# Path: openpathsampling/ops_logging.py
# def initialization_logging(logger, obj, entries):
#     # this works with either a list of attributes of a dictionary of
#     # variable name : attribute value
#     try:
#         entries.keys()
#         working_dict = entries
#     except AttributeError:
#         working_dict = {}
#         for entry in entries:
#             working_dict[entry] = obj.__dict__[entry]
# 
#     logger.info("Initializing <%s> (%s)",
#                 str(hex(id(obj))), str(obj.__class__.__name__))
#     for entry in working_dict.keys():
#         logger.info("Parameter: %s : %s", str(entry), str(working_dict[entry]))
# 
# Path: openpathsampling/deprecations.py
# def deprecate(deprecation):
#     """Decorator to deprecate a class/method
# 
#     Note
#     ----
#         Properties can be particularly challenging. First, you must put the
#         @property decorator outside (above) the @deprecate decorator.
#         Second, this does not (yet) change the docstrings of properties.
#         However, it will raise a warning when the property is used.
#     """
#     def decorator(dep_obj):
#         dep_obj.__new_docstring = update_docstring(dep_obj, deprecation)
#         wrap_class = isclass(dep_obj)
#         to_wrap = dep_obj.__init__ if wrap_class else dep_obj
# 
#         @wraps(to_wrap)
#         def wrapper(*args, **kwargs):
#             deprecation.warn()
#             return to_wrap(*args, **kwargs)
# 
#         if wrap_class:
#             dep_obj.__init__ = wrapper
#             return dep_obj
#         else:
#             return wrapper
#     return decorator
# 
# def has_deprecations(cls):
#     """Decorator to ensure that docstrings get updated for wrapped class"""
#     for obj in [cls] + list(vars(cls).values()):
#         if callable(obj) and hasattr(obj, '__new_docstring'):
#             try:
#                 obj.__doc__ = obj.__new_docstring
#             except AttributeError:
#                 # probably Python 2; we can't update docstring in Py2
#                 # see https://github.com/Chilipp/docrep/pull/9 and related
#                 pass
#             del obj.__new_docstring
#     return cls
# 
# Path: openpathsampling/deprecations.py
# SAMPLE_DETAILS = Deprecation(
#     problem="SampleDetails will be removed in {OPS} {version}.",
#     remedy="Use generic Details class instead.",
#     remove_version=(2, 0),
#     deprecated_in=(0, 9, 3)
# )
# 
# MOVE_DETAILS = Deprecation(
#     problem="MoveDetails will be removed in {OPS} {version}.",
#     remedy="Use generic Details class instead.",
#     remove_version=(2, 0),
#     deprecated_in=(0, 9, 3)
# )
# 
# NEW_SNAPSHOT_KWARG_SELECTOR = Deprecation(
#     problem=("'new_snapshot' should be a supported keyword in "
#              "selector.probability_ratio(); If snapshot has been copied or "
#              "modified we can't reliably find it in trial_trajectory. This "
#              "keyword must be supported in the expected signature: "
#              "(old_snapshot, old_trajectory, new_snapshot, new_trajectory) "
#              "in {OPS} {version}. "),
#     remedy=("kwarg 'new_snapshot' must to be supported, implement it as "
#             "new_snapshot=old_snapshot if new_traj is not used to calculate "
#             "the weight of old_snapshot"),
#     remove_version=(2, 0),
#     deprecated_in=(1, 6, 0)
# )


import abc
import logging
import numpy as np
import random
import openpathsampling as paths
from openpathsampling.netcdfplus import StorableNamedObject, StorableObject
from openpathsampling.pathmover_inout import InOutSet, InOut
from openpathsampling.rng import default_rng
from .ops_logging import initialization_logging
from .treelogic import TreeMixin
from openpathsampling.deprecations import deprecate, has_deprecations
from openpathsampling.deprecations import (SAMPLE_DETAILS, MOVE_DETAILS,
                                           NEW_SNAPSHOT_KWARG_SELECTOR)
from future.utils import with_metaclass
            'bias': bias
        }

        return [trial], details


# ****************************************************************************
#  SELECTION MOVERS
# ****************************************************************************

class SelectionMover(PathMover):
    """
    A general mover that selects a single mover from a set of possibilities

    This is a basic class for all sorts of selectors, like RandomChoice,
    RandomAllowedChoice. The way it works is to generate a list of weights
    and pick a random one using the weights. This is as general as possible
    and is chosen because it also allows to store the possibilities in a
    general way for better comparison

