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https://github.com/jpawlowski/hass.tibber_prices.git
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91efeed90f
New duration sensor showing time until next price trend change as hours (e.g., 2.25 h). Registered in MINUTE_UPDATE_ENTITY_KEYS for per-minute updates. Shares cached attributes with next_price_trend_change timestamp sensor. Added trend attributes to _unrecorded_attributes (threshold/volatility/diff attributes excluded from recorder). Updated timer group size test expectation from 6 to 7. Impact: Users can display a live countdown to the next trend change on dashboards and use it in automations (e.g., "if < 0.25 h, prepare").
42 lines
1.8 KiB
Python
42 lines
1.8 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_trend_") and cached_data.get("trend_attributes"):
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attributes.update(cached_data["trend_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 == "trend_change_in_minutes" 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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