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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.
541 lines
17 KiB
Python
541 lines
17 KiB
Python
"""Utility functions for calculating price averages."""
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from __future__ import annotations
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from datetime import datetime, timedelta
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from typing import TYPE_CHECKING
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from custom_components.tibber_prices.coordinator.helpers import get_intervals_for_day_offsets
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if TYPE_CHECKING:
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from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
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def calculate_median(prices: list[float]) -> float | None:
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"""
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Calculate median from a list of prices.
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Args:
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prices: List of price values
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Returns:
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Median price, or None if list is empty
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"""
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if not prices:
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return None
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sorted_prices = sorted(prices)
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n = len(sorted_prices)
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if n % 2 == 0:
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# Even number of elements: average of middle two
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return (sorted_prices[n // 2 - 1] + sorted_prices[n // 2]) / 2
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# Odd number of elements: middle element
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return sorted_prices[n // 2]
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def calculate_mean(prices: list[float]) -> float:
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"""
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Calculate arithmetic mean (average) from a list of prices.
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Args:
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prices: List of price values (must not be empty)
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Returns:
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Mean price
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Raises:
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ValueError: If prices list is empty
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"""
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if not prices:
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msg = "Cannot calculate mean of empty list"
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raise ValueError(msg)
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return sum(prices) / len(prices)
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def calculate_trailing_24h_mean(
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all_prices: list[dict],
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interval_start: datetime,
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*,
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time: TibberPricesTimeService,
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) -> tuple[float | None, float | None]:
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"""
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Calculate trailing 24-hour mean and median price for a given interval.
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Args:
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all_prices: List of all price data (yesterday, today, tomorrow combined)
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interval_start: Start time of the interval to calculate mean for
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time: TibberPricesTimeService instance (required)
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Returns:
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Tuple of (mean price, median price) for the 24 hours preceding the interval,
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or (None, None) if no data in window
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"""
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# Define the 24-hour window: from 24 hours before interval_start up to interval_start
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window_start = interval_start - timedelta(hours=24)
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window_end = interval_start
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# Filter prices within the 24-hour window
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prices_in_window = []
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for price_data in all_prices:
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starts_at = time.get_interval_time(price_data)
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if starts_at is None:
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continue
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# Include intervals that start within the window (not including the current interval's end)
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if window_start <= starts_at < window_end:
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prices_in_window.append(float(price_data["total"]))
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# Calculate mean and median
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# CRITICAL: Return None instead of 0.0 when no data available
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# With negative prices, 0.0 could be misinterpreted as a real mean value
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if prices_in_window:
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mean = calculate_mean(prices_in_window)
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median = calculate_median(prices_in_window)
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return mean, median
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return None, None
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def calculate_leading_24h_mean(
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all_prices: list[dict],
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interval_start: datetime,
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*,
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time: TibberPricesTimeService,
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) -> tuple[float | None, float | None]:
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"""
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Calculate leading 24-hour mean and median price for a given interval.
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Args:
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all_prices: List of all price data (yesterday, today, tomorrow combined)
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interval_start: Start time of the interval to calculate mean for
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time: TibberPricesTimeService instance (required)
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Returns:
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Tuple of (mean price, median price) for up to 24 hours following the interval,
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or (None, None) if no data in window
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"""
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# Define the 24-hour window: from interval_start up to 24 hours after
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window_start = interval_start
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window_end = interval_start + timedelta(hours=24)
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# Filter prices within the 24-hour window
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prices_in_window = []
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for price_data in all_prices:
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starts_at = time.get_interval_time(price_data)
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if starts_at is None:
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continue
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# Include intervals that start within the window
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if window_start <= starts_at < window_end:
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prices_in_window.append(float(price_data["total"]))
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# Calculate mean and median
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# CRITICAL: Return None instead of 0.0 when no data available
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# With negative prices, 0.0 could be misinterpreted as a real mean value
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if prices_in_window:
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mean = calculate_mean(prices_in_window)
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median = calculate_median(prices_in_window)
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return mean, median
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return None, None
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def calculate_current_trailing_mean(
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coordinator_data: dict,
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*,
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time: TibberPricesTimeService,
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) -> tuple[float | None, float | None]:
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"""
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Calculate the trailing 24-hour mean and median for the current time.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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time: TibberPricesTimeService instance (required)
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Returns:
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Tuple of (mean price, median price), or (None, None) if unavailable
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"""
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if not coordinator_data:
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return None, None
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# Get all intervals (yesterday, today, tomorrow) via helper
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all_prices = get_intervals_for_day_offsets(coordinator_data, [-1, 0, 1])
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if not all_prices:
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return None, None
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now = time.now()
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# calculate_trailing_24h_mean returns (mean, median) tuple
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return calculate_trailing_24h_mean(all_prices, now, time=time)
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def calculate_current_leading_mean(
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coordinator_data: dict,
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*,
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time: TibberPricesTimeService,
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) -> tuple[float | None, float | None]:
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"""
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Calculate the leading 24-hour mean and median for the current time.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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time: TibberPricesTimeService instance (required)
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Returns:
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Tuple of (mean price, median price), or (None, None) if unavailable
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"""
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if not coordinator_data:
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return None, None
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# Get all intervals (yesterday, today, tomorrow) via helper
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all_prices = get_intervals_for_day_offsets(coordinator_data, [-1, 0, 1])
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if not all_prices:
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return None, None
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now = time.now()
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# calculate_leading_24h_mean returns (mean, median) tuple
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return calculate_leading_24h_mean(all_prices, now, time=time)
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def calculate_trailing_24h_min(
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all_prices: list[dict],
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interval_start: datetime,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate trailing 24-hour minimum price for a given interval.
