def test_return_parameters_from_template(self):
atom_pairs = [[0, 1], [10, 14]]
atom_pair_op = op.MSMBFeaturizerCV("atom_pairs",
AtomPairsFeaturizer,
topology=self.topology,
pair_indices=atom_pairs)
# little trick. We just pretend the atom_pairs_op is a function we want
# to use it cannot be stored though, but for from_template it is enough
params = NetCDFPlus.get_value_parameters(
atom_pair_op(self.traj_topology[0]))
assert params['var_type'] == 'numpy.float32'
assert params['simtk_unit'] is None
assert params['dimensions'] == tuple([2])
def test_return_parameters_from_template(self):
atom_pairs = [[0, 1], [10, 14]]
atom_pair_op = op.MSMBFeaturizerCV(
"atom_pairs",
AtomPairsFeaturizer,
topology=self.topology,
pair_indices=atom_pairs)
# little trick. We just predent the atom_pairs_op is a function we want
# to use it cannot be stored though, but for from_template it is enough
params = NetCDFPlus.get_value_parameters(
atom_pair_op(self.traj_topology[0]))
assert params['var_type'] == 'numpy.float32'
assert params['simtk_unit'] is None
assert params['dimensions'] == tuple([2])
def add_cv(self, cv, template, allow_incomplete=None, chunksize=None):
"""
Parameters
----------
cv : :obj:`openpathsampling.CollectiveVariable`
template : :obj:`openpathsampling.engines.BaseSnapshot`
chunksize : int
allow_incomplete : bool
Returns
-------
:obj:`openpathsampling.netcdfplus.ObjectStore`
int
"""
if cv in self.cv_list:
return self.cv_list[cv]
if allow_incomplete is None:
allow_incomplete = cv.diskcache_allow_incomplete
if chunksize is None:
chunksize = cv.diskcache_chunksize
if template is None:
template = cv.diskcache_template
time_reversible = cv.cv_time_reversible
if not time_reversible:
# in the rare case of not time_reversible we use store partial
allow_incomplete = True
if not allow_incomplete:
# in complete mode we force chunk size one to match it to snapshots
chunksize = self.default_store_chunk_size
# determine value type and shape
params = NetCDFPlus.get_value_parameters(cv(template))
shape = params['dimensions']
if shape is None:
chunksizes = None
else:
chunksizes = tuple(params['dimensions'])
cv_idx = self.storage.cvs.index[cv.__uuid__]
store = SnapshotValueStore(time_reversible=time_reversible,
allow_incomplete=allow_incomplete,
chunksize=chunksize)
store_name = SnapshotWrapperStore._get_cv_name(cv_idx)
self.storage.create_store(store_name, store, False)
if store.allow_incomplete:
# we are not using the .initialize function here since we
# only have one variable and only here know its shape
self.storage.create_dimension(store.prefix, 0)
if shape is not None:
shape = tuple(list(shape))
chunksizes = tuple([chunksize] + list(chunksizes))
else:
shape = tuple()
chunksizes = tuple([chunksize])
# create the variable
store.create_variable(
'value',
var_type=params['var_type'],
dimensions=shape,
chunksizes=chunksizes,
simtk_unit=params['simtk_unit'],
)
store.create_variable('index', 'index')
else:
chunksize = self.default_store_chunk_size
# todo: Remove once netcdf4/python bug in Unidata/netcdf4-python#566 has
# been resolved. It seems that so far, after data has been written to a
# dimension you have to use chunksize 1 for any new variables in that
# dimension Even if the other variables all share the same chunksize
chunksize = 1
if shape is not None:
shape = tuple(['snapshots'] + list(shape))
chunksizes = tuple([chunksize] + list(chunksizes))
else:
shape = tuple(['snapshots'])
chunksizes = tuple([chunksize])
# create the variable
store.storage.create_variable(
store_name + '_value',
var_type=params['var_type'],
dimensions=shape,
chunksizes=chunksizes,
simtk_unit=params['simtk_unit'],
)
setattr(store, 'value', self.storage.vars[store_name + '_value'])
store.initialize()
store_idx = int(len(self.storage.dimensions['cvcache']))
self.cv_list[cv] = (store, store_idx)
