def low_pass_filter(self, filter_freq=None):
""" Low pass filter the data """
from gwpy.signal.filter_design import lowpass
from gwpy.timeseries import TimeSeries
if filter_freq is None:
logger.debug(
"Setting low pass filter_freq using given maximum frequency")
filter_freq = self.maximum_frequency
if 2 * filter_freq >= self.sampling_frequency:
logger.info(
"Low pass filter frequency of {}Hz requested, this is equal"
" or greater than the Nyquist frequency so no filter applied".
format(filter_freq))
return
logger.debug(
"Applying low pass filter with filter frequency {}".format(
filter_freq))
bp = lowpass(filter_freq, self.sampling_frequency)
strain = TimeSeries(self.time_domain_strain,
sample_rate=self.sampling_frequency)
strain = strain.filter(bp, filtfilt=True)
self._time_domain_strain = strain.value
def filter_gwe(data:TimeSeries):
from gwpy.signal import filter_design
bp = filter_design.bandpass(50, 250, data.sample_rate)
notches = [filter_design.notch(line, data.sample_rate) for line in (60, 120, 180)]
zpk = filter_design.concatenate_zpks(bp, *notches)
filt = data.filter(zpk, filtfilt=True)
data = data.crop(*data.span.contract(1))
filt = filt.crop(*filt.span.contract(1))
return filt
def plot_time_domain_data(self,
outdir='.',
label=None,
bandpass_frequencies=(50, 250),
notches=None,
start_end=None,
t0=None):
""" Plots the strain data in the time domain
Parameters
==========
outdir, label: str
Used in setting the saved filename.
bandpass: tuple, optional
A tuple of the (low, high) frequencies to use when bandpassing the
data, if None no bandpass is applied.
notches: list, optional
A list of frequencies specifying any lines to notch
start_end: tuple
A tuple of the (start, end) range of GPS times to plot
t0: float
If given, the reference time to subtract from the time series before
plotting.
"""
import matplotlib.pyplot as plt
from gwpy.timeseries import TimeSeries
from gwpy.signal.filter_design import bandpass, concatenate_zpks, notch
# We use the gwpy timeseries to perform bandpass and notching
if notches is None:
notches = list()
timeseries = TimeSeries(data=self.strain_data.time_domain_strain,
times=self.strain_data.time_array)
zpks = []
if bandpass_frequencies is not None:
zpks.append(
bandpass(bandpass_frequencies[0], bandpass_frequencies[1],
self.strain_data.sampling_frequency))
if notches is not None:
for line in notches:
zpks.append(notch(line, self.strain_data.sampling_frequency))
if len(zpks) > 0:
zpk = concatenate_zpks(*zpks)
strain = timeseries.filter(zpk, filtfilt=False)
else:
strain = timeseries
fig, ax = plt.subplots()
if t0:
x = self.strain_data.time_array - t0
xlabel = 'GPS time [s] - {}'.format(t0)
else:
x = self.strain_data.time_array
xlabel = 'GPS time [s]'
ax.plot(x, strain)
ax.set_xlabel(xlabel)
ax.set_ylabel('Strain')
if start_end is not None:
ax.set_xlim(*start_end)
fig.tight_layout()
if label is None:
fig.savefig('{}/{}_time_domain_data.png'.format(outdir, self.name))
else:
fig.savefig('{}/{}_{}_time_domain_data.png'.format(
outdir, self.name, label))
plt.close(fig)
