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)
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()
plot.show()
def test_inspiral_range(psd):
"""Test for :func:`gwpy.astro.inspiral_range_psd`
"""
r = astro.inspiral_range(psd, fmin=40)
utils.assert_quantity_almost_equal(r, TEST_RESULTS['inspiral_range'])
def test_inspiral_range(self):
r = astro.inspiral_range(self.psd, fmin=40)
self.assertEqual(r.unit, units.Mpc)
self.assertAlmostEqual(r.value, 19.63704209223392)
return r
t_unit = 'seconds'
sec_per_t_unit = 1.0
for i in range(0, num_points):
times[i] = times[i] / sec_per_t_unit
# Collect range data in arrays
ranges = []
medians = []
stds = []
for i in range(0, len(channel_list)):
ranges.append([])
for j in range(0, num_points):
data = TimeSeries.read(frame_cache_list[i], "%s:%s" % (ifo, channel_list[i]), start = gps_start_time + j * stride, end = gps_start_time + j * stride + integration_time)
PSD = data.psd(8, 4, method = 'lal_median')
BNS_range = float(inspiral_range(PSD, fmin=10).value)
ranges[i].append(BNS_range)
medians.append(numpy.median(ranges[i]))
stds.append(numpy.std(ranges[i]))
# Make plots
colors = ["blue", "green", "limegreen", "red", "yellow", "purple", "pink"] # Hopefully the user will not want to plot more than 7 datasets on one plot.
plt.figure(figsize = (12, 8))
for i in range(0, len(channel_list)):
plt.gcf().subplots_adjust(bottom=0.15)
plt.plot(times, ranges[i], colors[i % 6], linewidth = 1.5, label = r'%s:%s [median = %0.1f Mpc, $\sigma$ = %0.1f Mpc]' % (ifo, channel_list[i].replace('_', '\_'), medians[i], stds[i]))
if options.make_title:
plt.title("%s binary neutron star inspiral range" % ifo)
plt.ylabel('Angle-averaged range [Mpc]')
plt.xlabel('Time [%s] from %s UTC' % (t_unit, time.strftime("%b %d %Y %H:%M:%S", time.gmtime(gps_start_time + 315964782))))
plt.ylim(range_min, range_max)
plt.grid(True)
def test_inspiral_range(psd):
"""Test for :func:`gwpy.astro.inspiral_range_psd`
"""
r = astro.inspiral_range(psd, fmin=40)
utils.assert_quantity_almost_equal(r, TEST_RESULTS['inspiral_range'])