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Source code for galaxy.managers.workflows

from __future__ import absolute_import

import json
import logging
import os
import uuid
from collections import namedtuple

from gxformat2 import (
from six import string_types
from sqlalchemy import and_
from sqlalchemy.orm import joinedload, subqueryload

from galaxy import (
from galaxy.jobs.actions.post import ActionBox
from galaxy.model.item_attrs import UsesAnnotations
from galaxy.tools.parameters import (
from galaxy.tools.parameters.basic import (
from galaxy.util.json import safe_loads
from galaxy.util.sanitize_html import sanitize_html
from galaxy.web import url_for
from galaxy.workflow.modules import (
from galaxy.workflow.resources import get_resource_mapper_function
from galaxy.workflow.steps import attach_ordered_steps
from .base import decode_id
from .executables import artifact_class

log = logging.getLogger(__name__)

[docs]class WorkflowsManager(object): """ Handle CRUD type operations related to workflows. More interesting stuff regarding workflow execution, step sorting, etc... can be found in the galaxy.workflow module. """
[docs] def __init__(self, app): self.app = app
[docs] def get_stored_workflow(self, trans, workflow_id, by_stored_id=True): """ Use a supplied ID (UUID or encoded stored workflow ID) to find a workflow. """ if util.is_uuid(workflow_id): # see if they have passed in the UUID for a workflow that is attached to a stored workflow workflow_uuid = uuid.UUID(workflow_id) workflow_query = trans.sa_session.query(trans.app.model.StoredWorkflow).filter(and_( trans.app.model.StoredWorkflow.id == trans.app.model.Workflow.stored_workflow_id, trans.app.model.Workflow.uuid == workflow_uuid )) elif by_stored_id: workflow_id = decode_id(self.app, workflow_id) workflow_query = trans.sa_session.query(trans.app.model.StoredWorkflow).\ filter(trans.app.model.StoredWorkflow.id == workflow_id) else: workflow_id = decode_id(self.app, workflow_id) workflow_query = trans.sa_session.query(trans.app.model.StoredWorkflow).filter(and_( trans.app.model.StoredWorkflow.id == trans.app.model.Workflow.stored_workflow_id, trans.app.model.Workflow.id == workflow_id )) stored_workflow = workflow_query.options(joinedload('annotations'), joinedload('tags'), subqueryload('latest_workflow').joinedload('steps').joinedload('*')).first() if stored_workflow is None: raise exceptions.ObjectNotFound("No such workflow found.") return stored_workflow
[docs] def get_stored_accessible_workflow(self, trans, workflow_id, by_stored_id=True): """ Get a stored workflow from a encoded stored workflow id and make sure it accessible to the user. """ stored_workflow = self.get_stored_workflow(trans, workflow_id, by_stored_id=by_stored_id) # check to see if user has permissions to selected workflow if stored_workflow.user != trans.user and not trans.user_is_admin and not stored_workflow.published: if trans.sa_session.query(trans.app.model.StoredWorkflowUserShareAssociation).filter_by(user=trans.user, stored_workflow=stored_workflow).count() == 0: message = "Workflow is not owned by or shared with current user" raise exceptions.ItemAccessibilityException(message) return stored_workflow
[docs] def get_owned_workflow(self, trans, encoded_workflow_id): """ Get a workflow (non-stored) from a encoded workflow id and make sure it accessible to the user. """ workflow_id = decode_id(self.app, encoded_workflow_id) workflow = trans.sa_session.query(model.Workflow).get(workflow_id) self.check_security(trans, workflow, check_ownership=True) return workflow
[docs] def check_security(self, trans, has_workflow, check_ownership=True, check_accessible=True): """ check accessibility or ownership of workflows, storedworkflows, and workflowinvocations. Throw an exception or returns True if user has needed level of access. """ if not check_ownership and not check_accessible: return True # If given an invocation verify ownership of invocation if isinstance(has_workflow, model.WorkflowInvocation): # We use the the owner of the history that is associated to the invocation as a proxy # for the owner of the invocation. if trans.user != has_workflow.history.user and not trans.user_is_admin: raise exceptions.ItemOwnershipException() else: return True # stored workflow contains security stuff - follow that workflow to # that unless given a stored workflow. if isinstance(has_workflow, model.Workflow): stored_workflow = has_workflow.top_level_stored_workflow else: stored_workflow = has_workflow if stored_workflow.user != trans.user and not trans.user_is_admin: if check_ownership: raise exceptions.ItemOwnershipException() # else check_accessible... if trans.sa_session.query(model.StoredWorkflowUserShareAssociation).filter_by(user=trans.user, stored_workflow=stored_workflow).count() == 0: raise exceptions.ItemAccessibilityException() return True
[docs] def get_invocation(self, trans, decoded_invocation_id): workflow_invocation = trans.sa_session.query( self.app.model.WorkflowInvocation ).get(decoded_invocation_id) if not workflow_invocation: encoded_wfi_id = trans.security.encode_id(decoded_invocation_id) message = "'%s' is not a valid workflow invocation id" % encoded_wfi_id raise exceptions.ObjectNotFound(message) self.check_security(trans, workflow_invocation, check_ownership=True, check_accessible=False) return workflow_invocation
