voiage.schema.ValueArray

Container for probabilistic sensitivity analysis net-benefit samples.

Attributes

dataset : xarray.Dataset Canonical xarray Dataset with n_samples and n_strategies dimensions and a net_benefit data variable.

Examples

>>> import numpy as np >>> from voiage.schema import ValueArray >>> values = np.array([[10.0, 12.0], [11.0, 9.5]]) >>> va = ValueArray.from_numpy(values, [“A”, “B”]) >>> va.n_samples, va.n_strategies (2, 2)

Methods

from_dataset

from_dataset([positional or keyword] cls: None = None, [positional or keyword] dataset: xr.Dataset = None) -> ValueArray

Create a ValueArray from a canonical xarray Dataset.

Parameters:

• cls
• dataset xr.Dataset

Returns: ValueArray

to_dataset

to_dataset([positional or keyword] self: ValueArray = None) -> xr.Dataset

Return a deep copy of the canonical xarray Dataset.

Parameters:

• self ValueArray

Returns: xr.Dataset

copy

copy([positional or keyword] self: ValueArray = None) -> ValueArray

Return a deep copy of the ValueArray.

Parameters:

• self ValueArray

Returns: ValueArray

get_strategy_index

get_strategy_index([positional or keyword] self: ValueArray = None, [positional or keyword] strategy_name: str = None) -> int

Return the integer index for a strategy name.

Parameters:

• self ValueArray
• strategy_name str

Returns: int

slice_by_strategies

slice_by_strategies([positional or keyword] self: ValueArray = None, [positional or keyword] strategy_names: Sequence[str] = None) -> ValueArray

Return a new ValueArray containing only the requested strategies.

Parameters:

• self ValueArray
• strategy_names Sequence[str]

Returns: ValueArray

from_numpy

from_numpy([positional or keyword] cls: None = None, [positional or keyword] values: Union[np.ndarray, jnp.ndarray] = None, [positional or keyword] strategy_names: list[str] | None = None) -> ValueArray

Create a ValueArray from a numpy or JAX array.

Args: values: A 2D array of shape (n_samples, n_strategies). Supports both NumPy and JAX arrays. strategy_names: Optional list of strategy names

Returns

ValueArray: A new ValueArray instance

Parameters:

• cls
• values Union[np.ndarray, jnp.ndarray]
• strategy_names list[str] | None (default: None)

Returns: ValueArray

from_numpy_perspectives

from_numpy_perspectives([positional or keyword] cls: None = None, [positional or keyword] values: Union[np.ndarray, jnp.ndarray] = None, [positional or keyword] strategy_names: list[str] | None = None, [positional or keyword] perspective_names: list[str] | None = None) -> ValueArray

Create a multi-perspective ValueArray from a 3D array.

Parameters

values : numpy.ndarray or jax.numpy.ndarray Net-benefit values with shape (n_samples, n_strategies, n_perspectives). strategy_names : list[str], optional Strategy labels aligned to the second dimension. perspective_names : list[str], optional Perspective labels aligned to the third dimension.

Returns

ValueArray ValueArray with a perspective dimension.

Parameters:

• cls
• values Union[np.ndarray, jnp.ndarray]
• strategy_names list[str] | None (default: None)
• perspective_names list[str] | None (default: None)

Returns: ValueArray

from_jax

from_jax([positional or keyword] cls: None = None, [positional or keyword] values: jnp.ndarray = None, [positional or keyword] strategy_names: list[str] | None = None) -> ValueArray

Create a ValueArray from a JAX array.

Args: values: A 2D JAX array of shape (n_samples, n_strategies) strategy_names: Optional list of strategy names

Returns

ValueArray: A new ValueArray instance

Parameters:

• cls
• values jnp.ndarray
• strategy_names list[str] | None (default: None)

Returns: ValueArray