self.storage.vars['cvcache'][store_idx] = store
# use the cache and function of the CV to fill the store when it is made
if not allow_incomplete:
indices = self.vars['uuid'][:]
for pos, idx in enumerate(indices):
proxy = LoaderProxy(self.storage.snapshots, idx)
value = cv._cache_dict._get(proxy)
if value is None:
# not in cache so compute it if possible
if cv._eval_dict:
value = cv._eval_dict([proxy])[0]
else:
value = None
if value is not None:
store.vars['value'][pos] = value
store.cache[pos] = value
cv.set_cache_store(store)
return store, store_idx
def add_cv(self, cv, template, allow_incomplete=None, chunksize=None):
"""
Parameters
----------
cv : :obj:`openpathsampling.CollectiveVariable`
template : :obj:`openpathsampling.engines.BaseSnapshot`
chunksize : int
allow_incomplete : bool
Returns
-------
:obj:`openpathsampling.netcdfplus.ObjectStore`
int
"""
if cv in self.cv_list:
return self.cv_list[cv]
if allow_incomplete is None:
allow_incomplete = cv.diskcache_allow_incomplete
if chunksize is None:
chunksize = cv.diskcache_chunksize
if template is None:
template = cv.diskcache_template
time_reversible = cv.cv_time_reversible
if not time_reversible:
# in the rare case of not time_reversible we use store partial
allow_incomplete = True
if not allow_incomplete:
# in complete mode we force chunk size one to match it to snapshots
chunksize = self.default_store_chunk_size
# determine value type and shape
params = NetCDFPlus.get_value_parameters(cv(template))
shape = params['dimensions']
if shape is None:
chunksizes = None
else:
chunksizes = tuple(params['dimensions'])
cv_idx = self.storage.cvs.index[cv.__uuid__]
store = SnapshotValueStore(
time_reversible=time_reversible,
allow_incomplete=allow_incomplete,
chunksize=chunksize
)
store_name = SnapshotWrapperStore._get_cv_name(cv_idx)
self.storage.create_store(store_name, store, False)
if store.allow_incomplete:
# we are not using the .initialize function here since we
# only have one variable and only here know its shape
self.storage.create_dimension(store.prefix, 0)
if shape is not None:
shape = tuple(list(shape))
chunksizes = tuple([chunksize] + list(chunksizes))
else:
shape = tuple()
chunksizes = tuple([chunksize])
# create the variable
store.create_variable(
'value',
var_type=params['var_type'],
dimensions=shape,
chunksizes=chunksizes,
simtk_unit=params['simtk_unit'],
)
store.create_variable('index', 'index')
else:
chunksize = self.default_store_chunk_size
# todo: Remove once netcdf4/python bug in Unidata/netcdf4-python#566 has
# been resolved. It seems that so far, after data has been written to a
# dimension you have to use chunksize 1 for any new variables in that
# dimension Even if the other variables all share the same chunksize
chunksize = 1
if shape is not None:
shape = tuple(['snapshots'] + list(shape))
chunksizes = tuple([chunksize] + list(chunksizes))
else:
shape = tuple(['snapshots'])
chunksizes = tuple([chunksize])
# create the variable
store.storage.create_variable(
store_name + '_value',
var_type=params['var_type'],
dimensions=shape,
chunksizes=chunksizes,
simtk_unit=params['simtk_unit'],
)
setattr(store, 'value', self.storage.vars[store_name + '_value'])
store.initialize()
store_idx = int(len(self.storage.dimensions['cvcache']))
self.cv_list[cv] = (store, store_idx)
self.storage.vars['cvcache'][store_idx] = store
# use the cache and function of the CV to fill the store when it is made
if not allow_incomplete:
indices = self.vars['uuid'][:]
for pos, idx in enumerate(indices):
proxy = LoaderProxy(self.storage.snapshots, idx)
value = cv._cache_dict._get(proxy)
if value is None:
# not in cache so compute it if possible
if cv._eval_dict:
value = cv._eval_dict([proxy])[0]
else:
value = None
if value is not None:
store.vars['value'][pos] = value
store.cache[pos] = value
cv.set_cache_store(store)
return store, store_idx
