# 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.

   Change directory to provide relative paths for doctests
   >>> import os
   >>> filepath = os.path.dirname( os.path.realpath( __file__ ) )
   >>> datadir = os.path.realpath(os.path.join(filepath, '../testing/data'))
   >>> os.chdir(datadir)

"""

from datetime import datetime
from nipype.utils.misc import flatten, unflatten
try:
    from collections import OrderedDict
except ImportError:
    from ordereddict import OrderedDict
from copy import deepcopy
import cPickle
from glob import glob
import gzip
import inspect
import os
import os.path as op
import re
import shutil
import errno
from shutil import rmtree
from socket import gethostname
from string import Template
import sys
from tempfile import mkdtemp
from warnings import warn
from hashlib import sha1
from nipype.external import six

import numpy as np

from ..utils.misc import package_check, str2bool
package_check('networkx', '1.3')
import networkx as nx

from .. import config, logging
logger = logging.getLogger('workflow')
from ..interfaces.base import (traits, InputMultiPath, CommandLine,
                               Undefined, TraitedSpec, DynamicTraitedSpec,
                               Bunch, InterfaceResult, md5, Interface,
                               TraitDictObject, TraitListObject, isdefined)
from ..utils.misc import getsource, create_function_from_source
from ..utils.filemanip import (save_json, FileNotFoundError,
                               filename_to_list, list_to_filename,
                               copyfiles, fnames_presuffix, loadpkl,
                               split_filename, load_json, savepkl,
                               write_rst_header, write_rst_dict,
                               write_rst_list)

from .utils import (generate_expanded_graph, modify_paths,
                    export_graph, make_output_dir, write_workflow_prov,
                    clean_working_directory, format_dot, topological_sort,
                    get_print_name, merge_dict, evaluate_connect_function)


def _write_inputs(node):
    lines = []
    nodename = node.fullname.replace('.', '_')
    for key, _ in node.inputs.items():
        val = getattr(node.inputs, key)
        if isdefined(val):
            if type(val) == str:
                try:
                    func = create_function_from_source(val)
                except RuntimeError, e:
                    lines.append("%s.inputs.%s = '%s'" % (nodename, key, val))
                else:
                    funcname = [name for name in func.func_globals
                                if name != '__builtins__'][0]
                    lines.append(cPickle.loads(val))
                    if funcname == nodename:
                        lines[-1] = lines[-1].replace(' %s(' % funcname,
                                                      ' %s_1(' % funcname)
                        funcname = '%s_1' % funcname
                    lines.append('from nipype.utils.misc import getsource')
                    lines.append("%s.inputs.%s = getsource(%s)" % (nodename,
                                                                   key,
                                                                   funcname))
            else:
                lines.append('%s.inputs.%s = %s' % (nodename, key, val))
    return lines


def format_node(node, format='python', include_config=False):
    """Format a node in a given output syntax."""
    lines = []
    name = node.fullname.replace('.', '_')
    if format == 'python':
        klass = node._interface
        importline = 'from %s import %s' % (klass.__module__,
                                            klass.__class__.__name__)
        comment = '# Node: %s' % node.fullname
        spec = inspect.getargspec(node._interface.__init__)
        args = spec.args[1:]
        if args:
            filled_args = []
            for arg in args:
                if  hasattr(node._interface, '_%s' % arg):
                    filled_args.append('%s=%s' % (arg, getattr(node._interface,
                                                               '_%s' % arg)))
            args = ', '.join(filled_args)
        else:
            args = ''
        klass_name = klass.__class__.__name__
        if isinstance(node, MapNode):
            nodedef = '%s = MapNode(%s(%s), iterfield=%s, name="%s")' \
                      % (name, klass_name, args, node.iterfield, name)
        else:
            nodedef = '%s = Node(%s(%s), name="%s")' \
                      % (name, klass_name, args, name)
        lines = [importline, comment, nodedef]

        if include_config:
            lines = [importline, "from collections import OrderedDict",
                     comment, nodedef]
            lines.append('%s.config = %s' % (name, node.config))

        if node.iterables is not None:
            lines.append('%s.iterables = %s' % (name, node.iterables))
        lines.extend(_write_inputs(node))

    return lines


class WorkflowBase(object):
    """Defines common attributes and functions for workflows and nodes."""

    def __init__(self, name=None, base_dir=None):
        """ Initialize base parameters of a workflow or node

        Parameters
        ----------
        name : string (mandatory)
            Name of this node. Name must be alphanumeric and not contain any
            special characters (e.g., '.', '@').
        base_dir : string
            base output directory (will be hashed before creations)
            default=None, which results in the use of mkdtemp

        """
        self.base_dir = base_dir
        self.config = None
        self._verify_name(name)
        self.name = name
        # for compatibility with node expansion using iterables
        self._id = self.name
        self._hierarchy = None

    @property
    def inputs(self):
        raise NotImplementedError

    @property
    def outputs(self):
        raise NotImplementedError

    @property
    def fullname(self):
        fullname = self.name
        if self._hierarchy:
            fullname = self._hierarchy + '.' + self.name
        return fullname

    def clone(self, name):
        """Clone a workflowbase object

        Parameters
        ----------

        name : string (mandatory)
            A clone of node or workflow must have a new name
        """
        if (name is None) or (name == self.name):
            raise Exception('Cloning requires a new name')
        self._verify_name(name)
        clone = deepcopy(self)
        clone.name = name
        clone._id = name
        clone._hierarchy = None
        return clone

    def _check_outputs(self, parameter):
        return hasattr(self.outputs, parameter)

    def _check_inputs(self, parameter):
        if isinstance(self.inputs, DynamicTraitedSpec):
            return True
        return hasattr(self.inputs, parameter)

    def _verify_name(self, name):
        valid_name = bool(re.match('^[\w-]+$', name))
        if not valid_name:
            raise Exception('the name must not contain any special characters')

    def __repr__(self):
        if self._hierarchy:
            return '.'.join((self._hierarchy, self._id))
        else:
            return self._id

    def save(self, filename=None):
        if filename is None:
            filename = 'temp.pklz'
        savepkl(filename, self)

    def load(self, filename):
        if '.npz' in filename:
            DeprecationWarning(('npz files will be deprecated in the next '
                                'release. you can use numpy to open them.'))
            return np.load(filename)
        return loadpkl(filename)


class Workflow(WorkflowBase):
    """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()
        self.config = deepcopy(config._sections)

