panels.telemetry
================

.. py:module:: panels.telemetry

.. autoapi-nested-parse::

   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
----------

.. autoapisummary::

   panels.telemetry.JsonDict


Functions
---------

.. autoapisummary::

   panels.telemetry._parse_ts
   panels.telemetry._telemetry_anchor_ts
   panels.telemetry._tskr_pattern_window
   panels.telemetry._violation_bg
   panels.telemetry.build_fmea_latency_figure
   panels.telemetry.build_tskr_table_figure
   panels.telemetry.build_timeline_figure
   panels.telemetry.render_telemetry_panel


Module Contents
---------------

.. py:data:: JsonDict

.. py:function:: _parse_ts(value)

.. py:function:: _telemetry_anchor_ts(telemetry_summary)

.. py:function:: _tskr_pattern_window(p)

.. py:function:: _violation_bg(lat)

.. py:function:: build_fmea_latency_figure(telemetry_summary, kg_context)

   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


.. py:function:: build_tskr_table_figure(tskr_patterns)

   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


.. py:function:: build_timeline_figure(telemetry_summary, tskr_patterns)

   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


.. py:function:: render_telemetry_panel(telemetry_summary, tskr_patterns, kg_context = None)

   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