text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
  # vi: set ft=python sts=4 ts=4 sw=4 et:
  """Defines functionality for pipelined execution of interfaces
  
  The `Workflow` class provides core functionality for batch processing.
  """
  from __future__ import (print_function, division, unicode_literals,
                          absolute_import)
  from builtins import str, bytes, open
  
  import os
  import os.path as op
  import sys
  from datetime import datetime
  from copy import deepcopy
  import pickle
  import shutil
  
  import numpy as np
  import networkx as nx
  
  from ... import config, logging
  from ...utils.misc import str2bool
  from ...utils.functions import (getsource, create_function_from_source)
  
  from ...interfaces.base import (traits, TraitedSpec, TraitDictObject,
                                  TraitListObject)
  from ...utils.filemanip import save_json, makedirs, to_str
  from .utils import (generate_expanded_graph, export_graph, write_workflow_prov,
                      write_workflow_resources, format_dot, topological_sort,
                      get_print_name, merge_dict, format_node)
  
  from .base import EngineBase
  from .nodes import MapNode
  
  # Py2 compat: http://python-future.org/compatible_idioms.html#collections-counter-and-ordereddict
  from future import standard_library
  standard_library.install_aliases()
  
  logger = logging.getLogger('nipype.workflow')
  
  
  class Workflow(EngineBase):
      """Controls the setup and execution of a pipeline of processes."""
  
      def __init__(self, name, base_dir=None):
          """Create a workflow object.
  
          Parameters
          ----------
          name : alphanumeric string
              unique identifier for the workflow
          base_dir : string, optional
              path to workflow storage
  
          """
          super(Workflow, self).__init__(name, base_dir)
          self._graph = nx.DiGraph()
  
      # PUBLIC API
      def clone(self, name):
          """Clone a workflow
  
          .. note::
  
            Will reset attributes used for executing workflow. See
            _init_runtime_fields.
  
          Parameters
          ----------
  
          name: alphanumeric name
              unique name for the workflow
  
          """
          clone = super(Workflow, self).clone(name)
          clone._reset_hierarchy()
          return clone
  
      # Graph creation functions
      def connect(self, *args, **kwargs):
          """Connect nodes in the pipeline.
  
          This routine also checks if inputs and outputs are actually provided by
          the nodes that are being connected.
  
          Creates edges in the directed graph using the nodes and edges specified
          in the `connection_list`.  Uses the NetworkX method
          DiGraph.add_edges_from.
  
          Parameters
          ----------
  
          args : list or a set of four positional arguments
  
              Four positional arguments of the form::
  
                connect(source, sourceoutput, dest, destinput)
  
              source : nodewrapper node
              sourceoutput : string (must be in source.outputs)
              dest : nodewrapper node
              destinput : string (must be in dest.inputs)
  
              A list of 3-tuples of the following form::
  
               [(source, target,
                   [('sourceoutput/attribute', 'targetinput'),
                   ...]),
               ...]
  
              Or::
  
               [(source, target, [(('sourceoutput1', func, arg2, ...),
                                           'targetinput'), ...]),
               ...]
               sourceoutput1 will always be the first argument to func
               and func will be evaluated and the results sent ot targetinput
  
               currently func needs to define all its needed imports within the
               function as we use the inspect module to get at the source code
               and execute it remotely
          """
          if len(args) == 1:
              connection_list = args[0]
          elif len(args) == 4:
              connection_list = [(args[0], args[2], [(args[1], args[3])])]
          else:
              raise TypeError('connect() takes either 4 arguments, or 1 list of'
                              ' connection tuples (%d args given)' % len(args))
  
          disconnect = False
          if kwargs:
              disconnect = kwargs.get('disconnect', False)
  
          if disconnect:
              self.disconnect(connection_list)
              return
  
          newnodes = []
          for srcnode, destnode, _ in connection_list:
              if self in [srcnode, destnode]:
                  msg = ('Workflow connect cannot contain itself as node:'
                         ' src[%s] dest[%s] workflow[%s]') % (srcnode, destnode,
                                                              self.name)
  