    # 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 Exception('unknown set of parameters to connect function')
        if not kwargs:
            disconnect = False
        else:
            disconnect = kwargs['disconnect']
        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_iter(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__)):
                    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__)):
                    if isinstance(source, tuple):
                        # handles the case that source is specified
                        # with a function
                        sourcename = source[0]
                    elif isinstance(source, six.string_types):
                        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], six.string_types):
                    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, 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' % 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,
                                                          str(edge_data)))

    def disconnect(self, *args):
        """Disconnect two nodes

        See the docstring for connect for format.
        """
        # yoh: explicit **dict was introduced for compatibility with Python 2.5
        return self.connect(*args, **dict(disconnect=True))

    def add_nodes(self, nodes):
        """ Add nodes to a workflow

        Parameters
        ----------
        nodes : list
            A list of WorkflowBase-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__, WorkflowBase):
                raise Exception('Node %s must be a subclass of WorkflowBase' %
                                str(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 WorkflowBase-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 = make_output_dir(base_dir)
        if graph2use in ['hierarchical', 'colored']:
            dotfilename = op.join(base_dir, dotfilename)
            self.write_hierarchical_dotfile(dotfilename=dotfilename,
                                            colored=graph2use == "colored",
                                            simple_form=simple_form)
            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))
            export_graph(graph, base_dir, dotfilename=dotfilename,
                         format=format, simple_form=simple_form)

    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)

        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_iter(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.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(cPickle.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 type(plugin) is not str:
            runner = plugin
        else:
            name = 'nipype.pipeline.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)
        if 'crashdump_dir' in self.config:
            warn(("Deprecated: workflow.config['crashdump_dir']\n"
                  "Please use config['execution']['crashdump_dir']"))
            crash_dir = self.config['crashdump_dir']
            self.config['execution']['crashdump_dir'] = crash_dir
            del self.config['crashdump_dir']
        logger.info(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')
        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)
        if not op.exists(report_dir):
            os.makedirs(report_dir)
        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_iter():
            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')
        template = '%%0%dd_' % np.ceil(np.log10(len(nodes))).astype(int)
        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_iter(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_iter(node):
                data = graph.get_edge_data(*edge)
                for sourceinfo, _ in sorted(data['connect']):
                    if isinstance(sourceinfo, tuple):
                        input_name = sourceinfo[0]
                    else:
                        input_name = sourceinfo
                    if input_name not in node.needed_outputs:
                        node.needed_outputs += [input_name]
            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_iter(node):
                data = graph.get_edge_data(*edge)
                for sourceinfo, field in sorted(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)
                if node_lineage[idx] 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_iter(nbunch=node,
                                                         data=True):
                    for cd in d['connect']:
                        taken_inputs.append(cd[1])
                unconnectedinputs = TraitedSpec()
                for key, trait in 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 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, object, name, newvalue):
        """Trait callback function to update a node input
        """
        object.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, six.string_types):
            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, 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.
                for u, _, d in self._graph.in_edges(nbunch=node, data=True):
                    logger.debug('in: connections-> %s' % str(d['connect']))
                    for cd in deepcopy(d['connect']):
                        logger.debug("in: %s" % 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
                for _, v, d in self._graph.out_edges(nbunch=node, data=True):
                    logger.debug('out: connections-> %s' % str(d['connect']))
                    for cd in deepcopy(d['connect']):
                        logger.debug("out: %s" % 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','#0000FF','#B4B4FF','#E6E6FF','#FF0000',
                    '#FFB4B4','#FFE6E6','#00A300','#B4FFB4','#E6FFE6']

        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('}')
                if level==6:level=2
            else:
                for subnode in self._graph.successors_iter(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: ' + dotlist[-1])
        # add between workflow connections
        for u, v, d in self._graph.edges_iter(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: ' + dotlist[-1])
        return ('\n' + prefix).join(dotlist)


class Node(WorkflowBase):
    """Wraps interface objects for use in pipeline

    A Node creates a sandbox-like directory for executing the underlying
    interface. It will copy or link inputs into this directory to ensure that
    input data are not overwritten. A hash of the input state is used to
    determine if the Node inputs have changed and whether the node needs to be
    re-executed.

    Examples
    --------

    >>> from nipype import Node
    >>> from nipype.interfaces import spm
    >>> realign = Node(spm.Realign(), 'realign')
    >>> realign.inputs.in_files = 'functional.nii'
    >>> realign.inputs.register_to_mean = True
    >>> realign.run() # doctest: +SKIP

    """

    def __init__(self, interface, name, iterables=None, itersource=None,
                 synchronize=False, overwrite=None, needed_outputs=None,
                 run_without_submitting=False, **kwargs):
        """
        Parameters
        ----------

        interface : interface object
            node specific interface (fsl.Bet(), spm.Coregister())

        name : alphanumeric string
            node specific name

        iterables : generator
            Input field and list to iterate using the pipeline engine
            for example to iterate over different frac values in fsl.Bet()
            for a single field the input can be a tuple, otherwise a list
            of tuples
            node.iterables = ('frac',[0.5,0.6,0.7])
            node.iterables = [('fwhm',[2,4]),('fieldx',[0.5,0.6,0.7])]

            If this node has an itersource, then the iterables values
            is a dictionary which maps an iterable source field value
            to the target iterables field values, e.g.:
            inputspec.iterables = ('images',['img1.nii', 'img2.nii']])
            node.itersource = ('inputspec', ['frac'])
            node.iterables = ('frac', {'img1.nii': [0.5, 0.6],
                                       img2.nii': [0.6, 0.7]})

            If this node's synchronize flag is set, then an alternate
            form of the iterables is a [fields, values] list, where
            fields is the list of iterated fields and values is the
            list of value tuples for the given fields, e.g.:
            node.synchronize = True
            node.iterables = [('frac', 'threshold'),
                              [(0.5, True),
                               (0.6, False)]]

        itersource: tuple
            The (name, fields) iterables source which specifies the name
            of the predecessor iterable node and the input fields to use
            from that source node. The output field values comprise the
            key to the iterables parameter value mapping dictionary.

        synchronize: boolean
            Flag indicating whether iterables are synchronized.
            If the iterables are synchronized, then this iterable
            node is expanded once per iteration over all of the
            iterables values.
            Otherwise, this iterable node is expanded once per
            each permutation of the iterables values.

        overwrite : Boolean
            Whether to overwrite contents of output directory if it already
            exists. If directory exists and hash matches it
            assumes that process has been executed

        needed_outputs : list of output_names
            Force the node to keep only specific outputs. By default all
            outputs are kept. Setting this attribute will delete any output
            files and directories from the node's working directory that are
            not part of the `needed_outputs`.

        run_without_submitting : boolean
            Run the node without submitting to a job engine or to a
            multiprocessing pool

