mirror of
https://github.com/jpawlowski/hass.tibber_prices.git
synced 2026-03-30 05:13:40 +00:00
38ce1c4c50
Added optional diagnostic binary sensor that exposes get_chartdata service results as entity attributes for legacy dashboard tools. Key features: - Entity: binary_sensor.tibber_home_NAME_chart_data_export - Configurable via Options Flow Step 7 (YAML parameters) - Calls get_chartdata service with user configuration - Exposes response as attributes for chart cards - Disabled by default (opt-in) - Auto-refreshes on coordinator updates - Manual refresh via homeassistant.update_entity Implementation details: - Added chart_data_export entity description to definitions.py - Implemented state/attribute logic in binary_sensor/core.py - Added YAML configuration schema in schemas.py - Added validation in options_flow.py (Step 7) - Service call validation with detailed error messages - Attribute ordering: metadata first, descriptions next, service data last - Dynamic icon mapping (database-export/database-alert) Translations: - Added chart_data_export_config to all 5 languages - Added Step 7 descriptions with legacy warning - Added invalid_yaml_syntax/invalid_yaml_structure error messages - Added custom_translations for sensor descriptions Documentation: - Added Chart Data Export section to sensors.md - Added comprehensive service guide to services.md - Migration path from sensor to service - Configuration instructions via Options Flow Impact: Provides backward compatibility for dashboard tools that can only read entity attributes (e.g., older ApexCharts versions). New integrations should use tibber_prices.get_chartdata service directly.
1096 lines
45 KiB
Python
1096 lines
45 KiB
Python
"""Services for Tibber Prices integration."""
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from __future__ import annotations
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import re
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from datetime import timedelta
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from typing import Any, Final
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import voluptuous as vol
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from homeassistant.core import HomeAssistant, ServiceCall, SupportsResponse, callback
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from homeassistant.exceptions import ServiceValidationError
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from homeassistant.helpers.entity_registry import async_get as async_get_entity_registry
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from homeassistant.util import dt as dt_util
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from .api import (
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TibberPricesApiClientAuthenticationError,
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TibberPricesApiClientCommunicationError,
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TibberPricesApiClientError,
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)
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from .const import (
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CONF_PRICE_RATING_THRESHOLD_HIGH,
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CONF_PRICE_RATING_THRESHOLD_LOW,
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DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
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DEFAULT_PRICE_RATING_THRESHOLD_LOW,
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DOMAIN,
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PRICE_LEVEL_CHEAP,
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PRICE_LEVEL_EXPENSIVE,
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PRICE_LEVEL_NORMAL,
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PRICE_LEVEL_VERY_CHEAP,
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PRICE_LEVEL_VERY_EXPENSIVE,
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PRICE_RATING_HIGH,
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PRICE_RATING_LOW,
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PRICE_RATING_NORMAL,
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format_price_unit_minor,
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get_translation,
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)
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from .sensor.helpers import aggregate_level_data, aggregate_rating_data
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APEXCHARTS_YAML_SERVICE_NAME = "get_apexcharts_yaml"
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CHARTDATA_SERVICE_NAME = "get_chartdata"
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REFRESH_USER_DATA_SERVICE_NAME = "refresh_user_data"
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ATTR_DAY: Final = "day"
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ATTR_ENTRY_ID: Final = "entry_id"
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APEXCHARTS_SERVICE_SCHEMA: Final = vol.Schema(
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{
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vol.Required(ATTR_ENTRY_ID): str,
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vol.Optional("day", default="today"): vol.In(["yesterday", "today", "tomorrow"]),
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vol.Optional("level_type", default="rating_level"): vol.In(["rating_level", "level"]),
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}
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)
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def _normalize_level_filter(value: list[str] | None) -> list[str] | None:
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"""Convert level filter values to uppercase for case-insensitive comparison."""
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if value is None:
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return None
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return [v.upper() for v in value]
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def _normalize_rating_level_filter(value: list[str] | None) -> list[str] | None:
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"""Convert rating level filter values to uppercase for case-insensitive comparison."""
