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ynput__OpenPype | assignments_and_allocations.rst | Tutorial / Subdoc | Working with assignments and allocations | MIT License | ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/assignments_and_allocations.rst | [
"ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/source/ftrack_api/session.py"
] | Working with assignments and allocations
The API exposes assignments and allocations relationships on objects in
the project hierarchy. You can use these to retrieve the allocated or
assigned resources, which can be either groups or users.
Allocations can be used to allocate users or groups to a project team,
while assignments are more explicit and is used to assign users to
tasks. Both assignment and allocations are modelled as Appointment
objects, with a type attribute indicating the type of the appoinment.
The following example retrieves all users part of the project team:
# Retrieve a project
project = session.query('Project').first()
# Set to hold all users part of the project team
project_team = set()
# Add all allocated groups and users
for allocation in project['allocations']:
# Resource may be either a group or a user
resource = allocation['resource']
# If the resource is a group, add its members
if isinstance(resource, session.types['Group']):
for membership in resource['memberships']:
user = membership['user']
project_team.add(user)
# The resource is a user, add it.
else:
user = resource
project_team.add(user)
The next example shows how to assign the current user to a task:
# Retrieve a task and the current user
task = session.query('Task').first()
current_user = session.query(
u'User where username is {0}'.format(session.api_user)
).one()
# Create a new Appointment of type assignment.
session.create('Appointment', {
'context': task,
'resource': current_user,
'type': 'assignment'
})
# Finally, persist the new assignment
session.commit()
To list all users assigned to a task, see the following example:
task = session.query('Task').first()
users = session.query(
'select first_name, last_name from User '
'where assignments any (context_id = "{0}")'.format(task['id'])
)
for user in users:
print user['first_name'], user['last_name']
To list the current user's assigned tasks, see the example below:
assigned_tasks = session.query(
'select link from Task '
'where assignments any (resource.username = "{0}")'.format(session.api_user)
)
for task in assigned_tasks:
print u' / '.join(item['name'] for item in task['link'])
| # :coding: utf-8
# :copyright: Copyright (c) 2014 ftrack
from __future__ import absolute_import
import json
import logging
import collections
import datetime
import os
import getpass
import functools
import itertools
import distutils.version
import hashlib
import appdirs
import threading
import atexit
import requests
import requests.auth
import arrow
import clique
import ftrack_api
import ftrack_api.exception
import ftrack_api.entity.factory
import ftrack_api.entity.base
import ftrack_api.entity.location
import ftrack_api.cache
import ftrack_api.symbol
import ftrack_api.query
import ftrack_api.attribute
import ftrack_api.collection
import ftrack_api.event.hub
import ftrack_api.event.base
import ftrack_api.plugin
import ftrack_api.inspection
import ftrack_api.operation
import ftrack_api.accessor.disk
import ftrack_api.structure.origin
import ftrack_api.structure.entity_id
import ftrack_api.accessor.server
import ftrack_api._centralized_storage_scenario
import ftrack_api.logging
from ftrack_api.logging import LazyLogMessage as L
try:
from weakref import WeakMethod
except ImportError:
from ftrack_api._weakref import WeakMethod
class SessionAuthentication(requests.auth.AuthBase):
'''Attach ftrack session authentication information to requests.'''
def __init__(self, api_key, api_user):
'''Initialise with *api_key* and *api_user*.'''
self.api_key = api_key
self.api_user = api_user
super(SessionAuthentication, self).__init__()
def __call__(self, request):
'''Modify *request* to have appropriate headers.'''
request.headers.update({
'ftrack-api-key': self.api_key,
'ftrack-user': self.api_user
})
return request
class Session(object):
'''An isolated session for interaction with an ftrack server.'''
def __init__(
self, server_url=None, api_key=None, api_user=None, auto_populate=True,
plugin_paths=None, cache=None, cache_key_maker=None,
auto_connect_event_hub=None, schema_cache_path=None,
plugin_arguments=None
):
'''Initialise session.
*server_url* should be the URL of the ftrack server to connect to
including any port number. If not specified attempt to look up from
:envvar:`FTRACK_SERVER`.
*api_key* should be the API key to use for authentication whilst
*api_user* should be the username of the user in ftrack to record
operations against. If not specified, *api_key* should be retrieved
from :envvar:`FTRACK_API_KEY` and *api_user* from
:envvar:`FTRACK_API_USER`.
If *auto_populate* is True (the default), then accessing entity
attributes will cause them to be automatically fetched from the server
if they are not already. This flag can be changed on the session
directly at any time.
*plugin_paths* should be a list of paths to search for plugins. If not
specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`.
*cache* should be an instance of a cache that fulfils the
:class:`ftrack_api.cache.Cache` interface and will be used as the cache
for the session. It can also be a callable that will be called with the
session instance as sole argument. The callable should return ``None``
if a suitable cache could not be configured, but session instantiation
can continue safely.
.. note::
The session will add the specified cache to a pre-configured layered
cache that specifies the top level cache as a
:class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary
to construct a separate memory cache for typical behaviour. Working
around this behaviour or removing the memory cache can lead to
unexpected behaviour.
*cache_key_maker* should be an instance of a key maker that fulfils the
:class:`ftrack_api.cache.KeyMaker` interface and will be used to
generate keys for objects being stored in the *cache*. If not specified,
a :class:`~ftrack_api.cache.StringKeyMaker` will be used.
If *auto_connect_event_hub* is True then embedded event hub will be
automatically connected to the event server and allow for publishing and
subscribing to **non-local** events. If False, then only publishing and
subscribing to **local** events will be possible until the hub is
manually connected using :meth:`EventHub.connect
<ftrack_api.event.hub.EventHub.connect>`.
.. note::
The event hub connection is performed in a background thread to
improve session startup time. If a registered plugin requires a
connected event hub then it should check the event hub connection
status explicitly. Subscribing to events does *not* require a
connected event hub.
Enable schema caching by setting *schema_cache_path* to a folder path.
If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to
determine the path to store cache in. If the environment variable is
also not specified then a temporary directory will be used. Set to
`False` to disable schema caching entirely.
*plugin_arguments* should be an optional mapping (dict) of keyword
arguments to pass to plugin register functions upon discovery. If a
discovered plugin has a signature that is incompatible with the passed
arguments, the discovery mechanism will attempt to reduce the passed
arguments to only those that the plugin accepts. Note that a warning
will be logged in this case.
'''
super(Session, self).__init__()
self.logger = logging.getLogger(
__name__ + '.' + self.__class__.__name__
)
self._closed = False
if server_url is None:
server_url = os.environ.get('FTRACK_SERVER')
if not server_url:
raise TypeError(
'Required "server_url" not specified. Pass as argument or set '
'in environment variable FTRACK_SERVER.'
)
self._server_url = server_url
if api_key is None:
api_key = os.environ.get(
'FTRACK_API_KEY',
# Backwards compatibility
os.environ.get('FTRACK_APIKEY')
)
if not api_key:
raise TypeError(
'Required "api_key" not specified. Pass as argument or set in '
'environment variable FTRACK_API_KEY.'
)
self._api_key = api_key
if api_user is None:
api_user = os.environ.get('FTRACK_API_USER')
if not api_user:
try:
api_user = getpass.getuser()
except Exception:
pass
if not api_user:
raise TypeError(
'Required "api_user" not specified. Pass as argument, set in '
'environment variable FTRACK_API_USER or one of the standard '
'environment variables used by Python\'s getpass module.'
)
self._api_user = api_user
# Currently pending operations.
self.recorded_operations = ftrack_api.operation.Operations()
self.record_operations = True
self.cache_key_maker = cache_key_maker
if self.cache_key_maker is None:
self.cache_key_maker = ftrack_api.cache.StringKeyMaker()
# Enforce always having a memory cache at top level so that the same
# in-memory instance is returned from session.
self.cache = ftrack_api.cache.LayeredCache([
ftrack_api.cache.MemoryCache()
])
if cache is not None:
if callable(cache):
cache = cache(self)
if cache is not None:
self.cache.caches.append(cache)
self._managed_request = None
self._request = requests.Session()
self._request.auth = SessionAuthentication(
self._api_key, self._api_user
)
self.auto_populate = auto_populate
# Fetch server information and in doing so also check credentials.
self._server_information = self._fetch_server_information()
# Now check compatibility of server based on retrieved information.
self.check_server_compatibility()
# Construct event hub and load plugins.
self._event_hub = ftrack_api.event.hub.EventHub(
self._server_url,
self._api_user,
self._api_key,
)
self._auto_connect_event_hub_thread = None
if auto_connect_event_hub is True:
# Connect to event hub in background thread so as not to block main
# session usage waiting for event hub connection.
self._auto_connect_event_hub_thread = threading.Thread(
target=self._event_hub.connect
)
self._auto_connect_event_hub_thread.daemon = True
self._auto_connect_event_hub_thread.start()
# To help with migration from auto_connect_event_hub default changing
# from True to False.
self._event_hub._deprecation_warning_auto_connect = False
# Register to auto-close session on exit.
atexit.register(WeakMethod(self.close))
self._plugin_paths = plugin_paths
if self._plugin_paths is None:
self._plugin_paths = os.environ.get(
'FTRACK_EVENT_PLUGIN_PATH', ''
).split(os.pathsep)
self._discover_plugins(plugin_arguments=plugin_arguments)
# TODO: Make schemas read-only and non-mutable (or at least without
# rebuilding types)?
if schema_cache_path is not False:
if schema_cache_path is None:
schema_cache_path = appdirs.user_cache_dir()
schema_cache_path = os.environ.get(
'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path
)
schema_cache_path = os.path.join(
schema_cache_path, 'ftrack_api_schema_cache.json'
)
self.schemas = self._load_schemas(schema_cache_path)
self.types = self._build_entity_type_classes(self.schemas)
ftrack_api._centralized_storage_scenario.register(self)
self._configure_locations()
self.event_hub.publish(
ftrack_api.event.base.Event(
topic='ftrack.api.session.ready',
data=dict(
session=self
)
),
synchronous=True
)
def __enter__(self):
'''Return session as context manager.'''
return self
def __exit__(self, exception_type, exception_value, traceback):
'''Exit session context, closing session in process.'''
self.close()
@property
def _request(self):
'''Return request session.
Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has
been closed and connection unavailable.
'''
if self._managed_request is None:
raise ftrack_api.exception.ConnectionClosedError()
return self._managed_request
@_request.setter
def _request(self, value):
'''Set request session to *value*.'''
self._managed_request = value
@property
def closed(self):
'''Return whether session has been closed.'''
return self._closed
@property
def server_information(self):
'''Return server information such as server version.'''
return self._server_information.copy()
@property
def server_url(self):
'''Return server ulr used for session.'''
return self._server_url
@property
def api_user(self):
'''Return username used for session.'''
return self._api_user
@property
def api_key(self):
'''Return API key used for session.'''
return self._api_key
@property
def event_hub(self):
'''Return event hub.'''
return self._event_hub
@property
def _local_cache(self):
'''Return top level memory cache.'''
return self.cache.caches[0]
def check_server_compatibility(self):
'''Check compatibility with connected server.'''
server_version = self.server_information.get('version')
if server_version is None:
raise ftrack_api.exception.ServerCompatibilityError(
'Could not determine server version.'
)
# Perform basic version check.
if server_version!= 'dev':
min_server_version = '3.3.11'
if (
distutils.version.LooseVersion(min_server_version)
> distutils.version.LooseVersion(server_version)
):
raise ftrack_api.exception.ServerCompatibilityError(
'Server version {0} incompatible with this version of the '
'API which requires a server version >= {1}'.format(
server_version,
min_server_version
)
)
def close(self):
'''Close session.
Close connections to server. Clear any pending operations and local
cache.
Use this to ensure that session is cleaned up properly after use.
'''
if self.closed:
self.logger.debug('Session already closed.')
return
self._closed = True
self.logger.debug('Closing session.')
if self.recorded_operations:
self.logger.warning(
'Closing session with pending operations not persisted.'
)
# Clear pending operations.
self.recorded_operations.clear()
# Clear top level cache (expected to be enforced memory cache).
self._local_cache.clear()
# Close connections.
self._request.close()
self._request = None
try:
self.event_hub.disconnect()
if self._auto_connect_event_hub_thread:
self._auto_connect_event_hub_thread.join()
except ftrack_api.exception.EventHubConnectionError:
pass
self.logger.debug('Session closed.')
def reset(self):
'''Reset session clearing local state.
Clear all pending operations and expunge all entities from session.
Also clear the local cache. If the cache used by the session is a
:class:`~ftrack_api.cache.LayeredCache` then only clear top level cache.
Otherwise, clear the entire cache.
Plugins are not rediscovered or reinitialised, but certain plugin events
are re-emitted to properly configure session aspects that are dependant
on cache (such as location plugins).
.. warning::
Previously attached entities are not reset in memory and will retain
their state, but should not be used. Doing so will cause errors.
'''
if self.recorded_operations:
self.logger.warning(
'Resetting session with pending operations not persisted.'
)
# Clear pending operations.
self.recorded_operations.clear()
# Clear top level cache (expected to be enforced memory cache).
self._local_cache.clear()
# Re-configure certain session aspects that may be dependant on cache.
self._configure_locations()
self.event_hub.publish(
ftrack_api.event.base.Event(
topic='ftrack.api.session.reset',
data=dict(
session=self
)
),
synchronous=True
)
def auto_populating(self, auto_populate):
'''Temporarily set auto populate to *auto_populate*.
The current setting will be restored automatically when done.
Example::
with session.auto_populating(False):
print entity['name']
'''
return AutoPopulatingContext(self, auto_populate)
def operation_recording(self, record_operations):
'''Temporarily set operation recording to *record_operations*.
The current setting will be restored automatically when done.
Example::
with session.operation_recording(False):
entity['name'] = 'change_not_recorded'
'''
return OperationRecordingContext(self, record_operations)
@property
def created(self):
'''Return list of newly created entities.'''
entities = self._local_cache.values()
states = ftrack_api.inspection.states(entities)
return [
entity for (entity, state) in itertools.izip(entities, states)
if state is ftrack_api.symbol.CREATED
]
@property
def modified(self):
'''Return list of locally modified entities.'''
entities = self._local_cache.values()
states = ftrack_api.inspection.states(entities)
return [
entity for (entity, state) in itertools.izip(entities, states)
if state is ftrack_api.symbol.MODIFIED
]
@property
def deleted(self):
'''Return list of deleted entities.'''
entities = self._local_cache.values()
states = ftrack_api.inspection.states(entities)
return [
entity for (entity, state) in itertools.izip(entities, states)
if state is ftrack_api.symbol.DELETED
]
def reset_remote(self, reset_type, entity=None):
'''Perform a server side reset.
*reset_type* is a server side supported reset type,
passing the optional *entity* to perform the option upon.
Please refer to ftrack documentation for a complete list of
supported server side reset types.
'''
payload = {
'action':'reset_remote',
'reset_type': reset_type
}
if entity is not None:
payload.update({
'entity_type': entity.entity_type,
'entity_key': entity.get('id')
})
result = self.call(
[payload]
)
return result[0]['data']
def create(self, entity_type, data=None, reconstructing=False):
'''Create and return an entity of *entity_type* with initial *data*.
If specified, *data* should be a dictionary of key, value pairs that
should be used to populate attributes on the entity.
If *reconstructing* is False then create a new entity setting
appropriate defaults for missing data. If True then reconstruct an
existing entity.
Constructed entity will be automatically :meth:`merged <Session.merge>`
into the session.
'''
entity = self._create(entity_type, data, reconstructing=reconstructing)
entity = self.merge(entity)
return entity
def _create(self, entity_type, data, reconstructing):
'''Create and return an entity of *entity_type* with initial *data*.'''
try:
EntityTypeClass = self.types[entity_type]
except KeyError:
raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type)
return EntityTypeClass(self, data=data, reconstructing=reconstructing)
def ensure(self, entity_type, data, identifying_keys=None):
'''Retrieve entity of *entity_type* with *data*, creating if necessary.
*data* should be a dictionary of the same form passed to :meth:`create`.
