waveform.py
# Copyright (C) 2012 Alex Nitz
#
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# =============================================================================
#
# Preamble
#
# =============================================================================
#
"""Convenience functions to genenerate gravitational wave templates and
waveforms.
"""
import lal, lalsimulation, numpy, copy
from pycbc.types import TimeSeries, FrequencySeries, zeros, Array
from pycbc.types import real_same_precision_as, complex_same_precision_as
import pycbc.scheme as _scheme
import inspect
from pycbc.fft import fft
from pycbc import pnutils
from pycbc.waveform import utils as wfutils
from pycbc.waveform import parameters
from pycbc.filter import interpolate_complex_frequency, resample_to_delta_t
import pycbc
from spa_tmplt import spa_tmplt, spa_tmplt_norm, spa_tmplt_end, \
spa_tmplt_precondition, spa_amplitude_factor, \
spa_length_in_time
class NoWaveformError(Exception):
"""This should be raised if generating a waveform would just result in all
zeros being returned, e.g., if a requested `f_final` is <= `f_lower`.
"""
pass
# If this is set to True, waveform generation codes will try to regenerate
# waveforms with known failure conditions to try to avoid the failure. For
# example SEOBNRv3 waveforms would be regenerated with double the sample rate.
# If this is set to False waveform failures will always raise exceptions
fail_tolerant_waveform_generation = True
default_args = (parameters.fd_waveform_params.default_dict() + \
parameters.td_waveform_params).default_dict()
default_sgburst_args = {'eccentricity':0, 'polarization':0}
td_required_args = parameters.td_waveform_params.nodefaults.aslist
fd_required_args = parameters.fd_waveform_params.nodefaults.aslist
sgburst_required_args = ['q','frequency','hrss']
# td, fd, filter waveforms generated on the CPU
_lalsim_td_approximants = {}
_lalsim_fd_approximants = {}
_lalsim_enum = {}
_lalsim_sgburst_approximants = {}
def _check_lal_pars(p):
""" Create a laldict object from the dictionary of waveform parameters
Parameters
----------
p: dictionary
The dictionary of lalsimulation paramaters
Returns
-------
laldict: LalDict
The lal type dictionary to pass to the lalsimulation waveform functions.
"""
lal_pars = lal.CreateDict()
#nonGRparams can be straightforwardly added if needed, however they have to
# be invoked one by one
if p['phase_order']!=-1:
lalsimulation.SimInspiralWaveformParamsInsertPNPhaseOrder(lal_pars,int(p['phase_order']))
if p['amplitude_order']!=-1:
lalsimulation.SimInspiralWaveformParamsInsertPNAmplitudeOrder(lal_pars,int(p['amplitude_order']))
if p['spin_order']!=-1:
lalsimulation.SimInspiralWaveformParamsInsertPNSpinOrder(lal_pars,int(p['spin_order']))
if p['tidal_order']!=-1:
lalsimulation.SimInspiralWaveformParamsInsertPNTidalOrder(lal_pars, p['tidal_order'])
if p['eccentricity_order']!=-1:
lalsimulation.SimInspiralWaveformParamsInsertPNEccentricityOrder(lal_pars, p['eccentricity_order'])
if p['lambda1']:
lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(lal_pars, p['lambda1'])
if p['lambda2']:
lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(lal_pars, p['lambda2'])
if p['dquad_mon1']:
lalsimulation.SimInspiralWaveformParamsInsertdQuadMon1(lal_pars, p['dquad_mon1'])
if p['dquad_mon2']:
lalsimulation.SimInspiralWaveformParamsInsertdQuadMon2(lal_pars, p['dquad_mon2'])
if p['numrel_data']:
lalsimulation.SimInspiralWaveformParamsInsertNumRelData(lal_pars, str(p['numrel_data']))
if p['modes_choice']:
lalsimulation.SimInspiralWaveformParamsInsertModesChoice(lal_pars, p['modes_choice'])
if p['frame_axis']:
lalsimulation.SimInspiralWaveformParamsInsertFrameAxis(lal_pars, p['frame_axis'])
if p['side_bands']:
lalsimulation.SimInspiralWaveformParamsInsertSideband(lal_pars, p['side_bands'])
return lal_pars
def _lalsim_td_waveform(**p):
fail_tolerant_waveform_generation
lal_pars = _check_lal_pars(p)
#nonGRparams can be straightforwardly added if needed, however they have to
# be invoked one by one
try:
hp1, hc1 = lalsimulation.SimInspiralChooseTDWaveform(
float(pnutils.solar_mass_to_kg(p['mass1'])),
float(pnutils.solar_mass_to_kg(p['mass2'])),
float(p['spin1x']), float(p['spin1y']), float(p['spin1z']),
float(p['spin2x']), float(p['spin2y']), float(p['spin2z']),
pnutils.megaparsecs_to_meters(float(p['distance'])),
float(p['inclination']), float(p['coa_phase']),
float(p['long_asc_nodes']), float(p['eccentricity']), float(p['mean_per_ano']),
float(p['delta_t']), float(p['f_lower']), float(p['f_ref']),
lal_pars,
_lalsim_enum[p['approximant']])
except RuntimeError:
if not fail_tolerant_waveform_generation:
