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# VirtualEntityController

VirtualEntityController (abbreviated VEC) is a controller that creates and manages virtual entities (or devices) in Reactor. A virtual device/entity is one for which no physical device exists in the real world. Virtual devices are often used to help track states or provide configurable options within your automations. Some examples:

- A virtual switch called "Party Mode" that may set certain scenes and also disable timed-auto-off automations of indoor and outdoor lights (don't stop the party!);
- A virtual value (numeric) sensor canned "Pump Hours" may be used to accumulate the daily runtime of pool or fountain pump;
- A virtual string sensor may be used to store a multi-valued mode or state;
- A virtual binary sensor may be used to periodically query a remote RESTful API using an HTTP request and set the sensor state according to the response data.

Virtual entities can be used in automations just like any other entities.

The attributes of virtual entities can be either static or dynamic. Static attributes are those that just hold a value, like a variable. They can be modified using an action (described below) in your reactions. Dynamic attributes are derived by supplying source configuration, which may include an expression, that drives them. In these ways, the attributes of virtual entities are very much like Global Expressions.

## Creating Virtual Entities

The first step in creating virtual entities is to make sure VirtualEntityController is a configured controller in your system. Add the following to the `controllers` section of your `reactor.yaml` file (do not add the `controllers:` line shown below, that's just to help you indent the rest properly):

    controllers:  # do not copy this line
      - id: virtual
        name: VEC
        implementation: VirtualEntityController
        enabled: true
        config:
          entities:
            -
              id: A

After adding this configuration to your `reactor.yaml`, restart Reactor and hard-refresh any browser pages open to the Reactor UI. You can then go to the Entities list (left navigation), clear any previous filters, and enter "virtual" in the name search field — you should see a couple of new entities.

The configuration above creates a default virtual binary switch entity. This is done by the `entities` section, which is an array (the `-` in YAML indicates the start of a new array element). The `id: A` line tells VEC that the new virtual entity will have the ID `A`. You can assign any ID you want to a virtual entity, but you need to know:

1. The ID is required; you have to supply it; it cannot be generated for you;
2. The ID has to be unique; you can't use the same ID for another virtual entity (but it does not matter if an entity from another controller has the same ID);
3. The ID must be 1-64 characters long and only contain alphanumeric characters and underscore (i.e. upper- and lower-case A-Z, digits 0-9, `_`); IDs are case-sensitive (i.e. `a` and `A` are different IDs);
4. You should never change it — Reactor conditions and actions will find the entity by its ID, and if you change the ID, all of those conditions and actions will break.

When we only assign the `id` in configuration as we've done here, Reactor defaults the entity type to Binary Switch and supplies a default name. VEC has a few preconfigured entity types, and you can choose one by adding the `template` field with one of the following pre-defined values:

- `Binary Switch` — a typical on/off switch, implementing the `power_switch` and `toggle` capabilities with actions common to switches;
- `Dimmer` — a dimmer with switch capabilities extended with a settable (0%-100%) level;
- `Binary Sensor` — a binary state entity that has no actions;
- `Value Sensor` — an entity that stores a numeric value (and has no actions);
- `String Sensor` — an entity that stores a string value (and has no actions).

When setting the `template` value, make sure you spell the template name exactly as shown, including capitalization and spaces:

        config:
          entities:
            -
              id: A
              template: Binary Switch

You can supply a name for a virtual entity by adding the `name` field, like this:

        config:
          entities:
            -
              id: A
              template: Binary Switch
              name: Guest Mode

Since the Binary Switch and Dimmer entity types support the actions normally supported by switches and dimmers, you can use these actions to change the state of these entities. They also appear in the Reactor dashboard as switches and lights normally would, respectively, and can be operated the same way as well.

The attribute values of the sensor entity type have to be set using the `x_virtualentity.set_attribute` action. Your automations (specifically Reactions) can use an Entity Action on the virtual sensor entity, selecting this action, and then providing the attribute name and value to be set.

## Customizing Virtual Entities

Sometimes a template may not do exactly what you want, so you either need to modify it, or just create your own virtual entity with the capabilities, attributes and actions you want.

Let's say we want to add color temperature capability to a virtual dimmer for some reason. To do this, we would need to add the `color_temperature` capability to an entity. Here's how our new configuration would look:

        entities:
          -
            id: A
            template: Binary Switch
            name: Guest Mode
          -
            id: color_dimmer
            template: Dimmer
            name: Virtual Color Dimmer
            capabilities:
              - color_temperature

If you add this to your `reactor.yaml` and restart, you'll see the Virtual Color Dimmer entity with the capabilities assigned by the Dimmer template (`power_switch`, `toggle`, and `dimming`), and you will also see that added capability `color_temperature`. Now this is a trivial example, and in this case, only the attributes of `color_temperature` are available; none of the actions. The reason is that we would need to teach VEC what the actions do, and we're not quite ready for that yet.

