Merge branch 'master' into fsl_model

* master: (53 commits)
  Added a test for SPM bool conversion.
  Call super for all SPM _format_args()
  call super
  Make all Booleans to be formated as 1,0 in SPM
  updated changelog
  fix: added test for deepcopy bug
  fix: added back deepcopy
  fix: thanks to @rkern we now have a passing test with dynamic traits retaining their type through pickling
  fix: test that fails when traitedspec is not pickled properly
  updated changelog
  Added a default to maintain backwards compatibility
  utils.Merge option for not flattening the output list
  utils.Merge option for not flattening the output list
  utils.Merge option for not flattening the output list
  added mention about the new interface to the changelog
  Added tests stub files
  Make check-before-commit
  Significantly faster smoothing in tessellation_tutorial with MeshFix, CFF output option
  Interface for MeshFix by Marco Attene, Mirko Windhoff, Axel Thielscher
  Fixed deprecated config option.
  ...

Author: bpinsard

.travis.yml

@@ -0,0 +1,22 @@
+# vim ft=yaml
+# travis-ci.org definition for nipy build
+#
+# We pretend to be erlang because we need can't use the python support in
+# travis-ci; it uses virtualenvs, they do not have numpy, scipy, matplotlib,
+# and it is impractical to build them
+language: erlang
+env:
+    - PYTHON=python PYSUF=''
+    # - PYTHON=python3 PYSUF=3 : python3-numpy not currently available
+install:
+    - sudo apt-get install $PYTHON-dev
+    - sudo apt-get install $PYTHON-numpy
+    - sudo apt-get install $PYTHON-scipy
+    - sudo apt-get install $PYTHON-networkx
+    - sudo apt-get install $PYTHON-traits
+    - sudo apt-get install $PYTHON-setuptools
+    - sudo easy_install$PYSUF nibabel # Latest pypi
+    - sudo apt-get install $PYTHON-nose
+script:
+    # Change into an innocuous directory and find tests from installation
+    - make test

CHANGES

@@ -1,3 +1,11 @@
+Next release
+============
+
+*ENH: New interfaces: ICC, Meshfix
+*ENH: added no_flatten option to Merge
+
+*FIX: fixed dynamic traits bug
+
 Release 0.6.0 (Jun 30, 2012)
 ============================
 

Makefile

@@ -5,13 +5,8 @@
 PYTHON ?= python
 NOSETESTS ?= nosetests
 
-zipdoc:
-	@echo "Clean documentation directory."
-	python setup.py clean
-	@echo "Build documentation.zip..."
-	python setup.py build_sphinx
-	@echo "Clean documentation directory."
-	python setup.py clean
+zipdoc: html
+	zip documentation.zip doc/_build/html
 
 sdist: zipdoc
 	@echo "Building source distribution..."
@@ -75,6 +70,3 @@ check-before-commit: trailing-spaces html test
 	@echo "removed spaces"
 	@echo "built docs"
 	@echo "ran test"
-
-
-

build_docs.py

@@ -18,9 +18,6 @@
 from distutils.cmd import Command
 from distutils.command.clean import clean
 
-# Sphinx import.
-from sphinx.setup_command import BuildDoc
-
 _info_fname = pjoin(os.path.dirname(__file__), 'nipype', 'info.py')
 INFO_VARS = {}
 exec(open(_info_fname, 'rt').read(), {}, INFO_VARS)
@@ -104,61 +101,66 @@ def relative_path(filename):
 