raise
# For some cases failure modes can occur. Here we add waveform-specific
# instructions to try to work with waveforms that are known to fail.
if p['approximant'] == 'SEOBNRv3':
# In this case we'll try doubling the sample time and trying again
# Don't want to get stuck in a loop though!
if 'delta_t_orig' not in p:
p['delta_t_orig'] = p['delta_t']
p['delta_t'] = p['delta_t'] / 2.
if p['delta_t_orig'] / p['delta_t'] > 9:
raise
hp, hc = _lalsim_td_waveform(**p)
p['delta_t'] = p['delta_t_orig']
hp = resample_to_delta_t(hp, hp.delta_t*2)
hc = resample_to_delta_t(hc, hc.delta_t*2)
return hp, hc
raise
#lal.DestroyDict(lal_pars)
hp = TimeSeries(hp1.data.data[:], delta_t=hp1.deltaT, epoch=hp1.epoch)
hc = TimeSeries(hc1.data.data[:], delta_t=hc1.deltaT, epoch=hc1.epoch)
return hp, hc
def _spintaylor_aligned_prec_swapper(**p):
"""
SpinTaylorF2 is only single spin, it also struggles with anti-aligned spin
waveforms. This construct chooses between the aligned-twospin TaylorF2 model
and the precessing singlespin SpinTaylorF2 models. If aligned spins are
given, use TaylorF2, if nonaligned spins are given use SpinTaylorF2. In
the case of nonaligned doublespin systems the code will fail at the
waveform generator level.
"""
orig_approximant = p['approximant']
if p['spin2x'] == 0 and p['spin2y'] == 0 and p['spin1x'] == 0 and \
p['spin1y'] == 0:
p['approximant'] = 'TaylorF2'
else:
p['approximant'] = 'SpinTaylorF2'
hp, hc = _lalsim_fd_waveform(**p)
p['approximant'] = orig_approximant
return hp, hc
def _lalsim_fd_waveform(**p):
lal_pars = _check_lal_pars(p)
hp1, hc1 = lalsimulation.SimInspiralChooseFDWaveform(
float(pnutils.solar_mass_to_kg(p['mass1'])),
float(pnutils.solar_mass_to_kg(p['mass2'])),
float(p['spin1x']), float(p['spin1y']), float(p['spin1z']),
float(p['spin2x']), float(p['spin2y']), float(p['spin2z']),
pnutils.megaparsecs_to_meters(float(p['distance'])),
float(p['inclination']), float(p['coa_phase']),
float(p['long_asc_nodes']), float(p['eccentricity']), float(p['mean_per_ano']),
p['delta_f'], float(p['f_lower']), float(p['f_final']), float(p['f_ref']),
lal_pars,
_lalsim_enum[p['approximant']])
hp = FrequencySeries(hp1.data.data[:], delta_f=hp1.deltaF,
epoch=hp1.epoch)
hc = FrequencySeries(hc1.data.data[:], delta_f=hc1.deltaF,
epoch=hc1.epoch)
#lal.DestroyDict(lal_pars)
return hp, hc
def _lalsim_sgburst_waveform(**p):
hp, hc = lalsimulation.SimBurstSineGaussian(float(p['q']),
float(p['frequency']),
float(p['hrss']),
float(p['eccentricity']),
float(p['polarization']),
float(p['delta_t']))
hp = TimeSeries(hp.data.data[:], delta_t=hp.deltaT, epoch=hp.epoch)
hc = TimeSeries(hc.data.data[:], delta_t=hc.deltaT, epoch=hc.epoch)
return hp, hc
for approx_enum in xrange(0, lalsimulation.NumApproximants):
if lalsimulation.SimInspiralImplementedTDApproximants(approx_enum):
approx_name = lalsimulation.GetStringFromApproximant(approx_enum)
_lalsim_enum[approx_name] = approx_enum
_lalsim_td_approximants[approx_name] = _lalsim_td_waveform
