def from_string(psd_name, length, delta_f, low_freq_cutoff):
"""Generate a frequency series containing a LALSimulation PSD specified by name.
Parameters
----------
psd_name : string
PSD name as found in LALSimulation, minus the SimNoisePSD prefix.
length : int
Length of the frequency series in samples.
delta_f : float
Frequency resolution of the frequency series.
low_freq_cutoff : float
Frequencies below this value are set to zero.
Returns
-------
psd : FrequencySeries
The generated frequency series.
"""
if psd_name not in _psd_list:
raise ValueError(psd_name + ' not found among LALSimulation PSD functions.')
kmin = int(low_freq_cutoff / delta_f)
lalseries = lal.CreateREAL8FrequencySeries(
'', lal.LIGOTimeGPS(0), 0, delta_f, lal.DimensionlessUnit, length)
try:
func = lalsimulation.__dict__[_name_prefix + psd_name + _name_suffix]
except KeyError:
func = lalsimulation.__dict__[_name_prefix + psd_name]
func(lalseries, low_freq_cutoff)
else:
lalsimulation.SimNoisePSD(lalseries, 0, func)
psd = FrequencySeries(lalseries.data.data, delta_f=delta_f)
psd.data[:kmin] = 0
return psd
def td_waveform_to_fd_waveform(waveform, out=None, length=None,
buffer_length=100):
""" Convert a time domain into a frequency domain waveform by FFT.
As a waveform is assumed to "wrap" in the time domain one must be
careful to ensure the waveform goes to 0 at both "boundaries". To
ensure this is done correctly the waveform must have the epoch set such
the merger time is at t=0 and the length of the waveform should be
shorter than the desired length of the FrequencySeries (times 2 - 1)
so that zeroes can be suitably pre- and post-pended before FFTing.
If given, out is a memory array to be used as the output of the FFT.
If not given memory is allocated internally.
If present the length of the returned FrequencySeries is determined
from the length out. If out is not given the length can be provided
expicitly, or it will be chosen as the nearest power of 2. If choosing
length explicitly the waveform length + buffer_length is used when
choosing the nearest binary number so that some zero padding is always
added.
"""
# Figure out lengths and set out if needed
if out is None:
if length is None:
N = pnutils.nearest_larger_binary_number(len(waveform) + \
buffer_length)
n = int(N//2) + 1
else:
n = length
N = (n-1)*2
out = zeros(n, dtype=complex_same_precision_as(waveform))
else:
n = len(out)
N = (n-1)*2
delta_f = 1. / (N * waveform.delta_t)
# total duration of the waveform
tmplt_length = len(waveform) * waveform.delta_t
if len(waveform) > N:
err_msg = "The time domain template is longer than the intended "
err_msg += "duration in the frequency domain. This situation is "
err_msg += "not supported in this function. Please shorten the "
err_msg += "waveform appropriately before calling this function or "
err_msg += "increase the allowed waveform length. "
err_msg += "Waveform length (in samples): {}".format(len(waveform))
err_msg += ". Intended length: {}.".format(N)
raise ValueError(err_msg)
# for IMR templates the zero of time is at max amplitude (merger)
# thus the start time is minus the duration of the template from
# lower frequency cutoff to merger, i.e. minus the 'chirp time'
tChirp = - float( waveform.start_time ) # conversion from LIGOTimeGPS
waveform.resize(N)
k_zero = int(waveform.start_time / waveform.delta_t)
waveform.roll(k_zero)
htilde = FrequencySeries(out, delta_f=delta_f, copy=False)
fft(waveform.astype(real_same_precision_as(htilde)), htilde)
htilde.length_in_time = tmplt_length
htilde.chirp_length = tChirp
return htilde
def _shift_and_ifft(self, fdsinx, tshift, fseries=None):
"""Calls apply_fd_time_shift, and iFFTs to the time domain.
"""
start_time = self.time_series.start_time
tdshift = apply_fd_time_shift(fdsinx, start_time+tshift,
fseries=fseries)
if not isinstance(tdshift, FrequencySeries):
# cast to FrequencySeries so time series will work
tdshift = FrequencySeries(tdshift, delta_f=fdsinx.delta_f,
epoch=fdsinx.epoch)
return tdshift.to_timeseries()
def get_waveform_filter(out, template=None, **kwargs):
"""Return a frequency domain waveform filter for the specified approximant
"""
n = len(out)
input_params = props(template, **kwargs)
if input_params['approximant'] in filter_approximants(_scheme.mgr.state):
wav_gen = filter_wav[type(_scheme.mgr.state)]
htilde = wav_gen[input_params['approximant']](out=out, **input_params)
htilde.resize(n)
htilde.chirp_length = get_waveform_filter_length_in_time(**input_params)
htilde.length_in_time = htilde.chirp_length
return htilde
if input_params['approximant'] in fd_approximants(_scheme.mgr.state):
wav_gen = fd_wav[type(_scheme.mgr.state)]
hp, hc = wav_gen[input_params['approximant']](**input_params)
hp.resize(n)
hp.chirp_length = get_waveform_filter_length_in_time(**input_params)
hp.length_in_time = hp.chirp_length
return hp
elif input_params['approximant'] in td_approximants(_scheme.mgr.state):
# N: number of time samples required
N = (n-1)*2
delta_f = 1.0 / (N * input_params['delta_t'])
wav_gen = td_wav[type(_scheme.mgr.state)]
hp, hc = wav_gen[input_params['approximant']](**input_params)
# taper the time series hp if required
if ('taper' in input_params.keys() and \
input_params['taper'] is not None):
hp = wfutils.taper_timeseries(hp, input_params['taper'],
return_lal=False)
# total duration of the waveform
tmplt_length = len(hp) * hp.delta_t
# for IMR templates the zero of time is at max amplitude (merger)
# thus the start time is minus the duration of the template from
# lower frequency cutoff to merger, i.e. minus the 'chirp time'
tChirp = - float( hp.start_time ) # conversion from LIGOTimeGPS
hp.resize(N)
k_zero = int(hp.start_time / hp.delta_t)
hp.roll(k_zero)
htilde = FrequencySeries(out, delta_f=delta_f, copy=False)
fft(hp.astype(real_same_precision_as(htilde)), htilde)
htilde.length_in_time = tmplt_length
htilde.chirp_length = tChirp
return htilde
else:
raise ValueError("Approximant %s not available" %
(input_params['approximant']))
def from_string(psd_name, length, delta_f, low_freq_cutoff):
"""Generate a frequency series containing a LALSimulation PSD specified
by name.
Parameters
----------
psd_name : string
PSD name as found in LALSimulation, minus the SimNoisePSD prefix.
length : int
Length of the frequency series in samples.
delta_f : float
Frequency resolution of the frequency series.
low_freq_cutoff : float
Frequencies below this value are set to zero.
Returns
-------
psd : FrequencySeries
The generated frequency series.
"""
# check if valid PSD model
if psd_name not in get_psd_model_list():
raise ValueError(psd_name + ' not found among analytical '
'PSD functions.')
# if PSD model is in LALSimulation
if psd_name in get_lalsim_psd_list():
lalseries = lal.CreateREAL8FrequencySeries(
'', lal.LIGOTimeGPS(0), 0, delta_f, lal.DimensionlessUnit, length)
try:
func = lalsimulation.__dict__[
_name_prefix + psd_name + _name_suffix]
except KeyError:
func = lalsimulation.__dict__[_name_prefix + psd_name]
func(lalseries, low_freq_cutoff)
else:
lalsimulation.SimNoisePSD(lalseries, 0, func)
psd = FrequencySeries(lalseries.data.data, delta_f=delta_f)
# if PSD model is coded in PyCBC
else:
func = pycbc_analytical_psds[psd_name]
psd = func(length, delta_f, low_freq_cutoff)
# zero-out content below low-frequency cutoff
kmin = int(low_freq_cutoff / delta_f)
psd.data[:kmin] = 0
return psd
def get_waveform_filter(out, template=None, **kwargs):
"""Return a frequency domain waveform filter for the specified approximant
"""
n = len(out)
input_params = props(template, **kwargs)
if input_params['approximant'] in filter_approximants(_scheme.mgr.state):
wav_gen = filter_wav[type(_scheme.mgr.state)]
htilde = wav_gen[input_params['approximant']](out=out, **input_params)
htilde.resize(n)
htilde.chirp_length = get_waveform_filter_length_in_time(**input_params)
htilde.length_in_time = htilde.chirp_length
return htilde
if input_params['approximant'] in fd_approximants(_scheme.mgr.state):
wav_gen = fd_wav[type(_scheme.mgr.state)]
hp, hc = wav_gen[input_params['approximant']](**input_params)
hp.resize(n)
out[0:len(hp)] = hp[:]
hp = FrequencySeries(out, delta_f=hp.delta_f, copy=False)
hp.chirp_length = get_waveform_filter_length_in_time(**input_params)
hp.length_in_time = hp.chirp_length
return hp
elif input_params['approximant'] in td_approximants(_scheme.mgr.state):
# N: number of time samples required
N = (n-1)*2
delta_f = 1.0 / (N * input_params['delta_t'])
wav_gen = td_wav[type(_scheme.mgr.state)]
hp, hc = wav_gen[input_params['approximant']](**input_params)
# taper the time series hp if required
if ('taper' in input_params.keys() and \
input_params['taper'] is not None):
hp = wfutils.taper_timeseries(hp, input_params['taper'],
return_lal=False)
return td_waveform_to_fd_waveform(hp, out=out)
else:
raise ValueError("Approximant %s not available" %
(input_params['approximant']))
def flat_unity(length, delta_f, low_freq_cutoff):
""" Returns a FrequencySeries of ones above the low_frequency_cutoff.
Parameters
----------
length : int
Length of output Frequencyseries.
delta_f : float
Frequency step for output FrequencySeries.
low_freq_cutoff : int
Low-frequency cutoff for output FrequencySeries.
Returns
-------
FrequencySeries
Returns a FrequencySeries containing the unity PSD model.
"""
fseries = FrequencySeries(numpy.ones(length), delta_f=delta_f)
kmin = int(low_freq_cutoff / fseries.delta_f)
fseries.data[:kmin] = 0
return fseries
def _imrphenombfreq(**p):
import lalinspiral
params = lalinspiral.InspiralTemplate()
m1 = p['mass1']
m2 = p['mass2']
mc, et = pnutils.mass1_mass2_to_mchirp_eta(m1, m2)
params.approximant = lalsimulation.IMRPhenomB
params.fLower = p['f_lower']
params.eta = et
params.distance = p['distance'] * lal.PC_SI * 1e6
params.mass1 = m1
params.mass2 = m2
params.spin1[2] = p['spin1z']
params.spin2[2] = p['spin2z']
params.startPhase = p['coa_phase']*2 - lal.PI
params.startTime = 0
params.tSampling = 8192
N = int(params.tSampling / p['delta_f'])
n = N / 2
# Create temporary memory to hold the results and call the generator
hpt = zeros(N, dtype=float32)
hct = zeros(N, dtype=float32)
hpt=hpt.lal()
hct=hct.lal()
lalinspiral.BBHPhenWaveBFreqDomTemplates(hpt, hct, params)
# Copy the results to a complex frequencyseries format
hctc = FrequencySeries(zeros(n, dtype=complex64), delta_f=p['delta_f'])
hptc = FrequencySeries(zeros(n, dtype=complex64), delta_f=p['delta_f'])
hptc.data += hpt.data[0:n]
hptc.data[1:n] += hpt.data[N:N-n:-1] * 1j
hctc.data += hct.data[0:n]
hctc.data[1:n] += hct.data[N:N-n:-1] * 1j
return hptc.astype(complex128), hctc.astype(complex128)
def from_lalsimulation(func, length, delta_f, low_freq_cutoff):
"""Generate a frequency series containing the specified LALSimulation PSD.
Parameters
----------
func : function
LALSimulation PSD function.
length : int
Length of the frequency series in samples.
delta_f : float
Frequency resolution of the frequency series.
low_freq_cutoff : float
Frequencies below this value are set to zero.
Returns
-------
psd : FrequencySeries
The generated frequency series.
"""
psd = FrequencySeries(zeros(length), delta_f=delta_f)
kmin = int(low_freq_cutoff / delta_f)
psd.data[kmin:] = map(func, numpy.arange(length)[kmin:] * delta_f)
return psd
def spintaylorf2(**kwds):
""" Return a SpinTaylorF2 waveform using CUDA to generate the phase and amplitude
"""
#####Pull out the input arguments#####
f_lower = double(kwds['f_lower'])
delta_f = double(kwds['delta_f'])
distance = double(kwds['distance'])
mass1 = double(kwds['mass1'])
mass2 = double(kwds['mass2'])
spin1x = double(kwds['spin1x'])
spin1y = double(kwds['spin1y'])
spin1z = double(kwds['spin1z'])
phi0 = double(kwds['coa_phase']) #Orbital Phase at coalescence
phase_order = int(kwds['phase_order'])
amplitude_order = int(kwds['amplitude_order'])
inclination = double(kwds['inclination'])
lnhatx = sin(inclination)
lnhaty = 0.
lnhatz = cos(inclination)
psi = 0.
tC= -1.0 / delta_f
M = mass1 + mass2
eta = mass1 * mass2 / (M * M)
m_sec = M * lal.MTSUN_SI
piM = lal.PI * m_sec
vISCO = 1. / sqrt(6.)
fISCO = vISCO * vISCO * vISCO / piM
f_max = ceilpow2(fISCO)
n = int(f_max / delta_f + 1)
kmax = int(fISCO / delta_f)
kmin = int(numpy.ceil(f_lower / delta_f))
kmax = kmax if (kmax<n) else n
#####Calculate the Orientation#####
v0 = pow(piM * kmin * delta_f,1./3)
chi = sqrt(spin1x**2+spin1y**2+spin1z**2)
kappa = (lnhatx*spin1x+lnhaty*spin1y+lnhatz*spin1z)/chi if (chi > 0.) else 1.
Jx0 = mass1*mass2*lnhatx/v0 + mass1*mass1*spin1x
Jy0 = mass1*mass2*lnhaty/v0 + mass1*mass1*spin1y
Jz0 = mass1*mass2*lnhatz/v0 + mass1*mass1*spin1z
thetaJ = acos(Jz0 / sqrt(Jx0**2+Jy0**2+Jz0**2))
psiJ = atan2(Jy0, -Jx0) # FIXME: check that Jy0 and Jx0 are not both 0
# Rotate Lnhat back to frame where J is along z, to figure out initial alpha
rotLx = lnhatx*cos(thetaJ)*cos(psiJ) - lnhaty*cos(thetaJ)*sin(psiJ) + lnhatz*sin(thetaJ)
rotLy = lnhatx*sin(psiJ) + lnhaty*cos(psiJ)
alpha0 = atan2(rotLy, rotLx) # FIXME: check that rotLy and rotLx are not both 0
psiJ_P =psiJ + psi
psiJ_C =psiJ + psi + lal.PI/4.
#####Calculate the Coefficients#####
#quadparam = 1.
gamma0 = mass1*chi/mass2
#Calculate the spin corrections
# FIXME should use pycbc's function, but sigma has different expression
# in Andy's code, double check
# pn_beta, pn_sigma, pn_gamma = pycbc.pnutils.mass1_mass2_spin1z_spin2z_to_beta_sigma_gamma(
# mass1, mass2, chi*kappa, 0) # FIXME: spin2 is taken to be 0
pn_beta = (113.*mass1/(12.*M) - 19.*eta/6.)*chi*kappa
pn_sigma = ( (5.*(3.*kappa*kappa-1.)/2.) + (7. - kappa*kappa)/96. ) * (mass1*mass1*chi*chi/M/M)
pn_gamma = (5.*(146597. + 7056.*eta)*mass1/(2268.*M) - 10.*eta*(1276. + 153.*eta)/81.)*chi*kappa
prec_fac0 = 5.*(4. + 3.*mass2/mass1)/64.
dtdv2 = 743./336. + 11.*eta/4.
dtdv3 = -4.*lal.PI + pn_beta
dtdv4 = 3058673./1016064. + 5429.*eta/1008. + 617.*eta*eta/144. - pn_sigma
dtdv5 = (-7729./672.+13.*eta/8.)*lal.PI + 9.*pn_gamma/40.
#####Calculate the Initial Euler Angles alpha_ref, beta_ref=0 and zeta_ref#####
gam = gamma0*v0
sqrtfac = sqrt(1. + 2.*kappa*gam + gam*gam)
logv0 = log(v0)
logfac1 = log(1. + kappa*gam + sqrtfac)
logfac2 = log(kappa + gam + sqrtfac)
v02 = v0 * v0
v03 = v0 * v02
kappa2 = kappa * kappa
kappa3 = kappa2 * kappa
gamma02 = gamma0 * gamma0
gamma03 = gamma02 *gamma0
alpha_ref = prec_fac0*( logfac2 *( dtdv2*gamma0 + dtdv3*kappa - dtdv5*kappa/(2.*gamma02) + dtdv4/(2.*gamma0) - dtdv4*kappa2/(2.*gamma0) + (dtdv5*kappa3)/(2.*gamma02) ) + logfac1*( - dtdv2*gamma0*kappa - dtdv3 + kappa*gamma03/2. - gamma03*kappa3/2. ) + logv0 *( dtdv2*gamma0*kappa + dtdv3 - kappa*gamma03/2. + gamma03*kappa3/2. ) + sqrtfac *( dtdv3 + dtdv4*v0/2. + dtdv5/gamma02/3. + dtdv4*kappa/(2.*gamma0) + dtdv5*kappa*v0/(6.*gamma0) - dtdv5*kappa2/(2.*gamma02) - 1/(3.*v03) - gamma0*kappa/(6.*v02) - dtdv2/v0 - gamma02/(3.*v0) + gamma02*kappa2/(2.*v0) + dtdv5*v02/3. )) - alpha0
zeta_ref = prec_fac0*( dtdv3*gamma0*kappa*v0 + dtdv4*v0 + logfac2 *(-dtdv2*gamma0 - dtdv3*kappa + dtdv5*kappa/(2.*gamma02) - dtdv4/(2.*gamma0) + dtdv4*kappa2/(2.*gamma0) - dtdv5*kappa3/(2.*gamma02) ) + logv0 *( kappa*gamma03/2. - gamma03*kappa3/2. ) + logfac1 *( dtdv2*gamma0*kappa + dtdv3 - kappa*gamma03/2. + gamma03*kappa3/2. ) - 1/(3.*v03) - gamma0*kappa/(2.*v02) - dtdv2/v0 + dtdv4*gamma0*kappa*v02/2. + dtdv5*v02/2. + sqrtfac *( -dtdv3 - dtdv4*v0/2. - dtdv5/(3.*gamma02) - dtdv4*kappa/(2.*gamma0) - dtdv5*kappa*v0/(6.*gamma0) + dtdv5*kappa2/(2.*gamma02) + 1/(3.*v03) + gamma0*kappa/(6.*v02) + dtdv2/v0 + gamma02/(3.*v0) - gamma02*kappa2/(2.*v0) - dtdv5*v02/3. ) + dtdv5*gamma0*kappa*v03/3. )
#####Calculate the Complex sideband factors, mm=2 is first entry#####
RE_SBfac0= (1.+cos(thetaJ)**2)/2.
RE_SBfac1= sin(2.*thetaJ)
RE_SBfac2= 3.*sin(thetaJ)**2
RE_SBfac3= -sin(2.*thetaJ)
RE_SBfac4= (1.+cos(thetaJ)**2)/2.
IM_SBfac0= -cos(thetaJ)
IM_SBfac1= -2.*sin(thetaJ)
IM_SBfac2= 0.
IM_SBfac3= -2.*sin(thetaJ)
IM_SBfac4= cos(thetaJ)
#####Calculate the PN terms # FIXME replace with functions in lalsimulation #####
theta = -11831./9240.
lambdaa = -1987./3080.0
pfaN = 3.0/(128.0 * eta)
pfa2 = 5.0*(743.0/84 + 11.0 * eta)/9.0
pfa3 = -16.0*lal.PI + 4.0*pn_beta
pfa4 = 5.0*(3058.673/7.056 + 5429.0/7.0 * eta + 617.0 * eta*eta)/72.0 - \
10.0*pn_sigma
pfa5 = 5.0/9.0 * (7729.0/84.0 - 13.0 * eta) * lal.PI - pn_gamma
pfl5 = 5.0/3.0 * (7729.0/84.0 - 13.0 * eta) * lal.PI - pn_gamma * 3
pfa6 = (11583.231236531/4.694215680 - 640.0/3.0 * lal.PI * lal.PI- \
6848.0/21.0*lal.GAMMA) + \
eta * (-15335.597827/3.048192 + 2255./12. * lal.PI * \
lal.PI - 1760./3.*theta +12320./9.*lambdaa) + \
eta*eta * 76055.0/1728.0 - \
eta*eta*eta* 127825.0/1296.0
pfl6 = -6848.0/21.0
pfa7 = lal.PI * 5.0/756.0 * ( 15419335.0/336.0 + 75703.0/2.0 * eta - \
14809.0 * eta*eta)
FTaN = 32.0 * eta*eta / 5.0
FTa2 = -(12.47/3.36 + 3.5/1.2 * eta)
FTa3 = 4.0 * lal.PI
FTa4 = -(44.711/9.072 - 92.71/5.04 * eta - 6.5/1.8 * eta*eta)
FTa5 = -(81.91/6.72 + 58.3/2.4 * eta) * lal.PI
FTa6 = (664.3739519/6.9854400 + 16.0/3.0 * lal.PI*lal.PI -
17.12/1.05 * lal.GAMMA +
(4.1/4.8 * lal.PI*lal.PI - 134.543/7.776) * eta -
94.403/3.024 * eta*eta - 7.75/3.24 * eta*eta*eta)
FTl6 = -8.56/1.05
FTa7 = -(162.85/5.04 - 214.745/1.728 * eta - 193.385/3.024 * eta*eta) \
* lal.PI
dETaN = 2 * -eta/2.0
dETa1 = 2 * -(3.0/4.0 + 1.0/12.0 * eta)
dETa2 = 3 * -(27.0/8.0 - 19.0/8.0 * eta + 1./24.0 * eta*eta)
dETa3 = 4 * -(67.5/6.4 - (344.45/5.76 - 20.5/9.6 * lal.PI*lal.PI) *
eta + 15.5/9.6 * eta*eta + 3.5/518.4 * eta*eta*eta)
amp0 = -4. * mass1 * mass2 / (1.0e+06 * distance * lal.PC_SI ) * \
lal.MRSUN_SI * lal.MTSUN_SI * sqrt(lal.PI/12.0)
htildeP = FrequencySeries(zeros(n,dtype=complex128), delta_f=delta_f, copy=False)
htildeC = FrequencySeries(zeros(n,dtype=complex128), delta_f=delta_f, copy=False)
spintaylorf2_kernel(htildeP.data[kmin:kmax], htildeC.data[kmin:kmax],
kmin, phase_order, amplitude_order, delta_f, piM, pfaN,
pfa2, pfa3, pfa4, pfa5, pfl5,
pfa6, pfl6, pfa7, FTaN, FTa2,
FTa3, FTa4, FTa5, FTa6,
FTl6, FTa7, dETaN, dETa1, dETa2, dETa3,
amp0, tC, phi0,
kappa, prec_fac0, alpha_ref, zeta_ref,
dtdv2, dtdv3, dtdv4, dtdv5,
RE_SBfac0, RE_SBfac1, RE_SBfac2, RE_SBfac3, RE_SBfac4,
IM_SBfac0, IM_SBfac1, IM_SBfac2, IM_SBfac3, IM_SBfac4,
psiJ_P, psiJ_C, gamma0)
return htildeP, htildeC
def fd_decompress(amp,
phase,
sample_frequencies,
out=None,
df=None,
f_lower=None,
interpolation='inline_linear'):
"""Decompresses an FD waveform using the given amplitude, phase, and the
frequencies at which they are sampled at.
Parameters
----------
amp : array
The amplitude of the waveform at the sample frequencies.
phase : array
The phase of the waveform at the sample frequencies.
sample_frequencies : array
The frequency (in Hz) of the waveform at the sample frequencies.
out : {None, FrequencySeries}
The output array to save the decompressed waveform to. If this contains
slots for frequencies > the maximum frequency in sample_frequencies,
the rest of the values are zeroed. If not provided, must provide a df.
df : {None, float}
The frequency step to use for the decompressed waveform. Must be
provided if out is None.
f_lower : {None, float}
The frequency to start the decompression at. If None, will use whatever
the lowest frequency is in sample_frequencies. All values at
frequencies less than this will be 0 in the decompressed waveform.
interpolation : {'inline_linear', str}
The interpolation to use for the amplitude and phase. Default is
'inline_linear'. If 'inline_linear' a custom interpolater is used.
Otherwise, ``scipy.interpolate.interp1d`` is used; for other options,
see possible values for that function's ``kind`` argument.
Returns
-------
out : FrequencySeries
If out was provided, writes to that array. Otherwise, a new
FrequencySeries with the decompressed waveform.
"""
precision = _precision_map[sample_frequencies.dtype.name]
if _precision_map[amp.dtype.name] != precision or \
_precision_map[phase.dtype.name] != precision:
raise ValueError("amp, phase, and sample_points must all have the "
"same precision")
if out is None:
if df is None:
raise ValueError("Either provide output memory or a df")
hlen = int(numpy.ceil(sample_frequencies.max() / df + 1))
out = FrequencySeries(numpy.zeros(hlen,
dtype=_complex_dtypes[precision]),
copy=False,
delta_f=df)
else:
# check for precision compatibility
if out.precision == 'double' and precision == 'single':
raise ValueError("cannot cast single precision to double")
df = out.delta_f
hlen = len(out)
if f_lower is None:
imin = 0 # pylint:disable=unused-variable
f_lower = sample_frequencies[0]
start_index = 0
else:
if f_lower >= sample_frequencies.max():
raise ValueError("f_lower is > than the maximum sample frequency")
if f_lower < sample_frequencies.min():
raise ValueError("f_lower is < than the minimum sample frequency")
imin = int(
numpy.searchsorted(sample_frequencies, f_lower, side='right')) - 1 # pylint:disable=unused-variable
start_index = int(numpy.ceil(f_lower / df))
if start_index >= hlen:
raise ValueError('requested f_lower >= largest frequency in out')
# interpolate the amplitude and the phase
if interpolation == "inline_linear":
# Call the scheme-dependent function
inline_linear_interp(amp, phase, sample_frequencies, out, df, f_lower,
imin, start_index)
else:
# use scipy for fancier interpolation
sample_frequencies = numpy.array(sample_frequencies)
amp = numpy.array(amp)
phase = numpy.array(phase)
outfreq = out.sample_frequencies.numpy()
amp_interp = interpolate.interp1d(sample_frequencies,
amp,
kind=interpolation,
bounds_error=False,
fill_value=0.,
assume_sorted=True)
phase_interp = interpolate.interp1d(sample_frequencies,
phase,
kind=interpolation,
bounds_error=False,
fill_value=0.,
assume_sorted=True)
A = amp_interp(outfreq)
phi = phase_interp(outfreq)
out.data[:] = A * numpy.cos(phi) + (1j) * A * numpy.sin(phi)
return out
def _get_waveform_from_inspiral(**p):
import lalmetaio
# prefix with 'Inspiral-'
name = p['approximant'][9:]
if name.startswith('EOB'):
p['phase_order'] = -8
params = lalmetaio.SimInspiralTable()
params.waveform = name + string_from_order[p['phase_order']]
params.mass1= p['mass1']
params.mass2= p['mass2']
params.f_lower = p['f_lower']
params.spin1x = p['spin1x']
params.spin1y = p['spin1y']
params.spin1z = p['spin1z']
params.spin2x = p['spin2x']
params.spin2y = p['spin2y']
params.spin2z = p['spin2z']
params.inclination = p['inclination']
params.distance = p['distance']
params.coa_phase = p['coa_phase']
import lalinspiral
guess_length = lalinspiral.FindChirpChirpTime(params.mass1, params.mass2,
params.f_lower, 7)
guess_length = max(guess_length, 3)
params.geocent_end_time = guess_length * 1.5
params.taper = 'TAPER_NONE' #FIXME - either explain or don't hardcode this
bufferl = guess_length * 2
dt = p['delta_t']
df = 1.0 / bufferl
sample_rate = int(1.0 / dt)
epoch = lal.LIGOTimeGPS(0, 0)
N = bufferl * sample_rate
n = N / 2 + 1
resp = FrequencySeries(zeros(n), delta_f=df, epoch=epoch,
dtype=complex64) + 1
out = TimeSeries(zeros(N), delta_t=dt, epoch=epoch, dtype=float32)
outl = out.lal()
outl.sampleUnits = lal.ADCCountUnit
out2 = TimeSeries(zeros(N), delta_t=dt, epoch=epoch, dtype=float32)
outl2 = out.lal()
outl2.sampleUnits = lal.ADCCountUnit
respl = resp.lal()
respl.sampleUnits = lal.DimensionlessUnit
lalinspiral.FindChirpInjectSignals(outl, params, respl)
params.coa_phase -= lal.PI / 4
lalinspiral.FindChirpInjectSignals(outl2, params, respl)
seriesp = TimeSeries(outl.data.data, delta_t=dt,
epoch=epoch - params.geocent_end_time)
seriesc = TimeSeries(outl2.data.data, delta_t=dt,
epoch=epoch - params.geocent_end_time)
return seriesp, seriesc
def matched_filter_core(template,
data,
psd=None,
low_frequency_cutoff=None,
high_frequency_cutoff=None,
h_norm=None,
out=None,
corr_out=None):
""" Return the complex snr and normalization.
Return the complex snr, along with its associated normalization of the template,
matched filtered against the data.
Parameters
----------
template : TimeSeries or FrequencySeries
The template waveform
data : TimeSeries or FrequencySeries
The strain data to be filtered.
psd : {FrequencySeries}, optional
The noise weighting of the filter.
low_frequency_cutoff : {None, float}, optional
The frequency to begin the filter calculation. If None, begin at the
first frequency after DC.
high_frequency_cutoff : {None, float}, optional
The frequency to stop the filter calculation. If None, continue to the
the nyquist frequency.
h_norm : {None, float}, optional
The template normalization. If none, this value is calculated internally.
out : {None, Array}, optional
An array to use as memory for snr storage. If None, memory is allocated
internally.
corr_out : {None, Array}, optional
An array to use as memory for correlation storage. If None, memory is allocated
internally. If provided, management of the vector is handled externally by the
caller. No zero'ing is done internally.
Returns
-------
snr : TimeSeries
A time series containing the complex snr.
corrrelation: FrequencySeries
A frequency series containing the correlation vector.
norm : float
The normalization of the complex snr.
"""
if corr_out is not None:
_qtilde = corr_out
else:
global _qtilde_t
_qtilde = _qtilde_t
htilde = make_frequency_series(template)
stilde = make_frequency_series(data)
if len(htilde) != len(stilde):
raise ValueError("Length of template and data must match")
N = (len(stilde) - 1) * 2
kmin, kmax = get_cutoff_indices(low_frequency_cutoff,
high_frequency_cutoff, stilde.delta_f, N)
if out is None:
_q = zeros(N, dtype=complex_same_precision_as(data))
elif (len(out) == N) and type(out) is Array and out.kind == 'complex':
_q = out
else:
raise TypeError('Invalid Output Vector: wrong length or dtype')
if corr_out:
pass
elif (_qtilde is None) or (len(_qtilde) !=
N) or _qtilde.dtype != data.dtype:
_qtilde_t = _qtilde = zeros(N, dtype=complex_same_precision_as(data))
else:
_qtilde.clear()
correlate(htilde[kmin:kmax], stilde[kmin:kmax], _qtilde[kmin:kmax])
if psd is not None:
if isinstance(psd, FrequencySeries):
if psd.delta_f == stilde.delta_f:
_qtilde[kmin:kmax] /= psd[kmin:kmax]
else:
raise TypeError("PSD delta_f does not match data")
else:
raise TypeError("PSD must be a FrequencySeries")
ifft(_qtilde, _q)
if h_norm is None:
h_norm = sigmasq(htilde, psd, low_frequency_cutoff,
high_frequency_cutoff)
norm = (4.0 * stilde.delta_f) / sqrt(h_norm)
delta_t = 1.0 / (N * stilde.delta_f)
return (TimeSeries(_q, epoch=stilde._epoch, delta_t=delta_t, copy=False),
FrequencySeries(_qtilde,
epoch=stilde._epoch,
delta_f=htilde.delta_f,
copy=False), norm)
def detect_loud_glitches(strain, psd_duration=16, psd_stride=8,
psd_avg_method='median', low_freq_cutoff=30.,
threshold=50., cluster_window=5., corrupted_time=4.,
high_freq_cutoff=None, output_intermediates=False):
"""Automatic identification of loud transients for gating purposes."""
if output_intermediates:
strain.save_to_wav('strain_conditioned.wav')
# don't waste time trying to optimize a single FFT
pycbc.fft.fftw.set_measure_level(0)
logging.info('Autogating: estimating PSD')
psd = pycbc.psd.welch(strain, seg_len=psd_duration*strain.sample_rate,
seg_stride=psd_stride*strain.sample_rate,
avg_method=psd_avg_method)
logging.info('Autogating: time -> frequency')
strain_tilde = FrequencySeries(numpy.zeros(len(strain) / 2 + 1),
delta_f=1./strain.duration,
dtype=complex_same_precision_as(strain))
pycbc.fft.fft(strain, strain_tilde)
logging.info('Autogating: interpolating PSD')
psd = pycbc.psd.interpolate(psd, strain_tilde.delta_f)
logging.info('Autogating: whitening')
if high_freq_cutoff:
kmax = int(high_freq_cutoff / strain_tilde.delta_f)
strain_tilde[kmax:] = 0.
norm = high_freq_cutoff - low_freq_cutoff
else:
norm = strain.sample_rate/2. - low_freq_cutoff
strain_tilde /= (psd * norm) ** 0.5
kmin = int(low_freq_cutoff / strain_tilde.delta_f)
strain_tilde[0:kmin] = 0.
# FIXME at this point the strain can probably be downsampled
logging.info('Autogating: frequency -> time')
pycbc.fft.ifft(strain_tilde, strain)
pycbc.fft.fftw.set_measure_level(pycbc.fft.fftw._default_measurelvl)
logging.info('Autogating: stdev of whitened strain is %.4f', numpy.std(strain))
if output_intermediates:
strain.save_to_wav('strain_whitened.wav')
mag = abs(strain)
if output_intermediates:
mag.save('strain_whitened_mag.npy')
mag = numpy.array(mag, dtype=numpy.float32)
# remove corrupted strain at the ends
corrupted_idx = int(corrupted_time * strain.sample_rate)
mag[0:corrupted_idx] = 0
mag[-1:-corrupted_idx-1:-1] = 0
logging.info('Autogating: finding loud peaks')
indices = numpy.where(mag > threshold)[0]
cluster_idx = pycbc.events.findchirp_cluster_over_window(
indices, mag[indices], int(cluster_window*strain.sample_rate))
times = [idx * strain.delta_t + strain.start_time \
for idx in indices[cluster_idx]]
return times
def fd_decompress(amp, phase, sample_frequencies, out=None, df=None,
f_lower=None, interpolation='linear'):
"""Decompresses an FD waveform using the given amplitude, phase, and the
frequencies at which they are sampled at.