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Args:
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all_prices: List of all price data (yesterday, today, tomorrow combined)
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interval_start: Start time of the interval to calculate minimum for
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time: TibberPricesTimeService instance (required)
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Returns:
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Minimum price for the 24 hours preceding the interval, or None if no data in window
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"""
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# Define the 24-hour window: from 24 hours before interval_start up to interval_start
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window_start = interval_start - timedelta(hours=24)
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window_end = interval_start
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# Filter prices within the 24-hour window
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prices_in_window = []
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for price_data in all_prices:
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starts_at = time.get_interval_time(price_data)
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if starts_at is None:
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continue
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# Include intervals that start within the window (not including the current interval's end)
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if window_start <= starts_at < window_end:
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prices_in_window.append(float(price_data["total"]))
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# Calculate minimum
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# CRITICAL: Return None instead of 0.0 when no data available
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# With negative prices, 0.0 could be misinterpreted as a maximum value
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if prices_in_window:
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return min(prices_in_window)
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return None
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def calculate_trailing_24h_max(
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all_prices: list[dict],
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interval_start: datetime,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate trailing 24-hour maximum price for a given interval.
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Args:
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all_prices: List of all price data (yesterday, today, tomorrow combined)
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interval_start: Start time of the interval to calculate maximum for
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time: TibberPricesTimeService instance (required)
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Returns:
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Maximum price for the 24 hours preceding the interval, or None if no data in window
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"""
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# Define the 24-hour window: from 24 hours before interval_start up to interval_start
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window_start = interval_start - timedelta(hours=24)
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window_end = interval_start
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# Filter prices within the 24-hour window
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prices_in_window = []
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for price_data in all_prices:
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starts_at = time.get_interval_time(price_data)
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if starts_at is None:
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continue
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# Include intervals that start within the window (not including the current interval's end)
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if window_start <= starts_at < window_end:
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prices_in_window.append(float(price_data["total"]))
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# Calculate maximum
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# CRITICAL: Return None instead of 0.0 when no data available
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# With negative prices, 0.0 could be misinterpreted as a real price value
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if prices_in_window:
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return max(prices_in_window)
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return None
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def calculate_leading_24h_min(
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all_prices: list[dict],
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interval_start: datetime,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate leading 24-hour minimum price for a given interval.
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Args:
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all_prices: List of all price data (yesterday, today, tomorrow combined)
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interval_start: Start time of the interval to calculate minimum for
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time: TibberPricesTimeService instance (required)
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Returns:
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Minimum price for up to 24 hours following the interval, or None if no data in window
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"""
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# Define the 24-hour window: from interval_start up to 24 hours after
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window_start = interval_start
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window_end = interval_start + timedelta(hours=24)
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# Filter prices within the 24-hour window
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prices_in_window = []
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for price_data in all_prices:
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starts_at = time.get_interval_time(price_data)
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if starts_at is None:
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continue
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# Include intervals that start within the window
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if window_start <= starts_at < window_end:
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prices_in_window.append(float(price_data["total"]))
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# Calculate minimum
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# CRITICAL: Return None instead of 0.0 when no data available
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# With negative prices, 0.0 could be misinterpreted as a maximum value
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if prices_in_window:
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return min(prices_in_window)
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return None
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def calculate_leading_24h_max(
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all_prices: list[dict],
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interval_start: datetime,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate leading 24-hour maximum price for a given interval.