                  raise IOError(msg)
              if (srcnode not in newnodes) and not self._has_node(srcnode):
                  newnodes.append(srcnode)
              if (destnode not in newnodes) and not self._has_node(destnode):
                  newnodes.append(destnode)
          if newnodes:
              self._check_nodes(newnodes)
              for node in newnodes:
                  if node._hierarchy is None:
                      node._hierarchy = self.name
          not_found = []
          connected_ports = {}
          for srcnode, destnode, connects in connection_list:
              if destnode not in connected_ports:
                  connected_ports[destnode] = []
              # check to see which ports of destnode are already
              # connected.
              if not disconnect and (destnode in self._graph.nodes()):
                  for edge in self._graph.in_edges(destnode):
                      data = self._graph.get_edge_data(*edge)
                      for sourceinfo, destname in data['connect']:
                          if destname not in connected_ports[destnode]:
                              connected_ports[destnode] += [destname]
              for source, dest in connects:
                  # Currently datasource/sink/grabber.io modules
                  # determine their inputs/outputs depending on
                  # connection settings.  Skip these modules in the check
                  if dest in connected_ports[destnode]:
                      raise Exception("""\
  Trying to connect %s:%s to %s:%s but input '%s' of node '%s' is already
  connected.
  """ % (srcnode, source, destnode, dest, dest, destnode))
                  if not (hasattr(destnode, '_interface') and
                          ('.io' in str(destnode._interface.__class__) or any([
                              '.io' in str(val)
                              for val in destnode._interface.__class__.__bases__
                          ]))):
                      if not destnode._check_inputs(dest):
                          not_found.append(['in', destnode.name, dest])
                  if not (hasattr(srcnode, '_interface') and
                          ('.io' in str(srcnode._interface.__class__) or any([
                              '.io' in str(val)
                              for val in srcnode._interface.__class__.__bases__
                          ]))):
                      if isinstance(source, tuple):
                          # handles the case that source is specified
                          # with a function
                          sourcename = source[0]
                      elif isinstance(source, (str, bytes)):
                          sourcename = source
                      else:
                          raise Exception(
                              ('Unknown source specification in '
                               'connection from output of %s') % srcnode.name)
                      if sourcename and not srcnode._check_outputs(sourcename):
                          not_found.append(['out', srcnode.name, sourcename])
                  connected_ports[destnode] += [dest]
          infostr = []
          for info in not_found:
              infostr += [
                  "Module %s has no %sput called %s\n" % (info[1], info[0],
                                                          info[2])
              ]
          if not_found:
              raise Exception(
                  '\n'.join(['Some connections were not found'] + infostr))
  
          # turn functions into strings
          for srcnode, destnode, connects in connection_list:
              for idx, (src, dest) in enumerate(connects):
                  if isinstance(src,
                                tuple) and not isinstance(src[1], (str, bytes)):
                      function_source = getsource(src[1])
                      connects[idx] = ((src[0], function_source, src[2:]), dest)
  
          # add connections
          for srcnode, destnode, connects in connection_list:
              edge_data = self._graph.get_edge_data(srcnode, destnode, None)
              if edge_data:
                  logger.debug('(%s, %s): Edge data exists: %s', srcnode,
                               destnode, to_str(edge_data))
                  for data in connects:
                      if data not in edge_data['connect']:
                          edge_data['connect'].append(data)
                      if disconnect:
                          logger.debug('Removing connection: %s', to_str(data))
                          edge_data['connect'].remove(data)
                  if edge_data['connect']:
                      self._graph.add_edges_from([(srcnode, destnode,
                                                   edge_data)])
                  else:
                      # pass
                      logger.debug('Removing connection: %s->%s', srcnode,
                                   destnode)
                      self._graph.remove_edges_from([(srcnode, destnode)])
              elif not disconnect:
                  logger.debug('(%s, %s): No edge data', srcnode, destnode)
                  self._graph.add_edges_from([(srcnode, destnode, {
                      'connect': connects
                  })])
              edge_data = self._graph.get_edge_data(srcnode, destnode)
              logger.debug('(%s, %s): new edge data: %s', srcnode, destnode,
                           to_str(edge_data))
  
      def disconnect(self, *args):
          """Disconnect nodes
          See the docstring for connect for format.
          """
          if len(args) == 1:
              connection_list = args[0]
          elif len(args) == 4:
              connection_list = [(args[0], args[2], [(args[1], args[3])])]
          else:
              raise TypeError('disconnect() takes either 4 arguments, or 1 list '
                              'of connection tuples (%d args given)' % len(args))
  
          for srcnode, dstnode, conn in connection_list:
              logger.debug('disconnect(): %s->%s %s', srcnode, dstnode,
                           to_str(conn))
              if self in [srcnode, dstnode]:
                  raise IOError(
                      'Workflow connect cannot contain itself as node: src[%s] '
                      'dest[%s] workflow[%s]') % (srcnode, dstnode, self.name)
  
