# Then we can download a simulation of the GW150914 signal from GWOSC:
from astropy.utils.data import get_readable_fileobj
url = ("https://www.gw-openscience.org/s/events/GW150914/P150914/"
"fig2-unfiltered-waveform-H.txt")
with get_readable_fileobj(url) as f:
signal = TimeSeries.read(f, format='txt')
signal.t0 = .5 # make sure this intersects with noise time samples
# Note, since this simulation cuts off before a certain time, it is
# important to taper its ends to zero to avoid ringing artifacts.
# We can accomplish this using the
# :meth:`~gwpy.timeseries.TimeSeries.taper` method.
signal = signal.taper()
# Since the time samples overlap, we can inject this into our noise data
# using :meth:`~gwpy.types.series.Series.inject`:
data = noise.inject(signal)
# Finally, we can visualize the full process in the time domain:
from gwpy.plot import Plot
plot = Plot(noise, signal, data, separate=True, sharex=True, sharey=True)
plot.gca().set_epoch(0)
plot.show()
# We can clearly see that the loud GW150914-like signal has been layered
# on top of Gaussian noise with the correct amplitude and phase evolution.
delta = bm - am
ad = a + delta
dist = np.zeros(len(a))
xf = 1000000
xfi = 1 / xf
ad = a.copy()
bd = b.copy()
thresh = 10000000
for i in range(len(a)):
dist[i] = 1 / np.linalg.norm(a[i] - b[i])
if dist[i] < -1E-6:
ad[i] = am
bd[i] = bm
return dist, ad, bd
# time=np.arange(0, 100, 0.1);
# a= TimeSeries(np.sin(time))
# b= TimeSeries(np.sin(time*1.5))
# plot=Plot([a,b, dist])
# plot.show()
hdata = TimeSeries.fetch_open_data('H1', 1126259446, 1126259478, cache=True)
ldata = TimeSeries.fetch_open_data('L1', 1126259446, 1126259478, cache=True)
d, hl, ll = distance(hdata, ldata)
print(d)
plot = Plot([hdata, ldata], d, [hl, ll])
plot.show()
def zoom(ts: [TimeSeries], t, dt=0.5 * u.s):
print(t)
plot2 = Plot(ts)
ax = plot2.gca()
ax.set_xlim(t - dt, t + dt)
plot2.show()