Parameters
----------
amp : array
The amplitude of the waveform at the sample frequencies.
phase : array
The phase of the waveform at the sample frequencies.
sample_frequencies : array
The frequency (in Hz) of the waveform at the sample frequencies.
out : {None, FrequencySeries}
The output array to save the decompressed waveform to. If this contains
slots for frequencies > the maximum frequency in sample_frequencies,
the rest of the values are zeroed. If not provided, must provide a df.
df : {None, float}
The frequency step to use for the decompressed waveform. Must be
provided if out is None.
f_lower : {None, float}
The frequency to start the decompression at. If None, will use whatever
the lowest frequency is in sample_frequencies. All values at
frequencies less than this will be 0 in the decompressed waveform.
interpolation : {'linear', str}
The interpolation to use for the amplitude and phase. Default is
'linear'. If 'linear' a custom interpolater is used. Otherwise,
``scipy.interpolate.interp1d`` is used; for other options, see
possible values for that function's ``kind`` argument.
Returns
-------
out : FrqeuencySeries
If out was provided, writes to that array. Otherwise, a new
FrequencySeries with the decompressed waveform.
"""
if out is None:
if df is None:
raise ValueError("Either provide output memory or a df")
flen = int(numpy.ceil(sample_frequencies.max()/df+1))
out = FrequencySeries(numpy.zeros(flen,
dtype=numpy.complex128), copy=False, delta_f=df)
else:
df = out.delta_f
flen = len(out)
if f_lower is None:
jmin = 0
f_lower = sample_frequencies[0]
else:
if f_lower >= sample_frequencies.max():
raise ValueError("f_lower is > than the maximum sample frequency")
jmin = int(numpy.searchsorted(sample_frequencies, f_lower))
imin = int(numpy.floor(f_lower/df))
# interpolate the amplitude and the phase
if interpolation == "linear":
# use custom interpolation
sflen = len(sample_frequencies)
h = numpy.array(out.data, copy=False)
# make sure df is a float
df = float(df)
code = r"""
# include <math.h>
# include <stdio.h>
int j = jmin-1;
double sf = 0.;
double A = 0.;
double nextA = 0.;
double phi = 0.;
double nextPhi = 0.;
double next_sf = sample_frequencies[jmin];
double f = 0.;
double invsdf = 0.;
double mAmp = 0.;
double bAmp = 0.;
double mPhi = 0.;
double bPhi = 0.;
double interpAmp = 0.;
double interpPhi = 0.;
// zero-out beginning of array
std::fill(h, h+imin, std::complex<double>(0., 0.));
// cycle over desired samples
for (int i=imin; i<flen; i++){
f = i*df;
if (f >= next_sf){
// update linear interpolations
j += 1;
// if we have gone beyond the sampled frequencies, just break
if ((j+1) == sflen) {
// zero-out rest the rest of the array & exit
std::fill(h+i, h+flen, std::complex<double>(0., 0.));
break;
}
sf = (double) sample_frequencies[j];
next_sf = (double) sample_frequencies[j+1];
A = (double) amp[j];
nextA = (double) amp[j+1];
phi = (double) phase[j];
nextPhi = (double) phase[j+1];
invsdf = 1./(next_sf - sf);
mAmp = (nextA - A)*invsdf;
bAmp = A - mAmp*sf;
mPhi = (nextPhi - phi)*invsdf;
bPhi = phi - mPhi*sf;
}
interpAmp = mAmp * f + bAmp;
interpPhi = mPhi * f + bPhi;
h[i] = std::complex<double> (interpAmp*cos(interpPhi),
interpAmp*sin(interpPhi));
}
"""
inline(code, ['flen', 'sflen', 'df', 'sample_frequencies',
'amp', 'phase', 'h', 'imin', 'jmin'],
extra_compile_args=[WEAVE_FLAGS + '-march=native -O3 -w'] +\
omp_flags,
libraries=omp_libs)
else:
# use scipy for fancier interpolation
outfreq = out.sample_frequencies.numpy()
amp_interp = interpolate.interp1d(sample_frequencies, amp,
kind=interpolation, bounds_error=False, fill_value=0.,
assume_sorted=True)
phase_interp = interpolate.interp1d(sample_frequencies, phase,
kind=interpolation, bounds_error=False, fill_value=0.,
assume_sorted=True)
A = amp_interp(outfreq)
phi = phase_interp(outfreq)
out.data[:] = A*numpy.cos(phi) + (1j)*A*numpy.sin(phi)
return out
def get_fd_lm(template=None, **kwargs):
"""Return frequency domain lm mode with a given number of overtones.
Parameters
----------
template: object
An object that has attached properties. This can be used to substitute
for keyword arguments. A common example would be a row in an xml table.
freqs : dict
{lmn:f_lmn} Dictionary of the central frequencies for each overtone,
as many as number of modes.
taus : dict
{lmn:tau_lmn} Dictionary of the damping times for each overtone,
as many as number of modes.
l : int
l mode (lm modes available: 22, 21, 33, 44, 55).
m : int
m mode (lm modes available: 22, 21, 33, 44, 55).
nmodes: int
Number of overtones desired (maximum n=8)
amplmn : float
Amplitude of the lmn overtone, as many as the number of nmodes.
philmn : float
Phase of the lmn overtone, as many as the number of modes. Should also
include the information from the azimuthal angle (phi + m*Phi).
inclination : {0., float}, optional
Inclination of the system in radians. Default is 0 (face on).
delta_f : {None, float}, optional
The frequency step used to generate the ringdown.
If None, it will be set to the inverse of the time at which the
amplitude is 1/1000 of the peak amplitude (the minimum of all modes).
f_lower: {None, float}, optional
The starting frequency of the output frequency series.
If None, it will be set to delta_f.
f_final : {None, float}, optional
The ending frequency of the output frequency series.
If None, it will be set to the frequency at which the amplitude
is 1/1000 of the peak amplitude (the maximum of all modes).
Returns
-------
hplustilde: FrequencySeries
The plus phase of a lm mode with n overtones in frequency domain.
hcrosstilde: FrequencySeries
The cross phase of a lm mode with n overtones in frequency domain.
"""
input_params = props(template, lm_required_args, **kwargs)
# Get required args
amps, phis = lm_amps_phases(**input_params)
f_0 = input_params.pop('freqs')
tau = input_params.pop('taus')
l, m = input_params.pop('l'), input_params.pop('m')
inc = input_params.pop('inclination', 0.)
nmodes = input_params.pop('nmodes')
if int(nmodes) == 0:
raise ValueError('Number of overtones (nmodes) must be greater '
'than zero.')
# The following may not be in input_params
delta_f = input_params.pop('delta_f', None)
f_lower = input_params.pop('f_lower', None)
f_final = input_params.pop('f_final', None)
if delta_f is None:
delta_f = lm_deltaf(tau, ['%d%d%d' %(l,m,nmodes)])
if f_final is None:
f_final = lm_ffinal(f_0, tau, ['%d%d%d' %(l, m, nmodes)])
kmax = int(f_final / delta_f) + 1
outplus = FrequencySeries(zeros(kmax, dtype=complex128), delta_f=delta_f)
outcross = FrequencySeries(zeros(kmax, dtype=complex128), delta_f=delta_f)
for n in range(nmodes):
hplus, hcross = get_fd_qnm(template=None, f_0=f_0['%d%d%d' %(l,m,n)],
tau=tau['%d%d%d' %(l,m,n)],
amp=amps['%d%d%d' %(l,m,n)],
phi=phis['%d%d%d' %(l,m,n)],
inclination=inc, l=l, m=m, delta_f=delta_f,
f_lower=f_lower, f_final=f_final)
outplus.data += hplus.data
outcross.data += hcross.data
return outplus, outcross
dec_inj
]
snr_list = []
fig = pyplot.figure()
ii = 0
colors = {'H1': 'r', 'L1': 'g'}
for ifo in ['H1', 'L1']:
ii += 1
ax = fig.add_subplot(int('21{}'.format(ii)))
print ifo
# get the psd
print "loading psd"
psd = FrequencySeries(fp['{}/psds/0'.format(ifo)][:],
delta_f=fp['{}/psds/0'.format(ifo)].attrs['delta_f'])\
/DYN_RANGE_FAC**2
asd = FrequencySeries(numpy.sqrt(psd.numpy()), delta_f=psd.delta_f)
# get the strain
print "loading strain"
stilde = FrequencySeries(
fp['{}/stilde'.format(ifo)][:],
delta_f=fp['{}/stilde'.format(ifo)].attrs['delta_f'],
epoch=fp['{}/stilde'.format(ifo)].attrs['epoch'])
print "whitening"
wh_stilde = FrequencySeries(stilde / asd,
delta_f=stilde.delta_f,
epoch=stilde.epoch)
wh_strain = wh_stilde.to_timeseries()
def welch(timeseries,
seg_len=4096,
seg_stride=2048,
window='hann',
avg_method='median',
num_segments=None,
require_exact_data_fit=False):
"""PSD estimator based on Welch's method.
Parameters
----------
timeseries : TimeSeries
Time series for which the PSD is to be estimated.
seg_len : int
Segment length in samples.
seg_stride : int
Separation between consecutive segments, in samples.
window : {'hann', numpy.ndarray}
Function used to window segments before Fourier transforming, or
a `numpy.ndarray` that specifies the window.
avg_method : {'median', 'mean', 'median-mean'}
Method used for averaging individual segment PSDs.
Returns
-------
psd : FrequencySeries
Frequency series containing the estimated PSD.
Raises
------
ValueError
For invalid choices of `seg_len`, `seg_stride` `window` and
`avg_method` and for inconsistent combinations of len(`timeseries`),
`seg_len` and `seg_stride`.
Notes
-----
See arXiv:gr-qc/0509116 for details.
"""
window_map = {'hann': numpy.hanning}
# sanity checks
if isinstance(window, numpy.ndarray) and window.size != seg_len:
raise ValueError('Invalid window: incorrect window length')
if not isinstance(window, numpy.ndarray) and window not in window_map:
raise ValueError('Invalid window: unknown window {!r}'.format(window))
if avg_method not in ('mean', 'median', 'median-mean'):
raise ValueError('Invalid averaging method')
if type(seg_len) is not int or type(seg_stride) is not int \
or seg_len <= 0 or seg_stride <= 0:
raise ValueError('Segment length and stride must be positive integers')
if timeseries.precision == 'single':
fs_dtype = numpy.complex64
elif timeseries.precision == 'double':
fs_dtype = numpy.complex128
num_samples = len(timeseries)
if num_segments is None:
num_segments = int(num_samples // seg_stride)
# NOTE: Is this not always true?
if (num_segments - 1) * seg_stride + seg_len > num_samples:
num_segments -= 1
if not require_exact_data_fit:
data_len = (num_segments - 1) * seg_stride + seg_len
# Get the correct amount of data
if data_len < num_samples:
diff = num_samples - data_len
start = diff // 2
end = num_samples - diff // 2
# Want this to be integers so if diff is odd, catch it here.
if diff % 2:
start = start + 1
timeseries = timeseries[start:end]
num_samples = len(timeseries)
if data_len > num_samples:
err_msg = "I was asked to estimate a PSD on %d " % (data_len)
err_msg += "data samples. However the data provided only contains "
err_msg += "%d data samples." % (num_samples)
if num_samples != (num_segments - 1) * seg_stride + seg_len:
raise ValueError('Incorrect choice of segmentation parameters')
if not isinstance(window, numpy.ndarray):
window = window_map[window](seg_len)
w = Array(window.astype(timeseries.dtype))
# calculate psd of each segment
delta_f = 1. / timeseries.delta_t / seg_len
segment_tilde = FrequencySeries(numpy.zeros(seg_len / 2 + 1), \
delta_f=delta_f, dtype=fs_dtype)
segment_psds = []
for i in xrange(num_segments):
segment_start = i * seg_stride
segment_end = segment_start + seg_len
segment = timeseries[segment_start:segment_end]
assert len(segment) == seg_len
fft(segment * w, segment_tilde)
seg_psd = abs(segment_tilde * segment_tilde.conj()).numpy()
#halve the DC and Nyquist components to be consistent with TO10095
seg_psd[0] /= 2
seg_psd[-1] /= 2
segment_psds.append(seg_psd)
segment_psds = numpy.array(segment_psds)
if avg_method == 'mean':
psd = numpy.mean(segment_psds, axis=0)
elif avg_method == 'median':
psd = numpy.median(segment_psds, axis=0) / median_bias(num_segments)
elif avg_method == 'median-mean':
odd_psds = segment_psds[::2]
even_psds = segment_psds[1::2]
odd_median = numpy.median(odd_psds, axis=0) / \
median_bias(len(odd_psds))
even_median = numpy.median(even_psds, axis=0) / \
median_bias(len(even_psds))
psd = (odd_median + even_median) / 2
psd *= 2 * delta_f * seg_len / (w * w).sum()
return FrequencySeries(psd,
delta_f=delta_f,
dtype=timeseries.dtype,
epoch=timeseries.start_time)
def inverse_spectrum_truncation(psd,
max_filter_len,
low_frequency_cutoff=None,
trunc_method=None):
"""Modify a PSD such that the impulse response associated with its inverse
square root is no longer than `max_filter_len` time samples. In practice
this corresponds to a coarse graining or smoothing of the PSD.
Parameters
----------
psd : FrequencySeries
PSD whose inverse spectrum is to be truncated.
max_filter_len : int
Maximum length of the time-domain filter in samples.
low_frequency_cutoff : {None, int}
Frequencies below `low_frequency_cutoff` are zeroed in the output.
trunc_method : {None, 'hann'}
Function used for truncating the time-domain filter.
None produces a hard truncation at `max_filter_len`.
Returns
-------
psd : FrequencySeries
PSD whose inverse spectrum has been truncated.
Raises
------
ValueError
For invalid types or values of `max_filter_len` and `low_frequency_cutoff`.
Notes
-----
See arXiv:gr-qc/0509116 for details.
"""
# sanity checks
if type(max_filter_len) is not int or max_filter_len <= 0:
raise ValueError('max_filter_len must be a positive integer')
if low_frequency_cutoff is not None and low_frequency_cutoff < 0 \
or low_frequency_cutoff > psd.sample_frequencies[-1]:
raise ValueError(
'low_frequency_cutoff must be within the bandwidth of the PSD')
N = (len(psd) - 1) * 2
inv_asd = FrequencySeries((1. / psd)**0.5, delta_f=psd.delta_f, \
dtype=complex_same_precision_as(psd))
inv_asd[0] = 0
inv_asd[N / 2] = 0
q = TimeSeries(numpy.zeros(N), delta_t=(N / psd.delta_f), \
dtype=real_same_precision_as(psd))
if low_frequency_cutoff:
kmin = int(low_frequency_cutoff / psd.delta_f)
inv_asd[0:kmin] = 0
ifft(inv_asd, q)
trunc_start = max_filter_len / 2
trunc_end = N - max_filter_len / 2
if trunc_end < trunc_start:
raise ValueError('Invalid value in inverse_spectrum_truncation')
if trunc_method == 'hann':
trunc_window = Array(numpy.hanning(max_filter_len), dtype=q.dtype)
q[0:trunc_start] *= trunc_window[max_filter_len / 2:max_filter_len]
q[trunc_end:N] *= trunc_window[0:max_filter_len / 2]
if trunc_start < trunc_end:
q[trunc_start:trunc_end] = 0
psd_trunc = FrequencySeries(numpy.zeros(len(psd)), delta_f=psd.delta_f, \
dtype=complex_same_precision_as(psd))
fft(q, psd_trunc)
psd_trunc *= psd_trunc.conj()
psd_out = 1. / abs(psd_trunc)
return psd_out
def fd_decompress(amp,
phase,
sample_frequencies,
out=None,
df=None,
f_lower=None,
interpolation='linear'):
"""Decompresses an FD waveform using the given amplitude, phase, and the
frequencies at which they are sampled at.
Parameters
----------
amp : array
The amplitude of the waveform at the sample frequencies.
phase : array
The phase of the waveform at the sample frequencies.
sample_frequencies : array
The frequency (in Hz) of the waveform at the sample frequencies.
out : {None, FrequencySeries}
The output array to save the decompressed waveform to. If this contains
slots for frequencies > the maximum frequency in sample_frequencies,
the rest of the values are zeroed. If not provided, must provide a df.
df : {None, float}
The frequency step to use for the decompressed waveform. Must be
provided if out is None.
f_lower : {None, float}
The frequency to start the decompression at. If None, will use whatever
the lowest frequency is in sample_frequencies. All values at
frequencies less than this will be 0 in the decompressed waveform.
interpolation : {'linear', str}
The interpolation to use for the amplitude and phase. Default is
'linear'. If 'linear' a custom interpolater is used. Otherwise,
``scipy.interpolate.interp1d`` is used; for other options, see
possible values for that function's ``kind`` argument.
Returns
-------
out : FrqeuencySeries
If out was provided, writes to that array. Otherwise, a new
FrequencySeries with the decompressed waveform.
"""
if out is None:
if df is None:
raise ValueError("Either provide output memory or a df")
flen = int(numpy.ceil(sample_frequencies.max() / df + 1))
out = FrequencySeries(numpy.zeros(flen, dtype=numpy.complex128),
copy=False,
delta_f=df)
else:
df = out.delta_f
flen = len(out)
if f_lower is None:
jmin = 0
f_lower = sample_frequencies[0]
else:
if f_lower >= sample_frequencies.max():
raise ValueError("f_lower is > than the maximum sample frequency")
jmin = int(numpy.searchsorted(sample_frequencies, f_lower))
imin = int(numpy.floor(f_lower / df))
# interpolate the amplitude and the phase
if interpolation == "linear":
# use custom interpolation
sflen = len(sample_frequencies)
h = numpy.array(out.data, copy=False)
# make sure df is a float
df = float(df)
code = r"""
# include <math.h>
# include <stdio.h>
int j = jmin-1;
double sf = 0.;
double A = 0.;
double nextA = 0.;
double phi = 0.;
double nextPhi = 0.;
double next_sf = sample_frequencies[jmin];
double f = 0.;
double invsdf = 0.;
double mAmp = 0.;
double bAmp = 0.;
double mPhi = 0.;
double bPhi = 0.;
double interpAmp = 0.;
double interpPhi = 0.;
// zero-out beginning of array
std::fill(h, h+imin, std::complex<double>(0., 0.));
// cycle over desired samples
for (int i=imin; i<flen; i++){
f = i*df;
if (f >= next_sf){
// update linear interpolations
j += 1;
// if we have gone beyond the sampled frequencies, just break
if ((j+1) == sflen) {
// zero-out rest the rest of the array & exit
std::fill(h+i, h+flen, std::complex<double>(0., 0.));
break;
}
sf = (double) sample_frequencies[j];
next_sf = (double) sample_frequencies[j+1];
A = (double) amp[j];
nextA = (double) amp[j+1];
phi = (double) phase[j];
nextPhi = (double) phase[j+1];
invsdf = 1./(next_sf - sf);
mAmp = (nextA - A)*invsdf;
bAmp = A - mAmp*sf;
mPhi = (nextPhi - phi)*invsdf;
bPhi = phi - mPhi*sf;
}
interpAmp = mAmp * f + bAmp;
interpPhi = mPhi * f + bPhi;
h[i] = std::complex<double> (interpAmp*cos(interpPhi),
interpAmp*sin(interpPhi));
}
"""
inline(code, ['flen', 'sflen', 'df', 'sample_frequencies',
'amp', 'phase', 'h', 'imin', 'jmin'],
extra_compile_args=[WEAVE_FLAGS + '-march=native -O3 -w'] +\
omp_flags,
libraries=omp_libs)
else:
# use scipy for fancier interpolation
outfreq = out.sample_frequencies.numpy()
amp_interp = interpolate.interp1d(sample_frequencies,
amp,
kind=interpolation,
bounds_error=False,
fill_value=0.,
assume_sorted=True)
phase_interp = interpolate.interp1d(sample_frequencies,
phase,
kind=interpolation,
bounds_error=False,
fill_value=0.,
assume_sorted=True)
A = amp_interp(outfreq)
phi = phase_interp(outfreq)
out.data[:] = A * numpy.cos(phi) + (1j) * A * numpy.sin(phi)
return out
def adjust_strain(self,
strain,
delta_fs=None,
delta_qinv=None,
delta_fc=None,
kappa_c=1.0,
kappa_tst_re=1.0,
kappa_tst_im=0.0,
kappa_pu_re=1.0,
kappa_pu_im=0.0):
"""Adjust the FrequencySeries strain by changing the time-dependent
calibration parameters kappa_c(t), kappa_a(t), f_c(t), fs, and qinv.
Parameters
----------
strain : FrequencySeries
The strain data to be adjusted.
delta_fc : float
Change in coupled-cavity (CC) pole at time t.
kappa_c : float
Scalar correction factor for sensing function c0 at time t.
kappa_tst_re : float
Real part of scalar correction factor for actuation function
A_{tst0} at time t.
kappa_tst_im : float
Imaginary part of scalar correction factor for actuation function
A_tst0 at time t.
kappa_pu_re : float
Real part of scalar correction factor for actuation function
A_{pu0} at time t.
kappa_pu_im : float
Imaginary part of scalar correction factor for actuation function
A_{pu0} at time t.
fs : float
Spring frequency for signal recycling cavity.
qinv : float
Inverse quality factor for signal recycling cavity.
Returns
-------
strain_adjusted : FrequencySeries
The adjusted strain.
"""
fc = self.fc0 + delta_fc if delta_fc else self.fc0
fs = self.fs0 + delta_fs if delta_fs else self.fs0
qinv = self.qinv0 + delta_qinv if delta_qinv else self.qinv0
# calculate adjusted response function
r_adjusted = self.update_r(fs=fs,
qinv=qinv,
fc=fc,
kappa_c=kappa_c,
kappa_tst_re=kappa_tst_re,
kappa_tst_im=kappa_tst_im,
kappa_pu_re=kappa_pu_re,
kappa_pu_im=kappa_pu_im)
# calculate error function
k = r_adjusted / self.r0
# decompose into amplitude and unwrapped phase
k_amp = numpy.abs(k)
k_phase = numpy.unwrap(numpy.angle(k))
# convert to FrequencySeries by interpolating then resampling
order = 1
k_amp_off = UnivariateSpline(self.freq, k_amp, k=order, s=0)
k_phase_off = UnivariateSpline(self.freq, k_phase, k=order, s=0)
freq_even = strain.sample_frequencies.numpy()
k_even_sample = k_amp_off(freq_even) * \
numpy.exp(1.0j * k_phase_off(freq_even))
strain_adjusted = FrequencySeries(strain.numpy() * \
k_even_sample,
delta_f=strain.delta_f)
return strain_adjusted
m1 = m2 = 1.4
mchirp = float(pycbc.conversions.mchirp_from_mass1_mass2(m1, m2))
eta = float(pycbc.conversions.eta_from_mass1_mass2(m1, m2))
hp = numpy.zeros(flen, dtype=numpy.complex128)
hc = hp.copy()
f = lib.generate
f.argtypes = [
c_void_p, c_void_p, c_double, c_double, c_double, c_double, c_double,
c_double, c_double, c_double, c_double
]
_ = f(hp.ctypes.data, hc.ctypes.data, mchirp, eta, inc, ecc, lamc, lc, dist,
fend, df)
import pylab
# it appears that the plus / cross data is time inverted
hp = FrequencySeries(hp.conj(), delta_f=df, epoch=-int(1.0 / df))
hc = FrequencySeries(hc.conj(), delta_f=df, epoch=-int(1.0 / df))
kmin = int(flow / df)
hp[:kmin].clear()
hc[:kmin].clear()
t = hp.to_timeseries()
pylab.plot(t.sample_times, t)
#pylab.xscale('log')
pylab.show()
def calculate_acf(data, delta_t=1.0, unbiased=False):
r"""Calculates the one-sided autocorrelation function.
Calculates the autocorrelation function (ACF) and returns the one-sided
ACF. The ACF is defined as the autocovariance divided by the variance. The
ACF can be estimated using
.. math::
\hat{R}(k) = \frac{1}{n \sigma^{2}} \sum_{t=1}^{n-k} \left( X_{t} - \mu \right) \left( X_{t+k} - \mu \right)
Where :math:`\hat{R}(k)` is the ACF, :math:`X_{t}` is the data series at
time t, :math:`\mu` is the mean of :math:`X_{t}`, and :math:`\sigma^{2}` is
the variance of :math:`X_{t}`.
Parameters
-----------
data : TimeSeries or numpy.array
A TimeSeries or numpy.array of data.
delta_t : float
The time step of the data series if it is not a TimeSeries instance.
unbiased : bool
If True the normalization of the autocovariance function is n-k
instead of n. This is called the unbiased estimation of the
autocovariance. Note that this does not mean the ACF is unbiased.
Returns
-------
acf : numpy.array
If data is a TimeSeries then acf will be a TimeSeries of the
one-sided ACF. Else acf is a numpy.array.
"""
# if given a TimeSeries instance then get numpy.array
if isinstance(data, TimeSeries):
y = data.numpy()
delta_t = data.delta_t
else:
y = data
# Zero mean
y = y - y.mean()
ny_orig = len(y)
npad = 1
while npad < 2*ny_orig:
npad = npad << 1
ypad = numpy.zeros(npad)
ypad[:ny_orig] = y
# FFT data minus the mean
fdata = TimeSeries(ypad, delta_t=delta_t).to_frequencyseries()
# correlate
# do not need to give the congjugate since correlate function does it
cdata = FrequencySeries(zeros(len(fdata), dtype=fdata.dtype),
delta_f=fdata.delta_f, copy=False)
correlate(fdata, fdata, cdata)
# IFFT correlated data to get unnormalized autocovariance time series
acf = cdata.to_timeseries()
acf = acf[:ny_orig]
# normalize the autocovariance
# note that dividing by acf[0] is the same as ( y.var() * len(acf) )
if unbiased:
acf /= ( y.var() * numpy.arange(len(acf), 0, -1) )
else:
acf /= acf[0]
# return input datatype
if isinstance(data, TimeSeries):
return TimeSeries(acf, delta_t=delta_t)
else:
return acf
def template_segment_checker(self, bank, t_num, segment, start_time):
"""Test if injections in segment are worth filtering with template.
Using the current template, current segment, and injections within that
segment. Test if the injections and sufficiently "similar" to any of
the injections to justify actually performing a matched-filter call.
Ther are two parts to this test: First we check if the chirp time of
the template is within a provided window of any of the injections. If
not then stop here, it is not worth filtering this template, segment
combination for this injection set. If this check passes we compute a
match between a coarse representation of the template and a coarse
representation of each of the injections. If that match is above a
user-provided value for any of the injections then filtering can
proceed. This is currently only available if using frequency-domain
templates.
Parameters
-----------
FIXME
Returns
--------
FIXME
"""
if not self.enabled:
# If disabled, always filter (ie. return True)
return True
# Get times covered by segment analyze
sample_rate = 2. * (len(segment) - 1) * segment.delta_f
cum_ind = segment.cumulative_index
diff = segment.analyze.stop - segment.analyze.start
seg_start_time = cum_ind / sample_rate + start_time
seg_end_time = (cum_ind + diff) / sample_rate + start_time
# And add buffer
seg_start_time = seg_start_time - self.seg_buffer
seg_end_time = seg_end_time + self.seg_buffer
# Chirp time test
if self.chirp_time_window is not None:
m1 = bank.table[t_num]['mass1']
m2 = bank.table[t_num]['mass2']
tau0_temp, _ = mass1_mass2_to_tau0_tau3(m1, m2, self.f_lower)
for inj in self.injection_params.table:
end_time = inj.geocent_end_time + \
1E-9 * inj.geocent_end_time_ns
if not(seg_start_time <= end_time <= seg_end_time):
continue
tau0_inj, _ = \
mass1_mass2_to_tau0_tau3(inj.mass1, inj.mass2,
self.f_lower)
tau_diff = abs(tau0_temp - tau0_inj)
if tau_diff <= self.chirp_time_window:
break
else:
# Get's here if all injections are outside chirp-time window
return False
# Coarse match test
if self.match_threshold:
if self._short_template_mem is None:
# Set the memory for the short templates
wav_len = 1 + int(self.coarsematch_fmax /
self.coarsematch_deltaf)
self._short_template_mem = zeros(wav_len, dtype=np.complex64)
# Set the current short PSD to red_psd
try:
red_psd = self._short_psd_storage[id(segment.psd)]
except KeyError:
# PSD doesn't exist yet, so make it!
curr_psd = segment.psd.numpy()
step_size = int(self.coarsematch_deltaf / segment.psd.delta_f)
max_idx = int(self.coarsematch_fmax / segment.psd.delta_f) + 1
red_psd_data = curr_psd[:max_idx:step_size]
red_psd = FrequencySeries(red_psd_data, #copy=False,
delta_f=self.coarsematch_deltaf)
self._short_psd_storage[id(curr_psd)] = red_psd
# Set htilde to be the current short template
if not t_num == self._short_template_id:
# Set the memory for the short templates if unset
if self._short_template_mem is None:
wav_len = 1 + int(self.coarsematch_fmax /
self.coarsematch_deltaf)
self._short_template_mem = zeros(wav_len,
dtype=np.complex64)
# Generate short waveform
htilde = bank.generate_with_delta_f_and_max_freq(
t_num, self.coarsematch_fmax, self.coarsematch_deltaf,
low_frequency_cutoff=bank.table[t_num].f_lower,
cached_mem=self._short_template_mem)
self._short_template_id = t_num
self._short_template_wav = htilde
else:
htilde = self._short_template_wav
for inj in self.injection_params.table:
end_time = inj.geocent_end_time + \
1E-9 * inj.geocent_end_time_ns
if not(seg_start_time < end_time < seg_end_time):
continue
curr_inj = self.short_injections[inj.simulation_id]
o, _ = match(htilde, curr_inj, psd=red_psd,
low_frequency_cutoff=self.f_lower)
if o > self.match_threshold:
break
else:
# Get's here if all injections are outside match threshold
return False
return True
def get_fd_qnm(template=None, **kwargs):
"""Return a frequency domain damped sinusoid.
Parameters
----------
template: object
An object that has attached properties. This can be used to substitute
for keyword arguments. A common example would be a row in an xml table.
f_0 : float
The ringdown-frequency.
tau : float
The damping time of the sinusoid.
amp : float
The amplitude of the ringdown (constant for now).
phi : float
The initial phase of the ringdown. Should also include the information
from the azimuthal angle (phi_0 + m*Phi).
inclination : {0., float}, optional
Inclination of the system in radians. Default is 0 (face on).
l : {2, int}, optional
l mode for the spherical harmonics. Default is l=2.
m : {2, int}, optional
m mode for the spherical harmonics. Default is m=2.
t_0 : {0, float}, optional
The starting time of the ringdown.
delta_f : {None, float}, optional
The frequency step used to generate the ringdown.
If None, it will be set to the inverse of the time at which the
amplitude is 1/1000 of the peak amplitude.
f_lower: {None, float}, optional
The starting frequency of the output frequency series.
If None, it will be set to delta_f.
f_final : {None, float}, optional
The ending frequency of the output frequency series.
If None, it will be set to the frequency at which the amplitude is
1/1000 of the peak amplitude.
Returns
-------
hplustilde: FrequencySeries
The plus phase of the ringdown in frequency domain.
hcrosstilde: FrequencySeries
The cross phase of the ringdown in frequency domain.
"""
input_params = props(template, qnm_required_args, **kwargs)
f_0 = input_params.pop('f_0')
tau = input_params.pop('tau')
amp = input_params.pop('amp')
phi = input_params.pop('phi')
# the following have defaults, and so will be populated
t_0 = input_params.pop('t_0')
# the following may not be in input_params
inc = input_params.pop('inclination', 0.)
l = input_params.pop('l', 2)
m = input_params.pop('m', 2)
delta_f = input_params.pop('delta_f', None)
f_lower = input_params.pop('f_lower', None)
f_final = input_params.pop('f_final', None)
if delta_f is None:
delta_f = 1. / qnm_time_decay(tau, 1./1000)
if f_lower is None:
f_lower = delta_f
kmin = 0
else:
kmin = int(f_lower / delta_f)
if f_final is None:
f_final = qnm_freq_decay(f_0, tau, 1./1000)
if f_final > max_freq:
f_final = max_freq
kmax = int(f_final / delta_f) + 1
freqs = numpy.arange(kmin, kmax)*delta_f
# FIXME: we are using spin -2 weighted spherical harmonics for now,
# when possible switch to spheroidal harmonics.
sph_lm = lal.SpinWeightedSphericalHarmonic(inc, 0., -2, l, m).real
sph_lminusm = lal.SpinWeightedSphericalHarmonic(inc, 0., -2, l, -m).real
spherical_plus = sph_lm + (-1)**l * sph_lminusm
spherical_cross = sph_lm - (-1)**l * sph_lminusm
denominator = 1 + (4j * pi * freqs * tau) - (4 * pi_sq * ( freqs*freqs - f_0*f_0) * tau*tau)
norm = amp * tau / denominator
if t_0 != 0:
time_shift = numpy.exp(-1j * two_pi * freqs * t_0)
norm *= time_shift
# Analytical expression for the Fourier transform of the ringdown (damped sinusoid)
hp_tilde = norm * spherical_plus * ( (1 + 2j * pi * freqs * tau) * numpy.cos(phi)
- two_pi * f_0 * tau * numpy.sin(phi) )
hc_tilde = norm * spherical_cross * ( (1 + 2j * pi * freqs * tau) * numpy.sin(phi)
+ two_pi * f_0 * tau * numpy.cos(phi) )
hplustilde = FrequencySeries(zeros(kmax, dtype=complex128), delta_f=delta_f)
hcrosstilde = FrequencySeries(zeros(kmax, dtype=complex128), delta_f=delta_f)
hplustilde.data[kmin:kmax] = hp_tilde
hcrosstilde.data[kmin:kmax] = hc_tilde
return hplustilde, hcrosstilde
def spa_tmplt(**kwds):
""" Generate a minimal TaylorF2 approximant with optimations for the sin/cos
"""
# Pull out the input arguments
f_lower = kwds['f_lower']
delta_f = kwds['delta_f']
distance = kwds['distance']
mass1 = kwds['mass1']
mass2 = kwds['mass2']
s1z = kwds['spin1z']
s2z = kwds['spin2z']
phase_order = int(kwds['phase_order'])
#amplitude_order = int(kwds['amplitude_order'])
spin_order = int(kwds['spin_order'])
if 'out' in kwds:
out = kwds['out']
else:
out = None
amp_factor = spa_amplitude_factor(mass1=mass1, mass2=mass2) / distance
lal_pars = lal.CreateDict()
if phase_order != -1:
lalsimulation.SimInspiralWaveformParamsInsertPNPhaseOrder(
lal_pars, phase_order)
if spin_order != -1:
lalsimulation.SimInspiralWaveformParamsInsertPNSpinOrder(
lal_pars, spin_order)
#Calculate the PN terms
phasing = lalsimulation.SimInspiralTaylorF2AlignedPhasing(
float(mass1), float(mass2), float(s1z), float(s2z), lal_pars)
pfaN = phasing.v[0]
pfa2 = phasing.v[2] / pfaN
pfa3 = phasing.v[3] / pfaN
pfa4 = phasing.v[4] / pfaN
pfa5 = phasing.v[5] / pfaN
pfa6 = (phasing.v[6] - phasing.vlogv[6] * log(4)) / pfaN
pfa7 = phasing.v[7] / pfaN
pfl5 = phasing.vlogv[5] / pfaN
pfl6 = phasing.vlogv[6] / pfaN
piM = lal.PI * (mass1 + mass2) * lal.MTSUN_SI
kmin = int(f_lower / float(delta_f))
vISCO = 1. / sqrt(6.)
fISCO = vISCO * vISCO * vISCO / piM
kmax = int(fISCO / delta_f)
f_max = ceilpow2(fISCO)
n = int(f_max / delta_f) + 1
if not out:
htilde = FrequencySeries(zeros(n, dtype=numpy.complex64),
delta_f=delta_f,
copy=False)
else:
if type(out) is not Array:
raise TypeError("Output must be an instance of Array")
if len(out) < kmax:
kmax = len(out)
if out.dtype != complex64:
raise TypeError("Output array is the wrong dtype")
htilde = FrequencySeries(out, delta_f=delta_f, copy=False)
spa_tmplt_engine(htilde[kmin:kmax], kmin, phase_order, delta_f, piM, pfaN,
pfa2, pfa3, pfa4, pfa5, pfl5, pfa6, pfl6, pfa7,
amp_factor)
return htilde
def get_fd_from_freqtau(template=None, **kwargs):
"""Return frequency domain ringdown with all the modes specified.
