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Source code for galaxy.tools.actions
import json
import logging
import re
from json import dumps
from six import string_types
from galaxy import model
from galaxy.exceptions import ObjectInvalid
from galaxy.jobs.actions.post import ActionBox
from galaxy.model import LibraryDatasetDatasetAssociation, WorkflowRequestInputParameter
from galaxy.tools.parameters import update_dataset_ids
from galaxy.tools.parameters.basic import DataCollectionToolParameter, DataToolParameter, RuntimeValue
from galaxy.tools.parameters.wrapped import WrappedParameters
from galaxy.util import ExecutionTimer
from galaxy.util.none_like import NoneDataset
from galaxy.util.odict import odict
from galaxy.util.template import fill_template
from galaxy.web import url_for
log = logging.getLogger(__name__)
[docs]class ToolExecutionCache(object):
""" An object mean to cache calculation caused by repeatedly evaluting
the same tool by the same user with slightly different parameters.
"""
[docs] def __init__(self, trans):
self.trans = trans
self.current_user_roles = trans.get_current_user_roles()
[docs]class ToolAction(object):
"""
The actions to be taken when a tool is run (after parameters have
been converted and validated).
"""
[docs] def execute(self, tool, trans, incoming=None, set_output_hid=True):
incoming = incoming or {}
raise TypeError("Abstract method")
[docs]class DefaultToolAction(object):
"""Default tool action is to run an external command"""
def _collect_input_datasets(self, tool, param_values, trans, history, current_user_roles=None):
"""
Collect any dataset inputs from incoming. Returns a mapping from
parameter name to Dataset instance for each tool parameter that is
of the DataToolParameter type.
"""
if current_user_roles is None:
current_user_roles = trans.get_current_user_roles()
input_datasets = odict()
def visitor(input, value, prefix, parent=None, **kwargs):
def process_dataset(data, formats=None):
if not data or isinstance(data, RuntimeValue):
return None
if formats is None:
formats = input.formats
if not data.datatype.matches_any(formats):
# Need to refresh in case this conversion just took place, i.e. input above in tool performed the same conversion
trans.sa_session.refresh(data)
target_ext, converted_dataset = data.find_conversion_destination(formats)
if target_ext:
if converted_dataset:
data = converted_dataset
else:
data = data.get_converted_dataset(trans, target_ext, target_context=parent, history=history)
if not trans.app.security_agent.can_access_dataset(current_user_roles, data.dataset):
raise Exception("User does not have permission to use a dataset (%s) provided for input." % data.id)
return data
if isinstance(input, DataToolParameter):
if isinstance(value, list):
# If there are multiple inputs with the same name, they
# are stored as name1, name2, ...
for i, v in enumerate(value):
processed_dataset = process_dataset(v)
if i == 0:
# Allow copying metadata to output, first item will be source.
input_datasets[prefix + input.name] = processed_dataset
input_datasets[prefix + input.name + str(i + 1)] = processed_dataset
conversions = []
for conversion_name, conversion_extensions, conversion_datatypes in input.conversions:
new_data = process_dataset(input_datasets[prefix + input.name + str(i + 1)], conversion_datatypes)
if not new_data or new_data.datatype.matches_any(conversion_datatypes):
input_datasets[prefix + conversion_name + str(i + 1)] = new_data
conversions.append((conversion_name, new_data))
else:
raise Exception('A path for explicit datatype conversion has not been found: %s --/--> %s' % (input_datasets[prefix + input.name + str(i + 1)].extension, conversion_extensions))
if parent:
parent[input.name][i] = input_datasets[prefix + input.name + str(i + 1)]
for conversion_name, conversion_data in conversions:
# allow explicit conversion to be stored in job_parameter table
parent[conversion_name][i] = conversion_data.id # a more robust way to determine JSONable value is desired
else:
param_values[input.name][i] = input_datasets[prefix + input.name + str(i + 1)]
for conversion_name, conversion_data in conversions:
# allow explicit conversion to be stored in job_parameter table
param_values[conversion_name][i] = conversion_data.id # a more robust way to determine JSONable value is desired
else:
input_datasets[prefix + input.name] = process_dataset(value)
conversions = []
for conversion_name, conversion_extensions, conversion_datatypes in input.conversions:
new_data = process_dataset(input_datasets[prefix + input.name], conversion_datatypes)
if not new_data or new_data.datatype.matches_any(conversion_datatypes):
input_datasets[prefix + conversion_name] = new_data
conversions.append((conversion_name, new_data))
else:
raise Exception('A path for explicit datatype conversion has not been found: %s --/--> %s' % (input_datasets[prefix + input.name].extension, conversion_extensions))
target_dict = parent
if not target_dict:
target_dict = param_values
target_dict[input.name] = input_datasets[prefix + input.name]
for conversion_name, conversion_data in conversions:
# allow explicit conversion to be stored in job_parameter table
target_dict[conversion_name] = conversion_data.id # a more robust way to determine JSONable value is desired
elif isinstance(input, DataCollectionToolParameter):
if not value:
return
dataset_instances = []
if hasattr(value, 'child_collection'):
# if we are mapping a collection over a tool, we only require the child_collection
dataset_instances = value.child_collection.dataset_instances
else:
# else the tool takes a collection as input so we need everything
dataset_instances = value.collection.dataset_instances
for i, v in enumerate(dataset_instances):
data = v
if not trans.app.security_agent.can_access_dataset(current_user_roles, data.dataset):
raise Exception("User does not have permission to use a dataset (%s) provided for input." % data.id)
