imagine.likelihoods package¶
Submodules¶
imagine.likelihoods.ensemble_likelihood module¶
-
class
imagine.likelihoods.ensemble_likelihood.
EnsembleLikelihood
(measurement_dict, covariance_dict=None, mask_dict=None, cov_func=None, use_trace_approximation=False, **kwargs)[source]¶ Bases:
imagine.likelihoods.likelihood.Likelihood
Computes the likelihood accounting for the effects of stochastic fields
This is done by estimating the covariance associated the stochastic fields from an ensemble of simulations.
Parameters: - measurement_dict (imagine.observables.observable_dict.Measurements) – Measurements
- covariance_dict (imagine.observables.observable_dict.Covariances) – The covariances associated with the measurements. If the keyword argument is absent, the covariances will be read from the attribute measurement_dict.cov.
- mask_dict (imagine.observables.observable_dict.Masks) – Masks which will be applied to the Measurements, Covariances and Simulations, before computing the likelihood
- cov_func (func) – A function which takes a (Nens, Ndata) data array (potentially
MPI distributed) and returns a tuple comprising the mean and an
estimated covariance matrix.
If absent,
imagine.tools.covariance_estimator.oas_mcov()
will be used. - use_trace_approximation (bool) – If True, the determinant of the combined covariance matrix is approximated using \(\ln(|A+B)\approx \text{tr}\left[\ln\left(A+C\right)\right]\) (NB this assumes that the observed data covariance is diagonal). Otherwise (default), the determinant is calculated directly from the covariance matrix from the simulations.
-
class
imagine.likelihoods.ensemble_likelihood.
EnsembleLikelihoodDiagonal
(measurement_dict, covariance_dict=None, mask_dict=None, **kwargs)[source]¶ Bases:
imagine.likelihoods.likelihood.Likelihood
As EnsembleLikelihood but assuming that the covariance matrix is diagonal and well described by the sample variance. Likewise, only considers the diagonal of the observational covariance matrix.
Parameters: - measurement_dict (imagine.observables.observable_dict.Measurements) – Measurements
- covariance_dict (imagine.observables.observable_dict.Covariances) – The covariances associated with the measurements. If the keyword argument is absent, the covariances will be read from the attribute measurement_dict.cov.
- mask_dict (imagine.observables.observable_dict.Masks) – Masks which will be applied to the Measurements, Covariances and Simulations, before computing the likelihood
imagine.likelihoods.likelihood module¶
Likelihood class defines likelihood posterior function to be used in Bayesian analysis
member fuctions:
__init__
requires Measurements object Covariances object (optional) Masks object (optional)
call
running LOG-likelihood calculation requires ObservableDict object
-
class
imagine.likelihoods.likelihood.
Likelihood
(measurement_dict, covariance_dict=None, mask_dict=None, compute_dispersion=False, n_bootstrap=150)[source]¶ Bases:
imagine.tools.class_tools.BaseClass
Base class that defines likelihood posterior function to be used in Bayesian analysis
Parameters: - measurement_dict (imagine.observables.observable_dict.Measurements) – A
Measurements
dictionary containing observational data. - covariance_dict (imagine.observables.observable_dict.Covariances) – A
Covariances
dictionary containing observed covariance data. If set to None (the usual case), theLikelihood
will try to find theCovariances
in thecov
attribute of the supplied measurement_dict. - mask_dict (imagine.observables.observable_dict.Masks) – A
Masks
dictionary which should be applied to the measured and simulated data. - compute_dispersion (bool) – If True, calling the Likelihood object will return the likelihood value and the dispersion estimated by bootstrapping the simulations object and computing the sample standard deviation. If False (default), only the likelihood value is returned.
- n_bootstrap (int) – Number of resamples used in the bootstrapping of the simulations if compute_dispersion is set to True.
-
call
(observable_dict)[source]¶ Parameters: - observable_dict (imagine.observables.observable_dict)
- variables
-
covariance_dict
¶ Covariances
dictionary associated with this objectNB If a mask is used, only the masked version is stored
-
measurement_dict
¶ Measurements
dictionary associated with this objectNB If a mask is used, only the masked version is stored
- measurement_dict (imagine.observables.observable_dict.Measurements) – A
imagine.likelihoods.simple_likelihood module¶
-
class
imagine.likelihoods.simple_likelihood.
