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Source code for galaxy.managers.jobs

import json
import logging
import typing

from boltons.iterutils import remap
from pydantic import (
    BaseModel,
    Field,
)
from sqlalchemy import (
    and_,
    false,
    func,
    or_,
)
from sqlalchemy.orm import aliased
from sqlalchemy.sql import select

from galaxy import model
from galaxy.exceptions import (
    AdminRequiredException,
    ItemAccessibilityException,
    ObjectNotFound,
    RequestParameterInvalidException,
)
from galaxy.job_metrics import (
    RawMetric,
    Safety,
)
from galaxy.managers.collections import DatasetCollectionManager
from galaxy.managers.datasets import DatasetManager
from galaxy.managers.hdas import HDAManager
from galaxy.managers.lddas import LDDAManager
from galaxy.model.index_filter_util import (
    raw_text_column_filter,
    text_column_filter,
)
from galaxy.model.scoped_session import galaxy_scoped_session
from galaxy.schema.schema import (
    JobIndexQueryPayload,
    JobIndexSortByEnum,
)
from galaxy.security.idencoding import IdEncodingHelper
from galaxy.structured_app import StructuredApp
from galaxy.util import (
    defaultdict,
    ExecutionTimer,
    listify,
)
from galaxy.util.search import (
    FilteredTerm,
    parse_filters_structured,
    RawTextTerm,
)

log = logging.getLogger(__name__)


[docs]class JobLock(BaseModel): active: bool = Field(title="Job lock status", description="If active, jobs will not dispatch")
[docs]def get_path_key(path_tuple): path_key = "" tuple_elements = len(path_tuple) for i, p in enumerate(path_tuple): if isinstance(p, int): sep = "_" else: sep = "|" if i == (tuple_elements - 2) and p == "values": # dataset inputs are always wrapped in lists. To avoid 'rep_factorName_0|rep_factorLevel_2|countsFile|values_0', # we remove the last 2 items of the path tuple (values and list index) return path_key if path_key: path_key = f"{path_key}{sep}{p}" else: path_key = p return path_key
[docs]class JobManager:
[docs] def __init__(self, app: StructuredApp): self.app = app self.dataset_manager = DatasetManager(app)
[docs] def index_query(self, trans, payload: JobIndexQueryPayload): is_admin = trans.user_is_admin user_details = payload.user_details decoded_user_id = payload.user_id if is_admin: if decoded_user_id is not None: query = trans.sa_session.query(model.Job).filter(model.Job.user_id == decoded_user_id) else: query = trans.sa_session.query(model.Job) if user_details: query = query.outerjoin(model.Job.user) else: if user_details: raise AdminRequiredException("Only admins can index the jobs with user details enabled") if decoded_user_id is not None and decoded_user_id != trans.user.id: raise AdminRequiredException("Only admins can index the jobs of others") query = trans.sa_session.query(model.Job).filter(model.Job.user_id == trans.user.id) def build_and_apply_filters(query, objects, filter_func): if objects is not None: if isinstance(objects, str): query = query.filter(filter_func(objects)) elif isinstance(objects, list): t = [] for obj in objects: t.append(filter_func(obj)) query = query.filter(or_(*t)) return query query = build_and_apply_filters(query, payload.states, lambda s: model.Job.state == s) query = build_and_apply_filters(query, payload.tool_ids, lambda t: model.Job.tool_id == t) query = build_and_apply_filters(query, payload.tool_ids_like, lambda t: model.Job.tool_id.like(t)) query = build_and_apply_filters(query, payload.date_range_min, lambda dmin: model.Job.update_time >= dmin) query = build_and_apply_filters(query, payload.date_range_max, lambda dmax: model.Job.update_time <= dmax) history_id = payload.history_id workflow_id = payload.workflow_id invocation_id = payload.invocation_id if history_id is not None: query = query.filter(model.Job.history_id == history_id) if workflow_id or invocation_id: if workflow_id is not None: wfi_step = ( trans.sa_session.query(model.WorkflowInvocationStep) .join(model.WorkflowInvocation) .join(model.Workflow) .filter( model.Workflow.stored_workflow_id == workflow_id, ) .subquery() ) elif invocation_id is not None: wfi_step = ( trans.sa_session.query(model.WorkflowInvocationStep) .filter(model.WorkflowInvocationStep.workflow_invocation_id == invocation_id) .subquery() ) query1 = query.join(wfi_step) query2 = query.join(model.ImplicitCollectionJobsJobAssociation).join( wfi_step, model.ImplicitCollectionJobsJobAssociation.implicit_collection_jobs_id == wfi_step.c.implicit_collection_jobs_id, ) query = query1.union(query2) search = payload.search if search: search_filters = { "tool": "tool", "t": "tool", } if user_details: search_filters.update( { "user": "user", "u": "user", } ) if is_admin: search_filters.update( { "runner": "runner", "r": "runner", "handler": "handler", "h": "handler", } ) parsed_search = parse_filters_structured(search, search_filters) for term in parsed_search.terms: if isinstance(term, FilteredTerm): key = term.filter if key == "user": query = query.filter(text_column_filter(model.User.email, term)) elif key == "tool": query = query.filter(text_column_filter(model.Job.tool_id, term)) elif key == "handler": query = query.filter(text_column_filter(model.Job.handler, term)) elif key == "runner": query = query.filter(text_column_filter(model.Job.job_runner_name, term)) elif isinstance(term, RawTextTerm): columns = [model.Job.tool_id] if user_details: columns.append(model.User.email) if is_admin: columns.append(model.Job.handler) columns.append(model.Job.job_runner_name) query = query.filter(raw_text_column_filter(columns, term)) if payload.order_by == JobIndexSortByEnum.create_time: order_by = model.Job.create_time.desc() else: order_by = model.Job.update_time.desc() query = query.order_by(order_by) query = query.offset(payload.offset) query = query.limit(payload.limit) return query
[docs] def job_lock(self) -> JobLock: return JobLock(active=self.app.job_manager.job_lock)
[docs] def update_job_lock(self, job_lock: JobLock): self.app.queue_worker.send_control_task( "admin_job_lock", kwargs={"job_lock": job_lock.active}, get_response=True ) return self.job_lock()
[docs] def get_accessible_job(self, trans, decoded_job_id): job = trans.sa_session.query(trans.app.model.Job).filter(trans.app.model.Job.id == decoded_job_id).first() if job is None: raise ObjectNotFound() belongs_to_user = ( (job.user_id == trans.user.id) if job.user_id and trans.user else (job.session_id == trans.get_galaxy_session().id) ) if not trans.user_is_admin and not belongs_to_user: # Check access granted via output datasets. if not job.output_datasets: raise ItemAccessibilityException("Job has no output datasets.") for data_assoc in job.output_datasets: if not self.dataset_manager.is_accessible(data_assoc.dataset.dataset, trans.user): raise ItemAccessibilityException("You are not allowed to rerun this job.") trans.sa_session.refresh(job) return job
[docs] def stop(self, job, message=None): if not job.finished: job.mark_deleted(self.app.config.track_jobs_in_database) self.app.model.session.flush() self.app.job_manager.stop(job, message=message) return True else: return False
[docs]class JobSearch: """Search for jobs using tool inputs or other jobs"""
[docs] def __init__( self, sa_session: galaxy_scoped_session, hda_manager: HDAManager, dataset_collection_manager: DatasetCollectionManager, ldda_manager: LDDAManager, id_encoding_helper: IdEncodingHelper, ): self.sa_session = sa_session self.hda_manager = hda_manager self.dataset_collection_manager = dataset_collection_manager self.ldda_manager = ldda_manager self.decode_id = id_encoding_helper.decode_id
[docs] def by_tool_input(self, trans, tool_id, tool_version, param=None, param_dump=None, job_state="ok"): """Search for jobs producing same results using the 'inputs' part of a tool POST.""" user = trans.user input_data = defaultdict(list) def populate_input_data_input_id(path, key, value): """Traverses expanded incoming using remap and collects input_ids and input_data.""" if key == "id": path_key = get_path_key(path[:-2]) current_case = param_dump for p in path: current_case = current_case[p] src = current_case["src"] current_case = param for i, p in enumerate(path): if p == "values" and i == len(path) - 2: continue if isinstance(current_case, (list, dict)): current_case = current_case[p] identifier = getattr(current_case, "element_identifier", None) input_data[path_key].append( { "src": src, "id": value, "identifier": identifier, } ) return key, "__id_wildcard__" return key, value wildcard_param_dump = remap(param_dump, visit=populate_input_data_input_id) return self.__search( tool_id=tool_id, tool_version=tool_version, user=user, input_data=input_data, job_state=job_state, param_dump=param_dump, wildcard_param_dump=wildcard_param_dump, )
def __search( self, tool_id, tool_version, user, input_data, job_state=None, param_dump=None, wildcard_param_dump=None ): search_timer = ExecutionTimer() def replace_dataset_ids(path, key, value): """Exchanges dataset_ids (HDA, LDA, HDCA, not Dataset) in param_dump with dataset ids used in job.""" if key == "id": current_case = param_dump for p in path: current_case = current_case[p] src = current_case["src"] value = job_input_ids[src][value] return key, value return key, value job_conditions = [ and_( model.Job.tool_id == tool_id, model.Job.user == user, model.Job.copied_from_job_id.is_(None), # Always pick original job ) ] if tool_version: job_conditions.append(model.Job.tool_version == str(tool_version)) if job_state is None: job_conditions.append( model.Job.state.in_( [ model.Job.states.NEW, model.Job.states.QUEUED, model.Job.states.WAITING, model.Job.states.RUNNING, model.Job.states.OK, ] ) ) else: if isinstance(job_state, str): job_conditions.append(model.Job.state == job_state) elif isinstance(job_state, list): o = [] for s in job_state: o.append(model.Job.state == s) job_conditions.append(or_(*o)) for k, v in wildcard_param_dump.items(): wildcard_value = None if v == {"__class__": "RuntimeValue"}: # TODO: verify this is always None. e.g. run with runtime input input v = None elif k.endswith("|__identifier__"): # We've taken care of this while constructing the conditions based on ``input_data`` above continue elif k == "chromInfo" and "?.len" in v: continue wildcard_value = '"%?.len"' if not wildcard_value: value_dump = json.dumps(v, sort_keys=True) wildcard_value = value_dump.replace('"id": "__id_wildcard__"', '"id": %') a = aliased(model.JobParameter) if value_dump == wildcard_value: job_conditions.append( and_( model.Job.id == a.job_id, a.name == k, a.value == value_dump, ) ) else: job_conditions.append(and_(model.Job.id == a.job_id, a.name == k, a.value.like(wildcard_value))) job_conditions.append( and_( model.Job.any_output_dataset_collection_instances_deleted == false(), model.Job.any_output_dataset_deleted == false(), ) ) subq = self.sa_session.query(model.Job.id).filter(*job_conditions).subquery() data_conditions = [] # We now build the query filters that relate to the input datasets # that this job uses. We keep track of the requested dataset id in `requested_ids`, # the type (hda, hdca or lda) in `data_types` # and the ids that have been used in the job that has already been run in `used_ids`. requested_ids = [] data_types = [] used_ids = [] for k, input_list in input_data.items(): # k will be matched against the JobParameter.name column. This can be prefixed depending on whethter # the input is in a repeat, or not (section and conditional) k = {k, k.split("|")[-1]} for type_values in input_list: t = type_values["src"] v = type_values["id"] requested_ids.append(v) data_types.append(t) identifier = type_values["identifier"] if t == "hda": a = aliased(model.JobToInputDatasetAssociation) b = aliased(model.HistoryDatasetAssociation) c = aliased(model.HistoryDatasetAssociation) d = aliased(model.JobParameter) e = aliased(model.HistoryDatasetAssociationHistory) stmt = select([model.HistoryDatasetAssociation.id]).where( model.HistoryDatasetAssociation.id == e.history_dataset_association_id ) name_condition = [] if identifier: data_conditions.append( and_( model.Job.id == d.job_id, d.name.in_({f"{_}|__identifier__" for _ in k}), d.value == json.dumps(identifier), ) ) else: stmt = stmt.where(e.name == c.name) name_condition.append(b.name == c.name) stmt = ( stmt.where( e.extension == c.extension, ) .where( a.dataset_version == e.version, ) .where( e._metadata == c._metadata, ) ) data_conditions.append( and_( a.name.in_(k), a.dataset_id == b.id, # b is the HDA used for the job c.dataset_id == b.dataset_id, c.id == v, # c is the requested job input HDA # We need to make sure that the job we are looking for has been run with identical inputs. # Here we deal with 3 requirements: # - the jobs' input dataset (=b) version is 0, meaning the job's input dataset is not yet ready # - b's update_time is older than the job create time, meaning no changes occurred # - the job has a dataset_version recorded, and that