def highpass_fir(self, frequency, order, beta=5.0, remove_corrupted=True):
""" Highpass filter the time series using an FIR filtered generated from
the ideal response passed through a kaiser window (beta = 5.0)
Parameters
----------
Time Series: TimeSeries
The time series to be high-passed.
frequency: float
The frequency below which is suppressed.
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.
remove_corrupted : {True, boolean}
If True, the region of the time series corrupted by the filtering
is excised before returning. If false, the corrupted regions
are not excised and the full time series is returned.
"""
from pycbc.filter import highpass_fir
ts = highpass_fir(self, frequency, order, beta=beta)
if remove_corrupted:
ts = ts[order:len(ts) - order]
return ts
def highpass_fir(self, frequency, order, beta=5.0, remove_corrupted=True):
""" Highpass filter the time series using an FIR filtered generated from
the ideal response passed through a kaiser window (beta = 5.0)
Parameters
----------
Time Series: TimeSeries
The time series to be high-passed.
frequency: float
The frequency below which is suppressed.
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.
remove_corrupted : {True, boolean}
If True, the region of the time series corrupted by the filtering
is excised before returning. If false, the corrupted regions
are not excised and the full time series is returned.
"""
from pycbc.filter import highpass_fir
ts = highpass_fir(self, frequency, order, beta=beta)
if remove_corrupted:
ts = ts[order:len(ts)-order]
return ts
from pycbc.frame import read_frame
from pycbc.filter import highpass_fir, lowpass_fir
from pycbc.psd import welch, interpolate
from pycbc.types import TimeSeries
try:
from urllib.request import urlretrieve
except ImportError: # python < 3
from urllib import urlretrieve
# Read data and remove low frequency content
fname = 'H-H1_LOSC_4_V2-1126259446-32.gwf'
url = "https://www.gw-openscience.org/GW150914data/" + fname
urlretrieve(url, filename=fname)
h1 = highpass_fir(read_frame(fname, 'H1:LOSC-STRAIN'), 15.0, 8)
# Calculate the noise spectrum and whiten
psd = interpolate(welch(h1), 1.0 / 32)
white_strain = (h1.to_frequencyseries() / psd ** 0.5 * psd.delta_f).to_timeseries()
# remove some of the high and low frequencies
smooth = highpass_fir(white_strain, 25, 8)
smooth = lowpass_fir(white_strain, 250, 8)
#strech out and shift the frequency upwards to aid human hearing
fdata = smooth.to_frequencyseries()
fdata.roll(int(1200 / fdata.delta_f))
smooth = TimeSeries(fdata.to_timeseries(), delta_t=1.0/1024)
#Take slice around signal
smooth = smooth[len(smooth)/2 - 1500:len(smooth)/2 + 3000]
smooth.save_to_wav('gw150914_h1_chirp.wav')
from pycbc.frame import read_frame
from pycbc.filter import highpass_fir, matched_filter
from pycbc.waveform import get_fd_waveform
from pycbc.psd import welch, interpolate
import urllib
# Read data and remove low frequency content
fname = 'H-H1_LOSC_4_V2-1126259446-32.gwf'
url = "https://www.gw-openscience.org/GW150914data/" + fname
urllib.urlretrieve(url, filename=fname)
h1 = read_frame('H-H1_LOSC_4_V2-1126259446-32.gwf', 'H1:LOSC-STRAIN')
h1 = highpass_fir(h1, 15, 8)
# Calculate the noise spectrum
psd = interpolate(welch(h1), 1.0 / h1.duration)
# Generate a template to filter with
hp, hc = get_fd_waveform(approximant="IMRPhenomD",
mass1=40,
mass2=32,
f_lower=20,
delta_f=1.0 / h1.duration)
hp.resize(len(h1) / 2 + 1)
# Calculate the complex (two-phase SNR)
snr = matched_filter(hp, h1, psd=psd, low_frequency_cutoff=20.0)
# Remove regions corrupted by filter wraparound
snr = snr[len(snr) / 4:len(snr) * 3 / 4]
import pylab
from pycbc.filter import highpass_fir, lowpass_fir
from pycbc.psd import welch, interpolate
