Python toposort.toposort_flatten方法代码示例

# 需要导入模块: import toposort [as 别名]
# 或者: from toposort import toposort_flatten [as 别名]
def order_by_refs(envs):
    """
    Return topologicaly sorted list of environments.
    I.e. all referenced environments are placed before their references.
    """
    topology = {
        env['name']: set(env['refs'])
        for env in envs
    }
    by_name = {
        env['name']: env
        for env in envs
    }
    return [
        by_name[name]
        for name in toposort_flatten(topology)
    ] 

# 需要导入模块: import toposort [as 别名]
# 或者: from toposort import toposort_flatten [as 别名]
def sorted(self):
        if len(self.changes) == 0:
            return []

        changes = {}
        for change in self.changes:
            changes[change.id_] = set(change.requires)
        sorted_changes = toposort_flatten(changes)
        results = []
        for change_id in sorted_changes:
            for change in self.changes:
                if change_id == change.id_:
                    results.append(change)
                    break
        return results 

# 需要导入模块: import toposort [as 别名]
# 或者: from toposort import toposort_flatten [as 别名]
def expand(self, states):
        seen = set()
        depends = defaultdict(list)
        queue = deque()
        for state in states:
            queue.append(state)
            seen.add(state)
        while len(queue):
            state = queue.popleft()
            for arc in self.get_arcs(state, EPSILON):
                depends[arc[1]].append((arc[0], arc[3]))
                if arc[1] in seen:
                    continue
                queue.append(arc[1])
                seen.add(arc[1])

        depends_for_toposort = {key: {state for state, weight in value}
                                for key, value in depends.items()}
        order = toposort_flatten(depends_for_toposort)

        next_states = states
        for next_state in order:
            next_states[next_state] = self.combine_weights(
                *([next_states.get(next_state)] +
                  [next_states[prev_state] + weight
                   for prev_state, weight in depends[next_state]]))

        return next_states 

# 需要导入模块: import toposort [as 别名]
# 或者: from toposort import toposort_flatten [as 别名]
def find_joins_for_tables(joins, base_table, required_tables):
    """
    Given a set of tables required for a dataset query, this function finds the joins required for the query and
    sorts them topologically.

    :return:
        A list of joins in the order that they must be joined to the query.
    :raises:
        MissingTableJoinException - If a table is required but there is no join for that table
        CircularJoinsException - If there is a circular dependency between two or more joins
    """
    dependencies = defaultdict(set)
    slicer_joins = {join.table: join for join in joins}

    while required_tables:
        table = required_tables.pop()

        if table not in slicer_joins:
            raise MissingTableJoinException(
                "Could not find a join for table {}".format(str(table))
            )

        join = slicer_joins[table]
        tables_required_for_join = set(join.criterion.tables_) - {
            base_table,
            join.table,
        }

        dependencies[join] |= {
            slicer_joins[table] for table in tables_required_for_join
        }
        required_tables += tables_required_for_join - {d.table for d in dependencies}

    try:
        return toposort_flatten(dependencies, sort=True)
    except CircularDependencyError as e:
        raise CircularJoinsException(str(e)) 

# 需要导入模块: import toposort [as 别名]
# 或者: from toposort import toposort_flatten [as 别名]
def _apply_traits(self, pipeline_def):
        transformers = [trait.transformer() for trait in pipeline_def._traits_dict.values()]
        transformers_dict = {t.name: t for t in transformers}
        transformer_names = set(transformers_dict.keys())

        for transformer in transformers:
            if not set(transformer.dependencies()).issubset(transformer_names):
                missing = set(transformer.dependencies()) - transformer_names
                raise ModelValidationError(
                    f'{pipeline_def}: trait requires missing traits: ' + ', '.join(missing)
                )

        # order transformers according to dependencies
        transformer_dependencies = {
            t.name: t.order_dependencies() & transformer_names for t in transformers
        }

        ordered_transformers = []
        for name in toposort.toposort_flatten(transformer_dependencies):
            ordered_transformers.append(transformers_dict[name])

        # hardcode meta trait transformer
        ordered_transformers.append(MetaTraitTransformer())

        # inject new steps
        for transformer in ordered_transformers:
            for step in transformer.inject_steps():
                pipeline_def.add_step(step)

        # do remaining processing
        for transformer in ordered_transformers:
            transformer.process_pipeline_args(pipeline_def) 

# 需要导入模块: import toposort [as 别名]
# 或者: from toposort import toposort_flatten [as 别名]
def correlate(self,project):
        # Add procedures, interfaces and types from parent to our lists
        self.all_types = {}
        for dt in self.types:
            self.all_types[dt.name.lower()] = dt
        self.all_vars = {}
        for var in self.variables:
            self.all_vars[var.name.lower()] = var
        self.all_absinterfaces = {}
        self.all_procs = {}

