mirror of
https://github.com/jpawlowski/hass.tibber_prices.git
synced 2026-03-30 05:13:40 +00:00
Moved filter logic and all period attribute calculations from binary_sensor.py
to coordinator.py and period_utils.py, following Home Assistant best practices
for data flow architecture.
ARCHITECTURE CHANGES:
Binary Sensor Simplification (~225 lines removed):
- Removed _build_periods_summary, _add_price_diff_for_period (calculation logic)
- Removed _get_period_intervals_from_price_info (107 lines, interval reconstruction)
- Removed _should_show_periods, _check_volatility_filter, _check_level_filter
- Removed _build_empty_periods_result (filtering result builder)
- Removed _get_price_hours_attributes (24 lines, dead code)
- Removed datetime import (unused after cleanup)
- New: _build_final_attributes_simple (~20 lines, timestamp-only)
- Result: Pure display-only logic, reads pre-calculated data from coordinator
Coordinator Enhancement (+160 lines):
- Added _should_show_periods(): UND-Verknüpfung of volatility and level filters
- Added _check_volatility_filter(): Checks min_volatility threshold
- Added _check_level_filter(): Checks min/max level bounds
- Enhanced _calculate_periods_for_price_info(): Applies filters before period calculation
- Returns empty periods when filters don't match (instead of calculating unnecessarily)
- Passes volatility thresholds (moderate/high/very_high) to PeriodConfig
Period Utils Refactoring (+110 lines):
- Extended PeriodConfig with threshold_volatility_moderate/high/very_high
- Added PeriodData NamedTuple: Groups timing data (start, end, length, position)
- Added PeriodStatistics NamedTuple: Groups calculated stats (prices, volatility, ratings)
- Added ThresholdConfig NamedTuple: Groups all thresholds + reverse_sort flag
- New _calculate_period_price_statistics(): Extracts price_avg/min/max/spread calculation
- New _build_period_summary_dict(): Builds final dict with correct attribute ordering
- Enhanced _extract_period_summaries(): Now calculates ALL attributes (no longer lightweight):
* price_avg, price_min, price_max, price_spread (in minor units: ct/øre)
* volatility (low/moderate/high/very_high based on absolute thresholds)
* rating_difference_% (average of interval differences)
* period_price_diff_from_daily_min/max (period avg vs daily reference)
* aggregated level and rating_level
* period_interval_count (renamed from interval_count for clarity)
- Removed interval_starts array (redundant - start/end/count sufficient)
- Function signature refactored from 9→4 parameters using NamedTuples
Code Organization (HA Best Practice):
- Moved calculate_volatility_level() from const.py to price_utils.py
- Rule: const.py should contain only constants, no functions
- Removed duplicate VOLATILITY_THRESHOLD_* constants from const.py
- Updated imports in sensor.py, services.py, period_utils.py
DATA FLOW:
Before:
API → Coordinator (basic enrichment) → Binary Sensor (calculate everything on each access)
After:
API → Coordinator (enrichment + filtering + period calculation with ALL attributes) →
Cached Data → Binary Sensor (display + timestamp only)
ATTRIBUTE STRUCTURE:
Period summaries now contain (following copilot-instructions.md ordering):
1. Time: start, end, duration_minutes
2. Decision: level, rating_level, rating_difference_%
3. Prices: price_avg, price_min, price_max, price_spread, volatility
4. Differences: period_price_diff_from_daily_min/max (conditional)
5. Details: period_interval_count, period_position
6. Meta: periods_total, periods_remaining
BREAKING CHANGES: None
- Period data structure enhanced but backwards compatible
- Binary sensor API unchanged (state + attributes)
Impact: Binary sensors now display pre-calculated data from coordinator instead
of calculating on every access. Reduces complexity, improves performance, and
centralizes business logic following Home Assistant coordinator pattern. All
period filtering (volatility + level) now happens in coordinator before caching.
