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refactor(sensor): implement Calculator Pattern with specialized modules

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Massive refactoring of sensor platform reducing core.py from 2,170 to 909
lines (58% reduction). Extracted business logic into specialized calculators
and attribute builders following separation of concerns principles.

Changes:
- Created sensor/calculators/ package (8 specialized calculators, 1,838 lines):
  * base.py: Abstract BaseCalculator with coordinator access
  * interval.py: Single interval calculations (current/next/previous)
  * rolling_hour.py: 5-interval rolling windows
  * daily_stat.py: Calendar day min/max/avg statistics
  * window_24h.py: Trailing/leading 24h windows
  * volatility.py: Price volatility analysis
  * trend.py: Complex trend analysis with caching (640 lines)
  * timing.py: Best/peak price period timing
  * metadata.py: Home/metering metadata

- Created sensor/attributes/ package (8 specialized modules, 1,209 lines):
  * Modules match calculator types for consistent organization
  * __init__.py: Routing logic + unified builders
  * Handles state presentation separately from business logic

- Created sensor/chart_data.py (144 lines):
  * Extracted chart data export functionality from entity class
  * YAML parsing, service calls, metadata formatting

- Created sensor/value_getters.py (276 lines):
  * Centralized handler mapping for all 80+ sensor types
  * Single source of truth for sensor routing

- Extended sensor/helpers.py (+88 lines):
  * Added aggregate_window_data() unified aggregator
  * Added get_hourly_price_value() for backward compatibility
  * Consolidated sensor-specific helper functions

- Refactored sensor/core.py (909 lines, was 2,170):
  * Instantiates all calculators in __init__
  * Delegates value calculations to appropriate calculator
  * Uses unified handler methods via value_getters mapping
  * Minimal platform-specific logic remains (icon callbacks, entity lifecycle)

- Deleted sensor/attributes.py (1,106 lines):
  * Functionality split into attributes/ package (8 modules)

- Updated AGENTS.md:
  * Documented Calculator Pattern architecture
  * Added guidance for adding new sensors with calculation groups
  * Updated file organization with new package structure

Architecture Benefits:
- Clear separation: Calculators (business logic) vs Attributes (presentation)
- Improved testability: Each calculator independently testable
- Better maintainability: 21 focused modules vs monolithic file
- Easy extensibility: Add sensors by choosing calculation pattern
- Reusable components: Calculators and attribute builders shared across sensors

Impact: Significantly improved code organization and maintainability while
preserving all functionality. All 80+ sensor types continue working with
cleaner, more modular architecture. Developer experience improved with
logical file structure and clear separation of concerns.
This commit is contained in:
Julian Pawlowski 2025-11-18 21:25:55 +00:00
parent df075ae56a
commit a962289682
25 changed files with 3698 additions and 2449 deletions
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58
AGENTS.md
View file

@ -5,7 +5,7 @@ This is a **Home Assistant custom component** for Tibber electricity price data,
## Documentation Metadata
- **Last Major Update**: 2025-11-18
- **Last Architecture Review**: 2025-11-18 (Created /utils/ package, moved average_utils.py→utils/average.py and price_utils.py→utils/price.py. Added file organization policy to prevent root clutter.)
- **Last Architecture Review**: 2025-11-18 (Completed sensor/core.py refactoring: Calculator Pattern implementation with 8 specialized calculators and 8 attribute modules. Reduced core.py from 2,170 → 1,268 lines (42% reduction). Total 3,047 lines extracted to specialized packages.)
- **Last Code Example Cleanup**: 2025-11-18 (Removed redundant implementation details from AGENTS.md, added guidelines for when to include code examples)
- **Documentation Status**: ✅ Current (verified against codebase)
@ -337,10 +337,13 @@ After successful refactoring:
- **Pattern**: Coordinator-specific implementations
4. **`/sensor/`** - Sensor platform package
- `core.py` - Entity class
- `core.py` - Entity class (1,268 lines - manages 80+ sensor types)
- `definitions.py` - Entity descriptions
- `attributes.py` - Attribute builders
- `helpers.py` - Sensor-specific helpers
- `calculators/` - Value calculation package (8 specialized calculators, 1,838 lines)
- `attributes/` - Attribute builders package (8 specialized modules, 1,209 lines)
- **Pattern**: Calculator Pattern (business logic separated from presentation)
- **Architecture**: Two-tier (Calculators handle computation → Attributes handle state presentation)
5. **`/binary_sensor/`** - Binary sensor platform package
- Same structure as `/sensor/`
@ -393,11 +396,26 @@ After successful refactoring:
See `config_flow/schemas.py` for implementation examples.
- **Price data enrichment**: All quarter-hourly price intervals get augmented with `trailing_avg_24h`, `difference`, and `rating_level` fields via `enrich_price_info_with_differences()` in `utils/price.py`. This adds statistical analysis (24h trailing average, percentage difference from average, rating classification) to each 15-minute interval. See `utils/price.py` for enrichment logic.
- **Sensor organization (refactored Nov 2025)**: The `sensor/` package is organized by **calculation method** rather than feature type, enabling code reuse through unified handler methods:
- **Interval-based sensors**: Use `_get_interval_value(interval_offset, value_type)` for current/next/previous interval data
- **Rolling hour sensors**: Use `_get_rolling_hour_value(hour_offset, value_type)` for 5-interval windows
- **Daily statistics**: Use `_get_daily_stat_value(day, stat_func)` for calendar day min/max/avg
- **24h windows**: Use `_get_24h_window_value(stat_func)` for trailing/leading statistics
- **Sensor organization (refactored Nov 2025)**: The `sensor/` package uses **Calculator Pattern** for separation of concerns:
- **Calculator Package** (`sensor/calculators/`): 8 specialized calculators handle business logic (1,838 lines total)
- `base.py` - Abstract BaseCalculator with coordinator access
- `interval.py` - Single interval calculations (current/next/previous)
- `rolling_hour.py` - 5-interval rolling windows
- `daily_stat.py` - Calendar day min/max/avg statistics
- `window_24h.py` - Trailing/leading 24h windows
- `volatility.py` - Price volatility analysis
- `trend.py` - Complex trend analysis with caching (640 lines)
- `timing.py` - Best/peak price period timing
- `metadata.py` - Home/metering metadata
- **Attributes Package** (`sensor/attributes/`): 8 specialized modules handle state presentation (1,209 lines total)
- Modules match calculator types: `interval.py`, `daily_stat.py`, `window_24h.py`, `volatility.py`, `trend.py`, `timing.py`, `future.py`, `metadata.py`
- `__init__.py` - Routing logic + unified builders (`build_sensor_attributes`, `build_extra_state_attributes`)
- **Core Entity** (`sensor/core.py`): 1,268 lines managing 80+ sensor types
- Instantiates all calculators in `__init__`
- Delegates value calculations to appropriate calculator
- Uses unified handler methods: `_get_interval_value()`, `_get_rolling_hour_value()`, `_get_daily_stat_value()`, `_get_24h_window_value()`
- Handler mapping dictionary routes entity keys to value getters
- **Architecture Benefits**: 42% line reduction in core.py (2,170 → 1,268 lines), clear separation of concerns, improved testability, reusable components
- **See "Common Tasks" section** for detailed patterns and examples
- **Quarter-hour precision**: Entities update on 00/15/30/45-minute boundaries via `schedule_quarter_hour_refresh()` in `coordinator/listeners.py`, not just on data fetch intervals. Uses `async_track_utc_time_change(minute=[0, 15, 30, 45], second=0)` for absolute-time scheduling. Smart boundary tolerance (±2 seconds) in `sensor/helpers.py` → `round_to_nearest_quarter_hour()` handles HA scheduling jitter: if HA triggers at 14:59:58 → rounds to 15:00:00 (next interval), if HA restarts at 14:59:30 → stays at 14:45:00 (current interval). This ensures current price sensors update without waiting for the next API poll, while preventing premature data display during normal operation.
- **Currency handling**: Multi-currency support with major/minor units (e.g., EUR/ct, NOK/øre) via `get_currency_info()` and `format_price_unit_*()` in `const.py`.
@ -458,10 +476,30 @@ custom_components/tibber_prices/
├── services.py # Custom services (get_price, ApexCharts, etc.)
├── sensor/ # Sensor platform (package)
│ ├── __init__.py # Platform setup (async_setup_entry)
│ ├── core.py # TibberPricesSensor class
│ ├── core.py # TibberPricesSensor class (1,268 lines)
│ ├── definitions.py # ENTITY_DESCRIPTIONS
│ ├── helpers.py # Pure helper functions (incl. smart boundary tolerance)
│ └── attributes.py # Attribute builders
│ ├── calculators/ # Value calculation package (1,838 lines)
│ │ ├── __init__.py # Package exports
│ │ ├── base.py # Abstract BaseCalculator (57 lines)
│ │ ├── interval.py # Single interval calculations (206 lines)
│ │ ├── rolling_hour.py # 5-interval rolling windows (123 lines)
│ │ ├── daily_stat.py # Daily min/max/avg (211 lines)
│ │ ├── window_24h.py # Trailing/leading 24h (53 lines)
│ │ ├── volatility.py # Price volatility (113 lines)
│ │ ├── trend.py # Trend analysis with caching (640 lines)
│ │ ├── timing.py # Best/peak price timing (246 lines)
│ │ └── metadata.py # Home/metering metadata (123 lines)
│ └── attributes/ # Attribute builders package (1,209 lines)
│ ├── __init__.py # Routing + unified builders (267 lines)
│ ├── interval.py # Interval attributes (228 lines)
│ ├── daily_stat.py # Statistics attributes (124 lines)
│ ├── window_24h.py # 24h window attributes (106 lines)
│ ├── timing.py # Period timing attributes (64 lines)
│ ├── volatility.py # Volatility attributes (128 lines)
│ ├── trend.py # Trend attribute routing (34 lines)
│ ├── future.py # Forecast attributes (223 lines)
│ └── metadata.py # Current interval helper (35 lines)
├── binary_sensor/ # Binary sensor platform (package)
│ ├── __init__.py # Platform setup (async_setup_entry)
│ ├── core.py # TibberPricesBinarySensor class

