redback.likelihoods.PoissonLikelihood

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

Bases: _RedbackLikelihood

__init__(time: ndarray, counts: ndarray, function: callable, integrated_rate_function: bool = True, dt: Optional[Union[float, ndarray]] = None, kwargs: Optional[dict] = None) → None

Parameters:

• time (np.ndarray) – The time values.

• counts (np.ndarray) – The number of counts for the time value.

• function (callable) – The python function to fit to the data.

• integrated_rate_function (bool) – Whether the function returns an integrated rate over the dt in the bins. This should be true if you multiply the rate with dt in the function and false if the function returns a rate per unit time. (Default value = True)

• dt (Union[float, None, np.ndarray]) – Array of each bin size or single value if all bins are of the same size. If None, assume that dt is constant and calculate it from the first two elements of time.

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

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

Call self as a function.

Methods

__init__(time, counts, function[, ...])	param time: The time values.
log_likelihood()	return: The log-likelihood.
noise_log_likelihood()	return: The noise log-likelihood, i.e. the log-likelihood assuming the signal is just noise.

Attributes

background_rate
counts
dt
kwargs
n	Length of the x/y-values :rtype: int
time

log_likelihood() → float

Returns:

The log-likelihood.

Return type:

float

property n: int

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

Type:

return

noise_log_likelihood() → float

Returns:

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

Return type:

float