jpawlowski/hass.tibber_prices
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hass.tibber_prices/custom_components/tibber_prices/sensor.py
Julian Pawlowski c4f36d04de feat(icons): add dynamic icons and colors for all sensor types 
Implemented comprehensive dynamic icon and color system across all sensor types:

Price Sensors (5 sensors):
- Current/hour prices: Dynamic cash-family icons based on price level
  (cash-multiple/plus/cash/minus/remove)
- Next/previous: Static contextual icons (cash-fast, cash-refund, clock-fast)
- All have icon_color attribute for card-mod styling

Price Level Sensors (5 sensors):
- Dynamic gauge-family icons: gauge-empty → gauge-low → gauge → gauge-full → alert
- icon_color attribute with CSS variables (green/gray/orange/red)

Price Rating Sensors (5 sensors):
- Dynamic thumb-family icons: thumb-up → thumbs-up-down → thumb-down
- icon_color attribute for LOW/NORMAL/HIGH ratings

Volatility Sensors (4 sensors):
- Dynamic chart-family icons: chart-line-variant → chart-timeline-variant →
  chart-bar → chart-scatter-plot
- icon_color attribute for LOW/MODERATE/HIGH/VERY_HIGH levels

Trend Sensors (8 sensors):
- Dynamic trend icons: trending-up/down/neutral based on price movement
- icon_color attribute (red=rising, green=falling, gray=stable)

Binary Sensors (2 sensors):
- Best Price Period: piggy-bank (ON) / timer-sand or timer-sand-complete (OFF)
- Peak Price Period: alert-circle (ON) / shield-check or shield-check-outline (OFF)
- 6-hour lookahead window for intelligent OFF state icons
- icon_color attribute for all states

Technical implementation:
- PRICE_LEVEL_CASH_ICON_MAPPING in const.py for price sensor icons
- PRICE_SENSOR_ICON_MAPPING removed (static icons now in entity descriptions)
- Centralized icon logic in sensor.py icon property
- All color mappings use CSS variables for theme compatibility
- Binary sensors detect future periods within 6-hour window

Impact: Users now have visual indicators for all price-related states without
requiring card-mod. Optional card-mod styling available via icon_color attribute
for advanced customization. Icons update dynamically as price levels, ratings,
volatility, and trends change throughout the day.
2025-11-14 11:31:25 +00:00

2319 lines
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Python
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"""Sensor platform for tibber_prices."""
from __future__ import annotations
from datetime import date, datetime, timedelta
from typing import TYPE_CHECKING, Any
from homeassistant.components.sensor import (
SensorDeviceClass,
SensorEntity,
SensorEntityDescription,
)
from homeassistant.const import PERCENTAGE, EntityCategory
from homeassistant.core import callback
from homeassistant.util import dt as dt_util
from .average_utils import (
calculate_current_leading_avg,
calculate_current_leading_max,
calculate_current_leading_min,
calculate_current_rolling_5interval_avg,
calculate_current_trailing_avg,
calculate_current_trailing_max,
calculate_current_trailing_min,
calculate_next_hour_rolling_5interval_avg,
calculate_next_n_hours_avg,
)
from .const import (
CONF_EXTENDED_DESCRIPTIONS,
CONF_PRICE_RATING_THRESHOLD_HIGH,
CONF_PRICE_RATING_THRESHOLD_LOW,
CONF_PRICE_TREND_THRESHOLD_FALLING,
CONF_PRICE_TREND_THRESHOLD_RISING,
DEFAULT_EXTENDED_DESCRIPTIONS,
DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
DEFAULT_PRICE_RATING_THRESHOLD_LOW,
DEFAULT_PRICE_TREND_THRESHOLD_FALLING,
DEFAULT_PRICE_TREND_THRESHOLD_RISING,
DOMAIN,
PRICE_LEVEL_CASH_ICON_MAPPING,
PRICE_LEVEL_COLOR_MAPPING,
PRICE_LEVEL_ICON_MAPPING,
PRICE_LEVEL_MAPPING,
PRICE_RATING_COLOR_MAPPING,
PRICE_RATING_ICON_MAPPING,
PRICE_RATING_MAPPING,
VOLATILITY_COLOR_MAPPING,
VOLATILITY_ICON_MAPPING,
async_get_entity_description,
format_price_unit_minor,
get_entity_description,
get_price_level_translation,
)
from .coordinator import TIME_SENSITIVE_ENTITY_KEYS
from .entity import TibberPricesEntity
from .price_utils import (
MINUTES_PER_INTERVAL,
aggregate_price_levels,
aggregate_price_rating,
calculate_price_trend,
calculate_volatility_level,
find_price_data_for_interval,
)
if TYPE_CHECKING:
from collections.abc import Callable
from homeassistant.core import HomeAssistant
from homeassistant.helpers.entity_platform import AddEntitiesCallback
from .coordinator import TibberPricesDataUpdateCoordinator
from .data import TibberPricesConfigEntry
HOURS_IN_DAY = 24
LAST_HOUR_OF_DAY = 23
INTERVALS_PER_HOUR = 4 # 15-minute intervals
MAX_FORECAST_INTERVALS = 8 # Show up to 8 future intervals (2 hours with 15-min intervals)
MIN_HOURS_FOR_LATER_HALF = 3 # Minimum hours needed to calculate later half average
# Main price sensors that users will typically use in automations
PRICE_SENSORS = (
SensorEntityDescription(
key="current_price",
translation_key="current_price",
name="Current Electricity Price",
icon="mdi:cash", # Dynamic: will show cash-multiple/plus/cash/minus/remove based on level
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=2,
),
SensorEntityDescription(
key="next_interval_price",
translation_key="next_interval_price",
name="Next Price",
icon="mdi:cash-fast", # Static: motion lines indicate "coming soon"
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=2,
),
SensorEntityDescription(
key="previous_interval_price",
translation_key="previous_interval_price",
name="Previous Electricity Price",
icon="mdi:cash-refund", # Static: arrow back indicates "past"
device_class=SensorDeviceClass.MONETARY,
entity_registry_enabled_default=False,
suggested_display_precision=2,
),
SensorEntityDescription(
key="current_hour_average",
translation_key="current_hour_average",
name="Current Hour Average Price",
icon="mdi:cash", # Dynamic: will show cash-multiple/plus/cash/minus/remove based on level
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
),
SensorEntityDescription(
key="next_hour_average",
translation_key="next_hour_average",
name="Next Hour Average Price",
icon="mdi:clock-fast", # Static: clock indicates "next time period"
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
),
# NOTE: Enum options are defined inline (not imported from const.py) to avoid
# import timing issues with Home Assistant's entity platform initialization.
# Keep in sync with PRICE_LEVEL_OPTIONS in const.py!
SensorEntityDescription(
key="price_level",
translation_key="price_level",
name="Current Price Level",
icon="mdi:gauge",
device_class=SensorDeviceClass.ENUM,
options=["very_cheap", "cheap", "normal", "expensive", "very_expensive"],
),
SensorEntityDescription(
key="next_interval_price_level",
translation_key="next_interval_price_level",
name="Next Price Level",
icon="mdi:gauge-empty",
device_class=SensorDeviceClass.ENUM,
options=["very_cheap", "cheap", "normal", "expensive", "very_expensive"],
),
SensorEntityDescription(
key="previous_interval_price_level",
translation_key="previous_interval_price_level",
name="Previous Price Level",
icon="mdi:gauge-empty",
entity_registry_enabled_default=False,
device_class=SensorDeviceClass.ENUM,
options=["very_cheap", "cheap", "normal", "expensive", "very_expensive"],
),
SensorEntityDescription(
key="current_hour_price_level",
translation_key="current_hour_price_level",
name="Current Hour Price Level",
icon="mdi:gauge",
device_class=SensorDeviceClass.ENUM,
options=["very_cheap", "cheap", "normal", "expensive", "very_expensive"],
),
SensorEntityDescription(
key="next_hour_price_level",
translation_key="next_hour_price_level",
name="Next Hour Price Level",
icon="mdi:gauge-empty",
device_class=SensorDeviceClass.ENUM,
options=["very_cheap", "cheap", "normal", "expensive", "very_expensive"],
),
)
# Statistical price sensors
STATISTICS_SENSORS = (
SensorEntityDescription(
key="lowest_price_today",
translation_key="lowest_price_today",
name="Today's Lowest Price",
icon="mdi:arrow-collapse-down",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
),
SensorEntityDescription(
key="highest_price_today",
translation_key="highest_price_today",
name="Today's Highest Price",
icon="mdi:arrow-collapse-up",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
),
SensorEntityDescription(
key="average_price_today",
translation_key="average_price_today",
name="Today's Average Price",
icon="mdi:chart-line",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
),
SensorEntityDescription(
key="lowest_price_tomorrow",
translation_key="lowest_price_tomorrow",
name="Tomorrow's Lowest Price",
icon="mdi:arrow-collapse-down",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
),
SensorEntityDescription(
key="highest_price_tomorrow",
translation_key="highest_price_tomorrow",
name="Tomorrow's Highest Price",
icon="mdi:arrow-collapse-up",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
),
SensorEntityDescription(
key="average_price_tomorrow",
translation_key="average_price_tomorrow",
name="Tomorrow's Average Price",
icon="mdi:chart-line",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
),
SensorEntityDescription(
key="trailing_price_average",
translation_key="trailing_price_average",
name="Trailing 24h Average Price",
icon="mdi:chart-line",
device_class=SensorDeviceClass.MONETARY,
entity_registry_enabled_default=False,
suggested_display_precision=1,
),
SensorEntityDescription(
key="leading_price_average",
translation_key="leading_price_average",
name="Leading 24h Average Price",
icon="mdi:chart-line-variant",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
),
SensorEntityDescription(
key="trailing_price_min",
translation_key="trailing_price_min",
name="Trailing 24h Minimum Price",
icon="mdi:arrow-collapse-down",
device_class=SensorDeviceClass.MONETARY,
entity_registry_enabled_default=False,
suggested_display_precision=1,
),
SensorEntityDescription(
key="trailing_price_max",
translation_key="trailing_price_max",
name="Trailing 24h Maximum Price",
icon="mdi:arrow-collapse-up",
device_class=SensorDeviceClass.MONETARY,
entity_registry_enabled_default=False,
suggested_display_precision=1,
),
SensorEntityDescription(
key="leading_price_min",
translation_key="leading_price_min",
name="Leading 24h Minimum Price",
icon="mdi:arrow-collapse-down",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
),
SensorEntityDescription(
key="leading_price_max",
translation_key="leading_price_max",
name="Leading 24h Maximum Price",
icon="mdi:arrow-collapse-up",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
),
)
