def distance(a: TimeSeries, b: TimeSeries):
am = a.mean()
bm = b.mean()
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
# and then calculating the PSD spectrogram:
h1spec = h1.spectrogram(30, fftlength=4)
l1spec = l1.spectrogram(30, fftlength=4)
# To calculate the inspiral range variation, we need to create a
# :class:`~gwpy.timeseries.TimeSeries` in which to store the values, then
# loop over each PSD bin in the spectrogram, calculating the
# :func:`gwpy.astro.inspiral_range` for each one:
import numpy
from gwpy.astro import inspiral_range
h1range = TimeSeries(numpy.zeros(len(h1spec)),
dt=h1spec.dt, t0=h1spec.t0, unit='Mpc')
l1range = h1range.copy()
for i in range(h1range.size):
h1range[i] = inspiral_range(h1spec[i], fmin=10)
l1range[i] = inspiral_range(l1spec[i], fmin=10)
# We can now easily plot the timeseries to see the variation in LIGO
# sensitivity over the hour or so including GW150914:
plot = h1range.plot(label='LIGO-Hanford', color='gwpy:ligo-hanford',
figsize=(12, 5))
ax = plot.gca()
ax.plot(l1range, label='LIGO-Livingston', color='gwpy:ligo-livingston')
ax.set_ylabel('Angle-averaged sensitive distance [Mpc]')
ax.set_title('LIGO sensitivity to BNS around GW150914')
ax.set_epoch(1126259462) # <- set 0 on plot to GW150914
ax.legend()