mirror of
https://github.com/jpawlowski/hass.tibber_prices.git
synced 2026-03-30 05:13:40 +00:00
6e0310ef7c
Period data in array_of_arrays format now generates proper segment structure for stepline charts. Each period produces 2-3 data points depending on insert_nulls parameter: 1. Start time with price (begin period) 2. End time with price (hold price level) 3. End time with NULL (terminate segment, only if insert_nulls='segments'/'all') This enables ApexCharts to correctly display periods as continuous blocks with clean gaps between them. Previously only start point was generated, causing periods to render as single points instead of continuous segments. Changes: - formatters.py: Updated get_period_data() to generate 2-3 points per period - formatters.py: Added insert_nulls parameter to control NULL termination - get_chartdata.py: Pass insert_nulls parameter to get_period_data() - get_apexcharts_yaml.py: Set insert_nulls='segments' for period overlay - get_apexcharts_yaml.py: Preserve NULL values in data_generator mapping - get_apexcharts_yaml.py: Store original price for potential tooltip access - tests: Added comprehensive period data format tests Impact: Best price and peak price period overlays now display correctly as continuous blocks with proper segment separation in ApexCharts cards.
473 lines
17 KiB
Python
473 lines
17 KiB
Python
"""
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ApexCharts YAML generation service handler.
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This module implements the `get_apexcharts_yaml` service, which generates
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ready-to-use YAML configuration for ApexCharts cards with price level visualization.
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Features:
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- Automatic color-coded series per price level/rating
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- Server-side NULL insertion for clean gaps
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- Translated level names and titles
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- Responsive to user language settings
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- Configurable day selection (yesterday/today/tomorrow)
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Service: tibber_prices.get_apexcharts_yaml
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Response: YAML configuration dict for ApexCharts card
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"""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Final
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import voluptuous as vol
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from custom_components.tibber_prices.const import (
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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 homeassistant.exceptions import ServiceValidationError
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from homeassistant.helpers import config_validation as cv
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from homeassistant.helpers.entity_registry import (
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EntityRegistry,
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)
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from homeassistant.helpers.entity_registry import (
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async_get as async_get_entity_registry,
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)
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from .formatters import get_level_translation
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from .helpers import get_entry_and_data
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if TYPE_CHECKING:
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from homeassistant.core import ServiceCall
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# Service constants
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APEXCHARTS_YAML_SERVICE_NAME: Final = "get_apexcharts_yaml"
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ATTR_DAY: Final = "day"
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ATTR_ENTRY_ID: Final = "entry_id"
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# Service schema
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APEXCHARTS_SERVICE_SCHEMA = vol.Schema(
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{
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vol.Required(ATTR_ENTRY_ID): cv.string,
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vol.Optional("day"): 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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vol.Optional("highlight_best_price", default=True): cv.boolean,
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}
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)
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def _build_entity_map(
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entity_registry: EntityRegistry,
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entry_id: str,
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level_type: str,
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day: str,
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) -> dict[str, str]:
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"""
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Build entity mapping for price levels based on day.
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Maps price levels to appropriate sensor entities (min/max/avg for the selected day).
