/usr/share/pyshared/nipype/pipeline/utils.py is in python-nipype 0.9.2-1.
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# vi: set ft=python sts=4 ts=4 sw=4 et:
"""Utility routines for workflow graphs
"""
from copy import deepcopy
from glob import glob
from collections import defaultdict
import os
import pwd
import re
from uuid import uuid1
import numpy as np
from nipype.utils.misc import package_check
package_check('networkx', '1.3')
from socket import gethostname
import networkx as nx
from ..utils.filemanip import (fname_presuffix, FileNotFoundError,
filename_to_list, get_related_files)
from ..utils.misc import create_function_from_source, str2bool
from ..interfaces.base import (CommandLine, isdefined, Undefined, Bunch,
InterfaceResult)
from ..interfaces.utility import IdentityInterface
from ..utils.provenance import ProvStore, pm, nipype_ns, get_id
from .. import get_info
from .. import logging, config
logger = logging.getLogger('workflow')
try:
dfs_preorder = nx.dfs_preorder
except AttributeError:
dfs_preorder = nx.dfs_preorder_nodes
logger.debug('networkx 1.4 dev or higher detected')
try:
from os.path import relpath
except ImportError:
import os.path as op
def relpath(path, start=None):
"""Return a relative version of a path"""
if start is None:
start = os.curdir
if not path:
raise ValueError("no path specified")
start_list = op.abspath(start).split(op.sep)
path_list = op.abspath(path).split(op.sep)
if start_list[0].lower() != path_list[0].lower():
unc_path, rest = op.splitunc(path)
unc_start, rest = op.splitunc(start)
if bool(unc_path) ^ bool(unc_start):
raise ValueError(("Cannot mix UNC and non-UNC paths "
"(%s and %s)") % (path, start))
else:
raise ValueError("path is on drive %s, start on drive %s"
% (path_list[0], start_list[0]))
# Work out how much of the filepath is shared by start and path.
for i in range(min(len(start_list), len(path_list))):
if start_list[i].lower() != path_list[i].lower():
break
else:
i += 1
rel_list = [op.pardir] * (len(start_list) - i) + path_list[i:]
if not rel_list:
return os.curdir
return op.join(*rel_list)
def modify_paths(object, relative=True, basedir=None):
"""Convert paths in data structure to either full paths or relative paths
Supports combinations of lists, dicts, tuples, strs
Parameters
----------
relative : boolean indicating whether paths should be set relative to the
current directory
basedir : default os.getcwd()
what base directory to use as default
"""
if not basedir:
basedir = os.getcwd()
if isinstance(object, dict):
out = {}
for key, val in sorted(object.items()):
if isdefined(val):
out[key] = modify_paths(val, relative=relative,
basedir=basedir)
elif isinstance(object, (list, tuple)):
out = []
for val in object:
if isdefined(val):
out.append(modify_paths(val, relative=relative,
basedir=basedir))
if isinstance(object, tuple):
out = tuple(out)
else:
if isdefined(object):
if isinstance(object, str) and os.path.isfile(object):
if relative:
if config.getboolean('execution', 'use_relative_paths'):
out = relpath(object, start=basedir)
else:
out = object
else:
out = os.path.abspath(os.path.join(basedir, object))
if not os.path.exists(out):
raise FileNotFoundError('File %s not found' % out)
else:
out = object
return out
def get_print_name(node, simple_form=True):
"""Get the name of the node
For example, a node containing an instance of interfaces.fsl.BET
would be called nodename.BET.fsl
"""
name = node.fullname
if hasattr(node, '_interface'):
pkglist = node._interface.__class__.__module__.split('.')
interface = node._interface.__class__.__name__
destclass = ''
if len(pkglist) > 2:
destclass = '.%s' % pkglist[2]
if simple_form:
name = node.fullname + destclass
else:
name = '.'.join([node.fullname, interface]) + destclass
if simple_form:
parts = name.split('.')
if len(parts) > 2:
return ' ('.join(parts[1:])+')'
elif len(parts) == 2:
return parts[1]
return name
def _create_dot_graph(graph, show_connectinfo=False, simple_form=True):
"""Create a graph that can be pickled.
Ensures that edge info is pickleable.