# Volatility sensors (coefficient of variation analysis)
# NOTE: Enum options are defined inline (not imported from const.py) to avoid
# import timing issues with Home Assistant's entity platform initialization.
# Keep in sync with VOLATILITY_OPTIONS in const.py!
VOLATILITY_SENSORS = (
SensorEntityDescription(
key="today_volatility",
translation_key="today_volatility",
name="Today's Price Volatility",
icon="mdi:chart-bell-curve-cumulative",
device_class=SensorDeviceClass.ENUM,
options=["low", "moderate", "high", "very_high"],
),
SensorEntityDescription(
key="tomorrow_volatility",
translation_key="tomorrow_volatility",
name="Tomorrow's Price Volatility",
icon="mdi:chart-bell-curve-cumulative",
device_class=SensorDeviceClass.ENUM,
options=["low", "moderate", "high", "very_high"],
),
SensorEntityDescription(
key="next_24h_volatility",
translation_key="next_24h_volatility",
name="Next 24h Price Volatility",
icon="mdi:chart-bell-curve-cumulative",
device_class=SensorDeviceClass.ENUM,
options=["low", "moderate", "high", "very_high"],
),
SensorEntityDescription(
key="today_tomorrow_volatility",
translation_key="today_tomorrow_volatility",
name="Today + Tomorrow Price Volatility",
icon="mdi:chart-bell-curve-cumulative",
device_class=SensorDeviceClass.ENUM,
options=["low", "moderate", "high", "very_high"],
),
)
# Rating sensors
# NOTE: Enum options are defined inline (not imported from const.py) to avoid
# import timing issues with Home Assistant's entity platform initialization.
# Keep in sync with PRICE_RATING_OPTIONS in const.py!
RATING_SENSORS = (
SensorEntityDescription(
key="price_rating",
translation_key="price_rating",
name="Current Price Rating",
icon="mdi:star-outline",
device_class=SensorDeviceClass.ENUM,
options=["low", "normal", "high"],
),
SensorEntityDescription(
key="next_interval_price_rating",
translation_key="next_interval_price_rating",
name="Next Price Rating",
icon="mdi:star-half-full",
device_class=SensorDeviceClass.ENUM,
options=["low", "normal", "high"],
),
SensorEntityDescription(
key="previous_interval_price_rating",
translation_key="previous_interval_price_rating",
name="Previous Price Rating",
icon="mdi:star-half-full",
entity_registry_enabled_default=False,
device_class=SensorDeviceClass.ENUM,
options=["low", "normal", "high"],
),
SensorEntityDescription(
key="current_hour_price_rating",
translation_key="current_hour_price_rating",
name="Current Hour Price Rating",
icon="mdi:star-outline",
device_class=SensorDeviceClass.ENUM,
options=["low", "normal", "high"],
),
SensorEntityDescription(
key="next_hour_price_rating",
translation_key="next_hour_price_rating",
name="Next Hour Price Rating",
icon="mdi:star-half-full",
device_class=SensorDeviceClass.ENUM,
options=["low", "normal", "high"],
),
)
# Future average sensors (rolling N-hour windows from next interval)
FUTURE_AVERAGE_SENSORS = (
# Default enabled: 1h-5h
SensorEntityDescription(
key="next_avg_1h",
translation_key="next_avg_1h",
name="Next 1h Average Price",
icon="mdi:chart-line",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
entity_registry_enabled_default=True,
),
SensorEntityDescription(
key="next_avg_2h",
translation_key="next_avg_2h",
name="Next 2h Average Price",
icon="mdi:chart-line",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
entity_registry_enabled_default=True,
),
SensorEntityDescription(
key="next_avg_3h",
translation_key="next_avg_3h",
name="Next 3h Average Price",
icon="mdi:chart-line",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
entity_registry_enabled_default=True,
),
SensorEntityDescription(
key="next_avg_4h",
translation_key="next_avg_4h",
name="Next 4h Average Price",
icon="mdi:chart-line",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
entity_registry_enabled_default=True,
),
SensorEntityDescription(
key="next_avg_5h",
translation_key="next_avg_5h",
name="Next 5h Average Price",
icon="mdi:chart-line",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
entity_registry_enabled_default=True,
),
# Disabled by default: 6h, 8h, 12h (advanced use cases)
SensorEntityDescription(
key="next_avg_6h",
translation_key="next_avg_6h",
name="Next 6h Average Price",
icon="mdi:chart-line",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
entity_registry_enabled_default=False,
),
SensorEntityDescription(
key="next_avg_8h",
translation_key="next_avg_8h",
name="Next 8h Average Price",
icon="mdi:chart-line",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
entity_registry_enabled_default=False,
),
SensorEntityDescription(
key="next_avg_12h",
translation_key="next_avg_12h",
name="Next 12h Average Price",
icon="mdi:chart-line",
device_class=SensorDeviceClass.MONETARY,
suggested_display_precision=1,
entity_registry_enabled_default=False,
),
)
# Price trend sensors
TREND_SENSORS = (
# Default enabled: 1h-5h
SensorEntityDescription(
key="price_trend_1h",
translation_key="price_trend_1h",
name="Price Trend (1h)",
icon="mdi:trending-up",
device_class=SensorDeviceClass.ENUM,
options=["rising", "falling", "stable"],
entity_registry_enabled_default=True,
),
SensorEntityDescription(
key="price_trend_2h",
translation_key="price_trend_2h",
name="Price Trend (2h)",
icon="mdi:trending-up",
device_class=SensorDeviceClass.ENUM,
options=["rising", "falling", "stable"],
entity_registry_enabled_default=True,
),
SensorEntityDescription(
key="price_trend_3h",
translation_key="price_trend_3h",
name="Price Trend (3h)",
icon="mdi:trending-up",
device_class=SensorDeviceClass.ENUM,
options=["rising", "falling", "stable"],
entity_registry_enabled_default=True,
),
SensorEntityDescription(
key="price_trend_4h",
translation_key="price_trend_4h",
name="Price Trend (4h)",
icon="mdi:trending-up",
device_class=SensorDeviceClass.ENUM,
options=["rising", "falling", "stable"],
entity_registry_enabled_default=True,
),
SensorEntityDescription(
key="price_trend_5h",
translation_key="price_trend_5h",
name="Price Trend (5h)",
icon="mdi:trending-up",
device_class=SensorDeviceClass.ENUM,
options=["rising", "falling", "stable"],
entity_registry_enabled_default=True,
),
# Disabled by default: 6h, 8h, 12h
SensorEntityDescription(
key="price_trend_6h",
translation_key="price_trend_6h",
name="Price Trend (6h)",
icon="mdi:trending-up",
device_class=SensorDeviceClass.ENUM,
options=["rising", "falling", "stable"],
entity_registry_enabled_default=False,
),
SensorEntityDescription(
key="price_trend_8h",
translation_key="price_trend_8h",
name="Price Trend (8h)",
icon="mdi:trending-up",
device_class=SensorDeviceClass.ENUM,
options=["rising", "falling", "stable"],
entity_registry_enabled_default=False,
),
SensorEntityDescription(
key="price_trend_12h",
translation_key="price_trend_12h",
name="Price Trend (12h)",
icon="mdi:trending-up",
device_class=SensorDeviceClass.ENUM,
options=["rising", "falling", "stable"],
entity_registry_enabled_default=False,
),
)
# Diagnostic sensors for data availability
DIAGNOSTIC_SENSORS = (
SensorEntityDescription(
key="data_timestamp",
translation_key="data_timestamp",
name="Data Expiration",
icon="mdi:clock-check",
device_class=SensorDeviceClass.TIMESTAMP,
entity_category=EntityCategory.DIAGNOSTIC,
),
SensorEntityDescription(
key="price_forecast",
translation_key="price_forecast",
name="Price Forecast",
icon="mdi:chart-line",
entity_category=EntityCategory.DIAGNOSTIC,
),
)
# Combine all sensors
ENTITY_DESCRIPTIONS = (
*PRICE_SENSORS,
*STATISTICS_SENSORS,
*VOLATILITY_SENSORS,
*RATING_SENSORS,
*FUTURE_AVERAGE_SENSORS,
*TREND_SENSORS,
*DIAGNOSTIC_SENSORS,
)
async def async_setup_entry(
_hass: HomeAssistant,
entry: TibberPricesConfigEntry,
async_add_entities: AddEntitiesCallback,
) -> None:
"""Set up the sensor platform."""
async_add_entities(
TibberPricesSensor(
coordinator=entry.runtime_data.coordinator,
entity_description=entity_description,
)
for entity_description in ENTITY_DESCRIPTIONS
)
class TibberPricesSensor(TibberPricesEntity, SensorEntity):
"""tibber_prices Sensor class."""
def __init__(
self,
coordinator: TibberPricesDataUpdateCoordinator,
entity_description: SensorEntityDescription,
) -> None:
"""Initialize the sensor class."""
super().__init__(coordinator)
self.entity_description = entity_description
self._attr_unique_id = f"{coordinator.config_entry.entry_id}_{entity_description.key}"
self._attr_has_entity_name = True
self._value_getter: Callable | None = self._get_value_getter()
self._time_sensitive_remove_listener: Callable | None = None
self._trend_attributes: dict[str, Any] = {} # Sensor-specific trend attributes
self._cached_trend_value: str | None = None # Cache for trend state
async def async_added_to_hass(self) -> None:
"""When entity is added to hass."""
await super().async_added_to_hass()
# Register with coordinator for time-sensitive updates if applicable
if self.entity_description.key in TIME_SENSITIVE_ENTITY_KEYS:
self._time_sensitive_remove_listener = self.coordinator.async_add_time_sensitive_listener(
self._handle_time_sensitive_update
)
async def async_will_remove_from_hass(self) -> None:
"""When entity will be removed from hass."""
await super().async_will_remove_from_hass()
# Remove time-sensitive listener if registered
if self._time_sensitive_remove_listener:
self._time_sensitive_remove_listener()
self._time_sensitive_remove_listener = None
@callback
def _handle_time_sensitive_update(self) -> None:
"""Handle time-sensitive update from coordinator."""
# Clear cached trend values on time-sensitive updates
if self.entity_description.key.startswith("price_trend_"):
self._cached_trend_value = None
self._trend_attributes = {}
self.async_write_ha_state()
@callback
def _handle_coordinator_update(self) -> None:
"""Handle updated data from the coordinator."""
# Clear cached trend values when coordinator data changes
if self.entity_description.key.startswith("price_trend_"):
self._cached_trend_value = None
self._trend_attributes = {}
super()._handle_coordinator_update()
def _get_value_getter(self) -> Callable | None:
"""Return the appropriate value getter method based on the sensor type."""
key = self.entity_description.key
# Map sensor keys to their handler methods
handlers = {
# Price level sensors
"price_level": self._get_price_level_value,
"next_interval_price_level": lambda: self._get_interval_level_value(interval_offset=1),
"previous_interval_price_level": lambda: self._get_interval_level_value(interval_offset=-1),
"current_hour_price_level": lambda: self._get_rolling_hour_level_value(hour_offset=0),
"next_hour_price_level": lambda: self._get_rolling_hour_level_value(hour_offset=1),
# Price sensors
"current_price": lambda: self._get_interval_price_value(interval_offset=0, in_euro=False),
"next_interval_price": lambda: self._get_interval_price_value(interval_offset=1, in_euro=False),
"previous_interval_price": lambda: self._get_interval_price_value(interval_offset=-1, in_euro=False),
# Rolling hour average (5 intervals: 2 before + current + 2 after)
"current_hour_average": lambda: self._get_rolling_hour_average_value(
in_euro=False, decimals=2, hour_offset=0
),
"next_hour_average": lambda: self._get_rolling_hour_average_value(in_euro=False, decimals=2, hour_offset=1),
# Statistics sensors
"lowest_price_today": lambda: self._get_statistics_value(stat_func=min, in_euro=False, decimals=2),
"highest_price_today": lambda: self._get_statistics_value(stat_func=max, in_euro=False, decimals=2),
"average_price_today": lambda: self._get_statistics_value(
stat_func=lambda prices: sum(prices) / len(prices),
in_euro=False,
decimals=2,
),
# Tomorrow statistics sensors
"lowest_price_tomorrow": lambda: self._get_statistics_value(
stat_func=min, in_euro=False, decimals=2, day="tomorrow"
),
"highest_price_tomorrow": lambda: self._get_statistics_value(
stat_func=max, in_euro=False, decimals=2, day="tomorrow"
),
"average_price_tomorrow": lambda: self._get_statistics_value(
stat_func=lambda prices: sum(prices) / len(prices),
in_euro=False,
decimals=2,
day="tomorrow",
),
# Trailing and leading average sensors
"trailing_price_average": lambda: self._get_average_value(
average_type="trailing",
in_euro=False,
decimals=2,
),
"leading_price_average": lambda: self._get_average_value(
average_type="leading",
in_euro=False,
decimals=2,
),
# Trailing and leading min/max sensors
"trailing_price_min": lambda: self._get_minmax_value(
stat_type="trailing",
func_type="min",
in_euro=False,
decimals=2,
),
"trailing_price_max": lambda: self._get_minmax_value(
stat_type="trailing",
func_type="max",
in_euro=False,
decimals=2,
),
"leading_price_min": lambda: self._get_minmax_value(
stat_type="leading",
func_type="min",
in_euro=False,
decimals=2,
),
"leading_price_max": lambda: self._get_minmax_value(
stat_type="leading",
func_type="max",
in_euro=False,
decimals=2,
),
# Rating sensors
"price_rating": lambda: self._get_rating_value(rating_type="current"),
"next_interval_price_rating": lambda: self._get_interval_rating_value(interval_offset=1),
"previous_interval_price_rating": lambda: self._get_interval_rating_value(interval_offset=-1),
"current_hour_price_rating": lambda: self._get_rolling_hour_rating_value(hour_offset=0),
"next_hour_price_rating": lambda: self._get_rolling_hour_rating_value(hour_offset=1),
# Future average sensors (next N hours from next interval)
"next_avg_1h": lambda: self._get_next_avg_n_hours_value(hours=1),
"next_avg_2h": lambda: self._get_next_avg_n_hours_value(hours=2),
"next_avg_3h": lambda: self._get_next_avg_n_hours_value(hours=3),
"next_avg_4h": lambda: self._get_next_avg_n_hours_value(hours=4),
"next_avg_5h": lambda: self._get_next_avg_n_hours_value(hours=5),
"next_avg_6h": lambda: self._get_next_avg_n_hours_value(hours=6),
"next_avg_8h": lambda: self._get_next_avg_n_hours_value(hours=8),
"next_avg_12h": lambda: self._get_next_avg_n_hours_value(hours=12),
# Price trend sensors
"price_trend_1h": lambda: self._get_price_trend_value(hours=1),
"price_trend_2h": lambda: self._get_price_trend_value(hours=2),
"price_trend_3h": lambda: self._get_price_trend_value(hours=3),
"price_trend_4h": lambda: self._get_price_trend_value(hours=4),
"price_trend_5h": lambda: self._get_price_trend_value(hours=5),
"price_trend_6h": lambda: self._get_price_trend_value(hours=6),
"price_trend_8h": lambda: self._get_price_trend_value(hours=8),
"price_trend_12h": lambda: self._get_price_trend_value(hours=12),
# Diagnostic sensors
"data_timestamp": self._get_data_timestamp,
# Price forecast sensor
"price_forecast": self._get_price_forecast_value,
# Volatility sensors
"today_volatility": lambda: self._get_volatility_value(volatility_type="today"),
"tomorrow_volatility": lambda: self._get_volatility_value(volatility_type="tomorrow"),
"next_24h_volatility": lambda: self._get_volatility_value(volatility_type="next_24h"),
"today_tomorrow_volatility": lambda: self._get_volatility_value(volatility_type="today_tomorrow"),
}
return handlers.get(key)
def _get_current_interval_data(self) -> dict | None:
"""Get the price data for the current interval using coordinator utility."""
return self.coordinator.get_current_interval()
def _get_price_level_value(self) -> str | None:
"""Get the current price level value as enum string for the state."""
current_interval_data = self._get_current_interval_data()
if not current_interval_data or "level" not in current_interval_data:
return None
level = current_interval_data["level"]
self._last_price_level = level
# Convert API level (e.g., "NORMAL") to lowercase enum value (e.g., "normal")
return level.lower() if level else None
def _get_interval_level_value(self, *, interval_offset: int) -> str | None:
"""Get price level for an interval with offset (e.g., next or previous interval)."""
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
now = dt_util.now()
target_time = now + timedelta(minutes=MINUTES_PER_INTERVAL * interval_offset)
interval_data = find_price_data_for_interval(price_info, target_time)
if not interval_data or "level" not in interval_data:
return None
level = interval_data["level"]
# Convert API level to lowercase enum value
return level.lower() if level else None
def _get_rolling_hour_level_value(self, *, hour_offset: int) -> str | None:
"""Get aggregated price level for a 5-interval rolling window."""
