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Source code for galaxy.managers.datasets
"""
Manager and Serializer for Datasets.
"""
import glob
import logging
import os
from typing import Type
from galaxy import (
exceptions,
model
)
from galaxy.datatypes import sniff
from galaxy.managers import (
base,
deletable,
rbac_secured,
secured,
users
)
from galaxy.structured_app import MinimalManagerApp
from galaxy.util.checkers import check_binary
log = logging.getLogger(__name__)
[docs]class DatasetManager(base.ModelManager, secured.AccessibleManagerMixin, deletable.PurgableManagerMixin):
"""
Manipulate datasets: the components contained in DatasetAssociations/DatasetInstances/HDAs/LDDAs
"""
model_class = model.Dataset
foreign_key_name = 'dataset'
# TODO:?? get + error_if_uploading is common pattern, should upload check be worked into access/owed?
[docs] def __init__(self, app: MinimalManagerApp):
super().__init__(app)
self.permissions = DatasetRBACPermissions(app)
# needed for admin test
self.user_manager = users.UserManager(app)
[docs] def create(self, manage_roles=None, access_roles=None, flush=True, **kwargs):
"""
Create and return a new Dataset object.
"""
# default to NEW state on new datasets
kwargs.update(dict(state=(kwargs.get('state', model.Dataset.states.NEW))))
dataset = model.Dataset(**kwargs)
self.session().add(dataset)
self.permissions.set(dataset, manage_roles, access_roles, flush=False)
if flush:
self.session().flush()
return dataset
[docs] def copy(self, dataset, **kwargs):
raise exceptions.NotImplemented('Datasets cannot be copied')
[docs] def purge(self, dataset, flush=True):
"""
Remove the object_store/file for this dataset from storage and mark
as purged.
:raises exceptions.ConfigDoesNotAllowException: if the instance doesn't allow
"""
self.error_unless_dataset_purge_allowed(dataset)
# the following also marks dataset as purged and deleted
dataset.full_delete()
self.session().add(dataset)
if flush:
self.session().flush()
return dataset
# TODO: this may be more conv. somewhere else
# TODO: how to allow admin bypass?
[docs] def error_unless_dataset_purge_allowed(self, msg=None):
if not self.app.config.allow_user_dataset_purge:
msg = msg or 'This instance does not allow user dataset purging'
raise exceptions.ConfigDoesNotAllowException(msg)
# .... accessibility
# datasets can implement the accessible interface, but accessibility is checked in an entirely different way
# than those resources that have a user attribute (histories, pages, etc.)
[docs] def is_accessible(self, dataset, user, **kwargs):
"""
Is this dataset readable/viewable to user?
"""
if self.user_manager.is_admin(user, trans=kwargs.get("trans")):
return True
if self.has_access_permission(dataset, user):
return True
return False
[docs] def has_access_permission(self, dataset, user):
"""
Return T/F if the user has role-based access to the dataset.
"""
roles = user.all_roles_exploiting_cache() if user else []
return self.app.security_agent.can_access_dataset(roles, dataset)
