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https://github.com/jpawlowski/hass.tibber_prices.git
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6922e52368
Implemented new chart_metadata diagnostic sensor that provides essential chart configuration values (yaxis_min, yaxis_max, gradient_stop) as attributes, enabling dynamic chart configuration without requiring async service calls in templates. Sensor implementation: - New chart_metadata.py module with metadata-only service calls - Automatically calls get_chartdata with metadata="only" parameter - Refreshes on coordinator updates (new price data or user data) - State values: "pending", "ready", "error" - Enabled by default (critical for chart features) ApexCharts YAML generator integration: - Checks for chart_metadata sensor availability before generation - Uses template variables to read sensor attributes dynamically - Fallback to fixed values (gradient_stop=50%) if sensor unavailable - Creates separate notifications for two independent issues: 1. Chart metadata sensor disabled (reduced functionality warning) 2. Required custom cards missing (YAML won't work warning) - Both notifications explain YAML generation context and provide complete fix instructions with regeneration requirement Configuration: - Supports configuration.yaml: tibber_prices.chart_metadata_config - Optional parameters: day, minor_currency, resolution - Defaults to minor_currency=True for ApexCharts compatibility Translation additions: - Entity name and state translations (all 5 languages) - Notification messages for sensor unavailable and missing cards - best_price_period_name for tooltip formatter Binary sensor improvements: - tomorrow_data_available now enabled by default (critical for automations) - data_lifecycle_status now enabled by default (critical for debugging) Impact: Users get dynamic chart configuration with optimized Y-axis scaling and gradient positioning without manual calculations. ApexCharts YAML generation now provides clear, actionable notifications when issues occur, ensuring users understand why functionality is limited and how to fix it.
282 lines
16 KiB
Python
282 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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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_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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# Chart metadata sensor
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"chart_metadata": get_chart_metadata_value,
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}
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