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Source code for galaxy.job_execution.output_collect
""" Code allowing tools to define extra files associated with an output datset.
"""
import logging
import operator
import os
import re
from collections import OrderedDict
from tempfile import NamedTemporaryFile
import galaxy.model
from galaxy.model.dataset_collections import builder
from galaxy.model.dataset_collections.structure import UninitializedTree
from galaxy.model.dataset_collections.type_description import COLLECTION_TYPE_DESCRIPTION_FACTORY
from galaxy.model.store.discover import (
discover_target_directory,
DiscoveredFile,
JsonCollectedDatasetMatch,
ModelPersistenceContext,
persist_elements_to_folder,
persist_elements_to_hdca,
persist_hdas,
RegexCollectedDatasetMatch,
SessionlessModelPersistenceContext,
UNSET,
)
from galaxy.tool_util.parser.output_collection_def import (
DEFAULT_DATASET_COLLECTOR_DESCRIPTION,
INPUT_DBKEY_TOKEN,
ToolProvidedMetadataDatasetCollection,
)
from galaxy.tool_util.parser.output_objects import (
ToolOutput,
ToolOutputCollection,
)
from galaxy.util import (
unicodify
)
DATASET_ID_TOKEN = "DATASET_ID"
log = logging.getLogger(__name__)
# PermissionProvider and MetadataSourceProvider are abstractions over input data used to
# collect and produce dynamic outputs.
[docs]class PermissionProvider(object):
[docs] def __init__(self, inp_data, security_agent, job):
self._job = job
self._security_agent = security_agent
self._inp_data = inp_data
self._user = job.user
self._permissions = None
@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._security_agent.guess_derived_permissions_for_datasets(existing_datasets)
else:
# No valid inputs, we will use history defaults
permissions = self._security_agent.history_get_default_permissions(self._job.history)
self._permissions = permissions
return self._permissions
[docs] def set_default_hda_permissions(self, primary_data):
permissions = self.permissions
if permissions is not UNSET:
self._security_agent.set_all_dataset_permissions(primary_data.dataset, permissions, new=True, flush=False)
[docs] def copy_dataset_permissions(self, init_from, primary_data):
self._security_agent.copy_dataset_permissions(init_from.dataset, primary_data.dataset)
[docs]def collect_dynamic_outputs(
job_context,
output_collections,
):
# 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 job_context.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"]
# 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 = job_context.get_library_folder(destination)
persist_elements_to_folder(job_context, elements, library_folder)
elif destination_type == "hdca":
# create or populate a dataset collection in the history
assert "collection_type" in unnamed_output_dict
object_id = destination.get("object_id")
if object_id:
hdca = job_context.get_hdca(object_id)
else:
name = unnamed_output_dict.get("name", "unnamed collection")
collection_type = unnamed_output_dict["collection_type"]
collection_type_description = COLLECTION_TYPE_DESCRIPTION_FACTORY.for_collection_type(collection_type)
structure = UninitializedTree(collection_type_description)
hdca = job_context.create_hdca(name, structure)
persist_elements_to_hdca(job_context, elements, hdca, collector=DEFAULT_DATASET_COLLECTOR)
elif destination_type == "hdas":
persist_hdas(elements, job_context, final_job_state=job_context.final_job_state)
for name, has_collection in output_collections.items():
output_collection_def = job_context.output_collection_def(name)
if not output_collection_def:
continue
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 = builder.BoundCollectionBuilder(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,
final_job_state=job_context.final_job_state,
)
collection_builder.populate()
except Exception:
log.exception("Problem gathering output collection.")
collection.handle_population_failed("Problem building datasets for collection.")
