/usr/share/pyshared/nipype/pipeline/engine.py is in python-nipype 0.9.2-1.
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# vi: set ft=python sts=4 ts=4 sw=4 et:
"""Defines functionality for pipelined execution of interfaces
The `Pipeline` 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
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
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
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):
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.npz'
np.savez(filename, object=self)
def load(self, filename):
return np.load(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, str):
sourcename = source
else:
raise Exception(('Unknown source specification in '
'connection from output of %s') %
srcnode.name)
if sourcename and not srcnode._check_outputs(sourcename):
not_found.append(['out', srcnode.name, sourcename])
connected_ports[destnode] += [dest]
infostr = []
for info in not_found:
infostr += ["Module %s has no %sput called %s\n" % (info[1],
info[0],
info[2])]
if not_found:
raise Exception('\n'.join(['Some connections were not found'] +
infostr))
# turn functions into strings
for srcnode, destnode, connects in connection_list:
for idx, (src, dest) in enumerate(connects):
if isinstance(src, tuple) and not isinstance(src[1], str):
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'
orig - creates a top level graph without expanding internal
workflow nodes;
flat - expands workflow nodes recursively;
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']
if graph2use not in graphtypes:
raise ValueError('Unknown graph2use keyword. Must be one of: ' +
str(graphtypes))
base_dir, dotfilename = os.path.split(dotfilename)
if base_dir == '':
if self.base_dir:
base_dir = self.base_dir
if self.name:
base_dir = os.path.join(base_dir, self.name)
else:
base_dir = os.getcwd()
base_dir = make_output_dir(base_dir)
if graph2use == 'hierarchical':
dotfilename = os.path.join(base_dir, dotfilename)
self.write_hierarchical_dotfile(dotfilename=dotfilename,
colored=False,
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=True,
simple_form=True):
dotlist = ['digraph %s{' % self.name]
if colored:
dotlist.append(' ' + 'colorscheme=pastel28;')
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 = os.path.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 = os.path.join(workingdir, name)
if not os.path.exists(report_dir):
os.makedirs(report_dir)
shutil.copyfile(os.path.join(os.path.dirname(__file__),
'report_template.html'),
os.path.join(report_dir, 'index.html'))
shutil.copyfile(os.path.join(os.path.dirname(__file__),
'..', 'external', 'd3.v3.min.js'),
os.path.join(report_dir, 'd3.v3.min.js'))
nodes, groups = topological_sort(graph, depth_first=True)
graph_file = os.path.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 = os.path.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] = \
(os.path.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, str):
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=True,
simple_form=True):
"""Create a dot file with connection info
"""
if prefix is None:
prefix = ' '
if hierarchy is None:
hierarchy = []
level = (len(prefix) / 2) + 1
dotlist = ['%slabel="%s";' % (prefix, self.name)]
if colored:
dotlist.append('%scolor=%d;' % (prefix, level))
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", style=filled, colorscheme'
'=greys7 color=2];') % (nodename,
node_class_name))
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 + 'style=filled;')
dotlist.append(node._get_dot(prefix=prefix + prefix,
hierarchy=hierarchy + [self.name],
colored=colored,
simple_form=simple_form))
dotlist.append('}')
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, 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 = os.path.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 = os.path.join(outputdir, *param_dirs)
else:
outputdir = os.path.join(outputdir, *self.parameterization)
return os.path.abspath(os.path.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 os.path.exists(outdir):
logger.debug(os.listdir(outdir))
hashfiles = glob(os.path.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 = os.path.join(outdir, '_0x%s.json' % hashvalue)
logger.debug(hashfile)
if updatehash and os.path.exists(outdir):
logger.debug("Updating hash: %s" % hashvalue)
for file in glob(os.path.join(outdir, '_0x*.json')):
os.remove(file)
self._save_hashfile(hashfile, hashed_inputs)
return os.path.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 os.path.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 = (os.path.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(os.path.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 = os.path.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)
rmtree(outdir)
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(os.path.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(os.path.join(outdir, '_node.pklz'), self)
savepkl(os.path.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 os.path.exists(os.path.join(outdir, '_inputs.pklz')):
logger.debug('%s: creating inputs file' % self.name)
savepkl(os.path.join(outdir, '_inputs.pklz'),
self.inputs.get_traitsfree())
if not os.path.exists(os.path.join(outdir, '_node.pklz')):
logger.debug('%s: creating node file' % self.name)
savepkl(os.path.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 = os.path.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 = os.path.join(cwd, 'result_%s.pklz' % self.name)
result = None
attribute_error = False
if os.path.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(os.path.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 = os.path.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 = [os.path.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(os.path.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 = os.path.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(os.path.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 = os.path.join(cwd, '_report')
report_file = os.path.join(report_dir, 'report.rst')
if not os.path.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, str):
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, 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, **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
See Node docstring for additional keyword arguments.
"""
super(MapNode, self).__init__(interface, name, **kwargs)
if isinstance(iterfield, str):
iterfield = [iterfield]
self.iterfield = iterfield
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
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)
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)))
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()
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:
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 = os.path.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 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 = os.path.join(cwd, '_report')
report_file = os.path.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 + ' : ' +
os.path.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()
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:
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(os.path.join(cwd, 'mapflow', '*')):
if os.path.isdir(path):
if path.split(os.path.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)
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