Let's say you don't want to use any template, and just define your own virtual entity that has, for example, the `hvac_control`, `hvac_heating_unit` and `temperature_sensor` capabilities — the basis of a simple virtual thermostat. That might look like this:

        entities:
          -
            id: virtual_therm
            name: Virtual Heating Thermostat
            capabilities:
              - hvac_control
              - hvac_heating_unit
              - temperature_sensor
            primary_attribute: hvac_control.state

Here again, we are directing VEC to construct a virtual entity with the listed capabilities. It will have the attributes of those capabilities, and you'll be able to set them using `x_virtualentity.set_attribute`, but it won't have any of the actions until you define them (some day, later...). Remember, things like tracking the current thermostat mode, and turning the heating unit on when the temperature is below the setpoint, are functions performed in real life by the device behind the entity, but here in virtual entity world, there is no device, so there's nothing to manipulate the attributes automatically or perform an action requested.

## Defining Dynamic Attributes

The attributes of virtual devices don't need to be static. Let's say, for example, that we want to create a binary sensor that's true when our Hubitat Elevation's Mode is `Night`, but false otherwise. We'll start by using the Binary Sensor template, but we're going to customize just the `binary_sensor.state` attribute. To do that, we'll need a slightly different construction than we used in the previous section's examples.

        entities:
          -
            id: mode_night
            name: Mode is Night
            capabilities:
              binary_sensor:
                attributes:
                  state:
                    expr: getEntity( 'hubitat>mode' ).attributes.string_sensor.value == "night"

In the previous section's examples, the `capabilities` value was an array of capability names. In this example, we've changed it to an object; the keys of this object are capability names (e.g. `binary_sensor`). Within a capability, the key `attributes` is used to tell VEC we're configuring attributes in that capability, and finally, `state` is the name of the attribute in the `binary_sensor` capability that we are defining. The `expr` value is an expression that will drive the value of the attribute `binary_sensor.state` on this entity.

Dynamic attribute expressions are sensitive to dependencies. In the example above, VEC learns that the expression depends on the `hubitat>mode` entity, and will automatically re-evaluate the expression every time that entity changes, without using outside Rules or Reactions. And, these expressions can be as complex as you'd like.

If you have long expressions, use block text like this to keep your configuration more readable:

    expr: |
      day_of_week = dateparts().weekday,
      getEntity( 'hubitat>mode' ).attributes.string_sensor.value == "night" &&
      ( day_of_week == 0 || day_of_week == 6 )

In some cases, you may not want to update an entity's attribute based on some other condition. The `if_expr` key can be used to define an expression that determines whether the attribute should be updated at all, or skipped. If the result of the expression is true, or `if_expr` is not provided, the attribute will be updated; otherwise (i.e. when the `if_expr` result is false), the attribute is skipped.

## HTTP Request-driven Entities

VirtualEntityController incorporates a mechanism for making HTTP requests to fetch data that can then be used to set attributes on virtual entities. Here's an example configuration:

          entities:
            - id: "H"
              name: "IP Address Changed"
              template: Binary Sensor
              http_request:
                url: "https://api.ipify.org?format=json"
                interval: 3600
              capabilities:
                binary_sensor:
                  attributes:
                    state:
                      expr: "response.ip != '10.56.71.123'"
                string_sensor:
                  attributes:
                    value:
                      expr: response.ip

Here you can see we've added an `http_request` section to the configuration of an otherwise typical virtual binary sensor. With this configuration, VirtualEntityController will request our current public IP address from the ipify.org API every hour. VEC places the response from the server in a variable called `response` in the expression context that is used by each attribute's value `expr`ession. In this case, we've told api.ipify.org (via query parameter on the URL) to respond with a JSON object (i.e. `{ "ip": "10.56.71.123" }`), and VirtualEntityController knows to convert that into an real object for use in the value `expr`ession. We've set up the value expression for `binary_sensor.state` so that it's true if the IP address returned by the API is not the value we expect (that is, our public IP address has changed for some reason). Additionally, this entity is extended with the `string_sensor` capability, and its `value` attribute receives the IP address returned by the API (just so we can see it).

