Merge branch 'master' into spm_interfaces

* master: (138 commits)
  fix: CompCor PCA extraction was erroneous. fixed to save the correct components.
  fix: disabled check for current version due to portalocker issues
  doc: changed class to ref
  doc: minor spell checkings etc while reading
  fix: job submission should use info['node'] to get node
  fix: afni volreg to use gen_fname
  fix: spm new segment only outputs files as .nii
  fix: simplified format arg since trait catches errors
  fix: remove hash value computation on run unless requested explicitly by local_hash_check
  doc: pep8 fmrirealign
  fix: examples
  fix: FmriRealign4d
  fix: nitime test failure when display is missing
  fix: tests
  fixed doctest error with comma after -i in WIMT
  doctest files
  BF -- changed DARTELNorm2MNInputSpec.fwhm to match SmoothInputSpec.fwhm
  doc: added intro slides
  BUG: Explicitly declare arg to fix position
  ENH: Add opt for converting siemans PAR/REC files
  ...

Author: bpinsard

CHANGES

@@ -1,7 +1,11 @@
 Since last release
 ==================
 
-* ENH: New interfaces: MySQLSink, nipy.Similarity, WatershedBEM
+* API: display variable no longer encoded as inputs in commandline interfaces
+
+* ENH: New interfaces: MySQLSink, nipy.Similarity, WatershedBEM,
+       NetworkBasedStatistic, Atropos, N4BiasFieldCorrection, ApplyTransforms,
+       fs.MakeAverageSubject
 
 * FIX: Afni outputs should inherit from TraitedSpec
 

build_docs.py

@@ -21,7 +21,7 @@
 # Sphinx import.
 from sphinx.setup_command import BuildDoc
 
-_info_fname = pjoin('nipype', 'info.py')
+_info_fname = pjoin(os.path.dirname(__file__), 'nipype', 'info.py')
 INFO_VARS = {}
 exec(open(_info_fname, 'rt').read(), {}, INFO_VARS)
 

doc/quickstart.rst

@@ -15,8 +15,10 @@ Downloading and installing
 Beginner's guide
 ================
 
-By Michael Notter. `Available here`__
+Introductory slides. `Available here`__
+Michael Notter's guide. `Available here`__
 
+__ http://satra.github.com/intro2nipype
 __ http://miykael.github.com/nipype-beginner-s-guide/index.html
 
 User guides

doc/users/caching_tutorial.rst

@@ -130,18 +130,18 @@ rather than workflows. Use it: instead of data grabber nodes, use for
 instance the `glob` module. To vary parameters, use `for` loops. To make
 reusable code, write Python functions.
 
-One good rule of thumb to respect is to avoid the usage of explicite
-filenames appart from the outermost inputs and outputs of your
+One good rule of thumb to respect is to avoid the usage of explicit
+filenames apart from the outermost inputs and outputs of your
 processing. The reason being that the caching mechanism of
 :mod:`nipy.caching` takes care of generating the unique hashes, ensuring
-that, when you vary parameters, files are not overriden by the output of
+that, when you vary parameters, files are not overridden by the output of
 different computations.
 
 .. topic:: Debuging
 
     If you need to inspect the running environment of the nodes, it may
     be useful to know where they were executed. With `nipype.caching`,
-    you do not control this location as it it encoded by hashes.
+    you do not control this location as it is encoded by hashes.
 
     To find out where an operation has been persisted, simply look in
     it's output variable::

doc/users/config_file.rst

@@ -80,7 +80,9 @@ Execution
 *remove_unnecessary_outputs*
 	This will remove any interface outputs not needed by the workflow. If the
 	required outputs from a node changes, rerunning the workflow will rerun the
-	node. (possible values: ``true`` and ``false``; default value: ``true``)
+	node. Outputs of leaf nodes (nodes whose outputs are not connected to any 
+	other nodes) will never be deleted independent of this parameter. (possible 
+	values: ``true`` and ``false``; default value: ``true``)
 
 *use_relative_paths*
 	Should the paths stored in results (and used to look for inputs)

doc/users/debug.rst

@@ -17,11 +17,16 @@ performance issues.
 
 #. Use the debug config mode. This can be done by setting::
 
-      import config
+      from nipype import config
       config.enable_debug_mode()
 
    as the first import of your nipype script.
 
+   .. note::
+
+     Turning on debug will rerun your workflows and will rerun them after debugging
+     is turned off.
+
 #. There are several configuration options that can help with debugging. See
    :ref:`config_file` for more details::
 

doc/users/function_interface.rst

@@ -48,7 +48,7 @@ Which would print ``4``.
 
 Note that, if you are working interactively, the Function interface is
 unable to use functions that are defined within your interpreter session.
-(Specifcally, it can't use functions that live in the ``__main__`` namespace).
+(Specifically, it can't use functions that live in the ``__main__`` namespace).
 
 Using External Packages
 -----------------------
@@ -59,7 +59,7 @@ geared towards neuroimaging, such as Nibabel_, Nipy_, or PyMVPA_.
 
