# Path: pyparticleest/filter.py
# class ParticleApproximation(object):
#     """
#     Contains collection of particles approximating a pdf
# 
#     Use either seed and num or particles (and optionally weights,
#     if not uniform)
# 
#     Args:
#      - particles (array-like): collection of particles
#      - weights (array-like): weight for each particle
#      - seed (array-like): value to initialize all particles with
#      - num (int): number of particles
# 
#     """
#     def __init__(self, particles=None, logw=None, seed=None, num=None):
#         if (particles is not None):
#             self.part = numpy.copy(numpy.asarray(particles))
#             num = len(particles)
#         else:
#             self.part = numpy.empty(num, type(seed))
#             for k in range(num):
#                 self.part[k] = copy.deepcopy(seed)
# 
#         if (logw is not None):
#             self.w = numpy.copy(logw)
#         else:
#             self.w = -math.log(num) * numpy.ones(num)
# 
#         self.num = num
# 
#         # Used to keep track of the offest on all weights, this is continually updated
#         # when the weights are rescaled to avoid going to -Inf
#         self.w_offset = 0.0
# 
#     def __len__(self):
#         return len(self.part)
# 
#     def calc_Neff(self):
#         """
#         Calculate number of effective particles, common metric used to determine
#         when to resample
# 
#         Returns:
#          (float) number of effective particles
#         """
#         tmp = numpy.exp(self.w - numpy.max(self.w))
#         tmp /= numpy.sum(tmp)
#         return 1.0 / numpy.sum(numpy.square(tmp))
# 
#     def resample(self, model, N=None):
#         """
#         Resample approximation so all particles have the same weight
# 
#         Args:
#          - model: object containing problem specific methods
#          - N: new number of particles is N. If 'None' out the number of
#            particles remains the same
#         """
# 
#         if (N is None):
#             N = self.num
# 
#         # Alwyays keep the largest weight at 0 in logaritmic representation
#         tmp = self.w - numpy.max(self.w)
#         new_ind = sample(numpy.exp(tmp), N)
#         new_part = model.copy_ind(self.part, new_ind)
# 
#         self.w = numpy.log(numpy.ones(N, dtype=numpy.float) / N)
#         self.part = new_part
#         self.num = N
#         self.w_offset = 0.0
#         return new_ind
# 
#     def sample(self):
#         """
#         Draw one particle at random with probability corresponding to its weight
# 
#         Returns:
#          (array-like) sampled particle"""
#         return self.part[sample(numpy.exp(self.w), 1)[0]]
# 
#     def find_best_particles(self, n=1):
#         """
#         Return particles with largest weights
# 
#         Args:
#          - n (int): Number of particles to return
# 
#         Returns:
#          - (array-like) with len=n, representing the n most likely estimates """
#         indices = numpy.argsort(self.w)
#         return indices[range(n)]
# 
# class TrajectoryStep(object):
#     """
#     Store particle approximation, input, output and timestamp for
#     a single time index in a trajectory
# 
#     Args:
#      - pa (ParticleAppromixation): particle approximation
#      - u (array-like): input signals at time t
#        (u[t] contains the input for takin x[t] to x[t+1])
#      - y (array-like): measurements at time t
#        (y[t] is the measurment of x[t])
#      - t (float): time stamp for time t
#      - ancestors (array-like): indices for each particles ancestor
#     """
#     def __init__(self, pa, ancestors=None):
#         self.pa = pa
#         self.ancestors = ancestors


import numpy
import copy
from builtins import range
from . import filter as pf
from .filter import ParticleApproximation, TrajectoryStep
    def __len__(self):
        return len(self.traj)

    def perform_ancestors(self, pt, M):
        """
        Create smoothed trajectories by taking the forward trajectories

        Args:
         - pt (ParticleTrajectory): forward trajetories
         - M (int): number of trajectories to createa
        """
        self.traj = self.perform_ancestors_int(pt, M)

        if hasattr(self.model, 'post_smoothing'):
            # Do e.g. constrained smoothing for RBPS models
            self.traj = self.model.post_smoothing(self)

    def calculate_ancestors(self, pt, ind):
        T = len(pt)
        M = len(ind)
        ancestors = pt[T - 1].ancestors[ind]
        find = numpy.arange(M, dtype=int)
        last_part = self.model.sample_smooth(part=pt[T - 1].pa.part[ind],
                                             ptraj=pt[:(T - 1)], anc=ancestors,
                                             future_trajs=None, find=None,
                                             ut=self.u, yt=self.y,
                                             tt=self.t, cur_ind=T - 1)


        traj = numpy.empty((len(pt),), dtype=object)