By default, check for an entity that has matching *data*. If
*identifying_keys* is specified as a list of keys then only consider the
values from *data* for those keys when searching for existing entity. If
*data* is missing an identifying key then raise :exc:`KeyError`.
If no *identifying_keys* specified then use all of the keys from the
passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be
determined.
Each key should be a string.
.. note::
Currently only top level scalars supported. To ensure an entity by
looking at relationships, manually issue the :meth:`query` and
:meth:`create` calls.
If more than one entity matches the determined filter criteria then
raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`.
If no matching entity found then create entity using supplied *data*.
If a matching entity is found, then update it if necessary with *data*.
.. note::
If entity created or updated then a :meth:`commit` will be issued
automatically. If this behaviour is undesired, perform the
:meth:`query` and :meth:`create` calls manually.
Return retrieved or created entity.
Example::
# First time, a new entity with `username=martin` is created.
entity = session.ensure('User', {'username':'martin'})
# After that, the existing entity is retrieved.
entity = session.ensure('User', {'username':'martin'})
# When existing entity retrieved, entity may also be updated to
# match supplied data.
entity = session.ensure(
'User', {'username':'martin', 'email':'[email protected]'}
)
'''
if not identifying_keys:
identifying_keys = data.keys()
self.logger.debug(L(
'Ensuring entity {0!r} with data {1!r} using identifying keys '
'{2!r}', entity_type, data, identifying_keys
))
if not identifying_keys:
raise ValueError(
'Could not determine any identifying data to check against '
'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}'
.format(entity_type, data, identifying_keys)
)
expression = '{0} where'.format(entity_type)
criteria = []
for identifying_key in identifying_keys:
value = data[identifying_key]
if isinstance(value, basestring):
value = '"{0}"'.format(value)
elif isinstance(
value, (arrow.Arrow, datetime.datetime, datetime.date)
):
# Server does not store microsecond or timezone currently so
# need to strip from query.
# TODO: When datetime handling improved, update this logic.
value = (
arrow.get(value).naive.replace(microsecond=0).isoformat()
)
value = '"{0}"'.format(value)
criteria.append('{0} is {1}'.format(identifying_key, value))
expression = '{0} {1}'.format(
expression,'and '.join(criteria)
)
try:
entity = self.query(expression).one()
except ftrack_api.exception.NoResultFoundError:
self.logger.debug('Creating entity as did not already exist.')
# Create entity.
entity = self.create(entity_type, data)
self.commit()
else:
self.logger.debug('Retrieved matching existing entity.')
# Update entity if required.
updated = False
for key, target_value in data.items():
if entity[key]!= target_value:
entity[key] = target_value
updated = True
if updated:
self.logger.debug('Updating existing entity to match new data.')
self.commit()
return entity
def delete(self, entity):
'''Mark *entity* for deletion.'''
if self.record_operations:
self.recorded_operations.push(
ftrack_api.operation.DeleteEntityOperation(
entity.entity_type,
ftrack_api.inspection.primary_key(entity)
)
)
def get(self, entity_type, entity_key):
'''Return entity of *entity_type* with unique *entity_key*.
First check for an existing entry in the configured cache, otherwise
issue a query to the server.
If no matching entity found, return None.
'''
self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key))
primary_key_definition = self.types[entity_type].primary_key_attributes
if isinstance(entity_key, basestring):
entity_key = [entity_key]
if len(entity_key)!= len(primary_key_definition):
raise ValueError(
'Incompatible entity_key {0!r} supplied. Entity type {1} '
'expects a primary key composed of {2} values ({3}).'
.format(
entity_key, entity_type, len(primary_key_definition),
', '.join(primary_key_definition)
)
)
entity = None
try:
entity = self._get(entity_type, entity_key)
except KeyError:
# Query for matching entity.
self.logger.debug(
'Entity not present in cache. Issuing new query.'
)
condition = []
for key, value in zip(primary_key_definition, entity_key):
condition.append('{0} is "{1}"'.format(key, value))
expression = '{0} where ({1})'.format(
entity_type,'and '.join(condition)
)
results = self.query(expression).all()
if results:
entity = results[0]
return entity
def _get(self, entity_type, entity_key):
'''Return cached entity of *entity_type* with unique *entity_key*.
Raise :exc:`KeyError` if no such entity in the cache.
'''
# Check cache for existing entity emulating
# ftrack_api.inspection.identity result object to pass to key maker.
cache_key = self.cache_key_maker.key(
(str(entity_type), map(str, entity_key))
)
self.logger.debug(L(
'Checking cache for entity with key {0}', cache_key
))
entity = self.cache.get(cache_key)
self.logger.debug(L(
'Retrieved existing entity from cache: {0} at {1}',
entity, id(entity)
))
return entity
def query(self, expression, page_size=500):
'''Query against remote data according to *expression*.
*expression* is not executed directly. Instead return an
:class:`ftrack_api.query.QueryResult` instance that will execute remote
call on access.
*page_size* specifies the maximum page size that the returned query
result object should be configured with.
.. seealso:: :ref:`querying`
'''
self.logger.debug(L('Query {0!r}', expression))
# Add in sensible projections if none specified. Note that this is
# done here rather than on the server to allow local modification of the
# schema setting to include commonly used custom attributes for example.
# TODO: Use a proper parser perhaps?
if not expression.startswith('select'):
entity_type = expression.split(' ', 1)[0]
EntityTypeClass = self.types[entity_type]
projections = EntityTypeClass.default_projections
expression ='select {0} from {1}'.format(
', '.join(projections),
expression
)
query_result = ftrack_api.query.QueryResult(
self, expression, page_size=page_size
)
return query_result
def _query(self, expression):
'''Execute *query* and return (records, metadata).
Records will be a list of entities retrieved via the query and metadata
a dictionary of accompanying information about the result set.
'''
# TODO: Actually support batching several queries together.
# TODO: Should batches have unique ids to match them up later.
batch = [{
'action': 'query',
'expression': expression
}]
# TODO: When should this execute? How to handle background=True?
results = self.call(batch)
# Merge entities into local cache and return merged entities.
data = []
merged = dict()
for entity in results[0]['data']:
data.append(self._merge_recursive(entity, merged))
return data, results[0]['metadata']
def merge(self, value, merged=None):
'''Merge *value* into session and return merged value.
*merged* should be a mapping to record merges during run and should be
used to avoid infinite recursion. If not set will default to a
dictionary.
'''
if merged is None:
merged = {}
with self.operation_recording(False):
return self._merge(value, merged)
def _merge(self, value, merged):
'''Return merged *value*.'''
log_debug = self.logger.isEnabledFor(logging.DEBUG)
if isinstance(value, ftrack_api.entity.base.Entity):
log_debug and self.logger.debug(
'Merging entity into session: {0} at {1}'
.format(value, id(value))
)
return self._merge_entity(value, merged=merged)
elif isinstance(value, ftrack_api.collection.Collection):
log_debug and self.logger.debug(
'Merging collection into session: {0!r} at {1}'
.format(value, id(value))
)
merged_collection = []
for entry in value:
merged_collection.append(
self._merge(entry, merged=merged)
)
return merged_collection
elif isinstance(value, ftrack_api.collection.MappedCollectionProxy):
log_debug and self.logger.debug(
'Merging mapped collection into session: {0!r} at {1}'
.format(value, id(value))
)
merged_collection = []
for entry in value.collection:
merged_collection.append(
self._merge(entry, merged=merged)
)
return merged_collection
else:
return value
def _merge_recursive(self, entity, merged=None):
'''Merge *entity* and all its attributes recursivly.'''
log_debug = self.logger.isEnabledFor(logging.DEBUG)
if merged is None:
merged = {}
attached = self.merge(entity, merged)
for attribute in entity.attributes:
# Remote attributes.
remote_value = attribute.get_remote_value(entity)
if isinstance(
remote_value,
(
ftrack_api.entity.base.Entity,
ftrack_api.collection.Collection,
ftrack_api.collection.MappedCollectionProxy
)
):
log_debug and self.logger.debug(
'Merging remote value for attribute {0}.'.format(attribute)
)
if isinstance(remote_value, ftrack_api.entity.base.Entity):
self._merge_recursive(remote_value, merged=merged)
elif isinstance(
remote_value, ftrack_api.collection.Collection
):
for entry in remote_value:
self._merge_recursive(entry, merged=merged)
elif isinstance(
remote_value, ftrack_api.collection.MappedCollectionProxy
):
for entry in remote_value.collection:
self._merge_recursive(entry, merged=merged)
return attached
def _merge_entity(self, entity, merged=None):
'''Merge *entity* into session returning merged entity.
Merge is recursive so any references to other entities will also be
merged.
*entity* will never be modified in place. Ensure that the returned
merged entity instance is used.
'''
log_debug = self.logger.isEnabledFor(logging.DEBUG)
if merged is None:
merged = {}
with self.auto_populating(False):
entity_key = self.cache_key_maker.key(
ftrack_api.inspection.identity(entity)
)
# Check whether this entity has already been processed.
attached_entity = merged.get(entity_key)
if attached_entity is not None:
log_debug and self.logger.debug(
'Entity already processed for key {0} as {1} at {2}'
.format(entity_key, attached_entity, id(attached_entity))
)
return attached_entity
else:
log_debug and self.logger.debug(
'Entity not already processed for key {0}.'
.format(entity_key)
)
# Check for existing instance of entity in cache.
log_debug and self.logger.debug(
'Checking for entity in cache with key {0}'.format(entity_key)
)
try:
attached_entity = self.cache.get(entity_key)
log_debug and self.logger.debug(
'Retrieved existing entity from cache: {0} at {1}'
.format(attached_entity, id(attached_entity))
)
except KeyError:
# Construct new minimal instance to store in cache.
attached_entity = self._create(
entity.entity_type, {}, reconstructing=True
)
log_debug and self.logger.debug(
'Entity not present in cache. Constructed new instance: '
'{0} at {1}'.format(attached_entity, id(attached_entity))
)
# Mark entity as seen to avoid infinite loops.
merged[entity_key] = attached_entity
changes = attached_entity.merge(entity, merged=merged)
if changes:
self.cache.set(entity_key, attached_entity)
self.logger.debug('Cache updated with merged entity.')
else:
self.logger.debug(
'Cache not updated with merged entity as no differences '
'detected.'
)
return attached_entity
def populate(self, entities, projections):
'''Populate *entities* with attributes specified by *projections*.
Any locally set values included in the *projections* will not be
overwritten with the retrieved remote value. If this'synchronise'
behaviour is required, first clear the relevant values on the entity by
setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will
have the same effect::
>>> print(user['username'])
martin
>>> del user['username']
>>> print(user['username'])
Symbol(NOT_SET)
.. note::
Entities that have been created and not yet persisted will be
skipped as they have no remote values to fetch.
'''
self.logger.debug(L(
'Populate {0!r} projections for {1}.', projections, entities
))
if not isinstance(
entities, (list, tuple, ftrack_api.query.QueryResult)
):
entities = [entities]
# TODO: How to handle a mixed collection of different entity types
# Should probably fail, but need to consider handling hierarchies such
# as User and Group both deriving from Resource. Actually, could just
# proceed and ignore projections that are not present in entity type.
entities_to_process = []
for entity in entities:
if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED:
# Created entities that are not yet persisted have no remote
# values. Don't raise an error here as it is reasonable to
# iterate over an entities properties and see that some of them
# are NOT_SET.
self.logger.debug(L(
'Skipping newly created entity {0!r} for population as no '
'data will exist in the remote for this entity yet.', entity
))
continue
entities_to_process.append(entity)
if entities_to_process:
reference_entity = entities_to_process[0]
entity_type = reference_entity.entity_type
query ='select {0} from {1}'.format(projections, entity_type)
primary_key_definition = reference_entity.primary_key_attributes
entity_keys = [
ftrack_api.inspection.primary_key(entity).values()
for entity in entities_to_process
]
if len(primary_key_definition) > 1:
# Composite keys require full OR syntax unfortunately.
conditions = []
for entity_key in entity_keys:
condition = []
for key, value in zip(primary_key_definition, entity_key):
condition.append('{0} is "{1}"'.format(key, value))
conditions.append('({0})'.format('and '.join(condition)))
query = '{0} where {1}'.format(query,'or '.join(conditions))
else:
primary_key = primary_key_definition[0]
if len(entity_keys) > 1:
query = '{0} where {1} in ({2})'.format(
query, primary_key,
','.join([
str(entity_key[0]) for entity_key in entity_keys
])
)
else:
query = '{0} where {1} is {2}'.format(
query, primary_key, str(entity_keys[0][0])
)
result = self.query(query)
# Fetch all results now. Doing so will cause them to populate the
# relevant entities in the cache.
result.all()
# TODO: Should we check that all requested attributes were
# actually populated? If some weren't would we mark that to avoid
# repeated calls or perhaps raise an error?
# TODO: Make atomic.
def commit(self):
'''Commit all local changes to the server.'''
batch = []
with self.auto_populating(False):
for operation in self.recorded_operations:
# Convert operation to payload.
if isinstance(
operation, ftrack_api.operation.CreateEntityOperation
):
# At present, data payload requires duplicating entity
# type in data and also ensuring primary key added.
entity_data = {
'__entity_type__': operation.entity_type,
}
entity_data.update(operation.entity_key)
entity_data.update(operation.entity_data)
payload = OperationPayload({
'action': 'create',
'entity_type': operation.entity_type,
'entity_key': operation.entity_key.values(),
'entity_data': entity_data
})
elif isinstance(
operation, ftrack_api.operation.UpdateEntityOperation
):
entity_data = {
# At present, data payload requires duplicating entity
# type.
'__entity_type__': operation.entity_type,
operation.attribute_name: operation.new_value
}
payload = OperationPayload({
'action': 'update',
'entity_type': operation.entity_type,
'entity_key': operation.entity_key.values(),
'entity_data': entity_data
})
elif isinstance(
operation, ftrack_api.operation.DeleteEntityOperation
):
payload = OperationPayload({
'action': 'delete',
'entity_type': operation.entity_type,
'entity_key': operation.entity_key.values()
})
else:
raise ValueError(
'Cannot commit. Unrecognised operation type {0} '
'detected.'.format(type(operation))
)
batch.append(payload)
# Optimise batch.
# TODO: Might be better to perform these on the operations list instead
# so all operation contextual information available.
# If entity was created and deleted in one batch then remove all
# payloads for that entity.
created = set()
deleted = set()
for payload in batch:
if payload['action'] == 'create':
created.add(
(payload['entity_type'], str(payload['entity_key']))
)
elif payload['action'] == 'delete':
deleted.add(
(payload['entity_type'], str(payload['entity_key']))
)
created_then_deleted = deleted.intersection(created)
if created_then_deleted:
optimised_batch = []
for payload in batch:
entity_type = payload.get('entity_type')
entity_key = str(payload.get('entity_key'))
if (entity_type, entity_key) in created_then_deleted:
continue
optimised_batch.append(payload)
batch = optimised_batch
# Remove early update operations so that only last operation on
# attribute is applied server side.
updates_map = set()
for payload in reversed(batch):
if payload['action'] in ('update', ):
for key, value in payload['entity_data'].items():
if key == '__entity_type__':
continue
identity = (
payload['entity_type'], str(payload['entity_key']), key
)
if identity in updates_map:
del payload['entity_data'][key]
else:
updates_map.add(identity)
# Remove NOT_SET values from entity_data.
for payload in batch:
entity_data = payload.get('entity_data', {})
for key, value in entity_data.items():
if value is ftrack_api.symbol.NOT_SET:
del entity_data[key]
# Remove payloads with redundant entity_data.
optimised_batch = []
for payload in batch:
entity_data = payload.get('entity_data')
if entity_data is not None:
keys = entity_data.keys()
if not keys or keys == ['__entity_type__']:
continue
optimised_batch.append(payload)
batch = optimised_batch
# Collapse updates that are consecutive into one payload. Also, collapse
# updates that occur immediately after creation into the create payload.
optimised_batch = []
previous_payload = None
for payload in batch:
if (
previous_payload is not None
and payload['action'] == 'update'
and previous_payload['action'] in ('create', 'update')
and previous_payload['entity_type'] == payload['entity_type']
and previous_payload['entity_key'] == payload['entity_key']
):
previous_payload['entity_data'].update(payload['entity_data'])
continue
else:
optimised_batch.append(payload)
previous_payload = payload
batch = optimised_batch
# Process batch.
if batch:
result = self.call(batch)
# Clear recorded operations.
self.recorded_operations.clear()
# As optimisation, clear local values which are not primary keys to
# avoid redundant merges when merging references. Note: primary keys
# remain as needed for cache retrieval on new entities.
with self.auto_populating(False):
with self.operation_recording(False):
for entity in self._local_cache.values():
for attribute in entity:
if attribute not in entity.primary_key_attributes:
del entity[attribute]
# Process results merging into cache relevant data.
for entry in result:
if entry['action'] in ('create', 'update'):
# Merge returned entities into local cache.
self.merge(entry['data'])
elif entry['action'] == 'delete':
# TODO: Detach entity - need identity returned?
# TODO: Expunge entity from cache.
pass
# Clear remaining local state, including local values for primary
# keys on entities that were merged.
with self.auto_populating(False):
with self.operation_recording(False):
for entity in self._local_cache.values():
entity.clear()
def rollback(self):
'''Clear all recorded operations and local state.
Typically this would be used following a failed :meth:`commit` in order
to revert the session to a known good state.
Newly created entities not yet persisted will be detached from the
session / purged from cache and no longer contribute, but the actual
objects are not deleted from memory. They should no longer be used and
doing so could cause errors.
'''
with self.auto_populating(False):
with self.operation_recording(False):
# Detach all newly created entities and remove from cache. This
# is done because simply clearing the local values of newly
# created entities would result in entities with no identity as
# primary key was local while not persisted. In addition, it
# makes no sense for failed created entities to exist in session
# or cache.
for operation in self.recorded_operations:
if isinstance(
operation, ftrack_api.operation.CreateEntityOperation
):
entity_key = str((
str(operation.entity_type),
operation.entity_key.values()
))
try:
self.cache.remove(entity_key)
except KeyError:
pass
# Clear locally stored modifications on remaining entities.
for entity in self._local_cache.values():
entity.clear()
self.recorded_operations.clear()
def _fetch_server_information(self):
'''Return server information.'''
result = self.call([{'action': 'query_server_information'}])
return result[0]
def _discover_plugins(self, plugin_arguments=None):
'''Find and load plugins in search paths.
Each discovered module should implement a register function that
accepts this session as first argument. Typically the function should
register appropriate event listeners against the session's event hub.
def register(session):
session.event_hub.subscribe(
'topic=ftrack.api.session.construct-entity-type',
construct_entity_type
)
*plugin_arguments* should be an optional mapping of keyword arguments
and values to pass to plugin register functions upon discovery.
'''
plugin_arguments = plugin_arguments or {}
ftrack_api.plugin.discover(
self._plugin_paths, [self], plugin_arguments
)
def _read_schemas_from_cache(self, schema_cache_path):
'''Return schemas and schema hash from *schema_cache_path*.
*schema_cache_path* should be the path to the file containing the
schemas in JSON format.
'''
self.logger.debug(L(
'Reading schemas from cache {0!r}', schema_cache_path
))
if not os.path.exists(schema_cache_path):
self.logger.info(L(
'Cache file not found at {0!r}.', schema_cache_path
))
return [], None
with open(schema_cache_path, 'r') as schema_file:
schemas = json.load(schema_file)
hash_ = hashlib.md5(
json.dumps(schemas, sort_keys=True)
).hexdigest()
return schemas, hash_
def _write_schemas_to_cache(self, schemas, schema_cache_path):
'''Write *schemas* to *schema_cache_path*.
*schema_cache_path* should be a path to a file that the schemas can be
written to in JSON format.
'''
self.logger.debug(L(
'Updating schema cache {0!r} with new schemas.', schema_cache_path
))
with open(schema_cache_path, 'w') as local_cache_file:
json.dump(schemas, local_cache_file, indent=4)
def _load_schemas(self, schema_cache_path):
'''Load schemas.
First try to load schemas from cache at *schema_cache_path*. If the
cache is not available or the cache appears outdated then load schemas
from server and store fresh copy in cache.
If *schema_cache_path* is set to `False`, always load schemas from
server bypassing cache.
'''
local_schema_hash = None
schemas = []
if schema_cache_path:
try:
schemas, local_schema_hash = self._read_schemas_from_cache(
schema_cache_path
)
except (IOError, TypeError, AttributeError, ValueError):
# Catch any known exceptions when trying to read the local
# schema cache to prevent API from being unusable.
self.logger.exception(L(
'Schema cache could not be loaded from {0!r}',
schema_cache_path
))
# Use `dictionary.get` to retrieve hash to support older version of
# ftrack server not returning a schema hash.
server_hash = self._server_information.get(
'schema_hash', False
)
if local_schema_hash!= server_hash:
self.logger.debug(L(
'Loading schemas from server due to hash not matching.'
'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash
))
schemas = self.call([{'action': 'query_schemas'}])[0]
if schema_cache_path:
try:
self._write_schemas_to_cache(schemas, schema_cache_path)
except (IOError, TypeError):
self.logger.exception(L(
'Failed to update schema cache {0!r}.',
schema_cache_path
))
else:
self.logger.debug(L(
'Using cached schemas from {0!r}', schema_cache_path
))
return schemas
def _build_entity_type_classes(self, schemas):
'''Build default entity type classes.'''
fallback_factory = ftrack_api.entity.factory.StandardFactory()
classes = {}
for schema in schemas:
results = self.event_hub.publish(
ftrack_api.event.base.Event(
topic='ftrack.api.session.construct-entity-type',
data=dict(
schema=schema,
schemas=schemas
)
),
synchronous=True
)
results = [result for result in results if result is not None]
if not results:
self.logger.debug(L(
'Using default StandardFactory to construct entity type '
'class for "{0}"', schema['id']
))
entity_type_class = fallback_factory.create(schema)
elif len(results) > 1:
raise ValueError(
'Expected single entity type to represent schema "{0}" but '
'received {1} entity types instead.'
.format(schema['id'], len(results))
)
else:
entity_type_class = results[0]
classes[entity_type_class.entity_type] = entity_type_class
return classes
def _configure_locations(self):
'''Configure locations.'''
# First configure builtin locations, by injecting them into local cache.
# Origin.
location = self.create(
'Location',
data=dict(
name='ftrack.origin',
id=ftrack_api.symbol.ORIGIN_LOCATION_ID
),
reconstructing=True
)
ftrack_api.mixin(
location, ftrack_api.entity.location.OriginLocationMixin,
name='OriginLocation'
)
location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='')
location.structure = ftrack_api.structure.origin.OriginStructure()
location.priority = 100
# Unmanaged.
location = self.create(
'Location',
data=dict(
name='ftrack.unmanaged',
id=ftrack_api.symbol.UNMANAGED_LOCATION_ID
),
reconstructing=True
)
ftrack_api.mixin(
location, ftrack_api.entity.location.UnmanagedLocationMixin,
name='UnmanagedLocation'
)
location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='')
location.structure = ftrack_api.structure.origin.OriginStructure()
# location.resource_identifier_transformer = (
# ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session)
# )
location.priority = 90
# Review.
location = self.create(
'Location',
data=dict(
name='ftrack.review',
id=ftrack_api.symbol.REVIEW_LOCATION_ID
),
reconstructing=True
)
ftrack_api.mixin(
location, ftrack_api.entity.location.UnmanagedLocationMixin,
name='UnmanagedLocation'
)
location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='')
location.structure = ftrack_api.structure.origin.OriginStructure()
location.priority = 110
# Server.
location = self.create(
'Location',
data=dict(
name='ftrack.server',
id=ftrack_api.symbol.SERVER_LOCATION_ID
),
reconstructing=True
)
ftrack_api.mixin(
location, ftrack_api.entity.location.ServerLocationMixin,
name='ServerLocation'
)
location.accessor = ftrack_api.accessor.server._ServerAccessor(
session=self
)
location.structure = ftrack_api.structure.entity_id.EntityIdStructure()
location.priority = 150
# Master location based on server scenario.
storage_scenario = self.server_information.get('storage_scenario')
if (
storage_scenario and
storage_scenario.get('scenario')
):
self.event_hub.publish(
ftrack_api.event.base.Event(
topic='ftrack.storage-scenario.activate',
data=dict(
storage_scenario=storage_scenario
)
),
synchronous=True
)
# Next, allow further configuration of locations via events.
self.event_hub.publish(
ftrack_api.event.base.Event(
topic='ftrack.api.session.configure-location',
data=dict(
session=self
)
),
synchronous=True
)
@ftrack_api.logging.deprecation_warning(
'Session._call is now available as public method Session.call. The '
'private method will be removed in version 2.0.'
)
def _call(self, data):
'''Make request to server with *data* batch describing the actions.
.. note::
This private method is now available as public method
:meth:`entity_reference`. This alias remains for backwards
compatibility, but will be removed in version 2.0.
'''
return self.call(data)
def call(self, data):
'''Make request to server with *data* batch describing the actions.'''
url = self._server_url + '/api'
headers = {
'content-type': 'application/json',
'accept': 'application/json'
}
data = self.encode(data, entity_attribute_strategy='modified_only')
self.logger.debug(L('Calling server {0} with {1!r}', url, data))
response = self._request.post(
url,
headers=headers,
data=data
)
self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds()))
self.logger.debug(L('Response: {0!r}', response.text))
try:
result = self.decode(response.text)
except Exception:
error_message = (
'Server reported error in unexpected format. Raw error was: {0}'
.format(response.text)
)
self.logger.exception(error_message)
raise ftrack_api.exception.ServerError(error_message)
else:
if 'exception' in result:
# Handle exceptions.
error_message = 'Server reported error: {0}({1})'.format(
result['exception'], result['content']
)
self.logger.exception(error_message)
raise ftrack_api.exception.ServerError(error_message)
return result
def encode(self, data, entity_attribute_strategy='set_only'):
'''Return *data* encoded as JSON formatted string.
*entity_attribute_strategy* specifies how entity attributes should be
handled. The following strategies are available:
* *all* - Encode all attributes, loading any that are currently NOT_SET.
* *set_only* - Encode only attributes that are currently set without
loading any from the remote.
* *modified_only* - Encode only attributes that have been modified
locally.
* *persisted_only* - Encode only remote (persisted) attribute values.
'''
entity_attribute_strategies = (
'all','set_only','modified_only', 'persisted_only'
)
if entity_attribute_strategy not in entity_attribute_strategies:
raise ValueError(
'Unsupported entity_attribute_strategy "{0}". Must be one of '
'{1}'.format(
entity_attribute_strategy,
', '.join(entity_attribute_strategies)
)
)
return json.dumps(
data,
sort_keys=True,
default=functools.partial(
self._encode,
entity_attribute_strategy=entity_attribute_strategy
)
)
def _encode(self, item, entity_attribute_strategy='set_only'):
'''Return JSON encodable version of *item*.
*entity_attribute_strategy* specifies how entity attributes should be
handled. See :meth:`Session.encode` for available strategies.
'''
if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)):
return {
'__type__': 'datetime',
'value': item.isoformat()
}
if isinstance(item, OperationPayload):
data = dict(item.items())
if "entity_data" in data:
for key, value in data["entity_data"].items():
if isinstance(value, ftrack_api.entity.base.Entity):
data["entity_data"][key] = self.entity_reference(value)
return data
if isinstance(item, ftrack_api.entity.base.Entity):
data = self.entity_reference(item)
with self.auto_populating(True):
for attribute in item.attributes:
value = ftrack_api.symbol.NOT_SET
if entity_attribute_strategy == 'all':
value = attribute.get_value(item)
elif entity_attribute_strategy =='set_only':
if attribute.is_set(item):
value = attribute.get_local_value(item)
if value is ftrack_api.symbol.NOT_SET:
value = attribute.get_remote_value(item)
elif entity_attribute_strategy =='modified_only':
if attribute.is_modified(item):
value = attribute.get_local_value(item)
elif entity_attribute_strategy == 'persisted_only':
if not attribute.computed:
value = attribute.get_remote_value(item)
if value is not ftrack_api.symbol.NOT_SET:
if isinstance(
attribute, ftrack_api.attribute.ReferenceAttribute
):
if isinstance(value, ftrack_api.entity.base.Entity):
value = self.entity_reference(value)
data[attribute.name] = value
return data
if isinstance(
item, ftrack_api.collection.MappedCollectionProxy
):
# Use proxied collection for serialisation.
item = item.collection
if isinstance(item, ftrack_api.collection.Collection):
data = []
for entity in item:
data.append(self.entity_reference(entity))
return data
raise TypeError('{0!r} is not JSON serializable'.format(item))
def entity_reference(self, entity):
'''Return entity reference that uniquely identifies *entity*.
Return a mapping containing the __entity_type__ of the entity along with
the key, value pairs that make up it's primary key.
'''
reference = {
'__entity_type__': entity.entity_type
}
with self.auto_populating(False):
reference.update(ftrack_api.inspection.primary_key(entity))
return reference
@ftrack_api.logging.deprecation_warning(
'Session._entity_reference is now available as public method '
'Session.entity_reference. The private method will be removed '
'in version 2.0.'
)
def _entity_reference(self, entity):
'''Return entity reference that uniquely identifies *entity*.
Return a mapping containing the __entity_type__ of the entity along
with the key, value pairs that make up it's primary key.
.. note::
This private method is now available as public method
:meth:`entity_reference`. This alias remains for backwards
compatibility, but will be removed in version 2.0.
'''
return self.entity_reference(entity)
def decode(self, string):
'''Return decoded JSON *string* as Python object.'''
with self.operation_recording(False):
return json.loads(string, object_hook=self._decode)
def _decode(self, item):
'''Return *item* transformed into appropriate representation.'''
if isinstance(item, collections.Mapping):
if '__type__' in item:
if item['__type__'] == 'datetime':
item = arrow.get(item['value'])
elif '__entity_type__' in item:
item = self._create(
item['__entity_type__'], item, reconstructing=True
)
return item
def _get_locations(self, filter_inaccessible=True):
'''Helper to returns locations ordered by priority.
If *filter_inaccessible* is True then only accessible locations will be
included in result.
'''
# Optimise this call.
locations = self.query('Location')
# Filter.
if filter_inaccessible:
locations = filter(
lambda location: location.accessor,
locations
)
# Sort by priority.
locations = sorted(
locations, key=lambda location: location.priority
)
return locations
def pick_location(self, component=None):
'''Return suitable location to use.
If no *component* specified then return highest priority accessible
location. Otherwise, return highest priority accessible location that
*component* is available in.
Return None if no suitable location could be picked.
'''
if component:
return self.pick_locations([component])[0]
else:
locations = self._get_locations()
if locations:
return locations[0]
else:
return None
def pick_locations(self, components):
'''Return suitable locations for *components*.
Return list of locations corresponding to *components* where each
picked location is the highest priority accessible location for that
component. If a component has no location available then its
corresponding entry will be None.
'''
candidate_locations = self._get_locations()
availabilities = self.get_component_availabilities(
components, locations=candidate_locations
)
locations = []
for component, availability in zip(components, availabilities):
location = None
for candidate_location in candidate_locations:
if availability.get(candidate_location['id']) > 0.0:
location = candidate_location
break
locations.append(location)
return locations
def create_component(
self, path, data=None, location='auto'
):
'''Create a new component from *path* with additional *data*
.. note::
This is a helper method. To create components manually use the
standard :meth:`Session.create` method.
*path* can be a string representing a filesystem path to the data to
use for the component. The *path* can also be specified as a sequence
string, in which case a sequence component with child components for
each item in the sequence will be created automatically. The accepted
format for a sequence is '{head}{padding}{tail} [{ranges}]'. For
example::
'/path/to/file.%04d.ext [1-5, 7, 8, 10-20]'
.. seealso::
`Clique documentation <http://clique.readthedocs.org>`_
*data* should be a dictionary of any additional data to construct the
component with (as passed to :meth:`Session.create`).
If *location* is specified then automatically add component to that
location. The default of 'auto' will automatically pick a suitable
location to add the component to if one is available. To not add to any
location specifiy locations as None.
