iracema.aggregation

Some aggregation methods for time series.

iracema.aggregation.sliding_window(time_series, window_size, hop_size, function=None, window_name=None)

Use a sliding window to aggregate the data from time_series by applying the function to each analysis window. The content of each window will be passed as the first argument to the function. Return the aggregated data in an array.

Parameters

• time_series (TimeSeries) – Time series over which the sliding operation must be applied.

• window_size (int) – Size of the window.

• hop_size (int) – Number of samples to be skipped between two successive windowing operations.

• function (function) – Function to be applied to each window. If no function is specified, each window will contain an unaltered excerpt of the time series.

• window_name (str) – Name of the window function to be used. Options are: {“boxcar”, “triang”, “blackman”, “hamming”, “hann”, “bartlett”, “flattop”, “parzen”, “bohman”, “blackmanharris”, “nuttall”, “barthann”, “no_window”, None}.

iracema.aggregation.aggregate_features(time_series, func)

Aggregate the features within each sample from time_series.

iracema.aggregation.aggregate_sucessive_samples(time_series, func, padding='zeros')

Aggregate consecutive samples in time_series, and generate a new time series object.

Parameters

• time_series (TimeSeries) –

• padding ({'zeros', 'same', 'ones'}) –