def main(plot=True):
# make a graph representing a lattice in two dimensions
dim = [10,10]
grid_graph = nx.grid_graph(dim, periodic=False)
A = [(0,0)]
B = [(dim[0]-1, dim[1]-1)]
# make some random rates for each of the edges
rates = dict()
for u, v in grid_graph.edges_iter():
rates[(u,v)] = np.random.rand()
rates[(v,u)] = np.random.rand()
# set up the graph reduction object
reducer = GraphReduction(rates, A, B)
# make the kmc graph from the rates
kmc_graph = kmcgraph_from_rates(rates)
com_prob = reducer.compute_committor_probabilities(kmc_graph.nodes())
if plot:
# put it into matrix form and plot
P = np.zeros(dim)
for node, p in com_prob.iteritems():
x, y = node
P[x,y] = p
import matplotlib.pyplot as plt
plt.imshow(P, cmap="BrBG", vmin=0, vmax=1)
plt.title("probability of a trajectory reaching the lower right corner\n before reaching the upper left")
plt.colorbar()
plt.show()
def test_committor_probabilities(self, nnodes=10, nedges=20):
A = [0,1,2,3]
B = [8,9]
xx = 5
maker = _MakeRandomGraph(nnodes=nnodes, nedges=nedges, node_set=A+B)
graph = maker.run()
graph_backup = graph.copy()
kmc = KineticMonteCarlo(graph_backup, debug=False)
reducer = GraphReduction(maker.rates, A, B)
nodes = set(A + B + [xx])
PxB = reducer.compute_committor_probabilities(nodes)
for x in nodes:
self.assertIn(x, PxB)
for x in nodes:
PxB_kmc = kmc.committor_probability(x, A, B, niter=1000)
self.assertAlmostEqual(PxB[x], PxB_kmc, delta=.1)