# Path: pyparticleest/filter.py
# class ParticleApproximation(object):
#     """
#     Contains collection of particles approximating a pdf
# 
#     Use either seed and num or particles (and optionally weights,
#     if not uniform)
# 
#     Args:
#      - particles (array-like): collection of particles
#      - weights (array-like): weight for each particle
#      - seed (array-like): value to initialize all particles with
#      - num (int): number of particles
# 
#     """
#     def __init__(self, particles=None, logw=None, seed=None, num=None):
#         if (particles is not None):
#             self.part = numpy.copy(numpy.asarray(particles))
#             num = len(particles)
#         else:
#             self.part = numpy.empty(num, type(seed))
#             for k in range(num):
#                 self.part[k] = copy.deepcopy(seed)
# 
#         if (logw is not None):
#             self.w = numpy.copy(logw)
#         else:
#             self.w = -math.log(num) * numpy.ones(num)
# 
#         self.num = num
# 
#         # Used to keep track of the offest on all weights, this is continually updated
#         # when the weights are rescaled to avoid going to -Inf
#         self.w_offset = 0.0
# 
#     def __len__(self):
#         return len(self.part)
# 
#     def calc_Neff(self):
#         """
#         Calculate number of effective particles, common metric used to determine
#         when to resample
# 
#         Returns:
#          (float) number of effective particles
#         """
#         tmp = numpy.exp(self.w - numpy.max(self.w))
#         tmp /= numpy.sum(tmp)
#         return 1.0 / numpy.sum(numpy.square(tmp))
# 
#     def resample(self, model, N=None):
#         """
#         Resample approximation so all particles have the same weight
# 
#         Args:
#          - model: object containing problem specific methods
#          - N: new number of particles is N. If 'None' out the number of
#            particles remains the same
#         """
# 
#         if (N is None):
#             N = self.num
# 
#         # Alwyays keep the largest weight at 0 in logaritmic representation
#         tmp = self.w - numpy.max(self.w)
#         new_ind = sample(numpy.exp(tmp), N)
#         new_part = model.copy_ind(self.part, new_ind)
# 
#         self.w = numpy.log(numpy.ones(N, dtype=numpy.float) / N)
#         self.part = new_part
#         self.num = N
#         self.w_offset = 0.0
#         return new_ind
# 
#     def sample(self):
#         """
#         Draw one particle at random with probability corresponding to its weight
# 
#         Returns:
#          (array-like) sampled particle"""
#         return self.part[sample(numpy.exp(self.w), 1)[0]]
# 
#     def find_best_particles(self, n=1):
#         """
#         Return particles with largest weights
# 
#         Args:
#          - n (int): Number of particles to return
# 
#         Returns:
#          - (array-like) with len=n, representing the n most likely estimates """
#         indices = numpy.argsort(self.w)
#         return indices[range(n)]
# 
# class TrajectoryStep(object):
#     """
#     Store particle approximation, input, output and timestamp for
#     a single time index in a trajectory
# 
#     Args:
#      - pa (ParticleAppromixation): particle approximation
#      - u (array-like): input signals at time t
#        (u[t] contains the input for takin x[t] to x[t+1])
#      - y (array-like): measurements at time t
#        (y[t] is the measurment of x[t])
#      - t (float): time stamp for time t
#      - ancestors (array-like): indices for each particles ancestor
#     """
#     def __init__(self, pa, ancestors=None):
#         self.pa = pa
#         self.ancestors = ancestors


# Path: pyparticleest/smoother.py
import numpy
import copy
from builtins import range
from . import filter as pf
from .filter import ParticleApproximation, TrajectoryStep

    def __len__(self):
        return len(self.traj)

    def perform_ancestors(self, pt, M):
        """
        Create smoothed trajectories by taking the forward trajectories

        Args:
         - pt (ParticleTrajectory): forward trajetories
         - M (int): number of trajectories to createa
        """
        self.traj = self.perform_ancestors_int(pt, M)

        if hasattr(self.model, 'post_smoothing'):
            # Do e.g. constrained smoothing for RBPS models
            self.traj = self.model.post_smoothing(self)

    def calculate_ancestors(self, pt, ind):
        T = len(pt)
        M = len(ind)
        ancestors = pt[T - 1].ancestors[ind]
        find = numpy.arange(M, dtype=int)
        last_part = self.model.sample_smooth(part=pt[T - 1].pa.part[ind],
                                             ptraj=pt[:(T - 1)], anc=ancestors,
                                             future_trajs=None, find=None,
                                             ut=self.u, yt=self.y,
                                             tt=self.t, cur_ind=T - 1)


        traj = numpy.empty((len(pt),), dtype=object)

        traj[T - 1] = TrajectoryStep(ParticleApproximation(last_part),