redback.likelihoods.GaussianLikelihoodWithUpperLimits

class redback.likelihoods.GaussianLikelihoodWithUpperLimits(*args: Any, **kwargs: Any)

Bases: GaussianLikelihood

__init__(x: ndarray, y: ndarray, sigma: float | None | ndarray, function: callable, kwargs: dict = None, priors=None, fiducial_parameters=None, detections: ndarray | None = None, upper_limit_sigma: float | ndarray = 3.0, data_mode: str = 'flux') → None

A Gaussian likelihood that handles upper limits - extends the base GaussianLikelihood.

Parameters:

• x (np.ndarray) – The x values.

• y (np.ndarray) – The y values. For upper limits, these are the reported limit values.

• sigma (Union[float, None, np.ndarray]) – The standard deviation of the noise for detections.

• function (callable) – The python function to fit to the data. Note, this must take the dependent variable as its first argument. The other arguments will require a prior and will be sampled over (unless a fixed value is given).

• kwargs (dict) – Any additional keywords for ‘function’.

• priors – The priors for the parameters. Default to None if not provided.

Only necessary if using maximum likelihood estimation functionality. :type priors: Union[dict, None] :param fiducial_parameters: The starting guesses for model parameters to use in the optimization for maximum likelihood estimation. Default to None if not provided. :type fiducial_parameters: Union[dict, None] :param detections: Array indicating which data points are detections. Can be boolean (True/False) or integer (1/0). 1 = detection, 0 = upper limit. If None, all data points are treated as detections. :type detections: Union[np.ndarray, None] :param upper_limit_sigma: The sigma level for upper limits. Can be a single value (e.g., 3.0 for all 3-sigma limits) or an array with different sigma levels for each upper limit. Default is 3.0. :type upper_limit_sigma: Union[float, np.ndarray] :param data_mode: Data units for interpreting upper limits. ‘flux’, ‘flux_density’, and ‘luminosity’ all treat upper limits as “true value < limit”. ‘magnitude’ (or ‘mag’) treats upper limits as “true value > limit” (fainter than limit). Default is ‘flux’. :type data_mode: str

__call__(*args: Any, **kwargs: Any) → Any

Call self as a function.

Methods

__init__(x, y, sigma, function[, kwargs, ...])	A Gaussian likelihood that handles upper limits - extends the base GaussianLikelihood.
find_maximum_likelihood_parameters([...])	Estimate the maximum likelihood
get_bounds_from_priors(priors)
get_parameter_dictionary_from_list(...)
get_parameter_list_from_dictionary(...)
get_upper_limit_sigma_values()	Get the sigma values for upper limits only.
lnlike_scipy_maximize(parameter_list)
log_likelihood([parameters])	Override parent method to include upper limits.
noise_log_likelihood()	Override parent method to include upper limits in noise likelihood.
summary()	Provide a summary of the likelihood setup.

Attributes

data_mode
detections
kwargs
model_output	The model output for the given x values.
n	Length of the x/y-values :rtype: int
parameters
parameters_to_be_updated
residual
sigma
upper_limit_sigma
upper_limits	upper_limits is the inverse of detections

find_maximum_likelihood_parameters(iterations=5, maximization_kwargs=None, method='Nelder-Mead', break_threshold=0.001)

Estimate the maximum likelihood

Parameters:

• iterations – Iterations to run the minimizer for before stopping. Default is 5.

• maximization_kwargs – Any extra keyword arguments passed to the scipy minimize function

• method – Minimize method to use. Default is ‘Nelder-Mead’

• break_threshold – The threshold for the difference in log likelihood to break the loop. Default is 1e-3.

Returns:

Dictionary of maximum likelihood parameters

get_upper_limit_sigma_values() → ndarray

Get the sigma values for upper limits only.

Returns:

Array of sigma values for upper limit data points.

Return type:

np.ndarray

log_likelihood(parameters=None) → float

Override parent method to include upper limits.

Returns:

The log-likelihood including upper limits.

Return type:

float

property model_output: ndarray

The model output for the given x values. :rtype: np.ndarray

Type:

return

property n: int

Length of the x/y-values :rtype: int

Type:

return

noise_log_likelihood() → float

Override parent method to include upper limits in noise likelihood.

Returns:

The noise log-likelihood, i.e. the log-likelihood assuming the signal is just noise.

Return type:

float

summary() → dict

Provide a summary of the likelihood setup.

Returns:

Dictionary with summary information

property upper_limits: ndarray

upper_limits is the inverse of detections

Type:

Derived property