491 lines
18 KiB
Python
491 lines
18 KiB
Python
"""Binary sensor platform for tibber_prices."""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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from homeassistant.components.binary_sensor import (
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BinarySensorDeviceClass,
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BinarySensorEntity,
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BinarySensorEntityDescription,
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)
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from homeassistant.const import EntityCategory
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from homeassistant.core import callback
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from homeassistant.util import dt as dt_util
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from .coordinator import TIME_SENSITIVE_ENTITY_KEYS
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from .entity import TibberPricesEntity
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if TYPE_CHECKING:
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from collections.abc import Callable
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from homeassistant.core import HomeAssistant
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from homeassistant.helpers.entity_platform import AddEntitiesCallback
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from .coordinator import TibberPricesDataUpdateCoordinator
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from .data import TibberPricesConfigEntry
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from .const import (
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CONF_EXTENDED_DESCRIPTIONS,
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DEFAULT_EXTENDED_DESCRIPTIONS,
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async_get_entity_description,
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get_entity_description,
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)
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MINUTES_PER_INTERVAL = 15
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MIN_TOMORROW_INTERVALS_15MIN = 96
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ENTITY_DESCRIPTIONS = (
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BinarySensorEntityDescription(
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key="peak_price_period",
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translation_key="peak_price_period",
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name="Peak Price Interval",
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icon="mdi:clock-alert",
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),
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BinarySensorEntityDescription(
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key="best_price_period",
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translation_key="best_price_period",
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name="Best Price Interval",
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icon="mdi:clock-check",
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),
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BinarySensorEntityDescription(
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key="connection",
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translation_key="connection",
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name="Tibber API Connection",
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device_class=BinarySensorDeviceClass.CONNECTIVITY,
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entity_category=EntityCategory.DIAGNOSTIC,
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),
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BinarySensorEntityDescription(
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key="tomorrow_data_available",
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translation_key="tomorrow_data_available",
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name="Tomorrow's Data Available",
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icon="mdi:calendar-check",
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entity_category=EntityCategory.DIAGNOSTIC,
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),
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)
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async def async_setup_entry(
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_hass: HomeAssistant,
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entry: TibberPricesConfigEntry,
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async_add_entities: AddEntitiesCallback,
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) -> None:
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"""Set up the binary_sensor platform."""
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async_add_entities(
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TibberPricesBinarySensor(
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coordinator=entry.runtime_data.coordinator,
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entity_description=entity_description,
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)
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for entity_description in ENTITY_DESCRIPTIONS
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)
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class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
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"""tibber_prices binary_sensor class."""
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def __init__(
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self,
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coordinator: TibberPricesDataUpdateCoordinator,
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entity_description: BinarySensorEntityDescription,
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) -> None:
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"""Initialize the binary_sensor class."""
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super().__init__(coordinator)
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self.entity_description = entity_description
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self._attr_unique_id = f"{coordinator.config_entry.entry_id}_{entity_description.key}"
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self._state_getter: Callable | None = self._get_state_getter()
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self._attribute_getter: Callable | None = self._get_attribute_getter()
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self._time_sensitive_remove_listener: Callable | None = None
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async def async_added_to_hass(self) -> None:
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"""When entity is added to hass."""
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await super().async_added_to_hass()
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# Register with coordinator for time-sensitive updates if applicable
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if self.entity_description.key in TIME_SENSITIVE_ENTITY_KEYS:
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self._time_sensitive_remove_listener = self.coordinator.async_add_time_sensitive_listener(
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self._handle_time_sensitive_update
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)
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async def async_will_remove_from_hass(self) -> None:
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"""When entity will be removed from hass."""
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await super().async_will_remove_from_hass()
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# Remove time-sensitive listener if registered
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if self._time_sensitive_remove_listener:
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self._time_sensitive_remove_listener()
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self._time_sensitive_remove_listener = None
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@callback
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def _handle_time_sensitive_update(self) -> None:
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"""Handle time-sensitive update from coordinator."""
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self.async_write_ha_state()
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def _get_state_getter(self) -> Callable | None:
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"""Return the appropriate state getter method based on the sensor type."""
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key = self.entity_description.key
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if key == "peak_price_period":
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return self._peak_price_state
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if key == "best_price_period":
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return self._best_price_state
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if key == "connection":
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return lambda: True if self.coordinator.data else None
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if key == "tomorrow_data_available":
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return self._tomorrow_data_available_state
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return None
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def _best_price_state(self) -> bool | None:
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"""Return True if the current time is within a best price period."""
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if not self.coordinator.data:
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return None
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attrs = self._get_price_intervals_attributes(reverse_sort=False)
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if not attrs:
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return False # Should not happen, but safety fallback
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start = attrs.get("start")
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end = attrs.get("end")
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if not start or not end:
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return False # No period found = sensor is off
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now = dt_util.now()
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return start <= now < end
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def _peak_price_state(self) -> bool | None:
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"""Return True if the current time is within a peak price period."""
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if not self.coordinator.data:
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return None
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attrs = self._get_price_intervals_attributes(reverse_sort=True)
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if not attrs:
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return False # Should not happen, but safety fallback
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start = attrs.get("start")
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end = attrs.get("end")
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if not start or not end:
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return False # No period found = sensor is off
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now = dt_util.now()
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return start <= now < end
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def _tomorrow_data_available_state(self) -> bool | None:
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"""Return True if tomorrow's data is fully available, False if not, None if unknown."""