"""
logger.debug('creating dot graph')
pklgraph = nx.DiGraph()
for edge in graph.edges():
data = graph.get_edge_data(*edge)
srcname = get_print_name(edge[0], simple_form=simple_form)
destname = get_print_name(edge[1], simple_form=simple_form)
if show_connectinfo:
pklgraph.add_edge(srcname, destname, l=str(data['connect']))
else:
pklgraph.add_edge(srcname, destname)
return pklgraph
def _write_detailed_dot(graph, dotfilename):
"""Create a dot file with connection info
digraph structs {
node [shape=record];
struct1 [label="<f0> left|<f1> mid\ dle|<f2> right"];
struct2 [label="<f0> one|<f1> two"];
struct3 [label="hello\nworld |{ b |{c|<here> d|e}| f}| g | h"];
struct1:f1 -> struct2:f0;
struct1:f0 -> struct2:f1;
struct1:f2 -> struct3:here;
}
"""
text = ['digraph structs {', 'node [shape=record];']
# write nodes
edges = []
replacefunk = lambda x: x.replace('_', '').replace('.', ''). \
replace('@', '').replace('-', '')
for n in nx.topological_sort(graph):
nodename = str(n)
inports = []
for u, v, d in graph.in_edges_iter(nbunch=n, data=True):
for cd in d['connect']:
if isinstance(cd[0], str):
outport = cd[0]
else:
outport = cd[0][0]
inport = cd[1]
ipstrip = 'in' + replacefunk(inport)
opstrip = 'out' + replacefunk(outport)
edges.append('%s:%s:e -> %s:%s:w;' % (str(u).replace('.', ''),
opstrip,
str(v).replace('.', ''),
ipstrip))
if inport not in inports:
inports.append(inport)
inputstr = '{IN'
for ip in sorted(inports):
inputstr += '|<in%s> %s' % (replacefunk(ip), ip)
inputstr += '}'
outports = []
for u, v, d in graph.out_edges_iter(nbunch=n, data=True):
for cd in d['connect']:
if isinstance(cd[0], str):
outport = cd[0]
else:
outport = cd[0][0]
if outport not in outports:
outports.append(outport)
outputstr = '{OUT'
for op in sorted(outports):
outputstr += '|<out%s> %s' % (replacefunk(op), op)
outputstr += '}'
srcpackage = ''
if hasattr(n, '_interface'):
pkglist = n._interface.__class__.__module__.split('.')
if len(pkglist) > 2:
srcpackage = pkglist[2]
srchierarchy = '.'.join(nodename.split('.')[1:-1])
nodenamestr = '{ %s | %s | %s }' % (nodename.split('.')[-1],
srcpackage,
srchierarchy)
text += ['%s [label="%s|%s|%s"];' % (nodename.replace('.', ''),
inputstr,
nodenamestr,
outputstr)]
# write edges
for edge in sorted(edges):
text.append(edge)
text.append('}')
filep = open(dotfilename, 'wt')
filep.write('\n'.join(text))
filep.close()
return text
# Graph manipulations for iterable expansion
def _get_valid_pathstr(pathstr):
"""Remove disallowed characters from path
Removes: [][ (){}?:<>#!|"';]
Replaces: ',' -> '.'
"""
pathstr = pathstr.replace(os.sep, '..')
pathstr = re.sub(r'''[][ (){}?:<>#!|"';]''', '', pathstr)
pathstr = pathstr.replace(',', '.')
return pathstr
def expand_iterables(iterables, synchronize=False):
if synchronize:
return synchronize_iterables(iterables)
else:
return list(walk(iterables.items()))
def count_iterables(iterables, synchronize=False):
"""Return the number of iterable expansion nodes.
If synchronize is True, then the count is the maximum number
of iterables value lists.
Otherwise, the count is the product of the iterables value
list sizes.
"""
if synchronize:
op = max
else:
op = lambda x,y: x*y
return reduce(op, [len(func()) for _, func in iterables.iteritems()])
def walk(children, level=0, path=None, usename=True):
"""Generate all the full paths in a tree, as a dict.
Examples
--------
>>> from nipype.pipeline.utils import walk
>>> iterables = [('a', lambda: [1, 2]), ('b', lambda: [3, 4])]
>>> list(walk(iterables))
[{'a': 1, 'b': 3}, {'a': 1, 'b': 4}, {'a': 2, 'b': 3}, {'a': 2, 'b': 4}]
"""
# Entry point
if level == 0:
path = {}
# Exit condition
if not children:
yield path.copy()
return
# Tree recursion
head, tail = children[0], children[1:]
name, func = head
for child in func():
# We can use the arg name or the tree level as a key
if usename:
path[name] = child
else:
path[level] = child
# Recurse into the next level
for child_paths in walk(tail, level + 1, path, usename):
yield child_paths
def synchronize_iterables(iterables):
"""Synchronize the given iterables in item-wise order.
