Pimatic Plugin which provides various filtering functions to achieve digital filtering or smoothing of sensor data. It is useful, for example, if the sensor data is not accurate and you wish to disregard potential outliers at the minimum and maximum of sensor values processed.
To date, the plugin provides three filter types:
SimpleMovingAverageFilterprovides is the unweighted mean of a given number of previous sensor values processed.SimpleTruncatedMeanFilterprovides a truncated mean, where the highest and lowest value of a given number of previous sensor values is disregarded (truncated) and the remaining values are used to calculate the arithmetic mean.SimpleRateOfChangeFilterprovides the relative rate of value change per minute.
By default, all filters are updated when a variable used as part of the filter expression is updated. Alternatively, a time-based update scheme can be used which will evaluate the expression in regular time intervals (see section "Time-based Update" below).
More filters can be added on request. If you wish to get involved you're welcome to make contributions. If you're not familiar with programming please open issue to describe your request as detailed as possible including references to background material and possibly an algorithmic description. If you like this plugin, please consider ★ starring the project on github.
You can load the plugin by editing your config.json to include the following in the plugins section. However, it
is recommended to use the plugin editor instead which is provided with the pimatic web frontend
{
"plugin": "filter",
"debug": false
}
A filter basically is a pimatic device instance which takes an input value from another device which is processed to produce an output value. Depending on the type of filter the number of output values produced may different from the number of inputs provided.
The Simple Moving Average filter, is the unweighted mean of a given number of previous sensor values processed. For a general discussion see Wikipedia on Moving Average.
The number previous sensor values processed by the filter is called sliding window. You can specify the "size" of the sliding window. By default, the window takes five elements. Initially, when the number of data values processed is smaller than the window size, the mean will be calculated from the existing values.
The "output" property defines the attribute which represents the output produced by the filter. It must have a "name" and an "expression" which defines a reference to the input value. In the simplest case, the expression contains a variable, but it can also contain a calculation or a string interpolation which finally produces the input value. Note, however, the resulting value must be a number to be processed by the filter. The "output" property may also contain a "label", "acronym", and "unit". If the "unit" value is set to "auto" the unit will be derived from the input attribute.
{
"class": "SimpleMovingAverageFilter",
"id": "filter1",
"name": "Filter",
"size": 5,
"output": {
"name": "temperature",
"label": "Temperature",
"expression": "$unipi-2.temperature",
"acronym": "T",
"unit": "°C"
}
}The Simple Truncated Mean filter is a truncated mean, where the highest and lowest value of a given number of previous sensor values is disregarded (truncated) and the remaining values are used to calculate the arithmetic mean. For a general discussion see Wikipedia on Truncated Mean.
The number previous sensor values processed by the filter is called sliding window. You can specify the "size" of the sliding window. By default, the window takes five elements. Initially, when the number of data values processed is smaller than the window size the mean will be calculated, as follows:
- if there are less than three values, no truncating is performed and the mean is calculated from the existing values
- if there are three or more values, truncating is performed and the mean is calculated from the remaining values.
The "output" property defines the attribute which represents the output produced by the filter. It must have a "name" and an "expression" which defines a reference to the input value. In the simplest case, the expression contains a variable, but it can also contain a calculation or a string interpolation which finally produces the input value. Note, however, the resulting value must be a number to be processed by the filter. The "output" property may also contain a "label", "acronym", and "unit". If the "unit" value is set to "auto" the unit will be derived from the input attribute.
{
"class": "SimpleTruncatedMeanFilter",
"id": "filter1",
"name": "Filter",
"output": {
"name": "Temperature",
"expression": "$unipi-2.temperature",
"acronym": "T",
"unit": "°C"
}
}The Simple Rate of Change filter provides the relative rate of value change per minute. It calculates the difference of two attribute value updates and divides it by the time difference of the updates. The rate scale can be changed from "minute" to "millisecond", "second" or "hour" by setting the "timeBase" property (see example below). Generally, it can be used to detect an unusual value change. An example use case for the Simple Moving Average is a humidity sensor in the bathroom where the rate of value change is used to detect if someone is taking a shower or the bathroom window has been opened (in wintertime).
The "output" property defines the attribute which represents the output produced by the filter. It must have a "name" and an "expression" which defines a reference to the input value. In the simplest case, the expression contains a variable, but it can also contain a calculation or a string interpolation which finally produces the input value. Note, however, the resulting value must be a number to be processed by the filter. The "output" property may also contain a "label", "acronym", and "unit". If the "unit" value is set to "auto" the unit will be derived from the input attribute followed by the "timebase" fraction denominator.
{
"class": "SimpleRateOfChangeFilter",
"id": "filter2",
"name": "Filter",
"output": {
"name": "rateOfChange",
"expression": "$unipi-2.temperature",
"acronym": "roc"
},
"timeBase": "minute"
}
All device classes provide support for statistical attributes which can be added easily to the the device configuration:
- min - minimum value
- max - maximum value
- mean - arithmetic mean value
- increase - increase of the value in relation to the previous value
- percentChange - the percentage change of the value in relation to the previous value
- source - input value (provided here for convenience)
The attributes are added by setting the "stats" property of the device configuration which takes an array of string values. It is recommended to configure the attributes using the device editor provided with the pimatic web frontend as shown in the screenshot below.
Statistical attributes can be reset by executing the reset rule action on the device.
By default, the filter expression for the output attribute is only updated if one of the variables used as part of the
filter expression have been updated. By setting the device configuration property timeBasedUpdates to true a
time-based update scheme will be used. This will evaluate the expression in regular time intervals. The time
interval is defined by the property updateInterval which is set to a number and the property updateScale which
is one of "milliseconds", "seconds", "minutes", "hours", or "days".
See Release History.
Copyright (c) 2015-2019, Marcus Wittig and contributors. All rights reserved.