versions' metadata matches c's metadata. or_( and_( or_(a.dataset_version.in_([0, b.version]), b.update_time < model.Job.create_time), b.extension == c.extension, b.metadata == c.metadata, *name_condition, ), b.id.in_(stmt), ), or_(b.deleted == false(), c.deleted == false()), ) ) used_ids.append(a.dataset_id) elif t == "ldda": a = aliased(model.JobToInputLibraryDatasetAssociation) data_conditions.append(and_(model.Job.id == a.job_id, a.name.in_(k), a.ldda_id == v)) used_ids.append(a.ldda_id) elif t == "hdca": a = aliased(model.JobToInputDatasetCollectionAssociation) b = aliased(model.HistoryDatasetCollectionAssociation) c = aliased(model.HistoryDatasetCollectionAssociation) data_conditions.append( and_( model.Job.id == a.job_id, a.name.in_(k), b.id == a.dataset_collection_id, c.id == v, b.name == c.name, or_( and_(b.deleted == false(), b.id == v), and_( or_( c.copied_from_history_dataset_collection_association_id == b.id, b.copied_from_history_dataset_collection_association_id == c.id, ), c.deleted == false(), ), ), ) ) used_ids.append(a.dataset_collection_id) elif t == "dce": a = aliased(model.JobToInputDatasetCollectionElementAssociation) b = aliased(model.DatasetCollectionElement) c = aliased(model.DatasetCollectionElement) data_conditions.append( and_( model.Job.id == a.job_id, a.name.in_(k), a.dataset_collection_element_id == b.id, b.element_identifier == c.element_identifier, c.child_collection_id == b.child_collection_id, c.id == v, ) ) used_ids.append(a.dataset_collection_element_id) else: return [] query = ( self.sa_session.query(model.Job.id, *used_ids) .join(subq, model.Job.id == subq.c.id) .filter(*data_conditions) .group_by(model.Job.id, *used_ids) .order_by(model.Job.id.desc()) ) for job in query: # We found a job that is equal in terms of tool_id, user, state and input datasets, # but to be able to verify that the parameters match we need to modify all instances of # dataset_ids (HDA, LDDA, HDCA) in the incoming param_dump to point to those used by the # possibly equivalent job, which may have been run on copies of the original input data. job_input_ids = {} if len(job) > 1: # We do have datasets to check job_id, current_jobs_data_ids = job[0], job[1:] job_parameter_conditions = [model.Job.id == job_id] for src, requested_id, used_id in zip(data_types, requested_ids, current_jobs_data_ids): if src not in job_input_ids: job_input_ids[src] = {requested_id: used_id} else: job_input_ids[src][requested_id] = used_id new_param_dump = remap(param_dump, visit=replace_dataset_ids) # new_param_dump has its dataset ids remapped to those used by the job. # We now ask if the remapped job parameters match the current job. for k, v in new_param_dump.items(): if v == {"__class__": "RuntimeValue"}: # TODO: verify this is always None. e.g. run with runtime input input v = None elif k.endswith("|__identifier__"): # We've taken care of this while constructing the conditions based on ``input_data`` above continue elif k == "chromInfo" and "?.len" in v: continue wildcard_value = '"%?.len"' if not wildcard_value: wildcard_value = json.dumps(v, sort_keys=True).replace('"id": "__id_wildcard__"', '"id": %') a = aliased(model.JobParameter) job_parameter_conditions.append( and_(model.Job.id == a.job_id, a.name == k, a.value == json.dumps(v, sort_keys=True)) ) else: job_parameter_conditions = [model.Job.id == job] query = self.sa_session.query(model.Job).filter(*job_parameter_conditions) job = query.first() if job is None: continue n_parameters = 0 # Verify that equivalent jobs had the same number of job parameters # We skip chrominfo, dbkey, __workflow_invocation_uuid__ and identifer # parameter as these are not passed along when expanding tool parameters # and they can differ without affecting the resulting dataset. for parameter in job.parameters: if parameter.name.startswith("__"): continue if parameter.name in {"chromInfo", "dbkey"} or parameter.name.endswith("|__identifier__"): continue n_parameters += 1 if not n_parameters == sum( 1 for k in param_dump if not k.startswith("__") and not k.endswith("|__identifier__") and k not in {"chromInfo", "dbkey"} ): continue log.info("Found equivalent job %s", search_timer) return job log.info("No equivalent jobs found %s", search_timer) return None