Return: the {field: value} dictionary list
Examples
--------
>>> from nipype.pipeline.utils import synchronize_iterables
>>> iterables = dict(a=lambda: [1, 2], b=lambda: [3, 4])
>>> synced = synchronize_iterables(iterables)
>>> synced == [{'a': 1, 'b': 3}, {'a': 2, 'b': 4}]
True
>>> iterables = dict(a=lambda: [1, 2], b=lambda: [3], c=lambda: [4, 5, 6])
>>> synced = synchronize_iterables(iterables)
>>> synced == [{'a': 1, 'b': 3, 'c': 4}, {'a': 2, 'c': 5}, {'c': 6}]
True
"""
# Convert the (field, function) tuples into (field, value) lists
pair_lists = [[(field, value) for value in func()]
for field, func in iterables.iteritems()]
# A factory to make a dictionary from the mapped (field, value)
# key-value pairs. The filter removes any unmapped None items.
factory = lambda *pairs: dict(filter(None, pairs))
# Make a dictionary for each of the correlated (field, value) items
return map(factory, *pair_lists)
def evaluate_connect_function(function_source, args, first_arg):
func = create_function_from_source(function_source)
try:
output_value = func(first_arg,
*list(args))
except NameError as e:
if e.args[0].startswith("global name") and \
e.args[0].endswith("is not defined"):
e.args = (e.args[0],
("Due to engine constraints all imports have to be done "
"inside each function definition"))
raise e
return output_value
def get_levels(G):
levels = {}
for n in nx.topological_sort(G):
levels[n] = 0
for pred in G.predecessors_iter(n):
levels[n] = max(levels[n], levels[pred] + 1)
return levels
def _merge_graphs(supergraph, nodes, subgraph, nodeid, iterables,
prefix, synchronize=False):
"""Merges two graphs that share a subset of nodes.
If the subgraph needs to be replicated for multiple iterables, the
merge happens with every copy of the subgraph. Assumes that edges
between nodes of supergraph and subgraph contain data.
Parameters
----------
supergraph : networkx graph
Parent graph from which subgraph was selected
nodes : networkx nodes
Nodes of the parent graph from which the subgraph was initially
constructed.
subgraph : networkx graph
A subgraph that contains as a subset nodes from the supergraph.
These nodes connect the subgraph to the supergraph
nodeid : string
Identifier of a node for which parameterization has been sought
iterables : dict of functions
see `pipeline.NodeWrapper` for iterable requirements
Returns
-------
Returns a merged graph containing copies of the subgraph with
appropriate edge connections to the supergraph.
"""
# Retrieve edge information connecting nodes of the subgraph to other
# nodes of the supergraph.
supernodes = supergraph.nodes()
ids = [n._hierarchy + n._id for n in supernodes]
if len(np.unique(ids)) != len(ids):
# This should trap the problem of miswiring when multiple iterables are
# used at the same level. The use of the template below for naming
# updates to nodes is the general solution.
raise Exception(("Execution graph does not have a unique set of node "
"names. Please rerun the workflow"))
edgeinfo = {}
for n in subgraph.nodes():
nidx = ids.index(n._hierarchy + n._id)
for edge in supergraph.in_edges_iter(supernodes[nidx]):
#make sure edge is not part of subgraph
if edge[0] not in subgraph.nodes():
if n._hierarchy + n._id not in edgeinfo.keys():
edgeinfo[n._hierarchy + n._id] = []
edgeinfo[n._hierarchy + n._id].append((edge[0],
supergraph.get_edge_data(*edge)))
supergraph.remove_nodes_from(nodes)
# Add copies of the subgraph depending on the number of iterables
iterable_params = expand_iterables(iterables, synchronize)
# If there are no iterable subgraphs, then return
if not iterable_params:
return supergraph
# Make an iterable subgraph node id template
count = len(iterable_params)
template = '.%s%%0%dd' % (prefix, np.ceil(np.log10(count)))
# Copy the iterable subgraphs
for i, params in enumerate(iterable_params):
Gc = deepcopy(subgraph)
ids = [n._hierarchy + n._id for n in Gc.nodes()]
nodeidx = ids.index(nodeid)
rootnode = Gc.nodes()[nodeidx]
paramstr = ''
for key, val in sorted(params.items()):
paramstr = '_'.join((paramstr, _get_valid_pathstr(key),
_get_valid_pathstr(str(val))))
rootnode.set_input(key, val)
levels = get_levels(Gc)
for n in Gc.nodes():
"""
update parameterization of the node to reflect the location of
the output directory. For example, if the iterables along a
path of the directed graph consisted of the variables 'a' and
'b', then every node in the path including and after the node
with iterable 'b' will be placed in a directory
_a_aval/_b_bval/.
"""
path_length = levels[n]
# enter as negative numbers so that earlier iterables with longer
# path lengths get precedence in a sort
paramlist = [(-path_length, paramstr)]
if n.parameterization:
n.parameterization = paramlist + n.parameterization
else:
n.parameterization = paramlist
supergraph.add_nodes_from(Gc.nodes())
supergraph.add_edges_from(Gc.edges(data=True))
for node in Gc.nodes():
if node._hierarchy + node._id in edgeinfo.keys():
for info in edgeinfo[node._hierarchy + node._id]:
supergraph.add_edges_from([(info[0], node, info[1])])
node._id += template % i
return supergraph
def _connect_nodes(graph, srcnode, destnode, connection_info):
"""Add a connection between two nodes
"""
data = graph.get_edge_data(srcnode, destnode, default=None)
if not data:
data = {'connect': connection_info}
graph.add_edges_from([(srcnode, destnode, data)])
else:
data['connect'].extend(connection_info)
def _remove_nonjoin_identity_nodes(graph, keep_iterables=False):
"""Remove non-join identity nodes from the given graph
Iterable nodes are retained if and only if the keep_iterables
flag is set to True.
"""
# if keep_iterables is False, then include the iterable
# and join nodes in the nodes to delete
for node in _identity_nodes(graph, not keep_iterables):
if not hasattr(node, 'joinsource'):
_remove_identity_node(graph, node)
return graph
def _identity_nodes(graph, include_iterables):
"""Return the IdentityInterface nodes in the graph
The nodes are in topological sort order. The iterable nodes
are included if and only if the include_iterables flag is set
to True.
"""
return [node for node in nx.topological_sort(graph)
if isinstance(node._interface, IdentityInterface) and
(include_iterables or getattr(node, 'iterables') is None)]
def _remove_identity_node(graph, node):
"""Remove identity nodes from an execution graph
"""
portinputs, portoutputs = _node_ports(graph, node)
for field, connections in portoutputs.items():
if portinputs:
_propagate_internal_output(graph, node, field, connections,
portinputs)
else:
_propagate_root_output(graph, node, field, connections)
graph.remove_nodes_from([node])
logger.debug("Removed the identity node %s from the graph." % node)
def _node_ports(graph, node):
"""Return the given node's input and output ports
The return value is the (inputs, outputs) dictionaries.
The inputs is a {destination field: (source node, source field)}
dictionary.
The outputs is a {source field: destination items} dictionary,
where each destination item is a
(destination node, destination field, source field) tuple.
"""
portinputs = {}
portoutputs = {}
for u, _, d in graph.in_edges_iter(node, data=True):
for src, dest in d['connect']:
portinputs[dest] = (u, src)
for _, v, d in graph.out_edges_iter(node, data=True):
for src, dest in d['connect']:
if isinstance(src, tuple):
srcport = src[0]
else:
srcport = src
if srcport not in portoutputs:
portoutputs[srcport] = []
portoutputs[srcport].append((v, dest, src))
return (portinputs, portoutputs)
def _propagate_root_output(graph, node, field, connections):
"""Propagates the given graph root node output port
field connections to the out-edge destination nodes."""
for destnode, inport, src in connections:
value = getattr(node.inputs, field)
if isinstance(src, tuple):
value = evaluate_connect_function(src[1], src[2],
value)
destnode.set_input(inport, value)
def _propagate_internal_output(graph, node, field, connections, portinputs):
"""Propagates the given graph internal node output port
field connections to the out-edge source node and in-edge
destination nodes."""
for destnode, inport, src in connections:
if field in portinputs:
srcnode, srcport = portinputs[field]
if isinstance(srcport, tuple) and isinstance(src, tuple):
raise ValueError(("Does not support two inline functions "
"in series (\'%s\' and \'%s\'). "
"Please use a Function node") %
(srcport[1].split("\\n")[0][6:-1],
src[1].split("\\n")[0][6:-1]))
connect = graph.get_edge_data(srcnode, destnode,
default={'connect': []})
if isinstance(src, tuple):
connect['connect'].append(((srcport, src[1], src[2]), inport))
else:
connect = {'connect': [(srcport, inport)]}
old_connect = graph.get_edge_data(srcnode, destnode,
default={'connect': []})
old_connect['connect'] += connect['connect']
graph.add_edges_from([(srcnode, destnode, old_connect)])
else:
value = getattr(node.inputs, field)
if isinstance(src, tuple):
value = evaluate_connect_function(src[1], src[2], value)
destnode.set_input(inport, value)
def generate_expanded_graph(graph_in):
"""Generates an expanded graph based on node parameterization
Parameterization is controlled using the `iterables` field of the
pipeline elements. Thus if there are two nodes with iterables a=[1,2]
and b=[3,4] this procedure will generate a graph with sub-graphs
parameterized as (a=1,b=3), (a=1,b=4), (a=2,b=3) and (a=2,b=4).
"""
logger.debug("PE: expanding iterables")
graph_in = _remove_nonjoin_identity_nodes(graph_in, keep_iterables=True)
# standardize the iterables as {(field, function)} dictionaries
for node in graph_in.nodes_iter():
if node.iterables:
_standardize_iterables(node)
allprefixes = list('abcdefghijklmnopqrstuvwxyz')
# the iterable nodes
inodes = _iterable_nodes(graph_in)
logger.debug("Detected iterable nodes %s" % inodes)
# while there is an iterable node, expand the iterable node's
# subgraphs
while inodes:
inode = inodes[0]
logger.debug("Expanding the iterable node %s..." % inode)
# the join successor nodes of the current iterable node
jnodes = [node for node in graph_in.nodes_iter()
if hasattr(node, 'joinsource')
and inode.name == node.joinsource
and nx.has_path(graph_in, inode, node)]
# excise the join in-edges. save the excised edges in a
# {jnode: {source name: (destination name, edge data)}}
# dictionary
jedge_dict = {}
for jnode in jnodes:
in_edges = jedge_dict[jnode] = {}
for src, dest, data in graph_in.in_edges_iter(jnode, True):
in_edges[src._id] = data
graph_in.remove_edge(src, dest)
logger.debug("Excised the %s -> %s join node in-edge."
