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Source code for galaxy.tools.parameters.output_collect

""" Code allowing tools to define extra files associated with an output datset.
"""
import glob
import json
import logging
import operator
import os
import re
from collections import namedtuple

from galaxy import util
from galaxy.dataset_collections.structure import UninitializedTree
from galaxy.tools.parser.output_collection_def import (
    DEFAULT_DATASET_COLLECTOR_DESCRIPTION,
    INPUT_DBKEY_TOKEN,
    ToolProvidedMetadataDatasetCollection,
)
from galaxy.util import (
    ExecutionTimer,
    odict
)

DATASET_ID_TOKEN = "DATASET_ID"

log = logging.getLogger(__name__)


[docs]class NullToolProvidedMetadata(object):
[docs] def get_new_datasets(self, output_name): return []
[docs] def get_new_dataset_meta_by_basename(self, output_name, basename): return {}
[docs] def has_failed_outputs(self): return False
[docs] def get_unnamed_outputs(self): return []
[docs]class LegacyToolProvidedMetadata(object):
[docs] def __init__(self, job_wrapper, meta_file): self.job_wrapper = job_wrapper self.tool_provided_job_metadata = [] with open(meta_file, 'r') as f: for line in f: try: line = json.loads(line) assert 'type' in line except Exception: log.exception('(%s) Got JSON data from tool, but data is improperly formatted or no "type" key in data' % job_wrapper.job_id) log.debug('Offending data was: %s' % line) continue # Set the dataset id if it's a dataset entry and isn't set. # This isn't insecure. We loop the job's output datasets in # the finish method, so if a tool writes out metadata for a # dataset id that it doesn't own, it'll just be ignored. if line['type'] == 'dataset' and 'dataset_id' not in line: try: line['dataset_id'] = job_wrapper.get_output_file_id(line['dataset']) except KeyError: log.warning('(%s) Tool provided job dataset-specific metadata without specifying a dataset' % job_wrapper.job_id) continue self.tool_provided_job_metadata.append(line)
[docs] def get_meta_by_dataset_id(self, dataset_id): for meta in self.tool_provided_job_metadata: if meta['type'] == 'dataset' and meta['dataset_id'] == dataset_id: return meta
[docs] def get_new_dataset_meta_by_basename(self, output_name, basename): for meta in self.tool_provided_job_metadata: if meta['type'] == 'new_primary_dataset' and meta['filename'] == basename: return meta
[docs] def get_new_datasets(self, output_name): log.warning("Called get_new_datasets with legacy tool metadata provider - that is unimplemented.") return []
[docs] def has_failed_outputs(self): found_failed = False for meta in self.tool_provided_job_metadata: if meta.get("failed", False): found_failed = True return found_failed
[docs] def get_unnamed_outputs(self): return []
[docs]class ToolProvidedMetadata(object):
[docs] def __init__(self, job_wrapper, meta_file): self.job_wrapper = job_wrapper with open(meta_file, 'r') as f: self.tool_provided_job_metadata = json.load(f)
[docs] def get_meta_by_name(self, name): return self.tool_provided_job_metadata.get(name, {})
[docs] def get_new_dataset_meta_by_basename(self, output_name, basename): datasets = self.tool_provided_job_metadata.get(output_name, {}).get("datasets", []) for meta in datasets: if meta['filename'] == basename: return meta
[docs] def get_new_datasets(self, output_name): datasets = self.tool_provided_job_metadata.get(output_name, {}).get("datasets", []) if not datasets: elements = self.tool_provided_job_metadata.get(output_name, {}).get("elements", []) if elements: datasets = self._elements_to_datasets(elements) return datasets
def _elements_to_datasets(self, elements, level=0): for element in elements: extra_kwds = {"identifier_%d" % level: element["name"]} if "elements" in element: for inner_element in self._elements_to_datasets(element["elements"], level=level + 1): dataset = extra_kwds.copy() dataset.update(inner_element) yield dataset else: dataset = extra_kwds extra_kwds.update(element) yield extra_kwds
[docs] def has_failed_outputs(self): found_failed = False for output_name, meta in self.tool_provided_job_metadata.items(): if output_name == "__unnamed_outputs": continue if meta.get("failed", False): found_failed = True return found_failed
[docs] def get_unnamed_outputs(self): log.debug("unnamed outputs [%s]" % self.tool_provided_job_metadata) return self.tool_provided_job_metadata.get("__unnamed_outputs", [])
[docs]def collect_dynamic_outputs( tool, output_collections, tool_provided_metadata, job_working_directory, inp_data={}, job=None, input_dbkey="?", ): app = tool.app collections_service = tool.app.dataset_collections_service job_context = JobContext( tool, tool_provided_metadata, job, job_working_directory, inp_data, input_dbkey, ) # unmapped outputs do not correspond to explicit outputs of the tool, they were inferred entirely # from the tool provided metadata (e.g. galaxy.json). for unnamed_output_dict in tool_provided_metadata.get_unnamed_outputs(): assert "destination" in unnamed_output_dict assert "elements" in unnamed_output_dict destination = unnamed_output_dict["destination"] elements = unnamed_output_dict["elements"] assert "type" in destination destination_type = destination["type"] assert destination_type in ["library_folder", "hdca", "hdas"] trans = job_context.work_context # three destination types we need to handle here - "library_folder" (place discovered files in a library folder), # "hdca" (place discovered files in a history dataset collection), and "hdas" (place discovered files in a history # as stand-alone datasets). if destination_type == "library_folder": # populate a library folder (needs to be already have been created) library_folder_manager = app.library_folder_manager library_folder = library_folder_manager.get(trans, app.security.decode_id(destination.get("library_folder_id"))) def add_elements_to_folder(elements, library_folder): for element in elements: if "elements" in element: assert "name" in element name = element["name"] description = element.get("description") nested_folder = library_folder_manager.create(trans, library_folder.id, name, description) add_elements_to_folder(element["elements"], nested_folder) else: discovered_file = discovered_file_for_unnamed_output(element, job_working_directory) fields_match = discovered_file.match designation = fields_match.designation visible = fields_match.visible ext = fields_match.ext dbkey = fields_match.dbkey info = element.get("info", None) link_data = discovered_file.match.link_data # Create new primary dataset name = fields_match.name or designation job_context.create_dataset( ext=ext, designation=designation, visible=visible, dbkey=dbkey, name=name, filename=discovered_file.path, info=info, library_folder=library_folder, link_data=link_data ) add_elements_to_folder(elements, library_folder) elif destination_type == "hdca": # create or populate a dataset collection in the history history = job.history assert "collection_type" in unnamed_output_dict object_id = destination.get("object_id") if object_id: sa_session = tool.app.model.context hdca = sa_session.query(app.model.HistoryDatasetCollectionAssociation).get(int(object_id)) else: name = unnamed_output_dict.get("name", "unnamed collection") collection_type = unnamed_output_dict["collection_type"] collection_type_description = collections_service.collection_type_descriptions.for_collection_type(collection_type) structure = UninitializedTree(collection_type_description) hdca = collections_service.precreate_dataset_collection_instance( trans, history, name, structure=structure ) filenames = odict.odict() def add_to_discovered_files(elements, parent_identifiers=[]): for element in elements: if "elements" in element: add_to_discovered_files(element["elements"], parent_identifiers + [element["name"]]) else: discovered_file = discovered_file_for_unnamed_output(element, job_working_directory, parent_identifiers) filenames[discovered_file.path] = discovered_file add_to_discovered_files(elements) collection = hdca.collection collection_builder = collections_service.collection_builder_for( collection ) job_context.populate_collection_elements( collection, collection_builder, filenames, ) collection_builder.populate() elif destination_type == "hdas": # discover files as individual datasets for the target history history = job.history datasets = [] def collect_elements_for_history(elements): for element in elements: if "elements" in element: collect_elements_for_history(element["elements"]) else: discovered_file = discovered_file_for_unnamed_output(element, job_working_directory) fields_match = discovered_file.match designation = fields_match.designation ext = fields_match.ext dbkey = fields_match.dbkey info = element.get("info", None) link_data = discovered_file.match.link_data # Create new primary dataset name = fields_match.name or designation hda_id = discovered_file.match.object_id primary_dataset = None if hda_id: sa_session = tool.app.model.context primary_dataset = sa_session.query(app.model.HistoryDatasetAssociation).get(hda_id) dataset = job_context.create_dataset( ext=ext, designation=designation, visible=True, dbkey=dbkey, name=name, filename=discovered_file.path, info=info, link_data=link_data, primary_data=primary_dataset, ) dataset.raw_set_dataset_state('ok') if not hda_id: datasets.append(dataset) collect_elements_for_history(elements) job.history.add_datasets(job_context.sa_session, datasets) for name, has_collection in output_collections.items(): if name not in tool.output_collections: continue output_collection_def = tool.output_collections[name] if not output_collection_def.dynamic_structure: continue # Could be HDCA for normal jobs or a DC for mapping # jobs. if hasattr(has_collection, "collection"): collection = has_collection.collection else: collection = has_collection # We are adding dynamic collections, which may be precreated, but their actually state is still new! collection.populated_state = collection.populated_states.NEW try: collection_builder = collections_service.collection_builder_for( collection ) dataset_collectors = [dataset_collector(description) for description in output_collection_def.dataset_collector_descriptions] output_name = output_collection_def.name filenames = job_context.find_files(output_name, collection, dataset_collectors) job_context.populate_collection_elements( collection, collection_builder, filenames, name=output_collection_def.name, metadata_source_name=output_collection_def.metadata_source, ) collection_builder.populate() except Exception: log.exception("Problem gathering output collection.") collection.handle_population_failed("Problem building datasets for collection.")
