Next release

_docs/events.md

@@ -1,3 +1,23 @@
 # Events
 
-The following events are raised by the integration. These events power various entities and can also be used within automations.
+The following events are either raised or received by the integration. These events power various entities and can also be used within automations.
+
+## Update Data Source
+
+`target_timeframe_update_data_source`
+
+Data sources listen for this event and then update the data based on the provided data if the `data_source_id` matches the data source's id.
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `data_source_id` | `string` | The id of the data source the data belongs to |
+| `data` | `array` | The data to update the data source with |
+
+For each item in `data`, the following attributes should be present
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `start` | `datetime` | The start timestamp the value is effective from |
+| `end` | `datetime` | The end timestamp the value is effective to |
+| `value` | `float` | The value that is applicable for the timeframe. This could be something like an electricity rate |
+| `metadata` | `object` | Additional metadata that might describe how the value was created |

_docs/setup/data_source.md

@@ -10,12 +10,16 @@ The name of the data source. This is for informative purposes.
 
 ### Id
 
-The unique identifier of the data source. This is used by internal events to ensure sensors use the correct data as well as part of the entity name of all related entities. For example, if you were using data provided by Octopus Energy, then this might be `octopus_energy`.
+The unique identifier of the data source. This is used by internal events to ensure sensors use the correct data as well as part of the entity name of all related entities.
 
-Once the data source has been created, you'll have the following entities created. You'll then need to use the [available service](../services.md#target_timeframesupdate_target_timeframe_data_source) to configure the underlying data. There are also a collection of [blueprints](../blueprints.md#data-sources) available for loading popular data sources.
+The data source will listen for the [update data source event](../events.md#update-data-source) where the `data source id` matches this id. This event might be raised by other integrations. If this data source is being used for this purpose, then the id will need to be set to a certain value which should be highlighted in that integrations guide.
+
+Alternatively, you can use the [available service](../services.md#target_timeframesupdate_target_timeframe_data_source) to configure the underlying data. There is a collection of [blueprints](../blueprints.md#data-sources) available for loading data from popular data sources.
 
 ## Entities
 
+Once the data source has been created, you'll have the following entities created.
+
 ### Data Source Last Updated
 
 `sensor.target_timeframes_{{DATA_SOURCE_ID}}_data_source_last_updated`

_docs/setup/rolling_target_timeframe.md

@@ -100,7 +100,7 @@ There may be times that you want the target timeframe sensors to not take into a
 
 !!! info
 
-    This is only available for **continuous** target value sensors in **exact** hours mode.
+    This is only available for **continuous** target value sensors.
 
 There may be times when the device you're wanting the target value sensor to turn on doesn't have a consistent power draw. You can specify a weighting/multiplier which can be applied to the value of each discovered 30 minute slot. This can be specified in a few different ways. Take the following example weighting/multiplier for a required 2 hours.
 

_docs/setup/target_timeframe.md

@@ -128,7 +128,7 @@ There may be times that you want the target timeframe sensors to not take into a
 
 !!! info
 
-    This is only available for **continuous** target value sensors in **exact** hours mode.
+    This is only available for **continuous** target value sensors.
 
 There may be times when the device you're wanting the target value sensor to turn on doesn't have a consistent power draw. You can specify a weighting/multiplier which can be applied to the value of each discovered 30 minute slot. This can be specified in a few different ways. Take the following example weighting/multiplier for a required 2 hours.
 

custom_components/target_timeframes/config/rolling_target_timeframe.py

@@ -6,6 +6,7 @@
   CONFIG_TARGET_HOURS,
   CONFIG_TARGET_HOURS_MODE,
   CONFIG_TARGET_HOURS_MODE_EXACT,
+  CONFIG_TARGET_HOURS_MODE_MAXIMUM,
   CONFIG_TARGET_HOURS_MODE_MINIMUM,
   CONFIG_TARGET_MAX_VALUE,
   CONFIG_TARGET_MIN_VALUE,
@@ -120,14 +121,19 @@ def validate_rolling_target_timeframe_config(data):
       number_of_slots = int(data[CONFIG_TARGET_HOURS] * 2)
       weighting = create_weighting(data[CONFIG_TARGET_WEIGHTING], number_of_slots)
 
