Merge pull request #10 from aweinstein/enh/duecredit

enh/duecredit

Author: alexsavio

.travis.yml

@@ -57,8 +57,7 @@ install:
   pip install https://github.com/dmsurti/mayavi/archive/4d4aaf315a29d6a86707dd95149e27d9ed2225bf.zip;
   pip install -e git+https://github.com/enthought/ets.git#egg=ets;
   fi
-- pip install -r requirements.txt  # finish remaining requirements
-- python setup.py install
+- pip install -e .
 script:
 - python -W once:FSL:UserWarning:nipype `which nosetests` --with-doctest --with-cov --cover-package nipype --cov-config .coveragerc --logging-level=DEBUG --verbosity=3
 after_success:
@@ -72,4 +71,4 @@ deploy:
     tags: true
     repo: nipy/nipype
     branch: master
-  distributions: "sdist bdist_wheel"
+  distributions: "sdist"

CHANGES

@@ -1,6 +1,11 @@
-Release 0.12.0-rc1 (April 20, 2016)
-============
-
+Release 0.12.0 (July 12, 2016)
+==============================
+
+* ENH: New interface for Bruker to Nifti converter (https://github.com/nipy/nipype/pull/1523)
+* FIX: output file naming for FIRST outputs (https://github.com/nipy/nipype/pull/1524)
+* ENH: Adds `fslmaths -Tstd` to maths interfaces (https://github.com/nipy/nipype/pull/1518)
+* FIX: Selecting "gamma" in FSL Level1Design now does what the name says (https://github.com/nipy/nipype/pull/1500)
+* ENH: Added grad_dev input to fsl.dti.bedpostx5 interface(https://github.com/nipy/nipype/pull/1493)
 * ENH: ResourceMultiProc plugin to support resource allocation (https://github.com/nipy/nipype/pull/1372)
 * ENH: Added dcm2niix interface (https://github.com/nipy/nipype/pull/1435)
 * ENH: Add nipype_crash_search command (https://github.com/nipy/nipype/pull/1422)
@@ -36,7 +41,7 @@ Release 0.12.0-rc1 (April 20, 2016)
        (https://github.com/nipy/nipype/pull/1460)
 
 Release 0.11.0 (September 15, 2015)
-============
+===================================
 
 * API: Change how hash values are computed (https://github.com/nipy/nipype/pull/1174)
 * ENH: New algorithm: mesh.WarpPoints applies displacements fields to point sets
@@ -122,7 +127,7 @@ Release 0.11.0 (September 15, 2015)
        (https://github.com/nipy/nipype/pull/1142)
 
 Release 0.10.0 (October 10, 2014)
-============
+=================================
 
 * ENH: New miscelaneous interfaces: SplitROIs (mapper), MergeROIs (reducer)
        to enable parallel processing of very large images.
@@ -166,19 +171,19 @@ Release 0.10.0 (October 10, 2014)
 * FIX: Update for FSL 5.0.7 which deprecated Contrast Manager
 
 Release 0.9.2 (January 31, 2014)
-============
+================================
 
 * FIX: DataFinder was broken due to a typo
 * FIX: Order of DataFinder outputs was not guaranteed, it's human sorted now
 * ENH: New interfaces: Vnifti2Image, VtoMat
 
 Release 0.9.1 (December 25, 2013)
-============
+=================================
 
 * FIX: installation issues
 
 Release 0.9.0 (December 20, 2013)
-============
+=================================
 
 * ENH: SelectFiles: a streamlined version of DataGrabber
 * ENH: new tools for defining workflows: JoinNode, synchronize and itersource

CONTRIBUTING.md

@@ -10,10 +10,16 @@
 * Pull Requests should be tested, if feasible:
     - bugfixes should include regression tests
     - new behavior should at least get minimal exercise
-* Use a descriptive prefix for your PR: ENH, FIX, TST, DOC, STY, REF (refactor), WIP (Work in progress)
-* After submiting the PR, include an update to the CHANGES file: prefix: description (URL of pull request)
-* `make specs`
-* do: `make check-before-commit` before submitting the PR. This will require you to either install or be in developer mode with: `python setup.py install/develop`.
+* Use a descriptive prefix for your PR: ENH (enhancement), FIX, TST, DOC, STY, REF (refactor), WIP (Work in progress)
+* The person who accepts/merges your PR will include an update to the CHANGES file: prefix: description (URL of pull request)
+* Run `make check-before-commit` before submitting the PR.
+  This will require you to either install or be in developer mode with: `python setup.py install/develop`.
+* In general, do not catch exceptions without good reason. 
+  * catching non-fatal exceptions. 
+    Log the exception as a warning.
+  * adding more information about what may have caused the error.
+    Raise a new exception using ``raise_from(NewException("message"), oldException)`` from ``future``.
+    Do not log this, as it creates redundant/confusing logs.
 
 ## Contributing issues
 

Makefile

@@ -15,8 +15,6 @@ sdist: zipdoc
 	python setup.py sdist
 	@echo "Done building source distribution."
 	# XXX copy documentation.zip to dist directory.
-	# XXX Somewhere the doc/_build directory is removed and causes
-	# this script to fail.
 
 egg: zipdoc
 	@echo "Building egg..."
@@ -45,7 +43,10 @@ clean-build:
 clean-ctags:
 	rm -f tags
 
-clean: clean-build clean-pyc clean-so clean-ctags
+clean-doc:
+	rm -rf doc/_build
+
+clean: clean-build clean-pyc clean-so clean-ctags clean-doc
 
 in: inplace # just a shortcut
 inplace:

doc/conf.py

@@ -82,7 +82,7 @@
 # The short X.Y version.
 version = nipype.__version__
 # The full version, including alpha/beta/rc tags.
-release = "0.11.0"
+release = "0.12.0"
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

doc/users/grabbing_and_sinking.rst

@@ -58,6 +58,7 @@ DataGrabber stores its outputs in a field called outfiles.
   datasource1 = nio.DataGrabber()
   datasource1.inputs.base_directory = os.getcwd()
   datasource1.inputs.template = 'data/s1/f3.nii'
+  datasource1.inputs.sort_filelist = True
   results = datasource1.run()
 
 Or you can get at all uncompressed NIfTI files starting with the letter 'f' in
@@ -67,6 +68,7 @@ all directories starting with the letter 's'.
 
   datasource2.inputs.base_directory = '/mass'
   datasource2.inputs.template = 'data/s*/f*.nii'
+  datasource1.inputs.sort_filelist = True
 
 Two special inputs were used in these previous cases. The input `base_directory`
 indicates in which directory to search, while the input `template` indicates the
@@ -89,6 +91,7 @@ then used to set the template (see %d in the template).
   datasource3 = nio.DataGrabber(infields=['run'])
   datasource3.inputs.base_directory = os.getcwd()
   datasource3.inputs.template = 'data/s1/f%d.nii'
+  datasource1.inputs.sort_filelist = True
   datasource3.inputs.run = [3, 7]
 
 This will return files `basedir/data/s1/f3.nii` and `basedir/data/s1/f7.nii`. We
@@ -98,6 +101,7 @@ can take this a step further and pair subjects with runs.
 
   datasource4 = nio.DataGrabber(infields=['subject_id', 'run'])
   datasource4.inputs.template = 'data/%s/f%d.nii'
+  datasource1.inputs.sort_filelist = True
   datasource4.inputs.run = [3, 7]
   datasource4.inputs.subject_id = ['s1', 's3']
 
@@ -115,6 +119,7 @@ wish to retrieve all the functional runs and the structural image for the subjec
   datasource = nio.DataGrabber(infields=['subject_id'], outfields=['func', 'struct'])
   datasource.inputs.base_directory = 'data'
   datasource.inputs.template = '*'
+  datasource1.inputs.sort_filelist = True
   datasource.inputs.field_template = dict(func='%s/f%d.nii',
                                           struct='%s/struct.nii')
   datasource.inputs.template_args = dict(func=[['subject_id', [3,5,7,10]]],

doc/users/install.rst

@@ -9,10 +9,9 @@ This page covers the necessary steps to install Nipype.
 Download
 --------
 
-Release 0.10.0: [`zip <https://github.com/nipy/nipype/archive/0.10.0.zip>`__ `tar.gz
-<https://github.com/nipy/nipype/archive/0.10.0.tar.gz>`__]
+Current release: `<https://github.com/nipy/nipype/releases/latest>`_.
 
