weights_over_time_method

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Axis weights_over_time_method on sub-layer L4_5_E_ensemble_diagnostics (layer l4_5).

Sub-layer

L4_5_E_ensemble_diagnostics

Axis metadata

  • Default: 'stacked_area'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 3 option(s)

  • Future: 0 option(s)

Options

heatmap – operational

Heatmap of weights (member × time).

L4.5.E weights-over-time rendering heatmap.

This option configures the weights_over_time_method axis on the L4_5_E_ensemble_diagnostics sub-layer of L4.5; output is emitted under manifest.diagnostics/l4_5/L4_5_E_ensemble_diagnostics/ alongside the other selected views.

When to use

Many-member ensembles (> 20) where line / area plots become unreadable.

References

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

Related options: line_plot, stacked_area

Last reviewed 2026-05-05 by macroforecast author.

line_plot – operational

Line plot of weights per member over time.

L4.5.E weights-over-time rendering line_plot.

This option configures the weights_over_time_method axis on the L4_5_E_ensemble_diagnostics sub-layer of L4.5; output is emitted under manifest.diagnostics/l4_5/L4_5_E_ensemble_diagnostics/ alongside the other selected views.

When to use

Default reporting view; readable up to ~10 ensemble members.

References

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

Related options: stacked_area, heatmap

Last reviewed 2026-05-05 by macroforecast author.

stacked_area – operational

Stacked-area plot summing to 1.

L4.5.E weights-over-time rendering stacked_area.

This option configures the weights_over_time_method axis on the L4_5_E_ensemble_diagnostics sub-layer of L4.5; output is emitted under manifest.diagnostics/l4_5/L4_5_E_ensemble_diagnostics/ alongside the other selected views.

When to use

Emphasising member share; ideal for showcasing weight redistribution events.

References

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

Related options: line_plot, heatmap

Last reviewed 2026-05-05 by macroforecast author.