def create_waveform_generator(variable_params,
data,
recalibration=None,
gates=None,
**static_params):
"""Creates a waveform generator for use with a model.
Parameters
----------
variable_params : list of str
The names of the parameters varied.
data : dict
Dictionary mapping detector names to either a
:py:class:`<pycbc.types.TimeSeries TimeSeries>` or
:py:class:`<pycbc.types.FrequencySeries FrequencySeries>`.
recalibration : dict, optional
Dictionary mapping detector names to
:py:class:`<pycbc.calibration.Recalibrate>` instances for
recalibrating data.
gates : dict of tuples, optional
Dictionary of detectors -> tuples of specifying gate times. The
sort of thing returned by :py:func:`pycbc.gate.gates_from_cli`.
Returns
-------
pycbc.waveform.FDomainDetFrameGenerator
A waveform generator for frequency domain generation.
"""
# figure out what generator to use based on the approximant
try:
approximant = static_params['approximant']
except KeyError:
raise ValueError("no approximant provided in the static args")
generator_function = generator.select_waveform_generator(approximant)
# get data parameters; we'll just use one of the data to get the
# values, then check that all the others are the same
delta_f = None
for d in data.values():
if delta_f is None:
delta_f = d.delta_f
delta_t = d.delta_t
start_time = d.start_time
else:
if not all([
d.delta_f == delta_f, d.delta_t == delta_t, d.start_time
== start_time
]):
raise ValueError("data must all have the same delta_t, "
"delta_f, and start_time")
waveform_generator = generator.FDomainDetFrameGenerator(
generator_function,
epoch=start_time,
variable_args=variable_params,
detectors=list(data.keys()),
delta_f=delta_f,
delta_t=delta_t,
recalib=recalibration,
gates=gates,
**static_params)
return waveform_generator
def from_config(cls, cp, data=None, delta_f=None, delta_t=None,
gates=None, recalibration=None,
**kwargs):
"""Initializes an instance of this class from the given config file.
Parameters
----------
cp : WorkflowConfigParser
Config file parser to read.
data : dict
A dictionary of data, in which the keys are the detector names and
the values are the data. This is not retrieved from the config
file, and so must be provided.
delta_f : float
The frequency spacing of the data; needed for waveform generation.
delta_t : float
The time spacing of the data; needed for time-domain waveform
generators.
recalibration : dict of pycbc.calibration.Recalibrate, optional
Dictionary of detectors -> recalibration class instances for
recalibrating data.
gates : dict of tuples, optional
Dictionary of detectors -> tuples of specifying gate times. The
sort of thing returned by `pycbc.gate.gates_from_cli`.
\**kwargs :
All additional keyword arguments are passed to the class. Any
provided keyword will over ride what is in the config file.
"""
if data is None:
raise ValueError("must provide data")
args = cls._init_args_from_config(cp)
args['data'] = data
args.update(kwargs)
variable_params = args['variable_params']
try:
static_params = args['static_params']
except KeyError:
static_params = {}
# set up waveform generator
try:
approximant = static_params['approximant']
except KeyError:
raise ValueError("no approximant provided in the static args")
generator_function = generator.select_waveform_generator(approximant)
waveform_generator = generator.FDomainDetFrameGenerator(
generator_function, epoch=data.values()[0].start_time,
variable_args=variable_params, detectors=data.keys(),
delta_f=delta_f, delta_t=delta_t,
recalib=recalibration, gates=gates,
**static_params)
args['waveform_generator'] = waveform_generator
return cls(**args)
def create_waveform_generator(variable_params, data,
recalibration=None, gates=None,
**static_params):
"""Creates a waveform generator for use with a model.
Parameters
----------
variable_params : list of str
The names of the parameters varied.
data : dict
Dictionary mapping detector names to either a
:py:class:`<pycbc.types.TimeSeries TimeSeries>` or
:py:class:`<pycbc.types.FrequencySeries FrequencySeries>`.
recalibration : dict, optional
Dictionary mapping detector names to
:py:class:`<pycbc.calibration.Recalibrate>` instances for
recalibrating data.
gates : dict of tuples, optional
Dictionary of detectors -> tuples of specifying gate times. The
sort of thing returned by :py:func:`pycbc.gate.gates_from_cli`.
Returns
-------
pycbc.waveform.FDomainDetFrameGenerator
A waveform generator for frequency domain generation.
"""
# figure out what generator to use based on the approximant
try:
approximant = static_params['approximant']
except KeyError:
raise ValueError("no approximant provided in the static args")
generator_function = generator.select_waveform_generator(approximant)
# get data parameters; we'll just use one of the data to get the
# values, then check that all the others are the same
delta_f = None
for d in data.values():
if delta_f is None:
delta_f = d.delta_f
delta_t = d.delta_t
start_time = d.start_time
else:
if not all([d.delta_f == delta_f, d.delta_t == delta_t,
d.start_time == start_time]):
raise ValueError("data must all have the same delta_t, "
"delta_f, and start_time")
waveform_generator = generator.FDomainDetFrameGenerator(
generator_function, epoch=start_time,
variable_args=variable_params, detectors=list(data.keys()),
delta_f=delta_f, delta_t=delta_t,
recalib=recalibration, gates=gates,
**static_params)
return waveform_generator
def from_config(cls,
cp,
data=None,
delta_f=None,
delta_t=None,
gates=None,
recalibration=None,
**kwargs):
"""Initializes an instance of this class from the given config file.
