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# pipeline.engine.utils

## Module: `pipeline.engine.utils`

Utility routines for workflow graphs

## Functions

`nipype.pipeline.engine.utils.``clean_working_directory`(outputs, cwd, inputs, needed_outputs, config, files2keep=None, dirs2keep=None)

  Removes all files not needed for further analysis from the directory

`nipype.pipeline.engine.utils.``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.

`nipype.pipeline.engine.utils.``evaluate_connect_function`(function_source, args, first_arg)

`nipype.pipeline.engine.utils.``expand_iterables`(iterables, synchronize=False)

`nipype.pipeline.engine.utils.``export_graph`(graph_in, base_dir=None, show=False, use_execgraph=False, show_connectinfo=False, dotfilename=u'graph.dot', format=u'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] :

`nipype.pipeline.engine.utils.``format_dot`(dotfilename, format=u'png')

  Dump a directed graph (Linux only; install via brew on OSX)

`nipype.pipeline.engine.utils.``format_node`(node, format=u'python', include_config=False)

  Format a node in a given output syntax.

`nipype.pipeline.engine.utils.``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).

`nipype.pipeline.engine.utils.``get_all_files`(infile)

`nipype.pipeline.engine.utils.``get_levels`(G)

`nipype.pipeline.engine.utils.``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

`nipype.pipeline.engine.utils.``load_resultfile`(path, name)

  Load InterfaceResult file from path

  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

`nipype.pipeline.engine.utils.``merge_bundles`(g1, g2)

`nipype.pipeline.engine.utils.``merge_dict`(d1, d2, merge=<function <lambda>>)

  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}
      >>> d2 = merge_dict(d1, d1)
      >>> len(d2)
      3
      >>> [d2[k] for k in ['a', 'b', 'c']]
      [1, 2, 3]

      >>> d3 = merge_dict(d1, d1, lambda x,y: x+y)
      >>> len(d3)
      3
      >>> [d3[k] for k in ['a', 'b', 'c']]
      [2, 4, 6]

`nipype.pipeline.engine.utils.``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

`nipype.pipeline.engine.utils.``nodelist_runner`(nodes, updatehash=False, stop_first=False)

  A generator that iterates and over a list of `nodes` and executes them.

`nipype.pipeline.engine.utils.``save_hashfile`(hashfile, hashed_inputs)

  Store a hashfile

`nipype.pipeline.engine.utils.``save_resultfile`(result, cwd, name)

  Save a result pklz file to `cwd`

`nipype.pipeline.engine.utils.``strip_temp`(files, wd)

  Remove temp from a list of file paths

`nipype.pipeline.engine.utils.``synchronize_iterables`(iterables)

  Synchronize the given iterables in item-wise order.

  Return: the {field: value} dictionary list

  Examples

      >>> from nipype.pipeline.engine.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

`nipype.pipeline.engine.utils.``topological_sort`(graph, depth_first=False)

  Returns a depth first sorted order if depth_first is True

`nipype.pipeline.engine.utils.``walk`(children, level=0, path=None, usename=True)

  Generate all the full paths in a tree, as a dict.

  Examples

      >>> from nipype.pipeline.engine.utils import walk
      >>> iterables = [('a', lambda: [1, 2]), ('b', lambda: [3, 4])]
      >>> [val['a'] for val in walk(iterables)]
      [1, 1, 2, 2]
      >>> [val['b'] for val in walk(iterables)]
      [3, 4, 3, 4]

`nipype.pipeline.engine.utils.``walk_files`(cwd)

`nipype.pipeline.engine.utils.``walk_outputs`(object)

  Extract every file and directory from a python structure

`nipype.pipeline.engine.utils.``write_report`(node, report_type=None, is_mapnode=False)

  Write a report file for a node

`nipype.pipeline.engine.utils.``write_workflow_prov`(graph, filename=None, format=u'all')

  Write W3C PROV Model JSON file

`nipype.pipeline.engine.utils.``write_workflow_resources`(graph, filename=None, append=None)

  Generate a JSON file with profiling traces that can be loaded in a pandas DataFrame or processed with JavaScript like D3.js