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if not self.coordinator.data:
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return None
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price_info = self.coordinator.data.get("priceInfo", {})
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tomorrow_prices = price_info.get("tomorrow", [])
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interval_count = len(tomorrow_prices)
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if interval_count == MIN_TOMORROW_INTERVALS_15MIN:
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return True
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if interval_count == 0:
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return False
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return False
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def _get_tomorrow_data_available_attributes(self) -> dict | None:
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"""Return attributes for tomorrow_data_available binary sensor."""
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if not self.coordinator.data:
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return None
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price_info = self.coordinator.data.get("priceInfo", {})
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tomorrow_prices = price_info.get("tomorrow", [])
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interval_count = len(tomorrow_prices)
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if interval_count == 0:
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status = "none"
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elif interval_count == MIN_TOMORROW_INTERVALS_15MIN:
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status = "full"
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else:
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status = "partial"
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return {
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"intervals_available": interval_count,
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"data_status": status,
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}
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def _get_attribute_getter(self) -> Callable | None:
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"""Return the appropriate attribute getter method based on the sensor type."""
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key = self.entity_description.key
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if key == "peak_price_period":
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return lambda: self._get_price_intervals_attributes(reverse_sort=True)
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if key == "best_price_period":
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return lambda: self._get_price_intervals_attributes(reverse_sort=False)
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if key == "tomorrow_data_available":
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return self._get_tomorrow_data_available_attributes
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return None
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def _get_precomputed_period_data(self, *, reverse_sort: bool) -> dict | None:
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"""
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Get precomputed period data from coordinator.
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Returns lightweight period summaries (no full price data to avoid redundancy).
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"""
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if not self.coordinator.data:
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return None
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periods_data = self.coordinator.data.get("periods", {})
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period_type = "peak_price" if reverse_sort else "best_price"
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return periods_data.get(period_type)
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def _get_price_intervals_attributes(self, *, reverse_sort: bool) -> dict | None:
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"""
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Get price interval attributes using precomputed data from coordinator.
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All data is already calculated in the coordinator - we just need to:
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1. Get period summaries from coordinator (already filtered and fully calculated)
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2. Add the current timestamp
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3. Find current or next period based on time
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Note: All calculations (filtering, aggregations, level/rating) are done in coordinator.
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"""
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# Get precomputed period summaries from coordinator (already filtered and complete!)
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period_data = self._get_precomputed_period_data(reverse_sort=reverse_sort)
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if not period_data:
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return self._build_no_periods_result()
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period_summaries = period_data.get("periods", [])
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if not period_summaries:
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return self._build_no_periods_result()
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# Find current or next period based on current time
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now = dt_util.now()
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current_period = None
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# First pass: find currently active period
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for period in period_summaries:
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start = period.get("start")
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end = period.get("end")
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if start and end and start <= now < end:
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current_period = period
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break
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# Second pass: find next future period if none is active
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if not current_period:
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for period in period_summaries:
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start = period.get("start")
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if start and start > now:
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current_period = period
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break
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# Build final attributes
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return self._build_final_attributes_simple(current_period, period_summaries)
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def _build_no_periods_result(self) -> dict:
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"""
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Build result when no periods exist (not filtered, just none available).
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Returns:
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A dict with empty periods and timestamp.
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"""
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# Calculate timestamp: current time rounded down to last quarter hour
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now = dt_util.now()
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current_minute = (now.minute // 15) * 15
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timestamp = now.replace(minute=current_minute, second=0, microsecond=0)
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return {
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"timestamp": timestamp,
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"start": None,
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"end": None,
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"periods": [],
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}
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def _build_final_attributes_simple(
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self,
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current_period: dict | None,
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period_summaries: list[dict],
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) -> dict:
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"""
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Build the final attributes dictionary from coordinator's period summaries.
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All calculations are done in the coordinator - this just:
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1. Adds the current timestamp (only thing calculated every 15min)
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2. Uses the current/next period from summaries
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3. Adds nested period summaries
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Args:
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current_period: The current or next period (already complete from coordinator)
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period_summaries: All period summaries from coordinator
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"""
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now = dt_util.now()
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current_minute = (now.minute // 15) * 15
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timestamp = now.replace(minute=current_minute, second=0, microsecond=0)
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if current_period:
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# Start with complete period summary from coordinator (already has all attributes!)
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attributes = {
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"timestamp": timestamp, # ONLY thing we calculate here!
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**current_period, # All other attributes come from coordinator
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}
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# Add nested period summaries last (meta information)
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attributes["periods"] = period_summaries
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return attributes
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# No current/next period found - return all periods with timestamp
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return {
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"timestamp": timestamp,
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"periods": period_summaries,
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}
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@property
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def is_on(self) -> bool | None:
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"""Return true if the binary_sensor is on."""