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if value is None:
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return None
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return [v.upper() for v in value]
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CHARTDATA_SERVICE_SCHEMA: Final = vol.Schema(
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{
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vol.Required(ATTR_ENTRY_ID): str,
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vol.Optional(ATTR_DAY): vol.All(vol.Coerce(list), [vol.In(["yesterday", "today", "tomorrow"])]),
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vol.Optional("resolution", default="interval"): vol.In(["interval", "hourly"]),
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vol.Optional("output_format", default="array_of_objects"): vol.In(["array_of_objects", "array_of_arrays"]),
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vol.Optional("array_fields"): str,
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vol.Optional("minor_currency", default=False): bool,
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vol.Optional("round_decimals"): vol.All(vol.Coerce(int), vol.Range(min=0, max=10)),
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vol.Optional("include_level", default=False): bool,
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vol.Optional("include_rating_level", default=False): bool,
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vol.Optional("include_average", default=False): bool,
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vol.Optional("level_filter"): vol.All(
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vol.Coerce(list),
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_normalize_level_filter,
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[
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vol.In(
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[
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PRICE_LEVEL_VERY_CHEAP,
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PRICE_LEVEL_CHEAP,
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PRICE_LEVEL_NORMAL,
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PRICE_LEVEL_EXPENSIVE,
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PRICE_LEVEL_VERY_EXPENSIVE,
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]
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)
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],
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),
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vol.Optional("rating_level_filter"): vol.All(
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vol.Coerce(list),
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_normalize_rating_level_filter,
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[vol.In([PRICE_RATING_LOW, PRICE_RATING_NORMAL, PRICE_RATING_HIGH])],
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),
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vol.Optional("insert_nulls", default="none"): vol.In(["none", "segments", "all"]),
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vol.Optional("add_trailing_null", default=False): bool,
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vol.Optional("period_filter"): vol.In(["best_price", "peak_price"]),
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vol.Optional("start_time_field", default="start_time"): str,
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vol.Optional("end_time_field", default="end_time"): str,
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vol.Optional("price_field", default="price_per_kwh"): str,
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vol.Optional("level_field", default="level"): str,
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vol.Optional("rating_level_field", default="rating_level"): str,
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vol.Optional("average_field", default="average"): str,
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vol.Optional("data_key", default="data"): str,
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}
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)
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REFRESH_USER_DATA_SERVICE_SCHEMA: Final = vol.Schema(
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{
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vol.Required(ATTR_ENTRY_ID): str,
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}
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)
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# --- Entry point: Service handler ---
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def _aggregate_hourly_exact( # noqa: PLR0913, PLR0912, PLR0915
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intervals: list[dict],
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start_time_field: str,
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price_field: str,
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*,
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use_minor_currency: bool = False,
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round_decimals: int | None = None,
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include_level: bool = False,
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include_rating_level: bool = False,
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level_filter: list[str] | None = None,
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rating_level_filter: list[str] | None = None,
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include_average: bool = False,
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level_field: str = "level",
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rating_level_field: str = "rating_level",
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average_field: str = "average",
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day_average: float | None = None,
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threshold_low: float = DEFAULT_PRICE_RATING_THRESHOLD_LOW,
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threshold_high: float = DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
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period_timestamps: set[str] | None = None,
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) -> list[dict]:
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"""
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Aggregate 15-minute intervals to exact hourly averages.
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Each hour uses exactly 4 intervals (00:00, 00:15, 00:30, 00:45).
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Returns data points at the start of each hour.
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"""
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if not intervals:
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return []
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hourly_data = []
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i = 0
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while i < len(intervals):
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interval = intervals[i]
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start_time_str = interval.get("startsAt")
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if not start_time_str:
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i += 1
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continue
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# Parse the timestamp
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start_time = dt_util.parse_datetime(start_time_str)
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if not start_time:
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i += 1
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continue
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# Check if this is the start of an hour (:00)
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if start_time.minute != 0:
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i += 1
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continue
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# Collect 4 intervals for this hour (with optional filtering)
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hour_intervals = []
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hour_interval_data = [] # Complete interval data for aggregation functions
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for j in range(4):
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if i + j < len(intervals):
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interval = intervals[i + j]
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# Apply period filter if specified (check startsAt timestamp)
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if period_timestamps is not None:
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interval_start = interval.get("startsAt")
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if interval_start and interval_start not in period_timestamps:
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continue
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# Apply level filter if specified
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if level_filter is not None and "level" in interval and interval["level"] not in level_filter:
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continue
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# Apply rating_level filter if specified
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if (
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rating_level_filter is not None
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and "rating_level" in interval
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and interval["rating_level"] not in rating_level_filter
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):
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continue
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price = interval.get("total")
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if price is not None:
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hour_intervals.append(price)
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hour_interval_data.append(interval)
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# Calculate average if we have data
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if hour_intervals:
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avg_price = sum(hour_intervals) / len(hour_intervals)
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# Convert to minor currency (cents/øre) if requested
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avg_price = round(avg_price * 100, 2) if use_minor_currency else round(avg_price, 4)
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# Apply custom rounding if specified
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if round_decimals is not None:
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avg_price = round(avg_price, round_decimals)
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data_point = {start_time_field: start_time_str, price_field: avg_price}
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# Add aggregated level using same logic as sensors
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if include_level and hour_interval_data:
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aggregated_level = aggregate_level_data(hour_interval_data)
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if aggregated_level:
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data_point[level_field] = aggregated_level.upper() # Convert back to uppercase
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# Add aggregated rating_level using same logic as sensors
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if include_rating_level and hour_interval_data:
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aggregated_rating = aggregate_rating_data(hour_interval_data, threshold_low, threshold_high)
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if aggregated_rating:
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data_point[rating_level_field] = aggregated_rating.upper() # Convert back to uppercase
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# Add average if requested
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if include_average and day_average is not None:
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data_point[average_field] = day_average
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hourly_data.append(data_point)
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# Move to next hour (skip 4 intervals)
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i += 4
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return hourly_data
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def _get_period_data( # noqa: PLR0913, PLR0912, PLR0915
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*,
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coordinator: Any,
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period_filter: str,
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days: list[str],
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output_format: str,
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minor_currency: bool,
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round_decimals: int | None,
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level_filter: list[str] | None,
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rating_level_filter: list[str] | None,
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include_level: bool,
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include_rating_level: bool,
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start_time_field: str,
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end_time_field: str,
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price_field: str,
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level_field: str,
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rating_level_field: str,
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data_key: str,
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add_trailing_null: bool,
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) -> dict[str, Any]:
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"""
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Get period summary data instead of interval data.
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When period_filter is specified, returns the precomputed period summaries
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from the coordinator instead of filtering intervals.
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Note: Period prices (price_avg) are stored in minor currency units (ct/øre).
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They are converted to major currency unless minor_currency=True.
