dupin.preprocessing.signal#
Overview
Smooth out the highest frequencies of a signal. |
|
Smooth an array via a moving average. |
Details
Functions for smoothing a signal.
scipy.signal provides many additional methods for signal processing that can
be used to transform data in numerous ways.
- dupin.preprocessing.signal.fft_smoothing(y, max_frequency, sampling_frequency, **kwargs)[source]#
Smooth out the highest frequencies of a signal.
- Parameters:
y (\((N, M)\) numpy.ndarray of float) – The signal to remove low contributing frequencies from. The FFT is performed on the first dimension.
max_frequency (float) – The maximum frequency in the signal to allow. The unit for frequency must be consistent with
sampling_frequency.sampling_frequency (float) – The sampling frequency. The unit for frequency must be consistent with
max_frequency.**kwargs – Key word arguments to pass to
scipy.signal.ellip.
- dupin.preprocessing.signal.moving_average(y, span=1)[source]#
Smooth an array via a moving average.
For multidimensional arrays, the smoothing is done on the first axis. This is consistent when columns represent multiple variables or features and rows represent different instances.
- Parameters:
y (numpy.ndarray) – input array to smooth.
span (
int, optional) – The window size to compute the moving mean over. Defaults to 1 which does nothing.
- Returns:
smoothed_array
- Return type: