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RobustRectangularStandardization

Robustly standardize the signal in a sliding rectangular window.

This node estimates the running median and running median absolute deviation in a rectangular window (that is, with equal weighting for all samples in the window). Then, each sample is converted to robust z scores by subtracting the running median and dividing by the robust standard deviation, estimated from the median absolute deviation, both of which are estimated from the k samples prior to that sample. This filter maintains internal state to work seamlessly across chunk boundaries. On the first window length samples, the result will not yet be standardized fully since the filter first needs to collect sufficient data. Version 1.0.0

Ports/Properties

data

Data to process.

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

window_len

Window length for standardization. In seconds.

verbose name
Window Len
default value
30
port type
FloatPort
value type
float (can be None)

distribution

Type of distribution of the data. If gaussian, we assume tha the data is centered around 0. If half-normal, then we assume that the data is positive, and that we standardize it such that the minimum value is 0.

verbose name
Distribution
default value
gaussian
port type
EnumPort
value type
str (can be None)

use_caching

Use caching. Only applicable on offline data.

verbose name
Use Caching
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)