Bridge.SdAdapter

Bridge.SdAdapter#

Bridge.SdAdapter.py

Converts a legacy SerialData (sd) into a library SecSaxsData (ssd). This is an incremental step on the data-object consolidation track defined in Rule 13 of copilot-guidelines.md (target: molass-legacy GUI → molass-library).

Only the corrected-sd → ssd direction is implemented here. The reverse (ssd → sd) is not needed yet.

make_ssd_from_corrected_sd(corrected_sd)#

Build a library SecSaxsData from a baseline-corrected legacy SerialData.

The input must be a corrected (baseline-subtracted) sd, i.e. corrected_sd.baseline_corrected is True. The returned ssd has its trimmed and corrected flags set so that quick_decomposition() can be called directly without a redundant corrected_copy() step.

Parameters:

corrected_sd (molass_legacy.SerialAnalyzer.SerialData.SerialData) – A trimmed, baseline-corrected legacy data object.

Returns:

Library data object ready for quick_decomposition().

Return type:

molass.DataObjects.SecSaxsData.SecSaxsData

decomposition_from_optimizer_params(fullopt, params, base_decomp)#

Build a library Decomposition from a legacy optimizer + flat params vector.

This is the model-dependent update path used by JobStateCanvas._update_decomposition_to_current(). It reuses the existing ComponentUtils.get_xr_ccurves dispatch table, which already handles all five models (EGH, SDM, EDM/CEDM, LKM).

Parameters:

  • fullopt (molass_legacy.Optimizer.BasicOptimizer.BasicOptimizer) – The live legacy optimizer instance. Used only for split_params_simple(params) and get_model_name() — no heavy computation is triggered.

  • params (array-like) – Flat optimizer parameter vector at the desired snapshot (e.g. demo_info[1][curr_index]).

  • base_decomp (molass.LowRank.Decomposition.Decomposition) – The base decomposition that owns the ssd. Its ssd is reused as the data container; only the component curves are replaced.

Returns:

A new Decomposition whose XR/UV component curves reflect params.

Return type:

molass.LowRank.Decomposition.Decomposition

make_ssd_from_dsets(dsets, sd)#

Build a library SecSaxsData from a legacy OptDataSets + trimmed SerialData.

Used by JobStateCanvas in the legacy GUI path, where only dsets (already corrected) and the trimmed sd (for q-values and wavelengths) are available — corrected_sd is not stored in that path.

Parameters:

  • dsets (molass_legacy.Optimizer.OptDataSets.OptDataSets) – The prepared optimizer dataset. Iterated as ((xr_curve, D), rg_curve, (uv_curve, U)).

  • sd (molass_legacy.SerialAnalyzer.SerialData.SerialData) – Trimmed (uncorrected) legacy data object — used only for qvector and absorbance.wl_vector.

Returns:

Library data object ready for quick_decomposition().

Return type:

molass.DataObjects.SecSaxsData.SecSaxsData