# Source code for propertyestimator.protocols.gradients

    """
    A collection of protocols for reweighting cached simulation data.
    """
    import copy
    import logging
    import re
    from os import path

    import numpy as np
    from simtk import openmm
    from simtk.openmm import app

    from propertyestimator import unit
    from propertyestimator.forcefield import ForceFieldSource, SmirnoffForceFieldSource
    from propertyestimator.properties.properties import ParameterGradientKey, ParameterGradient
    from propertyestimator.substances import Substance
    from propertyestimator.thermodynamics import ThermodynamicState
    from propertyestimator.utils.exceptions import PropertyEstimatorException
    from propertyestimator.utils.openmm import pint_quantity_to_openmm, setup_platform_with_resources, \
        openmm_quantity_to_pint, disable_pbc
    from propertyestimator.utils.quantities import EstimatedQuantity
    from propertyestimator.utils.statistics import StatisticsArray, ObservableType
    from propertyestimator.workflow.decorators import protocol_input, protocol_output
    from propertyestimator.workflow.plugins import register_calculation_protocol
    from propertyestimator.workflow.protocols import BaseProtocol

    @register_calculation_protocol()
    class GradientReducedPotentials(BaseProtocol):
        """A protocol to estimates the gradient of an observable with
        respect to a number of specified force field parameters.
        """

        @protocol_input(list)
        def reference_force_field_paths(self):
            """A list of path to the force field file which were originally used
            to estimate the observable of interest."""
            pass

        @protocol_input(str)
        def reference_statistics_path(self):
            """An optional path to the statistics array which was generated
            alongside the observable of interest, which will be used to
            correct the potential energies at the reverse and forward states.

            This is only really needed when the observable of interest is an
            energy.
            """
            pass

        @protocol_input(str)
        def force_field_path(self):
            """A path to the force field which contains the parameters
            to differentiate the observable with respect to."""
            pass

        @protocol_input(bool)
        def enable_pbc(self):
            """If true, periodic boundary conditions will be enabled when
            re-evaluating the reduced potentials."""
            pass

        @protocol_input(Substance)
        def substance(self):
            """The substance which describes the composition
            of the system."""
            pass

        @protocol_input(ThermodynamicState)
        def thermodynamic_state(self):
            """The thermodynamic state to estimate the gradients at."""
            pass

        @protocol_input(str)
        def coordinate_file_path(self):
            """A path to the initial coordinates of the simulation trajectory which
            was used to estimate the observable of interest."""
            pass

        @protocol_input(str)
        def trajectory_file_path(self):
            """A path to the simulation trajectory which was used
            to estimate the observable of interest."""
            pass

        @protocol_input(ParameterGradientKey)
        def parameter_key(self):
            """A list of the parameters to differentiate with respect to."""
            pass

        @protocol_input(float)
        def perturbation_scale(self):
            """The amount to perturb the parameter by, such that
            p_new = p_old * (1 +/- perturbation_scale)"""
            pass

        @protocol_input(bool)
        def use_subset_of_force_field(self):
            """If true, the reduced potential will be estimated using an OpenMM
            system which only contains the parameter of interest.
            """

        @protocol_input(list)
        def effective_sample_indices(self):
            """NOTE - this is currently a placeholder input ONLY, and
            currently is not used for anything.
            """

        @protocol_output(list)
        def reference_potential_paths(self):
            pass

        @protocol_output(str)
        def reverse_potentials_path(self):
            pass

        @protocol_output(str)
        def forward_potentials_path(self):
            pass

        @protocol_output(unit.Quantity)
        def reverse_parameter_value(self):
            pass

        @protocol_output(unit.Quantity)
        def forward_parameter_value(self):
            pass

        def __init__(self, protocol_id):
            """Constructs a new EstimateParameterGradients object."""
            super().__init__(protocol_id)

            self._reference_force_field_paths = None
            self._reference_statistics_path = ''

            self._force_field_path = None
            self._enable_pbc = True

            self._substance = None
            self._thermodynamic_state = None

            self._statistical_inefficiency = None

            self._coordinate_file_path = None
            self._trajectory_file_path = None

            self._parameter_key = None
            self._perturbation_scale = 1.0e-4

            self._use_subset_of_force_field = True

            self._reference_potential_paths = []
            self._reverse_potentials_path = None
            self._forward_potentials_path = None

            self._reverse_parameter_value = None
            self._forward_parameter_value = None

            # This is currently a placeholder variable ONLY
            # and should not be used for anything.
            self._effective_sample_indices = []

        def _build_reduced_system(self, original_force_field, topology, scale_amount=None):
            """Produces an OpenMM system containing only forces for the specified parameter,
             optionally perturbed by the amount specified by `scale_amount`.

