Functions
Dict[str, Any]	load_metrics_summary (Path file_path)
Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]	extract_series_data (Dict[str, Any] series_dict, str series_name)
npt.NDArray[np.float64]	compute_timestamp_diffs (npt.NDArray[np.float64] timestamps)
npt.NDArray[np.float64]	compute_zscore (npt.NDArray[np.float64] values)
None	plot_metrics (Dict[str, Any] summary, Optional[str] session_id)
int	main ()

Detailed Description

Metrics Plotting Utility

This script loads and visualizes metrics data from the JSON summary file produced by
the redis_ipc_metrics_saving_client.py script (using MetricsCollector).

Plots:
1. Edge EDA (electrodermal activity)
2. Edge upper breathing trace
3. Core phasic blood pressure
4. Core upper breathing trace
5. Combined z-scores of all metrics
6. Edge upper breathing timestamp differences

All plots are displayed as subplots in a single figure.

Function Documentation

◆ compute_timestamp_diffs()

npt.NDArray[np.float64] plot_metrics.compute_timestamp_diffs ( npt.NDArray[np.float64] timestamps )

Compute differences between consecutive timestamps

◆ compute_zscore()

npt.NDArray[np.float64] plot_metrics.compute_zscore ( npt.NDArray[np.float64] values )

Compute z-score normalization of values

◆ extract_series_data()

Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]] plot_metrics.extract_series_data	(	Dict[str, Any]	series_dict,
		str	series_name )

Extract timestamps and values from a series in struct-of-arrays format

◆ load_metrics_summary()

Dict[str, Any] plot_metrics.load_metrics_summary ( Path file_path )

Load metrics summary JSON file produced by MetricsCollector

◆ plot_metrics()

None plot_metrics.plot_metrics	(	Dict[str, Any]	summary,
		Optional[str]	session_id )

Create a figure with all metric plots as subplots