def notch_fir(self, f1, f2, order, beta=5.0, remove_corrupted=True): """ notch filter the time series using an FIR filtered generated from the ideal response passed through a time-domain kaiser window (beta = 5.0) The suppression of the notch filter is related to the bandwidth and the number of samples in the filter length. For a few Hz bandwidth, a length corresponding to a few seconds is typically required to create significant suppression in the notched band. Parameters ---------- Time Series: TimeSeries The time series to be notched. f1: float The start of the frequency suppression. f2: float The end of the frequency suppression. order: int Number of corrupted samples on each side of the time series beta: float Beta parameter of the kaiser window that sets the side lobe attenuation. """ from pycbc.filter import notch_fir ts = notch_fir(self, f1, f2, order, beta=beta) if remove_corrupted: ts = ts[order:len(ts) - order] return ts
def notch_fir(self, f1, f2, order, beta=5.0, remove_corrupted=True): """ notch filter the time series using an FIR filtered generated from the ideal response passed through a time-domain kaiser window (beta = 5.0) The suppression of the notch filter is related to the bandwidth and the number of samples in the filter length. For a few Hz bandwidth, a length corresponding to a few seconds is typically required to create significant suppression in the notched band. Parameters ---------- Time Series: TimeSeries The time series to be notched. f1: float The start of the frequency suppression. f2: float The end of the frequency suppression. order: int Number of corrupted samples on each side of the time series beta: float Beta parameter of the kaiser window that sets the side lobe attenuation. """ from pycbc.filter import notch_fir ts = notch_fir(self, f1, f2, order, beta=beta) if remove_corrupted: ts = ts[order:len(ts)-order] return ts