# TODO: implement above for groups
# TODO: datatypes?
# .... data, object_store
# TODO: SecurityAgentDatasetRBACPermissions( object ):
[docs]class DatasetRBACPermissions:
[docs] def __init__(self, app):
self.app = app
self.access = rbac_secured.AccessDatasetRBACPermission(app)
self.manage = rbac_secured.ManageDatasetRBACPermission(app)
# TODO: temporary facade over security_agent
[docs] def available_roles(self, trans, dataset, controller='root'):
return self.app.security_agent.get_legitimate_roles(trans, dataset, controller)
[docs] def get(self, dataset, flush=True):
manage = self.manage.by_dataset(dataset)
access = self.access.by_dataset(dataset)
return (manage, access)
[docs] def set(self, dataset, manage_roles, access_roles, flush=True):
manage = self.manage.set(dataset, manage_roles or [], flush=False)
access = self.access.set(dataset, access_roles or [], flush=flush)
return (manage, access)
# ---- conv. settings
[docs] def set_public_with_single_manager(self, dataset, user, flush=True):
manage = self.manage.grant(dataset, user, flush=flush)
self.access.clear(dataset, flush=False)
return ([manage], [])
[docs] def set_private_to_one_user(self, dataset, user, flush=True):
manage = self.manage.grant(dataset, user, flush=False)
access = self.access.set_private(dataset, user, flush=flush)
return ([manage], access)
[docs]class DatasetSerializer(base.ModelSerializer, deletable.PurgableSerializerMixin):
model_manager_class = DatasetManager
[docs] def __init__(self, app: MinimalManagerApp, user_manager: users.UserManager):
super().__init__(app)
self.dataset_manager = self.manager
# needed for admin test
self.user_manager = user_manager
self.default_view = 'summary'
self.add_view('summary', [
'id',
'create_time',
'update_time',
'state',
'deleted',
'purged',
'purgable',
# 'object_store_id',
# 'external_filename',
# 'extra_files_path',
'file_size',
'total_size',
'uuid',
])
# could do visualizations and/or display_apps
[docs] def add_serializers(self):
super().add_serializers()
deletable.PurgableSerializerMixin.add_serializers(self)
self.serializers.update({
'create_time': self.serialize_date,
'update_time': self.serialize_date,
'uuid': lambda i, k, **c: str(i.uuid) if i.uuid else None,
'file_name': self.serialize_file_name,
'extra_files_path': self.serialize_extra_files_path,
'permissions': self.serialize_permissions,
'total_size': lambda i, k, **c: int(i.get_total_size()),
'file_size': lambda i, k, **c: int(i.get_size())
})
[docs] def serialize_file_name(self, dataset, key, user=None, **context):
"""
If the config allows or the user is admin, return the file name
of the file that contains this dataset's data.
"""
is_admin = self.user_manager.is_admin(user, trans=context.get("trans"))
# expensive: allow config option due to cost of operation
if is_admin or self.app.config.expose_dataset_path:
if not dataset.purged:
return dataset.file_name
self.skip()
[docs] def serialize_extra_files_path(self, dataset, key, user=None, **context):
"""
If the config allows or the user is admin, return the file path.
"""
is_admin = self.user_manager.is_admin(user, trans=context.get("trans"))
# expensive: allow config option due to cost of operation
if is_admin or self.app.config.expose_dataset_path:
if not dataset.purged:
return dataset.extra_files_path
self.skip()
[docs] def serialize_permissions(self, dataset, key, user=None, **context):
"""
"""
trans = context.get("trans")
if not self.dataset_manager.permissions.manage.is_permitted(dataset, user, trans=trans):
self.skip()
management_permissions = self.dataset_manager.permissions.manage.by_dataset(dataset)
access_permissions = self.dataset_manager.permissions.access.by_dataset(dataset)
permissions = {
'manage': [self.app.security.encode_id(perm.role.id) for perm in management_permissions],
'access': [self.app.security.encode_id(perm.role.id) for perm in access_permissions],
}
return permissions
# ============================================================================= AKA DatasetInstanceManager
[docs]class DatasetAssociationManager(base.ModelManager,
secured.AccessibleManagerMixin,
deletable.PurgableManagerMixin):
"""
DatasetAssociation/DatasetInstances are intended to be working
proxies to a Dataset, associated with either a library or a
user/history (HistoryDatasetAssociation).
"""
# DA's were meant to be proxies - but were never fully implemented as them
# Instead, a dataset association HAS a dataset but contains metadata specific to a library (lda) or user (hda)
model_class: Type[model.DatasetInstance] = model.DatasetInstance
# NOTE: model_manager_class should be set in HDA/LDA subclasses
[docs] def is_accessible(self, dataset_assoc, user, **kwargs):
"""
Is this DA accessible to `user`?
"""
# defer to the dataset
return self.dataset_manager.is_accessible(dataset_assoc.dataset, user, **kwargs)
[docs] def purge(self, dataset_assoc, flush=True):
"""
Purge this DatasetInstance and the dataset underlying it.