.. note::
A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be
automatically issued as part of the components registration in the
location.
'''
if data is None:
data = {}
if location == 'auto':
# Check if the component name matches one of the ftrackreview
# specific names. Add the component to the ftrack.review location if
# so. This is used to not break backwards compatibility.
if data.get('name') in (
'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image'
):
location = self.get(
'Location', ftrack_api.symbol.REVIEW_LOCATION_ID
)
else:
location = self.pick_location()
try:
collection = clique.parse(path)
except ValueError:
# Assume is a single file.
if'size' not in data:
data['size'] = self._get_filesystem_size(path)
data.setdefault('file_type', os.path.splitext(path)[-1])
return self._create_component(
'FileComponent', path, data, location
)
else:
# Calculate size of container and members.
member_sizes = {}
container_size = data.get('size')
if container_size is not None:
if len(collection.indexes) > 0:
member_size = int(
round(container_size / len(collection.indexes))
)
for item in collection:
member_sizes[item] = member_size
else:
container_size = 0
for item in collection:
member_sizes[item] = self._get_filesystem_size(item)
container_size += member_sizes[item]
# Create sequence component
container_path = collection.format('{head}{padding}{tail}')
data.setdefault('padding', collection.padding)
data.setdefault('file_type', os.path.splitext(container_path)[-1])
data.setdefault('size', container_size)
container = self._create_component(
'SequenceComponent', container_path, data, location=None
)
# Create member components for sequence.
for member_path in collection:
member_data = {
'name': collection.match(member_path).group('index'),
'container': container,
'size': member_sizes[member_path],
'file_type': os.path.splitext(member_path)[-1]
}
component = self._create_component(
'FileComponent', member_path, member_data, location=None
)
container['members'].append(component)
if location:
origin_location = self.get(
'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID
)
location.add_component(
container, origin_location, recursive=True
)
return container
def _create_component(self, entity_type, path, data, location):
'''Create and return component.
See public function :py:func:`createComponent` for argument details.
'''
component = self.create(entity_type, data)
# Add to special origin location so that it is possible to add to other
# locations.
origin_location = self.get(
'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID
)
origin_location.add_component(component, path, recursive=False)
if location:
location.add_component(component, origin_location, recursive=False)
return component
def _get_filesystem_size(self, path):
'''Return size from *path*'''
try:
size = os.path.getsize(path)
except OSError:
size = 0
return size
def get_component_availability(self, component, locations=None):
'''Return availability of *component*.
If *locations* is set then limit result to availability of *component*
in those *locations*.
Return a dictionary of {location_id:percentage_availability}
'''
return self.get_component_availabilities(
[component], locations=locations
)[0]
def get_component_availabilities(self, components, locations=None):
'''Return availabilities of *components*.
If *locations* is set then limit result to availabilities of
*components* in those *locations*.
Return a list of dictionaries of {location_id:percentage_availability}.
The list indexes correspond to those of *components*.
'''
availabilities = []
if locations is None:
locations = self.query('Location')
# Separate components into two lists, those that are containers and
# those that are not, so that queries can be optimised.
standard_components = []
container_components = []
for component in components:
if'members' in component.keys():
container_components.append(component)
else:
standard_components.append(component)
# Perform queries.
if standard_components:
self.populate(
standard_components, 'component_locations.location_id'
)
if container_components:
self.populate(
container_components,
'members, component_locations.location_id'
)
base_availability = {}
for location in locations:
base_availability[location['id']] = 0.0
for component in components:
availability = base_availability.copy()
availabilities.append(availability)
is_container ='members' in component.keys()
if is_container and len(component['members']):
member_availabilities = self.get_component_availabilities(
component['members'], locations=locations
)
multiplier = 1.0 / len(component['members'])
for member, member_availability in zip(
component['members'], member_availabilities
):
for location_id, ratio in member_availability.items():
availability[location_id] += (
ratio * multiplier
)
else:
for component_location in component['component_locations']:
location_id = component_location['location_id']
if location_id in availability:
availability[location_id] = 100.0
for location_id, percentage in availability.items():
# Avoid quantization error by rounding percentage and clamping
# to range 0-100.
adjusted_percentage = round(percentage, 9)
adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0))
availability[location_id] = adjusted_percentage
return availabilities
@ftrack_api.logging.deprecation_warning(
'Session.delayed_job has been deprecated in favour of session.call. '
'Please refer to the release notes for more information.'
)
def delayed_job(self, job_type):
'''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned.
*job_type* should be one of the allowed job types. There is currently
only one remote job type "SYNC_USERS_LDAP".
'''
if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ):
raise ValueError(
u'Invalid Job type: {0}.'.format(job_type)
)
operation = {
'action': 'delayed_job',
'job_type': job_type.name
}
try:
result = self.call(
[operation]
)[0]
except ftrack_api.exception.ServerError as error:
raise
return result['data']
def get_widget_url(self, name, entity=None, theme=None):
'''Return an authenticated URL for widget with *name* and given options.
The returned URL will be authenticated using a token which will expire
after 6 minutes.
*name* should be the name of the widget to return and should be one of
'info', 'tasks' or 'tasks_browser'.
Certain widgets require an entity to be specified. If so, specify it by
setting *entity* to a valid entity instance.
*theme* sets the theme of the widget and can be either 'light' or 'dark'
(defaulting to 'dark' if an invalid option given).
'''
operation = {
'action': 'get_widget_url',
'name': name,
'theme': theme
}
if entity:
operation['entity_type'] = entity.entity_type
operation['entity_key'] = (
ftrack_api.inspection.primary_key(entity).values()
)
try:
result = self.call([operation])
except ftrack_api.exception.ServerError as error:
# Raise informative error if the action is not supported.
if 'Invalid action u\'get_widget_url\'' in error.message:
raise ftrack_api.exception.ServerCompatibilityError(
'Server version {0!r} does not support "get_widget_url", '
'please update server and try again.'.format(
self.server_information.get('version')
)
)
else:
raise
else:
return result[0]['widget_url']
def encode_media(self, media, version_id=None, keep_original='auto'):
'''Return a new Job that encode *media* to make it playable in browsers.
*media* can be a path to a file or a FileComponent in the ftrack.server
location.
The job will encode *media* based on the file type and job data contains
information about encoding in the following format::
{
'output': [{
'format': 'video/mp4',
'component_id': 'e2dc0524-b576-11d3-9612-080027331d74'
}, {
'format': 'image/jpeg',
'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b'
}],
'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294',
'keep_original': True
}
The output components are associated with the job via the job_components
relation.
An image component will always be generated if possible that can be used
as a thumbnail.
If *media* is a file path, a new source component will be created and
added to the ftrack server location and a call to :meth:`commit` will be
issued. If *media* is a FileComponent, it will be assumed to be in
available in the ftrack.server location.
If *version_id* is specified, the new components will automatically be
associated with the AssetVersion. Otherwise, the components will not
be associated to a version even if the supplied *media* belongs to one.
A server version of 3.3.32 or higher is required for the version_id
argument to function properly.
If *keep_original* is not set, the original media will be kept if it
is a FileComponent, and deleted if it is a file path. You can specify
True or False to change this behavior.
'''
if isinstance(media, basestring):
# Media is a path to a file.
server_location = self.get(
'Location', ftrack_api.symbol.SERVER_LOCATION_ID
)
if keep_original == 'auto':
keep_original = False
component_data = None
if keep_original:
component_data = dict(version_id=version_id)
component = self.create_component(
path=media,
data=component_data,
location=server_location
)
# Auto commit to ensure component exists when sent to server.
self.commit()
elif (
hasattr(media, 'entity_type') and
media.entity_type in ('FileComponent',)
):
# Existing file component.
component = media
if keep_original == 'auto':
keep_original = True
else:
raise ValueError(
'Unable to encode media of type: {0}'.format(type(media))
)
operation = {
'action': 'encode_media',
'component_id': component['id'],
'version_id': version_id,
'keep_original': keep_original
}
try:
result = self.call([operation])
except ftrack_api.exception.ServerError as error:
# Raise informative error if the action is not supported.
if 'Invalid action u\'encode_media\'' in error.message:
raise ftrack_api.exception.ServerCompatibilityError(
'Server version {0!r} does not support "encode_media", '
'please update server and try again.'.format(
self.server_information.get('version')
)
)
else:
raise
return self.get('Job', result[0]['job_id'])
def get_upload_metadata(
self, component_id, file_name, file_size, checksum=None
):
'''Return URL and headers used to upload data for *component_id*.
*file_name* and *file_size* should match the components details.
The returned URL should be requested using HTTP PUT with the specified
headers.
The *checksum* is used as the Content-MD5 header and should contain
the base64-encoded 128-bit MD5 digest of the message (without the
headers) according to RFC 1864. This can be used as a message integrity
check to verify that the data is the same data that was originally sent.
'''
operation = {
'action': 'get_upload_metadata',
'component_id': component_id,
'file_name': file_name,
'file_size': file_size,
'checksum': checksum
}
try:
result = self.call([operation])
except ftrack_api.exception.ServerError as error:
# Raise informative error if the action is not supported.
if 'Invalid action u\'get_upload_metadata\'' in error.message:
raise ftrack_api.exception.ServerCompatibilityError(
'Server version {0!r} does not support '
'"get_upload_metadata", please update server and try '
'again.'.format(
self.server_information.get('version')
)
)
else:
raise
return result[0]
def send_user_invite(self, user):
'''Send a invitation to the provided *user*.
*user* is a User instance
'''
self.send_user_invites(
[user]
)
def send_user_invites(self, users):
'''Send a invitation to the provided *user*.
*users* is a list of User instances
'''
operations = []
for user in users:
operations.append(
{
'action':'send_user_invite',
'user_id': user['id']
}
)
try:
self.call(operations)
except ftrack_api.exception.ServerError as error:
# Raise informative error if the action is not supported.
if 'Invalid action u\'send_user_invite\'' in error.message:
raise ftrack_api.exception.ServerCompatibilityError(
'Server version {0!r} does not support '
'"send_user_invite", please update server and '
'try again.'.format(
self.server_information.get('version')
)
)
else:
raise
def send_review_session_invite(self, invitee):
'''Send an invite to a review session to *invitee*.
*invitee* is a instance of ReviewSessionInvitee.
.. note::
The *invitee* must be committed.
'''
self.send_review_session_invites([invitee])
def send_review_session_invites(self, invitees):
'''Send an invite to a review session to a list of *invitees*.
*invitee* is a list of ReviewSessionInvitee objects.
.. note::
All *invitees* must be committed.
'''
operations = []
for invitee in invitees:
operations.append(
{
'action':'send_review_session_invite',
'review_session_invitee_id': invitee['id']
}
)
try:
self.call(operations)
except ftrack_api.exception.ServerError as error:
# Raise informative error if the action is not supported.
if 'Invalid action u\'send_review_session_invite\'' in error.message:
raise ftrack_api.exception.ServerCompatibilityError(
'Server version {0!r} does not support '
'"send_review_session_invite", please update server and '
'try again.'.format(
self.server_information.get('version')
)
)
else:
raise
class AutoPopulatingContext(object):
'''Context manager for temporary change of session auto_populate value.'''
def __init__(self, session, auto_populate):
'''Initialise context.'''
super(AutoPopulatingContext, self).__init__()
self._session = session
self._auto_populate = auto_populate
self._current_auto_populate = None
def __enter__(self):
'''Enter context switching to desired auto populate setting.'''
self._current_auto_populate = self._session.auto_populate
self._session.auto_populate = self._auto_populate
def __exit__(self, exception_type, exception_value, traceback):
'''Exit context resetting auto populate to original setting.'''
self._session.auto_populate = self._current_auto_populate
class OperationRecordingContext(object):
'''Context manager for temporary change of session record_operations.'''
def __init__(self, session, record_operations):
'''Initialise context.'''
super(OperationRecordingContext, self).__init__()
self._session = session
self._record_operations = record_operations
self._current_record_operations = None
def __enter__(self):
'''Enter context.'''
self._current_record_operations = self._session.record_operations
self._session.record_operations = self._record_operations
def __exit__(self, exception_type, exception_value, traceback):
'''Exit context.'''
self._session.record_operations = self._current_record_operations
class OperationPayload(collections.MutableMapping):
'''Represent operation payload.'''
def __init__(self, *args, **kwargs):
'''Initialise payload.'''
super(OperationPayload, self).__init__()
self._data = dict()
self.update(dict(*args, **kwargs))
def __str__(self):
'''Return string representation.'''
return '<{0} {1}>'.format(
self.__class__.__name__, str(self._data)
)
def __getitem__(self, key):
'''Return value for *key*.'''
return self._data[key]
def __setitem__(self, key, value):
'''Set *value* for *key*.'''
self._data[key] = value
def __delitem__(self, key):
'''Remove *key*.'''
del self._data[key]
def __iter__(self):
'''Iterate over all keys.'''
return iter(self._data)
def __len__(self):
'''Return count of keys.'''
return len(self._data) |
|
ynput__OpenPype | custom_attribute.rst | Tutorial / Subdoc | Using custom attributes | MIT License | ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/custom_attribute.rst | [
"ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/source/ftrack_api/session.py"
] | Using custom attributes
Custom attributes can be written and read from entities using the
custom_attributes property.
The custom_attributes property provides a similar interface to a
dictionary.
Keys can be printed using the keys method:
>>> task['custom_attributes'].keys()
[u'my_text_field']
or access keys and values as items:
>>> print task['custom_attributes'].items()
[(u'my_text_field', u'some text')]
Read existing custom attribute values:
>>> print task['custom_attributes']['my_text_field']
'some text'
Updating a custom attributes can also be done similar to a dictionary:
task['custom_attributes']['my_text_field'] = 'foo'
To query for tasks with a custom attribute, my_text_field, you can use
the key from the configuration:
for task in session.query(
'Task where custom_attributes any '
'(key is "my_text_field" and value is "bar")'
):
print task['name']
Limitations
Expression attributes
Expression attributes are not yet supported and the reported value will
always be the non-evaluated expression.
Hierarchical attributes
Hierarchical attributes are not yet fully supported in the API.
Hierarchical attributes support both read and write, but when read they
are not calculated and instead the raw value is returned:
# The hierarchical attribute `my_attribute` is set on Shot but this will not
# be reflected on the children. Instead the raw value is returned.
print shot['custom_attributes']['my_attribute']
'foo'
print task['custom_attributes']['my_attribute']
None
To work around this limitation it is possible to use the legacy api for
hierarchical attributes or to manually query the parents for values and
use the first value that is set.
Validation
Custom attributes are validated on the ftrack server before persisted.
The validation will check that the type of the data is correct for the
custom attribute.
- number - int or float
- text - str or unicode
- enumerator - list
- boolean - bool
- date - datetime.datetime or datetime.date
If the value set is not valid a ftrack_api.exception.ServerError is
raised with debug information:
shot['custom_attributes']['fstart'] = 'test'
Traceback (most recent call last):
...
ftrack_api.exception.ServerError: Server reported error:
ValidationError(Custom attribute value for "fstart" must be of type number.
Got "test" of type <type 'unicode'>)
| # :coding: utf-8
# :copyright: Copyright (c) 2014 ftrack
from __future__ import absolute_import
import json
import logging
import collections
import datetime
import os
import getpass
import functools
import itertools
import distutils.version
import hashlib
import appdirs
import threading
import atexit
import requests
import requests.auth
import arrow
import clique
import ftrack_api
import ftrack_api.exception
import ftrack_api.entity.factory
import ftrack_api.entity.base
import ftrack_api.entity.location
import ftrack_api.cache
import ftrack_api.symbol
import ftrack_api.query
import ftrack_api.attribute
import ftrack_api.collection
import ftrack_api.event.hub
import ftrack_api.event.base
import ftrack_api.plugin
import ftrack_api.inspection
import ftrack_api.operation
import ftrack_api.accessor.disk
import ftrack_api.structure.origin
import ftrack_api.structure.entity_id
import ftrack_api.accessor.server
import ftrack_api._centralized_storage_scenario
import ftrack_api.logging
from ftrack_api.logging import LazyLogMessage as L
try:
from weakref import WeakMethod
except ImportError:
from ftrack_api._weakref import WeakMethod
class SessionAuthentication(requests.auth.AuthBase):
'''Attach ftrack session authentication information to requests.'''
def __init__(self, api_key, api_user):
'''Initialise with *api_key* and *api_user*.'''
self.api_key = api_key
self.api_user = api_user
super(SessionAuthentication, self).__init__()
def __call__(self, request):
'''Modify *request* to have appropriate headers.'''
request.headers.update({
'ftrack-api-key': self.api_key,
'ftrack-user': self.api_user
})
return request
class Session(object):
'''An isolated session for interaction with an ftrack server.'''
def __init__(
self, server_url=None, api_key=None, api_user=None, auto_populate=True,
plugin_paths=None, cache=None, cache_key_maker=None,
auto_connect_event_hub=None, schema_cache_path=None,
plugin_arguments=None
):
'''Initialise session.
*server_url* should be the URL of the ftrack server to connect to
including any port number. If not specified attempt to look up from
:envvar:`FTRACK_SERVER`.
*api_key* should be the API key to use for authentication whilst
*api_user* should be the username of the user in ftrack to record
operations against. If not specified, *api_key* should be retrieved
from :envvar:`FTRACK_API_KEY` and *api_user* from
:envvar:`FTRACK_API_USER`.
If *auto_populate* is True (the default), then accessing entity
attributes will cause them to be automatically fetched from the server
if they are not already. This flag can be changed on the session
directly at any time.
*plugin_paths* should be a list of paths to search for plugins. If not
specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`.
*cache* should be an instance of a cache that fulfils the
:class:`ftrack_api.cache.Cache` interface and will be used as the cache
for the session. It can also be a callable that will be called with the
session instance as sole argument. The callable should return ``None``
if a suitable cache could not be configured, but session instantiation
can continue safely.
.. note::
The session will add the specified cache to a pre-configured layered
cache that specifies the top level cache as a
:class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary
to construct a separate memory cache for typical behaviour. Working
around this behaviour or removing the memory cache can lead to
unexpected behaviour.
*cache_key_maker* should be an instance of a key maker that fulfils the
:class:`ftrack_api.cache.KeyMaker` interface and will be used to
generate keys for objects being stored in the *cache*. If not specified,
a :class:`~ftrack_api.cache.StringKeyMaker` will be used.
If *auto_connect_event_hub* is True then embedded event hub will be
automatically connected to the event server and allow for publishing and
subscribing to **non-local** events. If False, then only publishing and
subscribing to **local** events will be possible until the hub is
manually connected using :meth:`EventHub.connect
<ftrack_api.event.hub.EventHub.connect>`.
.. note::
The event hub connection is performed in a background thread to
improve session startup time. If a registered plugin requires a
connected event hub then it should check the event hub connection
status explicitly. Subscribing to events does *not* require a
connected event hub.
Enable schema caching by setting *schema_cache_path* to a folder path.
If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to
determine the path to store cache in. If the environment variable is
also not specified then a temporary directory will be used. Set to
`False` to disable schema caching entirely.
*plugin_arguments* should be an optional mapping (dict) of keyword
arguments to pass to plugin register functions upon discovery. If a
discovered plugin has a signature that is incompatible with the passed
arguments, the discovery mechanism will attempt to reduce the passed
arguments to only those that the plugin accepts. Note that a warning
will be logged in this case.
'''
super(Session, self).__init__()
self.logger = logging.getLogger(
__name__ + '.' + self.__class__.__name__
)
self._closed = False
if server_url is None:
server_url = os.environ.get('FTRACK_SERVER')
if not server_url:
raise TypeError(
'Required "server_url" not specified. Pass as argument or set '
'in environment variable FTRACK_SERVER.'
)
self._server_url = server_url
if api_key is None:
api_key = os.environ.get(
'FTRACK_API_KEY',
# Backwards compatibility
os.environ.get('FTRACK_APIKEY')
)
if not api_key:
raise TypeError(
'Required "api_key" not specified. Pass as argument or set in '
'environment variable FTRACK_API_KEY.'
)
self._api_key = api_key
if api_user is None:
api_user = os.environ.get('FTRACK_API_USER')
if not api_user:
try:
api_user = getpass.getuser()
except Exception:
pass
if not api_user:
raise TypeError(
'Required "api_user" not specified. Pass as argument, set in '
'environment variable FTRACK_API_USER or one of the standard '
'environment variables used by Python\'s getpass module.'
)
self._api_user = api_user
# Currently pending operations.
self.recorded_operations = ftrack_api.operation.Operations()
self.record_operations = True
self.cache_key_maker = cache_key_maker
if self.cache_key_maker is None:
self.cache_key_maker = ftrack_api.cache.StringKeyMaker()
# Enforce always having a memory cache at top level so that the same
# in-memory instance is returned from session.
self.cache = ftrack_api.cache.LayeredCache([
ftrack_api.cache.MemoryCache()
])
if cache is not None:
if callable(cache):
cache = cache(self)
if cache is not None:
self.cache.caches.append(cache)
self._managed_request = None
self._request = requests.Session()
self._request.auth = SessionAuthentication(
self._api_key, self._api_user
)
self.auto_populate = auto_populate
# Fetch server information and in doing so also check credentials.
self._server_information = self._fetch_server_information()
# Now check compatibility of server based on retrieved information.
self.check_server_compatibility()
# Construct event hub and load plugins.
self._event_hub = ftrack_api.event.hub.EventHub(
self._server_url,
self._api_user,
self._api_key,
)
self._auto_connect_event_hub_thread = None
if auto_connect_event_hub is True:
# Connect to event hub in background thread so as not to block main
# session usage waiting for event hub connection.
self._auto_connect_event_hub_thread = threading.Thread(
target=self._event_hub.connect
)
self._auto_connect_event_hub_thread.daemon = True
self._auto_connect_event_hub_thread.start()
# To help with migration from auto_connect_event_hub default changing
# from True to False.
self._event_hub._deprecation_warning_auto_connect = False
# Register to auto-close session on exit.
atexit.register(WeakMethod(self.close))
self._plugin_paths = plugin_paths
if self._plugin_paths is None:
self._plugin_paths = os.environ.get(
'FTRACK_EVENT_PLUGIN_PATH', ''
).split(os.pathsep)
self._discover_plugins(plugin_arguments=plugin_arguments)
# TODO: Make schemas read-only and non-mutable (or at least without
# rebuilding types)?
if schema_cache_path is not False:
if schema_cache_path is None:
schema_cache_path = appdirs.user_cache_dir()
schema_cache_path = os.environ.get(
'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path
)
schema_cache_path = os.path.join(
schema_cache_path, 'ftrack_api_schema_cache.json'
)
self.schemas = self._load_schemas(schema_cache_path)
self.types = self._build_entity_type_classes(self.schemas)
ftrack_api._centralized_storage_scenario.register(self)
self._configure_locations()
self.event_hub.publish(
ftrack_api.event.base.Event(
topic='ftrack.api.session.ready',
data=dict(
session=self
)
),
synchronous=True
)
def __enter__(self):
'''Return session as context manager.'''
return self
def __exit__(self, exception_type, exception_value, traceback):
'''Exit session context, closing session in process.'''
self.close()
@property
def _request(self):
'''Return request session.
Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has
been closed and connection unavailable.
'''
if self._managed_request is None:
raise ftrack_api.exception.ConnectionClosedError()
return self._managed_request
@_request.setter
def _request(self, value):
'''Set request session to *value*.'''
self._managed_request = value
@property
def closed(self):
'''Return whether session has been closed.'''
return self._closed
@property
def server_information(self):
'''Return server information such as server version.'''
return self._server_information.copy()
@property
def server_url(self):
'''Return server ulr used for session.'''
return self._server_url
@property
def api_user(self):
'''Return username used for session.'''
return self._api_user
@property
def api_key(self):
'''Return API key used for session.'''
return self._api_key
@property
def event_hub(self):
'''Return event hub.'''
return self._event_hub
@property
def _local_cache(self):
'''Return top level memory cache.'''
return self.cache.caches[0]
def check_server_compatibility(self):
'''Check compatibility with connected server.'''
server_version = self.server_information.get('version')
if server_version is None:
raise ftrack_api.exception.ServerCompatibilityError(
'Could not determine server version.'
)
# Perform basic version check.
if server_version!= 'dev':
min_server_version = '3.3.11'
if (
distutils.version.LooseVersion(min_server_version)
> distutils.version.LooseVersion(server_version)
):
raise ftrack_api.exception.ServerCompatibilityError(
'Server version {0} incompatible with this version of the '
'API which requires a server version >= {1}'.format(
server_version,
min_server_version
)
)
def close(self):
'''Close session.
Close connections to server. Clear any pending operations and local
cache.
Use this to ensure that session is cleaned up properly after use.
'''
if self.closed:
self.logger.debug('Session already closed.')
return
self._closed = True
self.logger.debug('Closing session.')
if self.recorded_operations:
self.logger.warning(
'Closing session with pending operations not persisted.'
)
# Clear pending operations.
self.recorded_operations.clear()
# Clear top level cache (expected to be enforced memory cache).
self._local_cache.clear()
# Close connections.
self._request.close()
self._request = None
try:
self.event_hub.disconnect()
if self._auto_connect_event_hub_thread:
self._auto_connect_event_hub_thread.join()
except ftrack_api.exception.EventHubConnectionError:
pass
self.logger.debug('Session closed.')
def reset(self):
'''Reset session clearing local state.
Clear all pending operations and expunge all entities from session.
Also clear the local cache. If the cache used by the session is a
:class:`~ftrack_api.cache.LayeredCache` then only clear top level cache.
Otherwise, clear the entire cache.
Plugins are not rediscovered or reinitialised, but certain plugin events
are re-emitted to properly configure session aspects that are dependant
on cache (such as location plugins).
.. warning::
Previously attached entities are not reset in memory and will retain
their state, but should not be used. Doing so will cause errors.
'''
if self.recorded_operations:
self.logger.warning(
'Resetting session with pending operations not persisted.'
)
# Clear pending operations.
self.recorded_operations.clear()
# Clear top level cache (expected to be enforced memory cache).
self._local_cache.clear()
# Re-configure certain session aspects that may be dependant on cache.
self._configure_locations()
self.event_hub.publish(
ftrack_api.event.base.Event(
topic='ftrack.api.session.reset',
data=dict(
session=self
)
),
synchronous=True
)
def auto_populating(self, auto_populate):
'''Temporarily set auto populate to *auto_populate*.
The current setting will be restored automatically when done.
Example::
with session.auto_populating(False):
print entity['name']
'''
return AutoPopulatingContext(self, auto_populate)
def operation_recording(self, record_operations):
'''Temporarily set operation recording to *record_operations*.
The current setting will be restored automatically when done.
Example::
with session.operation_recording(False):
entity['name'] = 'change_not_recorded'
'''
return OperationRecordingContext(self, record_operations)
@property
def created(self):
'''Return list of newly created entities.'''
entities = self._local_cache.values()
states = ftrack_api.inspection.states(entities)
return [
entity for (entity, state) in itertools.izip(entities, states)
if state is ftrack_api.symbol.CREATED
]
@property
def modified(self):
'''Return list of locally modified entities.'''
entities = self._local_cache.values()
states = ftrack_api.inspection.states(entities)
return [
entity for (entity, state) in itertools.izip(entities, states)
if state is ftrack_api.symbol.MODIFIED
]
@property
def deleted(self):
'''Return list of deleted entities.'''
entities = self._local_cache.values()
states = ftrack_api.inspection.states(entities)
return [
entity for (entity, state) in itertools.izip(entities, states)
if state is ftrack_api.symbol.DELETED
]
def reset_remote(self, reset_type, entity=None):
'''Perform a server side reset.
*reset_type* is a server side supported reset type,
passing the optional *entity* to perform the option upon.
Please refer to ftrack documentation for a complete list of
supported server side reset types.
'''
payload = {
'action':'reset_remote',
'reset_type': reset_type
}
if entity is not None:
payload.update({
'entity_type': entity.entity_type,
'entity_key': entity.get('id')
})
result = self.call(
[payload]
)
return result[0]['data']
def create(self, entity_type, data=None, reconstructing=False):
'''Create and return an entity of *entity_type* with initial *data*.
If specified, *data* should be a dictionary of key, value pairs that
should be used to populate attributes on the entity.
If *reconstructing* is False then create a new entity setting
appropriate defaults for missing data. If True then reconstruct an
existing entity.
Constructed entity will be automatically :meth:`merged <Session.merge>`
into the session.
'''
entity = self._create(entity_type, data, reconstructing=reconstructing)
entity = self.merge(entity)
return entity
def _create(self, entity_type, data, reconstructing):
'''Create and return an entity of *entity_type* with initial *data*.'''
try:
EntityTypeClass = self.types[entity_type]
except KeyError:
raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type)
return EntityTypeClass(self, data=data, reconstructing=reconstructing)
def ensure(self, entity_type, data, identifying_keys=None):
'''Retrieve entity of *entity_type* with *data*, creating if necessary.
*data* should be a dictionary of the same form passed to :meth:`create`.
By default, check for an entity that has matching *data*. If
*identifying_keys* is specified as a list of keys then only consider the
values from *data* for those keys when searching for existing entity. If
*data* is missing an identifying key then raise :exc:`KeyError`.
If no *identifying_keys* specified then use all of the keys from the
passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be
determined.
Each key should be a string.
.. note::
Currently only top level scalars supported. To ensure an entity by
looking at relationships, manually issue the :meth:`query` and
:meth:`create` calls.
If more than one entity matches the determined filter criteria then
raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`.
If no matching entity found then create entity using supplied *data*.
If a matching entity is found, then update it if necessary with *data*.
.. note::
If entity created or updated then a :meth:`commit` will be issued
automatically. If this behaviour is undesired, perform the
:meth:`query` and :meth:`create` calls manually.
Return retrieved or created entity.
Example::
# First time, a new entity with `username=martin` is created.
entity = session.ensure('User', {'username':'martin'})
# After that, the existing entity is retrieved.
entity = session.ensure('User', {'username':'martin'})
# When existing entity retrieved, entity may also be updated to
# match supplied data.
entity = session.ensure(
'User', {'username':'martin', 'email':'[email protected]'}
)
'''
if not identifying_keys:
identifying_keys = data.keys()
self.logger.debug(L(
'Ensuring entity {0!r} with data {1!r} using identifying keys '
'{2!r}', entity_type, data, identifying_keys
))
if not identifying_keys:
raise ValueError(
'Could not determine any identifying data to check against '
'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}'
.format(entity_type, data, identifying_keys)
)
expression = '{0} where'.format(entity_type)
criteria = []
for identifying_key in identifying_keys:
value = data[identifying_key]
if isinstance(value, basestring):
value = '"{0}"'.format(value)
elif isinstance(
value, (arrow.Arrow, datetime.datetime, datetime.date)
):
# Server does not store microsecond or timezone currently so
# need to strip from query.
# TODO: When datetime handling improved, update this logic.
value = (
arrow.get(value).naive.replace(microsecond=0).isoformat()
)
value = '"{0}"'.format(value)
criteria.append('{0} is {1}'.format(identifying_key, value))
expression = '{0} {1}'.format(
expression,'and '.join(criteria)
)
try:
entity = self.query(expression).one()
except ftrack_api.exception.NoResultFoundError:
self.logger.debug('Creating entity as did not already exist.')
# Create entity.
entity = self.create(entity_type, data)
self.commit()
else:
self.logger.debug('Retrieved matching existing entity.')
# Update entity if required.
updated = False
for key, target_value in data.items():
if entity[key]!= target_value:
entity[key] = target_value
updated = True
if updated:
self.logger.debug('Updating existing entity to match new data.')
self.commit()
return entity
def delete(self, entity):
'''Mark *entity* for deletion.'''
if self.record_operations:
self.recorded_operations.push(
ftrack_api.operation.DeleteEntityOperation(
entity.entity_type,
ftrack_api.inspection.primary_key(entity)
)
)
def get(self, entity_type, entity_key):
'''Return entity of *entity_type* with unique *entity_key*.
First check for an existing entry in the configured cache, otherwise
issue a query to the server.
If no matching entity found, return None.
'''
self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key))
primary_key_definition = self.types[entity_type].primary_key_attributes
if isinstance(entity_key, basestring):
entity_key = [entity_key]
if len(entity_key)!= len(primary_key_definition):
raise ValueError(
'Incompatible entity_key {0!r} supplied. Entity type {1} '
'expects a primary key composed of {2} values ({3}).'
.format(
entity_key, entity_type, len(primary_key_definition),
', '.join(primary_key_definition)
)
)
entity = None
try:
entity = self._get(entity_type, entity_key)
except KeyError:
# Query for matching entity.
self.logger.debug(
'Entity not present in cache. Issuing new query.'
)
condition = []
for key, value in zip(primary_key_definition, entity_key):
condition.append('{0} is "{1}"'.format(key, value))
expression = '{0} where ({1})'.format(
entity_type,'and '.join(condition)
)
results = self.query(expression).all()
if results:
entity = results[0]
return entity
def _get(self, entity_type, entity_key):
'''Return cached entity of *entity_type* with unique *entity_key*.
Raise :exc:`KeyError` if no such entity in the cache.
'''
# Check cache for existing entity emulating
# ftrack_api.inspection.identity result object to pass to key maker.
cache_key = self.cache_key_maker.key(
(str(entity_type), map(str, entity_key))
)
self.logger.debug(L(
'Checking cache for entity with key {0}', cache_key
))
entity = self.cache.get(cache_key)
self.logger.debug(L(
'Retrieved existing entity from cache: {0} at {1}',
entity, id(entity)
))
return entity
def query(self, expression, page_size=500):
'''Query against remote data according to *expression*.
*expression* is not executed directly. Instead return an
:class:`ftrack_api.query.QueryResult` instance that will execute remote
call on access.
*page_size* specifies the maximum page size that the returned query
result object should be configured with.
.. seealso:: :ref:`querying`
'''
self.logger.debug(L('Query {0!r}', expression))
# Add in sensible projections if none specified. Note that this is
# done here rather than on the server to allow local modification of the
# schema setting to include commonly used custom attributes for example.
# TODO: Use a proper parser perhaps?
if not expression.startswith('select'):
entity_type = expression.split(' ', 1)[0]
EntityTypeClass = self.types[entity_type]
projections = EntityTypeClass.default_projections
expression ='select {0} from {1}'.format(
', '.join(projections),
expression
)
query_result = ftrack_api.query.QueryResult(
self, expression, page_size=page_size
)
return query_result
def _query(self, expression):
'''Execute *query* and return (records, metadata).
Records will be a list of entities retrieved via the query and metadata
a dictionary of accompanying information about the result set.
'''
# TODO: Actually support batching several queries together.
# TODO: Should batches have unique ids to match them up later.
batch = [{
'action': 'query',
'expression': expression
}]
# TODO: When should this execute? How to handle background=True?
results = self.call(batch)
# Merge entities into local cache and return merged entities.
data = []
merged = dict()
for entity in results[0]['data']:
data.append(self._merge_recursive(entity, merged))
return data, results[0]['metadata']
def merge(self, value, merged=None):
'''Merge *value* into session and return merged value.
*merged* should be a mapping to record merges during run and should be
used to avoid infinite recursion. If not set will default to a
dictionary.
'''
if merged is None:
merged = {}
with self.operation_recording(False):
return self._merge(value, merged)
def _merge(self, value, merged):
'''Return merged *value*.'''
log_debug = self.logger.isEnabledFor(logging.DEBUG)
if isinstance(value, ftrack_api.entity.base.Entity):
log_debug and self.logger.debug(
'Merging entity into session: {0} at {1}'
.format(value, id(value))
)
return self._merge_entity(value, merged=merged)
elif isinstance(value, ftrack_api.collection.Collection):
log_debug and self.logger.debug(
'Merging collection into session: {0!r} at {1}'
.format(value, id(value))
)
merged_collection = []
for entry in value:
merged_collection.append(
self._merge(entry, merged=merged)
)
return merged_collection
elif isinstance(value, ftrack_api.collection.MappedCollectionProxy):
log_debug and self.logger.debug(
'Merging mapped collection into session: {0!r} at {1}'
.format(value, id(value))
)
merged_collection = []
for entry in value.collection:
merged_collection.append(
self._merge(entry, merged=merged)
)
return merged_collection
else:
return value
def _merge_recursive(self, entity, merged=None):
'''Merge *entity* and all its attributes recursivly.'''
log_debug = self.logger.isEnabledFor(logging.DEBUG)
if merged is None:
merged = {}
attached = self.merge(entity, merged)
for attribute in entity.attributes:
# Remote attributes.
remote_value = attribute.get_remote_value(entity)
if isinstance(
remote_value,
(
ftrack_api.entity.base.Entity,
ftrack_api.collection.Collection,
ftrack_api.collection.MappedCollectionProxy
)
):
log_debug and self.logger.debug(
'Merging remote value for attribute {0}.'.format(attribute)
)
if isinstance(remote_value, ftrack_api.entity.base.Entity):
self._merge_recursive(remote_value, merged=merged)
elif isinstance(
remote_value, ftrack_api.collection.Collection
):
for entry in remote_value:
self._merge_recursive(entry, merged=merged)
elif isinstance(
remote_value, ftrack_api.collection.MappedCollectionProxy
):
for entry in remote_value.collection:
self._merge_recursive(entry, merged=merged)
return attached
def _merge_entity(self, entity, merged=None):
'''Merge *entity* into session returning merged entity.
Merge is recursive so any references to other entities will also be
merged.
*entity* will never be modified in place. Ensure that the returned
merged entity instance is used.
'''
log_debug = self.logger.isEnabledFor(logging.DEBUG)
if merged is None:
merged = {}
with self.auto_populating(False):
entity_key = self.cache_key_maker.key(
ftrack_api.inspection.identity(entity)
)
# Check whether this entity has already been processed.
attached_entity = merged.get(entity_key)
if attached_entity is not None:
log_debug and self.logger.debug(
'Entity already processed for key {0} as {1} at {2}'
.format(entity_key, attached_entity, id(attached_entity))
)
return attached_entity
else:
log_debug and self.logger.debug(
'Entity not already processed for key {0}.'
.format(entity_key)
)
# Check for existing instance of entity in cache.
log_debug and self.logger.debug(
'Checking for entity in cache with key {0}'.format(entity_key)
)
try:
attached_entity = self.cache.get(entity_key)
log_debug and self.logger.debug(
'Retrieved existing entity from cache: {0} at {1}'
.format(attached_entity, id(attached_entity))
)
except KeyError:
# Construct new minimal instance to store in cache.
attached_entity = self._create(
entity.entity_type, {}, reconstructing=True
)
log_debug and self.logger.debug(
'Entity not present in cache. Constructed new instance: '
'{0} at {1}'.format(attached_entity, id(attached_entity))
)
# Mark entity as seen to avoid infinite loops.
merged[entity_key] = attached_entity
changes = attached_entity.merge(entity, merged=merged)
if changes:
self.cache.set(entity_key, attached_entity)
self.logger.debug('Cache updated with merged entity.')
else:
self.logger.debug(
'Cache not updated with merged entity as no differences '
'detected.'
)
return attached_entity
def populate(self, entities, projections):
'''Populate *entities* with attributes specified by *projections*.
Any locally set values included in the *projections* will not be
overwritten with the retrieved remote value. If this'synchronise'
behaviour is required, first clear the relevant values on the entity by
setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will
have the same effect::
>>> print(user['username'])
martin
>>> del user['username']
>>> print(user['username'])
Symbol(NOT_SET)
.. note::
Entities that have been created and not yet persisted will be
skipped as they have no remote values to fetch.
'''
self.logger.debug(L(
'Populate {0!r} projections for {1}.', projections, entities
))
if not isinstance(
entities, (list, tuple, ftrack_api.query.QueryResult)
):
entities = [entities]
# TODO: How to handle a mixed collection of different entity types
# Should probably fail, but need to consider handling hierarchies such
# as User and Group both deriving from Resource. Actually, could just
# proceed and ignore projections that are not present in entity type.
entities_to_process = []
for entity in entities:
if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED:
# Created entities that are not yet persisted have no remote
# values. Don't raise an error here as it is reasonable to
# iterate over an entities properties and see that some of them
# are NOT_SET.
self.logger.debug(L(
'Skipping newly created entity {0!r} for population as no '
'data will exist in the remote for this entity yet.', entity
))
continue
entities_to_process.append(entity)
if entities_to_process:
reference_entity = entities_to_process[0]
entity_type = reference_entity.entity_type
query ='select {0} from {1}'.format(projections, entity_type)
primary_key_definition = reference_entity.primary_key_attributes
entity_keys = [
ftrack_api.inspection.primary_key(entity).values()
for entity in entities_to_process
]
if len(primary_key_definition) > 1:
# Composite keys require full OR syntax unfortunately.
conditions = []
for entity_key in entity_keys:
condition = []
for key, value in zip(primary_key_definition, entity_key):
condition.append('{0} is "{1}"'.format(key, value))
conditions.append('({0})'.format('and '.join(condition)))
query = '{0} where {1}'.format(query,'or '.join(conditions))
else:
primary_key = primary_key_definition[0]
if len(entity_keys) > 1:
query = '{0} where {1} in ({2})'.format(
query, primary_key,
','.join([
str(entity_key[0]) for entity_key in entity_keys
])
)
else:
query = '{0} where {1} is {2}'.format(
query, primary_key, str(entity_keys[0][0])
)
result = self.query(query)
# Fetch all results now. Doing so will cause them to populate the
# relevant entities in the cache.
result.all()
# TODO: Should we check that all requested attributes were
# actually populated? If some weren't would we mark that to avoid
# repeated calls or perhaps raise an error?
# TODO: Make atomic.
def commit(self):
'''Commit all local changes to the server.'''
batch = []
with self.auto_populating(False):
for operation in self.recorded_operations:
# Convert operation to payload.
if isinstance(
operation, ftrack_api.operation.CreateEntityOperation
):
# At present, data payload requires duplicating entity
# type in data and also ensuring primary key added.
entity_data = {
'__entity_type__': operation.entity_type,
}
entity_data.update(operation.entity_key)
entity_data.update(operation.entity_data)
payload = OperationPayload({
'action': 'create',
'entity_type': operation.entity_type,
'entity_key': operation.entity_key.values(),
'entity_data': entity_data
})
elif isinstance(
operation, ftrack_api.operation.UpdateEntityOperation
):
entity_data = {
# At present, data payload requires duplicating entity
# type.
'__entity_type__': operation.entity_type,
operation.attribute_name: operation.new_value
}
payload = OperationPayload({
'action': 'update',
'entity_type': operation.entity_type,
'entity_key': operation.entity_key.values(),
'entity_data': entity_data
})
elif isinstance(
operation, ftrack_api.operation.DeleteEntityOperation
):
payload = OperationPayload({
'action': 'delete',
'entity_type': operation.entity_type,
'entity_key': operation.entity_key.values()
})
else:
raise ValueError(
'Cannot commit. Unrecognised operation type {0} '
'detected.'.format(type(operation))
)
batch.append(payload)
# Optimise batch.
# TODO: Might be better to perform these on the operations list instead
# so all operation contextual information available.
# If entity was created and deleted in one batch then remove all
# payloads for that entity.
created = set()
deleted = set()
for payload in batch:
if payload['action'] == 'create':
created.add(
(payload['entity_type'], str(payload['entity_key']))
)
elif payload['action'] == 'delete':
deleted.add(
(payload['entity_type'], str(payload['entity_key']))
)
created_then_deleted = deleted.intersection(created)
if created_then_deleted:
optimised_batch = []
for payload in batch:
entity_type = payload.get('entity_type')
entity_key = str(payload.get('entity_key'))
if (entity_type, entity_key) in created_then_deleted:
continue
optimised_batch.append(payload)
batch = optimised_batch
# Remove early update operations so that only last operation on
# attribute is applied server side.
updates_map = set()
for payload in reversed(batch):
if payload['action'] in ('update', ):
for key, value in payload['entity_data'].items():
if key == '__entity_type__':
continue
identity = (
payload['entity_type'], str(payload['entity_key']), key
)
if identity in updates_map:
del payload['entity_data'][key]
else:
updates_map.add(identity)
# Remove NOT_SET values from entity_data.
for payload in batch:
entity_data = payload.get('entity_data', {})
for key, value in entity_data.items():
if value is ftrack_api.symbol.NOT_SET:
del entity_data[key]
# Remove payloads with redundant entity_data.
optimised_batch = []
for payload in batch:
entity_data = payload.get('entity_data')
if entity_data is not None:
keys = entity_data.keys()
if not keys or keys == ['__entity_type__']:
continue
optimised_batch.append(payload)
batch = optimised_batch
# Collapse updates that are consecutive into one payload. Also, collapse
# updates that occur immediately after creation into the create payload.
optimised_batch = []
previous_payload = None
for payload in batch:
if (
previous_payload is not None
and payload['action'] == 'update'
and previous_payload['action'] in ('create', 'update')
and previous_payload['entity_type'] == payload['entity_type']
and previous_payload['entity_key'] == payload['entity_key']
):
previous_payload['entity_data'].update(payload['entity_data'])
continue
else:
optimised_batch.append(payload)
previous_payload = payload
batch = optimised_batch
# Process batch.
if batch:
result = self.call(batch)
# Clear recorded operations.
self.recorded_operations.clear()
# As optimisation, clear local values which are not primary keys to
# avoid redundant merges when merging references. Note: primary keys
# remain as needed for cache retrieval on new entities.
with self.auto_populating(False):
with self.operation_recording(False):
for entity in self._local_cache.values():
for attribute in entity:
if attribute not in entity.primary_key_attributes:
del entity[attribute]
# Process results merging into cache relevant data.
for entry in result:
if entry['action'] in ('create', 'update'):
# Merge returned entities into local cache.
self.merge(entry['data'])
elif entry['action'] == 'delete':
# TODO: Detach entity - need identity returned?
# TODO: Expunge entity from cache.
pass
# Clear remaining local state, including local values for primary
# keys on entities that were merged.
with self.auto_populating(False):
with self.operation_recording(False):
for entity in self._local_cache.values():
entity.clear()
def rollback(self):
'''Clear all recorded operations and local state.
Typically this would be used following a failed :meth:`commit` in order
to revert the session to a known good state.
Newly created entities not yet persisted will be detached from the
session / purged from cache and no longer contribute, but the actual
objects are not deleted from memory. They should no longer be used and
doing so could cause errors.
'''
with self.auto_populating(False):
with self.operation_recording(False):
# Detach all newly created entities and remove from cache. This
# is done because simply clearing the local values of newly
# created entities would result in entities with no identity as
# primary key was local while not persisted. In addition, it
# makes no sense for failed created entities to exist in session
# or cache.
for operation in self.recorded_operations:
if isinstance(
operation, ftrack_api.operation.CreateEntityOperation
):
entity_key = str((
str(operation.entity_type),
operation.entity_key.values()
))
try:
self.cache.remove(entity_key)
except KeyError:
pass
# Clear locally stored modifications on remaining entities.
for entity in self._local_cache.values():
entity.clear()
self.recorded_operations.clear()
def _fetch_server_information(self):
'''Return server information.'''
result = self.call([{'action': 'query_server_information'}])
return result[0]
def _discover_plugins(self, plugin_arguments=None):
'''Find and load plugins in search paths.
Each discovered module should implement a register function that
accepts this session as first argument. Typically the function should
register appropriate event listeners against the session's event hub.
def register(session):
session.event_hub.subscribe(
'topic=ftrack.api.session.construct-entity-type',
construct_entity_type
)
*plugin_arguments* should be an optional mapping of keyword arguments
and values to pass to plugin register functions upon discovery.
'''
plugin_arguments = plugin_arguments or {}
ftrack_api.plugin.discover(
self._plugin_paths, [self], plugin_arguments
)
def _read_schemas_from_cache(self, schema_cache_path):
'''Return schemas and schema hash from *schema_cache_path*.
