Source code for galaxy.tools.parameters.meta

from __future__ import print_function

import copy
import itertools
import logging
from collections import OrderedDict

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

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 process_key(incoming_key, incoming_value, d):
    key_parts = incoming_key.split('|')
    if len(key_parts) == 1:
        # Regular parameter
        d[incoming_key] = incoming_value
    elif key_parts[0].rsplit('_', 1)[-1].isdigit():
        # Repeat
        input_name_index = key_parts[0].rsplit('_', 1)
        input_name, index = input_name_index
        index = int(index)
        if input_name not in d:
            d[input_name] = []
        if len(d[input_name]) > index:
            subdict = d[input_name][index]
        else:
            subdict = {}
            d[input_name].append(subdict)
        process_key("|".join(key_parts[1:]), incoming_value=incoming_value, d=subdict)
    else:
        # Section / Conditional
        input_name = key_parts[0]
        subdict = {}
        d[input_name] = subdict
        process_key("|".join(key_parts[1:]), incoming_value=incoming_value, d=subdict)


[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)

    # If we're going to multiply input dataset combinations
    # order matters, so the following reorders incoming
    # according to tool.inputs (which is ordered).
    incoming_copy = incoming.copy()
    nested_dict = {}
    for incoming_key, incoming_value in incoming_copy.items():
        if not incoming_key.startswith('__'):
            process_key(incoming_key, incoming_value=incoming_value, d=nested_dict)

    reordered_incoming = OrderedDict()

    def visitor(input, value, prefix, prefixed_name, prefixed_label, error, **kwargs):
        if prefixed_name in incoming_copy:
            reordered_incoming[prefixed_name] = incoming_copy[prefixed_name]
            del incoming_copy[prefixed_name]

    visit_input_values(inputs=tool.inputs, input_values=nested_dict, callback=visitor)
    reordered_incoming.update(incoming_copy)

    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 = reordered_incoming

    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