feat(services): add hourly resolution option for chart data services

Add resolution parameter to get_chartdata and get_apexcharts_yaml services,
allowing users to choose between original 15-minute intervals or aggregated
hourly values for chart visualization.

Implementation uses rolling 5-interval window aggregation (-2, -1, 0, +1, +2
around :00 of each hour = 60 minutes total), matching the sensor rolling
hour methodology. Respects user's CONF_AVERAGE_SENSOR_DISPLAY setting for
mean vs median calculation.

Changes:
- formatters.py: Add aggregate_to_hourly() function preserving original
  field names (startsAt, total, level, rating_level) for unified processing
- get_chartdata.py: Pre-aggregate data before processing when resolution is
  'hourly', enabling same code path for filters/insert_nulls/connect_segments
- get_apexcharts_yaml.py: Add resolution parameter, pass to all 4 get_chartdata
  service calls in generated JavaScript
- services.yaml: Add resolution field with interval/hourly selector
- icons.json: Add section icons for get_apexcharts_yaml fields
- translations: Add highlight_peak_price and resolution field translations
  for all 5 languages (en, de, sv, nb, nl)

Impact: Users can now generate cleaner charts with 24 hourly data points
instead of 96 quarter-hourly intervals. The unified processing approach
ensures all chart features (filters, null insertion, segment connection)
work identically for both resolutions.

custom_components/tibber_prices/icons.json

@@ -16,7 +16,15 @@
             }
         },
         "get_apexcharts_yaml": {
-            "service": "mdi:chart-line"
+            "service": "mdi:chart-line",
+            "sections": {
+                "entry_id": "mdi:identifier",
+                "day": "mdi:calendar-range",
+                "level_type": "mdi:format-list-bulleted-type",
+                "resolution": "mdi:timer-sand",
+                "highlight_best_price": "mdi:battery-charging-low",
+                "highlight_peak_price": "mdi:battery-alert"
+            }
         },
         "refresh_user_data": {
             "service": "mdi:refresh"

custom_components/tibber_prices/services.yaml

@@ -46,6 +46,16 @@ get_apexcharts_yaml:
             - rating_level
             - level
           translation_key: level_type
+    resolution:
+      required: false
+      default: interval
+      example: interval
+      selector:
+        select:
+          options:
+            - interval
+            - hourly
+          translation_key: resolution
     highlight_best_price:
       required: false
       default: true

custom_components/tibber_prices/services/formatters.py

@@ -24,6 +24,8 @@ from datetime import datetime, time
 from typing import Any
 
 from custom_components.tibber_prices.const import (
+    CONF_AVERAGE_SENSOR_DISPLAY,
+    DEFAULT_AVERAGE_SENSOR_DISPLAY,
     DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
     DEFAULT_PRICE_RATING_THRESHOLD_LOW,
     get_translation,
@@ -32,6 +34,7 @@ from custom_components.tibber_prices.coordinator.helpers import (
     get_intervals_for_day_offsets,
 )
 from custom_components.tibber_prices.sensor.helpers import aggregate_level_data, aggregate_rating_data
+from custom_components.tibber_prices.utils.average import calculate_mean, calculate_median
 
 
 def normalize_level_filter(value: list[str] | None) -> list[str] | None:
@@ -48,6 +51,99 @@ def normalize_rating_level_filter(value: list[str] | None) -> list[str] | None:
     return [v.upper() for v in value]
 
