Analyzing a split MSTIS simulation

Included in this notebook:

Opening split files and look at the data



In [1]:

    
%matplotlib inline
import matplotlib.pyplot as plt
import openpathsampling as paths
import numpy as np



In [2]:

    
%%time
storage = paths.AnalysisStorage('mstis_data.nc')









    



CPU times: user 7.65 s, sys: 127 ms, total: 7.78 s
Wall time: 7.78 s

Analyze the rate with no snapshots present in the analyzed file



In [3]:

    
mstis = storage.networks.load(0)



In [4]:

    
mstis.hist_args['max_lambda'] = { 'bin_width' : 0.02, 'bin_range' : (0.0, 0.5) }
mstis.hist_args['pathlength'] = { 'bin_width' : 5, 'bin_range' : (0, 150) }



In [5]:

    
%%time
mstis.rate_matrix(storage.steps, force=True)









    



CPU times: user 4.87 s, sys: 245 ms, total: 5.12 s
Wall time: 4.96 s






    Out[5]:






  
    
      
      {x|opA(x) in [0.0, 0.2]}
      {x|opB(x) in [0.0, 0.2]}
      {x|opC(x) in [0.0, 0.2]}
    
  
  
    
      {x|opA(x) in [0.0, 0.2]}
      NaN
      0.00139595
      0
    
    
      {x|opB(x) in [0.0, 0.2]}
      0.00229702
      NaN
      0.0128833
    
    
      {x|opC(x) in [0.0, 0.2]}
      0.000852395
      0.00485865
      NaN

Move scheme analysis



In [6]:

    
scheme = storage.schemes[0]



In [7]:

    
scheme.move_summary(storage.steps)









    



Null moves for 1 cycles. Excluding null moves:
ms_outer_shooting ran 4.500% (expected 4.98%) of the cycles with acceptance 21/27 (77.78%)
repex ran 20.667% (expected 22.39%) of the cycles with acceptance 49/124 (39.52%)
shooting ran 47.333% (expected 44.78%) of the cycles with acceptance 207/284 (72.89%)
minus ran 2.500% (expected 2.99%) of the cycles with acceptance 11/15 (73.33%)
pathreversal ran 25.000% (expected 24.88%) of the cycles with acceptance 99/150 (66.00%)

Replica move history tree



In [8]:

    
import openpathsampling.visualize as vis
reload(vis)
from IPython.display import SVG



In [9]:

    
tree = vis.PathTree(
    storage.steps[0:200],
    vis.ReplicaEvolution(replica=2, accepted=False)
)

SVG(tree.svg())









    Out[9]:



In [10]:

    
decorrelated = tree.generator.decorrelated
print "We have " + str(len(decorrelated)) + " decorrelated trajectories."









    



We have 3 decorrelated trajectories.

Visualizing trajectories



In [11]:

    
from toy_plot_helpers import ToyPlot
background = ToyPlot()
background.contour_range = np.arange(-1.5, 1.0, 0.1)
background.add_pes(storage.engines[0].pes)



In [12]:

    
xval = paths.FunctionCV("xval", lambda snap : snap.xyz[0][0])
yval = paths.FunctionCV("yval", lambda snap : snap.xyz[0][1])
live_vis = paths.StepVisualizer2D(mstis, xval, yval, [-1.0, 1.0], [-1.0, 1.0])
live_vis.background = background.plot()

to make this work we need the actual snapshot coordinates! These are not present in the data file anymore so we attach the traj as a fallback. We are not using analysis storage since we do not cache anything.



In [14]:

    
storage.cvs









    Out[14]:





store.cvs[CollectiveVariable]



In [13]:

    
fallback = paths.Storage('mstis_traj.nc', 'r')



In [14]:

    
storage.fallback = fallback



In [15]:

    
live_vis.draw_samples(list(tree.samples))









    Out[15]:



In [ ]:

	{x\|opA(x) in [0.0, 0.2]}	{x\|opB(x) in [0.0, 0.2]}	{x\|opC(x) in [0.0, 0.2]}
{x\|opA(x) in [0.0, 0.2]}	NaN	0.00139595	0
{x\|opB(x) in [0.0, 0.2]}	0.00229702	NaN	0.0128833
{x\|opC(x) in [0.0, 0.2]}	0.000852395	0.00485865	NaN