        # Add procedures and types from USED modules to our lists
        for mod, extra in self.uses:
            if type(mod) is str: continue
            procs, absints, types, variables = mod.get_used_entities(extra)
            self.all_procs.update(procs)
            self.all_absinterfaces.update(absints)
            self.all_types.update(types)
            self.all_vars.update(variables)
        self.uses = [m[0] for m in self.uses]

        typelist = {}
        for dtype in self.types:
            if  dtype.extends and dtype.extends.lower() in self.all_types:
                dtype.extends = self.all_types[dtype.extends.lower()]
                typelist[dtype] = set([dtype.extends])
            else:
                typelist[dtype] = set([])
        typeorder = toposort.toposort_flatten(typelist)

        for dtype in typeorder:
            dtype.visible = True
            if dtype in self.types: dtype.correlate(project)
        for var in self.variables:
            var.correlate(project)
        for com in self.common:
            com.correlate(project)
        # Sort content
        self.sort() 

# 需要导入模块: import toposort [as 别名]
# 或者: from toposort import toposort_flatten [as 别名]
def ontologise(self):
        '''
        Takes the TermsToGenes and GenesToTerms dictionaries and
        corrects them for an AnnotationSet with a hierarchical ontology.
        If a gene is associated with a term, e.g. gene ENSG00000144061 is
        associated with the HPO term Nephropathy, it is also associated with
        all the ancestors of that term, e.g. ENSG00000144061 must also be
        associated with Abnormality of the Kidney.
        This function deals with this by taking the TermsToGenes
        dictionary and, for each term, taking the descendent terms,
        looking up their associated genes and adding them to the TermsToGenes
        set for the original term.

        '''
        TermsToGenes = self.TermsToGenes
        TermsToOntP = copy.copy(self.TermsToOnt)
        TermsToOntC = self.reverseDict(TermsToOntP)

        Adict = dict()
        # topologically sorts the terms in the ontology so that
        # every term is earlier in the list than all of its ancestors.
        sortedterms = toposort_flatten(self.TermsToOnt)
        sortedterms_p = []
        for s in sortedterms:
            if s in TermsToGenes:
                sortedterms_p.append(s)
        for term in sortedterms_p:
            Adict[term] = set()
            if term in TermsToOntC:
                # return descendents
                allids = getAllAncestorsDescendants(term, TermsToOntC)

                allids.add(term)
                for term2 in allids:
                    if term2 in TermsToGenes:
                        Adict[term] = Adict[term] | TermsToGenes[term2]

        self.TermsToGenes = Adict
        self.GenesToTerms = self.reverseDict(Adict) 

# 需要导入模块: import toposort [as 别名]
# 或者: from toposort import toposort_flatten [as 别名]
def delete_instance_filtered(instance, model_class, delete_nullable, skip_transitive_deletes):
    """
    Deletes the DB instance recursively, skipping any models in the skip_transitive_deletes set.

    Callers *must* ensure that any models listed in the skip_transitive_deletes must be capable
    of being directly deleted when the instance is deleted (with automatic sorting handling
    dependency order) - for example, the Manifest and ManifestBlob tables for Repository will
    always refer to the *same* repository when Manifest references ManifestBlob, so we can safely
    skip transitive deletion for the Manifest table.

    Callers *must* catch IntegrityError's raised, as this method will *not* delete the instance
    under a transaction, to avoid locking the database.
    """
    # We need to sort the ops so that models get cleaned in order of their dependencies
    ops = reversed(list(instance.dependencies(delete_nullable)))
    filtered_ops = []

    dependencies = defaultdict(set)

    for query, fk in ops:
        # We only want to skip transitive deletes, which are done using subqueries in the form of
        # DELETE FROM <table> in <subquery>. If an op is not using a subquery, we allow it to be
        # applied directly.
        if fk.model not in skip_transitive_deletes or query.op.lower() != "in":
            filtered_ops.append((query, fk))

        if query.op.lower() == "in":
            dependencies[fk.model.__name__].add(query.rhs.model.__name__)
        elif query.op == "=":
            dependencies[fk.model.__name__].add(model_class.__name__)
        else:
            raise RuntimeError("Unknown operator in recursive repository delete query")

    sorted_models = list(reversed(toposort.toposort_flatten(dependencies)))

    def sorted_model_key(query_fk_tuple):
        cmp_query, cmp_fk = query_fk_tuple
        if cmp_query.op.lower() == "in":
            return -1
        return sorted_models.index(cmp_fk.model.__name__)

    filtered_ops.sort(key=sorted_model_key)

