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https://github.com/jpawlowski/hass.tibber_prices.git
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189d3ba84d
Add comprehensive data_lifecycle_status sensor showing real-time cache vs fresh API data status with 6 states and 13+ detailed attributes. Key features: - 6 lifecycle states: cached, fresh, refreshing, searching_tomorrow, turnover_pending, error - Push-update system for instant state changes (refreshing→fresh→error) - Quarter-hour polling for turnover_pending detection at 23:45 - Accurate next_api_poll prediction using Timer #1 offset tracking - Tomorrow prediction with actual timer schedule (not fixed 13:00) - 13+ formatted attributes: cache_age, data_completeness, api_calls_today, next_api_poll, etc. Implementation: - sensor/calculators/lifecycle.py: New calculator with state logic - sensor/attributes/lifecycle.py: Attribute builders with formatting - coordinator/core.py: Lifecycle tracking + callback system (+16 lines) - sensor/core.py: Push callback registration (+3 lines) - coordinator/constants.py: Added to TIME_SENSITIVE_ENTITY_KEYS - Translations: All 5 languages (de, en, nb, nl, sv) Timing optimization: - Extended turnover warning: 5min → 15min (catches 23:45 quarter boundary) - No minute-timer needed: quarter-hour updates + push = optimal - Push-updates: <1sec latency for refreshing/fresh/error states - Timer offset tracking: Accurate tomorrow predictions Removed obsolete sensors: - data_timestamp (replaced by lifecycle attributes) - price_forecast (never implemented, removed from definitions) Impact: Users can monitor data freshness, API call patterns, cache age, and understand integration behavior. Perfect for troubleshooting and visibility into when data updates occur.
278 lines
16 KiB
Python
278 lines
16 KiB
Python
"""Value getter mapping for Tibber Prices sensors."""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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from custom_components.tibber_prices.utils.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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)
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if TYPE_CHECKING:
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from collections.abc import Callable
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from datetime import datetime
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from custom_components.tibber_prices.sensor.calculators.daily_stat import TibberPricesDailyStatCalculator
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from custom_components.tibber_prices.sensor.calculators.interval import TibberPricesIntervalCalculator
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from custom_components.tibber_prices.sensor.calculators.lifecycle import TibberPricesLifecycleCalculator
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from custom_components.tibber_prices.sensor.calculators.metadata import TibberPricesMetadataCalculator
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from custom_components.tibber_prices.sensor.calculators.rolling_hour import TibberPricesRollingHourCalculator
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from custom_components.tibber_prices.sensor.calculators.timing import TibberPricesTimingCalculator
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from custom_components.tibber_prices.sensor.calculators.trend import TibberPricesTrendCalculator
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from custom_components.tibber_prices.sensor.calculators.volatility import TibberPricesVolatilityCalculator
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from custom_components.tibber_prices.sensor.calculators.window_24h import TibberPricesWindow24hCalculator
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def get_value_getter_mapping( # noqa: PLR0913 - needs all calculators as parameters
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interval_calculator: TibberPricesIntervalCalculator,
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rolling_hour_calculator: TibberPricesRollingHourCalculator,
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daily_stat_calculator: TibberPricesDailyStatCalculator,
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window_24h_calculator: TibberPricesWindow24hCalculator,
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trend_calculator: TibberPricesTrendCalculator,
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timing_calculator: TibberPricesTimingCalculator,
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volatility_calculator: TibberPricesVolatilityCalculator,
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metadata_calculator: TibberPricesMetadataCalculator,
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lifecycle_calculator: TibberPricesLifecycleCalculator,
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get_next_avg_n_hours_value: Callable[[int], float | None],
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get_data_timestamp: Callable[[], datetime | None],
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get_chart_data_export_value: Callable[[], str | None],
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) -> dict[str, Callable]:
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"""
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Build mapping from entity key to value getter callable.
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This function centralizes the handler mapping logic, making it easier to maintain
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and understand the relationship between sensor types and their calculation methods.
