def validate(self, job_directory, string):
regex = "^%s$" % self._expression_to_regex(job_directory, self.xml_el)
match = compile(regex).match(string)
validated = match is not None
if validated:
for group in match.groups():
if not in_directory(group, join(job_directory.path, "inputs")) and \
not in_directory(group, join(job_directory.path, "outputs")):
validated = False
break
return validated
def validate(self, job_directory, string):
regex = "^%s$" % self._expression_to_regex(job_directory, self.xml_el)
match = compile(regex).match(string)
validated = match is not None
if validated:
for group in match.groups():
if not in_directory(group, join(job_directory.path, "inputs")) and \
not in_directory(group, join(job_directory.path, "outputs")):
validated = False
break
return validated
def discovered_file_for_element(dataset,
job_working_directory,
parent_identifiers=[],
collector=None):
target_directory = discover_target_directory(
getattr(collector, "directory", None), job_working_directory)
filename = dataset.get("filename")
error_message = dataset.get("error_message")
if error_message is None:
# 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, collector,
JsonCollectedDatasetMatch(dataset,
collector,
filename,
path=path,
parent_identifiers=parent_identifiers))
else:
assert "error_message" in dataset
return DiscoveredFileError(
dataset['error_message'], collector,
JsonCollectedDatasetMatch(dataset,
collector,
None,
parent_identifiers=parent_identifiers))
def get_work_dir_outputs( self, job_wrapper, job_working_directory=None ):
"""
Returns list of pairs (source_file, destination) describing path
to work_dir output file and ultimate destination.
"""
if not job_working_directory:
job_working_directory = os.path.abspath( job_wrapper.working_directory )
# Set up dict of dataset id --> output path; output path can be real or
# false depending on outputs_to_working_directory
output_paths = {}
for dataset_path in job_wrapper.get_output_fnames():
path = dataset_path.real_path
if self.app.config.outputs_to_working_directory:
path = dataset_path.false_path
output_paths[ dataset_path.dataset_id ] = path
output_pairs = []
# Walk job's output associations to find and use from_work_dir attributes.
job = job_wrapper.get_job()
job_tool = job_wrapper.tool
for (joda, dataset) in self._walk_dataset_outputs( job ):
if joda and job_tool:
hda_tool_output = job_tool.find_output_def( joda.name )
if hda_tool_output and hda_tool_output.from_work_dir:
# Copy from working dir to HDA.
# TODO: move instead of copy to save time?
source_file = os.path.join( job_working_directory, hda_tool_output.from_work_dir )
destination = job_wrapper.get_output_destination( output_paths[ dataset.dataset_id ] )
if in_directory( source_file, job_working_directory ):
output_pairs.append( ( source_file, destination ) )
else:
# Security violation.
log.exception( "from_work_dir specified a location not in the working directory: %s, %s" % ( source_file, job_wrapper.working_directory ) )
return output_pairs
def __in_working_directory(self, job, path, app):
working_directory = app.object_store.get_filename(job,
base_dir='job_work',
dir_only=True,
extra_dir=str(
job.id))
return util.in_directory(path, working_directory)
def get_work_dir_outputs( self, job_wrapper, job_working_directory=None ):
"""
Returns list of pairs (source_file, destination) describing path
to work_dir output file and ultimate destination.
"""
if not job_working_directory:
job_working_directory = os.path.abspath( job_wrapper.working_directory )
# Set up dict of dataset id --> output path; output path can be real or
# false depending on outputs_to_working_directory
output_paths = {}
for dataset_path in job_wrapper.get_output_fnames():
path = dataset_path.real_path
if self.app.config.outputs_to_working_directory:
path = dataset_path.false_path
output_paths[ dataset_path.dataset_id ] = path
output_pairs = []
# Walk job's output associations to find and use from_work_dir attributes.
job = job_wrapper.get_job()
job_tool = job_wrapper.tool
for (joda, dataset) in self._walk_dataset_outputs( job ):
if joda and job_tool:
hda_tool_output = job_tool.find_output_def( joda.name )
if hda_tool_output and hda_tool_output.from_work_dir:
# Copy from working dir to HDA.
# TODO: move instead of copy to save time?
source_file = os.path.join( job_working_directory, hda_tool_output.from_work_dir )
destination = job_wrapper.get_output_destination( output_paths[ dataset.dataset_id ] )
if in_directory( source_file, job_working_directory ):
output_pairs.append( ( source_file, destination ) )
else:
# Security violation.
log.exception( "from_work_dir specified a location not in the working directory: %s, %s" % ( source_file, job_wrapper.working_directory ) )
return output_pairs
def metadata_path_on_compute(path):
compute_path = path
log.info(compute_tmp_dir)
if compute_tmp_dir and tmp_dir and in_directory(path, tmp_dir):
path_relative = os.path.relpath(path, tmp_dir)
compute_path = os.path.join(compute_tmp_dir, path_relative)
return compute_path
def discover_target_directory(extra_file_collector, job_working_directory):
directory = job_working_directory
if extra_file_collector.directory:
directory = os.path.join(directory, extra_file_collector.directory)
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
def preprocess_volumes(volumes_raw_str, container_type):
"""Process Galaxy volume specification string to either Docker or Singularity specification.
Galaxy allows the mount try "default_ro" which translates to ro for Docker and
ro for Singularity iff no subdirectories are rw (Singularity does not allow ro
parent directories with rw subdirectories).
>>> preprocess_volumes("/a/b", DOCKER_CONTAINER_TYPE)
['/a/b:rw']
>>> preprocess_volumes("/a/b:ro,/a/b/c:rw", DOCKER_CONTAINER_TYPE)
['/a/b:ro', '/a/b/c:rw']
>>> preprocess_volumes("/a/b:/a:ro,/a/b/c:/a/b:rw", DOCKER_CONTAINER_TYPE)
['/a/b:/a:ro', '/a/b/c:/a/b:rw']
>>> preprocess_volumes("/a/b:default_ro,/a/b/c:rw", DOCKER_CONTAINER_TYPE)
['/a/b:ro', '/a/b/c:rw']
>>> preprocess_volumes("/a/b:default_ro,/a/b/c:ro", SINGULARITY_CONTAINER_TYPE)
['/a/b:ro', '/a/b/c:ro']
>>> preprocess_volumes("/a/b:default_ro,/a/b/c:rw", SINGULARITY_CONTAINER_TYPE)
['/a/b', '/a/b/c']
"""
volumes_raw_strs = [v.strip() for v in volumes_raw_str.split(",")]
volumes = []
rw_paths = []
for volume_raw_str in volumes_raw_strs:
volume_parts = volume_raw_str.split(":")
if len(volume_parts) > 3:
raise Exception("Unparsable volumes string in configuration [%s]" % volumes_raw_str)
if len(volume_parts) == 3:
volume_parts = ["{}:{}".format(volume_parts[0], volume_parts[1]), volume_parts[2]]
if len(volume_parts) == 2 and volume_parts[1] not in ("rw", "ro", "default_ro"):
volume_parts = ["{}:{}".format(volume_parts[0], volume_parts[1]), "rw"]
if len(volume_parts) == 1:
volume_parts.append("rw")
volumes.append(volume_parts)
if volume_parts[1] == "rw":
rw_paths.append(volume_parts[0])
for volume in volumes:
path = volume[0]
how = volume[1]
if how == "default_ro":
how = "ro"
if container_type == SINGULARITY_CONTAINER_TYPE:
for rw_path in rw_paths:
if in_directory(rw_path, path):
how = "rw"
volume[1] = how
# for a while singularity did not allow to specify the bind type rw
# (which is the default). so we omit this default
# see https://github.com/hpcng/singularity/pull/5487
if container_type == SINGULARITY_CONTAINER_TYPE and volume[1] == 'rw':
del volume[1]
return [":".join(v) for v in volumes]
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
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
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))
def get_work_dir_outputs(self, job_wrapper, job_working_directory=None):
"""
Returns list of pairs (source_file, destination) describing path
to work_dir output file and ultimate destination.
"""
if not job_working_directory:
job_working_directory = os.path.abspath(
job_wrapper.working_directory)
# Set up dict of dataset id --> output path; output path can be real or
# false depending on outputs_to_working_directory
output_paths = {}
for dataset_path in job_wrapper.get_output_fnames():
path = dataset_path.real_path
if self.app.config.outputs_to_working_directory:
path = dataset_path.false_path
output_paths[dataset_path.dataset_id] = path
output_pairs = []
# Walk job's output associations to find and use from_work_dir attributes.
job = job_wrapper.get_job()
job_tool = job_wrapper.tool
for dataset_assoc in job.output_datasets + job.output_library_datasets:
for dataset in dataset_assoc.dataset.dataset.history_associations + dataset_assoc.dataset.dataset.library_associations:
if isinstance(dataset,
self.app.model.HistoryDatasetAssociation):
joda = self.sa_session.query(
self.app.model.JobToOutputDatasetAssociation
).filter_by(job=job, dataset=dataset).first()
if joda and job_tool:
hda_tool_output = job_tool.outputs.get(joda.name, None)
if hda_tool_output and hda_tool_output.from_work_dir:
