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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 tempfile import NamedTemporaryFile
from typing import (
Callable,
Dict,
List,
Optional,
Union,
)
from sqlalchemy.orm.scoping import ScopedSession
from galaxy.model import (
DatasetInstance,
HistoryDatasetAssociation,
HistoryDatasetCollectionAssociation,
Job,
JobToOutputDatasetAssociation,
LibraryDatasetDatasetAssociation,
)
from galaxy.model.base import transaction
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,
MetadataSourceProvider as AbstractMetadataSourceProvider,
ModelPersistenceContext,
PermissionProvider as AbstractPermissionProvider,
persist_elements_to_folder,
persist_elements_to_hdca,
persist_hdas,
RegexCollectedDatasetMatch,
SessionlessModelPersistenceContext,
UNSET,
)
from galaxy.objectstore import (
ObjectStore,
persist_extra_files,
)
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.tool_util.provided_metadata import BaseToolProvidedMetadata
from galaxy.util import (
shrink_and_unicodify,
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(AbstractPermissionProvider):
[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, flush=False)
[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 have already been created)
library_folder = job_context.get_library_folder(destination)
persist_elements_to_folder(job_context, elements, library_folder)
job_context.persist_library_folder(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)
output_collections[name] = hdca
job_context.add_dataset_collection(hdca)
error_message = unnamed_output_dict.get("error_message")
if error_message:
hdca.collection.handle_population_failed(error_message)
else:
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(ModelPersistenceContext):
max_discovered_files: Union[int, float]
tool_provided_metadata: BaseToolProvidedMetadata
job_working_directory: str
[docs] def find_files(self, output_name, collection, dataset_collectors) -> list:
discovered_files = []
for discovered_file in discover_files(
output_name, self.tool_provided_metadata, dataset_collectors, self.job_working_directory, collection
):
self.increment_discovered_file_count()
discovered_files.append(discovered_file)
return discovered_files
[docs]class JobContext(BaseJobContext):
[docs] def __init__(
self,
tool,
tool_provided_metadata: BaseToolProvidedMetadata,
job,
job_working_directory,
permission_provider,
metadata_source_provider,
input_dbkey,
object_store,
final_job_state,
max_discovered_files: Optional[int],
flush_per_n_datasets=None,
):
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
self._flush_per_n_datasets = flush_per_n_datasets
self.max_discovered_files = float("inf") if max_discovered_files is None else max_discovered_files
self.discovered_file_count = 0
self._tag_handler = None
@property
def tag_handler(self):
if self._tag_handler is None:
self._tag_handler = self.app.tag_handler.create_tag_handler_session(self.job.galaxy_session)
return self._tag_handler
@property
def work_context(self):
from galaxy.work.context import WorkRequestContext
return WorkRequestContext(self.app, user=self.user, galaxy_session=self.job.galaxy_session)
@property
def sa_session(self) -> ScopedSession:
return self._sa_session
@property
def permission_provider(self) -> PermissionProvider:
return self._permission_provider
@property
def metadata_source_provider(self) -> MetadataSourceProvider:
return self._metadata_source_provider
@property
def job(self) -> Job:
return self._job
@property
def flush_per_n_datasets(self) -> Optional[int]:
return self._flush_per_n_datasets
@property
def input_dbkey(self) -> str:
return self._input_dbkey
@property
def object_store(self) -> ObjectStore:
return self._object_store
@property
def user(self):
if self.job:
user = self.job.user
else:
user = None
return user
[docs] def get_library_folder(self, destination):
app = self.app
library_folder_manager = app.library_folder_manager
folder_id = destination.get("library_folder_id")
decoded_folder_id = app.security.decode_id(folder_id) if isinstance(folder_id, str) else folder_id
library_folder = library_folder_manager.get(self.work_context, decoded_folder_id)
return library_folder
[docs] def get_hdca(self, object_id):
hdca = self.sa_session.query(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
collection_manager = self.app.dataset_collection_manager
hdca = collection_manager.precreate_dataset_collection_instance(trans, history, name, structure=structure)
return hdca
[docs] def add_output_dataset_association(self, name, dataset):
assoc = 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)
with transaction(trans.sa_session):
trans.sa_session.commit()
# 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), flush=False, new=True
)
library_folder.add_library_dataset(ld, genome_build=ldda.dbkey)
trans.sa_session.add(library_folder)
with transaction(trans.sa_session):
trans.sa_session.commit()
trans.sa_session.add(ld)
with transaction(trans.sa_session):
trans.sa_session.commit()
[docs] def add_datasets_to_history(self, datasets, for_output_dataset=None):
sa_session = self.sa_session
self.job.history.stage_addition(datasets)
pending_histories = {self.job.history}
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.stage_addition(new_data)
pending_histories.add(copied_dataset.history)
sa_session.add(new_data)
for history in pending_histories:
history.add_pending_items()
[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] def get_implicit_collection_jobs_association_id(self):
return self.job.implicit_collection_jobs_association and self.job.implicit_collection_jobs_association.id
[docs]class SessionlessJobContext(SessionlessModelPersistenceContext, BaseJobContext):
[docs] def __init__(
self,
metadata_params,
tool_provided_metadata: BaseToolProvidedMetadata,
object_store,
export_store,
import_store,
working_directory,
final_job_state,
max_discovered_files: Optional[int],
):
# TODO: use a metadata source provider... (pop from inputs and add parameter)
super().__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.final_job_state = final_job_state