Parameters
----------
template: object
An object that has attached properties. This can be used to substitute
for keyword arguments. A common example would be a row in an xml table.
lmns : list
Desired lmn modes as strings (lm modes available: 22, 21, 33, 44, 55).
The n specifies the number of overtones desired for the corresponding
lm pair (maximum n=8).
Example: lmns = ['223','331'] are the modes 220, 221, 222, and 330
f_lmn: float
Central frequency of the lmn overtone, as many as number of modes.
tau_lmn: float
Damping time of the lmn overtone, as many as number of modes.
amp220 : float
Amplitude of the fundamental 220 mode.
amplmn : float
Fraction of the amplitude of the lmn overtone relative to the
fundamental mode, as many as the number of subdominant modes.
philmn : float
Phase of the lmn overtone, as many as the number of modes. Should also
include the information from the azimuthal angle (phi + m*Phi).
inclination : {0., float}, optional
Inclination of the system in radians. Default is 0 (face on).
delta_f : {None, float}, optional
The frequency step used to generate the ringdown.
If None, it will be set to the inverse of the time at which the
amplitude is 1/1000 of the peak amplitude (the minimum of all modes).
f_lower: {None, float}, optional
The starting frequency of the output frequency series.
If None, it will be set to delta_f.
f_final : {None, float}, optional
The ending frequency of the output frequency series.
If None, it will be set to the frequency at which the amplitude
is 1/1000 of the peak amplitude (the maximum of all modes).
Returns
-------
hplustilde: FrequencySeries
The plus phase of a ringdown with the lm modes specified and
n overtones in frequency domain.
hcrosstilde: FrequencySeries
The cross phase of a ringdown with the lm modes specified and
n overtones in frequency domain.
"""
input_params = props(template, freqtau_required_args, **kwargs)
# Get required args
f_0, tau = lm_freqs_taus(**input_params)
lmns = input_params['lmns']
for lmn in lmns:
if int(lmn[2]) == 0:
raise ValueError('Number of overtones (nmodes) must be greater '
'than zero.')
# The following may not be in input_params
inc = input_params.pop('inclination', 0.)
delta_f = input_params.pop('delta_f', None)
f_lower = input_params.pop('f_lower', None)
f_final = input_params.pop('f_final', None)
if delta_f is None:
delta_f = lm_deltaf(tau, lmns)
if f_final is None:
f_final = lm_ffinal(f_0, tau, lmns)
if f_lower is None:
f_lower = delta_f
kmax = int(f_final / delta_f) + 1
outplustilde = FrequencySeries(zeros(kmax, dtype=complex128), delta_f=delta_f)
outcrosstilde = FrequencySeries(zeros(kmax, dtype=complex128), delta_f=delta_f)
for lmn in lmns:
l, m, nmodes = int(lmn[0]), int(lmn[1]), int(lmn[2])
hplustilde, hcrosstilde = get_fd_lm(freqs=f_0, taus=tau,
l=l, m=m, nmodes=nmodes,
inclination=inc,
delta_f=delta_f, f_lower=f_lower,
f_final=f_final, **input_params)
outplustilde.data += hplustilde.data
outcrosstilde.data += hcrosstilde.data
return outplustilde, outcrosstilde
def get_waveform_filter(out, template=None, **kwargs):
"""Return a frequency domain waveform filter for the specified approximant
"""
n = len(out)
input_params = props(template, **kwargs)
if input_params['approximant'] in filter_approximants(_scheme.mgr.state):
wav_gen = filter_wav[type(_scheme.mgr.state)]
htilde = wav_gen[input_params['approximant']](out=out, **input_params)
htilde.resize(n)
htilde.chirp_length = get_waveform_filter_length_in_time(
**input_params)
htilde.length_in_time = htilde.chirp_length
return htilde
if input_params['approximant'] in fd_approximants(_scheme.mgr.state):
wav_gen = fd_wav[type(_scheme.mgr.state)]
hp, hc = wav_gen[input_params['approximant']](**input_params)
hp.resize(n)
out[0:len(hp)] = hp[:]
hp = FrequencySeries(out, delta_f=hp.delta_f, copy=False)
hp.chirp_length = get_waveform_filter_length_in_time(**input_params)
hp.length_in_time = hp.chirp_length
return hp
elif input_params['approximant'] in td_approximants(_scheme.mgr.state):
# N: number of time samples required
N = (n - 1) * 2
delta_f = 1.0 / (N * input_params['delta_t'])
wav_gen = td_wav[type(_scheme.mgr.state)]
hp, hc = wav_gen[input_params['approximant']](**input_params)
# taper the time series hp if required
if ('taper' in input_params.keys() and \
input_params['taper'] is not None):
hp = wfutils.taper_timeseries(hp,
input_params['taper'],
return_lal=False)
# total duration of the waveform
tmplt_length = len(hp) * hp.delta_t
# for IMR templates the zero of time is at max amplitude (merger)
# thus the start time is minus the duration of the template from
# lower frequency cutoff to merger, i.e. minus the 'chirp time'
tChirp = -float(hp.start_time) # conversion from LIGOTimeGPS
hp.resize(N)
k_zero = int(hp.start_time / hp.delta_t)
hp.roll(k_zero)
htilde = FrequencySeries(out, delta_f=delta_f, copy=False)
fft(hp.astype(real_same_precision_as(htilde)), htilde)
htilde.length_in_time = tmplt_length
htilde.chirp_length = tChirp
return htilde
else:
raise ValueError("Approximant %s not available" %
(input_params['approximant']))
def calculate_acf(data, delta_t=1.0, unbiased=False):
r"""Calculates the one-sided autocorrelation function.
Calculates the autocorrelation function (ACF) and returns the one-sided
ACF. The ACF is defined as the autocovariance divided by the variance. The
ACF can be estimated using
.. math::
\hat{R}(k) = \frac{1}{n \sigma^{2}} \sum_{t=1}^{n-k} \left( X_{t} - \mu \right) \left( X_{t+k} - \mu \right)
Where :math:`\hat{R}(k)` is the ACF, :math:`X_{t}` is the data series at
time t, :math:`\mu` is the mean of :math:`X_{t}`, and :math:`\sigma^{2}` is
the variance of :math:`X_{t}`.
Parameters
-----------
data : TimeSeries or numpy.array
A TimeSeries or numpy.array of data.
delta_t : float
The time step of the data series if it is not a TimeSeries instance.
unbiased : bool
If True the normalization of the autocovariance function is n-k
instead of n. This is called the unbiased estimation of the
autocovariance. Note that this does not mean the ACF is unbiased.
Returns
-------
acf : numpy.array
If data is a TimeSeries then acf will be a TimeSeries of the
one-sided ACF. Else acf is a numpy.array.
"""
# if given a TimeSeries instance then get numpy.array
if isinstance(data, TimeSeries):
y = data.numpy()
delta_t = data.delta_t
else:
y = data
# Zero mean
y = y - y.mean()
ny_orig = len(y)
npad = 1
while npad < 2*ny_orig:
npad = npad << 1
ypad = numpy.zeros(npad)
ypad[:ny_orig] = y
# FFT data minus the mean
fdata = TimeSeries(ypad, delta_t=delta_t).to_frequencyseries()
# correlate
# do not need to give the congjugate since correlate function does it
cdata = FrequencySeries(zeros(len(fdata), dtype=fdata.dtype),
delta_f=fdata.delta_f, copy=False)
correlate(fdata, fdata, cdata)
# IFFT correlated data to get unnormalized autocovariance time series
acf = cdata.to_timeseries()
acf = acf[:ny_orig]
# normalize the autocovariance
# note that dividing by acf[0] is the same as ( y.var() * len(acf) )
if unbiased:
acf /= ( y.var() * numpy.arange(len(acf), 0, -1) )
else:
acf /= acf[0]
# return input datatype
if isinstance(data, TimeSeries):
return TimeSeries(acf, delta_t=delta_t)
else:
return acf
def fd_decompress(amp, phase, sample_frequencies, out=None, df=None,
f_lower=None, interpolation='linear'):
"""Decompresses an FD waveform using the given amplitude, phase, and the
frequencies at which they are sampled at.
Parameters
----------
amp : array
The amplitude of the waveform at the sample frequencies.
phase : array
The phase of the waveform at the sample frequencies.
sample_frequencies : array
The frequency (in Hz) of the waveform at the sample frequencies.
out : {None, FrequencySeries}
The output array to save the decompressed waveform to. If this contains
slots for frequencies > the maximum frequency in sample_frequencies,
the rest of the values are zeroed. If not provided, must provide a df.
df : {None, float}
The frequency step to use for the decompressed waveform. Must be
provided if out is None.
f_lower : {None, float}
The frequency to start the decompression at. If None, will use whatever
the lowest frequency is in sample_frequencies. All values at
frequencies less than this will be 0 in the decompressed waveform.
interpolation : {'linear', str}
The interpolation to use for the amplitude and phase. Default is
'linear'. If 'linear' a custom interpolater is used. Otherwise,
``scipy.interpolate.interp1d`` is used; for other options, see
possible values for that function's ``kind`` argument.
Returns
-------
out : FrqeuencySeries
If out was provided, writes to that array. Otherwise, a new
FrequencySeries with the decompressed waveform.
"""
precision = _precision_map[sample_frequencies.dtype.name]
if _precision_map[amp.dtype.name] != precision or \
_precision_map[phase.dtype.name] != precision:
raise ValueError("amp, phase, and sample_points must all have the "
"same precision")
if out is None:
if df is None:
raise ValueError("Either provide output memory or a df")
hlen = int(numpy.ceil(sample_frequencies.max()/df+1))
out = FrequencySeries(numpy.zeros(hlen,
dtype=_complex_dtypes[precision]), copy=False,
delta_f=df)
else:
# check for precision compatibility
if out.precision == 'double' and precision == 'single':
raise ValueError("cannot cast single precision to double")
df = out.delta_f
hlen = len(out)
if f_lower is None:
imin = 0
f_lower = sample_frequencies[0]
else:
if f_lower >= sample_frequencies.max():
raise ValueError("f_lower is > than the maximum sample frequency")
imin = int(numpy.searchsorted(sample_frequencies, f_lower))
start_index = int(numpy.floor(f_lower/df))
# interpolate the amplitude and the phase
if interpolation == "linear":
if precision == 'single':
code = _linear_decompress_code32
else:
code = _linear_decompress_code
# use custom interpolation
sflen = len(sample_frequencies)
h = numpy.array(out.data, copy=False)
delta_f = float(df)
inline(code, ['h', 'hlen', 'sflen', 'delta_f', 'sample_frequencies',
'amp', 'phase', 'start_index', 'imin'],
extra_compile_args=[WEAVE_FLAGS + '-march=native -O3 -w'] +\
omp_flags,
libraries=omp_libs)
else:
# use scipy for fancier interpolation
outfreq = out.sample_frequencies.numpy()
amp_interp = interpolate.interp1d(sample_frequencies.numpy(),
amp.numpy(), kind=interpolation, bounds_error=False, fill_value=0.,
assume_sorted=True)
phase_interp = interpolate.interp1d(sample_frequencies.numpy(),
phase.numpy(), kind=interpolation, bounds_error=False,
fill_value=0., assume_sorted=True)
A = amp_interp(outfreq)
phi = phase_interp(outfreq)
out.data[:] = A*numpy.cos(phi) + (1j)*A*numpy.sin(phi)
return out
def __init__(self,
low_frequency_cutoff,
high_frequency_cutoff,
snr_threshold,
tlen,
delta_f,
dtype,
segment_list,
template_output,
use_cluster,
downsample_factor=1,
upsample_threshold=1,
upsample_method='pruned_fft',
gpu_callback_method='none',
cluster_function='symmetric'):
""" Create a matched filter engine.
Parameters
----------
low_frequency_cutoff : {None, float}, optional
The frequency to begin the filter calculation. If None, begin at the
first frequency after DC.
high_frequency_cutoff : {None, float}, optional
The frequency to stop the filter calculation. If None, continue to the
the nyquist frequency.
snr_threshold : float
The minimum snr to return when filtering
segment_list : list
List of FrequencySeries that are the Fourier-transformed data segments
template_output : complex64
Array of memory given as the 'out' parameter to waveform.FilterBank
use_cluster : boolean
If true, cluster triggers above threshold using a window; otherwise,
only apply a threshold.
downsample_factor : {1, int}, optional
The factor by which to reduce the sample rate when doing a heirarchical
matched filter
upsample_threshold : {1, float}, optional
The fraction of the snr_threshold to trigger on the subsampled filter.
upsample_method : {pruned_fft, str}
The method to upsample or interpolate the reduced rate filter.
cluster_function : {symmetric, str}, optional
Which method is used to cluster triggers over time. If 'findchirp', a
sliding forward window; if 'symmetric', each window's peak is compared
to the windows before and after it, and only kept as a trigger if larger
than both.
"""
# Assuming analysis time is constant across templates and segments, also
# delta_f is constant across segments.
self.tlen = tlen
self.flen = self.tlen / 2 + 1
self.delta_f = delta_f
self.dtype = dtype
self.snr_threshold = snr_threshold
self.flow = low_frequency_cutoff
self.fhigh = high_frequency_cutoff
self.gpu_callback_method = gpu_callback_method
if cluster_function not in ['symmetric', 'findchirp']:
raise ValueError(
"MatchedFilter: 'cluster_function' must be either 'symmetric' or 'findchirp'"
)
self.cluster_function = cluster_function
self.segments = segment_list
self.htilde = template_output
if downsample_factor == 1:
self.snr_mem = zeros(self.tlen, dtype=self.dtype)
self.corr_mem = zeros(self.tlen, dtype=self.dtype)
if use_cluster and (cluster_function == 'symmetric'):
self.matched_filter_and_cluster = self.full_matched_filter_and_cluster_symm
# setup the threasholding/clustering operations for each segment
self.threshold_and_clusterers = []
for seg in self.segments:
thresh = events.ThresholdCluster(self.snr_mem[seg.analyze])
self.threshold_and_clusterers.append(thresh)
elif use_cluster and (cluster_function == 'findchirp'):
self.matched_filter_and_cluster = self.full_matched_filter_and_cluster_fc
else:
self.matched_filter_and_cluster = self.full_matched_filter_thresh_only
# Assuming analysis time is constant across templates and segments, also
# delta_f is constant across segments.
self.kmin, self.kmax = get_cutoff_indices(self.flow, self.fhigh,
self.delta_f, self.tlen)
# Set up the correlation operations for each analysis segment
corr_slice = slice(self.kmin, self.kmax)
self.correlators = []
for seg in self.segments:
corr = Correlator(self.htilde[corr_slice], seg[corr_slice],
self.corr_mem[corr_slice])
self.correlators.append(corr)
# setup up the ifft we will do
self.ifft = IFFT(self.corr_mem, self.snr_mem)
elif downsample_factor >= 1:
self.matched_filter_and_cluster = self.heirarchical_matched_filter_and_cluster
self.downsample_factor = downsample_factor
self.upsample_method = upsample_method
self.upsample_threshold = upsample_threshold
N_full = self.tlen
N_red = N_full / downsample_factor
self.kmin_full, self.kmax_full = get_cutoff_indices(
self.flow, self.fhigh, self.delta_f, N_full)
self.kmin_red, _ = get_cutoff_indices(self.flow, self.fhigh,
self.delta_f, N_red)
if self.kmax_full < N_red:
self.kmax_red = self.kmax_full
else:
self.kmax_red = N_red - 1
self.snr_mem = zeros(N_red, dtype=self.dtype)
self.corr_mem_full = FrequencySeries(zeros(N_full,
dtype=self.dtype),
delta_f=self.delta_f)
self.corr_mem = Array(self.corr_mem_full[0:N_red], copy=False)
self.inter_vec = zeros(N_full, dtype=self.dtype)
else:
raise ValueError("Invalid downsample factor")
def get_two_pol_waveform_filter(outplus, outcross, template, **kwargs):
"""Return a frequency domain waveform filter for the specified approximant.