# Copy from working dir to HDA.
# TODO: move instead of copy to save time?
source_file = os.path.join(
job_working_directory,
hda_tool_output.from_work_dir)
destination = job_wrapper.get_output_destination(
output_paths[dataset.dataset_id])
if in_directory(source_file,
job_working_directory):
output_pairs.append((source_file, destination))
else:
# Security violation.
log.exception(
"from_work_dir specified a location not in the working directory: %s, %s"
% (source_file,
job_wrapper.working_directory))
return output_pairs
def __is_output_dataset_path(self, job, path):
""" Check if is an output path for this job or a file in the an
output's extra files path.
"""
da_lists = [job.output_datasets, job.output_library_datasets]
for da_list in da_lists:
for job_dataset_association in da_list:
dataset = job_dataset_association.dataset
if not dataset:
continue
if os.path.abspath(dataset.file_name) == os.path.abspath(path):
return True
elif util.in_directory(path, dataset.extra_files_path):
return True
return False
def walk_over_extra_files( extra_file_collectors, job_working_directory, matchable ):
for extra_file_collector in extra_file_collectors:
directory = job_working_directory
if extra_file_collector.directory:
directory = os.path.join( directory, extra_file_collector.directory )
if not util.in_directory( directory, job_working_directory ):
raise Exception( "Problem with tool configuration, attempting to pull in datasets from outside working directory." )
if not os.path.isdir( directory ):
continue
for filename in sorted( os.listdir( directory ) ):
path = os.path.join( directory, filename )
if not os.path.isfile( path ):
continue
if extra_file_collector.match( matchable, filename ):
yield path, extra_file_collector
def __is_output_dataset_path( self, job, path ):
""" Check if is an output path for this job or a file in the an
output's extra files path.
"""
da_lists = [ job.output_datasets, job.output_library_datasets ]
for da_list in da_lists:
for job_dataset_association in da_list:
dataset = job_dataset_association.dataset
if not dataset:
continue
if os.path.abspath( dataset.file_name ) == os.path.abspath( path ):
return True
elif util.in_directory( path, dataset.extra_files_path ):
return True
return False
def ensure_in_working_directory(self, path, purge_source, in_place):
if in_directory(path, self.__workdir):
return path
new_path = self.__new_dataset_path()
if purge_source:
try:
shutil.move(path, new_path)
except OSError as e:
# We may not have permission to remove converted_path
if e.errno != errno.EACCES:
raise
else:
shutil.copy(path, new_path)
return new_path
def ensure_in_working_directory(self, path, purge_source, in_place):
if in_directory(path, self.__workdir):
return path
new_path = self.__new_dataset_path()
if purge_source:
try:
shutil.move(path, new_path)
except OSError as e:
# We may not have permission to remove converted_path
if e.errno != errno.EACCES:
raise
else:
shutil.copy(path, new_path)
return new_path
def preprocess_volumes(volumes_raw_str, container_type):
"""Process Galaxy volume specification string to either Docker or Singularity specification.
Galaxy allows the mount try "default_ro" which translates to ro for Docker and
ro for Singularity iff no subdirectories are rw (Singularity does not allow ro
parent directories with rw subdirectories).
