def test_fcross_project_strain(self):
""" Check consistency of project_strain() against antenna_pattern()
"""
coords = numpy.array([uniform(0,360), uniform(-90,90)])
psi = math.radians(uniform(0,180))
pt_eq = pb.skymaps.Skypoint(*numpy.radians(coords), COORD_SYS_EQUATORIAL)
d = pb.detectors.Detector(random.choice(DETECTORS))
antenna_pat = d.antenna_pattern(pt_eq, time=TIME, psi=psi)
hplus = TimeSeries(ZEROS_1_SEC, sample_rate=SAMPLING_RATE).to_lal()
hcross = TimeSeries(SIN_1_SEC, sample_rate=SAMPLING_RATE).to_lal()
hplus.epoch = lal.LIGOTimeGPS(TIME)
hcross.epoch = lal.LIGOTimeGPS(TIME)
# Project wave onto detector
response = d.project_strain(hplus, hcross, pt_eq, psi)
# Generate support timeseries
data = TimeSeries(ZEROS_5_SEC, \
sample_rate=SAMPLING_RATE, \
t0=TIME-2, unit=response._unit)
# Inject signal into timeseries
h = data.inject(response)
if antenna_pat[1] > 0:
estimated_pat = h.max().to_value()
else:
estimated_pat = h.min().to_value()
print("Exact antenna pattern = {} ; Estimated pattern from amplitude = {}".format(antenna_pat[1], estimated_pat))
self.assertAlmostEqual(antenna_pat[1], estimated_pat, places=2)
def test_delay_project_strain(self):
""" Check consistency of project_strain() against time_delay_earth_center()
"""
coords = numpy.array([uniform(0,360), uniform(-90,90)])
pt_eq = pb.skymaps.Skypoint(*numpy.radians(coords), COORD_SYS_EQUATORIAL)
d = pb.detectors.Detector(random.choice(DETECTORS))
delay = d.time_delay_from_earth_center(pt_eq, TIME)
hplus = TimeSeries(SIN_1_SEC, sample_rate=SAMPLING_RATE).to_lal()
hcross = TimeSeries(ZEROS_1_SEC, sample_rate=SAMPLING_RATE).to_lal()
hplus.epoch = lal.LIGOTimeGPS(TIME)
hcross.epoch = lal.LIGOTimeGPS(TIME)
# Project wave onto detector
response = d.project_strain(hplus, hcross, pt_eq, 0)
# Generate support timeseries
data = TimeSeries(ZEROS_5_SEC, \
sample_rate=SAMPLING_RATE, \
t0=TIME-2, unit=response._unit)
# Inject signal into timeseries
h = data.inject(response)
# Find end of the detector response
ix, = numpy.where(numpy.abs(h) > numpy.max(h)/10)
time_end = h.t0.value + ix[-1]/SAMPLING_RATE
estimated_delay = float(time_end - (TIME+1))
print("Exact delay = {} ; Estimated delay = {}".format(delay, estimated_delay))
# Estimate delay from timeseries
self.assertAlmostEqual(delay, estimated_delay, places=3)
def test_save_loudest_tile_features():
# prepare input data
channel = GW.channels[0]
noise = TimeSeries(numpy.random.normal(loc=1, scale=.5, size=16384 * 68),
sample_rate=16384,
epoch=-34).zpk([], [0], 1)
glitch = TimeSeries(signal.gausspulse(numpy.arange(-1, 1, 1. / 16384),
bw=100),
sample_rate=16384,
epoch=-1) * 1e-4
in_ = noise.inject(glitch)
_, _, _, qgram, _, _, _ = core.scan(gps=0,
channel=channel,
xoft=in_,
resample=4096,
fftlength=8)
# test loudest tiles
channel.save_loudest_tile_features(qgram, correlate=glitch)
assert channel.Q == numpy.around(qgram.plane.q, 1)
assert channel.energy == numpy.around(qgram.peak['energy'], 1)
assert channel.snr == numpy.around(qgram.peak['snr'], 1)