 While this is completely possible (and, indeed, an intended use of the
 Function interface), it does come with one important constraint. The
-function code you write is excecuted in a standalone environment, 
+function code you write is executed in a standalone environment, 
 which means that any external functions or classes you use have to
 be imported within the function itself::
 
@@ -77,7 +77,7 @@ Hello World - Function interface in a workflow
 Contributed by: Hänel Nikolaus Valentin 
 
 The following snippet of code demonstrates the use of the function interface in
-the context of a workflow. Note the use of `import os` within the function as
+the context of a workflow. Note the use of ``import os`` within the function as
 well as returning the absolute path from the Hello function. The `import` inside
 is necessary because functions are coded as strings and do not have to be on the
 PYTHONPATH. However any function called by this function has to be available on
@@ -139,7 +139,7 @@ the string would be
     add_two_str = "def add_two(val):\n    return val + 2\n"
 
 Unlike when using a function object, this input can be set like any other,
-meaning that you could write a function that outputs differnet function
+meaning that you could write a function that outputs different function
 strings depending on some run-time contingencies, and connect that output
 the the ``function_str`` input of a downstream Function interface. 
 

doc/users/grabbing_and_sinking.rst

@@ -37,7 +37,7 @@ to simply iterate over subjects.
 
 However, in the context of complex workflows and given that users typically
 arrange their imaging and other data in a semantically hierarchical data store,
-an alternate mechanism for reading and writing the data generated by a workflow
+an alternative mechanism for reading and writing the data generated by a workflow
 is often necessary. As the names suggest DataGrabber is used to get at data
 stored in a shared file system while DataSink is used to store the data
 generated by a workflow into a hierarchical structure on disk.
@@ -46,8 +46,8 @@ generated by a workflow into a hierarchical structure on disk.
 DataGrabber
 ===========
 
-Datagrabber is an interface for collecting files from hard drive. It is very
-flexible and supports almost any file organisation of your data you can imagine.
+DataGrabber is an interface for collecting files from hard drive. It is very
+flexible and supports almost any file organization of your data you can imagine.
 
 You can use it as a trivial use case of getting a fixed file. By default,
 DataGrabber stores its outputs in a field called outfiles.
@@ -60,7 +60,7 @@ DataGrabber stores its outputs in a field called outfiles.
   datasource1.inputs.template = 'data/s1/f3.nii'
   results = datasource1.run()
 
-Or you can get at all uncompressed nifti files starting with the letter 'f' in
+Or you can get at all uncompressed NIfTI files starting with the letter 'f' in
 all directories starting with the letter 's'.
 
 ::
@@ -75,7 +75,7 @@ path matches of the form `/mass/data/s*/f*`.
 
 .. note::
 
-  When used with wildcards (e.g., s* and f* above) Datagrabber does not return
+  When used with wildcards (e.g., s* and f* above) DataGrabber does not return
   data in sorted order. In order to force it to return data in sorted order, one
   needs to set the input `sorted = True`. However, when explicitly specifying an
   order as we will see below, `sorted` should be set to `False`.
@@ -108,7 +108,7 @@ A more realistic use-case
 
 In a typical study one often wants to grab different files for a given subject
 and store them in semantically meaningful outputs. In the following example, we
-wish to retrieve all the functional runs and the structural image for subject 's1'.
+wish to retrieve all the functional runs and the structural image for the subject 's1'.
 
 ::
 
@@ -147,7 +147,7 @@ iterables that have been used in the workflow. This makes navigating the working
 directory a not so pleasant experience. And typically the user is interested in
 preserving only a small percentage of these outputs. The DataSink interface can
 be used to extract components from this `cache` and store it at a different
-location. For XNAT-based storage, see XNATSink.
+location. For XNAT-based storage, see :ref:`nipype.interfaces.io.XNATSink` .
 
 .. note:: 
 

doc/users/install.rst

@@ -39,13 +39,13 @@ Debian and Ubuntu
 ~~~~~~~~~~~~~~~~~
 
 Add the `NeuroDebian <http://neuro.debian.org>`_ repository and install
-the ``python-nipype`` package using ``apt-get`` or your favourite package
+the ``python-nipype`` package using ``apt-get`` or your favorite package
 manager.
 
 Mac OS X
 ~~~~~~~~
 
-The easiest way to get nipype running on MacOSX is to install EPD_ and then add
+The easiest way to get nipype running on Mac OS X is to install EPD_ and then add
 nibabel and nipype by executing::
 
 	easy_install nibabel

examples/fmri_fsl_reuse.py

@@ -17,15 +17,6 @@
 
 import os                                    # system functions
 
-"""
-.. note::
-    config for logging should be set before anything else
-"""
-
-from nipype.utils.config import config
-config.set('logging', 'log_to_file', 'false')
-config.set_log_dir(os.getcwd())
-
 import nipype.interfaces.io as nio           # Data i/o
 import nipype.interfaces.fsl as fsl          # fsl
 import nipype.interfaces.utility as util     # utility