            Parameters
            ----------
            original_force_field: openforcefield.typing.engines.smirnoff.ForceField
                The force field to create the system from (and optionally perturb).
            topology: openforcefield.topology.Topology
                The topology of the system to apply the force field to.
            scale_amount: float, optional
                The optional amount to perturb the parameter by.

            Returns
            -------
            simtk.openmm.System
                The created system.
            simtk.unit.Quantity
                The new value of the perturbed parameter.
            """
            # As this method deals mainly with the toolkit, we stick to
            # simtk units here.
            from openforcefield.typing.engines.smirnoff import ForceField

            parameter_tag = self._parameter_key.tag
            parameter_smirks = self._parameter_key.smirks
            parameter_attribute = self._parameter_key.attribute

            original_handler = original_force_field.get_parameter_handler(parameter_tag)
            original_parameter = original_handler.parameters[parameter_smirks]

            if self._use_subset_of_force_field:

                force_field = ForceField()
                handler = copy.deepcopy(original_force_field.get_parameter_handler(parameter_tag))
                force_field.register_parameter_handler(handler)

            else:

                force_field = copy.deepcopy(original_force_field)
                handler = force_field.get_parameter_handler(parameter_tag)

            parameter_index = None
            value_list = None

            if hasattr(original_parameter, parameter_attribute):
                parameter_value = getattr(original_parameter, parameter_attribute)
            else:
                attribute_split = re.split(r'(\d+)', parameter_attribute)

                assert len(parameter_attribute) == 2
                assert hasattr(original_parameter, attribute_split[0])

                parameter_attribute = attribute_split[0]
                parameter_index = int(attribute_split[1]) - 1

                value_list = getattr(original_parameter, parameter_attribute)
                parameter_value = value_list[parameter_index]

            if scale_amount is not None:

                existing_parameter = handler.parameters[parameter_smirks]

                if np.isclose(parameter_value.value_in_unit(parameter_value.unit), 0.0):
                    # Careful thought needs to be given to this. Consider cases such as
                    # epsilon or sigma where negative values are not allowed.
                    parameter_value = (scale_amount if scale_amount > 0.0 else 0.0) * parameter_value.unit
                else:
                    parameter_value *= (1.0 + scale_amount)

                if value_list is None:
                    setattr(existing_parameter, parameter_attribute, parameter_value)
                else:
                    value_list[parameter_index] = parameter_value
                    setattr(existing_parameter, parameter_attribute, value_list)

            system = force_field.create_openmm_system(topology)

            if not self._enable_pbc:
                disable_pbc(system)

            return system, parameter_value

        def _evaluate_reduced_potential(self, system, trajectory, file_path,
                                        compute_resources, subset_energy_corrections=None):
            """Return the potential energy.
            Parameters
            ----------
            system: simtk.openmm.System
                The system which encodes the interaction forces for the
                specified parameter.
            trajectory: mdtraj.Trajectory
                A trajectory of configurations to evaluate.
            file_path: str
                The path to save the reduced potentials to.
            compute_resources: ComputeResources
                The compute resources available to execute on.
            subset_energy_corrections: unit.Quantity, optional
                A unit.Quantity wrapped numpy.ndarray which contains a set
                of energies to add to the re-evaluated potential energies.
                This is mainly used to correct the potential energies evaluated
                using a subset of the force field back to energies as if evaluated
                using the full thing.