assert channel.t == numpy.around(qgram.peak['time'], 3)
assert channel.f == numpy.around(qgram.peak['frequency'], 1)
assert channel.corr == numpy.around(glitch.max().value, 1)
assert channel.delay == 0.0
assert channel.stdev == glitch.std().value
def test_save_loudest_tile_features():
# prepare input data
channel = GW.channels[0]
noise = TimeSeries(
numpy.random.normal(loc=1, scale=.5, size=16384 * 68),
sample_rate=16384, epoch=-34).zpk([], [0], 1)
glitch = TimeSeries(
signal.gausspulse(numpy.arange(-1, 1, 1./16384), bw=100),
sample_rate=16384, epoch=-1) * 1e-4
in_ = noise.inject(glitch)
_, _, _, qgram, _, _, _ = core.scan(
gps=0, channel=channel, xoft=in_, resample=4096, fftlength=8)
# test loudest tiles
channel.save_loudest_tile_features(qgram, correlate=glitch)
assert channel.Q == numpy.around(qgram.plane.q, 1)
assert channel.energy == numpy.around(qgram.peak['energy'], 1)
assert channel.snr == numpy.around(qgram.peak['snr'], 1)
assert channel.t == numpy.around(qgram.peak['time'], 3)
assert channel.f == numpy.around(qgram.peak['frequency'], 1)
assert channel.corr == numpy.around(glitch.max().value, 1)
assert channel.delay == 0.0
assert channel.stdev == glitch.std().value
# 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.
# Then we can download a simulation of the GW150914 signal from LOSC:
from astropy.utils.data import get_readable_fileobj
source = 'https://losc.ligo.org/s/events/GW150914/P150914/'
url = '%s/fig2-unfiltered-waveform-H.txt' % source
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.
from .. import (config, core, plot)
__author__ = 'Alex Urban <*****@*****.**>'
# global test objects
FFTLENGTH = 8
NOISE = TimeSeries(numpy.random.normal(loc=1, scale=.5, size=16384 * 68),
sample_rate=16384,
epoch=-34).zpk([], [0], 1)
GLITCH = TimeSeries(signal.gausspulse(numpy.arange(-1, 1, 1. / 16384), bw=100),
sample_rate=16384,
epoch=-1) * 1e-4
INPUT = NOISE.inject(GLITCH)
CONFIGURATION = {
'q-range': '3.3166,150',
'frequency-range': '4.0,Inf',
'plot-time-durations': '4',
'always-plot': 'True',
}
CHANNEL = config.OmegaChannel(channelname='L1:TEST-STRAIN',
section='test',
**CONFIGURATION)
SERIES = core.scan(gps=0,
channel=CHANNEL,
xoft=INPUT,
resample=2048,
from .. import plot
__author__ = 'Alex Urban <*****@*****.**>'
# global test objects
TWOPI = 2 * numpy.pi
TIMES = numpy.arange(0, 16384 * 64)
NOISE = TimeSeries(
numpy.random.normal(loc=1, scale=.5, size=16384 * 64),
sample_rate=16384, epoch=-32).zpk([], [0], 1)
FRINGE = TimeSeries(
numpy.cos(TWOPI * TIMES), sample_rate=16384, epoch=-32)
DATA = NOISE.inject(FRINGE)
QSPECGRAM = DATA.q_transform(logf=True)
# -- make sure plots run end-to-end -------------------------------------------
def test_spectral_comparison(tmpdir):
outdir = str(tmpdir)
plot1 = os.path.join(outdir, 'test1.png')
plot2 = os.path.join(outdir, 'test2.png')
# test plotting
plot.spectral_comparison(0, QSPECGRAM, FRINGE, plot1)
plot.spectral_overlay(0, QSPECGRAM, FRINGE, plot2)
# clean up
shutil.rmtree(outdir, ignore_errors=True)