The following are the configuration keys known in `http_request`:

`url` (required):
  The HTTP(S) request URL. This is a required value, and it must be enclosed in quotes.
`interval`:
  The request interval in seconds (a positive, non-zero integer). There is no specific control over the basis of the interval; that is, an interval of one hour is not guaranteed to run at the top of every hour (0 minutes after the hour), and in fact, it's pure luck that it ever would. For strict control of request time, see `at` below.
`at`:

  An array of fixed times of day (24-hour form) at which the request should be made. Times must be specified as `"HH:MM"` strings, as shown below:

      at:
        - "17:30"  # 5:30pm
        - "00:00"  # midnight

  The times do not need to be specified in order. If the request is to be run only once per day, the abbreviated `at: "HH:MM"` single-line form may be used.

  If `at` is used, `interval` is ignored.

`method` (optional):
  The HTTP request method. If not specified, GET is used.
`auth_type` (optional):
  If HTTP authentication is required, indicate the type, which must be `basic` or `digest`. Omit this key and value if authentication is not required.
`auth_username` (optional):
  Username for HTTP authentication. This value should be enclosed in quotes.
`auth_password` (optional):
  Password for HTTP authentication. This value should be enclosed in quotes.
`headers` (optional):
  This can either be an array of strings formatted `key: value` (header name/key + colon + space(s) + value), or an object containing key/value pairs.

      # This is array form:
      headers:
        - "X-API-Key: 23904823985klj238"
        - "X-Auth-Type: simple"

      # This is object form:
      headers:
        X-API-Key: "23904823985klj238"
        X-Auth-Type: "simple"

`retry_interval` (optional):
  If a request fails, the next request attempt will be made on this interval rather than `interval` or next `at` time. Units are seconds, so the value must be a positive integer, and the default is 60 seconds. If the retry interval is 0, it is ignored and scheduling proceeds as if the request had succeeded.
`retry_limit` (optional):
  If a request fails, up to this many attempts will be made using the `retry_interval`. If the number of retries exceeds this limit, further retries will fall back to `interval` or `at` timing. This must be a positive integer or zero; the default is zero, meaning retries on `retry_interval` continue indefinitely.
`quiet_failure` (optional):
  If an HTTP request fails and this value is boolean true, VirtualEntityController will never issue a notification (shown in the Status page's Current Alerts widget, if present). If this value is an integer, then no notification is issued until the number of attempts exceeds the given value (e.g. if `3` is given, no notification is sent for the first three request attempts, but if the fourth attempt fails, a notification is then sent. Request failures are always logged in Reactor's log file(s) regardless of this setting. See Handling Request Failures below.
`skip_update_on_error` (optional):
  If a request fails and this flag is true, all of the capabilities and attributes of the entity will be left as-is (i.e. the entity will be treated as if the request had not occurred). The default is false, meaning each attribute will be updated and you need to handle the null `response` value in your expressions. See Handling Request Failures below.
`dump_response` (optional):
  This boolean flag, when true, logs the entire response from the server to the Reactor log file so that you can inspect it. This is helpful for debugging and navigating JSON objects for specific elements of data. JSON data may not be formatted nicely for human reading, but you can copy-paste it into tools like jsonlint.com to improve their readability.
`ignore_invalid_certs` (optional):
  When true, HTTPS requests to servers using self-signed or invalid certificates are permitted. Default is false.
`force_json` (optional):
  If your server returns a JSON response but does not declare it as such (i.e. by failing to provide the required `Content-Type: application/json` response header), this boolean flag can be used to force VirtualEntityController to handle the response as JSON. It can also be used in instances where a server-side failure produces a 200 status reply with a text or HTML error message rather than JSON containing an error indicator. Any non-JSON response will cause the request to fail (which can then be handled by other request flags or your expression(s)).
`body` (optional):
  The HTTP request body, if any. This can be a string or an object. If an object, it will be converted to a JSON string. It is required that you supply a `Content-Type` header if you are providing a request body. You do not, however, need to supply `Content-Length`; VirtualEntityController will compute and supply it based on the body given.

### Handling Request Failures

When an HTTP request fails, the reason for the failure is always logged, and the `x_virtualentity.last_http_status` attribute is updated. Since there is no valid response from the server, the `response` expression variable will be null.

The `quiet_failure` request configuration flag will control if (or when) a notification is sent to the Reactor UI (displayed in the Current Alerts widget on the Status panel). See the detail for that flag in the previous section, above.

If you have set the `skip_update_on_error` flag to true, your expressions will not be run and the attributes of your entity will not be affected. They will simply keep whatever value they last had until a successful request later updates them.

But if `skip_update_on_error` is not provided or is false, your expressions will still be run and receive the null value for `response`. Unless you handle this possibility, you will get additional errors from your expressions. To handle the null and avoid expression errors, here are some suggestions:

1. To avoid updating a single attribute, give it a `if_expr: "! isnull( response )"`. This will cause that attribute's value `expr`ession to be skipped, and that attribute will be left with its current value until a later request succeeds.