"""
# error here if disallowed - before jobs are stopped
# TODO: this check may belong in the controller
self.dataset_manager.error_unless_dataset_purge_allowed()
# We need to ignore a potential flush=False here and force the flush
# so that job cleanup associated with stop_creating_job will see
# the dataset as purged.
super().purge(dataset_assoc, flush=True)
# stop any jobs outputing the dataset_assoc
self.stop_creating_job(dataset_assoc)
# more importantly, purge underlying dataset as well
if dataset_assoc.dataset.user_can_purge:
self.dataset_manager.purge(dataset_assoc.dataset)
return dataset_assoc
# .... associated job
[docs] def creating_job(self, dataset_assoc):
"""
Return the `Job` that created this dataset or None if not found.
"""
# TODO: is this needed? Can't you use the dataset_assoc.creating_job attribute? When is this None?
# TODO: this would be even better if outputs and inputs were the underlying datasets
job = None
for job_output_assoc in dataset_assoc.creating_job_associations:
job = job_output_assoc.job
break
return job
[docs] def stop_creating_job(self, dataset_assoc):
"""
Stops an dataset_assoc's creating job if all the job's other outputs are deleted.
"""
# Optimize this to skip other checks if this dataset is terminal - we can infer the
# job is already complete.
if dataset_assoc.state in model.Dataset.terminal_states:
return False
if dataset_assoc.parent_id is None and len(dataset_assoc.creating_job_associations) > 0:
# Mark associated job for deletion
job = dataset_assoc.creating_job_associations[0].job
if not job.finished:
# Are *all* of the job's other output datasets deleted?
if job.check_if_output_datasets_deleted():
track_jobs_in_database = self.app.config.track_jobs_in_database
job.mark_deleted(track_jobs_in_database)
if not track_jobs_in_database:
self.app.job_manager.stop(job)
return True
return False
[docs] def is_composite(self, dataset_assoc):
"""
Return True if this hda/ldda is a composite type dataset.
.. note:: see also (whereever we keep information on composite datatypes?)
"""
return dataset_assoc.extension in self.app.datatypes_registry.get_composite_extensions()
[docs] def extra_files(self, dataset_assoc):
"""Return a list of file paths for composite files, an empty list otherwise."""
if not self.is_composite(dataset_assoc):
return []
return glob.glob(os.path.join(dataset_assoc.dataset.extra_files_path, '*'))
[docs] def serialize_dataset_association_roles(self, trans, dataset_assoc):
if hasattr(dataset_assoc, "library_dataset_dataset_association"):
library_dataset = dataset_assoc
dataset = library_dataset.library_dataset_dataset_association.dataset
else:
library_dataset = None
dataset = dataset_assoc.dataset
# Omit duplicated roles by converting to set
security_agent = trans.app.security_agent
access_roles = set(dataset.get_access_roles(security_agent))
manage_roles = set(dataset.get_manage_permissions_roles(security_agent))
access_dataset_role_list = [(access_role.name, trans.security.encode_id(access_role.id)) for access_role in access_roles]
manage_dataset_role_list = [(manage_role.name, trans.security.encode_id(manage_role.id)) for manage_role in manage_roles]
rval = dict(access_dataset_roles=access_dataset_role_list, manage_dataset_roles=manage_dataset_role_list)
if library_dataset is not None:
modify_roles = set(security_agent.get_roles_for_action(library_dataset, trans.app.security_agent.permitted_actions.LIBRARY_MODIFY))
modify_item_role_list = [(modify_role.name, trans.security.encode_id(modify_role.id)) for modify_role in modify_roles]
rval["modify_item_roles"] = modify_item_role_list
return rval
[docs] def detect_datatype(self, trans, dataset_assoc):
"""Sniff and assign the datatype to a given dataset association (ldda or hda)"""
data = trans.sa_session.query(self.model_class).get(dataset_assoc.id)
if data.datatype.is_datatype_change_allowed():
if not data.ok_to_edit_metadata():
raise exceptions.ItemAccessibilityException('This dataset is currently being used as input or output. You cannot change datatype until the jobs have completed or you have canceled them.')