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
yesterday_prices = price_info.get("yesterday", [])
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = yesterday_prices + today_prices + tomorrow_prices
if not all_prices:
return None
center_idx = self._find_rolling_hour_center_index(all_prices, hour_offset)
if center_idx is None:
return None
levels = self._collect_rolling_window_levels(all_prices, center_idx)
if not levels:
return None
aggregated_level = aggregate_price_levels(levels)
# Convert API level to lowercase enum value
return aggregated_level.lower() if aggregated_level else None
def _find_rolling_hour_center_index(self, all_prices: list, hour_offset: int) -> int | None:
"""Find the center index for the rolling hour window."""
now = dt_util.now()
current_idx = None
for idx, price_data in enumerate(all_prices):
starts_at = dt_util.parse_datetime(price_data["startsAt"])
if starts_at is None:
continue
starts_at = dt_util.as_local(starts_at)
interval_end = starts_at + timedelta(minutes=15)
if starts_at <= now < interval_end:
current_idx = idx
break
if current_idx is None:
return None
return current_idx + (hour_offset * 4)
def _collect_rolling_window_levels(self, all_prices: list, center_idx: int) -> list:
"""Collect levels from 2 intervals before to 2 intervals after."""
levels = []
for offset in range(-2, 3): # -2, -1, 0, 1, 2
idx = center_idx + offset
if 0 <= idx < len(all_prices):
level = all_prices[idx].get("level")
if level is not None:
levels.append(level)
return levels
def _translate_level(self, level: str) -> str:
"""Translate the level to the user's language."""
if not self.hass:
return level
language = self.hass.config.language or "en"
translated = get_price_level_translation(level, language)
if translated:
return translated
if language != "en":
fallback = get_price_level_translation(level, "en")
if fallback:
return fallback
return level
def _get_price_value(self, price: float, *, in_euro: bool) -> float:
"""Convert price based on unit."""
return price if in_euro else round((price * 100), 2)
def _get_hourly_price_value(self, *, hour_offset: int, in_euro: bool) -> float | None:
"""Get price for current hour or with offset."""
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
# Use HomeAssistant's dt_util to get the current time in the user's timezone
now = dt_util.now()
# Calculate the exact target datetime (not just the hour)
# This properly handles day boundaries
target_datetime = now.replace(microsecond=0) + timedelta(hours=hour_offset)
target_hour = target_datetime.hour
target_date = target_datetime.date()
# Determine which day's data we need
day_key = "tomorrow" if target_date > now.date() else "today"
for price_data in price_info.get(day_key, []):
# Parse the timestamp and convert to local time
starts_at = dt_util.parse_datetime(price_data["startsAt"])
if starts_at is None:
continue
# Make sure it's in the local timezone for proper comparison
starts_at = dt_util.as_local(starts_at)
# Compare using both hour and date for accuracy
if starts_at.hour == target_hour and starts_at.date() == target_date:
return self._get_price_value(float(price_data["total"]), in_euro=in_euro)
# If we didn't find the price in the expected day's data, check the other day
# This is a fallback for potential edge cases
other_day_key = "today" if day_key == "tomorrow" else "tomorrow"
for price_data in price_info.get(other_day_key, []):
starts_at = dt_util.parse_datetime(price_data["startsAt"])
if starts_at is None:
continue
starts_at = dt_util.as_local(starts_at)
if starts_at.hour == target_hour and starts_at.date() == target_date:
return self._get_price_value(float(price_data["total"]), in_euro=in_euro)
return None
def _get_interval_price_value(self, *, interval_offset: int, in_euro: bool) -> float | None:
"""Get price for the current interval or with offset, handling 15-minute intervals."""
if not self.coordinator.data:
return None
all_intervals = self.coordinator.get_all_intervals()
if not all_intervals:
return None
now = dt_util.now()
current_idx = None
for idx, interval in enumerate(all_intervals):
starts_at = interval.get("startsAt")
if starts_at:
ts = dt_util.parse_datetime(starts_at)
if ts and ts <= now < ts + timedelta(minutes=MINUTES_PER_INTERVAL):
current_idx = idx
break
if current_idx is None:
return None
target_idx = current_idx + interval_offset
if 0 <= target_idx < len(all_intervals):
price = float(all_intervals[target_idx]["total"])
return price if in_euro else round(price * 100, 2)
return None
def _get_statistics_value(
self,
*,
stat_func: Callable[[list[float]], float],
in_euro: bool,
decimals: int | None = None,
day: str = "today",
) -> float | None:
"""
Handle statistics sensor values using the provided statistical function.
Args:
stat_func: The statistical function to apply (min, max, avg, etc.)
in_euro: Whether to return the value in euros (True) or cents (False)
decimals: Number of decimal places to round to
day: Which day to calculate for - "today" or "tomorrow"
Returns:
The calculated value for the statistics sensor, or None if unavailable.
"""
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
# Get local midnight boundaries based on the requested day
local_midnight = dt_util.as_local(dt_util.start_of_local_day(dt_util.now()))
if day == "tomorrow":
local_midnight = local_midnight + timedelta(days=1)
local_midnight_next_day = local_midnight + timedelta(days=1)
# Collect all prices and their intervals from both today and tomorrow data that fall within the target day
price_intervals = []
for day_key in ["today", "tomorrow"]:
for price_data in price_info.get(day_key, []):
starts_at_str = price_data.get("startsAt")
if not starts_at_str:
continue
starts_at = dt_util.parse_datetime(starts_at_str)
if starts_at is None:
continue
# Convert to local timezone for comparison
starts_at = dt_util.as_local(starts_at)
# Include price if it starts within the target day's local date boundaries
if local_midnight <= starts_at < local_midnight_next_day:
total_price = price_data.get("total")
if total_price is not None:
price_intervals.append(
{
"price": float(total_price),
"interval": price_data,
}
)
if not price_intervals:
return None
# Find the extreme value and store its interval for later use in attributes
prices = [pi["price"] for pi in price_intervals]
value = stat_func(prices)
# Store the interval with the extreme price for use in attributes
for pi in price_intervals:
if pi["price"] == value:
self._last_extreme_interval = pi["interval"]
break
result = self._get_price_value(value, in_euro=in_euro)
if decimals is not None:
result = round(result, decimals)
return result
def _get_average_value(
self,
*,
average_type: str,
in_euro: bool,
decimals: int | None = None,
) -> float | None:
"""
Get trailing or leading 24-hour average price.
Args:
average_type: Either "trailing" or "leading"
in_euro: If True, return value in euros; if False, return in cents
decimals: Number of decimal places to round to, or None for no rounding
Returns:
The calculated average value, or None if unavailable
"""
if average_type == "trailing":
value = calculate_current_trailing_avg(self.coordinator.data)
elif average_type == "leading":
value = calculate_current_leading_avg(self.coordinator.data)
else:
return None
if value is None:
return None
result = self._get_price_value(value, in_euro=in_euro)
if decimals is not None:
result = round(result, decimals)
return result
def _get_rolling_hour_average_value(
self,
*,
in_euro: bool,
decimals: int | None = None,
hour_offset: int = 0,
) -> float | None:
"""
Get rolling 5-interval average (2 previous + current + 2 next).
This provides a smoothed "hour price" centered around a specific hour.
With hour_offset=0, it's centered on the current interval.
With hour_offset=1, it's centered on the interval 1 hour ahead.
Args:
in_euro: If True, return value in euros; if False, return in cents
decimals: Number of decimal places to round to, or None for no rounding
hour_offset: Number of hours to shift forward (0=current, 1=next hour)
Returns:
The calculated rolling average value, or None if unavailable
"""
if hour_offset == 0:
value = calculate_current_rolling_5interval_avg(self.coordinator.data)
elif hour_offset == 1:
value = calculate_next_hour_rolling_5interval_avg(self.coordinator.data)
else:
return None
if value is None:
return None
result = self._get_price_value(value, in_euro=in_euro)
if decimals is not None:
result = round(result, decimals)
return result
def _get_minmax_value(
self,
*,
stat_type: str,
func_type: str,
in_euro: bool,
decimals: int | None = None,
) -> float | None:
"""
Get trailing or leading 24-hour minimum or maximum price.
Args:
stat_type: Either "trailing" or "leading"
func_type: Either "min" or "max"
in_euro: If True, return value in euros; if False, return in cents
decimals: Number of decimal places to round to, or None for no rounding
Returns:
The calculated min/max value, or None if unavailable
"""
if stat_type == "trailing" and func_type == "min":
value = calculate_current_trailing_min(self.coordinator.data)
elif stat_type == "trailing" and func_type == "max":
value = calculate_current_trailing_max(self.coordinator.data)
elif stat_type == "leading" and func_type == "min":
value = calculate_current_leading_min(self.coordinator.data)
elif stat_type == "leading" and func_type == "max":
value = calculate_current_leading_max(self.coordinator.data)
else:
return None
if value is None:
return None
result = self._get_price_value(value, in_euro=in_euro)
if decimals is not None:
result = round(result, decimals)
return result
def _translate_rating_level(self, level: str) -> str:
"""Translate the rating level using custom translations, falling back to English or the raw value."""
if not self.hass or not level:
return level
language = self.hass.config.language or "en"
cache_key = f"{DOMAIN}_translations_{language}"
translations = self.hass.data.get(cache_key)
if (
translations
and "sensor" in translations
and "price_rating" in translations["sensor"]
and "price_levels" in translations["sensor"]["price_rating"]
and level in translations["sensor"]["price_rating"]["price_levels"]
):
return translations["sensor"]["price_rating"]["price_levels"][level]
# Fallback to English if not found
if language != "en":
en_cache_key = f"{DOMAIN}_translations_en"
en_translations = self.hass.data.get(en_cache_key)
if (
en_translations
and "sensor" in en_translations
and "price_rating" in en_translations
and "price_levels" in en_translations["sensor"]["price_rating"]
and level in en_translations["sensor"]["price_rating"]["price_levels"]
):
return en_translations["sensor"]["price_rating"]["price_levels"][level]
return level
def _get_rating_value(self, *, rating_type: str) -> str | None:
"""
Get the price rating level from the current price interval in priceInfo.