% (src, dest))
if inode.itersource:
# the itersource is a (node name, fields) tuple
src_name, src_fields = inode.itersource
# convert a single field to a list
if isinstance(src_fields, str):
src_fields = [src_fields]
# find the unique iterable source node in the graph
try:
iter_src = next((node for node in graph_in.nodes_iter()
if node.name == src_name
and nx.has_path(graph_in, node, inode)))
except StopIteration:
raise ValueError("The node %s itersource %s was not found"
" among the iterable predecessor nodes"
% (inode, src_name))
logger.debug("The node %s has iterable source node %s"
% (inode, iter_src))
# look up the iterables for this particular itersource descendant
# using the iterable source ancestor values as a key
iterables = {}
# the source node iterables values
src_values = [getattr(iter_src.inputs, field) for field in src_fields]
# if there is one source field, then the key is the the source value,
# otherwise the key is the tuple of source values
if len(src_values) == 1:
key = src_values[0]
else:
key = tuple(src_values)
# The itersource iterables is a {field: lookup} dictionary, where the
# lookup is a {source key: iteration list} dictionary. Look up the
# current iterable value using the predecessor itersource input values.
iter_dict = dict([(field, lookup[key]) for field, lookup in
inode.iterables if key in lookup])
# convert the iterables to the standard {field: function} format
iter_items = map(lambda(field, value): (field, lambda: value),
iter_dict.iteritems())
iterables = dict(iter_items)
else:
iterables = inode.iterables.copy()
inode.iterables = None
logger.debug('node: %s iterables: %s' % (inode, iterables))
# collect the subnodes to expand
subnodes = [s for s in dfs_preorder(graph_in, inode)]
prior_prefix = []
for s in subnodes:
prior_prefix.extend(re.findall('\.(.)I', s._id))
prior_prefix = sorted(prior_prefix)
if not len(prior_prefix):
iterable_prefix = 'a'
else:
if prior_prefix[-1] == 'z':
raise ValueError('Too many iterables in the workflow')
iterable_prefix =\
allprefixes[allprefixes.index(prior_prefix[-1]) + 1]
logger.debug(('subnodes:', subnodes))
# append a suffix to the iterable node id
inode._id += ('.' + iterable_prefix + 'I')
# merge the iterated subgraphs
subgraph = graph_in.subgraph(subnodes)
graph_in = _merge_graphs(graph_in, subnodes,
subgraph, inode._hierarchy + inode._id,
iterables, iterable_prefix, inode.synchronize)
# reconnect the join nodes
for jnode in jnodes:
# the {node id: edge data} dictionary for edges connecting
# to the join node in the unexpanded graph
old_edge_dict = jedge_dict[jnode]
# the edge source node replicates
expansions = defaultdict(list)
for node in graph_in.nodes_iter():
for src_id, edge_data in old_edge_dict.iteritems():
if node._id.startswith(src_id):
expansions[src_id].append(node)
for in_id, in_nodes in expansions.iteritems():
logger.debug("The join node %s input %s was expanded"
" to %d nodes." %(jnode, in_id, len(in_nodes)))
# preserve the node iteration order by sorting on the node id
for in_nodes in expansions.itervalues():
in_nodes.sort(key=lambda node: node._id)
# the number of join source replicates.
iter_cnt = count_iterables(iterables, inode.synchronize)
# make new join node fields to connect to each replicated
# join in-edge source node.
slot_dicts = [jnode._add_join_item_fields() for _ in range(iter_cnt)]