*schema_cache_path* should be the path to the file containing the
schemas in JSON format.
'''
self.logger.debug(L(
'Reading schemas from cache {0!r}', schema_cache_path
))
if not os.path.exists(schema_cache_path):
self.logger.info(L(
'Cache file not found at {0!r}.', schema_cache_path
))
return [], None
with open(schema_cache_path, 'r') as schema_file:
schemas = json.load(schema_file)
hash_ = hashlib.md5(
json.dumps(schemas, sort_keys=True)
).hexdigest()
return schemas, hash_
def _write_schemas_to_cache(self, schemas, schema_cache_path):
'''Write *schemas* to *schema_cache_path*.
*schema_cache_path* should be a path to a file that the schemas can be
written to in JSON format.
'''
self.logger.debug(L(
'Updating schema cache {0!r} with new schemas.', schema_cache_path
))
with open(schema_cache_path, 'w') as local_cache_file:
json.dump(schemas, local_cache_file, indent=4)
def _load_schemas(self, schema_cache_path):
'''Load schemas.
First try to load schemas from cache at *schema_cache_path*. If the
cache is not available or the cache appears outdated then load schemas
from server and store fresh copy in cache.
If *schema_cache_path* is set to `False`, always load schemas from
server bypassing cache.
'''
local_schema_hash = None
schemas = []
if schema_cache_path:
try:
schemas, local_schema_hash = self._read_schemas_from_cache(
schema_cache_path
)
except (IOError, TypeError, AttributeError, ValueError):
# Catch any known exceptions when trying to read the local
# schema cache to prevent API from being unusable.
self.logger.exception(L(
'Schema cache could not be loaded from {0!r}',
schema_cache_path
))
# Use `dictionary.get` to retrieve hash to support older version of
# ftrack server not returning a schema hash.
server_hash = self._server_information.get(
'schema_hash', False
)
if local_schema_hash!= server_hash:
self.logger.debug(L(
'Loading schemas from server due to hash not matching.'
'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash
))
schemas = self.call([{'action': 'query_schemas'}])[0]
if schema_cache_path:
try:
self._write_schemas_to_cache(schemas, schema_cache_path)
except (IOError, TypeError):
self.logger.exception(L(
'Failed to update schema cache {0!r}.',
schema_cache_path
))
else:
self.logger.debug(L(
'Using cached schemas from {0!r}', schema_cache_path
))
return schemas
def _build_entity_type_classes(self, schemas):
'''Build default entity type classes.'''
fallback_factory = ftrack_api.entity.factory.StandardFactory()
classes = {}
for schema in schemas:
results = self.event_hub.publish(
ftrack_api.event.base.Event(
topic='ftrack.api.session.construct-entity-type',
data=dict(
schema=schema,
schemas=schemas
)
),
synchronous=True
)
results = [result for result in results if result is not None]
if not results:
self.logger.debug(L(
'Using default StandardFactory to construct entity type '
'class for "{0}"', schema['id']
))
entity_type_class = fallback_factory.create(schema)
elif len(results) > 1:
raise ValueError(
'Expected single entity type to represent schema "{0}" but '
'received {1} entity types instead.'
.format(schema['id'], len(results))
)
else:
entity_type_class = results[0]
classes[entity_type_class.entity_type] = entity_type_class
return classes
def _configure_locations(self):
'''Configure locations.'''
# First configure builtin locations, by injecting them into local cache.
# Origin.
location = self.create(
'Location',
data=dict(
name='ftrack.origin',
id=ftrack_api.symbol.ORIGIN_LOCATION_ID
),
reconstructing=True
)
ftrack_api.mixin(
location, ftrack_api.entity.location.OriginLocationMixin,
name='OriginLocation'
)
location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='')
location.structure = ftrack_api.structure.origin.OriginStructure()
location.priority = 100
# Unmanaged.
location = self.create(
'Location',
data=dict(
name='ftrack.unmanaged',
id=ftrack_api.symbol.UNMANAGED_LOCATION_ID
),
reconstructing=True
)
ftrack_api.mixin(
location, ftrack_api.entity.location.UnmanagedLocationMixin,
name='UnmanagedLocation'
)
location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='')
location.structure = ftrack_api.structure.origin.OriginStructure()
# location.resource_identifier_transformer = (
# ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session)
# )
location.priority = 90
# Review.
location = self.create(
'Location',
data=dict(
name='ftrack.review',
id=ftrack_api.symbol.REVIEW_LOCATION_ID
),
reconstructing=True
)
ftrack_api.mixin(
location, ftrack_api.entity.location.UnmanagedLocationMixin,
name='UnmanagedLocation'
)
location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='')
location.structure = ftrack_api.structure.origin.OriginStructure()
location.priority = 110
# Server.
location = self.create(
'Location',
data=dict(
name='ftrack.server',
id=ftrack_api.symbol.SERVER_LOCATION_ID
),
reconstructing=True
)
ftrack_api.mixin(
location, ftrack_api.entity.location.ServerLocationMixin,
name='ServerLocation'
)
location.accessor = ftrack_api.accessor.server._ServerAccessor(
session=self
)
location.structure = ftrack_api.structure.entity_id.EntityIdStructure()
location.priority = 150
# Master location based on server scenario.
storage_scenario = self.server_information.get('storage_scenario')
if (
storage_scenario and
storage_scenario.get('scenario')
):
self.event_hub.publish(
ftrack_api.event.base.Event(
topic='ftrack.storage-scenario.activate',
data=dict(
storage_scenario=storage_scenario
)
),
synchronous=True
)
# Next, allow further configuration of locations via events.
self.event_hub.publish(
ftrack_api.event.base.Event(
topic='ftrack.api.session.configure-location',
data=dict(
session=self
)
),
synchronous=True
)
@ftrack_api.logging.deprecation_warning(
'Session._call is now available as public method Session.call. The '
'private method will be removed in version 2.0.'
)
def _call(self, data):
'''Make request to server with *data* batch describing the actions.
.. note::
This private method is now available as public method
:meth:`entity_reference`. This alias remains for backwards
compatibility, but will be removed in version 2.0.
'''
return self.call(data)
def call(self, data):
'''Make request to server with *data* batch describing the actions.'''
url = self._server_url + '/api'
headers = {
'content-type': 'application/json',
'accept': 'application/json'
}
data = self.encode(data, entity_attribute_strategy='modified_only')
self.logger.debug(L('Calling server {0} with {1!r}', url, data))
response = self._request.post(
url,
headers=headers,
data=data
)
self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds()))
self.logger.debug(L('Response: {0!r}', response.text))
try:
result = self.decode(response.text)
except Exception:
error_message = (
'Server reported error in unexpected format. Raw error was: {0}'
.format(response.text)
)
self.logger.exception(error_message)
raise ftrack_api.exception.ServerError(error_message)
else:
if 'exception' in result:
# Handle exceptions.
error_message = 'Server reported error: {0}({1})'.format(
result['exception'], result['content']
)
self.logger.exception(error_message)
raise ftrack_api.exception.ServerError(error_message)
return result
def encode(self, data, entity_attribute_strategy='set_only'):
'''Return *data* encoded as JSON formatted string.
*entity_attribute_strategy* specifies how entity attributes should be
handled. The following strategies are available:
* *all* - Encode all attributes, loading any that are currently NOT_SET.
* *set_only* - Encode only attributes that are currently set without
loading any from the remote.
* *modified_only* - Encode only attributes that have been modified
locally.
* *persisted_only* - Encode only remote (persisted) attribute values.
'''
entity_attribute_strategies = (
'all','set_only','modified_only', 'persisted_only'
)
if entity_attribute_strategy not in entity_attribute_strategies:
raise ValueError(
'Unsupported entity_attribute_strategy "{0}". Must be one of '
'{1}'.format(
entity_attribute_strategy,
', '.join(entity_attribute_strategies)
)
)
return json.dumps(
data,
sort_keys=True,
default=functools.partial(
self._encode,
entity_attribute_strategy=entity_attribute_strategy
)
)
def _encode(self, item, entity_attribute_strategy='set_only'):
'''Return JSON encodable version of *item*.
*entity_attribute_strategy* specifies how entity attributes should be
handled. See :meth:`Session.encode` for available strategies.
'''
if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)):
return {
'__type__': 'datetime',
'value': item.isoformat()
}
if isinstance(item, OperationPayload):
data = dict(item.items())
if "entity_data" in data:
for key, value in data["entity_data"].items():
if isinstance(value, ftrack_api.entity.base.Entity):
data["entity_data"][key] = self.entity_reference(value)
return data
if isinstance(item, ftrack_api.entity.base.Entity):
data = self.entity_reference(item)
with self.auto_populating(True):
for attribute in item.attributes:
value = ftrack_api.symbol.NOT_SET
if entity_attribute_strategy == 'all':
value = attribute.get_value(item)
elif entity_attribute_strategy =='set_only':
if attribute.is_set(item):
value = attribute.get_local_value(item)
if value is ftrack_api.symbol.NOT_SET:
value = attribute.get_remote_value(item)
elif entity_attribute_strategy =='modified_only':
if attribute.is_modified(item):
value = attribute.get_local_value(item)
elif entity_attribute_strategy == 'persisted_only':
if not attribute.computed:
value = attribute.get_remote_value(item)
if value is not ftrack_api.symbol.NOT_SET:
if isinstance(
attribute, ftrack_api.attribute.ReferenceAttribute
):
if isinstance(value, ftrack_api.entity.base.Entity):
value = self.entity_reference(value)
data[attribute.name] = value
return data
if isinstance(
item, ftrack_api.collection.MappedCollectionProxy
):
# Use proxied collection for serialisation.
item = item.collection
if isinstance(item, ftrack_api.collection.Collection):
data = []
for entity in item:
data.append(self.entity_reference(entity))
return data
raise TypeError('{0!r} is not JSON serializable'.format(item))
def entity_reference(self, entity):
'''Return entity reference that uniquely identifies *entity*.
Return a mapping containing the __entity_type__ of the entity along with
the key, value pairs that make up it's primary key.
'''
reference = {
'__entity_type__': entity.entity_type
}
with self.auto_populating(False):
reference.update(ftrack_api.inspection.primary_key(entity))
return reference
@ftrack_api.logging.deprecation_warning(
'Session._entity_reference is now available as public method '
'Session.entity_reference. The private method will be removed '
'in version 2.0.'
)
def _entity_reference(self, entity):
'''Return entity reference that uniquely identifies *entity*.
Return a mapping containing the __entity_type__ of the entity along
with the key, value pairs that make up it's primary key.
.. note::
This private method is now available as public method
:meth:`entity_reference`. This alias remains for backwards
compatibility, but will be removed in version 2.0.
'''
return self.entity_reference(entity)
def decode(self, string):
'''Return decoded JSON *string* as Python object.'''
with self.operation_recording(False):
return json.loads(string, object_hook=self._decode)
def _decode(self, item):
'''Return *item* transformed into appropriate representation.'''
if isinstance(item, collections.Mapping):
if '__type__' in item:
if item['__type__'] == 'datetime':
item = arrow.get(item['value'])
elif '__entity_type__' in item:
item = self._create(
item['__entity_type__'], item, reconstructing=True
)
return item
def _get_locations(self, filter_inaccessible=True):
'''Helper to returns locations ordered by priority.
If *filter_inaccessible* is True then only accessible locations will be
included in result.
'''
# Optimise this call.
locations = self.query('Location')
# Filter.
if filter_inaccessible:
locations = filter(
lambda location: location.accessor,
locations
)
# Sort by priority.
locations = sorted(
locations, key=lambda location: location.priority
)
return locations
def pick_location(self, component=None):
'''Return suitable location to use.
If no *component* specified then return highest priority accessible
location. Otherwise, return highest priority accessible location that
*component* is available in.
Return None if no suitable location could be picked.
'''
if component:
return self.pick_locations([component])[0]
else:
locations = self._get_locations()
if locations:
return locations[0]
else:
return None
def pick_locations(self, components):
'''Return suitable locations for *components*.
Return list of locations corresponding to *components* where each
picked location is the highest priority accessible location for that
component. If a component has no location available then its
corresponding entry will be None.
'''
candidate_locations = self._get_locations()
availabilities = self.get_component_availabilities(
components, locations=candidate_locations
)
locations = []
for component, availability in zip(components, availabilities):
location = None
for candidate_location in candidate_locations:
if availability.get(candidate_location['id']) > 0.0:
location = candidate_location
break
locations.append(location)
return locations
def create_component(
self, path, data=None, location='auto'
):
'''Create a new component from *path* with additional *data*
.. note::
This is a helper method. To create components manually use the
standard :meth:`Session.create` method.
*path* can be a string representing a filesystem path to the data to
use for the component. The *path* can also be specified as a sequence
string, in which case a sequence component with child components for
each item in the sequence will be created automatically. The accepted
format for a sequence is '{head}{padding}{tail} [{ranges}]'. For
example::
'/path/to/file.%04d.ext [1-5, 7, 8, 10-20]'
.. seealso::
`Clique documentation <http://clique.readthedocs.org>`_
*data* should be a dictionary of any additional data to construct the
component with (as passed to :meth:`Session.create`).
If *location* is specified then automatically add component to that
location. The default of 'auto' will automatically pick a suitable
location to add the component to if one is available. To not add to any
location specifiy locations as None.
.. note::
A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be
automatically issued as part of the components registration in the
location.
'''
if data is None:
data = {}
if location == 'auto':
# Check if the component name matches one of the ftrackreview
# specific names. Add the component to the ftrack.review location if
# so. This is used to not break backwards compatibility.
if data.get('name') in (
'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image'
):
location = self.get(
'Location', ftrack_api.symbol.REVIEW_LOCATION_ID
)
else:
location = self.pick_location()
try:
collection = clique.parse(path)
except ValueError:
# Assume is a single file.
if'size' not in data:
data['size'] = self._get_filesystem_size(path)
data.setdefault('file_type', os.path.splitext(path)[-1])
return self._create_component(
'FileComponent', path, data, location
)
else:
# Calculate size of container and members.
member_sizes = {}
container_size = data.get('size')
if container_size is not None:
if len(collection.indexes) > 0:
member_size = int(
round(container_size / len(collection.indexes))
)
for item in collection:
member_sizes[item] = member_size
else:
container_size = 0
for item in collection:
member_sizes[item] = self._get_filesystem_size(item)
container_size += member_sizes[item]
# Create sequence component
container_path = collection.format('{head}{padding}{tail}')
data.setdefault('padding', collection.padding)
data.setdefault('file_type', os.path.splitext(container_path)[-1])
data.setdefault('size', container_size)
container = self._create_component(
'SequenceComponent', container_path, data, location=None
)
# Create member components for sequence.
for member_path in collection:
member_data = {
'name': collection.match(member_path).group('index'),
'container': container,
'size': member_sizes[member_path],
'file_type': os.path.splitext(member_path)[-1]
}
component = self._create_component(
'FileComponent', member_path, member_data, location=None
)
container['members'].append(component)
if location:
origin_location = self.get(
'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID
)
location.add_component(
container, origin_location, recursive=True
)
return container
def _create_component(self, entity_type, path, data, location):
'''Create and return component.
See public function :py:func:`createComponent` for argument details.
'''
component = self.create(entity_type, data)
# Add to special origin location so that it is possible to add to other
# locations.
origin_location = self.get(
'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID
)
origin_location.add_component(component, path, recursive=False)
if location:
location.add_component(component, origin_location, recursive=False)
return component
def _get_filesystem_size(self, path):
'''Return size from *path*'''
try:
size = os.path.getsize(path)
except OSError:
size = 0
return size
def get_component_availability(self, component, locations=None):
'''Return availability of *component*.
If *locations* is set then limit result to availability of *component*
in those *locations*.
Return a dictionary of {location_id:percentage_availability}
'''
return self.get_component_availabilities(
[component], locations=locations
)[0]
def get_component_availabilities(self, components, locations=None):
'''Return availabilities of *components*.
If *locations* is set then limit result to availabilities of
*components* in those *locations*.
Return a list of dictionaries of {location_id:percentage_availability}.
The list indexes correspond to those of *components*.
