threeML.plugins.UnbinnedPoissonLike module
- class threeML.plugins.UnbinnedPoissonLike.EventObservation(events: ndarray, exposure: float, start: float | ndarray, stop: float | ndarray, for_timeseries: bool = False)[source]
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Bases:
object- property events: ndarray
- property exposure: float
- property for_timeseries: bool
- property is_multi_interval: bool
- property n_events: int
- property start: float | ndarray
- property stop: float | ndarray
- class threeML.plugins.UnbinnedPoissonLike.UnbinnedPoissonLike(name: str, observation: EventObservation, source_name: str | None = None)[source]
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Bases:
PluginPrototype- get_log_like() float[source]
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Return the value of the log-likelihood with the current values for the parameters
- get_number_of_data_points()[source]
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This returns the number of data points that are used to evaluate the likelihood. For binned measurements, this is the number of active bins used in the fit. For unbinned measurements, this would be the number of photons/particles that are evaluated on the likelihood
- inner_fit() float[source]
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This is used for the profile likelihood. Keeping fixed all parameters in the LikelihoodModel, this method minimize the logLike over the remaining nuisance parameters, i.e., the parameters belonging only to the model for this particular detector. If there are no nuisance parameters, simply return the logLike value.