valska.external_tools.bayeseor.report

Sweep-level post-processing reports for BayesEoR runs.

Functions

`generate_sweep_report`(*, sweep_dir[, ...])	Generate summary table(s) and plots for an existing sweep directory.
`parse_data_stats_evidence`(path)	Parse NS/INS log-evidence values from a BayesEoR `data-stats.dat` file.

Classes

`EvidenceValues`(ns_log_evidence, ...)	Evidence summary parsed from a BayesEoR `data-stats.dat` file.
`SweepPointReportRow`(perturb_parameter, ...)	Per-sweep-point summary row for report tables.
`SweepReportResult`(sweep_dir, out_dir, ...)	Paths and summary metadata for a generated sweep report.

class valska.external_tools.bayeseor.report.EvidenceValues(ns_log_evidence: float, ns_log_evidence_err: float | None, ins_log_evidence: float, ins_log_evidence_err: float | None, source_path: Path)

Evidence summary parsed from a BayesEoR data-stats.dat file.

ins_log_evidence: float

ins_log_evidence_err: float | None

ns_log_evidence: float

ns_log_evidence_err: float | None

source_path: Path

class valska.external_tools.bayeseor.report.SweepPointReportRow(perturb_parameter: str, perturb_frac: float, run_label: str, run_dir: str, status: str, signal_fit_ns_log_evidence: float | None, signal_fit_ns_log_evidence_err: float | None, signal_fit_ins_log_evidence: float | None, signal_fit_ins_log_evidence_err: float | None, no_signal_ns_log_evidence: float | None, no_signal_ns_log_evidence_err: float | None, no_signal_ins_log_evidence: float | None, no_signal_ins_log_evidence_err: float | None, selected_source: str, delta_log_evidence: float | None, bayes_factor_signal_over_no_signal: float | None, log10_bayes_factor_signal_over_no_signal: float | None, note: str | None)

Per-sweep-point summary row for report tables.

bayes_factor_signal_over_no_signal: float | None

delta_log_evidence: float | None

log10_bayes_factor_signal_over_no_signal: float | None

no_signal_ins_log_evidence: float | None

no_signal_ins_log_evidence_err: float | None

no_signal_ns_log_evidence: float | None

no_signal_ns_log_evidence_err: float | None

note: str | None

perturb_frac: float

perturb_parameter: str

run_dir: str

run_label: str

selected_source: str

signal_fit_ins_log_evidence: float | None

signal_fit_ins_log_evidence_err: float | None

signal_fit_ns_log_evidence: float | None

signal_fit_ns_log_evidence_err: float | None

status: str

class valska.external_tools.bayeseor.report.SweepReportResult(sweep_dir: Path, out_dir: Path, evidence_source: Literal['ns', 'ins'], rows_total: int, rows_complete: int, summary_csv: Path, summary_json: Path, delta_plot_png: Path | None, evidence_plot_png: Path | None, plot_analysis_results_png: Path | None, complete_analysis_json: Path | None, complete_analysis_csv: Path | None, complete_analysis_rows: list[dict[str, Any]])

Paths and summary metadata for a generated sweep report.

complete_analysis_csv: Path | None

complete_analysis_json: Path | None

complete_analysis_rows: list[dict[str, Any]]

delta_plot_png: Path | None

evidence_plot_png: Path | None

evidence_source: Literal['ns', 'ins']

out_dir: Path

plot_analysis_results_png: Path | None

rows_complete: int

rows_total: int

summary_csv: Path

summary_json: Path

sweep_dir: Path

valska.external_tools.bayeseor.report._compute_bf(delta_lnz: float) → tuple[float | None, float | None]

valska.external_tools.bayeseor.report._find_chain_root(run_dir: Path, hypothesis: Literal['signal_fit', 'no_signal']) → Path

valska.external_tools.bayeseor.report._find_single_mn_dir(hypothesis_output_dir: Path) → Path

valska.external_tools.bayeseor.report._parse_float_or_none(raw: str) → float | None

valska.external_tools.bayeseor.report._plot_delta_log_evidence(rows: list[SweepPointReportRow], out_path: Path) → None

valska.external_tools.bayeseor.report._plot_log_evidence_by_model(rows: list[SweepPointReportRow], *, out_path: Path, source: Literal['ns', 'ins']) → None

valska.external_tools.bayeseor.report._read_point_evidence(run_dir: Path, hypothesis: Literal['signal_fit', 'no_signal']) → EvidenceValues

valska.external_tools.bayeseor.report._read_point_perturbation(point: Mapping[str, Any]) → tuple[str, float]: Read perturbation metadata from current or legacy sweep manifests.

valska.external_tools.bayeseor.report._rows_to_dicts(rows: list[SweepPointReportRow]) → list[dict[str, Any]]

valska.external_tools.bayeseor.report._select_lnz(e: EvidenceValues, source: Literal['ns', 'ins']) → float

valska.external_tools.bayeseor.report._write_summary_csv(rows: list[SweepPointReportRow], out_path: Path) → None

valska.external_tools.bayeseor.report._write_summary_json(rows: list[SweepPointReportRow], out_path: Path) → None

valska.external_tools.bayeseor.report.generate_sweep_report(*, sweep_dir: Path, out_dir: Path | None = None, evidence_source: Literal['ns', 'ins'] = 'ins', make_plots: bool = True, include_plot_analysis_results: bool = False, include_complete_analysis_table: bool = False) → SweepReportResult: Generate summary table(s) and plots for an existing sweep directory.

valska.external_tools.bayeseor.report.parse_data_stats_evidence(path: Path) → EvidenceValues: Parse NS/INS log-evidence values from a BayesEoR data-stats.dat file.