Next release

_docs/blueprints.md

@@ -20,7 +20,7 @@ This blueprint will provide the data source for the UK Carbon Intensity as provi
 
 #### Default
 
-[Install blueprint)](https://my.home-assistant.io/redirect/blueprint_import/?blueprint_url=https%3A%2F%2Fbottlecapdave.github.io%2FHomeAssistant-TargetTimeframes%2Fblueprints%2Ftarget_timeframes_octopus_energy.yaml) | [Source](./blueprints/target_timeframes_octopus_energy.yaml)
+[Install blueprint](https://my.home-assistant.io/redirect/blueprint_import/?blueprint_url=https%3A%2F%2Fbottlecapdave.github.io%2FHomeAssistant-TargetTimeframes%2Fblueprints%2Ftarget_timeframes_octopus_energy.yaml) | [Source](./blueprints/target_timeframes_octopus_energy.yaml)
 
 This blueprint will provide the data source for Octopus Energy rates as provided by the [Octopus Energy](https://github.com/BottlecapDave/HomeAssistant-OctopusEnergy) integration. This is for accounts that don't have the [free electricity sensor](https://bottlecapdave.github.io/HomeAssistant-OctopusEnergy/entities/octoplus/#free-electricity-session-events) available.
 
@@ -46,4 +46,4 @@ This blueprint will provide the data source for Octopus Energy rates as provided
 
 !!! warning
 
-    This automation will run when any of the underlying entities update. This make take a while initially. If you want the data available immediately, then you'll need to run the automation manually.
+    This automation will run when any of the underlying entities update. This make take a while initially. If you want the data available immediately, then you'll need to run the automation manually.

custom_components/target_timeframes/entities/__init__.py

@@ -33,17 +33,17 @@ def apply_offset(date_time: datetime, offset: str, inverse = False):
 
   return date_time + timedelta(hours=hours, minutes=minutes, seconds=seconds)
 
-def is_target_timeframe_complete_in_period(current_date: datetime, applicable_time_periods: list | None, target_timeframes: list | None):
-  if applicable_time_periods is None or target_timeframes is None or len(applicable_time_periods) < 1 or len(target_timeframes) < 1:
+def is_target_timeframe_complete_in_period(current_date: datetime, start_time: datetime, end_time: datetime, target_timeframes: list | None):
+  if target_timeframes is None or len(target_timeframes) < 1:
     return False
 
   return (
-    applicable_time_periods[0]["start"] <= target_timeframes[0]["start"] and 
-    applicable_time_periods[-1]["end"] >= target_timeframes[-1]["end"] and
+    start_time <= target_timeframes[0]["start"] and 
+    end_time >= target_timeframes[-1]["end"] and
     target_timeframes[-1]["end"] <= current_date
   )
 
-def get_fixed_applicable_time_periods(current_date: datetime, target_start_time: str, target_end_time: str, time_period_values: list, is_rolling_target = True):
+def get_start_and_end_times(current_date: datetime, target_start_time: str, target_end_time: str, start_time_not_in_past = True):
   if (target_start_time is not None):
     target_start = parse_datetime(current_date.strftime(f"%Y-%m-%dT{target_start_time}:00%z"))
   else:
@@ -65,7 +65,7 @@ def get_fixed_applicable_time_periods(current_date: datetime, target_start_time:
       target_end = target_end + timedelta(days=1)
 
   # If our start date has passed, reset it to current_date to avoid picking a slot in the past
-  if (is_rolling_target == True and target_start < current_date and current_date < target_end):
+  if (start_time_not_in_past == True and target_start < current_date and current_date < target_end):
     _LOGGER.debug(f'Rolling target and {target_start} is in the past. Setting start to {current_date}')
     target_start = current_date
 
@@ -74,6 +74,9 @@ def get_fixed_applicable_time_periods(current_date: datetime, target_start_time:
     target_start = target_start + timedelta(days=1)
     target_end = target_end + timedelta(days=1)
 
+  return (target_start, target_end)
+
+def get_fixed_applicable_time_periods(target_start: datetime, target_end: datetime, time_period_values: list):
   _LOGGER.debug(f'Finding rates between {target_start} and {target_end}')
 
   # Retrieve the rates that are applicable for our target rate
@@ -434,4 +437,4 @@ def should_evaluate_target_timeframes(current_date: datetime, target_timeframes:
 
   return ((evaluation_mode == CONFIG_TARGET_TARGET_TIMES_EVALUATION_MODE_ALL_IN_PAST and all_rates_in_past) or
           (evaluation_mode == CONFIG_TARGET_TARGET_TIMES_EVALUATION_MODE_ALL_IN_FUTURE_OR_PAST and (one_rate_in_past == False or all_rates_in_past)) or
-          (evaluation_mode == CONFIG_TARGET_TARGET_TIMES_EVALUATION_MODE_ALWAYS))
+          (evaluation_mode == CONFIG_TARGET_TARGET_TIMES_EVALUATION_MODE_ALWAYS))

custom_components/target_timeframes/entities/target_timeframe.py

@@ -48,6 +48,7 @@
   create_weighting,
   extract_config,
   get_fixed_applicable_time_periods,
+  get_start_and_end_times,
   get_target_time_period_info,
   is_target_timeframe_complete_in_period,
   should_evaluate_target_timeframes
@@ -166,22 +167,24 @@ async def async_update(self):
         if CONFIG_TARGET_MAX_VALUE in self._config:
           max_rate = self._config[CONFIG_TARGET_MAX_VALUE]
 
+        target_start, target_end = get_start_and_end_times(current_local_date, start_time, end_time, True)
         applicable_time_periods = get_fixed_applicable_time_periods(
-          current_local_date,
-          start_time,
-          end_time,
-          self._data_source_data,
-          is_rolling_target
+          target_start,
+          target_end,
+          self._data_source_data
         )
 
-        is_target_timeframe_complete = is_rolling_target == False and is_target_timeframe_complete_in_period(current_local_date, applicable_time_periods, self._target_timeframes)
+        # Make sure we haven't already completed for the current target timeframe
+        applicable_target_start, applicable_target_end = get_start_and_end_times(current_local_date, start_time, end_time, False)
+        is_target_timeframe_complete = is_rolling_target == False and is_target_timeframe_complete_in_period(current_local_date, applicable_target_start, applicable_target_end, self._target_timeframes)
 
         if applicable_time_periods is not None and is_target_timeframe_complete == False:
           number_of_slots = math.ceil(target_hours * 2)
           weighting = create_weighting(self._config[CONFIG_TARGET_WEIGHTING] if CONFIG_TARGET_WEIGHTING in self._config else None, number_of_slots)
 
+          proposed_target_timeframes = None
           if (self._config[CONFIG_TARGET_TYPE] == CONFIG_TARGET_TYPE_CONTINUOUS):
-            self._target_timeframes = calculate_continuous_times(
+            proposed_target_timeframes = calculate_continuous_times(
               applicable_time_periods,
               target_hours,
               find_highest_values,
@@ -192,7 +195,7 @@ async def async_update(self):
               hours_mode = self._config[CONFIG_TARGET_HOURS_MODE]
             )
           elif (self._config[CONFIG_TARGET_TYPE] == CONFIG_TARGET_TYPE_INTERMITTENT):
-            self._target_timeframes = calculate_intermittent_times(
+            proposed_target_timeframes = calculate_intermittent_times(
               applicable_time_periods,
               target_hours,
               find_highest_values,
@@ -204,6 +207,7 @@ async def async_update(self):
           else:
             _LOGGER.error(f"Unexpected target type: {self._config[CONFIG_TARGET_TYPE]}")
 
+          self._target_timeframes = proposed_target_timeframes
           self._attributes["target_times"] = self._target_timeframes
           self._attributes["target_times_last_evaluated"] = current_date
           _LOGGER.debug(f"calculated rates: {self._target_timeframes}")

tests/unit/__init__.py

@@ -8,15 +8,15 @@
 _LOGGER = logging.getLogger(__name__)
 
 def create_data_source_data(period_from: datetime, period_to: datetime, expected_values: list):
-  rates = []
+  values = []
   current_valid_from = period_from
   current_valid_to = None
 
   rate_index = 0
   while current_valid_to is None or current_valid_to < period_to:
     current_valid_to = current_valid_from + timedelta(minutes=30)
 
-    rates.append({
+    values.append({
       "start": current_valid_from,
       "end": current_valid_to,
       "value": expected_values[rate_index],
@@ -28,19 +28,19 @@ def create_data_source_data(period_from: datetime, period_to: datetime, expected
     if (rate_index > (len(expected_values) - 1)):
       rate_index = 0
 
-  return rates
+  return values
 
 def get_start(rate):
   return rate["start"]
 
 def values_to_thirty_minute_increments(items: list, period_from: datetime, period_to: datetime):
-  """Process the collection of rates to ensure they're in 30 minute periods"""
+  """Process the collection of values to ensure they're in 30 minute periods"""
   starting_period_from = period_from
   results = []
 
   items.sort(key=get_start)
 
-  # We need to normalise our data into 30 minute increments so that all of our rates across all tariffs are the same and it's 
+  # We need to normalise our data into 30 minute increments so that all of our values across all tariffs are the same and it's 
   # easier to calculate our target rate sensors
   for item in items:
     value = float(item["value"])
@@ -53,7 +53,7 @@ def values_to_thirty_minute_increments(items: list, period_from: datetime, perio
     else:
       start = starting_period_from
 
-    # Some rates don't have end dates, so we should treat this as our period to target
+    # Some values don't have end dates, so we should treat this as our period to target
     if "end" in item and item["end"] is not None:
       target_date = as_utc(parse_datetime(item["end"]))