[docs] def cancel_invocation(self, trans, decoded_invocation_id): workflow_invocation = self.get_invocation(trans, decoded_invocation_id) cancelled = workflow_invocation.cancel() if cancelled: trans.sa_session.add(workflow_invocation) trans.sa_session.flush() else: # TODO: More specific exception? raise exceptions.MessageException("Cannot cancel an inactive workflow invocation.") return workflow_invocation
[docs] def get_invocation_step(self, trans, decoded_workflow_invocation_step_id): try: workflow_invocation_step = trans.sa_session.query( model.WorkflowInvocationStep ).get(decoded_workflow_invocation_step_id) except Exception: raise exceptions.ObjectNotFound() self.check_security(trans, workflow_invocation_step.workflow_invocation, check_ownership=True, check_accessible=False) return workflow_invocation_step
[docs] def update_invocation_step(self, trans, decoded_workflow_invocation_step_id, action): if action is None: raise exceptions.RequestParameterMissingException("Updating workflow invocation step requires an action parameter. ") workflow_invocation_step = self.get_invocation_step(trans, decoded_workflow_invocation_step_id) workflow_invocation = workflow_invocation_step.workflow_invocation if not workflow_invocation.active: raise exceptions.RequestParameterInvalidException("Attempting to modify the state of an completed workflow invocation.") step = workflow_invocation_step.workflow_step module = module_factory.from_workflow_step(trans, step) performed_action = module.do_invocation_step_action(step, action) workflow_invocation_step.action = performed_action trans.sa_session.add(workflow_invocation_step) trans.sa_session.flush() return workflow_invocation_step
[docs] def build_invocations_query(self, trans, stored_workflow_id=None, history_id=None, user_id=None, include_terminal=True, limit=None): """Get invocations owned by the current user.""" sa_session = trans.sa_session invocations_query = sa_session.query(model.WorkflowInvocation).order_by(model.WorkflowInvocation.table.c.id.desc()) if stored_workflow_id is not None: stored_workflow = sa_session.query(model.StoredWorkflow).get(stored_workflow_id) if not stored_workflow: raise exceptions.ObjectNotFound() invocations_query = invocations_query.join( model.Workflow ).filter( model.Workflow.table.c.stored_workflow_id == stored_workflow_id ) if user_id is not None: invocations_query = invocations_query.join( model.History ).filter( model.History.table.c.user_id == user_id ) if history_id is not None: invocations_query = invocations_query.filter( model.WorkflowInvocation.table.c.history_id == history_id ) if not include_terminal: invocations_query = invocations_query.filter( model.WorkflowInvocation.table.c.state.in_(model.WorkflowInvocation.non_terminal_states) ) if limit is not None: invocations_query = invocations_query.limit(limit) return [inv for inv in invocations_query if self.check_security(trans, inv, check_ownership=True, check_accessible=False)]
[docs] def serialize_workflow_invocation(self, invocation, **kwd): app = self.app view = kwd.get("view", "element") step_details = util.string_as_bool(kwd.get('step_details', False)) legacy_job_state = util.string_as_bool(kwd.get('legacy_job_state', False)) as_dict = invocation.to_dict(view, step_details=step_details, legacy_job_state=legacy_job_state) return app.security.encode_all_ids(as_dict, recursive=True)
[docs] def serialize_workflow_invocations(self, invocations, **kwd): if "view" not in kwd: kwd["view"] = "collection" return list(map(lambda i: self.serialize_workflow_invocation(i, **kwd), invocations))
CreatedWorkflow = namedtuple("CreatedWorkflow", ["stored_workflow", "workflow", "missing_tools"])
[docs]class WorkflowContentsManager(UsesAnnotations):
[docs] def __init__(self, app): self.app = app self._resource_mapper_function = get_resource_mapper_function(app)
[docs] def ensure_raw_description(self, dict_or_raw_description): if not isinstance(dict_or_raw_description, RawWorkflowDescription): dict_or_raw_description = RawWorkflowDescription(dict_or_raw_description) return dict_or_raw_description
[docs] def normalize_workflow_format(self, trans, as_dict): """Process incoming workflow descriptions for consumption by other methods. Currently this mostly means converting format 2 workflows into standard Galaxy workflow JSON for consumption for the rest of this module. In the future we will want to be a lot more percise about this - preserve the original description along side the data model and apply updates in a way that largely preserves YAML structure so workflows can be extracted. """ workflow_directory = None workflow_path = None if as_dict.get("src", None) == "from_path": if not trans.user_is_admin: raise exceptions.AdminRequiredException() workflow_path = as_dict.get("path") workflow_directory = os.path.normpath(os.path.dirname(workflow_path)) workflow_class, as_dict, object_id = artifact_class(trans, as_dict) if workflow_class == "GalaxyWorkflow" or "yaml_content" in as_dict: # Format 2 Galaxy workflow. galaxy_interface = Format2ConverterGalaxyInterface() import_options = ImportOptions() import_options.deduplicate_subworkflows = True as_dict = python_to_workflow(as_dict, galaxy_interface, workflow_directory=workflow_directory, import_options=import_options) return RawWorkflowDescription(as_dict, workflow_path)