 
+def aggregate_to_hourly(  # noqa: PLR0912
+    intervals: list[dict],
+    coordinator: Any,
+    threshold_low: float = DEFAULT_PRICE_RATING_THRESHOLD_LOW,
+    threshold_high: float = DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
+) -> list[dict]:
+    """
+    Aggregate 15-minute intervals to hourly using rolling 5-interval window.
+
+    Preserves original field names (startsAt, total, level, rating_level) so the
+    aggregated data can be processed by the same code path as interval data.
+
+    Uses the same methodology as sensor rolling hour calculations:
+    - 5-interval window: 2 before + center + 2 after (60 minutes total)
+    - Center interval is at :00 of each hour
+    - Respects user's CONF_AVERAGE_SENSOR_DISPLAY setting (mean vs median)
+
+    Example for 10:00 data point:
+    - Window includes: 09:30, 09:45, 10:00, 10:15, 10:30
+
+    Args:
+        intervals: List of 15-minute price intervals with startsAt, total, level, rating_level
+        coordinator: Data update coordinator instance
+        threshold_low: Rating level threshold (low/normal boundary)
+        threshold_high: Rating level threshold (normal/high boundary)
+
+    Returns:
+        List of hourly data points with same structure as input (startsAt, total, level, rating_level)
+
+    """
+    if not intervals:
+        return []
+
+    # Get user's average display preference (mean or median)
+    average_display = coordinator.config_entry.options.get(CONF_AVERAGE_SENSOR_DISPLAY, DEFAULT_AVERAGE_SENSOR_DISPLAY)
+    use_median = average_display == "median"
+
+    hourly_data = []
+
+    # Iterate through all intervals, only process those at :00
+    for i, interval in enumerate(intervals):
+        start_time = interval.get("startsAt")
+
+        if not start_time:
+            continue
+
+        # Check if this is the start of an hour (:00)
+        if start_time.minute != 0:
+            continue
+
+        # Collect 5-interval rolling window: -2, -1, 0, +1, +2
+        window_prices: list[float] = []
+        window_intervals: list[dict] = []
+
+        for offset in range(-2, 3):  # -2, -1, 0, +1, +2
+            target_idx = i + offset
+            if 0 <= target_idx < len(intervals):
+                target_interval = intervals[target_idx]
+                price = target_interval.get("total")
+                if price is not None:
+                    window_prices.append(price)
+                    window_intervals.append(target_interval)
+
+        # Calculate aggregated price based on user preference
+        if window_prices:
+            aggregated_price = calculate_median(window_prices) if use_median else calculate_mean(window_prices)
+
+            if aggregated_price is None:
+                continue
+
+            # Build data point with original field names
+            data_point: dict[str, Any] = {
+                "startsAt": start_time,
+                "total": aggregated_price,
+            }
+
+            # Add aggregated level
+            if window_intervals:
+                aggregated_level = aggregate_level_data(window_intervals)
+                if aggregated_level:
+                    data_point["level"] = aggregated_level.upper()
+
+            # Add aggregated rating_level
+            if window_intervals:
+                aggregated_rating = aggregate_rating_data(window_intervals, threshold_low, threshold_high)
+                if aggregated_rating:
+                    data_point["rating_level"] = aggregated_rating.upper()
+
+            hourly_data.append(data_point)
+
+    return hourly_data
+
+
 def aggregate_hourly_exact(  # noqa: PLR0913, PLR0912, PLR0915
     intervals: list[dict],
     start_time_field: str,

custom_components/tibber_prices/services/get_apexcharts_yaml.py

@@ -63,6 +63,7 @@ APEXCHARTS_SERVICE_SCHEMA = vol.Schema(
         vol.Required(ATTR_ENTRY_ID): cv.string,
         vol.Optional("day"): vol.In(["yesterday", "today", "tomorrow", "rolling_window", "rolling_window_autozoom"]),
         vol.Optional("level_type", default="rating_level"): vol.In(["rating_level", "level"]),
+        vol.Optional("resolution", default="interval"): vol.In(["interval", "hourly"]),
         vol.Optional("highlight_best_price", default=True): cv.boolean,
         vol.Optional("highlight_peak_price", default=False): cv.boolean,
     }
@@ -296,6 +297,7 @@ async def handle_apexcharts_yaml(call: ServiceCall) -> dict[str, Any]:  # noqa:
 
     day = call.data.get("day")  # Can be None (rolling window mode)
     level_type = call.data.get("level_type", "rating_level")
+    resolution = call.data.get("resolution", "interval")
     highlight_best_price = call.data.get("highlight_best_price", True)
     highlight_peak_price = call.data.get("highlight_peak_price", False)
 
@@ -361,7 +363,7 @@ async def handle_apexcharts_yaml(call: ServiceCall) -> dict[str, Any]:  # noqa:
             f"service: 'get_chartdata', "
             f"return_response: true, "
             f"service_data: {{ entry_id: '{entry_id}', {day_param}"
-            f"period_filter: 'best_price', "
+            f"period_filter: 'best_price', resolution: '{resolution}', "
             f"output_format: 'array_of_arrays', insert_nulls: 'segments', subunit_currency: {subunit_param} }} }}); "
             f"const originalData = response.response.data; "
             f"return originalData.map((point, i) => {{ "
@@ -400,7 +402,7 @@ async def handle_apexcharts_yaml(call: ServiceCall) -> dict[str, Any]:  # noqa:
             f"service: 'get_chartdata', "
             f"return_response: true, "
             f"service_data: {{ entry_id: '{entry_id}', {day_param}"
-            f"period_filter: 'peak_price', "
+            f"period_filter: 'peak_price', resolution: '{resolution}', "
             f"output_format: 'array_of_arrays', insert_nulls: 'segments', subunit_currency: {subunit_param} }} }}); "
             f"const originalData = response.response.data; "
             f"return originalData.map((point, i) => {{ "
@@ -455,7 +457,7 @@ async def handle_apexcharts_yaml(call: ServiceCall) -> dict[str, Any]:  # noqa:
                 f"domain: 'tibber_prices', "
                 f"service: 'get_chartdata', "
                 f"return_response: true, "
-                f"service_data: {{ entry_id: '{entry_id}', {day_param}{filter_param}, "
+                f"service_data: {{ entry_id: '{entry_id}', {day_param}{filter_param}, resolution: '{resolution}', "
                 f"output_format: 'array_of_arrays', insert_nulls: 'segments', subunit_currency: {subunit_param}, "
                 f"connect_segments: true }} }}); "
                 f"return response.response.data;"
@@ -468,7 +470,7 @@ async def handle_apexcharts_yaml(call: ServiceCall) -> dict[str, Any]:  # noqa:
                 f"domain: 'tibber_prices', "
                 f"service: 'get_chartdata', "
                 f"return_response: true, "
-                f"service_data: {{ entry_id: '{entry_id}', {day_param}{filter_param}, "
+                f"service_data: {{ entry_id: '{entry_id}', {day_param}{filter_param}, resolution: '{resolution}', "
                 f"output_format: 'array_of_arrays', insert_nulls: 'segments', subunit_currency: {subunit_param}, "
                 f"connect_segments: true }} }}); "
                 f"return response.response.data;"

custom_components/tibber_prices/services/get_chartdata.py

@@ -54,7 +54,12 @@ from custom_components.tibber_prices.coordinator.helpers import (
 )
 from homeassistant.exceptions import ServiceValidationError
 
-from .formatters import aggregate_hourly_exact, get_period_data, normalize_level_filter, normalize_rating_level_filter
+from .formatters import (
+    aggregate_to_hourly,
+    get_period_data,
+    normalize_level_filter,
+    normalize_rating_level_filter,
+)
 from .helpers import get_entry_and_data, has_tomorrow_data
 
 if TYPE_CHECKING:
@@ -529,6 +534,19 @@ async def handle_chartdata(call: ServiceCall) -> dict[str, Any]:  # noqa: PLR091
     day_offsets = [{"yesterday": -1, "today": 0, "tomorrow": 1}[day] for day in days]
     all_prices = get_intervals_for_day_offsets(coordinator.data, day_offsets)
 
