def record_success(self, execution_slice, job, outputs):
super(ToolExecutionTracker, self).record_success(execution_slice, job, outputs)
for output_name, output_dataset in outputs:
if ToolOutputCollectionPart.is_named_collection_part_name(output_name):
# Skip known collection outputs, these will be covered by
# output collections.
continue
self.outputs_by_output_name[output_name].append(output_dataset)
for job_output in job.output_dataset_collections:
self.outputs_by_output_name[job_output.name].append(job_output.dataset_collection)
def record_success(self, execution_slice, job, outputs):
super(ToolExecutionTracker, self).record_success(execution_slice, job, outputs)
for output_name, output_dataset in outputs:
if ToolOutputCollectionPart.is_named_collection_part_name(output_name):
# Skip known collection outputs, these will be covered by
# output collections.
continue
self.outputs_by_output_name[output_name].append(output_dataset)
for job_output in job.output_dataset_collections:
self.outputs_by_output_name[job_output.name].append(job_output.dataset_collection)
def record_success( self, job, outputs ):
self.successful_jobs.append( job )
self.output_datasets.extend( outputs )
for output_name, output_dataset in outputs:
if ToolOutputCollectionPart.is_named_collection_part_name( output_name ):
# Skip known collection outputs, these will be covered by
# output collections.
continue
self.outputs_by_output_name[ output_name ].append( output_dataset )
for job_output in job.output_dataset_collections:
self.outputs_by_output_name[ job_output.name ].append( job_output.dataset_collection )
for job_output in job.output_dataset_collection_instances:
self.output_collections.append( ( job_output.name, job_output.dataset_collection_instance ) )
def extract_steps(trans,
history=None,
job_ids=None,
dataset_ids=None,
dataset_collection_ids=None,
dataset_names=None,
dataset_collection_names=None):
# Ensure job_ids and dataset_ids are lists (possibly empty)
if job_ids is None:
job_ids = []
elif type(job_ids) is not list:
job_ids = [job_ids]
if dataset_ids is None:
dataset_ids = []
elif type(dataset_ids) is not list:
dataset_ids = [dataset_ids]
if dataset_collection_ids is None:
dataset_collection_ids = []
elif type(dataset_collection_ids) is not list:
dataset_collection_ids = [dataset_collection_ids]
# Convert both sets of ids to integers
job_ids = [int(_) for _ in job_ids]
dataset_ids = [int(_) for _ in dataset_ids]
dataset_collection_ids = [int(_) for _ in dataset_collection_ids]
# Find each job, for security we (implicitly) check that they are
# associated with a job in the current history.
summary = WorkflowSummary(trans, history)
jobs = summary.jobs
steps = []
hid_to_output_pair = {}
# Input dataset steps
for i, hid in enumerate(dataset_ids):
step = model.WorkflowStep()
step.type = 'data_input'
if dataset_names:
name = dataset_names[i]
else:
name = "Input Dataset"
step.tool_inputs = dict(name=name)
hid_to_output_pair[hid] = (step, 'output')
steps.append(step)
for i, hid in enumerate(dataset_collection_ids):
step = model.WorkflowStep()
step.type = 'data_collection_input'
if hid not in summary.collection_types:
raise exceptions.RequestParameterInvalidException(
"hid %s does not appear to be a collection" % hid)
collection_type = summary.collection_types[hid]
if dataset_collection_names:
name = dataset_collection_names[i]
else:
name = "Input Dataset Collection"
step.tool_inputs = dict(name=name, collection_type=collection_type)
hid_to_output_pair[hid] = (step, 'output')
steps.append(step)
# Tool steps
for job_id in job_ids:
if job_id not in summary.job_id2representative_job:
log.warning("job_id %s not found in job_id2representative_job %s" %
(job_id, summary.job_id2representative_job))
raise AssertionError(
"Attempt to create workflow with job not connected to current history"
)
job = summary.job_id2representative_job[job_id]
tool_inputs, associations = step_inputs(trans, job)
step = model.WorkflowStep()
step.type = 'tool'
step.tool_id = job.tool_id
step.tool_version = job.tool_version
step.tool_inputs = tool_inputs
# NOTE: We shouldn't need to do two passes here since only
# an earlier job can be used as an input to a later
# job.
for other_hid, input_name in associations:
if job in summary.implicit_map_jobs:
an_implicit_output_collection = jobs[job][0][1]
input_collection = an_implicit_output_collection.find_implicit_input_collection(
input_name)
if input_collection:
other_hid = input_collection.hid
else:
log.info("Cannot find implicit input collection for %s" %
input_name)
if other_hid in hid_to_output_pair:
other_step, other_name = hid_to_output_pair[other_hid]
conn = model.WorkflowStepConnection()
conn.input_step = step
conn.input_name = input_name
# Should always be connected to an earlier step
conn.output_step = other_step
conn.output_name = other_name
steps.append(step)
# Store created dataset hids
for assoc in (job.output_datasets +
job.output_dataset_collection_instances):
assoc_name = assoc.name
if ToolOutputCollectionPart.is_named_collection_part_name(
assoc_name):
continue
if job in summary.implicit_map_jobs:
hid = None
for implicit_pair in jobs[job]:
query_assoc_name, dataset_collection = implicit_pair
if query_assoc_name == assoc_name:
hid = dataset_collection.hid
if hid is None:
template = "Failed to find matching implicit job - job id is %s, implicit pairs are %s, assoc_name is %s."
