Warning

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

Source code for galaxy.managers.jobs

import json
import logging
import typing
from datetime import (
    date,
    datetime,
)

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 import (
    Job,
    JobParameter,
)
from galaxy.model.base import transaction
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, date, datetime)): 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) session = self.app.model.session with transaction(session): session.commit() 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 # build one subquery that selects a job with correct job parameters subq = select(model.Job.id).where( and_( model.Job.tool_id == tool_id, model.Job.user_id == user.id, model.Job.copied_from_job_id.is_(None), # Always pick original job ) ) if tool_version: subq = subq.where(Job.tool_version == str(tool_version)) if job_state is None: subq = subq.where( Job.state.in_( [Job.states.NEW, Job.states.QUEUED, Job.states.WAITING, Job.states.RUNNING, Job.states.OK] ) ) else: if isinstance(job_state, str): subq = subq.where(Job.state == job_state) elif isinstance(job_state, list): subq = subq.where(or_(*[Job.state == s for s in job_state])) # exclude jobs with deleted outputs subq = subq.where( and_( model.Job.any_output_dataset_collection_instances_deleted == false(), model.Job.any_output_dataset_deleted == false(), ) ) 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(JobParameter) if value_dump == wildcard_value: subq = subq.join(a).where( and_( Job.id == a.job_id, a.name == k, a.value == value_dump, ) ) else: subq = subq.join(a).where( and_( Job.id == a.job_id, a.name == k, a.value.like(wildcard_value), ) ) query = select(Job.id).select_from(Job.table.join(subq, subq.c.id == Job.id)) 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) query = query.add_columns(a.dataset_id) used_ids.append(a.dataset_id) query = query.join(a, a.job_id == model.Job.id) stmt = select([model.HistoryDatasetAssociation.id]).where( model.HistoryDatasetAssociation.id == e.history_dataset_association_id ) # b is the HDA used for the job query = query.join(b, a.dataset_id == b.id).join(c, c.dataset_id == b.dataset_id) name_condition = [] if identifier: query = query.join(d) data_conditions.append( and_( 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), 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()), ) ) elif t == "ldda": a = aliased(model.JobToInputLibraryDatasetAssociation) query = query.add_columns(a.ldda_id) query = query.join(a, a.job_id == model.Job.id) data_conditions.append(and_(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) query = query.add_columns(a.dataset_collection_id) query = ( query.join(a, a.job_id == model.Job.id) .join(b, b.id == a.dataset_collection_id) .join(c, b.name == c.name) ) data_conditions.append( and_( a.name.in_(k), c.id == v, 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) d = aliased(model.HistoryDatasetAssociation) e = aliased(model.HistoryDatasetAssociation) query = query.add_columns(a.dataset_collection_element_id) query = ( query.join(a, a.job_id == model.Job.id) .join(b, b.id == a.dataset_collection_element_id) .join( c, and_( c.element_identifier == b.element_identifier, or_(c.hda_id == b.hda_id, c.child_collection_id == b.child_collection_id), ), ) .outerjoin(d, d.id == c.hda_id) .outerjoin(e, e.dataset_id == d.dataset_id) ) data_conditions.append( and_( a.name.in_(k), or_( c.child_collection_id == b.child_collection_id, and_( c.hda_id == b.hda_id, d.id == c.hda_id, e.dataset_id == d.dataset_id, ), ), c.id == v, ) ) used_ids.append(a.dataset_collection_element_id) else: return [] query = query.where(*data_conditions).group_by(model.Job.id, *used_ids).order_by(model.Job.id.desc()) for job in self.sa_session.execute(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[0]] 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" or input.type == "data_collection": 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 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