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Source code for galaxy.workflow.modules

Modules used in building workflows

import json
import logging
import re
from collections import defaultdict
from typing import (

from typing_extensions import TypedDict

from galaxy import (
from galaxy.exceptions import (
from galaxy.job_execution.actions.post import ActionBox
from galaxy.model import (
from galaxy.model.base import ensure_object_added_to_session
from galaxy.model.dataset_collections import matching
from galaxy.schema.invocation import (
from galaxy.tool_util.cwl.util import set_basename_and_derived_properties
from galaxy.tool_util.parser.output_objects import ToolExpressionOutput
from galaxy.tools import (
from galaxy.tools.actions import filter_output
from galaxy.tools.execute import (
from galaxy.tools.expressions import do_eval
from galaxy.tools.parameters import (
from galaxy.tools.parameters.basic import (
from galaxy.tools.parameters.grouping import (
from galaxy.tools.parameters.history_query import HistoryQuery
from galaxy.tools.parameters.workflow_utils import (
from galaxy.tools.parameters.wrapped import make_dict_copy
from galaxy.util import (
from galaxy.util.bunch import Bunch
from galaxy.util.json import safe_loads
from galaxy.util.rules_dsl import RuleSet
from galaxy.util.template import fill_template
from galaxy.util.tool_shed.common_util import get_tool_shed_url_from_tool_shed_registry

    from galaxy.schema.invocation import InvocationMessageUnion
    from galaxy.workflow.run import WorkflowProgress

log = logging.getLogger(__name__)

# Key into Tool state to describe invocation-specific runtime properties.
# Key into step runtime state dict describing invocation-specific post job
# actions (i.e. PJA specified at runtime on top of the workflow-wide defined
# ones.

[docs]class NoReplacement: def __str__(self): return "NO_REPLACEMENT singleton"
NO_REPLACEMENT = NoReplacement()
[docs]class ConditionalStepWhen(BooleanToolParameter): pass
[docs]def to_cwl(value, hda_references, step): element_identifier = None if isinstance(value, model.HistoryDatasetCollectionAssociation): value = value.collection if isinstance(value, model.DatasetCollectionElement) and value.hda: element_identifier = value.element_identifier value = value.hda if isinstance(value, model.HistoryDatasetAssociation): # I think the following two checks are needed but they may # not be needed. if not value.dataset.in_ready_state(): why = f"dataset [{value.id}] is needed for valueFrom expression and is non-ready" raise DelayedWorkflowEvaluation(why=why) if not value.is_ok: raise FailWorkflowEvaluation( why=InvocationFailureDatasetFailed( reason=FailureReason.dataset_failed, hda_id=value.id, workflow_step_id=step.id ) ) if value.ext == "expression.json": with open(value.get_file_name()) as f: # OUR safe_loads won't work, will not load numbers, etc... return json.load(f) else: hda_references.append(value) properties = { "class": "File", "location": "step_input://%d" % len(hda_references), } set_basename_and_derived_properties( properties, value.dataset.created_from_basename or element_identifier or value.name ) return properties elif isinstance(value, model.DatasetCollection): if value.collection_type == "list": return [to_cwl(dce, hda_references=hda_references, step=step) for dce in value.dataset_elements] else: # Could be record or nested lists rval = {} for element in value.elements: rval[element.element_identifier] = to_cwl( element.element_object, hda_references=hda_references, step=step ) return rval elif isinstance(value, list): return [to_cwl(v, hda_references=hda_references, step=step) for v in value] elif is_runtime_value(value): return None elif isinstance(value, dict): # Nested tool state, such as conditionals return {k: to_cwl(v, hda_references=hda_references, step=step) for k, v in value.items()} else: return value
[docs]def from_cwl(value, hda_references, progress: "WorkflowProgress"): # TODO: turn actual files into HDAs here ... somehow I suppose. Things with # file:// locations for instance. if isinstance(value, dict) and "class" in value and "location" in value: if value["class"] == "File": # This is going to re-file -> HDA this each iteration I think, not a good # implementation. return progress.raw_to_galaxy(value) assert value["location"].startswith("step_input://"), f"Invalid location {value}" return hda_references[int(value["location"][len("step_input://") :]) - 1] elif isinstance(value, dict): raise NotImplementedError() else: return value
[docs]def evaluate_value_from_expressions(progress, step, execution_state, extra_step_state): when_expression = step.when_expression value_from_expressions = {} if execution_state: for key in execution_state.inputs.keys(): step_input = step.inputs_by_name.get(key) if step_input and step_input.value_from is not None: value_from_expressions[key] = step_input.value_from if not value_from_expressions and when_expression is None: return {} hda_references: List[model.HistoryDatasetAssociation] = [] step_state = {} for key, value in extra_step_state.items(): step_state[key] = to_cwl(value, hda_references=hda_references, step=step) if execution_state: for key, value in execution_state.inputs.items(): step_state[key] = to_cwl(value, hda_references=hda_references, step=step) if when_expression is not None: if when_expression == "${inputs.when}": # Fallback for workflows defined on 23.0 when_expression = "$(inputs.when)" try: as_cwl_value = do_eval( when_expression, step_state, ) except Exception: # Exception contains script and traceback, which could be helpful for debugging workflows, # but both could conceivably contain secrets. # CWL has a secret hint that should cause values to be sanitized, # but Galaxy does not, so we can't really display anything here at this point. # In any case I believe the CWL secret hint can be bypassed if the value is passed on # to another step input that doesn't have the secret set. # Complicated stuff, ignore for now. raise FailWorkflowEvaluation( InvocationFailureExpressionEvaluationFailed( reason=FailureReason.expression_evaluation_failed, workflow_step_id=step.id ) ) when_value = from_cwl(as_cwl_value, hda_references=hda_references, progress=progress) if not isinstance(when_value, bool): raise FailWorkflowEvaluation( InvocationFailureWhenNotBoolean( reason=FailureReason.when_not_boolean, workflow_step_id=step.id, details=f"Type is: {when_value.__class__.__name__}", ) ) return when_value
[docs]class WorkflowModule: label: str type: str name: str
[docs] def __init__(self, trans, content_id=None, **kwds): self.trans = trans self.content_id = content_id self.state = DefaultToolState()
# ---- Creating modules from various representations ---------------------
[docs] @classmethod def from_dict(Class, trans, d, **kwds): module = Class(trans, **kwds) input_connections = d.get("input_connections", {}) module.recover_state(d.get("tool_state"), input_connections=input_connections, **kwds) module.label = d.get("label") return module
[docs] @classmethod def from_workflow_step(Class, trans, step, **kwds): module = Class(trans, **kwds) module.recover_state(step.tool_inputs, from_tool_form=False) module.label = step.label return module
# ---- Saving in various forms ------------------------------------------
[docs] def save_to_step(self, step, detached=False): step.type = self.type step.tool_inputs = self.get_state()
# ---- General attributes -----------------------------------------------
[docs] def get_type(self): return self.type
[docs] def get_name(self): return self.name
[docs] def get_version(self): return None
[docs] def get_content_id(self): """If this component has an identifier external to the step (such as a tool or another workflow) return the identifier for that content. """ return None
[docs] def get_tooltip(self, static_path=""): return None
# ---- Configuration time -----------------------------------------------
[docs] def get_state(self, nested=True): """Return a serializable representation of the persistable state of the step. """ if inputs := self.get_inputs(): return self.state.encode(Bunch(inputs=inputs), self.trans.app, nested=nested) else: return self.state.inputs
[docs] def get_export_state(self): return self.get_state(nested=True)
[docs] def get_tool_state(self): return self.get_state(nested=False)
[docs] def recover_state(self, state, **kwds): """Recover tool state (as self.state and self.state.inputs) from dictionary describing configuration state (potentially from persisted step state). Sub-classes should supply a `default_state` method which contains the initial state `dict` with key, value pairs for all available attributes. Input parameter state may be a typed dictionary or a tool state dictionary generated by the web client (if from_tool_form in kwds is True). If classes need to distinguish between typed clean dictionary representations of state and the state generated for tool form (with extra nesting logic in the state for conditionals, un-typed string parameters, etc...) they should override the step_state_to_tool_state method to make this transformation. """ from_tool_form = kwds.get("from_tool_form", False) if not from_tool_form: # I've never seen state here be none except for unit tests so 'or {}' below may only be # needed due to test bugs. Doesn't hurt anything though. state = self.step_state_to_tool_state(state or {}) self.state = DefaultToolState() if inputs := self.get_inputs(): self.state.decode(state, Bunch(inputs=inputs), self.trans.app) else: self.state.inputs = safe_loads(state) or {}
[docs] def step_state_to_tool_state(self, state): return state
[docs] def get_errors(self, **kwargs): """This returns a step related error message as string or None""" return None
[docs] def get_inputs(self): """This returns inputs displayed in the workflow editor""" return {}
[docs] def get_all_inputs(self, data_only=False, connectable_only=False): return []
[docs] def get_data_inputs(self): """Get configure time data input descriptions.""" return self.get_all_inputs(data_only=True)
[docs] def get_all_outputs(self, data_only=False): return []
[docs] def get_data_outputs(self): return self.get_all_outputs(data_only=True)
[docs] def get_post_job_actions(self, incoming): return {}
[docs] def check_and_update_state(self): """ If the state is not in sync with the current implementation of the module, try to update. Returns a list of messages to be displayed """
[docs] def add_dummy_datasets(self, connections=None, steps=None): """Replace connected inputs with placeholder/dummy values."""