2. For attributes where you want to provide a signalling value to your automations that no data is available, handle the null `response` variable in your value `expr`ession as you would for any other variable. For example, you may use the coalesce operators to provide a default value: `response?.current?.temperature` (the result value will be null if `response` is null). Or, you could use the ternary operator or an `if-then-endif` block:

       isnull( response ) ? -1 : response.current.temperature

       if isnull( response ) then -1 else response.current.temperature endif

   Both of these examples return -1 when `response` is null. There are countless other ways to handle it, and the value that you use is entirely up to you.

Note that using `skip_update_on_error: true` is different from using `if_expr` in that the former applies to all attributes on the entity, where the latter applies only to the attributes having `if_expr` in their configurations. This gives you a lot of flexibility in handling request errors and what happens to the attributes.

## Time Series

It is often useful to collect data for analysis as a series of values over a period of time, referred to as a time-series. VEC allows you to create a time-series from the value of another attribute on another entity, or the value of a global variable. Some instances where a time-series may be helpful in handling a device may include:

- The device sends updates frequently and causes excessive rule evaluation. Using a time-series can dampen-out the frequent updates from the device and reduce them to a reasonable set to handle.
- You want to respond to trends in data, and you need a way to detect them. For example, you have a humidity sensor in a bathroom, and you want to use it to control an exhaust fan. A fixed trigger value isn't working, because the natural change in humidity inside your home over the course of a day and from season to season changes gradually, but you notice that there's a sudden change over short period when showering/bathing occurs (so the rate of change is more important than the actual values).

To define a time-series in VEC, add the following config to your virtual entity's attribute definition:

        config:
          entities:
            -
              id: time_series
              name: "Time Series Example"
              capabilities:
                value_sensor:
                  attributes:
                    value:
                      model: time series
                      entity: "weather>home"
                      attribute: "wx.temperature"
                      aggregate: sma
                      interval: 60  # minutes
                      depth: 3      # keep last three samples
                      precision: 2  # round the result to 2 digits right of decimal
              primary_attribute: value_sensor.value
              type: ValueSensor

The `model` key with the string value `time series` tells VEC that you want to collect data in a time-series. The source for the time-series is defined by `entity` and `attribute`, which are the entity (by canonical ID or name) and the attribute (specified as `capabilityName.attributeName`, e.g. `temperature_sensor.value`) on the entity for data collection. Alternately, you may use `global_variable: name` to use the named global variable as the source for the series.

The `interval` value, expressed in minutes, is the interval for sampling the source. On this interval, the source entity/attribute or global variable value will be collected and stored.

The `depth` value is the number of samples that should be stored and considered in the aggregate, and is always an integer >= 2. In the example given here, we are sampling the data on a 60-minute (one hour) interval, and with `depth: 3`, the samples stored will be the most recent, the sample taken 60 minutes prior to the most recent, and the sample taken 120 minutes prior to the most recent (so note that the depth of three in this case means a time span of two hours, not three). The `depth` value is not used by all aggregates, so refer to the individual aggregate descriptions below to find which use `depth` (and the default for that aggregate).

Info

Time is the essence of the time-series collection. Restarts of Reactor do not cause additional samples to be collected. Changes in the source value do not cause additional samples to be collected. Samples are only collected on the given `interval`, and the value collected is whatever the value is at that moment. That means sudden, short changes in value that occur between two intervals may be missed. Perhaps some day, Reactor will do event-based collection and resample the data, but for now, it's strictly managed by the clock.

The final step is to process (or aggregate) the time-series data to a scalar value, which is then stored on the virtual entity's designated attribute (in this case `value_sensor.value`). The `aggregate` configuration directive is given a (string) method name to define how that scalar value is computed. Additional configuration directives modify the method. The following table defines the supported methods:

