pycbc.inference.models package¶
Submodules¶
pycbc.inference.models.analytic module¶
This modules provides models that have analytic solutions for the log likelihood.

class
pycbc.inference.models.analytic.
TestEggbox
(variable_params, **kwargs)[source]¶ Bases:
pycbc.inference.models.base.BaseModel
The test distribution is an ‘eggbox’ function:
\[\log \mathcal{L}(\Theta) = \left[ 2+\prod_{i=1}^{n}\cos\left(\frac{\theta_{i}}{2}\right)\right]^{5}\]The number of dimensions is set by the number of
variable_params
that are passed.Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied.
 **kwargs – All other keyword arguments are passed to
BaseModel
.

name
= 'test_eggbox'¶

class
pycbc.inference.models.analytic.
TestNormal
(variable_params, mean=None, cov=None, **kwargs)[source]¶ Bases:
pycbc.inference.models.base.BaseModel
The test distribution is an multivariate normal distribution.
The number of dimensions is set by the number of
variable_params
that are passed. For details on the distribution used, seescipy.stats.multivariate_normal
.Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied.
 mean (arraylike, optional) – The mean values of the parameters. If None provide, will use 0 for all parameters.
 cov (arraylike, optional) – The covariance matrix of the parameters. If None provided, will use unit variance for all parameters, with crossterms set to 0.
 **kwargs – All other keyword arguments are passed to
BaseModel
.
Examples
Create a 2D model with zero mean and unit variance:
>>> m = TestNormal(['x', 'y'])
Set the current parameters and evaluate the log posterior:
>>> m.update(x=0.2, y=0.1) >>> m.logposterior 1.8628770664093453
See the current stats that were evaluated:
>>> m.current_stats {'logjacobian': 0.0, 'loglikelihood': 1.8628770664093453, 'logprior': 0.0}

name
= 'test_normal'¶

class
pycbc.inference.models.analytic.
TestPosterior
(variable_params, posterior_file, nsamples, **kwargs)[source]¶ Bases:
pycbc.inference.models.base.BaseModel
Build a test posterior from a set of samples using a kde
Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied.
 posterior_file (hdf file) – A compatible pycbc inference output file which posterior samples can be read from.
 nsamples (int) – Number of samples to draw from posterior file to build KDE.
 **kwargs – All other keyword arguments are passed to
BaseModel
.

name
= 'test_posterior'¶

class
pycbc.inference.models.analytic.
TestPrior
(variable_params, **kwargs)[source]¶ Bases:
pycbc.inference.models.base.BaseModel
Uses the prior as the test distribution.
Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied. Must have length 2.
 **kwargs – All other keyword arguments are passed to
BaseModel
.

name
= 'test_prior'¶

class
pycbc.inference.models.analytic.
TestRosenbrock
(variable_params, **kwargs)[source]¶ Bases:
pycbc.inference.models.base.BaseModel
The test distribution is the Rosenbrock function:
\[\log \mathcal{L}(\Theta) = \sum_{i=1}^{n1}[ (1\theta_{i})^{2}+100(\theta_{i+1}  \theta_{i}^{2})^{2}]\]The number of dimensions is set by the number of
variable_params
that are passed.Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied.
 **kwargs – All other keyword arguments are passed to
BaseModel
.

name
= 'test_rosenbrock'¶

class
pycbc.inference.models.analytic.
TestVolcano
(variable_params, **kwargs)[source]¶ Bases:
pycbc.inference.models.base.BaseModel
The test distribution is a twodimensional ‘volcano’ function:
\[\Theta = \sqrt{\theta_{1}^{2} + \theta_{2}^{2}} \log \mathcal{L}(\Theta) = 25\left(e^{\frac{\Theta}{35}} + \frac{1}{2\sqrt{2\pi}} e^{\frac{(\Theta5)^{2}}{8}}\right)\]Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied. Must have length 2.
 **kwargs – All other keyword arguments are passed to
BaseModel
.

name
= 'test_volcano'¶
pycbc.inference.models.base module¶
Base class for models.

class
pycbc.inference.models.base.
BaseModel
(variable_params, static_params=None, prior=None, sampling_transforms=None, waveform_transforms=None)[source]¶ Bases:
object
Base class for all models.
Given some model \(h\) with parameters \(\Theta\), Bayes Theorem states that the probability of observing parameter values \(\vartheta\) is:
\[p(\varthetah) = \frac{p(h\vartheta) p(\vartheta)}{p(h)}.\]Here:
 \(p(\varthetah)\) is the posterior probability;
 \(p(h\vartheta)\) is the likelihood;
 \(p(\vartheta)\) is the prior;
 \(p(h)\) is the evidence.
This class defines properties and methods for evaluating the log likelihood, log prior, and log posteror. A set of parameter values is set using the
update
method. Calling the class’slog(likelihoodpriorposterior)
properties will then evaluate the model at those parameter values.Classes that inherit from this class must implement a
_loglikelihood
function that can be called byloglikelihood
.Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied.
 static_params (dict, optional) – A dictionary of parameter names > values to keep fixed.
 prior (callable, optional) – A callable class or function that computes the log of the prior. If
None provided, will use
_noprior
, which returns 0 for all parameter values.  sampling_params (list, optional) – Replace one or more of the
variable_params
with the given parameters for sampling.  replace_parameters (list, optional) – The
variable_params
to replace with sampling parameters. Must be the same length assampling_params
.  sampling_transforms (list, optional) – List of transforms to use to go between the
variable_params
and the sampling parameters. Required ifsampling_params
is not None.  waveform_transforms (list, optional) – A list of transforms to convert the
variable_params
into something understood by the likelihood model. This is useful if the prior is more easily parameterized in parameters that are different than what the likelihood is most easily defined in. Since these are used solely for converting parameters, and not for rescaling the parameter space, a Jacobian is not required for these transforms.  Properties –
  –
 logjacobian – Returns the log of the jacobian needed to go from the parameter space
of the
variable_params
to the sampling params.  logprior – Returns the log of the prior.
 loglikelihood – A function that returns the log of the likelihood function.
 logposterior – A function that returns the log of the posterior.
 loglr – A function that returns the log of the likelihood ratio.
 logplr – A function that returns the log of the priorweighted likelihood ratio.

current_params
¶

current_stats
¶ Return the
default_stats
as a dict.This does no computation. It only returns what has already been calculated. If a stat hasn’t been calculated, it will be returned as
numpy.nan
.Returns: Dictionary of stat names > current stat values. Return type: dict

default_stats
¶ The stats that
get_current_stats
returns by default.

