trackpy.link¶

trackpy.link(f, search_range, pos_columns=None, t_column='frame', memory=0, predictor=None, adaptive_stop=None, adaptive_step=0.95, neighbor_strategy=None, link_strategy=None, dist_func=None, to_eucl=None)¶

Link a DataFrame of coordinates into trajectories.

Parameters:

fDataFrame: The DataFrame must include any number of column(s) for position and a column of frame numbers. By default, ‘x’ and ‘y’ are expected for position, and ‘frame’ is expected for frame number. See below for options to use custom column names.
search_rangefloat or tuple: the maximum distance features can move between frames, optionally per dimension
pos_columnslist of str, optional: Default is [‘y’, ‘x’], or [‘z’, ‘y’, ‘x’] when ‘z’ is present in f
t_columnstr, optional: Default is ‘frame’
memoryinteger, optional: the maximum number of frames during which a feature can vanish, then reappear nearby, and be considered the same particle. 0 by default.
predictorfunction, 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_stopfloat, 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_stepfloat, optional: Reduce search_range by multiplying it by this factor.
neighbor_strategy{‘KDTree’, ‘BTree’}: algorithm used to identify nearby features. Default ‘KDTree’.
link_strategy{‘recursive’, ‘nonrecursive’, ‘numba’, ‘hybrid’, ‘drop’, ‘auto’}: algorithm used to resolve subnetworks of nearby particles ‘auto’ uses hybrid (numba+recursive) if available ‘drop’ causes particles in subnetworks to go unlinked
dist_funcfunction or `sklearn.metrics.DistanceMetric` instance, optional: A custom python distance function or instance of the Scikit Learn DistanceMetric class. If a python distance function is passed, it must take two 1D arrays of coordinates and return a float. Must be used with the ‘BTree’ neighbor_strategy.
to_euclfunction, optional: function that transforms a N x ndim array of positions into coordinates in Euclidean space. Useful for instance to link by Euclidean distance starting from radial coordinates. If search_range is anisotropic, this parameter cannot be used.

Returns:

DataFrame with added column ‘particle’ containing trajectory labels.
The t_column (by default: ‘frame’) will be coerced to integer.