Test of CV functionality.

Most storage related tests are nosetests now. So look in testcollectivevariable.py please.



In [1]:

    
from __future__ import print_function
import openpathsampling as paths
import numpy as np
import openpathsampling.engines.openmm as peng



In [2]:

    
# force numpy print options for test comparison
np.set_printoptions(precision=6, formatter={'float_kind': lambda x: "{:.6f}".format(x)})

Load first frame from test pdb.



In [3]:

    
template = peng.snapshot_from_pdb('../resources/AD_initial_frame.pdb')

Create a simple CV without an underlying function.



In [4]:

    
cv0 = paths.CollectiveVariable('func0')

Create a complicated function.



In [5]:

    
center = 1
def dist(snapshot, center, np):
    return np.sum(snapshot.coordinates._value[0]) - center

Create collective variable from this function. Note that you have to specify center and np to make this work



In [6]:

    
cv1 = paths.FunctionCV('func1', dist, center=center, np=np, cv_time_reversible=False).with_diskcache()
cv2 = paths.FunctionCV('func2', dist, center=center, np=np, cv_wrap_numpy_array=True).with_diskcache()
cv3 = paths.FunctionCV('func3', dist, center=center, np=np, cv_wrap_numpy_array=True, cv_time_reversible=True).with_diskcache()

Create storage to test save and load.



In [7]:

    
#! lazy
storage = paths.Storage('can_be_deleted.nc', mode='w')
print(storage.snapshots.save(template))









    



283860163354172696480456782077640048652

Save CV



In [8]:

    
#! lazy
print(storage.save([cv0, cv1, cv2, cv3]))









    



[(store.attributes[PseudoAttribute] : 4 object(s), 20, 283860163354172696480456782077640048654L), (store.attributes[PseudoAttribute] : 4 object(s), 20, 283860163354172696480456782077640048656L), (store.attributes[PseudoAttribute] : 4 object(s), 20, 283860163354172696480456782077640048658L), (store.attributes[PseudoAttribute] : 4 object(s), 20, 283860163354172696480456782077640048660L)]



In [9]:

    
#! lazy
print(storage.cvs.index)









    



{283860163354172696480456782077640048656L: 1, 283860163354172696480456782077640048658L: 2, 283860163354172696480456782077640048660L: 3, 283860163354172696480456782077640048654L: 0}

Set the CV value for the storage.template



In [10]:

    
cv0[template] = 10.0

and create a storage for it



In [11]:

    
storage.cvs.add_diskcache(cv0, allow_incomplete=True)

And we should have a float store



In [12]:

    
storage.cvs.sync(cv0)



In [13]:

    
assert(cv0._store_dict.value_store.vars['value'][:] == [10.0])

Test function for reversed template.



In [14]:

    
dd = dist(template.reversed, center, np)
print("{:.8f}".format(dd))

Create another CV. This time using the from_template function.



In [15]:

    
print(cv0([template, template]))
print(cv1([template, template]))









    



[10.0, 10.0]
[3.3921003341674805, 3.3921003341674805]



In [16]:

    
print(type(cv0([template, template])))
print(type(cv0([template, template])[0]))
print(type(cv1([template, template])))
print(type(cv1([template, template])[0]))









    



<type 'list'>
<type 'float'>
<type 'list'>
<type 'numpy.float64'>



In [17]:

    
#! skip
print(storage.cvs.variables['json'][:])









    