-      if (len(weighting) != number_of_slots):
-        errors[CONFIG_TARGET_WEIGHTING] = "invalid_weighting_slots"
+      if (weighting is None or len(weighting) != number_of_slots):
+        if CONFIG_TARGET_HOURS_MODE in data and data[CONFIG_TARGET_HOURS_MODE] == CONFIG_TARGET_HOURS_MODE_MINIMUM:
+          errors[CONFIG_TARGET_WEIGHTING] = "invalid_minimum_weighting_slots"
+        elif CONFIG_TARGET_HOURS_MODE in data and data[CONFIG_TARGET_HOURS_MODE] == CONFIG_TARGET_HOURS_MODE_MAXIMUM:
+          errors[CONFIG_TARGET_WEIGHTING] = "invalid_maximum_weighting_slots"
+        else:
+          errors[CONFIG_TARGET_WEIGHTING] = "invalid_weighting_slots"
+
+      if CONFIG_TARGET_HOURS_MODE in data and data[CONFIG_TARGET_HOURS_MODE] != CONFIG_TARGET_HOURS_MODE_EXACT and "*" not in data[CONFIG_TARGET_WEIGHTING]:
+        errors[CONFIG_TARGET_WEIGHTING] = "weighting_not_varied_for_hour_mode"
 
     if data[CONFIG_TARGET_TYPE] != CONFIG_TARGET_TYPE_CONTINUOUS:
       errors[CONFIG_TARGET_WEIGHTING] = "weighting_not_supported_for_type"
-
-    if CONFIG_TARGET_HOURS_MODE in data and data[CONFIG_TARGET_HOURS_MODE] != CONFIG_TARGET_HOURS_MODE_EXACT:
-      errors[CONFIG_TARGET_WEIGHTING] = "weighting_not_supported_for_hour_mode"
 
   if CONFIG_TARGET_HOURS_MODE in data and data[CONFIG_TARGET_HOURS_MODE] == CONFIG_TARGET_HOURS_MODE_MINIMUM:
     if (CONFIG_TARGET_MIN_VALUE not in data or data[CONFIG_TARGET_MIN_VALUE] is None) and (CONFIG_TARGET_MAX_VALUE not in data or data[CONFIG_TARGET_MAX_VALUE] is None):

custom_components/target_timeframes/config/target_timeframe.py

@@ -9,6 +9,7 @@
   CONFIG_TARGET_HOURS,
   CONFIG_TARGET_HOURS_MODE,
   CONFIG_TARGET_HOURS_MODE_EXACT,
+  CONFIG_TARGET_HOURS_MODE_MAXIMUM,
   CONFIG_TARGET_HOURS_MODE_MINIMUM,
   CONFIG_TARGET_MAX_VALUE,
   CONFIG_TARGET_MIN_VALUE,
@@ -146,14 +147,19 @@ def validate_target_timeframe_config(data):
       number_of_slots = int(data[CONFIG_TARGET_HOURS] * 2)
       weighting = create_weighting(data[CONFIG_TARGET_WEIGHTING], number_of_slots)
 
-      if (len(weighting) != number_of_slots):
-        errors[CONFIG_TARGET_WEIGHTING] = "invalid_weighting_slots"
+      if (weighting is None or len(weighting) != number_of_slots):
+        if CONFIG_TARGET_HOURS_MODE in data and data[CONFIG_TARGET_HOURS_MODE] == CONFIG_TARGET_HOURS_MODE_MINIMUM:
+          errors[CONFIG_TARGET_WEIGHTING] = "invalid_minimum_weighting_slots"
+        elif CONFIG_TARGET_HOURS_MODE in data and data[CONFIG_TARGET_HOURS_MODE] == CONFIG_TARGET_HOURS_MODE_MAXIMUM:
+          errors[CONFIG_TARGET_WEIGHTING] = "invalid_maximum_weighting_slots"
+        else:
+          errors[CONFIG_TARGET_WEIGHTING] = "invalid_weighting_slots"
+
+      if CONFIG_TARGET_HOURS_MODE in data and data[CONFIG_TARGET_HOURS_MODE] != CONFIG_TARGET_HOURS_MODE_EXACT and "*" not in data[CONFIG_TARGET_WEIGHTING]:
+        errors[CONFIG_TARGET_WEIGHTING] = "weighting_not_varied_for_hour_mode"
 
