## output_analysis.plotly_confidence_interval_method()


Create an interactive Plotly visualisation of the cumulative mean and


Usage

``` python
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](output_analysis.confidence_interval_method.md#sim_tools.output_analysis.confidence_interval_method)).

`conf_ints`  
Results DataFrame from [confidence_interval_method](output_analysis.confidence_interval_method.md#sim_tools.output_analysis.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`