class JobController(BaseAPIController, UsesVisualizationMixin):
def __init__(self, app):
super(JobController, self).__init__(app)
self.job_manager = JobManager(app)
self.job_search = JobSearch(app)
self.hda_manager = hdas.HDAManager(app)
@expose_api
def index(self, trans, **kwd):
"""
index( trans, state=None, tool_id=None, history_id=None, date_range_min=None, date_range_max=None, user_details=False )
* GET /api/jobs:
return jobs for current user
!! if user is admin and user_details is True, then
return jobs for all galaxy users based on filtering - this is an extended service
:type state: string or list
:param state: limit listing of jobs to those that match one of the included states. If none, all are returned.
Valid Galaxy job states include:
'new', 'upload', 'waiting', 'queued', 'running', 'ok', 'error', 'paused', 'deleted', 'deleted_new'
:type tool_id: string or list
:param tool_id: limit listing of jobs to those that match one of the included tool_ids. If none, all are returned.
:type user_details: boolean
:param user_details: if true, and requestor is an admin, will return external job id and user email.
:type date_range_min: string '2014-01-01'
:param date_range_min: limit the listing of jobs to those updated on or after requested date
:type date_range_max: string '2014-12-31'
:param date_range_max: limit the listing of jobs to those updated on or before requested date
:type history_id: string
:param history_id: limit listing of jobs to those that match the history_id. If none, all are returned.
:rtype: list
:returns: list of dictionaries containing summary job information
"""
state = kwd.get('state', None)
is_admin = trans.user_is_admin
user_details = kwd.get('user_details', False)
if is_admin:
query = trans.sa_session.query(trans.app.model.Job)
else:
query = trans.sa_session.query(trans.app.model.Job).filter(trans.app.model.Job.user == trans.user)
def build_and_apply_filters(query, objects, filter_func):
if objects is not None:
if isinstance(objects, string_types):
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, state, lambda s: trans.app.model.Job.state == s)
query = build_and_apply_filters(query, kwd.get('tool_id', None), lambda t: trans.app.model.Job.tool_id == t)
query = build_and_apply_filters(query, kwd.get('tool_id_like', None), lambda t: trans.app.model.Job.tool_id.like(t))
query = build_and_apply_filters(query, kwd.get('date_range_min', None), lambda dmin: trans.app.model.Job.table.c.update_time >= dmin)
query = build_and_apply_filters(query, kwd.get('date_range_max', None), lambda dmax: trans.app.model.Job.table.c.update_time <= dmax)
history_id = kwd.get('history_id', None)
if history_id is not None:
try:
decoded_history_id = self.decode_id(history_id)
query = query.filter(trans.app.model.Job.history_id == decoded_history_id)
except Exception:
raise exceptions.ObjectAttributeInvalidException()
out = []
if kwd.get('order_by') == 'create_time':
order_by = trans.app.model.Job.create_time.desc()
else:
order_by = trans.app.model.Job.update_time.desc()
for job in query.order_by(order_by).all():
job_dict = job.to_dict('collection', system_details=is_admin)
j = self.encode_all_ids(trans, job_dict, True)
if user_details:
j['user_email'] = job.user.email
out.append(j)
return out
@expose_api_anonymous
def show(self, trans, id, **kwd):
"""
show( trans, id )
* GET /api/jobs/{id}:
return jobs for current user
:type id: string
:param id: Specific job id
:type full: boolean
:param full: whether to return extra information
:rtype: dictionary
:returns: dictionary containing full description of job data
"""
job = self.__get_job(trans, id)
is_admin = trans.user_is_admin
job_dict = self.encode_all_ids(trans, job.to_dict('element', system_details=is_admin), True)
full_output = util.asbool(kwd.get('full', 'false'))
if full_output:
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
))
if is_admin:
if job.user:
job_dict['user_email'] = job.user.email
else:
job_dict['user_email'] = None
job_dict['job_metrics'] = summarize_job_metrics(trans, job)
return job_dict
@expose_api
def common_problems(self, trans, id, **kwd):
"""
* GET /api/jobs/{id}/common_problems
check inputs and job for common potential problems to aid in error reporting
"""
job = self.__get_job(trans, id)
seen_ids = set()
has_empty_inputs = False
has_duplicate_inputs = False
for job_input_assoc in job.input_datasets:
input_dataset_instance = job_input_assoc.dataset
if input_dataset_instance is None:
continue
if input_dataset_instance.get_total_size() == 0:
has_empty_inputs = True
input_instance_id = input_dataset_instance.id
if input_instance_id in seen_ids:
has_duplicate_inputs = True
else:
seen_ids.add(input_instance_id)
