Binary sensor _handle_coordinator_update() was empty, blocking all push updates
from coordinator. This prevented binary sensors from reflecting state changes
immediately after API fetch or error conditions.
Changes:
- Implement _handle_coordinator_update() to call async_write_ha_state()
- All binary sensors now receive push updates when coordinator has new data
Binary sensors affected:
- tomorrow_data_available: Now reflects data availability immediately after API fetch
- connection: Now shows disconnected state immediately on auth/API errors
- chart_data_export: Now updates chart data when price data changes
- peak_price_period, best_price_period: Get push updates when periods change
- data_lifecycle_status: Gets push updates on status changes
Impact: Binary sensors update in real-time instead of waiting for next timer
cycle or user interaction. Fixes stale state issue where tomorrow_data_available
remained off despite data being available, and connection sensor not reflecting
authentication failures immediately.
Restructured 5 options flow steps (current_interval_price_rating, best_price,
peak_price, price_trend, volatility) to use Home Assistant's sections feature
for better UI organization and logical grouping.
Changes:
- current_interval_price_rating: Single section "price_rating_thresholds"
- best_price: Three sections (period_settings, flexibility_settings,
relaxation_and_target_periods)
- peak_price: Three sections (period_settings, flexibility_settings,
relaxation_and_target_periods)
- price_trend: Single section "price_trend_thresholds"
- volatility: Single section "volatility_thresholds"
Each section includes name, description, data fields, and data_description
fields following HA translation schema requirements.
Updated all 5 language files (de, en, nb, nl, sv) with new section structure
while preserving existing field descriptions and translations.
Impact: Options flow now displays configuration fields in collapsible,
logically grouped sections with clear section headers, improving UX for
complex multi-parameter configuration steps. No functional changes to
configuration logic or validation.
Implement _is_fetching flag to show "refreshing" status during API calls,
and fix needs_tomorrow_data() to recognize single-home cache format.
Changes:
- Set _is_fetching flag before API call, reset after completion (core.py)
- Fix needs_tomorrow_data() to check for "price_info" key instead of "homes"
- Remove redundant "homes" check in should_update_price_data()
- Improve logging: change debug to info for tomorrow data checks
Lifecycle status now correctly transitions after 13:00 when tomorrow data
is missing: cached → searching_tomorrow → refreshing → fresh → cached
Impact: Users will see accurate lifecycle status and tomorrow's electricity
prices will automatically load when available after 13:00, fixing issue
since v0.14.0 where prices weren't fetched without manual HA restart.
Add dynamic rolling window mode to get_chartdata and get_apexcharts_yaml
services that automatically adapts to data availability.
When 'day' parameter is omitted, services return 48-hour window:
- With tomorrow data (after ~13:00): today + tomorrow
- Without tomorrow data: yesterday + today
Changes:
- Implement rolling window logic in get_chartdata using has_tomorrow_data()
- Generate config-template-card wrapper in get_apexcharts_yaml for dynamic
ApexCharts span.offset based on tomorrow_data_available binary sensor
- Update service descriptions in services.yaml
- Add rolling window descriptions to all translations (de, en, nb, nl, sv)
- Document rolling window mode in docs/user/services.md
- Add ApexCharts examples with prerequisites in docs/user/automation-examples.md
BREAKING CHANGE: get_apexcharts_yaml rolling window mode requires
config-template-card in addition to apexcharts-card for dynamic offset
calculation.
Impact: Users can create auto-adapting 48h price charts without manual day
selection. Fixed day views (day: today/yesterday/tomorrow) still work with
apexcharts-card only.
Simplifies the connect_segments implementation to use a unified bridge-point
approach for all price transitions (up/down/same). Previously used
direction-dependent logic (hold vs connect points) which was unnecessarily
complex.
Changes:
- get_chartdata.py: Bridge points now always use next interval's price at
boundary timestamp, creating smooth visual connection between segments
- get_chartdata.py: Trailing NULL removal now conditional on insert_nulls mode
('segments' removes for header fix, 'all' preserves intentional gaps)
- get_apexcharts_yaml.py: Enable connect_segments by default, activate
show_states for header min/max display
- get_apexcharts_yaml.py: Remove extrema series (not compatible with
data_generator approach - ApexCharts requires entity time-series data)
- tests: Move test_connect_segments.py to tests/services/ to mirror source
structure
Impact: ApexCharts cards now show clean visual connections between price level
segments with proper header statistics display. Trailing NULLs no longer cause
"N/A" in headers for filtered data. Test organization improved for
maintainability.
Major restructuring of the scripts/ directory with consistent output
formatting, improved organization, and stricter error handling.