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try:
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if not self.coordinator.data or not self._state_getter:
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return None
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return self._state_getter()
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except (KeyError, ValueError, TypeError) as ex:
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self.coordinator.logger.exception(
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"Error getting binary sensor state",
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extra={
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"error": str(ex),
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"entity": self.entity_description.key,
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},
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)
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return None
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@property
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async def async_extra_state_attributes(self) -> dict | None:
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"""Return additional state attributes asynchronously."""
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try:
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# Get the dynamic attributes if the getter is available
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if not self.coordinator.data:
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return None
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attributes = {}
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if self._attribute_getter:
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dynamic_attrs = self._attribute_getter()
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if dynamic_attrs:
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# Copy and remove internal fields before exposing to user
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clean_attrs = {k: v for k, v in dynamic_attrs.items() if not k.startswith("_")}
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attributes.update(clean_attrs)
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# Add descriptions from the custom translations file
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if self.entity_description.translation_key and self.hass is not None:
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# Get user's language preference
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language = self.hass.config.language if self.hass.config.language else "en"
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# Add basic description
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description = await async_get_entity_description(
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self.hass,
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"binary_sensor",
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self.entity_description.translation_key,
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language,
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"description",
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)
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if description:
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attributes["description"] = description
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# Check if extended descriptions are enabled in the config
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extended_descriptions = self.coordinator.config_entry.options.get(
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CONF_EXTENDED_DESCRIPTIONS,
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self.coordinator.config_entry.data.get(CONF_EXTENDED_DESCRIPTIONS, DEFAULT_EXTENDED_DESCRIPTIONS),
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)
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# Add extended descriptions if enabled
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if extended_descriptions:
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# Add long description if available
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long_desc = await async_get_entity_description(
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self.hass,
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"binary_sensor",
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self.entity_description.translation_key,
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language,
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"long_description",
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)
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if long_desc:
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attributes["long_description"] = long_desc
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# Add usage tips if available
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usage_tips = await async_get_entity_description(
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self.hass,
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"binary_sensor",
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self.entity_description.translation_key,
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language,
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"usage_tips",
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)
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if usage_tips:
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attributes["usage_tips"] = usage_tips
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except (KeyError, ValueError, TypeError) as ex:
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self.coordinator.logger.exception(
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"Error getting binary sensor attributes",
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extra={
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"error": str(ex),
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"entity": self.entity_description.key,
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},
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)
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return None
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else:
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return attributes if attributes else None
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@property
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def extra_state_attributes(self) -> dict | None:
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"""Return additional state attributes synchronously."""
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try:
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# Start with dynamic attributes if available
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if not self.coordinator.data:
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return None
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attributes = {}
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if self._attribute_getter:
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dynamic_attrs = self._attribute_getter()
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if dynamic_attrs:
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# Copy and remove internal fields before exposing to user
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clean_attrs = {k: v for k, v in dynamic_attrs.items() if not k.startswith("_")}
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attributes.update(clean_attrs)
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# Add descriptions from the cache (non-blocking)
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if self.entity_description.translation_key and self.hass is not None:
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# Get user's language preference
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language = self.hass.config.language if self.hass.config.language else "en"
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# Add basic description from cache
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description = get_entity_description(
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"binary_sensor",
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self.entity_description.translation_key,
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language,
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"description",
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)
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if description:
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attributes["description"] = description
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# Check if extended descriptions are enabled in the config
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extended_descriptions = self.coordinator.config_entry.options.get(
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CONF_EXTENDED_DESCRIPTIONS,
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self.coordinator.config_entry.data.get(CONF_EXTENDED_DESCRIPTIONS, DEFAULT_EXTENDED_DESCRIPTIONS),
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)
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# Add extended descriptions if enabled (from cache only)
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if extended_descriptions:
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# Add long description if available in cache
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long_desc = get_entity_description(
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"binary_sensor",
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self.entity_description.translation_key,
|
|
language,
|
|
"long_description",
|
|
)
|
|
if long_desc:
|
|
attributes["long_description"] = long_desc
|
|
|
|
# Add usage tips if available in cache
|
|
usage_tips = get_entity_description(
|
|
"binary_sensor",
|
|
self.entity_description.translation_key,
|
|
language,
|
|
"usage_tips",
|
|
)
|
|
if usage_tips:
|
|
attributes["usage_tips"] = usage_tips
|
|
|
|
except (KeyError, ValueError, TypeError) as ex:
|
|
self.coordinator.logger.exception(
|
|
"Error getting binary sensor attributes",
|
|
extra={
|
|
"error": str(ex),
|
|
"entity": self.entity_description.key,
|
|
},
|
|
)
|
|
return None
|
|
else:
|
|
return attributes if attributes else None
|
|
|
|
async def async_update(self) -> None:
|
|
"""Force a refresh when homeassistant.update_entity is called."""
|
|
await self.coordinator.async_request_refresh()
|