        """
        base_dir = None
        if 'base_dir' in kwargs:
            base_dir = kwargs['base_dir']
        super(Node, self).__init__(name, base_dir)
        if interface is None:
            raise IOError('Interface must be provided')
        if not isinstance(interface, Interface):
            raise IOError('interface must be an instance of an Interface')
        self._interface = interface
        self.name = name
        self._result = None
        self.iterables = iterables
        self.synchronize = synchronize
        self.itersource = itersource
        self.overwrite = overwrite
        self.parameterization = None
        self.run_without_submitting = run_without_submitting
        self.input_source = {}
        self.needed_outputs = []
        self.plugin_args = {}
        if needed_outputs:
            self.needed_outputs = sorted(needed_outputs)
        self._got_inputs = False

    @property
    def interface(self):
        """Return the underlying interface object"""
        return self._interface

    @property
    def result(self):
        if self._result:
            return self._result
        else:
            cwd = self.output_dir()
            result, _, _ = self._load_resultfile(cwd)
            return result

    @property
    def inputs(self):
        """Return the inputs of the underlying interface"""
        return self._interface.inputs

    @property
    def outputs(self):
        """Return the output fields of the underlying interface"""
        return self._interface._outputs()

    def output_dir(self):
        """Return the location of the output directory for the node"""
        if self.base_dir is None:
            self.base_dir = mkdtemp()
        outputdir = self.base_dir
        if self._hierarchy:
            outputdir = op.join(outputdir, *self._hierarchy.split('.'))
        if self.parameterization:
            if not str2bool(self.config['execution']['parameterize_dirs']):
                param_dirs = [self._parameterization_dir(p) for p in
                              self.parameterization]
                outputdir = op.join(outputdir, *param_dirs)
            else:
                outputdir = op.join(outputdir, *self.parameterization)
        return op.abspath(op.join(outputdir,
                                            self.name))

    def set_input(self, parameter, val):
        """ Set interface input value"""
        logger.debug('setting nodelevel(%s) input %s = %s' % (str(self),
                                                              parameter,
                                                              str(val)))
        setattr(self.inputs, parameter, deepcopy(val))

    def get_output(self, parameter):
        """Retrieve a particular output of the node"""
        val = None
        if self._result:
            val = getattr(self._result.outputs, parameter)
        else:
            cwd = self.output_dir()
            result, _, _ = self._load_resultfile(cwd)
            if result and result.outputs:
                val = getattr(result.outputs, parameter)
        return val

    def help(self):
        """ Print interface help"""
        self._interface.help()

    def hash_exists(self, updatehash=False):
        # Get a dictionary with hashed filenames and a hashvalue
        # of the dictionary itself.
        hashed_inputs, hashvalue = self._get_hashval()
        outdir = self.output_dir()
        if op.exists(outdir):
            logger.debug(os.listdir(outdir))
        hashfiles = glob(op.join(outdir, '_0x*.json'))
        logger.debug(hashfiles)
        if len(hashfiles) > 1:
            logger.info(hashfiles)
            logger.info('Removing multiple hashfiles and forcing node to rerun')
            for hashfile in hashfiles:
                os.unlink(hashfile)
        hashfile = op.join(outdir, '_0x%s.json' % hashvalue)
        logger.debug(hashfile)
        if updatehash and op.exists(outdir):
            logger.debug("Updating hash: %s" % hashvalue)
            for file in glob(op.join(outdir, '_0x*.json')):
                os.remove(file)
            self._save_hashfile(hashfile, hashed_inputs)
        return op.exists(hashfile), hashvalue, hashfile, hashed_inputs

    def run(self, updatehash=False):
        """Execute the node in its directory.

        Parameters
        ----------

        updatehash: boolean
            Update the hash stored in the output directory
        """
        # check to see if output directory and hash exist
        if self.config is None:
            self.config = deepcopy(config._sections)
        else:
            self.config = merge_dict(deepcopy(config._sections), self.config)
        if not self._got_inputs:
            self._get_inputs()
            self._got_inputs = True
        outdir = self.output_dir()
        logger.info("Executing node %s in dir: %s" % (self._id, outdir))
        if op.exists(outdir):
            logger.debug(os.listdir(outdir))
        hash_info = self.hash_exists(updatehash=updatehash)
        hash_exists, hashvalue, hashfile, hashed_inputs = hash_info
        logger.debug(('updatehash, overwrite, always_run, hash_exists',
                      updatehash, self.overwrite, self._interface.always_run,
                      hash_exists))
        if (not updatehash and (((self.overwrite is None
                                  and self._interface.always_run)
                                 or self.overwrite) or
                                not hash_exists)):
            logger.debug("Node hash: %s" % hashvalue)

            # by rerunning we mean only nodes that did finish to run previously
            json_pat = op.join(outdir, '_0x*.json')
            json_unfinished_pat = op.join(outdir, '_0x*_unfinished.json')
            need_rerun = (op.exists(outdir)
                          and not isinstance(self, MapNode)
                          and len(glob(json_pat)) != 0
                          and len(glob(json_unfinished_pat)) == 0)
            if need_rerun:
                logger.debug("Rerunning node")
                logger.debug(("updatehash = %s, "
                              "self.overwrite = %s, "
                              "self._interface.always_run = %s, "
                              "os.path.exists(%s) = %s, "
                              "hash_method = %s") %
                             (str(updatehash),
                              str(self.overwrite),
                              str(self._interface.always_run),
                              hashfile,
                              str(op.exists(hashfile)),
                              self.config['execution']['hash_method'].lower()))
                log_debug = config.get('logging', 'workflow_level') == 'DEBUG'
                if log_debug and not op.exists(hashfile):
                    exp_hash_paths = glob(json_pat)
                    if len(exp_hash_paths) == 1:
                        split_out = split_filename(exp_hash_paths[0])
                        exp_hash_file_base = split_out[1]
                        exp_hash = exp_hash_file_base[len('_0x'):]
                        logger.debug("Previous node hash = %s" % exp_hash)
                        try:
                            prev_inputs = load_json(exp_hash_paths[0])
                        except:
                            pass
                        else:
                            logging.logdebug_dict_differences(prev_inputs,
                                                              hashed_inputs)
                cannot_rerun = (str2bool(
                    self.config['execution']['stop_on_first_rerun'])
                    and not (self.overwrite is None
                         and self._interface.always_run))
                if cannot_rerun:
                    raise Exception(("Cannot rerun when 'stop_on_first_rerun' "
                                     "is set to True"))
            hashfile_unfinished = op.join(outdir,
                                               '_0x%s_unfinished.json' %
                                               hashvalue)
            if op.exists(hashfile):
                os.remove(hashfile)
            rm_outdir = (op.exists(outdir)
                         and not (op.exists(hashfile_unfinished)
                                  and self._interface.can_resume)
                         and not isinstance(self, MapNode))
            if rm_outdir:
                logger.debug("Removing old %s and its contents" % outdir)
                try:
                    rmtree(outdir)
                except OSError as ex:
                    outdircont = os.listdir(outdir)
                    if ((ex.errno == errno.ENOTEMPTY) and (len(outdircont) == 0)):
                        logger.warn(('An exception was raised trying to remove old %s, '
                                    'but the path seems empty. Is it an NFS mount?. '
                                    'Passing the exception.') % outdir)
                        pass
                    elif ((ex.errno == errno.ENOTEMPTY) and (len(outdircont) != 0)):
                        logger.debug(('Folder contents (%d items): '
                                     '%s') % (len(outdircont), outdircont))
                        raise ex
                    else:
                        raise ex