+    # For hourly resolution, aggregate BEFORE processing
+    # This keeps the same data format (startsAt, total, level, rating_level)
+    # so all subsequent code (filters, insert_nulls, etc.) works unchanged
+    if resolution == "hourly":
+        all_prices = aggregate_to_hourly(
+            all_prices,
+            coordinator=coordinator,
+            threshold_low=threshold_low,
+            threshold_high=threshold_high,
+        )
+        # Also update all_timestamps for insert_nulls='all' mode
+        all_timestamps = sorted({interval["startsAt"] for interval in all_prices if interval.get("startsAt")})
+
     # Helper to get day key from interval timestamp for average lookup
     def _get_day_key_for_interval(interval_start: Any) -> str | None:
         """Determine which day key (yesterday/today/tomorrow) an interval belongs to."""
@@ -537,8 +555,9 @@ async def handle_chartdata(call: ServiceCall) -> dict[str, Any]:  # noqa: PLR091
         # Use pre-built mapping from actual interval data (TimeService-compatible)
         return date_to_day_key.get(interval_start.date())
 
-    if resolution == "interval":
+    # Process price data - same logic handles both interval and hourly resolution
-        # Original 15-minute intervals
+    # (hourly data was already aggregated above, but has the same format)
+    if resolution in ("interval", "hourly"):
         if insert_nulls == "all" and (level_filter or rating_level_filter):
             # Mode 'all': Insert NULL for all timestamps where filter doesn't match
             # Build a map of timestamp -> interval for quick lookup
@@ -865,32 +884,6 @@ async def handle_chartdata(call: ServiceCall) -> dict[str, Any]:  # noqa: PLR091
 
                     chart_data.append(data_point)
 
-    elif resolution == "hourly":
-        # Hourly averages (4 intervals per hour: :00, :15, :30, :45)
-        # Process all intervals together for hourly aggregation
-        chart_data.extend(
-            aggregate_hourly_exact(
-                all_prices,
-                start_time_field,
-                price_field,
-                coordinator=coordinator,
-                use_subunit_currency=subunit_currency,
-                round_decimals=round_decimals,
-                include_level=include_level,
-                include_rating_level=include_rating_level,
-                level_filter=level_filter,
-                rating_level_filter=rating_level_filter,
-                include_average=include_average,
-                level_field=level_field,
-                rating_level_field=rating_level_field,
-                average_field=average_field,
-                day_average=None,  # Not used when processing all days together
-                threshold_low=threshold_low,
-                period_timestamps=period_timestamps,
-                threshold_high=threshold_high,
-            )
-        )
-
     # Remove trailing null values ONLY for insert_nulls='segments' mode.
     # For 'all' mode, trailing nulls are intentional (show no-match until end of day).
     # For 'segments' mode, trailing nulls cause ApexCharts header to show "N/A".

custom_components/tibber_prices/translations/de.json

@@ -948,6 +948,14 @@
                 "highlight_best_price": {
                     "name": "Bestpreis-Zeiträume hervorheben",
                     "description": "Füge eine halbtransparente grüne Überlagerung hinzu, um die Bestpreis-Zeiträume im Diagramm hervorzuheben. Dies erleichtert die visuelle Identifizierung der optimalen Zeiten für den Energieverbrauch."
+                },
+                "highlight_peak_price": {
+                    "name": "Spitzenpreis-Zeiträume hervorheben",
+                    "description": "Füge eine halbtransparente rote Überlagerung hinzu, um die Spitzenpreis-Zeiträume im Diagramm hervorzuheben. Dies erleichtert die visuelle Identifizierung der Zeiten, in denen Energie am teuersten ist."
+                },
+                "resolution": {
+                    "name": "Auflösung",
+                    "description": "Zeitauflösung für die Diagrammdaten. 'interval' (Standard): Originale 15-Minuten-Intervalle (96 Punkte pro Tag). 'hourly': Aggregierte Stundenwerte mit einem rollierenden 60-Minuten-Fenster (24 Punkte pro Tag) für ein übersichtlicheres Diagramm."
                 }
             }
         },

