refactoring

custom_components/tibber_prices/binary_sensor.py

@@ -170,10 +170,10 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
         now = dt_util.now()
 
         # Detect interval granularity
-        interval_minutes = detect_interval_granularity(today_prices)
+        interval_length = detect_interval_granularity(today_prices)
 
         # Find price data for current interval
-        current_interval_data = find_price_data_for_interval({"today": today_prices}, now, interval_minutes)
+        current_interval_data = find_price_data_for_interval({"today": today_prices}, now, interval_length)
 
         if not current_interval_data:
             return None
@@ -182,15 +182,80 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
         prices.sort()
         return prices, float(current_interval_data["total"])
 
+    def _annotate_single_interval(
+        self,
+        interval: dict,
+        annotation_ctx: dict,
+    ) -> dict:
+        """Annotate a single interval with all required attributes."""
+        interval_copy = interval.copy()
+        interval_remaining = annotation_ctx["interval_count"] - annotation_ctx["interval_idx"]
+        # Extract all interval-related fields first
+        interval_start = interval_copy.pop("interval_start", None)
+        interval_end = interval_copy.pop("interval_end", None)
+        interval_hour = interval_copy.pop("interval_hour", None)
+        interval_minute = interval_copy.pop("interval_minute", None)
+        interval_time = interval_copy.pop("interval_time", None)
+        interval_length_minute = interval_copy.pop("interval_length_minute", annotation_ctx["interval_length"])
+        # Extract price
+        price = interval_copy.pop("price", None)
+        new_interval = {
+            "period_start": annotation_ctx["period_start"],
+            "period_end": annotation_ctx["period_end"],
+            "hour": annotation_ctx["period_start_hour"],
+            "minute": annotation_ctx["period_start_minute"],
+            "time": annotation_ctx["period_start_time"],
+            "period_length_minute": annotation_ctx["period_length"],
+            "period_remaining_minute_after_interval": interval_remaining * annotation_ctx["interval_length"],
+            "periods_total": annotation_ctx["period_count"],
+            "periods_remaining": annotation_ctx["periods_remaining"],
+            "period_position": annotation_ctx["period_idx"],
+            "interval_total": annotation_ctx["interval_count"],
+            "interval_remaining": interval_remaining,
+            "interval_position": annotation_ctx["interval_idx"],
+            "interval_start": interval_start,
+            "interval_end": interval_end,
+            "interval_hour": interval_hour,
+            "interval_minute": interval_minute,
+            "interval_time": interval_time,
+            "interval_length_minute": interval_length_minute,
+            "price": price,
+        }
+        # Add any remaining fields (should be only extra/unknowns)
+        new_interval.update(interval_copy)
+        new_interval["price_ct"] = round(new_interval["price"] * 100, 2)
+        price_diff = new_interval["price"] - annotation_ctx["ref_price"]
+        new_interval[annotation_ctx["diff_key"]] = round(price_diff, 4)
+        new_interval[annotation_ctx["diff_ct_key"]] = round(price_diff * 100, 2)
+        price_diff_percent = (
+            ((new_interval["price"] - annotation_ctx["ref_price"]) / annotation_ctx["ref_price"]) * 100
+            if annotation_ctx["ref_price"] != 0
+            else 0.0
+        )
+        new_interval[annotation_ctx["diff_pct_key"]] = round(price_diff_percent, 2)
+        avg_diff = new_interval["price"] - annotation_ctx["avg_price"]
+        new_interval["price_diff_from_avg"] = round(avg_diff, 4)
+        new_interval["price_diff_from_avg_ct"] = round(avg_diff * 100, 2)
+        avg_diff_percent = (
+            ((new_interval["price"] - annotation_ctx["avg_price"]) / annotation_ctx["avg_price"]) * 100
+            if annotation_ctx["avg_price"] != 0
+            else 0.0
+        )
+        new_interval["price_diff_from_avg_" + PERCENTAGE] = round(avg_diff_percent, 2)
+        return new_interval
+
     def _annotate_period_intervals(
         self,
         periods: list[list[dict]],
-        ref_price: float,
+        ref_prices: dict,
-        avg_price: float,
+        avg_price_by_day: dict,
-        interval_minutes: int,
+        interval_length: int,
     ) -> list[dict]:
-        """Return flattened and annotated intervals with period info and requested properties."""
+        """
-        # Determine reference type for naming
+        Return flattened and annotated intervals with period info and requested properties.
+
+        Uses the correct reference price for each interval's date.
+        """
         reference_type = None
         if self.entity_description.key == "best_price_period":
             reference_type = "min"
@@ -198,7 +263,6 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
             reference_type = "max"
         else:
             reference_type = "ref"
