def execute(self, tool, trans, incoming={}, set_output_hid=False):
for name, value in incoming.iteritems():
if isinstance(value, trans.app.model.HistoryDatasetAssociation):
dataset = value
dataset_name = name
break
else:
raise Exception(
'The dataset to set metadata on could not be determined.')
# Create the job object
job = trans.app.model.Job()
job.session_id = trans.get_galaxy_session().id
job.history_id = trans.history.id
job.tool_id = tool.id
start_job_state = job.state #should be job.states.NEW
try:
# For backward compatibility, some tools may not have versions yet.
job.tool_version = tool.version
except:
job.tool_version = "1.0.0"
job.state = job.states.WAITING #we need to set job state to something other than NEW, or else when tracking jobs in db it will be picked up before we have added input / output parameters
trans.sa_session.add(job)
trans.sa_session.flush() #ensure job.id is available
#add parameters to job_parameter table
# Store original dataset state, so we can restore it. A separate table might be better (no chance of 'losing' the original state)?
incoming['__ORIGINAL_DATASET_STATE__'] = dataset.state
external_metadata_wrapper = JobExternalOutputMetadataWrapper(job)
cmd_line = external_metadata_wrapper.setup_external_metadata(
dataset,
trans.sa_session,
exec_dir=None,
tmp_dir=trans.app.config.new_file_path,
dataset_files_path=trans.app.model.Dataset.file_path,
output_fnames=None,
config_root=None,
datatypes_config=None,
job_metadata=None,
kwds={'overwrite': True})
incoming['__SET_EXTERNAL_METADATA_COMMAND_LINE__'] = cmd_line
for name, value in tool.params_to_strings(incoming,
trans.app).iteritems():
job.add_parameter(name, value)
#add the dataset to job_to_input_dataset table
job.add_input_dataset(dataset_name, dataset)
#Need a special state here to show that metadata is being set and also allow the job to run
# i.e. if state was set to 'running' the set metadata job would never run, as it would wait for input (the dataset to set metadata on) to be in a ready state
dataset.state = dataset.states.SETTING_METADATA
job.state = start_job_state #job inputs have been configured, restore initial job state
trans.sa_session.flush()
# Queue the job for execution
trans.app.job_queue.put(job.id, tool)
trans.log_event(
"Added set external metadata job to the job queue, id: %s" %
str(job.id),
tool_id=job.tool_id)
return []
def execute( self, tool, trans, incoming = {}, set_output_hid = False ):
for name, value in incoming.iteritems():
if isinstance( value, trans.app.model.HistoryDatasetAssociation ):
dataset = value
dataset_name = name
break
else:
raise Exception( 'The dataset to set metadata on could not be determined.' )
# Create the job object
job = trans.app.model.Job()
job.session_id = trans.get_galaxy_session().id
job.history_id = trans.history.id
job.tool_id = tool.id
start_job_state = job.state #should be job.states.NEW
try:
# For backward compatibility, some tools may not have versions yet.
job.tool_version = tool.version
except:
job.tool_version = "1.0.0"
job.state = job.states.WAITING #we need to set job state to something other than NEW, or else when tracking jobs in db it will be picked up before we have added input / output parameters
trans.sa_session.add( job )
trans.sa_session.flush() #ensure job.id is available
#add parameters to job_parameter table
# Store original dataset state, so we can restore it. A separate table might be better (no chance of 'losing' the original state)?