Unlike get_waveform_filter this function returns both h_plus and h_cross
components of the waveform, which are needed for searches where h_plus
and h_cross are not related by a simple phase shift.
"""
n = len(outplus)
# If we don't have an inclination column alpha3 might be used
if not hasattr(template, 'inclination') and 'inclination' not in kwargs:
if hasattr(template, 'alpha3'):
kwargs['inclination'] = template.alpha3
input_params = props(template, **kwargs)
if input_params['approximant'] in fd_approximants(_scheme.mgr.state):
wav_gen = fd_wav[type(_scheme.mgr.state)]
hp, hc = wav_gen[input_params['approximant']](**input_params)
hp.resize(n)
hc.resize(n)
outplus[0:len(hp)] = hp[:]
hp = FrequencySeries(outplus, delta_f=hp.delta_f, copy=False)
outcross[0:len(hc)] = hc[:]
hc = FrequencySeries(outcross, delta_f=hc.delta_f, copy=False)
hp.chirp_length = get_waveform_filter_length_in_time(**input_params)
hp.length_in_time = hp.chirp_length
hc.chirp_length = hp.chirp_length
hc.length_in_time = hp.length_in_time
return hp, hc
elif input_params['approximant'] in td_approximants(_scheme.mgr.state):
# N: number of time samples required
N = (n-1)*2
delta_f = 1.0 / (N * input_params['delta_t'])
wav_gen = td_wav[type(_scheme.mgr.state)]
hp, hc = wav_gen[input_params['approximant']](**input_params)
# taper the time series hp if required
if 'taper' in input_params.keys() and \
input_params['taper'] is not None:
hp = wfutils.taper_timeseries(hp, input_params['taper'],
return_lal=False)
hc = wfutils.taper_timeseries(hc, input_params['taper'],
return_lal=False)
# total duration of the waveform
tmplt_length = len(hp) * hp.delta_t
# for IMR templates the zero of time is at max amplitude (merger)
# thus the start time is minus the duration of the template from
# lower frequency cutoff to merger, i.e. minus the 'chirp time'
tChirp = - float( hp.start_time ) # conversion from LIGOTimeGPS
hp.resize(N)
hc.resize(N)
k_zero = int(hp.start_time / hp.delta_t)
hp.roll(k_zero)
hc.roll(k_zero)
hp_tilde = FrequencySeries(outplus, delta_f=delta_f, copy=False)
hc_tilde = FrequencySeries(outcross, delta_f=delta_f, copy=False)
fft(hp.astype(real_same_precision_as(hp_tilde)), hp_tilde)
fft(hc.astype(real_same_precision_as(hc_tilde)), hc_tilde)
hp_tilde.length_in_time = tmplt_length
hp_tilde.chirp_length = tChirp
hc_tilde.length_in_time = tmplt_length
hc_tilde.chirp_length = tChirp
return hp_tilde, hc_tilde
else:
raise ValueError("Approximant %s not available" %
(input_params['approximant']))
def imrphenomc_tmplt(**kwds):
""" Return an IMRPhenomC waveform using CUDA to generate the phase and amplitude
Main Paper: arXiv:1005.3306
"""
# Pull out the input arguments
f_min = float128(kwds['f_lower'])
f_max = float128(kwds['f_final'])
delta_f = float128(kwds['delta_f'])
distance = float128(kwds['distance'])
mass1 = float128(kwds['mass1'])
mass2 = float128(kwds['mass2'])
spin1z = float128(kwds['spin1z'])
spin2z = float128(kwds['spin2z'])
if 'out' in kwds:
out = kwds['out']
else:
out = None
# Calculate binary parameters
M = mass1 + mass2
eta = mass1 * mass2 / (M * M)
Xi = (mass1 * spin1z / M) + (mass2 * spin2z / M)
Xisum = 2.*Xi
Xiprod = Xi*Xi
Xi2 = Xi*Xi
m_sec = M * lal.MTSUN_SI;
piM = lal.PI * m_sec;
## The units of distance given as input is taken to pe Mpc. Converting to SI
distance *= (1.0e6 * lal.PC_SI / (2. * sqrt(5. / (64.*lal.PI)) * M * lal.MRSUN_SI * M * lal.MTSUN_SI))
# Check if the value of f_max is correctly given, else replace with the fCut
# used in the PhenomB code in lalsimulation. The various coefficients come
# from Eq.(4.18) of http://arxiv.org/pdf/0710.2335 and
# Table I of http://arxiv.org/pdf/0712.0343
if not f_max:
f_max = (1.7086 * eta * eta - 0.26592 * eta + 0.28236) / piM
# Transform the eta, chi to Lambda parameters, using Eq 5.14, Table II of Main
# paper.
z101 = -2.417e-03
z102 = -1.093e-03
z111 = -1.917e-02
z110 = 7.267e-02
z120 = -2.504e-01
z201 = 5.962e-01
z202 = -5.600e-02
z211 = 1.520e-01
z210 = -2.970e+00
z220 = 1.312e+01
z301 = -3.283e+01
z302 = 8.859e+00
z311 = 2.931e+01
z310 = 7.954e+01
z320 = -4.349e+02
z401 = 1.619e+02
z402 = -4.702e+01
z411 = -1.751e+02
z410 = -3.225e+02
z420 = 1.587e+03
z501 = -6.320e+02
z502 = 2.463e+02
z511 = 1.048e+03
z510 = 3.355e+02
z520 = -5.115e+03
z601 = -4.809e+01
z602 = -3.643e+02
z611 = -5.215e+02
z610 = 1.870e+03
z620 = 7.354e+02
z701 = 4.149e+00
z702 = -4.070e+00
z711 = -8.752e+01
z710 = -4.897e+01
z720 = 6.665e+02
z801 = -5.472e-02
z802 = 2.094e-02
z811 = 3.554e-01
z810 = 1.151e-01
z820 = 9.640e-01
z901 = -1.235e+00
z902 = 3.423e-01
z911 = 6.062e+00
z910 = 5.949e+00
z920 = -1.069e+01
eta2 = eta*eta
Xi2 = Xiprod
# Calculate alphas, gamma, deltas from Table II and Eq 5.14 of Main paper
a1 = z101 * Xi + z102 * Xi2 + z111 * eta * Xi + z110 * eta + z120 * eta2
a2 = z201 * Xi + z202 * Xi2 + z211 * eta * Xi + z210 * eta + z220 * eta2
a3 = z301 * Xi + z302 * Xi2 + z311 * eta * Xi + z310 * eta + z320 * eta2
a4 = z401 * Xi + z402 * Xi2 + z411 * eta * Xi + z410 * eta + z420 * eta2
a5 = z501 * Xi + z502 * Xi2 + z511 * eta * Xi + z510 * eta + z520 * eta2
a6 = z601 * Xi + z602 * Xi2 + z611 * eta * Xi + z610 * eta + z620 * eta2
g1 = z701 * Xi + z702 * Xi2 + z711 * eta * Xi + z710 * eta + z720 * eta2
del1 = z801 * Xi + z802 * Xi2 + z811 * eta * Xi + z810 * eta + z820 * eta2
del2 = z901 * Xi + z902 * Xi2 + z911 * eta * Xi + z910 * eta + z920 * eta2
# Get the spin of the final BH
afin = FinalSpin( Xi, eta )
Q = Qa( abs(afin) )
# Get the fRD
frd = fRD( abs(afin), M)
Mfrd = frd * m_sec
# Define the frequencies where SPA->PM->RD
f1 = 0.1 * frd
Mf1 = m_sec * f1
f2 = frd
Mf2 = m_sec * f2
d1 = 0.005
d2 = 0.005
f0 = 0.98 * frd
Mf0 = m_sec * f0
d0 = 0.015
# Now use this frequency for calculation of betas
# calculate beta1 and beta2, that appear in Eq 5.7 in the main paper.
b2 = ((-5./3.)* a1 * pow(Mfrd,(-8./3.)) - a2/(Mfrd*Mfrd) - \
(a3/3.)*pow(Mfrd,(-4./3.)) + (2./3.)* a5 * pow(Mfrd,(-1./3.)) + a6)/eta
psiPMrd = (a1 * pow(Mfrd,(-5./3.)) + a2/Mfrd + a3 * pow(Mfrd,(-1./3.)) + \
a4 + a5 * pow(Mfrd,(2./3.)) + a6 * Mfrd)/eta
b1 = psiPMrd - (b2 * Mfrd)
### Calculate the PN coefficients, Eq A3 - A5 of main paper ###
pfaN = 3.0/(128.0 * eta)
pfa2 = (3715./756.) + (55.*eta/9.0)
pfa3 = -16.0*lal.PI + (113./3.)*Xi - 38.*eta*Xisum/3.
pfa4 = (152.93365/5.08032) - 50.*Xi2 + eta*(271.45/5.04 + 1.25*Xiprod) + \
3085.*eta2/72.
pfa5 = lal.PI*(386.45/7.56 - 65.*eta/9.) - \
Xi*(735.505/2.268 + 130.*eta/9.) + Xisum*(1285.0*eta/8.1 + 170.*eta2/9.) - \
10.*Xi2*Xi/3. + 10.*eta*Xi*Xiprod
pfa6 = 11583.231236531/4.694215680 - 640.0*lal.PI*lal.PI/3. - \
6848.0*lal.GAMMA/21. - 684.8*log(64.)/6.3 + \
eta*(2255.*lal.PI*lal.PI/12. - 15737.765635/3.048192) + \
76.055*eta2/1.728 - (127.825*eta2*eta/1.296) + \
2920.*lal.PI*Xi/3. - (175. - 1490.*eta)*Xi2/3. - \
(1120.*lal.PI/3. - 1085.*Xi/3.)*eta*Xisum + \
(269.45*eta/3.36 - 2365.*eta2/6.)*Xiprod
pfa6log = -6848./63.
pfa7 = lal.PI*(770.96675/2.54016 + 378.515*eta/1.512 - 740.45*eta2/7.56) - \
Xi*(20373.952415/3.048192 + 1509.35*eta/2.24 - 5786.95*eta2/4.32) + \
Xisum*(4862.041225*eta/1.524096 + 1189.775*eta2/1.008 - 717.05*eta2*eta/2.16 - 830.*eta*Xi2/3. + 35.*eta2*Xiprod/3.) - \
560.*lal.PI*Xi2 + 20.*lal.PI*eta*Xiprod + \
Xi2*Xi*(945.55/1.68 - 85.*eta) + Xi*Xiprod*(396.65*eta/1.68 + 255.*eta2)
xdotaN = 64.*eta/5.
xdota2 = -7.43/3.36 - 11.*eta/4.
xdota3 = 4.*lal.PI - 11.3*Xi/1.2 + 19.*eta*Xisum/6.
xdota4 = 3.4103/1.8144 + 5*Xi2 + eta*(13.661/2.016 - Xiprod/8.) + 5.9*eta2/1.8
xdota5 = -lal.PI*(41.59/6.72 + 189.*eta/8.) - Xi*(31.571/1.008 - 116.5*eta/2.4) + \
Xisum*(21.863*eta/1.008 - 79.*eta2/6.) - 3*Xi*Xi2/4. + \
9.*eta*Xi*Xiprod/4.
xdota6 = 164.47322263/1.39708800 - 17.12*lal.GAMMA/1.05 + \
16.*lal.PI*lal.PI/3 - 8.56*log(16.)/1.05 + \
eta*(45.1*lal.PI*lal.PI/4.8 - 561.98689/2.17728) + \
5.41*eta2/8.96 - 5.605*eta*eta2/2.592 - 80.*lal.PI*Xi/3. + \
eta*Xisum*(20.*lal.PI/3. - 113.5*Xi/3.6) + \
Xi2*(64.153/1.008 - 45.7*eta/3.6) - \
Xiprod*(7.87*eta/1.44 - 30.37*eta2/1.44)
xdota6log = -856./105.
xdota7 = -lal.PI*(4.415/4.032 - 358.675*eta/6.048 - 91.495*eta2/1.512) - \
Xi*(252.9407/2.7216 - 845.827*eta/6.048 + 415.51*eta2/8.64) + \
Xisum*(158.0239*eta/5.4432 - 451.597*eta2/6.048 + 20.45*eta2*eta/4.32 + 107.*eta*Xi2/6. - 5.*eta2*Xiprod/24.) + \
12.*lal.PI*Xi2 - Xi2*Xi*(150.5/2.4 + eta/8.) + \
Xi*Xiprod*(10.1*eta/2.4 + 3.*eta2/8.)
AN = 8.*eta*sqrt(lal.PI/5.)
A2 = (-107. + 55.*eta)/42.
A3 = 2.*lal.PI - 4.*Xi/3. + 2.*eta*Xisum/3.
A4 = -2.173/1.512 - eta*(10.69/2.16 - 2.*Xiprod) + 2.047*eta2/1.512
A5 = -10.7*lal.PI/2.1 + eta*(3.4*lal.PI/2.1)
A5imag = -24.*eta
A6 = 270.27409/6.46800 - 8.56*lal.GAMMA/1.05 + \
2.*lal.PI*lal.PI/3. + \
eta*(4.1*lal.PI*lal.PI/9.6 - 27.8185/3.3264) - \
20.261*eta2/2.772 + 11.4635*eta*eta2/9.9792 - \
4.28*log(16.)/1.05
A6log = -428./105.
A6imag = 4.28*lal.PI/1.05
### Define other parameters needed by waveform generation ###
kmin = int(f_min / delta_f)
kmax = int(f_max / delta_f)
n = kmax + 1;
if not out:
htilde = FrequencySeries(zeros(n,dtype=numpy.complex128), delta_f=delta_f, copy=False)
else:
if type(out) is not Array:
raise TypeError("Output must be an instance of Array")
if len(out) < kmax:
raise TypeError("Output array is too small")
if out.dtype != complex64:
raise TypeError("Output array is the wrong dtype")
htilde = FrequencySeries(out, delta_f=delta_f, copy=False)
phenomC_kernel(htilde.data[kmin:kmax], kmin, delta_f, eta, Xi, distance,
m_sec, piM, Mfrd,
pfaN, pfa2, pfa3, pfa4, pfa5, pfa6, pfa6log, pfa7,
a1, a2, a3, a4, a5, a6, b1, b2,
Mf1, Mf2, Mf0, d1, d2, d0,
xdota2, xdota3, xdota4, xdota5, xdota6, xdota6log,
xdota7, xdotaN, AN, A2, A3, A4, A5,
A5imag, A6, A6log, A6imag,
g1, del1, del2, Q )
hp = htilde
hc = htilde * 1j
return hp, hc
def inverse_spectrum_truncation(psd, max_filter_len, low_frequency_cutoff=None, trunc_method=None):
"""Modify a PSD such that the impulse response associated with its inverse
square root is no longer than `max_filter_len` time samples. In practice
this corresponds to a coarse graining or smoothing of the PSD.
Parameters
----------
psd : FrequencySeries
PSD whose inverse spectrum is to be truncated.
max_filter_len : int
Maximum length of the time-domain filter in samples.
low_frequency_cutoff : {None, int}
Frequencies below `low_frequency_cutoff` are zeroed in the output.
trunc_method : {None, 'hann'}
Function used for truncating the time-domain filter.
None produces a hard truncation at `max_filter_len`.
Returns
-------
psd : FrequencySeries
PSD whose inverse spectrum has been truncated.
Raises
------
ValueError
For invalid types or values of `max_filter_len` and `low_frequency_cutoff`.
Notes
-----
See arXiv:gr-qc/0509116 for details.