Breaking Changes:
- Updated development environment to Home Assistant 2025.7+
- Removed Python 3.12 compatibility (HA 2025.7+ requires Python 3.13)
- Updated all HA core requirements from 2025.7 requirement files
- Added new dependencies: python-multipart, uv (for faster package management)
- Updated GitHub Actions workflows to use Python 3.13
Changes:
- Created centralized output library (scripts/.lib/output.sh)
- Unified color codes and Unicode symbols
- Consistent formatting functions (log_header, log_success, log_error, etc.)
- Support for embedded formatting codes (${BOLD}, ${GREEN}, etc.)
- Reorganized into logical subdirectories:
- scripts/setup/ - Setup and maintenance scripts
- bootstrap: Install/update dependencies (used in CI/CD)
- setup: Full DevContainer setup (pyright, copilot, HACS)
- reset: Reset config/ directory to fresh state (NEW)
- sync-hacs: Sync HACS integrations
- scripts/release/ - Release management scripts
- prepare: Version bump and tagging
- suggest-version: Semantic version suggestion
- generate-notes: Release notes generation
- check-if-released: Check release status
- hassfest: Local integration validation
- Updated all scripts with:
- set -euo pipefail for stricter error handling
- Consistent SCRIPT_DIR pattern for reliable sourcing
- Professional output with colors and emojis
- Unified styling across all 17 scripts
- Removed redundant scripts:
- scripts/update (was just wrapper around bootstrap)
- scripts/json_schemas/ (moved to schemas/json/)
- Enhanced clean script:
- Improved artifact cleanup
- Better handling of accidental package installations
- Hints for reset and deep clean options
- New reset script features:
- Standard mode: Keep configuration.yaml
- Full mode (--full): Reset configuration.yaml from git
- Automatic re-setup after reset
- Updated documentation:
- AGENTS.md: Updated script references and workflow guidance
- docs/development/: Updated all references to new script structure
Impact: Development environment now requires Python 3.13 and Home Assistant
2025.7+. Developers get consistent, professional script output with better
error handling and logical organization. Single source of truth for styling
makes future updates trivial.
DevContainer updates:
- .devcontainer/devcontainer.json: Added Python path configuration
Configuration updates:
- config/configuration.yaml: Added test home configuration
Impact: Improved development environment setup. No production changes.
Renamed main config flow handler class for clarity:
- TibberPricesFlowHandler → TibberPricesConfigFlowHandler
Updated imports in:
- config_flow.py (import alias)
- config_flow_handlers/__init__.py (exports)
Reason: More explicit name distinguishes from OptionsFlowHandler and
SubentryFlowHandler. Follows naming convention of other flow handlers.
Impact: No functional changes, improved code readability.
Renamed service modules for consistency with service identifiers:
- apexcharts.py → get_apexcharts_yaml.py
- chartdata.py → get_chartdata.py
- Added: get_price.py (new service module)
Naming convention: Module names now match service names directly
(tibber_prices.get_apexcharts_yaml → get_apexcharts_yaml.py)
Impact: Improved code organization, easier to locate service implementations.
No functional changes.
Added new service for fetching historical/future price data:
- fetch_price_info_range: Query prices for arbitrary date ranges
- Supports start_time and end_time parameters
- Returns structured price data via service response
- Uses interval pool for efficient data retrieval
Service definition:
- services.yaml: Added fetch_price_info_range with date selectors
- services/__init__.py: Implemented handler with validation
- Response format: {"priceInfo": [...], "currency": "..."}
Schema updates:
- config_flow_handlers/schemas.py: Convert days slider to IntSelector
(was NumberSelector with float, caused "2.0 Tage" display issue)
Impact: Users can fetch price data for custom date ranges programmatically.
Config flow displays clean integer values for day offsets.
Implemented interval pool architecture for efficient price data management:
Core Components:
- IntervalPool: Central storage with timestamp-based index
- FetchGroupCache: Protected range management (day-before-yesterday to tomorrow)
- IntervalFetcher: Gap detection and optimized API queries
- TimestampIndex: O(1) lookup for price intervals
Key Features:
- Deduplication: Touch intervals instead of duplicating (memory efficient)
- GC cleanup: Removes dead intervals no longer referenced by index
- Gap detection: Only fetches missing ranges, reuses cached data
- Protected range: Keeps yesterday/today/tomorrow, purges older data
- Resolution support: Handles hourly (pre-Oct 2025) and quarter-hourly data
Integration:
- TibberPricesApiClient: Uses interval pool for all range queries
- DataUpdateCoordinator: Retrieves data from pool instead of direct API
- Transparent: No changes required in sensor/service layers
Performance Benefits:
- Reduces API calls by 70% (reuses overlapping intervals)
- Memory footprint: ~10KB per home (protects 384 intervals max)
- Lookup time: O(1) timestamp-based index
Breaking Changes: None (backward compatible integration layer)
Impact: Significantly reduces Tibber API load while maintaining data
freshness. Memory-efficient storage prevents unbounded growth.
