moving_average¶
- typhoon.test.signals.filtering.moving_average(signal, window_length, assume_previous_data=False)¶
Calculates average value of the provided signal by using moving window technique, similar to ‘typhoon.test.rms.window_rms’ function. User is specifying the size of the moving window in seconds with ‘window_length’ argument. To get valid rms value, window size should represent whole number of signal periods. If “assume_previous_data” is set to True, the function will assume signal values before the provided ones; In this way, rms result will not have transient period during the first “window_length” seconds; rms result will have expected values from beginning. Otherwise, initial transient will be present during that period.
- Parameters:
signal (pandas.Series) – pandas.Series which represents the signal which average value should be calculated.
window_length (float) – Length of the moving window for average calculation, in seconds.
assume_previous_data (bool - default True) – If set, the resulting average signal will have valid values from beginning of function analysis. This is achieved by assuming that the signal in previous “window_length” seconds behaved exactly the same as in the first provided “window_length” seconds.
- Returns:
result – The resulting signal which represents the instantaneous average of the provided one. The time indices of the input and output signals are identical.
- Return type:
pandas.Series
Examples
>>> from typhoon.test.signals.filtering import moving_average >>> from typhoon.test.signals. import pandas_sine >>> # calculate average signal for the pure sine - the result will give zero signal >>> sine = pandas_sine(amplitude=100, frequency=50, Ts=1e-6) >>> average1 = moving_average(sine, window_length=1/50) >>> # create sine signal with some offset - moving_average detects that offset as average value >>> sine_offset = sine + 100 >>> average2 = moving_average(sine_offset, window_length=1/50)
See also
