# Source code for propertyestimator.protocols.analysis

    """
    A collection of protocols for running analysing the results of molecular simulations.
    """

    import logging
    from os import path

    import numpy as np
    import typing

    from propertyestimator import unit
    from propertyestimator.utils import statistics, timeseries
    from propertyestimator.utils.exceptions import PropertyEstimatorException
    from propertyestimator.utils.quantities import EstimatedQuantity
    from propertyestimator.utils.statistics import StatisticsArray, bootstrap
    from propertyestimator.workflow.decorators import protocol_input, protocol_output, InequalityMergeBehaviour, UNDEFINED
    from propertyestimator.workflow.plugins import register_calculation_protocol
    from propertyestimator.workflow.protocols import BaseProtocol

    @register_calculation_protocol()
    class AveragePropertyProtocol(BaseProtocol):
        """An abstract base class for protocols which will calculate the
        average of a property and its uncertainty via bootstrapping.
        """

        bootstrap_iterations = protocol_input(
            docstring='The number of bootstrap iterations to perform.',
            type_hint=int,
            default_value=250,
            merge_behavior=InequalityMergeBehaviour.LargestValue
        )
        bootstrap_sample_size = protocol_input(
            docstring='The relative sample size to use for bootstrapping.',
            type_hint=float,
            default_value=1.0,
            merge_behavior=InequalityMergeBehaviour.LargestValue
        )

        equilibration_index = protocol_output(
            docstring='The index in the data set after which the data is stationary.',
            type_hint=int
        )
        statistical_inefficiency = protocol_output(
            docstring='The statistical inefficiency in the data set.',
            type_hint=float
        )

        value = protocol_output(
            docstring='The average value and its uncertainty.',
            type_hint=EstimatedQuantity
        )
        uncorrelated_values = protocol_output(
            docstring='The uncorrelated values which the average was calculated from.',
            type_hint=unit.Quantity
        )

        def _bootstrap_function(self, **sample_kwargs):
            """The function to perform on the data set being sampled by
            bootstrapping.

            Parameters
            ----------
            sample_kwargs: dict of str and np.ndarray
                A key words dictionary of the bootstrap sample data, where the
                sample data is a numpy array of shape=(num_frames, num_dimensions)
                with dtype=float.

            Returns
            -------
            float
                The result of evaluating the data.
            """

            assert len(sample_kwargs) == 1
            sample_data = next(iter(sample_kwargs.values()))

            return sample_data.mean()

        def execute(self, directory, available_resources):
            return self._get_output_dictionary()

    @register_calculation_protocol()
    class AverageTrajectoryProperty(AveragePropertyProtocol):
        """An abstract base class for protocols which will calculate the
        average of a property from a simulation trajectory.
        """

        input_coordinate_file = protocol_input(
            docstring='The file path to the starting coordinates of a trajectory.',
            type_hint=str,
            default_value=UNDEFINED
        )
        trajectory_path = protocol_input(
            docstring='The file path to the trajectory to average over.',
            type_hint=str,
            default_value=UNDEFINED
        )

        def execute(self, directory, available_resources):

            if self.trajectory_path is None:

                return PropertyEstimatorException(directory=directory,
                                                  message='The AverageTrajectoryProperty protocol '
                                                           'requires a previously calculated trajectory')

            return self._get_output_dictionary()

    @register_calculation_protocol()
    class ExtractAverageStatistic(AveragePropertyProtocol):
        """Extracts the average value from a statistics file which was generated
        during a simulation.
        """

        statistics_path = protocol_input(
            docstring='The file path to the statistics to average over.',
            type_hint=str,
            default_value=UNDEFINED
        )
        statistics_type = protocol_input(
            docstring='The type of statistic to average over.',
            type_hint=statistics.ObservableType,
            default_value=UNDEFINED
        )

        divisor = protocol_input(
            docstring='A value to divide the statistic by. This is useful if a statistic (such '
                      'as enthalpy) needs to be normalised by the number of molecules.',
            type_hint=typing.Union[int, float, unit.Quantity],
            default_value=1.0
        )

        def __init__(self, protocol_id):

            super().__init__(protocol_id)
            self._statistics = None

        def execute(self, directory, available_resources):

            logging.info('Extracting {}: {}'.format(self.statistics_type, self.id))

            if self.statistics_path is None:

                return PropertyEstimatorException(directory=directory,
                                                  message='The ExtractAverageStatistic protocol '
                                                           'requires a previously calculated statistics file')

            self._statistics = statistics.StatisticsArray.from_pandas_csv(self.statistics_path)

            if self.statistics_type not in self._statistics:

                return PropertyEstimatorException(directory=directory,
                                                  message=f'The {self.statistics_path} statistics file contains no '
                                                          f'data of type {self.statistics_type}.')

            values = self._statistics[self.statistics_type]

            statistics_unit = values[0].units
            unitless_values = values.to(statistics_unit).magnitude

            divisor = self.divisor

            if isinstance(self.divisor, unit.Quantity):
                statistics_unit /= self.divisor.units
                divisor = self.divisor.magnitude

            unitless_values = np.array(unitless_values) / divisor

            unitless_values, self.equilibration_index, self.statistical_inefficiency = \
                timeseries.decorrelate_time_series(unitless_values)

            final_value, final_uncertainty = bootstrap(self._bootstrap_function,
                                                       self.bootstrap_iterations,
                                                       self.bootstrap_sample_size,
                                                       values=unitless_values)

            self.uncorrelated_values = unitless_values * statistics_unit

            self.value = EstimatedQuantity(final_value * statistics_unit,
                                           final_uncertainty * statistics_unit, self.id)

            logging.info('Extracted {}: {}'.format(self.statistics_type, self.id))

            return self._get_output_dictionary()