Returns the rating level enum value, and stores the original
level and percentage difference as attributes.
"""
if not self.coordinator.data or rating_type != "current":
self._last_rating_difference = None
self._last_rating_level = None
return None
now = dt_util.now()
price_info = self.coordinator.data.get("priceInfo", {})
current_interval = find_price_data_for_interval(price_info, now)
if current_interval:
rating_level = current_interval.get("rating_level")
difference = current_interval.get("difference")
if rating_level is not None:
self._last_rating_difference = float(difference) if difference is not None else None
self._last_rating_level = rating_level
# Convert API rating (e.g., "NORMAL") to lowercase enum value (e.g., "normal")
return rating_level.lower() if rating_level else None
self._last_rating_difference = None
self._last_rating_level = None
return None
def _get_interval_rating_value(self, *, interval_offset: int) -> str | None:
"""Get price rating for an interval with offset (e.g., next or previous interval)."""
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
now = dt_util.now()
target_time = now + timedelta(minutes=MINUTES_PER_INTERVAL * interval_offset)
interval_data = find_price_data_for_interval(price_info, target_time)
if not interval_data:
return None
rating_level = interval_data.get("rating_level")
# Convert API rating to lowercase enum value
return rating_level.lower() if rating_level else None
def _get_rolling_hour_rating_value(self, *, hour_offset: int) -> str | None:
"""Get aggregated price rating for a 5-interval rolling window."""
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
yesterday_prices = price_info.get("yesterday", [])
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = yesterday_prices + today_prices + tomorrow_prices
if not all_prices:
return None
now = dt_util.now()
# Find the current interval
current_idx = None
for idx, price_data in enumerate(all_prices):
starts_at = dt_util.parse_datetime(price_data["startsAt"])
if starts_at is None:
continue
starts_at = dt_util.as_local(starts_at)
interval_end = starts_at + timedelta(minutes=15)
if starts_at <= now < interval_end:
current_idx = idx
break
if current_idx is None:
return None
# Shift by hour_offset * 4 intervals (4 intervals = 1 hour)
center_idx = current_idx + (hour_offset * 4)
# Collect differences from 2 intervals before to 2 intervals after (5 total)
differences = []
for offset in range(-2, 3): # -2, -1, 0, 1, 2
idx = center_idx + offset
if 0 <= idx < len(all_prices):
difference = all_prices[idx].get("difference")
if difference is not None:
differences.append(float(difference))
if not differences:
return None
# Get thresholds from config
threshold_low = self.coordinator.config_entry.options.get(
CONF_PRICE_RATING_THRESHOLD_LOW,
DEFAULT_PRICE_RATING_THRESHOLD_LOW,
)
threshold_high = self.coordinator.config_entry.options.get(
CONF_PRICE_RATING_THRESHOLD_HIGH,
DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
)
# Aggregate using average difference
aggregated_rating, _avg_diff = aggregate_price_rating(differences, threshold_low, threshold_high)
# Convert API rating to lowercase enum value
return aggregated_rating.lower() if aggregated_rating else None
def _get_next_avg_n_hours_value(self, *, hours: int) -> float | None:
"""
Get average price for next N hours starting from next interval.
Args:
hours: Number of hours to look ahead (1, 2, 3, 4, 5, 6, 8, 12)
Returns:
Average price in minor currency units (e.g., cents), or None if unavailable
"""
avg_price = calculate_next_n_hours_avg(self.coordinator.data, hours)
if avg_price is None:
return None
# Convert from major to minor currency units (e.g., EUR to cents)
return round(avg_price * 100, 2)
def _get_price_trend_value(self, *, hours: int) -> str | None:
"""
Calculate price trend comparing current interval vs next N hours average.
Args:
hours: Number of hours to look ahead for trend calculation
Returns:
Trend state: "rising" | "falling" | "stable", or None if unavailable
"""
# Return cached value if available to ensure consistency between
# native_value and extra_state_attributes
if self._cached_trend_value is not None and self._trend_attributes:
return self._cached_trend_value
if not self.coordinator.data:
return None
# Get current interval price and timestamp
current_interval = self._get_current_interval_data()
if not current_interval or "total" not in current_interval:
return None
current_price = float(current_interval["total"])
current_starts_at = dt_util.parse_datetime(current_interval["startsAt"])
if current_starts_at is None:
return None
current_starts_at = dt_util.as_local(current_starts_at)
# Get next interval timestamp (basis for calculation)
next_interval_start = current_starts_at + timedelta(minutes=MINUTES_PER_INTERVAL)
# Get future average price and detailed interval data
future_avg = calculate_next_n_hours_avg(self.coordinator.data, hours)
if future_avg is None:
return None
# Get configured thresholds from options
threshold_rising = self.coordinator.config_entry.options.get(
CONF_PRICE_TREND_THRESHOLD_RISING,
DEFAULT_PRICE_TREND_THRESHOLD_RISING,
)
threshold_falling = self.coordinator.config_entry.options.get(
CONF_PRICE_TREND_THRESHOLD_FALLING,
DEFAULT_PRICE_TREND_THRESHOLD_FALLING,
)
# Calculate trend with configured thresholds
trend_state, diff_pct = calculate_price_trend(
current_price, future_avg, threshold_rising=threshold_rising, threshold_falling=threshold_falling
)
# Determine icon color based on trend state
icon_color = {
"rising": "var(--error-color)", # Red/Orange for rising prices (expensive)
"falling": "var(--success-color)", # Green for falling prices (cheaper)
"stable": "var(--state-icon-color)", # Default gray for stable prices
}.get(trend_state, "var(--state-icon-color)")
# Store attributes in sensor-specific dictionary AND cache the trend value
self._trend_attributes = {
"timestamp": next_interval_start.isoformat(),
f"trend_{hours}h_%": round(diff_pct, 1),
f"next_{hours}h_avg": round(future_avg * 100, 2),
"interval_count": hours * 4,
"threshold_rising": threshold_rising,
"threshold_falling": threshold_falling,
"icon_color": icon_color,
}
# Calculate additional attributes for better granularity
if hours > MIN_HOURS_FOR_LATER_HALF:
# Get second half average for longer periods
later_half_avg = self._calculate_later_half_average(hours, next_interval_start)
if later_half_avg is not None:
self._trend_attributes[f"second_half_{hours}h_avg"] = round(later_half_avg * 100, 2)
# Calculate incremental change: how much does the later half differ from current?
if current_price > 0:
later_half_diff = ((later_half_avg - current_price) / current_price) * 100
self._trend_attributes[f"second_half_{hours}h_diff_from_current_%"] = round(later_half_diff, 1)
# Cache the trend value for consistency
self._cached_trend_value = trend_state
return trend_state
def _calculate_later_half_average(self, hours: int, next_interval_start: datetime) -> float | None:
"""
Calculate average price for the later half of the future time window.
This provides additional granularity by showing what happens in the second half
of the prediction window, helping distinguish between near-term and far-term trends.
Args:
hours: Total hours in the prediction window
next_interval_start: Start timestamp of the next interval
Returns:
Average price for the later half intervals, or None if insufficient data
"""
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = today_prices + tomorrow_prices
if not all_prices:
return None
# Calculate which intervals belong to the later half
total_intervals = hours * 4
first_half_intervals = total_intervals // 2
later_half_start = next_interval_start + timedelta(minutes=MINUTES_PER_INTERVAL * first_half_intervals)
later_half_end = next_interval_start + timedelta(minutes=MINUTES_PER_INTERVAL * total_intervals)
# Collect prices in the later half
later_prices = []
for price_data in all_prices:
starts_at = dt_util.parse_datetime(price_data["startsAt"])
if starts_at is None:
continue
starts_at = dt_util.as_local(starts_at)
if later_half_start <= starts_at < later_half_end:
price = price_data.get("total")
if price is not None:
later_prices.append(float(price))
if later_prices:
return sum(later_prices) / len(later_prices)
return None
def _get_data_timestamp(self) -> datetime | None:
"""Get the latest data timestamp."""
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
latest_timestamp = None
for day in ["today", "tomorrow"]:
for price_data in price_info.get(day, []):
timestamp = datetime.fromisoformat(price_data["startsAt"])
if not latest_timestamp or timestamp > latest_timestamp:
latest_timestamp = timestamp
return dt_util.as_utc(latest_timestamp) if latest_timestamp else None
def _get_prices_for_volatility(self, volatility_type: str, price_info: dict) -> list[float]:
"""
Get price list for volatility calculation based on type.
