booldog.bool_graph module

class booldog.bool_graph.BooleanGraph(graph, data_format='bnet', **kwargs)

Bases: object

A class to represent a Boolean graph.

n

The number of nodes/variables in the graph

Type: int

primes

Prime implicants of the Boolean graph. See PyBoolNet:prime implicants for more information.

Type: dict

nodes

Lists node names in the graph

Type: tuple of str

index

Dictionary of node name to integer index for indexing arrays

Type: dict

Initialise a Boolean graph.

Parameters

graph (str or dict) – A file path to the graph or a dictionary with the graph.
data_format ({'primes', 'interactions', 'bnet', 'graphml'}) – String specifying data format.
kwargs – #TODO Additional keyword arguments for the importer function.

primes_to_matrices()

Reduce graph to Activation and Inhibition Matrices.

Returns

Act (np.array) – n * n matrix with entry m_{i, j} = 1 iff node_j activates node_i
Inh (np.array) – n * n matrix with entry m_{i, j} = 1 iff node_j inhibits node_i

Notes

Only if graph is of type threshold (i.e. SQUAD) does this make sense. Used in SquadODE Create logic matrices

generate_states(fixed=None)

Generate all possible states of the graph.

Parameters: fixed (dict) – A dictionary of {node:state} to be kept fixed, with node in graph.nodes, and state in {0, 1}.
Yields: state (np.array) – length n array with a state of the graph.

boolean_simulation(initial_values=None, plot=True, export=False, **kwargs)

Compute a Boolean simulation (or state transition) from optional initial values.

Parameters

initial_values (int, str, list, dict, optional) – Initial state, see Notes for format
plot (bool, optional) – Whether to plot the simulation results
export (bool or path object or string, optional) – False, or a file path to save simulation results. Exports valu
**kwargs – If plot=True , additional keyword arguments are passed to plot_boolean_simulation().

Notes

This is a wrapper for pyboolnet.state_transition_graphs.primes2stg, and therefore takes the same argument format for initial states.

From PyBoolNet documentation:

Either a list of states in dict or str format::

    init = ["000", "111"]
    init = ["000", {"v1":1,"v2":1,"v3":1}]

or as a function that is called on every state and must return
either True or False to indicate whether the state ought to be initial::

    init = lambda x: x["v1"]>=x["v2"]

or by a subspace in which case all the states contained in it are
initial::

    init = "--1"
    init = {"v3":1}

plot_boolean_simulation(fig, booldog_style=True, plot_nodes=None)

Plot the state transition graph, from optional initial values.

Parameters

fig (str) – File name of generated figure
initial_values (int, str, list, dict) – Initial state, see Notes for format
new_style (bool) – Whether to use booldog style, or PyBoolNet style (default) to plot the state transition graph. Requires pygraphviz (If not installed, will default to PyBoolNet version.)
plot_nodes (None or list of str, optional) – Subset of nodes to plot. If None, plot all nodes. Only valid if new_style is True.

plot_state_transitions(fig, booldog_style=True, plot_nodes=None)

Plot the state transition graph, from optional initial values.

Parameters

fig (str) – File name of generated figure
initial_values (int, str, list, dict) – Initial state, see Notes for format
new_style (bool) – Whether to use booldog style, or PyBoolNet style (default) to plot the state transition graph. Requires pygraphviz (If not installed, will default to PyBoolNet version.)
plot_nodes (None or list of str, optional) – Subset of nodes to plot. If None, plot all nodes. Only valid if new_style is True.

steady_states(): All steady states of the Boolean graph.

get_parents(node)

Fetch regulators/inputs to a node

Parameters: node (str) – Node name
Returns: parents – Set of parent nodes
Return type: set

get_interactions(direction='out'): direction out: interactions[a][b] a –> b direction in: interactions[a][b] a <– b