def project_strain(self, hplus, hcross, skypoint, psi):
""" Project hplus and hcross onto the detector frame
assuming a given sky location and polarization of the source.
Apply consistent time delay due to wave propagation.
hplus, hcross: plus and cross polarisation (REAL8TimeSeries)
skypoint: Skypoint object
psi: polarization angle in radians
This function uses SimDetectorStrainREAL8TimeSeries() that takes equatorial
coordinates (RA and dec) and the epoch signal epoch to define the sky location
and corresponding mapping to an Earth-fixed coordinate system.
If the skypoint is given in the geographic coordinate system,
they are mapped to the equatorial coordinate system with
reference time set to the epoch of hplus and hcross.
"""
assert hplus.data.length == hcross.data.length, \
'The polarization time series must have same size'
assert hplus.deltaT == hcross.deltaT, \
'The polarization time series must have the same sampling step'
assert hplus.epoch == hcross.epoch, \
'The polarization time series must have the same epoch'
assert skypoint.coordsystem.is_valid(), "Unsupported coordinate system"
# If the skypoint is in the geographic coordinate system we transform
# to the equatorial frame at the signal epoch
if skypoint.coordsystem.name == 'geographic':
skypoint = skypoint.transformed_to(
Coordsystem('equatorial', hplus.epoch))
#print(skypoint.coords(fmt='lonlat',unit='radians'))
return TimeSeries.from_lal(SimDetectorStrainREAL8TimeSeries( \
hplus, hcross, \
*skypoint.coords(fmt='lonlat',unit='radians'), \
psi, self.descriptor))
def burst_inject(ts_data,
loc=0.5,
duration=0.1,
hrss=0.0275,
amp=100.,
plot=True,
sine=False):
'''
Inject burst signal in time series data
Parameters
----------
ts_data : TimeSeries
Time series magnetic field data
duration : float
Duration of the burst
hrss : float
hrss
Return
------
ts_data : TimeSeries
New time series data with injected burst
hp : TimeSeries
Time series data of the burst signal only
'''
# Define sampling rate
sample_rate = float(ts_data.sample_rate)
# Define time period from sampling rate
delta_t = 1.0 / sample_rate
# Define start time of the time series
t0 = float(ts_data.start_time)
# Define final time of the time series
t1 = t0 + len(ts_data) / sample_rate
if sine:
# First method to create sine gaussian burst
f_0 = 18
filter_band = 4
q = math.sqrt(2) * f_0 / filter_band * 2
# Create sine gaussian burst
hp, hx = SimBurstSineGaussian(q * 2, f_0, hrss, 1, 0, delta_t)
else:
# Create gaussian burst
hp, hx = SimBurstGaussian(duration, hrss, delta_t)
hp = TimeSeries.from_lal(hp)
hx = TimeSeries.from_lal(hx)
# We rescale the amplitude to hide or expose it in the data a bit better
hp *= amp
if plot:
# Plot fake burst signal
pyplot.plot(hp.times, hp, 'k-')
#pyplot.xlim([-0.5, 0.5])
#pyplot.ylim([-0.1, 0.1]);
pyplot.xlabel('Time (s)')
pyplot.ylabel('Magnitude')
pyplot.savefig('fakesignal.png')
pyplot.close()
# Define burst epoch
hp.epoch = int(t0 + loc * (t1 - t0))
# Define burst first timestamp
st = int((hp.epoch.value - t0) * sample_rate - len(hp) / 2)
# Define burst final timestamp
en = st + len(hp)
# Convert time series into gwpy.timeseries.TimeSeries format
ts_data = TimeSeries(ts_data, sample_rate=sample_rate, epoch=t0)
# Include burst in data
ts_data[st:en] += hp
# Convert back to pycbc.types.TimeSeries format
ts_data = types.TimeSeries(ts_data.value,
delta_t=1.0 / sample_rate,
epoch=t0)
# Return time series with burst included
return ts_data, hp