trackpy.link_df_iter#

trackpy.link_df_iter(f_iter, 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 an iterable of DataFrames into trajectories.

Parameters:

f_iteriterable of DataFrames

Each 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. For optimal performance, explicitly specify the column names using pos_columns and t_column kwargs.

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 If this is not supplied, f_iter will be investigated, which might cost performance. For optimal performance, always supply this parameter.

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.

Yields:

DataFrames with added column ‘particle’ containing trajectory labels

See also

link