"""
# sanity checks
if type(max_filter_len) is not int or max_filter_len <= 0:
raise ValueError('max_filter_len must be a positive integer')
if low_frequency_cutoff is not None and low_frequency_cutoff < 0 \
or low_frequency_cutoff > psd.sample_frequencies[-1]:
raise ValueError('low_frequency_cutoff must be within the bandwidth of the PSD')
N = (len(psd)-1)*2
inv_asd = FrequencySeries((1. / psd)**0.5, delta_f=psd.delta_f, \
dtype=complex_same_precision_as(psd))
inv_asd[0] = 0
inv_asd[N/2] = 0
q = TimeSeries(numpy.zeros(N), delta_t=(N / psd.delta_f), \
dtype=real_same_precision_as(psd))
if low_frequency_cutoff:
kmin = int(low_frequency_cutoff / psd.delta_f)
inv_asd[0:kmin] = 0
ifft(inv_asd, q)
trunc_start = max_filter_len / 2
trunc_end = N - max_filter_len / 2
if trunc_method == 'hann':
trunc_window = Array(numpy.hanning(max_filter_len), dtype=q.dtype)
q[0:trunc_start] *= trunc_window[max_filter_len/2:max_filter_len]
q[trunc_end:N] *= trunc_window[0:max_filter_len/2]
q[trunc_start:trunc_end] = 0
psd_trunc = FrequencySeries(numpy.zeros(len(psd)), delta_f=psd.delta_f, \
dtype=complex_same_precision_as(psd))
fft(q, psd_trunc)
psd_trunc *= psd_trunc.conj()
psd_out = 1. / abs(psd_trunc)
return psd_out
filter_delta_f = 1.0 / float(options.filter_signal_length)
print("Number of Signal Waveforms: ",len(signal_table))
print("Number of Templates : ",len(template_table))
print("Reading and Interpolating PSD")
if options.asd_file:
psd = pycbc.psd.read.from_txt(options.asd_file, filter_n, filter_delta_f,
options.filter_low_frequency_cutoff)
elif options.psd:
psd = pycbc.psd.analytic.from_string(options.psd, filter_n, filter_delta_f,
options.filter_low_frequency_cutoff)
psd *= DYN_RANGE_FAC **2
psd = FrequencySeries(psd,delta_f=psd.delta_f,dtype=float32)
with ctx:
print("Pregenerating Signals")
signals = []
index = 0
for signal_params in signal_table:
index += 1
update_progress(index/len(signal_table))
stilde = get_waveform(options.signal_approximant,
options.signal_phase_order,
options.signal_amplitude_order,
signal_params,
options.signal_start_frequency,
options.filter_sample_rate,
filter_N)
def fd_decompress(amp, phase, sample_frequencies, out=None, df=None,
f_lower=None, interpolation='inline_linear'):
"""Decompresses an FD waveform using the given amplitude, phase, and the
frequencies at which they are sampled at.
Parameters
----------
amp : array
The amplitude of the waveform at the sample frequencies.
phase : array
The phase of the waveform at the sample frequencies.
sample_frequencies : array
The frequency (in Hz) of the waveform at the sample frequencies.
out : {None, FrequencySeries}
The output array to save the decompressed waveform to. If this contains
slots for frequencies > the maximum frequency in sample_frequencies,
the rest of the values are zeroed. If not provided, must provide a df.
df : {None, float}
The frequency step to use for the decompressed waveform. Must be
provided if out is None.
f_lower : {None, float}
The frequency to start the decompression at. If None, will use whatever
the lowest frequency is in sample_frequencies. All values at
frequencies less than this will be 0 in the decompressed waveform.
interpolation : {'inline_linear', str}
The interpolation to use for the amplitude and phase. Default is
'inline_linear'. If 'inline_linear' a custom interpolater is used.
Otherwise, ``scipy.interpolate.interp1d`` is used; for other options,
see possible values for that function's ``kind`` argument.
Returns
-------
out : FrequencySeries
If out was provided, writes to that array. Otherwise, a new
FrequencySeries with the decompressed waveform.
"""
precision = _precision_map[sample_frequencies.dtype.name]
if _precision_map[amp.dtype.name] != precision or \
_precision_map[phase.dtype.name] != precision:
raise ValueError("amp, phase, and sample_points must all have the "
"same precision")
if out is None:
if df is None:
raise ValueError("Either provide output memory or a df")
hlen = int(numpy.ceil(sample_frequencies.max()/df+1))
out = FrequencySeries(numpy.zeros(hlen,
dtype=_complex_dtypes[precision]), copy=False,
delta_f=df)
else:
# check for precision compatibility
if out.precision == 'double' and precision == 'single':
raise ValueError("cannot cast single precision to double")
df = out.delta_f
hlen = len(out)
if f_lower is None:
imin = 0 # pylint:disable=unused-variable
f_lower = sample_frequencies[0]
start_index = 0
else:
if f_lower >= sample_frequencies.max():
raise ValueError("f_lower is > than the maximum sample frequency")
if f_lower < sample_frequencies.min():
raise ValueError("f_lower is < than the minimum sample frequency")
imin = int(numpy.searchsorted(sample_frequencies, f_lower,
side='right')) - 1 # pylint:disable=unused-variable
start_index = int(numpy.ceil(f_lower/df))
if start_index >= hlen:
raise ValueError('requested f_lower >= largest frequency in out')
# interpolate the amplitude and the phase
if interpolation == "inline_linear":
# Call the scheme-dependent function
inline_linear_interp(amp, phase, sample_frequencies, out,
df, f_lower, imin, start_index)
else:
# use scipy for fancier interpolation
sample_frequencies = numpy.array(sample_frequencies)
amp = numpy.array(amp)
phase = numpy.array(phase)
outfreq = out.sample_frequencies.numpy()
amp_interp = interpolate.interp1d(sample_frequencies, amp,
kind=interpolation,
bounds_error=False,
fill_value=0.,
assume_sorted=True)
phase_interp = interpolate.interp1d(sample_frequencies, phase,
kind=interpolation,
bounds_error=False,
fill_value=0.,
assume_sorted=True)
A = amp_interp(outfreq)
phi = phase_interp(outfreq)
out.data[:] = A*numpy.cos(phi) + (1j)*A*numpy.sin(phi)
return out
def calculate_acf(data, delta_t=1.0, unbiased=False):
""" Calculates the autocorrelation function (ACF) and returns the one-sided
ACF.
The ACF is defined as the autocovariance divided by the variance. The ACF
can be estimated using
\hat{R}(k) = \frac{1}{\left( n \sigma^{2}} \right) \sum_{t=1}^{n-k} \left( X_{t} - \mu \right) \left( X_{t+k} - \mu \right)
Where \hat{R}(k) is the ACF, X_{t} is the data series at time t, \mu is the
mean of X_{t}, and \sigma^{2} is the variance of X_{t}.
Parameters
-----------
data : {TimeSeries, numpy.array}
A TimeSeries or numpy.array of data.
delta_t : float
The time step of the data series if it is not a TimeSeries instance.
unbiased : bool
If True the normalization of the autocovariance function is n-k
instead of n. This is called the unbiased estimation of the
autocovariance. Note that this does not mean the ACF is unbiased.
Returns
-------
acf : numpy.array
If data is a TimeSeries then acf will be a TimeSeries of the
one-sided ACF. Else acf is a numpy.array.
"""
# if given a TimeSeries instance then get numpy.array
if isinstance(data, TimeSeries):
y = data.numpy()
delta_t = data.delta_t
else:
y = data
# FFT data minus the mean
fdata = TimeSeries(y-y.mean(), delta_t=delta_t).to_frequencyseries()
# correlate
# do not need to give the congjugate since correlate function does it
cdata = FrequencySeries(zeros(len(fdata), dtype=numpy.complex64),
delta_f=fdata.delta_f, copy=False)
correlate(fdata, fdata, cdata)
# IFFT correlated data to get unnormalized autocovariance time series
acf = cdata.to_timeseries()
# normalize the autocovariance
# note that dividing by acf[0] is the same as ( y.var() * len(acf) )
if unbiased:
acf /= ( y.var() * numpy.arange(len(acf), 0, -1) )
else:
acf /= acf[0]
# return input datatype
if isinstance(data, TimeSeries):
return TimeSeries(acf, delta_t=delta_t)
else:
return acf
def get_waveform(wav, approximant, f_min, dt, N):
"""This function will generate the waveform corresponding to the point
taken as input"""
#{{{
m1 = wav.mass1
m2 = wav.mass2
s1x = wav.spin1x
s1y = wav.spin1y
s1z = wav.spin1z
s2x = wav.spin2x
s2y = wav.spin2y
s2z = wav.spin2z
ecc = wav.alpha
mean_per_ano = wav.alpha1
long_asc_nodes = wav.alpha2
coa_phase = wav.coa_phase
inc = wav.inclination
dist = wav.distance
df = 1. / (dt * N)
f_max = min(1. / (2. * dt), 0.15 / ((m1 + m2) * lal.MTSUN_SI))
if approximant in fd_approximants():
try:
hptild, hctild = get_fd_waveform(approximant=approximant,
mass1=m1,
mass2=m2,
spin1x=s1x,
spin1y=s1y,
spin1z=s1z,
spin2x=s2x,
spin2y=s2y,
spin2z=s2z,
eccentricity=ecc,
mean_per_ano=mean_per_ano,
long_asc_nodes=long_asc_nodes,
coa_phase=coa_phase,
inclination=inc,
distance=dist,
f_lower=f_min,
f_final=f_max,
delta_f=df)
except RuntimeError as re:
for c in dir(wav):
if "__" not in c and "get" not in c and "set" not in c and hasattr(
wav, c):
print(c, getattr(wav, c))
raise RuntimeError(re)
hptilde = FrequencySeries(hptild,
delta_f=df,
dtype=np.complex128,
copy=True)
hpref_padded = FrequencySeries(zeros(N / 2 + 1),
delta_f=df,
dtype=np.complex128,
copy=True)
hpref_padded[0:len(hptilde)] = hptilde
hctilde = FrequencySeries(hctild,
delta_f=df,
dtype=np.complex128,
copy=True)
hcref_padded = FrequencySeries(zeros(N / 2 + 1),
delta_f=df,
dtype=np.complex128,
copy=True)
hcref_padded[0:len(hctilde)] = hctilde
href_padded = generate_detector_strain(wav, hpref_padded, hcref_padded)
elif approximant in td_approximants():
#raise IOError("Time domain approximants not supported at the moment..")
try:
hp, hc = get_td_waveform(approximant=approximant,
mass1=m1,
mass2=m2,
spin1x=s1x,
spin1y=s1y,
spin1z=s1z,
spin2x=s2x,
spin2y=s2y,
spin2z=s2z,
eccentricity=ecc,
mean_per_ano=mean_per_ano,
long_asc_nodes=long_asc_nodes,
coa_phase=coa_phase,
inclination=inc,
distance=dist,
f_lower=f_min,
delta_t=dt)
except RuntimeError as re:
for c in dir(wav):
if "__" not in c and "get" not in c and "set" not in c and hasattr(
wav, c):
print(c, getattr(wav, c))
raise RuntimeError(re)
hpref_padded = TimeSeries(zeros(N),
delta_t=dt,
dtype=hp.dtype,
copy=True)
hpref_padded[:len(hp)] = hp
hcref_padded = TimeSeries(zeros(N),
delta_t=dt,
dtype=hc.dtype,
copy=True)
hcref_padded[:len(hc)] = hc
href_padded_td = generate_detector_strain(wav, hpref_padded,
hcref_padded)
href_padded = make_frequency_series(href_padded_td)
return href_padded
def get_fd_qnm(template=None, **kwargs):
"""Return a frequency domain damped sinusoid.
Parameters
----------
template: object
An object that has attached properties. This can be used to substitute
for keyword arguments. A common example would be a row in an xml table.
f_0 : float
The ringdown-frequency.
tau : float
The damping time of the sinusoid.
t_0 : {0, float}, optional
The starting time of the ringdown.
phi_0 : {0, float}, optional
The initial phase of the ringdown.
amp : {1, float}, optional
The amplitude of the ringdown (constant for now).
delta_f : {None, float}, optional
The frequency step used to generate the ringdown.
If None, it will be set to the inverse of the time at which the
amplitude is 1/1000 of the peak amplitude.
f_lower: {None, float}, optional
The starting frequency of the output frequency series.
If None, it will be set to delta_f.
f_final : {None, float}, optional
The ending frequency of the output frequency series.
If None, it will be set to the frequency at which the amplitude is
1/1000 of the peak amplitude.
Returns
-------
hplustilde: FrequencySeries
The plus phase of the ringdown in frequency domain.
hcrosstilde: FrequencySeries
The cross phase of the ringdown in frequency domain.
"""
input_params = props_ringdown(template,**kwargs)
# get required args
try:
f_0 = input_params['f_0']
except KeyError:
raise ValueError('f_0 is required')
try:
tau = input_params['tau']
except KeyError:
raise ValueError('tau is required')
# get optional args
# the following have defaults, and so will be populated
t_0 = input_params.pop('t_0')
phi_0 = input_params.pop('phi_0')
amp = input_params.pop('amp')
# the following may not be in input_params
delta_f = input_params.pop('delta_f', None)
f_lower = input_params.pop('f_lower', None)
f_final = input_params.pop('f_final', None)
if delta_f is None:
delta_f = 1. / qnm_time_decay(tau, 1./1000)
if f_lower is None:
f_lower = delta_f
kmin = 0
else:
kmin = int(f_lower / delta_f)
if f_final is None:
f_final = qnm_freq_decay(f_0, tau, 1./1000)
kmax = int(f_final / delta_f) + 1
pi = numpy.pi
two_pi = 2 * numpy.pi
pi_sq = numpy.pi * numpy.pi
freqs = numpy.arange(kmin, kmax)*delta_f
denominator = 1 + (4j * pi * freqs * tau) - (4 * pi_sq * ( freqs*freqs - f_0*f_0) * tau*tau)
norm = amp * tau / denominator
if t_0 != 0:
time_shift = numpy.exp(-1j * two_pi * freqs * t_0)
norm *= time_shift
hp_tilde = norm * ( (1 + 2j * pi * freqs * tau) * numpy.cos(phi_0) - two_pi * f_0 * tau * numpy.sin(phi_0) )
hc_tilde = norm * ( (1 + 2j * pi * freqs * tau) * numpy.sin(phi_0) + two_pi * f_0 * tau * numpy.cos(phi_0) )
hplustilde = FrequencySeries(zeros(kmax, dtype=complex128), delta_f=delta_f)
hcrosstilde = FrequencySeries(zeros(kmax, dtype=complex128), delta_f=delta_f)
hplustilde.data[kmin:kmax] = hp_tilde
hcrosstilde.data[kmin:kmax] = hc_tilde
return hplustilde, hcrosstilde
def get_two_pol_waveform_filter(outplus, outcross, template, **kwargs):
"""Return a frequency domain waveform filter for the specified approximant.
Unlike get_waveform_filter this function returns both h_plus and h_cross
components of the waveform, which are needed for searches where h_plus
and h_cross are not related by a simple phase shift.
"""
n = len(outplus)
# If we don't have an inclination column alpha3 might be used
if not hasattr(template, 'inclination')\
and not kwargs.has_key('inclination'):
if hasattr(template, 'alpha3'):
kwargs['inclination'] = template.alpha3
input_params = props(template, **kwargs)
if input_params['approximant'] in fd_approximants(_scheme.mgr.state):
wav_gen = fd_wav[type(_scheme.mgr.state)]
hp, hc = wav_gen[input_params['approximant']](**input_params)
hp.resize(n)
hc.resize(n)
outplus[0:len(hp)] = hp[:]
hp = FrequencySeries(outplus, delta_f=hp.delta_f, copy=False)
outcross[0:len(hc)] = hc[:]
hc = FrequencySeries(outcross, delta_f=hc.delta_f, copy=False)
hp.chirp_length = get_waveform_filter_length_in_time(**input_params)
hp.length_in_time = hp.chirp_length
hc.chirp_length = hp.chirp_length
hc.length_in_time = hp.length_in_time
return hp, hc
elif input_params['approximant'] in td_approximants(_scheme.mgr.state):
# N: number of time samples required
N = (n - 1) * 2
delta_f = 1.0 / (N * input_params['delta_t'])
wav_gen = td_wav[type(_scheme.mgr.state)]
hp, hc = wav_gen[input_params['approximant']](**input_params)
# taper the time series hp if required
if ('taper' in input_params.keys() and \
input_params['taper'] is not None):
hp = wfutils.taper_timeseries(hp,
input_params['taper'],
return_lal=False)
hc = wfutils.taper_timeseries(hc,
input_params['taper'],
return_lal=False)
# total duration of the waveform
tmplt_length = len(hp) * hp.delta_t
# for IMR templates the zero of time is at max amplitude (merger)
# thus the start time is minus the duration of the template from
# lower frequency cutoff to merger, i.e. minus the 'chirp time'
tChirp = -float(hp.start_time) # conversion from LIGOTimeGPS
hp.resize(N)
hc.resize(N)
k_zero = int(hp.start_time / hp.delta_t)
hp.roll(k_zero)
hc.roll(k_zero)
hp_tilde = FrequencySeries(outplus, delta_f=delta_f, copy=False)
hc_tilde = FrequencySeries(outcross, delta_f=delta_f, copy=False)
fft(hp.astype(real_same_precision_as(hp_tilde)), hp_tilde)
fft(hc.astype(real_same_precision_as(hc_tilde)), hc_tilde)
hp_tilde.length_in_time = tmplt_length
hp_tilde.chirp_length = tChirp
hc_tilde.length_in_time = tmplt_length
hc_tilde.chirp_length = tChirp
return hp_tilde, hc_tilde
else:
raise ValueError("Approximant %s not available" %
(input_params['approximant']))
def cbrt_lookup(vmax, delta):
vec = numpy.arange(0, vmax * 1.2, delta)
return FrequencySeries(vec**(1.0 / 3.0), delta_f=delta).astype(float32)
def logv_lookup(vmax, delta):
vec = numpy.arange(0, vmax * 1.2, delta)
vec[1:len(vec)] = numpy.log(vec[1:len(vec)])
return FrequencySeries(vec, delta_f=delta).astype(float32)
def get_fd_qnm(template=None, **kwargs):
"""Return a frequency domain damped sinusoid.