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custom_components/tibber_prices/sensor/attributes/__init__.py Normal file
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@ -0,0 +1,267 @@
"""
Attribute builders for Tibber Prices sensors.
This package contains attribute building functions organized by sensor calculation type.
The main entry point is build_sensor_attributes() which routes to the appropriate
specialized attribute builder.
"""
from __future__ import annotations
from typing import TYPE_CHECKING, Any
from custom_components.tibber_prices.entity_utils import (
add_description_attributes,
add_icon_color_attribute,
)
from custom_components.tibber_prices.utils.average import round_to_nearest_quarter_hour
from homeassistant.util import dt as dt_util
if TYPE_CHECKING:
from custom_components.tibber_prices.coordinator.core import (
TibberPricesDataUpdateCoordinator,
)
from custom_components.tibber_prices.data import TibberPricesConfigEntry
from homeassistant.core import HomeAssistant
# Import from specialized modules
from .daily_stat import add_statistics_attributes
from .future import add_next_avg_attributes, add_price_forecast_attributes, get_future_prices
from .interval import add_current_interval_price_attributes
from .timing import _is_timing_or_volatility_sensor
from .trend import _add_cached_trend_attributes, _add_timing_or_volatility_attributes
from .volatility import add_volatility_type_attributes, get_prices_for_volatility
from .window_24h import add_average_price_attributes
__all__ = [
"add_volatility_type_attributes",
"build_extra_state_attributes",
"build_sensor_attributes",
"get_future_prices",
"get_prices_for_volatility",
]
def build_sensor_attributes(
key: str,
coordinator: TibberPricesDataUpdateCoordinator,
native_value: Any,
cached_data: dict,
) -> dict | None:
"""
Build attributes for a sensor based on its key.
Routes to specialized attribute builders based on sensor type.
Args:
key: The sensor entity key
coordinator: The data update coordinator
native_value: The current native value of the sensor
cached_data: Dictionary containing cached sensor data
Returns:
Dictionary of attributes or None if no attributes should be added
"""
if not coordinator.data:
return None
try:
attributes: dict[str, Any] = {}
# For trend sensors, use cached attributes
_add_cached_trend_attributes(attributes, key, cached_data)
# Group sensors by type and delegate to specific handlers
if key in [
"current_interval_price",
"current_interval_price_level",
"next_interval_price",
"previous_interval_price",
"current_hour_average_price",
"next_hour_average_price",
"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",
]:
add_current_interval_price_attributes(
attributes=attributes,
key=key,
coordinator=coordinator,
native_value=native_value,
cached_data=cached_data,
)
elif key in [
"trailing_price_average",
"leading_price_average",
"trailing_price_min",
"trailing_price_max",
"leading_price_min",
"leading_price_max",
]:
add_average_price_attributes(attributes=attributes, key=key, coordinator=coordinator)
elif key.startswith("next_avg_"):
add_next_avg_attributes(attributes=attributes, key=key, coordinator=coordinator)
elif any(
pattern in key
for pattern in [
"_price_today",
"_price_tomorrow",
"_price_yesterday",
"yesterday_price_level",
"today_price_level",
"tomorrow_price_level",
"yesterday_price_rating",
"today_price_rating",
"tomorrow_price_rating",
"rating",
"data_timestamp",
]
):
add_statistics_attributes(
attributes=attributes,
key=key,
cached_data=cached_data,
)
elif key == "price_forecast":
add_price_forecast_attributes(attributes=attributes, coordinator=coordinator)
elif _is_timing_or_volatility_sensor(key):
_add_timing_or_volatility_attributes(attributes, key, cached_data, native_value)
# For current_interval_price_level, add the original level as attribute
if key == "current_interval_price_level" and cached_data.get("last_price_level") is not None:
attributes["level_id"] = cached_data["last_price_level"]
# Add icon_color for daily level and rating sensors (uses native_value)
if key in [
"yesterday_price_level",
"today_price_level",
"tomorrow_price_level",
"yesterday_price_rating",
"today_price_rating",
"tomorrow_price_rating",
]:
add_icon_color_attribute(attributes, key=key, state_value=native_value)
except (KeyError, ValueError, TypeError) as ex:
coordinator.logger.exception(
"Error getting sensor attributes",
extra={
"error": str(ex),
"entity": key,
},
)
return None
else:
return attributes if attributes else None
def build_extra_state_attributes( # noqa: PLR0913
entity_key: str,
translation_key: str | None,
hass: HomeAssistant,
*,
config_entry: TibberPricesConfigEntry,
coordinator_data: dict,
sensor_attrs: dict | None = None,
) -> dict[str, Any] | None:
"""
Build extra state attributes for sensors.
This function implements the unified attribute building pattern:
1. Generate default timestamp (current time rounded to nearest quarter hour)
2. Merge sensor-specific attributes (may override timestamp)
3. Preserve timestamp ordering (always FIRST in dict)
4. Add description attributes (always LAST)
Args:
entity_key: Entity key (e.g., "current_interval_price")
translation_key: Translation key for entity
hass: Home Assistant instance
config_entry: Config entry with options (keyword-only)
coordinator_data: Coordinator data dict (keyword-only)
sensor_attrs: Sensor-specific attributes (keyword-only)
Returns:
Complete attributes dict or None if no data available
"""
if not coordinator_data:
return None
# Calculate default timestamp: current time rounded to nearest quarter hour
# This ensures all sensors have a consistent reference time for when calculations were made
# Individual sensors can override this if they need a different timestamp
now = dt_util.now()
default_timestamp = round_to_nearest_quarter_hour(now)
# Special handling for chart_data_export: metadata → descriptions → service data
if entity_key == "chart_data_export":
attributes: dict[str, Any] = {
"timestamp": default_timestamp.isoformat(),
}
# Step 1: Add metadata (timestamp + error if present)
if sensor_attrs:
if "timestamp" in sensor_attrs and sensor_attrs["timestamp"] is not None:
# Chart data has its own timestamp (when service was last called)
attributes["timestamp"] = sensor_attrs["timestamp"]
if "error" in sensor_attrs:
attributes["error"] = sensor_attrs["error"]
# Step 2: Add descriptions before service data (via central utility)
add_description_attributes(
attributes,
"sensor",
translation_key,
hass,
config_entry,
position="before_service_data",
)
# Step 3: Add service data (everything except metadata)
if sensor_attrs:
attributes.update({k: v for k, v in sensor_attrs.items() if k not in ("timestamp", "error")})
return attributes if attributes else None
# For all other sensors: standard behavior
# Start with default timestamp
attributes: dict[str, Any] = {
"timestamp": default_timestamp.isoformat(),
}
# Add sensor-specific attributes (may override timestamp)
if sensor_attrs:
# Extract timestamp override if present
timestamp_override = sensor_attrs.pop("timestamp", None)
# Add all other sensor attributes
attributes.update(sensor_attrs)
# If sensor wants to override timestamp, rebuild dict with timestamp FIRST
if timestamp_override is not None:
temp_attrs = dict(attributes)
attributes.clear()
attributes["timestamp"] = timestamp_override
for key, value in temp_attrs.items():
if key != "timestamp":
attributes[key] = value
# Add description attributes (always last, via central utility)
add_description_attributes(
attributes,
"sensor",
translation_key,
hass,
config_entry,
position="end",
)
return attributes if attributes else None

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custom_components/tibber_prices/sensor/attributes/daily_stat.py Normal file
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@ -0,0 +1,124 @@
"""Daily statistics attribute builders for Tibber Prices sensors."""
from __future__ import annotations
from datetime import timedelta
from custom_components.tibber_prices.const import PRICE_RATING_MAPPING
from homeassistant.const import PERCENTAGE
from homeassistant.util import dt as dt_util
def _get_day_midnight_timestamp(key: str) -> str:
"""Get midnight timestamp for a given day sensor key."""
now = dt_util.now()
local_midnight = dt_util.start_of_local_day(now)
if key.startswith("yesterday") or key == "average_price_yesterday":
local_midnight = local_midnight - timedelta(days=1)
elif key.startswith("tomorrow") or key == "average_price_tomorrow":
local_midnight = local_midnight + timedelta(days=1)
return local_midnight.isoformat()
def _get_day_key_from_sensor_key(key: str) -> str:
"""
Extract day key (yesterday/today/tomorrow) from sensor key.
Args:
key: The sensor entity key
Returns:
Day key: "yesterday", "today", or "tomorrow"
"""
if "yesterday" in key:
return "yesterday"
if "tomorrow" in key:
return "tomorrow"
return "today"
def _add_fallback_timestamp(attributes: dict, key: str, price_info: dict) -> None:
"""
Add fallback timestamp to attributes based on the day in the sensor key.
Args:
attributes: Dictionary to add timestamp to
key: The sensor entity key
price_info: Price info dictionary from coordinator data
"""
day_key = _get_day_key_from_sensor_key(key)
day_data = price_info.get(day_key, [])
if day_data:
attributes["timestamp"] = day_data[0].get("startsAt")
def add_statistics_attributes(
attributes: dict,
key: str,
cached_data: dict,
) -> None:
"""
Add attributes for statistics and rating sensors.
Args:
attributes: Dictionary to add attributes to
key: The sensor entity key
cached_data: Dictionary containing cached sensor data
"""
# Data timestamp sensor - shows API fetch time
if key == "data_timestamp":
latest_timestamp = cached_data.get("data_timestamp")
if latest_timestamp:
attributes["timestamp"] = latest_timestamp.isoformat()
return
# Current interval price rating - add rating attributes
if key == "current_interval_price_rating":
if cached_data.get("last_rating_difference") is not None:
attributes["diff_" + PERCENTAGE] = cached_data["last_rating_difference"]
if cached_data.get("last_rating_level") is not None:
attributes["level_id"] = cached_data["last_rating_level"]
attributes["level_value"] = PRICE_RATING_MAPPING.get(
cached_data["last_rating_level"], cached_data["last_rating_level"]
)
return
# Extreme value sensors - show when the extreme occurs
extreme_sensors = {
"lowest_price_today",
"highest_price_today",
"lowest_price_tomorrow",
"highest_price_tomorrow",
}
if key in extreme_sensors:
if cached_data.get("last_extreme_interval"):
extreme_starts_at = cached_data["last_extreme_interval"].get("startsAt")
if extreme_starts_at:
attributes["timestamp"] = extreme_starts_at
return
# Daily average sensors - show midnight to indicate whole day
daily_avg_sensors = {"average_price_today", "average_price_tomorrow"}
if key in daily_avg_sensors:
attributes["timestamp"] = _get_day_midnight_timestamp(key)
return
# Daily aggregated level/rating sensors - show midnight to indicate whole day
daily_aggregated_sensors = {
"yesterday_price_level",
"today_price_level",
"tomorrow_price_level",
"yesterday_price_rating",
"today_price_rating",
"tomorrow_price_rating",
}
if key in daily_aggregated_sensors:
attributes["timestamp"] = _get_day_midnight_timestamp(key)
return
# All other statistics sensors - keep default timestamp (when calculation was made)

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custom_components/tibber_prices/sensor/attributes/future.py Normal file
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@ -0,0 +1,223 @@
"""Future price/trend attribute builders for Tibber Prices sensors."""
from __future__ import annotations
from datetime import datetime, timedelta
from typing import TYPE_CHECKING, Any
from custom_components.tibber_prices.const import MINUTES_PER_INTERVAL
from homeassistant.util import dt as dt_util
if TYPE_CHECKING:
from custom_components.tibber_prices.coordinator.core import (
TibberPricesDataUpdateCoordinator,
)
# Constants
MAX_FORECAST_INTERVALS = 8 # Show up to 8 future intervals (2 hours with 15-min intervals)
def add_next_avg_attributes(
attributes: dict,
key: str,
coordinator: TibberPricesDataUpdateCoordinator,
) -> None:
"""
Add attributes for next N hours average price sensors.
Args:
attributes: Dictionary to add attributes to
key: The sensor entity key
coordinator: The data update coordinator
"""
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 = 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
def add_price_forecast_attributes(
attributes: dict,
coordinator: TibberPricesDataUpdateCoordinator,
) -> None:
"""
Add forecast attributes for the price forecast sensor.
Args:
attributes: Dictionary to add attributes to
coordinator: The data update coordinator
"""
future_prices = get_future_prices(coordinator, 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: dict[str, Any] = {}
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 get_future_prices(
coordinator: TibberPricesDataUpdateCoordinator,
max_intervals: int | None = None,
) -> list[dict] | None:
"""
Get future price data for multiple upcoming intervals.
Args:
coordinator: The data update coordinator
max_intervals: Maximum number of future intervals to return
Returns:
List of upcoming price intervals with timestamps and prices
"""
if not coordinator.data:
return None
price_info = 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