`aggregate` value | Method Description
--- | ---
`sma` | Simple Moving Average — the average of the time-series values is computed and given as the scalar result. The `depth: <integer >= 2>` value should be specified to control how far back to look when calculating the average (i.e. how many samples to consider).
`wma` | Weighted Moving Average — the average is computed, weighting each value considered with a factor. The `depth` value should be specified as described for `sma` above (default: 2). Weighting factors may be given in an array called `weight`, with the first element of the array applying to the most recent sample in the series, the second weight element applying to the most recent sample's predecessor, and so on. If no `weight` array is given, a default set that weights newer values higher than older values is used. To preserve the range of result values, the number of weights given should equal `depth` (i.e. if `depth` is 3, supply 3 weight values), and their sum should be 1.0 (the default set will follow these rules).
`ses` | Simple Exponential Smoothing — the time-series is put through an exponential smoothing algorithm with a given `alpha` (0.0 < `alpha` < 1.0; default 0.61803; closer to 0.0 means more aggressive smoothing). The scalar value is the last value in the smoothed series. This algorithm may be most useful for data that is given to sudden, short spikes in value. Hint: if you turn on debug level 5 for VirtualEntityController in `config/logging.yaml`, the time-series values and smoothed values will be written to the log in CSV format that you can import into Excel and plot in a graph. This may help you determine a suitable value for `alpha`.
`rate`, `derivative` | The rate of change of the time-series. The `depth` may be specified (see `sma`) to establish the "lookback" of the series; default `depth` for this method is 2 (meaning the reported rate of change will be that of the two most recent samples; if `depth` is 3, the rate will be determined from the most recent sample and its predecessor's predecessor; another way to look at this is to number the samples backwards, with the most recent sample being 1, its predecessor is 2, etc.). The rate is expressed in units of the source value per minute (i.e. if the source value is temperature reported in degrees Celsius, then the scalar value's units will be degrees Celsius per minute).
`accel`, `derivative2` | The acceleration of the recent samples (i.e. the rate of change of the rate of change, the second derivative). Here, too, `depth` may be applied, and like `rate`, the default `depth` is 2. The acceleration is computed by comparing the rate of change of the most recent `depth` values to the prior group of `depth` values. The time series must `2 * depth - 1` samples for this aggregate. See the detailed example and explanation below.
`first`, `oldest` | The first (i.e. oldest) value in the time-series is returned.
`last`, `latest` | The last (i.e. latest) value in the time-series is returned.
`max` | The maximum of the time-series' values is returned.
`min` | The minimum of the time-series' values is returned.
`median` | The median of the time-series' values is computed and returned as the scalar result.
`raw` | The time-series itself is handed back as the value. This exposes the time-series data to an `expr` configuration directive that allows you to write an aggregate algorithm of your own using the expression language. The time-series data will appear in that context in the `value` variable as an array of objects with keys `time` (milliseconds since the Epoch) and `value` (in units of the source).

The configuration keys `expr` and `precision` can be used to affect the aggregate's result value before it saved to the target entity's attribute. When using `expr`, the result value of the aggregations is available in a local variable called `value`. Example: `expr: "value*2"` will double the result of the aggregation, and `precision: 2` will round the result to two digits right of the decimal point.

### Time Series Example 1: weighted average

Here's an example of using `wma` (weighted average) to look back at the last four samples, with significant custom weighting on the two most recent samples:

                      model: time series
                      ...
                      aggregate: wma  # Weighted moving average
                      depth: 4
                      weight:
                        - 0.50  # most recent sample, 50% weight
                        - 0.35  # 35%
                        - 0.10  # 10%
                        - 0.05  # 5% oldest sample of the four considered

Again, if you don't supply a `weight` array, Reactor will compute default weights. If you supply a `weight` array but it's too short (i.e. fewer than `depth` values in the array), missing weights are treated as zeroes (0.0); if the array is too long, the excess weights are simply ignored.

Sum of Weights = 1.0

In order for the scalar result to be in a similar range of values to those in the time-series, it's very important that you supply all of the weights needed, no more no less, and that the sum of the weights is 1.0. If either of these is not true, then your scalar result will be skewed out of the range. That may be OK, and you may even find crafty ways to use that fact to produce useful results, but for most users, sticking to perfect size/perfect sum is the way to go.

### Time Series Example 2: `accel`

Here's an example of a time-series of 15 minute samples from am entity, comparing the acceleration of the most recent hour to that of the previous hour:

                  attributes:
                    value:
                      model: time series
                      entity: "mqtt>garage_humidity_sensor"
                      attribute: "humidity_sensor.value"
                      interval: 15  # minutes
                      aggregate: accel
                      depth: 5

The `interval` configured for this example is 15 minutes with a `depth` of 5 samples, so Reactor will store 9 samples (see below). With a 15-minute interval, that's a span of 120 minutes of data collected. As shown below, the samples can be numbered from 1 to 9, where 1 is the most recent sample and 9 is the oldest:

Sample | 9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
time | t-120 | t-105 | t-90 | t-75 | t-60 | t-45 | t-30 | t-15 | t

Acceleration is the change in rate, so two rates must be computed to be able to compute the change in rate. The meaning of `depth` for this aggregate is the depth of the rate samples. Given the depth of 5 here, Reactor will compare the rate of change for the most-recent hour's samples 1 through 5 with the prior hour's samples 5 through 9. Notice these overlap at sample 5, t-60, because it is considered to be the start of the most recent hour and also the end of the prior hour. Because two rates are computed using `depth`, this aggregate requires at least `(2*depth)-1` samples in the time series. Until it has collected sufficient samples, the result of the aggregate will be null.