else:
path = data.dataset.file_name
is_binary = check_binary(path)
datatype = sniff.guess_ext(path, trans.app.datatypes_registry.sniff_order, is_binary=is_binary)
trans.app.datatypes_registry.change_datatype(data, datatype)
trans.sa_session.flush()
self.set_metadata(trans, dataset_assoc)
else:
raise exceptions.InsufficientPermissionsException(f'Changing datatype "{data.extension}" is not allowed.')
[docs] def set_metadata(self, trans, dataset_assoc, overwrite=False, validate=True):
"""Trigger a job that detects and sets metadata on a given dataset association (ldda or hda)"""
data = trans.sa_session.query(self.model_class).get(dataset_assoc.id)
if not data.ok_to_edit_metadata():
raise exceptions.ItemAccessibilityException('This dataset is currently being used as input or output. You cannot edit metadata until the jobs have completed or you have canceled them.')
else:
if overwrite:
for name, spec in data.metadata.spec.items():
# We need to be careful about the attributes we are resetting
if name not in ['name', 'info', 'dbkey', 'base_name']:
if spec.get('default'):
setattr(data.metadata, name, spec.unwrap(spec.get('default')))
job, *_ = self.app.datatypes_registry.set_external_metadata_tool.tool_action.execute(
self.app.datatypes_registry.set_external_metadata_tool, trans, incoming={'input1': data, 'validate': validate},
overwrite=overwrite)
self.app.job_manager.enqueue(job, tool=self.app.datatypes_registry.set_external_metadata_tool)
[docs] def update_permissions(self, trans, dataset_assoc, **kwd):
action = kwd.get('action', 'set_permissions')
if action not in ['remove_restrictions', 'make_private', 'set_permissions']:
raise exceptions.RequestParameterInvalidException('The mandatory parameter "action" has an invalid value. '
'Allowed values are: "remove_restrictions", "make_private", "set_permissions"')
if hasattr(dataset_assoc, "library_dataset_dataset_association"):
library_dataset = dataset_assoc
dataset = library_dataset.library_dataset_dataset_association.dataset
else:
library_dataset = None
dataset = dataset_assoc.dataset
current_user_roles = trans.get_current_user_roles()
can_manage = trans.app.security_agent.can_manage_dataset(current_user_roles, dataset) or trans.user_is_admin
if not can_manage:
raise exceptions.InsufficientPermissionsException('You do not have proper permissions to manage permissions on this dataset.')
if action == 'remove_restrictions':
trans.app.security_agent.make_dataset_public(dataset)
if not trans.app.security_agent.dataset_is_public(dataset):
raise exceptions.InternalServerError('An error occurred while making dataset public.')
elif action == 'make_private':
if not trans.app.security_agent.dataset_is_private_to_user(trans, dataset):
private_role = trans.app.security_agent.get_private_user_role(trans.user)
dp = trans.app.model.DatasetPermissions(trans.app.security_agent.permitted_actions.DATASET_ACCESS.action, dataset, private_role)
trans.sa_session.add(dp)
trans.sa_session.flush()
if not trans.app.security_agent.dataset_is_private_to_user(trans, dataset):
# Check again and inform the user if dataset is not private.
raise exceptions.InternalServerError('An error occurred and the dataset is NOT private.')