Parameters
----------
template: object
An object that has attached properties. This can be used to substitute
for keyword arguments. A common example would be a row in an xml table.
f_0 : float
The ringdown-frequency.
tau : float
The damping time of the sinusoid.
amp : float
The amplitude of the ringdown (constant for now).
phi : float
The initial phase of the ringdown. Should also include the information
from the azimuthal angle (phi_0 + m*Phi).
inclination : {None, float}, optional
Inclination of the system in radians for the spherical harmonics.
l : {2, int}, optional
l mode for the spherical harmonics. Default is l=2.
m : {2, int}, optional
m mode for the spherical harmonics. Default is m=2.
t_0 : {0, float}, optional
The starting time of the ringdown.
delta_f : {None, float}, optional
The frequency step used to generate the ringdown.
If None, it will be set to the inverse of the time at which the
amplitude is 1/1000 of the peak amplitude.
f_lower: {None, float}, optional
The starting frequency of the output frequency series.
If None, it will be set to delta_f.
f_final : {None, float}, optional
The ending frequency of the output frequency series.
If None, it will be set to the frequency at which the amplitude is
1/1000 of the peak amplitude.
Returns
-------
hplustilde: FrequencySeries
The plus phase of the ringdown in frequency domain.
hcrosstilde: FrequencySeries
The cross phase of the ringdown in frequency domain.
"""
input_params = props(template, qnm_required_args, **kwargs)
f_0 = input_params.pop('f_0')
tau = input_params.pop('tau')
amp = input_params.pop('amp')
phi = input_params.pop('phi')
# the following have defaults, and so will be populated
t_0 = input_params.pop('t_0')
# the following may not be in input_params
inc = input_params.pop('inclination', None)
l = input_params.pop('l', 2)
m = input_params.pop('m', 2)
delta_f = input_params.pop('delta_f', None)
f_lower = input_params.pop('f_lower', None)
f_final = input_params.pop('f_final', None)
if not delta_f:
delta_f = 1. / qnm_time_decay(tau, 1./1000)
if not f_lower:
f_lower = delta_f
kmin = 0
else:
kmin = int(f_lower / delta_f)
if not f_final:
f_final = qnm_freq_decay(f_0, tau, 1./1000)
if f_final > max_freq:
f_final = max_freq
kmax = int(f_final / delta_f) + 1
freqs = numpy.arange(kmin, kmax)*delta_f
if inc is not None:
Y_plus, Y_cross = spher_harms(l, m, inc)
else:
Y_plus, Y_cross = 1, 1
denominator = 1 + (4j * pi * freqs * tau) - (4 * pi_sq * ( freqs*freqs - f_0*f_0) * tau*tau)
norm = amp * tau / denominator
if t_0 != 0:
time_shift = numpy.exp(-1j * two_pi * freqs * t_0)
norm *= time_shift
# Analytical expression for the Fourier transform of the ringdown (damped sinusoid)
hp_tilde = norm * Y_plus * ( (1 + 2j * pi * freqs * tau) * numpy.cos(phi)
- two_pi * f_0 * tau * numpy.sin(phi) )
hc_tilde = norm * Y_cross * ( (1 + 2j * pi * freqs * tau) * numpy.sin(phi)
+ two_pi * f_0 * tau * numpy.cos(phi) )
outplus = FrequencySeries(zeros(kmax, dtype=complex128), delta_f=delta_f)
outcross = FrequencySeries(zeros(kmax, dtype=complex128), delta_f=delta_f)
outplus.data[kmin:kmax] = hp_tilde
outcross.data[kmin:kmax] = hc_tilde
return outplus, outcross
def tensorboard_writer(
queue: mp.Queue,
log_dir: str,
parameters: List[str],
labels: List[str],
static_args_ini: str,
num_basis: int,
val_coefficients: Optional[torch.Tensor]=None,
val_gts: Optional[torch.Tensor]=None,
figure_titles: Optional[List[str]]=None,
):
# suppress luminosity distance debug messages
logger = logging.getLogger('bilby')
logger.propagate = False
logger.setLevel(logging.WARNING)
if log_dir is None:
tb = SummaryWriter()
else:
tb = SummaryWriter(log_dir)
_, static_args = read_ini_config(static_args_ini)
ifos = ('H1', 'L1')
interferometers = {'H1': 'Hanford', 'L1': 'Livingston', 'V1': 'Virgo', 'K1': 'KAGRA'}
basis_dir = Path('/mnt/datahole/daniel/gravflows/datasets/basis/')
basis = SVDBasis(basis_dir, static_args_ini, ifos, preload=False)
basis.load(time_translations=False, verbose=False)
basis.truncate(num_basis)
val_coefficients = val_coefficients.numpy()
for j in range(val_coefficients.shape[0]):
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(16, 4))
for i, ifo in enumerate(ifos):
reconstruction = val_coefficients[j, i] @ basis.Vh[i]
reconstruction = FrequencySeries(reconstruction, delta_f=static_args['delta_f'])
strain = reconstruction.to_timeseries(delta_t=static_args['delta_t'])
ax.plot(strain.sample_times, strain, label=interferometers[ifo], alpha=0.6)
ax.set_title(f'Reconstructed {figure_titles[j]} Strain')
ax.set_xlabel('Time (s)')
ax.set_ylabel('Strain') # units?
ax.set_xlim((static_args['seconds_before_event']-1, static_args['seconds_before_event']+1))
ax.legend(loc='upper left')
ax.grid('both')
# ax.axvline(static_args['seconds_before_event'], color='r', linestyle='--') # merger time marker
# ax.set_xticks([static_args['seconds_before_event']], minor=True) # add low frequency cutoff to ticks
# ax.set_xticklabels(['$t_{c}$'], minor=True, color='r')
tb.add_figure(f'reconstructions/{figure_titles[j]}', fig)
del reconstruction
del val_coefficients
del basis
# bilby setup - specify the output directory and the name of the bilby run
result = bilby.result.read_in_result(
outdir='bilby_runs/GW150914',
label='GW150914'
)
bilby_parameters = [
'mass_1', 'mass_2', 'phase', 'geocent_time',
'luminosity_distance', 'a_1', 'a_2', 'tilt_1', 'tilt_2',
'phi_12', 'phi_jl', 'theta_jn', 'psi', 'ra', 'dec'
]
bilby_samples = result.posterior[bilby_parameters].values
# # Shift the time of coalescence by the trigger time
bilby_samples[:,3] = bilby_samples[:,3] - Merger('GW150914').time
bilby_df = pd.DataFrame(bilby_samples.astype(np.float32), columns=bilby_parameters)
bilby_df = bilby_df.rename(columns={'luminosity_distance': 'distance', 'geocent_time': 'time'})
bilby_df = bilby_df.loc[:, parameters]
domain = [
[10, 80], # mass 1
[10, 80], # mass 2
[0, 2*np.pi], # phase
[0, 1], # a_1
[0, 1], # a 2
[0, np.pi], # tilt 1
[0, np.pi], # tilt 2
[0, 2*np.pi], # phi_12
[0, 2*np.pi], # phi_jl
[0, np.pi], # theta_jn
[0, np.pi], # psi
[0, 2*np.pi], # ra
[-np.pi/2, np.pi/2], # dec
# [0.005,0.055], # tc
[100,800], # distance
]
cosmoprior = bilby.gw.prior.UniformSourceFrame(
name='luminosity_distance',
minimum=1e2,
maximum=1e3,
)
while True:
try:
epoch, scalars, samples = queue.get()
if samples is not None:
# requires (batch, samples, parameters)
assert len(samples.shape) == 3, "samples must be of shape (batch, samples, parameters)"
if figure_titles is not None:
# to do - better handling of passing figure info through queue
assert samples.shape[0] == len(figure_titles), (
"sample.shape[0] and figure_titles must have matching lengths"
)
else:
figure_titles = ['']*samples.shape[0]
for key, value in scalars.items():
tb.add_scalar(key, value, epoch)
if samples is not None:
assert isinstance(samples, torch.Tensor)
for i in range(samples.shape[0]):
fig = plt.figure(figsize=(20,21))
if i == 0:
# GW150914 ONLY - hardcoded to first position
samples_df = pd.DataFrame(samples[i].numpy(), columns=parameters)
weights = cosmoprior.prob(samples_df['distance'])
weights = weights / np.mean(weights)
corner.corner(
bilby_df,
fig=fig,
labels=labels,
levels=[0.5, 0.9],
quantiles=[0.25, 0.75],
color='tab:orange',
scale_hist=True,
plot_datapoints=False,
)
corner.corner(
samples_df,
fig=fig,
levels=[0.5, 0.9],
quantiles=[0.25, 0.75],
color='tab:blue',
scale_hist=True,
plot_datapoints=False,
show_titles=True,
weights=weights * len(bilby_samples) / len(samples_df),
range=domain,
)
fig.legend(
handles=[
mpatches.Patch(color='tab:blue', label='Neural Spline Flow'),
mpatches.Patch(color='tab:orange', label='Bilby (dynesty)')],
loc='upper right',
fontsize=16,
)
else:
samples_df = pd.DataFrame(samples[i].numpy(), columns=parameters)
weights = cosmoprior.prob(samples_df['distance'])
weights = weights / np.mean(weights)
corner.corner(
samples_df,
fig=fig,
labels=labels,
levels=[0.5, 0.9],
quantiles=[0.25, 0.75],
color='tab:blue',
truth_color='tab:orange',
scale_hist=True,
plot_datapoints=False,
show_titles=True,
truths=val_gts[i].numpy() if val_gts is not None else None,
weights=weights * len(bilby_samples) / len(samples_df),
range=domain,
)
fig.legend(
handles=[
mpatches.Patch(color='tab:blue', label='Neural Spline Flow'),
mpatches.Patch(color='tab:orange', label='Ground Truth')],
loc='upper right',
fontsize=16,
)
fig.suptitle(f'{figure_titles[i]} Parameter Estimation', fontsize=18)
# fig.savefig(f'gwpe/figures/{figure_titles[i]}.png')
tb.add_figure(f'posteriors/{figure_titles[i]}', fig, epoch)
tb.flush()
except Exception as e:
# warning: assertions may not trigger exception to exit process
traceback.print_exc()
os.kill(os.getpid(), signal.SIGSTOP) # to do: check kill command
def welch(timeseries, seg_len=4096, seg_stride=2048, window='hann',
avg_method='median', num_segments=None, require_exact_data_fit=False):
"""PSD estimator based on Welch's method.
Parameters
----------
timeseries : TimeSeries
Time series for which the PSD is to be estimated.
seg_len : int
Segment length in samples.
seg_stride : int
Separation between consecutive segments, in samples.
window : {'hann'}
Function used to window segments before Fourier transforming.
avg_method : {'median', 'mean', 'median-mean'}
Method used for averaging individual segment PSDs.
Returns
-------
psd : FrequencySeries
Frequency series containing the estimated PSD.
Raises
------
ValueError
For invalid choices of `seg_len`, `seg_stride` `window` and
`avg_method` and for inconsistent combinations of len(`timeseries`),
`seg_len` and `seg_stride`.
Notes
-----
See arXiv:gr-qc/0509116 for details.
"""
window_map = {
'hann': numpy.hanning
}
# sanity checks
if not window in window_map:
raise ValueError('Invalid window')
if not avg_method in ('mean', 'median', 'median-mean'):
raise ValueError('Invalid averaging method')
if type(seg_len) is not int or type(seg_stride) is not int \
or seg_len <= 0 or seg_stride <= 0:
raise ValueError('Segment length and stride must be positive integers')
if timeseries.precision == 'single':
fs_dtype = numpy.complex64
elif timeseries.precision == 'double':
fs_dtype = numpy.complex128
num_samples = len(timeseries)
if num_segments is None:
num_segments = int(num_samples // seg_stride)
# NOTE: Is this not always true?
if (num_segments - 1) * seg_stride + seg_len > num_samples:
num_segments -= 1
if not require_exact_data_fit:
data_len = (num_segments - 1) * seg_stride + seg_len
# Get the correct amount of data
if data_len < num_samples:
diff = num_samples - data_len
start = diff // 2
end = num_samples - diff // 2
# Want this to be integers so if diff is odd, catch it here.
if diff % 2:
start = start + 1
timeseries = timeseries[start:end]
num_samples = len(timeseries)
if data_len > num_samples:
err_msg = "I was asked to estimate a PSD on %d " %(data_len)
err_msg += "data samples. However the data provided only contains "
err_msg += "%d data samples." %(num_samples)
if num_samples != (num_segments - 1) * seg_stride + seg_len:
raise ValueError('Incorrect choice of segmentation parameters')
w = Array(window_map[window](seg_len).astype(timeseries.dtype))
# calculate psd of each segment
delta_f = 1. / timeseries.delta_t / seg_len
segment_tilde = FrequencySeries(numpy.zeros(seg_len / 2 + 1), \
delta_f=delta_f, dtype=fs_dtype)
segment_psds = []
for i in xrange(num_segments):
segment_start = i * seg_stride
segment_end = segment_start + seg_len
segment = timeseries[segment_start:segment_end]
assert len(segment) == seg_len
fft(segment * w, segment_tilde)
seg_psd = abs(segment_tilde * segment_tilde.conj()).numpy()
#halve the DC and Nyquist components to be consistent with TO10095
seg_psd[0] /= 2
seg_psd[-1] /= 2
segment_psds.append(seg_psd)
segment_psds = numpy.array(segment_psds)
if avg_method == 'mean':
psd = numpy.mean(segment_psds, axis=0)
elif avg_method == 'median':
psd = numpy.median(segment_psds, axis=0) / median_bias(num_segments)
elif avg_method == 'median-mean':
odd_psds = segment_psds[::2]
even_psds = segment_psds[1::2]
odd_median = numpy.median(odd_psds, axis=0) / \
median_bias(len(odd_psds))
even_median = numpy.median(even_psds, axis=0) / \
median_bias(len(even_psds))
psd = (odd_median + even_median) / 2
psd *= 2 * delta_f * seg_len / (w*w).sum()
return FrequencySeries(psd, delta_f=delta_f, dtype=timeseries.dtype,
epoch=timeseries.start_time)
def fd_decompress(amp,
phase,
sample_frequencies,
out=None,
df=None,
f_lower=None,
interpolation='linear'):
"""Decompresses an FD waveform using the given amplitude, phase, and the
frequencies at which they are sampled at.
Parameters
----------
amp : array
The amplitude of the waveform at the sample frequencies.
phase : array
The phase of the waveform at the sample frequencies.
sample_frequencies : array
The frequency (in Hz) of the waveform at the sample frequencies.
out : {None, FrequencySeries}
The output array to save the decompressed waveform to. If this contains
slots for frequencies > the maximum frequency in sample_frequencies,
the rest of the values are zeroed. If not provided, must provide a df.
df : {None, float}
The frequency step to use for the decompressed waveform. Must be
provided if out is None.
f_lower : {None, float}
The frequency to start the decompression at. If None, will use whatever
the lowest frequency is in sample_frequencies. All values at
frequencies less than this will be 0 in the decompressed waveform.
interpolation : {'linear', str}
The interpolation to use for the amplitude and phase. Default is
'linear'. If 'linear' a custom interpolater is used. Otherwise,
``scipy.interpolate.interp1d`` is used; for other options, see
possible values for that function's ``kind`` argument.
Returns
-------
out : FrqeuencySeries
If out was provided, writes to that array. Otherwise, a new
FrequencySeries with the decompressed waveform.
"""
precision = _precision_map[sample_frequencies.dtype.name]
if _precision_map[amp.dtype.name] != precision or \
_precision_map[phase.dtype.name] != precision:
raise ValueError("amp, phase, and sample_points must all have the "
"same precision")
if out is None:
if df is None:
raise ValueError("Either provide output memory or a df")
hlen = int(numpy.ceil(sample_frequencies.max() / df + 1))
out = FrequencySeries(numpy.zeros(hlen,
dtype=_complex_dtypes[precision]),
copy=False,
delta_f=df)
else:
# check for precision compatibility
if out.precision == 'double' and precision == 'single':
raise ValueError("cannot cast single precision to double")
df = out.delta_f
hlen = len(out)
if f_lower is None:
imin = 0
f_lower = sample_frequencies[0]
else:
if f_lower >= sample_frequencies.max():
raise ValueError("f_lower is > than the maximum sample frequency")
imin = int(numpy.searchsorted(sample_frequencies, f_lower))
start_index = int(numpy.floor(f_lower / df))
# interpolate the amplitude and the phase
if interpolation == "linear":
if precision == 'single':
code = _linear_decompress_code32
else:
code = _linear_decompress_code
# use custom interpolation
sflen = len(sample_frequencies)
h = numpy.array(out.data, copy=False)
delta_f = float(df)
inline(code, ['h', 'hlen', 'sflen', 'delta_f', 'sample_frequencies',
'amp', 'phase', 'start_index', 'imin'],
extra_compile_args=[WEAVE_FLAGS + '-march=native -O3 -w'] +\
omp_flags,
libraries=omp_libs)
else:
# use scipy for fancier interpolation
outfreq = out.sample_frequencies.numpy()
amp_interp = interpolate.interp1d(sample_frequencies.numpy(),
amp.numpy(),
kind=interpolation,
bounds_error=False,
fill_value=0.,
assume_sorted=True)
phase_interp = interpolate.interp1d(sample_frequencies.numpy(),
phase.numpy(),
kind=interpolation,
bounds_error=False,
fill_value=0.,
assume_sorted=True)
A = amp_interp(outfreq)
phi = phase_interp(outfreq)
out.data[:] = A * numpy.cos(phi) + (1j) * A * numpy.sin(phi)
return out