static
extra_args_from_config
(cp, section, skip_args=None, dtypes=None)[source]¶ Gets any additional keyword in the given config file.
Parameters:  cp (WorkflowConfigParser) – Config file parser to read.
 section (str) – The name of the section to read.
 skip_args (list of str, optional) – Names of arguments to skip.
 dtypes (dict, optional) – A dictionary of arguments > data types. If an argument is found in the dict, it will be cast to the given datatype. Otherwise, the argument’s value will just be read from the config file (and thus be a string).
Returns: Dictionary of keyword arguments read from the config file.
Return type:

classmethod
from_config
(cp, **kwargs)[source]¶ Initializes an instance of this class from the given config file.
Parameters:  cp (WorkflowConfigParser) – Config file parser to read.
 **kwargs – All additional keyword arguments are passed to the class. Any provided keyword will over ride what is in the config file.

get_current_stats
(names=None)[source]¶ Return one or more of the current stats as a tuple.
This function does no computation. It only returns what has already been calculated. If a stat hasn’t been calculated, it will be returned as
numpy.nan
.Parameters: names (list of str, optional) – Specify the names of the stats to retrieve. If None
(the default), will returndefault_stats
.Returns: The current values of the requested stats, as a tuple. The order of the stats is the same as the names. Return type: tuple

logjacobian
¶ The log jacobian of the sampling transforms at the current postion.
If no sampling transforms were provided, will just return 0.
Parameters: **params – The keyword arguments should specify values for all of the variable args and all of the sampling args. Returns: The value of the jacobian. Return type: float

loglikelihood
¶ The log likelihood at the current parameters.
This will initially try to return the
current_stats.loglikelihood
. If that raises anAttributeError
, will call _loglikelihood` to calculate it and store it tocurrent_stats
.

logposterior
¶ Returns the log of the posterior of the current parameter values.
The logprior is calculated first. If the logprior returns
inf
(possibly indicating a nonphysical point), then theloglikelihood
is not called.

logprior
¶ Returns the log prior at the current parameters.

name
= None¶

static
prior_from_config
(cp, variable_params, prior_section, constraint_section)[source]¶ Gets arguments and keyword arguments from a config file.
Parameters:  cp (WorkflowConfigParser) – Config file parser to read.
 variable_params (list) – List of of model parameter names.
 prior_section (str) – Section to read prior(s) from.
 constraint_section (str) – Section to read constraint(s) from.
Returns: The prior.
Return type: pycbc.distributions.JointDistribution

prior_rvs
(size=1, prior=None)[source]¶ Returns random variates drawn from the prior.
If the
sampling_params
are different from thevariable_params
, the variates are transformed to the sampling_params parameter space before being returned.Parameters:  size (int, optional) – Number of random values to return for each parameter. Default is 1.
 prior (JointDistribution, optional) – Use the given prior to draw values rather than the saved prior.
Returns: A field array of the random values.
Return type:

sampling_params
¶ Returns the sampling parameters.
If
sampling_transforms
is None, this is the same as thevariable_params
.

static_params
¶ Returns the model’s static arguments.

update
(**params)[source]¶ Updates the current parameter positions and resets stats.
If any sampling transforms are specified, they are applied to the params before being stored.

variable_params
¶ Returns the model parameters.

class
pycbc.inference.models.base.
ModelStats
[source]¶ Bases:
object
Class to hold model’s current stat values.

getstats
(names, default=nan)[source]¶ Get the requested stats as a tuple.
If a requested stat is not an attribute (implying it hasn’t been stored), then the default value is returned for that stat.
Parameters:  names (list of str) – The names of the stats to get.
 default (float, optional) – What to return if a requested stat is not an attribute of self.
Default is
numpy.nan
.
Returns: A tuple of the requested stats.
Return type:

getstatsdict
(names, default=nan)[source]¶ Get the requested stats as a dictionary.
If a requested stat is not an attribute (implying it hasn’t been stored), then the default value is returned for that stat.
Parameters:  names (list of str) – The names of the stats to get.
 default (float, optional) – What to return if a requested stat is not an attribute of self.
Default is
numpy.nan
.
Returns: A dictionary of the requested stats.
Return type:

statnames
¶ Returns the names of the stats that have been stored.


class
pycbc.inference.models.base.
SamplingTransforms
(variable_params, sampling_params, replace_parameters, sampling_transforms)[source]¶ Bases:
object
Provides methods for transforming between sampling parameter space and model parameter space.

apply
(samples, inverse=False)[source]¶ Applies the sampling transforms to the given samples.
Parameters:  samples (dict or FieldArray) – The samples to apply the transforms to.
 inverse (bool, optional) – Whether to apply the inverse transforms (i.e., go from the sampling
args to the
variable_params
). Default is False.
Returns: The transformed samples, along with the original samples.
Return type: dict or FieldArray

classmethod
from_config
(cp, variable_params)[source]¶ Gets sampling transforms specified in a config file.
Sampling parameters and the parameters they replace are read from the
sampling_params
section, if it exists. Sampling transforms are read from thesampling_transforms
section(s), usingtransforms.read_transforms_from_config
.An
AssertionError
is raised if nosampling_params
section exists in the config file.Parameters:  cp (WorkflowConfigParser) – Config file parser to read.
 variable_params (list) – List of parameter names of the original variable params.
Returns: A sampling transforms class.
Return type:

logjacobian
(**params)[source]¶ Returns the log of the jacobian needed to transform pdfs in the
variable_params
parameter space to thesampling_params
parameter space.Let \(\mathbf{x}\) be the set of variable parameters, \(\mathbf{y} = f(\mathbf{x})\) the set of sampling parameters, and \(p_x(\mathbf{x})\) a probability density function defined over \(\mathbf{x}\). The corresponding pdf in \(\mathbf{y}\) is then:
\[p_y(\mathbf{y}) = p_x(\mathbf{x})\left\mathrm{det}\,\mathbf{J}_{ij}\right,\]where \(\mathbf{J}_{ij}\) is the Jacobian of the inverse transform \(\mathbf{x} = g(\mathbf{y})\). This has elements:
\[\mathbf{J}_{ij} = \frac{\partial g_i}{\partial{y_j}}\]This function returns \(\log \left\mathrm{det}\,\mathbf{J}_{ij}\right\).
Parameters: **params – The keyword arguments should specify values for all of the variable args and all of the sampling args. Returns: The value of the jacobian. Return type: float


pycbc.inference.models.base.
read_sampling_params_from_config
(cp, section_group=None, section='sampling_params')[source]¶ Reads sampling parameters from the given config file.
Parameters are read from the [({section_group}_){section}] section. The options should list the variable args to transform; the parameters they point to should list the parameters they are to be transformed to for sampling. If a multiple parameters are transformed together, they should be comma separated. Example:
[sampling_params] mass1, mass2 = mchirp, logitq spin1_a = logitspin1_a
Note that only the final sampling parameters should be listed, even if multiple intermediate transforms are needed. (In the above example, a transform is needed to go from mass1, mass2 to mchirp, q, then another one needed to go from q to logitq.) These transforms should be specified in separate sections; see
transforms.read_transforms_from_config
for details.Parameters:  cp (WorkflowConfigParser) – An open config parser to read from.
 section_group (str, optional) – Append {section_group}_ to the section name. Default is None.
 section (str, optional) – The name of the section. Default is ‘sampling_params’.
Returns:  sampling_params (list) – The list of sampling parameters to use instead.
 replaced_params (list) – The list of variable args to replace in the sampler.
pycbc.inference.models.base_data module¶
Base classes for models with data.

