"""
State Space Representation, Kalman Filter, Smoother, and Simulation Smoother

Author: Chad Fulton
License: Simplified-BSD
"""
from __future__ import division, absolute_import, print_function

import numpy as np
from .kalman_smoother import KalmanSmoother
from . import tools

SIMULATION_STATE = 0x01
SIMULATION_DISTURBANCE = 0x04
SIMULATION_ALL = (
    SIMULATION_STATE | SIMULATION_DISTURBANCE
)


class SimulationSmoother(KalmanSmoother):
    r"""
    State space representation of a time series process, with Kalman filter
    and smoother, and with simulation smoother.

    Parameters
    ----------
    k_endog : array_like or integer
        The observed time-series process :math:`y` if array like or the
        number of variables in the process if an integer.
    k_states : int
        The dimension of the unobserved state process.
    k_posdef : int, optional
        The dimension of a guaranteed positive definite covariance matrix
        describing the shocks in the measurement equation. Must be less than
        or equal to `k_states`. Default is `k_states`.
    simulation_smooth_results_class : class, optional
        Default results class to use to save output of simulation smoothing.
        Default is `SimulationSmoothResults`. If specified, class must extend
        from `SimulationSmoothResults`.
    simulation_smoother_classes : dict, optional
        Dictionary with BLAS prefixes as keys and classes as values.
    **kwargs
        Keyword arguments may be used to provide default values for state space
        matrices, for Kalman filtering options, for Kalman smoothing
        options, or for Simulation smoothing options.
        See `Representation`, `KalmanFilter`, and `KalmanSmoother` for more
        details.
    """

    simulation_outputs = [
        'simulate_state', 'simulate_disturbance', 'simulate_all'
    ]

    def __init__(self, k_endog, k_states, k_posdef=None,
                 simulation_smooth_results_class=None,
                 simulation_smoother_classes=None, **kwargs):
        super(SimulationSmoother, self).__init__(
            k_endog, k_states, k_posdef, **kwargs
        )

        if simulation_smooth_results_class is None:
            simulation_smooth_results_class = SimulationSmoothResults
        self.simulation_smooth_results_class = simulation_smooth_results_class

        self.prefix_simulation_smoother_map = (
            simulation_smoother_classes
            if simulation_smoother_classes is not None
            else tools.prefix_simulation_smoother_map.copy())

        # Holder for an model-level simulation smoother objects, to use in
        # simulating new time series.
        self._simulators = {}

    def get_simulation_output(self, simulation_output=None,
                              simulate_state=None, simulate_disturbance=None,
                              simulate_all=None, **kwargs):
        r"""
        Get simulation output bitmask

        Helper method to get final simulation output bitmask from a set of
        optional arguments including the bitmask itself and possibly boolean
        flags.

        Parameters
        ----------
        simulation_output : integer, optional
            Simulation output bitmask. If this is specified, it is simply
            returned and the other arguments are ignored.
        simulate_state : boolean, optional
            Whether or not to include the state in the simulation output.
        simulate_disturbance : boolean, optional
            Whether or not to include the state and observation disturbances
            in the simulation output.
        simulate_all : boolean, optional
            Whether or not to include all simulation output.
        \*\*kwargs
            Additional keyword arguments. Present so that calls to this method
            can use \*\*kwargs without clearing out additional arguments.
        """
        # If we don't explicitly have simulation_output, try to get it from
        # kwargs
        if simulation_output is None:
            simulation_output = 0

            if simulate_state:
                simulation_output |= SIMULATION_STATE
            if simulate_disturbance:
                simulation_output |= SIMULATION_DISTURBANCE
            if simulate_all:
                simulation_output |= SIMULATION_ALL

            # Handle case of no information in kwargs
            if simulation_output == 0:

                # If some arguments were passed, but we still don't have any
                # simulation output, raise an exception
                argument_set = not all([
                    simulate_state is None, simulate_disturbance is None,
                    simulate_all is None
                ])
                if argument_set:
                    raise ValueError("Invalid simulation output options:"
                                     " given options would result in no"
                                     " output.")

                # Otherwise set simulation output to be the same as smoother
                # output
                simulation_output = self.smoother_output

        return simulation_output

    def _simulate(self, nsimulations, measurement_shocks, state_shocks,
                  initial_state):

        if self._compatibility_mode:
            return super(SimulationSmoother, self)._simulate(
                nsimulations, measurement_shocks, state_shocks, initial_state)

        prefix = self.prefix

        # Create the simulator if necessary
        if (prefix not in self._simulators or
                not nsimulations == self._simulators[prefix].nobs):

            # Make sure we have the required Statespace representation
            prefix, dtype, create_statespace = (
                self._initialize_representation())

            # Initialize the state
            self._initialize_state(prefix=self.prefix)

            simulation_output = 0
            # Kalman smoother parameters
            smoother_output = -1
            # Kalman filter parameters
            filter_method = self.filter_method
            inversion_method = self.inversion_method
            stability_method = self.stability_method
            conserve_memory = self.conserve_memory
            filter_timing = self.filter_timing
            loglikelihood_burn = self.loglikelihood_burn
            tolerance = self.tolerance

            # Create a new simulation smoother object
            cls = self.prefix_simulation_smoother_map[prefix]
            self._simulators[prefix] = cls(
                self._statespaces[prefix],
                filter_method, inversion_method, stability_method,
                conserve_memory, filter_timing, tolerance, loglikelihood_burn,
                smoother_output, simulation_output, nsimulations
            )
        simulator = self._simulators[prefix]

        # Set the disturbance variates
        disturbance_variates = np.atleast_1d(np.array(
            np.r_[measurement_shocks.ravel(), state_shocks.ravel()],
            dtype=self.dtype
        ).squeeze())
        simulator.set_disturbance_variates(disturbance_variates,
                                           pretransformed=True)

        # Set the intial state vector
        initial_state = np.atleast_1d(np.array(
            initial_state, dtype=self.dtype
        ).squeeze())
        simulator.set_initial_state(initial_state)

        # Perform simulation smoothing
        # Note: simulation_output=-1 corresponds to whatever was setup when
        # the simulation smoother was constructed
        simulator.simulate(-1)

        simulated_obs = np.array(simulator.generated_obs, copy=True)
        simulated_state = np.array(simulator.generated_state, copy=True)

        return (
            simulated_obs[:, :nsimulations].T,
            simulated_state[:, :nsimulations].T
        )

    def simulation_smoother(self, simulation_output=None,
                            results_class=None, prefix=None, **kwargs):
        r"""
        Retrieve a simulation smoother for the statespace model.

        Parameters
        ----------
        simulation_output : int, optional
            Determines which simulation smoother output is calculated.
            Default is all (including state and disturbances).
        simulation_smooth_results_class : class, optional
            Default results class to use to save output of simulation
            smoothing. Default is `SimulationSmoothResults`. If specified,
            class must extend from `SimulationSmoothResults`.
        prefix : string
            The prefix of the datatype. Usually only used internally.
        **kwargs
            Additional keyword arguments, used to set the simulation output.
            See `set_simulation_output` for more details.

        Returns
        -------
        SimulationSmoothResults
        """

        if self._compatibility_mode:
            raise NotImplementedError('Simulation smoothing is not available.'
                                      ' Consider updating dependencies for'
                                      ' more options.')

        # Set the class to be the default results class, if None provided
        if results_class is None:
            results_class = self.simulation_smooth_results_class

        # Instantiate a new results object
        if not issubclass(results_class, SimulationSmoothResults):
            raise ValueError('Invalid results class provided.')

        # Make sure we have the required Statespace representation
        if prefix is None:
            prefix = self.prefix
        prefix, dtype, create_statespace = (
            self._initialize_representation(prefix)
        )

        # Simulation smoother parameters
        simulation_output = self.get_simulation_output(simulation_output,
                                                       **kwargs)

        # Kalman smoother parameters
        smoother_output = kwargs.get('smoother_output', simulation_output)

        # Kalman filter parameters
        filter_method = kwargs.get('filter_method', self.filter_method)
        inversion_method = kwargs.get('inversion_method',
                                      self.inversion_method)
        stability_method = kwargs.get('stability_method',
                                      self.stability_method)
        conserve_memory = kwargs.get('conserve_memory',
                                     self.conserve_memory)
        filter_timing = kwargs.get('filter_timing',
                                   self.filter_timing)
        loglikelihood_burn = kwargs.get('loglikelihood_burn',
                                        self.loglikelihood_burn)
        tolerance = kwargs.get('tolerance', self.tolerance)

        # Create a new simulation smoother object
        cls = self.prefix_simulation_smoother_map[prefix]
        simulation_smoother = cls(
            self._statespaces[prefix],
            filter_method, inversion_method, stability_method, conserve_memory,
            filter_timing, tolerance, loglikelihood_burn, smoother_output,
            simulation_output
        )

        # Create results object
        results = results_class(self, simulation_smoother)

        return results


class SimulationSmoothResults(object):
    r"""
    Results from applying the Kalman smoother and/or filter to a state space
    model.

    Parameters
    ----------
    model : Representation
        A Statespace representation
    simulation_smoother : {{prefix}}SimulationSmoother object
        The Cython simulation smoother object with which to simulation smooth.

    Attributes
    ----------
    model : Representation
        A Statespace representation
    dtype : dtype
        Datatype of representation matrices
    prefix : str
        BLAS prefix of representation matrices
    simulation_output : integer
        Bitmask controlling simulation output.
    simulate_state : boolean
        Flag for if the state is included in simulation output.
    simulate_disturbance : boolean
        Flag for if the state and observation disturbances are included in
        simulation output.
    simulate_all : boolean
        Flag for if simulation output should include everything.
    generated_measurement_disturbance : array
        Measurement disturbance variates used to genereate the observation
        vector.
    generated_state_disturbance : array
        State disturbance variates used to genereate the state and
        observation vectors.
    generated_obs : array
        Generated observation vector produced as a byproduct of simulation
        smoothing.
    generated_state : array
        Generated state vector produced as a byproduct of simulation smoothing.
    simulated_state : array
        Simulated state.
    simulated_measurement_disturbance : array
        Simulated measurement disturbance.
    simulated_state_disturbance : array
        Simulated state disturbance.
    """

    def __init__(self, model, simulation_smoother):
        self.model = model
        self.prefix = model.prefix
        self.dtype = model.dtype
        self._simulation_smoother = simulation_smoother

        # Output
        self._generated_measurement_disturbance = None
        self._generated_state_disturbance = None
        self._generated_obs = None
        self._generated_state = None
        self._simulated_state = None
        self._simulated_measurement_disturbance = None
        self._simulated_state_disturbance = None

    @property
    def simulation_output(self):
        return self._simulation_smoother.simulation_output

    @simulation_output.setter
    def simulation_output(self, value):
        self._simulation_smoother.simulation_output = value

    @property
    def simulate_state(self):
        return bool(self.simulation_output & SIMULATION_STATE)

    @simulate_state.setter
    def simulate_state(self, value):
        if bool(value):
            self.simulation_output = self.simulation_output | SIMULATION_STATE
        else:
            self.simulation_output = self.simulation_output & ~SIMULATION_STATE

    @property
    def simulate_disturbance(self):
        return bool(self.simulation_output & SIMULATION_DISTURBANCE)

    @simulate_disturbance.setter
    def simulate_disturbance(self, value):
        if bool(value):
            self.simulation_output = (
                self.simulation_output | SIMULATION_DISTURBANCE)
        else:
            self.simulation_output = (
                self.simulation_output & ~SIMULATION_DISTURBANCE)

    @property
    def simulate_all(self):
        return bool(self.simulation_output & SIMULATION_ALL)

    @simulate_all.setter
    def simulate_all(self, value):
        if bool(value):
            self.simulation_output = self.simulation_output | SIMULATION_ALL
        else:
            self.simulation_output = self.simulation_output & ~SIMULATION_ALL

    @property
    def generated_measurement_disturbance(self):
        """
        Randomly drawn measurement disturbance variates, used to construct
        `generated_obs`.

        Notes
        -----

        .. math::
            \varepsilon_t^+ ~ N(0, H_t)

        If `disturbance_variates` were provided to the `simulate()` method,
        then this returns those variates (which were N(0,1)) transformed to the
        distribution above.

        """
        if self._generated_measurement_disturbance is None:
            end = self.model.nobs * self.model.k_endog
            self._generated_measurement_disturbance = np.array(
                self._simulation_smoother.disturbance_variates[:end],
                copy=True).reshape(self.model.nobs, self.model.k_endog)
        return self._generated_measurement_disturbance

    @property
    def generated_state_disturbance(self):
        """
        Randomly drawn state disturbance variates, used to construct
        `generated_state` and `generated_obs`.

        Notes
        -----

        .. math::
            \eta_t^+ ~ N(0, Q_t)

        If `disturbance_variates` were provided to the `simulate()` method,
        then this returns those variates (which were N(0,1)) transformed to the
        distribution above.

