ezmsg.sigproc.scaler#

Functions

scaler(time_constant=1.0, axis=None)[source]#

Apply the adaptive standard scaler from https://riverml.xyz/latest/api/preprocessing/AdaptiveStandardScaler/ This is faster than scaler_np for single-channel data.

Parameters:

  • time_constant (float) – Decay constant tau in seconds.

  • axis (str | None) – The name of the axis to accumulate statistics over.

Returns:

A primed generator object that expects to be sent a AxisArray via .send(axis_array)

and yields an AxisArray with its data being a standardized, or “Z-scored” version of the input data.

Return type:

Generator[AxisArray, AxisArray, None]

scaler_np(time_constant=1.0, axis=None)[source]#
Parameters:

  • time_constant (float)

  • axis (str | None)

Return type:

AdaptiveStandardScalerTransformer

Classes

class AdaptiveStandardScaler(*args, settings=None, **kwargs)[source]#

Bases: BaseTransformerUnit[AdaptiveStandardScalerSettings, AxisArray, AxisArray, AdaptiveStandardScalerTransformer]

Parameters:

settings (Settings | None)

SETTINGS#

alias of AdaptiveStandardScalerSettings

class AdaptiveStandardScalerSettings(time_constant: float = 1.0, axis: str | None = None)[source]#

Bases: EWMASettings

Parameters:

  • time_constant (float)

  • axis (str | None)

__init__(time_constant=1.0, axis=None)#
Parameters:

  • time_constant (float)

  • axis (str | None)

Return type:

None

class AdaptiveStandardScalerState[source]#

Bases: object

samps_ewma: EWMATransformer | None = None#
vars_sq_ewma: EWMATransformer | None = None#
alpha: float | None = None#
class AdaptiveStandardScalerTransformer(*args, **kwargs)[source]#

Bases: BaseStatefulTransformer[AdaptiveStandardScalerSettings, AxisArray, AxisArray, AdaptiveStandardScalerState]

scaler(time_constant=1.0, axis=None)[source]#

Apply the adaptive standard scaler from https://riverml.xyz/latest/api/preprocessing/AdaptiveStandardScaler/ This is faster than scaler_np for single-channel data.

Parameters:

  • time_constant (float) – Decay constant tau in seconds.

  • axis (str | None) – The name of the axis to accumulate statistics over.

Returns:

A primed generator object that expects to be sent a AxisArray via .send(axis_array)

and yields an AxisArray with its data being a standardized, or “Z-scored” version of the input data.

Return type:

Generator[AxisArray, AxisArray, None]

class AdaptiveStandardScalerSettings(time_constant: float = 1.0, axis: str | None = None)[source]#

Bases: EWMASettings

Parameters:

  • time_constant (float)

  • axis (str | None)

__init__(time_constant=1.0, axis=None)#
Parameters:

  • time_constant (float)

  • axis (str | None)

Return type:

None

class AdaptiveStandardScalerState[source]#

Bases: object

samps_ewma: EWMATransformer | None = None#
vars_sq_ewma: EWMATransformer | None = None#
alpha: float | None = None#
class AdaptiveStandardScalerTransformer(*args, **kwargs)[source]#

Bases: BaseStatefulTransformer[AdaptiveStandardScalerSettings, AxisArray, AxisArray, AdaptiveStandardScalerState]

class AdaptiveStandardScaler(*args, settings=None, **kwargs)[source]#

Bases: BaseTransformerUnit[AdaptiveStandardScalerSettings, AxisArray, AxisArray, AdaptiveStandardScalerTransformer]

Parameters:

settings (Settings | None)

SETTINGS#

alias of AdaptiveStandardScalerSettings

scaler_np(time_constant=1.0, axis=None)[source]#
Parameters:

  • time_constant (float)

  • axis (str | None)

Return type:

AdaptiveStandardScalerTransformer