This configuration would work the same if you specified `retention: 120` in place of `depth: 5`. With a 15 minute sample interval, Reactor would calculate that it needs to collect 9 samples (as before), and would divide those samples evenly into the two 60-minute periods (samples 1-5 and 5-9). You may, therefore, specify either `depth` or `retention` for this aggregate; if you specify both, `depth` will override/control to the degree that if the specified `retention` is too small for the `depth`, it will be extended. When specified, `retention` should always be an even multiple of `interval`.

### Removing Data Outliers

Some sensors (in particular) can produce "spikey" results — their data often contains outlying values from momentary abberations in measurement. For example, a light sensor may report low values when someone walks in front of them. These abberations aren't important to the result and can throw off measurements in aggregation, so VirtualEntityController provides a couple of methods for controlling them before the aggregate is calculated.

The most basic is range restriction, which can be enabled through the use of `minimum` and `maximum` values in the attribute configuration. You may provide either or both; when a sourced value is outside the restriction range, it is simply pinned to that extreme (e.g. if a value is below the configured `minimum`, it is set to the `minimum` value instead). In the following example, humidity reports outside the range of 30-80% are simply pinned at these extremes:

                  attributes:
                    value:
                      model: time series
                      entity: "mqtt>garage_humidity_sensor"
                      attribute: "humidity_sensor.value"
                      interval: 15  # minutes
                      aggregate: sma
                      depth: 5
                      minimum: 30
                      maximum: 80

The other way to control outliers is threshold filtering, which removes non-compliant data from the time-series. VirtualEntityController provides three possible methods of threshold filtering, in order of complexity: Near Midline, Interquartile Range (IQR), and Z-Scoring.

To enable threshold filtering, add a `threshold` key to your attribute configuration with your required `type` as shown in the following example:

                  attributes:
                    value:
                      model: time series
                      entity: "mqtt>garage_humidity_sensor"
                      attribute: "humidity_sensor.value"
                      interval: 15  # minutes
                      aggregate: sma
                      depth: 5
                      retention: 360  # store extra data to allow for outlyer removal
                      threshold:
                        type: iqr
                        # additional filter parameters may go here, depending on type

Here, two important changes have been made to the configuration: first, the `threshold` configuration has been added to configure Interquartile Range (IQR) filtering; second, the `retention` period has been specified longer than the `depth` requires, so that additional values are stored. Storing additional values is necessary because applying the threshold will remove outlying data points, and if you only collect as many data points as the `depth` requires (the default behavior), there's no room for removal of data. If the thresholding operation removes just one data point, there is then insufficient data to compute the aggregate and a null value results, so threshold filtering almost always requires adjustment of `retention`.

`type` | Parameters | Description
--- | --- | ---
`near` | `value`, `threshold` | Near Midline uses the midline `value` and `threshold` to define the acceptable range `value`±`threshold`. For example, an acceptable range of 30 to 70 would be expressed with `value: 50` and `threshold: 20` (50 - 20 = 30, 50 + 20 = 70). The `threshold` can also be specified as a percentage by appending a `%` character to the value (e.g. `threshold: 10%` with `value: 50` would result in an effective range of 50±5 = 45 to 55.
`iqr` | none | Interquartile Range (IQR) removes data points that are outside the central 50% of values, effectively removing the largest and smallest values in the data set. This method requires no parameters.
`zscore` | `threshold` | Z-Scoring is a bit more sophisticated than IQR in that it removes data that is more than `threshold` standard deviations from the mean of the data set (default: 2, an integer in range of 1 to 5 inclusive).

The `minimum` and `maximum` range controls, if specified, are applied to new data values first. Then threshold filtering is applied, if configured. Finally, the aggregate is computed from the resulting data series.

## Defining Actions

Actions for virtual devices can do three things:

1. Computationally/procedurally modify attributes on the virtual entity;
2. Make HTTP(S) requests and, optionally, process the return data and update attributes on the virtual entity;
3. Start a Global Reaction.

Defining actions for a capabilities is done in an `actions` section underneath each configured capability. Consider the following trivial example where only attributes are modified (case 1 above):

          entities:
            - id: 'A'
              name: Virtual Switch A
              capabilities:
                power_switch:
                  attributes:
                    state:
                      value: false
                  actions:
                    "on":
                      target: state
                      value: true
                    "off":
                      target: state
                      value: false

In this simple case, a virtual switch entity `A` is defined having the `power_switch` capability. The initial state of the switch is off. When the `power_switch.on` action is performed, it writes the `state` attribute with the boolean value `true`. When the `power_switch.off` action is performed, the `state` attribute receives `false`. This makes it a fully-functional virtual switch that can be turned on and off like any other.

