jpawlowski/hass.tibber_prices
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hass.tibber_prices/custom_components/tibber_prices/utils/average.py
Julian Pawlowski c2b9908e69 refactor(naming): complete class naming convention alignment 
Renamed 25 public classes + 1 Enum to include TibberPrices prefix
following Home Assistant integration naming standards.

All classes now follow pattern: TibberPrices{SemanticPurpose}
No package hierarchy in names (import path is namespace).

Key changes:
- Coordinator module: DataFetcher, DataTransformer, ListenerManager,
  PeriodCalculator, TimeService (203 usages), CacheData
- Config flow: CannotConnectError, InvalidAuthError
- Entity utils: IconContext
- Sensor calculators: BaseCalculator + 8 subclasses
- Period handlers: 5 NamedTuples (PeriodConfig, PeriodData,
  PeriodStatistics, ThresholdConfig, IntervalCriteria)
- Period handlers: SpikeCandidateContext (dataclass → NamedTuple)
- API: QueryType Enum

Documentation updates:
- AGENTS.md: Added Pyright code generation guidelines
- planning/class-naming-refactoring-plan.md: Complete execution log

Quality metrics:
- 0 Pyright errors (strict type checking)
- 0 Ruff errors (linting + formatting)
- All hassfest checks passed
- 79 files validated

Impact: Aligns with HA Core standards (TibberDataCoordinator pattern).
No user-facing changes - internal refactor only.
2025-11-20 11:22:53 +00:00

489 lines
15 KiB
Python
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"""Utility functions for calculating price averages."""
from __future__ import annotations
from datetime import datetime, timedelta
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
def calculate_trailing_24h_avg(all_prices: list[dict], interval_start: datetime) -> float:
"""
Calculate trailing 24-hour average price for a given interval.
Args:
all_prices: List of all price data (yesterday, today, tomorrow combined)
interval_start: Start time of the interval to calculate average for
time: TibberPricesTimeService instance (required)
Returns:
Average price for the 24 hours preceding the interval (not including the interval itself)
"""
# Define the 24-hour window: from 24 hours before interval_start up to interval_start
window_start = interval_start - timedelta(hours=24)
window_end = interval_start
# Filter prices within the 24-hour window
prices_in_window = []
for price_data in all_prices:
starts_at = price_data["startsAt"] # Already datetime object in local timezone
if starts_at is None:
continue
# Include intervals that start within the window (not including the current interval's end)
if window_start <= starts_at < window_end:
prices_in_window.append(float(price_data["total"]))
# Calculate average
if prices_in_window:
return sum(prices_in_window) / len(prices_in_window)
return 0.0
def calculate_leading_24h_avg(all_prices: list[dict], interval_start: datetime) -> float:
"""
Calculate leading 24-hour average price for a given interval.
Args:
all_prices: List of all price data (yesterday, today, tomorrow combined)
interval_start: Start time of the interval to calculate average for
time: TibberPricesTimeService instance (required)
Returns:
Average price for up to 24 hours following the interval (including the interval itself)
"""
# Define the 24-hour window: from interval_start up to 24 hours after
window_start = interval_start
window_end = interval_start + timedelta(hours=24)
# Filter prices within the 24-hour window
prices_in_window = []
for price_data in all_prices:
starts_at = price_data["startsAt"] # Already datetime object in local timezone
if starts_at is None:
continue
# Include intervals that start within the window
if window_start <= starts_at < window_end:
prices_in_window.append(float(price_data["total"]))
# Calculate average
if prices_in_window:
return sum(prices_in_window) / len(prices_in_window)
return 0.0
def calculate_current_trailing_avg(
coordinator_data: dict,
*,
time: TibberPricesTimeService,
) -> float | None:
"""
Calculate the trailing 24-hour average for the current time.
Args:
coordinator_data: The coordinator data containing priceInfo
time: TibberPricesTimeService instance (required)
Returns:
Current trailing 24-hour average price, or None if unavailable
"""
if not coordinator_data:
return None
price_info = coordinator_data.get("priceInfo", {})
yesterday_prices = price_info.get("yesterday", [])
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = yesterday_prices + today_prices + tomorrow_prices
if not all_prices:
return None
now = time.now()
return calculate_trailing_24h_avg(all_prices, now)
def calculate_current_leading_avg(
coordinator_data: dict,
*,
time: TibberPricesTimeService,
) -> float | None:
"""
Calculate the leading 24-hour average for the current time.
Args:
coordinator_data: The coordinator data containing priceInfo
time: TibberPricesTimeService instance (required)
Returns:
Current leading 24-hour average price, or None if unavailable
"""
if not coordinator_data:
return None
price_info = coordinator_data.get("priceInfo", {})
yesterday_prices = price_info.get("yesterday", [])
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = yesterday_prices + today_prices + tomorrow_prices
if not all_prices:
return None
now = time.now()
return calculate_leading_24h_avg(all_prices, now)
def calculate_trailing_24h_min(
all_prices: list[dict],
interval_start: datetime,
*,
time: TibberPricesTimeService,
) -> float:
"""
Calculate trailing 24-hour minimum price for a given interval.
Args:
all_prices: List of all price data (yesterday, today, tomorrow combined)
interval_start: Start time of the interval to calculate minimum for
time: TibberPricesTimeService instance (required)
Returns:
Minimum price for the 24 hours preceding the interval (not including the interval itself)
"""
# Define the 24-hour window: from 24 hours before interval_start up to interval_start
window_start = interval_start - timedelta(hours=24)
window_end = interval_start
# Filter prices within the 24-hour window
prices_in_window = []
for price_data in all_prices:
starts_at = time.get_interval_time(price_data)
if starts_at is None:
continue
# Include intervals that start within the window (not including the current interval's end)
if window_start <= starts_at < window_end:
prices_in_window.append(float(price_data["total"]))
# Calculate minimum
if prices_in_window:
return min(prices_in_window)
return 0.0
def calculate_trailing_24h_max(
all_prices: list[dict],
interval_start: datetime,
*,
time: TibberPricesTimeService,
) -> float:
"""
Calculate trailing 24-hour maximum price for a given interval.
Args:
all_prices: List of all price data (yesterday, today, tomorrow combined)
interval_start: Start time of the interval to calculate maximum for
time: TibberPricesTimeService instance (required)
Returns:
Maximum price for the 24 hours preceding the interval (not including the interval itself)
"""
# Define the 24-hour window: from 24 hours before interval_start up to interval_start
window_start = interval_start - timedelta(hours=24)
window_end = interval_start
# Filter prices within the 24-hour window
prices_in_window = []
for price_data in all_prices:
starts_at = time.get_interval_time(price_data)
if starts_at is None:
continue
# Include intervals that start within the window (not including the current interval's end)
if window_start <= starts_at < window_end:
prices_in_window.append(float(price_data["total"]))
# Calculate maximum
if prices_in_window:
return max(prices_in_window)
return 0.0
def calculate_leading_24h_min(
all_prices: list[dict],
interval_start: datetime,
*,
time: TibberPricesTimeService,
) -> float:
"""
Calculate leading 24-hour minimum price for a given interval.
Args:
all_prices: List of all price data (yesterday, today, tomorrow combined)
interval_start: Start time of the interval to calculate minimum for
time: TibberPricesTimeService instance (required)
Returns:
Minimum price for up to 24 hours following the interval (including the interval itself)
"""
# Define the 24-hour window: from interval_start up to 24 hours after
window_start = interval_start
window_end = interval_start + timedelta(hours=24)
# Filter prices within the 24-hour window
prices_in_window = []
for price_data in all_prices:
starts_at = time.get_interval_time(price_data)
if starts_at is None:
continue
# Include intervals that start within the window
if window_start <= starts_at < window_end:
prices_in_window.append(float(price_data["total"]))
# Calculate minimum
if prices_in_window:
return min(prices_in_window)
return 0.0
def calculate_leading_24h_max(
all_prices: list[dict],
interval_start: datetime,
*,
time: TibberPricesTimeService,
) -> float:
"""
Calculate leading 24-hour maximum price for a given interval.
Args:
all_prices: List of all price data (yesterday, today, tomorrow combined)
interval_start: Start time of the interval to calculate maximum for
time: TibberPricesTimeService instance (required)
Returns:
Maximum price for up to 24 hours following the interval (including the interval itself)
"""
# Define the 24-hour window: from interval_start up to 24 hours after
window_start = interval_start
window_end = interval_start + timedelta(hours=24)
# Filter prices within the 24-hour window
prices_in_window = []
for price_data in all_prices:
starts_at = time.get_interval_time(price_data)
if starts_at is None:
continue
# Include intervals that start within the window
if window_start <= starts_at < window_end:
prices_in_window.append(float(price_data["total"]))
# Calculate maximum
if prices_in_window:
return max(prices_in_window)
return 0.0
def calculate_current_trailing_min(
coordinator_data: dict,
*,
time: TibberPricesTimeService,
) -> float | None:
"""
Calculate the trailing 24-hour minimum for the current time.
Args:
coordinator_data: The coordinator data containing priceInfo
time: TibberPricesTimeService instance (required)
Returns:
Current trailing 24-hour minimum price, or None if unavailable
"""
if not coordinator_data:
return None
price_info = coordinator_data.get("priceInfo", {})
yesterday_prices = price_info.get("yesterday", [])
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = yesterday_prices + today_prices + tomorrow_prices
if not all_prices:
return None
now = time.now()
return calculate_trailing_24h_min(all_prices, now, time=time)
def calculate_current_trailing_max(
coordinator_data: dict,
*,
time: TibberPricesTimeService,
) -> float | None:
"""
Calculate the trailing 24-hour maximum for the current time.
Args:
coordinator_data: The coordinator data containing priceInfo
time: TibberPricesTimeService instance (required)
Returns:
Current trailing 24-hour maximum price, or None if unavailable
"""
if not coordinator_data:
return None
price_info = coordinator_data.get("priceInfo", {})
yesterday_prices = price_info.get("yesterday", [])
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = yesterday_prices + today_prices + tomorrow_prices
if not all_prices:
return None
now = time.now()
return calculate_trailing_24h_max(all_prices, now, time=time)
def calculate_current_leading_min(
coordinator_data: dict,
*,
time: TibberPricesTimeService,
) -> float | None:
"""
Calculate the leading 24-hour minimum for the current time.
Args:
coordinator_data: The coordinator data containing priceInfo
time: TibberPricesTimeService instance (required)
Returns:
Current leading 24-hour minimum price, or None if unavailable
"""
if not coordinator_data:
return None
price_info = coordinator_data.get("priceInfo", {})
yesterday_prices = price_info.get("yesterday", [])
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = yesterday_prices + today_prices + tomorrow_prices
if not all_prices:
return None
now = time.now()
return calculate_leading_24h_min(all_prices, now, time=time)
def calculate_current_leading_max(
coordinator_data: dict,
*,
time: TibberPricesTimeService,
) -> float | None:
"""
Calculate the leading 24-hour maximum for the current time.
Args:
coordinator_data: The coordinator data containing priceInfo
time: TibberPricesTimeService instance (required)
Returns:
Current leading 24-hour maximum price, or None if unavailable
"""
if not coordinator_data:
return None
price_info = coordinator_data.get("priceInfo", {})
yesterday_prices = price_info.get("yesterday", [])
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = yesterday_prices + today_prices + tomorrow_prices
if not all_prices:
return None
now = time.now()
return calculate_leading_24h_max(all_prices, now, time=time)
def calculate_next_n_hours_avg(
coordinator_data: dict,
hours: int,
*,
time: TibberPricesTimeService,
) -> float | None:
"""
Calculate average price for the next N hours starting from the next interval.
This function computes the average of all 15-minute intervals starting from
the next interval (not current) up to N hours into the future.
Args:
coordinator_data: The coordinator data containing priceInfo
hours: Number of hours to look ahead (1, 2, 3, 4, 5, 6, 8, 12, etc.)
time: TibberPricesTimeService instance (required)
Returns:
Average price for the next N hours, or None if insufficient data
"""
if not coordinator_data or hours <= 0:
return None
price_info = coordinator_data.get("priceInfo", {})
yesterday_prices = price_info.get("yesterday", [])
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = yesterday_prices + today_prices + tomorrow_prices
if not all_prices:
return None
# Find the current interval index
current_idx = None
for idx, price_data in enumerate(all_prices):
starts_at = time.get_interval_time(price_data)
if starts_at is None:
continue
interval_end = starts_at + time.get_interval_duration()
if time.is_current_interval(starts_at, interval_end):
current_idx = idx
break
if current_idx is None:
return None
# Calculate how many intervals are in N hours
intervals_needed = time.minutes_to_intervals(hours * 60)
# Collect prices starting from NEXT interval (current_idx + 1)
prices_in_window = []
for offset in range(1, intervals_needed + 1):
idx = current_idx + offset
if idx >= len(all_prices):
# Not enough future data available
break
price = all_prices[idx].get("total")
if price is not None:
prices_in_window.append(float(price))
# Return None if no data at all
if not prices_in_window:
return None
# Return average (prefer full period, but allow graceful degradation)
return sum(prices_in_window) / len(prices_in_window)
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