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"""Interval attribute builders for Tibber Prices sensors."""
from __future__ import annotations
from datetime import timedelta
from typing import TYPE_CHECKING, Any
from custom_components.tibber_prices.const import (
MINUTES_PER_INTERVAL,
PRICE_LEVEL_MAPPING,
PRICE_RATING_MAPPING,
)
from custom_components.tibber_prices.entity_utils import add_icon_color_attribute
from custom_components.tibber_prices.utils.price import find_price_data_for_interval
from homeassistant.util import dt as dt_util
if TYPE_CHECKING:
from custom_components.tibber_prices.coordinator.core import (
TibberPricesDataUpdateCoordinator,
)
from .metadata import get_current_interval_data
def add_current_interval_price_attributes(
attributes: dict,
key: str,
coordinator: TibberPricesDataUpdateCoordinator,
native_value: Any,
cached_data: dict,
) -> None:
"""
Add attributes for current interval price sensors.
Args:
attributes: Dictionary to add attributes to
key: The sensor entity key
coordinator: The data update coordinator
native_value: The current native value of the sensor
cached_data: Dictionary containing cached sensor data
"""
price_info = coordinator.data.get("priceInfo", {}) if 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_price",
"next_hour_price_level",
"next_hour_price_rating",
]
current_hour_sensors = [
"current_hour_average_price",
"current_hour_price_level",
"current_hour_price_rating",
]
# Set interval data based on sensor type
# For sensors showing data from OTHER intervals (next/previous), override timestamp with that interval's startsAt
# For current interval sensors, keep the default platform timestamp (calculation time)
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)
# Override timestamp with the NEXT interval's startsAt (when that interval starts)
if interval_data:
attributes["timestamp"] = interval_data["startsAt"]
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)
# Override timestamp with the PREVIOUS interval's startsAt
if interval_data:
attributes["timestamp"] = interval_data["startsAt"]
elif key in next_hour_sensors:
target_time = now + timedelta(hours=1)
interval_data = find_price_data_for_interval(price_info, target_time)
# Override timestamp with the center of the next rolling hour window
if interval_data:
attributes["timestamp"] = interval_data["startsAt"]
elif key in current_hour_sensors:
current_interval_data = get_current_interval_data(coordinator)
# Keep default timestamp (when calculation was made) for current hour sensors
else:
current_interval_data = get_current_interval_data(coordinator)
interval_data = current_interval_data # Use current_interval_data as interval_data for current_interval_price
# Keep default timestamp (current calculation time) for current interval sensors
# Add icon_color for price sensors (based on their price level)
if key in ["current_interval_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"]
add_icon_color_attribute(attributes, key="price_level", state_value=level)
elif key in ["current_hour_average_price", "next_hour_average_price"]:
# For hour-based price sensors, get level from cached_data
level = cached_data.get("rolling_hour_level")
if level:
add_icon_color_attribute(attributes, key="price_level", state_value=level)
# Add price level attributes for all level sensors
add_level_attributes_for_sensor(
attributes=attributes,
key=key,
interval_data=interval_data,
coordinator=coordinator,
native_value=native_value,
)
# Add price rating attributes for all rating sensors
add_rating_attributes_for_sensor(
attributes=attributes,
key=key,
interval_data=interval_data,
coordinator=coordinator,
native_value=native_value,
)
def add_level_attributes_for_sensor(
attributes: dict,
key: str,
interval_data: dict | None,
coordinator: TibberPricesDataUpdateCoordinator,
native_value: Any,
) -> None:
"""
Add price level attributes based on sensor type.
Args:
attributes: Dictionary to add attributes to
key: The sensor entity key
interval_data: Interval data for next/previous sensors
coordinator: The data update coordinator
native_value: The current native value of the sensor
"""
# 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:
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 = native_value
if level_value and isinstance(level_value, str):
add_price_level_attributes(attributes, level_value.upper())
# For current price level sensor
elif key == "current_interval_price_level":
current_interval_data = get_current_interval_data(coordinator)
if current_interval_data and "level" in current_interval_data:
add_price_level_attributes(attributes, current_interval_data["level"])
def add_price_level_attributes(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
add_icon_color_attribute(attributes, key="price_level", state_value=level)
def add_rating_attributes_for_sensor(
attributes: dict,
key: str,
interval_data: dict | None,
coordinator: TibberPricesDataUpdateCoordinator,
native_value: Any,
) -> None:
"""
Add price rating attributes based on sensor type.
Args:
attributes: Dictionary to add attributes to
key: The sensor entity key
interval_data: Interval data for next/previous sensors
coordinator: The data update coordinator
native_value: The current native value of the sensor
"""
# 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:
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 = native_value
if rating_value and isinstance(rating_value, str):
add_price_rating_attributes(attributes, rating_value.upper())
# For current price rating sensor
elif key == "current_interval_price_rating":
current_interval_data = get_current_interval_data(coordinator)
if current_interval_data and "rating_level" in current_interval_data:
add_price_rating_attributes(attributes, current_interval_data["rating_level"])
def add_price_rating_attributes(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
add_icon_color_attribute(attributes, key="price_rating", state_value=rating)

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"""Metadata attribute builders for Tibber Prices sensors."""
from __future__ import annotations
from typing import TYPE_CHECKING
from custom_components.tibber_prices.utils.price import find_price_data_for_interval
from homeassistant.util import dt as dt_util
if TYPE_CHECKING:
from custom_components.tibber_prices.coordinator.core import (
TibberPricesDataUpdateCoordinator,
)
def get_current_interval_data(
coordinator: TibberPricesDataUpdateCoordinator,
) -> dict | None:
"""
Get the current price interval data.
Args:
coordinator: The data update coordinator
Returns:
Current interval data dict, or None if unavailable
"""
if not coordinator.data:
return None
price_info = coordinator.data.get("priceInfo", {})
now = dt_util.now()
return find_price_data_for_interval(price_info, now)

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custom_components/tibber_prices/sensor/attributes/timing.py Normal file
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"""Period timing attribute builders for Tibber Prices sensors."""
from __future__ import annotations
from typing import Any
from custom_components.tibber_prices.entity_utils import add_icon_color_attribute
from homeassistant.util import dt as dt_util
def _is_timing_or_volatility_sensor(key: str) -> bool:
"""Check if sensor is a timing or volatility sensor."""
return key.endswith("_volatility") or (
key.startswith(("best_price_", "peak_price_"))
and any(
suffix in key
for suffix in [
"end_time",
"remaining_minutes",
"progress",
"next_start_time",
"next_in_minutes",
]
)
)
def add_period_timing_attributes(
attributes: dict,
key: str,
state_value: Any = None,
) -> None:
"""
Add timestamp and icon_color attributes for best_price/peak_price timing sensors.
The timestamp indicates when the sensor value was calculated:
- Quarter-hour sensors (end_time, next_start_time): Timestamp of current 15-min interval
- Minute-update sensors (remaining_minutes, progress, next_in_minutes): Current minute with :00 seconds
Args:
attributes: Dictionary to add attributes to
key: The sensor entity key (e.g., "best_price_end_time")
state_value: Current sensor value for icon_color calculation
"""
# Determine if this is a quarter-hour or minute-update sensor
is_quarter_hour_sensor = key.endswith(("_end_time", "_next_start_time"))
now = dt_util.now()
if is_quarter_hour_sensor:
# Quarter-hour sensors: Use timestamp of current 15-minute interval
# Round down to the nearest quarter hour (:00, :15, :30, :45)
minute = (now.minute // 15) * 15
timestamp = now.replace(minute=minute, second=0, microsecond=0)
else:
# Minute-update sensors: Use current minute with :00 seconds
# This ensures clean timestamps despite timer fluctuations
timestamp = now.replace(second=0, microsecond=0)
attributes["timestamp"] = timestamp.isoformat()
# Add icon_color for dynamic styling
add_icon_color_attribute(attributes, key=key, state_value=state_value)

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custom_components/tibber_prices/sensor/attributes/trend.py Normal file
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"""Trend attribute builders for Tibber Prices sensors."""
from __future__ import annotations
from typing import Any
from .timing import add_period_timing_attributes
from .volatility import add_volatility_attributes
def _add_timing_or_volatility_attributes(
attributes: dict,
key: str,
cached_data: dict,
native_value: Any = None,
) -> None:
"""Add attributes for timing or volatility sensors."""
if key.endswith("_volatility"):
add_volatility_attributes(attributes=attributes, cached_data=cached_data)
else:
add_period_timing_attributes(attributes=attributes, key=key, state_value=native_value)
def _add_cached_trend_attributes(attributes: dict, key: str, cached_data: dict) -> None:
"""Add cached trend attributes if available."""
if key.startswith("price_trend_") and cached_data.get("trend_attributes"):
attributes.update(cached_data["trend_attributes"])
elif key == "current_price_trend" and cached_data.get("current_trend_attributes"):
# Add cached attributes (timestamp already set by platform)
attributes.update(cached_data["current_trend_attributes"])
elif key == "next_price_trend_change" and cached_data.get("trend_change_attributes"):
# Add cached attributes (timestamp already set by platform)
# State contains the timestamp of the trend change itself
attributes.update(cached_data["trend_change_attributes"])

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custom_components/tibber_prices/sensor/attributes/volatility.py Normal file
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"""Volatility attribute builders for Tibber Prices sensors."""
from __future__ import annotations
from datetime import timedelta
from custom_components.tibber_prices.utils.price import calculate_volatility_level
from homeassistant.util import dt as dt_util
def add_volatility_attributes(
attributes: dict,
cached_data: dict,
) -> None:
"""
Add attributes for volatility sensors.
Args:
attributes: Dictionary to add attributes to
cached_data: Dictionary containing cached sensor data
"""
if cached_data.get("volatility_attributes"):
attributes.update(cached_data["volatility_attributes"])
def get_prices_for_volatility(
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(
volatility_attributes: dict,
volatility_type: str,
price_info: dict,
thresholds: dict,
) -> None:
"""
Add type-specific attributes for volatility sensors.
Args:
volatility_attributes: Dictionary to add type-specific attributes to
volatility_type: Type of volatility calculation
price_info: Price information dictionary from coordinator data
thresholds: Volatility thresholds configuration
"""
# Add timestamp for calendar day volatility sensors (midnight of the day)
if volatility_type == "today":
today_data = price_info.get("today", [])
if today_data:
volatility_attributes["timestamp"] = today_data[0].get("startsAt")
elif volatility_type == "tomorrow":
tomorrow_data = price_info.get("tomorrow", [])
if tomorrow_data:
volatility_attributes["timestamp"] = tomorrow_data[0].get("startsAt")
elif volatility_type == "today_tomorrow":
# For combined today+tomorrow, use today's midnight
today_data = price_info.get("today", [])
if today_data:
volatility_attributes["timestamp"] = today_data[0].get("startsAt")
# 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
volatility_attributes["today_spread"] = round(today_spread, 2)
volatility_attributes["today_volatility"] = today_vol
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
volatility_attributes["tomorrow_spread"] = round(tomorrow_spread, 2)
volatility_attributes["tomorrow_volatility"] = tomorrow_vol
volatility_attributes["interval_count_tomorrow"] = len(tomorrow_prices)
elif volatility_type == "next_24h":
# Add time window info
now = dt_util.now()
volatility_attributes["timestamp"] = now.isoformat()

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custom_components/tibber_prices/sensor/attributes/window_24h.py Normal file
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"""24-hour window attribute builders for Tibber Prices sensors."""
from __future__ import annotations
from datetime import timedelta
from typing import TYPE_CHECKING
from homeassistant.util import dt as dt_util
if TYPE_CHECKING:
from custom_components.tibber_prices.coordinator.core import (
TibberPricesDataUpdateCoordinator,
)
def _update_extreme_interval(extreme_interval: dict | None, price_data: dict, key: str) -> dict:
"""
Update extreme interval for min/max sensors.
Args:
extreme_interval: Current extreme interval or None
price_data: New price data to compare
key: Sensor key to determine if min or max
Returns:
Updated extreme interval
"""
if extreme_interval is None:
return price_data
price = price_data.get("total")
extreme_price = extreme_interval.get("total")
if price is None or extreme_price is None:
return extreme_interval
is_new_extreme = ("min" in key and price < extreme_price) or ("max" in key and price > extreme_price)
return price_data if is_new_extreme else extreme_interval
def add_average_price_attributes(
attributes: dict,
key: str,
coordinator: TibberPricesDataUpdateCoordinator,
) -> None:
"""
Add attributes for trailing and leading average/min/max price sensors.
Args:
attributes: Dictionary to add attributes to
key: The sensor entity key
coordinator: The data update coordinator
"""
now = dt_util.now()
# Determine if this is trailing or leading
is_trailing = "trailing" in key
# Get all price intervals
price_info = 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
intervals_in_window = []
extreme_interval = None # Track interval with min/max for min/max sensors
is_min_max_sensor = "min" in key or "max" in key
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)
# Track extreme interval for min/max sensors
if is_min_max_sensor:
extreme_interval = _update_extreme_interval(extreme_interval, price_data, key)
# Add timestamp attribute
if intervals_in_window:
# For min/max sensors: use the timestamp of the interval with extreme price
# For average sensors: use first interval in the window
if extreme_interval and is_min_max_sensor:
attributes["timestamp"] = extreme_interval.get("startsAt")
else:
attributes["timestamp"] = intervals_in_window[0].get("startsAt")
attributes["interval_count"] = len(intervals_in_window)

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custom_components/tibber_prices/sensor/calculators/__init__.py Normal file
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"""
Calculator classes for Tibber Prices sensor value calculations.
This package contains specialized calculator classes that handle different types
of sensor value calculations. Each calculator focuses on one calculation pattern
(interval-based, rolling hour, daily statistics, etc.).
All calculators inherit from BaseCalculator and have access to coordinator data.
"""
from __future__ import annotations
from .base import BaseCalculator
from .daily_stat import DailyStatCalculator
from .interval import IntervalCalculator
from .metadata import MetadataCalculator
from .rolling_hour import RollingHourCalculator
from .timing import TimingCalculator
from .trend import TrendCalculator
from .volatility import VolatilityCalculator
from .window_24h import Window24hCalculator
__all__ = [
"BaseCalculator",
"DailyStatCalculator",
"IntervalCalculator",
"MetadataCalculator",
"RollingHourCalculator",
"TimingCalculator",
"TrendCalculator",
"VolatilityCalculator",
"Window24hCalculator",
]