from pycbc.catalog import Merger
import pylab
for ifo in ['H1', 'L1']:
# Read data and remove low frequency content
h1 = Merger("GW150914").strain(ifo)
h1 = highpass_fir(h1, 15, 8)
# Calculate the noise spectrum
psd = interpolate(welch(h1), 1.0 / h1.duration)
# whiten
white_strain = (h1.to_frequencyseries() / psd ** 0.5).to_timeseries()
# remove some of the high and low
smooth = highpass_fir(white_strain, 35, 8)
smooth = lowpass_fir(white_strain, 300, 8)
# time shift and flip L1
if ifo == 'L1':
smooth *= -1
smooth.roll(int(.007 / smooth.delta_t))
pylab.plot(smooth.sample_times, smooth, label=ifo)
pylab.legend()
pylab.xlim(1126259462.21, 1126259462.45)
pylab.ylim(-150, 150)
pylab.ylabel('Smoothed-Whitened Strain')
from pycbc.frame import read_frame
from pycbc.filter import highpass_fir, lowpass_fir
from pycbc.psd import welch, interpolate
from pycbc.types import TimeSeries
import urllib
# Read data and remove low frequency content
fname = 'H-H1_LOSC_4_V2-1126259446-32.gwf'
url = "https://losc.ligo.org/s/events/GW150914/" + fname
urllib.urlretrieve(url, filename=fname)
h1 = highpass_fir(read_frame(fname, 'H1:LOSC-STRAIN'), 15.0, 8)
# Calculate the noise spectrum and whiten
psd = interpolate(welch(h1), 1.0 / 32)
white_strain = (h1.to_frequencyseries() / psd ** 0.5 * psd.delta_f).to_timeseries()
# remove some of the high and low frequencies
smooth = highpass_fir(white_strain, 25, 8)
smooth = lowpass_fir(white_strain, 250, 8)
#strech out and shift the frequency upwards to aid human hearing
fdata = smooth.to_frequencyseries()
fdata.roll(int(1200 / fdata.delta_f))
smooth = TimeSeries(fdata.to_timeseries(), delta_t=1.0/1024)
#Take slice around signal
smooth = smooth[len(smooth)/2 - 1500:len(smooth)/2 + 3000]
smooth.save_to_wav('gw150914_h1_chirp.wav')
from pycbc.frame import read_frame
from pycbc.filter import highpass_fir, lowpass_fir, matched_filter
from pycbc.waveform import get_fd_waveform
from pycbc.psd import welch, interpolate
import urllib
import pylab
for ifo in ['H1', 'L1']:
# Read data and remove low frequency content
fname = '%s-%s_LOSC_4_V2-1126259446-32.gwf' % (ifo[0], ifo)
url = "https://losc.ligo.org/s/events/GW150914/" + fname
urllib.urlretrieve(url, filename=fname)
h1 = read_frame(fname, '%s:LOSC-STRAIN' % ifo)
h1 = highpass_fir(h1, 15, 8)
# Calculate the noise spectrum
psd = interpolate(welch(h1), 1.0 / h1.duration)
# whiten
white_strain = (h1.to_frequencyseries() / psd ** 0.5 * psd.delta_f).to_timeseries()
# remove some of the high and low
smooth = highpass_fir(white_strain, 35, 8)
smooth = lowpass_fir(white_strain, 300, 8)
# time shift and flip L1
if ifo == 'L1':
smooth *= -1
smooth.roll(int(.007 / smooth.delta_t))
pylab.plot(smooth.sample_times, smooth, label=ifo)
from pycbc.frame import read_frame
from pycbc.filter import highpass_fir, matched_filter
from pycbc.waveform import get_fd_waveform
from pycbc.psd import welch, interpolate
import urllib
# Read data and remove low frequency content
fname = 'H-H1_LOSC_4_V2-1126259446-32.gwf'
url = "https://losc.ligo.org/s/events/GW150914/" + fname
urllib.urlretrieve(url, filename=fname)
h1 = read_frame('H-H1_LOSC_4_V2-1126259446-32.gwf', 'H1:LOSC-STRAIN')
h1 = highpass_fir(h1, 15, 8)
# Calculate the noise spectrum
psd = interpolate(welch(h1), 1.0 / 32)
# Generate a template to filter with
hp, hc = get_fd_waveform(approximant="IMRPhenomD", mass1=40, mass2=32,
f_lower=20, delta_f=1.0/32)
hp.resize(len(h1) / 2 + 1)
# Calculate the complex (two-phase SNR)
snr = matched_filter(hp, h1, psd=psd, low_frequency_cutoff=20.0)
# Remove regions corrupted by filter wraparound
snr = snr[len(snr) / 4: len(snr) * 3 / 4]
import pylab
pylab.plot(snr.sample_times, abs(snr))
pylab.ylabel('signal-to-noise')
pylab.xlabel('GPS Time (s)')
