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https://github.com/jpawlowski/hass.tibber_prices.git
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625bc222ca
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.
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
"""
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Pure data transformation utilities for Tibber Prices integration.
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This package contains stateless, pure functions for data processing:
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- Time-window calculations (trailing/leading averages, min/max)
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- Price enrichment (differences, volatility, rating levels)
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- Statistical analysis (aggregation, trends)
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These functions operate on raw data structures (dicts, lists) and do NOT depend on:
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- Home Assistant entities or state management
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- Configuration entries or coordinators
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- Translation systems or UI-specific logic
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For entity-specific utilities (icons, colors, attributes), see entity_utils/ package.
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"""
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from __future__ import annotations
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from .average import (
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calculate_current_leading_avg,
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calculate_current_leading_max,
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calculate_current_leading_min,
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calculate_current_trailing_avg,
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calculate_current_trailing_max,
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calculate_current_trailing_min,
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calculate_next_n_hours_avg,
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)
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from .price import (
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aggregate_period_levels,
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aggregate_period_ratings,
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aggregate_price_levels,
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aggregate_price_rating,
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calculate_difference_percentage,
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calculate_price_trend,
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calculate_rating_level,
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calculate_trailing_average_for_interval,
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calculate_volatility_level,
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enrich_price_info_with_differences,
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find_price_data_for_interval,
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)
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__all__ = [
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"aggregate_period_levels",
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"aggregate_period_ratings",
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"aggregate_price_levels",
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"aggregate_price_rating",
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"calculate_current_leading_avg",
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"calculate_current_leading_max",
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"calculate_current_leading_min",
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"calculate_current_trailing_avg",
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"calculate_current_trailing_max",
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"calculate_current_trailing_min",
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"calculate_difference_percentage",
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"calculate_next_n_hours_avg",
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"calculate_price_trend",
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"calculate_rating_level",
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"calculate_trailing_average_for_interval",
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"calculate_volatility_level",
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"enrich_price_info_with_differences",
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"find_price_data_for_interval",
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]
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