`elasticai.preprocessor.thresholding.thresholding`#

Module Contents#

Classes#

SettingsThreshold

Dataclass for defining the funcs for determining properties to calculate thresholding Attributes: method: Applied method for thresholding [‘const’: constant given value, ‘abs_mean’: absolute mean value, ‘mad’: median absolute derivation, ‘mavg’, moving average, ‘mavg_abs’: absolute mean absolute value, ‘rms_norm’: Root-Mean-Squared, ‘rms_move’: Moving RMS, ‘rms_black’: RMS method used in Blackrock Neurotechnology Systems, ‘welford’: Welford Online Algorithm for STD Calculation] sampling_rate: Sampling rate of the transient signal [Hz] gain: Applied gain on threshold output window_sec: Window length in sec.

Thresholding

Data#

DefaultSettingsThreshold

API#

class elasticai.preprocessor.thresholding.thresholding.SettingsThreshold[source]#

Dataclass for defining the funcs for determining properties to calculate thresholding Attributes: method: Applied method for thresholding [‘const’: constant given value, ‘abs_mean’: absolute mean value, ‘mad’: median absolute derivation, ‘mavg’, moving average, ‘mavg_abs’: absolute mean absolute value, ‘rms_norm’: Root-Mean-Squared, ‘rms_move’: Moving RMS, ‘rms_black’: RMS method used in Blackrock Neurotechnology Systems, ‘welford’: Welford Online Algorithm for STD Calculation] sampling_rate: Sampling rate of the transient signal [Hz] gain: Applied gain on threshold output window_sec: Window length in sec.

method: str#: None

sampling_rate: float#: None

gain: float#: None

window_sec: float#: None

property window_steps: int#: Getting the stepsize of the window

elasticai.preprocessor.thresholding.thresholding.DefaultSettingsThreshold#: ‘SettingsThreshold(…)’

class elasticai.preprocessor.thresholding.thresholding.Thresholding(settings: elasticai.preprocessor.thresholding.thresholding.SettingsThreshold)[source]#

Initialization

Class for calculating the thresholding values based on the transient input signal

Parameters:: settings – Class SettingsThreshold for configuring the properties
Returns:: None

get_overview() → list[source]#

Getting an overview of available thresholding methods

Returns:: List with names of available methods

get_threshold(xin: numpy.ndarray, do_abs: bool = False, **kwargs) → numpy.ndarray[source]#

Function for getting the thresholding value from input

Parameters:

xin – Numpy array with transient raw signal
do_abs – Apply absolute xin for thresholding or not

Returns:

Numpy array with thresholding value from applied method

get_threshold_position(xin: numpy.ndarray, pre_time: float = 0.0, do_abs: bool = False, **kwargs) → numpy.ndarray[source]#

Function for getting the crosspoints of thresholding value and transient input