all(abs(complex(x(t))) < 1e-10 for t in domain)
        elif isinstance(x, str):
            try:
                complex_val = complex(x)
                is_zero = abs(complex_val) < 1e-10
            except Exception:
                is_zero = False
        else:
            is_zero = abs(complex(x)) < 1e-10

        return is_zero

    except Exception as e:
        logger.error(f"Error checking definiteness: {str(e)}")
        return False

check_triangle_inequality

check_triangle_inequality(x, y)

Check if the norm satisfies the triangle inequality.

The triangle inequality states that norm(x + y) <= norm(x) + norm(y).

Parameters

x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType] The first input. y : Union[VectorType, MatrixType, SequenceType, StringType, CallableType] The second input.

Returns

bool True if the norm satisfies the triangle inequality, False otherwise.

Source code in swarmauri_standard/norms/SupremumComplexNorm.py

def check_triangle_inequality(
    self,
    x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
    y: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
) -> bool:
    """
    Check if the norm satisfies the triangle inequality.

    The triangle inequality states that norm(x + y) <= norm(x) + norm(y).

    Parameters
    ----------
    x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
        The first input.
    y : Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
        The second input.

    Returns
    -------
    bool
        True if the norm satisfies the triangle inequality, False otherwise.
    """
    try:
        # Handle different types of inputs for addition
        if (
            isinstance(x, (list, tuple))
            and isinstance(y, (list, tuple))
            and len(x) == len(y)
        ):
            # For sequence types of the same length
            x_plus_y = [complex(x[i]) + complex(y[i]) for i in range(len(x))]
        elif (
            isinstance(x, np.ndarray)
            and isinstance(y, np.ndarray)
            and x.shape == y.shape
        ):
            # For numpy arrays of the same shape
            x_plus_y = x + y
        elif hasattr(x, "to_array") and hasattr(y, "to_array"):
            # For vector or matrix types with to_array method
            x_array = x.to_array()
            y_array = y.to_array()
            if len(x_array) == len(y_array):
                x_plus_y = [
                    complex(x_array[i]) + complex(y_array[i])
                    for i in range(len(x_array))
                ]
            else:
                raise TypeError("Inputs must have the same dimensions")
        elif callable(x) and callable(y):
            # For callable types, create a new callable that is the sum
            def sum_function(t):
                return complex(x(t)) + complex(y(t))

            x_plus_y = sum_function
        else:
            # For single values or incompatible types
            try:
                x_plus_y = complex(x) + complex(y)
            except TypeError:
                raise TypeError("Inputs cannot be added")

        # Compute norms
        norm_x = abs(self.compute(x))
        norm_y = abs(self.compute(y))
        norm_x_plus_y = abs(self.compute(x_plus_y))

        # Check triangle inequality
        # Use a small epsilon for floating-point comparison
        epsilon = 1e-10
        return norm_x_plus_y <= norm_x + norm_y + epsilon

    except Exception as e:
        logger.error(f"Error checking triangle inequality: {str(e)}")
        return False

check_absolute_homogeneity

check_absolute_homogeneity(x, scalar)

Check if the norm satisfies the absolute homogeneity property.

The absolute homogeneity property states that norm(ax) = |a|norm(x) for scalar a.

Parameters

x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType] The input. scalar : complex The scalar value.

Returns

bool True if the norm satisfies the absolute homogeneity property, False otherwise.

Source code in swarmauri_standard/norms/SupremumComplexNorm.py

def check_absolute_homogeneity(
    self,
    x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
    scalar: complex,
) -> bool:
    """
    Check if the norm satisfies the absolute homogeneity property.

    The absolute homogeneity property states that norm(a*x) = |a|*norm(x) for scalar a.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
        The input.
    scalar : complex
        The scalar value.

    Returns
    -------
    bool
        True if the norm satisfies the absolute homogeneity property, False otherwise.
    """
    try:
        # Convert scalar to complex
        scalar_complex = complex(scalar)

        # Handle different types of inputs for scalar multiplication
        if isinstance(x, (list, tuple)):
            # For sequence types
            scalar_times_x = [scalar_complex * complex(val) for val in x]
        elif isinstance(x, np.ndarray):
            # For numpy arrays
            scalar_times_x = scalar_complex * x
        elif hasattr(x, "to_array"):
            # For vector or matrix types with to_array method
            x_array = x.to_array()
            scalar_times_x = [scalar_complex * complex(val) for val in x_array]
        elif callable(x):
            # For callable types, create a new callable that is the scaled function
            def scaled_function(t):
                return scalar_complex * complex(x(t))

            scalar_times_x = scaled_function
        else:
            # For single values
            scalar_times_x = scalar_complex * complex(x)

        # Compute norms
        norm_x = abs(self.compute(x))
        norm_scalar_times_x = abs(self.compute(scalar_times_x))
        abs_scalar = abs(scalar_complex)

        # Check absolute homogeneity
        # Use a small epsilon for floating-point comparison
        epsilon = 1e-10
        return abs(norm_scalar_times_x - abs_scalar * norm_x) < epsilon

    except Exception as e:
        logger.error(f"Error checking absolute homogeneity: {str(e)}")
        return False

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS	DESCRIPTION
Callable	A decorator function that registers the model class. TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

PARAMETER	DESCRIPTION
resource_type	The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used. TYPE: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None
type_name	An optional custom type name for the subtype. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Callable	A decorator function that registers the subtype. TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(toml_content))
    except tomllib.TOMLDecodeError as e:
        raise ValueError(f"Invalid TOML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into TOML
    return toml.dumps(filtered_data)

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger