mirror of
https://github.com/jpawlowski/hass.tibber_prices.git
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abb02083a7
Implemented configurable display format (mean/median/both) while always calculating and exposing both price_mean and price_median attributes. Core changes: - utils/average.py: Refactored calculate_mean_median() to always return both values, added comprehensive None handling (117 lines changed) - sensor/attributes/helpers.py: Always include both attributes regardless of user display preference (41 lines) - sensor/core.py: Dynamic _unrecorded_attributes based on display setting (55 lines), extracted helper methods to reduce complexity - Updated all calculators (rolling_hour, trend, volatility, window_24h) to use new always-both approach Impact: Users can switch display format in UI without losing historical data. Automation authors always have access to both statistical measures.
65 lines
2 KiB
Python
65 lines
2 KiB
Python
"""
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Pure data transformation utilities for Tibber Prices integration.
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This package contains stateless, pure functions for data processing:
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- Time-window calculations (trailing/leading averages, min/max)
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- Price enrichment (differences, volatility, rating levels)
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- Statistical analysis (aggregation, trends)
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These functions operate on raw data structures (dicts, lists) and do NOT depend on:
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- Home Assistant entities or state management
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- Configuration entries or coordinators
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- Translation systems or UI-specific logic
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For entity-specific utilities (icons, colors, attributes), see entity_utils/ package.
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"""
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from __future__ import annotations
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from .average import (
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calculate_current_leading_max,
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calculate_current_leading_mean,
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calculate_current_leading_min,
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calculate_current_trailing_max,
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calculate_current_trailing_mean,
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calculate_current_trailing_min,
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calculate_mean,
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calculate_median,
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calculate_next_n_hours_mean,
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)
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from .price import (
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aggregate_period_levels,
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aggregate_period_ratings,
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aggregate_price_levels,
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aggregate_price_rating,
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calculate_difference_percentage,
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calculate_price_trend,
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calculate_rating_level,
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calculate_trailing_average_for_interval,
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calculate_volatility_level,
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enrich_price_info_with_differences,
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find_price_data_for_interval,
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)
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__all__ = [
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"aggregate_period_levels",
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"aggregate_period_ratings",
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"aggregate_price_levels",
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"aggregate_price_rating",
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"calculate_current_leading_max",
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"calculate_current_leading_mean",
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"calculate_current_leading_min",
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"calculate_current_trailing_max",
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"calculate_current_trailing_mean",
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"calculate_current_trailing_min",
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"calculate_difference_percentage",
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"calculate_mean",
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"calculate_median",
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"calculate_next_n_hours_mean",
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"calculate_price_trend",
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"calculate_rating_level",
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"calculate_trailing_average_for_interval",
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"calculate_volatility_level",
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"enrich_price_info_with_differences",
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"find_price_data_for_interval",
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]
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