    @register_calculation_protocol()
    class ExtractUncorrelatedData(BaseProtocol):
        """An abstract base class for protocols which will subsample
        a data set, yielding only equilibrated, uncorrelated data.
        """

        equilibration_index = protocol_input(
            docstring='The index in the data set after which the data is stationary.',
            type_hint=int,
            default_value=UNDEFINED,
            merge_behavior=InequalityMergeBehaviour.LargestValue
        )
        statistical_inefficiency = protocol_input(
            docstring='The statistical inefficiency in the data set.',
            type_hint=float,
            default_value=UNDEFINED,
            merge_behavior=InequalityMergeBehaviour.LargestValue
        )

        number_of_uncorrelated_samples = protocol_output(
            docstring='The number of uncorrelated samples.',
            type_hint=int
        )

        def execute(self, directory, available_resources):
            raise NotImplementedError

    @register_calculation_protocol()
    class ExtractUncorrelatedTrajectoryData(ExtractUncorrelatedData):
        """A protocol which will subsample frames from a trajectory, yielding only uncorrelated
        frames as determined from a provided statistical inefficiency and equilibration time.
        """

        input_coordinate_file = protocol_input(
            docstring='The file path to the starting coordinates of a trajectory.',
            type_hint=str,
            default_value=UNDEFINED
        )
        input_trajectory_path = protocol_input(
            docstring='The file path to the trajectory to subsample.',
            type_hint=str,
            default_value=UNDEFINED
        )

        output_trajectory_path = protocol_output(
            docstring='The file path to the subsampled trajectory.',
            type_hint=str
        )

        @staticmethod
        def _yield_frame(file, topology, stride):
            """A generator which yields frames of a DCD trajectory.

            Parameters
            ----------
            file: mdtraj.DCDTrajectoryFile
                The file object being used to read the trajectory.
            topology: mdtraj.Topology
                The object which describes the topology of the trajectory.
            stride
                Only read every stride-th frame.

            Returns
            -------
            mdtraj.Trajectory
                A trajectory containing only a single frame.
            """

            while True:

                frame = file.read_as_traj(topology, n_frames=1, stride=stride)

                if len(frame) == 0:
                    return

                yield frame

        def execute(self, directory, available_resources):

            import mdtraj
            from mdtraj.formats.dcd import DCDTrajectoryFile
            from mdtraj.utils import in_units_of

            logging.info('Subsampling trajectory: {}'.format(self.id))

            if self.input_trajectory_path is None:

                return PropertyEstimatorException(directory=directory,
                                                  message='The ExtractUncorrelatedTrajectoryData protocol '
                                                           'requires a previously calculated trajectory')

            # Set the output path.
            self.output_trajectory_path = path.join(directory, 'uncorrelated_trajectory.dcd')

            # Load in the trajectories topology.
            topology = mdtraj.load_frame(self.input_coordinate_file, 0).topology
            # Parse the internal mdtraj distance unit. While private access is undesirable,
            # this is never publicly defined and I believe this route to be preferable
            # over hard coding this unit.
            base_distance_unit = mdtraj.Trajectory._distance_unit

            # Determine the stride that needs to be taken to yield uncorrelated frames.
            stride = timeseries.get_uncorrelated_stride(self.statistical_inefficiency)
            frame_count = 0

            with DCDTrajectoryFile(self.input_trajectory_path, 'r') as input_file:

                # Skip the equilibration configurations.
                if self.equilibration_index > 0:
                    input_file.seek(self.equilibration_index)

                with DCDTrajectoryFile(self.output_trajectory_path, 'w') as output_file:

                    for frame in self._yield_frame(input_file, topology, stride):

                        output_file.write(
                            xyz=in_units_of(frame.xyz, base_distance_unit, output_file.distance_unit),
                            cell_lengths=in_units_of(frame.unitcell_lengths, base_distance_unit, output_file.distance_unit),
                            cell_angles=frame.unitcell_angles[0]
                        )

                        frame_count += 1

            self.number_of_uncorrelated_samples = frame_count

            logging.info('Trajectory subsampled: {}'.format(self.id))

            return self._get_output_dictionary()

    @register_calculation_protocol()
    class ExtractUncorrelatedStatisticsData(ExtractUncorrelatedData):
        """A protocol which will subsample entries from a statistics array, yielding only uncorrelated
        entries as determined from a provided statistical inefficiency and equilibration time.
        """

        input_statistics_path = protocol_input(
            docstring='The file path to the statistics to subsample.',
            type_hint=str,
            default_value=UNDEFINED
        )

        output_statistics_path = protocol_output(
            docstring='The file path to the subsampled statistics.',
            type_hint=str
        )

        def execute(self, directory, available_resources):

            logging.info('Subsampling statistics: {}'.format(self.id))

            if self.input_statistics_path is None:

                return PropertyEstimatorException(directory=directory,
                                                  message='The ExtractUncorrelatedStatisticsData protocol '
                                                           'requires a previously calculated statisitics file')

            statistics_array = StatisticsArray.from_pandas_csv(self.input_statistics_path)

            uncorrelated_indices = timeseries.get_uncorrelated_indices(len(statistics_array) - self.equilibration_index,
                                                                       self.statistical_inefficiency)

            uncorrelated_indices = [index + self.equilibration_index for index in uncorrelated_indices]
            uncorrelated_statistics = StatisticsArray.from_existing(statistics_array, uncorrelated_indices)

            self.output_statistics_path = path.join(directory, 'uncorrelated_statistics.csv')
            uncorrelated_statistics.to_pandas_csv(self.output_statistics_path)

            logging.info('Statistics subsampled: {}'.format(self.id))

            self.number_of_uncorrelated_samples = len(uncorrelated_statistics)

            return self._get_output_dictionary()