from typing import Optional

from autofit.mapper.prior_model.abstract import AbstractPriorModel
from autofit.non_linear.samples import Sample
from autofit.non_linear.samples.nest import NestSamples



class UltraNestSamples(NestSamples):

    @classmethod
    def from_results_internal(
            cls,
            results_internal,
            model: AbstractPriorModel,
            number_live_points: int,
            unconverged_sample_size: int = 100,
            time: Optional[float] = None,
    ):
        """
        The `Samples` classes in **PyAutoFit** provide an interface between the resultsof a `NonLinearSearch` (e.g.
        as files on your hard-disk) and Python.

        To create a `Samples` object after an `UltraNest` model-fit the results must be converted from the
        native format used by `UltraNest` to lists of values, the format used by the **PyAutoFit** `Samples` objects.
        This classmethod performs this conversion before creating a DyenstySamples` object.

        Parameters
        ----------
        results_internal
            The `UltraNest` results in their native internal format from which the samples are computed.
        model
            Maps input vectors of unit parameter values to physical values and model instances via priors.
        number_live_points
            The number of live points used by the `UltraNest` search.
        unconverged_sample_size
            If the samples are for a search that is yet to convergence, a reduced set of samples are used to provide
            a rough estimate of the parameters. The number of samples is set by this parameter.
        time
            The time taken to perform the model-fit, which is passed around `Samples` objects for outputting
            information on the overall fit.
        """
        
        parameters = results_internal["weighted_samples"]["points"]
        log_likelihood_list = results_internal["weighted_samples"]["logl"]
        log_prior_list = [
            sum(model.log_prior_list_from_vector(vector=vector)) for vector in parameters
        ]
        weight_list = results_internal["weighted_samples"]["weights"]

        sample_list = Sample.from_lists(
            model=model,
            parameter_lists=parameters,
            log_likelihood_list=log_likelihood_list,
            log_prior_list=log_prior_list,
            weight_list=weight_list
        )

        return UltraNestSamples(
            model=model,
            sample_list=sample_list,
            number_live_points=number_live_points,
            unconverged_sample_size=unconverged_sample_size,
            time=time,
            results_internal=results_internal,
        )

    @property
    def number_live_points(self):
        return self._number_live_points

    @property
    def total_samples(self):
        return self.results_internal["ncall"]

    @property
    def log_evidence(self):
        return self.results_internal["logz"]