self.max_discovered_files = float("inf") if max_discovered_files is None else max_discovered_files
self.discovered_file_count = 0
[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(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.add(collection_dataset.id)
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.add(collection_dataset.id)
[docs] def add_output_dataset_association(self, name, dataset_instance):
self.export_store.add_job_output_dataset_associations(self.get_job_id(), name, dataset_instance)
[docs] def get_implicit_collection_jobs_association_id(self):
return self.metadata_params.get("implicit_collection_jobs_association_id")
[docs]def collect_primary_datasets(job_context: Union[JobContext, SessionlessJobContext], 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: Dict[str, Dict[str, Union[HistoryDatasetAssociation, LibraryDatasetDatasetAssociation]]] = {}
storage_callbacks: List[Callable] = []
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 = {}
for discovered_file in discover_files(
name, job_context.tool_provided_metadata, dataset_collectors, job_working_directory, outdata
):
job_context.increment_discovered_file_count()
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(f"Problem parsing metadata fields for file {filename}")
designation = fields_match.designation
ext = fields_match.ext
if ext == "input":
ext = input_ext
dbkey = fields_match.dbkey
if dbkey == INPUT_DBKEY_TOKEN:
dbkey = job_context.input_dbkey
if filename_index == 0 and extra_file_collector.assign_primary_output and output_index == 0:
new_outdata_name = fields_match.name or f"{outdata.name} ({designation})"
outdata.change_datatype(ext)
outdata.dbkey = dbkey
outdata.designation = 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] = {}
visible = fields_match.visible
# Create new primary dataset
new_primary_name = fields_match.name or f"{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
)
extra_files = None
if new_primary_datasets_attributes:
extra_files_path = new_primary_datasets_attributes.get("extra_files", None)
if extra_files_path:
extra_files = os.path.join(job_working_directory, extra_files_path)
primary_data = job_context.create_dataset(
ext,
designation,
visible,
dbkey,
new_primary_name,
filename,
extra_files=extra_files,
info=info,
init_from=outdata,
dataset_attributes=new_primary_datasets_attributes,
creating_job_id=job_context.get_job_id() if job_context else None,
storage_callbacks=storage_callbacks,
)
# Associate new dataset with job
job_context.add_output_dataset_association(f"__new_primary_file_{name}|{designation}__", primary_data)
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()
outdata.discovered = True
sa_session = job_context.sa_session
if sa_session:
sa_session.add(outdata)
# Move discovered outputs to storage and set metdata / peeks
for callback in storage_callbacks:
callback()
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, parent_paths=None):
"""
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.
"""
parent_paths = parent_paths or []
def _walk(target_dir, extra_file_collector, job_working_directory, matchable, parent_paths):
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):
if extra_file_collector.recurse:
new_parent_paths = parent_paths[:]
new_parent_paths.append(filename)
# The current directory is already validated, so use that as the next job_working_directory when recursing
yield from _walk(
filename, extra_file_collector, directory, matchable, parent_paths=new_parent_paths
)
else:
match = extra_file_collector.match(matchable, filename, path=path, parent_paths=parent_paths)
if match:
yield match
yield from extra_file_collector.sort(
_walk(target_dir, extra_file_collector, job_working_directory, matchable, parent_paths)
)
[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:
[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:
[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_collection_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
self.match_relative_path = dataset_collection_description.match_relative_path
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, parent_paths=None):
pattern = self._pattern_for_dataset(dataset_instance)
if self.match_relative_path and parent_paths:
filename = os.path.join(*parent_paths, filename)
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).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(f"({galaxy_id_tag}) Exit code '{exit_code_str}' invalid. Using 0.")
exit_code = 0
return exit_code
[docs]def default_exit_code_file(files_dir, id_tag):
return os.path.join(files_dir, f"galaxy_{id_tag}.ec")
[docs]def collect_extra_files(
object_store: ObjectStore,
dataset: "DatasetInstance",
job_working_directory: str,
outputs_to_working_directory: bool = False,
):
# TODO: should this use compute_environment to determine the extra files path ?
assert dataset.dataset
real_file_name = file_name = dataset.dataset.extra_files_path_name_from(object_store)
if outputs_to_working_directory:
# OutputsToWorkingDirectoryPathRewriter always rewrites extra files to uuid path,
# so we have to collect from that path even if the real extra files path is dataset_N_files
file_name = f"dataset_{dataset.dataset.uuid}_files"
output_location = "outputs"
temp_file_path = os.path.join(job_working_directory, output_location, file_name)
if not os.path.exists(temp_file_path):
# Fall back to working dir, remove in 23.2
output_location = "working"
temp_file_path = os.path.join(job_working_directory, output_location, file_name)
if not os.path.exists(temp_file_path):
# no outputs to working directory, but may still need to push form cache to backend
temp_file_path = dataset.extra_files_path
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.
persist_extra_files(
object_store=object_store,
src_extra_files_path=temp_file_path,
primary_data=dataset,
extra_files_path_name=real_file_name,
)
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)
)
[docs]def collect_shrinked_content_from_path(path):
try:
with open(path, "rb") as fh:
return shrink_and_unicodify(fh.read().strip())
except FileNotFoundError:
return None