# TODO: check percent of failing jobs around a window on job.update_time for handler - report if high.
# TODO: check percent of failing jobs around a window on job.update_time for destination_id - report if high.
# TODO: sniff inputs (add flag to allow checking files?)
return {"has_empty_inputs": has_empty_inputs, "has_duplicate_inputs": has_duplicate_inputs}
@expose_api
def inputs(self, trans, id, **kwd):
"""
show( trans, id )
* GET /api/jobs/{id}/inputs
returns input datasets created by job
:type id: string
:param id: Encoded job id
:rtype: dictionary
:returns: dictionary containing input dataset associations
"""
job = self.__get_job(trans, id)
return self.__dictify_associations(trans, job.input_datasets, job.input_library_datasets)
@expose_api
def outputs(self, trans, id, **kwd):
"""
outputs( trans, id )
* GET /api/jobs/{id}/outputs
returns output datasets created by job
:type id: string
:param id: Encoded job id
:rtype: dictionary
:returns: dictionary containing output dataset associations
"""
job = self.__get_job(trans, id)
return self.__dictify_associations(trans, job.output_datasets, job.output_library_datasets)
@expose_api
def delete(self, trans, id, **kwd):
"""
delete( trans, id )
* Delete /api/jobs/{id}
cancels specified job
:type id: string
:param id: Encoded job id
:type message: string
:param message: Stop message.
"""
payload = kwd.get("payload") or {}
job = self.__get_job(trans, id)
message = payload.get("message", None)
return self.job_manager.stop(job, message=message)
@expose_api
def resume(self, trans, id, **kwd):
"""
* PUT /api/jobs/{id}/resume
Resumes a paused job
:type id: string
:param id: Encoded job id
:rtype: dictionary
:returns: dictionary containing output dataset associations
"""
job = self.__get_job(trans, id)
if not job:
raise exceptions.ObjectNotFound("Could not access job with id '%s'" % id)
if job.state == job.states.PAUSED:
job.resume()
else:
exceptions.RequestParameterInvalidException("Job with id '%s' is not paused" % (job.tool_id))
return self.__dictify_associations(trans, job.output_datasets, job.output_library_datasets)
@expose_api_anonymous
def metrics(self, trans, **kwd):
"""
* GET /api/jobs/{job_id}/metrics
* GET /api/datasets/{dataset_id}/metrics
Return job metrics for specified job. Job accessibility checks are slightly
different than dataset checks, so both methods are available.
:type job_id: string
:param job_id: Encoded job id
:type dataset_id: string
:param dataset_id: Encoded HDA or LDDA id
:type hda_ldda: string
:param hda_ldda: hda if dataset_id is an HDA id (default), ldda if
it is an ldda id.
:rtype: list
:returns: list containing job metrics
"""
job = self.__get_job(trans, **kwd)
return summarize_job_metrics(trans, job)
@require_admin
@expose_api
def destination_params(self, trans, **kwd):
"""
* GET /api/jobs/{job_id}/destination_params
Return destination parameters for specified job.
:type job_id: string
:param job_id: Encoded job id
:rtype: list
:returns: list containing job destination parameters
"""
job = self.__get_job(trans, **kwd)
return summarize_destination_params(trans, job)
@expose_api_anonymous
def parameters_display(self, trans, **kwd):
"""
* GET /api/jobs/{job_id}/parameters_display
* GET /api/datasets/{dataset_id}/parameters_display
Resolve parameters as a list for nested display. More client logic
here than is ideal but it is hard to reason about tool parameter
types on the client relative to the server. Job accessibility checks
are slightly different than dataset checks, so both methods are
available.
This API endpoint is unstable and tied heavily to Galaxy's JS client code,
this endpoint will change frequently.
:type job_id: string
:param job_id: Encoded job id
:type dataset_id: string
:param dataset_id: Encoded HDA or LDDA id
:type hda_ldda: string
:param hda_ldda: hda if dataset_id is an HDA id (default), ldda if
it is an ldda id.
:rtype: list
:returns: job parameters for for display
"""
job = self.__get_job(trans, **kwd)
return summarize_job_parameters(trans, job)
@expose_api_anonymous
def build_for_rerun(self, trans, id, **kwd):
"""
* GET /api/jobs/{id}/build_for_rerun
returns a tool input/param template prepopulated with this job's
information, suitable for rerunning or rendering parameters of the
job.
:type id: string
:param id: Encoded job id
:rtype: dictionary
:returns: dictionary containing output dataset associations
"""
job = self.__get_job(trans, id)
if not job:
raise exceptions.ObjectNotFound("Could not access job with id '%s'" % id)
tool = self.app.toolbox.get_tool(job.tool_id, kwd.get('tool_version') or job.tool_version)
if tool is None:
raise exceptions.ObjectNotFound("Requested tool not found")
if not tool.is_workflow_compatible:
raise exceptions.ConfigDoesNotAllowException("Tool '%s' cannot be rerun." % (job.tool_id))
return tool.to_json(trans, {}, job=job)
def __dictify_associations(self, trans, *association_lists):
rval = []
for association_list in association_lists:
rval.extend(self.__dictify_association(trans, a) for a in association_list)
return rval
def __dictify_association(self, trans, job_dataset_association):
dataset_dict = None
dataset = job_dataset_association.dataset
if dataset:
if isinstance(dataset, model.HistoryDatasetAssociation):
dataset_dict = dict(src="hda", id=trans.security.encode_id(dataset.id))
else:
dataset_dict = dict(src="ldda", id=trans.security.encode_id(dataset.id))
return dict(name=job_dataset_association.name, dataset=dataset_dict)
def __get_job(self, trans, job_id=None, dataset_id=None, **kwd):
if job_id is not None:
decoded_job_id = self.decode_id(job_id)
return self.job_manager.get_accessible_job(trans, decoded_job_id)
else:
hda_ldda = kwd.get("hda_ldda", "hda")
# Following checks dataset accessible
dataset_instance = self.get_hda_or_ldda(trans, hda_ldda=hda_ldda, dataset_id=dataset_id)
return dataset_instance.creating_job
@expose_api
def create(self, trans, payload, **kwd):
""" See the create method in tools.py in order to submit a job. """
raise exceptions.NotImplemented('Please POST to /api/tools instead.')
@expose_api
def search(self, trans, payload, **kwd):
"""
search( trans, payload )
* POST /api/jobs/search:
return jobs for current user
:type payload: dict
:param payload: Dictionary containing description of requested job. This is in the same format as
a request to POST /apt/tools would take to initiate a job
:rtype: list
:returns: list of dictionaries containing summary job information of the jobs that match the requested job run
This method is designed to scan the list of previously run jobs and find records of jobs that had
the exact some input parameters and datasets. This can be used to minimize the amount of repeated work, and simply
recycle the old results.
"""
tool_id = payload.get('tool_id')
if tool_id is None:
raise exceptions.RequestParameterMissingException("No tool id")
tool = trans.app.toolbox.get_tool(tool_id)
if tool is None:
raise exceptions.ObjectNotFound("Requested tool not found")
if 'inputs' not in payload:
raise exceptions.RequestParameterMissingException("No inputs defined")
inputs = payload.get('inputs', {})
# Find files coming in as multipart file data and add to inputs.
for k, v in payload.items():
if k.startswith('files_') or k.startswith('__files_'):
inputs[k] = v
request_context = WorkRequestContext(app=trans.app, user=trans.user, history=trans.history)
all_params, all_errors, _, _ = tool.expand_incoming(trans=trans, incoming=inputs, request_context=request_context)
if any(all_errors):
return []
params_dump = [tool.params_to_strings(param, self.app, nested=True) for param in all_params]
jobs = []
for param_dump, param in zip(params_dump, all_params):
job = self.job_search.by_tool_input(trans=trans,
tool_id=tool_id,
tool_version=tool.version,
param=param,
param_dump=param_dump,
job_state=payload.get('state'))
if job:
jobs.append(job)
return [self.encode_all_ids(trans, single_job.to_dict('element'), True) for single_job in jobs]
@expose_api_anonymous
def error(self, trans, id, payload, **kwd):
"""
error( trans, id )
* POST /api/jobs/{id}/error
submits a bug report via the API.
:type id: string
:param id: Encoded job id
:rtype: dictionary
:returns: dictionary containing information regarding where the error report was sent.
"""
# Get dataset on which this error was triggered
dataset_id = payload.get('dataset_id')
if not dataset_id:
raise exceptions.RequestParameterMissingException('No dataset_id')
decoded_dataset_id = self.decode_id(dataset_id)
dataset = self.hda_manager.get_accessible(decoded_dataset_id, trans.user)
# Get job
job = self.__get_job(trans, id)
if dataset.creating_job.id != job.id:
raise exceptions.RequestParameterInvalidException('dataset_id was not created by job_id')
tool = trans.app.toolbox.get_tool(job.tool_id, tool_version=job.tool_version) or None
email = payload.get('email')
if not email and not trans.anonymous:
email = trans.user.email
messages = trans.app.error_reports.default_error_plugin.submit_report(
dataset=dataset,
job=job,
tool=tool,
user_submission=True,
user=trans.user,
email=email,
message=payload.get('message')
)
return {'messages': messages}
@require_admin
@expose_api
def show_job_lock(self, trans, **kwd):
"""
* GET /api/job_lock
return boolean indicating if job lock active.
"""
return {"active": self.app.job_manager.job_lock}
@require_admin
@expose_api
def update_job_lock(self, trans, payload, **kwd):
"""
* PUT /api/job_lock
return boolean indicating if job lock active.
"""
job_lock = payload.get("active")
self.app.queue_worker.send_control_task('admin_job_lock', kwargs={'job_lock': job_lock}, get_response=True)
return {"active": self.app.job_manager.job_lock}
class JobController(BaseAPIController, UsesVisualizationMixin):
def __init__(self, app):
super(JobController, self).__init__(app)
self.job_manager = JobManager(app)
self.job_search = JobSearch(app)
@expose_api
def index(self, trans, **kwd):
"""
index( trans, state=None, tool_id=None, history_id=None, date_range_min=None, date_range_max=None, user_details=False )
* GET /api/jobs:
return jobs for current user
!! if user is admin and user_details is True, then
return jobs for all galaxy users based on filtering - this is an extended service
:type state: string or list
:param state: limit listing of jobs to those that match one of the included states. If none, all are returned.
Valid Galaxy job states include:
'new', 'upload', 'waiting', 'queued', 'running', 'ok', 'error', 'paused', 'deleted', 'deleted_new'
:type tool_id: string or list
:param tool_id: limit listing of jobs to those that match one of the included tool_ids. If none, all are returned.
:type user_details: boolean
:param user_details: if true, and requestor is an admin, will return external job id and user email.
:type date_range_min: string '2014-01-01'
:param date_range_min: limit the listing of jobs to those updated on or after requested date
:type date_range_max: string '2014-12-31'
:param date_range_max: limit the listing of jobs to those updated on or before requested date
:type history_id: string
:param history_id: limit listing of jobs to those that match the history_id. If none, all are returned.
