Implemented new chart_metadata diagnostic sensor that provides essential
chart configuration values (yaxis_min, yaxis_max, gradient_stop) as
attributes, enabling dynamic chart configuration without requiring
async service calls in templates.
Sensor implementation:
- New chart_metadata.py module with metadata-only service calls
- Automatically calls get_chartdata with metadata="only" parameter
- Refreshes on coordinator updates (new price data or user data)
- State values: "pending", "ready", "error"
- Enabled by default (critical for chart features)
ApexCharts YAML generator integration:
- Checks for chart_metadata sensor availability before generation
- Uses template variables to read sensor attributes dynamically
- Fallback to fixed values (gradient_stop=50%) if sensor unavailable
- Creates separate notifications for two independent issues:
1. Chart metadata sensor disabled (reduced functionality warning)
2. Required custom cards missing (YAML won't work warning)
- Both notifications explain YAML generation context and provide
complete fix instructions with regeneration requirement
Configuration:
- Supports configuration.yaml: tibber_prices.chart_metadata_config
- Optional parameters: day, minor_currency, resolution
- Defaults to minor_currency=True for ApexCharts compatibility
Translation additions:
- Entity name and state translations (all 5 languages)
- Notification messages for sensor unavailable and missing cards
- best_price_period_name for tooltip formatter
Binary sensor improvements:
- tomorrow_data_available now enabled by default (critical for automations)
- data_lifecycle_status now enabled by default (critical for debugging)
Impact: Users get dynamic chart configuration with optimized Y-axis scaling
and gradient positioning without manual calculations. ApexCharts YAML
generation now provides clear, actionable notifications when issues occur,
ensuring users understand why functionality is limited and how to fix it.
Add comprehensive data_lifecycle_status sensor showing real-time cache
vs fresh API data status with 6 states and 13+ detailed attributes.
Key features:
- 6 lifecycle states: cached, fresh, refreshing, searching_tomorrow,
turnover_pending, error
- Push-update system for instant state changes (refreshing→fresh→error)
- Quarter-hour polling for turnover_pending detection at 23:45
- Accurate next_api_poll prediction using Timer #1 offset tracking
- Tomorrow prediction with actual timer schedule (not fixed 13:00)
- 13+ formatted attributes: cache_age, data_completeness, api_calls_today,
next_api_poll, etc.
Implementation:
- sensor/calculators/lifecycle.py: New calculator with state logic
- sensor/attributes/lifecycle.py: Attribute builders with formatting
- coordinator/core.py: Lifecycle tracking + callback system (+16 lines)
- sensor/core.py: Push callback registration (+3 lines)
- coordinator/constants.py: Added to TIME_SENSITIVE_ENTITY_KEYS
- Translations: All 5 languages (de, en, nb, nl, sv)
Timing optimization:
- Extended turnover warning: 5min → 15min (catches 23:45 quarter boundary)
- No minute-timer needed: quarter-hour updates + push = optimal
- Push-updates: <1sec latency for refreshing/fresh/error states
- Timer offset tracking: Accurate tomorrow predictions
Removed obsolete sensors:
- data_timestamp (replaced by lifecycle attributes)
- price_forecast (never implemented, removed from definitions)
Impact: Users can monitor data freshness, API call patterns, cache age,
and understand integration behavior. Perfect for troubleshooting and
visibility into when data updates occur.