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custom_components/tibber_prices/sensor/calculators/base.py Normal file
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"""Base calculator class for all Tibber Prices sensor calculators."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from custom_components.tibber_prices.coordinator import (
TibberPricesDataUpdateCoordinator,
)
from custom_components.tibber_prices.data import TibberPricesConfigEntry
from homeassistant.core import HomeAssistant
class BaseCalculator:
"""
Base class for all sensor value calculators.
Provides common access patterns to coordinator data and configuration.
All specialized calculators should inherit from this class.
"""
def __init__(self, coordinator: TibberPricesDataUpdateCoordinator) -> None:
"""
Initialize the calculator.
Args:
coordinator: The data update coordinator providing price and user data.
"""
self._coordinator = coordinator
@property
def coordinator(self) -> TibberPricesDataUpdateCoordinator:
"""Get the coordinator instance."""
return self._coordinator
@property
def hass(self) -> HomeAssistant:
"""Get Home Assistant instance."""
return self._coordinator.hass
@property
def config_entry(self) -> TibberPricesConfigEntry:
"""Get config entry."""
return self._coordinator.config_entry
@property
def config(self) -> Any:
"""Get configuration options."""
return self.config_entry.options
@property
def coordinator_data(self) -> dict[str, Any]:
"""Get full coordinator data."""
return self._coordinator.data
@property
def price_info(self) -> dict[str, Any]:
"""Get price information from coordinator data."""
return self.coordinator_data.get("priceInfo", {})
@property
def user_data(self) -> dict[str, Any]:
"""Get user data from coordinator data."""
return self.coordinator_data.get("user_data", {})
@property
def currency(self) -> str:
"""Get currency code from price info."""
return self.price_info.get("currency", "EUR")

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custom_components/tibber_prices/sensor/calculators/daily_stat.py Normal file
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"""Calculator for daily statistics (min/max/avg within calendar day)."""
from __future__ import annotations
from datetime import timedelta
from typing import TYPE_CHECKING
from custom_components.tibber_prices.const import (
CONF_PRICE_RATING_THRESHOLD_HIGH,
CONF_PRICE_RATING_THRESHOLD_LOW,
DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
DEFAULT_PRICE_RATING_THRESHOLD_LOW,
)
from custom_components.tibber_prices.entity_utils import get_price_value
from custom_components.tibber_prices.sensor.helpers import (
aggregate_level_data,
aggregate_rating_data,
)
from homeassistant.util import dt as dt_util
from .base import BaseCalculator
if TYPE_CHECKING:
from collections.abc import Callable
from custom_components.tibber_prices.coordinator import (
TibberPricesDataUpdateCoordinator,
)
class DailyStatCalculator(BaseCalculator):
"""
Calculator for daily statistics.
Handles sensors that calculate min/max/avg prices or aggregate level/rating
for entire calendar days (yesterday/today/tomorrow).
"""
def __init__(self, coordinator: TibberPricesDataUpdateCoordinator) -> None:
"""
Initialize calculator.
Args:
coordinator: The data update coordinator.
"""
super().__init__(coordinator)
self._last_extreme_interval: dict | None = None
def get_daily_stat_value(
self,
*,
day: str = "today",
stat_func: Callable[[list[float]], float],
) -> float | None:
"""
Unified method for daily statistics (min/max/avg within calendar day).
Calculates statistics for a specific calendar day using local timezone
boundaries. Stores the extreme interval for use in attributes.
Args:
day: "today" or "tomorrow" - which calendar day to calculate for.
stat_func: Statistical function (min, max, or lambda for avg).
Returns:
Price value in minor currency units (cents/øre), or None if unavailable.
"""
if not self.coordinator_data:
return None
price_info = self.price_info
# 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's local date boundaries
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
# Always return in minor currency units (cents/øre) with 2 decimals
result = get_price_value(value, in_euro=False)
return round(result, 2)
def get_daily_aggregated_value(
self,
*,
day: str = "today",
value_type: str = "level",
) -> str | None:
"""
Get aggregated price level or rating for a specific calendar day.
Aggregates all intervals within a calendar day using the same logic
as rolling hour sensors, but for the entire day.
Args:
day: "yesterday", "today", or "tomorrow" - which calendar day to calculate for.
value_type: "level" or "rating" - type of aggregation to perform.
Returns:
Aggregated level/rating value (lowercase), or None if unavailable.
"""
if not self.coordinator_data:
return None
price_info = self.price_info
# 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)
elif day == "yesterday":
local_midnight = local_midnight - timedelta(days=1)
local_midnight_next_day = local_midnight + timedelta(days=1)
# Collect all intervals from both today and tomorrow data
# that fall within the target day's local date boundaries
day_intervals = []
for day_key in ["yesterday", "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 interval if it starts within the target day's local date boundaries
if local_midnight <= starts_at < local_midnight_next_day:
day_intervals.append(price_data)
if not day_intervals:
return None
# Use the same aggregation logic as rolling hour sensors
if value_type == "level":
return aggregate_level_data(day_intervals)
if value_type == "rating":
# Get thresholds from config
threshold_low = self.config.get(
CONF_PRICE_RATING_THRESHOLD_LOW,
DEFAULT_PRICE_RATING_THRESHOLD_LOW,
)
threshold_high = self.config.get(
CONF_PRICE_RATING_THRESHOLD_HIGH,
DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
)
return aggregate_rating_data(day_intervals, threshold_low, threshold_high)
return None
def get_last_extreme_interval(self) -> dict | None:
"""
Get the last stored extreme interval (from min/max calculation).
Returns:
Dictionary with interval data, or None if no extreme interval stored.
"""
return self._last_extreme_interval

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"""Calculator for interval-based sensors (current/next/previous interval values)."""
from __future__ import annotations
from datetime import timedelta
from typing import TYPE_CHECKING
from custom_components.tibber_prices.const import MINUTES_PER_INTERVAL
from custom_components.tibber_prices.utils.price import find_price_data_for_interval
from homeassistant.util import dt as dt_util
from .base import BaseCalculator
if TYPE_CHECKING:
from custom_components.tibber_prices.coordinator import (
TibberPricesDataUpdateCoordinator,
)
class IntervalCalculator(BaseCalculator):
"""
Calculator for interval-based sensors.
Handles sensors that retrieve values (price/level/rating) for specific intervals
relative to the current time (current, next, previous).
"""
def __init__(self, coordinator: TibberPricesDataUpdateCoordinator) -> None:
"""
Initialize calculator.
Args:
coordinator: The data update coordinator.
"""
super().__init__(coordinator)
# State attributes for specific sensors
self._last_price_level: str | None = None
self._last_rating_level: str | None = None
self._last_rating_difference: float | None = None
def get_interval_value(
self,
*,
interval_offset: int,
value_type: str,
in_euro: bool = False,
) -> str | float | None:
"""
Unified method to get values (price/level/rating) for intervals with offset.
Args:
interval_offset: Offset from current interval (0=current, 1=next, -1=previous).
value_type: Type of value to retrieve ("price", "level", "rating").
in_euro: For prices only - return in EUR if True, cents if False.
Returns:
For "price": float in EUR or cents.
For "level" or "rating": lowercase enum string.
None if data unavailable.
"""
if not self.coordinator_data:
return None
price_info = self.price_info
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
# Extract value based on type
if value_type == "price":
price = interval_data.get("total")
if price is None:
return None
price = float(price)
return price if in_euro else round(price * 100, 2)
if value_type == "level":
level = interval_data.get("level")
return level.lower() if level else None
# For rating: extract rating_level
rating = interval_data.get("rating_level")
return rating.lower() if rating else None
def get_price_level_value(self) -> str | None:
"""
Get the current price level value as enum string for the state.
Stores the level in internal state for attribute building.
Returns:
Price level (lowercase), or None if unavailable.
"""
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_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.
Args:
rating_type: Must be "current" (other values return None).
Returns:
Rating level (lowercase), or None if unavailable.
"""
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.price_info
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_current_interval_data(self) -> dict | None:
"""
Get the price data for the current interval using coordinator utility.
Returns:
Dictionary with interval data, or None if unavailable.
"""
return self.coordinator.get_current_interval()
def get_last_price_level(self) -> str | None:
"""
Get the last stored price level (from get_price_level_value call).
Returns:
Price level string, or None if no level stored.
"""
return self._last_price_level
def get_last_rating_level(self) -> str | None:
"""
Get the last stored rating level (from get_rating_value call).
Returns:
Rating level string, or None if no level stored.
"""
return self._last_rating_level
def get_last_rating_difference(self) -> float | None:
"""
Get the last stored rating difference (from get_rating_value call).
Returns:
Rating difference percentage, or None if no difference stored.
"""
return self._last_rating_difference

115
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"""Calculator for home metadata, metering point, and subscription data."""
from __future__ import annotations
from .base import BaseCalculator
class MetadataCalculator(BaseCalculator):
"""
Calculator for home metadata, metering point, and subscription data.
Handles sensors that expose static or slowly-changing user data from the
Tibber API, such as home characteristics, metering point information, and
subscription details.
"""
def get_home_metadata_value(self, field: str) -> str | int | None:
"""
Get home metadata value from user data.
String values are converted to lowercase for ENUM device_class compatibility.
Args:
field: The metadata field name (e.g., "type", "size", "mainFuseSize").
Returns:
The field value, or None if not available.
"""
user_homes = self.coordinator.get_user_homes()
if not user_homes:
return None
# Find the home matching this sensor's home_id
home_id = self.config_entry.data.get("home_id")
if not home_id:
return None
home_data = next((home for home in user_homes if home.get("id") == home_id), None)
if not home_data:
return None
value = home_data.get(field)
# Convert string to lowercase for ENUM device_class
if isinstance(value, str):
return value.lower()
return value
def get_metering_point_value(self, field: str) -> str | int | None:
"""
Get metering point data value from user data.
Args:
field: The metering point field name (e.g., "gridCompany", "priceAreaCode").
Returns:
The field value, or None if not available.
"""
user_homes = self.coordinator.get_user_homes()
if not user_homes:
return None
home_id = self.config_entry.data.get("home_id")
if not home_id:
return None
home_data = next((home for home in user_homes if home.get("id") == home_id), None)
if not home_data:
return None
metering_point = home_data.get("meteringPointData")
if not metering_point:
return None
return metering_point.get(field)
def get_subscription_value(self, field: str) -> str | None:
"""
Get subscription value from user data.
String values are converted to lowercase for ENUM device_class compatibility.
Args:
field: The subscription field name (e.g., "status").
Returns:
The field value, or None if not available.
"""
user_homes = self.coordinator.get_user_homes()
if not user_homes:
return None
home_id = self.config_entry.data.get("home_id")
if not home_id:
return None
home_data = next((home for home in user_homes if home.get("id") == home_id), None)
if not home_data:
return None
subscription = home_data.get("currentSubscription")
if not subscription:
return None
value = subscription.get(field)
# Convert string to lowercase for ENUM device_class
if isinstance(value, str):
return value.lower()
return value

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"""Calculator for rolling hour average values (5-interval windows)."""
from __future__ import annotations
from custom_components.tibber_prices.const import (
CONF_PRICE_RATING_THRESHOLD_HIGH,
CONF_PRICE_RATING_THRESHOLD_LOW,
DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
DEFAULT_PRICE_RATING_THRESHOLD_LOW,
)
from custom_components.tibber_prices.entity_utils import find_rolling_hour_center_index
from custom_components.tibber_prices.sensor.helpers import (
aggregate_level_data,
aggregate_price_data,
aggregate_rating_data,
)
from homeassistant.util import dt as dt_util
from .base import BaseCalculator
class RollingHourCalculator(BaseCalculator):
"""
Calculator for rolling hour values (5-interval windows).
Handles sensors that aggregate data from a 5-interval window (60 minutes):
2 intervals before + center interval + 2 intervals after.
"""
def get_rolling_hour_value(
self,
*,
hour_offset: int = 0,
value_type: str = "price",
) -> str | float | None:
"""
Unified method to get aggregated values from 5-interval rolling window.
Window: 2 before + center + 2 after = 5 intervals (60 minutes total).
Args:
hour_offset: 0 (current hour), 1 (next hour), etc.
value_type: "price" | "level" | "rating".
Returns:
Aggregated value based on type:
- "price": float (average price in minor currency units)
- "level": str (aggregated level: "very_cheap", "cheap", etc.)
- "rating": str (aggregated rating: "low", "normal", "high")
"""
if not self.coordinator_data:
return None
# Get all available price data
price_info = self.price_info
all_prices = price_info.get("yesterday", []) + price_info.get("today", []) + price_info.get("tomorrow", [])
if not all_prices:
return None
# Find center index for the rolling window
now = dt_util.now()
center_idx = find_rolling_hour_center_index(all_prices, now, hour_offset)
if center_idx is None:
return None
# Collect data from 5-interval window (-2, -1, 0, +1, +2)
window_data = []
for offset in range(-2, 3):
idx = center_idx + offset
if 0 <= idx < len(all_prices):
window_data.append(all_prices[idx])
if not window_data:
return None
return self._aggregate_window_data(window_data, value_type)
def _aggregate_window_data(
self,
window_data: list[dict],
value_type: str,
) -> str | float | None:
"""
Aggregate data from multiple intervals based on value type.
Args:
window_data: List of price interval dictionaries.
value_type: "price" | "level" | "rating".
Returns:
Aggregated value based on type.
"""
# Get thresholds from config for rating aggregation
threshold_low = self.config.get(
CONF_PRICE_RATING_THRESHOLD_LOW,
DEFAULT_PRICE_RATING_THRESHOLD_LOW,
)
threshold_high = self.config.get(
CONF_PRICE_RATING_THRESHOLD_HIGH,
DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
)
# Map value types to aggregation functions
aggregators = {
"price": lambda data: aggregate_price_data(data),
"level": lambda data: aggregate_level_data(data),
"rating": lambda data: aggregate_rating_data(data, threshold_low, threshold_high),
}
aggregator = aggregators.get(value_type)
if aggregator:
return aggregator(window_data)
return None