class
pycbc.inference.models.base_data.
BaseDataModel
(variable_params, data, recalibration=None, gates=None, injection_file=None, no_save_data=False, **kwargs)[source]¶ Bases:
pycbc.inference.models.base.BaseModel
Base class for models that require data and a waveform generator.
This adds propeties for the log of the likelihood that the data contain noise,
lognl
, and the log likelihood ratiologlr
.Classes that inherit from this class must define
_loglr
and_lognl
functions, in addition to the_loglikelihood
requirement inherited fromBaseModel
.Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied.
 data (dict) – A dictionary of data, in which the keys are the detector names and the values are the data.
 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.
 injection_file (str, optional) – If an injection was added to the data, the name of the injection file
used. If provided, the injection parameters will be written to
file when
write_metadata
is called.  **kwargs – All other keyword arguments are passed to
BaseModel
.

lognl
¶ Returns the log likelihood of the noise.

loglr
¶ Returns the log of the likelihood ratio.

logplr
¶ Returns the log of the priorweighted likelihood ratio.

See ``BaseModel`` for additional attributes and properties.

data
Dictionary mapping detector names to data.

detectors
Returns the detectors used.

loglr
The log likelihood ratio at the current parameters.
This will initially try to return the
current_stats.loglr
. If that raises anAttributeError
, will call _loglr` to calculate it and store it tocurrent_stats
.

lognl
The log likelihood of the model assuming the data is noise.
This will initially try to return the
current_stats.lognl
. If that raises anAttributeError
, will call _lognl` to calculate it and store it tocurrent_stats
.

logplr
Returns the log of the priorweighted likelihood ratio at the current parameter values.
The logprior is calculated first. If the logprior returns
inf
(possibly indicating a nonphysical point), thenloglr
is not called.
pycbc.inference.models.brute_marg module¶
This module provides model classes that do brute force marginalization using at the likelihood level.

class
pycbc.inference.models.brute_marg.
BruteParallelGaussianMarginalize
(variable_params, cores=10, base_model=None, marginalize_phase=None, **kwds)[source]¶ Bases:
pycbc.inference.models.gaussian_noise.BaseGaussianNoise

name
= 'brute_parallel_gaussian_marginalize'¶

pycbc.inference.models.data_utils module¶
Utilities for loading data for models.

exception
pycbc.inference.models.data_utils.
NoValidDataError
[source]¶ Bases:
Exception
This should be raised if a continous segment of valid data could not be found.

pycbc.inference.models.data_utils.
check_for_nans
(strain_dict)[source]¶ Checks if any data in a dictionary of strains has NaNs.
If any NaNs are found, a
ValueError
is raised.Parameters: strain_dict (dict) – Dictionary of detectors > pycbc.types.timeseries.TimeSeries
.

pycbc.inference.models.data_utils.
check_validtimes
(detector, gps_start, gps_end, shift_to_valid=False, max_shift=None, segment_name='DATA', **kwargs)[source]¶ Checks DQ server to see if the given times are in a valid segment.
If the
shift_to_valid
flag is provided, the times will be shifted left or right to try to find a continous valid block nearby. The shifting starts by shifting the times left by 1 second. If that does not work, it shifts the times right by one second. This continues, increasing the shift time by 1 second, until a valid block could be found, or until the shift size exceedsmax_shift
.If the given times are not in a continuous valid segment, or a valid block cannot be found nearby, a
NoValidDataError
is raised.Parameters:  detector (str) – The name of the detector to query; e.g., ‘H1’.
 gps_start (int) – The GPS start time of the segment to query.
 gps_end (int) – The GPS end time of the segment to query.
 shift_to_valid (bool, optional) – If True, will try to shift the gps start and end times to the nearest continous valid segment of data. Default is False.
 max_shift (int, optional) – The maximum number of seconds to try to shift left or right to find
a valid segment. Default is
gps_end  gps_start
.  segment_name (str, optional) – The status flag to query; passed to
pycbc.dq.query_flag()
. Default is “DATA”.  **kwargs – All other keyword arguments are passed to
pycbc.dq.query_flag()
.
Returns:  use_start (int) – The start time to use. If
shift_to_valid
is True, this may differ from the given GPS start time.  use_end (int) – The end time to use. If
shift_to_valid
is True, this may differ from the given GPS end time.

pycbc.inference.models.data_utils.
create_data_parser
()[source]¶ Creates an argument parser for loading GW data.

pycbc.inference.models.data_utils.
data_from_cli
(opts, check_for_valid_times=False, shift_psd_times_to_valid=False, err_on_missing_detectors=False)[source]¶ Loads the data needed for a model from the given commandline 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).
 check_for_valid_times (bool, optional) – Check that valid data exists in the requested gps times. Default is False.
 shift_psd_times_to_valid (bool, optional) – If estimating the PSD from data, shift the PSD times to a valid segment if needed. Default is False.
 err_on_missing_detectors (bool, optional) – Raise a NoValidDataError if any detector does not have valid data. Otherwise, a warning is printed, and that detector is skipped.
Returns:  strain_dict (dict) – Dictionary of detectors > time series strain.
 psd_strain_dict (dict or None) – If
opts.psd_(startend)_time
were set, a dctionary of detectors > time series data to use for PSD estimation. Otherwise,None
.

pycbc.inference.models.data_utils.
data_opts_from_config
(cp, section, filter_flow)[source]¶ Loads data options from a section in a config file.
Parameters:  cp (WorkflowConfigParser) – Config file to read.
 section (str) – The section to read. All options in the section will be loaded asif they wre commandline arguments.
 filter_flow (dict) – Dictionary of detectors > inner product low frequency cutoffs.
If a dataconditioninglowfreq cutoff wasn’t provided for any
of the detectors, these values will be used. Otherwise, the
dataconditioninglowfreq must be less than the inner product cutoffs.
If any are not, a
ValueError
is raised.
Returns: opts – An argument parser namespace that was constructed as if the options were specified on the command line.
Return type: parsed argparse.ArgumentParser