'''
availabilities = []
if locations is None:
locations = self.query('Location')
# Separate components into two lists, those that are containers and
# those that are not, so that queries can be optimised.
standard_components = []
container_components = []
for component in components:
if'members' in component.keys():
container_components.append(component)
else:
standard_components.append(component)
# Perform queries.
if standard_components:
self.populate(
standard_components, 'component_locations.location_id'
)
if container_components:
self.populate(
container_components,
'members, component_locations.location_id'
)
base_availability = {}
for location in locations:
base_availability[location['id']] = 0.0
for component in components:
availability = base_availability.copy()
availabilities.append(availability)
is_container ='members' in component.keys()
if is_container and len(component['members']):
member_availabilities = self.get_component_availabilities(
component['members'], locations=locations
)
multiplier = 1.0 / len(component['members'])
for member, member_availability in zip(
component['members'], member_availabilities
):
for location_id, ratio in member_availability.items():
availability[location_id] += (
ratio * multiplier
)
else:
for component_location in component['component_locations']:
location_id = component_location['location_id']
if location_id in availability:
availability[location_id] = 100.0
for location_id, percentage in availability.items():
# Avoid quantization error by rounding percentage and clamping
# to range 0-100.
adjusted_percentage = round(percentage, 9)
adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0))
availability[location_id] = adjusted_percentage
return availabilities
@ftrack_api.logging.deprecation_warning(
'Session.delayed_job has been deprecated in favour of session.call. '
'Please refer to the release notes for more information.'
)
def delayed_job(self, job_type):
'''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned.
*job_type* should be one of the allowed job types. There is currently
only one remote job type "SYNC_USERS_LDAP".
'''
if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ):
raise ValueError(
u'Invalid Job type: {0}.'.format(job_type)
)
operation = {
'action': 'delayed_job',
'job_type': job_type.name
}
try:
result = self.call(
[operation]
)[0]
except ftrack_api.exception.ServerError as error:
raise
return result['data']
def get_widget_url(self, name, entity=None, theme=None):
'''Return an authenticated URL for widget with *name* and given options.
The returned URL will be authenticated using a token which will expire
after 6 minutes.
*name* should be the name of the widget to return and should be one of
'info', 'tasks' or 'tasks_browser'.
Certain widgets require an entity to be specified. If so, specify it by
setting *entity* to a valid entity instance.
*theme* sets the theme of the widget and can be either 'light' or 'dark'
(defaulting to 'dark' if an invalid option given).
'''
operation = {
'action': 'get_widget_url',
'name': name,
'theme': theme
}
if entity:
operation['entity_type'] = entity.entity_type
operation['entity_key'] = (
ftrack_api.inspection.primary_key(entity).values()
)
try:
result = self.call([operation])
except ftrack_api.exception.ServerError as error:
# Raise informative error if the action is not supported.
if 'Invalid action u\'get_widget_url\'' in error.message:
raise ftrack_api.exception.ServerCompatibilityError(
'Server version {0!r} does not support "get_widget_url", '
'please update server and try again.'.format(
self.server_information.get('version')
)
)
else:
raise
else:
return result[0]['widget_url']
def encode_media(self, media, version_id=None, keep_original='auto'):
'''Return a new Job that encode *media* to make it playable in browsers.
*media* can be a path to a file or a FileComponent in the ftrack.server
location.
The job will encode *media* based on the file type and job data contains
information about encoding in the following format::
{
'output': [{
'format': 'video/mp4',
'component_id': 'e2dc0524-b576-11d3-9612-080027331d74'
}, {
'format': 'image/jpeg',
'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b'
}],
'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294',
'keep_original': True
}
The output components are associated with the job via the job_components
relation.
An image component will always be generated if possible that can be used
as a thumbnail.
If *media* is a file path, a new source component will be created and
added to the ftrack server location and a call to :meth:`commit` will be
issued. If *media* is a FileComponent, it will be assumed to be in
available in the ftrack.server location.
If *version_id* is specified, the new components will automatically be
associated with the AssetVersion. Otherwise, the components will not
be associated to a version even if the supplied *media* belongs to one.
A server version of 3.3.32 or higher is required for the version_id
argument to function properly.
If *keep_original* is not set, the original media will be kept if it
is a FileComponent, and deleted if it is a file path. You can specify
True or False to change this behavior.
'''
if isinstance(media, basestring):
# Media is a path to a file.
server_location = self.get(
'Location', ftrack_api.symbol.SERVER_LOCATION_ID
)
if keep_original == 'auto':
keep_original = False
component_data = None
if keep_original:
component_data = dict(version_id=version_id)
component = self.create_component(
path=media,
data=component_data,
location=server_location
)
# Auto commit to ensure component exists when sent to server.
self.commit()
elif (
hasattr(media, 'entity_type') and
media.entity_type in ('FileComponent',)
):
# Existing file component.
component = media
if keep_original == 'auto':
keep_original = True
else:
raise ValueError(
'Unable to encode media of type: {0}'.format(type(media))
)
operation = {
'action': 'encode_media',
'component_id': component['id'],
'version_id': version_id,
'keep_original': keep_original
}
try:
result = self.call([operation])
except ftrack_api.exception.ServerError as error:
# Raise informative error if the action is not supported.
if 'Invalid action u\'encode_media\'' in error.message:
raise ftrack_api.exception.ServerCompatibilityError(
'Server version {0!r} does not support "encode_media", '
'please update server and try again.'.format(
self.server_information.get('version')
)
)
else:
raise
return self.get('Job', result[0]['job_id'])
def get_upload_metadata(
self, component_id, file_name, file_size, checksum=None
):
'''Return URL and headers used to upload data for *component_id*.
*file_name* and *file_size* should match the components details.
The returned URL should be requested using HTTP PUT with the specified
headers.
The *checksum* is used as the Content-MD5 header and should contain
the base64-encoded 128-bit MD5 digest of the message (without the
headers) according to RFC 1864. This can be used as a message integrity
check to verify that the data is the same data that was originally sent.
'''
operation = {
'action': 'get_upload_metadata',
'component_id': component_id,
'file_name': file_name,
'file_size': file_size,
'checksum': checksum
}
try:
result = self.call([operation])
except ftrack_api.exception.ServerError as error:
# Raise informative error if the action is not supported.
if 'Invalid action u\'get_upload_metadata\'' in error.message:
raise ftrack_api.exception.ServerCompatibilityError(
'Server version {0!r} does not support '
'"get_upload_metadata", please update server and try '
'again.'.format(
self.server_information.get('version')
)
)
else:
raise
return result[0]
def send_user_invite(self, user):
'''Send a invitation to the provided *user*.
*user* is a User instance
'''
self.send_user_invites(
[user]
)
def send_user_invites(self, users):
'''Send a invitation to the provided *user*.
*users* is a list of User instances
'''
operations = []
for user in users:
operations.append(
{
'action':'send_user_invite',
'user_id': user['id']
}
)
try:
self.call(operations)
except ftrack_api.exception.ServerError as error:
# Raise informative error if the action is not supported.
if 'Invalid action u\'send_user_invite\'' in error.message:
raise ftrack_api.exception.ServerCompatibilityError(
'Server version {0!r} does not support '
'"send_user_invite", please update server and '
'try again.'.format(
self.server_information.get('version')
)
)
else:
raise
def send_review_session_invite(self, invitee):
'''Send an invite to a review session to *invitee*.
*invitee* is a instance of ReviewSessionInvitee.
.. note::
The *invitee* must be committed.
'''
self.send_review_session_invites([invitee])
def send_review_session_invites(self, invitees):
'''Send an invite to a review session to a list of *invitees*.
*invitee* is a list of ReviewSessionInvitee objects.
.. note::
All *invitees* must be committed.
'''
operations = []
for invitee in invitees:
operations.append(
{
'action':'send_review_session_invite',
'review_session_invitee_id': invitee['id']
}
)
try:
self.call(operations)
except ftrack_api.exception.ServerError as error:
# Raise informative error if the action is not supported.
if 'Invalid action u\'send_review_session_invite\'' in error.message:
raise ftrack_api.exception.ServerCompatibilityError(
'Server version {0!r} does not support '
'"send_review_session_invite", please update server and '
'try again.'.format(
self.server_information.get('version')
)
)
else:
raise
class AutoPopulatingContext(object):
'''Context manager for temporary change of session auto_populate value.'''
def __init__(self, session, auto_populate):
'''Initialise context.'''
super(AutoPopulatingContext, self).__init__()
self._session = session
self._auto_populate = auto_populate
self._current_auto_populate = None
def __enter__(self):
'''Enter context switching to desired auto populate setting.'''
self._current_auto_populate = self._session.auto_populate
self._session.auto_populate = self._auto_populate
def __exit__(self, exception_type, exception_value, traceback):
'''Exit context resetting auto populate to original setting.'''
self._session.auto_populate = self._current_auto_populate
class OperationRecordingContext(object):
'''Context manager for temporary change of session record_operations.'''
def __init__(self, session, record_operations):
'''Initialise context.'''
super(OperationRecordingContext, self).__init__()
self._session = session
self._record_operations = record_operations
self._current_record_operations = None
def __enter__(self):
'''Enter context.'''
self._current_record_operations = self._session.record_operations
self._session.record_operations = self._record_operations
def __exit__(self, exception_type, exception_value, traceback):
'''Exit context.'''
self._session.record_operations = self._current_record_operations
class OperationPayload(collections.MutableMapping):
'''Represent operation payload.'''
def __init__(self, *args, **kwargs):
'''Initialise payload.'''
super(OperationPayload, self).__init__()
self._data = dict()
self.update(dict(*args, **kwargs))
def __str__(self):
'''Return string representation.'''
return '<{0} {1}>'.format(
self.__class__.__name__, str(self._data)
)
def __getitem__(self, key):
'''Return value for *key*.'''
return self._data[key]
def __setitem__(self, key, value):
'''Set *value* for *key*.'''
self._data[key] = value
def __delitem__(self, key):
'''Remove *key*.'''
del self._data[key]
def __iter__(self):
'''Iterate over all keys.'''
return iter(self._data)
def __len__(self):
'''Return count of keys.'''
return len(self._data) |
|
ynput__OpenPype | encode_media.rst | Tutorial / Subdoc | Encoding media | MIT License | "ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/encode_media.r(...TRUNCATED) | ["ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/source/ftrack_api/session(...TRUNCATED) | "Encoding media\n\nMedia such as images and video can be encoded by the ftrack server to\nallow play(...TRUNCATED) | "# :coding: utf-8\n# :copyright: Copyright (c) 2014 ftrack\n\nfrom __future__ import absolute_import(...TRUNCATED) |
|
ynput__OpenPype | web_review.rst | Tutorial / Subdoc | Publishing for web review | MIT License | "ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/web_review.rst(...TRUNCATED) | ["ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/source/ftrack_api/session(...TRUNCATED) | "Publishing for web review\n\nFollow the example/encode_media example if you want to upload and enco(...TRUNCATED) | "# :coding: utf-8\n# :copyright: Copyright (c) 2014 ftrack\n\nfrom __future__ import absolute_import(...TRUNCATED) |
|
ynput__OpenPype | sync_ldap_users.rst | Tutorial / Subdoc | Sync users with LDAP | MIT License | "ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/sync_ldap_user(...TRUNCATED) | ["ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/source/ftrack_api/session(...TRUNCATED) | "Sync users with LDAP\n\nIf ftrack is configured to connect to LDAP you may trigger a\nsynchronizati(...TRUNCATED) | "# :coding: utf-8\n# :copyright: Copyright (c) 2014 ftrack\n\nfrom __future__ import absolute_import(...TRUNCATED) |
|
ynput__OpenPype | publishing.rst | Tutorial / Subdoc | Publishing versions | MIT License | "ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/publishing.rst(...TRUNCATED) | ["ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/source/ftrack_api/session(...TRUNCATED) | "Publishing versions\n\nTo know more about publishing and the concepts around publishing, read\nthe (...TRUNCATED) | "# :coding: utf-8\n# :copyright: Copyright (c) 2014 ftrack\n\nfrom __future__ import absolute_import(...TRUNCATED) |
|
ynput__OpenPype | security_roles.rst | Tutorial / Subdoc | Working with user security roles | MIT License | "ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/security_roles(...TRUNCATED) | ["ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/source/ftrack_api/session(...TRUNCATED) | "Working with user security roles\n\nThe API exposes SecurityRole and UserSecurityRole that can be u(...TRUNCATED) | "# :coding: utf-8\n# :copyright: Copyright (c) 2014 ftrack\n\nfrom __future__ import absolute_import(...TRUNCATED) |
|
ynput__OpenPype | list.rst | Tutorial / Subdoc | Using lists | MIT License | ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/list.rst | ["ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/source/ftrack_api/session(...TRUNCATED) | "Using lists\n\nLists can be used to create a collection of asset versions or objects\nsuch as tasks(...TRUNCATED) | "# :coding: utf-8\n# :copyright: Copyright (c) 2014 ftrack\n\nfrom __future__ import absolute_import(...TRUNCATED) |
|
ynput__OpenPype | review_session.rst | Tutorial / Subdoc | Using review sessions | MIT License | "ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/review_session(...TRUNCATED) | ["ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/source/ftrack_api/session(...TRUNCATED) | "Using review sessions\n\nClient review sessions can either be queried manually or by using a\nproje(...TRUNCATED) | "# :coding: utf-8\n# :copyright: Copyright (c) 2014 ftrack\n\nfrom __future__ import absolute_import(...TRUNCATED) |
|
ynput__OpenPype | timer.rst | Tutorial / Subdoc | Using timers | MIT License | ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/timer.rst | ["ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/source/ftrack_api/session(...TRUNCATED) | "Using timers\n\nTimers can be used to track how much time has been spend working on\nsomething.\n\n(...TRUNCATED) | "# :coding: utf-8\n# :copyright: Copyright (c) 2014 ftrack\n\nfrom __future__ import absolute_import(...TRUNCATED) |
End of preview. Expand
in Dataset Viewer.
🏟️ Long Code Arena (Module summarization)
This is the benchmark for Module summarization task as part of the 🏟️ Long Code Arena benchmark. The current version includes 216 manually curated text files describing different documentation of open-source permissive Python projects. The model is required to generate such description, given the relevant context code and the intent behind the documentation. All the repositories are published under permissive licenses (MIT, Apache-2.0, BSD-3-Clause, and BSD-2-Clause). The datapoints can be removed upon request.
How-to
Load the data via load_dataset:
```
from datasets import load_dataset
dataset = load_dataset("JetBrains-Research/lca-module-summarization")
```
Datapoint Structure
Each example has the following fields:
| Field | Description |
|---|---|
repo |
Name of the repository |
target_text |
Text of the target documentation file |
docfile_name |
Name of the file with target documentation |
intent |
One sentence description of what is expected in the documentation |
license |
License of the target repository |
relevant_code_files |
Paths to relevant code files (files that are mentioned in target documentation) |
relevant_code_dir |
Paths to relevant code directories (directories that are mentioned in target documentation) |
path_to_docfile |
Path to file with documentation (path to the documentation file in source repository) |
relevant_code_context |
Relevant code context collected from relevant code files and directories |
Note: you may collect and use your own relevant context. Our context may not be suitable. Zipped repositories can be found the repos directory.
Metric
To compare the predicted documentation and the ground truth documentation, we introduce the new metric based on LLM as an assessor. Our approach involves feeding the LLM with relevant code and two versions of documentation: the ground truth and the model-generated text. The LLM evaluates which documentation better explains and fits the code. To mitigate variance and potential ordering effects in model responses, we calculate the probability that the generated documentation is superior by averaging the results of two queries with the different order.
For more details about metric implementation, please refer to our GitHub repository.
Citing
@article{bogomolov2024long,
title={Long Code Arena: a Set of Benchmarks for Long-Context Code Models},
author={Bogomolov, Egor and Eliseeva, Aleksandra and Galimzyanov, Timur and Glukhov, Evgeniy and Shapkin, Anton and Tigina, Maria and Golubev, Yaroslav and Kovrigin, Alexander and van Deursen, Arie and Izadi, Maliheh and Bryksin, Timofey},
journal={arXiv preprint arXiv:2406.11612},
year={2024}
}
You can find the paper here.
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