def generate(self, **kwargs):
"""Generates a waveform, applies a time shift and the detector response
function from the given kwargs.
"""
self.current_params.update(kwargs)
rfparams = {
param: self.current_params[param]
for param in kwargs if param not in self.location_args
}
hp, hc = self.rframe_generator.generate(**rfparams)
if isinstance(hp, TimeSeries):
df = self.current_params['delta_f']
hp = hp.to_frequencyseries(delta_f=df)
hc = hc.to_frequencyseries(delta_f=df)
# time-domain waveforms will not be shifted so that the peak amp
# happens at the end of the time series (as they are for f-domain),
# so we add an additional shift to account for it
tshift = 1. / df - abs(hp._epoch)
else:
tshift = 0.
hp._epoch = hc._epoch = self._epoch
h = {}
if self.detector_names != ['RF']:
for detname, det in self.detectors.items():
# apply detector response function
fp, fc = det.antenna_pattern(
self.current_params['ra'], self.current_params['dec'],
self.current_params['polarization'],
self.current_params['tc'])
thish = fp * hp + fc * hc
# apply the time shift
tc = self.current_params['tc'] + \
det.time_delay_from_earth_center(self.current_params['ra'],
self.current_params['dec'], self.current_params['tc'])
h[detname] = apply_fd_time_shift(thish,
tc + tshift,
copy=False)
if self.recalib:
# recalibrate with given calibration model
h[detname] = \
self.recalib[detname].map_to_adjust(h[detname],
**self.current_params)
else:
# no detector response, just use the + polarization
if 'tc' in self.current_params:
hp = apply_fd_time_shift(hp,
self.current_params['tc'] + tshift,
copy=False)
h['RF'] = hp
if self.gates is not None:
# resize all to nearest power of 2
for d in h.values():
d.resize(ceilpow2(len(d) - 1) + 1)
h = gate.apply_gates_to_fd(h, self.gates)
return h
def generate(self, **kwargs):
"""Generates a waveform, applies a time shift and the detector response
function from the given kwargs.
"""
self.current_params.update(kwargs)
rfparams = {param: self.current_params[param]
for param in kwargs if param not in self.location_args}
hp, hc = self.rframe_generator.generate(**rfparams)
if isinstance(hp, TimeSeries):
df = self.current_params['delta_f']
hp = hp.to_frequencyseries(delta_f=df)
hc = hc.to_frequencyseries(delta_f=df)
# time-domain waveforms will not be shifted so that the peak amp
# happens at the end of the time series (as they are for f-domain),
# so we add an additional shift to account for it
tshift = 1./df - abs(hp._epoch)
else:
tshift = 0.
hp._epoch = hc._epoch = self._epoch
h = {}
if self.detector_names != ['RF']:
for detname, det in self.detectors.items():
# apply detector response function
fp, fc = det.antenna_pattern(self.current_params['ra'],
self.current_params['dec'],
self.current_params['polarization'],
self.current_params['tc'])
thish = fp*hp + fc*hc
# apply the time shift
tc = self.current_params['tc'] + \
det.time_delay_from_earth_center(self.current_params['ra'],
self.current_params['dec'], self.current_params['tc'])
h[detname] = apply_fd_time_shift(thish, tc+tshift, copy=False)
if self.recalib:
# recalibrate with given calibration model
h[detname] = \
self.recalib[detname].map_to_adjust(h[detname],
**self.current_params)
else:
# no detector response, just use the + polarization
if 'tc' in self.current_params:
hp = apply_fd_time_shift(hp, self.current_params['tc']+tshift,
copy=False)
h['RF'] = hp
if self.gates is not None:
# resize all to nearest power of 2
for d in h.values():
d.resize(ceilpow2(len(d)-1) + 1)
h = gate.apply_gates_to_fd(h, self.gates)
return h
def data_from_cli(opts):
"""Loads the data needed for a likelihood evaluator from the given
command-line options. Gates specifed on the command line are also applied.
Parameters
----------
opts : ArgumentParser parsed args
Argument options parsed from a command line string (the sort of thing
returned by `parser.parse_args`).
Returns
-------
strain_dict : dict
Dictionary of instruments -> `TimeSeries` strain.
stilde_dict : dict
Dictionary of instruments -> `FrequencySeries` strain.
psd_dict : dict
Dictionary of instruments -> `FrequencySeries` psds.
