EAA workflows and tools for APS 12-ID

This package provides workflow controllers, active-learning engines, and SAXS acquisition adapters for automated experiments at APS 12-ID. The spatial SAXS implementation separates experiment orchestration from the Bayesian decision logic so the two parts can be used together or integrated independently.

Organization

src/eaa_aps12id/
├── task_managers/
│   └── spatial_saxs_sampling.py   # acquisition workflow and run control
└── tools/
    ├── spatial_saxs_sampling.py   # Bayesian active-learning engine
    ├── aps12id_saxs.py            # APS 12-ID SAXS acquisition adapter
    └── spatial_saxs.py            # simulated spatial SAXS acquisition

Spatial SAXS task manager

SpatialSAXSAdaptiveSamplingTaskManager

The task manager is the high-level experiment controller. It owns the acquisition_tool and is responsible for:

• enforcing the total measurement budget and per-call iteration limit;
• requesting initial and adaptive positions from the engine;
• acquiring SAXS data at the returned positions, in the returned order, using the acquisition_tool;
• batching the measured positions and q-intensity arrays into one engine update;
• forwarding non-position acquisition arguments; and
• publishing progress and posterior visualizations to the EAA WebUI.

The manager does not implement any logic like Gaussian process to suggest new measurements or accept new data. These logics are implemented in the engine_tool and the task manager only queries the tool for suggestions and updates. It creates a SpatialSAXSAdaptiveSamplingEngineTool by default, or accepts a supplied engine through the engine_tool constructor argument.

Calling run(...) performs the following loop:

1. Initialize the engine with the candidate positions and active-learning configuration.
2. Ask the engine for path-optimized initial positions.
3. Acquire the initial SAXS spectra and update the engine once with the complete batch.
4. Ask the engine for the next path-optimized batch, acquire it, and update the engine.
5. Repeat until the manager-owned measurement budget or iteration limit is reached.

Spatial SAXS active-learning engine

SpatialSAXSAdaptiveSamplingEngineTool

The engine is a stateful EAA BaseTool that owns the complete measurement-suggestion logic. It never collects data and has no acquisition backend.

Its exposed interface is:

• initialize(...): configure the candidate positions, preprocessing, peak-detection, GP, and acquisition parameters.
• suggest_initial_measurements(): select initial candidate positions with scrambled Sobol sampling and return them in a travel-optimized order.
• update(positions, q_values, intensities): preprocess one or more new spectra, update the peak dictionary, and refit the GP models once for the batch.
• suggest(n_suggestions=1): select the next eligible position or batch and return it in a travel-optimized order.

Peak detection and GP model

For every supplied SAXS measurement, the engine:

1. interpolates the raw spectrum onto a common log-spaced q grid;
2. estimates and subtracts a smooth spectral background;
3. detects peaks using configurable height, prominence, and log-q width criteria; and
4. records either integrated peak area or peak height as the modeled observable.

The engine can use authoritative known_peak_q_values, or it can build and update a dynamic peak dictionary from measured spectra. It fits an independent Gaussian process for each active peak using normalized spatial coordinates and transformed peak observables.

Acquisition function

For each eligible, unmeasured candidate, the engine computes

acquisition = normalized_uncertainty × (
    epsilon_acquisition
    + w_peak × normalized_peak_observable
    + w_g × normalized_spatial_gradient
)

The peak-observable term favors positions where an eligible peak is predicted to be strong, while the gradient term favors spatial boundaries and rapid changes. Peak maps can be concentration-gated and spatially blurred before scoring. Configurable exclusion radii prevent suggestions near measured points or near other members of the same batch. Periodic farthest-point exploration can be enabled with exploration_interval.

After selecting a batch, the engine orders it with a low-travel path beginning at the latest measured position. Initial suggestions are also path optimized.

Standalone engine usage

A custom workflow can use the engine with any data-collection backend:

from eaa_aps12id.tools import SpatialSAXSAdaptiveSamplingEngineTool

engine = SpatialSAXSAdaptiveSamplingEngineTool()
engine.initialize(
    candidate_positions=[
        [0.0, 0.0],
        [0.0, 1.0],
        [1.0, 0.0],
        [1.0, 1.0],
    ],
)

positions = engine.suggest_initial_measurements()
q_values, intensities = acquire_with_custom_backend(positions)
engine.update(positions, q_values, intensities)

while more_measurements_are_needed():
    positions = engine.suggest(n_suggestions=2)
    q_values, intensities = acquire_with_custom_backend(positions)
    engine.update(positions, q_values, intensities)

q_values and intensities may contain arrays of different lengths for different measurements. Each update must also include the corresponding spatial positions from the configured candidate set. Candidate-position columns are ordered (y, x); coordinates otherwise use the same unit and frame as the acquisition tool.

Acquisition tools

APS12IDSAXSAcquisitionTool

Connects the logic-driven workflow to the APS 12-ID SAXS data-acquisition MCP server. It requests a reduced data file, loads the q and intensity arrays, and validates the requested q coverage.

SimulatedSpatialSAXS

Loads measured SAXS data and metadata from disk, constructs a spatial interpolation backend, and returns simulated q-intensity arrays through the same acquire_saxs interface used by the task manager.