add inputnode.in_data to all test workflows #1538

Author: oesteban

circle.yml

@@ -19,13 +19,13 @@ dependencies:
 test:
   override:
     - docker run -i -v /etc/localtime:/etc/localtime:ro -v ~/scratch:/scratch -w /scratch --entrypoint="/usr/bin/run_builddocs.sh" nipype/testbench
-    - docker run -i -v /etc/localtime:/etc/localtime:ro -v ~/scratch:/scratch -w /scratch nipype/testbench test_spm Linear workflow3d workflow4d :
+    - docker run -i -v /etc/localtime:/etc/localtime:ro -v ~/scratch:/scratch -w /scratch nipype/testbench test_spm Linear /root/examples/ workflow3d workflow4d :
         timeout: 1600
-    - docker run -i -v /etc/localtime:/etc/localtime:ro -v ~/scratch:/scratch -w /scratch nipype/testbench fmri_fsl_feeds Linear l1pipeline
-    - docker run -i -v /etc/localtime:/etc/localtime:ro -v ~/scratch:/scratch -w /scratch nipype/testbench fmri_spm_dartel Linear level1 l2pipeline :
+    - docker run -i -v /etc/localtime:/etc/localtime:ro -v ~/scratch:/scratch -w /scratch nipype/testbench fmri_fsl_feeds Linear /root/examples/ l1pipeline
+    - docker run -i -v /etc/localtime:/etc/localtime:ro -v ~/scratch:/scratch -w /scratch nipype/testbench fmri_spm_dartel Linear /root/examples/ level1 l2pipeline :
         timeout: 1600
-    - docker run -i -v /etc/localtime:/etc/localtime:ro -v ~/scratch:/scratch -w /scratch nipype/testbench fmri_fsl_reuse Linear level1_workflow
-    - docker run -i -v /etc/localtime:/etc/localtime:ro -v ~/scratch:/scratch -w /scratch nipype/testbench fmri_spm_nested Linear level1 l2pipeline
+    - docker run -i -v /etc/localtime:/etc/localtime:ro -v ~/scratch:/scratch -w /scratch nipype/testbench fmri_fsl_reuse Linear /root/examples/ level1_workflow
+    - docker run -i -v /etc/localtime:/etc/localtime:ro -v ~/scratch:/scratch -w /scratch nipype/testbench fmri_spm_nested Linear /root/examples/ level1 l2pipeline
     - docker run -i -v /etc/localtime:/etc/localtime:ro -v ~/scratch:/scratch -w /scratch --entrypoint="/usr/bin/run_nosetests.sh" nipype/testbench :
         timeout: 2600
   post:

docker/test-image-nipype/Dockerfile

@@ -53,18 +53,21 @@ RUN conda config --add channels conda-forge
 RUN conda create -y -n nipypetests-2.7 lockfile nipype && \
     echo '#!/bin/bash' >> /etc/profile.d/nipype.sh && \
     echo '#!/bin/bash' >> /etc/bashrc && \
-    echo 'source activate nipypetests-2.7' >> /etc/profile.d/nipype.sh && \
-    echo 'source activate nipypetests-2.7' >> /etc/bashrc
+    echo 'source activate nipypetests-2.7' >> /etc/profile.d/nipype.sh
 
 # Create conda environment
 RUN conda create -y -n nipypetests-3.4 lockfile nipype python=3.4 
 
 # Create conda environment
 RUN conda create -y -n nipypetests-3.5 lockfile nipype python=3.5
 
-# Install pip
-# RUN source activate nipypetests-2.7 && \
-#     pip install --upgrade pip && \
+# Install dipy
+RUN source activate nipypetests-2.7 && \
+    pip install dipy && \
+    source activate nipypetests-3.4 && \
+    pip install dipy && \
+    source activate nipypetests-3.5 && \
+    pip install dipy
 