[docs]class JobContext(object):
[docs] def __init__(self, tool, tool_provided_metadata, job, job_working_directory, inp_data, input_dbkey): self.inp_data = inp_data self.input_dbkey = input_dbkey self.app = tool.app self.sa_session = tool.sa_session self.job = job self.job_working_directory = job_working_directory self.tool_provided_metadata = tool_provided_metadata self._permissions = None
@property def work_context(self): from galaxy.work.context import WorkRequestContext return WorkRequestContext(self.app, user=self.job.user) @property def permissions(self): if self._permissions is None: inp_data = self.inp_data existing_datasets = [inp for inp in inp_data.values() if inp] if existing_datasets: permissions = self.app.security_agent.guess_derived_permissions_for_datasets(existing_datasets) else: # No valid inputs, we will use history defaults permissions = self.app.security_agent.history_get_default_permissions(self.job.history) self._permissions = permissions return self._permissions
[docs] def find_files(self, output_name, collection, dataset_collectors): filenames = odict.odict() for discovered_file in discover_files(output_name, self.tool_provided_metadata, dataset_collectors, self.job_working_directory, collection): filenames[discovered_file.path] = discovered_file return filenames
[docs] def populate_collection_elements(self, collection, root_collection_builder, filenames, name=None, metadata_source_name=None): # TODO: allow configurable sorting. # <sort by="lexical" /> <!-- default --> # <sort by="reverse_lexical" /> # <sort regex="example.(\d+).fastq" by="1:numerical" /> # <sort regex="part_(\d+)_sample_([^_]+).fastq" by="2:lexical,1:numerical" /> if name is None: name = "unnamed output" element_datasets = [] for filename, discovered_file in filenames.items(): create_dataset_timer = ExecutionTimer() fields_match = discovered_file.match if not fields_match: raise Exception("Problem parsing metadata fields for file %s" % filename) element_identifiers = fields_match.element_identifiers designation = fields_match.designation visible = fields_match.visible ext = fields_match.ext dbkey = fields_match.dbkey if dbkey == INPUT_DBKEY_TOKEN: dbkey = self.input_dbkey # Create new primary dataset dataset_name = fields_match.name or designation link_data = discovered_file.match.link_data tag_list = discovered_file.match.tag_list dataset = self.create_dataset( ext=ext, designation=designation, visible=visible, dbkey=dbkey, name=dataset_name, filename=filename, metadata_source_name=metadata_source_name, link_data=link_data, tag_list=tag_list, ) log.debug( "(%s) Created dynamic collection dataset for path [%s] with element identifier [%s] for output [%s] %s", self.job.id, filename, designation, name, create_dataset_timer, ) element_datasets.append((element_identifiers, dataset)) app = self.app sa_session = self.sa_session job = self.job if job: add_datasets_timer = ExecutionTimer() job.history.add_datasets(sa_session, [d for (ei, d) in element_datasets]) log.debug( "(%s) Add dynamic collection datasets to history for output [%s] %s", self.job.id, name, add_datasets_timer, ) for (element_identifiers, dataset) in element_datasets: current_builder = root_collection_builder for element_identifier in element_identifiers[:-1]: current_builder = current_builder.get_level(element_identifier) current_builder.add_dataset(element_identifiers[-1], dataset) # Associate new dataset with job if job: element_identifier_str = ":".join(element_identifiers) # Below was changed from '__new_primary_file_%s|%s__' % (name, designation ) assoc = app.model.JobToOutputDatasetAssociation('__new_primary_file_%s|%s__' % (name, element_identifier_str), dataset) assoc.job = self.job sa_session.add(assoc) dataset.raw_set_dataset_state('ok') sa_session.flush()