SimpleLikelihood
(measurement_dict, covariance_dict=None, mask_dict=None, compute_dispersion=False, n_bootstrap=150)[source]¶ Bases:
imagine.likelihoods.likelihood.Likelihood
A simple Likelihood class
Parameters: - measurement_dict (imagine.observables.observable_dict.Measurements) – Measurements
- covariance_dict (imagine.observables.observable_dict.Covariances) – Covariances
- mask_dict (imagine.observables.observable_dict.Masks) – Masks
Module contents¶
-
class
imagine.likelihoods.
EnsembleLikelihood
(measurement_dict, covariance_dict=None, mask_dict=None, cov_func=None, use_trace_approximation=False, **kwargs)[source]¶ Bases:
imagine.likelihoods.likelihood.Likelihood
Computes the likelihood accounting for the effects of stochastic fields
This is done by estimating the covariance associated the stochastic fields from an ensemble of simulations.
Parameters: - measurement_dict (imagine.observables.observable_dict.Measurements) – Measurements
- covariance_dict (imagine.observables.observable_dict.Covariances) – The covariances associated with the measurements. If the keyword argument is absent, the covariances will be read from the attribute measurement_dict.cov.
- mask_dict (imagine.observables.observable_dict.Masks) – Masks which will be applied to the Measurements, Covariances and Simulations, before computing the likelihood
- cov_func (func) – A function which takes a (Nens, Ndata) data array (potentially
MPI distributed) and returns a tuple comprising the mean and an
estimated covariance matrix.
If absent,
imagine.tools.covariance_estimator.oas_mcov()
will be used. - use_trace_approximation (bool) – If True, the determinant of the combined covariance matrix is approximated using \(\ln(|A+B)\approx \text{tr}\left[\ln\left(A+C\right)\right]\) (NB this assumes that the observed data covariance is diagonal). Otherwise (default), the determinant is calculated directly from the covariance matrix from the simulations.
-
class
imagine.likelihoods.
EnsembleLikelihoodDiagonal
(measurement_dict, covariance_dict=None, mask_dict=None, **kwargs)[source]¶ Bases:
imagine.likelihoods.likelihood.Likelihood
As EnsembleLikelihood but assuming that the covariance matrix is diagonal and well described by the sample variance. Likewise, only considers the diagonal of the observational covariance matrix.
Parameters: - measurement_dict (imagine.observables.observable_dict.Measurements) – Measurements
- covariance_dict (imagine.observables.observable_dict.Covariances) – The covariances associated with the measurements. If the keyword argument is absent, the covariances will be read from the attribute measurement_dict.cov.
- mask_dict (imagine.observables.observable_dict.Masks) – Masks which will be applied to the Measurements, Covariances and Simulations, before computing the likelihood
-
class
imagine.likelihoods.
Likelihood
(measurement_dict, covariance_dict=None, mask_dict=None, compute_dispersion=False, n_bootstrap=150)[source]¶ Bases:
imagine.tools.class_tools.BaseClass
Base class that defines likelihood posterior function to be used in Bayesian analysis
Parameters: - measurement_dict (imagine.observables.observable_dict.Measurements) – A
Measurements
dictionary containing observational data. - covariance_dict (imagine.observables.observable_dict.Covariances) – A
Covariances
dictionary containing observed covariance data. If set to None (the usual case), theLikelihood
will try to find theCovariances
in thecov
attribute of the supplied measurement_dict. - mask_dict (imagine.observables.observable_dict.Masks) – A
Masks
dictionary which should be applied to the measured and simulated data. - compute_dispersion (bool) – If True, calling the Likelihood object will return the likelihood value and the dispersion estimated by bootstrapping the simulations object and computing the sample standard deviation. If False (default), only the likelihood value is returned.
- n_bootstrap (int) – Number of resamples used in the bootstrapping of the simulations if compute_dispersion is set to True.
-
call
(observable_dict)[source]¶ Parameters: - observable_dict (imagine.observables.observable_dict)
- variables
-
covariance_dict
¶ Covariances
dictionary associated with this objectNB If a mask is used, only the masked version is stored
-
measurement_dict
¶ Measurements
dictionary associated with this objectNB If a mask is used, only the masked version is stored
- measurement_dict (imagine.observables.observable_dict.Measurements) – A
-
class
imagine.likelihoods.
SimpleLikelihood
(measurement_dict, covariance_dict=None, mask_dict=None, compute_dispersion=False, n_bootstrap=150)[source]¶ Bases:
imagine.likelihoods.likelihood.Likelihood
A simple Likelihood class
Parameters: - measurement_dict (imagine.observables.observable_dict.Measurements) – Measurements
- covariance_dict (imagine.observables.observable_dict.Covariances) – Covariances
- mask_dict (imagine.observables.observable_dict.Masks) – Masks