Args:
volatility_type: One of "today", "tomorrow", "next_24h", "today_tomorrow"
price_info: Price information dictionary from coordinator data
Returns:
List of prices to analyze
"""
if volatility_type == "today":
return [float(p["total"]) for p in price_info.get("today", []) if "total" in p]
if volatility_type == "tomorrow":
return [float(p["total"]) for p in price_info.get("tomorrow", []) if "total" in p]
if volatility_type == "next_24h":
# Rolling 24h from now
now = dt_util.now()
end_time = now + timedelta(hours=24)
prices = []
for day_key in ["today", "tomorrow"]:
for price_data in price_info.get(day_key, []):
starts_at = dt_util.parse_datetime(price_data.get("startsAt"))
if starts_at is None:
continue
starts_at = dt_util.as_local(starts_at)
if now <= starts_at < end_time and "total" in price_data:
prices.append(float(price_data["total"]))
return prices
if volatility_type == "today_tomorrow":
# Combined today + tomorrow
prices = []
for day_key in ["today", "tomorrow"]:
for price_data in price_info.get(day_key, []):
if "total" in price_data:
prices.append(float(price_data["total"]))
return prices
return []
def _add_volatility_type_attributes(
self,
volatility_type: str,
price_info: dict,
thresholds: dict,
) -> None:
"""Add type-specific attributes for volatility sensors."""
if volatility_type == "today_tomorrow":
# Add breakdown for today vs tomorrow
today_prices = [float(p["total"]) for p in price_info.get("today", []) if "total" in p]
tomorrow_prices = [float(p["total"]) for p in price_info.get("tomorrow", []) if "total" in p]
if today_prices:
today_vol = calculate_volatility_level(today_prices, **thresholds)
today_spread = (max(today_prices) - min(today_prices)) * 100
self._last_volatility_attributes["today_spread"] = round(today_spread, 2)
self._last_volatility_attributes["today_volatility"] = today_vol
self._last_volatility_attributes["interval_count_today"] = len(today_prices)
if tomorrow_prices:
tomorrow_vol = calculate_volatility_level(tomorrow_prices, **thresholds)
tomorrow_spread = (max(tomorrow_prices) - min(tomorrow_prices)) * 100
self._last_volatility_attributes["tomorrow_spread"] = round(tomorrow_spread, 2)
self._last_volatility_attributes["tomorrow_volatility"] = tomorrow_vol
self._last_volatility_attributes["interval_count_tomorrow"] = len(tomorrow_prices)
elif volatility_type == "next_24h":
# Add time window info
now = dt_util.now()
self._last_volatility_attributes["timestamp"] = now.isoformat()
def _get_volatility_value(self, *, volatility_type: str) -> str | None:
"""
Calculate price volatility using coefficient of variation for different time periods.
Args:
volatility_type: One of "today", "tomorrow", "next_24h", "today_tomorrow"
Returns:
Volatility level: "low", "moderate", "high", "very_high", or None if unavailable
"""
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
# Get volatility thresholds from config
thresholds = {
"threshold_moderate": self.coordinator.config_entry.options.get("volatility_threshold_moderate", 5.0),
"threshold_high": self.coordinator.config_entry.options.get("volatility_threshold_high", 15.0),
"threshold_very_high": self.coordinator.config_entry.options.get("volatility_threshold_very_high", 30.0),
}
# Get prices based on volatility type
prices_to_analyze = self._get_prices_for_volatility(volatility_type, price_info)
if not prices_to_analyze:
return None
# Calculate spread and basic statistics
price_min = min(prices_to_analyze)
price_max = max(prices_to_analyze)
spread = price_max - price_min
price_avg = sum(prices_to_analyze) / len(prices_to_analyze)
# Convert to minor currency units (ct/øre) for display
spread_minor = spread * 100
# Calculate volatility level with custom thresholds (pass price list, not spread)
volatility = calculate_volatility_level(prices_to_analyze, **thresholds)
# Store attributes for this sensor
self._last_volatility_attributes = {
"price_spread": round(spread_minor, 2),
"price_volatility": volatility,
"price_min": round(price_min * 100, 2),
"price_max": round(price_max * 100, 2),
"price_avg": round(price_avg * 100, 2),
"interval_count": len(prices_to_analyze),
}
# Add icon_color for dynamic styling
if volatility in VOLATILITY_COLOR_MAPPING:
self._last_volatility_attributes["icon_color"] = VOLATILITY_COLOR_MAPPING[volatility]
# Add type-specific attributes
self._add_volatility_type_attributes(volatility_type, price_info, thresholds)
# Return lowercase for ENUM device class
return volatility.lower()
# Add method to get future price intervals
def _get_price_forecast_value(self) -> str | None:
"""Get the highest or lowest price status for the price forecast entity."""
future_prices = self._get_future_prices(max_intervals=MAX_FORECAST_INTERVALS)
if not future_prices:
return "No forecast data available"
# Return a simple status message indicating how much forecast data is available
return f"Forecast available for {len(future_prices)} intervals"
def _get_future_prices(self, max_intervals: int | None = None) -> list[dict] | None:
"""
Get future price data for multiple upcoming intervals.
Args:
max_intervals: Maximum number of future intervals to return
Returns:
List of upcoming price intervals with timestamps and prices
"""
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = today_prices + tomorrow_prices
if not all_prices:
return None
now = dt_util.now()
# Initialize the result list
future_prices = []
# Track the maximum intervals to return
intervals_to_return = MAX_FORECAST_INTERVALS if max_intervals is None else max_intervals
for day_key in ["today", "tomorrow"]:
for price_data in price_info.get(day_key, []):
starts_at = dt_util.parse_datetime(price_data["startsAt"])
if starts_at is None:
continue
starts_at = dt_util.as_local(starts_at)
interval_end = starts_at + timedelta(minutes=MINUTES_PER_INTERVAL)
if starts_at > now:
future_prices.append(
{
"interval_start": starts_at.isoformat(),
"interval_end": interval_end.isoformat(),
"price": float(price_data["total"]),
"price_minor": round(float(price_data["total"]) * 100, 2),
"level": price_data.get("level", "NORMAL"),
"rating": price_data.get("difference", None),
"rating_level": price_data.get("rating_level"),
"day": day_key,
}
)
# Sort by start time
future_prices.sort(key=lambda x: x["interval_start"])
# Limit to the requested number of intervals
return future_prices[:intervals_to_return] if future_prices else None
def _add_price_forecast_attributes(self, attributes: dict) -> None:
"""Add forecast attributes for the price forecast sensor."""
future_prices = self._get_future_prices(max_intervals=MAX_FORECAST_INTERVALS)
if not future_prices:
attributes["intervals"] = []
attributes["intervals_by_hour"] = []
attributes["data_available"] = False
return
# Add timestamp attribute (first future interval)
if future_prices:
attributes["timestamp"] = future_prices[0]["interval_start"]
attributes["intervals"] = future_prices
attributes["data_available"] = True
# Group by hour for easier consumption in dashboards
hours = {}
for interval in future_prices:
starts_at = datetime.fromisoformat(interval["interval_start"])
hour_key = starts_at.strftime("%Y-%m-%d %H")
if hour_key not in hours:
hours[hour_key] = {
"hour": starts_at.hour,
"day": interval["day"],
"date": starts_at.date().isoformat(),
"intervals": [],
"min_price": None,
"max_price": None,
"avg_price": 0,
"avg_rating": None, # Initialize rating tracking
"ratings_available": False, # Track if any ratings are available
}
# Create interval data with both price and rating info
interval_data = {
"minute": starts_at.minute,
"price": interval["price"],
"price_minor": interval["price_minor"],
"level": interval["level"], # Price level from priceInfo
"time": starts_at.strftime("%H:%M"),
}
# Add rating data if available
if interval["rating"] is not None:
interval_data["rating"] = interval["rating"]
interval_data["rating_level"] = interval["rating_level"]
hours[hour_key]["ratings_available"] = True
hours[hour_key]["intervals"].append(interval_data)
# Track min/max/avg for the hour
price = interval["price"]
if hours[hour_key]["min_price"] is None or price < hours[hour_key]["min_price"]:
hours[hour_key]["min_price"] = price
if hours[hour_key]["max_price"] is None or price > hours[hour_key]["max_price"]:
hours[hour_key]["max_price"] = price
# Calculate averages
for hour_data in hours.values():
prices = [interval["price"] for interval in hour_data["intervals"]]
if prices:
hour_data["avg_price"] = sum(prices) / len(prices)
hour_data["min_price"] = hour_data["min_price"]
hour_data["max_price"] = hour_data["max_price"]
# Calculate average rating if ratings are available
if hour_data["ratings_available"]:
ratings = [interval.get("rating") for interval in hour_data["intervals"] if "rating" in interval]
if ratings:
hour_data["avg_rating"] = sum(ratings) / len(ratings)
# Convert to list sorted by hour
attributes["intervals_by_hour"] = [hour_data for _, hour_data in sorted(hours.items())]
def _add_volatility_attributes(self, attributes: dict) -> None:
"""Add attributes for volatility sensors."""
if hasattr(self, "_last_volatility_attributes") and self._last_volatility_attributes:
attributes.update(self._last_volatility_attributes)
@property
def native_value(self) -> float | str | datetime | None:
"""Return the native value of the sensor."""
try:
if not self.coordinator.data or not self._value_getter:
return None
# For price_level, ensure we return the translated value as state
if self.entity_description.key == "price_level":
return self._get_price_level_value()
return self._value_getter()
except (KeyError, ValueError, TypeError) as ex:
self.coordinator.logger.exception(
"Error getting sensor value",
extra={
"error": str(ex),
"entity": self.entity_description.key,
},
)
return None
@property
def native_unit_of_measurement(self) -> str | None:
"""Return the unit of measurement dynamically based on currency."""
if self.entity_description.device_class != SensorDeviceClass.MONETARY:
return None
currency = None
if self.coordinator.data:
price_info = self.coordinator.data.get("priceInfo", {})
currency = price_info.get("currency")
return format_price_unit_minor(currency)
@property
def icon(self) -> str | None:
"""Return the icon based on sensor type and state."""
key = self.entity_description.key
value = self.native_value
# Try to get icon from various sources
icon = (
self._get_trend_icon(key, value)
or self._get_price_sensor_icon(key)
or self._get_level_sensor_icon(key, value)
or self._get_rating_sensor_icon(key, value)
or self._get_volatility_sensor_icon(key, value)
)
# Fall back to static icon from entity description
return icon or self.entity_description.icon
def _get_trend_icon(self, key: str, value: Any) -> str | None:
"""Get icon for trend sensors."""
if not key.startswith("price_trend_") or not isinstance(value, str):
return None
trend_icons = {
"rising": "mdi:trending-up",
"falling": "mdi:trending-down",
"stable": "mdi:trending-neutral",
}
return trend_icons.get(value)
def _get_price_sensor_icon(self, key: str) -> str | None:
"""
Get icon for current price sensors (dynamic based on price level).