            else:
                logger.debug(("%s found and can_resume is True or Node is a "
                              "MapNode - resuming execution") %
                             hashfile_unfinished)
                if isinstance(self, MapNode):
                    # remove old json files
                    for filename in glob(op.join(outdir, '_0x*.json')):
                        os.unlink(filename)
            outdir = make_output_dir(outdir)
            self._save_hashfile(hashfile_unfinished, hashed_inputs)
            self.write_report(report_type='preexec', cwd=outdir)
            savepkl(op.join(outdir, '_node.pklz'), self)
            savepkl(op.join(outdir, '_inputs.pklz'),
                    self.inputs.get_traitsfree())
            try:
                self._run_interface()
            except:
                os.remove(hashfile_unfinished)
                raise
            shutil.move(hashfile_unfinished, hashfile)
            self.write_report(report_type='postexec', cwd=outdir)
        else:
            if not op.exists(op.join(outdir, '_inputs.pklz')):
                logger.debug('%s: creating inputs file' % self.name)
                savepkl(op.join(outdir, '_inputs.pklz'),
                        self.inputs.get_traitsfree())
            if not op.exists(op.join(outdir, '_node.pklz')):
                logger.debug('%s: creating node file' % self.name)
                savepkl(op.join(outdir, '_node.pklz'), self)
            logger.debug("Hashfile exists. Skipping execution")
            self._run_interface(execute=False, updatehash=updatehash)
        logger.debug('Finished running %s in dir: %s\n' % (self._id, outdir))
        return self._result

    # Private functions
    def _parameterization_dir(self, param):
        """
        Returns the directory name for the given parameterization string as follows:
            - If the parameterization is longer than 32 characters, then
              return the SHA-1 hex digest.
            - Otherwise, return the parameterization unchanged.
        """
        if len(param) > 32:
            return sha1(param).hexdigest()
        else:
            return param

    def _get_hashval(self):
        """Return a hash of the input state"""
        if not self._got_inputs:
            self._get_inputs()
            self._got_inputs = True
        hashed_inputs, hashvalue = self.inputs.get_hashval(
            hash_method=self.config['execution']['hash_method'])
        rm_extra = self.config['execution']['remove_unnecessary_outputs']
        if str2bool(rm_extra) and self.needed_outputs:
            hashobject = md5()
            hashobject.update(hashvalue)
            sorted_outputs = sorted(self.needed_outputs)
            hashobject.update(str(sorted_outputs))
            hashvalue = hashobject.hexdigest()
            hashed_inputs['needed_outputs'] = sorted_outputs
        return hashed_inputs, hashvalue

    def _save_hashfile(self, hashfile, hashed_inputs):
        try:
            save_json(hashfile, hashed_inputs)
        except (IOError, TypeError):
            err_type = sys.exc_info()[0]
            if err_type is TypeError:
                # XXX - SG current workaround is to just
                # create the hashed file and not put anything
                # in it
                fd = open(hashfile, 'wt')
                fd.writelines(str(hashed_inputs))
                fd.close()
                logger.debug(('Unable to write a particular type to the json '
                              'file'))
            else:
                logger.critical('Unable to open the file in write mode: %s' %
                                hashfile)

    def _get_inputs(self):
        """Retrieve inputs from pointers to results file

        This mechanism can be easily extended/replaced to retrieve data from
        other data sources (e.g., XNAT, HTTP, etc.,.)
        """
        logger.debug('Setting node inputs')
        for key, info in self.input_source.items():
            logger.debug('input: %s' % key)
            results_file = info[0]
            logger.debug('results file: %s' % results_file)
            results = loadpkl(results_file)
            output_value = Undefined
            if isinstance(info[1], tuple):
                output_name = info[1][0]
                value = getattr(results.outputs, output_name)
                if isdefined(value):
                    output_value = evaluate_connect_function(info[1][1],
                                                             info[1][2],
                                                             value)
            else:
                output_name = info[1]
                try:
                    output_value = results.outputs.get()[output_name]
                except TypeError:
                    output_value = results.outputs.dictcopy()[output_name]
            logger.debug('output: %s' % output_name)
            try:
                self.set_input(key, deepcopy(output_value))
            except traits.TraitError, e:
                msg = ['Error setting node input:',
                       'Node: %s' % self.name,
                       'input: %s' % key,
                       'results_file: %s' % results_file,
                       'value: %s' % str(output_value)]
                e.args = (e.args[0] + "\n" + '\n'.join(msg),)
                raise

    def _run_interface(self, execute=True, updatehash=False):
        if updatehash:
            return
        old_cwd = os.getcwd()
        os.chdir(self.output_dir())
        self._result = self._run_command(execute)
        os.chdir(old_cwd)

    def _save_results(self, result, cwd):
        resultsfile = op.join(cwd, 'result_%s.pklz' % self.name)
        if result.outputs:
            try:
                outputs = result.outputs.get()
            except TypeError:
                outputs = result.outputs.dictcopy()  # outputs was a bunch
            result.outputs.set(**modify_paths(outputs, relative=True,
                                              basedir=cwd))

        savepkl(resultsfile, result)
        logger.debug('saved results in %s' % resultsfile)

        if result.outputs:
            result.outputs.set(**outputs)

    def _load_resultfile(self, cwd):
        """Load results if it exists in cwd

        Parameter
        ---------

        cwd : working directory of node

        Returns
        -------

        result : InterfaceResult structure
        aggregate : boolean indicating whether node should aggregate_outputs
        attribute error : boolean indicating whether there was some mismatch in
            versions of traits used to store result and hence node needs to
            rerun
        """
        aggregate = True
        resultsoutputfile = op.join(cwd, 'result_%s.pklz' % self.name)
        result = None
        attribute_error = False
        if op.exists(resultsoutputfile):
            pkl_file = gzip.open(resultsoutputfile, 'rb')
            try:
                result = cPickle.load(pkl_file)
            except (traits.TraitError, AttributeError, ImportError), err:
                if isinstance(err, (AttributeError, ImportError)):
                    attribute_error = True
                    logger.debug(('attribute error: %s probably using '
                                  'different trait pickled file') % str(err))
                else:
                    logger.debug(('some file does not exist. hence trait '
                                  'cannot be set'))
            else:
                if result.outputs:
                    try:
                        outputs = result.outputs.get()
                    except TypeError:
                        outputs = result.outputs.dictcopy()  # outputs == Bunch
                    try:
                        result.outputs.set(**modify_paths(outputs,
                                                          relative=False,
                                                          basedir=cwd))
                    except FileNotFoundError:
                        logger.debug(('conversion to full path results in '
                                      'non existent file'))
                aggregate = False
            pkl_file.close()
        logger.debug('Aggregate: %s', aggregate)
        return result, aggregate, attribute_error