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Args:
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coordinator: Data coordinator with period summaries
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period_filter: "best_price" or "peak_price"
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days: List of days to include
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output_format: "array_of_objects" or "array_of_arrays"
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minor_currency: If False, convert prices from minor to major units
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round_decimals: Optional decimal rounding
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level_filter: Optional level filter
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rating_level_filter: Optional rating level filter
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include_level: Whether to include level field in output
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include_rating_level: Whether to include rating_level field in output
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start_time_field: Custom name for start time field
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end_time_field: Custom name for end time field
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price_field: Custom name for price field
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level_field: Custom name for level field
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rating_level_field: Custom name for rating_level field
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data_key: Top-level key name in response
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add_trailing_null: Whether to add trailing null point
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Returns:
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Dictionary with period data in requested format
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"""
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periods_data = coordinator.data.get("periods", {})
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period_data = periods_data.get(period_filter)
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if not period_data:
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return {data_key: []}
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period_summaries = period_data.get("periods", [])
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if not period_summaries:
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return {data_key: []}
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chart_data = []
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# Filter periods by day if requested
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filtered_periods = []
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if days:
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# Build set of allowed dates
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allowed_dates = set()
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for day in days:
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# Map day names to actual dates from coordinator
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price_info = coordinator.data.get("priceInfo", {})
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day_prices = price_info.get(day, [])
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if day_prices:
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# Extract date from first interval
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first_interval = day_prices[0]
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starts_at = first_interval.get("startsAt")
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if starts_at:
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dt = dt_util.parse_datetime(starts_at)
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if dt:
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dt = dt_util.as_local(dt)
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allowed_dates.add(dt.date())
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# Filter periods to those within allowed dates
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for period in period_summaries:
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start = period.get("start")
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if start and start.date() in allowed_dates:
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filtered_periods.append(period)
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else:
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filtered_periods = period_summaries
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# Apply level and rating_level filters
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for period in filtered_periods:
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# Apply level filter (normalize to uppercase for comparison)
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if level_filter and "level" in period and period["level"].upper() not in level_filter:
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continue
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# Apply rating_level filter (normalize to uppercase for comparison)
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if (
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rating_level_filter
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and "rating_level" in period
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and period["rating_level"].upper() not in rating_level_filter
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):
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continue
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# Build data point based on output format
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if output_format == "array_of_objects":
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# Map period fields to custom field names
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# Period has: start, end, level, rating_level, price_avg, price_min, price_max
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data_point = {}
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# Start time
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data_point[start_time_field] = period["start"]
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# End time
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data_point[end_time_field] = period.get("end")
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# Price (use price_avg from period, stored in minor units)
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price_avg = period.get("price_avg", 0.0)
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# Convert to major currency unless minor_currency=True
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if not minor_currency:
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price_avg = price_avg / 100
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if round_decimals is not None:
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price_avg = round(price_avg, round_decimals)
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data_point[price_field] = price_avg
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# Level (only if requested and present)
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if include_level and "level" in period:
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data_point[level_field] = period["level"].upper()
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# Rating level (only if requested and present)
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if include_rating_level and "rating_level" in period:
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data_point[rating_level_field] = period["rating_level"].upper()
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chart_data.append(data_point)
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else: # array_of_arrays
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# For array_of_arrays, include: [start, price_avg]
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price_avg = period.get("price_avg", 0.0)
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# Convert to major currency unless minor_currency=True
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if not minor_currency:
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price_avg = price_avg / 100
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if round_decimals is not None:
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price_avg = round(price_avg, round_decimals)
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chart_data.append([period["start"], price_avg])
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# Add trailing null point if requested
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if add_trailing_null and chart_data:
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if output_format == "array_of_objects":
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null_point = {start_time_field: None, end_time_field: None}
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for field in [price_field, level_field, rating_level_field]:
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null_point[field] = None
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chart_data.append(null_point)
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else: # array_of_arrays
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chart_data.append([None, None])
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return {data_key: chart_data}
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async def _get_chartdata(call: ServiceCall) -> dict[str, Any]: # noqa: PLR0912, PLR0915, C901
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"""Return price data in a simple chart-friendly format similar to Tibber Core integration."""
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hass = call.hass
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entry_id_raw = call.data.get(ATTR_ENTRY_ID)
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if entry_id_raw is None:
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raise ServiceValidationError(translation_domain=DOMAIN, translation_key="missing_entry_id")
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entry_id: str = str(entry_id_raw)
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days_raw = call.data.get(ATTR_DAY)
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# If no day specified, return all available data (today + tomorrow)
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if days_raw is None:
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days = ["today", "tomorrow"]
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# Convert single string to list for uniform processing
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elif isinstance(days_raw, str):
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days = [days_raw]
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else:
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days = days_raw
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start_time_field = call.data.get("start_time_field", "start_time")
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end_time_field = call.data.get("end_time_field", "end_time")
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price_field = call.data.get("price_field", "price_per_kwh")
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level_field = call.data.get("level_field", "level")
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rating_level_field = call.data.get("rating_level_field", "rating_level")
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average_field = call.data.get("average_field", "average")
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data_key = call.data.get("data_key", "data")
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resolution = call.data.get("resolution", "interval")
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output_format = call.data.get("output_format", "array_of_objects")
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minor_currency = call.data.get("minor_currency", False)
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round_decimals = call.data.get("round_decimals")
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include_level = call.data.get("include_level", False)
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include_rating_level = call.data.get("include_rating_level", False)
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include_average = call.data.get("include_average", False)
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insert_nulls = call.data.get("insert_nulls", "none")
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add_trailing_null = call.data.get("add_trailing_null", False)
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period_filter = call.data.get("period_filter")
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# Filter values are already normalized to uppercase by schema validators
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level_filter = call.data.get("level_filter")
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rating_level_filter = call.data.get("rating_level_filter")
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# If array_fields is specified, implicitly enable fields that are used
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array_fields_template = call.data.get("array_fields")
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if array_fields_template and output_format == "array_of_arrays":
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if level_field in array_fields_template:
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include_level = True
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if rating_level_field in array_fields_template:
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include_rating_level = True
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if average_field in array_fields_template:
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include_average = True
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_, coordinator, _ = _get_entry_and_data(hass, entry_id)
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# Get thresholds from config for rating aggregation
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threshold_low = coordinator.config_entry.options.get(
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CONF_PRICE_RATING_THRESHOLD_LOW, DEFAULT_PRICE_RATING_THRESHOLD_LOW
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)
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threshold_high = coordinator.config_entry.options.get(
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CONF_PRICE_RATING_THRESHOLD_HIGH, DEFAULT_PRICE_RATING_THRESHOLD_HIGH
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)
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|
|
# === SPECIAL HANDLING: Period Filter ===
|
|
# When period_filter is set, return period summaries instead of interval data
|
|
# Period summaries are already complete objects with aggregated data
|
|
if period_filter:
|
|
return _get_period_data(
|
|
coordinator=coordinator,
|
|
period_filter=period_filter,
|
|
days=days,
|
|
output_format=output_format,
|
|
minor_currency=minor_currency,
|
|
round_decimals=round_decimals,
|
|
level_filter=level_filter,
|
|
rating_level_filter=rating_level_filter,
|
|
include_level=include_level,
|
|
include_rating_level=include_rating_level,
|
|
start_time_field=start_time_field,
|
|
end_time_field=end_time_field,
|
|
price_field=price_field,
|
|
level_field=level_field,
|
|
rating_level_field=rating_level_field,
|
|
data_key=data_key,
|
|
add_trailing_null=add_trailing_null,
|
|
)
|
|
|
|
# === NORMAL HANDLING: Interval Data ===
|
|
# Get price data for all requested days
|
|
price_info = coordinator.data.get("priceInfo", {})
|
|
chart_data = []
|
|
|
|
# Build set of timestamps that match period_filter if specified
|
|
period_timestamps = None
|
|
if period_filter:
|
|
period_timestamps = set()
|
|
periods_data = coordinator.data.get("periods", {})
|
|
period_data = periods_data.get(period_filter)
|
|
if period_data:
|
|
period_summaries = period_data.get("periods", [])
|
|
# Period summaries don't contain intervals, only start/end timestamps
|
|
# Build set of all 15-minute intervals within period ranges
|
|
for period_summary in period_summaries:
|
|
start = period_summary.get("start")
|
|
end = period_summary.get("end")
|
|
if start and end:
|
|
# Generate all 15-minute timestamps within this period
|
|
current = start
|
|
while current < end:
|
|
period_timestamps.add(current.isoformat())
|
|
current = current + timedelta(minutes=15)
|
|
|
|
# Collect all timestamps if insert_nulls='all' (needed to insert NULLs for missing filter matches)
|
|
all_timestamps = set()
|
|
if insert_nulls == "all" and (level_filter or rating_level_filter):
|
|
for day in days:
|
|
day_prices = price_info.get(day, [])
|
|
for interval in day_prices:
|
|
start_time = interval.get("startsAt")
|
|
if start_time:
|
|
all_timestamps.add(start_time)
|
|
all_timestamps = sorted(all_timestamps)
|
|
|
|
# Calculate average if requested
|
|
day_averages = {}
|
|
if include_average:
|
|
for day in days:
|
|
day_prices = price_info.get(day, [])
|
|
if day_prices:
|
|
prices = [p["total"] for p in day_prices if p.get("total") is not None]
|
|
if prices:
|
|
avg = sum(prices) / len(prices)
|
|
# Apply same transformations as to regular prices
|
|
avg = round(avg * 100, 2) if minor_currency else round(avg, 4)
|
|
if round_decimals is not None:
|
|
avg = round(avg, round_decimals)
|
|
day_averages[day] = avg
|
|
|
|
for day in days:
|
|
day_prices = price_info.get(day, [])
|
|
|
|
if resolution == "interval":
|
|
# Original 15-minute intervals
|
|
if insert_nulls == "all" and (level_filter or rating_level_filter):
|
|
# Mode 'all': Insert NULL for all timestamps where filter doesn't match
|
|
# Build a map of timestamp -> interval for quick lookup
|
|
interval_map = {
|
|
interval.get("startsAt"): interval for interval in day_prices if interval.get("startsAt")
|
|
}
|
|
|
|
# Process all timestamps, filling gaps with NULL
|
|
for start_time in all_timestamps:
|
|
interval = interval_map.get(start_time)
|
|
|
|
if interval is None:
|
|
# No data for this timestamp - skip entirely
|
|
continue
|
|
|
|
price = interval.get("total")
|
|
if price is None:
|
|
continue
|
|
|
|
# Check if this interval matches the filter
|
|
matches_filter = False
|
|
if level_filter and "level" in interval:
|
|
matches_filter = interval["level"] in level_filter
|
|
elif rating_level_filter and "rating_level" in interval:
|
|
matches_filter = interval["rating_level"] in rating_level_filter
|
|
|
|
# If filter is set but doesn't match, insert NULL price
|
|
if not matches_filter:
|
|
price = None
|
|
elif price is not None:
|
|
# Convert to minor currency (cents/øre) if requested
|
|
price = round(price * 100, 2) if minor_currency else round(price, 4)
|
|
# Apply custom rounding if specified
|
|
if round_decimals is not None:
|
|
price = round(price, round_decimals)
|
|
|
|
data_point = {start_time_field: start_time, price_field: price}
|
|
|
|
# Add level if requested (only when price is not NULL)
|
|
if include_level and "level" in interval and price is not None:
|
|
data_point[level_field] = interval["level"]
|
|
|
|
# Add rating_level if requested (only when price is not NULL)
|
|
if include_rating_level and "rating_level" in interval and price is not None:
|
|
data_point[rating_level_field] = interval["rating_level"]
|
|
|
|
# Add average if requested
|
|
if include_average and day in day_averages:
|
|
data_point[average_field] = day_averages[day]
|
|
|
|
chart_data.append(data_point)
|
|
elif insert_nulls == "segments" and (level_filter or rating_level_filter):
|
|
# Mode 'segments': Add NULL points at segment boundaries for clean gaps
|
|
# Determine which field to check based on filter type
|
|
filter_field = "rating_level" if rating_level_filter else "level"
|
|
filter_values = rating_level_filter if rating_level_filter else level_filter
|
|
|
|
for i in range(len(day_prices) - 1):
|
|
interval = day_prices[i]
|
|
next_interval = day_prices[i + 1]
|
|
|
|
start_time = interval.get("startsAt")
|
|
price = interval.get("total")
|
|
next_start_time = next_interval.get("startsAt")
|
|
|
|
if start_time is None or price is None:
|
|
continue
|
|
|
|
interval_value = interval.get(filter_field)
|
|
next_value = next_interval.get(filter_field)
|
|
|
|
# Check if current interval matches filter
|
|
if interval_value in filter_values:
|
|
# Convert price
|
|
converted_price = round(price * 100, 2) if minor_currency else round(price, 4)
|
|
if round_decimals is not None:
|
|
converted_price = round(converted_price, round_decimals)
|
|
|
|
# Add current point
|
|
data_point = {start_time_field: start_time, price_field: converted_price}
|
|
|
|
if include_level and "level" in interval:
|
|
data_point[level_field] = interval["level"]
|
|
if include_rating_level and "rating_level" in interval:
|
|
data_point[rating_level_field] = interval["rating_level"]
|
|
if include_average and day in day_averages:
|
|
data_point[average_field] = day_averages[day]
|
|
|
|
chart_data.append(data_point)
|
|
|
|
# Check if next interval is different level (segment boundary)
|
|
if next_value != interval_value:
|
|
# Hold current price until next timestamp (stepline effect)
|
|
hold_point = {start_time_field: next_start_time, price_field: converted_price}
|
|
if include_level and "level" in interval:
|
|
hold_point[level_field] = interval["level"]
|
|
if include_rating_level and "rating_level" in interval:
|
|
hold_point[rating_level_field] = interval["rating_level"]
|
|
if include_average and day in day_averages:
|
|
hold_point[average_field] = day_averages[day]
|
|
chart_data.append(hold_point)
|
|
|
|
# Add NULL point to create gap
|
|
null_point = {start_time_field: next_start_time, price_field: None}
|
|
chart_data.append(null_point)
|
|
|
|
# Handle last interval of the day - extend to midnight
|
|
if day_prices:
|
|
last_interval = day_prices[-1]
|
|
last_start_time = last_interval.get("startsAt")
|
|
last_price = last_interval.get("total")
|
|
last_value = last_interval.get(filter_field)
|
|
|
|
if last_start_time and last_price is not None and last_value in filter_values:
|
|
# Parse timestamp and calculate midnight of next day
|
|
last_dt = dt_util.parse_datetime(last_start_time)
|
|
if last_dt:
|
|
last_dt = dt_util.as_local(last_dt)
|
|
# Calculate next day at 00:00
|
|
next_day = last_dt.replace(hour=0, minute=0, second=0, microsecond=0)
|
|
next_day = next_day + timedelta(days=1)
|
|
midnight_timestamp = next_day.isoformat()
|
|
|
|
# Try to get real price from tomorrow's first interval
|
|
next_day_name = None
|
|
if day == "yesterday":
|
|
next_day_name = "today"
|
|
elif day == "today":
|
|
next_day_name = "tomorrow"
|
|
# For "tomorrow", we don't have a "day after tomorrow"
|
|
|
|
midnight_price = None
|
|
midnight_interval = None
|
|
|
|
if next_day_name:
|
|
next_day_prices = price_info.get(next_day_name, [])
|
|
if next_day_prices:
|
|
first_next = next_day_prices[0]
|
|
first_next_value = first_next.get(filter_field)
|
|
# Only use tomorrow's price if it matches the same filter
|
|
if first_next_value == last_value:
|
|
midnight_price = first_next.get("total")
|
|
midnight_interval = first_next
|
|
|
|
# Fallback: use last interval's price if no tomorrow data or different level
|
|
if midnight_price is None:
|
|
midnight_price = last_price
|
|
midnight_interval = last_interval
|
|
|
|
# Convert price
|
|
converted_price = (
|
|
round(midnight_price * 100, 2) if minor_currency else round(midnight_price, 4)
|
|
)
|
|
if round_decimals is not None:
|
|
converted_price = round(converted_price, round_decimals)
|
|
|
|
# Add point at midnight with appropriate price (extends graph to end of day)
|
|
end_point = {start_time_field: midnight_timestamp, price_field: converted_price}
|
|
if midnight_interval is not None:
|
|
if include_level and "level" in midnight_interval:
|
|
end_point[level_field] = midnight_interval["level"]
|
|
if include_rating_level and "rating_level" in midnight_interval:
|
|
end_point[rating_level_field] = midnight_interval["rating_level"]
|
|
if include_average and day in day_averages:
|
|
end_point[average_field] = day_averages[day]
|
|
chart_data.append(end_point)
|
|
else:
|
|
# Mode 'none' (default): Only return matching intervals, no NULL insertion
|
|
for interval in day_prices:
|
|
start_time = interval.get("startsAt")
|
|
price = interval.get("total")
|
|
|
|
if start_time is not None and price is not None:
|
|
# Apply period filter if specified
|
|
if (
|
|
period_filter is not None
|
|
and period_timestamps is not None
|
|
and start_time not in period_timestamps
|
|
):
|
|
continue
|
|
|
|
# Apply level filter if specified
|
|
if level_filter is not None and "level" in interval and interval["level"] not in level_filter:
|
|
continue
|
|
|
|
# Apply rating_level filter if specified
|
|
if (
|
|
rating_level_filter is not None
|
|
and "rating_level" in interval
|
|
and interval["rating_level"] not in rating_level_filter
|
|
):
|
|
continue
|
|
|
|
# Convert to minor currency (cents/øre) if requested
|
|
price = round(price * 100, 2) if minor_currency else round(price, 4)
|
|
|
|
# Apply custom rounding if specified
|
|
if round_decimals is not None:
|
|
price = round(price, round_decimals)
|
|
|
|
data_point = {start_time_field: start_time, price_field: price}
|
|
|
|
# Add level if requested
|
|
if include_level and "level" in interval:
|
|
data_point[level_field] = interval["level"]
|
|
|
|
# Add rating_level if requested
|
|
if include_rating_level and "rating_level" in interval:
|
|
data_point[rating_level_field] = interval["rating_level"]
|
|
|
|
# Add average if requested
|
|
if include_average and day in day_averages:
|
|
data_point[average_field] = day_averages[day]
|
|
|
|
chart_data.append(data_point)
|
|
|
|
elif resolution == "hourly":
|
|
# Hourly averages (4 intervals per hour: :00, :15, :30, :45)
|
|
chart_data.extend(
|
|
_aggregate_hourly_exact(
|
|
day_prices,
|
|
start_time_field,
|
|
price_field,
|
|
use_minor_currency=minor_currency,
|
|
round_decimals=round_decimals,
|
|
include_level=include_level,
|
|
include_rating_level=include_rating_level,
|
|
level_filter=level_filter,
|
|
rating_level_filter=rating_level_filter,
|
|
include_average=include_average,
|
|
level_field=level_field,
|
|
rating_level_field=rating_level_field,
|
|
average_field=average_field,
|
|
day_average=day_averages.get(day),
|
|
threshold_low=threshold_low,
|
|
period_timestamps=period_timestamps,
|
|
threshold_high=threshold_high,
|
|
)
|
|
)
|
|
|
|
# Convert to array of arrays format if requested
|
|
if output_format == "array_of_arrays":
|
|
array_fields_template = call.data.get("array_fields")
|
|
|
|
# Default: nur timestamp und price
|
|
if not array_fields_template:
|
|
array_fields_template = f"{{{start_time_field}}}, {{{price_field}}}"
|
|
|
|
# Parse template to extract field names
|
|
field_pattern = re.compile(r"\{([^}]+)\}")
|
|
field_names = field_pattern.findall(array_fields_template)
|
|
|
|
if not field_names:
|
|
raise ServiceValidationError(
|
|
translation_domain=DOMAIN,
|
|
translation_key="invalid_array_fields",
|
|
translation_placeholders={"template": array_fields_template},
|
|
)
|
|
|
|
# Convert to [[field1, field2, ...], ...] format
|
|
points = []
|
|
for item in chart_data:
|
|
row = []
|
|
for field_name in field_names:
|
|
# Get value from item, or None if field doesn't exist
|
|
value = item.get(field_name)
|
|
row.append(value)
|
|
points.append(row)
|
|
|
|
# Add final null point for stepline rendering if requested
|
|
# (some chart libraries need this to prevent extrapolation to viewport edge)
|
|
if add_trailing_null and points:
|
|
null_row = [points[-1][0]] + [None] * (len(field_names) - 1)
|
|
points.append(null_row)
|
|
|
|
return {data_key: points}
|
|
|
|
# Add trailing null point for array_of_objects format if requested
|
|
if add_trailing_null and chart_data:
|
|
# Create a null point with only timestamp from last item, all other fields as None
|
|
last_item = chart_data[-1]
|
|
null_point = {start_time_field: last_item.get(start_time_field)}
|
|
# Set all other potential fields to None
|
|
for field in [price_field, level_field, rating_level_field, average_field]:
|
|
if field in last_item:
|
|
null_point[field] = None
|
|
chart_data.append(null_point)
|
|
|
|
return {data_key: chart_data}
|
|
|
|
|
|
def _get_level_translation(level_key: str, level_type: str, language: str) -> str:
|
|
"""Get translated name for a price level or rating level."""
