mirror of
https://github.com/jpawlowski/hass.tibber_prices.git
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1d065b11cd
_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
341 lines
19 KiB
Python
341 lines
19 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(
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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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# Day pattern sensors (via MetadataCalculator)
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"day_pattern_yesterday": lambda: metadata_calculator.get_day_pattern_value("yesterday"),
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"day_pattern_today": lambda: metadata_calculator.get_day_pattern_value("today"),
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"day_pattern_tomorrow": lambda: metadata_calculator.get_day_pattern_value("tomorrow"),
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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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# Price rank sensors (via VolatilityCalculator - reuses same price extraction)
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# Current interval rank
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"current_interval_price_rank_today": lambda: volatility_calculator.get_percentile_rank_value(
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subject="current_interval", percentile_type="today"
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),
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"current_interval_price_rank_tomorrow": lambda: volatility_calculator.get_percentile_rank_value(
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subject="current_interval", percentile_type="tomorrow"
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),
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"current_interval_price_rank_today_tomorrow": lambda: volatility_calculator.get_percentile_rank_value(
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subject="current_interval", percentile_type="today_tomorrow"
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),
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# Next interval rank
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"next_interval_price_rank_today": lambda: volatility_calculator.get_percentile_rank_value(
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subject="next_interval", percentile_type="today"
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),
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"next_interval_price_rank_today_tomorrow": lambda: volatility_calculator.get_percentile_rank_value(
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subject="next_interval", percentile_type="today_tomorrow"
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),
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# Previous interval rank
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"previous_interval_price_rank_today": lambda: volatility_calculator.get_percentile_rank_value(
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subject="previous_interval", percentile_type="today"
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),
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"previous_interval_price_rank_today_tomorrow": lambda: volatility_calculator.get_percentile_rank_value(
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subject="previous_interval", percentile_type="today_tomorrow"
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),
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# Rolling-hour rank (1h average)
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"current_hour_price_rank_today": lambda: volatility_calculator.get_percentile_rank_value(
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subject="current_hour", percentile_type="today"
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),
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"current_hour_price_rank_today_tomorrow": lambda: volatility_calculator.get_percentile_rank_value(
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subject="current_hour", percentile_type="today_tomorrow"
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),
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"next_hour_price_rank_today": lambda: volatility_calculator.get_percentile_rank_value(
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subject="next_hour", percentile_type="today"
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),
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"next_hour_price_rank_today_tomorrow": lambda: volatility_calculator.get_percentile_rank_value(
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subject="next_hour", percentile_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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