custom_components/tibber_prices/translations/en.json

@@ -948,6 +948,14 @@
                 "highlight_best_price": {
                     "name": "Highlight Best Price Periods",
                     "description": "Add a semi-transparent green overlay to highlight the best price periods on the chart. This makes it easy to visually identify the optimal times for energy consumption."
+                },
+                "highlight_peak_price": {
+                    "name": "Highlight Peak Price Periods",
+                    "description": "Add a semi-transparent red overlay to highlight the peak price periods on the chart. This makes it easy to visually identify times when energy is most expensive."
+                },
+                "resolution": {
+                    "name": "Resolution",
+                    "description": "Time resolution for the chart data. 'interval' (default): Original 15-minute intervals (96 points per day). 'hourly': Aggregated hourly values using a rolling 60-minute window (24 points per day) for a cleaner, less cluttered chart."
                 }
             }
         },

custom_components/tibber_prices/translations/nb.json

@@ -948,6 +948,14 @@
                 "highlight_best_price": {
                     "name": "Fremhev beste prisperioder",
                     "description": "Legg til et halvgjennomsiktig grønt overlegg for å fremheve de beste prisperiodene i diagrammet. Dette gjør det enkelt å visuelt identifisere de optimale tidene for energiforbruk."
+                },
+                "highlight_peak_price": {
+                    "name": "Fremhev høyeste prisperioder",
+                    "description": "Legg til et halvgjennomsiktig rødt overlegg for å fremheve de høyeste prisperiodene i diagrammet. Dette gjør det enkelt å visuelt identifisere tidene når energi er dyrest."
+                },
+                "resolution": {
+                    "name": "Oppløsning",
+                    "description": "Tidsoppløsning for diagramdata. 'interval' (standard): Opprinnelige 15-minutters intervaller (96 punkter per dag). 'hourly': Aggregerte timeverdier med et rullende 60-minutters vindu (24 punkter per dag) for et ryddigere og mindre rotete diagram."
                 }
             }
         },

custom_components/tibber_prices/translations/nl.json

@@ -948,6 +948,14 @@
                 "highlight_best_price": {
                     "name": "Beste prijsperiodes markeren",
                     "description": "Voeg een halfdo0rzichtige groene overlay toe om de beste prijsperiodes in de grafiek te markeren. Dit maakt het gemakkelijk om visueel de optimale tijden voor energieverbruik te identificeren."
+                },
+                "highlight_peak_price": {
+                    "name": "Piekprijsperiodes markeren",
+                    "description": "Voeg een halfdoorzichtige rode overlay toe om de piekprijsperiodes in de grafiek te markeren. Dit maakt het gemakkelijk om visueel de tijden te identificeren wanneer energie het duurst is."
+                },
+                "resolution": {
+                    "name": "Resolutie",
+                    "description": "Tijdresolutie voor de grafiekdata. 'interval' (standaard): Originele 15-minutenintervallen (96 punten per dag). 'hourly': Geaggregeerde uurwaarden met een rollend 60-minutenvenster (24 punten per dag) voor een overzichtelijkere grafiek."
                 }
             }
         },

custom_components/tibber_prices/translations/sv.json

@@ -948,6 +948,14 @@
                 "highlight_best_price": {
                     "name": "Markera bästa prisperioder",
                     "description": "Lägg till ett halvtransparent grönt överlag för att markera de bästa prisperioderna i diagrammet. Detta gör det enkelt att visuellt identifiera de optimala tiderna för energiförbrukning."
+                },
+                "highlight_peak_price": {
+                    "name": "Markera högsta prisperioder",
+                    "description": "Lägg till ett halvtransparent rött överlag för att markera de högsta prisperioderna i diagrammet. Detta gör det enkelt att visuellt identifiera tiderna när energi är som dyrast."
+                },
+                "resolution": {
+                    "name": "Upplösning",
+                    "description": "Tidsupplösning för diagramdata. 'interval' (standard): Ursprungliga 15-minutersintervall (96 punkter per dag). 'hourly': Aggregerade timvärden med ett rullande 60-minutersfönster (24 punkter per dag) för ett renare och mindre rörigt diagram."
                 }
             }
         },