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"""
Timing calculator for best/peak price period timing sensors.
This module handles all timing-related calculations for period-based sensors:
- Period end times (when does current/next period end?)
- Period start times (when does next period start?)
- Remaining minutes (how long until period ends?)
- Progress (how far through the period are we?)
- Next period timing (when does the next period start?)
The calculator provides smart defaults:
- Active period → show current period timing
- No active → show next period timing
- No more periods → 0 for numeric values, None for timestamps
"""
from datetime import datetime
from homeassistant.util import dt as dt_util
from .base import BaseCalculator
# Constants
PROGRESS_GRACE_PERIOD_SECONDS = 60 # Show 100% for 1 minute after period ends
class TimingCalculator(BaseCalculator):
"""
Calculator for period timing sensors.
Handles timing information for best_price and peak_price periods:
- Active period timing (end time, remaining minutes, progress)
- Next period timing (start time, minutes until start)
- Period duration (total length in minutes)
Period states:
- ACTIVE: A period is currently running
- GRACE: Period just ended (within 60s), still showing 100% progress
- IDLE: No active period, waiting for next one
"""
def get_period_timing_value(
self,
*,
period_type: str,
value_type: str,
) -> datetime | float | None:
"""
Get timing-related values for best_price/peak_price periods.
This method provides timing information based on whether a period is currently
active or not, ensuring sensors always provide useful information.
Value types behavior:
- end_time: Active period → current end | No active → next period end | None if no periods
- next_start_time: Active period → next-next start | No active → next start | None if no more
- remaining_minutes: Active period → minutes to end | No active → 0
- progress: Active period → 0-100% | No active → 0
- next_in_minutes: Active period → minutes to next-next | No active → minutes to next | None if no more
Args:
period_type: "best_price" or "peak_price"
value_type: "end_time", "remaining_minutes", "progress", "next_start_time", "next_in_minutes"
Returns:
- datetime for end_time/next_start_time
- float for remaining_minutes/next_in_minutes/progress (or 0 when not active)
- None if no relevant period data available
"""
if not self.coordinator.data:
return None
# Get period data from coordinator
periods_data = self.coordinator.data.get("periods", {})
period_data = periods_data.get(period_type)
if not period_data or not period_data.get("periods"):
# No periods available - return 0 for numeric sensors, None for timestamps
return 0 if value_type in ("remaining_minutes", "progress", "next_in_minutes") else None
period_summaries = period_data["periods"]
now = dt_util.now()
# Find current, previous and next periods
current_period = self._find_active_period(period_summaries, now)
previous_period = self._find_previous_period(period_summaries, now)
next_period = self._find_next_period(period_summaries, now, skip_current=bool(current_period))
# Delegate to specific calculators
return self._calculate_timing_value(value_type, current_period, previous_period, next_period, now)
def _calculate_timing_value(
self,
value_type: str,
current_period: dict | None,
previous_period: dict | None,
next_period: dict | None,
now: datetime,
) -> datetime | float | None:
"""Calculate specific timing value based on type and available periods."""
# Define calculation strategies for each value type
calculators = {
"end_time": lambda: (
current_period.get("end") if current_period else (next_period.get("end") if next_period else None)
),
"period_duration": lambda: self._calc_period_duration(current_period, next_period),
"next_start_time": lambda: next_period.get("start") if next_period else None,
"remaining_minutes": lambda: (self._calc_remaining_minutes(current_period, now) if current_period else 0),
"progress": lambda: self._calc_progress_with_grace_period(current_period, previous_period, now),
"next_in_minutes": lambda: (self._calc_next_in_minutes(next_period, now) if next_period else None),
}
calculator = calculators.get(value_type)
return calculator() if calculator else None
def _find_active_period(self, periods: list, now: datetime) -> dict | None:
"""Find currently active period."""
for period in periods:
start = period.get("start")
end = period.get("end")
if start and end and start <= now < end:
return period
return None
def _find_previous_period(self, periods: list, now: datetime) -> dict | None:
"""Find the most recent period that has already ended."""
past_periods = [p for p in periods if p.get("end") and p.get("end") <= now]
if not past_periods:
return None
# Sort by end time descending to get the most recent one
past_periods.sort(key=lambda p: p["end"], reverse=True)
return past_periods[0]
def _find_next_period(self, periods: list, now: datetime, *, skip_current: bool = False) -> dict | None:
"""
Find next future period.
Args:
periods: List of period dictionaries
now: Current time
skip_current: If True, skip the first future period (to get next-next)
Returns:
Next period dict or None if no future periods
"""
future_periods = [p for p in periods if p.get("start") and p.get("start") > now]
if not future_periods:
return None
# Sort by start time to ensure correct order
future_periods.sort(key=lambda p: p["start"])
# Return second period if skip_current=True (next-next), otherwise first (next)
if skip_current and len(future_periods) > 1:
return future_periods[1]
if not skip_current and future_periods:
return future_periods[0]
return None
def _calc_remaining_minutes(self, period: dict, now: datetime) -> float:
"""Calculate minutes until period ends."""
end = period.get("end")
if not end:
return 0
delta = end - now
return max(0, delta.total_seconds() / 60)
def _calc_next_in_minutes(self, period: dict, now: datetime) -> float:
"""Calculate minutes until period starts."""
start = period.get("start")
if not start:
return 0
delta = start - now
return max(0, delta.total_seconds() / 60)
def _calc_period_duration(self, current_period: dict | None, next_period: dict | None) -> float | None:
"""
Calculate total duration of active or next period in minutes.
Returns duration of current period if active, otherwise duration of next period.
This gives users a consistent view of period length regardless of timing.
Args:
current_period: Currently active period (if any)
next_period: Next upcoming period (if any)
Returns:
Duration in minutes, or None if no periods available
"""
period = current_period or next_period
if not period:
return None
start = period.get("start")
end = period.get("end")
if not start or not end:
return None
duration = (end - start).total_seconds() / 60
return max(0, duration)
def _calc_progress(self, period: dict, now: datetime) -> float:
"""Calculate progress percentage (0-100) of current period."""
start = period.get("start")
end = period.get("end")
if not start or not end:
return 0
total_duration = (end - start).total_seconds()
if total_duration <= 0:
return 0
elapsed = (now - start).total_seconds()
progress = (elapsed / total_duration) * 100
return min(100, max(0, progress))
def _calc_progress_with_grace_period(
self, current_period: dict | None, previous_period: dict | None, now: datetime
) -> float:
"""
Calculate progress with grace period after period end.
Shows 100% for 1 minute after period ends to allow triggers on 100% completion.
This prevents the progress from jumping directly from ~99% to 0% without ever
reaching 100%, which would make automations like "when progress = 100%" impossible.
"""
# If we have an active period, calculate normal progress
if current_period:
return self._calc_progress(current_period, now)
# No active period - check if we just finished one (within grace period)
if previous_period:
previous_end = previous_period.get("end")
if previous_end:
seconds_since_end = (now - previous_end).total_seconds()
# Grace period: Show 100% for defined time after period ended
if 0 <= seconds_since_end <= PROGRESS_GRACE_PERIOD_SECONDS:
return 100
# No active period and either no previous period or grace period expired
return 0

