def __module_from_dict(self, trans, step_dict, exact_tools=False):
""" Create a WorkflowStep model object and corresponding module
representing type-specific functionality from the incoming dictionary.
"""
step = model.WorkflowStep()
# TODO: Consider handling position inside module.
step.position = step_dict['position']
if "uuid" in step_dict and step_dict['uuid'] != "None":
step.uuid = step_dict["uuid"]
if "label" in step_dict:
step.label = step_dict["label"]
step_type = step_dict.get("type", None)
if step_type == "subworkflow":
subworkflow = self.__load_subworkflow_from_step_dict(
trans, step_dict)
step_dict["subworkflow"] = subworkflow
module = module_factory.from_dict(trans,
step_dict,
exact_tools=exact_tools)
module.save_to_step(step)
annotation = step_dict['annotation']
if annotation:
annotation = sanitize_html(annotation, 'utf-8', 'text/html')
self.add_item_annotation(trans.sa_session, trans.get_user(), step,
annotation)
# Stick this in the step temporarily
step.temp_input_connections = step_dict['input_connections']
return module, step
def __module_from_dict(self, trans, step_dict, secure):
""" Create a WorkflowStep model object and corrsponding module representing
type-specific functionality from the incoming dicitionary.
"""
step = model.WorkflowStep()
# TODO: Consider handling position inside module.
step.position = step_dict['position']
if "uuid" in step_dict:
step.uuid = step_dict["uuid"]
if "label" in step_dict:
step.label = step_dict["label"]
module = module_factory.from_dict(trans, step_dict, secure=secure)
module.save_to_step(step)
annotation = step_dict['annotation']
if annotation:
annotation = sanitize_html(annotation, 'utf-8', 'text/html')
self.add_item_annotation(trans.sa_session, trans.get_user(), step,
annotation)
# Stick this in the step temporarily
step.temp_input_connections = step_dict['input_connections']
return module, step
def __module_from_dict(self, trans, steps, steps_by_external_id, step_dict,
**kwds):
""" Create a WorkflowStep model object and corresponding module
representing type-specific functionality from the incoming dictionary.
"""
step = model.WorkflowStep()
# TODO: Consider handling position inside module.
step.position = step_dict['position']
if step_dict.get("uuid", None) and step_dict['uuid'] != "None":
step.uuid = step_dict["uuid"]
if "label" in step_dict:
step.label = step_dict["label"]
step_type = step_dict.get("type", None)
if step_type == "subworkflow":
subworkflow = self.__load_subworkflow_from_step_dict(
trans, step_dict)
step_dict["subworkflow"] = subworkflow
module = module_factory.from_dict(trans, step_dict, **kwds)
self.__set_default_label(step, module, step_dict.get('tool_state'))
module.save_to_step(step)
annotation = step_dict['annotation']
if annotation:
annotation = sanitize_html(annotation, 'utf-8', 'text/html')
self.add_item_annotation(trans.sa_session, trans.get_user(), step,
annotation)
# Stick this in the step temporarily
step.temp_input_connections = step_dict['input_connections']
# Create the model class for the step
steps.append(step)
steps_by_external_id[step_dict['id']] = step
if 'workflow_outputs' in step_dict:
workflow_outputs = step_dict['workflow_outputs']
found_output_names = set([])
for workflow_output in workflow_outputs:
# Allow workflow outputs as list of output_names for backward compatiblity.
if not isinstance(workflow_output, dict):
workflow_output = {"output_name": workflow_output}
output_name = workflow_output["output_name"]
if output_name in found_output_names:
raise exceptions.ObjectAttributeInvalidException(
"Duplicate workflow outputs with name [%s] found." %
output_name)
if not output_name:
raise exceptions.ObjectAttributeInvalidException(
"Workflow output with empty name encountered.")
found_output_names.add(output_name)
uuid = workflow_output.get("uuid", None)
label = workflow_output.get("label", None)
m = step.create_or_update_workflow_output(
output_name=output_name,
uuid=uuid,
label=label,
)
trans.sa_session.add(m)
return module, step
def test_annotations(self):
model = self.model
u = model.User(email="*****@*****.**", password="******")
self.persist(u)
def persist_and_check_annotation(annotation_class, **kwds):
annotated_association = annotation_class()
annotated_association.annotation = "Test Annotation"
annotated_association.user = u
for key, value in kwds.items():
setattr(annotated_association, key, value)
self.persist(annotated_association)
self.expunge()
stored_annotation = self.query(annotation_class).all()[0]
assert stored_annotation.annotation == "Test Annotation"
assert stored_annotation.user.email == "*****@*****.**"
sw = model.StoredWorkflow()
sw.user = u
self.persist(sw)
persist_and_check_annotation(model.StoredWorkflowAnnotationAssociation, stored_workflow=sw)
workflow = model.Workflow()
workflow.stored_workflow = sw
self.persist(workflow)
ws = model.WorkflowStep()
ws.workflow = workflow
self.persist(ws)
persist_and_check_annotation(model.WorkflowStepAnnotationAssociation, workflow_step=ws)
h = model.History(name="History for Annotation", user=u)
self.persist(h)
persist_and_check_annotation(model.HistoryAnnotationAssociation, history=h)
d1 = model.HistoryDatasetAssociation(extension="txt", history=h, create_dataset=True, sa_session=model.session)
self.persist(d1)
persist_and_check_annotation(model.HistoryDatasetAssociationAnnotationAssociation, hda=d1)
page = model.Page()
page.user = u
self.persist(page)
persist_and_check_annotation(model.PageAnnotationAssociation, page=page)
visualization = model.Visualization()
visualization.user = u
self.persist(visualization)
persist_and_check_annotation(model.VisualizationAnnotationAssociation, visualization=visualization)
dataset_collection = model.DatasetCollection(collection_type="paired")
history_dataset_collection = model.HistoryDatasetCollectionAssociation(collection=dataset_collection)
self.persist(history_dataset_collection)
persist_and_check_annotation(model.HistoryDatasetCollectionAssociationAnnotationAssociation, history_dataset_collection=history_dataset_collection)
library_dataset_collection = model.LibraryDatasetCollectionAssociation(collection=dataset_collection)
self.persist(library_dataset_collection)
persist_and_check_annotation(model.LibraryDatasetCollectionAnnotationAssociation, library_dataset_collection=library_dataset_collection)
def yaml_to_model(has_dict, id_offset=100):
if isinstance(has_dict, str):
has_dict = yaml.safe_load(has_dict)
workflow = model.Workflow()
workflow.steps = []
for i, step in enumerate(has_dict.get("steps", [])):
workflow_step = model.WorkflowStep()
if "order_index" not in step:
step["order_index"] = i
if "id" not in step:
# Fixed Offset ids just to test against assuption order_index != id
step["id"] = id_offset
id_offset += 1
step_type = step.get("type", None)
assert step_type is not None
if step_type == "subworkflow":
subworkflow_dict = step["subworkflow"]
del step["subworkflow"]
subworkflow = yaml_to_model(subworkflow_dict, id_offset=id_offset)
step["subworkflow"] = subworkflow
id_offset += len(subworkflow.steps)
for key, value in step.items():
if key == "input_connections":
raise NotImplementedError()
if key == "inputs":
inputs = []
for input_name, input_def in value.items():
step_input = model.WorkflowStepInput(workflow_step)
step_input.name = input_name
connections = []
for conn_dict in input_def.get("connections", []):
conn = model.WorkflowStepConnection()
for conn_key, conn_value in conn_dict.items():
if conn_key == "@output_step":
target_step = workflow.steps[conn_value]
conn_value = target_step
conn_key = "output_step"
if conn_key == "@input_subworkflow_step":
conn_value = step["subworkflow"].step_by_index(
conn_value)
conn_key = "input_subworkflow_step"
setattr(conn, conn_key, conn_value)
connections.append(conn)
step_input.connections = connections
inputs.append(step_input)
value = inputs
if key == "workflow_outputs":
value = [
partial(_dict_to_workflow_output, workflow_step)(_)
for _ in value
]
setattr(workflow_step, key, value)
workflow.steps.append(workflow_step)
return workflow
def test_workflow_export(self):
stored_workflow = model.StoredWorkflow()
stored_workflow.name = "My Cool Workflow"
workflow = model.Workflow()
stored_workflow.latest_workflow = workflow
workflow_step_0 = model.WorkflowStep()
workflow.steps = [workflow_step_0]
self.trans.app.workflow_manager.get_stored_accessible_workflow.return_value = stored_workflow
example = """# Example
```galaxy
workflow_display(workflow_id=1)
```
"""
result = self._to_basic(example)
assert "**Workflow:** My Cool Workflow\n" in result
assert "**Steps:**\n" in result
def test_workflows(self):
model = self.model
user = model.User(
email="*****@*****.**",
password="******"
)
def workflow_from_steps(steps):
stored_workflow = model.StoredWorkflow()
stored_workflow.user = user
workflow = model.Workflow()
workflow.steps = steps
workflow.stored_workflow = stored_workflow
return workflow
child_workflow = workflow_from_steps([])
self.persist(child_workflow)
workflow_step_1 = model.WorkflowStep()
workflow_step_1.order_index = 0
workflow_step_1.type = "data_input"
workflow_step_2 = model.WorkflowStep()
workflow_step_2.order_index = 1
workflow_step_2.type = "subworkflow"
workflow_step_2.subworkflow = child_workflow
workflow_step_1.get_or_add_input("moo1")
workflow_step_1.get_or_add_input("moo2")
workflow_step_2.get_or_add_input("moo")
workflow_step_1.add_connection("foo", "cow", workflow_step_2)
workflow = workflow_from_steps([workflow_step_1, workflow_step_2])
self.persist(workflow)
workflow_id = workflow.id
annotation = model.WorkflowStepAnnotationAssociation()
annotation.annotation = "Test Step Annotation"
annotation.user = user
annotation.workflow_step = workflow_step_1
self.persist(annotation)
assert workflow_step_1.id is not None
h1 = model.History(name="WorkflowHistory1", user=user)
invocation_uuid = uuid.uuid1()
workflow_invocation = model.WorkflowInvocation()
workflow_invocation.uuid = invocation_uuid
workflow_invocation.history = h1
workflow_invocation_step1 = model.WorkflowInvocationStep()
workflow_invocation_step1.workflow_invocation = workflow_invocation
workflow_invocation_step1.workflow_step = workflow_step_1
subworkflow_invocation = model.WorkflowInvocation()
workflow_invocation.attach_subworkflow_invocation_for_step(workflow_step_2, subworkflow_invocation)
workflow_invocation_step2 = model.WorkflowInvocationStep()
workflow_invocation_step2.workflow_invocation = workflow_invocation
workflow_invocation_step2.workflow_step = workflow_step_2
workflow_invocation.workflow = workflow
d1 = self.new_hda(h1, name="1")
workflow_request_dataset = model.WorkflowRequestToInputDatasetAssociation()
workflow_request_dataset.workflow_invocation = workflow_invocation
workflow_request_dataset.workflow_step = workflow_step_1
workflow_request_dataset.dataset = d1
self.persist(workflow_invocation)
assert workflow_request_dataset is not None
assert workflow_invocation.id is not None
history_id = h1.id
self.expunge()
loaded_invocation = self.query(model.WorkflowInvocation).get(workflow_invocation.id)
assert loaded_invocation.uuid == invocation_uuid, "%s != %s" % (loaded_invocation.uuid, invocation_uuid)
assert loaded_invocation
assert loaded_invocation.history.id == history_id
step_1, step_2 = loaded_invocation.workflow.steps
assert not step_1.subworkflow
assert step_2.subworkflow
assert len(loaded_invocation.steps) == 2
subworkflow_invocation_assoc = loaded_invocation.get_subworkflow_invocation_association_for_step(step_2)
assert subworkflow_invocation_assoc is not None
assert isinstance(subworkflow_invocation_assoc.subworkflow_invocation, model.WorkflowInvocation)
assert isinstance(subworkflow_invocation_assoc.parent_workflow_invocation, model.WorkflowInvocation)
assert subworkflow_invocation_assoc.subworkflow_invocation.history.id == history_id
loaded_workflow = self.query(model.Workflow).get(workflow_id)
assert len(loaded_workflow.steps[0].annotations) == 1
copied_workflow = loaded_workflow.copy(user=user)
annotations = copied_workflow.steps[0].annotations
assert len(annotations) == 1
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 __step(**kwds):
step = model.WorkflowStep()
for key, value in kwds.items():
setattr(step, key, value)
return step
def add_step(**kwds):
workflow_step = model.WorkflowStep()
for key, value in kwds.items():
setattr(workflow_step, key, value)
workflow.steps.append(workflow_step)
def _workflow_from_dict(self, trans, data, source=None):
"""
RPARK: copied from galaxy.webapps.galaxy.controllers.workflows.py
Creates a workflow from a dict. Created workflow is stored in the database and returned.
"""
# Put parameters in workflow mode
trans.workflow_building_mode = True
# Create new workflow from incoming dict
workflow = model.Workflow()
# If there's a source, put it in the workflow name.
if source:
name = "%s (imported from %s)" % (data['name'], source)
else:
name = data['name']
workflow.name = name
# Assume no errors until we find a step that has some
workflow.has_errors = False
# Create each step
steps = []
# The editor will provide ids for each step that we don't need to save,
# but do need to use to make connections
steps_by_external_id = {}
# Keep track of tools required by the workflow that are not available in
# the local Galaxy instance. Each tuple in the list of missing_tool_tups
# will be ( tool_id, tool_name, tool_version ).
missing_tool_tups = []
# First pass to build step objects and populate basic values
for key, step_dict in data['steps'].iteritems():
# Create the model class for the step
step = model.WorkflowStep()
steps.append(step)
steps_by_external_id[step_dict['id']] = step
# FIXME: Position should be handled inside module
step.position = step_dict['position']
module = module_factory.from_dict(trans, step_dict, secure=False)
if module.type == 'tool' and module.tool is None:
# A required tool is not available in the local Galaxy instance.
missing_tool_tup = (step_dict['tool_id'], step_dict['name'],
step_dict['tool_version'])
if missing_tool_tup not in missing_tool_tups:
missing_tool_tups.append(missing_tool_tup)
module.save_to_step(step)
if step.tool_errors:
workflow.has_errors = True
# Stick this in the step temporarily
step.temp_input_connections = step_dict['input_connections']
# Save step annotation.
#annotation = step_dict[ 'annotation' ]
#if annotation:
#annotation = sanitize_html( annotation, 'utf-8', 'text/html' )
# ------------------------------------------ #
# RPARK REMOVING: user annotation b/c of API
#self.add_item_annotation( trans.sa_session, trans.get_user(), step, annotation )
# ------------------------------------------ #
# Unpack and add post-job actions.
post_job_actions = step_dict.get('post_job_actions', {})
for name, pja_dict in post_job_actions.items():
model.PostJobAction(pja_dict['action_type'], step,
pja_dict['output_name'],
pja_dict['action_arguments'])
# Second pass to deal with connections between steps
for step in steps:
# Input connections
for input_name, conn_dict in step.temp_input_connections.iteritems(
):
if conn_dict:
conn = model.WorkflowStepConnection()
conn.input_step = step
conn.input_name = input_name
conn.output_name = conn_dict['output_name']
conn.output_step = steps_by_external_id[conn_dict['id']]
del step.temp_input_connections
# Order the steps if possible
attach_ordered_steps(workflow, steps)
# Connect up
stored = model.StoredWorkflow()
stored.name = workflow.name
workflow.stored_workflow = stored
stored.latest_workflow = workflow
stored.user = trans.user
# Persist
trans.sa_session.add(stored)
trans.sa_session.flush()
return stored, missing_tool_tups
def extract_steps(trans,
history=None,
job_ids=None,
dataset_ids=None,
dataset_collection_ids=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(id) for id in job_ids]
dataset_ids = [int(id) for id in dataset_ids]
dataset_collection_ids = [int(id) for id in dataset_collection_ids]
# Find each job, for security we (implicately) check that they are
# associated witha job in the current history.
summary = WorkflowSummary(trans, history)
jobs = summary.jobs
jobs_by_id = dict((job.id, job) for job in jobs.keys())
steps = []
steps_by_job_id = {}
hid_to_output_pair = {}
# Input dataset steps
for hid in dataset_ids:
step = model.WorkflowStep()
step.type = 'data_input'
step.tool_inputs = dict(name="Input Dataset")
hid_to_output_pair[hid] = (step, 'output')
steps.append(step)
for hid in 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]
step.tool_inputs = dict(name="Input Dataset Collection",
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 jobs_by_id:
log.warn("job_id %s not found in jobs_by_id %s" %
(job_id, jobs_by_id))
raise AssertionError(
"Attempt to create workflow with job not connected to current history"
)
job = jobs_by_id[job_id]
tool_inputs, associations = step_inputs(trans, job)
step = model.WorkflowStep()
step.type = 'tool'
step.tool_id = job.tool_id
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
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)
steps_by_job_id[job_id] = step
# Store created dataset hids
for assoc in job.output_datasets:
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:
log.warn("Failed to find matching implicit job.")
raise Exception("Failed to extract job.")
else:
hid = assoc.dataset.hid
hid_to_output_pair[hid] = (step, assoc.name)
return steps