              # If node is not in the graph, not connected
              if not self._has_node(srcnode) or not self._has_node(dstnode):
                  continue
  
              edge_data = self._graph.get_edge_data(srcnode, dstnode, {
                  'connect': []
              })
              ed_conns = [(c[0], c[1]) for c in edge_data['connect']]
  
              remove = []
              for edge in conn:
                  if edge in ed_conns:
                      # idx = ed_conns.index(edge)
                      remove.append((edge[0], edge[1]))
  
              logger.debug('disconnect(): remove list %s', to_str(remove))
              for el in remove:
                  edge_data['connect'].remove(el)
                  logger.debug('disconnect(): removed connection %s', to_str(el))
  
              if not edge_data['connect']:
                  self._graph.remove_edge(srcnode, dstnode)
              else:
                  self._graph.add_edges_from([(srcnode, dstnode, edge_data)])
  
      def add_nodes(self, nodes):
          """ Add nodes to a workflow
  
          Parameters
          ----------
          nodes : list
              A list of EngineBase-based objects
          """
          newnodes = []
          all_nodes = self._get_all_nodes()
          for node in nodes:
              if self._has_node(node):
                  raise IOError('Node %s already exists in the workflow' % node)
              if isinstance(node, Workflow):
                  for subnode in node._get_all_nodes():
                      if subnode in all_nodes:
                          raise IOError(('Subnode %s of node %s already exists '
                                         'in the workflow') % (subnode, node))
              newnodes.append(node)
          if not newnodes:
              logger.debug('no new nodes to add')
              return
          for node in newnodes:
              if not issubclass(node.__class__, EngineBase):
                  raise Exception('Node %s must be a subclass of EngineBase',
                                  node)
          self._check_nodes(newnodes)
          for node in newnodes:
              if node._hierarchy is None:
                  node._hierarchy = self.name
          self._graph.add_nodes_from(newnodes)
  
      def remove_nodes(self, nodes):
          """ Remove nodes from a workflow
  
          Parameters
          ----------
          nodes : list
              A list of EngineBase-based objects
          """
          self._graph.remove_nodes_from(nodes)
  
      # Input-Output access
      @property
      def inputs(self):
          return self._get_inputs()
  
      @property
      def outputs(self):
          return self._get_outputs()
  
      def get_node(self, name):
          """Return an internal node by name
          """
          nodenames = name.split('.')
          nodename = nodenames[0]
          outnode = [
              node for node in self._graph.nodes()
              if str(node).endswith('.' + nodename)
          ]
          if outnode:
              outnode = outnode[0]
              if nodenames[1:] and issubclass(outnode.__class__, Workflow):
                  outnode = outnode.get_node('.'.join(nodenames[1:]))
          else:
              outnode = None
          return outnode
  
      def list_node_names(self):
          """List names of all nodes in a workflow
          """
          outlist = []
          for node in nx.topological_sort(self._graph):
              if isinstance(node, Workflow):
                  outlist.extend([
                      '.'.join((node.name, nodename))
                      for nodename in node.list_node_names()
                  ])
              else:
                  outlist.append(node.name)
          return sorted(outlist)
  
      def write_graph(self,
                      dotfilename='graph.dot',
                      graph2use='hierarchical',
                      format="png",
                      simple_form=True):
          """Generates a graphviz dot file and a png file
  
          Parameters
          ----------
  
          graph2use: 'orig', 'hierarchical' (default), 'flat', 'exec', 'colored'
              orig - creates a top level graph without expanding internal
              workflow nodes;
              flat - expands workflow nodes recursively;
              hierarchical - expands workflow nodes recursively with a
              notion on hierarchy;
              colored - expands workflow nodes recursively with a
              notion on hierarchy in color;
              exec - expands workflows to depict iterables
  
          format: 'png', 'svg'
  
          simple_form: boolean (default: True)
              Determines if the node name used in the graph should be of the form
              'nodename (package)' when True or 'nodename.Class.package' when
              False.
  
          """
          graphtypes = ['orig', 'flat', 'hierarchical', 'exec', 'colored']
          if graph2use not in graphtypes:
              raise ValueError('Unknown graph2use keyword. Must be one of: ' +
                               str(graphtypes))
          base_dir, dotfilename = op.split(dotfilename)
          if base_dir == '':
              if self.base_dir:
                  base_dir = self.base_dir
                  if self.name:
                      base_dir = op.join(base_dir, self.name)
              else:
                  base_dir = os.getcwd()
          base_dir = makedirs(base_dir, exist_ok=True)
          if graph2use in ['hierarchical', 'colored']:
              if self.name[:1].isdigit():  # these graphs break if int
                  raise ValueError('{} graph failed, workflow name cannot begin '
                                   'with a number'.format(graph2use))
              dotfilename = op.join(base_dir, dotfilename)
              self.write_hierarchical_dotfile(
                  dotfilename=dotfilename,
                  colored=graph2use == "colored",
                  simple_form=simple_form)
              outfname = format_dot(dotfilename, format=format)
          else:
              graph = self._graph
              if graph2use in ['flat', 'exec']:
                  graph = self._create_flat_graph()
              if graph2use == 'exec':
                  graph = generate_expanded_graph(deepcopy(graph))
              outfname = export_graph(
                  graph,
                  base_dir,
                  dotfilename=dotfilename,
                  format=format,
                  simple_form=simple_form)
  