Only current_price and current_hour_average have dynamic icons.
Other price sensors (next/previous) use static icons from entity description.
"""
# Only current price sensors get dynamic icons
if key == "current_price":
level = self._get_price_level_for_sensor(key)
if level:
return PRICE_LEVEL_CASH_ICON_MAPPING.get(level.upper())
elif key == "current_hour_average":
level = self._get_hour_level_for_sensor(key)
if level:
return PRICE_LEVEL_CASH_ICON_MAPPING.get(level.upper())
# For all other price sensors, let entity description handle the icon
return None
def _get_level_sensor_icon(self, key: str, value: Any) -> str | None:
"""Get icon for price level sensors."""
if key not in [
"price_level",
"next_interval_price_level",
"previous_interval_price_level",
"current_hour_price_level",
"next_hour_price_level",
] or not isinstance(value, str):
return None
return PRICE_LEVEL_ICON_MAPPING.get(value.upper())
def _get_rating_sensor_icon(self, key: str, value: Any) -> str | None:
"""Get icon for price rating sensors."""
if key not in [
"price_rating",
"next_interval_price_rating",
"previous_interval_price_rating",
"current_hour_price_rating",
"next_hour_price_rating",
] or not isinstance(value, str):
return None
return PRICE_RATING_ICON_MAPPING.get(value.upper())
def _get_volatility_sensor_icon(self, key: str, value: Any) -> str | None:
"""Get icon for volatility sensors."""
if not key.endswith("_volatility") or not isinstance(value, str):
return None
return VOLATILITY_ICON_MAPPING.get(value.upper())
def _get_price_level_for_sensor(self, key: str) -> str | None:
"""Get the price level for a price sensor (current/next/previous interval)."""
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
now = dt_util.now()
# Map sensor key to interval offset
offset_map = {
"current_price": 0,
"next_interval_price": 1,
"previous_interval_price": -1,
}
interval_offset = offset_map.get(key)
if interval_offset is None:
return None
target_time = now + timedelta(minutes=MINUTES_PER_INTERVAL * interval_offset)
interval_data = find_price_data_for_interval(price_info, target_time)
if not interval_data or "level" not in interval_data:
return None
return interval_data["level"]
def _get_hour_level_for_sensor(self, key: str) -> str | None:
"""Get the price level for an hour average sensor (current/next hour)."""
if not self.coordinator.data:
return None
# Map sensor key to hour offset
offset_map = {
"current_hour_average": 0,
"next_hour_average": 1,
}
hour_offset = offset_map.get(key)
if hour_offset is None:
return None
# Use the same logic as _get_rolling_hour_level_value
price_info = self.coordinator.data.get("priceInfo", {})
yesterday_prices = price_info.get("yesterday", [])
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = yesterday_prices + today_prices + tomorrow_prices
if not all_prices:
return None
center_idx = self._find_rolling_hour_center_index(all_prices, hour_offset)
if center_idx is None:
return None
levels = self._collect_rolling_window_levels(all_prices, center_idx)
if not levels:
return None
return aggregate_price_levels(levels)
@property
async def async_extra_state_attributes(self) -> dict | None:
"""Return additional state attributes asynchronously."""
if not self.coordinator.data:
return None
attributes = self._get_sensor_attributes() or {}
# Add description from the custom translations file
if self.entity_description.translation_key and self.hass is not None:
# Get user's language preference
language = self.hass.config.language if self.hass.config.language else "en"
# Add basic description
description = await async_get_entity_description(
self.hass, "sensor", self.entity_description.translation_key, language, "description"
)
if description:
attributes["description"] = description
# Check if extended descriptions are enabled in the config
extended_descriptions = self.coordinator.config_entry.options.get(
CONF_EXTENDED_DESCRIPTIONS,
self.coordinator.config_entry.data.get(CONF_EXTENDED_DESCRIPTIONS, DEFAULT_EXTENDED_DESCRIPTIONS),
)
# Add extended descriptions if enabled
if extended_descriptions:
# Add long description if available
long_desc = await async_get_entity_description(
self.hass, "sensor", self.entity_description.translation_key, language, "long_description"
)
if long_desc:
attributes["long_description"] = long_desc
# Add usage tips if available
usage_tips = await async_get_entity_description(
self.hass, "sensor", self.entity_description.translation_key, language, "usage_tips"
)
if usage_tips:
attributes["usage_tips"] = usage_tips
return attributes if attributes else None
@property
def extra_state_attributes(self) -> dict | None:
"""
Return additional state attributes (synchronous version).
This synchronous method is required by Home Assistant and will
first return basic attributes, then add cached descriptions
without any blocking I/O operations.
"""
if not self.coordinator.data:
return None
# Start with the basic attributes
attributes = self._get_sensor_attributes() or {}
# Add descriptions from the cache if available (non-blocking)
if self.entity_description.translation_key and self.hass is not None:
# Get user's language preference
language = self.hass.config.language if self.hass.config.language else "en"
translation_key = self.entity_description.translation_key
# Add basic description from cache
description = get_entity_description("sensor", translation_key, language, "description")
if description:
attributes["description"] = description
# Check if extended descriptions are enabled in the config
extended_descriptions = self.coordinator.config_entry.options.get(
CONF_EXTENDED_DESCRIPTIONS,
self.coordinator.config_entry.data.get(CONF_EXTENDED_DESCRIPTIONS, DEFAULT_EXTENDED_DESCRIPTIONS),
)
# Add extended descriptions if enabled (from cache only)
if extended_descriptions:
# Add long description if available in cache
long_desc = get_entity_description("sensor", 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("sensor", translation_key, language, "usage_tips")
if usage_tips:
attributes["usage_tips"] = usage_tips
return attributes if attributes else None
def _get_sensor_attributes(self) -> dict | None:
"""Get attributes based on sensor type."""
try:
if not self.coordinator.data:
return None
key = self.entity_description.key
attributes = {}
# For trend sensors, use the cached _trend_attributes
# These are populated when native_value is calculated
if key.startswith("price_trend_") and hasattr(self, "_trend_attributes") and self._trend_attributes:
attributes.update(self._trend_attributes)
# Group sensors by type and delegate to specific handlers
if key in [
"current_price",
"price_level",
"next_interval_price",
"previous_interval_price",
"current_hour_average",
"next_hour_average",
"next_interval_price_level",
"previous_interval_price_level",
"current_hour_price_level",
"next_hour_price_level",
"next_interval_price_rating",
"previous_interval_price_rating",
"current_hour_price_rating",
"next_hour_price_rating",
]:
self._add_current_price_attributes(attributes)
elif key in [
"trailing_price_average",
"leading_price_average",
"trailing_price_min",
"trailing_price_max",
"leading_price_min",
"leading_price_max",
]:
self._add_average_price_attributes(attributes)
elif key.startswith("next_avg_"):
self._add_next_avg_attributes(attributes)
elif any(pattern in key for pattern in ["_price_today", "_price_tomorrow", "rating", "data_timestamp"]):
self._add_statistics_attributes(attributes)
elif key == "price_forecast":
self._add_price_forecast_attributes(attributes)
elif key.endswith("_volatility"):
self._add_volatility_attributes(attributes)
# For price_level, add the original level as attribute
if key == "price_level" and hasattr(self, "_last_price_level") and self._last_price_level is not None:
attributes["level_id"] = self._last_price_level
except (KeyError, ValueError, TypeError) as ex:
self.coordinator.logger.exception(
"Error getting sensor attributes",
extra={
"error": str(ex),
"entity": self.entity_description.key,
},
)
else:
return attributes if attributes else None
def _add_current_price_attributes(self, attributes: dict) -> None:
"""Add attributes for current price sensors."""