 RUN echo "source /etc/profile.d/nipype.sh" >> /etc/bash.bashrc
 CMD ["/bin/bash"]

examples/fmri_fsl_feeds.py

@@ -16,12 +16,12 @@
 from __future__ import division
 from builtins import range
 
-import os                                    # system functions
-
-import nipype.interfaces.io as nio           # Data i/o
-import nipype.interfaces.fsl as fsl          # fsl
-import nipype.pipeline.engine as pe          # pypeline engine
-import nipype.algorithms.modelgen as model   # model generation
+import os                                         # system functions
+from nipype.interfaces import io as nio           # Data i/o
+from nipype.interfaces import utility as niu      # Utilities
+from nipype.interfaces import fsl                 # fsl
+from nipype.pipeline import engine as pe          # pypeline engine
+from nipype.algorithms import modelgen as model   # model generation
 from nipype.workflows.fmri.fsl import (create_featreg_preproc,
                                        create_modelfit_workflow,
                                        create_reg_workflow)
@@ -48,7 +48,9 @@
 """
 
 # Specify the location of the FEEDS data. You can find it at http://www.fmrib.ox.ac.uk/fsl/feeds/doc/index.html
-feeds_data_dir = os.path.abspath('feeds/data')
+
+
+inputnode = pe.Node(niu.IdentityInterface(fields=['in_data']), name='inputnode')
 # Specify the subject directories
 # Map field names to individual subject runs.
 info = dict(func=[['fmri']],
@@ -63,7 +65,6 @@
 
 datasource = pe.Node(interface=nio.DataGrabber(outfields=['func', 'struct']),
                      name='datasource')
-datasource.inputs.base_directory = feeds_data_dir
 datasource.inputs.template = '%s.nii.gz'
 datasource.inputs.template_args = info
 datasource.inputs.sort_filelist = True
@@ -110,6 +111,7 @@
 l1pipeline.base_dir = os.path.abspath('./fsl_feeds/workingdir')
 l1pipeline.config = {"execution": {"crashdump_dir": os.path.abspath('./fsl_feeds/crashdumps')}}
 
+l1pipeline.connect(inputnode, 'in_data', datasource, 'base_directory')
 l1pipeline.connect(datasource, 'func', preproc, 'inputspec.func')
 l1pipeline.connect(preproc, 'outputspec.highpassed_files', modelspec, 'functional_runs')
 l1pipeline.connect(preproc, 'outputspec.motion_parameters', modelspec, 'realignment_parameters')
@@ -142,4 +144,5 @@
 """
 
 if __name__ == '__main__':
+    l1pipeline.inputs.inputnode.in_data = os.path.abspath('feeds/data')
     l1pipeline.run()

examples/fmri_fsl_reuse.py

@@ -20,10 +20,9 @@
 from builtins import range
 
 import os                                    # system functions
-
 import nipype.interfaces.io as nio           # Data i/o
 import nipype.interfaces.fsl as fsl          # fsl
-import nipype.interfaces.utility as util     # utility
+from nipype.interfaces import utility as niu # Utilities
 import nipype.pipeline.engine as pe          # pypeline engine
 import nipype.algorithms.modelgen as model   # model generation
 import nipype.algorithms.rapidart as ra      # artifact detection
@@ -56,7 +55,7 @@
 and modelfitting workflows.
 """
 
-art = pe.MapNode(interface=ra.ArtifactDetect(use_differences=[True, False],
+art = pe.MapNode(ra.ArtifactDetect(use_differences=[True, False],
                                              use_norm=True,
                                              norm_threshold=1,
                                              zintensity_threshold=3,
@@ -65,7 +64,7 @@
                  iterfield=['realigned_files', 'realignment_parameters', 'mask_file'],
                  name="art")
 
-modelspec = pe.Node(interface=model.SpecifyModel(), name="modelspec")
+modelspec = pe.Node(model.SpecifyModel(), name="modelspec")
 
 level1_workflow.connect([(preproc, art, [('outputspec.motion_parameters',
                                           'realignment_parameters'),
@@ -139,15 +138,15 @@ def num_copes(files):
 
 """
 
