output_analysis.plotly_confidence_interval_method()

Create an interactive Plotly visualisation of the cumulative mean and

Usage

output_analysis.plotly_confidence_interval_method(
    n_reps, conf_ints, metric_name, figsize=(1200, 400), shaded=True
)

confidence intervals for each replication.

This plot displays: - The running (cumulative) mean of a performance metric. - Lower and upper bounds of the confidence interval at each replication. - Annotated deviation (as % of mean) on hover. - A vertical dashed line at the minimum number of replications (n_reps) required to achieve the target precision.

Parameters

n_reps: Minimum number of replications needed to achieve desired precision (typically the output of confidence_interval_method).
conf_ints: Results DataFrame from confidence_interval_method, containing columns: "Cumulative Mean", "Lower Interval", "Upper Interval", etc.
metric_name: Name of the performance metric displayed in the y-axis label.
figsize=(1200, 400): Figure size in pixels: (width, height).
shaded=True: If True, use shaded CI region. If False, use dashed lines (legacy).

Returns

plotly.graph_objects.Figure