# Skipping implicit conversion stuff for now, revisit at
# some point and figure out if implicitly converting a
# dataset collection makes senese.
input_datasets[prefix + input.name + str(i + 1)] = data
tool.visit_inputs(param_values, visitor)
return input_datasets
[docs] def collect_input_dataset_collections(self, tool, param_values):
def append_to_key(the_dict, key, value):
if key not in the_dict:
the_dict[key] = []
the_dict[key].append(value)
input_dataset_collections = dict()
def visitor(input, value, prefix, parent=None, prefixed_name=None, **kwargs):
if isinstance(input, DataToolParameter):
values = value
if not isinstance(values, list):
values = [value]
for i, value in enumerate(values):
if isinstance(value, model.HistoryDatasetCollectionAssociation):
append_to_key(input_dataset_collections, prefixed_name, (value, True))
target_dict = parent
if not target_dict:
target_dict = param_values
# This is just a DataToolParameter, so replace this
# collection with individual datasets. Database will still
# record collection which should be enought for workflow
# extraction and tool rerun.
dataset_instances = value.collection.dataset_instances
if i == 0:
target_dict[input.name] = []
target_dict[input.name].extend(dataset_instances)
elif isinstance(input, DataCollectionToolParameter):
append_to_key(input_dataset_collections, prefix + input.name, (value, False))
tool.visit_inputs(param_values, visitor)
return input_dataset_collections
def _check_access(self, tool, trans):
assert tool.allow_user_access(trans.user), "User (%s) is not allowed to access this tool." % (trans.user)
def _collect_inputs(self, tool, trans, incoming, history, current_user_roles):
""" Collect history as well as input datasets and collections. """
app = trans.app
# Set history.
if not history:
history = tool.get_default_history_by_trans(trans, create=True)
if history not in trans.sa_session:
history = trans.sa_session.query(app.model.History).get(history.id)
# Track input dataset collections - but replace with simply lists so collect
# input datasets can process these normally.
inp_dataset_collections = self.collect_input_dataset_collections(tool, incoming)
# Collect any input datasets from the incoming parameters
inp_data = self._collect_input_datasets(tool, incoming, trans, history=history, current_user_roles=current_user_roles)
# grap tags from incoming HDAs
preserved_tags = {}
for data in inp_data.values():
if not data:
continue
for tag in [t for t in data.tags if t.user_tname == 'name']:
preserved_tags[tag.value] = tag
# grap tags from incoming HDCAs
for collection_pairs in inp_dataset_collections.values():
for collection, _ in collection_pairs:
# if sub-collection mapping, this will be an DC not an HDCA
# (e.g. part of collection not a collection instance) and thus won't have tags.
if hasattr(collection, "tags"):
for tag in [t for t in collection.tags if t.user_tname == 'name']:
preserved_tags[tag.value] = tag
return history, inp_data, inp_dataset_collections, preserved_tags
[docs] def execute(self, tool, trans, incoming=None, return_job=False, set_output_hid=True, history=None, job_params=None, rerun_remap_job_id=None, execution_cache=None, dataset_collection_elements=None, completed_job=None):
"""
Executes a tool, creating job and tool outputs, associating them, and
submitting the job to the job queue. If history is not specified, use
trans.history as destination for tool's output datasets.
