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Source code for galaxy.tools.parameters.meta

import copy
import itertools
import logging

from galaxy import (
    exceptions,
    model,
    util
)
from galaxy.util import permutations

log = logging.getLogger(__name__)


[docs]def expand_workflow_inputs(inputs): """ Expands incoming encoded multiple payloads, into the set of all individual payload combinations >>> params, param_keys = expand_workflow_inputs( {'1': {'input': {'batch': True, 'product': True, 'values': [{'hid': '1'}, {'hid': '2'}] }}} ) >>> print [ "%s" % ( p[ '1' ][ 'input' ][ 'hid' ] ) for p in params ] ['1', '2'] >>> params, param_keys = expand_workflow_inputs( {'1': {'input': {'batch': True, 'values': [{'hid': '1'}, {'hid': '2'}] }}} ) >>> print [ "%s" % ( p[ '1' ][ 'input' ][ 'hid' ] ) for p in params ] ['1', '2'] >>> params, param_keys = expand_workflow_inputs( {'1': {'input': {'batch': True, 'values': [{'hid': '1'}, {'hid': '2'}] }}, '2': {'input': {'batch': True, 'values': [{'hid': '3'}, {'hid': '4'}] }}} ) >>> print [ "%s%s" % ( p[ '1' ][ 'input' ][ 'hid' ], p[ '2' ][ 'input' ][ 'hid' ] ) for p in params ] ['13', '24'] >>> params, param_keys = expand_workflow_inputs( {'1': {'input': {'batch': True, 'product': True, 'values': [{'hid': '1'}, {'hid': '2'}] }}, '2': {'input': {'batch': True, 'values': [{'hid': '3'}, {'hid': '4'}, {'hid': '5'}] }}} ) >>> print [ "%s%s" % ( p[ '1' ][ 'input' ][ 'hid' ], p[ '2' ][ 'input' ][ 'hid' ] ) for p in params ] ['13', '23', '14', '24', '15', '25'] >>> params, param_keys = expand_workflow_inputs( {'1': {'input': {'batch': True, 'product': True, 'values': [{'hid': '1'}, {'hid': '2'}] }}, '2': {'input': {'batch': True, 'product': True, 'values': [{'hid': '3'}, {'hid': '4'}, {'hid': '5'}] }}, '3': {'input': {'batch': True, 'product': True, 'values': [{'hid': '6'}, {'hid': '7'}, {'hid': '8'}] }}} ) >>> print [ "%s%s%s" % ( p[ '1' ][ 'input' ][ 'hid' ], p[ '2' ][ 'input' ][ 'hid' ], p[ '3' ][ 'input' ][ 'hid' ] ) for p in params ] ['136', '137', '138', '146', '147', '148', '156', '157', '158', '236', '237', '238', '246', '247', '248', '256', '257', '258'] """ linked_n = None linked = [] product = [] linked_keys = [] product_keys = [] for step_id, step in sorted(inputs.items()): for key, value in sorted(step.items()): if isinstance(value, dict) and 'batch' in value and value['batch'] is True and 'values' in value and isinstance(value['values'], list): nval = len(value['values']) if 'product' in value and value['product'] is True: product.append(value['values']) product_keys.append((step_id, key)) else: if linked_n is None: linked_n = nval elif linked_n != nval or nval is 0: raise exceptions.RequestParameterInvalidException('Failed to match linked batch selections. Please select equal number of data files.') linked.append(value['values']) linked_keys.append((step_id, key)) params = [] params_keys = [] linked = linked or [[None]] product = product or [[None]] linked_keys = linked_keys or [(None, None)] product_keys = product_keys or [(None, None)] for linked_values, product_values in itertools.product(zip(*linked), itertools.product(*product)): new_params = copy.deepcopy(inputs) new_keys = [] for (step_id, key), value in list(zip(linked_keys, linked_values)) + list(zip(product_keys, product_values)): if step_id is not None: new_params[step_id][key] = value new_keys.append(str(value['hid'])) params_keys.append(new_keys) params.append(new_params) return params, params_keys
[docs]def expand_meta_parameters(trans, tool, incoming): """ Take in a dictionary of raw incoming parameters and expand to a list of expanded incoming parameters (one set of parameters per tool execution). """ to_remove = [] for key in incoming.keys(): if key.endswith("|__identifier__"): to_remove.append(key) for key in to_remove: incoming.pop(key) def classifier(input_key): value = incoming[input_key] if isinstance(value, dict) and 'values' in value: # Explicit meta wrapper for inputs... is_batch = value.get('batch', False) is_linked = value.get('linked', True) if is_batch and is_linked: classification = permutations.input_classification.MATCHED elif is_batch: classification = permutations.input_classification.MULTIPLIED else: classification = permutations.input_classification.SINGLE if __collection_multirun_parameter(value): collection_value = value['values'][0] values = __expand_collection_parameter(trans, input_key, collection_value, collections_to_match, linked=is_linked) else: values = value['values'] else: classification = permutations.input_classification.SINGLE values = value return classification, values from galaxy.dataset_collections import matching collections_to_match = matching.CollectionsToMatch() # Stick an unexpanded version of multirun keys so they can be replaced, # by expand_mult_inputs. incoming_template = incoming.copy() expanded_incomings = permutations.expand_multi_inputs(incoming_template, classifier) if collections_to_match.has_collections(): collection_info = trans.app.dataset_collections_service.match_collections(collections_to_match) else: collection_info = None return expanded_incomings, collection_info
def __expand_collection_parameter(trans, input_key, incoming_val, collections_to_match, linked=False): # If subcollectin multirun of data_collection param - value will # be "hdca_id|subcollection_type" else it will just be hdca_id if "|" in incoming_val: encoded_hdc_id, subcollection_type = incoming_val.split("|", 1) else: try: src = incoming_val["src"] if src != "hdca": raise exceptions.ToolMetaParameterException("Invalid dataset collection source type %s" % src) encoded_hdc_id = incoming_val["id"] subcollection_type = incoming_val.get('map_over_type', None) except TypeError: encoded_hdc_id = incoming_val subcollection_type = None hdc_id = trans.app.security.decode_id(encoded_hdc_id) hdc = trans.sa_session.query(model.HistoryDatasetCollectionAssociation).get(hdc_id) collections_to_match.add(input_key, hdc, subcollection_type=subcollection_type, linked=linked) if subcollection_type is not None: from galaxy.dataset_collections import subcollections subcollection_elements = subcollections.split_dataset_collection_instance(hdc, subcollection_type) return subcollection_elements else: hdas = [] for element in hdc.collection.dataset_elements: hda = element.dataset_instance hda.element_identifier = element.element_identifier hdas.append(hda) return hdas def __collection_multirun_parameter(value): is_batch = value.get('batch', False) if not is_batch: return False batch_values = util.listify(value['values']) if len(batch_values) == 1: batch_over = batch_values[0] if isinstance(batch_over, dict) and ('src' in batch_over) and (batch_over['src'] == 'hdca'): return True return False