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Args:
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interval_calculator: Calculator for current/next/previous interval values
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rolling_hour_calculator: Calculator for 5-interval rolling windows
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daily_stat_calculator: Calculator for daily min/max/avg statistics
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window_24h_calculator: Calculator for trailing/leading 24h windows
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trend_calculator: Calculator for price trend analysis
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timing_calculator: Calculator for best/peak price period timing
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volatility_calculator: Calculator for price volatility analysis
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metadata_calculator: Calculator for home/metering metadata
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lifecycle_calculator: Calculator for data lifecycle tracking
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get_next_avg_n_hours_value: Method for next N-hour average forecasts
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get_data_timestamp: Method for data timestamp sensor
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get_chart_data_export_value: Method for chart data export sensor
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Returns:
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Dictionary mapping entity keys to their value getter callables.
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"""
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return {
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# ================================================================
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# INTERVAL-BASED SENSORS - via IntervalCalculator
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# ================================================================
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# Price level sensors
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"current_interval_price_level": interval_calculator.get_price_level_value,
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"next_interval_price_level": lambda: interval_calculator.get_interval_value(
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interval_offset=1, value_type="level"
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),
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"previous_interval_price_level": lambda: interval_calculator.get_interval_value(
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interval_offset=-1, value_type="level"
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),
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# Price sensors (in cents)
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"current_interval_price": lambda: interval_calculator.get_interval_value(
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interval_offset=0, value_type="price", in_euro=False
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),
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"current_interval_price_major": lambda: interval_calculator.get_interval_value(
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interval_offset=0, value_type="price", in_euro=True
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),
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"next_interval_price": lambda: interval_calculator.get_interval_value(
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interval_offset=1, value_type="price", in_euro=False
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),
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"previous_interval_price": lambda: interval_calculator.get_interval_value(
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interval_offset=-1, value_type="price", in_euro=False
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),
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# Rating sensors
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"current_interval_price_rating": lambda: interval_calculator.get_rating_value(rating_type="current"),
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"next_interval_price_rating": lambda: interval_calculator.get_interval_value(
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interval_offset=1, value_type="rating"
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),
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"previous_interval_price_rating": lambda: interval_calculator.get_interval_value(
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interval_offset=-1, value_type="rating"
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),
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# ================================================================
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# ROLLING HOUR SENSORS (5-interval windows) - via RollingHourCalculator
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# ================================================================
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"current_hour_price_level": lambda: rolling_hour_calculator.get_rolling_hour_value(
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hour_offset=0, value_type="level"
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),
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"next_hour_price_level": lambda: rolling_hour_calculator.get_rolling_hour_value(
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hour_offset=1, value_type="level"
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),
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# Rolling hour average (5 intervals: 2 before + current + 2 after)
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"current_hour_average_price": lambda: rolling_hour_calculator.get_rolling_hour_value(
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hour_offset=0, value_type="price"
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),
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"next_hour_average_price": lambda: rolling_hour_calculator.get_rolling_hour_value(
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hour_offset=1, value_type="price"
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),
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"current_hour_price_rating": lambda: rolling_hour_calculator.get_rolling_hour_value(
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hour_offset=0, value_type="rating"
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),
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"next_hour_price_rating": lambda: rolling_hour_calculator.get_rolling_hour_value(
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hour_offset=1, value_type="rating"
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),
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# ================================================================
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# DAILY STATISTICS SENSORS - via DailyStatCalculator
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# ================================================================
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"lowest_price_today": lambda: daily_stat_calculator.get_daily_stat_value(day="today", stat_func=min),
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"highest_price_today": lambda: daily_stat_calculator.get_daily_stat_value(day="today", stat_func=max),
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"average_price_today": lambda: daily_stat_calculator.get_daily_stat_value(