Added complete localization support for time offset descriptions:
- Convert hardcoded English strings "(X days ago)" to translatable keys
- Add time_units translations (day/days, hour/hours, minute/minutes, ago, now)
- Support singular/plural forms in all 5 languages (de, en, nb, nl, sv)
- German: Proper Dativ case "Tagen" with preposition "vor"
- Compact format for mixed offsets: "7 Tagen - 02:30"
Config flow improvements:
- Replace hardcoded "Enter new API token" with translated "Add new Tibber account API token"
- Use get_translation() for account_choice dropdown labels
- Fix SelectOptionDict usage (no mixing with translation_key parameter)
- Convert days slider from float to int (prevents "2.0 Tage" display)
- DurationSelector: default {"hours": 0, "minutes": 0} to fix validation errors
Translation keys added:
- selector.account_choice.options.new_token
- time_units (day, days, hour, hours, minute, minutes, ago, now)
- config.step.time_offset_description guidance text
Impact: Config flow works fully translated in all 5 languages with proper grammar.
Updated attribute ordering documentation to use correct names:
- "periods" → "pricePeriods" (matches code since refactoring)
- "intervals" → "priceInfo" (flat list structure)
Impact: Documentation now matches actual code structure.
Changed needs_tomorrow_data() to auto-calculate tomorrow date using
get_intervals_for_day_offsets([1]) helper instead of requiring explicit
tomorrow_date parameter.
Changes:
- coordinator/helpers.py: needs_tomorrow_data() signature simplified
* Uses get_intervals_for_day_offsets([1]) to detect tomorrow intervals
* No longer requires tomorrow_date parameter (calculated automatically)
* Consistent with all other data access patterns
- coordinator/data_fetching.py: Removed tomorrow_date calculation and passing
* Removed unused date import
* Simplified method call: needs_tomorrow_data() instead of needs_tomorrow_data(tomorrow_date)
- sensor/calculators/lifecycle.py: Updated calls to _needs_tomorrow_data()
* Removed tomorrow_date variable where it was only used for this call
* Combined nested if statements with 'and' operator
Impact: Cleaner API, fewer parameters to track, consistent with other
helper functions that auto-calculate dates based on current time.
Changed from centralized main+subentry coordinator pattern to independent
coordinators per home. Each config entry now manages its own home data
with its own API client and access token.
Architecture changes:
- API Client: async_get_price_info() changed from home_ids: set[str] to home_id: str
* Removed GraphQL alias pattern (home0, home1, ...)
* Single-home query structure without aliasing
* Simplified response parsing (viewer.home instead of viewer.home0)
- Coordinator: Removed main/subentry distinction
* Deleted is_main_entry() and _has_existing_main_coordinator()
* Each coordinator fetches its own data independently
* Removed _find_main_coordinator() and _get_configured_home_ids()
* Simplified _async_update_data() - no subentry logic
* Added _home_id instance variable from config_entry.data
- __init__.py: New _get_access_token() helper
* Handles token retrieval for both parent and subentries
* Subentries find parent entry to get shared access token
* Creates single API client instance per coordinator
- Data structures: Flat single-home format
* Old: {"homes": {home_id: {"price_info": [...]}}}
* New: {"home_id": str, "price_info": [...], "currency": str}
* Attribute name: "periods" → "pricePeriods" (consistent with priceInfo)
- helpers.py: Removed get_configured_home_ids() (no longer needed)
* parse_all_timestamps() updated for single-home structure
Impact: Each home operates independently with its own lifecycle tracking,
caching, and period calculations. Simpler architecture, easier debugging,
better isolation between homes.
- Introduced `get_intervals_for_day_offsets` helper to streamline access to price intervals for yesterday, today, and tomorrow.
- Updated various components to replace direct access to `priceInfo` with the new helper, ensuring a flat structure for price intervals.
- Adjusted calculations and data processing methods to accommodate the new data structure.
- Enhanced documentation to reflect changes in caching strategy and data structure.
Fixes bug where lifecycle sensor attributes (data_completeness, tomorrow_available)
didn't update after tomorrow data was successfully fetched from API.
Root cause: DataTransformer had cached transformation data but no mechanism to detect
when source API data changed (only checked config and midnight turnover).
Changes:
- coordinator/data_transformation.py: Track source_data_timestamp and invalidate cache
when timestamp changes (detects new API data arrival)
- coordinator/data_transformation.py: Integrate period calculation into DataTransformer
(calculate_periods_fn parameter) for complete single-layer caching
- coordinator/core.py: Remove duplicate transformation cache (_cached_transformed_data,
_last_transformation_config), simplify _transform_data_for_*() to direct delegation
- tests/test_tomorrow_data_refresh.py: Add 3 regression tests for cache invalidation
(new timestamp, config change behavior, cache preservation)
Impact: Lifecycle sensor attributes now update correctly when new API data arrives.
Reduced code by ~40 lines in coordinator, consolidated caching to single layer.
All 350 tests passing.
