def run_exit_times(statePair, runs):
np.random.seed()
asympot = potentials.asym3Dpotential(scalefactor=2.0)
r1 = np.array(np.zeros(3))
p1 = mrd.particle(r1, 1.0)
sphereboundary = mrd.reflectiveSphere(4.)
integrator = integrators.brownianDynamicsSp(asympot, sphereboundary, p1,
0.001, 1.0)
sim = mrd.simulation(integrator)
exitTimes = []
exitPositions = []
fpts = []
exits = 0
run = 0
totalRuns = 0
while run < runs:
integrator.pa.position = np.array(minima[statePair[0]])
integrator.clock = 0.
totalRuns += 1
while True:
sim.integrator.integrate()
if np.linalg.norm(minima[statePair[1]] -
integrator.pa.position) < 0.2:
fpts.append(integrator.clock)
run += 1
break
elif np.linalg.norm(integrator.pa.position) > R:
exits += 1
exitTimes.append(integrator.clock)
exitPositions.append(integrator.pa.position)
break
print str(statePair[0]) + ' to ' + str(statePair[1])
return [np.array(fpts), totalRuns, exitTimes, exitPositions]
def run_mfpts_to_bath(state, runs, scalef, dt, runbound):
global MFPTS, minima
np.random.seed()
asympot3D = potentials.asym3Dpotential(scalefactor = scalef)
p1 = mrd.particle(np.zeros(3), 1.0)
sphereboundary = mrd.reflectiveSphere(4.0)
integrator = integrators.brownianDynamicsSp(asympot3D, sphereboundary, p1, dt, 1.0)
sim = mrd.simulation(integrator)
fpts = []
for run in range(runs):
integrator.pa.position = np.array(minima[state])
fpts.append(sim.run_mfpt(runbound))
print 'state '+str(state)+' done'
return np.array(fpts)
def run_mfpts_from_bath(bathRad, numpoints, scalefactor, dt):
np.random.seed()
asympot3D = potentials.asym3Dpotential(scalefactor=scalefactor)
p1 = mrd.particle(np.zeros(3), 1.0)
sphereboundary = mrd.reflectiveSphere(bathRad)
integrator = integrators.brownianDynamicsSp(asympot3D, sphereboundary, p1,
dt, 1.0)
sim = mrd.simulation(integrator)
startpoints = get_startpoints(numpoints, bathRad - radiusThreshold)
fpts = []
for startpoint in startpoints:
integrator.pa.position = startpoint
integrator.clock = 0.
fpts.append(sim.run_mfpt_points(np.array(minima), 0.2))
print 'bath to ' + str(bathRad)
return np.array(fpts)