-# Specify the location of the data.
-data_dir = os.path.abspath('data')
+inputnode = pe.Node(niu.IdentityInterface(fields=['in_data']), name='inputnode')
+
 # Specify the subject directories
 subject_list = ['s1']  # , 's3']
 # Map field names to individual subject runs.
 info = dict(func=[['subject_id', ['f3', 'f5', 'f7', 'f10']]],
             struct=[['subject_id', 'struct']])
 
-infosource = pe.Node(interface=util.IdentityInterface(fields=['subject_id']),
+infosource = pe.Node(niu.IdentityInterface(fields=['subject_id']),
                      name="infosource")
 
 """Here we set up iteration over all the subjects. The following line
@@ -169,10 +168,9 @@ def num_copes(files):
 functionality.
 """
 
-datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
+datasource = pe.Node(nio.DataGrabber(infields=['subject_id'],
                                                outfields=['func', 'struct']),
                      name='datasource')
-datasource.inputs.base_directory = data_dir
 datasource.inputs.template = '%s/%s.nii'
 datasource.inputs.template_args = info
 datasource.inputs.sort_filelist = True
@@ -182,12 +180,12 @@ def num_copes(files):
 iterables on this node to perform two different extents of smoothing.
 """
 
-inputnode = level1_workflow.get_node('featpreproc.inputspec')
-inputnode.iterables = ('fwhm', [5., 10.])
+featinput = level1_workflow.get_node('featpreproc.inputspec')
+featinput.iterables = ('fwhm', [5., 10.])
 
 hpcutoff = 120.
 TR = 3.
-inputnode.inputs.highpass = hpcutoff / (2. * TR)
+featinput.inputs.highpass = hpcutoff / (2. * TR)
 
 """
 Setup a function that returns subject-specific information about the
@@ -239,7 +237,8 @@ def subjectinfo(subject_id):
 level1_workflow.base_dir = os.path.abspath('./fsl/workingdir')
 level1_workflow.config['execution'] = dict(crashdump_dir=os.path.abspath('./fsl/crashdumps'))
 
-level1_workflow.connect([(infosource, datasource, [('subject_id', 'subject_id')]),
+level1_workflow.connect([(inputnode, datasource, [('in_data', 'base_directory')]),
+                         (infosource, datasource, [('subject_id', 'subject_id')]),
                          (infosource, modelspec, [(('subject_id', subjectinfo),
                                                    'subject_info')]),
                          (datasource, preproc, [('func', 'inputspec.func')]),

examples/fmri_spm_dartel.py

@@ -22,7 +22,7 @@
 import nipype.interfaces.spm as spm          # spm
 import nipype.workflows.fmri.spm as spm_wf          # spm
 import nipype.interfaces.fsl as fsl          # fsl
-import nipype.interfaces.utility as util     # utility
+from nipype.interfaces import utility as niu # Utilities
 import nipype.pipeline.engine as pe          # pypeline engine
 import nipype.algorithms.rapidart as ra      # artifact detection
 import nipype.algorithms.modelgen as model   # model specification
@@ -68,15 +68,15 @@
 and register all images to the mean image.
 """
 
-realign = pe.Node(interface=spm.Realign(), name="realign")
+realign = pe.Node(spm.Realign(), name="realign")
 realign.inputs.register_to_mean = True
 
 """Use :class:`nipype.algorithms.rapidart` to determine which of the
 images in the functional series are outliers based on deviations in
 intensity or movement.
 """
 
-art = pe.Node(interface=ra.ArtifactDetect(), name="art")
+art = pe.Node(ra.ArtifactDetect(), name="art")
 art.inputs.use_differences = [True, False]
 art.inputs.use_norm = True
 art.inputs.norm_threshold = 1
@@ -88,14 +88,14 @@
 :class:`nipype.interfaces.fsl.BET`.
 """
 
-skullstrip = pe.Node(interface=fsl.BET(), name="skullstrip")
+skullstrip = pe.Node(fsl.BET(), name="skullstrip")
 skullstrip.inputs.mask = True
 
 """Use :class:`nipype.interfaces.spm.Coregister` to perform a rigid
 body registration of the functional data to the structural data.
 """
 
-coregister = pe.Node(interface=spm.Coregister(), name="coregister")
+coregister = pe.Node(spm.Coregister(), name="coregister")
 coregister.inputs.jobtype = 'estimate'
 
 
@@ -134,40 +134,40 @@
 :class:`nipype.interfaces.spm.SpecifyModel`.
 """
 
-modelspec = pe.Node(interface=model.SpecifySPMModel(), name="modelspec")
+modelspec = pe.Node(model.SpecifySPMModel(), name="modelspec")
 modelspec.inputs.concatenate_runs = True
 
 """Generate a first level SPM.mat file for analysis
 :class:`nipype.interfaces.spm.Level1Design`.
 """
 
-level1design = pe.Node(interface=spm.Level1Design(), name="level1design")
+level1design = pe.Node(spm.Level1Design(), name="level1design")
 level1design.inputs.bases = {'hrf': {'derivs': [0, 0]}}
 
 """Use :class:`nipype.interfaces.spm.EstimateModel` to determine the
 parameters of the model.
 """
 
-level1estimate = pe.Node(interface=spm.EstimateModel(), name="level1estimate")
+level1estimate = pe.Node(spm.EstimateModel(), name="level1estimate")
 level1estimate.inputs.estimation_method = {'Classical': 1}
 
 """Use :class:`nipype.interfaces.spm.EstimateContrast` to estimate the
 first level contrasts specified in a few steps above.
 """
 
-contrastestimate = pe.Node(interface=spm.EstimateContrast(), name="contrastestimate")
+contrastestimate = pe.Node(spm.EstimateContrast(), name="contrastestimate")
 
 """Use :class: `nipype.interfaces.utility.Select` to select each contrast for
 reporting.
 """
 
-selectcontrast = pe.Node(interface=util.Select(), name="selectcontrast")
+selectcontrast = pe.Node(niu.Select(), name="selectcontrast")
 
 """Use :class:`nipype.interfaces.fsl.Overlay` to combine the statistical output of
 the contrast estimate and a background image into one volume.
 """
 
-overlaystats = pe.Node(interface=fsl.Overlay(), name="overlaystats")
+overlaystats = pe.Node(fsl.Overlay(), name="overlaystats")
 overlaystats.inputs.stat_thresh = (3, 10)
 overlaystats.inputs.show_negative_stats = True
 overlaystats.inputs.auto_thresh_bg = True
@@ -176,7 +176,7 @@
 statistical volumes for a report of the first-level results.
 """
 
-slicestats = pe.Node(interface=fsl.Slicer(), name="slicestats")
+slicestats = pe.Node(fsl.Slicer(), name="slicestats")
 slicestats.inputs.all_axial = True
 slicestats.inputs.image_width = 750
 
@@ -232,14 +232,14 @@
 """
 
 # Specify the location of the data.
-data_dir = os.path.abspath('data')
+# data_dir = os.path.abspath('data')
 # Specify the subject directories
 subject_list = ['s1', 's3']
 # Map field names to individual subject runs.
 info = dict(func=[['subject_id', ['f3', 'f5', 'f7', 'f10']]],
             struct=[['subject_id', 'struct']])
 
-infosource = pe.Node(interface=util.IdentityInterface(fields=['subject_id']), name="infosource")
+infosource = pe.Node(niu.IdentityInterface(fields=['subject_id']), name="infosource")
 
 """Here we set up iteration over all the subjects. The following line
 is a particular example of the flexibility of the system.  The
@@ -260,22 +260,21 @@
 functionality.
 """
 
-datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
+inputnode = pe.Node(niu.IdentityInterface(fields=['in_data']), name='inputnode')
+datasource = pe.Node(nio.DataGrabber(infields=['subject_id'],
                                                outfields=['func', 'struct']),
                      name='datasource')
-datasource.inputs.base_directory = data_dir
 datasource.inputs.template = '%s/%s.nii'
 datasource.inputs.template_args = info
 datasource.inputs.sort_filelist = True
 
