Source code for galaxy.managers.collections_util

import logging
import math

from galaxy import exceptions, model, web
from galaxy.util import string_as_bool

log = logging.getLogger(__name__)

ERROR_MESSAGE_UNKNOWN_SRC = "Unknown dataset source (src) %s."
ERROR_MESSAGE_NO_NESTED_IDENTIFIERS = "Dataset source new_collection requires nested element_identifiers for new collection."
ERROR_MESSAGE_NO_NAME = "Cannot load invalid dataset identifier - missing name - %s"
ERROR_MESSAGE_NO_COLLECTION_TYPE = "No collection_type define for nested collection %s."
ERROR_MESSAGE_INVALID_PARAMETER_FOUND = "Found invalid parameter %s in element identifier description %s."
ERROR_MESSAGE_DUPLICATED_IDENTIFIER_FOUND = "Found duplicated element identifier name %s."


[docs]def api_payload_to_create_params(payload): """ Cleanup API payload to pass into dataset_collections. """ required_parameters = ["collection_type", "element_identifiers"] missing_parameters = [p for p in required_parameters if p not in payload] if missing_parameters: message = "Missing required parameters %s" % missing_parameters raise exceptions.ObjectAttributeMissingException(message) params = dict( collection_type=payload.get("collection_type"), element_identifiers=payload.get("element_identifiers"), name=payload.get("name", None), hide_source_items=string_as_bool(payload.get("hide_source_items", False)), copy_elements=string_as_bool(payload.get("copy_elements", False)) ) return params
def validate_input_element_identifiers(element_identifiers): """ Scan through the list of element identifiers supplied by the API consumer and verify the structure is valid. """ log.debug("Validating %d element identifiers for collection creation." % len(element_identifiers)) identifier_names = set() for element_identifier in element_identifiers: if "__object__" in element_identifier: message = ERROR_MESSAGE_INVALID_PARAMETER_FOUND % ("__object__", element_identifier) raise exceptions.RequestParameterInvalidException(message) if "name" not in element_identifier: message = ERROR_MESSAGE_NO_NAME % element_identifier raise exceptions.RequestParameterInvalidException(message) name = element_identifier["name"] if name in identifier_names: message = ERROR_MESSAGE_DUPLICATED_IDENTIFIER_FOUND % name raise exceptions.RequestParameterInvalidException(message) else: identifier_names.add(name) src = element_identifier.get("src", "hda") if src not in ["hda", "hdca", "ldda", "new_collection"]: message = ERROR_MESSAGE_UNKNOWN_SRC % src raise exceptions.RequestParameterInvalidException(message) if src == "new_collection": if "element_identifiers" not in element_identifier: message = ERROR_MESSAGE_NO_NESTED_IDENTIFIERS raise exceptions.RequestParameterInvalidException(ERROR_MESSAGE_NO_NESTED_IDENTIFIERS) if "collection_type" not in element_identifier: message = ERROR_MESSAGE_NO_COLLECTION_TYPE % element_identifier raise exceptions.RequestParameterInvalidException(message) validate_input_element_identifiers(element_identifier["element_identifiers"]) def get_hda_and_element_identifiers(dataset_collection_instance): name = dataset_collection_instance.name collection = dataset_collection_instance.collection return get_collection(collection, name=name) def get_collection(collection, name=""): names = [] hdas = [] if collection.has_subcollections: for element in collection.elements: subnames, subhdas = get_collection_elements(element.child_collection, name="%s/%s" % (name, element.element_identifier)) names.extend(subnames) hdas.extend(subhdas) else: for element in collection.elements: names.append("%s/%s" % (name, element.element_identifier)) hdas.append(element.dataset_instance) return names, hdas def get_collection_elements(collection, name=""): names = [] hdas = [] for element in collection.elements: full_element_name = "%s/%s" % (name, element.element_identifier) if element.is_collection: subnames, subhdas = get_collection(element.child_collection, name=full_element_name) names.extend(subnames) hdas.extend(subhdas) else: names.append(full_element_name) hdas.append(element.dataset_instance) return names, hdas
[docs]def dictify_dataset_collection_instance(dataset_collection_instance, parent, security, view="element", fuzzy_count=None): hdca_view = "element" if view in ["element", "element-reference"] else "collection" dict_value = dataset_collection_instance.to_dict(view=hdca_view) encoded_id = security.encode_id(dataset_collection_instance.id) if isinstance(parent, model.History): encoded_history_id = security.encode_id(parent.id) dict_value['url'] = web.url_for('history_content_typed', history_id=encoded_history_id, id=encoded_id, type="dataset_collection") elif isinstance(parent, model.LibraryFolder): encoded_library_id = security.encode_id(parent.library.id) encoded_folder_id = security.encode_id(parent.id) # TODO: Work in progress - this end-point is not right yet... dict_value['url'] = web.url_for('library_content', library_id=encoded_library_id, id=encoded_id, folder_id=encoded_folder_id) if view in ["element", "element-reference"]: collection = dataset_collection_instance.collection rank_fuzzy_counts = gen_rank_fuzzy_counts(collection.collection_type, fuzzy_count) elements, rest_fuzzy_counts = get_fuzzy_count_elements(collection, rank_fuzzy_counts) if view == "element": dict_value['populated'] = collection.populated element_func = dictify_element else: element_func = dictify_element_reference dict_value['elements'] = [element_func(_, rank_fuzzy_counts=rest_fuzzy_counts) for _ in elements] security.encode_all_ids(dict_value, recursive=True) # TODO: Use Kyle's recursive formulation of this. return dict_value