[docs] def create_dataset( self, ext, designation, visible, dbkey, name, filename, metadata_source_name=None, info=None, library_folder=None, link_data=False, primary_data=None, tag_list=[], ): app = self.app sa_session = self.sa_session if primary_data is None: if not library_folder: primary_data = _new_hda(app, sa_session, ext, designation, visible, dbkey, self.permissions) else: primary_data = _new_ldda(self.work_context, name, ext, visible, dbkey, library_folder) else: primary_data.extension = ext primary_data.visible = visible primary_data.dbkey = dbkey # Copy metadata from one of the inputs if requested. metadata_source = None if metadata_source_name: metadata_source = self.inp_data[metadata_source_name] sa_session.flush() if tag_list: app.tag_handler.add_tags_from_list(self.job.user, primary_data, tag_list) # Move data from temp location to dataset location if not link_data: app.object_store.update_from_file(primary_data.dataset, file_name=filename, create=True) else: primary_data.link_to(filename) # We are sure there are no extra files, so optimize things that follow by settting total size also. primary_data.set_size(no_extra_files=True) # If match specified a name use otherwise generate one from # designation. primary_data.name = name if metadata_source: primary_data.init_meta(copy_from=metadata_source) else: primary_data.init_meta() if info is not None: primary_data.info = info primary_data.set_meta() primary_data.set_peek() return primary_data
[docs]def collect_primary_datasets(tool, output, tool_provided_metadata, job_working_directory, input_ext, input_dbkey="?"): app = tool.app sa_session = tool.sa_session # Loop through output file names, looking for generated primary # datasets in form specified by discover dataset patterns or in tool provided metadata. primary_output_assigned = False new_outdata_name = None primary_datasets = {} for output_index, (name, outdata) in enumerate(output.items()): dataset_collectors = [DEFAULT_DATASET_COLLECTOR] if name in tool.outputs: dataset_collectors = [dataset_collector(description) for description in tool.outputs[name].dataset_collector_descriptions] filenames = odict.odict() if 'new_file_path' in app.config.collect_outputs_from: if DEFAULT_DATASET_COLLECTOR in dataset_collectors: # 'new_file_path' collection should be considered deprecated, # only use old-style matching (glob instead of regex and only # using default collector - if enabled). for filename in glob.glob(os.path.join(app.config.new_file_path, "primary_%i_*" % outdata.id)): filenames[filename] = DiscoveredFile( filename, DEFAULT_DATASET_COLLECTOR, DEFAULT_DATASET_COLLECTOR.match(outdata, os.path.basename(filename)) ) if 'job_working_directory' in app.config.collect_outputs_from: for discovered_file in discover_files(name, tool_provided_metadata, dataset_collectors, job_working_directory, outdata): filenames[discovered_file.path] = discovered_file for filename_index, (filename, discovered_file) in enumerate(filenames.items()): extra_file_collector = discovered_file.collector fields_match = discovered_file.match if not fields_match: # Before I guess pop() would just have thrown an IndexError raise Exception("Problem parsing metadata fields for file %s" % filename) designation = fields_match.designation if filename_index == 0 and extra_file_collector.assign_primary_output and output_index == 0: new_outdata_name = fields_match.name or "%s (%s)" % (outdata.name, designation) # Move data from temp location to dataset location app.object_store.update_from_file(outdata.dataset, file_name=filename, create=True) primary_output_assigned = True continue if name not in primary_datasets: primary_datasets[name] = odict.odict() visible = fields_match.visible ext = fields_match.ext if ext == "input": ext = input_ext dbkey = fields_match.dbkey if dbkey == INPUT_DBKEY_TOKEN: dbkey = input_dbkey # Create new primary dataset primary_data = _new_hda(app, sa_session, ext, designation, visible, dbkey) app.security_agent.copy_dataset_permissions(outdata.dataset, primary_data.dataset) sa_session.flush() # Move data from temp location to dataset location app.object_store.update_from_file(primary_data.dataset, file_name=filename, create=True) # We are sure there are no extra files, so optimize things that follow by settting total size also. primary_data.set_size(no_extra_files=True) # If match specified a name use otherwise generate one from # designation. primary_data.name = fields_match.name or "%s (%s)" % (outdata.name, designation) primary_data.info = outdata.info primary_data.init_meta(copy_from=outdata) primary_data.dbkey = dbkey # Associate new dataset with job job = None for assoc in outdata.creating_job_associations: job = assoc.job break if job: assoc = app.model.JobToOutputDatasetAssociation('__new_primary_file_%s|%s__' % (name, designation), primary_data) assoc.job = job sa_session.add(assoc) sa_session.flush() primary_data.state = outdata.state # TODO: should be able to disambiguate files in different directories... new_primary_filename = os.path.split(filename)[-1] new_primary_datasets_attributes = tool_provided_metadata.get_new_dataset_meta_by_basename(name, new_primary_filename) # add tool/metadata provided information if new_primary_datasets_attributes: dataset_att_by_name = dict(ext='extension') for att_set in ['name', 'info', 'ext', 'dbkey']: dataset_att_name = dataset_att_by_name.get(att_set, att_set) setattr(primary_data, dataset_att_name, new_primary_datasets_attributes.get(att_set, getattr(primary_data, dataset_att_name))) extra_files_path = new_primary_datasets_attributes.get('extra_files', None) if extra_files_path: extra_files_path_joined = os.path.join(job_working_directory, extra_files_path) for root, dirs, files in os.walk(extra_files_path_joined): extra_dir = os.path.join(primary_data.extra_files_path, root.replace(extra_files_path_joined, '', 1).lstrip(os.path.sep)) extra_dir = os.path.normpath(extra_dir) for f in files: app.object_store.update_from_file( primary_data.dataset, extra_dir=extra_dir, alt_name=f, file_name=os.path.join(root, f), create=True, preserve_symlinks=True ) metadata_dict = new_primary_datasets_attributes.get('metadata', None) if metadata_dict: if "dbkey" in new_primary_datasets_attributes: metadata_dict["dbkey"] = new_primary_datasets_attributes["dbkey"] primary_data.metadata.from_JSON_dict(json_dict=metadata_dict) else: primary_data.set_meta() primary_data.set_peek() outdata.history.add_dataset(primary_data) # Add dataset to return dict primary_datasets[name][designation] = primary_data # Need to update all associated output hdas, i.e. history was # shared with job running for dataset in outdata.dataset.history_associations: if outdata == dataset: continue new_data = primary_data.copy() dataset.history.add_dataset(new_data) sa_session.add(new_data) sa_session.flush() if primary_output_assigned: outdata.name = new_outdata_name outdata.init_meta() outdata.set_meta() outdata.set_peek() sa_session.add(outdata) sa_session.flush() return primary_datasets
DiscoveredFile = namedtuple('DiscoveredFile', ['path', 'collector', 'match'])
[docs]def discover_files(output_name, tool_provided_metadata, extra_file_collectors, job_working_directory, matchable): extra_file_collectors = extra_file_collectors if extra_file_collectors and extra_file_collectors[0].discover_via == "tool_provided_metadata": # just load entries from tool provided metadata... assert len(extra_file_collectors) == 1 extra_file_collector = extra_file_collectors[0] target_directory = discover_target_directory(extra_file_collector.directory, job_working_directory) for dataset in tool_provided_metadata.get_new_datasets(output_name): filename = dataset["filename"] path = os.path.join(target_directory, filename) yield DiscoveredFile(path, extra_file_collector, JsonCollectedDatasetMatch(dataset, extra_file_collector, filename, path=path)) else: for (match, collector) in walk_over_file_collectors(extra_file_collectors, job_working_directory, matchable): yield DiscoveredFile(match.path, collector, match)
[docs]def discovered_file_for_unnamed_output(dataset, job_working_directory, parent_identifiers=[]): extra_file_collector = DEFAULT_TOOL_PROVIDED_DATASET_COLLECTOR target_directory = discover_target_directory(extra_file_collector.directory, job_working_directory) filename = dataset["filename"] # handle link_data_only here, verify filename is in directory if not linking... if not dataset.get("link_data_only"): path = os.path.join(target_directory, filename) if not util.in_directory(path, target_directory): raise Exception("Problem with tool configuration, attempting to pull in datasets from outside working directory.") else: path = filename return DiscoveredFile(path, extra_file_collector, JsonCollectedDatasetMatch(dataset, extra_file_collector, filename, path=path, parent_identifiers=parent_identifiers))