|
|
level_key_lower = level_key.lower()
|
|
# Use correct translation key based on level_type
|
|
if level_type == "rating_level":
|
|
name = get_translation(["selector", "rating_level_filter", "options", level_key_lower], language)
|
|
else:
|
|
name = get_translation(["selector", "level_filter", "options", level_key_lower], language)
|
|
# Fallback to original key if translation not found
|
|
return name or level_key
|
|
|
|
|
|
async def _get_apexcharts_yaml(call: ServiceCall) -> dict[str, Any]:
|
|
"""Return a YAML snippet for an ApexCharts card using the get_apexcharts_data service for each level."""
|
|
hass = call.hass
|
|
entry_id_raw = call.data.get(ATTR_ENTRY_ID)
|
|
if entry_id_raw is None:
|
|
raise ServiceValidationError(translation_domain=DOMAIN, translation_key="missing_entry_id")
|
|
entry_id: str = str(entry_id_raw)
|
|
|
|
day = call.data.get("day", "today")
|
|
level_type = call.data.get("level_type", "rating_level")
|
|
|
|
# Get user's language from hass config
|
|
user_language = hass.config.language or "en"
|
|
|
|
# Get coordinator to access price data (for currency)
|
|
_, coordinator, _ = _get_entry_and_data(hass, entry_id)
|
|
price_info = coordinator.data.get("priceInfo", {})
|
|
currency = price_info.get("currency", "EUR")
|
|
price_unit = format_price_unit_minor(currency)
|
|
|
|
# Get a sample entity_id for the series (first sensor from this entry)
|
|
entity_registry = async_get_entity_registry(hass)
|
|
sample_entity = None
|
|
for entity in entity_registry.entities.values():
|
|
if entity.config_entry_id == entry_id and entity.domain == "sensor":
|
|
sample_entity = entity.entity_id
|
|
break
|
|
|
|
if level_type == "rating_level":
|
|
series_levels = [
|
|
(PRICE_RATING_LOW, "#2ecc71"),
|
|
(PRICE_RATING_NORMAL, "#f1c40f"),
|
|
(PRICE_RATING_HIGH, "#e74c3c"),
|
|
]
|
|
else:
|
|
series_levels = [
|
|
(PRICE_LEVEL_VERY_CHEAP, "#2ecc71"),
|
|
(PRICE_LEVEL_CHEAP, "#27ae60"),
|
|
(PRICE_LEVEL_NORMAL, "#f1c40f"),
|
|
(PRICE_LEVEL_EXPENSIVE, "#e67e22"),
|
|
(PRICE_LEVEL_VERY_EXPENSIVE, "#e74c3c"),
|
|
]
|
|
series = []
|
|
for level_key, color in series_levels:
|
|
# Get translated name for the level using helper function
|
|
name = _get_level_translation(level_key, level_type, user_language)
|
|
# Use server-side insert_nulls='segments' for clean gaps
|
|
if level_type == "rating_level":
|
|
filter_param = f"rating_level_filter: ['{level_key}']"
|
|
else:
|
|
filter_param = f"level_filter: ['{level_key}']"
|
|
|
|
data_generator = (
|
|
f"const response = await hass.callWS({{ "
|
|
f"type: 'call_service', "
|
|
f"domain: 'tibber_prices', "
|
|
f"service: 'get_chartdata', "
|
|
f"return_response: true, "
|
|
f"service_data: {{ entry_id: '{entry_id}', day: ['{day}'], {filter_param}, "
|
|
f"output_format: 'array_of_arrays', insert_nulls: 'segments', minor_currency: true }} }}); "
|
|
f"return response.response.data;"
|
|
)
|
|
# Only show extremas for HIGH and LOW levels (not NORMAL)
|
|
show_extremas = level_key != "NORMAL"
|
|
series.append(
|
|
{
|
|
"entity": sample_entity or "sensor.tibber_prices",
|
|
"name": name,
|
|
"type": "area",
|
|
"color": color,
|
|
"yaxis_id": "price",
|
|
"show": {"extremas": show_extremas, "legend_value": False},
|
|
"data_generator": data_generator,
|
|
"stroke_width": 1,
|
|
}
|
|
)
|
|
|
|
# Get translated title based on level_type
|
|
title_key = "title_rating_level" if level_type == "rating_level" else "title_level"
|
|
title = get_translation(["apexcharts", title_key], user_language) or (
|
|
"Price Phases Daily Progress" if level_type == "rating_level" else "Price Level"
|
|
)
|
|
|
|
# Add translated day to title
|
|
day_translated = get_translation(["selector", "day", "options", day], user_language) or day.capitalize()
|
|
title = f"{title} - {day_translated}"
|
|
|
|
# Configure span based on selected day
|
|
if day == "yesterday":
|
|
span_config = {"start": "day", "offset": "-1d"}
|
|
elif day == "tomorrow":
|
|
span_config = {"start": "day", "offset": "+1d"}
|
|
else: # today
|
|
span_config = {"start": "day"}
|
|
|
|
return {
|
|
"type": "custom:apexcharts-card",
|
|
"update_interval": "5m",
|
|
"span": span_config,
|
|
"header": {
|
|
"show": True,