    def _load_results(self, cwd):
        result, aggregate, attribute_error = self._load_resultfile(cwd)
        # try aggregating first
        if aggregate:
            logger.debug('aggregating results')
            if attribute_error:
                old_inputs = loadpkl(op.join(cwd, '_inputs.pklz'))
                self.inputs.set(**old_inputs)
            if not isinstance(self, MapNode):
                self._copyfiles_to_wd(cwd, True, linksonly=True)
                aggouts = self._interface.aggregate_outputs(
                    needed_outputs=self.needed_outputs)
                runtime = Bunch(cwd=cwd,
                                returncode=0,
                                environ=deepcopy(os.environ.data),
                                hostname=gethostname())
                result = InterfaceResult(
                    interface=self._interface.__class__,
                    runtime=runtime,
                    inputs=self._interface.inputs.get_traitsfree(),
                    outputs=aggouts)
                self._save_results(result, cwd)
            else:
                logger.debug('aggregating mapnode results')
                self._run_interface()
                result = self._result
        return result

    def _run_command(self, execute, copyfiles=True):
        cwd = os.getcwd()
        if execute and copyfiles:
            self._originputs = deepcopy(self._interface.inputs)
        if execute:
            runtime = Bunch(returncode=1,
                            environ=deepcopy(os.environ.data),
                            hostname=gethostname())
            result = InterfaceResult(
                interface=self._interface.__class__,
                runtime=runtime,
                inputs=self._interface.inputs.get_traitsfree())
            self._result = result
            logger.debug('Executing node')
            if copyfiles:
                self._copyfiles_to_wd(cwd, execute)
            if issubclass(self._interface.__class__, CommandLine):
                try:
                    cmd = self._interface.cmdline
                except Exception, msg:
                    self._result.runtime.stderr = msg
                    raise
                cmdfile = op.join(cwd, 'command.txt')
                fd = open(cmdfile, 'wt')
                fd.writelines(cmd + "\n")
                fd.close()
                logger.info('Running: %s' % cmd)
            try:
                result = self._interface.run()
            except Exception, msg:
                self._result.runtime.stderr = msg
                raise

            dirs2keep = None
            if isinstance(self, MapNode):
                dirs2keep = [op.join(cwd, 'mapflow')]
            result.outputs = clean_working_directory(result.outputs, cwd,
                                                     self._interface.inputs,
                                                     self.needed_outputs,
                                                     self.config,
                                                     dirs2keep=dirs2keep)
            self._save_results(result, cwd)
        else:
            logger.info("Collecting precomputed outputs")
            try:
                result = self._load_results(cwd)
            except (FileNotFoundError, AttributeError):
                # if aggregation does not work, rerun the node
                logger.info(("Some of the outputs were not found: "
                             "rerunning node."))
                result = self._run_command(execute=True, copyfiles=False)
        return result

    def _strip_temp(self, files, wd):
        out = []
        for f in files:
            if isinstance(f, list):
                out.append(self._strip_temp(f, wd))
            else:
                out.append(f.replace(op.join(wd, '_tempinput'), wd))
        return out

    def _copyfiles_to_wd(self, outdir, execute, linksonly=False):
        """ copy files over and change the inputs"""
        if hasattr(self._interface, '_get_filecopy_info'):
            logger.debug('copying files to wd [execute=%s, linksonly=%s]' %
                         (str(execute), str(linksonly)))
            if execute and linksonly:
                olddir = outdir
                outdir = op.join(outdir, '_tempinput')
                os.makedirs(outdir)
            for info in self._interface._get_filecopy_info():
                files = self.inputs.get().get(info['key'])
                if not isdefined(files):
                    continue
                if files:
                    infiles = filename_to_list(files)
                    if execute:
                        if linksonly:
                            if not info['copy']:
                                newfiles = copyfiles(infiles,
                                                     [outdir],
                                                     copy=info['copy'],
                                                     create_new=True)
                            else:
                                newfiles = fnames_presuffix(infiles,
                                                            newpath=outdir)
                            newfiles = self._strip_temp(
                                newfiles,
                                op.abspath(olddir).split(op.sep)[-1])
                        else:
                            newfiles = copyfiles(infiles,
                                                 [outdir],
                                                 copy=info['copy'],
                                                 create_new=True)
                    else:
                        newfiles = fnames_presuffix(infiles, newpath=outdir)
                    if not isinstance(files, list):
                        newfiles = list_to_filename(newfiles)
                    setattr(self.inputs, info['key'], newfiles)
            if execute and linksonly:
                rmtree(outdir)

    def update(self, **opts):
        self.inputs.update(**opts)

    def write_report(self, report_type=None, cwd=None):
        if not str2bool(self.config['execution']['create_report']):
            return
        report_dir = op.join(cwd, '_report')
        report_file = op.join(report_dir, 'report.rst')
        if not op.exists(report_dir):
            os.makedirs(report_dir)
        if report_type == 'preexec':
            logger.debug('writing pre-exec report to %s' % report_file)
            fp = open(report_file, 'wt')
            fp.writelines(write_rst_header('Node: %s' % get_print_name(self),
                                           level=0))
            fp.writelines(write_rst_list(['Hierarchy : %s' % self.fullname,
                                          'Exec ID : %s' % self._id]))
            fp.writelines(write_rst_header('Original Inputs', level=1))
            fp.writelines(write_rst_dict(self.inputs.get()))
        if report_type == 'postexec':
            logger.debug('writing post-exec report to %s' % report_file)
            fp = open(report_file, 'at')
            fp.writelines(write_rst_header('Execution Inputs', level=1))
            fp.writelines(write_rst_dict(self.inputs.get()))
            exit_now = (not hasattr(self.result, 'outputs')
                        or self.result.outputs is None)
            if exit_now:
                return
            fp.writelines(write_rst_header('Execution Outputs', level=1))
            if isinstance(self.result.outputs, Bunch):
                fp.writelines(write_rst_dict(self.result.outputs.dictcopy()))
            elif self.result.outputs:
                fp.writelines(write_rst_dict(self.result.outputs.get()))
            if isinstance(self, MapNode):
                fp.close()
                return
            fp.writelines(write_rst_header('Runtime info', level=1))
            if hasattr(self.result.runtime, 'cmdline'):
                fp.writelines(write_rst_dict(
                    {'hostname': self.result.runtime.hostname,
                     'duration': self.result.runtime.duration,
                     'command': self.result.runtime.cmdline}))
            else:
                fp.writelines(write_rst_dict(
                    {'hostname': self.result.runtime.hostname,
                     'duration': self.result.runtime.duration}))
            if hasattr(self.result.runtime, 'merged'):
                fp.writelines(write_rst_header('Terminal output', level=2))
                fp.writelines(write_rst_list(self.result.runtime.merged))
            if hasattr(self.result.runtime, 'environ'):
                fp.writelines(write_rst_header('Environment', level=2))
                fp.writelines(write_rst_dict(self.result.runtime.environ))
        fp.close()


class JoinNode(Node):
    """Wraps interface objects that join inputs into a list.

