mirror of
https://github.com/jpawlowski/hass.tibber_prices.git
synced 2026-05-28 18:43:40 +00:00
b1b41be9aa
Renamed trend_change_in_minutes → next_price_trend_change_in to align with its sibling sensor next_price_trend_change (timestamp variant). Follows the established best/peak price naming pattern where related sensors share a common prefix (e.g. best_price_next_start_time / best_price_next_in_minutes). Updated entity key, translation key, friendly names (all 5 languages), custom translations, coordinator constants, attribute routing, and cache-clear mapping. BREAKING CHANGE: Entity ID changes from sensor.<home>_trend_change_in_minutes to sensor.<home>_next_price_trend_change_in. Automations and dashboards referencing the old entity ID must be updated. Impact: Users with automations or dashboard cards referencing the old sensor name need to update references. The sensor retains identical functionality and attributes.
44 lines
2 KiB
Python
44 lines
2 KiB
Python
"""Trend attribute builders for Tibber Prices sensors."""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any
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if TYPE_CHECKING:
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from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
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from .timing import add_period_timing_attributes
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from .volatility import add_volatility_attributes
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def _add_timing_or_volatility_attributes(
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attributes: dict,
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key: str,
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cached_data: dict,
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native_value: Any = None,
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*,
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time: TibberPricesTimeService,
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) -> None:
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"""Add attributes for timing or volatility sensors."""
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if key.endswith("_volatility"):
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add_volatility_attributes(attributes=attributes, cached_data=cached_data, time=time)
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else:
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add_period_timing_attributes(attributes=attributes, key=key, state_value=native_value, time=time)
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def _add_cached_trend_attributes(attributes: dict, key: str, cached_data: dict) -> None:
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"""Add cached trend attributes if available."""
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if key.startswith("price_outlook_") and cached_data.get("trend_attributes"):
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attributes.update(cached_data["trend_attributes"])
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elif key.startswith("price_trajectory_") and cached_data.get("trajectory_attributes"):
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attributes.update(cached_data["trajectory_attributes"])
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elif key == "current_price_trend" and cached_data.get("current_trend_attributes"):
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# Add cached attributes (timestamp already set by platform)
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attributes.update(cached_data["current_trend_attributes"])
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elif key == "next_price_trend_change" and cached_data.get("trend_change_attributes"):
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# Add cached attributes (timestamp already set by platform)
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# State contains the timestamp of the trend change itself
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attributes.update(cached_data["trend_change_attributes"])
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elif key == "next_price_trend_change_in" and cached_data.get("trend_change_attributes"):
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# Duration sensor shares same cached attributes as the timestamp sensor
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attributes.update(cached_data["trend_change_attributes"])
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