        """
        if self._generated_state_disturbance is None:
            start = self.model.nobs * self.model.k_endog
            self._generated_state_disturbance = np.array(
                self._simulation_smoother.disturbance_variates[start:],
                copy=True).reshape(self.model.nobs, self.model.k_posdef)
        return self._generated_state_disturbance

    @property
    def generated_obs(self):
        """
        Generated vector of observations by iterating on the observation and
        transition equations, given a random initial state draw and random
        disturbance draws.

        Notes
        -----

        .. math::
            y_t^+ = d_t + Z_t \alpha_t^+ + \varepsilon_t^+

        """
        if self._generated_obs is None:
            self._generated_obs = np.array(
                self._simulation_smoother.generated_obs, copy=True
            )
        return self._generated_obs

    @property
    def generated_state(self):
        """
        Generated vector of states by iterating on the transition equation,
        given a random initial state draw and random disturbance draws.

        Notes
        -----

        .. math::
            \alpha_{t+1}^+ = c_t + T_t \alpha_t^+ + \eta_t^+

        """
        if self._generated_state is None:
            self._generated_state = np.array(
                self._simulation_smoother.generated_state, copy=True
            )
        return self._generated_state

    @property
    def simulated_state(self):
        """
        Random draw of the state vector from its conditional distribution.

        Notes
        -----

        .. math::
            \alpha ~ p(\alpha \mid Y_n)

        """
        if self._simulated_state is None:
            self._simulated_state = np.array(
                self._simulation_smoother.simulated_state, copy=True
            )
        return self._simulated_state

    @property
    def simulated_measurement_disturbance(self):
        """
        Random draw of the measurement disturbance vector from its conditional
        distribution.

        Notes
        -----

        .. math::
            \varepsilon ~ N(\hat \varepsilon, Var(\hat \varepsilon \mid Y_n))

        """
        if self._simulated_measurement_disturbance is None:
            self._simulated_measurement_disturbance = np.array(
                self._simulation_smoother.simulated_measurement_disturbance,
                copy=True
            )
        return self._simulated_measurement_disturbance

    @property
    def simulated_state_disturbance(self):
        """
        Random draw of the state disturbance vector from its conditional
        distribution.

        Notes
        -----

        .. math::
            \eta ~ N(\hat \eta, Var(\hat \eta \mid Y_n))

        """
        if self._simulated_state_disturbance is None:
            self._simulated_state_disturbance = np.array(
                self._simulation_smoother.simulated_state_disturbance,
                copy=True
            )
        return self._simulated_state_disturbance

    def simulate(self, simulation_output=-1, disturbance_variates=None,
                 initial_state_variates=None):
        r"""
        Perform simulation smoothing

        Does not return anything, but populates the object's `simulated_*`
        attributes, as specified by simulation output.

        Parameters
        ----------
        simulation_output : integer, optional
            Bitmask controlling simulation output. Default is to use the
            simulation output defined in object initialization.
        disturbance_variates : array_likes, optional
            Random values to use as disturbance variates, distributed standard
            Normal. Usually only specified if results are to be replicated
            (e.g. to enforce a seed) or for testing. If not specified, random
            variates are drawn.
        initial_state_variates : array_likes, optional
            Random values to use as initial state variates. Usually only
            specified if results are to be replicated (e.g. to enforce a seed)
            or for testing. If not specified, random variates are drawn.
        """
        # Clear any previous output
        self._generated_measurement_disturbance = None
        self._generated_state_disturbance = None
        self._generated_state = None
        self._generated_obs = None
        self._generated_state = None
        self._simulated_state = None
        self._simulated_measurement_disturbance = None
        self._simulated_state_disturbance = None

        # Re-initialize the _statespace representation
        self.model._initialize_representation(prefix=self.prefix)

        # Initialize the state
        self.model._initialize_state(prefix=self.prefix)

        # Draw the (independent) random variates for disturbances in the
        # simulation
        if disturbance_variates is not None:
            self._simulation_smoother.set_disturbance_variates(
                np.array(disturbance_variates, dtype=self.dtype)
            )
        else:
            self._simulation_smoother.draw_disturbance_variates()

        # Draw the (independent) random variates for the initial states in the
        # simulation
        if initial_state_variates is not None:
            self._simulation_smoother.set_initial_state_variates(
                np.array(initial_state_variates, dtype=self.dtype)
            )
        else:
            self._simulation_smoother.draw_initial_state_variates()

        # Perform simulation smoothing
        # Note: simulation_output=-1 corresponds to whatever was setup when
        # the simulation smoother was constructed
        self._simulation_smoother.simulate(simulation_output)
