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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 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]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_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
new_primary_datasets = {}
try:
galaxy_json_path = os.path.join(job_working_directory, "working", tool.provide_metadata_file)
# LEGACY: Remove in 17.XX
if not os.path.exists(galaxy_json_path):
# Maybe this is a legacy job, use the job working directory instead
galaxy_json_path = os.path.join(job_working_directory, tool.provide_metadata_file)
json_file = open(galaxy_json_path, 'r')
for line in json_file:
line = json.loads(line)
if line.get('type') == 'new_primary_dataset':
new_primary_datasets[os.path.split(line.get('filename'))[-1]] = line
except Exception:
# This should not be considered an error or warning condition, this file is optional
pass
# 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))
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,
dir_only=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()
sa_session.add(primary_data)
sa_session.flush()
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())