upxo.pxtal.twinned_simple_3d.selfrepr_qualification module

selfrepr_qualification.py

Threshold-based qualification of representativeness-assessment results (twinned_simple_3d’s Self Repr.-1 page): a subset’s morphological parameter is “qualified” under a given representativeness metric when that metric’s score is at or above a user-set threshold.

upxo.pxtal.twinned_simple_3d.selfrepr_qualification.qualifies(value, threshold)

A representativeness score qualifies when it’s >= threshold (both are “higher = more representative” scores, so this is a plain one-sided comparison).

upxo.pxtal.twinned_simple_3d.selfrepr_qualification.build_param_legend(selected_params, param_labels, param_abbrevs)

Maps each selected morphological parameter to its fixed abbreviation (e.g. “A” for Area, “AR” for Aspect Ratio – from selfrepr_morphology.MORPH_PARAM_ABBREV, NOT a sequential A/B/C tied to selection order, which read as arbitrary/confusing) and returns the ordered [(abbrev, param_key, param_label), …] list plus a formatted legend string (“A = Area, AR = Aspect Ratio, …”) – both the table-building code and the on-screen legend line use the SAME ordering, so the abbreviations always agree.

upxo.pxtal.twinned_simple_3d.selfrepr_qualification.build_metric_legend(selected_metrics, metric_labels)

Maps each selected representativeness metric to a positional “RM1”, “RM2”, … abbreviation (metric names like “Wasserstein Similarity” are too long for a table header) and returns the ordered [(abbrev, metric_key, metric_label), …] list plus a formatted legend string (“RM1 = Wasserstein Similarity, …”).

upxo.pxtal.twinned_simple_3d.selfrepr_qualification.build_qualification_table(assessment, selected_params, selected_metrics, thresholds, subsets, param_labels, param_abbrevs, metric_labels)

Builds the Block [8] qualification table: one row per (subset, parameter) pair, one column per selected metric.

Parameters:

• assessment (dict[(subset_index, param_key), dict[metric_key, float]]) – Block [6]’s “Assess” output – pipeline['selfrepr1_assessment'].

• selected_params (list of str) – Morphological parameter keys, in the order rows are grouped.

• selected_metrics (list of str) – Representativeness metric keys, in column order (“RM1”, “RM2”, …).

• thresholds (dict[(metric_key, param_key), float]) – Per-(metric, parameter) qualification threshold.

• subsets (list of dict) – Each with ‘index’ (tuple) and ‘centroid_um’ (tuple) – e.g. the tiles from subsetting_2d.generate_subset_tiles_2d plus a precomputed ‘centroid_um’.

• param_labels (dict[str, str]) – Display label per parameter key (e.g. selfrepr_morphology.MORPH_PARAM_LABELS).

• param_abbrevs (dict[str, str]) – Fixed abbreviation per parameter key (e.g. selfrepr_morphology.MORPH_PARAM_ABBREV).

• metric_labels (dict[str, str]) – Display label per metric key (e.g. {k: label for k, (label, _fn) in representativeness_metrics.METRIC_REGISTRY.items()}).

Returns:

(rows, legend) – rows: one dict per (subset, parameter), keys ‘subset’, ‘centroid’, ‘row_name’ (the parameter’s abbreviation), plus one bool (or None if not computable) per metric key in selected_metrics. legend: “MP = Morphological Parameter (A = Area, …). RM = Representativeness Metric (RM1 = Wasserstein Similarity, …).”

Return type:

(list of dict, str)

upxo.pxtal.twinned_simple_3d.selfrepr_qualification.rank_subsets_by_qualification_count(assessment, metric_key, selected_params, thresholds, subsets, default_threshold=0.6)

Ranks every sub-set by how many of selected_params it QUALIFIES on, for ONE representativeness metric – “which sub-set looks most like the parent domain across the most properties, under this one metric” (as opposed to [8]’s table, which shows every metric x parameter pair without collapsing them into a single ranking).

Parameters:

• assessment (dict[(subset_index, param_key), dict[metric_key, float]]) – Block [6]’s “Assess” output – pipeline['selfrepr1_assessment'].

• metric_key (str) – The single representativeness metric to rank by.

• selected_params (list of str) – Morphological parameter keys to consider.

• thresholds (dict[(metric_key, param_key), float]) – Per-(metric, parameter) qualification threshold – typically [8]’s qual_threshold_vars. A (metric_key, param_key) pair with no configured threshold falls back to default_threshold, so ranking works even for a metric/parameter combination [8] was never configured with.

• subsets (list of dict) – Each with ‘index’ (tuple) and ‘centroid_um’ (tuple).

• default_threshold (float) – Fallback threshold for any (metric_key, param_key) pair not present in thresholds.

Returns:

• list of dict, sorted by ‘score’ descending (ties broken by ‘subset’

• index for a stable, reproducible order) –

  {‘subset’: (i, j), ‘centroid’: (x, y), ‘score’: int,

  ’total’: int, ‘qualifying_params’: [param_key, …]}