def execute(self, tool, trans, incoming={}, return_job=False, set_output_hid=True, set_output_history=True, history=None, job_params=None, rerun_remap_job_id=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. """ def make_dict_copy(from_dict): """ Makes a copy of input dictionary from_dict such that all values that are dictionaries result in creation of a new dictionary ( a sort of deepcopy ). We may need to handle other complex types ( e.g., lists, etc ), but not sure... Yes, we need to handle lists (and now are)... """ copy_from_dict = {} for key, value in from_dict.items(): if type(value).__name__ == 'dict': copy_from_dict[key] = make_dict_copy(value) elif isinstance(value, list): copy_from_dict[key] = make_list_copy(value) else: copy_from_dict[key] = value return copy_from_dict def make_list_copy(from_list): new_list = [] for value in from_list: if isinstance(value, dict): new_list.append(make_dict_copy(value)) elif isinstance(value, list): new_list.append(make_list_copy(value)) else: new_list.append(value) return new_list def wrap_values(inputs, input_values, skip_missing_values=False): # Wrap tool inputs as necessary for input in inputs.itervalues(): if input.name not in input_values and skip_missing_values: continue if isinstance(input, Repeat): for d in input_values[input.name]: wrap_values(input.inputs, d, skip_missing_values=skip_missing_values) elif isinstance(input, Conditional): values = input_values[input.name] current = values["__current_case__"] wrap_values(input.cases[current].inputs, values, skip_missing_values=skip_missing_values) elif isinstance(input, DataToolParameter) and input.multiple: input_values[ input.name ] = \ galaxy.tools.DatasetListWrapper( input_values[ input.name ], datatypes_registry = trans.app.datatypes_registry, tool = tool, name = input.name ) elif isinstance(input, DataToolParameter): input_values[ input.name ] = \ galaxy.tools.DatasetFilenameWrapper( input_values[ input.name ], datatypes_registry = trans.app.datatypes_registry, tool = tool, name = input.name ) elif isinstance(input, SelectToolParameter): input_values[ input.name] = galaxy.tools.SelectToolParameterWrapper( input, input_values[input.name], tool.app, other_values=incoming) else: input_values[input.name] = galaxy.tools.InputValueWrapper( input, input_values[input.name], incoming) # Set history. if not history: history = tool.get_default_history_by_trans(trans, create=True) out_data = odict() # Collect any input datasets from the incoming parameters inp_data = self.collect_input_datasets(tool, incoming, trans) # Deal with input dataset names, 'dbkey' and types input_names = [] input_ext = 'data' input_dbkey = incoming.get("dbkey", "?") for name, data in inp_data.items(): if not data: data = NoneDataset( datatypes_registry=trans.app.datatypes_registry) continue # Convert LDDA to an HDA. if isinstance(data, LibraryDatasetDatasetAssociation): data = data.to_history_dataset_association(None) inp_data[name] = data else: # HDA if data.hid: input_names.append('data %s' % data.hid) input_ext = data.ext if data.dbkey not in [None, '?']: input_dbkey = data.dbkey # Collect chromInfo dataset and add as parameters to incoming db_datasets = {} db_dataset = trans.db_dataset_for(input_dbkey) if db_dataset: db_datasets["chromInfo"] = db_dataset incoming["chromInfo"] = db_dataset.file_name else: # For custom builds, chrom info resides in converted dataset; for built-in builds, chrom info resides in tool-data/shared. chrom_info = None if trans.user and ('dbkeys' in trans.user.preferences) and ( input_dbkey in from_json_string( trans.user.preferences['dbkeys'])): # Custom build. custom_build_dict = from_json_string( trans.user.preferences['dbkeys'])[input_dbkey] if 'fasta' in custom_build_dict: build_fasta_dataset = trans.sa_session.query( trans.app.model.HistoryDatasetAssociation).get( custom_build_dict['fasta']) chrom_info = build_fasta_dataset.get_converted_dataset( trans, 'len').file_name if not chrom_info: # Default to built-in build. chrom_info = os.path.join(trans.app.config.len_file_path, "%s.len" % input_dbkey) incoming["chromInfo"] = chrom_info inp_data.update(db_datasets) # Determine output dataset permission/roles list existing_datasets = [inp for inp in inp_data.values() if inp] if existing_datasets: output_permissions = trans.app.security_agent.guess_derived_permissions_for_datasets( existing_datasets) else: # No valid inputs, we will use history defaults output_permissions = trans.app.security_agent.history_get_default_permissions( history) # 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 = "" # Add the dbkey to the incoming parameters incoming["dbkey"] = input_dbkey params = None #wrapped params are used by change_format action and by output.label; only perform this wrapping once, as needed # 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_id = None for name, output in tool.outputs.items(): for filter in output.filters: try: if not eval(filter.text.strip(), globals(), incoming): break #do not create this dataset except Exception, e: log.debug('Dataset output filter failed: %s' % e) else: #all filters passed 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? ([email protected]) # 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( trans.app.model.HistoryDatasetAssociation).get(dataid) assert data != None out_data[name] = data else: # the type should match the input ext = output.format if ext == "input": ext = input_ext if output.format_source is not None and output.format_source in inp_data: try: input_dataset = inp_data[output.format_source] input_extension = input_dataset.ext ext = input_extension except Exception, e: pass #process change_format tags if output.change_format: if params is None: params = make_dict_copy(incoming) wrap_values( tool.inputs, params, skip_missing_values=not tool.check_values) 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=params) ) == 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 = inp_data.get(check, None) if check is not None: if str( getattr( check, when_elem.get( 'attribute')) ) == when_elem.get('value', None): ext = when_elem.get( 'format', ext) data = trans.app.model.HistoryDatasetAssociation( extension=ext, create_dataset=True, sa_session=trans.sa_session) if output.hidden: data.visible = False # Commit the dataset immediately so it gets database assigned unique id trans.sa_session.add(data) trans.sa_session.flush() trans.app.security_agent.set_all_dataset_permissions( data.dataset, output_permissions) # 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 = object_store_id try: trans.app.object_store.create(data.dataset) except ObjectInvalid: raise Exception( 'Unable to create output dataset: object store is full' ) object_store_id = data.dataset.object_store_id # these will be the same thing after the first output # This may not be neccesary with the new parent/child associations data.designation = name # Copy metadata from one of the inputs if requested. if output.metadata_source: data.init_meta(copy_from=inp_data[output.metadata_source]) else: data.init_meta() # Take dbkey from LAST input data.dbkey = str(input_dbkey) # Set state # FIXME: shouldn't this be NEW until the job runner changes it? data.state = data.states.QUEUED data.blurb = "queued" # Set output label if output.label: if params is None: params = make_dict_copy(incoming) # wrapping the params allows the tool config to contain things like # <outputs> # <data format="input" name="output" label="Blat on ${<input_param>.name}" /> # </outputs> wrap_values(tool.inputs, params, skip_missing_values=not tool.check_values) #tool (only needing to be set once) and on_string (set differently for each label) are overwritten for each output dataset label being determined params['tool'] = tool params['on_string'] = on_text data.name = fill_template(output.label, context=params) else: if params is None: params = make_dict_copy(incoming) wrap_values(tool.inputs, params, skip_missing_values=not tool.check_values) data.name = self._get_default_data_name( data, tool, on_text=on_text, trans=trans, incoming=incoming, history=history, params=params, job_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) # Store all changes to database trans.sa_session.flush()
def execute(self, tool, trans, incoming={}, return_job=False, set_output_hid=True, set_output_history=True, history=None, job_params=None, rerun_remap_job_id=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. """ # Set history. if not history: history = tool.get_default_history_by_trans(trans, create=True) out_data = odict() # 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, trans) # Collect any input datasets from the incoming parameters inp_data = self.collect_input_datasets(tool, incoming, trans) # Deal with input dataset names, 'dbkey' and types input_names = [] input_ext = 'data' input_dbkey = incoming.get("dbkey", "?") for name, data in inp_data.items(): if not data: data = NoneDataset( datatypes_registry=trans.app.datatypes_registry) continue # Convert LDDA to an HDA. if isinstance(data, LibraryDatasetDatasetAssociation): data = data.to_history_dataset_association(None) inp_data[name] = data else: # HDA if data.hid: input_names.append('data %s' % data.hid) input_ext = data.ext if data.dbkey not in [None, '?']: input_dbkey = data.dbkey # Collect chromInfo dataset and add as parameters to incoming db_datasets = {} (chrom_info, db_dataset) = trans.