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https://github.com/jpawlowski/hass.tibber_prices.git
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4876a2cc29
Created entity_utils/helpers.py with platform-agnostic utility functions: - get_price_value(): Price unit conversion (major/minor currency) - translate_level(): Price level translation - translate_rating_level(): Rating level translation - find_rolling_hour_center_index(): Rolling hour window calculations These functions moved from sensor/helpers.py as they are used by both sensor and binary_sensor platforms. Remaining sensor/helpers.py now contains only sensor-specific helpers (aggregate_price_data, etc.). Updated imports: - sensor/core.py: Import from entity_utils instead of sensor.helpers - entity_utils/icons.py: Fixed find_rolling_hour_center_index import - binary_sensor platforms: Can now use shared helpers Added clear docstrings explaining: - entity_utils/helpers.py: Platform-agnostic utilities - sensor/helpers.py: Sensor-specific aggregation functions Impact: Better code reuse, clearer responsibility boundaries between platform-specific and shared utilities.
83 lines
2.5 KiB
Python
83 lines
2.5 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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- translate_level: Price level translation
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- translate_rating_level: Rating level translation
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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 custom_components.tibber_prices.utils.price import (
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aggregate_price_levels,
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aggregate_price_rating,
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)
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def aggregate_price_data(window_data: list[dict]) -> float | None:
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"""
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Calculate average 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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Returns:
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Average price in minor currency units (cents/øre), or 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
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# Return in minor currency units (cents/øre)
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return round((sum(prices) / len(prices)) * 100, 2)
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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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