Next release

_docs/blueprints.md

@@ -47,3 +47,9 @@ 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.
+
+### Weather
+
+[Install blueprint](https://my.home-assistant.io/redirect/blueprint_import/?blueprint_url=https%3A%2F%2Fbottlecapdave.github.io%2FHomeAssistant-TargetTimeframes%2Fblueprints%2Ftarget_timeframes_weather.yaml) | [Source](./blueprints/target_timeframes_weather.yaml)
+
+This blueprint will provide the data source based on the forecast of the provide weather sensor and will use the specified `Forecast attribute` as the value for the calculation of the target timeframes. The forecast will be refreshed every 30 minutes.

_docs/blueprints/target_timeframes_weather.yaml

@@ -0,0 +1,78 @@
+blueprint:
+  name: Target Timeframes - Weather source
+  description: Configures a target timeframe data source from Weather
+  domain: automation
+  author: BottlecapDave
+  input:
+    target_timeframe_data_source_sensor:
+      name: Target timeframe data source sensor
+      description: The data source sensor which represents the data source to update
+      selector:
+        entity:
+          filter:
+          - domain:
+            - sensor
+            integration: target_timeframes
+          multiple: false
+    weather_sensor:
+      name: Weather sensor
+      description: The weather sensor to get the forecast for.
+      selector:
+        entity:
+          filter:
+          - domain:
+            - weather
+          multiple: false
+    forecast_attribute:
+      name: Forecast attribute
+      description: Type in the name of the desired forecast attribute to use as the value (e.g. "precipitation", "uv_index" or "temperature"). See the metadata of the result for possible options
+      default: "condition"
+      selector:
+        text:
+variables:
+  target_timeframe_data_source_sensor: !input target_timeframe_data_source_sensor
+  weather_sensor: !input weather_sensor
+  forecast_attribute: !input forecast_attribute
+  millisecond_jitter: >
+    {{ range(1, 1000) | random }}
+mode: queued
+max: 4
+triggers:
+- trigger: time_pattern
+  minutes: '/30'
+condition: []
+action:
+# Add a bit of jitter so the API isn't hit at once
+- delay:
+    milliseconds: >
+        {{ millisecond_jitter }}
+- action: weather.get_forecasts
+  target:
+    entity_id: !input weather_sensor
+  data:
+    type: hourly
+  response_variable: weather_forecast
+- variables:
+    data_source_data: >
+      {%- set forecast_items = weather_forecast[weather_sensor]["forecast"] -%}
+      {%- set data = namespace(items=[]) -%}
+      {%- for forecast in forecast_items -%}
+        {%- set start = forecast["datetime"] | as_timestamp | timestamp_utc -%}
+        {%- set end = ((start | as_datetime) + timedelta(minutes=30)) | as_timestamp | timestamp_utc -%}
+        {%- set value = forecast[forecast_attribute] | float -%}
+        {%- set new_item = [{ 'start': start , 'end': end, 'value': value, 'metadata': forecast }] -%}
+
+        {%- set data.items = data.items + new_item -%}
+
+        {%- set start = end -%}
+        {%- set end = ((start | as_datetime) + timedelta(minutes=30)) | as_timestamp | timestamp_utc -%}
+        {%- set new_item = [{ 'start': start , 'end': end, 'value': value, 'metadata': forecast }] -%}
+
+        {%- set data.items = data.items + new_item -%}
+      {%- endfor -%}
+      {{ data.items }}
+- action: target_timeframes.update_target_timeframe_data_source
+  data: >
+    {{ { 'data': data_source_data } }}
+  target:
+    entity_id: !input target_timeframe_data_source_sensor

_docs/setup/target_timeframe.md

@@ -64,9 +64,27 @@ This will only evaluate target times if no target times have been calculated or
 
 For example, lets say we have a continuous target which looks between `00:00` and `08:00` has existing target times from `2023-01-02T01:00` to `2023-01-02T02:00`. 
 