# for each join in-edge, connect every expanded source node
# which matches on the in-edge source name to the destination
# join node. Qualify each edge connect join field name by
# appending the next join slot index, e.g. the connect
# from two expanded nodes from field 'out_file' to join
# field 'in' are qualified as ('out_file', 'in1') and
# ('out_file', 'in2'), resp. This preserves connection port
# integrity.
for old_id, in_nodes in expansions.iteritems():
# reconnect each replication of the current join in-edge
# source
for in_idx, in_node in enumerate(in_nodes):
olddata = old_edge_dict[old_id]
newdata = deepcopy(olddata)
# the (source, destination) field tuples
connects = newdata['connect']
# the join fields connected to the source
join_fields = [field for _, field in connects
if field in jnode.joinfield]
# the {field: slot fields} maps assigned to the input
# node, e.g. {'image': 'imageJ3', 'mask': 'maskJ3'}
# for the third join source expansion replicate of a
# join node with join fields image and mask
slots = slot_dicts[in_idx]
for con_idx, connect in enumerate(connects):
src_field, dest_field = connect
# qualify a join destination field name
if dest_field in slots:
slot_field = slots[dest_field]
connects[con_idx] = (src_field, slot_field)
logger.debug("Qualified the %s -> %s join field"
" %s as %s." %
(in_node, jnode, dest_field, slot_field))
graph_in.add_edge(in_node, jnode, newdata)
logger.debug("Connected the join node %s subgraph to the"
" expanded join point %s" % (jnode, in_node))
#nx.write_dot(graph_in, '%s_post.dot' % node)
# the remaining iterable nodes
inodes = _iterable_nodes(graph_in)
for node in graph_in.nodes():
if node.parameterization:
node.parameterization = [param for _, param in
sorted(node.parameterization)]
logger.debug("PE: expanding iterables ... done")
return _remove_nonjoin_identity_nodes(graph_in)
def _iterable_nodes(graph_in):
"""Returns the iterable nodes in the given graph and their join
dependencies.
The nodes are ordered as follows:
- nodes without an itersource precede nodes with an itersource
- nodes without an itersource are sorted in reverse topological order
- nodes with an itersource are sorted in topological order
This order implies the following:
- every iterable node without an itersource is expanded before any
node with an itersource
- every iterable node without an itersource is expanded before any
of it's predecessor iterable nodes without an itersource
- every node with an itersource is expanded before any of it's
successor nodes with an itersource
Return the iterable nodes list
"""
nodes = nx.topological_sort(graph_in)
inodes = [node for node in nodes if node.iterables is not None]
inodes_no_src = [node for node in inodes if not node.itersource]
inodes_src = [node for node in inodes if node.itersource]
inodes_no_src.reverse()
return inodes_no_src + inodes_src
def _standardize_iterables(node):
"""Converts the given iterables to a {field: function} dictionary,
if necessary, where the function returns a list."""
# trivial case
if not node.iterables:
return
iterables = node.iterables
# The candidate iterable fields
fields = set(node.inputs.copyable_trait_names())
# Flag indicating whether the iterables are in the alternate
# synchronize form and are not converted to a standard format.
synchronize = False
# A synchronize iterables node without an itersource can be in
# [fields, value tuples] format rather than
# [(field, value list), (field, value list), ...]
if node.synchronize:
if len(iterables) == 2:
first, last = iterables
if all((isinstance(item, str) and item in fields
for item in first)):
iterables = _transpose_iterables(first, last)
# Convert a tuple to a list
if isinstance(iterables, tuple):
iterables = [iterables]
# Validate the standard [(field, values)] format
_validate_iterables(node, iterables, fields)
# Convert a list to a dictionary
if isinstance(iterables, list):
# Convert a values list to a function. This is a legacy
# Nipype requirement with unknown rationale.
if not node.itersource:
iter_items = map(lambda(field, value): (field, lambda: value),
iterables)
iterables = dict(iter_items)
node.iterables = iterables
def _validate_iterables(node, iterables, fields):
"""
Raise TypeError if an iterables member is not iterable.
Raise ValueError if an iterables member is not a (field, values) pair.
Raise ValueError if an iterable field is not in the inputs.
"""
# The iterables can be a {field: value list} dictionary.
if isinstance(iterables, dict):
iterables = iterables.items()
elif not isinstance(iterables, tuple) and not isinstance(iterables, list):
raise ValueError("The %s iterables type is not a list or a dictionary:"
" %s" % (node.name, iterables.__class__))
for item in iterables:
try:
if len(item) != 2:
raise ValueError("The %s iterables is not a [(field, values)]"
" list" % node.name)
except TypeError, e:
raise TypeError("A %s iterables member is not iterable: %s"
% (node.name, e))
field, _ = item
if field not in fields:
raise ValueError("The %s iterables field is unrecognized: %s"
% (node.name, field))
def _transpose_iterables(fields, values):
"""
Converts the given fields and tuple values into a standardized
iterables value.
If the input values is a synchronize iterables dictionary, then
the result is a (field, {key: values}) list.
Otherwise, the result is a list of (field: value list) pairs.
"""
if isinstance(values, dict):
transposed = dict([(field, defaultdict(list)) for field in fields])
for key, tuples in values.iteritems():
for kvals in tuples:
for idx, val in enumerate(kvals):
if val != None:
transposed[fields[idx]][key].append(val)
return transposed.items()
else:
return zip(fields, [filter(lambda(v): v != None, list(transpose))
for transpose in zip(*values)])
def export_graph(graph_in, base_dir=None, show=False, use_execgraph=False,
show_connectinfo=False, dotfilename='graph.dot', format='png',
simple_form=True):
""" Displays the graph layout of the pipeline
This function requires that pygraphviz and matplotlib are available on
the system.