[docs]def discover_target_directory(dir_name, job_working_directory): if dir_name: directory = os.path.join(job_working_directory, dir_name) if not util.in_directory(directory, job_working_directory): raise Exception("Problem with tool configuration, attempting to pull in datasets from outside working directory.") return directory else: return job_working_directory
[docs]def walk_over_file_collectors(extra_file_collectors, job_working_directory, matchable): for extra_file_collector in extra_file_collectors: assert extra_file_collector.discover_via == "pattern" for match in walk_over_extra_files(extra_file_collector.directory, extra_file_collector, job_working_directory, matchable): yield match, extra_file_collector
[docs]def walk_over_extra_files(target_dir, extra_file_collector, job_working_directory, matchable): """ Walks through all files in a given directory, and returns all files that match the given collector's match criteria. If the collector has the recurse flag enabled, will also recursively descend into child folders. """ matches = [] directory = discover_target_directory(target_dir, job_working_directory) if os.path.isdir(directory): for filename in os.listdir(directory): path = os.path.join(directory, filename) if os.path.isdir(path) and extra_file_collector.recurse: # The current directory is already validated, so use that as the next job_working_directory when recursing for match in walk_over_extra_files(filename, extra_file_collector, directory, matchable): yield match else: match = extra_file_collector.match(matchable, filename, path=path) if match: matches.append(match) for match in extra_file_collector.sort(matches): yield match
[docs]def dataset_collector(dataset_collection_description): if dataset_collection_description is DEFAULT_DATASET_COLLECTOR_DESCRIPTION: # Use 'is' and 'in' operators, so lets ensure this is # treated like a singleton. return DEFAULT_DATASET_COLLECTOR else: if dataset_collection_description.discover_via == "pattern": return DatasetCollector(dataset_collection_description) else: return ToolMetadataDatasetCollector(dataset_collection_description)
[docs]class ToolMetadataDatasetCollector(object):
[docs] def __init__(self, dataset_collection_description): self.discover_via = dataset_collection_description.discover_via self.default_dbkey = dataset_collection_description.default_dbkey self.default_ext = dataset_collection_description.default_ext self.default_visible = dataset_collection_description.default_visible self.directory = dataset_collection_description.directory self.assign_primary_output = dataset_collection_description.assign_primary_output
[docs]class DatasetCollector(object):
[docs] def __init__(self, dataset_collection_description): self.discover_via = dataset_collection_description.discover_via # dataset_collection_description is an abstract description # built from the tool parsing module - see galaxy.tools.parser.output_colleciton_def self.sort_key = dataset_collection_description.sort_key self.sort_reverse = dataset_collection_description.sort_reverse self.sort_comp = dataset_collection_description.sort_comp self.pattern = dataset_collection_description.pattern self.default_dbkey = dataset_collection_description.default_dbkey self.default_ext = dataset_collection_description.default_ext self.default_visible = dataset_collection_description.default_visible self.directory = dataset_collection_description.directory self.assign_primary_output = dataset_collection_description.assign_primary_output self.recurse = dataset_collection_description.recurse
def _pattern_for_dataset(self, dataset_instance=None): token_replacement = r'\d+' if dataset_instance: token_replacement = str(dataset_instance.id) return self.pattern.replace(DATASET_ID_TOKEN, token_replacement)
[docs] def match(self, dataset_instance, filename, path=None): pattern = self._pattern_for_dataset(dataset_instance) re_match = re.match(pattern, filename) match_object = None if re_match: match_object = RegexCollectedDatasetMatch(re_match, self, filename, path=path) return match_object