-        # Set attribute name suffixes
         if reference_type == "min":
             diff_key = "price_diff_from_min"
             diff_ct_key = "price_diff_from_min_ct"
@@ -213,123 +277,121 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
             diff_pct_key = "price_diff_" + PERCENTAGE
         result = []
         period_count = len(periods)
-        for idx, period in enumerate(periods, 1):
+        for period_idx, period in enumerate(periods, 1):
             period_start = period[0]["interval_start"] if period else None
             period_start_hour = period_start.hour if period_start else None
             period_start_minute = period_start.minute if period_start else None
             period_start_time = f"{period_start_hour:02d}:{period_start_minute:02d}" if period_start else None
             period_end = period[-1]["interval_end"] if period else None
             interval_count = len(period)
-            period_length = interval_count * interval_minutes
+            period_length = interval_count * interval_length
-            periods_remaining = len(periods) - idx
+            periods_remaining = len(periods) - period_idx
             for interval_idx, interval in enumerate(period, 1):
-                interval_copy = interval.copy()
+                interval_start = interval.get("interval_start")
-                interval_remaining = interval_count - interval_idx
+                interval_date = interval_start.date() if interval_start else None
-                # Compose new dict with period-related keys first, then interval timing, then price info
+                avg_price = avg_price_by_day.get(interval_date, 0)
-                new_interval = {
+                ref_price = ref_prices.get(interval_date, 0)
+                annotation_ctx = {
                     "period_start": period_start,
                     "period_end": period_end,
-                    "hour": period_start_hour,
+                    "period_start_hour": period_start_hour,
-                    "minute": period_start_minute,
+                    "period_start_minute": period_start_minute,
-                    "time": period_start_time,
+                    "period_start_time": period_start_time,
-                    "period_length_minute": period_length,
+                    "period_length": period_length,
-                    "period_remaining_minute_after_interval": interval_remaining * interval_minutes,
+                    "interval_count": interval_count,
-                    "periods_total": period_count,
+                    "interval_idx": interval_idx,
+                    "interval_length": interval_length,
+                    "period_count": period_count,
                     "periods_remaining": periods_remaining,
-                    "interval_total": interval_count,
+                    "period_idx": period_idx,
-                    "interval_remaining": interval_remaining,
+                    "ref_price": ref_price,
-                    "interval_position": interval_idx,
+                    "avg_price": avg_price,
+                    "diff_key": diff_key,
+                    "diff_ct_key": diff_ct_key,
+                    "diff_pct_key": diff_pct_key,
                 }
-                # Add interval timing
+                new_interval = self._annotate_single_interval(
-                new_interval["interval_start"] = interval_copy.pop("interval_start", None)
+                    interval,
-                new_interval["interval_end"] = interval_copy.pop("interval_end", None)
+                    annotation_ctx,
-                # Add hour, minute, time, price if present in interval_copy
+                )
-                for k in ("interval_hour", "interval_minute", "interval_time", "price"):
-                    if k in interval_copy:
-                        new_interval[k] = interval_copy.pop(k)
-                # Add the rest of the interval info (e.g. price_ct, price_difference_*, etc.)
-                new_interval.update(interval_copy)
-                new_interval["price_ct"] = round(new_interval["price"] * 100, 2)
-                price_diff = new_interval["price"] - ref_price
-                new_interval[diff_key] = round(price_diff, 4)
-                new_interval[diff_ct_key] = round(price_diff * 100, 2)
-                price_diff_percent = ((new_interval["price"] - ref_price) / ref_price) * 100 if ref_price != 0 else 0.0
-                new_interval[diff_pct_key] = round(price_diff_percent, 2)
-                # Add difference to average price of the day (avg_price is now passed in)
-                avg_diff = new_interval["price"] - avg_price
-                new_interval["price_diff_from_avg"] = round(avg_diff, 4)
-                new_interval["price_diff_from_avg_ct"] = round(avg_diff * 100, 2)
-                avg_diff_percent = ((new_interval["price"] - avg_price) / avg_price) * 100 if avg_price != 0 else 0.0
-                new_interval["price_diff_from_avg_" + PERCENTAGE] = round(avg_diff_percent, 2)
                 result.append(new_interval)
         return result
 
