mirror of
https://github.com/jpawlowski/hass.tibber_prices.git
synced 2026-05-28 18:43:40 +00:00
a4ad506e01
Add get_display_precision() to const.py returning DISPLAY_PRECISION_SUBUNIT (2) or DISPLAY_PRECISION_BASE (4) based on config. Replace hardcoded round(..., 2) with get_display_precision() in all calculators and attribute builders. Add _update_suggested_precision() to sensor core; syncs entity registry suggested_display_precision on every coordinator update. Interval price sensors get full precision (2 or 4 dp); other MONETARY sensors get half precision (1 or 2 dp) as sensible default. Impact: Price sensor states and attributes now correctly use 4 decimal places in base-currency mode (was always 2). Display precision in dashboards updates automatically when currency mode changes.
95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
"""
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Sensor platform-specific helper functions.
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This module contains helper functions specific to the sensor platform:
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- aggregate_price_data: Calculate average price from window data
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- aggregate_level_data: Aggregate price levels from intervals
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- aggregate_rating_data: Aggregate price ratings from intervals
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For shared helper functions (used by both sensor and binary_sensor platforms),
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see entity_utils/helpers.py:
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- get_price_value: Price unit conversion
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- find_rolling_hour_center_index: Rolling hour window calculations
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"""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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from custom_components.tibber_prices.const import get_display_precision, get_display_unit_factor
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from custom_components.tibber_prices.utils.average import calculate_mean, calculate_median
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from custom_components.tibber_prices.utils.price import aggregate_price_levels, aggregate_price_rating
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if TYPE_CHECKING:
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from homeassistant.config_entries import ConfigEntry
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def aggregate_average_data(
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window_data: list[dict],
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config_entry: ConfigEntry,
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) -> tuple[float | None, float | None]:
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"""
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Calculate average and median price from window data.
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Args:
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window_data: List of price interval dictionaries with 'total' key.
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config_entry: Config entry to get display unit configuration.
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Returns:
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Tuple of (average price, median price) in display currency units,
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or (None, None) if no prices.
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"""
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prices = [float(i["total"]) for i in window_data if "total" in i]
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if not prices:
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return None, None
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# Calculate both mean and median
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mean = calculate_mean(prices)
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median = calculate_median(prices)
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# Convert to display currency unit based on configuration
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factor = get_display_unit_factor(config_entry)
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precision = get_display_precision(config_entry)
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return round(mean * factor, precision), round(median * factor, precision) if median is not None else None
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def aggregate_level_data(window_data: list[dict]) -> str | None:
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"""
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Aggregate price levels from window data.
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Args:
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window_data: List of price interval dictionaries with 'level' key
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Returns:
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Aggregated price level (lowercase), or None if no levels
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"""
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levels = [i["level"] for i in window_data if "level" in i]
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if not levels:
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return None
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aggregated = aggregate_price_levels(levels)
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return aggregated.lower() if aggregated else None
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def aggregate_rating_data(
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window_data: list[dict],
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threshold_low: float,
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threshold_high: float,
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) -> str | None:
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"""
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Aggregate price ratings from window data.
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Args:
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window_data: List of price interval dictionaries with 'difference' and 'rating_level'
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threshold_low: Low threshold for rating calculation
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threshold_high: High threshold for rating calculation
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Returns:
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Aggregated price rating (lowercase), or None if no ratings
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"""
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differences = [i["difference"] for i in window_data if "difference" in i and "rating_level" in i]
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if not differences:
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return None
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aggregated, _ = aggregate_price_rating(differences, threshold_low, threshold_high)
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return aggregated.lower() if aggregated else None
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