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
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
"""Helper functions for sensor attributes."""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from custom_components.tibber_prices.data import TibberPricesConfigEntry
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def add_alternate_average_attribute(
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attributes: dict,
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cached_data: dict,
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base_key: str,
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*,
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config_entry: TibberPricesConfigEntry,
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) -> None:
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"""
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Add both average values (mean and median) as attributes.
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This ensures automations work consistently regardless of which value
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is displayed in the state. Both values are always available as attributes.
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Note: To avoid duplicate recording, the value used as state should be
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excluded from recorder via dynamic _unrecorded_attributes in sensor core.
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Args:
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attributes: Dictionary to add attribute to
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cached_data: Cached calculation data containing mean/median values
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base_key: Base key for cached values (e.g., "average_price_today", "rolling_hour_0")
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config_entry: Config entry for user preferences (used to determine which value is in state)
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"""
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# Always add both mean and median values as attributes
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mean_value = cached_data.get(f"{base_key}_mean")
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if mean_value is not None:
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attributes["price_mean"] = mean_value
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median_value = cached_data.get(f"{base_key}_median")
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if median_value is not None:
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attributes["price_median"] = median_value
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