[docs] def get_config_form(self, step=None): """Serializes input parameters of a module into input dictionaries.""" return {"title": self.name, "inputs": [param.to_dict(self.trans) for param in self.get_inputs().values()]}
# ---- Run time ---------------------------------------------------------
[docs] def get_runtime_state(self) -> DefaultToolState: raise TypeError("Abstract method")
[docs] def get_runtime_inputs(self, step, connections: Optional[Iterable[WorkflowStepConnection]] = None): """Used internally by modules and when displaying inputs in workflow editor and run workflow templates. """ return {}
[docs] def compute_runtime_state(self, trans, step=None, step_updates=None): """Determine the runtime state (potentially different from self.state which describes configuration state). This (again unlike self.state) is currently always a `DefaultToolState` object. If `step` is not `None`, it will be used to search for default values defined in workflow input steps. If `step_updates` is `None`, this is likely for rendering the run form for instance and no runtime properties are available and state must be solely determined by the default runtime state described by the step. If `step_updates` are available they describe the runtime properties supplied by the workflow runner. """ state = self.get_runtime_state() step_errors = {} if step is not None: def update_value(input, context, prefixed_name, **kwargs): step_input = step.get_input(prefixed_name) if step_input is None: return NO_REPLACEMENT if step_input.default_value_set: return step_input.default_value return NO_REPLACEMENT visit_input_values( self.get_runtime_inputs(step, connections=step.output_connections), state.inputs, update_value, no_replacement_value=NO_REPLACEMENT, ) if step_updates: def update_value(input, context, prefixed_name, **kwargs): if prefixed_name in step_updates: value, error = check_param(trans, input, step_updates.get(prefixed_name), context) if error is not None: step_errors[prefixed_name] = error return value return NO_REPLACEMENT visit_input_values( self.get_runtime_inputs(step), state.inputs, update_value, no_replacement_value=NO_REPLACEMENT ) return state, step_errors
[docs] def encode_runtime_state(self, step, runtime_state): """Takes the computed runtime state and serializes it during run request creation.""" return runtime_state.encode(Bunch(inputs=self.get_runtime_inputs(step)), self.trans.app)
[docs] def decode_runtime_state(self, step, runtime_state): """Takes the serialized runtime state and decodes it when running the workflow.""" state = DefaultToolState() state.decode(runtime_state, Bunch(inputs=self.get_runtime_inputs(step)), self.trans.app) return state
[docs] def execute( self, trans, progress: "WorkflowProgress", invocation_step, use_cached_job: bool = False ) -> Optional[bool]: """Execute the given workflow invocation step. Use the supplied workflow progress object to track outputs, find inputs, etc.... Return a False if there is additional processing required to on subsequent workflow scheduling runs, None or True means the workflow step executed properly. """ raise TypeError("Abstract method")
[docs] def do_invocation_step_action(self, step, action): """Update or set the workflow invocation state action - generic extension point meant to allows users to interact with interactive workflow modules. The action object returned from this method will be attached to the WorkflowInvocationStep and be available the next time the workflow scheduler visits the workflow. """ raise exceptions.RequestParameterInvalidException( "Attempting to perform invocation step action on module that does not support actions." )
[docs] def recover_mapping(self, invocation_step, progress): """Re-populate progress object with information about connections from previously executed steps recorded via invocation_steps. """ outputs = {} for output_dataset_assoc in invocation_step.output_datasets: outputs[output_dataset_assoc.output_name] = output_dataset_assoc.dataset for output_dataset_collection_assoc in invocation_step.output_dataset_collections: outputs[output_dataset_collection_assoc.output_name] = output_dataset_collection_assoc.dataset_collection progress.set_step_outputs(invocation_step, outputs, already_persisted=True)
[docs] def get_informal_replacement_parameters(self, step) -> List[str]: """Return a list of informal replacement parameters. If replacement is handled via formal workflow inputs - do not include it in this list. """ return []
[docs] def compute_collection_info(self, progress: "WorkflowProgress", step, all_inputs): """ Use get_all_inputs (if implemented) to determine collection mapping for execution. """ collections_to_match = self._find_collections_to_match(progress, step, all_inputs) # Have implicit collections... collection_info = self.trans.app.dataset_collection_manager.match_collections(collections_to_match) if collection_info: if progress.subworkflow_collection_info: # We've mapped over a subworkflow. Slices of the invocation might be conditional # and progress.subworkflow_collection_info.when_values holds the appropriate when_values collection_info.when_values = progress.subworkflow_collection_info.when_values else: # The invocation is not mapped over, but it might still be conditional. # Multiplication and linking should be handled by slice_collection() collection_info.when_values = progress.when_values return collection_info or progress.subworkflow_collection_info
def _find_collections_to_match(self, progress: "WorkflowProgress", step, all_inputs) -> matching.CollectionsToMatch: collections_to_match = matching.CollectionsToMatch() dataset_collection_type_descriptions = self.trans.app.dataset_collection_manager.collection_type_descriptions for input_dict in all_inputs: name = input_dict["name"] data = progress.replacement_for_input(self.trans, step, input_dict) can_map_over = hasattr(data, "collection") # and data.collection.allow_implicit_mapping if not can_map_over: continue is_data_param = input_dict["input_type"] == "dataset" is_data_collection_param = input_dict["input_type"] == "dataset_collection" if is_data_param or is_data_collection_param: multiple = input_dict["multiple"] if is_data_param: if multiple: # multiple="true" data input, acts like "list" collection_type. effective_input_collection_type = ["list"] else: collections_to_match.add(name, data) continue else: effective_input_collection_type = input_dict.get("collection_types") if not effective_input_collection_type: if progress.subworkflow_structure: effective_input_collection_type = [ progress.subworkflow_structure.collection_type_description.collection_type ] elif input_dict.get("collection_type"): effective_input_collection_type = [input_dict.get("collection_type")] type_list = [] if progress.subworkflow_structure: # If we have progress.subworkflow_structure were mapping a subworkflow invocation over a higher-dimension input # e.g an outer list:list over an inner list. Whatever we do, the inner workflow cannot reduce the outer list. # This is what we're setting up here. type_list = progress.subworkflow_structure.collection_type_description.collection_type.split(":") leaf_type = type_list.pop(0) if type_list and type_list[-1] == effective_input_collection_type: effective_input_collection_type = [":".join(type_list[:-1])] if ":".join(type_list[:-1]) else [] history_query = HistoryQuery.from_collection_types( effective_input_collection_type, dataset_collection_type_descriptions, ) subcollection_type_description = history_query.can_map_over(data) or None if subcollection_type_description: subcollection_type_list = subcollection_type_description.collection_type.split(":") for collection_type in reversed(subcollection_type_list): if type_list: leaf_type = type_list.pop(0) assert collection_type == leaf_type if type_list: subcollection_type_description = dataset_collection_type_descriptions.for_collection_type( ":".join(type_list) ) collections_to_match.add(name, data, subcollection_type=subcollection_type_description) elif is_data_param and progress.subworkflow_structure: collections_to_match.add(name, data, subcollection_type=subcollection_type_description) continue if data is not NO_REPLACEMENT: collections_to_match.add(name, data) continue known_input_names = {input_dict["name"] for input_dict in all_inputs} if step.when_expression: for step_input in step.inputs: step_input_name = step_input.name input_in_execution_state = step_input_name not in known_input_names if input_in_execution_state: maybe_collection = progress.replacement_for_connection( step.input_connections_by_name[step_input_name][0] ) if hasattr(maybe_collection, "collection"): # Is that always right ? collections_to_match.add(step_input_name, maybe_collection) return collections_to_match
[docs]class SubWorkflowModule(WorkflowModule): # Two step improvements to build runtime inputs for subworkflow modules # - First pass verify nested workflow doesn't have an RuntimeInputs # - Second pass actually turn RuntimeInputs into inputs if possible. type = "subworkflow" name = "Subworkflow" _modules: Optional[List[Any]] = None subworkflow: Workflow
[docs] def __init__(self, trans, content_id=None, **kwds): super().__init__(trans, content_id, **kwds) self.post_job_actions: Optional[Dict[str, Any]] = None
[docs] @classmethod def from_dict(Class, trans, d, **kwds): module = super().from_dict(trans, d, **kwds) if "subworkflow" in d: detached = kwds.get("detached", False) assert not detached # dry run requires content_id module.subworkflow = d["subworkflow"] elif "content_id" in d: module.subworkflow = trans.app.workflow_manager.get_owned_workflow(trans, d["content_id"]) else: raise Exception("Step associated subworkflow could not be found.") return module
[docs] @classmethod def from_workflow_step(Class, trans, step, **kwds): module = super().from_workflow_step(trans, step, **kwds) module.subworkflow = step.subworkflow return module
[docs] def save_to_step(self, step, **kwd): step.type = self.type step.subworkflow = self.subworkflow ensure_object_added_to_session(step, object_in_session=self.subworkflow)
[docs] def get_name(self): if hasattr(self.subworkflow, "name"): return self.subworkflow.name return self.name
[docs] def get_all_inputs(self, data_only=False, connectable_only=False): """Get configure time data input descriptions.""" # Filter subworkflow steps and get inputs inputs = [] if hasattr(self.subworkflow, "input_steps"): for step in self.subworkflow.input_steps: name = step.label if not name: step_module = module_factory.from_workflow_step(self.trans, step) name = f"{step.order_index}:{step_module.get_name()}" input = dict( input_subworkflow_step_id=step.order_index, name=name, label=name, multiple=False, extensions=["data"], input_type=step.input_type, ) step_type = step.type if step_type == "data_collection_input": input["collection_type"] = step.tool_inputs.get("collection_type") if step.tool_inputs else None if step_type == "parameter_input": input["type"] = step.tool_inputs["parameter_type"] input["optional"] = step.tool_inputs.get("optional", False) inputs.append(input) return inputs