"""
incoming = incoming or {}
self._check_access(tool, trans)
app = trans.app
if execution_cache is None:
execution_cache = ToolExecutionCache(trans)
current_user_roles = execution_cache.current_user_roles
history, inp_data, inp_dataset_collections, preserved_tags = self._collect_inputs(tool, trans, incoming, history, current_user_roles)
# Build name for output datasets based on tool name and input names
on_text = self._get_on_text(inp_data)
# format='input" previously would give you a random extension from
# the input extensions, now it should just give "input" as the output
# format.
input_ext = 'data' if tool.profile < 16.04 else "input"
input_dbkey = incoming.get("dbkey", "?")
for name, data in reversed(inp_data.items()):
if not data:
data = NoneDataset(datatypes_registry=app.datatypes_registry)
continue
# Convert LDDA to an HDA.
if isinstance(data, LibraryDatasetDatasetAssociation) and not completed_job:
data = data.to_history_dataset_association(None)
inp_data[name] = data
if tool.profile < 16.04:
input_ext = data.ext
if data.dbkey not in [None, '?']:
input_dbkey = data.dbkey
identifier = getattr(data, "element_identifier", None)
if identifier is not None:
incoming["%s|__identifier__" % name] = identifier
# Collect chromInfo dataset and add as parameters to incoming
(chrom_info, db_dataset) = app.genome_builds.get_chrom_info(input_dbkey, trans=trans, custom_build_hack_get_len_from_fasta_conversion=tool.id != 'CONVERTER_fasta_to_len')
if db_dataset:
inp_data.update({"chromInfo": db_dataset})
incoming["chromInfo"] = chrom_info
if not completed_job:
# Determine output dataset permission/roles list
existing_datasets = [inp for inp in inp_data.values() if inp]
if existing_datasets:
output_permissions = app.security_agent.guess_derived_permissions_for_datasets(existing_datasets)
else:
# No valid inputs, we will use history defaults
output_permissions = app.security_agent.history_get_default_permissions(history)
# Add the dbkey to the incoming parameters
incoming["dbkey"] = input_dbkey
# wrapped params are used by change_format action and by output.label; only perform this wrapping once, as needed
wrapped_params = self._wrapped_params(trans, tool, incoming, inp_data)
out_data = odict()
input_collections = dict((k, v[0][0]) for k, v in inp_dataset_collections.items())
output_collections = OutputCollections(
trans,
history,
tool=tool,
tool_action=self,
input_collections=input_collections,
dataset_collection_elements=dataset_collection_elements,
on_text=on_text,
incoming=incoming,
params=wrapped_params.params,
job_params=job_params,
tags=preserved_tags,
)
# Keep track of parent / child relationships, we'll create all the
# datasets first, then create the associations
parent_to_child_pairs = []
child_dataset_names = set()
object_store_populator = ObjectStorePopulator(app)
def handle_output(name, output, hidden=None):
if output.parent:
parent_to_child_pairs.append((output.parent, name))
child_dataset_names.add(name)
# What is the following hack for? Need to document under what
# conditions can the following occur? (james@bx.psu.edu)
# HACK: the output data has already been created
# this happens i.e. as a result of the async controller
if name in incoming:
dataid = incoming[name]
data = trans.sa_session.query(app.model.HistoryDatasetAssociation).get(dataid)
assert data is not None
out_data[name] = data
else:
ext = determine_output_format(
output,
wrapped_params.params,
inp_data,
inp_dataset_collections,
input_ext
)
create_datasets = True
dataset = None
if completed_job:
for output_dataset in completed_job.output_datasets:
if output_dataset.name == name:
create_datasets = False
completed_data = output_dataset.dataset
dataset = output_dataset.dataset.dataset
break
data = app.model.HistoryDatasetAssociation(extension=ext, dataset=dataset, create_dataset=create_datasets, flush=False)
if hidden is None:
hidden = output.hidden
if not hidden and dataset_collection_elements is not None: # Mapping over a collection - hide datasets
hidden = True
if hidden:
data.visible = False
if dataset_collection_elements is not None and name in dataset_collection_elements:
dataset_collection_elements[name].hda = data
trans.sa_session.add(data)
if not completed_job:
trans.app.security_agent.set_all_dataset_permissions(data.dataset, output_permissions, new=True)
data.copy_tags_to(preserved_tags)
# Must flush before setting object store id currently.
# TODO: optimize this.
trans.sa_session.flush()
if not completed_job:
object_store_populator.set_object_store_id(data)
# This may not be neccesary with the new parent/child associations
data.designation = name
# Copy metadata from one of the inputs if requested.
# metadata source can be either a string referencing an input
# or an actual object to copy.
metadata_source = output.metadata_source
if metadata_source:
if isinstance(metadata_source, string_types):
metadata_source = inp_data.get(metadata_source)
if metadata_source is not None:
data.init_meta(copy_from=metadata_source)
else:
data.init_meta()
# Take dbkey from LAST input
data.dbkey = str(input_dbkey)
# Set state
if completed_job:
data.blurb = completed_data.blurb
data.peek = completed_data.peek
data._metadata = completed_data._metadata
else:
data.blurb = "queued"
# Set output label
data.name = self.get_output_name(output, data, tool, on_text, trans, incoming, history, wrapped_params.params, job_params)
# Store output
out_data[name] = data
if output.actions:
# Apply pre-job tool-output-dataset actions; e.g. setting metadata, changing format
output_action_params = dict(out_data)
output_action_params.update(incoming)
output.actions.apply_action(data, output_action_params)
# Also set the default values of actions of type metadata
self.set_metadata_defaults(output, data, tool, on_text, trans, incoming, history, wrapped_params.params, job_params)
# Flush all datasets at once.
return data
for name, output in tool.outputs.items():
if not filter_output(output, incoming):
handle_output_timer = ExecutionTimer()
if output.collection:
collections_manager = app.dataset_collections_service
element_identifiers = []
known_outputs = output.known_outputs(input_collections, collections_manager.type_registry)
# Just to echo TODO elsewhere - this should be restructured to allow
# nested collections.
for output_part_def in known_outputs:
# Add elements to top-level collection, unless nested...
current_element_identifiers = element_identifiers
current_collection_type = output.structure.collection_type
for parent_id in (output_part_def.parent_ids or []):
# TODO: replace following line with formal abstractions for doing this.
current_collection_type = ":".join(current_collection_type.split(":")[1:])
name_to_index = dict((value["name"], index) for (index, value) in enumerate(current_element_identifiers))
if parent_id not in name_to_index:
if parent_id not in current_element_identifiers:
index = len(current_element_identifiers)
current_element_identifiers.append(dict(
name=parent_id,
collection_type=current_collection_type,
src="new_collection",
element_identifiers=[],
))
else:
index = name_to_index[parent_id]
current_element_identifiers = current_element_identifiers[index]["element_identifiers"]
effective_output_name = output_part_def.effective_output_name
element = handle_output(effective_output_name, output_part_def.output_def, hidden=True)
# TODO: this shouldn't exist in the top-level of the history at all
# but for now we are still working around that by hiding the contents
# there.
# Following hack causes dataset to no be added to history...
child_dataset_names.add(effective_output_name)
history.add_dataset(element, set_hid=set_output_hid, quota=False)
trans.sa_session.add(element)
trans.sa_session.flush()
current_element_identifiers.append({
"__object__": element,
"name": output_part_def.element_identifier,
})
log.info(element_identifiers)
if output.dynamic_structure:
assert not element_identifiers # known_outputs must have been empty
element_kwds = dict(elements=collections_manager.ELEMENTS_UNINITIALIZED)
else:
element_kwds = dict(element_identifiers=element_identifiers)
output_collections.create_collection(
output=output,
name=name,
**element_kwds
)
log.info("Handled collection output named %s for tool %s %s" % (name, tool.id, handle_output_timer))
else:
handle_output(name, output)
log.info("Handled output named %s for tool %s %s" % (name, tool.id, handle_output_timer))
add_datasets_timer = ExecutionTimer()
# Add all the top-level (non-child) datasets to the history unless otherwise specified
datasets_to_persist = []
for name in out_data.keys():
if name not in child_dataset_names and name not in incoming: # don't add children; or already existing datasets, i.e. async created
data = out_data[name]
datasets_to_persist.append(data)
# Set HID and add to history.
# This is brand new and certainly empty so don't worry about quota.
# TOOL OPTIMIZATION NOTE - from above loop to the job create below 99%+
# of execution time happens within in history.add_datasets.
history.add_datasets(trans.sa_session, datasets_to_persist, set_hid=set_output_hid, quota=False, flush=False)
# Add all the children to their parents
for parent_name, child_name in parent_to_child_pairs:
parent_dataset = out_data[parent_name]
child_dataset = out_data[child_name]
parent_dataset.children.append(child_dataset)
log.info("Added output datasets to history %s" % add_datasets_timer)
job_setup_timer = ExecutionTimer()
# Create the job object
job, galaxy_session = self._new_job_for_session(trans, tool, history)
self._record_inputs(trans, tool, job, incoming, inp_data, inp_dataset_collections, current_user_roles)
self._record_outputs(job, out_data, output_collections)
job.object_store_id = object_store_populator.object_store_id
if job_params:
job.params = dumps(job_params)
job.set_handler(tool.get_job_handler(job_params))
if completed_job:
job.set_copied_from_job_id(completed_job.id)
trans.sa_session.add(job)
# Now that we have a job id, we can remap any outputs if this is a rerun and the user chose to continue dependent jobs
# This functionality requires tracking jobs in the database.
if app.config.track_jobs_in_database and rerun_remap_job_id is not None:
self._remap_job_on_rerun(trans=trans,
galaxy_session=galaxy_session,
rerun_remap_job_id=rerun_remap_job_id,
current_job=job,
out_data=out_data)
log.info("Setup for job %s complete, ready to flush %s" % (job.log_str(), job_setup_timer))
job_flush_timer = ExecutionTimer()
trans.sa_session.flush()
log.info("Flushed transaction for job %s %s" % (job.log_str(), job_flush_timer))
# Some tools are not really executable, but jobs are still created for them ( for record keeping ).
# Examples include tools that redirect to other applications ( epigraph ). These special tools must
# include something that can be retrieved from the params ( e.g., REDIRECT_URL ) to keep the job
# from being queued.
if 'REDIRECT_URL' in incoming:
# Get the dataset - there should only be 1
for name in inp_data.keys():
dataset = inp_data[name]
redirect_url = tool.parse_redirect_url(dataset, incoming)
# GALAXY_URL should be include in the tool params to enable the external application
# to send back to the current Galaxy instance
GALAXY_URL = incoming.get('GALAXY_URL', None)
assert GALAXY_URL is not None, "GALAXY_URL parameter missing in tool config."
redirect_url += "&GALAXY_URL=%s" % GALAXY_URL
# Job should not be queued, so set state to ok
job.set_state(app.model.Job.states.OK)
job.info = "Redirected to: %s" % redirect_url
trans.sa_session.add(job)
trans.sa_session.flush()
trans.response.send_redirect(url_for(controller='tool_runner', action='redirect', redirect_url=redirect_url))
else:
# Put the job in the queue if tracking in memory
app.job_manager.job_queue.put(job.id, job.tool_id)
trans.log_event("Added job to the job queue, id: %s" % str(job.id), tool_id=job.tool_id)
return job, out_data
def _remap_job_on_rerun(self, trans, galaxy_session, rerun_remap_job_id, current_job, out_data):
"""
Re-connect dependent datasets for a job that is being rerun (because it failed initially).
If a job fails, the user has the option to try the job again with changed parameters.
To be able to resume jobs that depend on this jobs output datasets we change the dependent's job
input datasets to be those of the job that is being rerun.
"""
try:
old_job = trans.sa_session.query(trans.app.model.Job).get(rerun_remap_job_id)
assert old_job is not None, '(%s/%s): Old job id is invalid' % (rerun_remap_job_id, current_job.id)
assert old_job.tool_id == current_job.tool_id, '(%s/%s): Old tool id (%s) does not match rerun tool id (%s)' % (old_job.id, current_job.id, old_job.tool_id, current_job.tool_id)
if trans.user is not None:
assert old_job.user_id == trans.user.id, '(%s/%s): Old user id (%s) does not match rerun user id (%s)' % (old_job.id, current_job.id, old_job.user_id, trans.user.id)
elif trans.user is None and type(galaxy_session) == trans.model.GalaxySession:
assert old_job.session_id == galaxy_session.id, '(%s/%s): Old session id (%s) does not match rerun session id (%s)' % (old_job.id, current_job.id, old_job.session_id, galaxy_session.id)
else:
raise Exception('(%s/%s): Remapping via the API is not (yet) supported' % (old_job.id, current_job.id))
# Duplicate PJAs before remap.
for pjaa in old_job.post_job_actions:
current_job.add_post_job_action(pjaa.post_job_action)
if old_job.workflow_invocation_step:
replacement_dict = {}
for parameter in old_job.workflow_invocation_step.workflow_invocation.input_parameters:
if parameter.type == WorkflowRequestInputParameter.types.REPLACEMENT_PARAMETERS:
replacement_dict[parameter.name] = parameter.value
for pja in old_job.workflow_invocation_step.workflow_step.post_job_actions:
# execute immediate actions here, with workflow context.
if pja.action_type in ActionBox.immediate_actions:
ActionBox.execute(trans.app, trans.sa_session, pja, current_job, replacement_dict)
for p in old_job.parameters:
if p.name.endswith('|__identifier__'):
current_job.parameters.append(p.copy())
remapped_hdas = self.__remap_data_inputs(old_job=old_job, current_job=current_job)
for jtod in old_job.output_datasets:
for (job_to_remap, jtid) in [(jtid.job, jtid) for jtid in jtod.dataset.dependent_jobs]:
if (trans.user is not None and job_to_remap.user_id == trans.user.id) or (
trans.user is None and job_to_remap.session_id == galaxy_session.id):
self.__remap_parameters(job_to_remap, jtid, jtod, out_data)
trans.sa_session.add(job_to_remap)
trans.sa_session.add(jtid)
jtod.dataset.visible = False
trans.sa_session.add(jtod)
for jtodc in old_job.output_dataset_collection_instances:
# Update JobToOutputDatasetCollectionAssociation to the current job
jtodc.job = current_job
hdca = jtodc.dataset_collection_instance
hdca.collection.replace_failed_elements(remapped_hdas)
if hdca.implicit_collection_jobs:
for job in hdca.implicit_collection_jobs.jobs:
if job.job_id == old_job.id:
job.job_id = current_job.id
except Exception:
log.exception('Cannot remap rerun dependencies.')
def __remap_data_inputs(self, old_job, current_job):
"""Record output datasets from old_job and build a dictionary that maps the old output HDAs to the new output HDAs."""
remapped_hdas = {}
old_output_datasets = {jtod.name: jtod.dataset for jtod in old_job.output_datasets}
for jtod in current_job.output_datasets:
remapped_hdas[old_output_datasets[jtod.name]] = jtod.dataset
return remapped_hdas
def __remap_parameters(self, job_to_remap, jtid, jtod, out_data):
if job_to_remap.state == job_to_remap.states.PAUSED:
job_to_remap.state = job_to_remap.states.NEW
for hda in [dep_jtod.dataset for dep_jtod in job_to_remap.output_datasets]:
if hda.state == hda.states.PAUSED:
hda.state = hda.states.NEW
hda.info = None
input_values = dict([(p.name, json.loads(p.value)) for p in job_to_remap.parameters])
old_dataset_id = jtod.dataset_id
new_dataset_id = out_data[jtod.name].id
input_values = update_dataset_ids(input_values, {old_dataset_id: new_dataset_id}, src='hda')
for p in job_to_remap.parameters:
p.value = json.dumps(input_values[p.name])
jtid.dataset = out_data[jtod.name]
jtid.dataset.hid = jtod.dataset.hid
log.info('Job %s input HDA %s remapped to new HDA %s' % (job_to_remap.id, jtod.dataset.id, jtid.dataset.id))
def _wrapped_params(self, trans, tool, incoming, input_datasets=None):
wrapped_params = WrappedParameters(trans, tool, incoming, input_datasets=input_datasets)
return wrapped_params
def _get_on_text(self, inp_data):
input_names = []
for data in reversed(inp_data.values()):
if getattr(data, "hid", None):
input_names.append('data %s' % data.hid)
return on_text_for_names(input_names)
def _new_job_for_session(self, trans, tool, history):
job = trans.app.model.Job()
galaxy_session = None
if hasattr(trans, "get_galaxy_session"):
galaxy_session = trans.get_galaxy_session()
# If we're submitting from the API, there won't be a session.
if type(galaxy_session) == trans.model.GalaxySession:
job.session_id = galaxy_session.id
if trans.user is not None:
job.user_id = trans.user.id
job.history_id = history.id
job.tool_id = tool.id
try:
# For backward compatibility, some tools may not have versions yet.
job.tool_version = tool.version
except AttributeError:
job.tool_version = "1.0.0"
return job, galaxy_session
def _record_inputs(self, trans, tool, job, incoming, inp_data, inp_dataset_collections, current_user_roles):
# FIXME: Don't need all of incoming here, just the defined parameters
# from the tool. We need to deal with tools that pass all post
# parameters to the command as a special case.
reductions = {}
for name, dataset_collection_info_pairs in inp_dataset_collections.items():
for (dataset_collection, reduced) in dataset_collection_info_pairs:
if reduced:
if name not in reductions:
reductions[name] = []
reductions[name].append(dataset_collection)
# TODO: verify can have multiple with same name, don't want to lose traceability
if isinstance(dataset_collection, model.HistoryDatasetCollectionAssociation):
# FIXME: when recording inputs for special tools (e.g. ModelOperationToolAction),
# dataset_collection is actually a DatasetCollectionElement, which can't be added
# to a jobs' input_dataset_collection relation, which expects HDCA instances
job.add_input_dataset_collection(name, dataset_collection)
# If this an input collection is a reduction, we expanded it for dataset security, type
# checking, and such, but the persisted input must be the original collection
# so we can recover things like element identifier during tool command evaluation.
def restore_reduction_visitor(input, value, prefix, parent=None, prefixed_name=None, **kwargs):
if prefixed_name in reductions and isinstance(input, DataToolParameter):
target_dict = parent
if not target_dict:
target_dict = incoming
target_dict[input.name] = []
for reduced_collection in reductions[prefixed_name]:
target_dict[input.name].append({'id': reduced_collection.id, 'src': 'hdca'})
if reductions:
tool.visit_inputs(incoming, restore_reduction_visitor)
for name, value in tool.params_to_strings(incoming, trans.app).items():
job.add_parameter(name, value)
self._check_input_data_access(trans, job, inp_data, current_user_roles)
def _record_outputs(self, job, out_data, output_collections):
out_collections = output_collections.out_collections
out_collection_instances = output_collections.out_collection_instances
for name, dataset in out_data.items():
job.add_output_dataset(name, dataset)
for name, dataset_collection in out_collections.items():
job.add_implicit_output_dataset_collection(name, dataset_collection)
for name, dataset_collection_instance in out_collection_instances.items():
job.add_output_dataset_collection(name, dataset_collection_instance)
dataset_collection_instance.job = job
def _check_input_data_access(self, trans, job, inp_data, current_user_roles):
access_timer = ExecutionTimer()
for name, dataset in inp_data.items():
if dataset:
if not trans.app.security_agent.can_access_dataset(current_user_roles, dataset.dataset):
raise Exception("User does not have permission to use a dataset (%s) provided for input." % dataset.id)
if dataset in trans.sa_session:
job.add_input_dataset(name, dataset=dataset)
else:
job.add_input_dataset(name, dataset_id=dataset.id)
else:
job.add_input_dataset(name, None)
job_str = job.log_str()
log.info("Verified access to datasets for %s %s" % (job_str, access_timer))
[docs] def get_output_name(self, output, dataset, tool, on_text, trans, incoming, history, params, job_params):
if output.label:
params['tool'] = tool
params['on_string'] = on_text
return fill_template(output.label, context=params)
else:
return self._get_default_data_name(dataset, tool, on_text=on_text, trans=trans, incoming=incoming, history=history, params=params, job_params=job_params)
[docs] def set_metadata_defaults(self, output, dataset, tool, on_text, trans, incoming, history, params, job_params):
"""
This allows to map names of input files to metadata default values. Example:
<data format="tabular" name="output" label="Tabular output, aggregates data from individual_inputs" >
<actions>
<action name="column_names" type="metadata" default="${','.join([input.name for input in $individual_inputs ])}" />
</actions>
</data>
"""
if output.actions:
for action in output.actions.actions:
if action.tag == "metadata" and action.default:
metadata_new_value = fill_template(action.default, context=params).split(",")
dataset.metadata.__setattr__(str(action.name), metadata_new_value)
def _get_default_data_name(self, dataset, tool, on_text=None, trans=None, incoming=None, history=None, params=None, job_params=None, **kwd):
name = tool.name
if on_text:
name += (" on " + on_text)
return name
[docs]class ObjectStorePopulator(object):
""" Small helper for interacting with the object store and making sure all
datasets from a job end up with the same object_store_id.
"""
[docs] def set_object_store_id(self, data):
# Create an empty file immediately. The first dataset will be
# created in the "default" store, all others will be created in
# the same store as the first.
data.dataset.object_store_id = self.object_store_id
try:
self.object_store.create(data.dataset)
except ObjectInvalid:
raise Exception('Unable to create output dataset: object store is full')
self.object_store_id = data.dataset.object_store_id # these will be the same thing after the first output
[docs]class OutputCollections(object):
""" Keeps track of collections (DC or HDCA) created by actions.
Actions do fairly different things depending on whether we are creating
just part of an collection or a whole output collection (mapping_over_collection
parameter).
"""
[docs] def __init__(self, trans, history, tool, tool_action, input_collections, dataset_collection_elements, on_text, incoming, params, job_params, tags):
self.trans = trans
self.history = history
self.tool = tool
self.tool_action = tool_action
self.input_collections = input_collections
self.dataset_collection_elements = dataset_collection_elements
self.on_text = on_text
self.incoming = incoming
self.params = params
self.job_params = job_params
self.out_collections = {}
self.out_collection_instances = {}
self.tags = tags
[docs] def create_collection(self, output, name, **element_kwds):
input_collections = self.input_collections
collections_manager = self.trans.app.dataset_collections_service
collection_type = output.structure.collection_type
if collection_type is None:
collection_type_source = output.structure.collection_type_source
if collection_type_source is None:
# TODO: Not a new problem, but this should be determined
# sooner.
raise Exception("Could not determine collection type to create.")
if collection_type_source not in input_collections:
raise Exception("Could not find collection type source with name [%s]." % collection_type_source)
collection_type = input_collections[collection_type_source].collection.collection_type
if "elements" in element_kwds:
elements = element_kwds["elements"]
if hasattr(elements, "items"): # else it is ELEMENTS_UNINITIALIZED object.