[docs] def build_workflow_from_raw_description( self, trans, raw_workflow_description, source=None, add_to_menu=False, publish=False, create_stored_workflow=True, exact_tools=True, fill_defaults=False, from_tool_form=False, ): data = raw_workflow_description.as_dict # Put parameters in workflow mode trans.workflow_building_mode = workflow_building_modes.ENABLED # 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, missing_tool_tups = self._workflow_from_raw_description( trans, raw_workflow_description, name=name, exact_tools=exact_tools, fill_defaults=fill_defaults, from_tool_form=from_tool_form, ) if 'uuid' in data: workflow.uuid = data['uuid'] if create_stored_workflow: # Connect up stored = model.StoredWorkflow() stored.from_path = raw_workflow_description.workflow_path stored.name = workflow.name workflow.stored_workflow = stored stored.latest_workflow = workflow stored.user = trans.user stored.published = publish if data['annotation']: annotation = sanitize_html(data['annotation']) self.add_item_annotation(trans.sa_session, stored.user, stored, annotation) workflow_tags = data.get('tags', []) trans.app.tag_handler.set_tags_from_list(user=trans.user, item=stored, new_tags_list=workflow_tags) # Persist trans.sa_session.add(stored) if add_to_menu: if trans.user.stored_workflow_menu_entries is None: trans.user.stored_workflow_menu_entries = [] menuEntry = model.StoredWorkflowMenuEntry() menuEntry.stored_workflow = stored trans.user.stored_workflow_menu_entries.append(menuEntry) else: stored = None # Persist trans.sa_session.add(workflow) trans.sa_session.flush() return CreatedWorkflow( stored_workflow=stored, workflow=workflow, missing_tools=missing_tool_tups )
[docs] def update_workflow_from_raw_description(self, trans, stored_workflow, raw_workflow_description, **kwds): raw_workflow_description = self.ensure_raw_description(raw_workflow_description) # Put parameters in workflow mode trans.workflow_building_mode = workflow_building_modes.ENABLED workflow, missing_tool_tups = self._workflow_from_raw_description( trans, raw_workflow_description, name=stored_workflow.name, **kwds ) if missing_tool_tups: errors = [] for missing_tool_tup in missing_tool_tups: errors.append("Step %i: Requires tool '%s'." % (int(missing_tool_tup[3]) + 1, missing_tool_tup[0])) raise MissingToolsException(workflow, errors) # Connect up workflow.stored_workflow = stored_workflow stored_workflow.latest_workflow = workflow # Persist trans.sa_session.flush() if stored_workflow.from_path: self._sync_stored_workflow(trans, stored_workflow) # Return something informative errors = [] if workflow.has_errors: errors.append("Some steps in this workflow have validation errors") if workflow.has_cycles: errors.append("This workflow contains cycles") return workflow, errors
def _workflow_from_raw_description(self, trans, raw_workflow_description, name, **kwds): data = raw_workflow_description.as_dict if isinstance(data, string_types): data = json.loads(data) # Create new workflow from source data workflow = model.Workflow() workflow.name = name if 'report' in data: workflow.reports_config = data['report'] # 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 = {} # Preload dependent workflows with locally defined content_ids. subworkflows = data.get("subworkflows") subworkflow_id_map = None if subworkflows: subworkflow_id_map = {} for key, subworkflow_dict in subworkflows.items(): subworkflow = self.__build_embedded_subworkflow(trans, subworkflow_dict, **kwds) subworkflow_id_map[key] = subworkflow # 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 = [] for step_dict in self.__walk_step_dicts(data): self.__load_subworkflows(trans, step_dict, subworkflow_id_map, **kwds) for step_dict in self.__walk_step_dicts(data): module, step = self.__module_from_dict(trans, steps, steps_by_external_id, step_dict, **kwds) is_tool = is_tool_module_type(module.type) if is_tool and module.tool is None: missing_tool_tup = (module.tool_id, module.get_name(), module.tool_version, step_dict['id']) if missing_tool_tup not in missing_tool_tups: missing_tool_tups.append(missing_tool_tup) if module.get_errors(): workflow.has_errors = True # Second pass to deal with connections between steps self.__connect_workflow_steps(steps, steps_by_external_id) # Order the steps if possible attach_ordered_steps(workflow, steps) return workflow, missing_tool_tups
[docs] def workflow_to_dict(self, trans, stored, style="export", version=None): """ Export the workflow contents to a dictionary ready for JSON-ification and to be sent out via API for instance. There are three styles of export allowed 'export', 'instance', and 'editor'. The Galaxy team will do its best to preserve the backward compatibility of the 'export' style - this is the export method meant to be portable across Galaxy instances and over time. The 'editor' style is subject to rapid and unannounced changes. The 'instance' export option describes the workflow in a context more tied to the current Galaxy instance and includes fields like 'url' and 'url' and actual unencoded step ids instead of 'order_index'. """ def to_format_2(wf_dict, **kwds): return from_galaxy_native(wf_dict, None, **kwds) if version == '': version = None if version is not None: version = int(version) workflow = stored.get_internal_version(version) if style == "export": style = self.app.config.default_workflow_export_format if style == "editor": wf_dict = self._workflow_to_dict_editor(trans, stored, workflow) elif style == "legacy": wf_dict = self._workflow_to_dict_instance(stored, workflow=workflow, legacy=True) elif style == "instance": wf_dict = self._workflow_to_dict_instance(stored, workflow=workflow, legacy=False) elif style == "run": wf_dict = self._workflow_to_dict_run(trans, stored, workflow=workflow) elif style == "format2": wf_dict = self._workflow_to_dict_export(trans, stored, workflow=workflow) wf_dict = to_format_2(wf_dict) elif style == "format2_wrapped_yaml": wf_dict = self._workflow_to_dict_export(trans, stored, workflow=workflow) wf_dict = to_format_2(wf_dict, json_wrapper=True) elif style == "ga": wf_dict = self._workflow_to_dict_export(trans, stored, workflow=workflow) else: raise exceptions.RequestParameterInvalidException('Unknown workflow style [%s]' % style) if version is not None: wf_dict['version'] = version else: wf_dict['version'] = len(stored.workflows) - 1 return wf_dict