def dictify_element_reference(element, rank_fuzzy_counts=None): """Load minimal details of elements required to show outline of contents in history panel. History panel can use this reference to expand to full details if individual dataset elements are clicked. """ dictified = element.to_dict(view="element") element_object = element.element_object if element_object is not None: object_details = dict( id=element_object.id, model_class=element_object.__class__.__name__, ) if element.child_collection: object_details["collection_type"] = element_object.collection_type child_collection = element.child_collection elements, rest_fuzzy_counts = get_fuzzy_count_elements(child_collection, rank_fuzzy_counts) # Recursively yield elements for each nested collection... object_details["elements"] = [dictify_element_reference(_, rank_fuzzy_counts=rest_fuzzy_counts) for _ in elements] object_details["element_count"] = child_collection.element_count else: object_details["state"] = element_object.state object_details["hda_ldda"] = 'hda' object_details["history_id"] = element_object.history_id else: object_details = None dictified["object"] = object_details return dictified def dictify_element(element, rank_fuzzy_counts=None): dictified = element.to_dict(view="element") element_object = element.element_object if element_object is not None: object_details = element.element_object.to_dict() if element.child_collection: child_collection = element.child_collection elements, rest_fuzzy_counts = get_fuzzy_count_elements(child_collection, rank_fuzzy_counts) # Recursively yield elements for each nested collection... child_collection = element.child_collection object_details["elements"] = [dictify_element(_, rank_fuzzy_counts=rest_fuzzy_counts) for _ in elements] object_details["populated"] = child_collection.populated object_details["element_count"] = child_collection.element_count else: object_details = None dictified["object"] = object_details return dictified def get_fuzzy_count_elements(collection, rank_fuzzy_counts): if rank_fuzzy_counts and rank_fuzzy_counts[0]: rank_fuzzy_count = rank_fuzzy_counts[0] elements = collection.elements[0:rank_fuzzy_count] else: elements = collection.elements if rank_fuzzy_counts is not None: rest_fuzzy_counts = rank_fuzzy_counts[1:] else: rest_fuzzy_counts = None return elements, rest_fuzzy_counts def gen_rank_fuzzy_counts(collection_type, fuzzy_count=None): """Turn a global estimate on elements to return to per nested level based on collection type. This takes an arbitrary constant and generates an arbitrary constant and is quite messy. None of this should be relied on as a stable API - it is more of a general guideline to restrict within broad ranges the amount of objects returned. >>> def is_around(x, y): ... return y - 1 < x and y + 1 > y ... >>> gen_rank_fuzzy_counts("list", None) [None] >>> gen_rank_fuzzy_counts("list", 500) [500] >>> gen_rank_fuzzy_counts("paired", 500) [2] >>> gen_rank_fuzzy_counts("list:paired", None) [None, None] >>> gen_rank_fuzzy_counts("list:list", 101) # 100 would be edge case at 10 so bump to ensure 11 [11, 11] >>> ll, pl = gen_rank_fuzzy_counts("list:paired", 100) >>> pl 2 >>> is_around(ll, 50) True >>> pl, ll = gen_rank_fuzzy_counts("paired:list", 100) >>> pl 2 >>> is_around(ll, 50) True >>> gen_rank_fuzzy_counts("list:list:list", 1001) [11, 11, 11] >>> l1l, l2l, l3l, pl = gen_rank_fuzzy_counts("list:list:list:paired", 2000) >>> pl 2 >>> is_around(10, l1l) True >>> gen_rank_fuzzy_counts("list:list:list", 1) [1, 1, 1] >>> gen_rank_fuzzy_counts("list:list:list", 2) [2, 2, 2] >>> gen_rank_fuzzy_counts("paired:paired", 400) [2, 2] >>> gen_rank_fuzzy_counts("paired:paired", 5) [2, 2] >>> gen_rank_fuzzy_counts("paired:paired", 3) [2, 2] >>> gen_rank_fuzzy_counts("paired:paired", 1) [1, 1] >>> gen_rank_fuzzy_counts("paired:paired", 2) [2, 2] """ rank_collection_types = collection_type.split(":") if fuzzy_count is None: return [None for rt in rank_collection_types] else: # This is a list... paired_count = sum([1 if rt == "paired" else 0 for rt in rank_collection_types]) list_count = len(rank_collection_types) - paired_count paired_fuzzy_count_mult = 1 if paired_count == 0 else 2 << (paired_count - 1) list_fuzzy_count_mult = math.floor((fuzzy_count * 1.0) / paired_fuzzy_count_mult) list_rank_fuzzy_count = int(math.floor(math.pow(list_fuzzy_count_mult, 1.0 / list_count)) + 1) if list_count > 0 else 1.0 pair_rank_fuzzy_count = 2 if list_rank_fuzzy_count > fuzzy_count: list_rank_fuzzy_count = fuzzy_count if pair_rank_fuzzy_count > fuzzy_count: pair_rank_fuzzy_count = fuzzy_count rank_fuzzy_counts = [pair_rank_fuzzy_count if rt == "paired" else list_rank_fuzzy_count for rt in rank_collection_types] return rank_fuzzy_counts __all__ = ('api_payload_to_create_params', 'dictify_dataset_collection_instance')