>>> preprocess_volumes("/a/b", DOCKER_CONTAINER_TYPE)
['/a/b:rw']
>>> preprocess_volumes("/a/b:ro,/a/b/c:rw", DOCKER_CONTAINER_TYPE)
['/a/b:ro', '/a/b/c:rw']
>>> preprocess_volumes("/a/b:default_ro,/a/b/c:rw", DOCKER_CONTAINER_TYPE)
['/a/b:ro', '/a/b/c:rw']
>>> preprocess_volumes("/a/b:default_ro,/a/b/c:rw", SINGULARITY_CONTAINER_TYPE)
['/a/b:rw', '/a/b/c:rw']
"""
volumes_raw_strs = [v.strip() for v in volumes_raw_str.split(",")]
volumes = []
rw_paths = []
for volume_raw_str in volumes_raw_strs:
volume_parts = volume_raw_str.split(":")
if len(volume_parts) > 2:
raise Exception("Unparsable volumes string in configuration [%s]" %
volumes_raw_str)
if len(volume_parts) == 1:
volume_parts.append("rw")
volumes.append(volume_parts)
if volume_parts[1] == "rw":
rw_paths.append(volume_parts[0])
for volume in volumes:
path = volume[0]
how = volume[1]
if how == "default_ro":
how = "ro"
if container_type == SINGULARITY_CONTAINER_TYPE:
for rw_path in rw_paths:
if in_directory(rw_path, path):
how = "rw"
volume[1] = how
return [":".join(v) for v in volumes]
def preprocess_volumes(volumes_raw_str, container_type):
"""Process Galaxy volume specification string to either Docker or Singularity specification.
Galaxy allows the mount try "default_ro" which translates to ro for Docker and
ro for Singularity iff no subdirectories are rw (Singularity does not allow ro
parent directories with rw subdirectories).
>>> preprocess_volumes("/a/b", DOCKER_CONTAINER_TYPE)
'/a/b:rw'
>>> preprocess_volumes("/a/b:ro,/a/b/c:rw", DOCKER_CONTAINER_TYPE)
'/a/b:ro,/a/b/c:rw'
>>> preprocess_volumes("/a/b:default_ro,/a/b/c:rw", DOCKER_CONTAINER_TYPE)
'/a/b:ro,/a/b/c:rw'
>>> preprocess_volumes("/a/b:default_ro,/a/b/c:rw", SINGULARITY_CONTAINER_TYPE)
'/a/b:rw,/a/b/c:rw'
"""
volumes_raw_strs = [v.strip() for v in volumes_raw_str.split(",")]
volumes = []
rw_paths = []
for volume_raw_str in volumes_raw_strs:
volume_parts = volume_raw_str.split(":")
if len(volume_parts) > 2:
raise Exception("Unparsable volumes string in configuration [%s]" % volumes_raw_str)
if len(volume_parts) == 1:
volume_parts.append("rw")
volumes.append(volume_parts)
if volume_parts[1] == "rw":
rw_paths.append(volume_parts[0])
for volume in volumes:
path = volume[0]
how = volume[1]
if how == "default_ro":
how = "ro"
if container_type == SINGULARITY_CONTAINER_TYPE:
for rw_path in rw_paths:
if in_directory(rw_path, path):
how = "rw"
volume[1] = how
return ",".join([":".join(v) for v in volumes])
def walk_over_extra_files(extra_file_collectors, job_working_directory,
matchable):
for extra_file_collector in extra_file_collectors:
directory = job_working_directory
if extra_file_collector.directory:
directory = os.path.join(directory, extra_file_collector.directory)
if not util.in_directory(directory, job_working_directory):
raise Exception(
"Problem with tool configuration, attempting to pull in datasets from outside working directory."
)
if not os.path.isdir(directory):
continue
for filename in sorted(os.listdir(directory)):
path = os.path.join(directory, filename)
if not os.path.isfile(path):
continue
if extra_file_collector.match(matchable, filename):
yield path, extra_file_collector
def ensure_in_working_directory(self, path, purge_source, in_place):
if in_directory(path, self.__workdir):
return path
new_path = self.__new_dataset_path()
if purge_source:
try:
shutil.move(path, new_path)
# Drop .info file if it exists
try:
os.remove(f"{path}.info")
except FileNotFoundError:
pass
except OSError as e:
# We may not have permission to remove converted_path
if e.errno != errno.EACCES:
raise
else:
shutil.copy(path, new_path)
return new_path
def get_work_dir_outputs( self, job_wrapper, job_working_directory=None ):
"""
Returns list of pairs (source_file, destination) describing path
to work_dir output file and ultimate destination.