Parameters
----------
cp : WorkflowConfigParser
Config file parser to read.
data : dict
A dictionary of data, in which the keys are the detector names and
the values are the data. This is not retrieved from the config
file, and so must be provided.
delta_f : float
The frequency spacing of the data; needed for waveform generation.
delta_t : float
The time spacing of the data; needed for time-domain waveform
generators.
recalibration : dict of pycbc.calibration.Recalibrate, optional
Dictionary of detectors -> recalibration class instances for
recalibrating data.
gates : dict of tuples, optional
Dictionary of detectors -> tuples of specifying gate times. The
sort of thing returned by `pycbc.gate.gates_from_cli`.
\**kwargs :
All additional keyword arguments are passed to the class. Any
provided keyword will over ride what is in the config file.
"""
prior_section = "marginalized_prior"
args = cls._init_args_from_config(cp)
marg_prior = read_distributions_from_config(cp, prior_section)
if len(marg_prior) == 0:
raise AttributeError("No priors are specified for the "
"marginalization. Please specify this in a "
"section in the config file with heading "
"{}-variable".format(prior_section))
params = [i.params[0] for i in marg_prior]
marg_args = [k for k, v in args.items() if "_marginalization" in k]
if len(marg_args) != len(params):
raise ValueError("There is not a prior for each keyword argument")
kwargs['marg_prior'] = marg_prior
for i in params:
kwargs[i + "_marginalization"] = True
args.update(kwargs)
variable_params = args['variable_params']
args["data"] = data
try:
static_params = args['static_params']
except KeyError:
static_params = {}
# set up waveform generator
try:
approximant = static_params['approximant']
except KeyError:
raise ValueError("no approximant provided in the static args")
generator_function = generator.select_waveform_generator(approximant)
waveform_generator = generator.FDomainDetFrameGenerator(
generator_function,
epoch=data.values()[0].start_time,
variable_args=variable_params,
detectors=data.keys(),
delta_f=delta_f,
delta_t=delta_t,
recalib=recalibration,
gates=gates,
**static_params)
args['waveform_generator'] = waveform_generator
args["f_lower"] = static_params["f_lower"]
return cls(**args)
def from_config(cls, cp, data=None, delta_f=None, delta_t=None,
gates=None, recalibration=None, **kwargs):
"""Initializes an instance of this class from the given config file.
Parameters
----------
cp : WorkflowConfigParser
Config file parser to read.
data : dict
A dictionary of data, in which the keys are the detector names and
the values are the data. This is not retrieved from the config
file, and so must be provided.
delta_f : float
The frequency spacing of the data; needed for waveform generation.
delta_t : float
The time spacing of the data; needed for time-domain waveform
generators.
recalibration : dict of pycbc.calibration.Recalibrate, optional
Dictionary of detectors -> recalibration class instances for
recalibrating data.
gates : dict of tuples, optional
Dictionary of detectors -> tuples of specifying gate times. The
sort of thing returned by `pycbc.gate.gates_from_cli`.
\**kwargs :
All additional keyword arguments are passed to the class. Any
provided keyword will over ride what is in the config file.
"""
prior_section = "marginalized_prior"
args = cls._init_args_from_config(cp)
marg_prior = read_distributions_from_config(cp, prior_section)
if len(marg_prior) == 0:
raise AttributeError("No priors are specified for the "
"marginalization. Please specify this in a "
"section in the config file with heading "
"{}-variable".format(prior_section))
params = [i.params[0] for i in marg_prior]
marg_args = [k for k, v in args.items() if "_marginalization" in k]
if len(marg_args) != len(params):
raise ValueError("There is not a prior for each keyword argument")
kwargs['marg_prior'] = marg_prior
for i in params:
kwargs[i+"_marginalization"] = True
args.update(kwargs)
variable_params = args['variable_params']
args["data"] = data
try:
static_params = args['static_params']
except KeyError:
static_params = {}
# set up waveform generator
try:
approximant = static_params['approximant']
except KeyError:
raise ValueError("no approximant provided in the static args")
generator_function = generator.select_waveform_generator(approximant)
waveform_generator = generator.FDomainDetFrameGenerator(
generator_function, epoch=data.values()[0].start_time,
variable_args=variable_params, detectors=data.keys(),
delta_f=delta_f, delta_t=delta_t,
recalib=recalibration, gates=gates,
**static_params)
args['waveform_generator'] = waveform_generator
args["f_lower"] = static_params["f_lower"]
return cls(**args)
epoch=stilde.epoch)
wh_strain = wh_stilde.to_timeseries()
#load map values
llrs = fp.read_likelihood_stats(iteration=opts.iteration,
thin_start=opts.thin_start,
thin_end=opts.thin_end,
thin_interval=opts.thin_interval)
map_idx = (llrs.loglr + llrs.prior).argmax()
map_values = samples[map_idx]
varargs = fp.variable_args
sargs = fp.static_args
print "generating injected waveforms"
genclass = generator.select_waveform_generator(
fp.static_args['approximant'])
gen = generator.FDomainDetFrameGenerator(genclass,
detectors=['H1', 'L1'],
epoch=stilde.epoch,
variable_args=varargs,
**sargs)
fis = gen.generate(tc=injvals[0],
mass1=injvals[1],
mass2=injvals[2],
spin1_a=injvals[3],
spin1_azimuthal=injvals[4],
spin1_polar=injvals[5],
spin2_a=injvals[6],
spin2_azimuthal=injvals[7],
spin2_polar=injvals[8],
distance=injvals[9],