pycbc.inference.models.data_utils.
detectors_with_valid_data
(detectors, gps_start_times, gps_end_times, pad_data=None, err_on_missing_detectors=False, **kwargs)[source]¶ Determines which detectors have valid data.
Parameters:  detectors (list of str) – Names of the detector names to check.
 gps_start_times (dict) – Dictionary of detector name > start time listing the GPS start times of the segment to check for each detector.
 gps_end_times (dict) – Dictionary of detector name > end time listing the GPS end times of the segment to check for each detector.
 pad_data (dict, optional) – Dictionary of detector name > pad time to add to the beginning/end of
the GPS start/end times before checking. A pad time for every detector
in
detectors
must be given. Default (None) is to apply no pad to the times.  err_on_missing_detectors (bool, optional) – If True, a
NoValidDataError
will be raised if any detector does not have continous valid data in its requested segment. Otherwise, the detector will not be included in the returned list of detectors with valid data. Default is False.  **kwargs – All other keyword arguments are passed to
check_validtimes
.
Returns: A dictionary of detector name > valid times giving the detectors with valid data and their segments. If
shift_to_valid
was passed tocheck_validtimes
this may not be the same as the input segments. If no valid times could be found for a detector (anderr_on_missing_detectors
is False), it will not be included in the returned dictionary.Return type:

pycbc.inference.models.data_utils.
fd_data_from_strain_dict
(opts, strain_dict, psd_strain_dict=None)[source]¶ Converts a dictionary of time series to the frequency domain, and gets the PSDs.
Parameters:  opts (ArgumentParser parsed args) – Argument options parsed from a command line string (the sort of thing returned by parser.parse_args).
 strain_dict (dict) – Dictionary of detectors > time series data.
 psd_strain_dict (dict, optional) – Dictionary of detectors > time series data to use for PSD estimation.
If not provided, will use the
strain_dict
. This is ignored ifopts.psd_estimation
is not set. Seepycbc.psd.psd_from_cli_multi_ifos()
for details.
Returns:  stilde_dict (dict) – Dictionary of detectors > frequency series data.
 psd_dict (dict) – Dictionary of detectors > frequencydomain PSDs.

pycbc.inference.models.data_utils.
gate_overwhitened_data
(stilde_dict, psd_dict, gates)[source]¶ Applies gates to overwhitened data.
Parameters: Returns: Dictionary of detectors > frequency series data with the gates applied after overwhitening. The returned data is not overwhitened.
Return type:

pycbc.inference.models.data_utils.
strain_from_cli_multi_ifos
(*args, **kwargs)[source]¶ Wrapper around strain.from_cli_multi_ifos that tries a few times before quiting.
When running in a parallel environment, multiple concurrent queries to the segment data base can cause time out errors. If that happens, this will sleep for a few seconds, then try again a few times before giving up.
pycbc.inference.models.gaussian_noise module¶
This module provides model classes that assume the noise is Gaussian.

class
pycbc.inference.models.gaussian_noise.
BaseGaussianNoise
(variable_params, data, low_frequency_cutoff, psds=None, high_frequency_cutoff=None, normalize=False, static_params=None, ignore_failed_waveforms=False, no_save_data=False, **kwargs)[source]¶ Bases:
pycbc.inference.models.base_data.BaseDataModel
Model for analyzing GW data with assuming a widesense stationary Gaussian noise model.
This model will load gravitational wave data and calculate the log noise likelihood
_lognl
and normalization. It also implements the_loglikelihood
function as the sum of the log likelihood ratio and thelognl
. It does not implement a log likelihood ratio function_loglr
, however, since that can differ depending on the signal model. Models that analyze GW data assuming it is stationary Gaussian should therefore inherit from this class and implement their own_loglr
function.For more details on the inner product used, the log likelihood of the noise, and the normalization factor, see
GaussianNoise
.Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied.
 data (dict) – A dictionary of data, in which the keys are the detector names and the values are the data (assumed to be unwhitened). All data must have the same frequency resolution.
 low_frequency_cutoff (dict) – A dictionary of starting frequencies, in which the keys are the detector names and the values are the starting frequencies for the respective detectors to be used for computing inner products.
 psds (dict, optional) – A dictionary of FrequencySeries keyed by the detector names. The dictionary must have a psd for each detector specified in the data dictionary. If provided, the inner products in each detector will be weighted by 1/psd of that detector.
 high_frequency_cutoff (dict, optional) – A dictionary of ending frequencies, in which the keys are the detector names and the values are the ending frequencies for the respective detectors to be used for computing inner products. If not provided, the minimum of the largest frequency stored in the data and a given waveform will be used.
 normalize (bool, optional) – If True, the normalization factor \(alpha\) will be included in the
log likelihood. See
GaussianNoise
for details. Default is to not include it.  static_params (dict, optional) – A dictionary of parameter names > values to keep fixed.
 ignore_failed_waveforms (bool, optional) – If the waveform generator raises an error when it tries to generate, treat the point as having zero likelihood. This allows the parameter estimation to continue. Otherwise, an error will be raised, stopping the run. Default is False.
 **kwargs – All other keyword arguments are passed to
BaseDataModel
.

ignore_failed_waveforms
¶ If True, points in parameter space that cause waveform generation to fail (i.e., they raise a
FailedWaveformError
) will be treated as points with zero likelihood. Otherwise, such points will cause the model to raise aFailedWaveformError
.Type: bool

low_frequency_cutoff
¶

high_frequency_cutoff
¶

kmin
¶

kmax
¶

psds
¶

psd_segments
¶

weight
¶

whitened_data
¶

normalize
¶

lognorm
¶

det_lognl
(det)[source]¶ Returns the log likelihood of the noise in the given detector:
\[\log p(d_in_i) = \log \alpha_i  \frac{1}{2} \left<d_i  d_i\right>.\]Parameters: det (str) – The name of the detector. Returns: The log likelihood of the noise in the requested detector. Return type: float

det_lognorm
(det)[source]¶ The log of the likelihood normalization in the given detector.
If
self.normalize
is False, will just return 0.

classmethod
from_config
(cp, data_section='data', data=None, psds=None, **kwargs)[source]¶ Initializes an instance of this class from the given config file.
In addition to
[model]
, adata_section
(default[data]
) must be in the configuration file. The data section specifies settings for loading data and estimating PSDs. See the online documentation for more details.The following options are read from the
[model]
section, in addition toname
(which must be set):{{DET}}lowfrequencycutoff = FLOAT
: The low frequency cutoff to use for each detector {{DET}}. A cutoff must be provided for every detector that may be analyzed (any additional detectors are ignored).{{DET}}highfrequencycutoff = FLOAT
: (Optional) A high frequency cutoff for each detector. If not provided, the Nyquist frequency is used.checkforvalidtimes =
: (Optional) If provided, will check that there are no data quality flags on during the analysis segment and the segment used for PSD estimation in each detector. To check for flags,pycbc.dq.query_flag()
is used, with settings pulled from thedq*
options in the[data]
section. If a detector has bad data quality during either the analysis segment or PSD segment, it will be removed from the analysis.shiftpsdtimestovalid =
: (Optional) If provided, the segment used for PSD estimation will automatically be shifted left or right until a continous block of data with no data quality issues can be found. If no block can be found with a maximum shift of +/ the requested psd segment length, the detector will not be analyzed.erronmissingdetectors =
: Raises an error if any detector is removed from the analysis because a valid time could not be found. Otherwise, a warning is printed to screen and the detector is removed from the analysis.normalize =
: (Optional) Turn on the normalization factor.ignorefailedwaveforms =
: Sets theignore_failed_waveforms
attribute.
Parameters:  cp (WorkflowConfigParser) – Config file parser to read.
 data_section (str, optional) – The name of the section to load data options from.
 **kwargs – All additional keyword arguments are passed to the class. Any provided keyword will over ride what is in the config file.