"""
if not job_working_directory:
job_working_directory = os.path.abspath( job_wrapper.working_directory )
# Set up dict of dataset id --> output path; output path can be real or
# false depending on outputs_to_working_directory
output_paths = {}
for dataset_path in job_wrapper.get_output_fnames():
path = dataset_path.real_path
if self.app.config.outputs_to_working_directory:
path = dataset_path.false_path
output_paths[ dataset_path.dataset_id ] = path
output_pairs = []
# Walk job's output associations to find and use from_work_dir attributes.
job = job_wrapper.get_job()
job_tool = self.app.toolbox.tools_by_id.get( job.tool_id, None )
for dataset_assoc in job.output_datasets + job.output_library_datasets:
for dataset in dataset_assoc.dataset.dataset.history_associations + dataset_assoc.dataset.dataset.library_associations:
if isinstance( dataset, self.app.model.HistoryDatasetAssociation ):
joda = self.sa_session.query( self.app.model.JobToOutputDatasetAssociation ).filter_by( job=job, dataset=dataset ).first()
if joda and job_tool:
hda_tool_output = job_tool.outputs.get( joda.name, None )
if hda_tool_output and hda_tool_output.from_work_dir:
# Copy from working dir to HDA.
# TODO: move instead of copy to save time?
source_file = os.path.join( job_working_directory, hda_tool_output.from_work_dir )
destination = job_wrapper.get_output_destination( output_paths[ dataset.dataset_id ] )
if in_directory( source_file, job_working_directory ):
output_pairs.append( ( source_file, destination ) )
log.debug( "Copying %s to %s as directed by from_work_dir" % ( source_file, destination ) )
else:
# Security violation.
log.exception( "from_work_dir specified a location not in the working directory: %s, %s" % ( source_file, job_wrapper.working_directory ) )
return output_pairs
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))
def remote_output_path_rewrite(self, local_path):
output_type = path_type.OUTPUT
if in_directory(local_path, self.local_working_directory):
output_type = path_type.OUTPUT_WORKDIR
remote_path = self.__remote_path_rewrite(local_path, output_type)
return remote_path
def exists(self, filename):
path = os.path.abspath(self.path(filename))
return os.path.exists(path) and in_directory(path, self.file_dir)
def exists(self, filename):
path = os.path.abspath(self.path(filename))
return os.path.exists(path) and in_directory(path, self.file_dir)
def __is_allowed_temp_dir_file(self, app, job, path):
# grrr.. need to get away from new_file_path - these should be written
# to job working directory like metadata files.
in_temp_dir = util.in_directory(path, app.config.new_file_path)
return in_temp_dir and os.path.split(path)[-1].startswith(
"GALAXY_VERSION_")
def metadata_path_on_compute(path):
compute_path = path
if compute_tmp_dir and tmp_dir and in_directory(path, tmp_dir):
path_relative = os.path.relpath(path, tmp_dir)
compute_path = os.path.join(compute_tmp_dir, path_relative)
return compute_path
def __in_working_directory( self, job, path, app ):
working_directory = app.object_store.get_filename(job, base_dir='job_work', dir_only=True, extra_dir=str(job.id))
return util.in_directory( path, working_directory )
def __is_allowed_temp_dir_file( self, app, job, path ):
# grrr.. need to get away from new_file_path - these should be written
# to job working directory like metadata files.
in_temp_dir = util.in_directory( path, app.config.new_file_path )
return in_temp_dir and os.path.split( path )[ -1 ].startswith( "GALAXY_VERSION_")
def verify_is_in_directory(path, directory, local_path_module=os.path):
if not in_directory(path, directory, local_path_module):
msg = "Attempt to read or write file outside an authorized directory."
log.warn("%s Attempted path: %s, valid directory: %s" %
(msg, path, directory))
raise Exception(msg)
def remote_output_path_rewrite(self, local_path):
output_type = path_type.OUTPUT
if in_directory(local_path, self.local_working_directory):
output_type = path_type.OUTPUT_WORKDIR
remote_path = self.__remote_path_rewrite(local_path, output_type)
return remote_path
def verify_is_in_directory(path, directory, local_path_module=os.path):
if not in_directory(path, directory, local_path_module):
msg = "Attempt to read or write file outside an authorized directory."