-Development: [`zip <http://github.com/nipy/nipype/zipball/master>`__ `tar.gz
+Development version: [`zip <http://github.com/nipy/nipype/zipball/master>`__ `tar.gz
 <http://github.com/nipy/nipype/tarball/master>`__]
 
 `Prior downloads <http://github.com/nipy/nipype/tags>`_
@@ -25,13 +24,14 @@ or::
 
         git clone https://github.com/nipy/nipype.git
 
+Check out the list of nipype's `current dependencies <https://github.com/shoshber/nipype/blob/master/nipype/info.py#L105>`_.
+
 Install
 -------
 
 The installation process is similar to other Python packages.
 
-If you already have a Python environment setup that has the dependencies listed
-below, you can do::
+If you already have a Python environment set up, you can do::
 
 	easy_install nipype
 
@@ -61,8 +61,7 @@ If you downloaded the source distribution named something
 like ``nipype-x.y.tar.gz``, then unpack the tarball, change into the
 ``nipype-x.y`` directory and install nipype using::
 
-    pip install -r requirements.txt
-    python setup.py install
+    pip install -e .
 
 **Note:** Depending on permissions you may need to use ``sudo``.
 
@@ -76,8 +75,18 @@ nose_ installed, then do the following::
 
 you can also test with nosetests::
 
-    nosetests --with-doctest /software/nipy-repo/masternipype/nipype
-    --exclude=external --exclude=testing
+    nosetests --with-doctest <installation filepath>/nipype  --exclude=external --exclude=testing
+
+or::
+
+    nosetests --with-doctest nipype
+
+A successful test run should complete in a few minutes and end with
+something like::
+
+    Ran 13053 tests in 126.618s
+
+    OK (SKIP=66)
 
 All tests should pass (unless you're missing a dependency). If SUBJECTS_DIR
 variable is not set some FreeSurfer related tests will fail. If any tests
@@ -89,9 +98,9 @@ tests::
 
        export MATLABCMD=$pathtomatlabdir/bin/$platform/MATLAB
 
-where, $pathtomatlabdir is the path to your matlab installation and
-$platform is the directory referring to x86 or x64 installations
-(typically glnxa64 on 64-bit installations).
+where ``$pathtomatlabdir`` is the path to your matlab installation and
+``$platform`` is the directory referring to x86 or x64 installations
+(typically ``glnxa64`` on 64-bit installations).
 
 Avoiding any MATLAB calls from testing
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -102,38 +111,9 @@ On unix systems, set an empty environment variable::
 
 This will skip any tests that require matlab.
 
