swh:1:snp:3a699297f000109a1bc833f294a54171df990207
Tip revision: 4606ff7e39717505df757ac7e0f5538ec9e25cb3 authored by Ian Harry on 22 February 2024, 14:14:35 UTC
V236 release (#4648)
V236 release (#4648)
Tip revision: 4606ff7
data.py
import matplotlib.pyplot as pp
import pycbc.catalog
m = pycbc.catalog.Merger("GW170817", source='gwtc-1')
fig, axs = pp.subplots(2, 1, sharex=True, sharey=True)
for ifo, ax in zip(["L1", "H1"], axs):
pp.sca(ax)
pp.title(ifo)
# Retreive data around the BNS merger
ts = m.strain(ifo).time_slice(m.time - 15, m.time + 6)
# Whiten the data with a 4s filter
white = ts.whiten(4, 4)
times, freqs, power = white.qtransform(.01, logfsteps=200,
qrange=(110, 110),
frange=(20, 512))
pp.pcolormesh(times, freqs, power**0.5, vmax=5)
pp.yscale('log')
pp.ylabel("Frequency (Hz)")
pp.xlabel("Time (s)")
pp.show()