-    def _get_price_intervals_attributes(self, *, reverse_sort: bool) -> dict | None:
+    def _split_intervals_by_day(self, all_prices: list[dict]) -> tuple[dict, dict, dict]:
-        """
+        """Split intervals by day, calculate interval minutes and average price per day."""
-        Get price interval attributes with support for 15-minute intervals and period grouping.
+        intervals_by_day: dict = {}
+        interval_length_by_day: dict = {}
+        avg_price_by_day: dict = {}
+        for price_data in all_prices:
+            dt = dt_util.parse_datetime(price_data["startsAt"])
+            if dt is None:
+                continue
+            date = dt.date()
+            intervals_by_day.setdefault(date, []).append(price_data)
+        for date, intervals in intervals_by_day.items():
+            interval_length_by_day[date] = detect_interval_granularity(intervals)
+            avg_price_by_day[date] = sum(float(p["total"]) for p in intervals) / len(intervals)
+        return intervals_by_day, interval_length_by_day, avg_price_by_day
 
-        Args:
+    def _calculate_reference_prices(self, intervals_by_day: dict, *, reverse_sort: bool) -> dict:
-            reverse_sort: Whether to sort prices in reverse (high to low)
+        """Calculate reference prices for each day."""
-
+        ref_prices: dict = {}
-        Returns:
+        for date, intervals in intervals_by_day.items():
-            Dictionary with interval data or None if not available
+            prices = [float(p["total"]) for p in intervals]
-
+            if reverse_sort is False:
-        """
+                ref_prices[date] = min(prices)
-        if not self.coordinator.data:
-            return None
-
-        price_info = self.coordinator.data["data"]["viewer"]["homes"][0]["currentSubscription"]["priceInfo"]
-        today_prices = price_info.get("today", [])
-
-        if not today_prices:
-            return None
-
-        interval_minutes = detect_interval_granularity(today_prices)
-
-        # Use entity type to determine flex and logic, but always use 'price_intervals' as attribute name
-        if reverse_sort is False:  # best_price_period entity
-            flex = self._get_flex_option(CONF_BEST_PRICE_FLEX, DEFAULT_BEST_PRICE_FLEX)
-            prices = [float(p["total"]) for p in today_prices]
-            min_price = min(prices)
-
-            def in_range(price: float) -> bool:
-                return price <= min_price * (1 + flex)
-
-            ref_price = min_price
-        elif reverse_sort is True:  # peak_price_period entity
-            flex = self._get_flex_option(CONF_PEAK_PRICE_FLEX, DEFAULT_PEAK_PRICE_FLEX)
-            prices = [float(p["total"]) for p in today_prices]
-            max_price = max(prices)
-
-            def in_range(price: float) -> bool:
-                return price >= max_price * (1 - flex)
-
-            ref_price = max_price
             else:
-            return None
+                ref_prices[date] = max(prices)
+        return ref_prices
 
-        # Calculate average price for the day (all intervals, not just periods)
+    def _build_periods(
-        all_prices = [float(p["total"]) for p in today_prices]
+        self,
-        avg_price = sum(all_prices) / len(all_prices) if all_prices else 0.0
+        all_prices: list[dict],
+        ref_prices: dict,
+        interval_length_by_day: dict,
+        flex: float,
+        *,
+        reverse_sort: bool,
+    ) -> list[list[dict]]:
+        """
+        Build periods, allowing periods to cross midnight (day boundary).
 
-        # Build intervals with period grouping
+        Strictly enforce flex threshold by percent diff, matching attribute calculation.
-        periods = []
+        """
-        current_period = []
+        periods: list[list[dict]] = []
-        for price_data in today_prices:
+        current_period: list[dict] = []
+        last_ref_date = None
+        for price_data in all_prices:
             starts_at = dt_util.parse_datetime(price_data["startsAt"])
             if starts_at is None:
                 continue
             starts_at = dt_util.as_local(starts_at)
+            date = starts_at.date()
+            ref_price = ref_prices[date]
+            interval_length = interval_length_by_day[date]
             price = float(price_data["total"])
-            if in_range(price):
+            percent_diff = ((price - ref_price) / ref_price) * 100 if ref_price != 0 else 0.0
+            percent_diff = round(percent_diff, 2)
+            # For best price: percent_diff <= flex*100; for peak: percent_diff >= -flex*100
+            in_flex = percent_diff <= flex * 100 if not reverse_sort else percent_diff >= -flex * 100
+            # Split period if day or interval length changes
+            if (
+                last_ref_date is not None
+                and (date != last_ref_date or interval_length != interval_length_by_day[last_ref_date])
+                and current_period
+            ):
+                periods.append(current_period)
+                current_period = []
+            last_ref_date = date
+            if in_flex:
                 current_period.append(
                     {
                         "interval_hour": starts_at.hour,
                         "interval_minute": starts_at.minute,
                         "interval_time": f"{starts_at.hour:02d}:{starts_at.minute:02d}",
+                        "interval_length_minute": interval_length,
                         "price": price,
                         "interval_start": starts_at,
-                        # interval_end will be filled later
                     }
                 )
             elif current_period:
@@ -337,37 +399,95 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
                 current_period = []
         if current_period:
             periods.append(current_period)
+        return periods
 