log.warn("%s Attempted path: %s, valid directory: %s" % (msg, path, directory))
raise Exception(msg)
def collect_primary_datasets( tool, output, job_working_directory, input_ext ):
app = tool.app
sa_session = tool.sa_session
new_primary_datasets = {}
try:
json_file = open( os.path.join( job_working_directory, jobs.TOOL_PROVIDED_JOB_METADATA_FILE ), '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 of:
# 'primary_associatedWithDatasetID_designation_visibility_extension(_DBKEY)'
primary_datasets = {}
for name, outdata in output.items():
dataset_collectors = tool.outputs[ name ].dataset_collectors if name in tool.outputs else [ DEFAULT_DATASET_COLLECTOR ]
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 ] = DEFAULT_DATASET_COLLECTOR
if 'job_working_directory' in app.config.collect_outputs_from:
for extra_file_collector in dataset_collectors:
directory = job_working_directory
if extra_file_collector.directory:
directory = os.path.join( directory, extra_file_collector.directory )
if not util.in_directory( directory, job_working_directory ):
raise Exception( "Problem with tool configuration, attempting to pull in datasets from outside working directory." )
if not os.path.isdir( directory ):
continue
for filename in os.listdir( directory ):
path = os.path.join( directory, filename )
if not os.path.isfile( path ):
continue
if extra_file_collector.match( outdata, filename ):
filenames[ path ] = extra_file_collector
for filename, extra_file_collector in filenames.iteritems():
if not name in primary_datasets:
primary_datasets[name] = {}
fields_match = extra_file_collector.match( outdata, os.path.basename( filename ) )
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
visible = fields_match.visible
ext = fields_match.ext
if ext == "input":
ext = input_ext
dbkey = fields_match.dbkey
# Create new primary dataset
primary_data = app.model.HistoryDatasetAssociation( extension=ext,
designation=designation,
visible=visible,
dbkey=dbkey,
create_dataset=True,
sa_session=sa_session )
app.security_agent.copy_dataset_permissions( outdata.dataset, primary_data.dataset )
sa_session.add( primary_data )
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)
primary_data.set_size()
# 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
#add tool/metadata provided information
new_primary_datasets_attributes = new_primary_datasets.get( os.path.split( filename )[-1], {} )
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
)
# FIXME:
# since these are placed into the job working dir, let the standard
# Galaxy cleanup methods handle this (for now?)
# there was an extra_files_path dir, attempt to remove it
#shutil.rmtree( extra_files_path_joined )
metadata_dict = new_primary_datasets_attributes.get( 'metadata', None )
if metadata_dict:
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()
return primary_datasets
def collect_primary_datasets(tool, output, job_working_directory, input_ext):
app = tool.app
sa_session = tool.sa_session
new_primary_datasets = {}
try:
json_file = open(
os.path.join(job_working_directory,
jobs.TOOL_PROVIDED_JOB_METADATA_FILE), '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 of:
# 'primary_associatedWithDatasetID_designation_visibility_extension(_DBKEY)'
primary_datasets = {}
for name, outdata in output.items():
dataset_collectors = tool.outputs[
name].dataset_collectors if name in tool.outputs else [
DEFAULT_DATASET_COLLECTOR
]
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] = DEFAULT_DATASET_COLLECTOR
if 'job_working_directory' in app.config.collect_outputs_from:
for extra_file_collector in dataset_collectors:
directory = job_working_directory
if extra_file_collector.directory:
directory = os.path.join(directory,
extra_file_collector.directory)
if not util.in_directory(directory, job_working_directory):
raise Exception(
"Problem with tool configuration, attempting to pull in datasets from outside working directory."
)
if not os.path.isdir(directory):
continue
for filename in os.listdir(directory):
path = os.path.join(directory, filename)
if not os.path.isfile(path):
continue
if extra_file_collector.match(outdata, filename):
filenames[path] = extra_file_collector
for filename, extra_file_collector in filenames.iteritems():
if not name in primary_datasets:
primary_datasets[name] = {}
fields_match = extra_file_collector.match(
outdata, os.path.basename(filename))
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
visible = fields_match.visible
ext = fields_match.ext
if ext == "input":
ext = input_ext
dbkey = fields_match.dbkey
# Create new primary dataset
primary_data = app.model.HistoryDatasetAssociation(
extension=ext,
designation=designation,
visible=visible,
dbkey=dbkey,
create_dataset=True,
sa_session=sa_session)
app.security_agent.copy_dataset_permissions(
outdata.dataset, primary_data.dataset)
sa_session.add(primary_data)
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)
primary_data.set_size()
# 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
#add tool/metadata provided information
new_primary_datasets_attributes = new_primary_datasets.get(
os.path.split(filename)[-1], {})
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)
# FIXME:
# since these are placed into the job working dir, let the standard
# Galaxy cleanup methods handle this (for now?)
# there was an extra_files_path dir, attempt to remove it
#shutil.rmtree( extra_files_path_joined )
metadata_dict = new_primary_datasets_attributes.get(
'metadata', None)
if metadata_dict:
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()
return primary_datasets
