Base helpers#

Base functions for diversity calculations.

phypanda.base.diversity(network: DirectedPhyNetwork, taxa: set[str], measure: DiversityMeasure, **kwargs: Any) → float#

Compute diversity for a fixed taxon set.

Parameters:

  • network (DirectedPhyNetwork) – Input phylogenetic network.

  • taxa (set[str]) – Taxa for which diversity is evaluated.

  • measure (DiversityMeasure) – Diversity measure implementation.

  • **kwargs (Any) – Measure-specific keyword arguments.

Returns:

Diversity value for taxa.

Return type:

float

Raises:

PhyloZooValueError – If one or more taxa are not present in the network.

Examples

>>> import phypanda as pp
>>> # value = pp.diversity(network, {"a", "b"}, measure=pp.all_paths)
phypanda.base.greedy_max_diversity(network: DirectedPhyNetwork, budget: int, measure: DiversityMeasure, costs: Mapping[str, int] | None = None, **kwargs: Any) → tuple[float, Set[str]]#

Greedily maximize diversity under an integer budget.

The selected taxon at each step is the affordable taxon with highest normalized gain marginal_diversity / cost.

Parameters:

  • network (DirectedPhyNetwork) – Input phylogenetic network.

  • budget (int) – Total integer budget.

  • measure (DiversityMeasure) – Diversity measure implementation.

  • costs (Mapping[str, int] | None, optional) – Integer cost per taxon. If None, unit costs are used.

  • **kwargs (Any) – Measure-specific keyword arguments.

Returns:

Greedy objective value and selected taxa.

Return type:

tuple[float, Set[str]]

Examples

>>> import phypanda as pp
>>> # value, taxa = pp.greedy_max_diversity(network, 5, measure=pp.all_paths)
>>> # sorted(taxa)
phypanda.base.marginal_diversities(network: DirectedPhyNetwork, saved_taxa: set[str], measure: DiversityMeasure, **kwargs: Any) → Dict[str, float]#

Compute marginal diversity contributions for all taxa.

Parameters:

  • network (DirectedPhyNetwork) – Input phylogenetic network.

  • saved_taxa (set[str]) – Current selected taxa.

  • measure (DiversityMeasure) – Diversity measure implementation.

  • **kwargs (Any) – Measure-specific keyword arguments.

Returns:

Mapping from taxon to marginal gain/loss relative to saved_taxa.

Return type:

Dict[str, float]

Examples

>>> import phypanda as pp
>>> # marg = pp.marginal_diversities(network, {"a"}, measure=pp.all_paths)
phypanda.base.solve_max_diversity(network: DirectedPhyNetwork, budget: int, measure: DiversityMeasure, costs: Mapping[str, int] | None = None, **kwargs: Any) → tuple[float, Set[str]]#

Solve maximum diversity under an integer budget.

Parameters:

  • network (DirectedPhyNetwork) – Input phylogenetic network.

  • budget (int) – Total integer budget.

  • measure (DiversityMeasure) – Diversity measure implementation.

  • costs (Mapping[str, int] | None, optional) – Integer cost per taxon. If None, unit costs are used.

  • **kwargs (Any) – Measure-specific keyword arguments.

Returns:

Optimal objective value and selected taxa.

Return type:

tuple[float, Set[str]]

Examples

>>> import phypanda as pp
>>> # value, taxa = pp.solve_max_diversity(network, 5, measure=pp.all_paths)
>>> # len(taxa) <= 5