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Standardization

Scale the data such that it is standardized.

Standardization will scale the data such that the standard deviation (or some other measure of choice) is 1.0. Note that this does not set the mean of the data to zero, though (that is handled by the Centering node, and should be done beforehand, unless the data is already zero, for instance after highpass filtering, or you do not want the mean to be zero). Note that, if your data is segmented or otherwise comes in the form of multiple instances, you can decide whether the statistics should be taken over the instances or over the time axis in each instance. You also can decide whether the statistics should be updated on every chunk, or if it should only be updated on the calibration data (non-streaming chunks), and whether they should be recomputed on subsequent non-streaming chunks ( e.g., subsequent test data). Note that there is also a moving-window standardization node available, though that one will by default also correct the mean. Version 1.1.0

Ports/Properties

data

Data to process.

  • verbose name: Data
  • default value: None
  • port type: DataPort
  • value type: Packet (can be None)
  • data direction: INOUT

axis

Axis over which the statistic should be aggregated. For instance, if this is set to time, then the mean/standard deviation will be computed over time. If a comma-separated list of axes are given (e.g., time, instance), then the data are pooled along those axes before the statistic is taken.

  • verbose name: Take Statistics Along Axis
  • default value: instance
  • port type: ComboPort
  • value type: str (can be None)

measure

Measure to use for standardization. After standardizing, the measure will yield 1.0 on the standardized data. Mad is the median absolute deviation (a robust alternative to the standard deviation).

  • verbose name: Standardize Using Measure
  • default value: std
  • port type: EnumPort
  • value type: str (can be None)

adapt_on_streaming

Adapt state parameters on streaming chunks. If this is False (the default), then this node will not adapt itself on streaming chunks, i.e., only on non-streaming chunks, such as imported calibration recordings. This is useful if the streaming chunks would be too short to meaningfully adapt the parameters on and the calibration recording is sufficietly similar to the streaming data for it to be meaningful to adapt the state only on these data. Or if set to True, the node will simply adapt itself on every chunk it receives, whether it is streaming or not.

  • verbose name: Adapt On Streaming Chunks
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

initialize_once

Initialize (adapt state parameters) only once. If False, this node will recalibrate itself on any qualifying chunk (based on the setting of adapt on streaming chunks).

  • verbose name: Adapt Only Once
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

ignore_resets

Ignore signal resets.

  • verbose name: Ignore Resets
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

set_breakpoint

Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.

  • verbose name: Set Breakpoint (Debug Only)
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

metadata

User-definable meta-data associated with the node. Usually reserved for technical purposes.

  • verbose name: Metadata
  • default value: {}
  • port type: DictPort
  • value type: dict (can be None)