# `ragraph.analysis.sequence.utils`#

Sequencing utils.

## Module Contents#

### Functions#

 `branchsort`(→ Tuple[ragraph.graph.Graph, ...) `markov_decision`(→ int) Make a decision based on a Markov flow analysis of the adjacency matrix. The node

### Attributes#

ragraph.analysis.sequence.utils.branchsort_analysis#
ragraph.analysis.sequence.utils.branchsort(graph: ragraph.graph.Graph, algo: Callable, algo_args: Optional[Dict[str, Any]] = None, root: Optional[Union[str, ragraph.graph.Node]] = None, nodes: Optional[Union[List[ragraph.graph.Node], List[str]]] = None, leafs: Optional[Union[List[ragraph.graph.Node], List[str]]] = None, edge_weights: Optional[List[str]] = None, inherit: bool = True, loops: bool = False, inplace: bool = True, recurse: bool = True, names: bool = False, safe: bool = True) Tuple[ragraph.graph.Graph, List[ragraph.graph.Node], List[ragraph.graph.Node]]#
ragraph.analysis.sequence.utils.markov_decision(graph: ragraph.graph.Graph, options: List[ragraph.graph.Node], inherit: bool = True, loops: bool = False, inf: float = 1.0, dep: float = 1.0, mu: float = 1.5, context: Optional[List[ragraph.graph.Node]] = None) int#

Make a decision based on a Markov flow analysis of the adjacency matrix. The node with the lowest net markov flow is picked.

Parameters:
• graph – Graph data.

• options – Nodes to decide between.

• inf – The weight to subtract outgoing flow by.

• dep – The weight to add incoming flow by.

• mu – Evaporation constant when calculating flow through nodes.

• context – Optional superset of nodes with respect to the options argument that constitutes the “complete” Markov chain that should be taken into account.

Returns:

Index of node to pick.