incoming[ '__ORIGINAL_DATASET_STATE__' ] = dataset.state
external_metadata_wrapper = JobExternalOutputMetadataWrapper( job )
cmd_line = external_metadata_wrapper.setup_external_metadata( dataset,
trans.sa_session,
exec_dir = None,
tmp_dir = trans.app.config.new_file_path,
dataset_files_path = trans.app.model.Dataset.file_path,
output_fnames = None,
config_root = None,
datatypes_config = None,
job_metadata = None,
kwds = { 'overwrite' : True } )
incoming[ '__SET_EXTERNAL_METADATA_COMMAND_LINE__' ] = cmd_line
for name, value in tool.params_to_strings( incoming, trans.app ).iteritems():
job.add_parameter( name, value )
#add the dataset to job_to_input_dataset table
job.add_input_dataset( dataset_name, dataset )
#Need a special state here to show that metadata is being set and also allow the job to run
# i.e. if state was set to 'running' the set metadata job would never run, as it would wait for input (the dataset to set metadata on) to be in a ready state
dataset.state = dataset.states.SETTING_METADATA
job.state = start_job_state #job inputs have been configured, restore initial job state
trans.sa_session.flush()
# Queue the job for execution
trans.app.job_queue.put( job.id, tool )
trans.log_event( "Added set external metadata job to the job queue, id: %s" % str(job.id), tool_id=job.tool_id )
return []
def execute_via_app( self, tool, app, session_id, history_id, user=None,
incoming={}, set_output_hid=False, overwrite=True,
history=None, job_params=None ):
"""
Execute using application.
"""
for name, value in incoming.iteritems():
if isinstance( value, app.model.HistoryDatasetAssociation ):
dataset = value
dataset_name = name
type = 'hda'
break
elif isinstance( value, app.model.LibraryDatasetDatasetAssociation ):
dataset = value
dataset_name = name
type = 'ldda'
break
else:
raise Exception( 'The dataset to set metadata on could not be determined.' )
sa_session = app.model.context
# Create the job object
job = app.model.Job()
job.session_id = session_id
job.history_id = history_id
job.tool_id = tool.id
if user:
job.user_id = user.id
if job_params:
job.params = dumps( job_params )
start_job_state = job.state # should be job.states.NEW
try:
# For backward compatibility, some tools may not have versions yet.
job.tool_version = tool.version
except:
job.tool_version = "1.0.1"
job.state = job.states.WAITING # we need to set job state to something other than NEW, or else when tracking jobs in db it will be picked up before we have added input / output parameters
job.set_handler(tool.get_job_handler( job_params ))
sa_session.add( job )
sa_session.flush() # ensure job.id is available
# add parameters to job_parameter table
# Store original dataset state, so we can restore it. A separate table might be better (no chance of 'losing' the original state)?
incoming[ '__ORIGINAL_DATASET_STATE__' ] = dataset.state
input_paths = [DatasetPath( dataset.id, real_path=dataset.file_name, mutable=False )]
app.object_store.create(job, base_dir='job_work', dir_only=True, extra_dir=str(job.id))
job_working_dir = app.object_store.get_filename(job, base_dir='job_work', dir_only=True, extra_dir=str(job.id))
external_metadata_wrapper = JobExternalOutputMetadataWrapper( job )
cmd_line = external_metadata_wrapper.setup_external_metadata( dataset,
sa_session,
exec_dir=None,
tmp_dir=job_working_dir,
dataset_files_path=app.model.Dataset.file_path,
output_fnames=input_paths,
config_root=app.config.root,
config_file=app.config.config_file,
datatypes_config=app.datatypes_registry.integrated_datatypes_configs,
job_metadata=None,
include_command=False,
max_metadata_value_size=app.config.max_metadata_value_size,
kwds={ 'overwrite' : overwrite } )
incoming[ '__SET_EXTERNAL_METADATA_COMMAND_LINE__' ] = cmd_line
for name, value in tool.params_to_strings( incoming, app ).iteritems():
job.add_parameter( name, value )
# add the dataset to job_to_input_dataset table
if type == 'hda':
job.add_input_dataset( dataset_name, dataset )
elif type == 'ldda':
job.add_input_library_dataset( dataset_name, dataset )
# Need a special state here to show that metadata is being set and also allow the job to run
# i.e. if state was set to 'running' the set metadata job would never run, as it would wait for input (the dataset to set metadata on) to be in a ready state
dataset._state = dataset.states.SETTING_METADATA
job.state = start_job_state # job inputs have been configured, restore initial job state
sa_session.flush()
# Queue the job for execution
app.job_queue.put( job.id, tool.id )