for value in elements.values():
# Either a HDA (if) or a DatasetCollection (the else)
if getattr(value, "history_content_type", None) == "dataset":
assert value.history is not None
else:
for dataset in value.dataset_instances:
assert dataset.history is not None
if self.dataset_collection_elements is not None:
dc = collections_manager.create_dataset_collection(
self.trans,
collection_type=collection_type,
**element_kwds
)
if name in self.dataset_collection_elements:
self.dataset_collection_elements[name].child_collection = dc
# self.trans.sa_session.add(self.dataset_collection_elements[name])
self.out_collections[name] = dc
else:
hdca_name = self.tool_action.get_output_name(
output,
None,
self.tool,
self.on_text,
self.trans,
self.incoming,
self.history,
self.params,
self.job_params,
)
hdca = collections_manager.create(
self.trans,
self.history,
name=hdca_name,
collection_type=collection_type,
trusted_identifiers=True,
tags=self.tags,
**element_kwds
)
# name here is name of the output element - not name
# of the hdca.
self.out_collection_instances[name] = hdca
[docs]def on_text_for_names(input_names):
# input_names may contain duplicates... this is because the first value in
# multiple input dataset parameters will appear twice once as param_name
# and once as param_name1.
unique_names = []
for name in input_names:
if name not in unique_names:
unique_names.append(name)
input_names = unique_names
# Build name for output datasets based on tool name and input names
if len(input_names) == 1:
on_text = input_names[0]
elif len(input_names) == 2:
on_text = '%s and %s' % tuple(input_names[0:2])
elif len(input_names) == 3:
on_text = '%s, %s, and %s' % tuple(input_names[0:3])
elif len(input_names) > 3:
on_text = '%s, %s, and others' % tuple(input_names[0:2])
else:
on_text = ""
return on_text
[docs]def filter_output(output, incoming):
for filter in output.filters:
try:
if not eval(filter.text.strip(), globals(), incoming):
return True # do not create this dataset
except Exception as e:
log.debug('Dataset output filter failed: %s' % e)
return False
[docs]def determine_output_format(output, parameter_context, input_datasets, input_dataset_collections, random_input_ext):
""" Determines the output format for a dataset based on an abstract
description of the output (galaxy.tools.parser.ToolOutput), the parameter
wrappers, a map of the input datasets (name => HDA), and the last input
extensions in the tool form.
TODO: Don't deal with XML here - move this logic into ToolOutput.
TODO: Make the input extension used deterministic instead of random.
"""
# the type should match the input
ext = output.format
if ext == "input":
ext = random_input_ext
format_source = output.format_source
if format_source is not None and format_source in input_datasets:
try:
input_dataset = input_datasets[output.format_source]
input_extension = input_dataset.ext
ext = input_extension
except Exception:
pass
elif format_source is not None:
if re.match(r"^[^\[\]]*\[[^\[\]]*\]$", format_source):
collection_name, element_index = format_source[0:-1].split("[")
# Treat as json to interpret "forward" vs 0 with type
# Make it feel more like Python, single quote better in XML also.
element_index = element_index.replace("'", '"')
element_index = json.loads(element_index)
if collection_name in input_dataset_collections:
try:
input_collection = input_dataset_collections[collection_name][0][0]
input_collection_collection = input_collection.collection
try:
input_element = input_collection_collection[element_index]
except KeyError:
for element in input_collection_collection.dataset_elements:
if element.element_identifier == element_index:
input_element = element
break
input_dataset = input_element.element_object
input_extension = input_dataset.ext
ext = input_extension
except Exception as e:
log.debug("Exception while trying to determine format_source: %s", e)
pass
# process change_format tags
if output.change_format is not None:
new_format_set = False
for change_elem in output.change_format:
for when_elem in change_elem.findall('when'):
check = when_elem.get('input', None)
if check is not None:
try:
if '$' not in check:
# allow a simple name or more complex specifications
check = '${%s}' % check
if str(fill_template(check, context=parameter_context)) == when_elem.get('value', None):
ext = when_elem.get('format', ext)
except Exception: # bad tag input value; possibly referencing a param within a different conditional when block or other nonexistent grouping construct
continue
else:
check = when_elem.get('input_dataset', None)
if check is not None:
check = input_datasets.get(check, None)
# At this point check is a HistoryDatasetAssociation object.
check_format = when_elem.get('format', ext)
check_value = when_elem.get('value', None)
check_attribute = when_elem.get('attribute', None)
if check is not None and check_value is not None and check_attribute is not None:
# See if the attribute to be checked belongs to the HistoryDatasetAssociation object.
if hasattr(check, check_attribute):
if str(getattr(check, check_attribute)) == str(check_value):
ext = check_format
new_format_set = True
break
# See if the attribute to be checked belongs to the metadata associated with the
# HistoryDatasetAssociation object.
if check.metadata is not None:
metadata_value = check.metadata.get(check_attribute, None)
if metadata_value is not None:
if str(metadata_value) == str(check_value):
ext = check_format
new_format_set = True
break
if new_format_set:
break
return ext