The `target` value tells VEC which attribute is going to be written. The `value` key provides a literal value to be written. There are other ways to get the value for the target attribute, such as `parameter` (the value of a parameter passed to the action), `entity` (the entity object, allowing you to plunge the depths of values in its structure for a particular source), and `attribute` (a synonym for `entity: attributes.<value>`, that is, the value of a specific attribute on the entity). Here's an implementation of `power_switch.set` to add to our virtual switch that shows how `parameter` can be used. Note that the parameter is called `state` on the `power_switch.set` action:

                    "set":
                      target: state
                      parameter: state

If this case, if `power_switch.set` is performed with a parameter `state` set to `true`, the `true` value will be written to the target `power_switch.state` attribute. Likewise if the action is performed with `state` passed as `false`, the target attribute will receive `false`.

Did you know?

Did you know you don't need to provide an implementation of `power_switch.set`? If your entity doesn't have an implementation for this action, Reactor's default implementation will use `power_switch.on` and `power_switch.off` to emulate it. The same is also true in reverse: if you implement `power_switch.set` but not `.on` and `.off`, Reactor will emulate `.on` and `.off` using `.set`. So we're actually doing a little more work in this example than is necessary, but it's good for a simple illustration of principles.

Actions can also make HTTP requests. This is useful for using virtual entities to map to actual devices or external data. As an example, let's say we have a Shelly relay that we can access via HTTP:

          entities:
            - id: 'vec_range_hood_shelly'
              name: Range Hood
              capabilities:
                power_switch:
                  attributes:
                    state:
                      value: false
                  actions:
                    "on":
                      http_request: "http://192.168.0.125/relay/0?turn=on"
                      target: state
                      expr: "response?.ison"
                    "off":
                      http_request: "http://192.168.0.125/relay/0?turn=off"
                      target: state
                      expr: "response?.ison"

In this case, when we perform the `power_switch.on` action, an HTTP request with the given URL is made. Shelly relays respond with a JSON data structure that includes an `ison` key/value pair that provides the new state of the relay. The `expr` expression uses that to derive the value for the target `power_switch.state` attribute. Details of how `expr` functions and works with other directives is further explained below.

The URL string specified allows substitution using `%expression%` syntax. The string between `%`s is evaluated as an expression, and its result is replaced in the URL string. Note that if this is being used for URL query parameter values, it is recommended that the expression be wrapped in the `urlencode()` function so that special characters are correctly escaped/encoded.

The following example shows substitition in a URL with an implementation of `power_switch.set` for a Shelly relay, where the (boolean) `state` parameter passed to the action is mapped to the words `on` or `off` in the URL query parameter as appropriate:

                    "set":
                      http_request: "http://192.168.0.125/relay/0?turn=%urlencode(parameters.state ? 'on' : 'off')%"
                      target: state
                      expr: "response?.ison"

The final thing an action can do is run a Reaction. This is pretty straightforward:

                  actions:
                    "on":
                      run: "MyOnReaction"  # supply Global Reaction name or ID here
                    "off":
                      run:
                        reaction: "MyOffReaction"  # name or ID
                        wait: true

This example shows two different forms of the `run` directive, which runs a specified Reaction. The former form, where the value of `run` is the Reaction name or ID, enqueues the specified reaction and continues execution immediately (it doesn't wait for the Reaction to complete). The latter form specifies an object containing `reaction` and `wait` key/value pairs, where `reaction` specifies the name or ID of the Reaction to be run, and `wait`, when true specifies that VEC should wait at this point for the Reaction to complete.

Here's the full description of the possible key/value pairs used for an action implementation:

                  actions:
                    "action-name":
                      target: "<attribute_name>" or "<capability_name.attribute_name>"  # default capability is action's capability
                      run: "<reaction_id or name>"
                      run:           # alternate object form gives more options/control:
                        reaction: "<reaction_id or name>"
                        wait: true|false  # default false
                      http_request: "<url>"
                      http_request:  # alternate object form gives more options/control:
                        url: <url>
                        method: <GET|POST|PUT>  # optional
                        #  ...and other options -- see HTTP Request-driven Entities above
                      value: <constant num, string, or bool>
                      parameter: "<parameter_name>"
                      attribute: "<attribute_name>"
                      entity: "dereference_expression"
                      system: "dereference_expression"
                      expr: "expression"
                      map:
                        "key1": "value1"
                        "keyn": "valuen"
                      map_unmatched: <constant num, string, or bool>
                      skip_empty: true|false  # default false
                      skip_null: true|false   # default false
                      default: <constant num, string, or bool>

