levels : iterable of iterables containing Points objects
e.g., a list containing lists with the Points in each frame
search_range : float
the maximum distance features can move between frames
memory : integer
the maximum number of frames during which a feature can vanish,
then reppear nearby, and be considered the same particle. 0 by default.
neighbor_strategy : {‘KDTree’, ‘BTree’}
algorithm used to identify nearby features
link_strategy : {‘recursive’, ‘nonrecursive’, ‘numba’, ‘drop’, ‘auto’}
algorithm used to resolve subnetworks of nearby particles
‘auto’ uses numba if available
‘drop’ causes particles in subnetworks to go unlinked
hash_size : sequence
For ‘BTree’ mode only. Define the shape of the search region.
(Higher-level wrappers of link infer this from the data.)
box_size : sequence
For ‘BTree’ mode only. Define the parition size to optimize
performance. If None (default), the search_range is used, which is
a reasonable guess for best performance.
predictor : function, optional
Improve performance by guessing where a particle will be in the
next frame.
For examples of how this works, see the “predict” module.
adaptive_stop : float, optional
If not None, when encountering an oversize subnet, retry by progressively
reducing search_range until the subnet is solvable. If search_range
becomes <= adaptive_stop, give up and raise a SubnetOversizeException.
adaptive_step : float, optional
Reduce search_range by multiplying it by this factor.
track_cls : class, optional
for special uses, you can specify a custom class that holds
each Track
hash_generator : function, optional
a function that returns a HashTable, included for legacy support.
Specifying hash_size and box_size (above) fully defined a HashTable.
|