:rtype: list
:returns: list of dictionaries containing summary job information
"""
state = kwd.get('state', None)
is_admin = trans.user_is_admin
user_details = kwd.get('user_details', False)
if is_admin:
query = trans.sa_session.query(trans.app.model.Job)
else:
query = trans.sa_session.query(trans.app.model.Job).filter(
trans.app.model.Job.user == trans.user)
def build_and_apply_filters(query, objects, filter_func):
if objects is not None:
if isinstance(objects, string_types):
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, state, lambda s: trans.app.model.Job.state == s)
query = build_and_apply_filters(
query, kwd.get('tool_id', None),
lambda t: trans.app.model.Job.tool_id == t)
query = build_and_apply_filters(
query, kwd.get('tool_id_like', None),
lambda t: trans.app.model.Job.tool_id.like(t))
query = build_and_apply_filters(
query, kwd.get('date_range_min', None),
lambda dmin: trans.app.model.Job.table.c.update_time >= dmin)
query = build_and_apply_filters(
query, kwd.get('date_range_max', None),
lambda dmax: trans.app.model.Job.table.c.update_time <= dmax)
history_id = kwd.get('history_id', None)
if history_id is not None:
try:
decoded_history_id = self.decode_id(history_id)
query = query.filter(
trans.app.model.Job.history_id == decoded_history_id)
except Exception:
raise exceptions.ObjectAttributeInvalidException()
out = []
if kwd.get('order_by') == 'create_time':
order_by = trans.app.model.Job.create_time.desc()
else:
order_by = trans.app.model.Job.update_time.desc()
for job in query.order_by(order_by).all():
job_dict = job.to_dict('collection', system_details=is_admin)
j = self.encode_all_ids(trans, job_dict, True)
if user_details:
j['user_email'] = job.user.email
out.append(j)
return out
@expose_api_anonymous
def show(self, trans, id, **kwd):
"""
show( trans, id )
* GET /api/jobs/{id}:
return jobs for current user
:type id: string
:param id: Specific job id
:type full: boolean
:param full: whether to return extra information
:rtype: dictionary
:returns: dictionary containing full description of job data
"""
job = self.__get_job(trans, id)
is_admin = trans.user_is_admin
job_dict = self.encode_all_ids(
trans, job.to_dict('element', system_details=is_admin), True)
full_output = util.asbool(kwd.get('full', 'false'))
if full_output:
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))
if is_admin:
if job.user:
job_dict['user_email'] = job.user.email
else:
job_dict['user_email'] = None
def metric_to_dict(metric):
metric_name = metric.metric_name
metric_value = metric.metric_value
metric_plugin = metric.plugin
title, value = trans.app.job_metrics.format(
metric_plugin, metric_name, metric_value)
return dict(
title=title,
value=value,
plugin=metric_plugin,
name=metric_name,
raw_value=str(metric_value),
)
job_dict['job_metrics'] = self._metrics_as_dict(trans, job)
return job_dict
@expose_api
def common_problems(self, trans, id, **kwd):
"""
* GET /api/jobs/{id}/common_problems
check inputs and job for common potential problems to aid in error reporting
"""
job = self.__get_job(trans, id)
seen_ids = set()
has_empty_inputs = False
has_duplicate_inputs = False
for job_input_assoc in job.input_datasets:
input_dataset_instance = job_input_assoc.dataset
if input_dataset_instance is None:
continue
if input_dataset_instance.get_total_size() == 0:
has_empty_inputs = True
input_instance_id = input_dataset_instance.id
if input_instance_id in seen_ids:
has_duplicate_inputs = True
else:
seen_ids.add(input_instance_id)
# TODO: check percent of failing jobs around a window on job.update_time for handler - report if high.
# TODO: check percent of failing jobs around a window on job.update_time for destination_id - report if high.
# TODO: sniff inputs (add flag to allow checking files?)
return {
"has_empty_inputs": has_empty_inputs,
"has_duplicate_inputs": has_duplicate_inputs
}
@expose_api
def inputs(self, trans, id, **kwd):
"""
show( trans, id )
* GET /api/jobs/{id}/inputs
returns input datasets created by job
:type id: string
:param id: Encoded job id
:rtype: dictionary
:returns: dictionary containing input dataset associations
"""
job = self.__get_job(trans, id)
return self.__dictify_associations(trans, job.input_datasets,
job.input_library_datasets)
@expose_api
def outputs(self, trans, id, **kwd):
"""
outputs( trans, id )
* GET /api/jobs/{id}/outputs
returns output datasets created by job
:type id: string
:param id: Encoded job id
:rtype: dictionary
:returns: dictionary containing output dataset associations
"""
job = self.__get_job(trans, id)
return self.__dictify_associations(trans, job.output_datasets,
job.output_library_datasets)
@expose_api
def delete(self, trans, id, **kwd):
"""
delete( trans, id )
* Delete /api/jobs/{id}
cancels specified job
:type id: string
:param id: Encoded job id
"""
job = self.__get_job(trans, id)
if not job.finished:
job.mark_deleted(self.app.config.track_jobs_in_database)
trans.sa_session.flush()
self.app.job_manager.stop(job)
return True
else:
return False
@expose_api
def resume(self, trans, id, **kwd):
"""
* PUT /api/jobs/{id}/resume
Resumes a paused job
:type id: string
:param id: Encoded job id
:rtype: dictionary
:returns: dictionary containing output dataset associations
"""
job = self.__get_job(trans, id)
if not job:
raise exceptions.ObjectNotFound(
"Could not access job with id '%s'" % id)
if job.state == job.states.PAUSED:
job.resume()
else:
exceptions.RequestParameterInvalidException(
"Job with id '%s' is not paused" % (job.tool_id))
return self.__dictify_associations(trans, job.output_datasets,
job.output_library_datasets)
@expose_api_anonymous
def metrics(self, trans, **kwd):
"""
* GET /api/jobs/{job_id}/metrics
* GET /api/datasets/{dataset_id}/metrics
Return job metrics for specified job. Job accessibility checks are slightly
different than dataset checks, so both methods are available.
:type job_id: string
:param job_id: Encoded job id
:type dataset_id: string
:param dataset_id: Encoded HDA or LDDA id
:type hda_ldda: string
:param hda_ldda: hda if dataset_id is an HDA id (default), ldda if
it is an ldda id.
:rtype: list
:returns: list containing job metrics
"""
job = self.__get_job(trans, **kwd)
if not trans.user_is_admin and not trans.app.config.expose_potentially_sensitive_job_metrics:
return []
return self._metrics_as_dict(trans, job)
def _metrics_as_dict(self, trans, job):
def metric_to_dict(metric):
metric_name = metric.metric_name
metric_value = metric.metric_value
metric_plugin = metric.plugin
title, value = trans.app.job_metrics.format(
metric_plugin, metric_name, metric_value)
return dict(
title=title,
value=value,
plugin=metric_plugin,
name=metric_name,
raw_value=str(metric_value),
)
metrics = [
m for m in job.metrics if m.plugin != 'env' or trans.user_is_admin
]
return list(map(metric_to_dict, metrics))
@expose_api_anonymous
def parameters_display(self, trans, **kwd):
"""
* GET /api/jobs/{job_id}/parameters_display
* GET /api/datasets/{dataset_id}/parameters_display
Resolve parameters as a list for nested display. More client logic
here than is ideal but it is hard to reason about tool parameter
types on the client relative to the server. Job accessibility checks
are slightly different than dataset checks, so both methods are
available.
This API endpoint is unstable and tied heavily to Galaxy's JS client code,
this endpoint will change frequently.
:type job_id: string
:param job_id: Encoded job id
:type dataset_id: string
:param dataset_id: Encoded HDA or LDDA id
:type hda_ldda: string
:param hda_ldda: hda if dataset_id is an HDA id (default), ldda if
it is an ldda id.
:rtype: list
:returns: job parameters for for display
"""
job = self.__get_job(trans, **kwd)
def inputs_recursive(input_params,
param_values,
depth=1,
upgrade_messages=None):
if upgrade_messages is None:
upgrade_messages = {}
rval = []
for input_index, input in enumerate(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="%s uploaded datasets" %
len(param_values[input.name])))
elif input.type == "data":
value = []
for i, element in enumerate(
util.listify(param_values[input.name])):
if element.history_content_type == "dataset":
hda = element
encoded_id = trans.security.encode_id(hda.id)
value.append({
"src": "hda",
"id": encoded_id,
"hid": hda.hid,
"name": hda.name
})
else:
value.append({
"hid": element.hid,
"name": element.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
toolbox = self.app.toolbox
tool = toolbox.get_tool(job.tool_id, job.tool_version)
assert tool is not None, 'Requested tool has not been loaded.'
params_objects = None
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
try:
params_objects = job.get_param_values(self.app,
ignore_errors=False)
except Exception:
params_objects = job.get_param_values(self.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(self.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)
return {
"parameters": parameters,
"has_parameter_errors": has_parameter_errors
}
@expose_api_anonymous
def build_for_rerun(self, trans, id, **kwd):
"""
* GET /api/jobs/{id}/build_for_rerun
returns a tool input/param template prepopulated with this job's
information, suitable for rerunning or rendering parameters of the
job.
:type id: string
:param id: Encoded job id
:rtype: dictionary
:returns: dictionary containing output dataset associations
"""
job = self.__get_job(trans, id)
if not job:
raise exceptions.ObjectNotFound(
"Could not access job with id '%s'" % id)
tool = self.app.toolbox.get_tool(
job.tool_id,
kwd.get('tool_version') or job.tool_version)
if tool is None:
raise exceptions.ObjectNotFound("Requested tool not found")
if not tool.is_workflow_compatible:
raise exceptions.ConfigDoesNotAllowException(
"Tool '%s' cannot be rerun." % (job.tool_id))
return tool.to_json(trans, {}, job=job)
def __dictify_associations(self, trans, *association_lists):
rval = []
for association_list in association_lists:
rval.extend(
self.__dictify_association(trans, a) for a in association_list)
return rval
def __dictify_association(self, trans, job_dataset_association):
dataset_dict = None
dataset = job_dataset_association.dataset
if dataset:
if isinstance(dataset, model.HistoryDatasetAssociation):
dataset_dict = dict(src="hda",
id=trans.security.encode_id(dataset.id))
else:
dataset_dict = dict(src="ldda",
id=trans.security.encode_id(dataset.id))
return dict(name=job_dataset_association.name, dataset=dataset_dict)
def __get_job(self, trans, job_id=None, dataset_id=None, **kwd):
if job_id is not None:
try:
decoded_job_id = self.decode_id(job_id)
except Exception:
raise exceptions.MalformedId()
return self.job_manager.get_accessible_job(trans, decoded_job_id)
else:
hda_ldda = kwd.get("hda_ldda", "hda")
# Following checks dataset accessible
dataset_instance = self.get_hda_or_ldda(trans,
hda_ldda=hda_ldda,
dataset_id=dataset_id)
return dataset_instance.creating_job
@expose_api
def create(self, trans, payload, **kwd):
""" See the create method in tools.py in order to submit a job. """
raise exceptions.NotImplemented('Please POST to /api/tools instead.')
@expose_api
def search(self, trans, payload, **kwd):
"""
search( trans, payload )
* POST /api/jobs/search:
return jobs for current user
:type payload: dict
:param payload: Dictionary containing description of requested job. This is in the same format as
a request to POST /apt/tools would take to initiate a job
:rtype: list
:returns: list of dictionaries containing summary job information of the jobs that match the requested job run
This method is designed to scan the list of previously run jobs and find records of jobs that had
the exact some input parameters and datasets. This can be used to minimize the amount of repeated work, and simply
recycle the old results.
"""
tool_id = payload.get('tool_id')
if tool_id is None:
raise exceptions.ObjectAttributeMissingException("No tool id")
tool = trans.app.toolbox.get_tool(tool_id)
if tool is None:
raise exceptions.ObjectNotFound("Requested tool not found")
if 'inputs' not in payload:
raise exceptions.ObjectAttributeMissingException(
"No inputs defined")
inputs = payload.get('inputs', {})
# Find files coming in as multipart file data and add to inputs.
for k, v in payload.items():
if k.startswith('files_') or k.startswith('__files_'):
inputs[k] = v
request_context = WorkRequestContext(app=trans.app,
user=trans.user,
history=trans.history)
all_params, all_errors, _, _ = tool.expand_incoming(
trans=trans, incoming=inputs, request_context=request_context)
if any(all_errors):
return []
params_dump = [
tool.params_to_strings(param, self.app, nested=True)
for param in all_params
]
jobs = []
for param_dump, param in zip(params_dump, all_params):
job = self.job_search.by_tool_input(trans=trans,
tool_id=tool_id,
tool_version=tool.version,
param=param,
param_dump=param_dump,
job_state=payload.get('state'))
if job:
jobs.append(job)
return [
self.encode_all_ids(trans, single_job.to_dict('element'), True)
for single_job in jobs
]
@expose_api_anonymous
def error(self, trans, id, **kwd):
"""
error( trans, id )
* POST /api/jobs/{id}/error
submits a bug report via the API.
