# Path: emcee3/moves/red_blue.py
# class RedBlueMove(object):
#     """
#     An abstract red-blue ensemble move with parallelization as described in
#     `Foreman-Mackey et al. (2013) <http://arxiv.org/abs/1202.3665>`_.
# 
#     Args:
#         nsplits (Optional[int]): The number of sub-ensembles to use. Each
#             sub-ensemble is updated in parallel using the other sets as the
#             complementary ensemble. The default value is ``2`` and you
#             probably won't need to change that.
# 
#         randomize_split (Optional[bool]): Randomly shuffle walkers between
#             sub-ensembles. The same number of walkers will be assigned to
#             each sub-ensemble on each iteration. By default, this is ``False``.
# 
#         live_dangerously (Optional[bool]): By default, an update will fail with
#             a ``RuntimeError`` if the number of walkers is smaller than twice
#             the dimension of the problem because the walkers would then be
#             stuck on a low dimensional subspace. This can be avoided by
#             switching between the stretch move and, for example, a
#             Metropolis-Hastings step. If you want to do this and suppress the
#             error, set ``live_dangerously = True``. Thanks goes (once again)
#             to @dstndstn for this wonderful terminology.
# 
#     """
#     def __init__(self,
#                  nsplits=2,
#                  randomize_split=False,
#                  live_dangerously=False):
#         self.nsplits = int(nsplits)
#         self.live_dangerously = live_dangerously
#         self.randomize_split = randomize_split
# 
#     def setup(self, ensemble):
#         pass
# 
#     def get_proposal(self, ensemble, sample, complement):
#         raise NotImplementedError("The proposal must be implemented by "
#                                   "subclasses")
# 
#     def finalize(self, ensemble):
#         pass
# 
#     def update(self, ensemble):
#         """
#         Execute a move starting from the given :class:`Ensemble` and updating
#         it in-place.
# 
#         :param ensemble:
#             The starting :class:`Ensemble`.
# 
#         :return ensemble:
#             The same ensemble updated in-place.
# 
#         """
#         # Check that the dimensions are compatible.
#         nwalkers, ndim = ensemble.nwalkers, ensemble.ndim
#         if nwalkers < 2 * ndim and not self.live_dangerously:
#             raise RuntimeError("It is unadvisable to use a red-blue move "
#                                "with fewer walkers than twice the number of "
#                                "dimensions.")
# 
#         # Run any move-specific setup.
#         self.setup(ensemble)
# 
#         # Split the ensemble in half and iterate over these two halves.
#         inds = np.arange(nwalkers) % self.nsplits
#         if self.randomize_split:
#             ensemble.random.shuffle(inds)
#         for i in range(self.nsplits):
#             S1 = inds == i
#             S2 = inds != i
# 
#             # Get the two halves of the ensemble.
#             s = ensemble.coords[S1]
#             c = ensemble.coords[S2]
# 
#             # Get the move-specific proposal.
#             q, factors = self.get_proposal(ensemble, s, c)
# 
#             # Compute the lnprobs of the proposed position.
#             states = ensemble.propose(q)
# 
#             # Loop over the walkers and update them accordingly.
#             for i, (j, f, state) in enumerate(zip(
#                     np.arange(len(ensemble))[S1], factors, states)):
#                 lnpdiff = (
#                     f +
#                     state.log_probability -
#                     ensemble.walkers[j].log_probability
#                 )
#                 if lnpdiff > np.log(ensemble.random.rand()):
#                     state.accepted = True
# 
#             # Update the ensemble with the accepted walkers.
#             ensemble.update(states, subset=S1)
# 
#         # Do any move-specific cleanup.
#         self.finalize(ensemble)
# 
#         return ensemble


import numpy as np
    from scipy.stats import gaussian_kde
from .red_blue import RedBlueMove
# -*- coding: utf-8 -*-