 ################################################################################
 # Distutils Command class build the docs
-class MyBuildDoc(BuildDoc):
-    """ Sub-class the standard sphinx documentation building system, to
-        add logics for API generation and matplotlib's plot directive.
-    """
-
-    def run(self):
-        self.run_command('api_docs')
-        # We need to be in the doc directory for to plot_directive
-        # and API generation to work
-        """
-        os.chdir('doc')
-        try:
-            BuildDoc.run(self)
-        finally:
-            os.chdir('..')
-        """
-        # It put's the build in a doc/doc/_build directory with the
-        # above?!?!  I'm leaving the code above here but commented out
-        # in case I'm missing something?
-        BuildDoc.run(self)
-        self.zip_docs()
-
-    def zip_docs(self):
-        if not os.path.exists(DOC_BUILD_DIR):
-            raise OSError, 'Doc directory does not exist.'
-        target_file = os.path.join('doc', 'documentation.zip')
-        # ZIP_DEFLATED actually compresses the archive. However, there
-        # will be a RuntimeError if zlib is not installed, so we check
-        # for it. ZIP_STORED produces an uncompressed zip, but does not
-        # require zlib.
-        try:
-            zf = zipfile.ZipFile(target_file, 'w',
-                                        compression=zipfile.ZIP_DEFLATED)
-        except RuntimeError:
-            warnings.warn('zlib not installed, storing the docs '
-                            'without compression')
-            zf = zipfile.ZipFile(target_file, 'w',
-                                        compression=zipfile.ZIP_STORED)
-
-        for root, dirs, files in os.walk(DOC_BUILD_DIR):
-            relative = relative_path(root)
-            if not relative.startswith('.doctrees'):
-                for f in files:
-                    zf.write(os.path.join(root, f),
-                            os.path.join(relative, 'html_docs', f))
-        zf.close()
-
-
-    def finalize_options(self):
-        """ Override the default for the documentation build
-            directory.
+# Sphinx import.
+try:
+    from sphinx.setup_command import BuildDoc
+except:
+    MyBuildDoc = None
+else:
+    class MyBuildDoc(BuildDoc):
+        """ Sub-class the standard sphinx documentation building system, to
+            add logics for API generation and matplotlib's plot directive.
         """
-        self.build_dir = os.path.join(*DOC_BUILD_DIR.split(os.sep)[:-1])
-        BuildDoc.finalize_options(self)
 
+        def run(self):
+            self.run_command('api_docs')
+            # We need to be in the doc directory for to plot_directive
+            # and API generation to work
+            """
+            os.chdir('doc')
+            try:
+                BuildDoc.run(self)
+            finally:
+                os.chdir('..')
+            """
+            # It put's the build in a doc/doc/_build directory with the
+            # above?!?!  I'm leaving the code above here but commented out
+            # in case I'm missing something?
+            BuildDoc.run(self)
+            self.zip_docs()
+
+        def zip_docs(self):
+            if not os.path.exists(DOC_BUILD_DIR):
+                raise OSError, 'Doc directory does not exist.'
+            target_file = os.path.join('doc', 'documentation.zip')
+            # ZIP_DEFLATED actually compresses the archive. However, there
+            # will be a RuntimeError if zlib is not installed, so we check
+            # for it. ZIP_STORED produces an uncompressed zip, but does not
+            # require zlib.
+            try:
+                zf = zipfile.ZipFile(target_file, 'w',
+                                            compression=zipfile.ZIP_DEFLATED)
+            except RuntimeError:
+                warnings.warn('zlib not installed, storing the docs '
+                                'without compression')
+                zf = zipfile.ZipFile(target_file, 'w',
+                                            compression=zipfile.ZIP_STORED)
+
+            for root, dirs, files in os.walk(DOC_BUILD_DIR):
+                relative = relative_path(root)
+                if not relative.startswith('.doctrees'):
+                    for f in files:
+                        zf.write(os.path.join(root, f),
+                                os.path.join(relative, 'html_docs', f))
+            zf.close()
+
+
+        def finalize_options(self):
+            """ Override the default for the documentation build
+                directory.
+            """
+            self.build_dir = os.path.join(*DOC_BUILD_DIR.split(os.sep)[:-1])
+            BuildDoc.finalize_options(self)
 