-Dependencies
+Recommended Software
 ------------
 
-Below is a list of required dependencies, along with additional software
-recommendations.
-
-Must Have
-~~~~~~~~~
-
-.. note::
-
-    Full distributions of Nipype, such as the ones in  Anaconda_ or Canopy_, provide
-    the following packages automatically.
-
-Nibabel_ 1.0 - 1.4
-  Neuroimaging file i/o library.
-
-Python_ 2.7
-
-NetworkX_ 1.0 - 1.8
-  Python package for working with complex networks.
-
-NumPy_ 1.3 - 1.7
-
-SciPy_ 0.7 - 0.12
-  Numpy and Scipy are high-level, optimized scientific computing libraries.
-
-Enthought_ Traits_ 4.0.0 - 4.3.0
-
-Dateutil 1.5 -
-
-
 Strong Recommendations
 ~~~~~~~~~~~~~~~~~~~~~~
 

doc/users/pipeline_tutorial.rst

@@ -49,7 +49,7 @@ Requirements
   - FSL_, FreeSurfer_, Camino_, ConnectomeViewer and MATLAB_ are available and
     callable from the command line
 
-  - SPM_ 5/8 is installed and callable in matlab
+  - SPM_ 5/8/12 is installed and callable in matlab
 
   - Space: 3-10 GB
 
@@ -59,29 +59,12 @@ Checklist for analysis tutorials
 For the analysis tutorials, we will be using a slightly modified version of the
 FBIRN Phase I travelling data set.
 
-Step 0
-~~~~~~
+1. Download and extract the `Pipeline tutorial data (429MB).
+<https://figshare.com/articles/nipype_tutorial_data/3395806>`_
+(md5: d175083784c5167de4ea11b43b37c166)
 
-Download and extract the `Pipeline tutorial data (429MB).
-<https://dl.dropbox.com/s/jzgq2nupxyz36bp/nipype-tutorial.tar.bz2>`_
-
-(checksum: 56ed4b7e0aac5627d1724e9c10cd26a7)
-
-
-Step 1.
-~~~~~~~
-
-Ensure that all programs are available by calling ``bet``, ``matlab``
-and then ``which spm`` within matlab to ensure you have spm5/8 in your
+2. Ensure that all programs are available by calling ``bet``, ``matlab``
+and then ``which spm`` within matlab to ensure you have spm5/8/12 in your
 matlab path.
 
-Step 2.
-~~~~~~~
-
-You can now run the tutorial by typing ``python tutorial_script.py``
-within the nipype-tutorial directory. This will run a full first level
-analysis on two subjects following by a 1-sample t-test on their first
-level results. The next section goes through each section of the
-tutorial script and describes what it is doing.
-
 .. include:: ../links_names.txt

doc/users/resource_sched_profiler.rst

@@ -144,17 +144,17 @@ The pandas_ Python package is required to use this feature.
 	from nipype.pipeline.plugins.callback_log import log_nodes_cb
 	args_dict = {'n_procs' : 8, 'memory_gb' : 10, 'status_callback' : log_nodes_cb}
 	workflow.run(plugin='MultiProc', plugin_args=args_dict)
-	
+
 	# ...workflow finishes and writes callback log to '/home/user/run_stats.log'
-	
+
 	from nipype.utils.draw_gantt_chart import generate_gantt_chart
 	generate_gantt_chart('/home/user/run_stats.log', cores=8)
 	# ...creates gantt chart in '/home/user/run_stats.log.html'
 
 The ``generate_gantt_chart`` function will create an html file that can be viewed
 in a browser. Below is an example of the gantt chart displayed in a web browser.
 Note that when the cursor is hovered over any particular node bubble or resource
-bubble, some additional information is shown in a pop-up. 
+bubble, some additional information is shown in a pop-up.
 
  * -  .. image:: images/gantt_chart.png
          :width: 100 %

doc/users/tutorial_101.rst

@@ -75,7 +75,8 @@ realigner to the smoother in step 5.
 **3. Creating and configuring a workflow**
 
 Here we create an instance of a workflow and indicate that it should operate in
-the current directory.
+the current directory. The workflow's output will be placed in the ``preproc``
+directory.
 
 .. testcode::
 
@@ -128,11 +129,13 @@ above were generated using this.
 
    workflow.write_graph()
 
-This creates two files graph.dot and graph_detailed.dot and if
+This creates two files ``graph.dot`` and ``graph_detailed.dot`` inside
+``./preproc`` and if
 graphviz_ is installed on your system it automatically converts it
 to png files. If graphviz is not installed you can take the dot files
 and load them in a graphviz visualizer elsewhere. You can specify how detailed
-the graph is going to be, by using "graph2use" argument which takes the following
+the graph is going to be, by using the ``graph2use`` argument which takes
+the following
 options:
 
 * hierarchical - creates a graph showing all embedded workflows (default)
@@ -152,9 +155,11 @@ above pipeline.
    import nipype.algorithms.rapidart as ra
    artdetect = pe.Node(interface=ra.ArtifactDetect(), name='artdetect')
    artdetect.inputs.use_differences  = [True, False]
-   art.inputs.use_norm = True
-   art.inputs.norm_threshold = 0.5
-   art.inputs.zintensity_threshold = 3
+   artdetect.inputs.use_norm = True
+   artdetect.inputs.norm_threshold = 0.5
+   artdetect.inputs.zintensity_threshold = 3
+   artdetect.inputs.parameter_source = "SPM"
+   artdetect.inputs.mask_type = "spm_global"
    workflow.connect([(realigner, artdetect,
                       [('realigned_files', 'realigned_files'),
                        ('realignment_parameters','realignment_parameters')]