job_context.add_dataset_collection(has_collection)
[docs]class BaseJobContext(object):
[docs] def find_files(self, output_name, collection, dataset_collectors):
filenames = OrderedDict()
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]class JobContext(ModelPersistenceContext, BaseJobContext):
[docs] def __init__(self, tool, tool_provided_metadata, job, job_working_directory, permission_provider, metadata_source_provider, input_dbkey, object_store, final_job_state):
self.tool = tool
self.metadata_source_provider = metadata_source_provider
self.permission_provider = permission_provider
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.object_store = object_store
self.final_job_state = final_job_state
@property
def work_context(self):
from galaxy.work.context import WorkRequestContext
return WorkRequestContext(self.app, user=self.user)
@property
def user(self):
if self.job:
user = self.job.user
else:
user = None
return user
@property
def tag_handler(self):
return self.app.tag_handler
[docs] def get_library_folder(self, destination):
app = self.app
library_folder_manager = app.library_folder_manager
library_folder = library_folder_manager.get(self.work_context, app.security.decode_id(destination.get("library_folder_id")))
return library_folder
[docs] def get_hdca(self, object_id):
hdca = self.sa_session.query(galaxy.model.HistoryDatasetCollectionAssociation).get(int(object_id))
return hdca
[docs] def create_library_folder(self, parent_folder, name, description):
assert parent_folder.id
library_folder_manager = self.app.library_folder_manager
nested_folder = library_folder_manager.create(self.work_context, parent_folder.id, name, description)
return nested_folder
[docs] def create_hdca(self, name, structure):
history = self.job.history
trans = self.work_context
collections_service = self.app.dataset_collections_service
hdca = collections_service.precreate_dataset_collection_instance(
trans, history, name, structure=structure
)
return hdca
[docs] def add_output_dataset_association(self, name, dataset):
assoc = galaxy.model.JobToOutputDatasetAssociation(name, dataset)
assoc.job = self.job
self.sa_session.add(assoc)
[docs] def add_library_dataset_to_folder(self, library_folder, ld):
trans = self.work_context
ldda = ld.library_dataset_dataset_association
trans.sa_session.add(ldda)
trans = self.work_context
trans.app.security_agent.copy_library_permissions(trans, library_folder, ld)
trans.sa_session.add(ld)
trans.sa_session.flush()
# 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=ldda.dbkey)
trans.sa_session.add(library_folder)
trans.sa_session.flush()
trans.sa_session.add(ld)
trans.sa_session.flush()
[docs] def add_datasets_to_history(self, datasets, for_output_dataset=None):
sa_session = self.sa_session
self.job.history.add_datasets(sa_session, datasets)
if for_output_dataset is not None:
# Need to update all associated output hdas, i.e. history was
# shared with job running
for copied_dataset in for_output_dataset.dataset.history_associations:
if copied_dataset == for_output_dataset:
continue
for dataset in datasets:
new_data = dataset.copy()
copied_dataset.history.add_dataset(new_data)
sa_session.add(new_data)
sa_session.flush()
[docs] def output_collection_def(self, name):
tool = self.tool
if name not in tool.output_collections:
return None
output_collection_def = tool.output_collections[name]
return output_collection_def
[docs] def output_def(self, name):
tool = self.tool
if name not in tool.outputs:
return None
output_collection_def = tool.outputs[name]
return output_collection_def
[docs]class SessionlessJobContext(SessionlessModelPersistenceContext, BaseJobContext):
[docs] def __init__(self, metadata_params, tool_provided_metadata, object_store, export_store, import_store, working_directory, final_job_state):
# TODO: use a metadata source provider... (pop from inputs and add parameter)
# TODO: handle input_dbkey...
input_dbkey = "?"
super(SessionlessJobContext, self).__init__(object_store, export_store, working_directory)
self.metadata_params = metadata_params
self.tool_provided_metadata = tool_provided_metadata
self.import_store = import_store
self.input_dbkey = input_dbkey
self.final_job_state = final_job_state
[docs] def output_collection_def(self, name):
tool_as_dict = self.metadata_params["tool"]
output_collection_defs = tool_as_dict["output_collections"]
if name not in output_collection_defs:
return False
output_collection_def_dict = output_collection_defs[name]
output_collection_def = ToolOutputCollection.from_dict(name, output_collection_def_dict)
return output_collection_def
[docs] def output_def(self, name):
tool_as_dict = self.metadata_params["tool"]
output_defs = tool_as_dict["outputs"]
if name not in output_defs:
return None
output_def_dict = output_defs[name]
output_def = ToolOutput.from_dict(name, output_def_dict)
return output_def
[docs] def get_hdca(self, object_id):
hdca = self.import_store.sa_session.query(galaxy.model.HistoryDatasetCollectionAssociation).find(int(object_id))
if hdca:
self.export_store.add_dataset_collection(hdca)
for collection_dataset in hdca.dataset_instances:
include_files = True
self.export_store.add_dataset(collection_dataset, include_files=include_files)
self.export_store.collection_datasets[collection_dataset.id] = True
return hdca
[docs] def add_dataset_collection(self, collection):
self.export_store.add_dataset_collection(collection)
for collection_dataset in collection.dataset_instances:
include_files = True
self.export_store.add_dataset(collection_dataset, include_files=include_files)
self.export_store.collection_datasets[collection_dataset.id] = True
[docs] def add_output_dataset_association(self, name, dataset_instance):
job_id = self.metadata_params["job_id_tag"]
self.export_store.add_job_output_dataset_associations(job_id, name, dataset_instance)
[docs]def collect_primary_datasets(job_context, output, input_ext):
job_working_directory = job_context.job_working_directory
# 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]
output_def = job_context.output_def(name)
if output_def is not None:
dataset_collectors = [dataset_collector(description) for description in output_def.dataset_collector_descriptions]
filenames = OrderedDict()
for discovered_file in discover_files(name, job_context.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)
outdata.dataset.external_filename = None # resets filename_override
# Move data from temp location to dataset location
job_context.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] = OrderedDict()
visible = fields_match.visible
ext = fields_match.ext
if ext == "input":
ext = input_ext
dbkey = fields_match.dbkey
if dbkey == INPUT_DBKEY_TOKEN:
dbkey = job_context.input_dbkey
# Create new primary dataset
new_primary_name = fields_match.name or "%s (%s)" % (outdata.name, designation)