     if data[CONFIG_TARGET_TYPE] != CONFIG_TARGET_TYPE_CONTINUOUS:
       errors[CONFIG_TARGET_WEIGHTING] = "weighting_not_supported_for_type"
-
-    if CONFIG_TARGET_HOURS_MODE in data and data[CONFIG_TARGET_HOURS_MODE] != CONFIG_TARGET_HOURS_MODE_EXACT:
-      errors[CONFIG_TARGET_WEIGHTING] = "weighting_not_supported_for_hour_mode"
 
   if CONFIG_TARGET_HOURS_MODE in data and data[CONFIG_TARGET_HOURS_MODE] == CONFIG_TARGET_HOURS_MODE_MINIMUM:
     if (CONFIG_TARGET_MIN_VALUE not in data or data[CONFIG_TARGET_MIN_VALUE] is None) and (CONFIG_TARGET_MAX_VALUE not in data or data[CONFIG_TARGET_MAX_VALUE] is None):

custom_components/target_timeframes/config_flow.py

@@ -22,6 +22,12 @@
 
 from .config.data_source import validate_source_config
 
+description_placeholders = {
+  "setup_data_source_docs_url": "https://bottlecapdave.github.io/HomeAssistant-TargetTimeframes/setup/data_source",
+  "setup_target_timeframe_docs_url": "https://bottlecapdave.github.io/HomeAssistant-TargetTimeframes/setup/target_timeframe",
+  "setup_rolling_target_timeframe_docs_url": "https://bottlecapdave.github.io/HomeAssistant-TargetTimeframes/setup/rolling_target_timeframe",
+}
+
 class TargetTimeframesConfigFlow(ConfigFlow, domain=DOMAIN): 
   """Config flow."""
 
@@ -48,7 +54,8 @@ async def async_step_user(self, user_input):
     return self.async_show_form(
       step_id="user",
       data_schema=DATA_SCHEMA_SOURCE,
-      errors=errors
+      errors=errors,
+      description_placeholders=description_placeholders
     )
 
   async def async_step_reconfigure(self, user_input: dict[str, Any] | None = None):
@@ -76,7 +83,8 @@ async def async_step_reconfigure(self, user_input: dict[str, Any] | None = None)
         DATA_SCHEMA_SOURCE,
         config
       ),
-      errors=errors
+      errors=errors,
+      description_placeholders=description_placeholders
     )
 
   @classmethod
@@ -112,7 +120,8 @@ async def async_step_user(
         DATA_SCHEMA_TARGET_TIME_PERIOD,
         user_input if user_input is not None else {}
       ),
-      errors=errors
+      errors=errors,
+      description_placeholders=description_placeholders
     )
 
   async def async_step_reconfigure(self, user_input: dict[str, Any] | None = None):
@@ -132,7 +141,8 @@ async def async_step_reconfigure(self, user_input: dict[str, Any] | None = None)
         DATA_SCHEMA_TARGET_TIME_PERIOD,
         config
       ),
-      errors=errors
+      errors=errors,
+      description_placeholders=description_placeholders
     )
 
 class RollingTargetTimePeriodSubentryFlowHandler(ConfigSubentryFlow):
@@ -157,7 +167,8 @@ async def async_step_user(
         DATA_SCHEMA_ROLLING_TARGET_TIME_PERIOD,
         user_input if user_input is not None else {}
       ),
-      errors=errors
+      errors=errors,
+      description_placeholders=description_placeholders
     )
 
   async def async_step_reconfigure(self, user_input: dict[str, Any] | None = None):
@@ -177,5 +188,6 @@ async def async_step_reconfigure(self, user_input: dict[str, Any] | None = None)
         DATA_SCHEMA_ROLLING_TARGET_TIME_PERIOD,
         config
       ),
-      errors=errors
+      errors=errors,
+      description_placeholders=description_placeholders
     )

custom_components/target_timeframes/const.py

@@ -185,4 +185,5 @@
   ),
 })
 
-EVENT_DATA_SOURCE = "target_time_period_data_source_updated"
+EVENT_DATA_SOURCE = "target_time_period_data_source_updated"
+EVENT_UPDATE_DATA_SOURCE = "target_timeframe_update_data_source"

custom_components/target_timeframes/entities/__init__.py

@@ -141,7 +141,7 @@ def calculate_continuous_times(
     find_latest_values = False,
     min_value = None,
     max_value = None,
-    weighting: list = None,
+    weighting: str = None,
     hours_mode = CONFIG_TARGET_HOURS_MODE_EXACT,
     context: str = None
   ):
@@ -151,9 +151,6 @@ def calculate_continuous_times(
   applicable_time_periods_count = len(applicable_time_periods)
   total_required_time_periods = math.ceil(target_hours * 2)
 
-  if weighting is not None and len(weighting) != total_required_time_periods:
-    raise ValueError(f"{context} - Weighting does not match target hours")
-
   best_continuous_time_periods = None
   best_continuous_time_periods_total = None
 
@@ -169,8 +166,6 @@ def calculate_continuous_times(
       continue
 
     continuous_time_periods = [time_period]
-    value_weight = Decimal(time_period["weighting"]) if "weighting" in time_period else 1
-    continuous_rates_total = Decimal(time_period["value"]) * value_weight * (weighting[0] if weighting is not None and len(weighting) > 0 else 1)
 
     for offset in range(1, total_required_time_periods if hours_mode != CONFIG_TARGET_HOURS_MODE_MINIMUM else applicable_time_periods_count):
       if (index + offset) < applicable_time_periods_count:
@@ -183,21 +178,12 @@ def calculate_continuous_times(
           break
 
         continuous_time_periods.append(offset_time_period)
-        value_weight = Decimal(offset_time_period["weighting"]) if "weighting" in offset_time_period else 1
-        continuous_rates_total += Decimal(offset_time_period["value"]) * value_weight * (weighting[offset] if weighting is not None else 1)
       else:
         break
 
     current_continuous_time_periods_length = len(continuous_time_periods)
     best_continuous_time_periods_length = len(best_continuous_time_periods) if best_continuous_time_periods is not None else 0
 
-    is_best_continuous_rates = False
-    if best_continuous_time_periods is not None:
-      if search_for_highest_value:
-        is_best_continuous_rates = (continuous_rates_total >= best_continuous_time_periods_total if find_latest_values else continuous_rates_total > best_continuous_time_periods_total)
-      else:
-        is_best_continuous_rates = (continuous_rates_total <= best_continuous_time_periods_total if find_latest_values else continuous_rates_total < best_continuous_time_periods_total)
-
     has_required_hours = False
     if hours_mode == CONFIG_TARGET_HOURS_MODE_EXACT:
       has_required_hours = current_continuous_time_periods_length == total_required_time_periods
@@ -206,12 +192,26 @@ def calculate_continuous_times(
     elif hours_mode == CONFIG_TARGET_HOURS_MODE_MAXIMUM:
       has_required_hours = current_continuous_time_periods_length <= total_required_time_periods and current_continuous_time_periods_length >= best_continuous_time_periods_length
 
