valska.notebook_helpers
Reusable helpers for validation notebooks.
Functions
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Parse Airy sweep points and compute per-point Bayes factors. |
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Plot $Deltaln Z$ and $ln Z$ diagnostics from sweep report summary. |
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Plot complementary signal-fit chain comparison directly from point run dirs. |
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Run Airy BaNTER summary and display a wide, non-truncating summary table. |
- valska.notebook_helpers.extract_airy_point_bayes_factors(chains_dir: str | Path, sweep_relative_dir: str = 'airy_diam14m/GSM_plus_GLEAM/_sweeps/sweep_airy_init') dict[str, Any]
Parse Airy sweep points and compute per-point Bayes factors.
- valska.notebook_helpers.plot_report_summary_diagnostics(sweep_dir: str | Path, title_fs: int = 24, axis_fs: int = 18, tick_fs: int = 14, legend_fs: int = 14) tuple[dict[str, Any], pandas.DataFrame]
Plot $Deltaln Z$ and $ln Z$ diagnostics from sweep report summary.
- valska.notebook_helpers.plot_signal_fit_chain_comparison(points: list[dict[str, Any]], title: str = 'Sweep signal fit chain comparison', title_fs: int = 24, axis_fs: int = 18, tick_fs: int = 14, legend_fs: int = 11) Any
Plot complementary signal-fit chain comparison directly from point run dirs.
- valska.notebook_helpers.run_airy_banter_summary(points: list[dict[str, Any]]) dict[str, Any]
Run Airy BaNTER summary and display a wide, non-truncating summary table.