info = outdata.info
# TODO: should be able to disambiguate files in different directories...
new_primary_filename = os.path.split(filename)[-1]
new_primary_datasets_attributes = job_context.tool_provided_metadata.get_new_dataset_meta_by_basename(name, new_primary_filename)
primary_data = job_context.create_dataset(
ext,
designation,
visible,
dbkey,
new_primary_name,
filename,
info=info,
init_from=outdata,
dataset_attributes=new_primary_datasets_attributes,
)
# Associate new dataset with job
job_context.add_output_dataset_association('__new_primary_file_%s|%s__' % (name, designation), primary_data)
if new_primary_datasets_attributes:
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)
primary_data.dataset.create_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:
job_context.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
)
job_context.add_datasets_to_history([primary_data], for_output_dataset=outdata)
# Add dataset to return dict
primary_datasets[name][designation] = primary_data
if primary_output_assigned:
outdata.name = new_outdata_name
outdata.init_meta()
outdata.set_meta()
outdata.set_peek()
sa_session = job_context.sa_session
if sa_session:
sa_session.add(outdata)
job_context.flush()
return primary_datasets
[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 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.tool_util.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))
DEFAULT_DATASET_COLLECTOR = DatasetCollector(DEFAULT_DATASET_COLLECTOR_DESCRIPTION)
DEFAULT_TOOL_PROVIDED_DATASET_COLLECTOR = ToolMetadataDatasetCollector(ToolProvidedMetadataDatasetCollection())
[docs]def read_exit_code_from(exit_code_file, id_tag):
"""Read exit code reported for a Galaxy job."""
try:
# This should be an 8-bit exit code, but read ahead anyway:
exit_code_str = open(exit_code_file, "r").read(32)
except Exception:
# By default, the exit code is 0, which typically indicates success.
exit_code_str = "0"
try:
# Decode the exit code. If it's bogus, then just use 0.
exit_code = int(exit_code_str)
except ValueError:
galaxy_id_tag = id_tag
log.warning("(%s) Exit code '%s' invalid. Using 0." % (galaxy_id_tag, exit_code_str))
exit_code = 0
return exit_code
[docs]def default_exit_code_file(files_dir, id_tag):
return os.path.join(files_dir, 'galaxy_%s.ec' % id_tag)
[docs]def collect_extra_files(object_store, dataset, job_working_directory):
store_by = getattr(object_store, "store_by", "id")
file_name = "dataset_%s_files" % getattr(dataset.dataset, store_by)
temp_file_path = os.path.join(job_working_directory, "working", file_name)
extra_dir = None
try:
# This skips creation of directories - object store
# automatically creates them. However, empty directories will
# not be created in the object store at all, which might be a
# problem.
for root, dirs, files in os.walk(temp_file_path):
extra_dir = root.replace(os.path.join(job_working_directory, "working"), '', 1).lstrip(os.path.sep)
for f in files:
object_store.update_from_file(
dataset.dataset,
extra_dir=extra_dir,
alt_name=f,
file_name=os.path.join(root, f),
create=True,
preserve_symlinks=True
)
except Exception as e:
log.debug("Error in collect_associated_files: %s", unicodify(e))
# Handle composite datatypes of auto_primary_file type
if dataset.datatype.composite_type == 'auto_primary_file' and not dataset.has_data():
try:
with NamedTemporaryFile(mode='w') as temp_fh:
temp_fh.write(dataset.datatype.generate_primary_file(dataset))
temp_fh.flush()
object_store.update_from_file(dataset.dataset, file_name=temp_fh.name, create=True)
dataset.set_size()
except Exception as e:
log.warning('Unable to generate primary composite file automatically for %s: %s', dataset.dataset.id, unicodify(e))