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