key = self.entity_description.key
price_info = self.coordinator.data.get("priceInfo", {}) if self.coordinator.data else {}
now = dt_util.now()
# Determine which interval to use based on sensor type
next_interval_sensors = [
"next_interval_price",
"next_interval_price_level",
"next_interval_price_rating",
]
previous_interval_sensors = [
"previous_interval_price",
"previous_interval_price_level",
"previous_interval_price_rating",
]
next_hour_sensors = [
"next_hour_average",
"next_hour_price_level",
"next_hour_price_rating",
]
current_hour_sensors = [
"current_hour_average",
"current_hour_price_level",
"current_hour_price_rating",
]
# Set timestamp and interval data based on sensor type
interval_data = None
if key in next_interval_sensors:
target_time = now + timedelta(minutes=MINUTES_PER_INTERVAL)
interval_data = find_price_data_for_interval(price_info, target_time)
attributes["timestamp"] = interval_data["startsAt"] if interval_data else None
elif key in previous_interval_sensors:
target_time = now - timedelta(minutes=MINUTES_PER_INTERVAL)
interval_data = find_price_data_for_interval(price_info, target_time)
attributes["timestamp"] = interval_data["startsAt"] if interval_data else None
elif key in next_hour_sensors:
target_time = now + timedelta(hours=1)
interval_data = find_price_data_for_interval(price_info, target_time)
attributes["timestamp"] = interval_data["startsAt"] if interval_data else None
elif key in current_hour_sensors:
current_interval_data = self._get_current_interval_data()
attributes["timestamp"] = current_interval_data["startsAt"] if current_interval_data else None
else:
current_interval_data = self._get_current_interval_data()
attributes["timestamp"] = current_interval_data["startsAt"] if current_interval_data else None
# Add icon_color for price sensors (based on their price level)
if key in ["current_price", "next_interval_price", "previous_interval_price"]:
# For interval-based price sensors, get level from interval_data
if interval_data and "level" in interval_data:
level = interval_data["level"]
if level in PRICE_LEVEL_COLOR_MAPPING:
attributes["icon_color"] = PRICE_LEVEL_COLOR_MAPPING[level]
elif key in ["current_hour_average", "next_hour_average"]:
# For hour-based price sensors, get level from the corresponding level sensor
level = self._get_hour_level_for_sensor(key)
if level and level in PRICE_LEVEL_COLOR_MAPPING:
attributes["icon_color"] = PRICE_LEVEL_COLOR_MAPPING[level]
# Add price level attributes for all level sensors
self._add_level_attributes_for_sensor(attributes, key, interval_data)
# Add price rating attributes for all rating sensors
self._add_rating_attributes_for_sensor(attributes, key, interval_data)
def _add_level_attributes_for_sensor(self, attributes: dict, key: str, interval_data: dict | None) -> None:
"""Add price level attributes based on sensor type."""
# For interval-based level sensors (next/previous), use interval data
if key in ["next_interval_price_level", "previous_interval_price_level"]:
if interval_data and "level" in interval_data:
self._add_price_level_attributes(attributes, interval_data["level"])
# For hour-aggregated level sensors, use native_value
elif key in ["current_hour_price_level", "next_hour_price_level"]:
level_value = self.native_value
if level_value and isinstance(level_value, str):
self._add_price_level_attributes(attributes, level_value.upper())
# For current price level sensor
elif key == "price_level":
current_interval_data = self._get_current_interval_data()
if current_interval_data and "level" in current_interval_data:
self._add_price_level_attributes(attributes, current_interval_data["level"])
def _add_price_level_attributes(self, attributes: dict, level: str) -> None:
"""
Add price level specific attributes.
Args:
attributes: Dictionary to add attributes to
level: The price level value (e.g., VERY_CHEAP, NORMAL, etc.)
"""
if level in PRICE_LEVEL_MAPPING:
attributes["level_value"] = PRICE_LEVEL_MAPPING[level]
attributes["level_id"] = level
# Add icon_color for dynamic styling
if level in PRICE_LEVEL_COLOR_MAPPING:
attributes["icon_color"] = PRICE_LEVEL_COLOR_MAPPING[level]
def _add_rating_attributes_for_sensor(self, attributes: dict, key: str, interval_data: dict | None) -> None:
"""Add price rating attributes based on sensor type."""
# For interval-based rating sensors (next/previous), use interval data
if key in ["next_interval_price_rating", "previous_interval_price_rating"]:
if interval_data and "rating_level" in interval_data:
self._add_price_rating_attributes(attributes, interval_data["rating_level"])
# For hour-aggregated rating sensors, use native_value
elif key in ["current_hour_price_rating", "next_hour_price_rating"]:
rating_value = self.native_value
if rating_value and isinstance(rating_value, str):
self._add_price_rating_attributes(attributes, rating_value.upper())
# For current price rating sensor
elif key == "price_rating":
current_interval_data = self._get_current_interval_data()
if current_interval_data and "rating_level" in current_interval_data:
self._add_price_rating_attributes(attributes, current_interval_data["rating_level"])
def _add_price_rating_attributes(self, attributes: dict, rating: str) -> None:
"""
Add price rating specific attributes.
Args:
attributes: Dictionary to add attributes to
rating: The price rating value (e.g., LOW, NORMAL, HIGH)
"""
if rating in PRICE_RATING_MAPPING:
attributes["rating_value"] = PRICE_RATING_MAPPING[rating]
attributes["rating_id"] = rating
# Add icon_color for dynamic styling
if rating in PRICE_RATING_COLOR_MAPPING:
attributes["icon_color"] = PRICE_RATING_COLOR_MAPPING[rating]
def _find_price_timestamp(
self,
attributes: dict,
price_info: Any,
day_key: str,
target_hour: int,
target_date: date,
) -> None:
"""Find a price timestamp for a specific hour and date."""
for price_data in price_info.get(day_key, []):
starts_at = dt_util.parse_datetime(price_data["startsAt"])
if starts_at is None:
continue
starts_at = dt_util.as_local(starts_at)
if starts_at.hour == target_hour and starts_at.date() == target_date:
attributes["timestamp"] = price_data["startsAt"]
break
def _add_statistics_attributes(self, attributes: dict) -> None:
"""Add attributes for statistics and rating sensors."""
key = self.entity_description.key
price_info = self.coordinator.data.get("priceInfo", {})
now = dt_util.now()
if key == "data_timestamp":
# For data_timestamp sensor, use the latest timestamp (same as the sensor value)
latest_timestamp = self._get_data_timestamp()
if latest_timestamp:
attributes["timestamp"] = latest_timestamp.isoformat()
elif key == "price_rating":
interval_data = find_price_data_for_interval(price_info, now)
attributes["timestamp"] = interval_data["startsAt"] if interval_data else None
if hasattr(self, "_last_rating_difference") and self._last_rating_difference is not None:
attributes["diff_" + PERCENTAGE] = self._last_rating_difference
if hasattr(self, "_last_rating_level") and self._last_rating_level is not None:
attributes["level_id"] = self._last_rating_level
attributes["level_value"] = PRICE_RATING_MAPPING.get(self._last_rating_level, self._last_rating_level)
elif key in [
"lowest_price_today",
"highest_price_today",
"lowest_price_tomorrow",
"highest_price_tomorrow",
]:
# Use the timestamp from the interval that has the extreme price (already stored during value calculation)
if hasattr(self, "_last_extreme_interval") and self._last_extreme_interval:
attributes["timestamp"] = self._last_extreme_interval.get("startsAt")
else:
# Fallback: use the first timestamp of the appropriate day
day_key = "tomorrow" if "tomorrow" in key else "today"
day_data = price_info.get(day_key, [])
if day_data:
attributes["timestamp"] = day_data[0].get("startsAt")
else:
# Fallback: use the first timestamp of the appropriate day
day_key = "tomorrow" if "tomorrow" in key else "today"
day_data = price_info.get(day_key, [])
if day_data:
attributes["timestamp"] = day_data[0].get("startsAt")
def _add_average_price_attributes(self, attributes: dict) -> None:
"""Add attributes for trailing and leading average price sensors."""
key = self.entity_description.key
now = dt_util.now()
# Determine if this is trailing or leading
is_trailing = "trailing" in key
# Get all price intervals
price_info = self.coordinator.data.get("priceInfo", {})
yesterday_prices = price_info.get("yesterday", [])
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = yesterday_prices + today_prices + tomorrow_prices
if not all_prices:
return
# Calculate the time window
if is_trailing:
window_start = now - timedelta(hours=24)
window_end = now
else:
window_start = now
window_end = now + timedelta(hours=24)
# Find all intervals in the window and get first/last timestamps
intervals_in_window = []
for price_data in all_prices:
starts_at = dt_util.parse_datetime(price_data["startsAt"])
if starts_at is None:
continue
starts_at = dt_util.as_local(starts_at)
if window_start <= starts_at < window_end:
intervals_in_window.append(price_data)
# Add timestamp attribute (first interval in the window)
if intervals_in_window:
attributes["timestamp"] = intervals_in_window[0].get("startsAt")
attributes["interval_count"] = len(intervals_in_window)
def _add_next_avg_attributes(self, attributes: dict) -> None:
"""Add attributes for next N hours average price sensors."""
key = self.entity_description.key
now = dt_util.now()
# Extract hours from sensor key (e.g., "next_avg_3h" -> 3)
try:
hours = int(key.replace("next_avg_", "").replace("h", ""))
except (ValueError, AttributeError):
return
# Get next interval start time (this is where the calculation begins)
next_interval_start = now + timedelta(minutes=MINUTES_PER_INTERVAL)
# Calculate the end of the time window
window_end = next_interval_start + timedelta(hours=hours)
# Get all price intervals
price_info = self.coordinator.data.get("priceInfo", {})
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = today_prices + tomorrow_prices
if not all_prices:
return
# Find all intervals in the window
intervals_in_window = []
for price_data in all_prices:
starts_at = dt_util.parse_datetime(price_data["startsAt"])
if starts_at is None:
continue
starts_at = dt_util.as_local(starts_at)
if next_interval_start <= starts_at < window_end:
intervals_in_window.append(price_data)
# Add timestamp attribute (start of next interval - where calculation begins)
if intervals_in_window:
attributes["timestamp"] = intervals_in_window[0].get("startsAt")
attributes["interval_count"] = len(intervals_in_window)
attributes["hours"] = hours
async def async_update(self) -> None:
"""Force a refresh when homeassistant.update_entity is called."""
await self.coordinator.async_request_refresh()
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