# FIXME: need to add event logging to app and log events there rather than trans.
# trans.log_event( "Added set external metadata job to the job queue, id: %s" % str(job.id), tool_id=job.tool_id )
# clear e.g. converted files
dataset.datatype.before_setting_metadata( dataset )
return job, odict()
def execute_via_app( self, tool, app, session_id, history_id, user=None,
incoming = {}, set_output_hid = False, overwrite = True,
history=None, job_params=None ):
"""
Execute using application.
"""
for name, value in incoming.iteritems():
if isinstance( value, app.model.HistoryDatasetAssociation ):
dataset = value
dataset_name = name
type = 'hda'
break
elif isinstance( value, app.model.LibraryDatasetDatasetAssociation ):
dataset = value
dataset_name = name
type = 'ldda'
break
else:
raise Exception( 'The dataset to set metadata on could not be determined.' )
sa_session = app.model.context
# Create the job object
job = app.model.Job()
job.session_id = session_id
job.history_id = history_id
job.tool_id = tool.id
if user:
job.user_id = user.id
if job_params:
job.params = dumps( job_params )
start_job_state = job.state #should be job.states.NEW
try:
# For backward compatibility, some tools may not have versions yet.
job.tool_version = tool.version
except:
job.tool_version = "1.0.1"
job.state = job.states.WAITING #we need to set job state to something other than NEW, or else when tracking jobs in db it will be picked up before we have added input / output parameters
job.set_handler(tool.get_job_handler( job_params ))
sa_session.add( job )
sa_session.flush() #ensure job.id is available
#add parameters to job_parameter table
# Store original dataset state, so we can restore it. A separate table might be better (no chance of 'losing' the original state)?
incoming[ '__ORIGINAL_DATASET_STATE__' ] = dataset.state
input_paths = [DatasetPath( dataset.id, real_path=dataset.file_name, mutable=False )]
app.object_store.create(job, base_dir='job_work', dir_only=True, extra_dir=str(job.id))
job_working_dir = app.object_store.get_filename(job, base_dir='job_work', dir_only=True, extra_dir=str(job.id))
external_metadata_wrapper = JobExternalOutputMetadataWrapper( job )
cmd_line = external_metadata_wrapper.setup_external_metadata( dataset,
sa_session,
exec_dir = None,
tmp_dir = job_working_dir,
dataset_files_path = app.model.Dataset.file_path,
output_fnames = input_paths,
config_root = app.config.root,
config_file = app.config.config_file,
datatypes_config = app.datatypes_registry.integrated_datatypes_configs,
job_metadata = None,
include_command = False,
kwds = { 'overwrite' : overwrite } )
incoming[ '__SET_EXTERNAL_METADATA_COMMAND_LINE__' ] = cmd_line
for name, value in tool.params_to_strings( incoming, app ).iteritems():
job.add_parameter( name, value )
#add the dataset to job_to_input_dataset table
if type == 'hda':
job.add_input_dataset( dataset_name, dataset )
elif type == 'ldda':
job.add_input_library_dataset( dataset_name, dataset )
#Need a special state here to show that metadata is being set and also allow the job to run
# i.e. if state was set to 'running' the set metadata job would never run, as it would wait for input (the dataset to set metadata on) to be in a ready state
dataset._state = dataset.states.SETTING_METADATA
job.state = start_job_state #job inputs have been configured, restore initial job state
sa_session.flush()
# Queue the job for execution
app.job_queue.put( job.id, tool.id )
# FIXME: need to add event logging to app and log events there rather than trans.
#trans.log_event( "Added set external metadata job to the job queue, id: %s" % str(job.id), tool_id=job.tool_id )
#clear e.g. converted files
dataset.datatype.before_setting_metadata( dataset )
return job, odict()