    Examples
    --------

    >>> import nipype.pipeline.engine as pe
    >>> from nipype import Node, JoinNode, Workflow
    >>> from nipype.interfaces.utility import IdentityInterface
    >>> from nipype.interfaces import (ants, dcm2nii, fsl)
    >>> wf = Workflow(name='preprocess')
    >>> inputspec = Node(IdentityInterface(fields=['image']),
    ...                     name='inputspec')
    >>> inputspec.iterables = [('image',
    ...                        ['img1.nii', 'img2.nii', 'img3.nii'])]
    >>> img2flt = Node(fsl.ImageMaths(out_data_type='float'),
    ...                   name='img2flt')
    >>> wf.connect(inputspec, 'image', img2flt, 'in_file')
    >>> average = JoinNode(ants.AverageImages(), joinsource='inputspec',
    ...                       joinfield='images', name='average')
    >>> wf.connect(img2flt, 'out_file', average, 'images')
    >>> realign = Node(fsl.FLIRT(), name='realign')
    >>> wf.connect(img2flt, 'out_file', realign, 'in_file')
    >>> wf.connect(average, 'output_average_image', realign, 'reference')
    >>> strip = Node(fsl.BET(), name='strip')
    >>> wf.connect(realign, 'out_file', strip, 'in_file')

    """

    def __init__(self, interface, name, joinsource, joinfield=None,
        unique=False, **kwargs):
        """

        Parameters
        ----------
        interface : interface object
            node specific interface (fsl.Bet(), spm.Coregister())
        name : alphanumeric string
            node specific name
        joinsource : node name
            name of the join predecessor iterable node
        joinfield : string or list of strings
            name(s) of list input fields that will be aggregated.
            The default is all of the join node input fields.
        unique : flag indicating whether to ignore duplicate input values

        See Node docstring for additional keyword arguments.
        """
        super(JoinNode, self).__init__(interface, name, **kwargs)

        self.joinsource = joinsource
        """the join predecessor iterable node"""

        if not joinfield:
            # default is the interface fields
            joinfield = self._interface.inputs.copyable_trait_names()
        elif isinstance(joinfield, six.string_types):
            joinfield = [joinfield]
        self.joinfield = joinfield
        """the fields to join"""

        self._inputs = self._override_join_traits(self._interface.inputs,
                                                  self.joinfield)
        """the override inputs"""

        self._unique = unique
        """flag indicating whether to ignore duplicate input values"""

        self._next_slot_index = 0
        """the joinfield index assigned to an iterated input"""

    @property
    def joinsource(self):
        return self._joinsource

    @joinsource.setter
    def joinsource(self, value):
        """Set the joinsource property. If the given value is a Node,
        then the joinsource is set to the node name.
        """
        if isinstance(value, Node):
            value = value.name
        self._joinsource = value

    @property
    def inputs(self):
        """The JoinNode inputs include the join field overrides."""
        return self._inputs

    def _add_join_item_fields(self):
        """Add new join item fields assigned to the next iterated
        input

        This method is intended solely for workflow graph expansion.

        Examples
        --------

        >>> from nipype.interfaces.utility import IdentityInterface
        >>> import nipype.pipeline.engine as pe
        >>> from nipype import Node, JoinNode, Workflow
        >>> inputspec = Node(IdentityInterface(fields=['image']),
        ...    name='inputspec'),
        >>> join = JoinNode(IdentityInterface(fields=['images', 'mask']),
        ...    joinsource='inputspec', joinfield='images', name='join')
        >>> join._add_join_item_fields()
        {'images': 'imagesJ1'}

        Return the {base field: slot field} dictionary
        """
        # create the new join item fields
        idx = self._next_slot_index
        newfields = dict([(field, self._add_join_item_field(field, idx))
                          for field in self.joinfield])
        # increment the join slot index
        logger.debug("Added the %s join item fields %s." % (self, newfields))
        self._next_slot_index += 1
        return newfields

    def _add_join_item_field(self, field, index):
        """Add new join item fields qualified by the given index

        Return the new field name
        """
        # the new field name
        name = self._join_item_field_name(field, index)
        # make a copy of the join trait
        trait = self._inputs.trait(field, False, True)
        # add the join item trait to the override traits
        self._inputs.add_trait(name, trait)

        return name

    def _join_item_field_name(self, field, index):
        """Return the field suffixed by the index + 1"""
        return "%sJ%d" % (field, index + 1)

    def _override_join_traits(self, basetraits, fields):
        """Convert the given join fields to accept an input that
        is a list item rather than a list. Non-join fields
        delegate to the interface traits.

        Return the override DynamicTraitedSpec
        """
        dyntraits = DynamicTraitedSpec()
        if fields is None:
            fields = basetraits.copyable_trait_names()
        else:
            # validate the fields
            for field in fields:
                if not basetraits.trait(field):
                    raise ValueError("The JoinNode %s does not have a field"
                                     " named %s" % (self.name, field))
        for name, trait in basetraits.items():
            # if a join field has a single inner trait, then the item
            # trait is that inner trait. Otherwise, the item trait is
            # a new Any trait.
            if name in fields and len(trait.inner_traits) == 1:
                item_trait = trait.inner_traits[0]
                dyntraits.add_trait(name, item_trait)
                logger.debug("Converted the join node %s field %s"
                             " trait type from %s to %s"
                             % (self, name, trait.trait_type.info(),
                                item_trait.info()))
            else:
                dyntraits.add_trait(name, traits.Any)
                setattr(dyntraits, name, Undefined)
        return dyntraits

    def _run_command(self, execute, copyfiles=True):
        """Collates the join inputs prior to delegating to the superclass."""
        self._collate_join_field_inputs()
        return super(JoinNode, self)._run_command(execute, copyfiles)

    def _collate_join_field_inputs(self):
        """
        Collects each override join item field into the interface join
        field input."""
        for field in self.inputs.copyable_trait_names():
            if field in self.joinfield:
                # collate the join field
                val = self._collate_input_value(field)
                try:
                    setattr(self._interface.inputs, field, val)
                except Exception as e:
                    raise ValueError(">>JN %s %s %s %s %s: %s" % (self, field, val, self.inputs.copyable_trait_names(), self.joinfield, e))
            elif hasattr(self._interface.inputs, field):
                # copy the non-join field
                val = getattr(self._inputs, field)
                if isdefined(val):
                    setattr(self._interface.inputs, field, val)
        logger.debug("Collated %d inputs into the %s node join fields"
                     % (self._next_slot_index, self))

    def _collate_input_value(self, field):
        """
        Collects the join item field values into a list or set value for
        the given field, as follows:

        - If the field trait is a Set, then the values are collected into
        a set.

