This is file runs the main calculation for the fixed length TPS simulation. It requires the file alanine_dipeptide_fixed_tps_traj.nc, which is written in the notebook alanine_dipeptide_fixed_tps_traj.ipynb.

In this file, you will learn:

how to set up and run a fixed length TPS simulation

NB: This is a long calculation. In practice, it would be best to export the Python from this notebook, remove the live_visualizer, and run non-interactively on a computing node.



In [1]:

    
import openpathsampling as paths

Load engine, trajectory, and states from file



In [2]:

    
old_storage = paths.Storage("tps_nc_files/alanine_dipeptide_fixed_tps_traj.nc", "r")



In [3]:

    
engine = old_storage.engines[0]
C_7eq = old_storage.volumes.find('C_7eq')
alpha_R = old_storage.volumes.find('alpha_R')
traj = old_storage.trajectories[0]
phi = old_storage.cvs.find('phi')
psi = old_storage.cvs.find('psi')
template = old_storage.snapshots[0]



In [4]:

    
print engine.name
print engine.snapshot_timestep

TPS

The only difference between this and the flexible path length example in alanine_dipeptide_tps_run.ipynb is that we used a FixedLengthTPSNetwork. We selected the length=400 (8 ps) as a maximum length based on the results from a flexible path length run.



In [5]:

    
network = paths.FixedLengthTPSNetwork(C_7eq, alpha_R, length=400)



In [6]:

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



In [7]:

    
initial_conditions = scheme.initial_conditions_from_trajectories(traj)









    



No missing ensembles.
No extra ensembles.



In [8]:

    
sampler = paths.PathSampling(storage=paths.Storage("tps_nc_files/alanine_dipeptide_fixed_tps.nc", "w", template),
                             move_scheme=scheme,
                             sample_set=initial_conditions)
#sampler.live_visualizer = paths.StepVisualizer2D(network, phi, psi, [-3.14, 3.14], [-3.14, 3.14])



In [9]:

    
#sampler.live_visualizer = None



In [10]:

    
sampler.run(10000)









    



Working on Monte Carlo cycle number 10000
Running for 124921 seconds -  0.08 steps per second
Expected time to finish: 12 seconds
DONE! Completed 10000 Monte Carlo cycles.

With this done, you can go on to do the fixed-length parts of the analysis in alanine_dipeptide_tps_analysis.ipynb.