    Attributes
    ----------
    movers : list of openpathsampling.PathMover
        the PathMovers to choose from
    """

    def __init__(self, movers):
        super(SelectionMover, self).__init__()

        self.movers = movers

# Path: openpathsampling/ops_logging.py
# def initialization_logging(logger, obj, entries):
#     # this works with either a list of attributes of a dictionary of
#     # variable name : attribute value
#     try:
#         entries.keys()
#         working_dict = entries
#     except AttributeError:
#         working_dict = {}
#         for entry in entries:
#             working_dict[entry] = obj.__dict__[entry]
# 
#     logger.info("Initializing <%s> (%s)",
#                 str(hex(id(obj))), str(obj.__class__.__name__))
#     for entry in working_dict.keys():
#         logger.info("Parameter: %s : %s", str(entry), str(working_dict[entry]))
# 
# Path: openpathsampling/deprecations.py
# def deprecate(deprecation):
#     """Decorator to deprecate a class/method
# 
#     Note
#     ----
#         Properties can be particularly challenging. First, you must put the
#         @property decorator outside (above) the @deprecate decorator.
#         Second, this does not (yet) change the docstrings of properties.
#         However, it will raise a warning when the property is used.
#     """
#     def decorator(dep_obj):
#         dep_obj.__new_docstring = update_docstring(dep_obj, deprecation)
#         wrap_class = isclass(dep_obj)
#         to_wrap = dep_obj.__init__ if wrap_class else dep_obj
# 
#         @wraps(to_wrap)
#         def wrapper(*args, **kwargs):
#             deprecation.warn()
#             return to_wrap(*args, **kwargs)
# 
#         if wrap_class:
#             dep_obj.__init__ = wrapper
#             return dep_obj
#         else:
#             return wrapper
#     return decorator
# 
# def has_deprecations(cls):
#     """Decorator to ensure that docstrings get updated for wrapped class"""
#     for obj in [cls] + list(vars(cls).values()):
#         if callable(obj) and hasattr(obj, '__new_docstring'):
#             try:
#                 obj.__doc__ = obj.__new_docstring
#             except AttributeError:
#                 # probably Python 2; we can't update docstring in Py2
#                 # see https://github.com/Chilipp/docrep/pull/9 and related
#                 pass
#             del obj.__new_docstring
#     return cls
# 
# Path: openpathsampling/deprecations.py
# SAMPLE_DETAILS = Deprecation(
#     problem="SampleDetails will be removed in {OPS} {version}.",
#     remedy="Use generic Details class instead.",
#     remove_version=(2, 0),
#     deprecated_in=(0, 9, 3)
# )
# 
# MOVE_DETAILS = Deprecation(
#     problem="MoveDetails will be removed in {OPS} {version}.",
#     remedy="Use generic Details class instead.",
#     remove_version=(2, 0),
#     deprecated_in=(0, 9, 3)
# )
# 
# NEW_SNAPSHOT_KWARG_SELECTOR = Deprecation(
#     problem=("'new_snapshot' should be a supported keyword in "
#              "selector.probability_ratio(); If snapshot has been copied or "
#              "modified we can't reliably find it in trial_trajectory. This "
#              "keyword must be supported in the expected signature: "
#              "(old_snapshot, old_trajectory, new_snapshot, new_trajectory) "
#              "in {OPS} {version}. "),
#     remedy=("kwarg 'new_snapshot' must to be supported, implement it as "
#             "new_snapshot=old_snapshot if new_traj is not used to calculate "
#             "the weight of old_snapshot"),
#     remove_version=(2, 0),
#     deprecated_in=(1, 6, 0)
# )


# Path: openpathsampling/pathmover.py
import abc
import logging
import numpy as np
import random
import openpathsampling as paths
from openpathsampling.netcdfplus import StorableNamedObject, StorableObject
from openpathsampling.pathmover_inout import InOutSet, InOut
from openpathsampling.rng import default_rng
from .ops_logging import initialization_logging
from .treelogic import TreeMixin
from openpathsampling.deprecations import deprecate, has_deprecations
from openpathsampling.deprecations import (SAMPLE_DETAILS, MOVE_DETAILS,
                                           NEW_SNAPSHOT_KWARG_SELECTOR)
from future.utils import with_metaclass

            'bias': bias
        }

        return [trial], details


# ****************************************************************************
#  SELECTION MOVERS
# ****************************************************************************

class SelectionMover(PathMover):
    """
    A general mover that selects a single mover from a set of possibilities

    This is a basic class for all sorts of selectors, like RandomChoice,
    RandomAllowedChoice. The way it works is to generate a list of weights
    and pick a random one using the weights. This is as general as possible
    and is chosen because it also allows to store the possibilities in a
    general way for better comparison

    Attributes
    ----------
    movers : list of openpathsampling.PathMover
        the PathMovers to choose from
    """

    def __init__(self, movers):
        super(SelectionMover, self).__init__()

        self.movers = movers

        initialization_logging(init_log, self,