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.
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.
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.
Introduce TimeService as single source of truth for all datetime operations,
replacing direct dt_util calls throughout the codebase. This establishes
consistent time context across update cycles and enables future time-travel
testing capability.
Core changes:
- NEW: coordinator/time_service.py with timezone-aware datetime API
- Coordinator now creates TimeService per update cycle, passes to calculators
- Timer callbacks (#2, #3) inject TimeService into entity update flow
- All sensor calculators receive TimeService via coordinator reference
- Attribute builders accept time parameter for timestamp calculations
Key patterns replaced:
- dt_util.now() → time.now() (single reference time per cycle)
- dt_util.parse_datetime() + as_local() → time.get_interval_time()
- Manual interval arithmetic → time.get_interval_offset_time()
- Manual day boundaries → time.get_day_boundaries()
- round_to_nearest_quarter_hour() → time.round_to_nearest_quarter()
Import cleanup:
- Removed dt_util imports from ~30 files (calculators, attributes, utils)
- Restricted dt_util to 3 modules: time_service.py (operations), api/client.py
(rate limiting), entity_utils/icons.py (cosmetic updates)
- datetime/timedelta only for TYPE_CHECKING (type hints) or duration arithmetic
Interval resolution abstraction:
- Removed hardcoded MINUTES_PER_INTERVAL constant from 10+ files
- New methods: time.minutes_to_intervals(), time.get_interval_duration()
- Supports future 60-minute resolution (legacy data) via TimeService config
Timezone correctness:
- API timestamps (startsAt) already localized by data transformation
- TimeService operations preserve HA user timezone throughout
- DST transitions handled via get_expected_intervals_for_day() (future use)
Timestamp ordering preserved:
- Attribute builders generate default timestamp (rounded quarter)
- Sensors override when needed (next interval, daily midnight, etc.)
- Platform ensures timestamp stays FIRST in attribute dict
Timer integration:
- Timer #2 (quarter-hour): Creates TimeService, calls _handle_time_sensitive_update(time)
- Timer #3 (30-second): Creates TimeService, calls _handle_minute_update(time)
- Consistent time reference for all entities in same update batch
Time-travel readiness:
- TimeService.with_reference_time() enables time injection (not yet used)
- All calculations use time.now() → easy to simulate past/future states
- Foundation for debugging period calculations with historical data
Impact: Eliminates timestamp drift within update cycles (previously 60+ independent
dt_util.now() calls could differ by milliseconds). Establishes architecture for
time-based testing and debugging features.