elif action == 'set_permissions':
def to_role_id(encoded_role_id):
role_id = base.decode_id(self.app, encoded_role_id)
return role_id
def parameters_roles_or_none(role_type):
encoded_role_ids = kwd.get(role_type, kwd.get(f"{role_type}_ids[]", None))
if encoded_role_ids is not None:
return list(map(to_role_id, encoded_role_ids))
else:
return None
access_roles = parameters_roles_or_none('access')
manage_roles = parameters_roles_or_none('manage')
modify_roles = parameters_roles_or_none('modify')
role_ids_dict = {
'DATASET_MANAGE_PERMISSIONS': manage_roles,
'DATASET_ACCESS': access_roles,
}
if library_dataset is not None:
role_ids_dict["LIBRARY_MODIFY"] = modify_roles
self._set_permissions(trans, dataset_assoc, role_ids_dict)
def _set_permissions(self, trans, dataset_assoc, roles_dict):
raise exceptions.NotImplemented()
class _UnflattenedMetadataDatasetAssociationSerializer(base.ModelSerializer,
deletable.PurgableSerializerMixin):
def __init__(self, app):
self.dataset_serializer = app[DatasetSerializer]
super().__init__(app)
def add_serializers(self):
super().add_serializers()
deletable.PurgableSerializerMixin.add_serializers(self)
self.serializers.update({
'create_time': self.serialize_date,
'update_time': self.serialize_date,
# underlying dataset
'dataset': lambda i, k, **c: self.dataset_serializer.serialize_to_view(i.dataset, view='summary', **c),
'dataset_id': self._proxy_to_dataset(key='id'),
# TODO: why is this named uuid!? The da doesn't have a uuid - it's the underlying dataset's uuid!
'uuid': self._proxy_to_dataset(key='uuid'),
# 'dataset_uuid': self._proxy_to_dataset( key='uuid' ),
'file_name': self._proxy_to_dataset(serializer=self.dataset_serializer.serialize_file_name),
'extra_files_path': self._proxy_to_dataset(serializer=self.dataset_serializer.serialize_extra_files_path),
'permissions': self._proxy_to_dataset(serializer=self.dataset_serializer.serialize_permissions),
# TODO: do the sizes proxy accurately/in the same way?
'size': lambda i, k, **c: int(i.get_size()),
'file_size': lambda i, k, **c: self.serializers['size'](i, k, **c),
'nice_size': lambda i, k, **c: i.get_size(nice_size=True),
# common to lddas and hdas - from mapping.py
'copied_from_history_dataset_association_id': self.serialize_id,
'copied_from_library_dataset_dataset_association_id': self.serialize_id,
'info': lambda i, k, **c: i.info.strip() if isinstance(i.info, str) else i.info,
'blurb': lambda i, k, **c: i.blurb,
'peek': lambda i, k, **c: i.display_peek() if i.peek and i.peek != 'no peek' else None,
'meta_files': self.serialize_meta_files,
'metadata': self.serialize_metadata,
'creating_job': self.serialize_creating_job,
'rerunnable': self.serialize_rerunnable,
'parent_id': self.serialize_id,
'designation': lambda i, k, **c: i.designation,
# 'extended_metadata': self.serialize_extended_metadata,
# 'extended_metadata_id': self.serialize_id,
# remapped
'genome_build': lambda i, k, **c: i.dbkey,
# derived (not mapped) attributes
'data_type': lambda i, k, **c: f"{i.datatype.__class__.__module__}.{i.datatype.__class__.__name__}",
'converted': self.serialize_converted_datasets,
# TODO: metadata/extra files
})
# this an abstract superclass, so no views created
# because of that: we need to add a few keys that will use the default serializer
self.serializable_keyset.update(['name', 'state', 'tool_version', 'extension', 'visible', 'dbkey'])
def _proxy_to_dataset(self, serializer=None, key=None):
# dataset associations are (rough) proxies to datasets - access their serializer using this remapping fn
# remapping done by either kwarg key: IOW dataset attr key (e.g. uuid)
# or by kwarg serializer: a function that's passed in (e.g. permissions)
if key:
serializer = self.dataset_serializer.serializers.get(key)
if serializer:
return lambda i, k, **c: serializer(i.dataset, key or k, **c)
raise TypeError('kwarg serializer or key needed')
def serialize_meta_files(self, dataset_assoc, key, **context):
"""
Cycle through meta files and return them as a list of dictionaries.