"""
# get gates to apply
gates = gates_from_cli(opts)
psd_gates = psd_gates_from_cli(opts)
# get strain time series
strain_dict = strain_from_cli_multi_ifos(opts, opts.instruments,
precision="double")
# apply gates if not waiting to overwhiten
if not opts.gate_overwhitened:
logging.info("Applying gates to strain data")
strain_dict = apply_gates_to_td(strain_dict, gates)
# get strain time series to use for PSD estimation
# if user has not given the PSD time options then use same data as analysis
if opts.psd_start_time and opts.psd_end_time:
logging.info("Will generate a different time series for PSD "
"estimation")
psd_opts = opts
psd_opts.gps_start_time = psd_opts.psd_start_time
psd_opts.gps_end_time = psd_opts.psd_end_time
psd_strain_dict = strain_from_cli_multi_ifos(psd_opts,
opts.instruments,
precision="double")
# apply any gates
logging.info("Applying gates to PSD data")
psd_strain_dict = apply_gates_to_td(psd_strain_dict, psd_gates)
elif opts.psd_start_time or opts.psd_end_time:
raise ValueError("Must give --psd-start-time and --psd-end-time")
else:
psd_strain_dict = strain_dict
# FFT strain and save each of the length of the FFT, delta_f, and
# low frequency cutoff to a dict
logging.info("FFT strain")
stilde_dict = {}
length_dict = {}
delta_f_dict = {}
low_frequency_cutoff_dict = low_frequency_cutoff_from_cli(opts)
for ifo in opts.instruments:
stilde_dict[ifo] = strain_dict[ifo].to_frequencyseries()
length_dict[ifo] = len(stilde_dict[ifo])
delta_f_dict[ifo] = stilde_dict[ifo].delta_f
# get PSD as frequency series
psd_dict = psd_from_cli_multi_ifos(opts, length_dict, delta_f_dict,
low_frequency_cutoff_dict, opts.instruments,
strain_dict=psd_strain_dict, precision="double")
# apply any gates to overwhitened data, if desired
if opts.gate_overwhitened and opts.gate is not None:
logging.info("Applying gates to overwhitened data")
# overwhiten the data
for ifo in gates:
stilde_dict[ifo] /= psd_dict[ifo]
stilde_dict = apply_gates_to_fd(stilde_dict, gates)
# unwhiten the data for the likelihood generator
for ifo in gates:
stilde_dict[ifo] *= psd_dict[ifo]
return strain_dict, stilde_dict, psd_dict
def data_from_cli(opts):
"""Loads the data needed for a likelihood evaluator from the given
command-line options. Gates specifed on the command line are also applied.
Parameters
----------
opts : ArgumentParser parsed args
Argument options parsed from a command line string (the sort of thing
returned by `parser.parse_args`).
Returns
-------
strain_dict : dict
Dictionary of instruments -> `TimeSeries` strain.
stilde_dict : dict
Dictionary of instruments -> `FrequencySeries` strain.
psd_dict : dict
Dictionary of instruments -> `FrequencySeries` psds.
"""
# get gates to apply
gates = gates_from_cli(opts)
psd_gates = psd_gates_from_cli(opts)
# get strain time series
strain_dict = strain_from_cli_multi_ifos(opts, opts.instruments,
precision="double")
# apply gates if not waiting to overwhiten
if not opts.gate_overwhitened:
logging.info("Applying gates to strain data")
strain_dict = apply_gates_to_td(strain_dict, gates)
# get strain time series to use for PSD estimation
# if user has not given the PSD time options then use same data as analysis
if opts.psd_start_time and opts.psd_end_time:
logging.info("Will generate a different time series for PSD "
"estimation")
psd_opts = opts
psd_opts.gps_start_time = psd_opts.psd_start_time
psd_opts.gps_end_time = psd_opts.psd_end_time
psd_strain_dict = strain_from_cli_multi_ifos(psd_opts,
opts.instruments,
precision="double")
# apply any gates
logging.info("Applying gates to PSD data")
psd_strain_dict = apply_gates_to_td(psd_strain_dict, psd_gates)
elif opts.psd_start_time or opts.psd_end_time:
raise ValueError("Must give --psd-start-time and --psd-end-time")
else:
psd_strain_dict = strain_dict
# FFT strain and save each of the length of the FFT, delta_f, and
# low frequency cutoff to a dict
logging.info("FFT strain")
stilde_dict = {}
length_dict = {}
delta_f_dict = {}
low_frequency_cutoff_dict = low_frequency_cutoff_from_cli(opts)
for ifo in opts.instruments:
stilde_dict[ifo] = strain_dict[ifo].to_frequencyseries()
length_dict[ifo] = len(stilde_dict[ifo])
delta_f_dict[ifo] = stilde_dict[ifo].delta_f
# get PSD as frequency series
psd_dict = psd_from_cli_multi_ifos(opts, length_dict, delta_f_dict,
low_frequency_cutoff_dict, opts.instruments,
strain_dict=psd_strain_dict, precision="double")
# apply any gates to overwhitened data, if desired
if opts.gate_overwhitened and opts.gate is not None:
logging.info("Applying gates to overwhitened data")
# overwhiten the data
for ifo in gates:
stilde_dict[ifo] /= psd_dict[ifo]
stilde_dict = apply_gates_to_fd(stilde_dict, gates)
# unwhiten the data for the likelihood generator
for ifo in gates:
stilde_dict[ifo] *= psd_dict[ifo]
return strain_dict, stilde_dict, psd_dict