galaxy.model.dataset_collections package

Subpackages

galaxy.model.dataset_collections.types package

Submodules

galaxy.model.dataset_collections.builder module

galaxy.model.dataset_collections.builder.build_collection(type, dataset_instances, collection=None, associated_identifiers=None)[source]: Build DatasetCollection with populated DatasetcollectionElement objects corresponding to the supplied dataset instances or throw exception if this is not a valid collection of the specified type.

galaxy.model.dataset_collections.builder.set_collection_elements(dataset_collection, type, dataset_instances, associated_identifiers)[source]

class galaxy.model.dataset_collections.builder.CollectionBuilder(collection_type_description)[source]

Bases: object

Purely functional builder pattern for building a dataset collection.

__init__(collection_type_description)[source]

replace_elements_in_collection(template_collection, replacement_dict)[source]

get_level(identifier)[source]

add_dataset(identifier, dataset_instance)[source]

build_elements()[source]

build()[source]

class galaxy.model.dataset_collections.builder.BoundCollectionBuilder(dataset_collection)[source]

Bases: CollectionBuilder

More stateful builder that is bound to a particular model object.

__init__(dataset_collection)[source]

populate_partial()[source]

populate()[source]

galaxy.model.dataset_collections.matching module

class galaxy.model.dataset_collections.matching.CollectionsToMatch[source]

Bases: object

Structure representing a set of collections that need to be matched up when running tools (possibly workflows in the future as well).

__init__()[source]

add(input_name, hdca, subcollection_type=None, linked=True)[source]

has_collections()[source]

items()[source]

class galaxy.model.dataset_collections.matching.MatchingCollections[source]

Bases: object

Structure holding the result of matching a list of collections together. This class being different than the class above and being created in the DatasetCollectionManager layer may seem like overkill but I suspect in the future plugins will be subtypable for instance so matching collections will need to make heavy use of the dataset collection type registry managed by the dataset collections service - hence the complexity now.

__init__()[source]

slice_collections()[source]

subcollection_mapping_type(input_name)[source]

property structure: Yield cross product of all unlinked collections structures to linked collection structure.

map_over_action_tuples(input_name)[source]

is_mapped_over(input_name)[source]

static for_collections(collections_to_match, collection_type_descriptions) → MatchingCollections | None[source]

galaxy.model.dataset_collections.registry module

class galaxy.model.dataset_collections.registry.DatasetCollectionTypesRegistry[source]

Bases: object

__init__()[source]

get(plugin_type)[source]

prototype(plugin_type)[source]

galaxy.model.dataset_collections.structure module

Module for reasoning about structure of and matching hierarchical collections of data.

class galaxy.model.dataset_collections.structure.Leaf[source]

Bases: object

children_known = True

property is_leaf

clone()[source]

multiply(other_structure)[source]

sliced_collection_type(collection)[source]

class galaxy.model.dataset_collections.structure.BaseTree(collection_type_description)[source]

Bases: object

__init__(collection_type_description)[source]

class galaxy.model.dataset_collections.structure.UninitializedTree(collection_type_description)[source]

Bases: BaseTree

children_known = False

clone()[source]

property is_leaf

multiply(other_structure)[source]

class galaxy.model.dataset_collections.structure.Tree(children, collection_type_description, when_values=None)[source]

Bases: BaseTree

children_known = True

__init__(children, collection_type_description, when_values=None)[source]

static for_dataset_collection(dataset_collection, collection_type_description)[source]

walk_collections(hdca_dict)[source]

property is_leaf

can_match(other_structure)[source]

multiply(other_structure)[source]

clone()[source]

galaxy.model.dataset_collections.structure.tool_output_to_structure(get_sliced_input_collection_structure, tool_output, collections_manager)[source]

galaxy.model.dataset_collections.structure.dict_map(func, input_dict)[source]

galaxy.model.dataset_collections.structure.get_structure(dataset_collection_instance, collection_type_description, leaf_subcollection_type=None)[source]

galaxy.model.dataset_collections.subcollections module

galaxy.model.dataset_collections.subcollections.split_dataset_collection_instance(dataset_collection_instance, collection_type)[source]: Split up collection into collection.

galaxy.model.dataset_collections.type_description module

class galaxy.model.dataset_collections.type_description.CollectionTypeDescriptionFactory(type_registry=<galaxy.model.dataset_collections.registry.DatasetCollectionTypesRegistry object>)[source]

Bases: object

__init__(type_registry=<galaxy.model.dataset_collections.registry.DatasetCollectionTypesRegistry object>)[source]

for_collection_type(collection_type)[source]

class galaxy.model.dataset_collections.type_description.CollectionTypeDescription(collection_type: str | CollectionTypeDescription, collection_type_description_factory)[source]

Bases: object

Abstraction over dataset collection type that ties together string reprentation in database/model with type registry.

>>> factory = CollectionTypeDescriptionFactory(None)
>>> nested_type_description = factory.for_collection_type("list:paired")
>>> paired_type_description = factory.for_collection_type("paired")
>>> nested_type_description.has_subcollections_of_type("list")
False
>>> nested_type_description.has_subcollections_of_type("list:paired")
False
>>> nested_type_description.has_subcollections_of_type("paired")
True
>>> nested_type_description.has_subcollections_of_type(paired_type_description)
True
>>> nested_type_description.has_subcollections()
True
>>> paired_type_description.has_subcollections()
False
>>> paired_type_description.rank_collection_type()
'paired'
>>> nested_type_description.rank_collection_type()
'list'
>>> nested_type_description.effective_collection_type(paired_type_description)
'list'
>>> nested_type_description.effective_collection_type_description(paired_type_description).collection_type
'list'
>>> nested_type_description.child_collection_type()
'paired'

__init__(collection_type: str | CollectionTypeDescription, collection_type_description_factory)[source]

collection_type: str

child_collection_type()[source]

child_collection_type_description()[source]

effective_collection_type_description(subcollection_type)[source]

effective_collection_type(subcollection_type)[source]

has_subcollections_of_type(other_collection_type)[source]

Take in another type (either flat string or another CollectionTypeDescription) and determine if this collection contains subcollections matching that type.

The way this is used in map/reduce it seems to make the most sense for this to return True if these subtypes are proper (i.e. a type is not considered to have subcollections of its own type).

is_subcollection_of_type(other_collection_type)[source]

can_match_type(other_collection_type)[source]

subcollection_type_description()[source]

has_subcollections()[source]

rank_collection_type()[source]: Return the top-level collection type corresponding to this collection type. For instance the “rank” type of a list of paired data (“list:paired”) is “list”.

rank_type_plugin()[source]

property dimension

multiply(other_collection_type)[source]

galaxy.model.dataset_collections.type_description.map_over_collection_type(mapped_over_collection_type, target_collection_type)[source]