def normal(start, end, seed=0):
""" Generate data with a white Gaussian (normal) distribution
Parameters
----------
start_time : int
Start time in GPS seconds to generate noise
end_time : int
End time in GPS seconds to generate nosie
seed : {None, int}
The seed to generate the noise.
Returns
--------
noise : TimeSeries
A TimeSeries containing gaussian noise
"""
# This is reproduceable because we used fixed seeds from known values
s = int(start / BLOCK_SIZE)
e = int(end / BLOCK_SIZE)
# The data evenly divides so the last block would be superfluous
if end % BLOCK_SIZE == 0:
e -= 1
sv = RandomState(seed).randint(-2**50, 2**50)
data = numpy.concatenate([block(i + sv) for i in numpy.arange(s, e + 1, 1)])
ts = TimeSeries(data, delta_t=1.0 / SAMPLE_RATE, epoch=start)
return ts.time_slice(start, end)
def normal(start, end, seed=0):
""" Generate data with a white Gaussian (normal) distribution
Parameters
----------
start_time : int
Start time in GPS seconds to generate noise
end_time : int
End time in GPS seconds to generate nosie
seed : {None, int}
The seed to generate the noise.
Returns
--------
noise : TimeSeries
A TimeSeries containing gaussian noise
"""
# This is reproduceable because we used fixed seeds from known values
s = int(start / BLOCK_SIZE)
e = int(end / BLOCK_SIZE)
# The data evenly divides so the last block would be superfluous
if end % BLOCK_SIZE == 0:
e -= 1
sv = RandomState(seed).randint(-2**50, 2**50)
data = numpy.concatenate(
[block(i + sv) for i in numpy.arange(s, e + 1, 1)])
ts = TimeSeries(data, delta_t=1.0 / SAMPLE_RATE, epoch=start)
return ts.time_slice(start, end)
def normal(start, end, sample_rate=16384, seed=0):
""" Generate data with a white Gaussian (normal) distribution
Parameters
----------
start_time : int
Start time in GPS seconds to generate noise
end_time : int
End time in GPS seconds to generate noise
sample-rate: float
Sample rate to generate the data at. Keep constant if you want to
ensure continuity between disjoint time spans.
seed : {None, int}
The seed to generate the noise.
Returns
--------
noise : TimeSeries
A TimeSeries containing gaussian noise
"""
# This is reproduceable because we used fixed seeds from known values
block_dur = BLOCK_SAMPLES / sample_rate
s = int(numpy.floor(start / block_dur))
e = int(numpy.floor(end / block_dur))
# The data evenly divides so the last block would be superfluous
if end % block_dur == 0:
e -= 1
sv = RandomState(seed).randint(-2**50, 2**50)
data = numpy.concatenate(
[block(i + sv, sample_rate) for i in numpy.arange(s, e + 1, 1)])
ts = TimeSeries(data, delta_t=1.0 / sample_rate, epoch=(s * block_dur))
return ts.time_slice(start, end)
waveform[merger_index:start_index] = hp
signal = noise + waveform
pylab.figure(figsize=(10, 5))
pylab.plot(signal.sample_times, signal, label='Waveform + Noise')
pylab.plot(waveform.sample_times, waveform, label='Waveform')
pylab.legend()
pylab.xlabel('Time (s)')
pylab.ylabel('Strain')
pylab.title('Injected Waveform')
zoom_signal = signal.time_slice(merger_time - 0.5, merger_time + 0.5)
zoom_waveform = waveform.time_slice(merger_time - 0.5, merger_time + 0.5)
pylab.figure(figsize=(10, 5))
pylab.plot(zoom_signal.sample_times, zoom_signal, label='Signal')
pylab.plot(zoom_waveform.sample_times, zoom_waveform, label='Waveform')
pylab.xlabel('Time (s)')
pylab.ylabel('Strain')
pylab.title('Zoomed View')
pylab.legend()
hp.resize(time_duration * f_sample)
snr = matched_filter(hp, signal, psd=psd, low_frequency_cutoff=20)
pylab.figure(figsize=[10, 4])
pylab.plot(snr.sample_times, abs(snr))
pylab.ylabel('Signal-to-noise')
pylab.xlabel('Time (s)')
