Simple readings data transformation plugin that calculates the RMS value of data points over a variable data range. The duratoin of the RMS sample is defined by a trigger asset and datapoint. When the trigger value crosses the zero value, from negiative to positive, then an RMS calculation is done for each of the data points and readings the filter is processing.
It may optionally also include peak to peak measurements (i.e. the maximum swing) within the same data period as the RMS value is calculated.
Note, peak values may be less than individual values of the input if the asset value does not fall to or below zero. Where a data value swings between negative and positive values then the peak value will be greater than the maximum value in the data stream. For example if the minimum value of a data point in the sample set is 0.3 and the maximum is 3.4 then the peak value will be 3.1. If the maximum value is 2.4 and the minimum is zero then the peak will be 2.4. If the maximum value is 1.7 and the minimum is -0.5 then the peak value will be 2.2.
The user may also choose to include or not the raw data that is used to calculate the RMS values via a switch in the configuration.
Where a datastream has multiple assets within it the RMS filter may be limited to work only on those assets whose name matches a regular expression given in the configuration of the filter. The default for this expression is .*, i.e. all assets are processed.
A number of configuration options exist:
- triggerAsset
- The name of the asset that triggers the RMS calculation
- triggerDatapoint
- The name of the datapoint within the trigger asset that triggers the RMS calculation
- triggerType
- The type of event that triggers the generation of the RMS value. This may be crossign the zero point or reaching a peak (maximum or minimum).
- triggerEdge
- The type of edge that triggers the generation, either a rising (increasing value) edge or a falling edge (data values decreasing).
- assetName
- The asset name to use to output the RMS values. "%a" will be replaced with the original asset name.
- rawData
- A switch to include the raw input data in the output
- peak
- A switch to include peak to peak measurements for the same data set as the RMS measurement
- match
- A regular expression to limit the asset names on which this filter operations
To build FogLAMP "RMS" C++ filter plugin:
$ mkdir build
$ cd build
$ cmake ..
$ make- By default the FogLAMP develop package header files and libraries are expected to be located in /usr/include/foglamp and /usr/lib/foglamp
- If FOGLAMP_ROOT env var is set and no -D options are set, the header files and libraries paths are pulled from the ones under the FOGLAMP_ROOT directory. Please note that you must first run 'make' in the FOGLAMP_ROOT directory.
You may also pass one or more of the following options to cmake to override this default behaviour:
- FOGLAMP_SRC sets the path of a FogLAMP source tree
- FOGLAMP_INCLUDE sets the path to FogLAMP header files
- FOGLAMP_LIB sets the path to FogLAMP libraries
- FOGLAMP_INSTALL sets the installation path of Random plugin
- NOTE:
- The FOGLAMP_INCLUDE option should point to a location where all the FogLAMP header files have been installed in a single directory.
- The FOGLAMP_LIB option should point to a location where all the FogLAMP libraries have been installed in a single directory.
- 'make install' target is defined only when FOGLAMP_INSTALL is set
Examples:
no options
$ cmake ..
no options and FOGLAMP_ROOT set
$ export FOGLAMP_ROOT=/some_foglamp_setup
$ cmake ..
set FOGLAMP_SRC
$ cmake -DFOGLAMP_SRC=/home/source/develop/FogLAMP ..
set FOGLAMP_INCLUDE
$ cmake -DFOGLAMP_INCLUDE=/dev-package/include ..
set FOGLAMP_LIB
$ cmake -DFOGLAMP_LIB=/home/dev/package/lib ..
set FOGLAMP_INSTALL
$ cmake -DFOGLAMP_INSTALL=/home/source/develop/FogLAMP ..
$ cmake -DFOGLAMP_INSTALL=/usr/local/foglamp ..