[docs]def view_show_job(trans, job, full: bool) -> typing.Dict: is_admin = trans.user_is_admin job_dict = trans.app.security.encode_all_ids(job.to_dict("element", system_details=is_admin), True) if trans.app.config.expose_dataset_path and "command_line" not in job_dict: job_dict["command_line"] = job.command_line if full: job_dict.update( dict( tool_stdout=job.tool_stdout, tool_stderr=job.tool_stderr, job_stdout=job.job_stdout, job_stderr=job.job_stderr, stderr=job.stderr, stdout=job.stdout, job_messages=job.job_messages, dependencies=job.dependencies, ) ) if is_admin: job_dict["user_email"] = job.get_user_email() job_dict["job_metrics"] = summarize_job_metrics(trans, job) return job_dict
[docs]def invocation_job_source_iter(sa_session, invocation_id): # TODO: Handle subworkflows. join = model.WorkflowInvocationStep.table.join(model.WorkflowInvocation) statement = ( select( [ model.WorkflowInvocationStep.job_id, model.WorkflowInvocationStep.implicit_collection_jobs_id, model.WorkflowInvocationStep.state, ] ) .select_from(join) .where(model.WorkflowInvocation.id == invocation_id) ) for row in sa_session.execute(statement): if row[0]: yield ("Job", row[0], row[2]) if row[1]: yield ("ImplicitCollectionJobs", row[1], row[2])
[docs]def fetch_job_states(sa_session, job_source_ids, job_source_types): assert len(job_source_ids) == len(job_source_types) job_ids = set() implicit_collection_job_ids = set() workflow_invocations_job_sources = {} workflow_invocation_states = ( {} ) # should be set before we walk step states to be conservative on whether things are done expanding yet for job_source_id, job_source_type in zip(job_source_ids, job_source_types): if job_source_type == "Job": job_ids.add(job_source_id) elif job_source_type == "ImplicitCollectionJobs": implicit_collection_job_ids.add(job_source_id) elif job_source_type == "WorkflowInvocation": invocation_state = sa_session.query(model.WorkflowInvocation).get(job_source_id).state workflow_invocation_states[job_source_id] = invocation_state workflow_invocation_job_sources = [] for ( invocation_step_source_type, invocation_step_source_id, invocation_step_state, ) in invocation_job_source_iter(sa_session, job_source_id): workflow_invocation_job_sources.append( (invocation_step_source_type, invocation_step_source_id, invocation_step_state) ) if invocation_step_source_type == "Job": job_ids.add(invocation_step_source_id) elif invocation_step_source_type == "ImplicitCollectionJobs": implicit_collection_job_ids.add(invocation_step_source_id) workflow_invocations_job_sources[job_source_id] = workflow_invocation_job_sources else: raise RequestParameterInvalidException(f"Invalid job source type {job_source_type} found.") job_summaries = {} implicit_collection_jobs_summaries = {} for job_id in job_ids: job_summaries[job_id] = summarize_jobs_to_dict(sa_session, sa_session.query(model.Job).get(job_id)) for implicit_collection_jobs_id in implicit_collection_job_ids: implicit_collection_jobs_summaries[implicit_collection_jobs_id] = summarize_jobs_to_dict( sa_session, sa_session.query(model.ImplicitCollectionJobs).get(implicit_collection_jobs_id) ) rval = [] for job_source_id, job_source_type in zip(job_source_ids, job_source_types): if job_source_type == "Job": rval.append(job_summaries[job_source_id]) elif job_source_type == "ImplicitCollectionJobs": rval.append(implicit_collection_jobs_summaries[job_source_id]) else: invocation_state = workflow_invocation_states[job_source_id] invocation_job_summaries = [] invocation_implicit_collection_job_summaries = [] invocation_step_states = [] for ( invocation_step_source_type, invocation_step_source_id, invocation_step_state, ) in workflow_invocations_job_sources[job_source_id]: invocation_step_states.append(invocation_step_state) if invocation_step_source_type == "Job": invocation_job_summaries.append(job_summaries[invocation_step_source_id]) else: invocation_implicit_collection_job_summaries.append( implicit_collection_jobs_summaries[invocation_step_source_id] ) rval.append( summarize_invocation_jobs( job_source_id, invocation_job_summaries, invocation_implicit_collection_job_summaries, invocation_state, invocation_step_states, ) ) return rval
[docs]def summarize_invocation_jobs( invocation_id, job_summaries, implicit_collection_job_summaries, invocation_state, invocation_step_states ): states = {} if invocation_state == "scheduled": all_scheduled = True for invocation_step_state in invocation_step_states: all_scheduled = all_scheduled and invocation_step_state == "scheduled" if all_scheduled: populated_state = "ok" else: populated_state = "new" elif invocation_state in ["cancelled", "failed"]: populated_state = "failed" else: # call new, ready => new populated_state = "new" def merge_states(component_states): for key, value in component_states.items(): if key not in states: states[key] = value else: states[key] += value for job_summary in job_summaries: merge_states(job_summary["states"]) for implicit_collection_job_summary in implicit_collection_job_summaries: # 'new' (un-populated collections might not yet have a states entry) if "states" in implicit_collection_job_summary: merge_states(implicit_collection_job_summary["states"]) component_populated_state = implicit_collection_job_summary["populated_state"] if component_populated_state == "failed": populated_state = "failed" elif component_populated_state == "new" and populated_state != "failed": populated_state = "new" rval = { "id": invocation_id, "model": "WorkflowInvocation", "states": states, "populated_state": populated_state, } return rval
[docs]def summarize_jobs_to_dict(sa_session, jobs_source): """Produce a summary of jobs for job summary endpoints. :type jobs_source: a Job or ImplicitCollectionJobs or None :param jobs_source: the object to summarize :rtype: dict :returns: dictionary containing job summary information """ rval = None if jobs_source is None: pass elif isinstance(jobs_source, model.Job): rval = { "populated_state": "ok", "states": {jobs_source.state: 1}, "model": "Job", "id": jobs_source.id, } else: populated_state = jobs_source.populated_state rval = { "id": jobs_source.id, "populated_state": populated_state, "model": "ImplicitCollectionJobs", } if populated_state == "ok": # produce state summary... states = {} join = model.ImplicitCollectionJobs.table.join( model.ImplicitCollectionJobsJobAssociation.table.join(model.Job) ) statement = ( select([model.Job.state, func.count("*")]) .select_from(join) .where(model.ImplicitCollectionJobs.id == jobs_source.id) .group_by(model.Job.state) ) for row in sa_session.execute(statement): states[row[0]] = row[1] rval["states"] = states return rval
[docs]def summarize_job_metrics(trans, job): """Produce a dict-ified version of job metrics ready for tabular rendering. Precondition: the caller has verified the job is accessible to the user represented by the trans parameter. """ safety_level = Safety.SAFE if trans.user_is_admin: safety_level = Safety.UNSAFE elif trans.app.config.expose_potentially_sensitive_job_metrics: safety_level = Safety.POTENTIALLY_SENSITVE raw_metrics = [ RawMetric( m.metric_name, m.metric_value, m.plugin, ) for m in job.metrics ] dictifiable_metrics = trans.app.job_metrics.dictifiable_metrics(raw_metrics, safety_level) return [d.dict() for d in dictifiable_metrics]
[docs]def summarize_destination_params(trans, job): """Produce a dict-ified version of job destination parameters ready for tabular rendering. Precondition: the caller has verified the job is accessible to the user represented by the trans parameter. """ destination_params = { "Runner": job.job_runner_name, "Runner Job ID": job.job_runner_external_id, "Handler": job.handler, } job_destination_params = job.destination_params if job_destination_params: destination_params.update(job_destination_params) return destination_params
[docs]def summarize_job_parameters(trans, job): """Produce a dict-ified version of job parameters ready for tabular rendering. Precondition: the caller has verified the job is accessible to the user represented by the trans parameter. """ def inputs_recursive(input_params, param_values, depth=1, upgrade_messages=None): if upgrade_messages is None: upgrade_messages = {} rval = [] for input in input_params.values(): if input.name in param_values: if input.type == "repeat": for i in range(len(param_values[input.name])): rval.extend(inputs_recursive(input.inputs, param_values[input.name][i], depth=depth + 1)) elif input.type == "section": # Get the value of the current Section parameter rval.append(dict(text=input.name, depth=depth)) rval.extend( inputs_recursive( input.inputs, param_values[input.name], depth=depth + 