def _sync_stored_workflow(self, trans, stored_workflow): workflow_path = stored_workflow.from_path workflow = stored_workflow.latest_workflow with open(workflow_path, "w") as f: if workflow_path.endswith(".ga"): wf_dict = self._workflow_to_dict_export(trans, stored_workflow, workflow=workflow) json.dump(wf_dict, f, indent=4) else: wf_dict = self._workflow_to_dict_export(trans, stored_workflow, workflow=workflow) wf_dict = from_galaxy_native(wf_dict, None, json_wrapper=True) f.write(wf_dict["yaml_content"]) def _workflow_to_dict_run(self, trans, stored, workflow): """ Builds workflow dictionary used by run workflow form """ if len(workflow.steps) == 0: raise exceptions.MessageException('Workflow cannot be run because it does not have any steps.') if attach_ordered_steps(workflow, workflow.steps): raise exceptions.MessageException('Workflow cannot be run because it contains cycles.') trans.workflow_building_mode = workflow_building_modes.USE_HISTORY module_injector = WorkflowModuleInjector(trans) has_upgrade_messages = False step_version_changes = [] missing_tools = [] errors = {} for step in workflow.steps: try: module_injector.inject(step, steps=workflow.steps, exact_tools=False) except exceptions.ToolMissingException as e: # FIXME: if a subworkflow lacks multiple tools we report only the first missing tool if e.tool_id not in missing_tools: missing_tools.append(e.tool_id) continue if step.upgrade_messages: has_upgrade_messages = True if step.type == 'tool' or step.type is None: if step.module.version_changes: step_version_changes.extend(step.module.version_changes) step_errors = step.module.get_errors() if step_errors: errors[step.id] = step_errors if missing_tools: workflow.annotation = self.get_item_annotation_str(trans.sa_session, trans.user, workflow) raise exceptions.MessageException('Following tools missing: %s' % ', '.join(missing_tools)) workflow.annotation = self.get_item_annotation_str(trans.sa_session, trans.user, workflow) step_order_indices = {} for step in workflow.steps: step_order_indices[step.id] = step.order_index step_models = [] for step in workflow.steps: step_model = None if step.type == 'tool': incoming = {} tool = trans.app.toolbox.get_tool(step.tool_id, tool_version=step.tool_version, tool_uuid=step.tool_uuid) params_to_incoming(incoming, tool.inputs, step.state.inputs, trans.app) step_model = tool.to_json(trans, incoming, workflow_building_mode=workflow_building_modes.USE_HISTORY) step_model['post_job_actions'] = [{ 'short_str' : ActionBox.get_short_str(pja), 'action_type' : pja.action_type, 'output_name' : pja.output_name, 'action_arguments' : pja.action_arguments } for pja in step.post_job_actions] else: inputs = step.module.get_runtime_inputs(connections=step.output_connections) step_model = { 'inputs' : [input.to_dict(trans) for input in inputs.values()] } step_model['replacement_parameters'] = step.module.get_replacement_parameters(step) step_model['step_type'] = step.type step_model['step_label'] = step.label step_model['step_name'] = step.module.get_name() step_model['step_version'] = step.module.get_version() step_model['step_index'] = step.order_index step_model['output_connections'] = [{ 'input_step_index' : step_order_indices.get(oc.input_step_id), 'output_step_index' : step_order_indices.get(oc.output_step_id), 'input_name' : oc.input_name, 'output_name' : oc.output_name } for oc in step.output_connections] if step.annotations: step_model['annotation'] = step.annotations[0].annotation if step.upgrade_messages: step_model['messages'] = step.upgrade_messages step_models.append(step_model) return { 'id': trans.app.security.encode_id(stored.id), 'history_id': trans.app.security.encode_id(trans.history.id) if trans.history else None, 'name': stored.name, 'steps': step_models, 'step_version_changes': step_version_changes, 'has_upgrade_messages': has_upgrade_messages, 'workflow_resource_parameters': self._workflow_resource_parameters(trans, stored, workflow), } def _workflow_resource_parameters(self, trans, stored, workflow): """Get workflow scheduling resource parameters for this user and workflow or None if not configured. """ return self._resource_mapper_function(trans=trans, stored_workflow=stored, workflow=workflow) def _workflow_to_dict_editor(self, trans, stored, workflow, tooltip=True, is_subworkflow=False): # Pack workflow data into a dictionary and return data = {} data['name'] = workflow.name data['steps'] = {} data['upgrade_messages'] = {} data['report'] = workflow.reports_config input_step_types = set(workflow.input_step_types) # For each step, rebuild the form and encode the state for step in workflow.steps: # Load from database representation module = module_factory.from_workflow_step(trans, step, exact_tools=False) if not module: raise exceptions.MessageException('Unrecognized step type: %s' % step.type) # Load label from state of data input modules, necessary for backward compatibility self.__set_default_label(step, module, step.tool_inputs) # Fix any missing parameters upgrade_message = module.check_and_update_state() if upgrade_message: data['upgrade_messages'][step.order_index] = upgrade_message if (hasattr(module, "version_changes")) and (module.version_changes): if step.order_index in data['upgrade_messages']: data['upgrade_messages'][step.order_index][module.tool.name] = "\n".join(module.version_changes) else: data['upgrade_messages'][step.order_index] = {module.tool.name: "\n".join(module.version_changes)} # Get user annotation. annotation_str = self.get_item_annotation_str(trans.sa_session, trans.user, step) or '' config_form = module.get_config_form(step=step) # Pack attributes into plain dictionary step_dict = { 'id': step.order_index, 'type': module.type, 'label': module.label, 'content_id': module.get_content_id(), 'name': module.get_name(), 'tool_state': module.get_tool_state(), 'errors': module.get_errors(), 'inputs': module.get_all_inputs(connectable_only=True), 'outputs': module.get_all_outputs(), 'config_form': config_form, 'annotation': annotation_str, 'post_job_actions': {}, 'uuid': str(step.uuid) if step.uuid else None, 'workflow_outputs': [] } if tooltip: step_dict['tooltip'] = module.get_tooltip(static_path=url_for('/static')) # Connections input_connections = step.input_connections input_connections_type = {} multiple_input = {} # Boolean value indicating if this can be multiple if (step.type is None or step.type == 'tool') and module.tool: # Determine full (prefixed) names of valid input datasets data_input_names = {} def callback(input, prefixed_name, **kwargs): if isinstance(input, DataToolParameter) or isinstance(input, DataCollectionToolParameter): data_input_names[prefixed_name] = True multiple_input[prefixed_name] = input.multiple if isinstance(input, DataToolParameter): input_connections_type[input.name] = "dataset" if isinstance(input, DataCollectionToolParameter): input_connections_type[input.name] = "dataset_collection" visit_input_values(module.tool.inputs, module.state.inputs, callback) # post_job_actions pja_dict = {} for pja in step.post_job_actions: pja_dict[pja.action_type + pja.output_name] = dict( action_type=pja.action_type, output_name=pja.output_name, action_arguments=pja.action_arguments ) if pja.action_type == 'ChangeDatatypeAction': for output in step_dict['outputs']: if output['name'] == pja.output_name: output['force_datatype'] = [pja.action_arguments['newtype']] step_dict['post_job_actions'] = pja_dict # workflow outputs outputs = [] for output in step.unique_workflow_outputs: if output.workflow_step.type not in input_step_types: output_label = output.label output_name = output.output_name output_uuid = str(output.uuid) if output.uuid else None outputs.append({"output_name": output_name, "uuid": output_uuid, "label": output_label}) step_dict['workflow_outputs'] = outputs # Encode input connections as dictionary input_conn_dict = {} for conn in input_connections: input_type = "dataset" if conn.input_name in input_connections_type: input_type = input_connections_type[conn.input_name] conn_dict = dict(id=conn.output_step.order_index, output_name=conn.output_name, input_type=input_type) if conn.input_name in multiple_input: if conn.input_name in input_conn_dict: input_conn_dict[conn.input_name].append(conn_dict) else: input_conn_dict[conn.input_name] = [conn_dict] else: input_conn_dict[conn.input_name] = conn_dict step_dict['input_connections'] = input_conn_dict # Position step_dict['position'] = step.position # Add to return value data['steps'][step.order_index] = step_dict if is_subworkflow: data['steps'] = self._resolve_collection_type(data['steps']) return data
[docs] @staticmethod def get_step_map_over(current_step, steps): """ Given a tool step and its input steps guess that maximum level of mapping over. All data outputs of a step need to be mapped over to this level. """ max_map_over = '' for input_name, input_connections in current_step['input_connections'].items(): if isinstance(input_connections, dict): # if input does not accept multiple inputs input_connections = [input_connections] for input_value in input_connections: current_data_input = None for current_input in current_step['inputs']: if current_input['name'] == input_name: current_data_input = current_input # we've got one of the tools' input data definitions break input_step = steps[input_value['id']] for input_step_data_output in input_step['outputs']: if input_step_data_output['name'] == input_value['output_name']: collection_type = input_step_data_output.get('collection_type') # This is the defined incoming collection type, in reality there may be additional # mapping over of the workflows' data input, but this should be taken care of by the workflow editor / # outer workflow. if collection_type: if current_data_input.get('input_type') == 'dataset' and current_data_input.get('multiple'): # We reduce the innermost collection if ':' in collection_type: # more than one layer of nesting and multiple="true" input, # we consume the innermost collection collection_type = ":".join(collection_type.rsplit(':')[:-1]) else: # We've reduced a list or a pair collection_type = None elif current_data_input.get('input_type') == 'dataset_collection': current_collection_types = current_data_input['collection_types'] if not current_collection_types: # Accepts any input dataset collection, no mapping collection_type = None elif collection_type in current_collection_types: # incoming collection type is an exact match, no mapping over collection_type = None else: outer_map_over = collection_type for accepted_collection_type in current_data_input['collection_types']: # need to find the lowest level of mapping over, # for collection_type = 'list:list:list' and accepted_collection_type = ['list:list', 'list'] # it'd be outer_map_over == 'list' if collection_type.endswith(accepted_collection_type): _outer_map_over = collection_type[:-(len(accepted_collection_type) + 1)] if len(_outer_map_over.split(':')) < len(outer_map_over.split(':')): outer_map_over = _outer_map_over collection_type = outer_map_over # If there is mapping over, we're going to assume it is linked, everything else is (probably) # too hard to display in the workflow editor. With this assumption we should be able to # set the maximum mapping over level to the most deeply nested map_over if collection_type and len(collection_type.split(':')) >= len(max_map_over.split(':')): max_map_over = collection_type if max_map_over: return max_map_over return None
def _resolve_collection_type(self, steps): """ Fill in collection type for step outputs. This can either be via collection_type_source and / or "inherited" from the step's input. This information is only needed in the workflow editor. """ for order_index in sorted(steps): step = steps[order_index] if step['type'] == 'tool' and not step.get('errors'): map_over = self.get_step_map_over(step, steps) for step_data_output in step['outputs']: if step_data_output.get('collection_type_source') and step_data_output['collection_type'] is None: collection_type_source = step_data_output['collection_type_source'] for input_connection in step['input_connections'].get(collection_type_source, []): input_step = steps[input_connection['id']] for input_step_data_output in input_step['outputs']: if input_step_data_output['name'] == input_connection['output_name']: step_data_output['collection_type'] = input_step_data_output.get('collection_type') if map_over: collection_type = map_over step_data_output['collection'] = True if step_data_output.get('collection_type'): collection_type = "%s:%s" % (map_over, step_data_output['collection_type']) step_data_output['collection_type'] = collection_type return steps def _workflow_to_dict_export(self, trans, stored=None, workflow=None): """ Export the workflow contents to a dictionary ready for JSON-ification and export. """ annotation_str = "" tag_str = "" if stored is not None: annotation_str = self.get_item_annotation_str(trans.sa_session, trans.user, stored) or '' tag_str = stored.make_tag_string_list() # Pack workflow data into a dictionary and return data = {} data['a_galaxy_workflow'] = 'true' # Placeholder for identifying galaxy workflow data['format-version'] = "0.1" data['name'] = workflow.name data['annotation'] = annotation_str data['tags'] = tag_str if workflow.uuid is not None: data['uuid'] = str(workflow.uuid) data['steps'] = {} if workflow.reports_config: data['report'] = workflow.reports_config # For each step, rebuild the form and encode the state for step in workflow.steps: # Load from database representation module = module_factory.from_workflow_step(trans, step) if not module: raise exceptions.MessageException('Unrecognized step type: %s' % step.type) # Get user annotation. annotation_str = self.get_item_annotation_str(trans.sa_session, trans.user, step) or '' content_id = module.get_content_id() # Export differences for backward compatibility tool_state = module.get_export_state() # Step info step_dict = { 'id': step.order_index, 'type': module.type, 'content_id': content_id, 'tool_id': content_id, # For workflows exported to older Galaxies, # eliminate after a few years... 'tool_version': step.tool_version, 'name': module.get_name(), 'tool_state': json.dumps(tool_state), 'errors': module.get_errors(), 'uuid': str(step.uuid), 'label': step.label or None, 'annotation': annotation_str } # Add tool shed repository information and post-job actions to step dict. if module.type == 'tool': if module.tool and module.tool.tool_shed: step_dict["tool_shed_repository"] = { 'name': module.tool.repository_name, 'owner': module.tool.repository_owner, 'changeset_revision': module.tool.changeset_revision, 'tool_shed': module.tool.tool_shed } tool_representation = None dynamic_tool = step.dynamic_tool if dynamic_tool: tool_representation = dynamic_tool.value step_dict['tool_representation'] = tool_representation if util.is_uuid(step_dict['content_id']): step_dict['content_id'] = None step_dict['tool_id'] = None pja_dict = {} for pja in step.post_job_actions: pja_dict[pja.action_type + pja.output_name] = dict( action_type=pja.action_type, output_name=pja.output_name, action_arguments=pja.action_arguments) step_dict['post_job_actions'] = pja_dict if module.type == 'subworkflow': del step_dict['content_id'] del step_dict['errors'] del step_dict['tool_version'] del step_dict['tool_state'] subworkflow = step.subworkflow subworkflow_as_dict = self._workflow_to_dict_export( trans, stored=None, workflow=subworkflow ) step_dict['subworkflow'] = subworkflow_as_dict # Data inputs, legacy section not used anywhere within core input_dicts = [] step_state = module.state.inputs or {} if module.type != 'tool': name = step_state.get("name") or module.label if name: input_dicts.append({"name": name, "description": annotation_str}) for name, val in step_state.items(): input_type = type(val) if input_type == RuntimeValue: input_dicts.append({"name": name, "description": "runtime parameter for tool %s" % module.get_name()}) elif input_type == dict: # Input type is described by a dict, e.g. indexed parameters. for partval in val.values(): if type(partval) == RuntimeValue: input_dicts.append({"name": name, "description": "runtime parameter for tool %s" % module.get_name()}) step_dict['inputs'] = input_dicts # User outputs workflow_outputs_dicts = [] for workflow_output in