high_frequency_cutoff
The high frequency cutoff of the inner product.

kmax
Dictionary of ending indices for the inner product.
This is determined from the high frequency cutoff and the
delta_f
of the data usingpycbc.filter.matchedfilter.get_cutoff_indices()
. If no high frequency cutoff was provided, this will be the indice corresponding to the Nyquist frequency.

kmin
Dictionary of starting indices for the inner product.
This is determined from the lower frequency cutoff and the
delta_f
of the data usingpycbc.filter.matchedfilter.get_cutoff_indices()
.

lognorm
The log of the normalization of the log likelihood.

low_frequency_cutoff
The low frequency cutoff of the inner product.

normalize
Determines if the loglikelihood includes the normalization term.

psd_segments
Dictionary giving times used for PSD estimation for each detector.
If a detector’s PSD was not estimated from data, or the segment wasn’t provided, that detector will not be in the dictionary.

psds
Dictionary of detectors > PSD frequency series.
If no PSD was provided for a detector, this will just be a frequency series of ones.

set_psd_segments
(psds)[source]¶ Sets the PSD segments from a dictionary of PSDs.
This attempts to get the PSD segment from a
psd_segment
attribute of each detector’s PSD frequency series. If that attribute isn’t set, then that detector is not added to the dictionary of PSD segments.Parameters: psds (dict) – Dictionary of detector name > PSD frequency series. The segment used for each PSD will try to be retrieved from the PSD’s .psd_segment
attribute.

weight
Dictionary of detectors > frequency series of innerproduct weights.
The weights are \(\sqrt{4 \Delta f / S_n(f)}\). This is set when the PSDs are set.

whitened_data
Dictionary of detectors > whitened data frequency series.
The whitened data is the data multiplied by the innerproduct weight. Note that this includes the \(\sqrt{4 \Delta f}\) factor. This is set when the PSDs are set.

write_metadata
(fp)[source]¶ Adds writing the psds and lognl, since it’s a constant.
The lognl is written to the sample group’s
attrs
.The analyzed detectors, their analysis segments, and the segments used for psd estimation are written to the file’s
attrs
, asanalyzed_detectors
,{{detector}}_analysis_segment
, and{{detector}}_psd_segment
, respectively.Parameters: fp (pycbc.inference.io.BaseInferenceFile instance) – The inference file to write to.