[docs] def sort(self, matches): reverse = self.sort_reverse sort_key = self.sort_key sort_comp = self.sort_comp assert sort_key in ["filename", "dbkey", "name", "designation"] assert sort_comp in ["lexical", "numeric"] key = operator.attrgetter(sort_key) if sort_comp == "numeric": key = _compose(int, key) return sorted(matches, key=key, reverse=reverse)
def _compose(f, g): return lambda x: f(g(x))
[docs]class JsonCollectedDatasetMatch(object):
[docs] def __init__(self, as_dict, collector, filename, path=None, parent_identifiers=[]): self.as_dict = as_dict self.collector = collector self.filename = filename self.path = path self._parent_identifiers = parent_identifiers
@property def designation(self): # If collecting nested collection, grab identifier_0, # identifier_1, etc... and join on : to build designation. element_identifiers = self.raw_element_identifiers if element_identifiers: return ":".join(element_identifiers) elif "designation" in self.as_dict: return self.as_dict.get("designation") elif "name" in self.as_dict: return self.as_dict.get("name") else: return None @property def element_identifiers(self): return self._parent_identifiers + (self.raw_element_identifiers or [self.designation]) @property def raw_element_identifiers(self): identifiers = [] i = 0 while True: key = "identifier_%d" % i if key in self.as_dict: identifiers.append(self.as_dict.get(key)) else: break i += 1 return identifiers @property def name(self): """ Return name or None if not defined by the discovery pattern. """ return self.as_dict.get("name") @property def dbkey(self): return self.as_dict.get("dbkey", self.collector.default_dbkey) @property def ext(self): return self.as_dict.get("ext", self.collector.default_ext) @property def visible(self): try: return self.as_dict["visible"].lower() == "visible" except KeyError: return self.collector.default_visible @property def link_data(self): return bool(self.as_dict.get("link_data_only", False)) @property def tag_list(self): return self.as_dict.get("tags", []) @property def object_id(self): return self.as_dict.get("object_id", None)
[docs]class RegexCollectedDatasetMatch(JsonCollectedDatasetMatch):
[docs] def __init__(self, re_match, collector, filename, path=None): super(RegexCollectedDatasetMatch, self).__init__( re_match.groupdict(), collector, filename, path=path )
UNSET = object() def _new_ldda( trans, name, ext, visible, dbkey, library_folder, ): ld = trans.app.model.LibraryDataset(folder=library_folder, name=name) trans.sa_session.add(ld) trans.sa_session.flush() trans.app.security_agent.copy_library_permissions(trans, library_folder, ld) ldda = trans.app.model.LibraryDatasetDatasetAssociation(name=name, extension=ext, dbkey=dbkey, library_dataset=ld, user=trans.user, create_dataset=True, sa_session=trans.sa_session) trans.sa_session.add(ldda) ldda.state = ldda.states.OK # Permissions must be the same on the LibraryDatasetDatasetAssociation and the associated LibraryDataset trans.app.security_agent.copy_library_permissions(trans, ld, ldda) # Copy the current user's DefaultUserPermissions to the new LibraryDatasetDatasetAssociation.dataset trans.app.security_agent.set_all_dataset_permissions(ldda.dataset, trans.app.security_agent.user_get_default_permissions(trans.user)) library_folder.add_library_dataset(ld, genome_build=dbkey) trans.sa_session.add(library_folder) trans.sa_session.flush() ld.library_dataset_dataset_association_id = ldda.id trans.sa_session.add(ld) trans.sa_session.flush() return ldda def _new_hda( app, sa_session, ext, designation, visible, dbkey, permissions=UNSET, ): """Return a new unflushed HDA with dataset and permissions setup. """ # Create new primary dataset primary_data = app.model.HistoryDatasetAssociation(extension=ext, designation=designation, visible=visible, dbkey=dbkey, create_dataset=True, flush=False, sa_session=sa_session) if permissions is not UNSET: app.security_agent.set_all_dataset_permissions(primary_data.dataset, permissions, new=True, flush=False) sa_session.add(primary_data) return primary_data DEFAULT_DATASET_COLLECTOR = DatasetCollector(DEFAULT_DATASET_COLLECTOR_DESCRIPTION) DEFAULT_TOOL_PROVIDED_DATASET_COLLECTOR = ToolMetadataDatasetCollector(ToolProvidedMetadataDatasetCollection())