for approx_enum in xrange(0, lalsimulation.NumApproximants):
if lalsimulation.SimInspiralImplementedFDApproximants(approx_enum):
approx_name = lalsimulation.GetStringFromApproximant(approx_enum)
_lalsim_enum[approx_name] = approx_enum
_lalsim_fd_approximants[approx_name] = _lalsim_fd_waveform
# sine-Gaussian burst
for approx_enum in xrange(0, lalsimulation.NumApproximants):
if lalsimulation.SimInspiralImplementedFDApproximants(approx_enum):
approx_name = lalsimulation.GetStringFromApproximant(approx_enum)
_lalsim_enum[approx_name] = approx_enum
_lalsim_sgburst_approximants[approx_name] = _lalsim_sgburst_waveform
cpu_sgburst = _lalsim_sgburst_approximants
cpu_td = dict(_lalsim_td_approximants.items())
cpu_fd = _lalsim_fd_approximants
# Waveforms written in CUDA
_cuda_td_approximants = {}
_cuda_fd_approximants = {}
if pycbc.HAVE_CUDA:
from pycbc.waveform.TaylorF2 import taylorf2 as cuda_taylorf2
from pycbc.waveform.pycbc_phenomC_tmplt import imrphenomc_tmplt
from pycbc.waveform.SpinTaylorF2 import spintaylorf2 as cuda_spintaylorf2
_cuda_fd_approximants["IMRPhenomC"] = imrphenomc_tmplt
_cuda_fd_approximants["SpinTaylorF2"] = cuda_spintaylorf2
cuda_td = dict(_lalsim_td_approximants.items() + _cuda_td_approximants.items())
cuda_fd = dict(_lalsim_fd_approximants.items() + _cuda_fd_approximants.items())
# List the various available approximants ####################################
def print_td_approximants():
print("LalSimulation Approximants")
for approx in _lalsim_td_approximants.keys():
print(" " + approx)
print("CUDA Approximants")
for approx in _cuda_td_approximants.keys():
print(" " + approx)
def print_fd_approximants():
print("LalSimulation Approximants")
for approx in _lalsim_fd_approximants.keys():
print(" " + approx)
print("CUDA Approximants")
for approx in _cuda_fd_approximants.keys():
print(" " + approx)
def print_sgburst_approximants():
print("LalSimulation Approximants")
for approx in _lalsim_sgburst_approximants.keys():
print(" " + approx)
def td_approximants(scheme=_scheme.mgr.state):
"""Return a list containing the available time domain approximants for
the given processing scheme.
"""
return td_wav[type(scheme)].keys()
def fd_approximants(scheme=_scheme.mgr.state):
"""Return a list containing the available fourier domain approximants for
the given processing scheme.
"""
return fd_wav[type(scheme)].keys()
def sgburst_approximants(scheme=_scheme.mgr.state):
"""Return a list containing the available time domain sgbursts for
the given processing scheme.
"""
return sgburst_wav[type(scheme)].keys()
def filter_approximants(scheme=_scheme.mgr.state):
"""Return a list of fourier domain approximants including those
written specifically as templates.
"""
return filter_wav[type(scheme)].keys()
# Input parameter handling ###################################################
def get_obj_attrs(obj):
""" Return a dictionary built from the attributes of the given object.