class
pycbc.inference.models.gaussian_noise.
GaussianNoise
(variable_params, data, low_frequency_cutoff, psds=None, high_frequency_cutoff=None, normalize=False, static_params=None, **kwargs)[source]¶ Bases:
pycbc.inference.models.gaussian_noise.BaseGaussianNoise
Model that assumes data is stationary Gaussian noise.
With Gaussian noise the log likelihood functions for signal \(\log p(d\Theta, h)\) and for noise \(\log p(dn)\) are given by:
\[\begin{split}\log p(d\Theta, h) &= \log\alpha \frac{1}{2} \sum_i \left< d_i  h_i(\Theta)  d_i  h_i(\Theta) \right> \\ \log p(dn) &= \log\alpha \frac{1}{2} \sum_i \left<d_i  d_i\right>\end{split}\]where the sum is over the number of detectors, \(d_i\) is the data in each detector, and \(h_i(\Theta)\) is the model signal in each detector. The (discrete) inner product is given by:
\[\left<a_i  b_i\right> = 4\Re \Delta f \sum_{k=k_{\mathrm{min}}}^{k_{\mathrm{max}}} \frac{\tilde{a}_i^{*}[k] \tilde{b}_i[k]}{S^{(i)}_n[k]},\]where \(\Delta f\) is the frequency resolution (given by 1 / the observation time \(T\)), \(k\) is an index over the discretely sampled frequencies \(f = k \Delta_f\), and \(S^{(i)}_n[k]\) is the PSD in the given detector. The upper cutoff on the inner product \(k_{\max}\) is by default the Nyquist frequency \(k_{\max} = N/2+1\), where \(N = \lfloor T/\Delta t \rfloor\) is the number of samples in the time domain, but this can be set manually to a smaller value.
The normalization factor \(\alpha\) is:
\[\alpha = \prod_{i} \frac{1}{\left(\pi T\right)^{N/2} \prod_{k=k_\mathrm{min}}^{k_{\mathrm{max}}} S^{(i)}_n[k]},\]where the product is over the number of detectors. By default, the normalization constant is not included in the log likelihood, but it can be turned on using the
normalize
keyword argument.Note that the log likelihood ratio has fewer terms than the log likelihood, since the normalization and \(\left<d_id_i\right>\) terms cancel:
\[\log \mathcal{L}(\Theta) = \sum_i \left[ \left<h_i(\Theta)d_i\right>  \frac{1}{2} \left<h_i(\Theta)h_i(\Theta)\right> \right]\]Upon initialization, the data is whitened using the given PSDs. If no PSDs are given the data and waveforms returned by the waveform generator are assumed to be whitened.
For more details on initialization parameters and definition of terms, see
models.BaseDataModel
.Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied.
 data (dict) – A dictionary of data, in which the keys are the detector names and the values are the data (assumed to be unwhitened). The list of keys must match the waveform generator’s detectors keys, and the epoch of every data set must be the same as the waveform generator’s epoch.
 low_frequency_cutoff (dict) – A dictionary of starting frequencies, in which the keys are the detector names and the values are the starting frequencies for the respective detectors to be used for computing inner products.
 psds (dict, optional) – A dictionary of FrequencySeries keyed by the detector names. The dictionary must have a psd for each detector specified in the data dictionary. If provided, the inner products in each detector will be weighted by 1/psd of that detector.
 high_frequency_cutoff (dict, optional) – A dictionary of ending frequencies, in which the keys are the detector names and the values are the ending frequencies for the respective detectors to be used for computing inner products. If not provided, the minimum of the largest frequency stored in the data and a given waveform will be used.
 normalize (bool, optional) – If True, the normalization factor \(alpha\) will be included in the log likelihood. Default is to not include it.
 static_params (dict, optional) – A dictionary of parameter names > values to keep fixed.
 **kwargs – All other keyword arguments are passed to
BaseDataModel
.
Examples
Create a signal, and set up the model using that signal:
>>> from pycbc import psd as pypsd >>> from pycbc.inference.models import GaussianNoise >>> from pycbc.waveform.generator import (FDomainDetFrameGenerator, ... FDomainCBCGenerator) >>> seglen = 4 >>> sample_rate = 2048 >>> N = seglen*sample_rate/2+1 >>> fmin = 30. >>> static_params = {'approximant': 'IMRPhenomD', 'f_lower': fmin, ... 'mass1': 38.6, 'mass2': 29.3, ... 'spin1z': 0., 'spin2z': 0., 'ra': 1.37, 'dec': 1.26, ... 'polarization': 2.76, 'distance': 3*500.} >>> variable_params = ['tc'] >>> tsig = 3.1 >>> generator = FDomainDetFrameGenerator( ... FDomainCBCGenerator, 0., detectors=['H1', 'L1'], ... variable_args=variable_params, ... delta_f=1./seglen, **static_params) >>> signal = generator.generate(tc=tsig) >>> psd = pypsd.aLIGOZeroDetHighPower(N, 1./seglen, 20.) >>> psds = {'H1': psd, 'L1': psd} >>> low_frequency_cutoff = {'H1': fmin, 'L1': fmin} >>> model = GaussianNoise(variable_params, signal, low_frequency_cutoff, psds=psds, static_params=static_params)
Set the current position to the coalescence time of the signal:
>>> model.update(tc=tsig)
Now compute the log likelihood ratio and priorweighted likelihood ratio; since we have not provided a prior, these should be equal to each other:
>>> print('{:.2f}'.format(model.loglr)) 282.43 >>> print('{:.2f}'.format(model.logplr)) 282.43
Print all of the default_stats:
>>> print(',\n'.join(['{}: {:.2f}'.format(s, v) ... for (s, v) in sorted(model.current_stats.items())])) H1_cplx_loglr: 177.76+0.00j, H1_optimal_snrsq: 355.52, L1_cplx_loglr: 104.67+0.00j, L1_optimal_snrsq: 209.35, logjacobian: 0.00, loglikelihood: 0.00, loglr: 282.43, logprior: 0.00
Compute the SNR; for this system and PSD, this should be approximately 24:
>>> from pycbc.conversions import snr_from_loglr >>> x = snr_from_loglr(model.loglr) >>> print('{:.2f}'.format(x)) 23.77
Since there is no noise, the SNR should be the same as the quadrature sum of the optimal SNRs in each detector:
>>> x = (model.det_optimal_snrsq('H1') + ... model.det_optimal_snrsq('L1'))**0.5 >>> print('{:.2f}'.format(x)) 23.77
Toggle on the normalization constant:
>>> model.normalize = True >>> model.loglikelihood 835397.8757405131
Using the same model, evaluate the log likelihood ratio at several points in time and check that the max is at tsig:
>>> import numpy >>> times = numpy.linspace(tsig1, tsig+1, num=101) >>> loglrs = numpy.zeros(len(times)) >>> for (ii, t) in enumerate(times): ... model.update(tc=t) ... loglrs[ii] = model.loglr >>> print('tsig: {:.2f}, time of max loglr: {:.2f}'.format( ... tsig, times[loglrs.argmax()])) tsig: 3.10, time of max loglr: 3.10
Create a prior and use it (see distributions module for more details):
>>> from pycbc import distributions >>> uniform_prior = distributions.Uniform(tc=(tsig0.2,tsig+0.2)) >>> prior = distributions.JointDistribution(variable_params, uniform_prior) >>> model = GaussianNoise(variable_params, ... signal, low_frequency_cutoff, psds=psds, prior=prior, ... static_params=static_params) >>> model.update(tc=tsig) >>> print('{:.2f}'.format(model.logplr)) 283.35 >>> print(',\n'.join(['{}: {:.2f}'.format(s, v) ... for (s, v) in sorted(model.current_stats.items())])) H1_cplx_loglr: 177.76+0.00j, H1_optimal_snrsq: 355.52, L1_cplx_loglr: 104.67+0.00j, L1_optimal_snrsq: 209.35, logjacobian: 0.00, loglikelihood: 0.00, loglr: 282.43, logprior: 0.92

det_cplx_loglr
(det)[source]¶ Returns the complex log likelihood ratio in the given detector.
Parameters: det (str) – The name of the detector. Returns: The complex log likelihood ratio. Return type: complex float

det_optimal_snrsq
(det)[source]¶ Returns the opitmal SNR squared in the given detector.
Parameters: det (str) – The name of the detector. Returns: The opimtal SNR squared. Return type: float

name
= 'gaussian_noise'¶

pycbc.inference.models.gaussian_noise.
create_waveform_generator
(variable_params, data, waveform_transforms=None, recalibration=None, gates=None, generator_class=<class 'pycbc.waveform.generator.FDomainDetFrameGenerator'>, **static_params)[source]¶ 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
<pycbc.types.TimeSeries TimeSeries>
or<pycbc.types.FrequencySeries FrequencySeries>
.  waveform_transforms (list, optional) – The list of transforms applied to convert variable parameters into parameters that will be understood by the waveform generator.
 recalibration (dict, optional) – Dictionary mapping detector names to
<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
pycbc.gate.gates_from_cli()
.  generator_class (detectorframe fdomain generator, optional) – Class to use for generating waveforms. Default is
waveform.generator.FDomainDetFrameGenerator
.  **static_params – All other keyword arguments are passed as static parameters to the waveform generator.
Returns: A waveform generator for frequency domain generation.
Return type: pycbc.waveform.FDomainDetFrameGenerator

pycbc.inference.models.gaussian_noise.
get_values_from_injection
(cp, injection_file, update_cp=True)[source]¶ Replaces all FROM_INJECTION values in a config file with the corresponding value from the injection.
This looks for any options that start with
FROM_INJECTION[:ARG]
in a config file. It then replaces that value with the corresponding value from the injection file. An argument may be optionally provided, in which case the argument will be retrieved from the injection file. Functions of parameters in the injection file may be used; the syntax and functions available is the same as theparameters
argument in executables such aspycbc_inference_extract_samples
. If noARG
is provided, then the option name will try to be retrieved from the injection.For example,
mass1 = FROM_INJECTION
will cause
mass1
to be retrieved from the injection file, while:will cause the larger of mass1 and mass2 to be retrieved from the injection file. Note that if spaces are in the argument, it must be encased in single quotes.
The injection file may contain only one injection. Otherwise, a ValueError will be raised.
Parameters:  cp (ConfigParser) – The config file within which to replace values.
 injection_file (str or None) – The injection file to get values from. A ValueError will be raised
if there are any
FROM_INJECTION
values in the config file, and injection file is None, or if there is more than one injection.  update_cp (bool, optional) – Update the config parser with the replaced parameters. If False, will just retrieve the parameter values to update, without updating the config file. Default is True.
Returns: The parameters that were replaced, as a tuple of section name, option, value.
Return type:
pycbc.inference.models.marginalized_gaussian_noise module¶
This module provides model classes that assume the noise is Gaussian and allows for the likelihood to be marginalized over phase and/or time and/or distance.

class
pycbc.inference.models.marginalized_gaussian_noise.
MarginalizedHMPolPhase
(variable_params, data, low_frequency_cutoff, psds=None, high_frequency_cutoff=None, normalize=False, polarization_samples=100, coa_phase_samples=100, static_params=None, **kwargs)[source]¶ Bases:
pycbc.inference.models.gaussian_noise.BaseGaussianNoise
Numerically marginalizes waveforms with higher modes over polarization and phase.
This class implements the Gaussian likelihood with an explicit numerical marginalization over polarization angle and orbital phase. This is accomplished using a fixed set of integration points distributed uniformly between 0 and 2:math:pi for both the polarization and phase. By default, 100 integration points are used for each parameter, giving \(10^4\) evaluation points in total. This can be modified using the
polarization_samples
andcoa_phase_samples
arguments.This only works with waveforms that return separate spherical harmonic modes for each waveform. For a list of currently supported approximants, see
pycbc.waveform.waveform_modes.fd_waveform_mode_approximants()
andpycbc.waveform.waveform_modes.td_waveform_mode_approximants()
.Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied.
 data (dict) – A dictionary of data, in which the keys are the detector names and the values are the data (assumed to be unwhitened). All data must have the same frequency resolution.
 low_frequency_cutoff (dict) – A dictionary of starting frequencies, in which the keys are the detector names and the values are the starting frequencies for the respective detectors to be used for computing inner products.
 psds (dict, optional) – A dictionary of FrequencySeries keyed by the detector names. The dictionary must have a psd for each detector specified in the data dictionary. If provided, the inner products in each detector will be weighted by 1/psd of that detector.
 high_frequency_cutoff (dict, optional) – A dictionary of ending frequencies, in which the keys are the detector names and the values are the ending frequencies for the respective detectors to be used for computing inner products. If not provided, the minimum of the largest frequency stored in the data and a given waveform will be used.
 normalize (bool, optional) – If True, the normalization factor \(alpha\) will be included in the
log likelihood. See
GaussianNoise
for details. Default is to not include it.  polarization_samples (int, optional) – How many points to use in polarization. Default is 100.
 coa_phase_samples (int, optional) – How many points to use in phase. Defaults is 100.
 **kwargs – All other keyword arguments are passed to
gaussian_noise.BaseGaussianNoisei
.

name
= 'marginalized_hmpolphase'¶

class
pycbc.inference.models.marginalized_gaussian_noise.
MarginalizedPhaseGaussianNoise
(variable_params, data, low_frequency_cutoff, psds=None, high_frequency_cutoff=None, normalize=False, static_params=None, **kwargs)[source]¶ Bases:
pycbc.inference.models.gaussian_noise.BaseGaussianNoise
The likelihood is analytically marginalized over phase.
This class can be used with signal models that can be written as:
\[\tilde{h}(f; \Theta, \phi) = A(f; \Theta)e^{i\Psi(f; \Theta) + i \phi},\]where \(\phi\) is an arbitrary phase constant. This phase constant can be analytically marginalized over with a uniform prior as follows: assuming the noise is stationary and Gaussian (see GaussianNoise for details), the posterior is:
\[\begin{split}p(\Theta,\phid) &\propto p(\Theta)p(\phi)p(d\Theta,\phi) \\ &\propto p(\Theta)\frac{1}{2\pi}\exp\left[ \frac{1}{2}\sum_{i}^{N_D} \left< h_i(\Theta,\phi)  d_i, h_i(\Theta,\phi)  d_i \right>\right].\end{split}\]Here, the sum is over the number of detectors \(N_D\), \(d_i\) and \(h_i\) are the data and signal in detector \(i\), respectively, and we have assumed a uniform prior on \(phi \in [0, 2\pi)\). With the form of the signal model given above, the inner product in the exponent can be written as:
\[\begin{split}\frac{1}{2}\left<h_i  d_i, h_i d_i\right> &= \left<h_i, d_i\right>  \frac{1}{2}\left<h_i, h_i\right>  \frac{1}{2}\left<d_i, d_i\right> \\ &= \Re\left\{O(h^0_i, d_i)e^{i\phi}\right\}  \frac{1}{2}\left<h^0_i, h^0_i\right>  \frac{1}{2}\left<d_i, d_i\right>,\end{split}\]where:
\[\begin{split}h_i^0 &\equiv \tilde{h}_i(f; \Theta, \phi=0); \\ O(h^0_i, d_i) &\equiv 4 \int_0^\infty \frac{\tilde{h}_i^*(f; \Theta,0)\tilde{d}_i(f)}{S_n(f)}\mathrm{d}f.\end{split}\]Gathering all of the terms that are not dependent on \(\phi\) together:
\[\alpha(\Theta, d) \equiv \exp\left[\frac{1}{2}\sum_i \left<h^0_i, h^0_i\right> + \left<d_i, d_i\right>\right],\]we can marginalize the posterior over \(\phi\):
\[\begin{split}p(\Thetad) &\propto p(\Theta)\alpha(\Theta,d)\frac{1}{2\pi} \int_{0}^{2\pi}\exp\left[\Re \left\{ e^{i\phi} \sum_i O(h^0_i, d_i) \right\}\right]\mathrm{d}\phi \\ &\propto p(\Theta)\alpha(\Theta, d)\frac{1}{2\pi} \int_{0}^{2\pi}\exp\left[ x(\Theta,d)\cos(\phi) + y(\Theta, d)\sin(\phi) \right]\mathrm{d}\phi.\end{split}\]The integral in the last line is equal to \(2\pi I_0(\sqrt{x^2+y^2})\), where \(I_0\) is the modified Bessel function of the first kind. Thus the marginalized log posterior is:
\[\log p(\Thetad) \propto \log p(\Theta) + I_0\left(\left\sum_i O(h^0_i, d_i)\right\right)  \frac{1}{2}\sum_i\left[ \left<h^0_i, h^0_i\right>  \left<d_i, d_i\right> \right]\]
name
= 'marginalized_phase'¶


class
pycbc.inference.models.marginalized_gaussian_noise.
MarginalizedPolarization
(variable_params, data, low_frequency_cutoff, psds=None, high_frequency_cutoff=None, normalize=False, polarization_samples=1000, static_params=None, **kwargs)[source]¶ Bases:
pycbc.inference.models.gaussian_noise.BaseGaussianNoise
This likelihood numerically marginalizes over polarization angle
This class implements the Gaussian likelihood with an explicit numerical marginalization over polarization angle. This is accomplished using a fixed set of integration points distribution uniformation between 0 and 2pi. By default, 1000 integration points are used. The ‘polarization_samples’ argument can be passed to set an alternate number of integration points.

