mirror of
https://github.com/jpawlowski/hass.tibber_prices.git
synced 2026-03-29 21:03:40 +00:00
a962289682
Massive refactoring of sensor platform reducing core.py from 2,170 to 909 lines (58% reduction). Extracted business logic into specialized calculators and attribute builders following separation of concerns principles. Changes: - Created sensor/calculators/ package (8 specialized calculators, 1,838 lines): * base.py: Abstract BaseCalculator with coordinator access * interval.py: Single interval calculations (current/next/previous) * rolling_hour.py: 5-interval rolling windows * daily_stat.py: Calendar day min/max/avg statistics * window_24h.py: Trailing/leading 24h windows * volatility.py: Price volatility analysis * trend.py: Complex trend analysis with caching (640 lines) * timing.py: Best/peak price period timing * metadata.py: Home/metering metadata - Created sensor/attributes/ package (8 specialized modules, 1,209 lines): * Modules match calculator types for consistent organization * __init__.py: Routing logic + unified builders * Handles state presentation separately from business logic - Created sensor/chart_data.py (144 lines): * Extracted chart data export functionality from entity class * YAML parsing, service calls, metadata formatting - Created sensor/value_getters.py (276 lines): * Centralized handler mapping for all 80+ sensor types * Single source of truth for sensor routing - Extended sensor/helpers.py (+88 lines): * Added aggregate_window_data() unified aggregator * Added get_hourly_price_value() for backward compatibility * Consolidated sensor-specific helper functions - Refactored sensor/core.py (909 lines, was 2,170): * Instantiates all calculators in __init__ * Delegates value calculations to appropriate calculator * Uses unified handler methods via value_getters mapping * Minimal platform-specific logic remains (icon callbacks, entity lifecycle) - Deleted sensor/attributes.py (1,106 lines): * Functionality split into attributes/ package (8 modules) - Updated AGENTS.md: * Documented Calculator Pattern architecture * Added guidance for adding new sensors with calculation groups * Updated file organization with new package structure Architecture Benefits: - Clear separation: Calculators (business logic) vs Attributes (presentation) - Improved testability: Each calculator independently testable - Better maintainability: 21 focused modules vs monolithic file - Easy extensibility: Add sensors by choosing calculation pattern - Reusable components: Calculators and attribute builders shared across sensors Impact: Significantly improved code organization and maintainability while preserving all functionality. All 80+ sensor types continue working with cleaner, more modular architecture. Developer experience improved with logical file structure and clear separation of concerns.
34 lines
1.4 KiB
Python
34 lines
1.4 KiB
Python
"""Trend attribute builders for Tibber Prices sensors."""
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import Any
|
|
|
|
from .timing import add_period_timing_attributes
|
|
from .volatility import add_volatility_attributes
|
|
|
|
|
|
def _add_timing_or_volatility_attributes(
|
|
attributes: dict,
|
|
key: str,
|
|
cached_data: dict,
|
|
native_value: Any = None,
|
|
) -> None:
|
|
"""Add attributes for timing or volatility sensors."""
|
|
if key.endswith("_volatility"):
|
|
add_volatility_attributes(attributes=attributes, cached_data=cached_data)
|
|
else:
|
|
add_period_timing_attributes(attributes=attributes, key=key, state_value=native_value)
|
|
|
|
|
|
def _add_cached_trend_attributes(attributes: dict, key: str, cached_data: dict) -> None:
|
|
"""Add cached trend attributes if available."""
|
|
if key.startswith("price_trend_") and cached_data.get("trend_attributes"):
|
|
attributes.update(cached_data["trend_attributes"])
|
|
elif key == "current_price_trend" and cached_data.get("current_trend_attributes"):
|
|
# Add cached attributes (timestamp already set by platform)
|
|
attributes.update(cached_data["current_trend_attributes"])
|
|
elif key == "next_price_trend_change" and cached_data.get("trend_change_attributes"):
|
|
# Add cached attributes (timestamp already set by platform)
|
|
# State contains the timestamp of the trend change itself
|
|
attributes.update(cached_data["trend_change_attributes"])
|