Modules
		numpy numba matplotlib.pyplot scipy skimage sklearn

Classes
		builtins.object DistanceMatrix   class DistanceMatrix(builtins.object)     DistanceMatrix(size1, size2=None, dtype=&lt;class 'numpy.float32'&gt;, value=None)       Methods defined here: __getitem__(self, indices) __init__(self, size1, size2=None, dtype=<class 'numpy.float32'>, value=None) Initialize self.  See help(type(self)) for accurate signature. __setitem__(self, indices, item) Data descriptors defined here: __dict__ dictionary for instance variables (if defined) __weakref__ list of weak references to the object (if defined)

Functions
		fastnorm(x) fastnorm_overload(x) filter_pairs1(poses1, poses2, matches, n_neighbors=25) float_string_dist(vec1, vec2, penalty=10) match_dots(position_list, min_dots=2, pair_dots_func=<function pair_dots2 at 0x7f2e1f003f40>, **kwargs) Matches groups of points between the sets given in position_list Uses the provided pair_dots_func to match dots into pairs between each set, then combines these pairs into larger groups pair_dots1(poses1, poses2) pair_dots2(poses1, poses2, n_neighbors=10, penalty=5, progress=False, debug=True, max_distance=None) pair_dots3(poses1, poses2, n_neighbors=50) pair_dots4(poses1, poses2) pair_dots_func(poses1, poses2, dist_func, pass_indices=False, progress=False, debug=True) Finds pairs of dots where each one is the closest to the other, based on the given distance function.     poses1, poses2: numpy arrays of positions that will be passed to the dist_func     dist_func: a function that accepts either two poses from poses1 and poses2         or two indices (see pass_indices) and returns a single float, the distance score     pass_indices: if true, the indices into poses1, poses2 are passed to dist_func     progress: if true, a progress bar is displayed     debug: if true, debug info is printed to the console pair_dots_nn_func(poses1, poses2, dist_func, neighbors=100, pass_indices=False, progress=False, debug=True) Finds pairs of dots where each one is the closest to the other, based on the given distance function. Only considers matches in the n nearest neighbors of each point, where n is the argument neighbors.     neighbors: number of nearest neighbors to consider when searching for pairs. This         can also be the actual neighbors already calculated, in this case it would be         a numpy array of shape (len(poses1), n_neighbors) of indices into poses2.     See pair_dots_func for documentation of other args. prepare_dists(poses)