        - Otherwise, the values are collected into a list which preserves
        the iterables order. If the ``unique`` flag is set, then duplicate
        values are removed but the iterables order is preserved.
        """
        val = [self._slot_value(field, idx)
               for idx in range(self._next_slot_index)]
        basetrait = self._interface.inputs.trait(field)
        if isinstance(basetrait.trait_type, traits.Set):
            return set(val)
        elif self._unique:
            return list(OrderedDict.fromkeys(val))
        else:
            return val

    def _slot_value(self, field, index):
        slot_field = self._join_item_field_name(field, index)
        try:
            return getattr(self._inputs, slot_field)
        except AttributeError as e:
            raise AttributeError("The join node %s does not have a slot field %s"
                         " to hold the %s value at index %d: %s"
                         % (self, slot_field, field, index, e))

class MapNode(Node):
    """Wraps interface objects that need to be iterated on a list of inputs.

    Examples
    --------

    >>> from nipype import MapNode
    >>> from nipype.interfaces import fsl
    >>> realign = MapNode(fsl.MCFLIRT(), 'in_file', 'realign')
    >>> realign.inputs.in_file = ['functional.nii',
    ...                           'functional2.nii',
    ...                           'functional3.nii']
    >>> realign.run() # doctest: +SKIP

    """

    def __init__(self, interface, iterfield, name, serial=False, nested=False, **kwargs):
        """

        Parameters
        ----------
        interface : interface object
            node specific interface (fsl.Bet(), spm.Coregister())
        iterfield : string or list of strings
            name(s) of input fields that will receive a list of whatever kind
            of input they take. the node will be run separately for each
            value in these lists. for more than one input, the values are
            paired (i.e. it does not compute a combinatorial product).
        name : alphanumeric string
            node specific name
        serial : boolean
            flag to enforce executing the jobs of the mapnode in a serial manner rather than parallel
        nested : boolea
            support for nested lists, if set the input list will be flattened before running, and the
            nested list structure of the outputs will be resored
        See Node docstring for additional keyword arguments.
        """


        super(MapNode, self).__init__(interface, name, **kwargs)
        if isinstance(iterfield, six.string_types):
            iterfield = [iterfield]
        self.iterfield = iterfield
        self.nested = nested
        self._inputs = self._create_dynamic_traits(self._interface.inputs,
                                                   fields=self.iterfield)
        self._inputs.on_trait_change(self._set_mapnode_input)
        self._got_inputs = False
        self._serial = serial

    def _create_dynamic_traits(self, basetraits, fields=None, nitems=None):
        """Convert specific fields of a trait to accept multiple inputs
        """
        output = DynamicTraitedSpec()
        if fields is None:
            fields = basetraits.copyable_trait_names()
        for name, spec in basetraits.items():
            if name in fields and ((nitems is None) or (nitems > 1)):
                logger.debug('adding multipath trait: %s' % name)
                if self.nested:
                    output.add_trait(name, InputMultiPath(traits.Any()))
                else:
                    output.add_trait(name, InputMultiPath(spec.trait_type))
            else:
                output.add_trait(name, traits.Trait(spec))
            setattr(output, name, Undefined)
            value = getattr(basetraits, name)
            if isdefined(value):
                setattr(output, name, value)
            value = getattr(output, name)
        return output

    def set_input(self, parameter, val):
        """ Set interface input value or nodewrapper attribute

        Priority goes to interface.
        """
        logger.debug('setting nodelevel(%s) input %s = %s' % (str(self),
                                                              parameter,
                                                              str(val)))
        self._set_mapnode_input(self.inputs, parameter, deepcopy(val))

    def _set_mapnode_input(self, object, name, newvalue):
        logger.debug('setting mapnode(%s) input: %s -> %s' % (str(self),
                                                              name,
                                                              str(newvalue)))
        if name in self.iterfield:
            setattr(self._inputs, name, newvalue)
        else:
            setattr(self._interface.inputs, name, newvalue)

    def _get_hashval(self):
        """ Compute hash including iterfield lists."""
        if not self._got_inputs:
            self._get_inputs()
            self._got_inputs = True
        self._check_iterfield()
        hashinputs = deepcopy(self._interface.inputs)
        for name in self.iterfield:
            hashinputs.remove_trait(name)
            hashinputs.add_trait(
                name,
                InputMultiPath(
                    self._interface.inputs.traits()[name].trait_type))
            logger.debug('setting hashinput %s-> %s' %
                         (name, getattr(self._inputs, name)))
            if self.nested:
                setattr(hashinputs, name, flatten(getattr(self._inputs, name)))
            else:
                setattr(hashinputs, name, getattr(self._inputs, name))
        hashed_inputs, hashvalue = hashinputs.get_hashval(
            hash_method=self.config['execution']['hash_method'])
        rm_extra = self.config['execution']['remove_unnecessary_outputs']
        if str2bool(rm_extra) and self.needed_outputs:
            hashobject = md5()
            hashobject.update(hashvalue)
            sorted_outputs = sorted(self.needed_outputs)
            hashobject.update(str(sorted_outputs))
            hashvalue = hashobject.hexdigest()
            hashed_inputs['needed_outputs'] = sorted_outputs
        return hashed_inputs, hashvalue