-* If the current time is `2023-01-02T00:59`, then the target times will be re-evaluated and might change if the target period (i.e. `2023-01-02T00:30` to `2023-01-02T08:30`) has better values than the existing target times (e.g. the external weightings have changed).
+* If the current time is `2023-01-02T00:59`, then the target times will be re-evaluated and might change if the target period (i.e. `2023-01-02T00:30` to `2023-01-02T08:00`) has better values than the existing target times (e.g. the external weightings have changed).
 * If the current time is `2023-01-02T01:00`, the the target times will not be re-evaluated because we've entered our current target times, even if the evaluation period has cheaper times. 
-* If the current time is `2023-01-02T02:01`, the the target times will be re-evaluated because our existing target times are in the past and will find the best times in the new rolling target period (i.e. `2023-01-02T02:00` to `2023-01-02T10:00`). 
+* If the current time is `2023-01-02T02:01`, the the target times will be re-evaluated because our existing target times are in the past and will find the best times in the new target period (i.e. `2023-01-02T02:00` to `2023-01-02T08:00`). 
+
+#### Always
+
+This will always evaluate the best target times for the target period, even if the sensor is in the middle of an existing target time period.
+
+For example, lets say we have a continuous target which looks between `00:00` and `08:00` and has existing target times from `2023-01-02T01:00` to `2023-01-02T02:00`. 
+
+* If the current time is `2023-01-02T00:59`, then the target times will be re-evaluated and might change if the new target period (i.e. `2023-01-02T00:30` to `2023-01-02T08:30`) has better times than the existing target times.
+* If the current time is `2023-01-02T01:31`, then the target times will be re-evaluated and might change if the new target period (i.e. `2023-01-02T01:30` to `2023-01-02T08:30`) has better times than the existing target times.
+* If the current time is `2023-01-02T02:01`, the the target times will be re-evaluated because our existing target times are in the past and will find the best times in the new target period (i.e. `2023-01-02T02:00` to `2023-01-02T08:00`).
+
+!!! note
+
+    This is only supported when [Re-evaluate within time frame](#re-evaluate-within-time-frame) is enabled, otherwise it will behave the same as the other options.
+
+!!! warning
+
+    This setting means that you could end up with the sensor not turning on for the fully requested hours as the target times might be moved ahead half way through the picked times. It also could mean that the sensor doesn't come on at all during the requested look ahead hours (e.g. 8) because the lowest period kept moving back. 
 
 ### Offset
 

custom_components/target_timeframes/config/target_timeframe.py

@@ -15,7 +15,10 @@
   CONFIG_TARGET_MINIMUM_REQUIRED_MINUTES_IN_SLOT,
   CONFIG_TARGET_NAME,
   CONFIG_TARGET_OFFSET,
+  CONFIG_TARGET_ROLLING_TARGET,
   CONFIG_TARGET_START_TIME,
+  CONFIG_TARGET_TARGET_TIMES_EVALUATION_MODE,
+  CONFIG_TARGET_TARGET_TIMES_EVALUATION_MODE_ALWAYS,
   CONFIG_TARGET_TYPE,
   CONFIG_TARGET_TYPE_CONTINUOUS,
   CONFIG_TARGET_WEIGHTING,
@@ -186,4 +189,8 @@ def validate_target_timeframe_config(data):
   if minimum_required_minutes_in_slot is not None and (minimum_required_minutes_in_slot < 1 or minimum_required_minutes_in_slot > 30):
     errors[CONFIG_TARGET_DANGEROUS_SETTINGS] = "invalid_minimum_required_minutes_in_slot"
 
+  if (data[CONFIG_TARGET_TARGET_TIMES_EVALUATION_MODE] == CONFIG_TARGET_TARGET_TIMES_EVALUATION_MODE_ALWAYS and
+      (CONFIG_TARGET_ROLLING_TARGET not in data or data[CONFIG_TARGET_ROLLING_TARGET] == False)):
+    errors[CONFIG_TARGET_TARGET_TIMES_EVALUATION_MODE] = "always_evaluation_not_supported"
+
   return errors

custom_components/target_timeframes/const.py

@@ -112,6 +112,7 @@
           options=[
             selector.SelectOptionDict(value=CONFIG_TARGET_TARGET_TIMES_EVALUATION_MODE_ALL_IN_PAST, label="All existing target rates are in the past"),
             selector.SelectOptionDict(value=CONFIG_TARGET_TARGET_TIMES_EVALUATION_MODE_ALL_IN_FUTURE_OR_PAST, label="Existing target rates haven't started or finished"),
+            selector.SelectOptionDict(value=CONFIG_TARGET_TARGET_TIMES_EVALUATION_MODE_ALWAYS, label="Always"),
           ],
           mode=selector.SelectSelectorMode.DROPDOWN,
       )

custom_components/target_timeframes/entities/target_timeframe.py

@@ -190,7 +190,8 @@ async def async_update(self):
         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,
+          applicable_target_end,
+          self._target_timeframes,
           self._config[CONFIG_TARGET_NAME]
         )
 

custom_components/target_timeframes/translations/en.json

@@ -133,7 +133,8 @@
         "minimum_or_maximum_value_not_specified": "Either minimum and/or maximum value must be specified for minimum hours mode",
         "minimum_value_not_less_than_maximum_value": "Minimum value must be less or equal to the maximum value if both are specified",
         "invalid_integer": "Value must be a number with no decimal places",
-        "invalid_minimum_required_minutes_in_slot": "Value must be between 1 and 30"
+        "invalid_minimum_required_minutes_in_slot": "Value must be between 1 and 30",
+        "always_evaluation_not_supported": "Always evaluation mode is only supported when \"Re-evaluate multiple times a day\" is enabled"
       }
     },
     "rolling_target_time_period": {