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Args:
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all_prices: List of all price data (yesterday, today, tomorrow combined)
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interval_start: Start time of the interval to calculate maximum for
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time: TibberPricesTimeService instance (required)
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Returns:
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Maximum price for up to 24 hours following the interval, or None if no data in window
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"""
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# Define the 24-hour window: from interval_start up to 24 hours after
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window_start = interval_start
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window_end = interval_start + timedelta(hours=24)
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# Filter prices within the 24-hour window
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prices_in_window = []
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for price_data in all_prices:
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starts_at = time.get_interval_time(price_data)
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if starts_at is None:
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continue
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# Include intervals that start within the window
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if window_start <= starts_at < window_end:
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prices_in_window.append(float(price_data["total"]))
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# Calculate maximum
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# CRITICAL: Return None instead of 0.0 when no data available
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# With negative prices, 0.0 could be misinterpreted as a real price value
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if prices_in_window:
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return max(prices_in_window)
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return None
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def calculate_current_trailing_min(
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coordinator_data: dict,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate the trailing 24-hour minimum for the current time.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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time: TibberPricesTimeService instance (required)
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Returns:
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Current trailing 24-hour minimum price, or None if unavailable
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"""
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if not coordinator_data:
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return None
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# Get all intervals (yesterday, today, tomorrow) via helper
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all_prices = get_intervals_for_day_offsets(coordinator_data, [-1, 0, 1])
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if not all_prices:
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return None
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now = time.now()
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return calculate_trailing_24h_min(all_prices, now, time=time)
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def calculate_current_trailing_max(
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coordinator_data: dict,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate the trailing 24-hour maximum for the current time.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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time: TibberPricesTimeService instance (required)
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Returns:
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Current trailing 24-hour maximum price, or None if unavailable
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"""
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if not coordinator_data:
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return None
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# Get all intervals (yesterday, today, tomorrow) via helper
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all_prices = get_intervals_for_day_offsets(coordinator_data, [-1, 0, 1])
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if not all_prices:
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return None
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now = time.now()
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return calculate_trailing_24h_max(all_prices, now, time=time)
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def calculate_current_leading_min(
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coordinator_data: dict,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate the leading 24-hour minimum for the current time.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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time: TibberPricesTimeService instance (required)
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Returns:
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Current leading 24-hour minimum price, or None if unavailable
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"""
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if not coordinator_data:
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return None
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# Get all intervals (yesterday, today, tomorrow) via helper
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all_prices = get_intervals_for_day_offsets(coordinator_data, [-1, 0, 1])
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if not all_prices:
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return None
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now = time.now()
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return calculate_leading_24h_min(all_prices, now, time=time)
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def calculate_current_leading_max(
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coordinator_data: dict,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate the leading 24-hour maximum for the current time.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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time: TibberPricesTimeService instance (required)
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Returns:
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Current leading 24-hour maximum price, or None if unavailable
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"""
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if not coordinator_data:
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return None
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# Get all intervals (yesterday, today, tomorrow) via helper
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all_prices = get_intervals_for_day_offsets(coordinator_data, [-1, 0, 1])
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if not all_prices:
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return None
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now = time.now()
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return calculate_leading_24h_max(all_prices, now, time=time)
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def calculate_next_n_hours_mean(
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coordinator_data: dict,
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hours: int,
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*,
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time: TibberPricesTimeService,
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) -> tuple[float | None, float | None]:
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"""
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Calculate mean and median price for the next N hours starting from the next interval.
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This function computes the mean and median of all 15-minute intervals starting from
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the next interval (not current) up to N hours into the future.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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hours: Number of hours to look ahead (1, 2, 3, 4, 5, 6, 8, 12, etc.)
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time: TibberPricesTimeService instance (required)
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Returns:
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Tuple of (mean price, median price) for the next N hours,
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or (None, None) if insufficient data
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"""
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if not coordinator_data or hours <= 0:
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return None, None
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# Get all intervals (yesterday, today, tomorrow) via helper
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all_prices = get_intervals_for_day_offsets(coordinator_data, [-1, 0, 1])
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if not all_prices:
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return None, None
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# Find the current interval index
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current_idx = None
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for idx, price_data in enumerate(all_prices):
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starts_at = time.get_interval_time(price_data)
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if starts_at is None:
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continue
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interval_end = starts_at + time.get_interval_duration()
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if time.is_current_interval(starts_at, interval_end):
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current_idx = idx
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break
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if current_idx is None:
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return None, None
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# Calculate how many intervals are in N hours
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intervals_needed = time.minutes_to_intervals(hours * 60)
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# Collect prices starting from NEXT interval (current_idx + 1)
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prices_in_window = []
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for offset in range(1, intervals_needed + 1):
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idx = current_idx + offset
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if idx >= len(all_prices):
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# Not enough future data available
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break
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price = all_prices[idx].get("total")
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if price is not None:
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prices_in_window.append(float(price))
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# Return None if no data at all
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if not prices_in_window:
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return None, None
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# Return mean and median (prefer full period, but allow graceful degradation)
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mean = calculate_mean(prices_in_window)
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median = calculate_median(prices_in_window)
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return mean, median
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