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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.model import LibraryDatasetDatasetAssociation
from galaxy.tools.parameters import update_param
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={}, set_output_hid=True): 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) return history, inp_data, inp_dataset_collections
[docs] def execute(self, tool, trans, incoming={}, return_job=False, set_output_hid=True, history=None, job_params=None, rerun_remap_job_id=None, mapping_over_collection=False, execution_cache=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. """ 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 = 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", "?") preserved_tags = {} 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): 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 for tag in [t for t in data.tags if t.user_tname == 'name']: preserved_tags[tag.value] = tag # 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 # 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, mapping_over_collection=mapping_over_collection, on_text=on_text, incoming=incoming, params=wrapped_params.params, job_params=job_params, ) # 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 ) data = app.model.HistoryDatasetAssociation(extension=ext, create_dataset=True, flush=False) if hidden is None: hidden = output.hidden if hidden: data.visible = False trans.sa_session.add(data) trans.app.security_agent.set_all_dataset_permissions(data.dataset, output_permissions, new=True) for _, tag in preserved_tags.items(): data.tags.append(tag.copy()) # Must flush before setting object store id currently. # TODO: optimize this. trans.sa_session.flush() 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 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): 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, tags=preserved_tags, **element_kwds ) else: handle_output_timer = ExecutionTimer() 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)) 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: try: old_job = trans.sa_session.query(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, job.id) assert old_job.tool_id == job.tool_id, '(%s/%s): Old tool id (%s) does not match rerun tool id (%s)' % (old_job.id, job.id, old_job.tool_id, 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, 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, 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, job.id)) # Duplicate PJAs before remap. for pjaa in old_job.post_job_actions: job.add_post_job_action(pjaa.post_job_action) 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): 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]) update_param(jtid.name, input_values, str(out_data[jtod.name].id)) 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)) trans.sa_session.add(job_to_remap) trans.sa_session.add(jtid) jtod.dataset.visible = False trans.sa_session.add(jtod) except Exception: log.exception('Cannot remap rerun dependencies.') 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_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 _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 name, data in reversed(inp_data.items()): 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: 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 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) 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 __init__(self, app): self.object_store = app.object_store self.object_store_id = None
[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, mapping_over_collection, on_text, incoming, params, job_params): self.trans = trans self.history = history self.tool = tool self.tool_action = tool_action self.input_collections = input_collections self.mapping_over_collection = mapping_over_collection self.on_text = on_text self.incoming = incoming self.params = params self.job_params = job_params self.out_collections = {} self.out_collection_instances = {}
[docs] def create_collection(self, output, name, tags=None, **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 key, value in elements.items(): # 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.mapping_over_collection: dc = collections_manager.create_dataset_collection( self.trans, collection_type=collection_type, **element_kwds ) 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=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: # 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