def particle_trajectories(self, indices, fields=None, suppress_logging=False, ptype=None):
    'Create a collection of particle trajectories in time over a series of\n        datasets.\n\n        Parameters\n        ----------\n        indices : array_like\n            An integer array of particle indices whose trajectories we\n            want to track. If they are not sorted they will be sorted.\n        fields : list of strings, optional\n            A set of fields that is retrieved when the trajectory\n            collection is instantiated. Default: None (will default\n            to the fields \'particle_position_x\', \'particle_position_y\',\n            \'particle_position_z\')\n        suppress_logging : boolean\n            Suppress yt\'s logging when iterating over the simulation time\n            series. Default: False\n        ptype : str, optional\n            Only use this particle type. Default: None, which uses all particle type.\n\n        Examples\n        --------\n        >>> my_fns = glob.glob("orbit_hdf5_chk_00[0-9][0-9]")\n        >>> my_fns.sort()\n        >>> fields = ["particle_position_x", "particle_position_y",\n        >>>           "particle_position_z", "particle_velocity_x",\n        >>>           "particle_velocity_y", "particle_velocity_z"]\n        >>> ds = load(my_fns[0])\n        >>> init_sphere = ds.sphere(ds.domain_center, (.5, "unitary"))\n        >>> indices = init_sphere["particle_index"].astype("int")\n        >>> ts = DatasetSeries(my_fns)\n        >>> trajs = ts.particle_trajectories(indices, fields=fields)\n        >>> for t in trajs :\n        >>>     print t["particle_velocity_x"].max(), t["particle_velocity_x"].min()\n\n        Note\n        ----\n        This function will fail if there are duplicate particle ids or if some of the particle\n        disappear.\n        '
    return ParticleTrajectories(self, indices, fields=fields, suppress_logging=suppress_logging, ptype=ptype)
Create a collection of particle trajectories in time over a series of
datasets.

Parameters
----------
indices : array_like
    An integer array of particle indices whose trajectories we
    want to track. If they are not sorted they will be sorted.
fields : list of strings, optional
    A set of fields that is retrieved when the trajectory
    collection is instantiated. Default: None (will default
    to the fields 'particle_position_x', 'particle_position_y',
    'particle_position_z')
suppress_logging : boolean
    Suppress yt's logging when iterating over the simulation time
    series. Default: False
ptype : str, optional
    Only use this particle type. Default: None, which uses all particle type.

Examples
--------
>>> my_fns = glob.glob("orbit_hdf5_chk_00[0-9][0-9]")
>>> my_fns.sort()
>>> fields = ["particle_position_x", "particle_position_y",
>>>           "particle_position_z", "particle_velocity_x",
>>>           "particle_velocity_y", "particle_velocity_z"]
>>> ds = load(my_fns[0])
>>> init_sphere = ds.sphere(ds.domain_center, (.5, "unitary"))
>>> indices = init_sphere["particle_index"].astype("int")
>>> ts = DatasetSeries(my_fns)
>>> trajs = ts.particle_trajectories(indices, fields=fields)
>>> for t in trajs :
>>>     print t["particle_velocity_x"].max(), t["particle_velocity_x"].min()

Note
----
This function will fail if there are duplicate particle ids or if some of the particle
disappear.
def particle_trajectories(self, indices, fields=None, suppress_logging=False, ptype=None):
    'Create a collection of particle trajectories in time over a series of\n        datasets.\n\n        Parameters\n        ----------\n        indices : array_like\n            An integer array of particle indices whose trajectories we\n            want to track. If they are not sorted they will be sorted.\n        fields : list of strings, optional\n            A set of fields that is retrieved when the trajectory\n            collection is instantiated. Default: None (will default\n            to the fields \'particle_position_x\', \'particle_position_y\',\n            \'particle_position_z\')\n        suppress_logging : boolean\n            Suppress yt\'s logging when iterating over the simulation time\n            series. Default: False\n        ptype : str, optional\n            Only use this particle type. Default: None, which uses all particle type.\n\n        Examples\n        --------\n        >>> my_fns = glob.glob("orbit_hdf5_chk_00[0-9][0-9]")\n        >>> my_fns.sort()\n        >>> fields = ["particle_position_x", "particle_position_y",\n        >>>           "particle_position_z", "particle_velocity_x",\n        >>>           "particle_velocity_y", "particle_velocity_z"]\n        >>> ds = load(my_fns[0])\n        >>> init_sphere = ds.sphere(ds.domain_center, (.5, "unitary"))\n        >>> indices = init_sphere["particle_index"].astype("int")\n        >>> ts = DatasetSeries(my_fns)\n        >>> trajs = ts.particle_trajectories(indices, fields=fields)\n        >>> for t in trajs :\n        >>>     print t["particle_velocity_x"].max(), t["particle_velocity_x"].min()\n\n        Note\n        ----\n        This function will fail if there are duplicate particle ids or if some of the particle\n        disappear.\n        '
    return ParticleTrajectories(self, indices, fields=fields, suppress_logging=suppress_logging, ptype=ptype)