pele.rates._kmc

pele.rates._kmc.weighted_pick()

Source

def weighted_pick(weights):

sample uniformly from the objects with given weights

Parameters:

weightsdict

a dictionary with objects as the keys and weights as the values

Returns:

the object selected uniformly from the weights

class pele.rates._kmc.KineticMonteCarlo

Source

class KineticMonteCarlo:

class to do kinetic Monte Carlo runs

Parameters:

graphnetworkx.DiGraph()

see GraphReduction for a description of how to attach transition probabilities and waiting times

KineticMonteCarlo.first_passage()

Source

def first_passage(self, a, B, maxiter=100000):

start at a and stop when you get to B, return the time elapsed

Parameters:

anode

the start node

Biterable

the set of final states

Returns:

total_time, niter

KineticMonteCarlo.mean_first_passage_time()

Source

def mean_first_passage_time(self, a, B, niter=1000):

compute the mean first passage time from node a to nodes B

KineticMonteCarlo.mean_rate()

Source

def mean_rate(self, A, B, niter=1000, weights=None):

return the mean rate from A to B

Parameters:

A, Biteratables

groups of nodes

weightsdict

The equilibrium occupation probabilities of the states in group A. For doing the weighted average over the states in A

Notes

the mean rate is the inverse mean first passage time averaged over the states in A

KineticMonteCarlo.committor()

Source

def committor(self, x, A, B, maxiter=100000):

starting from x return True if the trajectory ends up B before it enters A