    @property
    def inputs(self):
        return self._inputs

    @property
    def outputs(self):
        if self._interface._outputs():
            return Bunch(self._interface._outputs().get())
        else:
            return None

    def _make_nodes(self, cwd=None):
        if cwd is None:
            cwd = self.output_dir()
        if self.nested:
            nitems = len(flatten(filename_to_list(getattr(self.inputs, self.iterfield[0]))))
        else:
            nitems = len(filename_to_list(getattr(self.inputs, self.iterfield[0])))
        for i in range(nitems):
            nodename = '_' + self.name + str(i)
            node = Node(deepcopy(self._interface), name=nodename)
            node.overwrite = self.overwrite
            node.run_without_submitting = self.run_without_submitting
            node.plugin_args = self.plugin_args
            node._interface.inputs.set(
                **deepcopy(self._interface.inputs.get()))
            for field in self.iterfield:
                if self.nested:
                    fieldvals = flatten(filename_to_list(getattr(self.inputs, field)))
                else:
                    fieldvals = filename_to_list(getattr(self.inputs, field))
                logger.debug('setting input %d %s %s' % (i, field,
                                                         fieldvals[i]))
                setattr(node.inputs, field,
                        fieldvals[i])
            node.config = self.config
            node.base_dir = op.join(cwd, 'mapflow')
            yield i, node

    def _node_runner(self, nodes, updatehash=False):
        for i, node in nodes:
            err = None
            try:
                node.run(updatehash=updatehash)
            except Exception, err:
                if str2bool(self.config['execution']['stop_on_first_crash']):
                    self._result = node.result
                    raise
            yield i, node, err

    def _collate_results(self, nodes):
        self._result = InterfaceResult(interface=[], runtime=[],
                                       provenance=[], inputs=[],
                                       outputs=self.outputs)
        returncode = []
        for i, node, err in nodes:
            self._result.runtime.insert(i, None)
            if node.result:
                if hasattr(node.result, 'runtime'):
                    self._result.interface.insert(i, node.result.interface)
                    self._result.inputs.insert(i, node.result.inputs)
                    self._result.runtime[i] = node.result.runtime
                if hasattr(node.result, 'provenance'):
                    self._result.provenance.insert(i, node.result.provenance)
            returncode.insert(i, err)
            if self.outputs:
                for key, _ in self.outputs.items():
                    rm_extra = (self.config['execution']
                                ['remove_unnecessary_outputs'])
                    if str2bool(rm_extra) and self.needed_outputs:
                        if key not in self.needed_outputs:
                            continue
                    values = getattr(self._result.outputs, key)
                    if not isdefined(values):
                        values = []
                    if node.result.outputs:
                        values.insert(i, node.result.outputs.get()[key])
                    else:
                        values.insert(i, None)
                    defined_vals = [isdefined(val) for val in values]
                    if any(defined_vals) and self._result.outputs:
                        setattr(self._result.outputs, key, values)

        if self.nested:
            for key, _ in self.outputs.items():
                values = getattr(self._result.outputs, key)
                if isdefined(values):
                    values = unflatten(values, filename_to_list(getattr(self.inputs, self.iterfield[0])))
                setattr(self._result.outputs, key, values)

        if returncode and any([code is not None for code in returncode]):
            msg = []
            for i, code in enumerate(returncode):
                if code is not None:
                    msg += ['Subnode %d failed' % i]
                    msg += ['Error:', str(code)]
            raise Exception('Subnodes of node: %s failed:\n%s' %
                            (self.name, '\n'.join(msg)))

    def write_report(self, report_type=None, cwd=None):
        if not str2bool(self.config['execution']['create_report']):
            return
        if report_type == 'preexec':
            super(MapNode, self).write_report(report_type=report_type, cwd=cwd)
        if report_type == 'postexec':
            super(MapNode, self).write_report(report_type=report_type, cwd=cwd)
            report_dir = op.join(cwd, '_report')
            report_file = op.join(report_dir, 'report.rst')
            fp = open(report_file, 'at')
            fp.writelines(write_rst_header('Subnode reports', level=1))
            nitems = len(filename_to_list(
                getattr(self.inputs, self.iterfield[0])))
            subnode_report_files = []
            for i in range(nitems):
                nodename = '_' + self.name + str(i)
                subnode_report_files.insert(i, 'subnode %d' % i + ' : ' +
                                               op.join(cwd,
                                                            'mapflow',
                                                            nodename,
                                                            '_report',
                                                            'report.rst'))
            fp.writelines(write_rst_list(subnode_report_files))
            fp.close()

    def get_subnodes(self):
        if not self._got_inputs:
            self._get_inputs()
            self._got_inputs = True
        self._check_iterfield()
        self.write_report(report_type='preexec', cwd=self.output_dir())
        return [node for _, node in self._make_nodes()]

    def num_subnodes(self):
        if not self._got_inputs:
            self._get_inputs()
            self._got_inputs = True
        self._check_iterfield()
        if self._serial :
            return 1
        else:
            if self.nested:
                return len(filename_to_list(flatten(getattr(self.inputs, self.iterfield[0]))))
            else:
                return len(filename_to_list(getattr(self.inputs, self.iterfield[0])))

    def _get_inputs(self):
        old_inputs = self._inputs.get()
        self._inputs = self._create_dynamic_traits(self._interface.inputs,
                                                   fields=self.iterfield)
        self._inputs.set(**old_inputs)
        super(MapNode, self)._get_inputs()

    def _check_iterfield(self):
        """Checks iterfield

        * iterfield must be in inputs
        * number of elements must match across iterfield
        """
        for iterfield in self.iterfield:
            if not isdefined(getattr(self.inputs, iterfield)):
                raise ValueError(("Input %s was not set but it is listed "
                                  "in iterfields.") % iterfield)
        if len(self.iterfield) > 1:
            first_len = len(filename_to_list(getattr(self.inputs,
                                                     self.iterfield[0])))
            for iterfield in self.iterfield[1:]:
                if first_len != len(filename_to_list(getattr(self.inputs,
                                                             iterfield))):
                    raise ValueError(("All iterfields of a MapNode have to "
                                      "have the same length. %s") %
                                     str(self.inputs))

    def _run_interface(self, execute=True, updatehash=False):
        """Run the mapnode interface

        This is primarily intended for serial execution of mapnode. A parallel
        execution requires creation of new nodes that can be spawned
        """
        old_cwd = os.getcwd()
        cwd = self.output_dir()
        os.chdir(cwd)
        self._check_iterfield()
        if execute:
            if self.nested:
                nitems = len(filename_to_list(flatten(getattr(self.inputs,
                                                      self.iterfield[0]))))
            else:
                nitems = len(filename_to_list(getattr(self.inputs,
                                                      self.iterfield[0])))
            nodenames = ['_' + self.name + str(i) for i in range(nitems)]
            # map-reduce formulation
            self._collate_results(self._node_runner(self._make_nodes(cwd),
                                                    updatehash=updatehash))
            self._save_results(self._result, cwd)
            # remove any node directories no longer required
            dirs2remove = []
            for path in glob(op.join(cwd, 'mapflow', '*')):
                if op.isdir(path):
                    if path.split(op.sep)[-1] not in nodenames:
                        dirs2remove.append(path)
            for path in dirs2remove:
                shutil.rmtree(path)
        else:
            self._result = self._load_results(cwd)
        os.chdir(old_cwd)