from __future__ import division, print_function


try:
except ImportError:
    gaussian_kde = None


__all__ = ["KDEMove"]



# Path: emcee3/moves/red_blue.py
# class RedBlueMove(object):
#     """
#     An abstract red-blue ensemble move with parallelization as described in
#     `Foreman-Mackey et al. (2013) <http://arxiv.org/abs/1202.3665>`_.
# 
#     Args:
#         nsplits (Optional[int]): The number of sub-ensembles to use. Each
#             sub-ensemble is updated in parallel using the other sets as the
#             complementary ensemble. The default value is ``2`` and you
#             probably won't need to change that.
# 
#         randomize_split (Optional[bool]): Randomly shuffle walkers between
#             sub-ensembles. The same number of walkers will be assigned to
#             each sub-ensemble on each iteration. By default, this is ``False``.
# 
#         live_dangerously (Optional[bool]): By default, an update will fail with
#             a ``RuntimeError`` if the number of walkers is smaller than twice
#             the dimension of the problem because the walkers would then be
#             stuck on a low dimensional subspace. This can be avoided by
#             switching between the stretch move and, for example, a
#             Metropolis-Hastings step. If you want to do this and suppress the
#             error, set ``live_dangerously = True``. Thanks goes (once again)
#             to @dstndstn for this wonderful terminology.
# 
#     """
#     def __init__(self,
#                  nsplits=2,
#                  randomize_split=False,
#                  live_dangerously=False):
#         self.nsplits = int(nsplits)
#         self.live_dangerously = live_dangerously
#         self.randomize_split = randomize_split
# 
#     def setup(self, ensemble):
#         pass
# 
#     def get_proposal(self, ensemble, sample, complement):
#         raise NotImplementedError("The proposal must be implemented by "
#                                   "subclasses")
# 
#     def finalize(self, ensemble):
#         pass
# 
#     def update(self, ensemble):
#         """
#         Execute a move starting from the given :class:`Ensemble` and updating
#         it in-place.
# 
#         :param ensemble:
#             The starting :class:`Ensemble`.
# 
#         :return ensemble:
#             The same ensemble updated in-place.
# 
#         """
#         # Check that the dimensions are compatible.
#         nwalkers, ndim = ensemble.nwalkers, ensemble.ndim
#         if nwalkers < 2 * ndim and not self.live_dangerously:
#             raise RuntimeError("It is unadvisable to use a red-blue move "
#                                "with fewer walkers than twice the number of "
#                                "dimensions.")
# 
#         # Run any move-specific setup.
#         self.setup(ensemble)
# 
#         # Split the ensemble in half and iterate over these two halves.
#         inds = np.arange(nwalkers) % self.nsplits
#         if self.randomize_split:
#             ensemble.random.shuffle(inds)
#         for i in range(self.nsplits):
#             S1 = inds == i
#             S2 = inds != i
# 
#             # Get the two halves of the ensemble.
#             s = ensemble.coords[S1]
#             c = ensemble.coords[S2]
# 
#             # Get the move-specific proposal.
#             q, factors = self.get_proposal(ensemble, s, c)
# 
#             # Compute the lnprobs of the proposed position.
#             states = ensemble.propose(q)
# 
#             # Loop over the walkers and update them accordingly.
#             for i, (j, f, state) in enumerate(zip(
#                     np.arange(len(ensemble))[S1], factors, states)):
#                 lnpdiff = (
#                     f +
#                     state.log_probability -
#                     ensemble.walkers[j].log_probability
#                 )
#                 if lnpdiff > np.log(ensemble.random.rand()):
#                     state.accepted = True
# 
#             # Update the ensemble with the accepted walkers.
#             ensemble.update(states, subset=S1)
# 
#         # Do any move-specific cleanup.
#         self.finalize(ensemble)
# 
#         return ensemble


# Path: emcee3/moves/kde.py
import numpy as np
    from scipy.stats import gaussian_kde
from .red_blue import RedBlueMove

# -*- coding: utf-8 -*-

from __future__ import division, print_function


try:
except ImportError:
    gaussian_kde = None


__all__ = ["KDEMove"]



class KDEMove(RedBlueMove):