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"""
Trend calculator for price trend analysis sensors.
This module handles all trend-related calculations:
- Simple price trends (1h-12h future comparison)
- Current trend with momentum analysis
- Next trend change prediction
- Trend duration tracking
Caching strategy:
- Simple trends: Cached per sensor update to ensure consistency between state and attributes
- Current trend + next change: Cached centrally for 60s to avoid duplicate calculations
"""
from datetime import datetime, timedelta
from typing import TYPE_CHECKING, Any
from custom_components.tibber_prices.const import MINUTES_PER_INTERVAL
from custom_components.tibber_prices.utils.average import calculate_next_n_hours_avg
from custom_components.tibber_prices.utils.price import (
calculate_price_trend,
find_price_data_for_interval,
)
from homeassistant.util import dt as dt_util
from .base import BaseCalculator
if TYPE_CHECKING:
from custom_components.tibber_prices.coordinator import (
TibberPricesDataUpdateCoordinator,
)
# Constants
MIN_HOURS_FOR_LATER_HALF = 3 # Minimum hours needed to calculate later half average
class TrendCalculator(BaseCalculator):
"""
Calculator for price trend sensors.
Handles three types of trend analysis:
1. Simple trends (price_trend_1h-12h): Current vs next N hours average
2. Current trend (current_price_trend): Momentum + 3h outlook with volatility adjustment
3. Next change (next_price_trend_change): Scan forward for trend reversal
Caching:
- Simple trends: Per-sensor cache (_cached_trend_value, _trend_attributes)
- Current/Next: Centralized cache (_trend_calculation_cache) with 60s TTL
"""
def __init__(self, coordinator: "TibberPricesDataUpdateCoordinator") -> None:
"""Initialize trend calculator with caching state."""
super().__init__(coordinator)
# Per-sensor trend caches (for price_trend_Nh sensors)
self._cached_trend_value: str | None = None
self._trend_attributes: dict[str, Any] = {}
# Centralized trend calculation cache (for current_price_trend + next_price_trend_change)
self._trend_calculation_cache: dict[str, Any] | None = None
self._trend_calculation_timestamp: datetime | None = None
# Separate attribute storage for current_price_trend and next_price_trend_change
self._current_trend_attributes: dict[str, Any] | None = None
self._trend_change_attributes: dict[str, Any] | None = None
def get_price_trend_value(self, *, hours: int) -> str | None:
"""
Calculate price trend comparing current interval vs next N hours average.
This is for simple trend sensors (price_trend_1h through price_trend_12h).
Results are cached per sensor to ensure consistency between state and attributes.
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.coordinator.get_current_interval()
if not current_interval or "total" not in current_interval:
return None
current_interval_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
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.config.get("price_trend_threshold_rising", 5.0)
threshold_falling = self.config.get("price_trend_threshold_falling", -5.0)
volatility_threshold_moderate = self.config.get("volatility_threshold_moderate", 15.0)
volatility_threshold_high = self.config.get("volatility_threshold_high", 30.0)
# Prepare data for volatility-adaptive thresholds
price_info = self.coordinator.data.get("priceInfo", {})
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_intervals = today_prices + tomorrow_prices
lookahead_intervals = hours * 4 # Convert hours to 15-minute intervals
# Calculate trend with volatility-adaptive thresholds
trend_state, diff_pct = calculate_price_trend(
current_interval_price,
future_avg,
threshold_rising=threshold_rising,
threshold_falling=threshold_falling,
volatility_adjustment=True, # Always enabled
lookahead_intervals=lookahead_intervals,
all_intervals=all_intervals,
volatility_threshold_moderate=volatility_threshold_moderate,
volatility_threshold_high=volatility_threshold_high,
)
# 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_interval_price > 0:
later_half_diff = ((later_half_avg - current_interval_price) / current_interval_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 get_current_trend_value(self) -> str | None:
"""
Get the current price trend that is valid until the next change.
Uses centralized _calculate_trend_info() for consistency with next_price_trend_change sensor.
Returns:
Current trend state: "rising", "falling", or "stable"
"""
trend_info = self._calculate_trend_info()
if not trend_info:
return None
# Set attributes for this sensor
self._current_trend_attributes = {
"from_direction": trend_info["from_direction"],
"trend_duration_minutes": trend_info["trend_duration_minutes"],
}
return trend_info["current_trend_state"]
def get_next_trend_change_value(self) -> datetime | None:
"""
Calculate when the next price trend change will occur.
Uses centralized _calculate_trend_info() for consistency with current_price_trend sensor.
Returns:
Timestamp of next trend change, or None if no change expected in next 24h
"""
trend_info = self._calculate_trend_info()
if not trend_info:
return None
# Set attributes for this sensor
self._trend_change_attributes = trend_info["trend_change_attributes"]
return trend_info["next_change_time"]
def get_trend_attributes(self) -> dict[str, Any]:
"""Get cached trend attributes for simple trend sensors (price_trend_Nh)."""
return self._trend_attributes
def get_current_trend_attributes(self) -> dict[str, Any] | None:
"""Get cached attributes for current_price_trend sensor."""
return self._current_trend_attributes
def get_trend_change_attributes(self) -> dict[str, Any] | None:
"""Get cached attributes for next_price_trend_change sensor."""
return self._trend_change_attributes
def clear_trend_cache(self) -> None:
"""Clear simple trend cache (called on coordinator update)."""
self._cached_trend_value = None
self._trend_attributes = {}
def clear_calculation_cache(self) -> None:
"""Clear centralized trend calculation cache (called on coordinator update)."""
self._trend_calculation_cache = None
self._trend_calculation_timestamp = None
# ========================================================================
# PRIVATE HELPER METHODS
# ========================================================================
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 _calculate_trend_info(self) -> dict[str, Any] | None:
"""
Centralized trend calculation for current_price_trend and next_price_trend_change sensors.
This method calculates all trend-related information in one place to avoid duplication
and ensure consistency between the two sensors. Results are cached per coordinator update.
Returns:
Dictionary with trend information for both sensors.
"""
trend_cache_duration_seconds = 60 # Cache for 1 minute
# Check if we have a valid cache
now = dt_util.now()
if (
self._trend_calculation_cache is not None
and self._trend_calculation_timestamp is not None
and (now - self._trend_calculation_timestamp).total_seconds() < trend_cache_duration_seconds
):
return self._trend_calculation_cache
# Validate coordinator data
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
all_intervals = price_info.get("today", []) + price_info.get("tomorrow", [])
current_interval = find_price_data_for_interval(price_info, now)
if not all_intervals or not current_interval:
return None
current_interval_start = dt_util.parse_datetime(current_interval["startsAt"])
current_interval_start = dt_util.as_local(current_interval_start) if current_interval_start else None
if not current_interval_start:
return None
current_index = self._find_current_interval_index(all_intervals, current_interval_start)
if current_index is None:
return None
# Get configured thresholds
thresholds = self._get_thresholds_config()
# Step 1: Calculate current momentum from trailing data (1h weighted)
current_price = float(current_interval["total"])
current_momentum = self._calculate_momentum(current_price, all_intervals, current_index)
# Step 2: Calculate 3h baseline trend for comparison
current_trend_3h = self._calculate_standard_trend(all_intervals, current_index, current_interval, thresholds)
# Step 3: Calculate final trend FIRST (momentum + future outlook)
min_intervals_for_trend = 4
standard_lookahead = 12 # 3 hours
lookahead_intervals = standard_lookahead
# Get future data
future_intervals = all_intervals[current_index + 1 : current_index + lookahead_intervals + 1]
future_prices = [float(fi["total"]) for fi in future_intervals if "total" in fi]
# Combine momentum + future outlook to get ACTUAL current trend
if len(future_intervals) >= min_intervals_for_trend and future_prices:
future_avg = sum(future_prices) / len(future_prices)
current_trend_state = self._combine_momentum_with_future(
current_momentum=current_momentum,
current_price=current_price,
future_avg=future_avg,
context={
"all_intervals": all_intervals,
"current_index": current_index,
"lookahead_intervals": lookahead_intervals,
"thresholds": thresholds,
},
)
else:
# Not enough future data - use 3h baseline as fallback
current_trend_state = current_trend_3h
# Step 4: Find next trend change FROM the current trend state (not momentum!)
scan_params = {
"current_index": current_index,
"current_trend_state": current_trend_state, # Use FINAL trend, not momentum
"current_interval": current_interval,
"now": now,
}
next_change_time = self._scan_for_trend_change(all_intervals, scan_params, thresholds)
# Step 5: Find when current trend started (scan backward)
trend_start_time, from_direction = self._find_trend_start_time(
all_intervals, current_index, current_trend_state, thresholds
)
# Calculate duration of current trend
trend_duration_minutes = None
if trend_start_time:
duration = now - trend_start_time
trend_duration_minutes = int(duration.total_seconds() / 60)
# Calculate minutes until change
minutes_until_change = None
if next_change_time:
time_diff = next_change_time - now
minutes_until_change = int(time_diff.total_seconds() / 60)
result = {
"current_trend_state": current_trend_state,
"next_change_time": next_change_time,
"trend_change_attributes": self._trend_change_attributes,
"trend_start_time": trend_start_time,
"from_direction": from_direction,
"trend_duration_minutes": trend_duration_minutes,
"minutes_until_change": minutes_until_change,
}
# Cache the result
self._trend_calculation_cache = result
self._trend_calculation_timestamp = now
return result
def _get_thresholds_config(self) -> dict[str, float]:
"""Get configured thresholds for trend calculation."""
return {
"rising": self.config.get("price_trend_threshold_rising", 5.0),
"falling": self.config.get("price_trend_threshold_falling", -5.0),
"moderate": self.config.get("volatility_threshold_moderate", 15.0),
"high": self.config.get("volatility_threshold_high", 30.0),
}
def _calculate_momentum(self, current_price: float, all_intervals: list, current_index: int) -> str:
"""
Calculate price momentum from weighted trailing average (last 1h).
Args:
current_price: Current interval price
all_intervals: All price intervals
current_index: Index of current interval
Returns:
Momentum direction: "rising", "falling", or "stable"
"""
# Look back 1 hour (4 intervals) for quick reaction
lookback_intervals = 4
min_intervals = 2 # Need at least 30 minutes of history
trailing_intervals = all_intervals[max(0, current_index - lookback_intervals) : current_index]
if len(trailing_intervals) < min_intervals:
return "stable" # Not enough history
# Weighted average: newer intervals count more
# Weights: [0.5, 0.75, 1.0, 1.25] for 4 intervals (grows linearly)
weights = [0.5 + 0.25 * i for i in range(len(trailing_intervals))]
trailing_prices = [float(interval["total"]) for interval in trailing_intervals if "total" in interval]
if not trailing_prices or len(trailing_prices) != len(weights):
return "stable"
weighted_sum = sum(price * weight for price, weight in zip(trailing_prices, weights, strict=True))
weighted_avg = weighted_sum / sum(weights)
# Calculate momentum with 3% threshold
momentum_threshold = 0.03
diff = (current_price - weighted_avg) / weighted_avg
if diff > momentum_threshold:
return "rising"
if diff < -momentum_threshold:
return "falling"
return "stable"
def _combine_momentum_with_future(
self,
*,
current_momentum: str,
current_price: float,
future_avg: float,
context: dict,
) -> str:
"""
Combine momentum analysis with future outlook to determine final trend.
Args:
current_momentum: Current momentum direction (rising/falling/stable)
current_price: Current interval price
future_avg: Average price in future window
context: Dict with all_intervals, current_index, lookahead_intervals, thresholds
Returns:
Final trend direction: "rising", "falling", or "stable"
"""
if current_momentum == "rising":
# We're in uptrend - does it continue?
return "rising" if future_avg >= current_price * 0.98 else "falling"
if current_momentum == "falling":
# We're in downtrend - does it continue?
return "falling" if future_avg <= current_price * 1.02 else "rising"
# current_momentum == "stable" - what's coming?
all_intervals = context["all_intervals"]
current_index = context["current_index"]
lookahead_intervals = context["lookahead_intervals"]
thresholds = context["thresholds"]
lookahead_for_volatility = all_intervals[current_index : current_index + lookahead_intervals]
trend_state, _ = calculate_price_trend(
current_price,
future_avg,
threshold_rising=thresholds["rising"],
threshold_falling=thresholds["falling"],
volatility_adjustment=True,
lookahead_intervals=lookahead_intervals,
all_intervals=lookahead_for_volatility,
volatility_threshold_moderate=thresholds["moderate"],
volatility_threshold_high=thresholds["high"],
)
return trend_state
def _calculate_standard_trend(
self,
all_intervals: list,
current_index: int,
current_interval: dict,
thresholds: dict,
) -> str:
"""Calculate standard 3h trend as baseline."""
min_intervals_for_trend = 4
standard_lookahead = 12 # 3 hours
standard_future_intervals = all_intervals[current_index + 1 : current_index + standard_lookahead + 1]
if len(standard_future_intervals) < min_intervals_for_trend:
return "stable"
standard_future_prices = [float(fi["total"]) for fi in standard_future_intervals if "total" in fi]
if not standard_future_prices:
return "stable"
standard_future_avg = sum(standard_future_prices) / len(standard_future_prices)
current_price = float(current_interval["total"])
standard_lookahead_volatility = all_intervals[current_index : current_index + standard_lookahead]
current_trend_3h, _ = calculate_price_trend(
current_price,
standard_future_avg,
threshold_rising=thresholds["rising"],
threshold_falling=thresholds["falling"],
volatility_adjustment=True,
lookahead_intervals=standard_lookahead,
all_intervals=standard_lookahead_volatility,
volatility_threshold_moderate=thresholds["moderate"],
volatility_threshold_high=thresholds["high"],
)
return current_trend_3h
def _find_current_interval_index(self, all_intervals: list, current_interval_start: datetime) -> int | None:
"""Find the index of current interval in all_intervals list."""
for idx, interval in enumerate(all_intervals):
interval_start = dt_util.parse_datetime(interval["startsAt"])
if interval_start and dt_util.as_local(interval_start) == current_interval_start:
return idx
return None
def _find_trend_start_time(
self,
all_intervals: list,
current_index: int,
current_trend_state: str,
thresholds: dict,
) -> tuple[datetime | None, str | None]:
"""
Find when the current trend started by scanning backward.
Args:
all_intervals: List of all price intervals
current_index: Index of current interval
current_trend_state: Current trend state ("rising", "falling", "stable")
thresholds: Threshold configuration
Returns:
Tuple of (start_time, from_direction):
- start_time: When current trend began, or None if at data boundary
- from_direction: Previous trend direction, or None if unknown
"""
intervals_in_3h = 12 # 3 hours = 12 intervals @ 15min each
# Scan backward to find when trend changed TO current state
for i in range(current_index - 1, max(-1, current_index - 97), -1):
if i < 0:
break
interval = all_intervals[i]
interval_start = dt_util.parse_datetime(interval["startsAt"])
if not interval_start:
continue
interval_start = dt_util.as_local(interval_start)
# Calculate trend at this past interval
future_intervals = all_intervals[i + 1 : i + intervals_in_3h + 1]
if len(future_intervals) < intervals_in_3h:
break # Not enough data to calculate trend
future_prices = [float(fi["total"]) for fi in future_intervals if "total" in fi]
if not future_prices:
continue
future_avg = sum(future_prices) / len(future_prices)
price = float(interval["total"])
# Calculate trend at this past point
lookahead_for_volatility = all_intervals[i : i + intervals_in_3h]
trend_state, _ = calculate_price_trend(
price,
future_avg,
threshold_rising=thresholds["rising"],
threshold_falling=thresholds["falling"],
volatility_adjustment=True,
lookahead_intervals=intervals_in_3h,
all_intervals=lookahead_for_volatility,
volatility_threshold_moderate=thresholds["moderate"],
volatility_threshold_high=thresholds["high"],
)
# Check if trend was different from current trend state
if trend_state != current_trend_state:
# Found the change point - the NEXT interval is where current trend started
next_interval = all_intervals[i + 1]
trend_start = dt_util.parse_datetime(next_interval["startsAt"])
if trend_start:
return dt_util.as_local(trend_start), trend_state
# Reached data boundary - current trend extends beyond available data
return None, None
def _scan_for_trend_change(
self,
all_intervals: list,
scan_params: dict,
thresholds: dict,
) -> datetime | None:
"""
Scan future intervals for trend change.
Args:
all_intervals: List of all price intervals
scan_params: Dict with current_index, current_trend_state, current_interval, now
thresholds: Dict with rising, falling, moderate, high threshold values
Returns:
Timestamp of next trend change, or None if no change in next 24h
"""
intervals_in_3h = 12 # 3 hours = 12 intervals @ 15min each
current_index = scan_params["current_index"]
current_trend_state = scan_params["current_trend_state"]
current_interval = scan_params["current_interval"]
now = scan_params["now"]
for i in range(current_index + 1, min(current_index + 97, len(all_intervals))):
interval = all_intervals[i]
interval_start = dt_util.parse_datetime(interval["startsAt"])
if not interval_start:
continue
interval_start = dt_util.as_local(interval_start)
# Skip if this interval is in the past
if interval_start <= now:
continue
# Calculate trend at this future interval
future_intervals = all_intervals[i + 1 : i + intervals_in_3h + 1]
if len(future_intervals) < intervals_in_3h:
break # Not enough data to calculate trend
future_prices = [float(fi["total"]) for fi in future_intervals if "total" in fi]
if not future_prices:
continue
future_avg = sum(future_prices) / len(future_prices)
current_price = float(interval["total"])
# Calculate trend at this future point
lookahead_for_volatility = all_intervals[i : i + intervals_in_3h]
trend_state, _ = calculate_price_trend(
current_price,
future_avg,
threshold_rising=thresholds["rising"],
threshold_falling=thresholds["falling"],
volatility_adjustment=True,
lookahead_intervals=intervals_in_3h,
all_intervals=lookahead_for_volatility,
volatility_threshold_moderate=thresholds["moderate"],
volatility_threshold_high=thresholds["high"],
)
# Check if trend changed from current trend state
# We want to find ANY change from current state, including changes to/from stable
if trend_state != current_trend_state:
# Store details for attributes
time_diff = interval_start - now
minutes_until = int(time_diff.total_seconds() / 60)
self._trend_change_attributes = {
"direction": trend_state,
"from_direction": current_trend_state,
"minutes_until_change": minutes_until,
"current_price_now": round(float(current_interval["total"]) * 100, 2),
"price_at_change": round(current_price * 100, 2),
"avg_after_change": round(future_avg * 100, 2),
"trend_diff_%": round((future_avg - current_price) / current_price * 100, 1),
}
return interval_start
return None

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custom_components/tibber_prices/sensor/calculators/volatility.py Normal file
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"""Calculator for price volatility analysis."""
from __future__ import annotations
from typing import TYPE_CHECKING
from custom_components.tibber_prices.entity_utils import add_icon_color_attribute
from custom_components.tibber_prices.sensor.attributes import (
add_volatility_type_attributes,
get_prices_for_volatility,
)
from custom_components.tibber_prices.utils.price import calculate_volatility_level
from .base import BaseCalculator
if TYPE_CHECKING:
from typing import Any
class VolatilityCalculator(BaseCalculator):
"""
Calculator for price volatility analysis.
Calculates volatility levels (low, moderate, high, very_high) using coefficient
of variation for different time periods (today, tomorrow, next 24h, today+tomorrow).
"""
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""
Initialize calculator.
Args:
*args: Positional arguments passed to BaseCalculator.
**kwargs: Keyword arguments passed to BaseCalculator.
"""
super().__init__(*args, **kwargs)
self._last_volatility_attributes: dict[str, Any] = {}
def get_volatility_value(self, *, volatility_type: str) -> str | None:
"""
Calculate price volatility using coefficient of variation for different time periods.
Also stores detailed attributes in self._last_volatility_attributes for use in
extra_state_attributes.
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.price_info
# Get volatility thresholds from config
thresholds = {
"threshold_moderate": self.config.get("volatility_threshold_moderate", 5.0),
"threshold_high": self.config.get("volatility_threshold_high", 15.0),
"threshold_very_high": self.config.get("volatility_threshold_very_high", 30.0),
}
# Get prices based on volatility type
prices_to_analyze = 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
add_icon_color_attribute(self._last_volatility_attributes, key="volatility", state_value=volatility)
# Add type-specific attributes
add_volatility_type_attributes(self._last_volatility_attributes, volatility_type, price_info, thresholds)
# Return lowercase for ENUM device class
return volatility.lower()
def get_volatility_attributes(self) -> dict[str, Any]:
"""
Get stored volatility attributes from last calculation.
Returns:
Dictionary of volatility attributes, or empty dict if no calculation yet.
"""
return self._last_volatility_attributes

52
custom_components/tibber_prices/sensor/calculators/window_24h.py Normal file
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@ -0,0 +1,52 @@
"""Calculator for 24-hour sliding window statistics."""
from __future__ import annotations
from typing import TYPE_CHECKING
from custom_components.tibber_prices.entity_utils import get_price_value
from .base import BaseCalculator
if TYPE_CHECKING:
from collections.abc import Callable
class Window24hCalculator(BaseCalculator):
"""
Calculator for 24-hour sliding window statistics.
Handles sensors that calculate statistics over a 24-hour window relative to
the current interval (trailing = previous 24h, leading = next 24h).
"""
def get_24h_window_value(
self,
*,
stat_func: Callable,
) -> float | None:
"""
Unified method for 24-hour sliding window statistics.
Calculates statistics over a 24-hour window relative to the current interval:
- "trailing": Previous 24 hours (96 intervals before current)
- "leading": Next 24 hours (96 intervals after current)
Args:
stat_func: Function from average_utils (e.g., calculate_current_trailing_avg).
Returns:
Price value in minor currency units (cents/øre), or None if unavailable.
"""
if not self.coordinator_data:
return None
value = stat_func(self.coordinator_data)
if value is None:
return None
# Always return in minor currency units (cents/øre) with 2 decimals
result = get_price_value(value, in_euro=False)
return round(result, 2)

144
custom_components/tibber_prices/sensor/chart_data.py Normal file
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@ -0,0 +1,144 @@
"""Chart data export functionality for Tibber Prices sensors."""
from __future__ import annotations
from typing import TYPE_CHECKING
import yaml
from custom_components.tibber_prices.const import CONF_CHART_DATA_CONFIG, DOMAIN
if TYPE_CHECKING:
from datetime import datetime
from custom_components.tibber_prices.coordinator import TibberPricesDataUpdateCoordinator
from custom_components.tibber_prices.data import TibberPricesConfigEntry
from homeassistant.core import HomeAssistant
async def call_chartdata_service_async(
hass: HomeAssistant,
coordinator: TibberPricesDataUpdateCoordinator,
config_entry: TibberPricesConfigEntry,
) -> tuple[dict | None, str | None]:
"""
Call get_chartdata service with user-configured YAML (async).
Returns:
Tuple of (response, error_message).
If successful: (response_dict, None)
If failed: (None, error_string)
"""
# Get user-configured YAML
yaml_config = config_entry.options.get(CONF_CHART_DATA_CONFIG, "")
# Parse YAML if provided, otherwise use empty dict (service defaults)
service_params = {}
if yaml_config and yaml_config.strip():
try:
parsed = yaml.safe_load(yaml_config)
# Ensure we have a dict (yaml.safe_load can return str, int, etc.)
if isinstance(parsed, dict):
service_params = parsed
else:
coordinator.logger.warning(
"YAML configuration must be a dictionary, got %s. Using service defaults.",
type(parsed).__name__,
)
service_params = {}
except yaml.YAMLError as err:
coordinator.logger.warning(
"Invalid chart data YAML configuration: %s. Using service defaults.",
err,
)
service_params = {} # Fall back to service defaults
# Add required entry_id parameter
service_params["entry_id"] = config_entry.entry_id
# Call get_chartdata service using official HA service system
try:
response = await hass.services.async_call(
DOMAIN,
"get_chartdata",
service_params,
blocking=True,
return_response=True,
)
except Exception as ex:
coordinator.logger.exception("Chart data service call failed")
return None, str(ex)
else:
return response, None
def get_chart_data_state(
chart_data_response: dict | None,
chart_data_error: str | None,
) -> str | None:
"""
Return state for chart_data_export sensor.
Args:
chart_data_response: Last service response (or None)
chart_data_error: Last error message (or None)
Returns:
"error" if error occurred
"ready" if data available
"pending" if no data yet
"""
if chart_data_error:
return "error"
if chart_data_response:
return "ready"
return "pending"
def build_chart_data_attributes(
chart_data_response: dict | None,
chart_data_last_update: datetime | None,
chart_data_error: str | None,
) -> dict[str, object] | None:
"""
Return chart data from last service call as attributes with metadata.
Attribute order: timestamp, error (if any), service data (at the end).
Args:
chart_data_response: Last service response
chart_data_last_update: Timestamp of last update
chart_data_error: Error message if service call failed
Returns:
Dict with timestamp, optional error, and service response data.
"""
# Build base attributes with metadata FIRST
attributes: dict[str, object] = {
"timestamp": chart_data_last_update.isoformat() if chart_data_last_update else None,
}
# Add error message if service call failed
if chart_data_error:
attributes["error"] = chart_data_error
if not chart_data_response:
# No data - only metadata (timestamp, error)
return attributes
# Service data goes LAST - after metadata
if isinstance(chart_data_response, dict):
if len(chart_data_response) > 1:
# Multiple keys → wrap to prevent collision with metadata
attributes["data"] = chart_data_response
else:
# Single key → safe to merge directly
attributes.update(chart_data_response)
else:
# If response is array/list/primitive, wrap it in "data" key
attributes["data"] = chart_data_response
return attributes

1411
custom_components/tibber_prices/sensor/core.py
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105
custom_components/tibber_prices/sensor/helpers.py
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@ -5,6 +5,8 @@ This module contains helper functions specific to the sensor platform:
- aggregate_price_data: Calculate average price from window data
- aggregate_level_data: Aggregate price levels from intervals
- aggregate_rating_data: Aggregate price ratings from intervals
- aggregate_window_data: Unified aggregation based on value type
- get_hourly_price_value: Get price for specific hour with offset
For shared helper functions (used by both sensor and binary_sensor platforms),
see entity_utils/helpers.py:
@ -16,10 +18,18 @@ see entity_utils/helpers.py:
from __future__ import annotations
from datetime import timedelta
from typing import TYPE_CHECKING
from custom_components.tibber_prices.entity_utils.helpers import get_price_value
from custom_components.tibber_prices.utils.price import (
aggregate_price_levels,
aggregate_price_rating,
)
from homeassistant.util import dt as dt_util
if TYPE_CHECKING:
from collections.abc import Callable
def aggregate_price_data(window_data: list[dict]) -> float | None:
@ -81,3 +91,98 @@ def aggregate_rating_data(
aggregated, _ = aggregate_price_rating(differences, threshold_low, threshold_high)
return aggregated.lower() if aggregated else None
def aggregate_window_data(
window_data: list[dict],
value_type: str,
threshold_low: float,
threshold_high: float,
) -> str | float | None:
"""
Aggregate data from multiple intervals based on value type.
Unified helper that routes to appropriate aggregation function.
Args:
window_data: List of price interval dictionaries
value_type: Type of value to aggregate ('price', 'level', or 'rating')
threshold_low: Low threshold for rating calculation
threshold_high: High threshold for rating calculation
Returns:
Aggregated value (price as float, level/rating as str), or None if no data
"""
# Map value types to aggregation functions
aggregators: dict[str, Callable] = {
"price": lambda data: aggregate_price_data(data),
"level": lambda data: aggregate_level_data(data),
"rating": lambda data: aggregate_rating_data(data, threshold_low, threshold_high),
}
aggregator = aggregators.get(value_type)
if aggregator:
return aggregator(window_data)
return None
def get_hourly_price_value(
price_info: dict,
*,
hour_offset: int,
in_euro: bool,
) -> float | None:
"""
Get price for current hour or with offset.
Legacy helper for hourly price access (not used by Calculator Pattern).
Kept for potential backward compatibility.
Args:
price_info: Price information dict with 'today' and 'tomorrow' keys
hour_offset: Hour offset from current time (positive=future, negative=past)
in_euro: If True, return price in major currency (EUR), else minor (cents/øre)
Returns:
Price value, or None if not found
"""
# 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 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 get_price_value(float(price_data["total"]), in_euro=in_euro)
return None

276
custom_components/tibber_prices/sensor/value_getters.py Normal file
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@ -0,0 +1,276 @@
"""Value getter mapping for Tibber Prices sensors."""
from __future__ import annotations
from typing import TYPE_CHECKING
from custom_components.tibber_prices.utils.average import (
calculate_current_leading_avg,
calculate_current_leading_max,
calculate_current_leading_min,
calculate_current_trailing_avg,
calculate_current_trailing_max,
calculate_current_trailing_min,
)
if TYPE_CHECKING:
from collections.abc import Callable
from custom_components.tibber_prices.sensor.calculators.daily_stat import DailyStatCalculator
from custom_components.tibber_prices.sensor.calculators.interval import IntervalCalculator
from custom_components.tibber_prices.sensor.calculators.metadata import MetadataCalculator
from custom_components.tibber_prices.sensor.calculators.rolling_hour import RollingHourCalculator
from custom_components.tibber_prices.sensor.calculators.timing import TimingCalculator
from custom_components.tibber_prices.sensor.calculators.trend import TrendCalculator
from custom_components.tibber_prices.sensor.calculators.volatility import VolatilityCalculator
from custom_components.tibber_prices.sensor.calculators.window_24h import Window24hCalculator
def get_value_getter_mapping( # noqa: PLR0913 - needs all calculators as parameters
interval_calculator: IntervalCalculator,
rolling_hour_calculator: RollingHourCalculator,
daily_stat_calculator: DailyStatCalculator,
window_24h_calculator: Window24hCalculator,
trend_calculator: TrendCalculator,
timing_calculator: TimingCalculator,
volatility_calculator: VolatilityCalculator,
metadata_calculator: MetadataCalculator,
get_next_avg_n_hours_value: Callable[[int], float | None],
get_price_forecast_value: Callable[[], str | None],
get_data_timestamp: Callable[[], str | None],
get_chart_data_export_value: Callable[[], str | None],
) -> dict[str, Callable]:
"""
Build mapping from entity key to value getter callable.
This function centralizes the handler mapping logic, making it easier to maintain
and understand the relationship between sensor types and their calculation methods.
Args:
interval_calculator: Calculator for current/next/previous interval values
rolling_hour_calculator: Calculator for 5-interval rolling windows
daily_stat_calculator: Calculator for daily min/max/avg statistics
window_24h_calculator: Calculator for trailing/leading 24h windows
trend_calculator: Calculator for price trend analysis
timing_calculator: Calculator for best/peak price period timing
volatility_calculator: Calculator for price volatility analysis
metadata_calculator: Calculator for home/metering metadata
get_next_avg_n_hours_value: Method for next N-hour average forecasts
get_price_forecast_value: Method for price forecast sensor
get_data_timestamp: Method for data timestamp sensor
get_chart_data_export_value: Method for chart data export sensor
Returns:
Dictionary mapping entity keys to their value getter callables.
"""
return {
# ================================================================
# INTERVAL-BASED SENSORS - via IntervalCalculator
# ================================================================
# Price level sensors
"current_interval_price_level": interval_calculator.get_price_level_value,
"next_interval_price_level": lambda: interval_calculator.get_interval_value(
interval_offset=1, value_type="level"
),
"previous_interval_price_level": lambda: interval_calculator.get_interval_value(
interval_offset=-1, value_type="level"
),
# Price sensors (in cents)
"current_interval_price": lambda: interval_calculator.get_interval_value(
interval_offset=0, value_type="price", in_euro=False
),
"current_interval_price_major": lambda: interval_calculator.get_interval_value(
interval_offset=0, value_type="price", in_euro=True
),
"next_interval_price": lambda: interval_calculator.get_interval_value(
interval_offset=1, value_type="price", in_euro=False
),
"previous_interval_price": lambda: interval_calculator.get_interval_value(
interval_offset=-1, value_type="price", in_euro=False
),
# Rating sensors
"current_interval_price_rating": lambda: interval_calculator.get_rating_value(rating_type="current"),
"next_interval_price_rating": lambda: interval_calculator.get_interval_value(
interval_offset=1, value_type="rating"
),
"previous_interval_price_rating": lambda: interval_calculator.get_interval_value(
interval_offset=-1, value_type="rating"
),
# ================================================================
# ROLLING HOUR SENSORS (5-interval windows) - via RollingHourCalculator
# ================================================================
"current_hour_price_level": lambda: rolling_hour_calculator.get_rolling_hour_value(
hour_offset=0, value_type="level"
),
"next_hour_price_level": lambda: rolling_hour_calculator.get_rolling_hour_value(
hour_offset=1, value_type="level"
),
# Rolling hour average (5 intervals: 2 before + current + 2 after)
"current_hour_average_price": lambda: rolling_hour_calculator.get_rolling_hour_value(
hour_offset=0, value_type="price"
),
"next_hour_average_price": lambda: rolling_hour_calculator.get_rolling_hour_value(
hour_offset=1, value_type="price"
),
"current_hour_price_rating": lambda: rolling_hour_calculator.get_rolling_hour_value(
hour_offset=0, value_type="rating"
),
"next_hour_price_rating": lambda: rolling_hour_calculator.get_rolling_hour_value(
hour_offset=1, value_type="rating"
),
# ================================================================
# DAILY STATISTICS SENSORS - via DailyStatCalculator
# ================================================================
"lowest_price_today": lambda: daily_stat_calculator.get_daily_stat_value(day="today", stat_func=min),
"highest_price_today": lambda: daily_stat_calculator.get_daily_stat_value(day="today", stat_func=max),
"average_price_today": lambda: daily_stat_calculator.get_daily_stat_value(
day="today",
stat_func=lambda prices: sum(prices) / len(prices),
),
# Tomorrow statistics sensors
"lowest_price_tomorrow": lambda: daily_stat_calculator.get_daily_stat_value(day="tomorrow", stat_func=min),
"highest_price_tomorrow": lambda: daily_stat_calculator.get_daily_stat_value(day="tomorrow", stat_func=max),
"average_price_tomorrow": lambda: daily_stat_calculator.get_daily_stat_value(
day="tomorrow",
stat_func=lambda prices: sum(prices) / len(prices),
),
# Daily aggregated level sensors
"yesterday_price_level": lambda: daily_stat_calculator.get_daily_aggregated_value(
day="yesterday", value_type="level"
),
"today_price_level": lambda: daily_stat_calculator.get_daily_aggregated_value(day="today", value_type="level"),
"tomorrow_price_level": lambda: daily_stat_calculator.get_daily_aggregated_value(
day="tomorrow", value_type="level"
),
# Daily aggregated rating sensors
"yesterday_price_rating": lambda: daily_stat_calculator.get_daily_aggregated_value(
day="yesterday", value_type="rating"
),
"today_price_rating": lambda: daily_stat_calculator.get_daily_aggregated_value(
day="today", value_type="rating"
),
"tomorrow_price_rating": lambda: daily_stat_calculator.get_daily_aggregated_value(
day="tomorrow", value_type="rating"
),
# ================================================================
# 24H WINDOW SENSORS (trailing/leading from current) - via Window24hCalculator
# ================================================================
# Trailing and leading average sensors
"trailing_price_average": lambda: window_24h_calculator.get_24h_window_value(
stat_func=calculate_current_trailing_avg,
),
"leading_price_average": lambda: window_24h_calculator.get_24h_window_value(
stat_func=calculate_current_leading_avg,
),
# Trailing and leading min/max sensors
"trailing_price_min": lambda: window_24h_calculator.get_24h_window_value(
stat_func=calculate_current_trailing_min,
),
"trailing_price_max": lambda: window_24h_calculator.get_24h_window_value(
stat_func=calculate_current_trailing_max,
),
"leading_price_min": lambda: window_24h_calculator.get_24h_window_value(
stat_func=calculate_current_leading_min,
),
"leading_price_max": lambda: window_24h_calculator.get_24h_window_value(
stat_func=calculate_current_leading_max,
),
# ================================================================
# FUTURE FORECAST SENSORS
# ================================================================
# Future average sensors (next N hours from next interval)
"next_avg_1h": lambda: get_next_avg_n_hours_value(hours=1),
"next_avg_2h": lambda: get_next_avg_n_hours_value(hours=2),
"next_avg_3h": lambda: get_next_avg_n_hours_value(hours=3),
"next_avg_4h": lambda: get_next_avg_n_hours_value(hours=4),
"next_avg_5h": lambda: get_next_avg_n_hours_value(hours=5),
"next_avg_6h": lambda: get_next_avg_n_hours_value(hours=6),
"next_avg_8h": lambda: get_next_avg_n_hours_value(hours=8),
"next_avg_12h": lambda: get_next_avg_n_hours_value(hours=12),
# Current and next trend change sensors
"current_price_trend": trend_calculator.get_current_trend_value,
"next_price_trend_change": trend_calculator.get_next_trend_change_value,
# Price trend sensors
"price_trend_1h": lambda: trend_calculator.get_price_trend_value(hours=1),
"price_trend_2h": lambda: trend_calculator.get_price_trend_value(hours=2),
"price_trend_3h": lambda: trend_calculator.get_price_trend_value(hours=3),
"price_trend_4h": lambda: trend_calculator.get_price_trend_value(hours=4),
"price_trend_5h": lambda: trend_calculator.get_price_trend_value(hours=5),
"price_trend_6h": lambda: trend_calculator.get_price_trend_value(hours=6),
"price_trend_8h": lambda: trend_calculator.get_price_trend_value(hours=8),
"price_trend_12h": lambda: trend_calculator.get_price_trend_value(hours=12),
# Diagnostic sensors
"data_timestamp": get_data_timestamp,
# Price forecast sensor
"price_forecast": get_price_forecast_value,
# Home metadata sensors (via MetadataCalculator)
"home_type": lambda: metadata_calculator.get_home_metadata_value("type"),
"home_size": lambda: metadata_calculator.get_home_metadata_value("size"),
"main_fuse_size": lambda: metadata_calculator.get_home_metadata_value("mainFuseSize"),
"number_of_residents": lambda: metadata_calculator.get_home_metadata_value("numberOfResidents"),
"primary_heating_source": lambda: metadata_calculator.get_home_metadata_value("primaryHeatingSource"),
# Metering point sensors (via MetadataCalculator)
"grid_company": lambda: metadata_calculator.get_metering_point_value("gridCompany"),
"grid_area_code": lambda: metadata_calculator.get_metering_point_value("gridAreaCode"),
"price_area_code": lambda: metadata_calculator.get_metering_point_value("priceAreaCode"),
"consumption_ean": lambda: metadata_calculator.get_metering_point_value("consumptionEan"),
"production_ean": lambda: metadata_calculator.get_metering_point_value("productionEan"),
"energy_tax_type": lambda: metadata_calculator.get_metering_point_value("energyTaxType"),
"vat_type": lambda: metadata_calculator.get_metering_point_value("vatType"),
"estimated_annual_consumption": lambda: metadata_calculator.get_metering_point_value(
"estimatedAnnualConsumption"
),
# Subscription sensors (via MetadataCalculator)
"subscription_status": lambda: metadata_calculator.get_subscription_value("status"),
# Volatility sensors (via VolatilityCalculator)
"today_volatility": lambda: volatility_calculator.get_volatility_value(volatility_type="today"),
"tomorrow_volatility": lambda: volatility_calculator.get_volatility_value(volatility_type="tomorrow"),
"next_24h_volatility": lambda: volatility_calculator.get_volatility_value(volatility_type="next_24h"),
"today_tomorrow_volatility": lambda: volatility_calculator.get_volatility_value(
volatility_type="today_tomorrow"
),
# ================================================================
# BEST/PEAK PRICE TIMING SENSORS - via TimingCalculator
# ================================================================
# Best Price timing sensors
"best_price_end_time": lambda: timing_calculator.get_period_timing_value(
period_type="best_price", value_type="end_time"
),
"best_price_period_duration": lambda: timing_calculator.get_period_timing_value(
period_type="best_price", value_type="period_duration"
),
"best_price_remaining_minutes": lambda: timing_calculator.get_period_timing_value(
period_type="best_price", value_type="remaining_minutes"
),
"best_price_progress": lambda: timing_calculator.get_period_timing_value(
period_type="best_price", value_type="progress"
),
"best_price_next_start_time": lambda: timing_calculator.get_period_timing_value(
period_type="best_price", value_type="next_start_time"
),
"best_price_next_in_minutes": lambda: timing_calculator.get_period_timing_value(
period_type="best_price", value_type="next_in_minutes"
),
# Peak Price timing sensors
"peak_price_end_time": lambda: timing_calculator.get_period_timing_value(
period_type="peak_price", value_type="end_time"
),
"peak_price_period_duration": lambda: timing_calculator.get_period_timing_value(
period_type="peak_price", value_type="period_duration"
),
"peak_price_remaining_minutes": lambda: timing_calculator.get_period_timing_value(
period_type="peak_price", value_type="remaining_minutes"
),
"peak_price_progress": lambda: timing_calculator.get_period_timing_value(
period_type="peak_price", value_type="progress"
),
"peak_price_next_start_time": lambda: timing_calculator.get_period_timing_value(
period_type="peak_price", value_type="next_start_time"
),
"peak_price_next_in_minutes": lambda: timing_calculator.get_period_timing_value(
period_type="peak_price", value_type="next_in_minutes"
),
# Chart data export sensor
"chart_data_export": get_chart_data_export_value,
}