/usr/share/pyshared/nipype/pipeline/utils.py is in python-nipype 0.5.3-2wheezy2.
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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
import os
import re
import numpy as np
from nipype.utils.misc import package_check
package_check('networkx', '1.3')
import networkx as nx
from nipype.interfaces.base import CommandLine, isdefined, Undefined
from nipype.utils.filemanip import fname_presuffix, FileNotFoundError,\
filename_to_list
from nipype.utils.misc import create_function_from_source, str2bool
from nipype.interfaces.utility import IdentityInterface
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
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('.')
interface = n._interface.__class__.__name__
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 walk(children, level=0, path=None, usename=True):
"""Generate all the full paths in a tree, as a dict.
"""
# 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 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):
"""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
count = 0
for i, params in enumerate(walk(iterables.items())):
count += 1
template = '.%s%%0%dd' % (prefix, np.ceil(np.log10(count)))
for i, params in enumerate(walk(iterables.items())):
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_identity_nodes(graph, keep_iterables=False):
"""Remove identity nodes from an execution graph
"""
identity_nodes = []
for node in nx.topological_sort(graph):
if isinstance(node._interface, IdentityInterface):
if keep_iterables and getattr(node, 'iterables') is not None:
pass
else:
identity_nodes.append(node)
if identity_nodes:
for node in identity_nodes:
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))
if not portoutputs:
pass
elif not portinputs:
for key, connections in portoutputs.items():
for destnode, inport, src in connections:
value = getattr(node.inputs, key)
if isinstance(src, tuple):
value = evaluate_connect_function(src[1], src[2],
value)
destnode.set_input(inport, value)
else:
for key, connections in portoutputs.items():
for destnode, inport, src in connections:
if key not in portinputs:
value = getattr(node.inputs, key)
if isinstance(src, tuple):
value = evaluate_connect_function(src[1],
src[2],
value)
destnode.set_input(inport, value)
else:
srcnode, srcport = portinputs[key]
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)])
graph.remove_nodes_from([node])
return graph
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_identity_nodes(graph_in, keep_iterables=True)
moreiterables = True
# convert list of tuples to dict fields
for node in graph_in.nodes():
if isinstance(node.iterables, tuple):
node.iterables = [node.iterables]
for node in graph_in.nodes():
if isinstance(node.iterables, list):
node.iterables = dict(map(lambda(x): (x[0],
lambda: x[1]),
node.iterables))
allprefixes = list('abcdefghijklmnopqrstuvwxyz')
while moreiterables:
nodes = nx.topological_sort(graph_in)
nodes.reverse()
inodes = [node for node in nodes if node.iterables is not None]
if inodes:
node = inodes[0]
iterables = node.iterables.copy()
node.iterables = None
logger.debug('node: %s iterables: %s' % (node, iterables))
subnodes = [s for s in dfs_preorder(graph_in, node)]
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]
node._id += ('.' + iterable_prefix + 'I')
logger.debug(('subnodes:', subnodes))
subgraph = graph_in.subgraph(subnodes)
graph_in = _merge_graphs(graph_in, subnodes,
subgraph, node._hierarchy + node._id,
iterables, iterable_prefix)
#nx.write_dot(graph_in, '%s_post.dot'%node)
else:
moreiterables = False
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_identity_nodes(graph_in)
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).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).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, str):
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.xml', '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)))
logger.debug('Needed files: %s' % (';'.join(needed_files)))
logger.debug('Needed dirs: %s' % (';'.join(needed_dirs)))
files2remove = []
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(dirname) for dirname in needed_dirs]):
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)
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
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