            Returns
            ---------
            propertyestimator.unit.Quantity
                A unit bearing `np.ndarray` which contains the reduced potential.
            PropertyEstimatorException, optional
                Any exceptions that were raised.
            """
            from simtk import unit as simtk_unit

            integrator = openmm.VerletIntegrator(0.1 * simtk_unit.femtoseconds)

            platform = setup_platform_with_resources(compute_resources, True)
            openmm_context = openmm.Context(system, integrator, platform)

            potentials = np.zeros(trajectory.n_frames, dtype=np.float64)
            reduced_potentials = np.zeros(trajectory.n_frames, dtype=np.float64)

            temperature = pint_quantity_to_openmm(self._thermodynamic_state.temperature)
            beta = 1.0 / (simtk_unit.BOLTZMANN_CONSTANT_kB * temperature)

            pressure = pint_quantity_to_openmm(self._thermodynamic_state.pressure)

            for frame_index in range(trajectory.n_frames):

                positions = trajectory.xyz[frame_index]
                box_vectors = trajectory.openmm_boxes(frame_index)

                if self._enable_pbc:
                    openmm_context.setPeriodicBoxVectors(*box_vectors)

                openmm_context.setPositions(positions)

                state = openmm_context.getState(getEnergy=True)

                unreduced_potential = state.getPotentialEnergy() / simtk_unit.AVOGADRO_CONSTANT_NA

                if pressure is not None and self.enable_pbc:
                    unreduced_potential += pressure * state.getPeriodicBoxVolume()

                potentials[frame_index] = state.getPotentialEnergy().value_in_unit(simtk_unit.kilojoule_per_mole)
                reduced_potentials[frame_index] = unreduced_potential * beta

            potentials *= unit.kilojoule / unit.mole
            reduced_potentials *= unit.dimensionless

            if subset_energy_corrections is not None:
                potentials += subset_energy_corrections

            statistics_array = StatisticsArray()
            statistics_array[ObservableType.ReducedPotential] = reduced_potentials
            statistics_array[ObservableType.PotentialEnergy] = potentials
            statistics_array.to_pandas_csv(file_path)

        def execute(self, directory, available_resources):

            import mdtraj

            from openforcefield.topology import Molecule, Topology

            logging.info(f'Calculating the reduced gradient potentials for {self._parameter_key}: {self._id}')

            if len(self._reference_force_field_paths) != 1 and self._use_subset_of_force_field:

                return PropertyEstimatorException(directory, 'A single reference force field must be '
                                                             'provided when calculating the reduced '
                                                             'potentials using a subset of the full force')

            if len(self._reference_statistics_path) <= 0 and self._use_subset_of_force_field:

                return PropertyEstimatorException(directory, 'The path to the statistics evaluated using '
                                                             'the full force field must be provided.')

            with open(self._force_field_path) as file:
                target_force_field_source = ForceFieldSource.parse_json(file.read())

            if not isinstance(target_force_field_source, SmirnoffForceFieldSource):

                return PropertyEstimatorException(directory, 'Only SMIRNOFF force fields are supported by '
                                                             'this protocol.')

            target_force_field = target_force_field_source.to_force_field()

            trajectory = mdtraj.load_dcd(self._trajectory_file_path,
                                         self._coordinate_file_path)

            unique_molecules = []

            for component in self._substance.components:

                molecule = Molecule.from_smiles(smiles=component.smiles)
                unique_molecules.append(molecule)

            pdb_file = app.PDBFile(self._coordinate_file_path)
            topology = Topology.from_openmm(pdb_file.topology, unique_molecules=unique_molecules)

            # If we are using only a subset of the system object, load in the reference
            # statistics containing the full system energies to correct the output
            # forward and reverse potential energies.
            reference_statistics = None
            subset_energy_corrections = None

            if self._use_subset_of_force_field:
                reference_statistics = StatisticsArray.from_pandas_csv(self._reference_statistics_path)

            # Compute the reduced reference energy if any reference force field files
            # have been provided.
            for index, reference_force_field_path in enumerate(self._reference_force_field_paths):

                with open(reference_force_field_path) as file:
                    reference_force_field_source = ForceFieldSource.parse_json(file.read())

                if not isinstance(reference_force_field_source, SmirnoffForceFieldSource):
                    return PropertyEstimatorException(directory, 'Only SMIRNOFF force fields are supported by '
                                                                 'this protocol.')