Parameters
----------
show : boolean
Indicate whether to generate pygraphviz output fromn
networkx. default [False]
use_execgraph : boolean
Indicates whether to use the specification graph or the
execution graph. default [False]
show_connectioninfo : boolean
Indicates whether to show the edge data on the graph. This
makes the graph rather cluttered. default [False]
"""
graph = deepcopy(graph_in)
if use_execgraph:
graph = generate_expanded_graph(graph)
logger.debug('using execgraph')
else:
logger.debug('using input graph')
if base_dir is None:
base_dir = os.getcwd()
if not os.path.exists(base_dir):
os.makedirs(base_dir)
outfname = fname_presuffix(dotfilename,
suffix='_detailed.dot',
use_ext=False,
newpath=base_dir)
logger.info('Creating detailed dot file: %s' % outfname)
_write_detailed_dot(graph, outfname)
cmd = 'dot -T%s -O %s' % (format, outfname)
res = CommandLine(cmd, terminal_output='allatonce').run()
if res.runtime.returncode:
logger.warn('dot2png: %s', res.runtime.stderr)
pklgraph = _create_dot_graph(graph, show_connectinfo, simple_form)
outfname = fname_presuffix(dotfilename,
suffix='.dot',
use_ext=False,
newpath=base_dir)
nx.write_dot(pklgraph, outfname)
logger.info('Creating dot file: %s' % outfname)
cmd = 'dot -T%s -O %s' % (format, outfname)
res = CommandLine(cmd, terminal_output='allatonce').run()
if res.runtime.returncode:
logger.warn('dot2png: %s', res.runtime.stderr)
if show:
pos = nx.graphviz_layout(pklgraph, prog='dot')
nx.draw(pklgraph, pos)
if show_connectinfo:
nx.draw_networkx_edge_labels(pklgraph, pos)
def format_dot(dotfilename, format=None):
cmd = 'dot -T%s -O %s' % (format, dotfilename)
CommandLine(cmd).run()
logger.info('Converting dotfile: %s to %s format' % (dotfilename, format))
def make_output_dir(outdir):
"""Make the output_dir if it doesn't exist.
Parameters
----------
outdir : output directory to create
"""
if not os.path.exists(os.path.abspath(outdir)):
logger.debug("Creating %s" % outdir)
os.makedirs(outdir)
return outdir
def get_all_files(infile):
files = [infile]
if infile.endswith(".img"):
files.append(infile[:-4] + ".hdr")
files.append(infile[:-4] + ".mat")
if infile.endswith(".img.gz"):
files.append(infile[:-7] + ".hdr.gz")
return files
def walk_outputs(object):
"""Extract every file and directory from a python structure
"""
out = []
if isinstance(object, dict):
for key, val in sorted(object.items()):
if isdefined(val):
out.extend(walk_outputs(val))
elif isinstance(object, (list, tuple)):
for val in object:
if isdefined(val):
out.extend(walk_outputs(val))
else:
if isdefined(object) and isinstance(object, basestring):
if os.path.islink(object) or os.path.isfile(object):
out = [(filename, 'f') for filename in get_all_files(object)]
elif os.path.isdir(object):
out = [(object, 'd')]
return out
def walk_files(cwd):
for path, _, files in os.walk(cwd):
for f in files:
yield os.path.join(path, f)
def clean_working_directory(outputs, cwd, inputs, needed_outputs, config,
files2keep=None, dirs2keep=None):
"""Removes all files not needed for further analysis from the directory
"""
if not outputs:
return
outputs_to_keep = outputs.get().keys()
if needed_outputs and \
str2bool(config['execution']['remove_unnecessary_outputs']):
outputs_to_keep = needed_outputs
# build a list of needed files
output_files = []
outputdict = outputs.get()
for output in outputs_to_keep:
output_files.extend(walk_outputs(outputdict[output]))
needed_files = [path for path, type in output_files if type == 'f']
if str2bool(config['execution']['keep_inputs']):
input_files = []
inputdict = inputs.get()
input_files.extend(walk_outputs(inputdict))
needed_files += [path for path, type in input_files if type == 'f']
for extra in ['_0x*.json', 'provenance.*', 'pyscript*.m',
'command.txt', 'result*.pklz', '_inputs.pklz', '_node.pklz']:
needed_files.extend(glob(os.path.join(cwd, extra)))
if files2keep:
needed_files.extend(filename_to_list(files2keep))
needed_dirs = [path for path, type in output_files if type == 'd']
if dirs2keep:
needed_dirs.extend(filename_to_list(dirs2keep))
for extra in ['_nipype', '_report']:
needed_dirs.extend(glob(os.path.join(cwd, extra)))
temp = []
for filename in needed_files:
temp.extend(get_related_files(filename))
needed_files = temp
logger.debug('Needed files: %s' % (';'.join(needed_files)))
logger.debug('Needed dirs: %s' % (';'.join(needed_dirs)))
files2remove = []
if str2bool(config['execution']['remove_unnecessary_outputs']):
for f in walk_files(cwd):
if f not in needed_files:
if len(needed_dirs) == 0:
files2remove.append(f)
elif not any([f.startswith(dname) for dname in needed_dirs]):
files2remove.append(f)
else:
if not str2bool(config['execution']['keep_inputs']):
input_files = []
inputdict = inputs.get()
input_files.extend(walk_outputs(inputdict))
input_files = [path for path, type in input_files if type == 'f']
for f in walk_files(cwd):
if f in input_files and f not in needed_files:
files2remove.append(f)
logger.debug('Removing files: %s' % (';'.join(files2remove)))
for f in files2remove:
os.remove(f)
for key in outputs.copyable_trait_names():
if key not in outputs_to_keep:
setattr(outputs, key, Undefined)
return outputs
def merge_dict(d1, d2, merge=lambda x, y: y):
"""
Merges two dictionaries, non-destructively, combining
values on duplicate keys as defined by the optional merge
function. The default behavior replaces the values in d1
with corresponding values in d2. (There is no other generally
applicable merge strategy, but often you'll have homogeneous
types in your dicts, so specifying a merge technique can be
valuable.)
