def pathways(self, fraction=1.0, maxiter=1000):
r"""Decompose flux network into dominant reaction paths.
Parameters
----------
fraction : float, optional
Fraction of total flux to assemble in pathway decomposition
maxiter : int, optional
Maximum number of pathways for decomposition
Returns
-------
paths : list
List of dominant reaction pathways
capacities: list
List of capacities corresponding to each reactions pathway in paths
References
----------
.. [1] P. Metzner, C. Schuette and E. Vanden-Eijnden.
Transition Path Theory for Markov Jump Processes.
Multiscale Model Simul 7: 1192-1219 (2009)
"""
return tptapi.pathways(self.net_flux,
self.A,
self.B,
fraction=fraction,
maxiter=maxiter)
def test_pathways_sparse_incomplete(self):
paths, capacities = pathways(self.F_sparse, self.A, self.B, fraction=0.5)
self.assertTrue(len(paths) == len(self.paths[0:2]))
self.assertTrue(len(capacities) == len(self.capacities[0:2]))
for i in range(len(paths)):
assert_allclose(paths[i], self.paths[i])
assert_allclose(capacities[i], self.capacities[i])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
paths, capacities = pathways(self.F, self.A, self.B, fraction=1.0, maxiter=1)
for i in range(len(paths)):
assert_allclose(paths[i], self.paths[i])
assert_allclose(capacities[i], self.capacities[i])
assert issubclass(w[-1].category, RuntimeWarning)
def test_pathways_dense(self):
paths, capacities = pathways(self.F, self.A, self.B)
self.assertTrue(len(paths) == len(self.paths))
self.assertTrue(len(capacities) == len(self.capacities))
for i in range(len(paths)):
assert_allclose(paths[i], self.paths[i])
assert_allclose(capacities[i], self.capacities[i])
def pathways(self, fraction=1.0, maxiter=1000):
"""Decompose flux network into dominant reaction paths.
Parameters
----------
fraction : float, optional
Fraction of total flux to assemble in pathway decomposition
maxiter : int, optional
Maximum number of pathways for decomposition
Returns
-------
paths : list
List of dominant reaction pathways
capacities: list
List of capacities corresponding to each reactions pathway in paths
"""
return pathways(self._flux,
self.A,
self.B,
fraction=fraction,
maxiter=maxiter)