step.unique_workflow_outputs: workflow_output_dict = dict( output_name=workflow_output.output_name, label=workflow_output.label, uuid=str(workflow_output.uuid) if workflow_output.uuid is not None else None, ) workflow_outputs_dicts.append(workflow_output_dict) step_dict['workflow_outputs'] = workflow_outputs_dicts # All step outputs step_dict['outputs'] = [] if type(module) is ToolModule: for output in module.get_data_outputs(): step_dict['outputs'].append({'name': output['name'], 'type': output['extensions'][0]}) step_in = {} for step_input in step.inputs: if step_input.default_value_set: step_in[step_input.name] = {"default": step_input.default_value} if step_in: step_dict["in"] = step_in # Connections input_connections = step.input_connections if step.type is None or step.type == 'tool': # Determine full (prefixed) names of valid input datasets data_input_names = {} def callback(input, prefixed_name, **kwargs): if isinstance(input, DataToolParameter) or isinstance(input, DataCollectionToolParameter): data_input_names[prefixed_name] = True # FIXME: this updates modules silently right now; messages from updates should be provided. module.check_and_update_state() if module.tool: # If the tool is installed we attempt to verify input values # and connections, otherwise the last known state will be dumped without modifications. visit_input_values(module.tool.inputs, module.state.inputs, callback) # Encode input connections as dictionary input_conn_dict = {} unique_input_names = set([conn.input_name for conn in input_connections]) for input_name in unique_input_names: input_conn_dicts = [] for conn in input_connections: if conn.input_name != input_name: continue input_conn = dict( id=conn.output_step.order_index, output_name=conn.output_name ) if conn.input_subworkflow_step is not None: subworkflow_step_id = conn.input_subworkflow_step.order_index input_conn["input_subworkflow_step_id"] = subworkflow_step_id input_conn_dicts.append(input_conn) input_conn_dict[input_name] = input_conn_dicts # Preserve backward compatibility. Previously Galaxy # assumed input connections would be dictionaries not # lists of dictionaries, so replace any singleton list # with just the dictionary so that workflows exported from # newer Galaxy instances can be used with older Galaxy # instances if they do no include multiple input # tools. This should be removed at some point. Mirrored # hack in _workflow_from_raw_description should never be removed so # existing workflow exports continue to function. for input_name, input_conn in dict(input_conn_dict).items(): if len(input_conn) == 1: input_conn_dict[input_name] = input_conn[0] step_dict['input_connections'] = input_conn_dict # Position step_dict['position'] = step.position # Add to return value data['steps'][step.order_index] = step_dict return data def _workflow_to_dict_instance(self, stored, workflow, legacy=True): encode = self.app.security.encode_id sa_session = self.app.model.context item = stored.to_dict(view='element', value_mapper={'id': encode}) item['name'] = workflow.name item['url'] = url_for('workflow', id=item['id']) item['owner'] = stored.user.username inputs = {} for step in workflow.input_steps: step_type = step.type step_label = step.label or step.tool_inputs.get('name') if step_label: label = step_label elif step_type == "data_input": label = "Input Dataset" elif step_type == "data_collection_input": label = "Input Dataset Collection" elif step_type == 'parameter_input': label = "Input Parameter" else: raise ValueError("Invalid step_type %s" % step_type) if legacy: index = step.id else: index = step.order_index step_uuid = str(step.uuid) if step.uuid else None inputs[index] = {'label': label, 'value': '', 'uuid': step_uuid} item['inputs'] = inputs item['annotation'] = self.get_item_annotation_str(sa_session, stored.user, stored) steps = {} steps_to_order_index = {} for step in workflow.steps: steps_to_order_index[step.id] = step.order_index for step in workflow.steps: step_id = step.id if legacy else step.order_index step_type = step.type step_dict = {'id': step_id, 'type': step_type, 'tool_id': step.tool_id, 'tool_version': step.tool_version, 'annotation': self.get_item_annotation_str(sa_session, stored.user, step), 'tool_inputs': step.tool_inputs, 'input_steps': {}} if step_type == 'subworkflow': del step_dict['tool_id'] del step_dict['tool_version'] del step_dict['tool_inputs'] step_dict['workflow_id'] = encode(step.subworkflow.id) for conn in step.input_connections: step_id = step.id if legacy else step.order_index source_id = conn.output_step_id source_step = source_id if legacy else steps_to_order_index[source_id] step_dict['input_steps'][conn.input_name] = {'source_step': source_step, 'step_output': conn.output_name} steps[step_id] = step_dict item['steps'] = steps return item def __walk_step_dicts(self, data): """ Walk over the supplied step dictionaries and return them in a way designed to preserve step order when possible. """ supplied_steps = data['steps'] # Try to iterate through imported workflow in such a way as to # preserve step order. step_indices = list(supplied_steps.keys()) try: step_indices = sorted(step_indices, key=int) except ValueError: # to defensive, were these ever or will they ever not be integers? pass discovered_labels = set() discovered_uuids = set() discovered_output_labels = set() discovered_output_uuids = set() # First pass to build step objects and populate basic values for step_index in step_indices: step_dict = supplied_steps[step_index] uuid = step_dict.get("uuid", None) if uuid and uuid != "None": if