          logger.info(
              'Generated workflow graph: %s (graph2use=%s, simple_form=%s).' %
              (outfname, graph2use, simple_form))
          return outfname
  
      def write_hierarchical_dotfile(self,
                                     dotfilename=None,
                                     colored=False,
                                     simple_form=True):
          dotlist = ['digraph %s{' % self.name]
          dotlist.append(
              self._get_dot(
                  prefix='  ', colored=colored, simple_form=simple_form))
          dotlist.append('}')
          dotstr = '\n'.join(dotlist)
          if dotfilename:
              fp = open(dotfilename, 'wt')
              fp.writelines(dotstr)
              fp.close()
          else:
              logger.info(dotstr)
  
      def export(self,
                 filename=None,
                 prefix="output",
                 format="python",
                 include_config=False):
          """Export object into a different format
  
          Parameters
          ----------
          filename: string
             file to save the code to; overrides prefix
          prefix: string
             prefix to use for output file
          format: string
             one of "python"
          include_config: boolean
             whether to include node and workflow config values
  
          """
          formats = ["python"]
          if format not in formats:
              raise ValueError('format must be one of: %s' % '|'.join(formats))
          flatgraph = self._create_flat_graph()
          nodes = nx.topological_sort(flatgraph)
  
          all_lines = None
          lines = ['# Workflow']
          importlines = [
              'from nipype.pipeline.engine import Workflow, '
              'Node, MapNode'
          ]
          functions = {}
          if format == "python":
              connect_template = '%s.connect(%%s, %%s, %%s, "%%s")' % self.name
              connect_template2 = '%s.connect(%%s, "%%s", %%s, "%%s")' \
                                  % self.name
              wfdef = '%s = Workflow("%s")' % (self.name, self.name)
              lines.append(wfdef)
              if include_config:
                  lines.append('%s.config = %s' % (self.name, self.config))
              for idx, node in enumerate(nodes):
                  nodename = node.fullname.replace('.', '_')
                  # write nodes
                  nodelines = format_node(
                      node, format='python', include_config=include_config)
                  for line in nodelines:
                      if line.startswith('from'):
                          if line not in importlines:
                              importlines.append(line)
                      else:
                          lines.append(line)
                  # write connections
                  for u, _, d in flatgraph.in_edges(nbunch=node, data=True):
                      for cd in d['connect']:
                          if isinstance(cd[0], tuple):
                              args = list(cd[0])
                              if args[1] in functions:
                                  funcname = functions[args[1]]
                              else:
                                  func = create_function_from_source(args[1])
                                  funcname = [
                                      name for name in func.__globals__
                                      if name != '__builtins__'
                                  ][0]
                                  functions[args[1]] = funcname
                              args[1] = funcname
                              args = tuple([arg for arg in args if arg])
                              line_args = (u.fullname.replace('.', '_'), args,
                                           nodename, cd[1])
                              line = connect_template % line_args
                              line = line.replace("'%s'" % funcname, funcname)
                              lines.append(line)
                          else:
                              line_args = (u.fullname.replace('.', '_'), cd[0],
                                           nodename, cd[1])
                              lines.append(connect_template2 % line_args)
              functionlines = ['# Functions']
              for function in functions:
                  functionlines.append(pickle.loads(function).rstrip())
              all_lines = importlines + functionlines + lines
  
              if not filename:
                  filename = '%s%s.py' % (prefix, self.name)
              with open(filename, 'wt') as fp:
                  fp.writelines('\n'.join(all_lines))
          return all_lines
  
      def run(self, plugin=None, plugin_args=None, updatehash=False):
          """ Execute the workflow
  
