pygwb.preprocessing.preprocessing_data_timeseries_array

pygwb.preprocessing.preprocessing_data_timeseries_array(t0: int, tf: int, array: ndarray, new_sample_rate: int, cutoff_frequency: float, segment_duration: int, sample_rate: int = 4096, number_cropped_seconds: int = 2, window_downsampling: str = 'hamming', ftype: str = 'fir', time_shift: int = 0)[source]

Function performing the pre-processing of a time-series array to be used in the remainder of the code.

Parameters:

  • t0 (int) – GPS time of the start of the data taking.

  • tf (int) – GPS time of the end of the data taking.

  • array (np.ndarray) – Array containing a timeseries.

  • new_sample_rate (int) – Sampling rate of the downsampled-timeseries in Hz.

  • cutoff_frequency (float) – Frequency (in Hz) from which to start applying the high pass filter.

  • segment_duration (int) – Duration (in seconds) of each segment (argument of set_start_time).

  • sample_rate (int, optional) – Sampling rate of the original timeseries. Default is 4096 Hz.

  • number_cropped_seconds (int, optional) – Number of seconds to remove at the beginning and end of the high-passed data. Default is 2.

  • window_downsampling (str, optional) – Type of window used to downsample. Default is “hamming”.

  • ftype (str, optional) – Type of filter to use in the downsampling. Default is “fir”.

  • time_shift (int, optional) – Value of the time shift (in seconds). Default is no time shift.

Returns:
pre_processed_data: gwpy.timeseries.TimeSeries

Timeseries containing the filtered and high passed data (shifted if time_shift>0).