1, upgrade_messages=upgrade_messages.get(input.name), ) ) elif input.type == "conditional": try: current_case = param_values[input.name]["__current_case__"] is_valid = True except Exception: current_case = None is_valid = False if is_valid: rval.append( dict(text=input.test_param.label, depth=depth, value=input.cases[current_case].value) ) rval.extend( inputs_recursive( input.cases[current_case].inputs, param_values[input.name], depth=depth + 1, upgrade_messages=upgrade_messages.get(input.name), ) ) else: rval.append( dict( text=input.name, depth=depth, notes="The previously used value is no longer valid.", error=True, ) ) elif input.type == "upload_dataset": rval.append( dict( text=input.group_title(param_values), depth=depth, value=f"{len(param_values[input.name])} uploaded datasets", ) ) elif input.type == "data": value = [] for element in listify(param_values[input.name]): encoded_id = trans.security.encode_id(element.id) if isinstance(element, model.HistoryDatasetAssociation): hda = element value.append({"src": "hda", "id": encoded_id, "hid": hda.hid, "name": hda.name}) elif isinstance(element, model.DatasetCollectionElement): value.append({"src": "dce", "id": encoded_id, "name": element.element_identifier}) elif isinstance(element, model.HistoryDatasetCollectionAssociation): value.append({"src": "hdca", "id": encoded_id, "hid": element.hid, "name": element.name}) else: raise Exception( f"Unhandled data input parameter type encountered {element.__class__.__name__}" ) rval.append(dict(text=input.label, depth=depth, value=value)) elif input.visible: if hasattr(input, "label") and input.label: label = input.label else: # value for label not required, fallback to input name (same as tool panel) label = input.name rval.append( dict( text=label, depth=depth, value=input.value_to_display_text(param_values[input.name]), notes=upgrade_messages.get(input.name, ""), ) ) else: # Parameter does not have a stored value. # Get parameter label. if input.type == "conditional": label = input.test_param.label elif input.type == "repeat": label = input.label() else: label = input.label or input.name rval.append( dict(text=label, depth=depth, notes="not used (parameter was added after this job was run)") ) return rval # Load the tool app = trans.app toolbox = app.toolbox tool = toolbox.get_tool(job.tool_id, job.tool_version) params_objects = None parameters = [] upgrade_messages = {} has_parameter_errors = False # Load parameter objects, if a parameter type has changed, it's possible for the value to no longer be valid if tool: try: params_objects = job.get_param_values(app, ignore_errors=False) except Exception: params_objects = job.get_param_values(app, ignore_errors=True) # use different param_objects in the following line, since we want to display original values as much as possible upgrade_messages = tool.check_and_update_param_values( job.get_param_values(app, ignore_errors=True), trans, update_values=False ) has_parameter_errors = True parameters = inputs_recursive(tool.inputs, params_objects, depth=1, upgrade_messages=upgrade_messages) else: has_parameter_errors = True return { "parameters": parameters, "has_parameter_errors": has_parameter_errors, "outputs": summarize_job_outputs(job=job, tool=tool, params=params_objects, security=trans.security), }
[docs]def get_output_name(tool, output, params): try: return tool.tool_action.get_output_name( output, tool=tool, params=params, ) except Exception: pass
[docs]def summarize_job_outputs(job: model.Job, tool, params, security): outputs = defaultdict(list) output_labels = {} possible_outputs = ( ("hda", "dataset_id", job.output_datasets), ("ldda", "ldda_id", job.output_library_datasets), ("hdca", "dataset_collection_id", job.output_dataset_collection_instances), ) for src, attribute, output_associations in possible_outputs: for output_association in output_associations: output_name = output_association.name if output_name not in output_labels and tool: tool_output = tool.output_collections if src == "hdca" else tool.outputs output_labels[output_name] = get_output_name( tool=tool, output=tool_output.get(output_name), params=params ) label = output_labels.get(output_name) outputs[output_name].append( { "label": label, "value": {"src": src, "id": security.encode_id(getattr(output_association, attribute))}, } ) return outputs