[docs] def get_modules(self): if self._modules is None: self._modules = [module_factory.from_workflow_step(self.trans, step) for step in self.subworkflow.steps] return self._modules
@property def version_changes(self): version_changes = [] for m in self.get_modules(): if hasattr(m, "version_changes"): version_changes.extend(m.version_changes) return version_changes
[docs] def check_and_update_state(self): states = (m.check_and_update_state() for m in self.get_modules()) # TODO: key ("Step N:") is not currently consumed in UI return {f"Step {i + 1}": upgrade_message for i, upgrade_message in enumerate(states) if upgrade_message} or None
[docs] def get_errors(self, **kwargs): errors1 = (module.get_errors(include_tool_id=True) for module in self.get_modules()) errors2 = [e for e in errors1 if e] if any(errors2): return errors2 return None
[docs] def get_all_outputs(self, data_only=False): outputs = [] self.post_job_actions = {} if hasattr(self.subworkflow, "workflow_outputs"): from galaxy.managers.workflows import WorkflowContentsManager workflow_contents_manager = WorkflowContentsManager(self.trans.app) subworkflow_dict = workflow_contents_manager._workflow_to_dict_editor( trans=self.trans, stored=self.subworkflow.stored_workflow, workflow=self.subworkflow, tooltip=False, is_subworkflow=True, ) for order_index in sorted(subworkflow_dict["steps"]): step = subworkflow_dict["steps"][order_index] data_outputs = step["outputs"] for workflow_output in step["workflow_outputs"]: label = workflow_output["label"] if not label: label = f"{order_index}:{workflow_output['output_name']}" workflow_output_uuid = workflow_output.get("uuid") or object() for data_output in data_outputs: data_output_uuid = data_output.get("uuid") or object() if ( data_output["name"] == workflow_output["output_name"] or data_output_uuid == workflow_output_uuid ): change_datatype_action = step["post_job_actions"].get( f"ChangeDatatypeAction{data_output['name']}" ) if change_datatype_action: self.post_job_actions[f"ChangeDatatypeAction{label}"] = change_datatype_action data_output["name"] = label # That's the right data_output break else: # This can happen when importing workflows with missing tools. # We can't raise an exception here, as that would prevent loading # the workflow. log.error( f"Workflow output '{workflow_output['output_name']}' defined, but not listed among data outputs" ) continue outputs.append(data_output) return outputs
[docs] def get_post_job_actions(self, incoming): return self.post_job_actions
[docs] def get_content_id(self): return self.trans.security.encode_id(self.subworkflow.id)
[docs] def execute( self, trans, progress: "WorkflowProgress", invocation_step, use_cached_job: bool = False ) -> Optional[bool]: """Execute the given workflow step in the given workflow invocation. Use the supplied workflow progress object to track outputs, find inputs, etc... """ step = invocation_step.workflow_step all_inputs = self.get_all_inputs() collection_info = self.compute_collection_info(progress, step, all_inputs) if collection_info: iteration_elements_iter = collection_info.slice_collections() else: if progress.when_values: # If we have more than one item in when_values it must have come from an expression.json # collection, so we'd have a collection_info instance ... I think. assert len(progress.when_values) == 1, "Got more than 1 when value, this shouldn't be possible" iteration_elements_iter = [(None, progress.when_values[0] if progress.when_values else None)] when_values: List[Union[bool, None]] = [] for iteration_elements, when_value in iteration_elements_iter: if when_value is False or not step.when_expression: # We're skipping this step (when==False) or we keep # the current when_value if there is no explicit when_expression on this step. when_values.append(when_value) else: # Got a conditional step and we could potentially run it, # so we have to build the step state and evaluate the expression extra_step_state = {} for step_input in step.inputs: step_input_name = step_input.name if iteration_elements and step_input_name in iteration_elements: # noqa: B023 value = iteration_elements[step_input_name] # noqa: B023 else: value = progress.replacement_for_connection(step_input.connections[0], is_data=True) extra_step_state[step_input_name] = value when_values.append( evaluate_value_from_expressions( progress, step, execution_state={}, extra_step_state=extra_step_state ) ) if collection_info: collection_info.when_values = when_values subworkflow_invoker = progress.subworkflow_invoker( trans, step, use_cached_job=use_cached_job, subworkflow_collection_info=collection_info, when_values=when_values, ) subworkflow_invoker.invoke() subworkflow = subworkflow_invoker.workflow subworkflow_progress = subworkflow_invoker.progress outputs = {} for workflow_output in subworkflow.workflow_outputs: workflow_output_label = ( workflow_output.label or f"{workflow_output.workflow_step.order_index}:{workflow_output.output_name}" ) replacement = subworkflow_progress.get_replacement_workflow_output(workflow_output) outputs[workflow_output_label] = replacement progress.set_step_outputs(invocation_step, outputs) return None
[docs] def get_runtime_state(self): state = DefaultToolState() state.inputs = {} return state
[docs] def get_runtime_inputs(self, step, connections: Optional[Iterable[WorkflowStepConnection]] = None): inputs = {} for step in self.subworkflow.steps: if step.type == "tool": assert isinstance(step.module, ToolModule) tool = step.module.tool if tool is None: raise ToolMissingException("Tool in subworkflow missing.", tool_id=step.module.tool_id) tool_inputs = step.module.state def callback(input, prefixed_name, prefixed_label, value=None, **kwds): # All data parameters are represented as runtime values, skip them # here. if input.type in ["data", "data_collection"]: return if is_runtime_value(value) and runtime_to_json(value)["__class__"] != "ConnectedValue": input_name = f"{step.order_index}|{prefixed_name}" # noqa: B023 inputs[input_name] = InputProxy(input, input_name) visit_input_values(tool.inputs, tool_inputs.inputs, callback) return inputs
[docs] def get_informal_replacement_parameters(self, step) -> List[str]: """Return a list of replacement parameters.""" replacement_parameters = set() formal_parameters = set() for step in self.subworkflow.input_steps: if step.label: formal_parameters.add(step.label) for subworkflow_step in self.subworkflow.steps: module = subworkflow_step.module assert module for replacement_parameter in module.get_informal_replacement_parameters(subworkflow_step): if replacement_parameter not in formal_parameters: replacement_parameters.add(replacement_parameter) return list(replacement_parameters)
[docs]class InputProxy: """Provide InputParameter-interfaces over inputs but renamed for workflow context."""
[docs] def __init__(self, input, prefixed_name): self.input = input self.prefixed_name = prefixed_name
@property def name(self): return self.prefixed_name @property def label(self): return self.prefixed_name
[docs] def to_dict(self, *args, **kwds): as_dict = self.input.to_dict(*args, **kwds) as_dict["name"] = self.prefixed_name return as_dict
[docs]def optional_param(optional): bool_source = dict(name="optional", label="Optional", type="boolean", checked=optional) optional_value = BooleanToolParameter(None, bool_source) return optional_value
[docs]def format_param(trans, formats): formats_val = "" if not formats else ",".join(formats) source = dict( type="text", label="Format(s)", name="format", value=formats_val, optional=True, options=formats, help="Leave empty to auto-generate filtered list at runtime based on connections.", ) source["options"] = [{"value": v, "label": v} for v in trans.app.datatypes_registry.datatypes_by_extension.keys()] format_value = TextToolParameter(None, source) return format_value
[docs]class InputModuleState(TypedDict, total=False): optional: bool format: List[str] tag: str
[docs]class InputModule(WorkflowModule): default_optional = False
[docs] def get_runtime_state(self): state = DefaultToolState() state.inputs = dict(input=None) return state
[docs] def get_all_inputs(self, data_only=False, connectable_only=False): return []
[docs] def execute( self, trans, progress: "WorkflowProgress", invocation_step, use_cached_job: bool = False ) -> Optional[bool]: invocation = invocation_step.workflow_invocation step = invocation_step.workflow_step input_value = step.state.inputs["input"] if input_value is None: default_value = step.get_input_default_value(NO_REPLACEMENT) if default_value is not NO_REPLACEMENT: input_value = raw_to_galaxy(trans.app, trans.history, default_value) step_outputs = dict(output=input_value) # Web controller may set copy_inputs_to_history, API controller always sets # inputs. if progress.copy_inputs_to_history: for input_dataset_hda in list(step_outputs.values()): content_type = input_dataset_hda.history_content_type if content_type == "dataset": new_hda = input_dataset_hda.copy() invocation.history.add_dataset(new_hda) step_outputs["input_ds_copy"] = new_hda elif content_type == "dataset_collection": new_hdca = input_dataset_hda.copy() invocation.history.add_dataset_collection(new_hdca) step_outputs["input_ds_copy"] = new_hdca else: raise Exception("Unknown history content encountered") # If coming from UI - we haven't registered invocation inputs yet, # so do that now so dependent steps can be recalculated. In the future # everything should come in from the API and this can be eliminated. if not invocation.has_input_for_step(step.id): content = next(iter(step_outputs.values())) if content: invocation.add_input(content, step.id) progress.set_outputs_for_input(invocation_step, step_outputs) return None
[docs] def recover_mapping(self, invocation_step, progress): progress.set_outputs_for_input(invocation_step, already_persisted=True)
[docs] def get_export_state(self): return self._parse_state_into_dict()
def _parse_state_into_dict(self): inputs = self.state.inputs rval: InputModuleState = {} if "optional" in inputs: optional = bool(inputs["optional"]) else: optional = self.default_optional rval["optional"] = optional if "format" in inputs: formats: Optional[List[str]] = listify(inputs["format"]) else: formats = None if formats: rval["format"] = formats if "tag" in inputs: tag = inputs["tag"] else: tag = None rval["tag"] = tag return rval
[docs] def step_state_to_tool_state(self, state): state = safe_loads(state) if "format" in state: formats = state["format"] if formats: formats = ",".join(listify(formats)) state["format"] = formats state = json.dumps(state) return state
[docs] def save_to_step(self, step, **kwd): step.type = self.type step.tool_inputs = self._parse_state_into_dict()
[docs]class InputDataModule(InputModule): type = "data_input" name = "Input dataset"
[docs] def get_all_outputs(self, data_only=False): parameter_def = self._parse_state_into_dict() format_def = parameter_def.get("format") optional = parameter_def["optional"] if format_def is None: extensions = ["input"] else: extensions = listify(format_def) return [dict(name="output", extensions=extensions, optional=optional)]
[docs] def get_filter_set(self, connections=None): filter_set = [] if connections: for oc in connections: for ic in oc.input_step.module.get_data_inputs(): if "extensions" in ic and ic["extensions"] != "input" and ic["name"] == oc.input_name: filter_set += ic["extensions"] if not filter_set: filter_set = ["data"] return ", ".join(filter_set)
[docs] def get_runtime_inputs(self, step, connections: Optional[Iterable[WorkflowStepConnection]] = None): parameter_def = self._parse_state_into_dict() optional = parameter_def["optional"] tag = parameter_def["tag"] formats = parameter_def.get("format") if not formats: formats = self.get_filter_set(connections) else: formats = ",".join(listify(formats)) data_src = dict( name="input", label=self.label, multiple=False, type="data", format=formats, tag=tag, optional=optional ) default_unset = object() default = step.get_input_default_value(default_unset) if default is not default_unset: data_src["default"] = default input_param = DataToolParameter(None, data_src, self.trans) return dict(input=input_param)
[docs] def get_inputs(self): parameter_def = self._parse_state_into_dict() tag = parameter_def["tag"] tag_source = dict( name="tag", label="Tag filter", type="text", optional="true", value=tag, help="Tags to automatically filter inputs", ) input_tag = TextToolParameter(None, tag_source) optional = parameter_def["optional"] inputs = {} inputs["optional"] = optional_param(optional) inputs["format"] = format_param(self.trans, parameter_def.get("format")) inputs["tag"] = input_tag return inputs
[docs]class InputDataCollectionModule(InputModule): type = "data_collection_input" name = "Input dataset collection" default_collection_type = "list" collection_type = default_collection_type
[docs] def get_inputs(self): parameter_def = self._parse_state_into_dict() collection_type = parameter_def["collection_type"] tag = parameter_def["tag"] optional = parameter_def["optional"] collection_type_source = dict( name="collection_type", label="Collection type", type="text", value=collection_type ) collection_type_source["options"] = [ {"value": "list", "label": "List of Datasets"}, {"value": "paired", "label": "Dataset Pair"}, {"value": "list:paired", "label": "List of Dataset Pairs"}, ] input_collection_type = TextToolParameter(None, collection_type_source) tag_source = dict( name="tag", label="Tag filter", type="text", optional="true", value=tag, help="Tags to automatically filter inputs", ) input_tag = TextToolParameter(None, tag_source) inputs = {} inputs["collection_type"] = input_collection_type inputs["optional"] = optional_param(optional) inputs["format"] = format_param(self.trans, parameter_def.get("format")) inputs["tag"] = input_tag return inputs
[docs] def get_runtime_inputs(self, step, connections: Optional[Iterable[WorkflowStepConnection]] = None): parameter_def = self._parse_state_into_dict() collection_type = parameter_def["collection_type"] optional = parameter_def["optional"] tag = parameter_def["tag"] collection_param_source = dict( name="input", label=self.label, type="data_collection", collection_type=collection_type, tag=tag, optional=optional, ) if formats := parameter_def.get("format"): collection_param_source["format"] = ",".join(listify(formats)) input_param = DataCollectionToolParameter(None, collection_param_source, self.trans) return dict(input=input_param)
[docs] def get_all_outputs(self, data_only=False): parameter_def = self._parse_state_into_dict() format_def = parameter_def.get("format") optional = parameter_def["optional"] if format_def is None: extensions = ["input"] else: extensions = listify(format_def) return [ dict( name="output", extensions=extensions, collection=True, collection_type=parameter_def.get("collection_type", self.default_collection_type), optional=optional, ) ]
def _parse_state_into_dict(self): state_as_dict = super()._parse_state_into_dict() if "collection_type" in (inputs := self.state.inputs): collection_type = inputs["collection_type"] else: collection_type = self.default_collection_type state_as_dict["collection_type"] = collection_type return state_as_dict
[docs]class InputParameterModule(WorkflowModule): POSSIBLE_PARAMETER_TYPES = ["text", "integer", "float", "boolean", "color"] type = "parameter_input" name = "Input parameter" default_parameter_type = "text" default_optional = False default_default_value = None parameter_type = default_parameter_type optional = default_optional default_value = default_default_value
[docs] def get_inputs(self): parameter_def = self._parse_state_into_dict() parameter_type = parameter_def["parameter_type"] optional = parameter_def["optional"] select_source = dict(name="parameter_type", label="Parameter type", type="select", value=parameter_type) select_source["options"] = [ {"value": "text", "label": "Text"}, {"value": "integer", "label": "Integer"}, {"value": "float", "label": "Float"}, {"value": "boolean", "label": "Boolean (True or False)"}, {"value": "color", "label": "Color"}, ] input_parameter_type = SelectToolParameter(None, select_source) # encode following loop in description above instead for i, option in enumerate(input_parameter_type.static_options): option = list(option) if option[1] == parameter_type: # item 0 is option description, item 1 is value, item 2 is "selected" option[2] = True input_parameter_type.static_options[i] = tuple(option) parameter_type_cond = Conditional() parameter_type_cond.name = "parameter_definition" parameter_type_cond.test_param = input_parameter_type cases = [] for param_type in ["text", "integer", "float", "boolean", "color"]: default_source: Dict[str, Union[int, float, bool, str]] = dict( name="default", label="Default Value", type=param_type ) if param_type == "text": if parameter_type == "text": text_default = parameter_def.get("default") or "" else: text_default = "" default_source["value"] = text_default input_default_value: Union[ TextToolParameter, IntegerToolParameter, FloatToolParameter, BooleanToolParameter, ColorToolParameter, ] = TextToolParameter(None, default_source) elif param_type == "integer": if parameter_type == "integer": integer_default = parameter_def.get("default") or 0 else: integer_default = 0 default_source["value"] = integer_default input_default_value = IntegerToolParameter(None, default_source) elif param_type == "float": if parameter_type == "float": float_default = parameter_def.get("default") or 0.0 else: float_default = 0.0 default_source["value"] = float_default input_default_value = FloatToolParameter(None, default_source) elif param_type == "boolean": if parameter_type == "boolean": boolean_default = parameter_def.get("default") or False else: boolean_default = False default_source["value"] = boolean_default default_source["checked"] = boolean_default input_default_value = BooleanToolParameter(None, default_source) elif param_type == "color": if parameter_type == "color": color_default = parameter_def.get("default") or "#000000" else: color_default = "#000000" default_source["value"] = color_default input_default_value = ColorToolParameter(None, default_source) optional_value = optional_param(optional) optional_cond = Conditional() optional_cond.name = "optional" optional_cond.test_param = optional_value when_this_type = ConditionalWhen() when_this_type.value = param_type when_this_type.inputs = {} when_this_type.inputs["optional"] = optional_cond specify_default_checked = "default" in parameter_def specify_default_source = dict( name="specify_default", label="Specify a default value", type="boolean", checked=specify_default_checked ) specify_default = BooleanToolParameter(None, specify_default_source) specify_default_cond = Conditional() specify_default_cond.name = "specify_default" specify_default_cond.test_param = specify_default when_specify_default_true = ConditionalWhen() when_specify_default_true.value = "true" when_specify_default_true.inputs = {} when_specify_default_true.inputs["default"] = input_default_value when_specify_default_false = ConditionalWhen() when_specify_default_false.value = "false" when_specify_default_false.inputs = {} specify_default_cond_cases = [when_specify_default_true, when_specify_default_false] specify_default_cond.cases = specify_default_cond_cases when_true = ConditionalWhen() when_true.value = "true" when_true.inputs = {} when_true.inputs["default"] = specify_default_cond when_false = ConditionalWhen() when_false.value = "false" when_false.inputs = {} optional_cases = [when_true, when_false] optional_cond.cases = optional_cases if param_type == "text": restrict_how_source: Dict[str, Union[str, List[Dict[str, Union[str, bool]]]]] = dict( name="how", label="Restrict Text Values?", type="select" ) if parameter_def.get("restrictions") is not None: restrict_how_value = "staticRestrictions" elif parameter_def.get("restrictOnConnections") is True: restrict_how_value = "onConnections" elif parameter_def.get("suggestions") is not None: restrict_how_value = "staticSuggestions" else: restrict_how_value = "none" restrict_how_source["options"] = [ { "value": "none", "label": "Do not specify restrictions (default).", "selected": restrict_how_value == "none", }, { "value": "onConnections", "label": "Attempt restriction based on connections.", "selected": restrict_how_value == "onConnections", }, { "value": "staticRestrictions", "label": "Provide list of all possible values.", "selected": restrict_how_value == "staticRestrictions", }, { "value": "staticSuggestions", "label": "Provide list of suggested values.", "selected": restrict_how_value == "staticSuggestions", }, ] restrictions_cond = Conditional() restrictions_how = SelectToolParameter(None, restrict_how_source) restrictions_cond.name = "restrictions" restrictions_cond.test_param = restrictions_how when_restrict_none = ConditionalWhen() when_restrict_none.value = "none" when_restrict_none.inputs = {} when_restrict_connections = ConditionalWhen() when_restrict_connections.value = "onConnections" when_restrict_connections.inputs = {} when_restrict_static_restrictions = ConditionalWhen() when_restrict_static_restrictions.value = "staticRestrictions" when_restrict_static_restrictions.inputs = {} when_restrict_static_suggestions = ConditionalWhen() when_restrict_static_suggestions.value = "staticSuggestions" when_restrict_static_suggestions.inputs = {} # Repeats don't work - so use common separated list for now. # Use both restrictions and suggestions as each other's default so value preserved. restrictions_list = parameter_def.get("restrictions") or parameter_def.get("suggestions") if restrictions_list is None: restrictions_list = [] restriction_values = self._parameter_option_def_to_tool_form_str(restrictions_list) restrictions_source = dict( name="restrictions", label="Restricted Values", value=restriction_values, help="Comma-separated list of all permitted values", ) restrictions = TextToolParameter(None, restrictions_source) suggestions_source = dict( name="suggestions", label="Suggested Values", value=restriction_values, help="Comma-separated list of some potential values", ) suggestions = TextToolParameter(None, suggestions_source) when_restrict_static_restrictions.inputs["restrictions"] = restrictions when_restrict_static_suggestions.inputs["suggestions"] = suggestions restrictions_cond_cases = [ when_restrict_none, when_restrict_connections, when_restrict_static_restrictions, when_restrict_static_suggestions, ] restrictions_cond.cases = restrictions_cond_cases when_this_type.inputs["restrictions"] = restrictions_cond cases.append(when_this_type) parameter_type_cond.cases = cases return {"parameter_definition": parameter_type_cond}
[docs] def restrict_options(self, step, connections: Iterable[WorkflowStepConnection], default_value): try: static_options = [] # Retrieve possible runtime options for 'select' type inputs for connection in connections: # Well this isn't a great assumption... assert connection.input_step module = connection.input_step.module assert isinstance(module, (ToolModule, SubWorkflowModule)) if isinstance(module, ToolModule): assert module.tool tool_inputs = module.tool.inputs # may not be set, but we're catching the Exception below. def callback(input, prefixed_name, context, **kwargs): if prefixed_name == connection.input_name and hasattr(input, "get_options"): # noqa: B023 static_options.append(input.get_options(self.trans, {})) visit_input_values(tool_inputs, module.state.inputs, callback) elif isinstance(module, SubWorkflowModule): subworkflow_input_name = connection.input_name for step in module.subworkflow.input_steps: if step.input_type == "parameter" and step.label == subworkflow_input_name: static_options.append( step.module.get_runtime_inputs(step, connections=step.output_connections)[ "input" ].static_options ) options = None if static_options and len(static_options) == 1: # If we are connected to a single option, just use it as is so order is preserved cleanly and such. options = [ {"label": o[0], "value": o[1], "selected": bool(default_value and o[1] == default_value)} for o in static_options[0] ] elif static_options: # Intersection based on values of multiple option connections. intxn_vals = set.intersection(*({option[1] for option in options} for options in static_options)) intxn_opts = {option for options in static_options for option in options if option[1] in intxn_vals} d = defaultdict(set) # Collapse labels with same values for label, value, _ in intxn_opts: d[value].add(label) options = [ { "label": ", ".join(label), "value": value, "selected": bool(default_value and value == default_value), } for value, label in d.items() ] return options except Exception: log.debug("Failed to generate options for text parameter, falling back to free text.", exc_info=True)
[docs] def get_runtime_inputs(self, step, connections: Optional[Iterable[WorkflowStepConnection]] = None): parameter_def = self._parse_state_into_dict() parameter_type = parameter_def["parameter_type"] optional = parameter_def["optional"] default_value = parameter_def.get("default", self.default_default_value) if parameter_type not in ["text", "boolean", "integer", "float", "color"]: raise ValueError("Invalid parameter type for workflow parameters encountered.") # Optional parameters for tool input source definition. parameter_kwds: Dict[str, Union[str, List[Dict[str, Any]]]] = {} is_text = parameter_type == "text" restricted_inputs = False # Really is just an attempt - tool module may not be available (small problem), get_options may really depend on other # values we are not setting, so this isn't great. Be sure to just fallback to text in this case. attemptRestrictOnConnections = is_text and parameter_def.get("restrictOnConnections") and connections if attemptRestrictOnConnections: connections = cast(Iterable[WorkflowStepConnection], connections) restricted_options = self.restrict_options(step, connections=connections, default_value=default_value) if restricted_options is not None: restricted_inputs = True parameter_kwds["options"] = restricted_options def _parameter_def_list_to_options(parameter_value): options = [] for item in parameter_value: option = {} if isinstance(item, dict): value = item["value"] option["value"] = value if "label" in item: option["label"] = item["label"] else: option["label"] = value else: option["value"] = item option["label"] = item options.append(option) return options if is_text and not restricted_inputs and parameter_def.get("restrictions"): restriction_values = parameter_def.get("restrictions") parameter_kwds["options"] = _parameter_def_list_to_options(restriction_values) restricted_inputs = True client_parameter_type = parameter_type if restricted_inputs: client_parameter_type = "select" parameter_class = parameter_types[client_parameter_type] if optional: if client_parameter_type == "select": parameter_kwds["selected"] = default_value else: parameter_kwds["value"] = default_value if parameter_type == "boolean": parameter_kwds["checked"] = default_value if "value" not in parameter_kwds and parameter_type in ["integer", "float"]: parameter_kwds["value"] = str(0) if is_text and parameter_def.get("suggestions") is not None: suggestion_values = parameter_def.get("suggestions") parameter_kwds["options"] = _parameter_def_list_to_options(suggestion_values) input_source = dict( name="input", label=self.label, type=client_parameter_type, optional=optional, **parameter_kwds ) input = parameter_class(None, input_source) return dict(input=input)
[docs] def get_runtime_state(self): state = DefaultToolState() state.inputs = dict(input=None) return state
[docs] def get_all_outputs(self, data_only=False): if data_only: return [] parameter_def = self._parse_state_into_dict() return [ dict( name="output", label=self.label, type=parameter_def.get("parameter_type"), optional=parameter_def["optional"], parameter=True, ) ]
[docs] def execute( self, trans, progress: "WorkflowProgress", invocation_step, use_cached_job: bool = False ) -> Optional[bool]: step = invocation_step.workflow_step input_value = step.state.inputs["input"] if input_value is None: default_value = step.get_input_default_value(NO_REPLACEMENT) # TODO: look at parameter type and infer if value should be a dictionary # instead. Guessing only field parameter types in CWL branch would have # default as dictionary like this. if not isinstance(default_value, dict): default_value = {"value": default_value} input_value = default_value.get("value", NO_REPLACEMENT) step_outputs = dict(output=input_value) progress.set_outputs_for_input(invocation_step, step_outputs) return None
[docs] def step_state_to_tool_state(self, state): state = safe_loads(state) default_set, default_value = False, None if "default" in state: default_set = True default_value = state["default"] state["optional"] = True restrictions = state.get("restrictions") restrictOnConnections = state.get("restrictOnConnections") suggestions = state.get("suggestions") restrictions_how = "none" if restrictions is not None: restrictions_how = "staticRestrictions" if suggestions is not None: restrictions_how = "staticSuggestions" elif restrictOnConnections: restrictions_how = "onConnections" state = { "parameter_definition": { "parameter_type": state["parameter_type"], "optional": {"optional": str(state.get("optional", False))}, } } state["parameter_definition"]["restrictions"] = {} state["parameter_definition"]["restrictions"]["how"] = restrictions_how if restrictions_how == "staticRestrictions": state["parameter_definition"]["restrictions"]["restrictions"] = self._parameter_option_def_to_tool_form_str( restrictions ) if restrictions_how == "staticSuggestions": state["parameter_definition"]["restrictions"]["suggestions"] = self._parameter_option_def_to_tool_form_str( suggestions ) if default_set: state["parameter_definition"]["optional"]["specify_default"] = {} state["parameter_definition"]["optional"]["specify_default"]["specify_default"] = True state["parameter_definition"]["optional"]["specify_default"]["default"] = default_value state = json.dumps(state) return state
def _parameter_option_def_to_tool_form_str(self, parameter_def): return ",".join(f"{o['value']}:{o['label']}" if isinstance(o, dict) else o for o in parameter_def)
[docs] def get_export_state(self): export_state = self._parse_state_into_dict() return export_state
def _parse_state_into_dict(self): inputs = self.state.inputs rval = {} # 19.0X tool state... if "parameter_type" in inputs: rval.update({"parameter_type": inputs["parameter_type"], "optional": False}) # expanded tool state... elif "parameter_definition" in inputs: parameters_def = inputs["parameter_definition"] if "optional" in parameters_def: optional_state = parameters_def["optional"] optional = bool(optional_state["optional"]) if "specify_default" in optional_state and bool(optional_state["specify_default"]["specify_default"]): rval["default"] = optional_state["specify_default"]["default"] else: optional = False restrictions_cond_values = parameters_def.get("restrictions") if restrictions_cond_values: def _string_to_parameter_def_option(str_value): items = [] for option_part in str_value.split(","): option_part = option_part.strip() if ":" not in option_part: items.append(option_part) else: value, label = option_part.split(":", 1) items.append({"value": value, "label": label}) return items # how better be in here. how = restrictions_cond_values["how"] if how == "none": pass elif how == "onConnections": rval["restrictOnConnections"] = True elif how == "staticRestrictions": restriction_values = restrictions_cond_values["restrictions"] rval.update({"restrictions": _string_to_parameter_def_option(restriction_values)}) elif how == "staticSuggestions": suggestion_values = restrictions_cond_values["suggestions"] rval.update({"suggestions": _string_to_parameter_def_option(suggestion_values)}) else: log.warning("Unknown restriction conditional type encountered for workflow parameter.") rval.update({"parameter_type": parameters_def["parameter_type"], "optional": optional}) else: rval.update({"parameter_type": self.default_parameter_type, "optional": self.default_optional}) return rval
[docs] def save_to_step(self, step, **kwd): step.type = self.type step.tool_inputs = self._parse_state_into_dict()
[docs]class PauseModule(WorkflowModule): """Initially this module will unconditionally pause a workflow - will aim to allow conditional pausing later on. """ type = "pause" name = "Pause for dataset review"
[docs] def get_all_inputs(self, data_only=False, connectable_only=False): input = dict( name="input", label="Dataset for Review", multiple=False, extensions="input", input_type="dataset", ) return [input] if not data_only else []
[docs] def get_all_outputs(self, data_only=False): return [dict(name="output", label="Reviewed Dataset", extensions=["input"])]
[docs] def get_runtime_state(self): state = DefaultToolState() state.inputs = {} return state
[docs] def execute( self, trans, progress: "WorkflowProgress", invocation_step, use_cached_job: bool = False ) -> Optional[bool]: step = invocation_step.workflow_step progress.mark_step_outputs_delayed(step, why="executing pause step") return None