:type id: string
:param id: Encoded job id
:rtype: dictionary
:returns: dictionary containing information regarding where the error report was sent.
"""
# Get dataset on which this error was triggered
try:
decoded_dataset_id = self.decode_id(kwd['dataset_id'])
except Exception:
raise exceptions.MalformedId()
dataset = trans.sa_session.query(
trans.app.model.HistoryDatasetAssociation).get(decoded_dataset_id)
# Get job
job = self.__get_job(trans, id)
tool = trans.app.toolbox.get_tool(
job.tool_id, tool_version=job.tool_version) or None
email = kwd.get('email')
if not email and not trans.anonymous:
email = trans.user.email
messages = trans.app.error_reports.default_error_plugin.submit_report(
dataset=dataset,
job=job,
tool=tool,
user_submission=True,
user=trans.user,
email=email,
message=kwd.get('message'))
return {'messages': messages}
def _remap(container, line):
id_match = re.search(ID_PATTERN, line)
object_id = None
encoded_id = None
if id_match:
object_id = int(id_match.group(2))
encoded_id = trans.security.encode_id(object_id)
line = line.replace(id_match.group(),
"%s=%s" % (id_match.group(1), encoded_id))
def ensure_rendering_data_for(object_type, encoded_id):
if object_type not in extra_rendering_data:
extra_rendering_data[object_type] = {}
object_type_data = extra_rendering_data[object_type]
if encoded_id not in object_type_data:
object_type_data[encoded_id] = {}
return object_type_data[encoded_id]
def extend_history_dataset_rendering_data(key, val, default_val):
ensure_rendering_data_for("history_datasets",
encoded_id)[key] = val or default_val
if container == "history_dataset_display":
assert object_id is not None
hda = hdas_manager.get_accessible(object_id, trans.user)
if "history_datasets" not in extra_rendering_data:
extra_rendering_data["history_datasets"] = {}
extend_history_dataset_rendering_data("name", hda.name, "")
elif container == "history_dataset_peek":
assert object_id is not None
hda = hdas_manager.get_accessible(object_id, trans.user)
peek = hda.peek
extend_history_dataset_rendering_data(
"peek", peek, "*No Dataset Peek Available*")
elif container == "history_dataset_info":
hda = hdas_manager.get_accessible(object_id, trans.user)
info = hda.info
extend_history_dataset_rendering_data(
"info", info, "*No Dataset Peek Available*")
elif container == "workflow_display":
# TODO: should be workflow id...
stored_workflow = workflows_manager.get_stored_accessible_workflow(
trans, encoded_id)
ensure_rendering_data_for(
"workflows", encoded_id)["name"] = stored_workflow.name
elif container == "history_dataset_collection_display":
collection_manager = DatasetCollectionManager(trans.app)
hdca = collection_manager.get_dataset_collection_instance(
trans, "history", encoded_id)
hdca_serializer = HDCASerializer(trans.app)
hdca_view = hdca_serializer.serialize_to_view(hdca,
user=trans.user,
trans=trans,
view="summary")
if "history_dataset_collections" not in extra_rendering_data:
extra_rendering_data["history_dataset_collections"] = {}
ensure_rendering_data_for("history_dataset_collections",
encoded_id).update(hdca_view)
elif container == "tool_stdout":
job_manager = JobManager(trans.app)
job = job_manager.get_accessible_job(trans, object_id)
ensure_rendering_data_for(
"jobs", encoded_id
)["tool_stdout"] = job.tool_stdout or "*No Standard Output Available*"
elif container == "tool_stderr":
job_manager = JobManager(trans.app)
job = job_manager.get_accessible_job(trans, object_id)
ensure_rendering_data_for(
"jobs", encoded_id
)["tool_stderr"] = job.tool_stderr or "*No Standard Error Available*"
return (line, False)