solution

MacSyFinder find lot of potential systems for the same model, all these systems are saved in “all_systems.xxx” files. This module allow to explore among of all systems which combination seems to be more probable.

solution API reference

Solution

class macsypy.solution.Solution(systems: list[System])

Handle Solution, a solution is a set of compatible Systems

when compare solutions we check the following criteria

1. The number of hits

2. The number of systems

3. The average of wholeness

4. The hits position (is used ti give predictable output for unit tests)

__eq__(other) → bool

Return self==value.

__gt__(other) → bool

Return self>value.

__hash__ = None

__init__(systems: list[System]) → None

Parameters:

systems – The list of system that composed this solution

__iter__() → Generator

Solution allow to iterate over the systems

Returns:

generator

__lt__(other) → bool

Return self<value.

__weakref__

list of weak references to the object (if defined)

_sorted_systems(systems: list[System]) → list[System]

sort the systems following the positions of th hits that composed the systems

Parameters:

systems (list of mcsypy.system.System objects) – the systems to sort

Returns:

a sorted copy of the systems

Return type:

list of mcsypy.system.System objects

property average_wholeness: float

The average of the systems wholeness

property hits_number: int

The sum of the hits of each system in this solution

property hits_positions: list[int]

The list of position of all hits of the solution

property score: float

The score of this solution

property systems: list[System]

“a sorted list of the systems that composed the solution

combine_clusters

macsypy.solution.combine_clusters(clusters: list[~macsypy.cluster.Cluster], true_loners: dict[slice(<class 'str'>, macsypy.hit.Loner | macsypy.hit.LonerMultiSystem, None)], multi_loci: bool = False) → list[tuple[Cluster]]

generate the combinations of clusters, with loners and multi systems

Parameters:

• clusters – the clusters to combines

• true_loners (dict the name of the function code by hit gene_ref.alternate_of as key and 1 macsypy.cluster.Cluster with the best a macsypy.hit.Loner or macsypy.hit.LonerMultiSystem hit as value) – the multi-systems hits

• multi_loci – True if the model is multi_loci false otherwise

Returns:

all available combination of clusters

Return type:

List of combination. a combination is a tuple of macsypy.cluster.Cluster objects

combine_multisystems

macsypy.solution.combine_multisystems(rejected_candidates: list[RejectedCandidate], multi_systems: list[Cluster])

Parameters:

• rejected_candidates –

• multi_systems – sequence of macsypy.cluster.Cluster each cluster must be composed of only one macsypy.hit.MultiSystem object

Returns:

list of cluster combination with the multisystem

Return type:

[(macsypy.cluster.Cluster cluster1, cluster2, …), (macsypy.cluster.Cluster cluster3, cluster4, …)]

find_best_solutions

macsypy.solution.find_best_solutions(systems: list[System]) → tuple[list[Solution], float]

Among the systems choose the combination of systems which does not share macsypy.hit.CoreHit and maximize the sum of systems scores

Parameters:

systems – the systems to analyse

Returns:

the list of macsypy.solution.Solution which represent one best solution then it’s score.

Return type:

tuple of 2 elements the best solutions and it’s score