          Parameters
          ----------
  
          plugin: plugin name or object
              Plugin to use for execution. You can create your own plugins for
              execution.
          plugin_args : dictionary containing arguments to be sent to plugin
              constructor. see individual plugin doc strings for details.
          """
          if plugin is None:
              plugin = config.get('execution', 'plugin')
          if not isinstance(plugin, (str, bytes)):
              runner = plugin
          else:
              name = '.'.join(__name__.split('.')[:-2] + ['plugins'])
              try:
                  __import__(name)
              except ImportError:
                  msg = 'Could not import plugin module: %s' % name
                  logger.error(msg)
                  raise ImportError(msg)
              else:
                  plugin_mod = getattr(sys.modules[name], '%sPlugin' % plugin)
                  runner = plugin_mod(plugin_args=plugin_args)
          flatgraph = self._create_flat_graph()
          self.config = merge_dict(deepcopy(config._sections), self.config)
          logger.info('Workflow %s settings: %s', self.name,
                      to_str(sorted(self.config)))
          self._set_needed_outputs(flatgraph)
          execgraph = generate_expanded_graph(deepcopy(flatgraph))
          for index, node in enumerate(execgraph.nodes()):
              node.config = merge_dict(deepcopy(self.config), node.config)
              node.base_dir = self.base_dir
              node.index = index
              if isinstance(node, MapNode):
                  node.use_plugin = (plugin, plugin_args)
          self._configure_exec_nodes(execgraph)
          if str2bool(self.config['execution']['create_report']):
              self._write_report_info(self.base_dir, self.name, execgraph)
          runner.run(execgraph, updatehash=updatehash, config=self.config)
          datestr = datetime.utcnow().strftime('%Y%m%dT%H%M%S')
          if str2bool(self.config['execution']['write_provenance']):
              prov_base = op.join(self.base_dir,
                                  'workflow_provenance_%s' % datestr)
              logger.info('Provenance file prefix: %s' % prov_base)
              write_workflow_prov(execgraph, prov_base, format='all')
  
          if config.resource_monitor:
              base_dir = self.base_dir or os.getcwd()
              write_workflow_resources(
                  execgraph,
                  filename=op.join(base_dir, self.name, 'resource_monitor.json'))
          return execgraph
  
      # PRIVATE API AND FUNCTIONS
  
      def _write_report_info(self, workingdir, name, graph):
          if workingdir is None:
              workingdir = os.getcwd()
          report_dir = op.join(workingdir, name)
          makedirs(report_dir, exist_ok=True)
          shutil.copyfile(
              op.join(op.dirname(__file__), 'report_template.html'),
              op.join(report_dir, 'index.html'))
          shutil.copyfile(
              op.join(op.dirname(__file__), '..', '..', 'external', 'd3.js'),
              op.join(report_dir, 'd3.js'))
          nodes, groups = topological_sort(graph, depth_first=True)
          graph_file = op.join(report_dir, 'graph1.json')
          json_dict = {'nodes': [], 'links': [], 'groups': [], 'maxN': 0}
          for i, node in enumerate(nodes):
              report_file = "%s/_report/report.rst" % \
                            node.output_dir().replace(report_dir, '')
              result_file = "%s/result_%s.pklz" % \
                            (node.output_dir().replace(report_dir, ''),
                             node.name)
              json_dict['nodes'].append(
                  dict(
                      name='%d_%s' % (i, node.name),
                      report=report_file,
                      result=result_file,
                      group=groups[i]))
          maxN = 0
          for gid in np.unique(groups):
              procs = [i for i, val in enumerate(groups) if val == gid]
              N = len(procs)
              if N > maxN:
                  maxN = N
              json_dict['groups'].append(
                  dict(procs=procs, total=N, name='Group_%05d' % gid))
          json_dict['maxN'] = maxN
          for u, v in graph.in_edges():
              json_dict['links'].append(
                  dict(source=nodes.index(u), target=nodes.index(v), value=1))
          save_json(graph_file, json_dict)
          graph_file = op.join(report_dir, 'graph.json')
          # Avoid RuntimeWarning: divide by zero encountered in log10
          num_nodes = len(nodes)
          if num_nodes > 0:
              index_name = np.ceil(np.log10(num_nodes)).astype(int)
          else:
              index_name = 0
          template = '%%0%dd_' % index_name
  
          def getname(u, i):
              name_parts = u.fullname.split('.')
              # return '.'.join(name_parts[:-1] + [template % i + name_parts[-1]])
              return template % i + name_parts[-1]
  
          json_dict = []
          for i, node in enumerate(nodes):
              imports = []
              for u, v in graph.in_edges(nbunch=node):
                  imports.append(getname(u, nodes.index(u)))
              json_dict.append(
                  dict(
                      name=getname(node, i),
                      size=1,
                      group=groups[i],
                      imports=imports))
          save_json(graph_file, json_dict)
  