message = template % (job.id, jobs[job], assoc_name)
log.warning(message)
raise Exception("Failed to extract job.")
else:
if hasattr(assoc, "dataset"):
hid = assoc.dataset.hid
else:
hid = assoc.dataset_collection_instance.hid
hid_to_output_pair[hid] = (step, assoc.name)
return steps
def extract_steps(trans, history=None, job_ids=None, dataset_ids=None, dataset_collection_ids=None, dataset_names=None, dataset_collection_names=None):
# Ensure job_ids and dataset_ids are lists (possibly empty)
if job_ids is None:
job_ids = []
elif type(job_ids) is not list:
job_ids = [job_ids]
if dataset_ids is None:
dataset_ids = []
elif type(dataset_ids) is not list:
dataset_ids = [dataset_ids]
if dataset_collection_ids is None:
dataset_collection_ids = []
elif type(dataset_collection_ids) is not list:
dataset_collection_ids = [dataset_collection_ids]
# Convert both sets of ids to integers
job_ids = [int(_) for _ in job_ids]
dataset_ids = [int(_) for _ in dataset_ids]
dataset_collection_ids = [int(_) for _ in dataset_collection_ids]
# Find each job, for security we (implicitly) check that they are
# associated with a job in the current history.
summary = WorkflowSummary(trans, history)
jobs = summary.jobs
steps = []
hid_to_output_pair = {}
# Input dataset steps
for i, hid in enumerate(dataset_ids):
step = model.WorkflowStep()
step.type = 'data_input'
if dataset_names:
name = dataset_names[i]
else:
name = "Input Dataset"
step.tool_inputs = dict(name=name)
hid_to_output_pair[hid] = (step, 'output')
steps.append(step)
for i, hid in enumerate(dataset_collection_ids):
step = model.WorkflowStep()
step.type = 'data_collection_input'
if hid not in summary.collection_types:
raise exceptions.RequestParameterInvalidException("hid %s does not appear to be a collection" % hid)
collection_type = summary.collection_types[hid]
if dataset_collection_names:
name = dataset_collection_names[i]
else:
name = "Input Dataset Collection"
step.tool_inputs = dict(name=name, collection_type=collection_type)
hid_to_output_pair[hid] = (step, 'output')
steps.append(step)
# Tool steps
for job_id in job_ids:
if job_id not in summary.job_id2representative_job:
log.warning("job_id %s not found in job_id2representative_job %s" % (job_id, summary.job_id2representative_job))
raise AssertionError("Attempt to create workflow with job not connected to current history")
job = summary.job_id2representative_job[job_id]
tool_inputs, associations = step_inputs(trans, job)
step = model.WorkflowStep()
step.type = 'tool'
step.tool_id = job.tool_id
step.tool_version = job.tool_version
step.tool_inputs = tool_inputs
# NOTE: We shouldn't need to do two passes here since only
# an earlier job can be used as an input to a later
# job.
for other_hid, input_name in associations:
if job in summary.implicit_map_jobs:
an_implicit_output_collection = jobs[job][0][1]
input_collection = an_implicit_output_collection.find_implicit_input_collection(input_name)
if input_collection:
other_hid = input_collection.hid
else:
log.info("Cannot find implicit input collection for %s" % input_name)
if other_hid in hid_to_output_pair:
other_step, other_name = hid_to_output_pair[other_hid]
conn = model.WorkflowStepConnection()
conn.input_step = step
conn.input_name = input_name
# Should always be connected to an earlier step
conn.output_step = other_step
conn.output_name = other_name
steps.append(step)
# Store created dataset hids
for assoc in (job.output_datasets + job.output_dataset_collection_instances):
assoc_name = assoc.name
if ToolOutputCollectionPart.is_named_collection_part_name(assoc_name):
continue
if job in summary.implicit_map_jobs:
hid = None
for implicit_pair in jobs[job]:
query_assoc_name, dataset_collection = implicit_pair
if query_assoc_name == assoc_name:
hid = dataset_collection.hid
if hid is None:
template = "Failed to find matching implicit job - job id is %s, implicit pairs are %s, assoc_name is %s."
message = template % (job.id, jobs[job], assoc_name)
log.warning(message)
raise Exception("Failed to extract job.")
else:
if hasattr(assoc, "dataset"):
hid = assoc.dataset.hid
else:
hid = assoc.dataset_collection_instance.hid
hid_to_output_pair[hid] = (step, assoc.name)
return steps
