Class swarmauri_standard.inner_products.SobolevH1InnerProduct.SobolevH1InnerProduct

swarmauri_standard.inner_products.SobolevH1InnerProduct.SobolevH1InnerProduct

SobolevH1InnerProduct(alpha=1.0, beta=1.0, **kwargs)

Bases: InnerProductBase

Implementation of the H1 Sobolev space inner product.

The H1 Sobolev inner product combines the L2 inner product of functions with the L2 inner product of their first derivatives. This makes it particularly useful for problems where both function values and their smoothness (derivatives) are important.

Attributes

type : Literal["SobolevH1InnerProduct"] The type identifier for this inner product alpha : float Weight for the function value component (L2 norm part) beta : float Weight for the derivative component (H1 semi-norm part)

Initialize the SobolevH1InnerProduct with specified weights.

Parameters

alpha : float, optional Weight for the function value component, by default 1.0 beta : float, optional Weight for the derivative component, by default 1.0

Source code in swarmauri_standard/inner_products/SobolevH1InnerProduct.py

def __init__(self, alpha: float = 1.0, beta: float = 1.0, **kwargs):
    """
    Initialize the SobolevH1InnerProduct with specified weights.

    Parameters
    ----------
    alpha : float, optional
        Weight for the function value component, by default 1.0
    beta : float, optional
        Weight for the derivative component, by default 1.0
    """
    # Pass alpha and beta to the parent constructor
    kwargs["alpha"] = alpha
    kwargs["beta"] = beta
    super().__init__(**kwargs)
    logger.info(
        f"Initialized SobolevH1InnerProduct with alpha={alpha}, beta={beta}"
    )

type class-attribute instance-attribute

type = 'SobolevH1InnerProduct'

alpha instance-attribute

alpha

beta instance-attribute

beta

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 = INNER_PRODUCT.value

version class-attribute instance-attribute

version = '0.1.0'

compute

compute(a, b)

Compute the H1 Sobolev inner product between two objects.

For functions f and g, computes: _H1 = alpha * ∫ f(x)·g(x) dx + beta * ∫ f'(x)·g'(x) dx

Parameters

a : Union[Vector, Matrix, Callable] The first object for inner product calculation b : Union[Vector, Matrix, Callable] The second object for inner product calculation

Returns

float The H1 inner product value

Raises

TypeError If the inputs are not of compatible types or don't provide derivative information

Source code in swarmauri_standard/inner_products/SobolevH1InnerProduct.py

def compute(
    self, a: Union[Vector, Matrix, Callable], b: Union[Vector, Matrix, Callable]
) -> float:
    """
    Compute the H1 Sobolev inner product between two objects.

    For functions f and g, computes:
    <f, g>_H1 = alpha * ∫ f(x)·g(x) dx + beta * ∫ f'(x)·g'(x) dx

    Parameters
    ----------
    a : Union[Vector, Matrix, Callable]
        The first object for inner product calculation
    b : Union[Vector, Matrix, Callable]
        The second object for inner product calculation

    Returns
    -------
    float
        The H1 inner product value

    Raises
    ------
    TypeError
        If the inputs are not of compatible types or don't provide derivative information
    """
    logger.debug(f"Computing H1 inner product between {type(a)} and {type(b)}")

    # Handle different input types
    if isinstance(a, Callable) and isinstance(b, Callable):
        return self._compute_for_functions(a, b)
    elif isinstance(a, np.ndarray) and isinstance(b, np.ndarray):
        return self._compute_for_arrays(a, b)
    elif isinstance(a, Vector) and isinstance(b, Vector):
        # Use the concrete Vector class
        return self._compute_for_vectors(a, b)
    else:
        error_msg = (
            f"Cannot compute H1 inner product for types {type(a)} and {type(b)}"
        )
        logger.error(error_msg)
        raise TypeError(error_msg)

check_conjugate_symmetry

check_conjugate_symmetry(a, b)

Check if the H1 inner product satisfies the conjugate symmetry property.

Parameters

a : Union[Vector, Matrix, Callable] The first object b : Union[Vector, Matrix, Callable] The second object

Returns

bool True if conjugate symmetry holds, False otherwise

Source code in swarmauri_standard/inner_products/SobolevH1InnerProduct.py

def check_conjugate_symmetry(
    self, a: Union[Vector, Matrix, Callable], b: Union[Vector, Matrix, Callable]
) -> bool:
    """
    Check if the H1 inner product satisfies the conjugate symmetry property.

    Parameters
    ----------
    a : Union[Vector, Matrix, Callable]
        The first object
    b : Union[Vector, Matrix, Callable]
        The second object

    Returns
    -------
    bool
        True if conjugate symmetry holds, False otherwise
    """
    logger.debug(f"Checking conjugate symmetry for {type(a)} and {type(b)}")

    # Compute <a, b> and <b, a>
    inner_ab = self.compute(a, b)
    inner_ba = self.compute(b, a)

    # For real-valued functions, <a, b> should equal <b, a>
    # For complex-valued functions, <a, b> should equal conjugate(<b, a>)
    if np.iscomplex(inner_ab) or np.iscomplex(inner_ba):
        is_symmetric = np.isclose(inner_ab, np.conj(inner_ba))
    else:
        is_symmetric = np.isclose(inner_ab, inner_ba)

    logger.debug(
        f"Conjugate symmetry check result: {is_symmetric} (<a,b>={inner_ab}, <b,a>={inner_ba})"
    )
    return is_symmetric

check_linearity_first_argument

check_linearity_first_argument(a1, a2, b, alpha, beta)

Check if the H1 inner product satisfies linearity in the first argument.

Verifies if = alpha + beta

Parameters

a1 : Union[Vector, Matrix, Callable] First component of the first argument a2 : Union[Vector, Matrix, Callable] Second component of the first argument b : Union[Vector, Matrix, Callable] The second object alpha : float Scalar multiplier for a1 beta : float Scalar multiplier for a2

Returns

bool True if linearity in the first argument holds, False otherwise

Raises

TypeError If the inputs cannot be linearly combined

Source code in swarmauri_standard/inner_products/SobolevH1InnerProduct.py

def check_linearity_first_argument(
    self,
    a1: Union[Vector, Matrix, Callable],
    a2: Union[Vector, Matrix, Callable],
    b: Union[Vector, Matrix, Callable],
    alpha: float,
    beta: float,
) -> bool:
    """
    Check if the H1 inner product satisfies linearity in the first argument.

    Verifies if <alpha*a1 + beta*a2, b> = alpha*<a1, b> + beta*<a2, b>

    Parameters
    ----------
    a1 : Union[Vector, Matrix, Callable]
        First component of the first argument
    a2 : Union[Vector, Matrix, Callable]
        Second component of the first argument
    b : Union[Vector, Matrix, Callable]
        The second object
    alpha : float
        Scalar multiplier for a1
    beta : float
        Scalar multiplier for a2

    Returns
    -------
    bool
        True if linearity in the first argument holds, False otherwise

    Raises
    ------
    TypeError
        If the inputs cannot be linearly combined
    """
    logger.debug(
        f"Checking linearity in first argument with alpha={alpha}, beta={beta}"
    )

    # Compute individual inner products
    inner_a1b = self.compute(a1, b)
    inner_a2b = self.compute(a2, b)

    # Compute the right side of the linearity equation
    right_side = alpha * inner_a1b + beta * inner_a2b

    # Compute the left side by creating the linear combination first
    # This implementation depends on the type of inputs
    if isinstance(a1, np.ndarray) and isinstance(a2, np.ndarray):
        linear_combo = alpha * a1 + beta * a2
        left_side = self.compute(linear_combo, b)
    elif isinstance(a1, Callable) and isinstance(a2, Callable):
        # Create a new function representing the linear combination
        def linear_combo(x):
            val1, der1 = a1(x)
            val2, der2 = a2(x)
            return (alpha * val1 + beta * val2, alpha * der1 + beta * der2)

        left_side = self.compute(linear_combo, b)
    elif hasattr(a1, "get_values") and hasattr(a2, "get_values"):
        # This is a simplified approach - a real implementation would need
        # to create a proper Vector object that combines a1 and a2
        a1_values = a1.get_values()
        a2_values = a2.get_values()
        a1_derivatives = a1.get_derivatives()
        a2_derivatives = a2.get_derivatives()

        # Create a mock object with the combined values
        class CombinedVector:
            def get_values(self):
                return alpha * a1_values + beta * a2_values

            def get_derivatives(self):
                return alpha * a1_derivatives + beta * a2_derivatives

        linear_combo = CombinedVector()
        left_side = self.compute(linear_combo, b)
    else:
        error_msg = (
            f"Cannot create linear combination of types {type(a1)} and {type(a2)}"
        )
        logger.error(error_msg)
        raise TypeError(error_msg)

    # Check if the two sides are approximately equal
    is_linear = np.isclose(left_side, right_side)
    logger.debug(
        f"Linearity check result: {is_linear} (left side: {left_side}, right side: {right_side})"
    )

    return is_linear

check_positivity

check_positivity(a)

Check if the H1 inner product satisfies the positivity property.

Verifies if >= 0 and = 0 iff a = 0

Parameters

a : Union[Vector, Matrix, Callable] The object to check positivity for

Returns

bool True if positivity holds, False otherwise

Source code in swarmauri_standard/inner_products/SobolevH1InnerProduct.py

def check_positivity(self, a: Union[Vector, Matrix, Callable]) -> bool:
    """
    Check if the H1 inner product satisfies the positivity property.

    Verifies if <a, a> >= 0 and <a, a> = 0 iff a = 0

    Parameters
    ----------
    a : Union[Vector, Matrix, Callable]
        The object to check positivity for

    Returns
    -------
    bool
        True if positivity holds, False otherwise
    """
    logger.debug(f"Checking positivity for {type(a)}")

    # Compute <a, a>
    inner_aa = self.compute(a, a)

    # Check if it's non-negative
    is_non_negative = inner_aa >= 0

    # Check if it's zero only when a is zero
    is_zero_for_zero_only = True

    # This check depends on the type of input
    if isinstance(a, np.ndarray):
        # For arrays, check if a is non-zero when inner product is non-zero
        if np.isclose(inner_aa, 0) and not np.allclose(a, 0):
            is_zero_for_zero_only = False
    elif isinstance(a, Callable):
        # For functions, this is hard to verify in general
        # We would need to sample the function at multiple points
        # For now, we assume this part of the check passes
        pass
    elif hasattr(a, "get_values"):
        # For vector objects, check values
        a_values = a.get_values()
        a_derivatives = a.get_derivatives()
        if np.isclose(inner_aa, 0) and (
            not np.allclose(a_values, 0) or not np.allclose(a_derivatives, 0)
        ):
            is_zero_for_zero_only = False

    is_positive = is_non_negative and is_zero_for_zero_only
    logger.debug(
        f"Positivity check result: {is_positive} (inner product: {inner_aa})"
    )

    return is_positive

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