openpathsampling.storage.stores.TrajectoryStore

class openpathsampling.storage.stores.TrajectoryStore

__init__()

Parameters:

• content_class

• json (bool or str json or jsonobj) – if False the store will not create a json variable for serialization if True the store will use the json pickling to store objects and a single storable object will be serialized and not referenced. If a string is given the string is taken as the variable type of the json variable. Here only two values are allowed: jsonobj (equivalent to True) or json which will also reference directly given storable objects.

• nestable (bool) – if True this marks the content_class to be saved as nested dict objects and not a pointing to saved objects. So the saved complex object is only stored once and not split into several objects that are referenced by each other in a tree-like fashion

Notes

Usually you want caching, but limited. Recommended is to use an LRUCache with a reasonable maximum number of objects that depends on the typical number of objects to cache and their size

The class that takes care of storing data in a file is called a Storage, so the netCDF+ subclassed Storage is a storage. The classes that know how to load and save an object from the storage are called Store, like ObjectStore, SampleStore, etc…

The difference between json and jsonobj is subtle. Consider storing a complex object. Then there are two ways to do that. 1. json: Store a reference to the object (provided) it is stored and 2. jsonobj: serialize the object and only use references for contained objects. All inner objects will always be stored using references. The only exception is using nestable. Consider objects that contain references to objects of the same type, like e.g. operations in an equation (2*3 + 3). Each operation represents a value but each operation needs values to operate on. To save such an object you have again two options: 1. nestable=False. Store all single objects and always reference the contained objects. For an equation that would mean to store several objects op1 = plus(op2, 3), op2 = times(2, 3). Since this is correct though not intuitive you can also use 2. nestable=True. Store all the serialized objects nested into one object (string). For our example this corresponds to plus(times(2,3), 3).

Methods

__init__()
add_attribute(store_cls, attribute, template)
add_single_to_cache(idx, snaps)	Add a single object to cache by json
args()	Return a list of args of the __init__ function of a class
base()	Return the most parent class actually derived from StorableObject
cache_all()	Load all samples as fast as possible into the cache
clear_cache()	Clear the cache and force reloading
complete_attribute(attribute)	Compute all missing values of a CV and store them
count_weaks()	Return number of objects subclassed from StorableObject still in memory
create_uuid_index()
create_variable(var_name, var_type[, ...])	Create a new variable in the netCDF storage.
descendants()	Return a list of all subclassed objects
fix_name()	Set the objects name to be immutable.
forget(obj)	This will revert remembering non-stored objects.
free()	Return the number of the next free index for this store
from_dict(dct)	Reconstruct an object from a dictionary representaiton
get(item)
get_uuid()
idx(obj)	Return the index in this store for a given object
initialize([units])	Initialize the associated storage to allow for object storage.
is_created()
iter_snapshot_indices()	Return an iterator over the lists of snapshot indices for all trajectories in the storage
load(idx)	Returns an object from the storage.
load_indices()
mention(trajectory)	Save a trajectory and store its snapshots only shallow
named(name)	Name an unnamed object.
objects()	Returns a dictionary of all storable objects
pos(obj)
pos_uuid(uid)
prefix_delegate(dct)
proxy(item)	Return a proxy of a object for this store
reference(obj)
register(storage, prefix)	Associate the object store to a specific storage with a given prefix
register_fallback(store)
remember(obj)	Tell a store that an obj should be assumed as stored
restore()
reverse_uuid()
ruuid(uid)
save(obj[, idx])	Saves an object to the storage.
set_caching(caching)	Set the caching mode for this store
set_dimension_prefix_store([prefix_store])	Select which store or none should be used to prefix dimension names
set_observer(active)	(De-)Activate observing creation of storable objects
snapshot_indices(idx)	Load snapshot indices for trajectory with ID 'idx' from the storage
sync_attribute(attribute)	Store all cached values of a CV in the diskcache
to_dict()	Convert object into a dictionary representation
write(variable, idx, obj[, attribute])

Attributes

ACTIVE_LONG
CREATION_COUNT
INSTANCE_UUID
allowed_types
base_cls	Return the base class
base_cls_name	Return the name of the base class
cls	Return the class name as a string
default_cache
default_name	Return the default name.
default_store_chunk_size
dimension_prefix
first	Returns the first stored object.
is_named	True if this object has a custom name.
last	Returns the last generated trajectory.
name	Return the current name of the object.
observe_objects
simplifier	Return the simplifier instance used to create JSON serialization
storage	Return the associated storage object