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%matplotlib inline
import openpathsampling as paths
import numpy as np
As always, we load things from files so we don't have to set them up again.
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old = paths.Storage("mistis.nc", 'r')
engine = old.engines[0]
network = old.networks[0]
states = set(network.initial_states + network.final_states)
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# must ensure that the diskcache is disabled in order to save,
# otherwise it looks for things that aren't there!
cvs = old.cvs[:]
for cv in cvs:
cv.disable_diskcache()
The flux_pairs variable is a list of 2-tuples, where the first element is the state we're calculating the flux out of, and the second element is the interface we're calculating the flux through.
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flux_pairs = [(t.stateA, t.interfaces[0]) for t in network.transitions.values()]
Set up the simulation and run it!
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sim = paths.DirectSimulation(
storage=None,
engine=engine,
states=states,
flux_pairs=flux_pairs,
initial_snapshot=old.snapshots[0]
)
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%%time
sim.run(150000) # 30 sec
#sim.run(1500000) # 6 min
#sim.run(15000000) # 60 min
#sim.run(150000000) # 10 hr
#sim.run(800000000) # 2 days
Now we move on to the analysis.
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sim.rate_matrix
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sim.n_transitions
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fluxes = sim.fluxes
for f in fluxes:
print f[0].name, f[1].name, fluxes[f]
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sim.n_flux_events
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sim.results
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output = paths.Storage("direct_simulation.nc", 'w')
output.save(old.snapshots[0])
output.save(sim)
output.tag['direct_results'] = sim.results
output.close()