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← signal_processing package

ExponentialStandardization

Standardize the given data using an exponentially decaying window.

This filter aims to produce standardized output signals (that is, each channel/feature has approximately mean zero and variance one). This is based on the statistics of the past data, where more recent data is weighted more strongly. The time constant of this exponential weighting is an adjustible parameter. This filter is meant to be run on continuous data. That is, it should not be preceded by a Segmentation node. 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

forget_halftime

Time constant of filter, in seconds. Data that lies this many seconds in the past will be weighted half as much as the most recent data when designing the filter, and data twice as old will be weighted by 1/4, etc. Can also be thought of as the half-time of an exponentially weighted filter.

verbose name
Adaptive Filter Time Constant
default value
60
port type
FloatPort
value type
float (can be None)

ignore_nans

If activated the data slices containing nan values are ignored.

verbose name
Ignore Data With Nan Values
default value
False
port type
BoolPort
value type
bool (can be None)

across_all_chs

If activated the standardization is performed across all entries in the non-time axes, ie., the mean and standard deviation is calculated over the entire tensor snapshot.

verbose name
Standardize Across Entire Tensor
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)