      def _set_needed_outputs(self, graph):
          """Initialize node with list of which outputs are needed."""
          rm_outputs = self.config['execution']['remove_unnecessary_outputs']
          if not str2bool(rm_outputs):
              return
          for node in graph.nodes():
              node.needed_outputs = []
              for edge in graph.out_edges(node):
                  data = graph.get_edge_data(*edge)
                  sourceinfo = [
                      v1[0] if isinstance(v1, tuple) else v1
                      for v1, v2 in data['connect']
                  ]
                  node.needed_outputs += [
                      v for v in sourceinfo if v not in node.needed_outputs
                  ]
              if node.needed_outputs:
                  node.needed_outputs = sorted(node.needed_outputs)
  
      def _configure_exec_nodes(self, graph):
          """Ensure that each node knows where to get inputs from
          """
          for node in graph.nodes():
              node.input_source = {}
              for edge in graph.in_edges(node):
                  data = graph.get_edge_data(*edge)
                  for sourceinfo, field in data['connect']:
                      node.input_source[field] = \
                          (op.join(edge[0].output_dir(),
                                   'result_%s.pklz' % edge[0].name),
                           sourceinfo)
  
      def _check_nodes(self, nodes):
          """Checks if any of the nodes are already in the graph
  
          """
          node_names = [node.name for node in self._graph.nodes()]
          node_lineage = [node._hierarchy for node in self._graph.nodes()]
          for node in nodes:
              if node.name in node_names:
                  idx = node_names.index(node.name)
                  try:
                      this_node_lineage = node_lineage[idx]
                  except IndexError:
                      raise IOError(
                          'Duplicate node name "%s" found.' % node.name)
                  else:
                      if this_node_lineage in [node._hierarchy, self.name]:
                          raise IOError(
                              'Duplicate node name "%s" found.' % node.name)
              else:
                  node_names.append(node.name)
  
      def _has_attr(self, parameter, subtype='in'):
          """Checks if a parameter is available as an input or output
          """
          if subtype == 'in':
              subobject = self.inputs
          else:
              subobject = self.outputs
          attrlist = parameter.split('.')
          cur_out = subobject
          for attr in attrlist:
              if not hasattr(cur_out, attr):
                  return False
              cur_out = getattr(cur_out, attr)
          return True
  
      def _get_parameter_node(self, parameter, subtype='in'):
          """Returns the underlying node corresponding to an input or
          output parameter
          """
          if subtype == 'in':
              subobject = self.inputs
          else:
              subobject = self.outputs
          attrlist = parameter.split('.')
          cur_out = subobject
          for attr in attrlist[:-1]:
              cur_out = getattr(cur_out, attr)
          return cur_out.traits()[attrlist[-1]].node
  
      def _check_outputs(self, parameter):
          return self._has_attr(parameter, subtype='out')
  
      def _check_inputs(self, parameter):
          return self._has_attr(parameter, subtype='in')
  
      def _get_inputs(self):
          """Returns the inputs of a workflow
  
          This function does not return any input ports that are already
          connected
          """
          inputdict = TraitedSpec()
          for node in self._graph.nodes():
              inputdict.add_trait(node.name, traits.Instance(TraitedSpec))
              if isinstance(node, Workflow):
                  setattr(inputdict, node.name, node.inputs)
              else:
                  taken_inputs = []
                  for _, _, d in self._graph.in_edges(nbunch=node, data=True):
                      for cd in d['connect']:
                          taken_inputs.append(cd[1])
                  unconnectedinputs = TraitedSpec()
                  for key, trait in list(node.inputs.items()):
                      if key not in taken_inputs:
                          unconnectedinputs.add_trait(key,
                                                      traits.Trait(
                                                          trait, node=node))
                          value = getattr(node.inputs, key)
                          setattr(unconnectedinputs, key, value)
                  setattr(inputdict, node.name, unconnectedinputs)
                  getattr(inputdict, node.name).on_trait_change(self._set_input)
          return inputdict
  
      def _get_outputs(self):
          """Returns all possible output ports that are not already connected
          """
          outputdict = TraitedSpec()
          for node in self._graph.nodes():
              outputdict.add_trait(node.name, traits.Instance(TraitedSpec))
              if isinstance(node, Workflow):
                  setattr(outputdict, node.name, node.outputs)
              elif node.outputs:
                  outputs = TraitedSpec()
                  for key, _ in list(node.outputs.items()):
                      outputs.add_trait(key, traits.Any(node=node))
                      setattr(outputs, key, None)
                  setattr(outputdict, node.name, outputs)
          return outputdict
  
      def _set_input(self, objekt, name, newvalue):
          """Trait callback function to update a node input
          """
          objekt.traits()[name].node.set_input(name, newvalue)
  