                reference_force_field = reference_force_field_source.to_force_field()
                reference_system, _ = self._build_reduced_system(reference_force_field, topology)

                reference_potentials_path = path.join(directory, f'reference_{index}.csv')

                self._evaluate_reduced_potential(reference_system, trajectory,
                                                 reference_potentials_path,
                                                 available_resources)

                self._reference_potential_paths.append(reference_potentials_path)

                if reference_statistics is not None:

                    subset_energies = StatisticsArray.from_pandas_csv(reference_potentials_path)
                    subset_energy_corrections = (reference_statistics[ObservableType.PotentialEnergy] -
                                                 subset_energies[ObservableType.PotentialEnergy])

                    subset_energies[ObservableType.PotentialEnergy] = reference_statistics[ObservableType.PotentialEnergy]
                    subset_energies.to_pandas_csv(reference_potentials_path)

            # Build the slightly perturbed system.
            reverse_system, self._reverse_parameter_value = self._build_reduced_system(target_force_field,
                                                                                       topology,
                                                                                       -self._perturbation_scale)

            forward_system, self._forward_parameter_value = self._build_reduced_system(target_force_field,
                                                                                       topology,
                                                                                       self._perturbation_scale)

            self._reverse_parameter_value = openmm_quantity_to_pint(self._reverse_parameter_value)
            self._forward_parameter_value = openmm_quantity_to_pint(self._forward_parameter_value)

            # Calculate the reduced potentials.
            self._reverse_potentials_path = path.join(directory, 'reverse.csv')
            self._forward_potentials_path = path.join(directory, 'forward.csv')

            self._evaluate_reduced_potential(reverse_system, trajectory, self._reverse_potentials_path,
                                             available_resources, subset_energy_corrections)
            self._evaluate_reduced_potential(forward_system, trajectory, self._forward_potentials_path,
                                             available_resources, subset_energy_corrections)

            logging.info(f'Finished calculating the reduced gradient potentials.')

            return self._get_output_dictionary()

    @register_calculation_protocol()
    class CentralDifferenceGradient(BaseProtocol):
        """A protocol which employs the central diference method
        to estimate the gradient of an observable A, such that

        grad = (A(x-h) - A(x+h)) / (2h)

        Notes
        -----
        The `values` input must either be a list of unit.Quantity, a ProtocolPath to a list
        of unit.Quantity, or a list of ProtocolPath which each point to a unit.Quantity.
        """

        @protocol_input(ParameterGradientKey)
        def parameter_key(self):
            """The key that describes which parameters this
            gradient was estimated for."""
            pass

        @protocol_input(EstimatedQuantity)
        def reverse_observable_value(self):
            """The value of A(x-h)."""
            pass

        @protocol_input(EstimatedQuantity)
        def forward_observable_value(self):
            """The value of A(x+h)."""
            pass

        @protocol_input(unit.Quantity)
        def reverse_parameter_value(self):
            """The value of x-h."""
            pass

        @protocol_input(unit.Quantity)
        def forward_parameter_value(self):
            """The value of x+h."""
            pass

        @protocol_output(ParameterGradient)
        def gradient(self):
            """The estimated gradient."""
            pass

        def __init__(self, protocol_id):
            """Constructs a new CentralDifferenceGradient object."""
            super().__init__(protocol_id)

            self._parameter_key = None

            self._reverse_observable_value = None
            self._forward_observable_value = None

            self._reverse_parameter_value = None
            self._forward_parameter_value = None

            self._gradient = None

        def execute(self, directory, available_resources):

            if self._forward_parameter_value < self._reverse_parameter_value:

                return PropertyEstimatorException(f'The forward parameter value ({self._forward_parameter_value}) must '
                                                  f'be larger than the reverse value ({self._reverse_parameter_value}).')

            gradient = ((self._forward_observable_value.value - self._reverse_observable_value.value) /
                        (self._forward_parameter_value - self._reverse_parameter_value))

            self._gradient = ParameterGradient(self._parameter_key, gradient)

            return self._get_output_dictionary()