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"""ESIOS API handler for HomeAssistant. Hourly price attributes."""
import zoneinfo
from contextlib import suppress
from datetime import datetime
from typing import Any
from .const import EsiosApiData, KEY_ADJUSTMENT, KEY_INDEXED, KEY_INJECTION, KEY_PVPC
def _is_tomorrow_price(ts: datetime, ref: datetime) -> bool:
return any(
ts_comp > ts_tz_ref
for ts_comp, ts_tz_ref in zip(ts.isocalendar(), ref.isocalendar())
)
def _split_today_tomorrow_prices(
current_prices: dict[datetime, float],
utc_time: datetime,
timezone: zoneinfo.ZoneInfo,
) -> tuple[dict[datetime, float], dict[datetime, float]]:
local_time = utc_time.astimezone(timezone)
today, tomorrow = {}, {}
for ts_utc, price_h in current_prices.items():
ts_local = ts_utc.astimezone(timezone)
if _is_tomorrow_price(ts_local, local_time):
tomorrow[ts_utc] = price_h
else:
today[ts_utc] = price_h
return today, tomorrow
def _make_price_tag_attributes(
prices: dict[datetime, float], timezone: zoneinfo.ZoneInfo, tomorrow: bool
) -> dict[str, Any]:
prefix = "price_next_day_" if tomorrow else "price_"
attributes = {}
for ts_utc, price_h in prices.items():
ts_local = ts_utc.astimezone(timezone)
attr_key = f"{prefix}{ts_local.hour:02d}h"
if attr_key in attributes: # DST change with duplicated hour :)
attr_key += "_d"
attributes[attr_key] = price_h
return attributes
def _make_price_stats_attributes(
sensor_key: str,
current_price: float,
current_prices: dict[datetime, float],
utc_time: datetime,
timezone: zoneinfo.ZoneInfo,
) -> dict[str, Any]:
attributes: dict[str, Any] = {}
sign_is_best = 1 if sensor_key != KEY_INJECTION else -1
prices_sorted = dict(
sorted(current_prices.items(), key=lambda x: sign_is_best * x[1])
)
better_prices_ahead = [
(ts, price)
for ts, price in current_prices.items()
if ts > utc_time and price * sign_is_best < current_price * sign_is_best
]
if better_prices_ahead:
next_better_ts, next_better_price = better_prices_ahead[0]
delta_better = next_better_ts - utc_time
attributes["next_better_price"] = next_better_price
attributes["hours_to_better_price"] = int(delta_better.total_seconds()) // 3600
attributes["num_better_prices_ahead"] = len(better_prices_ahead)
with suppress(ValueError):
attributes["price_position"] = (
list(prices_sorted.values()).index(current_price) + 1
)
max_price = max(current_prices.values())
min_price = min(current_prices.values())
with suppress(ZeroDivisionError):
attributes["price_ratio"] = round(
(current_price - min_price) / (max_price - min_price), 2
)
attributes["max_price"] = max_price
first_price_at = next(iter(prices_sorted)).astimezone(timezone).hour
last_price_at = next(iter(reversed(prices_sorted))).astimezone(timezone).hour
attributes["max_price_at"] = last_price_at if sign_is_best == 1 else first_price_at
attributes["min_price"] = min_price
attributes["min_price_at"] = first_price_at if sign_is_best == 1 else last_price_at
attributes["next_best_at"] = [
ts.astimezone(timezone).hour for ts in prices_sorted if ts >= utc_time
]
return attributes
def make_price_sensor_attributes(
sensor_key: str,
current_prices: dict[datetime, float],
utc_time: datetime,
timezone: zoneinfo.ZoneInfo,
) -> dict[str, Any]:
"""Generate sensor attributes for hourly prices variables."""
current_price = current_prices[utc_time]
today, tomorrow = _split_today_tomorrow_prices(current_prices, utc_time, timezone)
price_attrs = _make_price_stats_attributes(
sensor_key, current_price, today, utc_time, timezone
)
price_tags = _make_price_tag_attributes(today, timezone, False)
if tomorrow:
tomorrow_prices = {
f"{key} (next day)": value
for key, value in _make_price_stats_attributes(
sensor_key, current_price, tomorrow, utc_time, timezone
).items()
}
tomorrow_price_tags = _make_price_tag_attributes(tomorrow, timezone, True)
price_attrs = {**price_attrs, **tomorrow_prices}
price_tags = {**price_tags, **tomorrow_price_tags}
return {**price_attrs, **price_tags}
def add_composed_price_sensors(data: EsiosApiData):
"""Calculate price sensors derived from multiple data series."""
if (
data.availability.get(KEY_PVPC, False)
and data.availability.get(KEY_ADJUSTMENT, False)
and (
common_ts_prices := set(data.sensors[KEY_PVPC]).intersection(
set(data.sensors[KEY_ADJUSTMENT])
)
)
):
# generate 'indexed tariff' as: PRICE = PVPC - ADJUSTMENT
pvpc = data.sensors[KEY_PVPC]
adjustment = data.sensors[KEY_ADJUSTMENT]
data.sensors[KEY_INDEXED] = {
ts_hour: round(pvpc[ts_hour] - adjustment[ts_hour], 5)
for ts_hour in sorted(common_ts_prices)
}
data.availability[KEY_INDEXED] = True
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