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Args:
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entity_registry: Entity registry
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entry_id: Config entry ID
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level_type: "rating_level" or "level"
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day: "today", "tomorrow", or "yesterday"
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Returns:
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Dictionary mapping level keys to entity IDs
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"""
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entity_map = {}
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# Define mapping patterns for each combination of level_type and day
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# Note: Match by entity key (in unique_id), not entity_id (user can rename)
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# Note: For "yesterday", we use "today" sensors as they show current state
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pattern_map = {
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("rating_level", "today"): [
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("lowest_price_today", [PRICE_RATING_LOW]),
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("average_price_today", [PRICE_RATING_NORMAL]),
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("highest_price_today", [PRICE_RATING_HIGH]),
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],
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("rating_level", "yesterday"): [
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("lowest_price_today", [PRICE_RATING_LOW]),
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("average_price_today", [PRICE_RATING_NORMAL]),
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("highest_price_today", [PRICE_RATING_HIGH]),
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],
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("rating_level", "tomorrow"): [
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("lowest_price_tomorrow", [PRICE_RATING_LOW]),
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("average_price_tomorrow", [PRICE_RATING_NORMAL]),
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("highest_price_tomorrow", [PRICE_RATING_HIGH]),
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],
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("level", "today"): [
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("lowest_price_today", [PRICE_LEVEL_VERY_CHEAP, PRICE_LEVEL_CHEAP]),
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("average_price_today", [PRICE_LEVEL_NORMAL]),
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("highest_price_today", [PRICE_LEVEL_EXPENSIVE, PRICE_LEVEL_VERY_EXPENSIVE]),
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],
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("level", "yesterday"): [
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("lowest_price_today", [PRICE_LEVEL_VERY_CHEAP, PRICE_LEVEL_CHEAP]),
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("average_price_today", [PRICE_LEVEL_NORMAL]),
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("highest_price_today", [PRICE_LEVEL_EXPENSIVE, PRICE_LEVEL_VERY_EXPENSIVE]),
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],
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("level", "tomorrow"): [
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("lowest_price_tomorrow", [PRICE_LEVEL_VERY_CHEAP, PRICE_LEVEL_CHEAP]),
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("average_price_tomorrow", [PRICE_LEVEL_NORMAL]),
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("highest_price_tomorrow", [PRICE_LEVEL_EXPENSIVE, PRICE_LEVEL_VERY_EXPENSIVE]),
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],
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}
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patterns = pattern_map.get((level_type, day), [])
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for entity in entity_registry.entities.values():
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if entity.config_entry_id != entry_id or entity.domain != "sensor":
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continue
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# Match entity against patterns using unique_id (contains entry_id_key)
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# Extract key from unique_id: format is "{entry_id}_{key}"
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if entity.unique_id and "_" in entity.unique_id:
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entity_key = entity.unique_id.split("_", 1)[1] # Get everything after first underscore
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for pattern, levels in patterns:
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if pattern == entity_key:
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for level in levels:
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entity_map[level] = entity.entity_id
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break
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return entity_map
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def _get_current_price_entity(entity_registry: EntityRegistry, entry_id: str) -> str | None:
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"""Get current interval price entity for header display."""
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return next(
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(
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entity.entity_id
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for entity in entity_registry.entities.values()
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if entity.config_entry_id == entry_id
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and entity.unique_id
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and entity.unique_id.endswith("_current_interval_price")
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),
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None,
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)
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async def handle_apexcharts_yaml(call: ServiceCall) -> dict[str, Any]: # noqa: PLR0912, PLR0915
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"""
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Return YAML snippet for ApexCharts card.
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Generates a complete ApexCharts card configuration with:
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- Separate series for each price level/rating (color-coded)
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- Automatic data fetching via get_chartdata service
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- Translated labels and titles
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- Clean gap visualization with NULL insertion
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See services.yaml for detailed parameter documentation.
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Args:
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call: Service call with parameters
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Returns:
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Dictionary with ApexCharts card configuration
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Raises:
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ServiceValidationError: If entry_id is missing or invalid
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"""
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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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day = call.data.get("day") # Can be None (rolling window mode)
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level_type = call.data.get("level_type", "rating_level")
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highlight_best_price = call.data.get("highlight_best_price", True)
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# Get user's language from hass config
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user_language = hass.config.language or "en"
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# Get coordinator to access price data (for currency)
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_, coordinator, _ = get_entry_and_data(hass, entry_id)
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# Get currency from coordinator data
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currency = coordinator.data.get("currency", "EUR")
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price_unit = format_price_unit_minor(currency)
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# Get entity registry for mapping
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entity_registry = async_get_entity_registry(hass)
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# Build entity mapping based on level_type and day for clickable states
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# When day is None, use "today" as fallback for entity mapping
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entity_map = _build_entity_map(entity_registry, entry_id, level_type, day or "today")
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if level_type == "rating_level":
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series_levels = [
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(PRICE_RATING_LOW, "#2ecc71"),
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(PRICE_RATING_NORMAL, "#f1c40f"),
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(PRICE_RATING_HIGH, "#e74c3c"),
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]
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else:
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series_levels = [
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(PRICE_LEVEL_VERY_CHEAP, "#2ecc71"),
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(PRICE_LEVEL_CHEAP, "#27ae60"),
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(PRICE_LEVEL_NORMAL, "#f1c40f"),
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(PRICE_LEVEL_EXPENSIVE, "#e67e22"),
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(PRICE_LEVEL_VERY_EXPENSIVE, "#e74c3c"),
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]
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series = []
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# Only create series for levels that have a matching entity (filter out missing levels)
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for level_key, color in series_levels:
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# Skip levels that don't have a corresponding sensor
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if level_key not in entity_map:
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continue
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# Get translated name for the level using helper function
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name = get_level_translation(level_key, level_type, user_language)
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# Use server-side insert_nulls='segments' for clean gaps
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if level_type == "rating_level":
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filter_param = f"rating_level_filter: ['{level_key}']"
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else:
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filter_param = f"level_filter: ['{level_key}']"
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# Conditionally include day parameter (omit for rolling window mode)
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day_param = f"day: ['{day}'], " if day else ""
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data_generator = (
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f"const response = await hass.callWS({{ "
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f"type: 'call_service', "
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f"domain: 'tibber_prices', "
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f"service: 'get_chartdata', "
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f"return_response: true, "
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f"service_data: {{ entry_id: '{entry_id}', {day_param}{filter_param}, "
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f"output_format: 'array_of_arrays', insert_nulls: 'segments', minor_currency: true, "
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f"connect_segments: true }} }}); "
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f"return response.response.data;"
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)
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# All series use same configuration (no extremas on data_generator series)
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# Hide all levels in header since data_generator series don't show meaningful state values
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# (the entity state is the min/max/avg price, not the current price for this level)
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show_config = {"legend_value": False, "in_header": False}
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series.append(
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{
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"entity": entity_map[level_key], # Use entity_map directly (no fallback needed)
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"name": name,
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"type": "area",
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"color": color,
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"yaxis_id": "price",
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"show": show_config,
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"data_generator": data_generator,
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"stroke_width": 1,
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}
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)
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# Note: Extrema markers don't work with data_generator approach
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# ApexCharts requires entity time-series data for extremas feature
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# Min/Max sensors are single values, not time-series
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# Get translated name for best price periods (needed for tooltip formatter)
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best_price_name = (
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get_translation(["binary_sensor", "best_price_period", "name"], user_language) or "Best Price Period"
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)
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# Add best price period highlight overlay (vertical bands from top to bottom)
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if highlight_best_price and entity_map:
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# Create vertical highlight bands using separate Y-axis (0-1 range)
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# This creates a semi-transparent overlay from bottom to top without affecting price scale
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# Conditionally include day parameter (omit for rolling window mode)
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day_param = f"day: ['{day}'], " if day else ""
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# Store original prices for tooltip, but map to 1 for full-height overlay
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# We use a custom tooltip formatter to show the real price
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best_price_generator = (
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f"const response = await hass.callWS({{ "
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f"type: 'call_service', "
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f"domain: 'tibber_prices', "
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f"service: 'get_chartdata', "
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f"return_response: true, "
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f"service_data: {{ entry_id: '{entry_id}', {day_param}"
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f"period_filter: 'best_price', "
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f"output_format: 'array_of_arrays', insert_nulls: 'segments', minor_currency: true }} }}); "
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f"const originalData = response.response.data; "
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f"return originalData.map((point, i) => {{ "
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f"const result = [point[0], point[1] === null ? null : 1]; "
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f"result.originalPrice = point[1]; "
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f"return result; "
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f"}});"
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)
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# Use first entity from entity_map (reuse existing entity to avoid extra header entries)
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best_price_entity = next(iter(entity_map.values()))
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series.append(
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{
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"entity": best_price_entity,
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"name": best_price_name,
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"type": "area",
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"color": "rgba(46, 204, 113, 0.2)", # Semi-transparent green
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"yaxis_id": "highlight", # Use separate Y-axis (0-1) for full-height overlay
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"show": {"legend_value": False, "in_header": False, "in_legend": False},
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"data_generator": best_price_generator,
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"stroke_width": 0,
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"curve": "stepline",
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}
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)
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# Get translated title based on level_type
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title_key = "title_rating_level" if level_type == "rating_level" else "title_level"
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title = get_translation(["apexcharts", title_key], user_language) or (
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"Price Phases Daily Progress" if level_type == "rating_level" else "Price Level"
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)
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# Add translated day to title (only if day parameter was provided)
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if day:
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day_translated = get_translation(["selector", "day", "options", day], user_language) or day.capitalize()
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title = f"{title} - {day_translated}"
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# Configure span based on selected day
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# For rolling window mode, use config-template-card for dynamic offset
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if day == "yesterday":
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span_config = {"start": "day", "offset": "-1d"}
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graph_span_value = None
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use_template = False
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elif day == "tomorrow":
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span_config = {"start": "day", "offset": "+1d"}
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graph_span_value = None
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use_template = False
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elif day: # today (explicit)
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span_config = {"start": "day"}
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graph_span_value = None
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use_template = False
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else: # Rolling window mode (None)
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# Use config-template-card to dynamically set offset based on data availability
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span_config = None # Will be set in template
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graph_span_value = "48h"
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use_template = True
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result = {
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"type": "custom:apexcharts-card",
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"update_interval": "5m",
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"header": {
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"show": True,
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"title": title,
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"show_states": True,
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},
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"apex_config": {
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"chart": {
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"animations": {"enabled": False},
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"toolbar": {"show": True, "tools": {"zoom": True, "pan": True}},
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"zoom": {"enabled": True},
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},
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"stroke": {"curve": "stepline", "width": 2},
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"fill": {
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"type": "gradient",
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"opacity": 0.4,
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"gradient": {
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"shade": "dark",
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"type": "vertical",
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"shadeIntensity": 0.5,
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"opacityFrom": 0.7,
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"opacityTo": 0.2,
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},
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},
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"dataLabels": {"enabled": False},
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"tooltip": {
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"x": {"format": "HH:mm"},
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"y": {"title": {"formatter": f"function() {{ return '{price_unit}'; }}"}},
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},
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"legend": {
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"show": False,
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"position": "top",
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"horizontalAlign": "left",
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"markers": {"radius": 2},
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},
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"grid": {
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"show": True,
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"borderColor": "#40475D",
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"strokeDashArray": 4,
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"xaxis": {"lines": {"show": True}},
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"yaxis": {"lines": {"show": True}},
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},
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"markers": {"size": 0},
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},
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"yaxis": [
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{
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"id": "price",
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"decimals": 2,
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"min": 0,
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"apex_config": {"title": {"text": price_unit}},
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},
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{
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"id": "highlight",
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"min": 0,
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"max": 1,
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"show": False, # Hide this axis (only for highlight overlay)
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"opposite": True,
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},
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],
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"now": {"show": True, "color": "#8e24aa", "label": "🕒 LIVE"},
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"all_series_config": {
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"stroke_width": 1,
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"group_by": {"func": "raw", "duration": "15min"},
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},
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"series": series,
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}
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# For rolling window mode, wrap in config-template-card for dynamic offset
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if use_template:
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# Add graph_span to base config (48h window)
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result["graph_span"] = graph_span_value
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# Find tomorrow_data_available binary sensor
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tomorrow_data_sensor = next(
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(
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entity.entity_id
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for entity in entity_registry.entities.values()
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if entity.config_entry_id == entry_id
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and entity.unique_id
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and entity.unique_id.endswith("_tomorrow_data_available")
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),
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None,
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)
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if tomorrow_data_sensor:
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# Wrap in config-template-card with dynamic offset calculation
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# Template checks if tomorrow data is available (binary sensor state)
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# If 'on' (tomorrow data available) → offset +1d (show today+tomorrow)
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# If 'off' (no tomorrow data) → offset +0d (show yesterday+today)
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template_value = f"states['{tomorrow_data_sensor}'].state === 'on' ? '+1d' : '+0d'"
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return {
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"type": "custom:config-template-card",
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"variables": {
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"v_offset": template_value,
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},
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"entities": [tomorrow_data_sensor],
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"card": {
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**result,
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"span": {
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"end": "day",
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"offset": "${v_offset}",
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},
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},
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}
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# Fallback if sensor not found: just use +1d offset
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result["span"] = {"end": "day", "offset": "+1d"}
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return result
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# Add span for fixed-day views
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if span_config:
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result["span"] = span_config
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# Add graph_span if needed
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if graph_span_value:
|
|
result["graph_span"] = graph_span_value
|
|
|
|
return result
|