name
= 'marginalized_polarization'¶

pycbc.inference.models.relbin module¶
This module provides model classes and functions for implementing a relative binning likelihood for parameter estimation.

class
pycbc.inference.models.relbin.
Relative
(variable_params, data, low_frequency_cutoff, fiducial_params=None, gammas=None, epsilon=0.5, earth_rotation=False, **kwargs)[source]¶ Bases:
pycbc.inference.models.gaussian_noise.BaseGaussianNoise
Model that assumes the likelihood in a region around the peak is slowly varying such that a linear approximation can be made, and likelihoods can be calculated at a coarser frequency resolution. For more details on the implementation, see https://arxiv.org/abs/1806.08792.
This model requires the use of a fiducial waveform whose parameters are near the peak of the likelihood. The fiducial waveform and all template waveforms used in likelihood calculation are currently generated using the SPAtmplt approximant.
For more details on initialization parameters and definition of terms, see
BaseGaussianNoise
.Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied.
 data (dict) – A dictionary of data, in which the keys are the detector names and the values are the data (assumed to be unwhitened). All data must have the same frequency resolution.
 low_frequency_cutoff (dict) – A dictionary of starting frequencies, in which the keys are the detector names and the values are the starting frequencies for the respective detectors to be used for computing inner products.
 figucial_params (dict) – A dictionary of waveform parameters to be used for generating the fiducial waveform. Keys must be parameter names in the form ‘PARAM_ref’ where PARAM is a recognized extrinsic parameter or an intrinsic parameter compatible with the chosen approximant.
 gammas (array of floats, optional) – Frequency powerlaw indices to be used in computing frequency bins.
 epsilon (float, optional) – Tuning parameter used in calculating the frequency bins. Lower values will result in higher resolution and more bins.
 earth_rotation (boolean, optional) – Default is False. If True, then vary the fp/fc polarization values as a function of frequency bin, using a predetermined PN approximation for the time offsets.
 **kwargs – All other keyword arguments are passed to
BaseGaussianNoise
.

static
extra_args_from_config
(cp, section, skip_args=None, dtypes=None)[source]¶ Adds reading fiducial waveform parameters from config file.

name
= 'relative'¶

pycbc.inference.models.relbin.
setup_bins
(f_full, f_lo, f_hi, chi=1.0, eps=0.5, gammas=None)[source]¶ Construct frequency bins for use in a relative likelihood model. For details, see [Barak, Dai & Venumadhav 2018].
Parameters:  f_full (array) – The full resolution array of frequencies being used in the analysis.
 f_lo (float) – The starting frequency used in matched filtering. This will be the left edge of the first frequency bin.
 f_hi (float) – The ending frequency used in matched filtering. This will be the right edge of the last frequency bin.
 chi (float, optional) – Tunable parameter, see [Barak, Dai & Venumadhav 2018]
 eps (float, optional) – Tunable parameter, see [Barak, Dai & Venumadhav 2018]. Lower values result in larger number of bins.
 gammas (array, optional) – Frequency powerlaw indices to be used in computing bins.
Returns:  nbin (int) – Number of bins.
 fbin (numpy.array of floats) – Bin edge frequencies.
 fbin_ind (numpy.array of ints) – Indices of bin edges in full frequency array.
pycbc.inference.models.relbin_cpu module¶
Optimized inner loop functions for the relative likelihood model

pycbc.inference.models.relbin_cpu.
likelihood_parts
()¶

pycbc.inference.models.relbin_cpu.
likelihood_parts_v
()¶
pycbc.inference.models.single_template module¶
This module provides model classes that assume the noise is Gaussian.

class
pycbc.inference.models.single_template.
SingleTemplate
(variable_params, data, low_frequency_cutoff, sample_rate=32768, polarization_samples=None, **kwargs)[source]¶ Bases:
pycbc.inference.models.gaussian_noise.BaseGaussianNoise
Model that assumes we know all the intrinsic parameters.
This model assumes we know all the intrinsic parameters, and are only maximizing over the extrinsic ones. We also assume a dominant mode waveform approximant only and nonprecessing.
Parameters:  variable_params ((tuple of) string(s)) – A tuple of parameter names that will be varied.
 data (dict) – A dictionary of data, in which the keys are the detector names and the values are the data (assumed to be unwhitened). All data must have the same frequency resolution.
 low_frequency_cutoff (dict) – A dictionary of starting frequencies, in which the keys are the detector names and the values are the starting frequencies for the respective detectors to be used for computing inner products.
 sample_rate (int, optional) – The sample rate to use. Default is 32768.
 polarization_samples (int, optional) – Parameter to specify how finely to marginalize over polarization angle. If None, then polarization must be a parameter.
 **kwargs – All other keyword arguments are passed to
BaseGaussianNoise
; see that class for details.

name
= 'single_template'¶
Module contents¶
This package provides classes and functions for evaluating Bayesian statistics assuming various noise models.

class
pycbc.inference.models.
CallModel
(model, callstat, return_all_stats=True)[source]¶ Bases:
object
Wrapper class for calling models from a sampler.
This class can be called like a function, with the parameter values to evaluate provided as a list in the same order as the model’s
variable_params
. In that case, the model is updated with the provided parameters and then thecallstat
retrieved. Ifreturn_all_stats
is set toTrue
, then all of the stats specified by the model’sdefault_stats
will be returned as a tuple, in addition to the stat value.The model’s attributes are promoted to this class’s namespace, so that any attribute and method of
model
may be called directly from this class.This class must be initalized prior to the creation of a
Pool
object.Parameters: Examples
Create a wrapper around an instance of the
TestNormal
model, with thecallstat
set tologposterior
:>>> from pycbc.inference.models import TestNormal, CallModel >>> model = TestNormal(['x', 'y']) >>> call_model = CallModel(model, 'logposterior')
Now call on a set of parameter values:
>>> call_model([0.1, 0.2]) (1.8628770664093453, (0.0, 0.0, 1.8628770664093453))
Note that a tuple of all of the model’s
default_stats
were returned in addition to thelogposterior
value. We can shut this off by togglingreturn_all_stats
:>>> call_model.return_all_stats = False >>> call_model([0.1, 0.2]) 1.8628770664093453
Attributes of the model can be called from the call model. For example:
>>> call_model.variable_params ('x', 'y')

pycbc.inference.models.
read_from_config
(cp, **kwargs)[source]¶ Initializes a model from the given config file.
The section must have a
name
argument. The name argument corresponds to the name of the class to initialize.Parameters:  cp (WorkflowConfigParser) – Config file parser to read.
 **kwargs – All other keyword arguments are passed to the
from_config
method of the class specified by the name argument.
Returns: The initialized model.
Return type: cls