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day="today",
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stat_func=lambda prices: sum(prices) / len(prices),
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),
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# Tomorrow statistics sensors
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"lowest_price_tomorrow": lambda: daily_stat_calculator.get_daily_stat_value(day="tomorrow", stat_func=min),
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"highest_price_tomorrow": lambda: daily_stat_calculator.get_daily_stat_value(day="tomorrow", stat_func=max),
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"average_price_tomorrow": lambda: daily_stat_calculator.get_daily_stat_value(
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day="tomorrow",
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stat_func=lambda prices: sum(prices) / len(prices),
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),
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# Daily aggregated level sensors
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"yesterday_price_level": lambda: daily_stat_calculator.get_daily_aggregated_value(
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day="yesterday", value_type="level"
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),
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"today_price_level": lambda: daily_stat_calculator.get_daily_aggregated_value(day="today", value_type="level"),
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"tomorrow_price_level": lambda: daily_stat_calculator.get_daily_aggregated_value(
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day="tomorrow", value_type="level"
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),
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# Daily aggregated rating sensors
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"yesterday_price_rating": lambda: daily_stat_calculator.get_daily_aggregated_value(
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day="yesterday", value_type="rating"
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),
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"today_price_rating": lambda: daily_stat_calculator.get_daily_aggregated_value(
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day="today", value_type="rating"
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),
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"tomorrow_price_rating": lambda: daily_stat_calculator.get_daily_aggregated_value(
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day="tomorrow", value_type="rating"
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),
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# ================================================================
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# 24H WINDOW SENSORS (trailing/leading from current) - via TibberPricesWindow24hCalculator
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# ================================================================
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# Trailing and leading average sensors
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"trailing_price_average": lambda: window_24h_calculator.get_24h_window_value(
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stat_func=calculate_current_trailing_avg,
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),
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"leading_price_average": lambda: window_24h_calculator.get_24h_window_value(
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stat_func=calculate_current_leading_avg,
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),
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# Trailing and leading min/max sensors
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"trailing_price_min": lambda: window_24h_calculator.get_24h_window_value(
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stat_func=calculate_current_trailing_min,
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),
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"trailing_price_max": lambda: window_24h_calculator.get_24h_window_value(
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stat_func=calculate_current_trailing_max,
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),
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"leading_price_min": lambda: window_24h_calculator.get_24h_window_value(
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stat_func=calculate_current_leading_min,
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),
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"leading_price_max": lambda: window_24h_calculator.get_24h_window_value(
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stat_func=calculate_current_leading_max,
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),
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# ================================================================
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# FUTURE FORECAST SENSORS
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# ================================================================
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# Future average sensors (next N hours from next interval)
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"next_avg_1h": lambda: get_next_avg_n_hours_value(1),
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"next_avg_2h": lambda: get_next_avg_n_hours_value(2),
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"next_avg_3h": lambda: get_next_avg_n_hours_value(3),
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"next_avg_4h": lambda: get_next_avg_n_hours_value(4),
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"next_avg_5h": lambda: get_next_avg_n_hours_value(5),
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"next_avg_6h": lambda: get_next_avg_n_hours_value(6),
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"next_avg_8h": lambda: get_next_avg_n_hours_value(8),
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"next_avg_12h": lambda: get_next_avg_n_hours_value(12),
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# Current and next trend change sensors
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"current_price_trend": trend_calculator.get_current_trend_value,
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"next_price_trend_change": trend_calculator.get_next_trend_change_value,
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# Price trend sensors
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"price_trend_1h": lambda: trend_calculator.get_price_trend_value(hours=1),
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"price_trend_2h": lambda: trend_calculator.get_price_trend_value(hours=2),
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"price_trend_3h": lambda: trend_calculator.get_price_trend_value(hours=3),
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"price_trend_4h": lambda: trend_calculator.get_price_trend_value(hours=4),
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"price_trend_5h": lambda: trend_calculator.get_price_trend_value(hours=5),
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"price_trend_6h": lambda: trend_calculator.get_price_trend_value(hours=6),
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"price_trend_8h": lambda: trend_calculator.get_price_trend_value(hours=8),
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"price_trend_12h": lambda: trend_calculator.get_price_trend_value(hours=12),
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# Diagnostic sensors
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"data_timestamp": get_data_timestamp,
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# Data lifecycle status sensor
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"data_lifecycle_status": lambda: lifecycle_calculator.get_lifecycle_state(),
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# Home metadata sensors (via MetadataCalculator)
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"home_type": lambda: metadata_calculator.get_home_metadata_value("type"),
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"home_size": lambda: metadata_calculator.get_home_metadata_value("size"),
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"main_fuse_size": lambda: metadata_calculator.get_home_metadata_value("mainFuseSize"),
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"number_of_residents": lambda: metadata_calculator.get_home_metadata_value("numberOfResidents"),
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"primary_heating_source": lambda: metadata_calculator.get_home_metadata_value("primaryHeatingSource"),
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# Metering point sensors (via MetadataCalculator)
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"grid_company": lambda: metadata_calculator.get_metering_point_value("gridCompany"),
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"grid_area_code": lambda: metadata_calculator.get_metering_point_value("gridAreaCode"),
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"price_area_code": lambda: metadata_calculator.get_metering_point_value("priceAreaCode"),
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"consumption_ean": lambda: metadata_calculator.get_metering_point_value("consumptionEan"),
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"production_ean": lambda: metadata_calculator.get_metering_point_value("productionEan"),
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"energy_tax_type": lambda: metadata_calculator.get_metering_point_value("energyTaxType"),
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"vat_type": lambda: metadata_calculator.get_metering_point_value("vatType"),
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"estimated_annual_consumption": lambda: metadata_calculator.get_metering_point_value(
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"estimatedAnnualConsumption"
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),
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# Subscription sensors (via MetadataCalculator)
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"subscription_status": lambda: metadata_calculator.get_subscription_value("status"),
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# Volatility sensors (via VolatilityCalculator)
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"today_volatility": lambda: volatility_calculator.get_volatility_value(volatility_type="today"),
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"tomorrow_volatility": lambda: volatility_calculator.get_volatility_value(volatility_type="tomorrow"),
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"next_24h_volatility": lambda: volatility_calculator.get_volatility_value(volatility_type="next_24h"),
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"today_tomorrow_volatility": lambda: volatility_calculator.get_volatility_value(
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volatility_type="today_tomorrow"
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),
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# ================================================================
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# BEST/PEAK PRICE TIMING SENSORS - via TimingCalculator
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# ================================================================
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# Best Price timing sensors
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"best_price_end_time": lambda: timing_calculator.get_period_timing_value(
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period_type="best_price", value_type="end_time"
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),
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"best_price_period_duration": lambda: timing_calculator.get_period_timing_value(
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period_type="best_price", value_type="period_duration"
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),
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"best_price_remaining_minutes": lambda: timing_calculator.get_period_timing_value(
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period_type="best_price", value_type="remaining_minutes"
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),
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"best_price_progress": lambda: timing_calculator.get_period_timing_value(
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period_type="best_price", value_type="progress"
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),
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"best_price_next_start_time": lambda: timing_calculator.get_period_timing_value(
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period_type="best_price", value_type="next_start_time"
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),
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"best_price_next_in_minutes": lambda: timing_calculator.get_period_timing_value(
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period_type="best_price", value_type="next_in_minutes"
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),
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# Peak Price timing sensors
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"peak_price_end_time": lambda: timing_calculator.get_period_timing_value(
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period_type="peak_price", value_type="end_time"
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),
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"peak_price_period_duration": lambda: timing_calculator.get_period_timing_value(
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period_type="peak_price", value_type="period_duration"
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),
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"peak_price_remaining_minutes": lambda: timing_calculator.get_period_timing_value(
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period_type="peak_price", value_type="remaining_minutes"
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),
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"peak_price_progress": lambda: timing_calculator.get_period_timing_value(
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period_type="peak_price", value_type="progress"
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),
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"peak_price_next_start_time": lambda: timing_calculator.get_period_timing_value(
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period_type="peak_price", value_type="next_start_time"
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),
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"peak_price_next_in_minutes": lambda: timing_calculator.get_period_timing_value(
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period_type="peak_price", value_type="next_in_minutes"
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),
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# Chart data export sensor
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"chart_data_export": get_chart_data_export_value,
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}
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