sit2standpy.AccelerationFilter¶
-
class
sit2standpy.AccelerationFilter(continuous_wavelet='gaus1', power_band=None, power_peak_kw=None, power_std_height=True, power_std_trim=0, reconstruction_method='moving average', lowpass_order=4, lowpass_cutoff=5, window=0.25, discrete_wavelet='dmey', extension_mode='constant', reconstruction_level=1)¶ Object for filtering and reconstructing raw acceleration data
Parameters: - continuous_wavelet : str, optional
Continuous wavelet to use for signal deconstruction. Default is ‘gaus1’. CWT coefficients will be summed in frequency bands that will be used for detecting approximate STS locations.
- power_band : {array_like, int, float}, optional
Frequency band in which to sum the CWT coefficients. Either an array_like of length 2, with the lower and upper limits, or a number, which will be taken as the upper limit, and the lower limit will be set to 0. Default is [0, 0.5].
- power_peak_kw : {None, dict}, optional
Extra key-word arguments to pass to scipy.signal.find_peaks when finding peaks in the summed CWT coefficient power band data. Default is None, which will use the default parameters except setting minimum height to 90, unless std_height is True.
- power_std_height : bool, optional
Use the standard deviation of the power for peak finding. Default is True. If True, the standard deviation height will overwrite the setting in power_peak_kw.
- power_std_trim : int, optional
Number of seconds to trim off the start and end of the power signal before computing the standard deviation for power_std_height. Default is 0s, which will not trim anything. Recommended value if trimming is 1s.
- reconstruction_method : {‘moving average’, ‘dwt’}, optional
Method for computing the reconstructed acceleration. Default is ‘moving average’, which takes the moving average over the specified window. Other option is ‘dwt’, which uses the discrete wavelet transform to deconstruct and reconstruct the signal while filtering noise out.
- lowpass_order : int, optional
Initial low-pass filtering order. Default is 4.
- lowpass_cutoff : float, optional
Initial low-pass filtering cuttoff, in Hz. Default is 5Hz.
- window : float, optional
Window to use for moving average, in seconds. Default is 0.25s. Ignored if reconstruction_method is ‘dwt’.
- discrete_wavelet : str, optional
Discrete wavelet to use if reconstruction_method is ‘dwt’. Default is ‘dmey’. See pywt.wavelist(kind=’discrete’) for a complete list of options. Ignored if reconstruction_method is ‘moving average’.
- extension_mode : str, optional
Signal extension mode to use in the DWT de- and re-construction of the signal. Default is ‘constant’, see pywt.Modes.modes for a list of options. Ignored if reconstruction_method is ‘moving average’.
- reconstruction_level : int, optional
Reconstruction level of the DWT processed signal. Default is 1. Ignored if reconstruction_method is ‘moving average’.
Methods
apply(accel, fs)Apply the desired filtering to the provided signal. -
apply(accel, fs)¶ Apply the desired filtering to the provided signal.
Parameters: - accel : numpy.ndarray
(N, 3) array of raw acceleration values.
- fs : float, optional
Sampling frequency for the acceleration data.
Returns: - mag_acc_f : numpy.ndarray
(N, ) array of the filtered (low-pass only) acceleration magnitude.
- mag_acc_r : numpy.ndarray
(N, ) array of the reconstructed acceleration magnitude. This is either filtered and then moving averaged, or filtered, and then passed through the DWT and inverse DWT with more filtering, depending on the reconstruction_method specified.
- power : numpy.ndarray
(N, ) array of the CWT power approximation in the band specified by power_band.
- power_peaks : numpy.ndarray
Indices of the peaks detected in the power signal.