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 = trans.app.security_agent.guess_derived_permissions_for_datasets( existing_datasets) else: # No valid inputs, we will use history defaults output_permissions = trans.app.security_agent.history_get_default_permissions( history) # Build name for output datasets based on tool name and input names on_text = on_text_for_names(input_names) # 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 = WrappedParameters(trans, tool, incoming) # 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_id = None for name, output in tool.outputs.items(): for filter in output.filters: try: if not eval(filter.text.strip(), globals(), incoming): break # do not create this dataset except Exception, e: log.debug('Dataset output filter failed: %s' % e) else: # all filters passed 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? ([email protected]) # 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( trans.app.model.HistoryDatasetAssociation).get(dataid) assert data != None out_data[name] = data else: # the type should match the input ext = output.format if ext == "input": ext = input_ext if output.format_source is not None and output.format_source in inp_data: try: input_dataset = inp_data[output.format_source] input_extension = input_dataset.ext ext = input_extension except Exception, e: pass #process change_format tags if output.change_format: 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=wrapped_params. params)) == 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 = inp_data.get(check, None) if check is not None: if str( getattr( check, when_elem.get( 'attribute')) ) == when_elem.get('value', None): ext = when_elem.get( 'format', ext) data = trans.app.model.HistoryDatasetAssociation( extension=ext, create_dataset=True, sa_session=trans.sa_session) if output.hidden: data.visible = False # Commit the dataset immediately so it gets database assigned unique id trans.sa_session.add(data) trans.sa_session.flush() trans.app.security_agent.set_all_dataset_permissions( data.dataset, output_permissions) # 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 = object_store_id try: trans.app.object_store.create(data.dataset) except ObjectInvalid: raise Exception( 'Unable to create output dataset: object store is full' ) object_store_id = data.dataset.object_store_id # these will be the same thing after the first output # This may not be neccesary with the new parent/child associations data.designation = name # Copy metadata from one of the inputs if requested. if output.metadata_source: data.init_meta(copy_from=inp_data[output.metadata_source]) else: data.init_meta() # Take dbkey from LAST input data.dbkey = str(input_dbkey) # Set state # FIXME: shouldn't this be NEW until the job runner changes it? data.state = data.states.QUEUED 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) # Store all changes to database trans.sa_session.flush()
def execute(self, tool, trans, incoming={}, return_job=False, set_output_hid=True, set_output_history=True, history=None, job_params=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. """ def make_dict_copy( from_dict ): """ Makes a copy of input dictionary from_dict such that all values that are dictionaries result in creation of a new dictionary ( a sort of deepcopy ). We may need to handle other complex types ( e.g., lists, etc ), but not sure... Yes, we need to handle lists (and now are)... """ copy_from_dict = {} for key, value in from_dict.items(): if type( value ).__name__ == 'dict': copy_from_dict[ key ] = make_dict_copy( value ) elif isinstance( value, list ): copy_from_dict[ key ] = make_list_copy( value ) else: copy_from_dict[ key ] = value return copy_from_dict def make_list_copy( from_list ): new_list = [] for value in from_list: if isinstance( value, dict ): new_list.append( make_dict_copy( value ) ) elif isinstance( value, list ): new_list.append( make_list_copy( value ) ) else: new_list.append( value ) return new_list def wrap_values( inputs, input_values, skip_missing_values = False ): # Wrap tool inputs as necessary for input in inputs.itervalues(): if input.name not in input_values and skip_missing_values: continue if isinstance( input, Repeat ): for d in input_values[ input.name ]: wrap_values( input.inputs, d, skip_missing_values = skip_missing_values ) elif isinstance( input, Conditional ): values = input_values[ input.name ] current = values[ "__current_case__" ] wrap_values( input.cases[current].inputs, values, skip_missing_values = skip_missing_values ) elif isinstance( input, DataToolParameter ) and input.multiple: input_values[ input.name ] = \ galaxy.tools.DatasetListWrapper( input_values[ input.name ], datatypes_registry = trans.app.datatypes_registry, tool = tool, name = input.name ) elif isinstance( input, DataToolParameter ): input_values[ input.name ] = \ galaxy.tools.DatasetFilenameWrapper( input_values[ input.name ], datatypes_registry = trans.app.datatypes_registry, tool = tool, name = input.name ) elif isinstance( input, SelectToolParameter ): input_values[ input.name ] = galaxy.tools.SelectToolParameterWrapper( input, input_values[ input.name ], tool.app, other_values = incoming ) else: input_values[ input.name ] = galaxy.tools.InputValueWrapper( input, input_values[ input.name ], incoming ) # Set history. if not history: history = trans.history out_data = odict() # Collect any input datasets from the incoming parameters inp_data = self.collect_input_datasets( tool, incoming, trans ) # Deal with input dataset names, 'dbkey' and types input_names = [] input_ext = 'data' input_dbkey = incoming.get( "dbkey", "?" ) for name, data in inp_data.items(): if not data: data = NoneDataset( datatypes_registry = trans.app.datatypes_registry ) continue # Convert LDDA to an HDA. if isinstance(data, LibraryDatasetDatasetAssociation): data = data.to_history_dataset_association( None ) inp_data[name] = data else: # HDA if data.hid: input_names.append( 'data %s' % data.hid ) input_ext = data.ext if data.dbkey not in [None, '?']: input_dbkey = data.dbkey # Collect chromInfo dataset and add as parameters to incoming db_datasets = {} db_dataset = trans.db_dataset_for( input_dbkey ) if db_dataset: db_datasets[ "chromInfo" ] = db_dataset incoming[ "chromInfo" ] = db_dataset.file_name else: # For custom builds, chrom info resides in converted dataset; for built-in builds, chrom info resides in tool-data/shared. chrom_info = None if trans.user and ( 'dbkeys' in trans.user.preferences ) and ( input_dbkey in from_json_string( trans.user.preferences[ 'dbkeys' ] ) ): # Custom build. custom_build_dict = from_json_string( trans.user.preferences[ 'dbkeys' ] )[ input_dbkey ] if 'fasta' in custom_build_dict: build_fasta_dataset = trans.app.model.HistoryDatasetAssociation.get( custom_build_dict[ 'fasta' ] ) chrom_info = build_fasta_dataset.get_converted_dataset( trans, 'len' ).file_name if not chrom_info: # Default to built-in build. chrom_info = os.path.join( trans.app.config.tool_data_path, 'shared','ucsc','chrom', "%s.len" % input_dbkey ) incoming[ "chromInfo" ] = chrom_info inp_data.update( db_datasets ) # Determine output dataset permission/roles list existing_datasets = [ inp for inp in inp_data.values() if inp ] if existing_datasets: output_permissions = trans.app.security_agent.guess_derived_permissions_for_datasets( existing_datasets ) else: # No valid inputs, we will use history defaults output_permissions = trans.app.security_agent.history_get_default_permissions( history ) # 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 = "" # Add the dbkey to the incoming parameters incoming[ "dbkey" ] = input_dbkey params = None #wrapped params are used by change_format action and by output.label; only perform this wrapping once, as needed # 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_id = None for name, output in tool.outputs.items(): for filter in output.filters: try: if not eval( filter.text.strip(), globals(), incoming ): break #do not create this dataset except Exception, e: log.debug( 'Dataset output filter failed: %s' % e ) else: #all filters passed 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? ([email protected]) # 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( trans.app.model.HistoryDatasetAssociation ).get( dataid ) assert data != None out_data[name] = data else: # the type should match the input ext = output.format if ext == "input": ext = input_ext if output.format_source is not None and output.format_source in inp_data: try: ext = inp_data[output.format_source].ext except Exception, e: pass #process change_format tags if output.change_format: if params is None: params = make_dict_copy( incoming ) wrap_values( tool.inputs, params, skip_missing_values = not tool.check_values ) 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 = params ) ) == 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 = inp_data.get( check, None ) if check is not None: if str( getattr( check, when_elem.get( 'attribute' ) ) ) == when_elem.get( 'value', None ): ext = when_elem.get( 'format', ext ) data = trans.app.model.HistoryDatasetAssociation( extension=ext, create_dataset=True, sa_session=trans.sa_session ) if output.hidden: data.visible = False # Commit the dataset immediately so it gets database assigned unique id trans.sa_session.add( data ) trans.sa_session.flush() trans.app.security_agent.set_all_dataset_permissions( data.dataset, output_permissions ) # 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 = object_store_id try: trans.app.object_store.create( data.dataset ) except ObjectInvalid: raise Exception('Unable to create output dataset: object store is full') object_store_id = data.dataset.object_store_id # these will be the same thing after the first output # This may not be neccesary with the new parent/child associations data.designation = name # Copy metadata from one of the inputs if requested. if output.metadata_source: data.init_meta( copy_from=inp_data[output.metadata_source] ) else: data.init_meta() # Take dbkey from LAST input data.dbkey = str(input_dbkey) # Set state # FIXME: shouldn't this be NEW until the job runner changes it? data.state = data.states.QUEUED data.blurb = "queued" # Set output label if output.label: if params is None: params = make_dict_copy( incoming ) # wrapping the params allows the tool config to contain things like # <outputs> # <data format="input" name="output" label="Blat on ${<input_param>.name}" /> # </outputs> wrap_values( tool.inputs, params, skip_missing_values = not tool.check_values ) #tool (only needing to be set once) and on_string (set differently for each label) are overwritten for each output dataset label being determined params['tool'] = tool params['on_string'] = on_text params['time']=logging.time.strftime('%X %x', logging.time.gmtime()) data.name = fill_template( output.label, context=params ) else: data.name = tool.name if on_text: data.name += ( " on " + on_text ) # 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 ) # Store all changes to database trans.sa_session.flush()
def execute(self, tool, trans, incoming={}, return_job=False, set_output_hid=True, set_output_history=True, history=None, job_params=None, rerun_remap_job_id=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. """ # Set history. if not history: history = tool.get_default_history_by_trans( trans, create=True ) out_data = odict() # 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 ) # Deal with input dataset names, 'dbkey' and types input_names = [] input_ext = 'data' input_dbkey = incoming.get( "dbkey", "?" ) for name, data in inp_data.items(): if not data: data = NoneDataset( datatypes_registry=trans.app.datatypes_registry ) continue # Convert LDDA to an HDA. if isinstance(data, LibraryDatasetDatasetAssociation): data = data.to_history_dataset_association( None ) inp_data[name] = data else: # HDA if data.hid: input_names.append( 'data %s' % data.hid ) input_ext = data.ext if data.dbkey not in [None, '?']: input_dbkey = data.dbkey # Collect chromInfo dataset and add as parameters to incoming db_datasets = {} ( chrom_info, db_dataset ) = trans.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 = trans.app.security_agent.guess_derived_permissions_for_datasets( existing_datasets ) else: # No valid inputs, we will use history defaults output_permissions = trans.app.security_agent.history_get_default_permissions( history ) # Build name for output datasets based on tool name and input names on_text = on_text_for_names( input_names ) # 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 = WrappedParameters( trans, tool, incoming ) # 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_id = None for name, output in tool.outputs.items(): for filter in output.filters: try: if not eval( filter.text.strip(), globals(), incoming ): break # do not create this dataset except Exception, e: log.debug( 'Dataset output filter failed: %s' % e ) else: # all filters passed 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? ([email protected]) # 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( trans.app.model.HistoryDatasetAssociation ).get( dataid ) assert data != None out_data[name] = data else: # the type should match the input ext = output.format if ext == "input": ext = input_ext if output.format_source is not None and output.format_source in inp_data: try: input_dataset = inp_data[output.format_source] input_extension = input_dataset.ext ext = input_extension except Exception, e: pass #process change_format tags if output.change_format: 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=wrapped_params.params ) ) == 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 = inp_data.get( check, None ) if check is not None: if str( getattr( check, when_elem.get( 'attribute' ) ) ) == when_elem.get( 'value', None ): ext = when_elem.get( 'format', ext ) data = trans.app.model.HistoryDatasetAssociation( extension=ext, create_dataset=True, sa_session=trans.sa_session ) if output.hidden: data.visible = False # Commit the dataset immediately so it gets database assigned unique id trans.sa_session.add( data ) trans.sa_session.flush() trans.app.security_agent.set_all_dataset_permissions( data.dataset, output_permissions ) # 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 = object_store_id try: trans.app.object_store.create( data.dataset ) except ObjectInvalid: raise Exception('Unable to create output dataset: object store is full') object_store_id = data.dataset.object_store_id # these will be the same thing after the first output # This may not be neccesary with the new parent/child associations data.designation = name # Copy metadata from one of the inputs if requested. if output.metadata_source: data.init_meta( copy_from=inp_data[output.metadata_source] ) else: data.init_meta() # Take dbkey from LAST input data.dbkey = str(input_dbkey) # Set state # FIXME: shouldn't this be NEW until the job runner changes it? data.state = data.states.QUEUED 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 ) # Store all changes to database trans.sa_session.flush()