panels.telemetry

Telemetry & Temporal panel: anomaly timeline, TSKR patterns, FMEA brackets.

Reads the telemetry_summary input (per-signal anomalies and changepoints) and the tskr_patterns artifact to render an anomaly-window Gantt (overlaid with TSKR pattern windows), a latency-colored TSKR table, an optional FMEA expected-latency strip from kg_context, and per-signal statistics.

Attributes

JsonDict

Functions

_parse_ts(value)

_telemetry_anchor_ts(telemetry_summary)

_tskr_pattern_window(p)

_violation_bg(lat)

build_fmea_latency_figure(telemetry_summary, kg_context)

Build a Gantt of expected failure-mode latency windows vs the event anchor.

build_tskr_table_figure(tskr_patterns)

Build a table figure of TSKR patterns, row-colored by latency violation.

build_timeline_figure(telemetry_summary, tskr_patterns)

Build the anomaly-window Gantt, overlaid with changepoints and TSKR windows.

render_telemetry_panel(telemetry_summary, tskr_patterns)

Render the Telemetry & Temporal section.

Module Contents

panels.telemetry.JsonDict[source]
panels.telemetry._parse_ts(value)[source]
Parameters:

value (Optional[str])

Return type:

Optional[pandas.Timestamp]

panels.telemetry._telemetry_anchor_ts(telemetry_summary)[source]
Parameters:

telemetry_summary (JsonDict)

Return type:

Optional[pandas.Timestamp]

panels.telemetry._tskr_pattern_window(p)[source]
Parameters:

p (JsonDict)

Return type:

tuple[Optional[pandas.Timestamp], Optional[pandas.Timestamp]]

panels.telemetry._violation_bg(lat)[source]
Parameters:

lat (Any)

Return type:

str

panels.telemetry.build_fmea_latency_figure(telemetry_summary, kg_context)[source]

Build a Gantt of expected failure-mode latency windows vs the event anchor.

The anchor is the telemetry window start, or the earliest anomaly timestamp when no window is given.

@ In, telemetry_summary, dict, the telemetry_summary input, used only to

derive the time anchor

@ In, kg_context, dict, optional; reads failure_modes[]

expected_latency_min_hours/expected_latency_max_hours

@ Out, fig, plotly.graph_objects.Figure, the strip, or None if no KG context,

no anchor, or no failure mode carries a latency bracket

Parameters:
Return type:

Optional[plotly.graph_objects.Figure]

panels.telemetry.build_tskr_table_figure(tskr_patterns)[source]

Build a table figure of TSKR patterns, row-colored by latency violation.

@ In, tskr_patterns, dict, the tskr_patterns artifact; reads

patterns (first 40), each supplying pattern_id/target_id/ relation/mean_lag_hours/latency_violation_type/confidence

@ Out, fig, plotly.graph_objects.Figure, the table, or None if no patterns

Parameters:

tskr_patterns (JsonDict)

Return type:

Optional[plotly.graph_objects.Figure]

panels.telemetry.build_timeline_figure(telemetry_summary, tskr_patterns)[source]

Build the anomaly-window Gantt, overlaid with changepoints and TSKR windows.

@ In, telemetry_summary, dict, the telemetry_summary input; reads

signals[].anomalies[] (window, pattern, severity_score/legacy severity, id) and signals[].changepoints[] (dashed verticals)

@ In, tskr_patterns, dict, optional; its patterns[] windows (first 16)

are drawn as shaded regions over the timeline

@ Out, fig, plotly.graph_objects.Figure, the timeline, or None if no

anomaly has a valid timestamp

Parameters:
Return type:

Optional[plotly.graph_objects.Figure]

panels.telemetry.render_telemetry_panel(telemetry_summary, tskr_patterns, kg_context=None)[source]

Render the Telemetry & Temporal section.

@ In, telemetry_summary, dict, the telemetry_summary input (signals,

anomalies, changepoints, per-signal stats); None shows an info note

@ In, tskr_patterns, dict, optional tskr_patterns artifact for the

pattern table and timeline overlay

@ In, kg_context, dict, optional; enables the FMEA expected-latency strip @ Out, None

Parameters:
Return type:

None