      def _set_node_input(self, node, param, source, sourceinfo):
          """Set inputs of a node given the edge connection"""
          if isinstance(sourceinfo, (str, bytes)):
              val = source.get_output(sourceinfo)
          elif isinstance(sourceinfo, tuple):
              if callable(sourceinfo[1]):
                  val = sourceinfo[1](source.get_output(sourceinfo[0]),
                                      *sourceinfo[2:])
          newval = val
          if isinstance(val, TraitDictObject):
              newval = dict(val)
          if isinstance(val, TraitListObject):
              newval = val[:]
          logger.debug('setting node input: %s->%s', param, to_str(newval))
          node.set_input(param, deepcopy(newval))
  
      def _get_all_nodes(self):
          allnodes = []
          for node in self._graph.nodes():
              if isinstance(node, Workflow):
                  allnodes.extend(node._get_all_nodes())
              else:
                  allnodes.append(node)
          return allnodes
  
      def _has_node(self, wanted_node):
          for node in self._graph.nodes():
              if wanted_node == node:
                  return True
              if isinstance(node, Workflow):
                  if node._has_node(wanted_node):
                      return True
          return False
  
      def _create_flat_graph(self):
          """Make a simple DAG where no node is a workflow."""
          logger.debug('Creating flat graph for workflow: %s', self.name)
          workflowcopy = deepcopy(self)
          workflowcopy._generate_flatgraph()
          return workflowcopy._graph
  
      def _reset_hierarchy(self):
          """Reset the hierarchy on a graph
          """
          for node in self._graph.nodes():
              if isinstance(node, Workflow):
                  node._reset_hierarchy()
                  for innernode in node._graph.nodes():
                      innernode._hierarchy = '.'.join((self.name,
                                                       innernode._hierarchy))
              else:
                  node._hierarchy = self.name
  
      def _generate_flatgraph(self):
          """Generate a graph containing only Nodes or MapNodes
          """
          logger.debug('expanding workflow: %s', self)
          nodes2remove = []
          if not nx.is_directed_acyclic_graph(self._graph):
              raise Exception(('Workflow: %s is not a directed acyclic graph '
                               '(DAG)') % self.name)
          nodes = nx.topological_sort(self._graph)
          for node in nodes:
              logger.debug('processing node: %s', node)
              if isinstance(node, Workflow):
                  nodes2remove.append(node)
                  # use in_edges instead of in_edges_iter to allow
                  # disconnections to take place properly. otherwise, the
                  # edge dict is modified.
                  # dj: added list() for networkx ver.2
                  for u, _, d in list(
                          self._graph.in_edges(nbunch=node, data=True)):
                      logger.debug('in: connections-> %s', to_str(d['connect']))
                      for cd in deepcopy(d['connect']):
                          logger.debug("in: %s", to_str(cd))
                          dstnode = node._get_parameter_node(cd[1], subtype='in')
                          srcnode = u
                          srcout = cd[0]
                          dstin = cd[1].split('.')[-1]
                          logger.debug('in edges: %s %s %s %s', srcnode, srcout,
                                       dstnode, dstin)
                          self.disconnect(u, cd[0], node, cd[1])
                          self.connect(srcnode, srcout, dstnode, dstin)
                  # do not use out_edges_iter for reasons stated in in_edges
                  # dj: for ver 2 use list(out_edges)
                  for _, v, d in list(
                          self._graph.out_edges(nbunch=node, data=True)):
                      logger.debug('out: connections-> %s', to_str(d['connect']))
                      for cd in deepcopy(d['connect']):
                          logger.debug("out: %s", to_str(cd))
                          dstnode = v
                          if isinstance(cd[0], tuple):
                              parameter = cd[0][0]
                          else:
                              parameter = cd[0]
                          srcnode = node._get_parameter_node(
                              parameter, subtype='out')
                          if isinstance(cd[0], tuple):
                              srcout = list(cd[0])
                              srcout[0] = parameter.split('.')[-1]
                              srcout = tuple(srcout)
                          else:
                              srcout = parameter.split('.')[-1]
                          dstin = cd[1]
                          logger.debug('out edges: %s %s %s %s', srcnode, srcout,
                                       dstnode, dstin)
                          self.disconnect(node, cd[0], v, cd[1])
                          self.connect(srcnode, srcout, dstnode, dstin)
                  # expand the workflow node
                  # logger.debug('expanding workflow: %s', node)
                  node._generate_flatgraph()
                  for innernode in node._graph.nodes():
                      innernode._hierarchy = '.'.join((self.name,
                                                       innernode._hierarchy))
                  self._graph.add_nodes_from(node._graph.nodes())
                  self._graph.add_edges_from(node._graph.edges(data=True))
          if nodes2remove:
              self._graph.remove_nodes_from(nodes2remove)
          logger.debug('finished expanding workflow: %s', self)
  