-    if ((best_continuous_time_periods is None or is_best_continuous_rates) and has_required_hours):
-      best_continuous_time_periods = continuous_time_periods
-      best_continuous_time_periods_total = continuous_rates_total
-      _LOGGER.debug(f'{context} - New best block discovered {continuous_rates_total} ({continuous_time_periods[0]["start"] if len(continuous_time_periods) > 0 else None} - {continuous_time_periods[-1]["end"] if len(continuous_time_periods) > 0 else None})')
-    else:
-      _LOGGER.debug(f'{context} - Total rates for current block {continuous_rates_total} ({continuous_time_periods[0]["start"] if len(continuous_time_periods) > 0 else None} - {continuous_time_periods[-1]["end"] if len(continuous_time_periods) > 0 else None}). Total rates for best block {best_continuous_time_periods_total}')
+    if has_required_hours:
+      weighting_values = create_weighting(weighting, len(continuous_time_periods))
+      if weighting_values is not None:
+        continuous_rates_total = sum([Decimal(rate["value"]) * weighting_values[index] for index, rate in enumerate(continuous_time_periods)])
+      else:
+        continuous_rates_total = sum([Decimal(rate["value"]) for rate in continuous_time_periods])
+
+      is_best_continuous_rates = False
+      if best_continuous_time_periods is not None:
+        if search_for_highest_value:
+          is_best_continuous_rates = (continuous_rates_total >= best_continuous_time_periods_total if find_latest_values else continuous_rates_total > best_continuous_time_periods_total)
+        else:
+          is_best_continuous_rates = (continuous_rates_total <= best_continuous_time_periods_total if find_latest_values else continuous_rates_total < best_continuous_time_periods_total)
+
+      if is_best_continuous_rates or best_continuous_time_periods is None:
+        best_continuous_time_periods = continuous_time_periods
+        best_continuous_time_periods_total = continuous_rates_total
+        _LOGGER.debug(f'{context} - New best block discovered {continuous_rates_total} ({continuous_time_periods[0]["start"] if len(continuous_time_periods) > 0 else None} - {continuous_time_periods[-1]["end"] if len(continuous_time_periods) > 0 else None})')
+      else:
+        _LOGGER.debug(f'{context} - Total rates for current block {continuous_rates_total} ({continuous_time_periods[0]["start"] if len(continuous_time_periods) > 0 else None} - {continuous_time_periods[-1]["end"] if len(continuous_time_periods) > 0 else None}). Total rates for best block {best_continuous_time_periods_total}')
 
   if best_continuous_time_periods is not None:
     # Make sure our rates are in ascending order before returning
@@ -409,6 +409,9 @@ def create_weighting(config: str, number_of_slots: int):
 
   parts = config.split(',')
   parts_length = len(parts)
+  if parts_length > number_of_slots:
+    return None
+
   weighting = []
   for index in range(parts_length):
     if (parts[index] == "*"):

custom_components/target_timeframes/entities/data_source.py

@@ -17,7 +17,7 @@
 from homeassistant.helpers.entity import generate_entity_id
 
 from ..utils.attributes import dict_to_typed_dict
-from ..const import DOMAIN, EVENT_DATA_SOURCE
+from ..const import DOMAIN, EVENT_DATA_SOURCE, EVENT_UPDATE_DATA_SOURCE
 from ..storage.data_source_data import async_save_cached_data_source_data
 from ..utils.data_source_data import DataSourceItem, merge_data_source_data, validate_data_source_data
 
@@ -67,6 +67,11 @@ def extra_state_attributes(self):
   @property
   def native_value(self):
     return self._state
+
+  @callback
+  async def _async_handle_event(self, event) -> None:
+    if event.data.get("data_source_id", '').lower() == self._source_id.lower():
+      await self.async_update_target_timeframe_data_source(event.data.get("data", []))
 
   async def async_added_to_hass(self):
     """Call when entity about to be added to hass."""
@@ -81,6 +86,10 @@ async def async_added_to_hass(self):
 
       _LOGGER.debug(f'Restored state: {self._state}')
 
+    self.async_on_remove(
+      self._hass.bus.async_listen(EVENT_UPDATE_DATA_SOURCE, self._async_handle_event)
+    )
+
   @callback
   async def async_update_target_timeframe_data_source(self, data, replace_all_existing_data = False):
     """Update target timeframe data source"""

custom_components/target_timeframes/entities/rolling_target_timeframe.py

@@ -163,8 +163,7 @@ async def async_update(self):
         )
 
         if applicable_time_periods is not None:
-          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)
+          weighting = self._config[CONFIG_TARGET_WEIGHTING] if CONFIG_TARGET_WEIGHTING in self._config else None
 
           if (self._config[CONFIG_TARGET_TYPE] == CONFIG_TARGET_TYPE_CONTINUOUS):
             self._target_timeframes = calculate_continuous_times(

custom_components/target_timeframes/entities/target_timeframe.py

@@ -195,8 +195,7 @@ async def async_update(self):
         )
 
         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)
+          weighting = self._config[CONFIG_TARGET_WEIGHTING] if CONFIG_TARGET_WEIGHTING in self._config else None
 
           proposed_target_timeframes = None
           if (self._config[CONFIG_TARGET_TYPE] == CONFIG_TARGET_TYPE_CONTINUOUS):