 ################################################################################
 # Distutils Command class to clean

examples/fmri_fsl.py

@@ -612,7 +612,7 @@ def subjectinfo(subject_id):
 
 l1pipeline = pe.Workflow(name= "level1")
 l1pipeline.base_dir = os.path.abspath('./fsl/workingdir')
-l1pipeline.config = dict(crashdump_dir=os.path.abspath('./fsl/crashdumps'))
+l1pipeline.config = {"execution": {"crashdump_dir":os.path.abspath('./fsl/crashdumps')}}
 
 l1pipeline.connect([(infosource, datasource, [('subject_id', 'subject_id')]),
                     (infosource, firstlevel, [(('subject_id', subjectinfo), 'modelfit.modelspec.subject_info')]),

examples/tessellation_tutorial.py

@@ -35,6 +35,7 @@
 Import the necessary modules and workflow from nipype.
 """
 import nipype.pipeline.engine as pe          # pypeline engine
+import nipype.interfaces.cmtk as cmtk
 import nipype.interfaces.io as nio           # Data i/o
 import os, os.path as op
 from nipype.workflows.smri.freesurfer import create_tessellation_flow
@@ -56,13 +57,27 @@
 ======
 
 Create the tessellation workflow and set inputs
+Here we will choose Gifti (gii) as the output format, because
+we want to able to view the surface in ConnectomeViewer.
+
+In you intend to view the meshes in gmsh or Blender, you should change
+the workflow creation to use stereolithographic (stl) format.
 """
 
-tessflow = create_tessellation_flow(name='tessflow')
+tessflow = create_tessellation_flow(name='tessflow', out_format='gii')
 tessflow.inputs.inputspec.subject_id = 'fsaverage'
 tessflow.inputs.inputspec.subjects_dir = subjects_dir
 tessflow.inputs.inputspec.lookup_file = lookup_file
 
+"""
+We also create a conditional node to package the surfaces for ConnectomeViewer.
+Simply set cff to "False" to ignore this step.
+"""
+
+cff = True
+if cff:
+    cff = pe.Node(interface=cmtk.CFFConverter(), name='cff')
+    cff.inputs.out_file = 'Meshes.cff'
 """
 Outputs
 =======
@@ -85,4 +100,14 @@
 tesspipe = pe.Workflow(name='tessellate_tutorial')
 tesspipe.base_dir = output_dir
 tesspipe.connect([(tessflow, datasink,[('outputspec.meshes', '@meshes.all')])])
+
+"""
+If the surfaces are to be packaged, this will connect the CFFConverter
+node to the tessellation and smoothing workflow, as well as to the datasink.
+"""
+
+if cff:
+    tesspipe.connect([(tessflow, cff,[('outputspec.meshes', 'gifti_surfaces')])])
+    tesspipe.connect([(cff, datasink,[('connectome_file', '@cff')])])
+
 tesspipe.run()

nipype/algorithms/icc.py

@@ -0,0 +1,129 @@
+from numpy import ones, kron, mean, eye, hstack, dot, tile
+from scipy.linalg import pinv
+from ..interfaces.base import BaseInterfaceInputSpec, TraitedSpec, \
+    BaseInterface, traits, File
+import nibabel as nb
+import numpy as np
+import os
+
+
+class ICCInputSpec(BaseInterfaceInputSpec):
+    subjects_sessions = traits.List(traits.List(File(exists=True)),
+                           desc="n subjects m sessions 3D stat files",
+                           mandatory=True)
+    mask = File(exists=True, mandatory=True)
+
+
+class ICCOutputSpec(TraitedSpec):
+    icc_map = File(exists=True)
+    session_var_map = File(exists=True, desc="variance between sessions")
+    subject_var_map = File(exists=True, desc="variance between subjects")
+
+
+class ICC(BaseInterface):
+    '''
+    Calculates Interclass Correlation Coefficient (3,1) as defined in
+    P. E. Shrout & Joseph L. Fleiss (1979). "Intraclass Correlations: Uses in
+    Assessing Rater Reliability". Psychological Bulletin 86 (2): 420-428. This
+    particular implementation is aimed at relaibility (test-retest) studies.
+    '''
+    input_spec = ICCInputSpec
+    output_spec = ICCOutputSpec
+
+    def _run_interface(self, runtime):
+        maskdata = nb.load(self.inputs.mask).get_data()
+        maskdata = np.logical_not(np.logical_or(maskdata == 0, np.isnan(maskdata)))
+
+        session_datas = [[nb.load(fname).get_data()[maskdata].reshape(-1, 1) for fname in sessions] for sessions in self.inputs.subjects_sessions]
+        list_of_sessions = [np.dstack(session_data) for session_data in session_datas]
+        all_data = np.hstack(list_of_sessions)
+        icc = np.zeros(session_datas[0][0].shape)
+        session_F = np.zeros(session_datas[0][0].shape)
+        session_var = np.zeros(session_datas[0][0].shape)
+        subject_var = np.zeros(session_datas[0][0].shape)
+
+        for x in range(icc.shape[0]):
+            Y = all_data[x, :, :]
+            icc[x], subject_var[x], session_var[x],  session_F[x], _, _ = ICC_rep_anova(Y)
+
+        nim = nb.load(self.inputs.subjects_sessions[0][0])
+        new_data = np.zeros(nim.get_shape())
+        new_data[maskdata] = icc.reshape(-1,)
+        new_img = nb.Nifti1Image(new_data, nim.get_affine(), nim.get_header())
+        nb.save(new_img, 'icc_map.nii')
+        
+        new_data = np.zeros(nim.get_shape())
+        new_data[maskdata] = session_var.reshape(-1,)
+        new_img = nb.Nifti1Image(new_data, nim.get_affine(), nim.get_header())
+        nb.save(new_img, 'session_var_map.nii')
+        
+        new_data = np.zeros(nim.get_shape())
+        new_data[maskdata] = subject_var.reshape(-1,)
+        new_img = nb.Nifti1Image(new_data, nim.get_affine(), nim.get_header())
+        nb.save(new_img, 'subject_var_map.nii')
+
+        return runtime
+
+    def _list_outputs(self):
+        outputs = self._outputs().get()
+        outputs['icc_map'] = os.path.abspath('icc_map.nii')
+        outputs['sessions_F_map'] = os.path.abspath('sessions_F_map.nii')
+        outputs['session_var_map'] = os.path.abspath('session_var_map.nii')
+        outputs['subject_var_map'] = os.path.abspath('subject_var_map.nii')
+        return outputs
+
+
+def ICC_rep_anova(Y):
+    '''
+    the data Y are entered as a 'table' ie subjects are in rows and repeated
+    measures in columns
+
+    --------------------------------------------------------------------------
+                       One Sample Repeated measure ANOVA
+                       Y = XB + E with X = [FaTor / Subjects]
+    --------------------------------------------------------------------------
+    '''
+
+    [nb_subjects, nb_conditions] = Y.shape
+    dfc = nb_conditions - 1
+    dfe = (nb_subjects - 1) * dfc
+    dfr = nb_subjects - 1
+
+    # Compute the repeated measure effect
+    # ------------------------------------
+
+    # Sum Square Total
+    mean_Y = mean(Y)
+    SST = ((Y - mean_Y) ** 2).sum()
+
+    # create the design matrix for the different levels
+    x = kron(eye(nb_conditions), ones((nb_subjects, 1)))  # sessions
+    x0 = tile(eye(nb_subjects), (nb_conditions, 1))  # subjects
+    X = hstack([x, x0])
+
+    # Sum Square Error
+    predicted_Y = dot(dot(dot(X, pinv(dot(X.T, X))), X.T), Y.flatten('F'))
+    residuals = Y.flatten('F') - predicted_Y
+    SSE = (residuals ** 2).sum()
+
+    residuals.shape = Y.shape
+
+    MSE = SSE / dfe
+
+    # Sum square session effect - between colums/sessions
+    SSC = ((mean(Y, 0) - mean_Y) ** 2).sum() * nb_subjects
+    MSC = SSC / dfc / nb_subjects
+
+    session_effect_F = MSC / MSE
+
+    # Sum Square subject effect - between rows/subjects
+    SSR = SST - SSC - SSE
+    MSR = SSR / dfr
+
+    # ICC(3,1) = (mean square subjeT - mean square error) / (mean square subjeT + (k-1)*-mean square error)
+    ICC = (MSR - MSE) / (MSR + dfc * MSE)
+    
+    e_var = MSE #variance of error
+    r_var = (MSR - MSE)/nb_conditions #variance between subjects
+
+    return ICC, r_var, e_var, session_effect_F, dfc, dfe