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This document is for an in-development version of Galaxy. You can alternatively view this page in the latest release if it exists or view the top of the latest release's documentation.

Source code for galaxy.managers.workflows

import json
import logging
import os
import uuid
from collections import namedtuple
from typing import (
    Any,
    cast,
    Dict,
    List,
    Optional,
)

from gxformat2 import (
    from_galaxy_native,
    ImporterGalaxyInterface,
    ImportOptions,
    python_to_workflow,
)
from pydantic import BaseModel
from sqlalchemy import and_
from sqlalchemy.orm import joinedload, subqueryload

from galaxy import (
    exceptions,
    model,
    util
)
from galaxy.job_execution.actions.post import ActionBox
from galaxy.model.item_attrs import UsesAnnotations
from galaxy.structured_app import MinimalManagerApp
from galaxy.tools.parameters import (
    params_to_incoming,
    visit_input_values
)
from galaxy.tools.parameters.basic import (
    DataCollectionToolParameter,
    DataToolParameter,
    RuntimeValue,
    workflow_building_modes
)
from galaxy.util.json import (
    safe_dumps,
    safe_loads,
)
from galaxy.util.sanitize_html import sanitize_html
from galaxy.web import url_for
from galaxy.workflow.modules import (
    is_tool_module_type,
    module_factory,
    ToolModule,
    WorkflowModuleInjector
)
from galaxy.workflow.refactor.execute import WorkflowRefactorExecutor
from galaxy.workflow.refactor.schema import (
    RefactorActionExecution,
    RefactorActions,
)
from galaxy.workflow.reports import generate_report
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: """ 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: MinimalManagerApp): 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: if not by_stored_id: # May have a subworkflow without attached StoredWorkflow object, this was the default prior to 20.09 release. workflow = trans.sa_session.query(trans.app.model.Workflow).get(workflow_id) stored_workflow = self.attach_stored_workflow(trans=trans, workflow=workflow) if stored_workflow: return stored_workflow 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 attach_stored_workflow(self, trans, workflow): """Attach and return stored workflow if possible.""" # Imported Subworkflows are not created with a StoredWorkflow association # To properly serialize them we do need a StoredWorkflow, so we create and attach one here. # We hide the new StoredWorkflow to avoid cluttering the default workflow view. if workflow and workflow.stored_workflow is None and self.check_security(trans, has_workflow=workflow): stored_workflow = trans.app.model.StoredWorkflow(user=trans.user, name=workflow.name, workflow=workflow, hidden=True) trans.sa_session.add(stored_workflow) trans.sa_session.flush() 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, eager=False): q = trans.sa_session.query( self.app.model.WorkflowInvocation ) if eager: q = q.options(subqueryload(self.app.model.WorkflowInvocation.steps).joinedload( 'implicit_collection_jobs').joinedload( 'jobs').joinedload( 'job').joinedload( 'input_datasets') ) workflow_invocation = q.get(decoded_invocation_id) if not workflow_invocation: encoded_wfi_id = trans.security.encode_id(decoded_invocation_id) message = f"'{encoded_wfi_id}' is not a valid workflow invocation id" raise exceptions.ObjectNotFound(message) self.check_security(trans, workflow_invocation, check_ownership=True, check_accessible=False) return workflow_invocation
[docs] def get_invocation_report(self, trans, invocation_id, **kwd): decoded_workflow_invocation_id = trans.security.decode_id(invocation_id) workflow_invocation = self.get_invocation(trans, decoded_workflow_invocation_id) generator_plugin_type = kwd.get("generator_plugin_type") runtime_report_config_json = kwd.get("runtime_report_config_json") invocation_markdown = kwd.get("invocation_markdown", None) target_format = kwd.get("format", "json") if invocation_markdown: runtime_report_config_json = {"markdown": invocation_markdown} return generate_report( trans, workflow_invocation, runtime_report_config_json=runtime_report_config_json, plugin_type=generator_plugin_type, target_format=target_format, )
[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 a 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, job_id=None, user_id=None, include_terminal=True, limit=None, offset=None, sort_by=None, sort_desc=None): """Get invocations owned by the current user.""" sa_session = trans.sa_session invocations_query = sa_session.query(model.WorkflowInvocation) 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 job_id is not None: invocations_query = invocations_query.join( model.WorkflowInvocationStep ).filter(model.WorkflowInvocationStep.table.c.job_id == job_id) if not include_terminal: invocations_query = invocations_query.filter( model.WorkflowInvocation.table.c.state.in_(model.WorkflowInvocation.non_terminal_states) ) total_matches = invocations_query.count() if sort_by: sort_column = getattr(model.WorkflowInvocation, sort_by) if sort_desc: sort_column = sort_column.desc() invocations_query = invocations_query.order_by(sort_column) else: invocations_query = invocations_query.order_by(model.WorkflowInvocation.table.c.id.desc()) if limit is not None: invocations_query = invocations_query.limit(limit) if offset is not None: invocations_query = invocations_query.offset(offset) invocations = [inv for inv in invocations_query if self.check_security(trans, inv, check_ownership=True, check_accessible=False)] return invocations, total_matches
[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: MinimalManagerApp): 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 precise 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, workflow_create_options, source=None, add_to_menu=False, hidden=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 'name' not in data: raise exceptions.RequestParameterInvalidException(f"Invalid workflow format detected [{data}]") workflow_input_name = data['name'] imported_sufix = f"(imported from {source})" if source and imported_sufix not in workflow_input_name: name = f"{workflow_input_name} {imported_sufix}" else: name = workflow_input_name workflow, missing_tool_tups = self._workflow_from_raw_description( trans, raw_workflow_description, workflow_create_options, name=name, ) if 'uuid' in data: workflow.uuid = data['uuid'] # 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 = workflow_create_options.publish stored.hidden = hidden 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) 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, workflow_update_options): raw_workflow_description = self.ensure_raw_description(raw_workflow_description) # Put parameters in workflow mode trans.workflow_building_mode = workflow_building_modes.ENABLED dry_run = workflow_update_options.dry_run workflow, missing_tool_tups = self._workflow_from_raw_description( trans, raw_workflow_description, workflow_update_options, name=stored_workflow.name, dry_run=dry_run, ) if missing_tool_tups and not workflow_update_options.allow_missing_tools: 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 if not dry_run: workflow.stored_workflow = stored_workflow stored_workflow.latest_workflow = workflow else: stored_workflow = model.StoredWorkflow() # detached stored_workflow.latest_workflow = workflow workflow.stored_workflow = stored_workflow if workflow_update_options.update_stored_workflow_attributes: update_dict = raw_workflow_description.as_dict if 'name' in update_dict: sanitized_name = sanitize_html(update_dict['name']) workflow.name = sanitized_name stored_workflow.name = sanitized_name if 'annotation' in update_dict: newAnnotation = sanitize_html(update_dict['annotation']) sa_session = None if dry_run else trans.sa_session self.add_item_annotation(sa_session, stored_workflow.user, stored_workflow, newAnnotation) # Persist if not dry_run: 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, workflow_state_resolution_options, name, **kwds): # don't commit the workflow or attach its part to the sa session - just build a # a transient model to operate on or render. dry_run = kwds.pop("dry_run", False) data = raw_workflow_description.as_dict if isinstance(data, str): 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'] workflow.license = data.get('license') workflow.creator_metadata = data.get('creator') if 'license' in data: workflow.license = data['license'] if 'creator' in data: workflow.creator_metadata = data['creator'] # Assume no errors until we find a step that has some workflow.has_errors = False # Create each step steps: List[model.WorkflowStep] = [] # 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: Dict[str, model.WorkflowStep] = {} # 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, workflow_state_resolution_options) 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): if not dry_run: self.__load_subworkflows(trans, step_dict, subworkflow_id_map, workflow_state_resolution_options, dry_run=dry_run) module_kwds = workflow_state_resolution_options.dict() module_kwds.update(kwds) # TODO: maybe drop this? for step_dict in self.__walk_step_dicts(data): module, step = self.__module_from_dict(trans, steps, steps_by_external_id, step_dict, **module_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, dry_run) # 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, history=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, history=history or trans.history) elif style == "preview": wf_dict = self._workflow_to_dict_preview(trans, 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(f'Unknown workflow style {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, history=None): """ 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 in ('tool', 'subworkflow', 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(f"Following tools missing: {', '.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: Dict[str, Any] = {} 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, history=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(history.id) if 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_to_dict_preview(self, trans, workflow): """ Builds workflow dictionary containing input labels and values. Used to create embedded workflow previews. """ 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.') # Ensure that the user has a history trans.get_history(most_recent=True, create=True) def row_for_param(input_dict, param, raw_value, other_values, prefix, step): input_dict["label"] = param.get_label() value = None if isinstance(param, DataToolParameter) or isinstance(param, DataCollectionToolParameter): if (prefix + param.name) in step.input_connections_by_name: conns = step.input_connections_by_name[prefix + param.name] if not isinstance(conns, list): conns = [conns] value_list = ["Output '%s' from Step %d." % (conn.output_name, int(conn.output_step.order_index) + 1) for conn in conns] value = ",".join(value_list) else: value = "Select at Runtime." else: value = param.value_to_display_text(raw_value) or 'Unavailable.' input_dict["value"] = value if hasattr(step, 'upgrade_messages') and step.upgrade_messages and param.name in step.upgrade_messages: input_dict["upgrade_messages"] = step.upgrade_messages[param.name] def do_inputs(inputs, values, prefix, step, other_values=None): input_dicts = [] for input in inputs.values(): input_dict = {} input_dict["type"] = input.type if input.type == "repeat": repeat_values = values[input.name] if len(repeat_values) > 0: input_dict["title"] = input.title_plural nested_input_dicts = [] for i in range(len(repeat_values)): nested_input_dict = {} index = repeat_values[i]['__index__'] nested_input_dict["title"] = "%i. %s" % (i + 1, input.title) nested_input_dict["inputs"] = do_inputs(input.inputs, repeat_values[i], f"{prefix + input.name}_{str(index)}|", step, other_values) nested_input_dicts.append(nested_input_dict) input_dict["inputs"] = nested_input_dicts elif input.type == "conditional": group_values = values[input.name] current_case = group_values['__current_case__'] new_prefix = f"{prefix + input.name}|" row_for_param(input_dict, input.test_param, group_values[input.test_param.name], other_values, prefix, step) input_dict["inputs"] = do_inputs(input.cases[current_case].inputs, group_values, new_prefix, step, other_values) elif input.type == "section": new_prefix = f"{prefix + input.name}|" group_values = values[input.name] input_dict["title"] = input.title input_dict["inputs"] = do_inputs(input.inputs, group_values, new_prefix, step, other_values) else: row_for_param(input_dict, input, values[input.name], other_values, prefix, step) input_dicts.append(input_dict) return input_dicts step_dicts = [] for step in workflow.steps: module_injector = WorkflowModuleInjector(trans) step_dict = {} step_dict["order_index"] = step.order_index if hasattr(step, "annotation") and step.annotation is not None: step_dict["annotation"] = step.annotation try: module_injector.inject(step, steps=workflow.steps, exact_tools=False) except exceptions.ToolMissingException as e: step_dict["label"] = f"Unknown Tool with id '{e.tool_id}'" step_dicts.append(step_dict) continue if step.type == 'tool' or step.type is None: tool = trans.app.toolbox.get_tool(step.tool_id) if tool: step_dict["label"] = step.label or tool.name else: step_dict["label"] = f"Unknown Tool with id '{step.tool_id}'" step_dict["inputs"] = do_inputs(tool.inputs, step.state.inputs, "", step) elif step.type == 'subworkflow': step_dict["label"] = step.label or (step.subworkflow.name if step.subworkflow else "Missing workflow.") errors = step.module.get_errors() if errors: step_dict["errors"] = errors subworkflow_dict = self._workflow_to_dict_preview(trans, step.subworkflow) step_dict["inputs"] = subworkflow_dict["steps"] else: module = step.module step_dict["label"] = module.name step_dict["inputs"] = do_inputs(module.get_runtime_inputs(), step.state.inputs, "", step) step_dicts.append(step_dict) return { "steps": step_dicts, } 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 or {} data['license'] = workflow.license data['creator'] = workflow.creator_metadata data['annotation'] = self.get_item_annotation_str(trans.sa_session, trans.user, stored) or '' output_label_index = set() 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(f'Unrecognized step type: {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_dict = module.check_and_update_state() or {} if hasattr(module, "version_changes") and module.version_changes: upgrade_message_dict[module.get_name()] = "\n".join(module.version_changes) # Get user annotation. config_form = module.get_config_form(step=step) annotation_str = self.get_item_annotation_str(trans.sa_session, trans.user, step) or '' # 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 ) step_dict['post_job_actions'] = pja_dict # workflow outputs outputs = [] output_label_duplicate = set() 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}) if output_label is not None: if output_label in output_label_index: if output_label not in output_label_duplicate: output_label_duplicate.add(output_label) else: output_label_index.add(output_label) step_dict['workflow_outputs'] = outputs if len(output_label_duplicate) > 0: output_label_duplicate_string = ", ".join(output_label_duplicate) upgrade_message_dict['output_label_duplicate'] = f"Ignoring duplicate labels: {output_label_duplicate_string}." if upgrade_message_dict: data['upgrade_messages'][step.order_index] = upgrade_message_dict # Encode input connections as dictionary input_conn_dict: model.InputConnDictType = {} 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: cast(list, 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 if current_data_input is None: log.info(f"failed to find input {input_name} for get_step_map_over") continue 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 = f"{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, internal=False): """ Export the workflow contents to a dictionary ready for JSON-ification and export. If internal, use content_ids instead subworkflow definitions. """ annotation_str = "" tag_str = "" if stored is not None: if stored.id: annotation_str = self.get_item_annotation_str(trans.sa_session, trans.user, stored) or '' tag_str = stored.make_tag_string_list() else: # dry run with flushed workflow objects, just use the annotation annotations = stored.annotations if annotations and len(annotations) > 0: annotation_str = util.unicodify(annotations[0].annotation) # Pack workflow data into a dictionary and return data: Dict[str, Any] = {} 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) steps: Dict[int, Dict[str, Any]] = {} data['steps'] = steps if workflow.reports_config: data['report'] = workflow.reports_config if workflow.creator_metadata: data['creator'] = workflow.creator_metadata if workflow.license: data['license'] = workflow.license # 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(f'Unrecognized step type: {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' and not internal: 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": f"runtime parameter for tool {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": f"runtime parameter for tool {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: Dict[str, List[Dict[str, Any]]] = {} unique_input_names = {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. back_compat_input_conn_dict: model.InputConnDictType = {} for input_name, input_conn_list in dict(input_conn_dict).items(): if len(input_conn_list) == 1: back_compat_input_conn_dict[input_name] = input_conn_list[0] else: back_compat_input_conn_dict[input_name] = input_conn_list step_dict['input_connections'] = back_compat_input_conn_dict # Position step_dict['position'] = step.position # Add to return value 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(f"Invalid step_type {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) item['license'] = workflow.license item['creator'] = workflow.creator_metadata 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(f"Duplicate step UUID '{uuid}' in request.") discovered_uuids.add(uuid) label = step_dict.get("label", None) if label: if label in discovered_labels: raise exceptions.DuplicatedIdentifierException(f"Duplicated step label '{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(f"Duplicated workflow output label '{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(f"Duplicate workflow output UUID '{output_uuid}' in request.") discovered_output_uuids.add(uuid) yield step_dict def __load_subworkflows(self, trans, step_dict, subworkflow_id_map, workflow_state_resolution_options, dry_run=False): step_type = step_dict.get("type", None) if step_type == "subworkflow": subworkflow = self.__load_subworkflow_from_step_dict( trans, step_dict, subworkflow_id_map, workflow_state_resolution_options, dry_run=dry_run ) step_dict["subworkflow"] = subworkflow def __module_from_dict(self, trans, steps: List[model.WorkflowStep], steps_by_external_id: Dict[str, model.WorkflowStep], step_dict, **kwds): """ Create a WorkflowStep model object and corresponding module representing type-specific functionality from the incoming dictionary. """ dry_run = kwds.get("dry_run", False) step = model.WorkflowStep() step.position = step_dict.get('position', model.WorkflowStep.DEFAULT_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, detached=dry_run, **kwds) self.__set_default_label(step, module, step_dict.get('tool_state')) module.save_to_step(step, detached=dry_run) annotation = step_dict.get('annotation') if annotation: annotation = sanitize_html(annotation) sa_session = None if dry_run else trans.sa_session self.add_item_annotation(sa_session, trans.get_user(), step, annotation) # Stick this in the step temporarily step.temp_input_connections = step_dict.get('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(f"Duplicate workflow outputs with name [{output_name}] found.") 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, ) if not dry_run: 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 if dry_run and step in trans.sa_session: trans.sa_session.expunge(step) return module, step def __load_subworkflow_from_step_dict(self, trans, step_dict, subworkflow_id_map, workflow_state_resolution_options, dry_run=False): 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: assert not dry_run subworkflow = self.__build_embedded_subworkflow(trans, embedded_subworkflow, workflow_state_resolution_options) elif subworkflow_id_map is not None: assert not dry_run # Interpret content_id as a workflow local thing. subworkflow = subworkflow_id_map[subworkflow_id[1:]] else: subworkflow = self.app.workflow_manager.get_owned_workflow( trans, subworkflow_id ) return subworkflow def __build_embedded_subworkflow(self, trans, data, workflow_state_resolution_options): raw_workflow_description = self.ensure_raw_description(data) subworkflow = self.build_workflow_from_raw_description( trans, raw_workflow_description, workflow_state_resolution_options, hidden=True ).workflow return subworkflow def __connect_workflow_steps(self, steps: List[model.WorkflowStep], steps_by_external_id, dry_run: bool) -> None: """ 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 if step.temp_input_connections: # populated by __module_from_dict 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(f"Failed to find external id {external_id} in {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) if dry_run: input_subworkflow_step_index = None step.add_connection(input_name, output_name, output_step, input_subworkflow_step_index) step.temp_input_connections = None 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] def do_refactor(self, trans, stored_workflow, refactor_request): """Apply supplied actions to stored_workflow.latest_workflow to build a new version. """ workflow = stored_workflow.latest_workflow as_dict = self._workflow_to_dict_export(trans, stored_workflow, workflow=workflow, internal=True) raw_workflow_description = self.normalize_workflow_format(trans, as_dict) workflow_update_options = WorkflowUpdateOptions( fill_defaults=False, allow_missing_tools=True, dry_run=refactor_request.dry_run, ) module_injector = WorkflowModuleInjector(trans) refactor_executor = WorkflowRefactorExecutor(raw_workflow_description, workflow, module_injector) action_executions = refactor_executor.refactor(refactor_request) refactored_workflow, errors = self.update_workflow_from_raw_description( trans, stored_workflow, raw_workflow_description, workflow_update_options, ) # errors could be three things: # - we allow missing tools so it won't be that. # - might have cycles or might have state validation issues, but it still saved... # so this is really more of a warning - we disregard it the other two places # it is used also. These same messages will appear in the dictified version we # we send back anyway return refactored_workflow, action_executions
[docs] def refactor(self, trans, stored_workflow, refactor_request): refactored_workflow, action_executions = self.do_refactor(trans, stored_workflow, refactor_request) return RefactorResponse( action_executions=action_executions, workflow=self.workflow_to_dict(trans, refactored_workflow.stored_workflow, style=refactor_request.style), dry_run=refactor_request.dry_run, )
[docs] def get_all_tools(self, workflow): tools = [] for step in workflow.steps: if step.type == 'tool': if step.tool_id: if {"tool_id": step.tool_id, "tool_version": step.tool_version} not in tools: tools.append({"tool_id": step.tool_id, "tool_version": step.tool_version}) elif step.type == "subworkflow": tools.extend(self.get_all_tools(step.subworkflow)) return tools
[docs]class RefactorRequest(RefactorActions): style: str = "export"
[docs]class RefactorResponse(BaseModel): action_executions: List[RefactorActionExecution] workflow: dict dry_run: bool
[docs] class Config: # Workflows have dictionaries with integer keys, which pydantic doesn't coerce to strings. # Integer object keys aren't valid JSON, so the client fails. json_dumps = safe_dumps
[docs]class WorkflowStateResolutionOptions(BaseModel): # fill in default tool state when updating, may change tool_state fill_defaults: bool = False # If True, assume all tool state coming from generated form instead of potentially simpler json stored in DB/exported from_tool_form: bool = False # If False, allow running with less exact tool versions exact_tools: bool = True
[docs]class WorkflowUpdateOptions(WorkflowStateResolutionOptions): # Only used internally, don't set. If using the API assume updating the workflows # representation with name or annotation for instance, updates the corresponding # stored workflow update_stored_workflow_attributes: bool = True allow_missing_tools: bool = False dry_run: bool = False
# Workflow update options but with some different defaults - we allow creating # workflows with missing tools by default but not updating.
[docs]class WorkflowCreateOptions(WorkflowStateResolutionOptions): import_tools: bool = False publish: bool = False # true or false, effectively defaults to ``publish`` if None/unset importable: Optional[bool] = None # following are install options, only used if import_tools is true install_repository_dependencies: bool = False install_resolver_dependencies: bool = False install_tool_dependencies: bool = False new_tool_panel_section_label: str = '' tool_panel_section_id: str = '' tool_panel_section_mapping: Dict = {} shed_tool_conf: Optional[str] = None @property def is_importable(self): # if self.importable is None, use self.publish that has a default. if self.importable is None: return self.publish else: return self.importable @property def install_options(self): return { 'install_repository_dependencies': self.install_repository_dependencies, 'install_resolver_dependencies': self.install_resolver_dependencies, 'install_tool_dependencies': self.install_tool_dependencies, 'new_tool_panel_section_label': self.new_tool_panel_section_label, 'tool_panel_section_id': self.tool_panel_section_id, 'tool_panel_section_mapping': self.tool_panel_section_mapping, 'shed_tool_conf': self.shed_tool_conf }
[docs]class MissingToolsException(exceptions.MessageException):
[docs] def __init__(self, workflow, errors): self.workflow = workflow self.errors = errors
[docs]class RawWorkflowDescription:
[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.")