uuid in discovered_uuids: raise exceptions.DuplicatedIdentifierException("Duplicate step UUID in request.") discovered_uuids.add(uuid) label = step_dict.get("label", None) if label: if label in discovered_labels: raise exceptions.DuplicatedIdentifierException("Duplicated step label in request.") discovered_labels.add(label) if 'workflow_outputs' in step_dict: outputs = step_dict['workflow_outputs'] # outputs may be list of name (deprecated legacy behavior) # or dictionary of names to {uuid: <uuid>, label: <label>} if isinstance(outputs, dict): for output_name in outputs: output_dict = outputs[output_name] output_label = output_dict.get("label", None) if output_label: if label in discovered_output_labels: raise exceptions.DuplicatedIdentifierException("Duplicated workflow output label in request.") discovered_output_labels.add(label) output_uuid = step_dict.get("output_uuid", None) if output_uuid: if output_uuid in discovered_output_uuids: raise exceptions.DuplicatedIdentifierException("Duplicate workflow output UUID in request.") discovered_output_uuids.add(uuid) yield step_dict def __load_subworkflows(self, trans, step_dict, subworkflow_id_map, **kwds): step_type = step_dict.get("type", None) if step_type == "subworkflow": subworkflow = self.__load_subworkflow_from_step_dict( trans, step_dict, subworkflow_id_map, **kwds ) step_dict["subworkflow"] = subworkflow 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"] 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) 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) external_id = step_dict["id"] steps_by_external_id[external_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 compatibility. 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) if "in" in step_dict: for input_name, input_dict in step_dict["in"].items(): step_input = step.get_or_add_input(input_name) NO_DEFAULT_DEFINED = object() default = input_dict.get("default", NO_DEFAULT_DEFINED) if default is not NO_DEFAULT_DEFINED: step_input.default_value = default step_input.default_value_set = True return module, step def __load_subworkflow_from_step_dict(self, trans, step_dict, subworkflow_id_map, **kwds): embedded_subworkflow = step_dict.get("subworkflow", None) subworkflow_id = step_dict.get("content_id", None) if embedded_subworkflow and subworkflow_id: raise Exception("Subworkflow step defines both subworkflow and content_id, only one may be specified.") if not embedded_subworkflow and not subworkflow_id: raise Exception("Subworkflow step must define either subworkflow or content_id.") if embedded_subworkflow: subworkflow = self.__build_embedded_subworkflow(trans, embedded_subworkflow, **kwds) elif subworkflow_id_map is not None: # Interpret content_id as a workflow local thing. subworkflow = subworkflow_id_map[subworkflow_id[1:]] else: workflow_manager = WorkflowsManager(self.app) subworkflow = workflow_manager.get_owned_workflow( trans, subworkflow_id ) return subworkflow def __build_embedded_subworkflow(self, trans, data, **kwds): raw_workflow_description = self.ensure_raw_description(data) subworkflow = self.build_workflow_from_raw_description( trans, raw_workflow_description, create_stored_workflow=False, fill_defaults=kwds.get("fill_defaults", False) ).workflow return subworkflow def __connect_workflow_steps(self, steps, steps_by_external_id): """ Second pass to deal with connections between steps. Create workflow connection objects using externally specified ids using during creation or update. """ for step in steps: # Input connections for input_name, conn_list in step.temp_input_connections.items(): if not conn_list: continue if not isinstance(conn_list, list): # Older style singleton connection conn_list = [conn_list] for conn_dict in conn_list: if 'output_name' not in conn_dict or 'id' not in conn_dict: template = "Invalid connection [%s] - must be dict with output_name and id fields." message = template % conn_dict raise exceptions.MessageException(message) external_id = conn_dict['id'] if external_id not in steps_by_external_id: raise KeyError("Failed to find external id %s in %s" % (external_id, steps_by_external_id.keys())) output_step = steps_by_external_id[external_id] output_name = conn_dict["output_name"] input_subworkflow_step_index = conn_dict.get('input_subworkflow_step_id', None) step.add_connection(input_name, output_name, output_step, input_subworkflow_step_index) del step.temp_input_connections def __set_default_label(self, step, module, state): """ Previously data input modules had a `name` attribute to rename individual steps. Here, this value is transferred to the actual `label` attribute which is available for all module types, unique, and mapped to its own database column. """ if not module.label and module.type in ['data_input', 'data_collection_input']: new_state = safe_loads(state) default_label = new_state.get('name') if default_label and util.unicodify(default_label).lower() not in ['input dataset', 'input dataset collection']: step.label = module.label = default_label
[docs]class MissingToolsException(exceptions.MessageException):
[docs] def __init__(self, workflow, errors): self.workflow = workflow self.errors = errors
[docs]class RawWorkflowDescription(object):
[docs] def __init__(self, as_dict, workflow_path=None): self.as_dict = as_dict self.workflow_path = workflow_path
[docs]class Format2ConverterGalaxyInterface(ImporterGalaxyInterface):
[docs] def import_workflow(self, workflow, **kwds): raise NotImplementedError("Direct format 2 import of nested workflows is not yet implemented, use bioblend client.")