[u'{"_cls":"CollectiveVariable","_dict":{"cv_time_reversible":false,"name":"func0"}}'
 u'{"_cls":"FunctionCV","_dict":{"name":"func1","f":{"_marshal":"YwMAAAADAAAAAwAAAEMAAQBzGwAAAHwCAGoAAHwAAGoBAGoCAGQBABmDAQB8AQAYUygCAAAATmkAAAAAKAMAAAB0AwAAAHN1bXQLAAAAY29vcmRpbmF0ZXN0BgAAAF92YWx1ZSgDAAAAdAgAAABzbmFwc2hvdHQGAAAAY2VudGVydAIAAABucCgAAAAAKAAAAABzHgAAADxpcHl0aG9uLWlucHV0LTUtNzlhNzEwNDZhYjU1PnQEAAAAZGlzdAIAAABzAgAAAAAB","_module_vars":[],"_global_vars":[]},"cv_time_reversible":false,"cv_wrap_numpy_array":false,"cv_requires_lists":false,"cv_scalarize_numpy_singletons":false,"kwargs":{"np":{"_import":"numpy"},"center":1}}}'
 u'{"_cls":"FunctionCV","_dict":{"name":"func2","f":{"_marshal":"YwMAAAADAAAAAwAAAEMAAQBzGwAAAHwCAGoAAHwAAGoBAGoCAGQBABmDAQB8AQAYUygCAAAATmkAAAAAKAMAAAB0AwAAAHN1bXQLAAAAY29vcmRpbmF0ZXN0BgAAAF92YWx1ZSgDAAAAdAgAAABzbmFwc2hvdHQGAAAAY2VudGVydAIAAABucCgAAAAAKAAAAABzHgAAADxpcHl0aG9uLWlucHV0LTUtNzlhNzEwNDZhYjU1PnQEAAAAZGlzdAIAAABzAgAAAAAB","_module_vars":[],"_global_vars":[]},"cv_time_reversible":false,"cv_wrap_numpy_array":true,"cv_requires_lists":false,"cv_scalarize_numpy_singletons":false,"kwargs":{"np":{"_import":"numpy"},"center":1}}}'
 u'{"_cls":"FunctionCV","_dict":{"name":"func3","f":{"_marshal":"YwMAAAADAAAAAwAAAEMAAQBzGwAAAHwCAGoAAHwAAGoBAGoCAGQBABmDAQB8AQAYUygCAAAATmkAAAAAKAMAAAB0AwAAAHN1bXQLAAAAY29vcmRpbmF0ZXN0BgAAAF92YWx1ZSgDAAAAdAgAAABzbmFwc2hvdHQGAAAAY2VudGVydAIAAABucCgAAAAAKAAAAABzHgAAADxpcHl0aG9uLWlucHV0LTUtNzlhNzEwNDZhYjU1PnQEAAAAZGlzdAIAAABzAgAAAAAB","_module_vars":[],"_global_vars":[]},"cv_time_reversible":true,"cv_wrap_numpy_array":true,"cv_requires_lists":false,"cv_scalarize_numpy_singletons":false,"kwargs":{"np":{"_import":"numpy"},"center":1}}}']



In [18]:

    
cv0j = storage.cvs.vars['json'][0]
cv1j = storage.cvs.vars['json'][1]
cv2j = storage.cvs.vars['json'][2]
cv3j = storage.cvs.vars['json'][3]



In [19]:

    
res = cv0j([template, template, template])
assert(res[0] == res[1] == res[2])
res = cv1j([template, template, template])
assert(res[0] == res[1] == res[2])
res = cv2j([template, template, template])
assert(res[0] == res[1] == res[2])
res = cv3j([template, template, template])
assert(res[0] == res[1] == res[2])

PseudoAttributes



In [20]:

    
t = paths.Trajectory([template] * 4)



In [21]:

    
#! lazy
print(storage.save(t))









    



(store.trajectories[Trajectory] : 1 object(s), 0, 283860163354172696480456782077640048728L)



In [22]:

    
def ff(t, cv3):
    return max(cv3(t))



In [23]:

    
a = paths.netcdfplus.FunctionPseudoAttribute('max_cv', paths.Trajectory, ff, cv3=cv3)



In [24]:

    
#! lazy
print(storage.attributes.save(a))









    



283860163354172696480456782077640048730



In [25]:

    
print("{:.8f}".format(a(t)))



In [26]:

    
p = paths.netcdfplus.LoaderProxy(storage.trajectories, t.__uuid__)



In [27]:

    
print("{:.8f}".format(a(p)))



In [28]:

    
print(storage.trajectories.add_attribute(paths.netcdfplus.ValueStore, a, t, allow_incomplete=False))









    



store.trajectories_max_cv[None/ANY] : (not created)



In [29]:

    
ats = storage.trajectories.attribute_list[a]



In [30]:

    
print(ats.vars['value'][:])



In [31]:

    
print(a._store_dict.value_store.vars['value'][:])



In [32]:

    
storage.trajectories.sync_attribute(a)



In [33]:

    
storage.attributes.has_cache(a)









    Out[33]:





True



In [34]:

    
storage.attributes.sync(a)



In [35]:

    
storage.close()



In [36]:

    
storage = paths.Storage('can_be_deleted.nc', mode='r')



In [37]:

    
storage.attributes[4]









    Out[37]:





<openpathsampling.netcdfplus.attribute.FunctionPseudoAttribute at 0x7f2957bf4d50>



In [38]:

    
storage.close()



In [ ]: