This document is for an old release 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.workflow.run_request

import logging
import uuid

from galaxy import (
from galaxy.managers import histories
from galaxy.tools.parameters.meta import expand_workflow_inputs

INPUT_STEP_TYPES = ['data_input', 'data_collection_input', 'parameter_input']

log = logging.getLogger(__name__)

[docs]class WorkflowRunConfig(object): """ Wrapper around all the ways a workflow execution can be parameterized. :param target_history: History to execute workflow in. :type target_history: galaxy.model.History. :param replacement_dict: Workflow level parameters used for renaming post job actions. :type replacement_dict: dict :param copy_inputs_to_history: Should input data parameters be copied to target_history. (Defaults to False) :type copy_inputs_to_history: bool :param inputs: Map from step ids to dict's containing HDA for these steps. :type inputs: dict :param inputs_by: How inputs maps to inputs (datasets/collections) to workflows steps - by unencoded database id ('step_id'), index in workflow 'step_index' (independent of database), or by input name for that step ('name'). :type inputs_by: str :param param_map: Override step parameters - should be dict with step id keys and tool param name-value dicts as values. :type param_map: dict """
[docs] def __init__(self, target_history, replacement_dict, copy_inputs_to_history=False, inputs=None, param_map=None, allow_tool_state_corrections=False, use_cached_job=False): self.target_history = target_history self.replacement_dict = replacement_dict self.copy_inputs_to_history = copy_inputs_to_history self.inputs = inputs or {} self.param_map = param_map or {} self.allow_tool_state_corrections = allow_tool_state_corrections self.use_cached_job = use_cached_job
def _normalize_inputs(steps, inputs, inputs_by): normalized_inputs = {} for step in steps: if step.type not in INPUT_STEP_TYPES: continue possible_input_keys = [] for inputs_by_el in inputs_by.split("|"): if inputs_by_el == "step_id": possible_input_keys.append(str(step.id)) elif inputs_by_el == "step_index": possible_input_keys.append(str(step.order_index)) elif inputs_by_el == "step_uuid": possible_input_keys.append(str(step.uuid)) elif inputs_by_el == "name": possible_input_keys.append(step.label or step.tool_inputs.get('name')) else: raise exceptions.MessageException("Workflow cannot be run because unexpected inputs_by value specified.") inputs_key = None for possible_input_key in possible_input_keys: if possible_input_key in inputs: inputs_key = possible_input_key if not inputs_key: message = "Workflow cannot be run because an expected input step '%s' has no input dataset." % step.id raise exceptions.MessageException(message) normalized_inputs[step.id] = inputs[inputs_key] return normalized_inputs def _normalize_step_parameters(steps, param_map, legacy=False, already_normalized=False): """ Take a complex param_map that can reference parameters by step_id in the new flexible way or in the old one-parameter per tep fashion or by tool id and normalize the parameters so everything is referenced by a numeric step id. """ normalized_param_map = {} for step in steps: if already_normalized: param_dict = param_map.get(str(step.order_index), {}) else: param_dict = _step_parameters(step, param_map, legacy=legacy) if param_dict: normalized_param_map[step.id] = param_dict return normalized_param_map def _step_parameters(step, param_map, legacy=False): """ Update ``step`` parameters based on the user-provided ``param_map`` dict. ``param_map`` should be structured as follows:: PARAM_MAP = {STEP_ID_OR_UUID: PARAM_DICT, ...} PARAM_DICT = {NAME: VALUE, ...} For backwards compatibility, the following (deprecated) format is also supported for ``param_map``:: PARAM_MAP = {TOOL_ID: PARAM_DICT, ...} in which case PARAM_DICT affects all steps with the given tool id. If both by-tool-id and by-step-id specifications are used, the latter takes precedence. Finally (again, for backwards compatibility), PARAM_DICT can also be specified as:: PARAM_DICT = {'param': NAME, 'value': VALUE} Note that this format allows only one parameter to be set per step. """ param_dict = param_map.get(step.tool_id, {}).copy() if legacy: param_dict.update(param_map.get(str(step.id), {})) else: param_dict.update(param_map.get(str(step.order_index), {})) step_uuid = step.uuid if step_uuid: uuid_params = param_map.get(str(step_uuid), {}) param_dict.update(uuid_params) if param_dict: if 'param' in param_dict and 'value' in param_dict: param_dict[param_dict['param']] = param_dict['value'] del param_dict['param'] del param_dict['value'] # Inputs can be nested dict, but Galaxy tool code wants nesting of keys (e.g. # cond1|moo=4 instead of cond1: {moo: 4} ). new_params = _flatten_step_params(param_dict) return new_params def _flatten_step_params(param_dict, prefix=""): # TODO: Temporary work around until tool code can process nested data # structures. This should really happen in there so the tools API gets # this functionality for free and so that repeats can be handled # properly. Also the tool code walks the tool inputs so it nows what is # a complex value object versus something that maps to child parameters # better than the hack or searching for src and id here. new_params = {} for key in list(param_dict.keys()): if prefix: effective_key = "%s|%s" % (prefix, key) else: effective_key = key value = param_dict[key] if isinstance(value, dict) and (not ('src' in value and 'id' in value) and key != "__POST_JOB_ACTIONS__"): new_params.update(_flatten_step_params(value, effective_key)) else: new_params[effective_key] = value return new_params def _get_target_history(trans, workflow, payload, param_keys=None, index=0): param_keys = param_keys or [] history_name = payload.get('new_history_name', None) history_id = payload.get('history_id', None) history_param = payload.get('history', None) if [history_name, history_id, history_param].count(None) < 2: raise exceptions.RequestParameterInvalidException("Specified workflow target history multiple ways - at most one of 'history', 'history_id', and 'new_history_name' may be specified.") if history_param: if history_param.startswith('hist_id='): history_id = history_param[8:] else: history_name = history_param if history_id: history_manager = histories.HistoryManager(trans.app) target_history = history_manager.get_owned(trans.security.decode_id(history_id), trans.user, current_history=trans.history) else: if history_name: nh_name = history_name else: nh_name = 'History from %s workflow' % workflow.name if len(param_keys) <= index: raise exceptions.MessageException("Incorrect expansion of workflow batch parameters.") ids = param_keys[index] nids = len(ids) if nids == 1: nh_name = '%s on %s' % (nh_name, ids[0]) elif nids > 1: nh_name = '%s on %s and %s' % (nh_name, ', '.join(ids[0:-1]), ids[-1]) new_history = trans.app.model.History(user=trans.user, name=nh_name) trans.sa_session.add(new_history) target_history = new_history return target_history
[docs]def build_workflow_run_configs(trans, workflow, payload): app = trans.app allow_tool_state_corrections = payload.get('allow_tool_state_corrections', False) use_cached_job = payload.get('use_cached_job', False) # Sanity checks. if len(workflow.steps) == 0: raise exceptions.MessageException("Workflow cannot be run because it does not have any steps") if workflow.has_cycles: raise exceptions.MessageException("Workflow cannot be run because it contains cycles") if 'step_parameters' in payload and 'parameters' in payload: raise exceptions.RequestParameterInvalidException("Cannot specify both legacy parameters and step_parameters attributes.") if 'inputs' in payload and 'ds_map' in payload: raise exceptions.RequestParameterInvalidException("Cannot specify both legacy ds_map and input attributes.") add_to_history = 'no_add_to_history' not in payload legacy = payload.get('legacy', False) already_normalized = payload.get('parameters_normalized', False) raw_parameters = payload.get('parameters', {}) run_configs = [] unexpanded_param_map = _normalize_step_parameters(workflow.steps, raw_parameters, legacy=legacy, already_normalized=already_normalized) expanded_params, expanded_param_keys = expand_workflow_inputs(unexpanded_param_map) for index, param_map in enumerate(expanded_params): history = _get_target_history(trans, workflow, payload, expanded_param_keys, index) inputs = payload.get('inputs', None) inputs_by = payload.get('inputs_by', None) # New default is to reference steps by index of workflow step # which is intrinsic to the workflow and independent of the state # of Galaxy at the time of workflow import. default_inputs_by = 'step_index|step_uuid' if inputs is None: # Default to legacy behavior - read ds_map and reference steps # by unencoded step id (a raw database id). inputs = payload.get('ds_map', {}) if legacy: default_inputs_by = 'step_id|step_uuid' inputs_by = inputs_by or default_inputs_by else: inputs = inputs or {} inputs_by = inputs_by or default_inputs_by if inputs or not already_normalized: normalized_inputs = _normalize_inputs(workflow.steps, inputs, inputs_by) else: # Only allow dumping IDs directly into JSON database instead of properly recording the # inputs with referential integrity if parameters are already normalized (coming from tool form). normalized_inputs = {} steps_by_id = workflow.steps_by_id # Set workflow inputs. for key, input_dict in normalized_inputs.items(): step = steps_by_id[key] if step.type == 'parameter_input': continue if 'src' not in input_dict: raise exceptions.RequestParameterInvalidException("Not input source type defined for input '%s'." % input_dict) if 'id' not in input_dict: raise exceptions.RequestParameterInvalidException("Not input id defined for input '%s'." % input_dict) if 'content' in input_dict: raise exceptions.RequestParameterInvalidException("Input cannot specify explicit 'content' attribute %s'." % input_dict) input_source = input_dict['src'] input_id = input_dict['id'] try: if input_source == 'ldda': ldda = trans.sa_session.query(app.model.LibraryDatasetDatasetAssociation).get(trans.security.decode_id(input_id)) assert trans.user_is_admin() or trans.app.security_agent.can_access_dataset(trans.get_current_user_roles(), ldda.dataset) content = ldda.to_history_dataset_association(history, add_to_history=add_to_history) elif input_source == 'ld': ldda = trans.sa_session.query(app.model.LibraryDataset).get(trans.security.decode_id(input_id)).library_dataset_dataset_association assert trans.user_is_admin() or trans.app.security_agent.can_access_dataset(trans.get_current_user_roles(), ldda.dataset) content = ldda.to_history_dataset_association(history, add_to_history=add_to_history) elif input_source == 'hda': # Get dataset handle, add to dict and history if necessary content = trans.sa_session.query(app.model.HistoryDatasetAssociation).get(trans.security.decode_id(input_id)) assert trans.user_is_admin() or trans.app.security_agent.can_access_dataset(trans.get_current_user_roles(), content.dataset) elif input_source == 'uuid': dataset = trans.sa_session.query(app.model.Dataset).filter(app.model.Dataset.uuid == input_id).first() if dataset is None: # this will need to be changed later. If federation code is avalible, then a missing UUID # could be found amoung fereration partners raise exceptions.RequestParameterInvalidException("Input cannot find UUID: %s." % input_id) assert trans.user_is_admin() or trans.app.security_agent.can_access_dataset(trans.get_current_user_roles(), dataset) content = history.add_dataset(dataset) elif input_source == 'hdca': content = app.dataset_collections_service.get_dataset_collection_instance(trans, 'history', input_id) else: raise exceptions.RequestParameterInvalidException("Unknown workflow input source '%s' specified." % input_source) if add_to_history and content.history != history: content = content.copy() if isinstance(content, app.model.HistoryDatasetAssociation): history.add_dataset(content) else: history.add_dataset_collection(content) input_dict['content'] = content except AssertionError: raise exceptions.ItemAccessibilityException("Invalid workflow input '%s' specified" % input_id) for key in set(normalized_inputs.keys()): value = normalized_inputs[key] if isinstance(value, dict) and 'content' in value: normalized_inputs[key] = value['content'] else: normalized_inputs[key] = value run_configs.append(WorkflowRunConfig( target_history=history, replacement_dict=payload.get('replacement_params', {}), inputs=normalized_inputs, param_map=param_map, allow_tool_state_corrections=allow_tool_state_corrections, use_cached_job=use_cached_job, )) return run_configs
[docs]def workflow_run_config_to_request(trans, run_config, workflow): param_types = model.WorkflowRequestInputParameter.types workflow_invocation = model.WorkflowInvocation() workflow_invocation.uuid = uuid.uuid1() workflow_invocation.history = run_config.target_history def add_parameter(name, value, type): parameter = model.WorkflowRequestInputParameter( name=name, value=value, type=type, ) workflow_invocation.input_parameters.append(parameter) steps_by_id = {} for step in workflow.steps: steps_by_id[step.id] = step serializable_runtime_state = step.module.encode_runtime_state(step.state) step_state = model.WorkflowRequestStepState() step_state.workflow_step = step log.info("Creating a step_state for step.id %s" % step.id) step_state.value = serializable_runtime_state workflow_invocation.step_states.append(step_state) if step.type == "subworkflow": subworkflow_run_config = WorkflowRunConfig( target_history=run_config.target_history, replacement_dict=run_config.replacement_dict, copy_inputs_to_history=False, use_cached_job=run_config.use_cached_job, inputs={}, param_map={}, allow_tool_state_corrections=run_config.allow_tool_state_corrections ) subworkflow_invocation = workflow_run_config_to_request( trans, subworkflow_run_config, step.subworkflow, ) workflow_invocation.attach_subworkflow_invocation_for_step( step, subworkflow_invocation, ) replacement_dict = run_config.replacement_dict for name, value in replacement_dict.items(): add_parameter( name=name, value=value, type=param_types.REPLACEMENT_PARAMETERS, ) for step_id, content in run_config.inputs.items(): workflow_invocation.add_input(content, step_id) add_parameter("copy_inputs_to_history", "true" if run_config.copy_inputs_to_history else "false", param_types.META_PARAMETERS) add_parameter("use_cached_job", "true" if run_config.use_cached_job else "false", param_types.META_PARAMETERS) return workflow_invocation
[docs]def workflow_request_to_run_config(work_request_context, workflow_invocation): param_types = model.WorkflowRequestInputParameter.types history = workflow_invocation.history replacement_dict = {} inputs = {} param_map = {} copy_inputs_to_history = None use_cached_job = False for parameter in workflow_invocation.input_parameters: parameter_type = parameter.type if parameter_type == param_types.REPLACEMENT_PARAMETERS: replacement_dict[parameter.name] = parameter.value elif parameter_type == param_types.META_PARAMETERS: if parameter.name == "copy_inputs_to_history": copy_inputs_to_history = (parameter.value == "true") if parameter.name == 'use_cached_job': use_cached_job = (parameter.value == 'true') for input_association in workflow_invocation.input_datasets: inputs[input_association.workflow_step_id] = input_association.dataset for input_association in workflow_invocation.input_dataset_collections: inputs[input_association.workflow_step_id] = input_association.dataset_collection for input_association in workflow_invocation.input_step_parameters: inputs[input_association.workflow_step_id] = input_association.parameter_value if copy_inputs_to_history is None: raise exceptions.InconsistentDatabase("Failed to find copy_inputs_to_history parameter loading workflow_invocation from database.") workflow_run_config = WorkflowRunConfig( target_history=history, replacement_dict=replacement_dict, inputs=inputs, param_map=param_map, copy_inputs_to_history=copy_inputs_to_history, use_cached_job=use_cached_job, ) return workflow_run_config
def __decode_id(trans, workflow_id, model_type="workflow"): try: return trans.security.decode_id(workflow_id) except Exception: message = "Malformed %s id ( %s ) specified, unable to decode" % (model_type, workflow_id) raise exceptions.MalformedId(message)