LineageProblem.compute_time_point_distances(source, intermediate, target, posterior_marginals=True, backend='ott', **kwargs)

Compute Wasserstein distance between time points.

See also

  • TODO(MUCDK): create an example showing the usage.

  • source (TypeVar(K, bound= Hashable)) – Key identifying the source distribution.

  • intermediate (TypeVar(K, bound= Hashable)) – Key identifying the intermediate distribution.

  • target (TypeVar(K, bound= Hashable)) – Key identifying the target distribution.

  • posterior_marginals (bool) – Whether to use posterior_growth_rates or prior_growth_rates. TODO(MUCDK): needs more explanation

  • backend (Literal['ott']) – Backend used for the distance computation.

  • kwargs (Any) –

    Keyword arguments for the distance function, depending on the backend:

  • self (TemporalMixinProtocol[K, B])

Return type:

tuple[float, float]


: The distance between source and intermediate cells and intermediate and target cells, respectively.