DetrendedFluctuation
DFA exponent: how far a signal remembers its own past.
data
exponent
- Type name
signal:DetrendedFluctuation- Plane
signal- Tags
analysis- Language
python- Tier
in-process- Bundle
complexity- Source
node-bundles/complexity/detrended_fluctuation.py- Availability
- available
Slots
| Slot | Direction | Type | |
|---|---|---|---|
data | input | ARRAY | |
exponent | output | ARRAY |
Parameters
common
| Name | Type | Default | Range | Doc |
|---|---|---|---|---|
autotrigger | bool | false | Run on the node's own schedule, instead of waiting for an input frame. Turn this on for sources; leave it off for transforms driven by their input. | |
max_frequency | float | 0 | 0 … 100 | Rate cap for this node, read through `frequency_mode`. 0 means uncapped — the node runs as often as the scheduler and its inputs allow. |
frequency_mode | string | updates_per_second | updates_per_second | seconds_per_update | How to read `max_frequency`: as a rate in Hz (updates per second), or as a period in seconds between updates — convenient for very slow nodes. |
Source
The current source of this node, as it stands in the goofi repository at node-bundles/complexity/detrended_fluctuation.py.
"""DetrendedFluctuation — the DFA exponent, from antropy.
Says how a signal's fluctuation grows with the window it is measured over: near 0.5 is noise with
no memory, near 1.0 is long-range correlated, above 1.5 is a random walk. The last axis is time and
is consumed; every axis before it survives.
"""
import antropy
import numpy as np
import goofi
class DetrendedFluctuation(goofi.Node):
"""DFA exponent: how far a signal remembers its own past."""
TAGS = ["analysis"]
INPUTS = {"data": goofi.InputSlot(goofi.DataType.ARRAY, required=True)}
OUTPUTS = {"exponent": goofi.DataType.ARRAY}
def process(self, data):
return np.apply_along_axis(
antropy.detrended_fluctuation, -1, np.asarray(data.data, dtype=np.float64)
).astype(np.float32)This reference describes goofi 3.1.0(537cd394), generated from a running instance on 2026-09-06.