"""
pr = {}
if obj is not None:
if isinstance(obj, numpy.core.records.record):
for name in obj.dtype.names:
pr[name] = getattr(obj, name)
elif hasattr(obj, '__dict__'):
pr = obj.__dict__
elif hasattr(obj, '__slots__'):
for slot in obj.__slots__:
if hasattr(obj, slot):
pr[slot] = getattr(obj, slot)
elif isinstance(obj, dict):
pr = obj.copy()
else:
for name in dir(obj):
try:
value = getattr(obj, name)
if not name.startswith('__') and not inspect.ismethod(value):
pr[name] = value
except:
continue
return pr
def props(obj, **kwargs):
""" Return a dictionary built from the combination of defaults, kwargs,
and the attributes of the given object.
"""
pr = get_obj_attrs(obj)
# Get the parameters to generate the waveform
# Note that keyword arguments override values in the template object
input_params = default_args.copy()
input_params.update(pr)
input_params.update(kwargs)
return input_params
# Input parameter handling for bursts ########################################
def props_sgburst(obj, **kwargs):
pr = {}
if obj is not None:
for name in dir(obj):
try:
value = getattr(obj, name)
if not name.startswith('__') and not inspect.ismethod(value):
pr[name] = value
except:
continue
# Get the parameters to generate the waveform
# Note that keyword arguments override values in the template object
input_params = default_sgburst_args.copy()
input_params.update(pr)
input_params.update(kwargs)
return input_params
# Waveform generation ########################################################
def get_fd_waveform_sequence(template=None, **kwds):
"""Return values of the waveform evaluated at the sequence of frequency
points.
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.
{params}
Returns
-------
hplustilde: Array
The plus phase of the waveform in frequency domain evaluated at the
frequency points.
hcrosstilde: Array
The cross phase of the waveform in frequency domain evaluated at the
frequency points.
"""
kwds['delta_f'] = -1
kwds['f_lower'] = -1
p = props(template, **kwds)
lal_pars = _check_lal_pars(p)
flags = lalsimulation.SimInspiralCreateWaveformFlags()
lalsimulation.SimInspiralSetSpinOrder(flags, p['spin_order'])
lalsimulation.SimInspiralSetTidalOrder(flags, p['tidal_order'])
hp, hc = lalsimulation.SimInspiralChooseFDWaveformSequence(float(p['coa_phase']),
float(pnutils.solar_mass_to_kg(p['mass1'])),
float(pnutils.solar_mass_to_kg(p['mass2'])),
float(p['spin1x']), float(p['spin1y']), float(p['spin1z']),
float(p['spin2x']), float(p['spin2y']), float(p['spin2z']),
float(p['f_ref']),
pnutils.megaparsecs_to_meters(float(p['distance'])),
float(p['inclination']),
lal_pars,
_lalsim_enum[p['approximant']],
p['sample_points'].lal())
return Array(hp.data.data), Array(hc.data.data)
get_fd_waveform_sequence.__doc__ = get_fd_waveform_sequence.__doc__.format(
params=parameters.fd_waveform_sequence_params.docstr(prefix=" ",
include_label=False).lstrip(' '))
def get_td_waveform(template=None, **kwargs):
"""Return the plus and cross polarizations of a time domain waveform.
Parameters
----------
template: object
An object that has attached properties. This can be used to subsitute
for keyword arguments. A common example would be a row in an xml table.
{params}
Returns
-------
hplus: TimeSeries
The plus polarization of the waveform.
hcross: TimeSeries
The cross polarization of the waveform.
"""
input_params = props(template,**kwargs)
wav_gen = td_wav[type(_scheme.mgr.state)]
if 'approximant' not in input_params or input_params['approximant'] is None:
raise ValueError("Please provide an approximant name")
elif input_params['approximant'] not in wav_gen:
raise ValueError("Approximant %s not available" %
(input_params['approximant']))
for arg in td_required_args:
if arg not in input_params:
raise ValueError("Please provide " + str(arg) )
return wav_gen[input_params['approximant']](**input_params)
get_td_waveform.__doc__ = get_td_waveform.__doc__.format(
params=parameters.td_waveform_params.docstr(prefix=" ",
include_label=False).lstrip(' '))
def get_fd_waveform(template=None, **kwargs):
"""Return a frequency domain gravitational waveform.
