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galaxy.model.dataset_collections package

Subpackages

• galaxy.model.dataset_collections.types package
  • Submodules
  • galaxy.model.dataset_collections.types.list module
  • galaxy.model.dataset_collections.types.paired module

Submodules

galaxy.model.dataset_collections.builder module

galaxy.model.dataset_collections.builder.build_collection(type, dataset_instances)

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)

class galaxy.model.dataset_collections.builder.CollectionBuilder(collection_type_description)

Bases: object

Purely functional builder pattern for building a dataset collection.

__init__(collection_type_description)

Initialize self. See help(type(self)) for accurate signature.

replace_elements_in_collection(template_collection, replacement_dict)

get_level(identifier)

add_dataset(identifier, dataset_instance)

build_elements()

build()

class galaxy.model.dataset_collections.builder.BoundCollectionBuilder(dataset_collection)

Bases: galaxy.model.dataset_collections.builder.CollectionBuilder

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

__init__(dataset_collection)

Initialize self. See help(type(self)) for accurate signature.

populate()

galaxy.model.dataset_collections.matching module

class galaxy.model.dataset_collections.matching.CollectionsToMatch

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__()

Initialize self. See help(type(self)) for accurate signature.

add(input_name, hdca, subcollection_type=None, linked=True)

has_collections()

items()

class galaxy.model.dataset_collections.matching.MatchingCollections

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 dataset_collections_service 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 sevice - hence the complexity now.

__init__()

Initialize self. See help(type(self)) for accurate signature.

slice_collections()

subcollection_mapping_type(input_name)

property structure

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

map_over_action_tuples(input_name)

is_mapped_over(input_name)

static for_collections(collections_to_match, collection_type_descriptions)

galaxy.model.dataset_collections.registry module

class galaxy.model.dataset_collections.registry.DatasetCollectionTypesRegistry

Bases: object

__init__()

Initialize self. See help(type(self)) for accurate signature.

get(plugin_type)

prototype(plugin_type)

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

Bases: object

children_known = True

property is_leaf

clone()

multiply(other_structure)

sliced_collection_type(collection)

class galaxy.model.dataset_collections.structure.BaseTree(collection_type_description)

Bases: object

__init__(collection_type_description)

Initialize self. See help(type(self)) for accurate signature.

class galaxy.model.dataset_collections.structure.UninitializedTree(collection_type_description)

Bases: galaxy.model.dataset_collections.structure.BaseTree

children_known = False

clone()

property is_leaf

multiply(other_structure)

class galaxy.model.dataset_collections.structure.Tree(children, collection_type_description)

Bases: galaxy.model.dataset_collections.structure.BaseTree

children_known = True

__init__(children, collection_type_description)

Initialize self. See help(type(self)) for accurate signature.

static for_dataset_collection(dataset_collection, collection_type_description)

walk_collections(hdca_dict)

property is_leaf

can_match(other_structure)

multiply(other_structure)

clone()

galaxy.model.dataset_collections.structure.tool_output_to_structure(get_sliced_input_collection_structure, tool_output, collections_manager)

galaxy.model.dataset_collections.structure.dict_map(func, input_dict)

galaxy.model.dataset_collections.structure.get_structure(dataset_collection_instance, collection_type_description, leaf_subcollection_type=None)

galaxy.model.dataset_collections.subcollections module

galaxy.model.dataset_collections.subcollections.split_dataset_collection_instance(dataset_collection_instance, collection_type)

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>)

Bases: object

__init__(type_registry=<galaxy.model.dataset_collections.registry.DatasetCollectionTypesRegistry object>)

Initialize self. See help(type(self)) for accurate signature.

for_collection_type(collection_type)

class galaxy.model.dataset_collections.type_description.CollectionTypeDescription(collection_type, collection_type_description_factory)

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, collection_type_description_factory)

Initialize self. See help(type(self)) for accurate signature.

child_collection_type()

child_collection_type_description()

effective_collection_type_description(subcollection_type)

effective_collection_type(subcollection_type)

has_subcollections_of_type(other_collection_type)

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)

can_match_type(other_collection_type)

subcollection_type_description()

has_subcollections()

rank_collection_type()

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()

property dimension

multiply(other_collection_type)

galaxy.model.dataset_collections.type_description.map_over_collection_type(mapped_over_collection_type, target_collection_type)