|
|
"title": title,
|
|
"show_states": False,
|
|
},
|
|
"apex_config": {
|
|
"chart": {
|
|
"animations": {"enabled": False},
|
|
"toolbar": {"show": True, "tools": {"zoom": True, "pan": True}},
|
|
"zoom": {"enabled": True},
|
|
},
|
|
"stroke": {"curve": "stepline", "width": 2},
|
|
"fill": {
|
|
"type": "gradient",
|
|
"opacity": 0.4,
|
|
"gradient": {
|
|
"shade": "dark",
|
|
"type": "vertical",
|
|
"shadeIntensity": 0.5,
|
|
"opacityFrom": 0.7,
|
|
"opacityTo": 0.2,
|
|
},
|
|
},
|
|
"dataLabels": {"enabled": False},
|
|
"tooltip": {
|
|
"x": {"format": "HH:mm"},
|
|
"y": {"title": {"formatter": f"function() {{ return '{price_unit}'; }}"}},
|
|
},
|
|
"legend": {
|
|
"show": True,
|
|
"position": "top",
|
|
"horizontalAlign": "left",
|
|
"markers": {"radius": 2},
|
|
},
|
|
"grid": {
|
|
"show": True,
|
|
"borderColor": "#40475D",
|
|
"strokeDashArray": 4,
|
|
"xaxis": {"lines": {"show": True}},
|
|
"yaxis": {"lines": {"show": True}},
|
|
},
|
|
"markers": {"size": 0},
|
|
},
|
|
"yaxis": [
|
|
{
|
|
"id": "price",
|
|
"decimals": 2,
|
|
"min": 0,
|
|
"apex_config": {"title": {"text": price_unit}},
|
|
},
|
|
],
|
|
"now": {"show": True, "color": "#8e24aa", "label": "🕒 LIVE"},
|
|
"all_series_config": {
|
|
"stroke_width": 1,
|
|
"group_by": {"func": "raw", "duration": "15min"},
|
|
},
|
|
"series": series,
|
|
}
|
|
|
|
|
|
async def _refresh_user_data(call: ServiceCall) -> dict[str, Any]:
|
|
"""Refresh user data for a specific config entry and return updated information."""
|
|
entry_id = call.data.get(ATTR_ENTRY_ID)
|
|
hass = call.hass
|
|
|
|
if not entry_id:
|
|
return {
|
|
"success": False,
|
|
"message": "Entry ID is required",
|
|
}
|
|
|
|
# Get the entry and coordinator
|
|
try:
|
|
_, coordinator, _ = _get_entry_and_data(hass, entry_id)
|
|
except ServiceValidationError as ex:
|
|
return {
|
|
"success": False,
|
|
"message": f"Invalid entry ID: {ex}",
|
|
}
|
|
|
|
# Force refresh user data using the public method
|
|
try:
|
|
updated = await coordinator.refresh_user_data()
|
|
except (
|
|
TibberPricesApiClientAuthenticationError,
|
|
TibberPricesApiClientCommunicationError,
|
|
TibberPricesApiClientError,
|
|
) as ex:
|
|
return {
|
|
"success": False,
|
|
"message": f"API error refreshing user data: {ex!s}",
|
|
}
|
|
else:
|
|
if updated:
|
|
user_profile = coordinator.get_user_profile()
|
|
homes = coordinator.get_user_homes()
|
|
|
|
return {
|
|
"success": True,
|
|
"message": "User data refreshed successfully",
|
|
"user_profile": user_profile,
|
|
"homes_count": len(homes),
|
|
"homes": homes,
|
|
"last_updated": user_profile.get("last_updated"),
|
|
}
|
|
return {
|
|
"success": False,
|
|
"message": "User data was already up to date",
|
|
}
|
|
|
|
|
|
# --- Helpers ---
|
|
|
|
|
|
def _get_entry_and_data(hass: HomeAssistant, entry_id: str) -> tuple[Any, Any, dict]:
|
|
"""Validate entry and extract coordinator and data."""
|
|
if not entry_id:
|
|
raise ServiceValidationError(translation_domain=DOMAIN, translation_key="missing_entry_id")
|
|
entry = next(
|
|
(e for e in hass.config_entries.async_entries(DOMAIN) if e.entry_id == entry_id),
|
|
None,
|
|
)
|
|
if not entry or not hasattr(entry, "runtime_data") or not entry.runtime_data:
|
|
raise ServiceValidationError(translation_domain=DOMAIN, translation_key="invalid_entry_id")
|
|
coordinator = entry.runtime_data.coordinator
|
|
data = coordinator.data or {}
|
|
return entry, coordinator, data
|
|
|
|
|
|
# --- Service registration ---
|
|
|
|
|
|
@callback
|
|
def async_setup_services(hass: HomeAssistant) -> None:
|
|
"""Set up services for Tibber Prices integration."""
|
|
hass.services.async_register(
|
|
DOMAIN,
|
|
APEXCHARTS_YAML_SERVICE_NAME,
|
|
_get_apexcharts_yaml,
|
|
schema=APEXCHARTS_SERVICE_SCHEMA,
|
|
supports_response=SupportsResponse.ONLY,
|
|
)
|
|
hass.services.async_register(
|
|
DOMAIN,
|
|
CHARTDATA_SERVICE_NAME,
|
|
_get_chartdata,
|
|
schema=CHARTDATA_SERVICE_SCHEMA,
|
|
supports_response=SupportsResponse.ONLY,
|
|
)
|
|
hass.services.async_register(
|
|
DOMAIN,
|
|
REFRESH_USER_DATA_SERVICE_NAME,
|
|
_refresh_user_data,
|
|
schema=REFRESH_USER_DATA_SERVICE_SCHEMA,
|
|
supports_response=SupportsResponse.ONLY,
|
|
)
|