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fix(periods): restore relaxation metadata marking with correct sign handling
Restored mark_periods_with_relaxation() function and added call in relax_all_prices() to properly mark periods found through relaxation. Problem: Periods found via relaxation were missing metadata attributes: - relaxation_active - relaxation_level - relaxation_threshold_original_% - relaxation_threshold_applied_% These attributes are expected by: - period_overlap.py: For merging periods with correct relaxation info - binary_sensor/attributes.py: For displaying relaxation info to users Implementation: - Added reverse_sort parameter to preserve sign semantics - For Best Price: Store positive thresholds (e.g., +15%, +18%) - For Peak Price: Store negative thresholds (e.g., -20%, -23%) - Mark periods immediately after calculate_periods() and before resolve_period_overlaps() so metadata is preserved during merging Impact: Users can now see which periods were found through relaxation and at what flex threshold. Peak Price periods show negative thresholds matching the user's configuration semantics (negative = below maximum).
This commit is contained in:
Julian Pawlowski
parent
14b68a504b
commit
f6b553d90e
1 changed files with 44 additions and 82 deletions
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@ -60,6 +60,41 @@ def group_periods_by_day(periods: list[dict]) -> dict[date, list[dict]]:
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return periods_by_day
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def mark_periods_with_relaxation(
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periods: list[dict],
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relaxation_level: str,
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original_threshold: float,
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applied_threshold: float,
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*,
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reverse_sort: bool = False,
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) -> None:
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"""
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Mark periods with relaxation information (mutates period dicts in-place).
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Uses consistent 'relaxation_*' prefix for all relaxation-related attributes.
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These attributes are read by period_overlap.py and binary_sensor/attributes.py.
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For Peak Price periods (reverse_sort=True), thresholds are stored as negative
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values to match the user's configuration semantics (negative flex = below maximum).
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Args:
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periods: List of period dicts to mark
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relaxation_level: String describing the relaxation level (e.g., "flex=18.0% +level_any")
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original_threshold: Original flex threshold value (decimal, e.g., 0.15 for 15%)
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applied_threshold: Actually applied threshold value (decimal, e.g., 0.18 for 18%)
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reverse_sort: True for Peak Price (negative values), False for Best Price (positive values)
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"""
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for period in periods:
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period["relaxation_active"] = True
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period["relaxation_level"] = relaxation_level
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# Convert decimal to percentage for display
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# For Peak Prices: Store as negative to match user's config semantics
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sign = -1 if reverse_sort else 1
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period["relaxation_threshold_original_%"] = round(original_threshold * 100 * sign, 1)
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period["relaxation_threshold_applied_%"] = round(applied_threshold * 100 * sign, 1)
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def group_prices_by_day(all_prices: list[dict], *, time: TibberPricesTimeService) -> dict[date, list[dict]]:
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"""
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Group price intervals by the day they belong to (today and future only).
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@ -86,88 +121,6 @@ def group_prices_by_day(all_prices: list[dict], *, time: TibberPricesTimeService
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return prices_by_day
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def check_min_periods_per_day(
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periods: list[dict], min_periods: int, all_prices: list[dict], *, time: TibberPricesTimeService
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) -> bool:
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"""
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Check if minimum periods requirement is met for each day individually.
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Returns True if we should STOP relaxation (enough periods found per day).
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Returns False if we should CONTINUE relaxation (not enough periods yet).
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Args:
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periods: List of period summary dicts
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min_periods: Minimum number of periods required per day
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all_prices: All available price intervals (used to determine which days have data)
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time: TibberPricesTimeService instance (required)
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Returns:
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True if every day with price data has at least min_periods, False otherwise
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"""
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if not periods:
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return False # No periods at all, continue relaxation
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# Get all days that have price data (today and future only, not yesterday)
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today = time.now().date()
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available_days = set()
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for price in all_prices:
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starts_at = time.get_interval_time(price)
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if starts_at:
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price_date = starts_at.date()
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# Only count today and future days (not yesterday)
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if price_date >= today:
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available_days.add(price_date)
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if not available_days:
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return False # No price data for today/future, continue relaxation
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# Group found periods by day
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periods_by_day = group_periods_by_day(periods)
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# Check each day with price data: ALL must have at least min_periods
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for day in available_days:
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day_periods = periods_by_day.get(day, [])
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period_count = len(day_periods)
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if period_count < min_periods:
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_LOGGER.debug(
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"Day %s has only %d periods (need %d) - continuing relaxation",
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day,
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period_count,
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min_periods,
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)
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return False # This day doesn't have enough, continue relaxation
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# All days with price data have enough periods, stop relaxation
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return True
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def mark_periods_with_relaxation(
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periods: list[dict],
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relaxation_level: str,
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original_threshold: float,
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applied_threshold: float,
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) -> None:
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"""
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Mark periods with relaxation information (mutates period dicts in-place).
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Uses consistent 'relaxation_*' prefix for all relaxation-related attributes.
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Args:
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periods: List of period dicts to mark
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relaxation_level: String describing the relaxation level
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original_threshold: Original flex threshold value (decimal, e.g., 0.19 for 19%)
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applied_threshold: Actually applied threshold value (decimal, e.g., 0.25 for 25%)
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"""
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for period in periods:
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period["relaxation_active"] = True
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period["relaxation_level"] = relaxation_level
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# Convert decimal to percentage for display (0.19 → 19.0)
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period["relaxation_threshold_original_%"] = round(original_threshold * 100, 1)
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period["relaxation_threshold_applied_%"] = round(applied_threshold * 100, 1)
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def calculate_periods_with_relaxation( # noqa: PLR0913, PLR0915 - Per-day relaxation requires many parameters and statements
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all_prices: list[dict],
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*,
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@ -494,6 +447,15 @@ def relax_all_prices( # noqa: PLR0913 - Comprehensive filter relaxation require
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len(new_periods),
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)
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# Mark newly found periods with relaxation metadata BEFORE merging
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mark_periods_with_relaxation(
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new_periods,
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relaxation_level=phase_label_full,
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original_threshold=base_flex,
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applied_threshold=current_flex,
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reverse_sort=config.reverse_sort,
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)
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# Resolve overlaps between existing and new periods
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combined, standalone_count = resolve_period_overlaps(
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existing_periods=existing_periods,
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