      def _get_dot(self,
                   prefix=None,
                   hierarchy=None,
                   colored=False,
                   simple_form=True,
                   level=0):
          """Create a dot file with connection info
          """
          if prefix is None:
              prefix = '  '
          if hierarchy is None:
              hierarchy = []
          colorset = [
              '#FFFFC8',  # Y
              '#0000FF',
              '#B4B4FF',
              '#E6E6FF',  # B
              '#FF0000',
              '#FFB4B4',
              '#FFE6E6',  # R
              '#00A300',
              '#B4FFB4',
              '#E6FFE6',  # G
              '#0000FF',
              '#B4B4FF'
          ]  # loop B
          if level > len(colorset) - 2:
              level = 3  # Loop back to blue
  
          dotlist = ['%slabel="%s";' % (prefix, self.name)]
          for node in nx.topological_sort(self._graph):
              fullname = '.'.join(hierarchy + [node.fullname])
              nodename = fullname.replace('.', '_')
              if not isinstance(node, Workflow):
                  node_class_name = get_print_name(node, simple_form=simple_form)
                  if not simple_form:
                      node_class_name = '.'.join(node_class_name.split('.')[1:])
                  if hasattr(node, 'iterables') and node.iterables:
                      dotlist.append(('%s[label="%s", shape=box3d,'
                                      'style=filled, color=black, colorscheme'
                                      '=greys7 fillcolor=2];') %
                                     (nodename, node_class_name))
                  else:
                      if colored:
                          dotlist.append(
                              ('%s[label="%s", style=filled,'
                               ' fillcolor="%s"];') % (nodename, node_class_name,
                                                       colorset[level]))
                      else:
                          dotlist.append(('%s[label="%s"];') % (nodename,
                                                                node_class_name))
  
          for node in nx.topological_sort(self._graph):
              if isinstance(node, Workflow):
                  fullname = '.'.join(hierarchy + [node.fullname])
                  nodename = fullname.replace('.', '_')
                  dotlist.append('subgraph cluster_%s {' % nodename)
                  if colored:
                      dotlist.append(prefix + prefix + 'edge [color="%s"];' %
                                     (colorset[level + 1]))
                      dotlist.append(prefix + prefix + 'style=filled;')
                      dotlist.append(prefix + prefix + 'fillcolor="%s";' %
                                     (colorset[level + 2]))
                  dotlist.append(
                      node._get_dot(
                          prefix=prefix + prefix,
                          hierarchy=hierarchy + [self.name],
                          colored=colored,
                          simple_form=simple_form,
                          level=level + 3))
                  dotlist.append('}')
              else:
                  for subnode in self._graph.successors(node):
                      if node._hierarchy != subnode._hierarchy:
                          continue
                      if not isinstance(subnode, Workflow):
                          nodefullname = '.'.join(hierarchy + [node.fullname])
                          subnodefullname = '.'.join(
                              hierarchy + [subnode.fullname])
                          nodename = nodefullname.replace('.', '_')
                          subnodename = subnodefullname.replace('.', '_')
                          for _ in self._graph.get_edge_data(node,
                                                             subnode)['connect']:
                              dotlist.append('%s -> %s;' % (nodename,
                                                            subnodename))
                          logger.debug('connection: %s', dotlist[-1])
          # add between workflow connections
          for u, v, d in self._graph.edges(data=True):
              uname = '.'.join(hierarchy + [u.fullname])
              vname = '.'.join(hierarchy + [v.fullname])
              for src, dest in d['connect']:
                  uname1 = uname
                  vname1 = vname
                  if isinstance(src, tuple):
                      srcname = src[0]
                  else:
                      srcname = src
                  if '.' in srcname:
                      uname1 += '.' + '.'.join(srcname.split('.')[:-1])
                  if '.' in dest and '@' not in dest:
                      if not isinstance(v, Workflow):
                          if 'datasink' not in \
                             str(v._interface.__class__).lower():
                              vname1 += '.' + '.'.join(dest.split('.')[:-1])
                      else:
                          vname1 += '.' + '.'.join(dest.split('.')[:-1])
                  if uname1.split('.')[:-1] != vname1.split('.')[:-1]:
                      dotlist.append('%s -> %s;' % (uname1.replace('.', '_'),
                                                    vname1.replace('.', '_')))
                      logger.debug('cross connection: %s', dotlist[-1])
          return ('\n' + prefix).join(dotlist)
  