TE Curve Verification Pipeline Mean-Centered Collage Diagnostics

Build TE Curve Verification Pipeline mean-centered collage diagnostics.

scripts.reports.analysis.build_track2_mean_centered_collage_report.build_argument_parser()[source]

Build the command-line argument parser.

Return type:

ArgumentParser

scripts.reports.analysis.build_track2_mean_centered_collage_report.parse_command_line_arguments()[source]

Parse command-line arguments.

Return type:

Namespace

scripts.reports.analysis.build_track2_mean_centered_collage_report.resolve_timestamped_output_paths(output_root, report_topic_root, report_date)[source]

Resolve timestamped output and report directories.

Parameters:
  • output_root (Path)

  • report_topic_root (Path)

  • report_date (str | None)

Return type:

tuple[str, Path, Path]

scripts.reports.analysis.build_track2_mean_centered_collage_report.compute_curve_mean_centering_metrics(entry_dictionary)[source]

Compute raw and mean-centered metrics for one predicted curve.

Parameters:

entry_dictionary (dict[str, Any])

Return type:

dict[str, float]

scripts.reports.analysis.build_track2_mean_centered_collage_report.compute_improvement_pct(raw_metric_value, adjusted_metric_value)[source]

Compute percentage improvement from one raw metric to one adjusted metric.

Parameters:
  • raw_metric_value (float)

  • adjusted_metric_value (float)

Return type:

float

scripts.reports.analysis.build_track2_mean_centered_collage_report.append_mean_centering_metrics(entry_dictionary)[source]

Return one entry enriched with mean-centering metrics.

Parameters:

entry_dictionary (dict[str, Any])

Return type:

dict[str, Any]

scripts.reports.analysis.build_track2_mean_centered_collage_report.summarize_mean_centering_metrics(entry_list)[source]

Summarize mean-centering metrics across one candidate/surface.

Parameters:

entry_list (list[dict[str, Any]])

Return type:

dict[str, float]

scripts.reports.analysis.build_track2_mean_centered_collage_report.save_mean_centered_candidate_collage(collage_path, candidate_id, selected_entry_list)[source]

Save one four-curve mean-centered collage for a candidate.

Parameters:
  • collage_path (Path)

  • candidate_id (str)

  • selected_entry_list (list[dict[str, Any]])

Return type:

None

scripts.reports.analysis.build_track2_mean_centered_collage_report.save_per_curve_metrics_csv(csv_path, entry_list)[source]

Save raw and mean-centered metrics for every evaluated curve.

Parameters:
  • csv_path (Path)

  • entry_list (list[dict[str, Any]])

Return type:

None

scripts.reports.analysis.build_track2_mean_centered_collage_report.save_candidate_metrics_csv(csv_path, candidate_summary_list)[source]

Save aggregate raw and mean-centered metrics for every candidate.

Parameters:
  • csv_path (Path)

  • candidate_summary_list (list[dict[str, Any]])

Return type:

None

scripts.reports.analysis.build_track2_mean_centered_collage_report.append_group_metric_table(report_line_list, group_summary_list)[source]

Append one mean-centered candidate metric table.

Parameters:
  • report_line_list (list[str])

  • group_summary_list (list[dict[str, Any]])

Return type:

None

scripts.reports.analysis.build_track2_mean_centered_collage_report.append_top_improvement_table(report_line_list, candidate_summary_list, row_count=12)[source]

Append top candidates by MAE improvement.

Parameters:
  • report_line_list (list[str])

  • candidate_summary_list (list[dict[str, Any]])

  • row_count (int)

Return type:

None

scripts.reports.analysis.build_track2_mean_centered_collage_report.build_report_markdown(report_path, output_directory, candidate_summary_list, group_list, candidate_metrics_csv_path, per_curve_metrics_csv_path, validation_summary_path)[source]

Build the Markdown report body.

Parameters:
  • report_path (Path)

  • output_directory (Path)

  • candidate_summary_list (list[dict[str, Any]])

  • group_list (list[ReportCandidateGroup])

  • candidate_metrics_csv_path (Path)

  • per_curve_metrics_csv_path (Path)

  • validation_summary_path (Path)

Return type:

str

scripts.reports.analysis.build_track2_mean_centered_collage_report.build_curve_key(entry_dictionary)[source]

Build a stable key for one curve entry.

Parameters:

entry_dictionary (dict[str, Any])

Return type:

tuple[str, str]

scripts.reports.analysis.build_track2_mean_centered_collage_report.load_source_collage_summary(summary_path)[source]

Load the source best-model collage summary.

Parameters:

summary_path (Path)

Return type:

dict[str, Any]

scripts.reports.analysis.build_track2_mean_centered_collage_report.run_track2_mean_centered_collage_report(arguments)[source]

Run the full TE Curve Verification Pipeline mean-centered collage report generation.

Parameters:

arguments (Namespace)

Return type:

dict[str, Any]

scripts.reports.analysis.build_track2_mean_centered_collage_report.main()[source]

Run the command-line entry point.

Return type:

None