[docs] def recover_mapping(self, invocation_step, progress): if invocation_step: step = invocation_step.workflow_step action = invocation_step.action if action: connection = step.input_connections_by_name["input"][0] replacement = progress.replacement_for_connection(connection) progress.set_step_outputs(invocation_step, {"output": replacement}) return elif action is False: raise CancelWorkflowEvaluation( why=InvocationCancellationReviewFailed( reason=CancelReason.cancelled_on_review, workflow_step_id=step.id ) ) delayed_why = "workflow paused at this step waiting for review" raise DelayedWorkflowEvaluation(why=delayed_why)
[docs] def do_invocation_step_action(self, step, action): """Update or set the workflow invocation state action - generic extension point meant to allows users to interact with interactive workflow modules. The action object returned from this method will be attached to the WorkflowInvocationStep and be available the next time the workflow scheduler visits the workflow. """ return bool(action)
[docs]class ToolModule(WorkflowModule): type = "tool" name = "Tool"
[docs] def __init__(self, trans, tool_id, tool_version=None, exact_tools=True, tool_uuid=None, **kwds): super().__init__(trans, content_id=tool_id, **kwds) self.tool_id = tool_id self.tool_version = str(tool_version) if tool_version else None self.tool_uuid = tool_uuid self.tool = None if getattr(trans.app, "toolbox", None): self.tool = trans.app.toolbox.get_tool( tool_id, tool_version=tool_version, exact=exact_tools, tool_uuid=tool_uuid ) if self.tool: current_tool_version = str(self.tool.version) if exact_tools and self.tool_version and self.tool_version != current_tool_version: safe_version = get_safe_version(self.tool, self.tool_version) if safe_version: self.tool = trans.app.toolbox.get_tool( tool_id, tool_version=safe_version, exact=True, tool_uuid=tool_uuid ) else: log.info( f"Exact tool specified during workflow module creation for [{tool_id}] but couldn't find correct version [{tool_version}]." ) self.tool = None self.post_job_actions = {} self.runtime_post_job_actions = {} self.workflow_outputs = [] self.version_changes = []
# ---- Creating modules from various representations ---------------------
[docs] @classmethod def from_dict(Class, trans, d, **kwds): tool_id = d.get("content_id") or d.get("tool_id") tool_version = d.get("tool_version") if tool_version: tool_version = str(tool_version) tool_uuid = d.get("tool_uuid", None) if tool_id is None and tool_uuid is None: tool_representation = d.get("tool_representation") if tool_representation: create_request = { "representation": tool_representation, } if not trans.user_is_admin: raise exceptions.AdminRequiredException("Only admin users can create tools dynamically.") dynamic_tool = trans.app.dynamic_tool_manager.create_tool(trans, create_request, allow_load=False) tool_uuid = dynamic_tool.uuid if tool_id is None and tool_uuid is None: raise exceptions.RequestParameterInvalidException(f"No content id could be located for for step [{d}]") module = super().from_dict(trans, d, tool_id=tool_id, tool_version=tool_version, tool_uuid=tool_uuid, **kwds) module.post_job_actions = d.get("post_job_actions", {}) module.workflow_outputs = d.get("workflow_outputs", []) if module.tool: message = "" if tool_id != module.tool_id: message += f"The tool (id '{tool_id}') specified in this step is not available. Using the tool with id {module.tool_id} instead." if d.get("tool_version", "Unspecified") != module.get_version(): message += f"{tool_id}: using version '{module.get_version()}' instead of version '{d.get('tool_version', 'Unspecified')}' specified in this workflow." if message: log.debug(message) module.version_changes.append(message) return module
[docs] @classmethod def from_workflow_step(Class, trans, step, **kwds): tool_version = step.tool_version tool_uuid = step.tool_uuid kwds["exact_tools"] = False module = super().from_workflow_step( trans, step, tool_id=step.tool_id, tool_version=tool_version, tool_uuid=tool_uuid, **kwds ) module.workflow_outputs = step.workflow_outputs module.post_job_actions = {} for pja in step.post_job_actions: module.post_job_actions[pja.action_type] = pja if module.tool: message = "" if ( step.tool_id and step.tool_id != module.tool.id or step.tool_version and step.tool_version != module.tool.version ): # This means the exact version of the tool is not installed. We inform the user. old_tool_shed = step.tool_id.split("/repos/")[0] if ( old_tool_shed not in module.tool.id ): # Only display the following warning if the tool comes from a different tool shed old_tool_shed_url = get_tool_shed_url_from_tool_shed_registry(trans.app, old_tool_shed) if ( not old_tool_shed_url ): # a tool from a different tool_shed has been found, but the original tool shed has been deactivated old_tool_shed_url = f"http://{old_tool_shed}" # let's just assume it's either http, or a http is forwarded to https. old_url = f"{old_tool_shed_url}/view/{module.tool.repository_owner}/{module.tool.repository_name}/" new_url = f"{module.tool.sharable_url}/{module.tool.changeset_revision}/" new_tool_shed_url = new_url.split("/view")[0] message += f'The tool \'{module.tool.name}\', version {tool_version} by the owner {module.tool.repository_owner} installed from <a href="{old_url}" target="_blank">{old_tool_shed_url}</a> is not available. ' message += f'A derivation of this tool installed from <a href="{new_url}" target="_blank">{new_tool_shed_url}</a> will be used instead. ' if step.tool_version and ( step.tool_version != module.tool.version and not get_safe_version(module.tool, step.tool_version) ): message += f"<span title=\"tool id '{step.tool_id}'\">Using version '{module.tool.version}' instead of version '{step.tool_version}' specified in this workflow. " if message: log.debug(message) module.version_changes.append(message) else: log.warning(f"The tool '{step.tool_id}' is missing. Cannot build workflow module.") return module
# ---- Saving in various forms ------------------------------------------
[docs] def save_to_step(self, step, detached=False): super().save_to_step(step, detached=detached) step.tool_id = self.tool_id if self.tool: step.tool_version = self.get_version() else: step.tool_version = self.tool_version if tool_uuid := getattr(self, "tool_uuid", None): step.dynamic_tool = self.trans.app.dynamic_tool_manager.get_tool_by_uuid(tool_uuid) if not detached: for k, v in self.post_job_actions.items(): pja = self.__to_pja(k, v, step) self.trans.sa_session.add(pja)
# ---- General attributes ------------------------------------------------
[docs] def get_name(self): return self.tool.name if self.tool else self.tool_id
[docs] def get_content_id(self): return self.tool.id if self.tool else self.tool_id
[docs] def get_version(self): return self.tool.version if self.tool else self.tool_version
[docs] def get_tooltip(self, static_path=None): if self.tool and self.tool.help: host_url = self.trans.url_builder("/") static_path = self.trans.url_builder(static_path) if static_path else "" return self.tool.help.render(host_url=host_url, static_path=static_path)
# ---- Configuration time -----------------------------------------------
[docs] def get_errors(self, include_tool_id=False, **kwargs): if not self.tool: if include_tool_id: return f"{self.tool_id} is not installed" else: return "Tool is not installed"
[docs] def get_inputs(self): return self.tool.inputs if self.tool else {}
[docs] def get_all_inputs(self, data_only=False, connectable_only=False): if data_only and connectable_only: raise Exception("Must specify at most one of data_only and connectable_only as True.") inputs = [] if self.tool: def callback(input, prefixed_name, prefixed_label, value=None, **kwargs): visible = not hasattr(input, "hidden") or not input.hidden input_type = input.type is_data = isinstance(input, DataToolParameter) or isinstance(input, DataCollectionToolParameter) is_connectable = is_runtime_value(value) and runtime_to_json(value)["__class__"] == "ConnectedValue" if data_only: skip = not visible or not is_data elif connectable_only: skip = not visible or not (is_data or is_connectable) elif isinstance(input, HiddenToolParameter): skip = False else: skip = not visible if not skip: if isinstance(input, DataToolParameter): inputs.append( dict( name=prefixed_name, label=prefixed_label, multiple=input.multiple, extensions=input.extensions, optional=input.optional, input_type="dataset", ) ) elif isinstance(input, DataCollectionToolParameter): inputs.append( dict( name=prefixed_name, label=prefixed_label, multiple=input.multiple, input_type="dataset_collection", collection_types=input.collection_types, optional=input.optional, extensions=input.extensions, ) ) else: inputs.append( dict( name=prefixed_name, label=prefixed_label, multiple=False, input_type="parameter", optional=getattr(input, "optional", False), type=input_type, ) ) visit_input_values(self.tool.inputs, self.state.inputs, callback) return inputs
[docs] def get_all_outputs(self, data_only=False): data_outputs = [] if self.tool: for name, tool_output in self.tool.outputs.items(): if filter_output(self.tool, tool_output, self.state.inputs): continue extra_kwds = {} if isinstance(tool_output, ToolExpressionOutput): extra_kwds["parameter"] = True if tool_output.collection: extra_kwds["collection"] = True collection_type = tool_output.structure.collection_type if not collection_type and tool_output.structure.collection_type_from_rules: rule_param = tool_output.structure.collection_type_from_rules if rule_param in self.state.inputs: rules = self.state.inputs[rule_param] if rules: rule_set = RuleSet(rules) collection_type = rule_set.collection_type extra_kwds["collection_type"] = collection_type extra_kwds["collection_type_source"] = tool_output.structure.collection_type_source formats = ["input"] # TODO: fix elif tool_output.format_source is not None: formats = ["input"] # default to special name "input" which remove restrictions on connections else: formats = [tool_output.format] for change_format_model in tool_output.change_format: format = change_format_model["format"] if format and format not in formats: formats.append(format) if tool_output.label: try: params = make_dict_copy(self.state.inputs) params["on_string"] = "input dataset(s)" params["tool"] = self.tool extra_kwds["label"] = fill_template( tool_output.label, context=params, python_template_version=self.tool.python_template_version ) except Exception: pass data_outputs.append( dict(name=name, extensions=formats, type=tool_output.output_type, optional=False, **extra_kwds) ) return data_outputs
[docs] def get_config_form(self, step=None): if self.tool: self.add_dummy_datasets(connections=step and step.input_connections) incoming: Dict[str, str] = {} params_to_incoming(incoming, self.tool.inputs, self.state.inputs, self.trans.app) return self.tool.to_json(self.trans, incoming, workflow_building_mode=True)
