small updates to documentation: install, tutorials, make

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/users/install.rst

@@ -76,8 +76,14 @@ 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
+
+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 +95,9 @@ 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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -119,7 +125,7 @@ Must Have
 Nibabel_ 1.0 - 1.4
   Neuroimaging file i/o library.
 
-Python_ 2.7
+Python_ 2.7 or Python_ 3
 
 NetworkX_ 1.0 - 1.8
   Python package for working with complex networks.

doc/users/pipeline_tutorial.rst

@@ -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
-~~~~~~
+# 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
+# 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/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')]