Key | Value & Usage
--- | ---
`target` | (string, optional) Target attribute to be written. May be specified as a simple attribute name, in which case the capability name is the same as the action's, or in `capability_name.attribute_name` form to write an attribute in a different capability from the action.
`run` | (string or object; optional) Runs the given Reaction. If a string is given, the Reaction with that ID or name is run, and the action doesn't wait for the Reaction to complete. Using the object form, the `reaction` specifies the Reaction, and the `wait` key (boolean) determines if the action pauses until the Reaction completes. When the `run` key is used, no value is produced, none of the other keys is considered, and nothing is written to any attribute.
`http_request` | (string or object, optional) Causes an HTTP request to be made to the given URL. When given as a string only, the string is the URL to be fetched (via HTTP GET). When given as an object, additional control over the form and content of the request is possible. The key/value pairs allowed by the object are the same as those described for `http_request` in HTTP Request-driven Entities above.
`value` | (string, number, or boolean; optional) Value to be assigned to the `target` attribute.
`parameter` | (string; optional) Name of a parameter defined for the action; the value to be assigned to `target` will be taken from the named action parameter.
`attribute` | (string; optional) Name of an attribute on the entity on which the action is being performed. The value of this attribute will be assigned to the `target` attribute. For example, specifying `attribute: "dimming.level"` could cause the current dimming level on the entity to be written to the `target` attribute.
`entity` | (string; optional) A dereferencing expression to draw a value from within the current entity object. Specifing `attribute: "dimming.level"` is the same as specifying `entity: "attributes.dimming.level"` (so the former is a shortcut, really).
`system` | (string; optional) A dereferencing expression to draw a value from within VEC's system object.
`expr` | (string/expression; optional) An expression to be evaluated. The expression is evaluated after the above keys have been processed, so it is possible for the expression to manipulate the value derived from them. The current derived value is available in the expression as the variable `value`. For example, the current dimming level of an entity could be doubled by taking the value from the `dimming.level` attribute with `attribute: "dimming.level"` followed by the expression `min(1, value*2)` (the use of the `min()` function ensures that the new dimming value is not greater than 1.0).
`map` | (object; optional) A set of key/value pairs. If the current value matches a key in the map, it is replaced with the key's corresponding value.
`map_unmatched` | (string, number, or boolean; optional) A value to be used if the current value does not match any key in the given `map`. This optional directive is only considered when `map` is also used.
`skip_empty` | (boolean; optional) If true and the value is an empty string or null, it is not written to the `target` attribute.
`skip_null` | (boolean; optional) If true and the value is null, it is not written to the `target` attribute.
`default` | (string, number, or boolean; optional) If the value ends up being null, replace it with this `default` value.

Basically, the keys `value`, `parameter`, `attribute`, `system`, `entity` and `http_request` all determine a value from their respective sources. From there, the `expr` expression can manipulate the value if necessary. The `map` is then applied, followed by the `skip_` directives and finally `default`. The `target` determines what attribute the final value is written to, if any. When the `run` key is used, it does not produce a value and all other keys are ignored (and no attribute is written). Consider the following rules and recommendations:

### Multi-Step Actions

The action configuration can also be an array of objects conforming to the above description, and each element of the array is performed in sequence. This makes it possible to write multiple attributes in a single action, for example.

                hvac_heating_unit:
                  actions:
                    "set_setpoint":
                      -
                        target: setpoint
                        parameter: setpoint
                      -
                        target: units
                        value: "C"
                      -
                        http_request: "http://192.168.0.33/thermocontrol/api/set?target=%parameters.setpoint%"
                      -
                        run: "Notify Setpoint Adjusted"

In this example, the `hvac_heating_unit.set_setpoint` action performs four distinct steps:

1. It sets the `hvac_heating_unit.setpoint` attribute to the value of the `setpoint` parameter given to the action;
2. It sets the `hvac_heating_unit.units` attribute to "C" to indicate temperature is in Celsius;
3. It makes an HTTP request to a remote endpoint, presumably to set the target temperature of the related device using its local API. Any response from the endpoint is not written to an attribute, because no `target` has been given in this step.
4. Finally, a Reaction called Notify Setpoint Adjusted is started.

### Actions That Return Responses

If you are providing an action implementation for an action that is defined to return a response, the final value is the returned response data (whether it is written to a target attribute or not). If an action is meant only to generate a response and not write an attribute, `target: null` is supported (no attribute will be written, but all other processing occurs).

Updated: 2025-Dec-22