"""
meta_files = []
for meta_type in dataset_assoc.metadata_file_types:
if getattr(dataset_assoc.metadata, meta_type, None):
meta_files.append(
dict(file_type=meta_type,
download_url=self.url_for('history_contents_metadata_file',
history_id=self.app.security.encode_id(dataset_assoc.history_id),
history_content_id=self.app.security.encode_id(dataset_assoc.id),
metadata_file=meta_type)))
return meta_files
def serialize_metadata(self, dataset_assoc, key, excluded=None, **context):
"""
Cycle through metadata and return as dictionary.
"""
# dbkey is a repeat actually (metadata_dbkey == genome_build)
# excluded = [ 'dbkey' ] if excluded is None else excluded
excluded = [] if excluded is None else excluded
metadata = {}
for name, spec in dataset_assoc.metadata.spec.items():
if name in excluded:
continue
val = dataset_assoc.metadata.get(name)
# NOTE: no files
if isinstance(val, model.MetadataFile):
# only when explicitly set: fetching filepaths can be expensive
if not self.app.config.expose_dataset_path:
continue
val = val.file_name
# TODO:? possibly split this off?
# If no value for metadata, look in datatype for metadata.
elif val is None and hasattr(dataset_assoc.datatype, name):
val = getattr(dataset_assoc.datatype, name)
if val is None and spec.get("optional"):
continue
metadata[name] = val
return metadata
def serialize_creating_job(self, dataset, key, **context):
"""
Return the id of the Job that created this dataset (or its original)
or None if no `creating_job` is found.
"""
if dataset.creating_job:
return self.serialize_id(dataset.creating_job, 'id')
else:
return None
def serialize_rerunnable(self, dataset, key, **context):
"""
Return False if this tool that created this dataset can't be re-run
(e.g. upload).
"""
if dataset.creating_job:
tool = self.app.toolbox.get_tool(dataset.creating_job.tool_id, dataset.creating_job.tool_version)
if tool and tool.is_workflow_compatible:
return True
return False
def serialize_converted_datasets(self, dataset_assoc, key, **context):
"""
Return a file extension -> converted dataset encoded id map with all
the existing converted datasets associated with this instance.
This filters out deleted associations.
"""
id_map = {}
for converted in dataset_assoc.implicitly_converted_datasets:
if not converted.deleted and converted.dataset:
id_map[converted.type] = self.serialize_id(converted.dataset, 'id')
return id_map
[docs]class DatasetAssociationSerializer(_UnflattenedMetadataDatasetAssociationSerializer):
# TODO: remove this class - metadata should be a sub-object instead as in the superclass
[docs] def add_serializers(self):
super().add_serializers()
# remove the single nesting key here
del self.serializers['metadata']
[docs] def serialize(self, dataset_assoc, keys, **context):
"""
Override to add metadata as flattened keys on the serialized DatasetInstance.
"""
# if 'metadata' isn't removed from keys here serialize will retrieve the un-serializable MetadataCollection
# TODO: remove these when metadata is sub-object
KEYS_HANDLED_SEPARATELY = ('metadata', )
left_to_handle = self._pluck_from_list(keys, KEYS_HANDLED_SEPARATELY)
serialized = super().serialize(dataset_assoc, keys, **context)
# add metadata directly to the dict instead of as a sub-object
if 'metadata' in left_to_handle:
metadata = self._prefixed_metadata(dataset_assoc)
serialized.update(metadata)
return serialized
# TODO: this is more util/gen. use
def _pluck_from_list(self, list_, elems):
"""
Removes found elems from list list_ and returns list of found elems if found.
"""
found = []
for elem in elems:
try:
index = list_.index(elem)
found.append(list_.pop(index))
except ValueError:
pass
return found
def _prefixed_metadata(self, dataset_assoc):
"""
Adds (a prefixed version of) the DatasetInstance metadata to the dict,
prefixing each key with 'metadata_'.
