miniBN

class colomoto.minibn.BaseNetwork(data=None, Symbol_class=<class 'boolean.boolean.Symbol'>, allowed_in_name=('.', '_', ':', '-'), **kwargs)

Bases: dict

classmethod auto_cast(obj)

biolqm_format = None

copy() → a shallow copy of D

import_data(data)

classmethod load(filename, **kwargs)

rename(a, b)

rewrite(a, tr, simplify=True)

sanitize_names()

source(sep)

to_biolqm()

v(name)

vars(*names)

zero()

class colomoto.minibn.BlockParallelDynamics(spec, model, **opts)

Bases: BlockSequentialDynamics

class colomoto.minibn.BlockSequentialDynamics(sequence, model, loops=True)

Bases: PeriodicDynamics

class colomoto.minibn.BooleanNetwork(data=None, Symbol_class=<class 'boolean.boolean.Symbol'>, allowed_in_name=('.', '_', ':', '-'), **kwargs)

Bases: BaseNetwork

as_dnf()

biolqm_format = 'bnet'

constants()

dynamics(update_mode='asynchronous', init=None, loops=None)

Returns a directed graph (networkx.DiGraph object) of the dynamics with the update_mode.

Parameters:

• update_mode – either “asynchronous” (or equivalently “fully-asynchronous”), “synchronous” (or equivalently “parallel”), “general”. Alternatively, it can be a function returning an UpdateModeDynamics object.

• init (dict[str,int]) – Optional initial state from which the dynamics is computed.

import_data(data)

influence_graph()

inputs()

make_hash(simplify=False)

compute a hash for the BN based on its DNF representation

propagate_constants()

For each node having a constant function, replace references to that

node by the constant in the expression of all the other nodes.

Performs simple function simplification. Modifies the Boolean network in-place. The set of constant nodes can be accessed with the constants() method.

simplify(in_place=False, suspect_dnf=True)

if in_place, modifies the network in place, otherwise works on a copy returns it. if suspect_dnf, try harder simplifications for functions that are in DNF and have literals with opposite signs in clauses (will make CNF and DNF transformations).

source(sep=', ')

to_pint()

to_pyboolnet()

class colomoto.minibn.ElementaryUpdateModeDynamics(model, min_u, max_u, **opts)

Bases: UpdateModeDynamics

class colomoto.minibn.FAsyncRun(model, init, k, seed=None)

Bases: _RandomRun

Fully-asynchronous update run

Run at most k steps of an execution of given model from initial configuration init.

Stops at fixpoints.

select_for_update(nodes)

Return the sub-sequence of nodes to actually update

class colomoto.minibn.FullyAsynchronousDynamics(model, **opts)

Bases: ElementaryUpdateModeDynamics

class colomoto.minibn.GAsyncRun(model, init, k, seed=None)

Bases: _RandomRun

(General) asynchronous update run

Run at most k steps of an execution of given model from initial configuration init.

Stops at fixpoints.

select_for_update(nodes)

Return the sub-sequence of nodes to actually update

class colomoto.minibn.GeneralAsynchronousDynamics(model, **opts)

Bases: ElementaryUpdateModeDynamics

class colomoto.minibn.MVVar(obj)

Bases: Symbol

is_instanciated()

level()

nodevar()

class colomoto.minibn.MultiValuedNetwork(*args, **kwargs)

Bases: BaseNetwork

append(a, f)

biolqm_format = 'mnet'

import_data(data)

influence_graph()

rewrite(a, tr)

simplify()

source(sep=' <- ')

class colomoto.minibn.NOT(*args)

Bases: NOT

colomoto.minibn.ParallelDynamics

alias of SynchronousDynamics

class colomoto.minibn.PeriodicDynamics(sequence, model, loops=True)

Bases: UpdateModeDynamics

Periodic (deterministic) updating mode.

It is parameterized by a sequence of sets of nodes to update simultaneously in order to compute the next configuration.

class colomoto.minibn.SequentialDynamics(sequence, model, **opts)

Bases: BlockSequentialDynamics

class colomoto.minibn.SyncRun(model, init, k)

Bases: _Run

Synchronous update run

Run at most k steps of an execution of given model from initial configuration init.

Stops at fixpoints.

select_for_update(nodes)

Return the sub-sequence of nodes to actually update

class colomoto.minibn.SynchronousDynamics(model, loops=True, **opts)

Bases: ElementaryUpdateModeDynamics

class colomoto.minibn.UpdateModeDynamics(model, loops=False)

Bases: object

Abstract class for the updating mode of a BooleanNetwork object

dynamics()

partial_dynamics(init)

push(d, x)

random_walk(init, steps=0, stop_condition=None, stop_at=None)

colomoto.minibn.is_constant(f)

colomoto.minibn.simplify_dnf(ba, f)

colomoto.minibn.struct_of_dnf(ba, f, container=<class 'frozenset'>, sort=False)