    # NOTE: We do not use a transaction here, as it can be a VERY long transaction, potentially
    # locking up the database. Instead, we expect cleanup code to have run before this point, and
    # if this fails with an IntegrityError, callers are expected to catch and retry.
    for query, fk in filtered_ops:
        _model = fk.model
        if fk.null and not delete_nullable:
            _model.update(**{fk.name: None}).where(query).execute()
        else:
            _model.delete().where(query).execute()

    return instance.delete().where(instance._pk_expr()).execute() 

# 需要导入模块: import toposort [as 别名]
# 或者: from toposort import toposort_flatten [as 别名]
def get_relation_view():
        _views = PreferenceRelationView.get_by(to_dict=True)
        views = []
        if current_app.config.get("USE_ACL"):
            for i in _views:
                try:
                    if ACLManager().has_permission(i.get('name'),
                                                   ResourceTypeEnum.RELATION_VIEW,
                                                   PermEnum.READ):
                        views.append(i)
                except AbortException:
                    pass
        else:
            views = _views

        view2cr_ids = dict()
        result = dict()
        name2id = list()
        for view in views:
            view2cr_ids.setdefault(view['name'], []).extend(json.loads(view['cr_ids']))
            name2id.append([view['name'], view['id']])

        id2type = dict()
        for view_name in view2cr_ids:
            for i in view2cr_ids[view_name]:
                id2type[i['parent_id']] = None
                id2type[i['child_id']] = None
            topo = {i['child_id']: {i['parent_id']} for i in view2cr_ids[view_name]}
            leaf = list(set(toposort.toposort_flatten(topo)) - set([j for i in topo.values() for j in i]))

            leaf2show_types = {i: [t['child_id'] for t in CITypeRelation.get_by(parent_id=i)] for i in leaf}
            node2show_types = copy.deepcopy(leaf2show_types)

            def _find_parent(_node_id):
                parents = topo.get(_node_id, {})
                for parent in parents:
                    node2show_types.setdefault(parent, []).extend(node2show_types.get(_node_id, []))
                    _find_parent(parent)
                if not parents:
                    return

            for l in leaf:
                _find_parent(l)

            for node_id in node2show_types:
                node2show_types[node_id] = [CITypeCache.get(i).to_dict() for i in set(node2show_types[node_id])]

            result[view_name] = dict(topo=list(map(list, toposort.toposort(topo))),
                                     topo_flatten=list(toposort.toposort_flatten(topo)),
                                     leaf=leaf,
                                     leaf2show_types=leaf2show_types,
                                     node2show_types=node2show_types,
                                     show_types=[CITypeCache.get(j).to_dict()
                                                 for i in leaf2show_types.values() for j in i])

        for type_id in id2type:
            id2type[type_id] = CITypeCache.get(type_id).to_dict()

        return result, id2type, sorted(name2id, key=lambda x: x[1]) 

# 需要导入模块: import toposort [as 别名]
# 或者: from toposort import toposort_flatten [as 别名]
def __init__(self, thread_id, name, experiment, component_id, max_results, cache_results):
        threading.Thread.__init__(self)
        self.threadID = thread_id
        self.name = name
        self.experiment = experiment
        self.comp_id = component_id
        self.result = {}
        self.max_results = max_results
        self.cache_results = cache_results
        print "Submitting topology to storm. End component", self.comp_id
        exp = Experiment.objects.get(pk=self.experiment)
        graph = exp.workflow.graph_data
        graph_data = {}
        print graph
        tmp = graph.split(',')
        for elem in tmp:
            first_node = elem.split(":")[0]
            second_node = elem.split(":")[1]
            if second_node in graph_data:
                depend_nodes = graph_data[second_node]
                depend_nodes.add(first_node)
            else:
                graph_data[second_node] = set()
                graph_data[second_node].add(first_node)
        topological_graph = toposort_flatten(graph_data)
        print "Graph after topological sort", topological_graph
        message = {
            'exp_id': self.experiment, 'result': self.comp_id,
            'graph': topological_graph, 'components': defaultdict()}

        for data in topological_graph:
            component_id = int(data)
            comp = Component.objects.get(pk=component_id)
            if comp.operation_type.function_type == 'Create':
                if comp.operation_type.function_arg == 'Table':
                        filename = comp.operation_type.function_subtype_arg
                        input_data = read_csv(filename)
                        message['input'] = {}
                        for elem in list(input_data.columns):
                            message['input'][elem] = list(input_data[elem])
                        message['cols'] = list(input_data.columns)
                        # message['input'] = input_data.to_dict()

            serialized_obj = serializers.serialize('json', [comp.operation_type, ])
            print "Component_id", component_id, " ", comp.operation_type
            message['components'][data] = serialized_obj

        print "Message ", message
        r = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=0)
        self.pubsub = r.pubsub(ignore_subscribe_messages=True)
        self.pubsub.subscribe("Exp " + str(self.experiment))
        ret = r.publish('workflow', json.dumps(message))
        print "return", ret