 """We need to create a separate workflow to make the DARTEL template
 """
 
-datasource_dartel = pe.MapNode(interface=nio.DataGrabber(infields=['subject_id'],
+datasource_dartel = pe.MapNode(nio.DataGrabber(infields=['subject_id'],
                                                          outfields=['struct']),
                                name='datasource_dartel',
                                iterfield=['subject_id'])
-datasource_dartel.inputs.base_directory = data_dir
 datasource_dartel.inputs.template = '%s/%s.nii'
 datasource_dartel.inputs.template_args = dict(struct=[['subject_id', 'struct']])
 datasource_dartel.inputs.sort_filelist = True
@@ -285,7 +284,7 @@
 This way we will be able to pick the right field flows later.
 """
 
-rename_dartel = pe.MapNode(util.Rename(format_string="subject_id_%(subject_id)s_struct"),
+rename_dartel = pe.MapNode(niu.Rename(format_string="subject_id_%(subject_id)s_struct"),
                            iterfield=['in_file', 'subject_id'],
                            name='rename_dartel')
 rename_dartel.inputs.subject_id = subject_list
@@ -307,7 +306,7 @@ def pickFieldFlow(dartel_flow_fields, subject_id):
 
     raise Exception
 
-pick_flow = pe.Node(util.Function(input_names=['dartel_flow_fields',
+pick_flow = pe.Node(niu.Function(input_names=['dartel_flow_fields',
                                                'subject_id'],
                                   output_names=['dartel_flow_field'],
                                   function=pickFieldFlow),
@@ -399,7 +398,9 @@ def subjectinfo(subject_id):
 level1 = pe.Workflow(name="level1")
 level1.base_dir = os.path.abspath('spm_dartel_tutorial/workingdir')
 
-level1.connect([(datasource_dartel, rename_dartel, [('struct', 'in_file')]),
+level1.connect([(inputnode, datasource, [('in_data', 'base_directory')]),
+                (inputnode, datasource_dartel, [('in_data', 'base_directory')]),
+                (datasource_dartel, rename_dartel, [('struct', 'in_file')]),
                 (rename_dartel, dartel_workflow, [('out_file', 'inputspec.structural_files')]),
 
                 (infosource, datasource, [('subject_id', 'subject_id')]),
@@ -437,9 +438,9 @@ def subjectinfo(subject_id):
 the mean image would be copied to that directory.
 """
 
-datasink = pe.Node(interface=nio.DataSink(), name="datasink")
+datasink = pe.Node(nio.DataSink(), name="datasink")
 datasink.inputs.base_directory = os.path.abspath('spm_dartel_tutorial/l1output')
-report = pe.Node(interface=nio.DataSink(), name='report')
+report = pe.Node(nio.DataSink(), name='report')
 report.inputs.base_directory = os.path.abspath('spm_dartel_tutorial/report')
 report.inputs.parameterization = False
 
@@ -501,10 +502,10 @@ def getstripdir(subject_id):
 """
 
 # setup a 1-sample t-test node
-onesamplettestdes = pe.Node(interface=spm.OneSampleTTestDesign(), name="onesampttestdes")
-l2estimate = pe.Node(interface=spm.EstimateModel(), name="level2estimate")
+onesamplettestdes = pe.Node(spm.OneSampleTTestDesign(), name="onesampttestdes")
+l2estimate = pe.Node(spm.EstimateModel(), name="level2estimate")
 l2estimate.inputs.estimation_method = {'Classical': 1}
-l2conestimate = pe.Node(interface=spm.EstimateContrast(), name="level2conestimate")
+l2conestimate = pe.Node(spm.EstimateContrast(), name="level2conestimate")
 cont1 = ('Group', 'T', ['mean'], [1])
 l2conestimate.inputs.contrasts = [cont1]
 l2conestimate.inputs.group_contrast = True