cleaning_summary_view

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Axis cleaning_summary_view on sub-layer L2_5_D_cleaning_effect_summary (layer l2_5).

Sub-layer

L2_5_D_cleaning_effect_summary

Axis metadata

  • Default: 'multi'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 4 option(s)

  • Future: 0 option(s)

Options

multi – operational

Render all three counts together.

L2.5.D cleaning effect view multi.

This option configures the cleaning_summary_view axis on the L2_5_D_cleaning_effect_summary sub-layer of L2.5; output is emitted under manifest.diagnostics/l2_5/L2_5_D_cleaning_effect_summary/ alongside the other selected views.

When to use

Default; full cleaning footprint summary.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: n_imputed_per_series, n_outliers_flagged, n_truncated_obs

Last reviewed 2026-05-05 by macroforecast author.

n_imputed_per_series – operational

Count of imputed cells per series.

L2.5.D cleaning effect view n_imputed_per_series.

This option configures the cleaning_summary_view axis on the L2_5_D_cleaning_effect_summary sub-layer of L2.5; output is emitted under manifest.diagnostics/l2_5/L2_5_D_cleaning_effect_summary/ alongside the other selected views.

When to use

Auditing imputation footprint; series with > 30% imputed cells warrant inspection.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: n_outliers_flagged, n_truncated_obs, multi

Last reviewed 2026-05-05 by macroforecast author.

n_outliers_flagged – operational

Count of outlier-flagged cells per series.

L2.5.D cleaning effect view n_outliers_flagged.

This option configures the cleaning_summary_view axis on the L2_5_D_cleaning_effect_summary sub-layer of L2.5; output is emitted under manifest.diagnostics/l2_5/L2_5_D_cleaning_effect_summary/ alongside the other selected views.

When to use

Auditing outlier-handler aggressiveness; very high counts may indicate threshold mis-calibration.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: n_imputed_per_series, n_truncated_obs, multi

Last reviewed 2026-05-05 by macroforecast author.

n_truncated_obs – operational

Count of observations dropped by L2.E frame-edge handling.

L2.5.D cleaning effect view n_truncated_obs.

This option configures the cleaning_summary_view axis on the L2_5_D_cleaning_effect_summary sub-layer of L2.5; output is emitted under manifest.diagnostics/l2_5/L2_5_D_cleaning_effect_summary/ alongside the other selected views.

When to use

Auditing edge truncation effects on the available sample size.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: n_imputed_per_series, n_outliers_flagged, multi

Last reviewed 2026-05-05 by macroforecast author.