"""
# build the original, nested dictionary
metadata = self.serialize_metadata(dataset_assoc, 'metadata')
# prefix each key within and return
prefixed = {}
for key, val in metadata.items():
prefixed_key = f"metadata_{key}"
prefixed[prefixed_key] = val
return prefixed
[docs]class DatasetAssociationDeserializer(base.ModelDeserializer, deletable.PurgableDeserializerMixin):
[docs] def add_deserializers(self):
super().add_deserializers()
deletable.PurgableDeserializerMixin.add_deserializers(self)
self.deserializers.update({
'name': self.deserialize_basestring,
'info': self.deserialize_basestring,
'datatype': self.deserialize_datatype,
})
self.deserializable_keyset.update(self.deserializers.keys())
# TODO: untested
[docs] def deserialize_metadata(self, dataset_assoc, metadata_key, metadata_dict, **context):
"""
"""
self.validate.type(metadata_key, metadata_dict, dict)
returned = {}
for key, val in metadata_dict.items():
returned[key] = self.deserialize_metadatum(dataset_assoc, key, val, **context)
return returned
[docs] def deserialize_metadatum(self, dataset_assoc, key, val, **context):
"""
"""
if key not in dataset_assoc.datatype.metadata_spec:
return
metadata_specification = dataset_assoc.datatype.metadata_spec[key]
if metadata_specification.get('readonly'):
return
unwrapped_val = metadata_specification.unwrap(val)
setattr(dataset_assoc.metadata, key, unwrapped_val)
# ...?
return unwrapped_val
[docs] def deserialize_datatype(self, item, key, val, **context):
if not item.datatype.is_datatype_change_allowed():
raise exceptions.RequestParameterInvalidException("The current datatype does not allow datatype changes.")
target_datatype = self.app.datatypes_registry.get_datatype_by_extension(val)
if not target_datatype:
raise exceptions.RequestParameterInvalidException("The target datatype does not exist.")
if not target_datatype.is_datatype_change_allowed():
raise exceptions.RequestParameterInvalidException("The target datatype does not allow datatype changes.")
if not item.ok_to_edit_metadata():
raise exceptions.RequestParameterInvalidException("Dataset metadata could not be updated because it is used as input or output of a running job.")
item.change_datatype(val)
sa_session = self.app.model.context
sa_session.flush()
trans = context.get("trans")
job, *_ = self.app.datatypes_registry.set_external_metadata_tool.tool_action.execute(self.app.datatypes_registry.set_external_metadata_tool, trans, incoming={'input1': item}, overwrite=False) # overwrite is False as per existing behavior
trans.app.job_manager.enqueue(job, tool=trans.app.datatypes_registry.set_external_metadata_tool)
return item.datatype
[docs]class DatasetAssociationFilterParser(base.ModelFilterParser, deletable.PurgableFiltersMixin):
def _add_parsers(self):
super()._add_parsers()
deletable.PurgableFiltersMixin._add_parsers(self)
self.orm_filter_parsers.update({
'name': {'op': ('eq', 'contains', 'like')},
'state': {'column': '_state', 'op': ('eq', 'in')},
'visible': {'op': ('eq'), 'val': self.parse_bool},
})
self.fn_filter_parsers.update({
'genome_build': self.string_standard_ops('dbkey'),
'data_type': {
'op': {
'eq': self.eq_datatype,
'isinstance': self.isinstance_datatype
}
}
})
[docs] def eq_datatype(self, dataset_assoc, class_str):
"""
Is the `dataset_assoc` datatype equal to the registered datatype `class_str`?
"""
comparison_class = self.app.datatypes_registry.get_datatype_class_by_name(class_str)
return comparison_class and dataset_assoc.datatype.__class__ == comparison_class
[docs] def isinstance_datatype(self, dataset_assoc, class_strs):
"""
Is the `dataset_assoc` datatype derived from any of the registered
datatypes in the comma separated string `class_strs`?
"""
parse_datatype_fn = self.app.datatypes_registry.get_datatype_class_by_name
comparison_classes = []
for class_str in class_strs.split(','):
datatype_class = parse_datatype_fn(class_str)
if datatype_class:
comparison_classes.append(datatype_class)
return comparison_classes and isinstance(dataset_assoc.datatype, comparison_classes)