parameter

Parameter

Bases: Leaf

Parameter that can be changed at runtime without recompilation.

Parameters are symbolic variables with initial values that can be updated through the problem's parameter dictionary. They allow for efficient parameter sweeps without needing to recompile the optimization problem.

Example

obs_center = ox.Parameter("obs_center", shape=(3,), value=np.array([1.0, 0.0, 0.0]))

Later: problem.parameters["obs_center"] = new_value

Source code in openscvx/symbolic/expr/parameter.py

class Parameter(Leaf):
    """Parameter that can be changed at runtime without recompilation.

    Parameters are symbolic variables with initial values that can be updated
    through the problem's parameter dictionary. They allow for efficient
    parameter sweeps without needing to recompile the optimization problem.

    Example:
        obs_center = ox.Parameter("obs_center", shape=(3,), value=np.array([1.0, 0.0, 0.0]))
        # Later: problem.parameters["obs_center"] = new_value
    """

    def __init__(
        self,
        name: str,
        shape: tuple = (),
        value: Union[float, int, np.ndarray, None] = None,
    ):
        """Initialize a Parameter node.

        Args:
            name: Name identifier for the parameter
            shape: Shape of the parameter (default: scalar)
            value: Initial value for the parameter (required)
        """
        super().__init__(name, shape)
        if value is None:
            raise ValueError(f"Parameter '{name}' requires an initial value")
        self.value = np.asarray(value, dtype=float)

    def _hash_into(self, hasher: "hashlib._Hash") -> None:
        """Hash Parameter by its shape only (value-invariant).

        Parameters are hashed by shape only, not by value. This allows the same
        compiled solver to be reused across parameter sweeps - only the structure
        matters for compilation, not the actual values.

        Args:
            hasher: A hashlib hash object to update
        """
        hasher.update(b"Parameter")
        hasher.update(str(self._shape).encode())

__init__(name: str, shape: tuple = (), value: Union[float, int, np.ndarray, None] = None)

Initialize a Parameter node.

Parameters:

Name	Type	Description	Default
name	str	Name identifier for the parameter	required
shape	tuple	Shape of the parameter (default: scalar)	()
value	Union[float, int, ndarray, None]	Initial value for the parameter (required)	None

Source code in openscvx/symbolic/expr/parameter.py

def __init__(
    self,
    name: str,
    shape: tuple = (),
    value: Union[float, int, np.ndarray, None] = None,
):
    """Initialize a Parameter node.

    Args:
        name: Name identifier for the parameter
        shape: Shape of the parameter (default: scalar)
        value: Initial value for the parameter (required)
    """
    super().__init__(name, shape)
    if value is None:
        raise ValueError(f"Parameter '{name}' requires an initial value")
    self.value = np.asarray(value, dtype=float)

ParameterSpec

Bases: BaseModel

Validates Parameter configuration from YAML/JSON/dict input.

Source code in openscvx/symbolic/expr/parameter.py

class ParameterSpec(BaseModel):
    """Validates Parameter configuration from YAML/JSON/dict input."""

    name: str
    shape: List[int]
    value: Any

    model_config = ConfigDict(extra="forbid")

    def to_parameter(self) -> "Parameter":
        return Parameter(
            self.name,
            shape=tuple(self.shape),
            value=np.asarray(self.value, dtype=float),
        )