Examples:
>>> d1 = {'a': 1, 'c': 3, 'b': 2}
>>> merge_dict(d1, d1)
{'a': 1, 'c': 3, 'b': 2}
>>> merge_dict(d1, d1, lambda x,y: x+y)
{'a': 2, 'c': 6, 'b': 4}
"""
if not isinstance(d1, dict):
return merge(d1, d2)
result = dict(d1)
if d2 is None:
return result
for k, v in d2.iteritems():
if k in result:
result[k] = merge_dict(result[k], v, merge=merge)
else:
result[k] = v
return result
def merge_bundles(g1, g2):
for rec in g2.get_records():
g1._add_record(rec)
return g1
def write_workflow_prov(graph, filename=None, format='turtle'):
"""Write W3C PROV Model JSON file
"""
if not filename:
filename = os.path.join(os.getcwd(), 'workflow_provenance')
ps = ProvStore()
processes = []
nodes = graph.nodes()
for idx, node in enumerate(nodes):
result = node.result
classname = node._interface.__class__.__name__
_, hashval, _, _ = node.hash_exists()
attrs = {pm.PROV["type"]: nipype_ns[classname],
pm.PROV["label"]: '_'.join((classname, node.name)),
nipype_ns['hashval']: hashval}
process = ps.g.activity(get_id(), None, None, attrs)
if isinstance(result.runtime, list):
process.add_extra_attributes({pm.PROV["type"]: nipype_ns["MapNode"]})
# add info about sub processes
for idx, runtime in enumerate(result.runtime):
subresult = InterfaceResult(result.interface[idx],
runtime, outputs={})
if result.inputs:
subresult.inputs = result.inputs[idx]
if result.outputs:
for key, value in result.outputs.items():
values = getattr(result.outputs, key)
if isdefined(values):
subresult.outputs[key] = values[idx]
sub_bundle = ProvStore().add_results(subresult)
ps.g = merge_bundles(ps.g, sub_bundle)
ps.g.wasGeneratedBy(sub_bundle, process)
else:
process.add_extra_attributes({pm.PROV["type"]: nipype_ns["Node"]})
result_bundle = ProvStore().add_results(result)
ps.g = merge_bundles(ps.g, result_bundle)
ps.g.wasGeneratedBy(result_bundle, process)
processes.append(process)
# add dependencies (edges)
# Process->Process
for idx, edgeinfo in enumerate(graph.in_edges_iter()):
ps.g.wasStartedBy(processes[nodes.index(edgeinfo[1])],
starter=processes[nodes.index(edgeinfo[0])])
# write provenance
try:
if format in ['turtle', 'all']:
ps.g.rdf().serialize(filename + '.ttl', format='turtle')
except (ImportError, NameError):
format = 'all'
finally:
if format in ['provn', 'all']:
with open(filename + '.provn', 'wt') as fp:
fp.writelines(ps.g.get_provn())
if format in ['json', 'all']:
with open(filename + '.json', 'wt') as fp:
pm.json.dump(ps.g, fp, cls=pm.ProvBundle.JSONEncoder)
return ps.g
def topological_sort(graph, depth_first=True):
nodesort = nx.topological_sort(graph)
if not depth_first:
return nodesort, None
logger.debug("Performing depth first search")
nodes=[]
groups=[]
group=0
G = nx.Graph()
G.add_nodes_from(graph.nodes())
G.add_edges_from(graph.edges())
components = nx.connected_components(G)
for desc in components:
group += 1
indices = []
for node in desc:
indices.append(nodesort.index(node))
nodes.extend(np.array(nodesort)[np.array(indices)[np.argsort(indices)]].tolist())
for node in desc:
nodesort.remove(node)
groups.extend([group] * len(desc))
return nodes, groups
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