-        # Add interval_end to each interval (next interval's start or None)
+    def _add_interval_ends(self, periods: list[list[dict]]) -> None:
+        """Add interval_end to each interval using per-interval interval_length."""
         for period in periods:
             for idx, interval in enumerate(period):
                 if idx + 1 < len(period):
                     interval["interval_end"] = period[idx + 1]["interval_start"]
                 else:
-                    # Try to estimate end as start + interval_minutes
+                    interval["interval_end"] = interval["interval_start"] + timedelta(
-                    interval["interval_end"] = interval["interval_start"] + timedelta(minutes=interval_minutes)
+                        minutes=interval["interval_length_minute"]
+                    )
 
-        result = self._annotate_period_intervals(periods, ref_price, avg_price, interval_minutes)
+    def _filter_intervals_today_tomorrow(self, result: list[dict]) -> list[dict]:
+        """Filter intervals to only include those from today and tomorrow."""
+        today = dt_util.now().date()
+        tomorrow = today + timedelta(days=1)
+        return [
+            interval
+            for interval in result
+            if interval.get("interval_start") and today <= interval["interval_start"].date() <= tomorrow
+        ]
 
-        # Find the current or next interval (by time) from the annotated result
+    def _find_current_or_next_interval(self, filtered_result: list[dict]) -> dict | None:
+        """Find the current or next interval from the filtered list."""
         now = dt_util.now()
-        current_interval = None
+        for interval in filtered_result:
-        for interval in result:
             start = interval.get("interval_start")
             end = interval.get("interval_end")
             if start and end and start <= now < end:
-                current_interval = interval.copy()
+                return interval.copy()
-                break
+        for interval in filtered_result:
-        else:
-            # If no current interval, show the next period's first interval (if available)
-            for interval in result:
             start = interval.get("interval_start")
             if start and start > now:
-                    current_interval = interval.copy()
+                return interval.copy()
-                    break
+        return None
 
+    def _filter_periods_today_tomorrow(self, periods: list[list[dict]]) -> list[list[dict]]:
+        """Filter periods to only those with at least one interval in today or tomorrow."""
+        today = dt_util.now().date()
+        tomorrow = today + timedelta(days=1)
+        return [
+            period
+            for period in periods
+            if any(
+                interval.get("interval_start") and today <= interval["interval_start"].date() <= tomorrow
+                for interval in period
+            )
+        ]
+
+    def _get_price_intervals_attributes(self, *, reverse_sort: bool) -> dict | None:
+        """Get price interval attributes with support for 15-minute intervals and period grouping."""
+        if not self.coordinator.data:
+            return None
+        price_info = self.coordinator.data["data"]["viewer"]["homes"][0]["currentSubscription"]["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
+        all_prices.sort(key=lambda p: p["startsAt"])
+        intervals_by_day, interval_length_by_day, avg_price_by_day = self._split_intervals_by_day(all_prices)
+        ref_prices = self._calculate_reference_prices(intervals_by_day, reverse_sort=reverse_sort)
+        flex = self._get_flex_option(
+            CONF_BEST_PRICE_FLEX if not reverse_sort else CONF_PEAK_PRICE_FLEX,
+            DEFAULT_BEST_PRICE_FLEX if not reverse_sort else DEFAULT_PEAK_PRICE_FLEX,
+        )
+        periods = self._build_periods(
+            all_prices,
+            ref_prices,
+            interval_length_by_day,
+            flex,
+            reverse_sort=reverse_sort,
+        )
+        self._add_interval_ends(periods)
+        # Only use periods relevant for today/tomorrow for annotation and attribute calculation
+        filtered_periods = self._filter_periods_today_tomorrow(periods)
+        # Use the last interval's interval_length for period annotation (approximate)
+        result = self._annotate_period_intervals(
+            filtered_periods,
+            ref_prices,
+            avg_price_by_day,
+            filtered_periods[-1][-1]["interval_length_minute"] if filtered_periods and filtered_periods[-1] else 60,
+        )
+        filtered_result = self._filter_intervals_today_tomorrow(result)
+        current_interval = self._find_current_or_next_interval(filtered_result)
         attributes = {**current_interval} if current_interval else {}
-        attributes["intervals"] = result
+        attributes["intervals"] = filtered_result
         return attributes
 
     def _get_price_hours_attributes(self, *, attribute_name: str, reverse_sort: bool) -> dict | None: