mirror of
https://github.com/jpawlowski/hass.tibber_prices.git
synced 2026-05-28 18:43:40 +00:00
faa3b2b71a
Changed native_unit_of_measurement from HOURS to MINUTES for all 7 duration sensors. HA auto-converts to hours for display via suggested_unit_of_measurement=HOURS. Sensors affected: - next_price_trend_change_in - best_price_period_duration, best_price_remaining_minutes, best_price_next_in_minutes - peak_price_period_duration, peak_price_remaining_minutes, peak_price_next_in_minutes Removed _minutes_to_hours() conversion function — calculator values (minutes) are now passed through directly. BREAKING CHANGE: State values for all duration sensors change from hours to minutes (e.g. 1.5 → 90). The display unit remains hours (suggested_unit_of_measurement). Automations using numeric state comparisons must be updated (multiply old thresholds by 60). Impact: Users with automations comparing duration sensor states numerically need to update thresholds. Dashboard display is unchanged for new installations. Existing installations retain their configured display unit but the underlying numeric value changes.
299 lines
17 KiB
Python
299 lines
17 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, cast
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from custom_components.tibber_prices.utils.average import (
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calculate_current_leading_max,
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calculate_current_leading_mean,
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calculate_current_leading_min,
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calculate_current_trailing_max,
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calculate_current_trailing_mean,
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calculate_current_trailing_min,
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calculate_mean,
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calculate_median,
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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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get_chart_metadata_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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get_chart_metadata_value: Method for chart metadata 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_base": 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: (calculate_mean(prices), calculate_median(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: (calculate_mean(prices), calculate_median(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_mean,
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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_mean,
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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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"next_price_trend_change_in": trend_calculator.get_trend_change_in_minutes_value,
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# Price outlook sensors (current price vs average of next Xh)
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"price_outlook_1h": lambda: trend_calculator.get_price_outlook_value(hours=1),
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"price_outlook_2h": lambda: trend_calculator.get_price_outlook_value(hours=2),
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"price_outlook_3h": lambda: trend_calculator.get_price_outlook_value(hours=3),
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"price_outlook_4h": lambda: trend_calculator.get_price_outlook_value(hours=4),
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"price_outlook_5h": lambda: trend_calculator.get_price_outlook_value(hours=5),
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"price_outlook_6h": lambda: trend_calculator.get_price_outlook_value(hours=6),
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"price_outlook_8h": lambda: trend_calculator.get_price_outlook_value(hours=8),
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"price_outlook_12h": lambda: trend_calculator.get_price_outlook_value(hours=12),
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# Price trajectory sensors (first-half vs second-half window, reveals turning points)
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"price_trajectory_2h": lambda: trend_calculator.get_price_trajectory_value(hours=2),
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"price_trajectory_3h": lambda: trend_calculator.get_price_trajectory_value(hours=3),
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"price_trajectory_4h": lambda: trend_calculator.get_price_trajectory_value(hours=4),
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"price_trajectory_5h": lambda: trend_calculator.get_price_trajectory_value(hours=5),
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"price_trajectory_6h": lambda: trend_calculator.get_price_trajectory_value(hours=6),
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"price_trajectory_8h": lambda: trend_calculator.get_price_trajectory_value(hours=8),
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"price_trajectory_12h": lambda: trend_calculator.get_price_trajectory_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": 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: cast(
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"float | None",
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timing_calculator.get_period_timing_value(period_type="best_price", value_type="period_duration"),
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),
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"best_price_remaining_minutes": lambda: cast(
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"float | None",
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timing_calculator.get_period_timing_value(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: cast(
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"float | None",
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timing_calculator.get_period_timing_value(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: cast(
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"float | None",
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timing_calculator.get_period_timing_value(period_type="peak_price", value_type="period_duration"),
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),
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"peak_price_remaining_minutes": lambda: cast(
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"float | None",
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timing_calculator.get_period_timing_value(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: cast(
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"float | None",
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timing_calculator.get_period_timing_value(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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# Chart metadata sensor
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"chart_metadata": get_chart_metadata_value,
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}
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