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Add config flow for Derivative (#22063)
Co-authored-by: Erik Montnemery <erik@montnemery.com>
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@ -12,16 +12,35 @@ ha_qa_scale: internal
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ha_codeowners:
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- '@afaucogney'
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ha_domain: derivative
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ha_config_flow: true
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ha_platforms:
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- sensor
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---
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The `derivative` platform creates a sensor that estimates the derivative of the values provided by a source sensor.
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Derivative sensors are updated upon changes of the **source**.
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The derivative integration creates a sensor that estimates the derivative of the
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values provided by another sensor (the **source sensor**). Derivative sensors are updated upon changes of the **source sensor**.
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## Configuration
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{% include integrations/config_flow.md %}
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{% configuration_basic %}
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Name:
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description: The name the sensor should have. You can change it again later.
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Input sensor:
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description: The entity providing numeric readings to create the derivative of.
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Precision:
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description: Round the calculated integration value to at most N decimal places.
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Time window:
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description: The time window in which to calculate the derivative. Derivatives in this window will be averaged with a simple moving average algorithm (SMA) weighted by time. This is for instance useful for a sensor that outputs discrete values, or to filter out short duration noise. By default the derivative is calculated between two consecutive updates without any smoothing.
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Metric Prefix:
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description: Metric unit to prefix the derivative result ([Wikipedia](https://en.wikipedia.org/wiki/Unit_prefix)).
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Time unit:
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description: SI unit of time of the derivative. If this parameter is set, the unit of measurement will be set to **x/y** where **x** is the unit of the source sensor and **y** is the value of this parameter.
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{% endconfiguration_basic %}
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To enable Derivative Sensor in your installation, add the following to your `configuration.yaml` file:
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## YAML Configuration
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Alternatlively, this integration can be configured and set up manually via YAML
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instead. To enable the Integration sensor in your installation, add the
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following to your `configuration.yaml` file:
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```yaml
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# Example configuration.yaml entry
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@ -72,7 +91,7 @@ For example, you have a temperature sensor `sensor.temperature` that outputs a v
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That means that two consecutive output values might be the same (so the derivative is `Δy/Δx=0` because `Δy=0` !)
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However, the temperature might actually be changing over time.
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In order to capture this, you should use a `time_window`, such that immediate jumps don't result in high derivatives and that after the next sensor update, the derivatives doesn't vanish to zero.
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An example configuration that uses `time_window` is
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An example YAML configuration that uses `time_window` is
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```yaml
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sensor:
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