find_cheapest_block, find_cheapest_hours, and find_cheapest_schedule
accept an optional power_profile (fixed per-interval watt array
matching the requested duration/task length). When allow_relaxation
was enabled and no full-duration window could be found, the
duration-reduction relaxation phase silently reduced the interval
count without shrinking power_profile accordingly. Downstream code
(find_cheapest_contiguous_window, calculate_window_statistics,
_find_cheapest_window_in_pool) then truncated the profile from the
front to match, dropping trailing appliance-cycle phases and using
the wrong per-interval weights for both window selection and the
reported cost estimate.
Disable duration-reduction relaxation whenever a power_profile is
supplied (find_cheapest_schedule guards per-task via
any_task_has_power_profile); distance and level-filter relaxation
phases remain available since they don't affect interval count.
Impact: Services with a power_profile no longer silently pick a
shorter window with mismatched power weighting during relaxation;
they now correctly report no window found if the full duration isn't
available, preserving the accuracy of appliance-cycle cost estimates.
Add a real (non-mocked) end-to-end test exercising apply_must_finish_by
and resolve_search_range together with a range-filtering fake pool,
reproducing the exact scenario from GH #168: a naive must_finish_by
datetime combined with search_start_day_offset: 0. The underlying fix
already landed in db5d172 but had no test covering the full request
pipeline (only individual helpers were mocked in existing tests).
Release-Notes: skip
User-Impact: none
* fix(services): preserve service call data through coordinator data fetch
In `_handle_find_block` and `_handle_find_hours`, the local `data`
variable holding the resolved service call data was rebound to the
coordinator data dict returned by `get_entry_and_data()`. As a result,
the subsequent calls to `validate_search_params(data)`,
`apply_must_finish_by(data, ...)` and `resolve_search_range(...)` read
search-range parameters from coordinator data instead of from the
service call, silently ignoring:
- must_finish_by
- search_scope
- search_start, search_end
- search_start_time, search_end_time
- search_start_day_offset, search_end_day_offset
- search_start_offset_minutes, search_end_offset_minutes
- include_current_interval
The functions fell back to the default range ("now → end of tomorrow")
for every call that depended on these parameters.
Rename the third return value of `get_entry_and_data()` to
`coordinator_data` so the service call `data` survives, restoring
deadline and search-scope semantics. `find_cheapest_schedule.py`
already uses `data_dict` for the same purpose and was not affected.
Verified locally against v0.31.0: a call with
`must_finish_by: 2026-06-01T20:00:00+02:00` now correctly produces
`search_end: 2026-06-01T20:00:00+02:00` (was end-of-tomorrow before).
* refactor(services): update data handling in find_cheapest_schedule service
Refactor the data retrieval process to use coordinator data instead of entry data for improved clarity and consistency.
Impact: Enhances maintainability of the service code without altering user-facing functionality.
---------
Co-authored-by: “WouterK” <kwaken.geringd0w@icloud.com”git config --global user.name “WouterK”git config --global user.email kwaken.geringd0w@icloud.com”>
Co-authored-by: Julian Pawlowski <jpawlowski@users.noreply.github.com>
Implement a new service that progressively relaxes user-defined filters to ensure a result is always returned when price data is available. This includes three phases: halving the minimum distance from average, expanding level filters, and reducing duration.
Impact: Users will receive results even when strict filters would otherwise yield no matches, improving the reliability of scheduling actions.
feat(pricing): enhance scheduling actions with new parameters
Introduce new parameters `smooth_outliers`, `min_distance_from_avg`, and `allow_relaxation` to scheduling actions, allowing for better control over price selection and ensuring results are meaningfully different from average prices.
Impact: Users can now fine-tune their scheduling actions to avoid marginal savings and ensure more uniform pricing within selected windows.
docs(scheduling): update documentation for new features
Revise the scheduling actions documentation to include new parameters and their effects, such as outlier smoothing and minimum distance from average, along with examples for better user understanding.
Impact: Users will have clearer guidance on how to utilize new features effectively in their automations.
test(scheduling): add tests for new relaxation logic
Implement unit tests to verify the behavior of the new relaxation logic in scheduling actions, ensuring that filters are correctly relaxed and results are returned as expected.
Impact: Increased test coverage and reliability of the scheduling features.
_resolve_time_with_day_offset() was calling dt_util.now() internally
instead of using the injected now parameter. This caused incorrect date
calculations in tests and any caller that passes a specific reference time.
Also add missing price_rank_* sensor keys to TIME_SENSITIVE_ENTITY_KEYS
in coordinator/constants.py so quarter-hour refresh is registered for all
11 price rank sensors (current/next/previous interval and hour variants).
Rename dt as dt_utils → dt as dt_util (ICN001) across 11 files to follow
the project-wide import alias convention. Apply ruff auto-fixes for import
ordering and collapsing single-item imports throughout the codebase.
Released-Bug: no
Add structured reason codes to no-result responses for find_cheapest_block,
find_cheapest_hours, and find_cheapest_schedule. Each handler now classifies
why no result was returned: no_data_in_range, no_intervals_matching_level_filter,
insufficient_intervals_after_filter, or insufficient_contiguous_window.
Add include_comparison_details flag to find_cheapest_schedule. When enabled,
each scheduled task includes a price_comparison field showing the most expensive
alternative window (mean, min, max, start, end) for cost-savings context.
Document stable reason code contracts in en.json service descriptions.
Add corresponding field translations to all locales (de, nb, nl, sv).
Impact: Automations and scripts can now react to why no window was found,
and schedules can display concrete savings vs. worst-case pricing.
