types

data.types

Classes

Name	Description
AlgorithmResult	Result of an iterative algorithm (FIC, EIB, etc.).
BaseTimeSeries	Base time-series dataType.
ExperimentResult	Result from a complete experiment run.
ExplorationResult	Result of parameter exploration (grid search).
LegacySimulationResult	Legacy simulation result class. Use SimulationResult instead.
ObservationResult	Result from an observation pipeline with named outputs.
OptimizationResult	Result of gradient-based optimization.
SimulationResult	Wrapper for simulation result with attached observations.

AlgorithmResult

data.types.AlgorithmResult(
    name=None,
    state=None,
    history=None,
    pre_tuning=None,
    post_tuning=None,
    post_tuning_observations=None,
    n_iterations=None,
    hyperparameters=None,
    **kwargs,
)

Result of an iterative algorithm (FIC, EIB, etc.).

Provides structured access to algorithm outputs with consistent naming regardless of which algorithm was run.

Attributes

name : str Algorithm name state : Bunch Final state with tuned parameters history : Bunch Per-iteration tracking: parameters, observations, metrics Access pattern: history.{param_name}[i], history.{obs_name}[i] pre_tuning : SimulationResult Simulation BEFORE algorithm (for comparison) post_tuning : SimulationResult Simulation AFTER algorithm with attached observations n_iterations : int Number of iterations run hyperparameters : Bunch Algorithm hyperparameters used (eta, window_size, etc.) convergence : Bunch Convergence metrics (final values, deltas, etc.)

BaseTimeSeries

data.types.BaseTimeSeries(
    time,
    data,
    network=None,
    title='TimeSeries',
    sample_period=None,
    labels_dimensions={},
    units=None,
)

Base time-series dataType.

Attributes

Name	Description
sample_period_ms	:returns sample_period is ms
sample_rate	:returns samples per second [Hz]

Methods

Name	Description
convert_units	Convert units for a specific dimension and return a new TimeSeries.
copy	Return a deep copy of the current instance.
duplicate	Fast shallow-copy-based duplication with attribute update.
summary_info	Gather scientifically interesting summary information from an instance of this datatype.

convert_units

data.types.BaseTimeSeries.convert_units(dimension, target_unit)

Convert units for a specific dimension and return a new TimeSeries.

Parameters:

dimension : str Dimension to convert (‘time’, ‘state’, ‘region’, ‘mode’) target_unit : str Target unit to convert to

Returns:

TimeSeries New TimeSeries with converted values

copy

data.types.BaseTimeSeries.copy()

Return a deep copy of the current instance.

duplicate

data.types.BaseTimeSeries.duplicate(**kwargs)

Fast shallow-copy-based duplication with attribute update.

summary_info

data.types.BaseTimeSeries.summary_info()

Gather scientifically interesting summary information from an instance of this datatype.

ExperimentResult

data.types.ExperimentResult(results=None, experiment_name=None, **kwargs)

Result from a complete experiment run.

Wraps the raw results from code execution and provides: - Tree-structured view showing outputs with their contents - Schema-aligned access (results.integration.main, results.algorithms.fic, etc.)

ExplorationResult

data.types.ExplorationResult(
    name=None,
    grid=None,
    results=None,
    axes=None,
    observable=None,
    **kwargs,
)

Result of parameter exploration (grid search).

A thin wrapper around tvboptim exploration outputs that provides: - Access to raw results (flat or grid-shaped) - Axis information for parameter values - Utility methods for finding optimal points and slicing

Designed to work with tvboptim’s Space and ParallelResult directly, while also supporting other exploration backends.

Attributes

name : str Exploration name grid : Space Parameter grid specification (tvboptim Space object) results : jnp.ndarray Observable values at each grid point (flat or grid-shaped) axes : list List of axis info (Bunch with name, lo, hi, n, values) observable : str Name of observable computed optimal : Bunch Best point found (parameters, value, index) shape : tuple Grid shape derived from axes

Methods

Name	Description
as_grid	Reshape flat results to grid shape.
slice	Get a slice of results with some parameters fixed.

as_grid

data.types.ExplorationResult.as_grid()

Reshape flat results to grid shape.

Returns

jnp.ndarray Results reshaped to (n_axis1, n_axis2, …) matching axes order

slice

data.types.ExplorationResult.slice(**fixed_params)

Get a slice of results with some parameters fixed.

Example: result.slice(G=0.5) returns 1D slice at G=0.5

LegacySimulationResult

data.types.LegacySimulationResult()

Legacy simulation result class. Use SimulationResult instead.

ObservationResult

data.types.ObservationResult(**kwargs)

Result from an observation pipeline with named outputs.

Exposes pipeline outputs as attributes (e.g., result.psd, result.frequencies) while maintaining NativeSolution-like interface (.data, .time, .dt).

Attributes

Name	Description
data	Primary data output (alias for ys).
time	Time array (alias for ts).

OptimizationResult

data.types.OptimizationResult(
    name=None,
    state=None,
    history=None,
    simulation=None,
    n_steps=None,
    hyperparameters=None,
    **kwargs,
)

Result of gradient-based optimization.

Provides structured access to optimization outputs including loss trajectory, parameter evolution, and final simulation.

Attributes

name : str Optimization/loss function name state : Bunch Final optimized state (alias: fitted_params) history : Bunch Per-step tracking: loss values, states, gradients Access: history.loss[i], history.state[i] simulation : SimulationResult Post-optimization simulation with attached observations loss_trajectory : jnp.ndarray Loss values at each step (convenience accessor) n_steps : int Number of optimization steps final_loss : float Final loss value hyperparameters : Bunch Optimizer settings (learning_rate, algorithm, etc.)

SimulationResult

data.types.SimulationResult(result=None, observations=None, **kwargs)

Wrapper for simulation result with attached observations.

Mirrors the YAML structure by attaching observations to the simulation they derive from. This provides consistent access regardless of mode.

Attributes

data : jnp.ndarray Raw simulation data (time, state, nodes) time : jnp.ndarray Time vector observations : Bunch Computed observations from this simulation (bold, fc, etc.)