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.
{params}
Returns
-------
hplustilde: FrequencySeries
The plus phase of the waveform in frequency domain.
hcrosstilde: FrequencySeries
The cross phase of the waveform in frequency domain.
"""
input_params = props(template,**kwargs)
wav_gen = fd_wav[type(_scheme.mgr.state)]
if 'approximant' not in input_params:
raise ValueError("Please provide an approximant name")
elif input_params['approximant'] not in wav_gen:
raise ValueError("Approximant %s not available" %
(input_params['approximant']))
for arg in fd_required_args:
if arg not in input_params:
raise ValueError("Please provide " + str(arg) )
try:
ffunc = input_params.pop('f_final_func')
if ffunc != '':
# convert the frequency function to a value
input_params['f_final'] = pnutils.named_frequency_cutoffs[ffunc](
input_params)
# if the f_final is < f_lower, raise a NoWaveformError
if 'f_final' in input_params and (
input_params['f_lower']+input_params['delta_f']
>= input_params['f_final']):
raise NoWaveformError("cannot generate waveform: f_lower >= f_final")
except KeyError:
pass
return wav_gen[input_params['approximant']](**input_params)
get_fd_waveform.__doc__ = get_fd_waveform.__doc__.format(
params=parameters.fd_waveform_params.docstr(prefix=" ",
include_label=False).lstrip(' '))
def get_interpolated_fd_waveform(dtype=numpy.complex64, return_hc=True,
**params):
""" Return a fourier domain waveform approximant, using interpolation
"""
def rulog2(val):
return 2.0 ** numpy.ceil(numpy.log2(float(val)))
orig_approx = params['approximant']
params['approximant'] = params['approximant'].replace('_INTERP', '')
df = params['delta_f']
if 'duration' not in params:
duration = get_waveform_filter_length_in_time(**params)
elif params['duration'] > 0:
duration = params['duration']
else:
err_msg = "Waveform duration must be greater than 0."
raise ValueError(err_msg)
#FIXME We should try to get this length directly somehow
# I think this number should be conservative
ringdown_padding = 0.5
df_min = 1.0 / rulog2(duration + ringdown_padding)
# FIXME: I don't understand this, but waveforms with df_min < 0.5 will chop
# off the inspiral when using ringdown_padding - 0.5.
# Also, if ringdown_padding is set to a very small
# value we can see cases where the ringdown is chopped.
if df_min > 0.5:
df_min = 0.5
params['delta_f'] = df_min
hp, hc = get_fd_waveform(**params)
hp = hp.astype(dtype)
if return_hc:
hc = hc.astype(dtype)
else:
hc = None
f_end = get_waveform_end_frequency(**params)
if f_end is None:
f_end = (len(hp) - 1) * hp.delta_f
if 'f_final' in params and params['f_final'] > 0:
f_end_params = params['f_final']
if f_end is not None:
f_end = min(f_end_params, f_end)
n_min = int(rulog2(f_end / df_min)) + 1
if n_min < len(hp):
hp = hp[:n_min]
if hc is not None:
hc = hc[:n_min]
offset = int(ringdown_padding * (len(hp)-1)*2 * hp.delta_f)
hp = interpolate_complex_frequency(hp, df, zeros_offset=offset, side='left')
if hc is not None:
hc = interpolate_complex_frequency(hc, df, zeros_offset=offset,
side='left')
params['approximant'] = orig_approx
return hp, hc
def get_sgburst_waveform(template=None, **kwargs):
"""Return the plus and cross polarizations of a time domain
sine-Gaussian burst waveform.
Parameters
----------
template: object
An object that has attached properties. This can be used to subsitute
for keyword arguments. A common example would be a row in an xml table.
approximant : string
A string that indicates the chosen approximant. See `td_approximants`
for available options.
q : float
The quality factor of a sine-Gaussian burst
frequency : float
The centre-frequency of a sine-Gaussian burst
delta_t : float
The time step used to generate the waveform
hrss : float
The strain rss
amplitude: float
The strain amplitude
Returns
-------
hplus: TimeSeries
The plus polarization of the waveform.
hcross: TimeSeries
The cross polarization of the waveform.
"""
input_params = props_sgburst(template,**kwargs)
for arg in sgburst_required_args:
if arg not in input_params:
raise ValueError("Please provide " + str(arg))
return _lalsim_sgburst_waveform(**input_params)
# Waveform filter routines ###################################################
# Organize Filter Generators
_inspiral_fd_filters = {}
_cuda_fd_filters = {}
_cuda_fd_filters['SPAtmplt'] = spa_tmplt
_inspiral_fd_filters['SPAtmplt'] = spa_tmplt
filter_wav = _scheme.ChooseBySchemeDict()
filter_wav.update( {_scheme.CPUScheme:_inspiral_fd_filters,
_scheme.CUDAScheme:_cuda_fd_filters,
} )
# Organize functions for function conditioning/precalculated values
_filter_norms = {}
_filter_ends = {}
_filter_preconditions = {}
_template_amplitude_norms = {}
_filter_time_lengths = {}
def seobnrv2_final_frequency(**kwds):
return pnutils.get_final_freq("SEOBNRv2", kwds['mass1'], kwds['mass2'],
kwds['spin1z'], kwds['spin2z'])
def get_imr_length(approx, **kwds):
"""Call through to pnutils to obtain IMR waveform durations
"""
m1 = float(kwds['mass1'])
m2 = float(kwds['mass2'])
s1z = float(kwds['spin1z'])
s2z = float(kwds['spin2z'])
f_low = float(kwds['f_lower'])
# 10% margin of error is incorporated in the pnutils function
return pnutils.get_imr_duration(m1, m2, s1z, s2z, f_low, approximant=approx)
def seobnrv2_length_in_time(**kwds):
"""Stub for holding the calculation of SEOBNRv2* waveform duration.
"""
return get_imr_length("SEOBNRv2", **kwds)
def seobnrv4_length_in_time(**kwds):
"""Stub for holding the calculation of SEOBNRv4* waveform duration.
"""
return get_imr_length("SEOBNRv4", **kwds)
def imrphenomd_length_in_time(**kwds):
"""Stub for holding the calculation of IMRPhenomD waveform duration.
"""
return get_imr_length("IMRPhenomD", **kwds)
_filter_norms["SPAtmplt"] = spa_tmplt_norm
_filter_preconditions["SPAtmplt"] = spa_tmplt_precondition
_filter_ends["SPAtmplt"] = spa_tmplt_end
_filter_ends["TaylorF2"] = spa_tmplt_end
#_filter_ends["SEOBNRv1_ROM_EffectiveSpin"] = seobnrv2_final_frequency
#_filter_ends["SEOBNRv1_ROM_DoubleSpin"] = seobnrv2_final_frequency
#_filter_ends["SEOBNRv2_ROM_EffectiveSpin"] = seobnrv2_final_frequency
#_filter_ends["SEOBNRv2_ROM_DoubleSpin"] = seobnrv2_final_frequency
#_filter_ends["SEOBNRv2_ROM_DoubleSpin_HI"] = seobnrv2_final_frequency
# PhenomD returns higher frequencies than this, so commenting this out for now
#_filter_ends["IMRPhenomC"] = seobnrv2_final_frequency
#_filter_ends["IMRPhenomD"] = seobnrv2_final_frequency
_template_amplitude_norms["SPAtmplt"] = spa_amplitude_factor
_filter_time_lengths["SPAtmplt"] = spa_length_in_time
_filter_time_lengths["TaylorF2"] = spa_length_in_time
_filter_time_lengths["SEOBNRv1_ROM_EffectiveSpin"] = seobnrv2_length_in_time
_filter_time_lengths["SEOBNRv1_ROM_DoubleSpin"] = seobnrv2_length_in_time
_filter_time_lengths["SEOBNRv2_ROM_EffectiveSpin"] = seobnrv2_length_in_time
_filter_time_lengths["SEOBNRv2_ROM_DoubleSpin"] = seobnrv2_length_in_time
_filter_time_lengths["EOBNRv2HM_ROM"] = seobnrv2_length_in_time
_filter_time_lengths["SEOBNRv2_ROM_DoubleSpin_HI"] = seobnrv2_length_in_time
_filter_time_lengths["SEOBNRv4_ROM"] = seobnrv4_length_in_time
_filter_time_lengths["IMRPhenomC"] = imrphenomd_length_in_time
_filter_time_lengths["IMRPhenomD"] = imrphenomd_length_in_time
_filter_time_lengths["IMRPhenomPv2"] = imrphenomd_length_in_time
_filter_time_lengths["SpinTaylorF2"] = spa_length_in_time
# Also add generators for switching between approximants
apx_name = "SpinTaylorF2_SWAPPER"
cpu_fd[apx_name] = _spintaylor_aligned_prec_swapper
_filter_time_lengths[apx_name] = _filter_time_lengths["SpinTaylorF2"]
# We can do interpolation for waveforms that have a time length
for apx in copy.copy(_filter_time_lengths):
if apx in cpu_fd:
apx_int = apx + '_INTERP'
cpu_fd[apx_int] = get_interpolated_fd_waveform
_filter_time_lengths[apx_int] = _filter_time_lengths[apx]
td_wav = _scheme.ChooseBySchemeDict()
fd_wav = _scheme.ChooseBySchemeDict()
td_wav.update({_scheme.CPUScheme:cpu_td,_scheme.CUDAScheme:cuda_td})
fd_wav.update({_scheme.CPUScheme:cpu_fd,_scheme.CUDAScheme:cuda_fd})
sgburst_wav = {_scheme.CPUScheme:cpu_sgburst}
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)]
duration = get_waveform_filter_length_in_time(**input_params)
hp, _ = wav_gen[input_params['approximant']](duration=duration,
return_hc=False, **input_params)
hp.resize(n)
out[0:len(hp)] = hp[:]
hp = FrequencySeries(out, delta_f=hp.delta_f, copy=False)
hp.length_in_time = hp.chirp_length = duration
return hp
elif input_params['approximant'] in td_approximants(_scheme.mgr.state):
wav_gen = td_wav[type(_scheme.mgr.state)]
hp, _ = 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 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
# 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 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 waveform_norm_exists(approximant):
if approximant in _filter_norms:
return True
else:
return False
def get_template_amplitude_norm(template=None, **kwargs):
""" Return additional constant template normalization. This only affects
the effective distance calculation. Returns None for all templates with a
physically meaningful amplitude.
"""
input_params = props(template,**kwargs)
approximant = kwargs['approximant']
if approximant in _template_amplitude_norms:
return _template_amplitude_norms[approximant](**input_params)
else:
return None
def get_waveform_filter_precondition(approximant, length, delta_f):
"""Return the data preconditioning factor for this approximant.
"""
if approximant in _filter_preconditions:
return _filter_preconditions[approximant](length, delta_f)
else:
return None
def get_waveform_filter_norm(approximant, psd, length, delta_f, f_lower):
""" Return the normalization vector for the approximant
"""
if approximant in _filter_norms:
return _filter_norms[approximant](psd, length, delta_f, f_lower)
else:
return None
def get_waveform_end_frequency(template=None, **kwargs):
"""Return the stop frequency of a template
"""
input_params = props(template,**kwargs)
approximant = kwargs['approximant']
if approximant in _filter_ends:
return _filter_ends[approximant](**input_params)
else:
return None
def get_waveform_filter_length_in_time(approximant, template=None, **kwargs):
"""For filter templates, return the length in time of the template.
"""
kwargs = props(template, **kwargs)
if approximant in _filter_time_lengths:
return _filter_time_lengths[approximant](**kwargs)
else:
return None
__all__ = ["get_td_waveform", "get_fd_waveform", "get_fd_waveform_sequence",
"print_td_approximants", "print_fd_approximants",
"td_approximants", "fd_approximants",
"get_waveform_filter", "filter_approximants",
"get_waveform_filter_norm", "get_waveform_end_frequency",
"waveform_norm_exists", "get_template_amplitude_norm",
"get_waveform_filter_length_in_time", "get_sgburst_waveform",
"print_sgburst_approximants", "sgburst_approximants",
"td_waveform_to_fd_waveform", "get_two_pol_waveform_filter",
"NoWaveformError"]
