In [1]:

    

import openpathsampling as paths


    

In [5]:

    

# always the most complicated bit
import openpathsampling.engines.toy as toys
import numpy as np

pes = (toys.OuterWalls([1.0,1.0], [0.0,0.0]) + 
       toys.Gaussian(-0.7, [12.0, 0.5], [-0.5, 0.0]) +
       toys.Gaussian(-0.7, [12.0, 0.5], [0.5, 0.0]))
topology = toys.Topology(n_spatial=2, masses=[1.0, 1.0], pes=pes)
engine = toys.Engine({'integ': toys.LangevinBAOABIntegrator(dt=0.02, temperature=0.1, gamma=2.5),
                              'n_frames_max': 5000,
                              'n_steps_per_frame': 10}, topology)
template = toys.Snapshot(coordinates=np.array([[0.0, 0.0]]),
                         velocities=np.array([[0.0, 0.0]]),
                        engine=engine)


    

In [6]:

    

# states are volumes in a CV space: define the CV
def xval(snapshot):
    return snapshot.xyz[0][0]

cv = paths.FunctionCV("xval", xval)


    

In [7]:

    

stateA = paths.CVDefinedVolume(cv, float("-inf"), -0.5).named("A")
stateB = paths.CVDefinedVolume(cv, 0.5, float("inf")).named("B")


    

In [8]:

    

network = paths.TPSNetwork(stateA, stateB)


    

In [9]:

    

scheme = paths.OneWayShootingMoveScheme(network, engine=engine)


    

In [11]:

    

# I'll fake an initial trajectory
trajectory = paths.Trajectory([
    toys.Snapshot(coordinates=np.array([[-.55+k*0.1, 0.0]]),
                  velocities=np.array([[0.1, 0.0]]),
                  engine=engine)
    for k in range(12)
])


    

In [12]:

    

initial_conditions = scheme.initial_conditions_from_trajectories(trajectory)


    

    




# finished generating: still missing 0 samples
1

In [13]:

    

storage = paths.Storage("simple.nc", "w", template=template)
simulation = paths.PathSampling(storage, scheme, initial_conditions)


    

In [14]:

    

simulation.run(1000)


    

    




Working on Monte Carlo cycle number 1000
Running for 328 seconds -  3.04 steps per second
Expected time to finish: 0 seconds
DONE! Completed 1000 Monte Carlo cycles.

In [15]:

    

from collections import Counter


    

In [17]:

    

q = Counter()
for ch in storage.movechanges:
    q.update([ch.__class__])


    

In [18]:

    

q


    

    
Out[18]:





Counter({openpathsampling.movechange.EmptyMoveChange: 1,
         openpathsampling.movechange.AcceptedSampleMoveChange: 578,
         openpathsampling.movechange.RejectedSampleMoveChange: 422,
         openpathsampling.movechange.RandomChoiceMoveChange: 3000,
         openpathsampling.movechange.PathSimulatorMoveChange: 1000})

In [11]:

    

# 1. path tree


    

In [12]:

    

# 2. committor based on shooting points


    

In [13]:

    

# 3. scheme.move_summary(storage)


    

In [14]:

    

# 4. path length histogram?