[docs] def check_and_update_state(self): if self.tool: return self.tool.check_and_update_param_values(self.state.inputs, self.trans, workflow_building_mode=True)
[docs] def add_dummy_datasets(self, connections=None, steps=None): if self.tool: if connections: # Store connections by input name input_connections_by_name = {conn.input_name: conn for conn in connections} else: input_connections_by_name = {} # Any input needs to have value RuntimeValue or obtain the value from connected steps def callback(input, prefixed_name, context, **kwargs): input_type = input.type is_data = input_type in ["data", "data_collection"] if ( is_data and connections is not None and steps is not None and self.trans.workflow_building_mode is workflow_building_modes.USE_HISTORY ): if prefixed_name in input_connections_by_name: return ConnectedValue() else: return input.get_initial_value(self.trans, context) elif (is_data and connections is None) or prefixed_name in input_connections_by_name: return ConnectedValue() visit_input_values(self.tool.inputs, self.state.inputs, callback) else: raise ToolMissingException(f"Tool {self.tool_id} missing. Cannot add dummy datasets.", tool_id=self.tool_id)
[docs] def get_post_job_actions(self, incoming): return ActionBox.handle_incoming(incoming)
# ---- Run time ---------------------------------------------------------
[docs] def recover_state(self, state, **kwds): """Recover state `dict` from simple dictionary describing configuration state (potentially from persisted step state). Sub-classes should supply a `default_state` method which contains the initial state `dict` with key, value pairs for all available attributes. """ super().recover_state(state, **kwds) if kwds.get("fill_defaults", False) and self.tool: self.compute_runtime_state(self.trans, step=None, step_updates=None) self.augment_tool_state_for_input_connections(**kwds) self.tool.check_and_update_param_values(self.state.inputs, self.trans, workflow_building_mode=True)
[docs] def augment_tool_state_for_input_connections(self, **kwds): """Update tool state to accommodate specified input connections. Top-level and conditional inputs will automatically get populated with connected data outputs at runtime, but if there are not enough repeat instances in the tool state - the runtime replacement code will never visit the input elements it needs to in order to connect the data parameters to the tool state. This code then populates the required repeat instances in the tool state in order for these instances to be visited and inputs properly connected at runtime. I believe this should be run before check_and_update_param_values in recover_state so non-data parameters are properly populated with default values. The need to populate defaults is why this is done here instead of at runtime - but this might also be needed at runtime at some point (for workflows installed before their corresponding tools?). See the test case test_inputs_to_steps for an example of a workflow test case that exercises this code. """ # Ensure any repeats defined only by input_connections are populated. input_connections = kwds.get("input_connections", {}) expected_replacement_keys = input_connections.keys() def augment(expected_replacement_key, inputs_states): if self.tool is None: raise ToolMissingException( f"Tool {self.tool_id} missing. Cannot augment tool state for input connections.", tool_id=self.tool_id, ) if "|" not in expected_replacement_key: return prefix, rest = expected_replacement_key.split("|", 1) if "_" not in prefix: return repeat_name, index = prefix.rsplit("_", 1) if not index.isdigit(): return index = int(index) repeat = self.tool.inputs[repeat_name] if repeat.type != "repeat": return if repeat_name not in inputs_states: inputs_states[repeat_name] = [] repeat_values = inputs_states[repeat_name] repeat_instance_state = None while index >= len(repeat_values): repeat_instance_state = {"__index__": len(repeat_values)} repeat_values.append(repeat_instance_state) if repeat_instance_state: # TODO: untest branch - no test case for nested repeats yet... augment(rest, repeat_instance_state) for expected_replacement_key in expected_replacement_keys: inputs_states = self.state.inputs augment(expected_replacement_key, inputs_states)
[docs] def get_runtime_state(self): state = DefaultToolState() state.inputs = self.state.inputs return state
[docs] def get_runtime_inputs(self, step, connections: Optional[Iterable[WorkflowStepConnection]] = None): return self.get_inputs()
[docs] def compute_runtime_state(self, trans, step=None, step_updates=None): # Warning: This method destructively modifies existing step state. if self.tool: step_errors = {} state = self.state self.runtime_post_job_actions = {} state, step_errors = super().compute_runtime_state(trans, step, step_updates) if step_updates: self.runtime_post_job_actions = step_updates.get(RUNTIME_POST_JOB_ACTIONS_KEY, {}) step_metadata_runtime_state = self.__step_meta_runtime_state() if step_metadata_runtime_state: state.inputs[RUNTIME_STEP_META_STATE_KEY] = step_metadata_runtime_state return state, step_errors else: raise ToolMissingException( f"Tool {self.tool_id} missing. Cannot compute runtime state.", tool_id=self.tool_id )
[docs] def decode_runtime_state(self, step, runtime_state): """Take runtime state from persisted invocation and convert it into a DefaultToolState object for use during workflow invocation. """ if self.tool: state = super().decode_runtime_state(step, runtime_state) if RUNTIME_STEP_META_STATE_KEY in runtime_state: self.__restore_step_meta_runtime_state(json.loads(runtime_state[RUNTIME_STEP_META_STATE_KEY])) return state else: raise ToolMissingException( f"Tool {self.tool_id} missing. Cannot recover runtime state.", tool_id=self.tool_id )
[docs] def execute( self, trans, progress: "WorkflowProgress", invocation_step, use_cached_job: bool = False ) -> Optional[bool]: invocation = invocation_step.workflow_invocation step = invocation_step.workflow_step tool = trans.app.toolbox.get_tool(step.tool_id, tool_version=step.tool_version, tool_uuid=step.tool_uuid) if not tool.is_workflow_compatible: # TODO: why do we even create an invocation, seems like something we could check on submit? message = f"Specified tool [{tool.id}] in step {step.order_index + 1} is not workflow-compatible." raise exceptions.MessageException(message) tool_state = step.state tool_inputs = tool.inputs.copy() # Not strictly needed - but keep Tool state clean by stripping runtime # metadata parameters from it. if RUNTIME_STEP_META_STATE_KEY in tool_state.inputs: del tool_state.inputs[RUNTIME_STEP_META_STATE_KEY] all_inputs = self.get_all_inputs() all_inputs_by_name = {} for input_dict in all_inputs: all_inputs_by_name[input_dict["name"]] = input_dict collection_info = self.compute_collection_info(progress, step, all_inputs) param_combinations = [] if collection_info: iteration_elements_iter = collection_info.slice_collections() else: if progress.when_values: assert len(progress.when_values) == 1, "Got more than 1 when value, this shouldn't be possible" iteration_elements_iter = [(None, progress.when_values[0] if progress.when_values else None)] resource_parameters = invocation.resource_parameters for iteration_elements, when_value in iteration_elements_iter: execution_state = tool_state.copy() # TODO: Move next step into copy() execution_state.inputs = make_dict_copy(execution_state.inputs) expected_replacement_keys = set(step.input_connections_by_name.keys()) found_replacement_keys = set() # Connect up def callback(input, prefixed_name: str, **kwargs): input_dict = all_inputs_by_name[prefixed_name] replacement: Union[model.Dataset, NoReplacement] = NO_REPLACEMENT if iteration_elements and prefixed_name in iteration_elements: # noqa: B023 replacement = iteration_elements[prefixed_name] # noqa: B023 else: replacement = progress.replacement_for_input(trans, step, input_dict) if replacement is not NO_REPLACEMENT: if not isinstance(input, BaseDataToolParameter): # Probably a parameter that can be replaced dataset_instance: Optional[model.DatasetInstance] = None if isinstance(replacement, model.DatasetCollectionElement): dataset_instance = replacement.hda elif isinstance(replacement, model.DatasetInstance): dataset_instance = replacement if dataset_instance and dataset_instance.extension == "expression.json": with open(dataset_instance.get_file_name()) as f: replacement = json.load(f) found_replacement_keys.add(prefixed_name) # noqa: B023 # bool cast should be fine, can only have true/false on ConditionalStepWhen # also terrible of course and it's not needed for API requests if isinstance(input, ConditionalStepWhen) and bool(replacement) is False: raise SkipWorkflowStepEvaluation return replacement try: # Replace DummyDatasets with historydatasetassociations visit_input_values( tool_inputs, execution_state.inputs, callback, no_replacement_value=NO_REPLACEMENT, replace_optional_connections=True, ) except KeyError as k: message = f"Error due to input mapping of '{unicodify(k)}' in tool '{tool.id}'. A common cause of this is conditional outputs that cannot be determined until runtime, please review workflow step {step.order_index + 1}." raise exceptions.MessageException(message) if step.when_expression and when_value is not False: extra_step_state = {} for step_input in step.inputs: step_input_name = step_input.name input_in_execution_state = step_input_name not in execution_state.inputs if input_in_execution_state: if step_input_name in all_inputs_by_name: if iteration_elements and step_input_name in iteration_elements: # noqa: B023 value = iteration_elements[step_input_name] # noqa: B023 else: value = progress.replacement_for_input(trans, step, all_inputs_by_name[step_input_name]) # TODO: only do this for values... is everything with a default # this way a field parameter? I guess not? extra_step_state[step_input_name] = value # Might be needed someday... # elif step_input.default_value_set: # extra_step_state[step_input_name] = step_input.default_value else: if iteration_elements and step_input_name in iteration_elements: # noqa: B023 value = iteration_elements[step_input_name] # noqa: B023 else: value = progress.replacement_for_connection(step_input.connections[0], is_data=True) extra_step_state[step_input_name] = value if when_value is not False: when_value = evaluate_value_from_expressions( progress, step, execution_state=execution_state, extra_step_state=extra_step_state ) if when_value is not None: # Track this more formally ? execution_state.inputs["__when_value__"] = when_value unmatched_input_connections = expected_replacement_keys - found_replacement_keys if unmatched_input_connections: log.warning(f"Failed to use input connections for inputs [{unmatched_input_connections}]") param_combinations.append(execution_state.inputs) complete = False completed_jobs = {} for i, param in enumerate(param_combinations): if use_cached_job: completed_jobs[i] = tool.job_search.by_tool_input( trans=trans, tool_id=tool.id, tool_version=tool.version, param=param, param_dump=tool.params_to_strings(param, trans.app, nested=True), job_state=None, ) else: completed_jobs[i] = None try: mapping_params = MappingParameters(tool_state.inputs, param_combinations) max_num_jobs = progress.maximum_jobs_to_schedule_or_none validate_outputs = False for pja in step.post_job_actions: if pja.action_type == "ValidateOutputsAction": validate_outputs = True execution_tracker = execute( trans=self.trans, tool=tool, mapping_params=mapping_params, history=invocation.history, collection_info=collection_info, workflow_invocation_uuid=invocation.uuid.hex, invocation_step=invocation_step, max_num_jobs=max_num_jobs, validate_outputs=validate_outputs, job_callback=lambda job: self._handle_post_job_actions( step, job, progress.effective_replacement_dict() ), completed_jobs=completed_jobs, workflow_resource_parameters=resource_parameters, ) complete = True except PartialJobExecution as pje: execution_tracker = pje.execution_tracker except ToolInputsNotReadyException: delayed_why = f"tool [{tool.id}] inputs are not ready, this special tool requires inputs to be ready" raise DelayedWorkflowEvaluation(why=delayed_why) progress.record_executed_job_count(len(execution_tracker.successful_jobs)) if collection_info: step_outputs = dict(execution_tracker.implicit_collections) else: step_outputs = dict(execution_tracker.output_datasets) step_outputs.update(execution_tracker.output_collections) progress.set_step_outputs(invocation_step, step_outputs, already_persisted=not invocation_step.is_new) if collection_info: step_inputs = mapping_params.param_template step_inputs.update(collection_info.collections) self._handle_mapped_over_post_job_actions( step, step_inputs, step_outputs, progress.effective_replacement_dict() ) if progress.when_values == [False] and not progress.subworkflow_collection_info: # Step skipped entirely. We hide the output to avoid confusion. # Could be revisited if we have a nice visual way to say these are skipped ? for output in step_outputs.values(): if isinstance(output, (model.HistoryDatasetAssociation, model.HistoryDatasetCollectionAssociation)): output.visible = False if execution_tracker.execution_errors: # TODO: formalize into InvocationFailure ? message = f"Failed to create {len(execution_tracker.execution_errors)} job(s) for workflow step {step.order_index + 1}: {str(execution_tracker.execution_errors[0])}" for error in execution_tracker.execution_errors: # try first to raise a structured invocation error message if isinstance(error, exceptions.ToolInputsNotOKException) and error.src == "hda": raise FailWorkflowEvaluation( why=InvocationFailureDatasetFailed( reason=FailureReason.dataset_failed, hda_id=error.id, workflow_step_id=step.id, ) ) raise exceptions.MessageException(message) return complete
def _effective_post_job_actions(self, step): effective_post_job_actions = step.post_job_actions[:] for key, value in self.runtime_post_job_actions.items(): effective_post_job_actions.append(self.__to_pja(key, value, None)) return effective_post_job_actions def _handle_mapped_over_post_job_actions(self, step, step_inputs, step_outputs, replacement_dict): effective_post_job_actions = self._effective_post_job_actions(step) for pja in effective_post_job_actions: if pja.action_type in ActionBox.mapped_over_output_actions: ActionBox.execute_on_mapped_over( self.trans, self.trans.sa_session, pja, step_inputs, step_outputs, replacement_dict ) def _handle_post_job_actions(self, step, job, replacement_dict): # Create new PJA associations with the created job, to be run on completion. # PJA Parameter Replacement (only applies to immediate actions-- rename specifically, for now) # Pass along replacement dict with the execution of the PJA so we don't have to modify the object. # Combine workflow and runtime post job actions into the effective post # job actions for this execution. effective_post_job_actions = self._effective_post_job_actions(step) for pja in effective_post_job_actions: if pja.action_type in ActionBox.immediate_actions or isinstance(self.tool, DatabaseOperationTool): ActionBox.execute(self.trans.app, self.trans.sa_session, pja, job, replacement_dict) else: if job.id: pjaa = model.PostJobActionAssociation(pja, job_id=job.id) else: pjaa = model.PostJobActionAssociation(pja, job=job) self.trans.sa_session.add(pjaa) def __restore_step_meta_runtime_state(self, step_runtime_state): if RUNTIME_POST_JOB_ACTIONS_KEY in step_runtime_state: self.runtime_post_job_actions = step_runtime_state[RUNTIME_POST_JOB_ACTIONS_KEY] def __step_meta_runtime_state(self): """Build a dictionary a of meta-step runtime state (state about how the workflow step - not the tool state) to be serialized with the Tool state. """ return {RUNTIME_POST_JOB_ACTIONS_KEY: self.runtime_post_job_actions} def __to_pja(self, key, value, step): if "output_name" in value: output_name = value["output_name"] else: output_name = None if "action_arguments" in value: action_arguments = value["action_arguments"] else: action_arguments = None return PostJobAction(value["action_type"], step, output_name, action_arguments)
[docs] def get_informal_replacement_parameters(self, step: WorkflowStep) -> List[str]: """Return a list of replacement parameters.""" replacement_parameters = set() for pja in step.post_job_actions: if action_arguments := pja.action_arguments: for argument in action_arguments.values(): for match in re.findall(r"\$\{(.+?)\}", unicodify(argument)): replacement_parameters.add(match) return list(replacement_parameters)
[docs]class WorkflowModuleFactory:
[docs] def __init__(self, module_types: Dict[str, Type[WorkflowModule]]): self.module_types = module_types
[docs] def from_dict(self, trans, d, **kwargs) -> WorkflowModule: """ Return module initialized from the data in dictionary `d`. """ type = d["type"] assert ( type in self.module_types ), f"Unexpected workflow step type [{type}] not found in [{self.module_types.keys()}]" return self.module_types[type].from_dict(trans, d, **kwargs)
[docs] def from_workflow_step(self, trans, step: WorkflowStep, **kwargs) -> WorkflowModule: """ Return module initialized from the WorkflowStep object `step`. """ type = step.type assert type return self.module_types[type].from_workflow_step(trans, step, **kwargs)
module_types = dict( data_input=InputDataModule, data_collection_input=InputDataCollectionModule, parameter_input=InputParameterModule, pause=PauseModule, tool=ToolModule, subworkflow=SubWorkflowModule, ) module_factory = WorkflowModuleFactory(module_types)
[docs]def load_module_sections(trans): """Get abstract description of the workflow modules this Galaxy instance is configured with. """ module_sections = {} module_sections["inputs"] = { "name": "inputs", "title": "Inputs", "modules": [ {"name": "data_input", "title": "Input Dataset", "description": "Input dataset"}, { "name": "data_collection_input", "title": "Input Dataset Collection", "description": "Input dataset collection", }, { "name": "parameter_input", "title": "Parameter Input", "description": "Simple inputs used for workflow logic", }, ], } if trans.app.config.enable_beta_workflow_modules: module_sections["experimental"] = { "name": "experimental", "title": "Experimental", "modules": [ {"name": "pause", "title": "Pause Workflow for Dataset Review", "description": "Pause for Review"} ], } return module_sections
[docs]class DelayedWorkflowEvaluation(Exception):
[docs] def __init__(self, why=None): self.why = why
[docs]class CancelWorkflowEvaluation(Exception):
[docs] def __init__(self, why: "InvocationMessageUnion"): self.why = why
[docs]class FailWorkflowEvaluation(Exception):
[docs] def __init__(self, why: "InvocationMessageUnion"): self.why = why
[docs]class SkipWorkflowStepEvaluation(Exception): pass
[docs]class WorkflowModuleInjector: """Injects workflow step objects from the ORM with appropriate module and module generated/influenced state."""
[docs] def __init__(self, trans, allow_tool_state_corrections=False): self.trans = trans self.allow_tool_state_corrections = allow_tool_state_corrections
[docs] def inject(self, step: WorkflowStep, step_args=None, steps=None, **kwargs): """Pre-condition: `step` is an ORM object coming from the database, if supplied `step_args` is the representation of the inputs for that step supplied via web form. Post-condition: The supplied `step` has new non-persistent attributes useful during workflow invocation. These include 'upgrade_messages', 'state', 'input_connections_by_name', and 'module'. If step_args is provided from a web form this is applied to generate 'state' else it is just obtained from the database. """ step.upgrade_messages = {} # Make connection information available on each step by input name. step.setup_input_connections_by_name() # Populate module. module = step.module = module_factory.from_workflow_step(self.trans, step, **kwargs) # Any connected input needs to have value DummyDataset (these # are not persisted so we need to do it every time) module.add_dummy_datasets(connections=step.input_connections, steps=steps) # Populate subworkflow components if step.type == "subworkflow": subworkflow_param_map = step_args or {} unjsonified_subworkflow_param_map = {} for key, value in subworkflow_param_map.items(): unjsonified_subworkflow_param_map[int(key)] = value subworkflow = step.subworkflow assert subworkflow populate_module_and_state(self.trans, subworkflow, param_map=unjsonified_subworkflow_param_map)
[docs] def inject_all(self, workflow: Workflow, param_map=None, ignore_tool_missing_exception=False, **kwargs): param_map = param_map or {} for step in workflow.steps: step_args = param_map.get(step.id, {}) try: self.inject(step, steps=workflow.steps, step_args=step_args, **kwargs) except ToolMissingException: if not ignore_tool_missing_exception: raise
[docs] def compute_runtime_state(self, step: WorkflowStep, step_args=None): assert step.module, "module must be injected before computing runtime state" state, step_errors = step.module.compute_runtime_state(self.trans, step, step_args) step.state = state # Fix any missing parameters step.upgrade_messages = step.module.check_and_update_state() return step_errors
[docs]def populate_module_and_state( trans, workflow: Workflow, param_map, allow_tool_state_corrections=False, module_injector=None ): """Used by API but not web controller, walks through a workflow's steps and populates transient module and state attributes on each. """ if module_injector is None: module_injector = WorkflowModuleInjector(trans, allow_tool_state_corrections) module_injector.inject_all(workflow, param_map=param_map) for step in workflow.steps: step_args = param_map.get(step.id, {}) step_errors = module_injector.compute_runtime_state(step, step_args=step_args) if step_errors: raise exceptions.MessageException( "Error computing workflow step runtime state", err_data={step.order_index: step_errors} ) if step.upgrade_messages: if allow_tool_state_corrections: log.debug('Workflow step "%i" had upgrade messages: %s', step.id, step.upgrade_messages) else: raise exceptions.MessageException( "Workflow step has upgrade messages", err_data={step.order_index: step.upgrade_messages} )