In [1]:
from __future__ import print_function
import openpathsampling as paths
import numpy as np
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#! lazy
import pyemma.coordinates as coor
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#! lazy
ref_storage = paths.Storage('engine_store_test.nc', mode='r')
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#! lazy
storage = paths.Storage('delete.nc', 'w')
storage.trajectories.save(ref_storage.trajectories[0])
Out[4]:
Import a PyEmma Coordinates Module
Using of pyemma featurizers or general other complex code requires a little trick to be storable. Since storing of code only works if we are not dependend on the context (scope) we need to wrap the construction of our featurizer in a function, that gets all it needs from the global scope as a parameter
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def pyemma_generator(f):
f.add_inverse_distances(f.pairs(f.select_Backbone()))
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cv = paths.collectivevariable.PyEMMAFeaturizerCV(
'pyemma',
pyemma_generator,
topology=ref_storage.snapshots[0].topology
).with_diskcache()
Now use this featurizer generating function to build a collective variable out of it. All we need for that is a name as usual, the generating function, the list of parameters - here only the topology and at best a test snapshot, a template.
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cv(ref_storage.trajectories[0]);
Let's save it to the storage
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#! lazy
print(storage.save(cv))
and apply the featurizer to a trajectory
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cv(storage.trajectories[0]);
Sync to make sure the cache is written to the netCDF file.
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cv(storage.snapshots.all());
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py_cv = storage.cvs['pyemma']
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store = storage.stores['cv%d' % storage.idx(py_cv)]
nc_var = store.variables['value']
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assert(nc_var.shape[1] == 15)
print(nc_var.shape[1])
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assert(nc_var.var_type == 'numpy.float32')
print(nc_var.var_type)
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#! ignore
print(storage.variables['attributes_json'][:])
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py_cv_idx = storage.idx(py_cv)
print(py_cv_idx)
py_emma_feat = storage.vars['attributes_json'][py_cv_idx]
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erg = py_emma_feat(storage.snapshots);
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#! lazy
print(erg[:,2:4])
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storage.close()
ref_storage.close()
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#! lazy
storage = paths.Storage('delete.nc', 'r')
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cv = storage.cvs[0]
Make sure that we get the same result
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assert np.allclose(erg, cv(storage.snapshots))
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storage.close()
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