Merge pull request #17 from satra/enh/package

WIP - RF: turning bips into a package with a single command line executable

Author: satra

LICENSE

@@ -0,0 +1,13 @@
+Copyright [2012] [INCF, MIT]
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

README

@@ -0,0 +1,6 @@
+Optimized Nipype_ pipelines for brain imaging
+=============================================
+
+`Detailed description of the pipelines <https://docs.google.com/document/d/1du0c1ltvNUvH5F3paIi1V3TUWV_-SarHsrId_eEII6I/edit>`_
+
+.. _Nipype: nipy.org/nipype

README.rst

@@ -0,0 +1,32 @@
+#!/usr/bin/env python
+"""Meta routine to configure and run various brain imaging workflows
+"""
+
+import argparse
+
+
+# modality
+# subtype
+# workflow
+# config
+
+"""
+Examples
+bips -m fmri -t task -w preproc -c config.py
+bips -m fmri -t task -w qa -c config.py
+bips -m fmri -t resting -w qa -c config.py
+
+bips -m dmri -w preproc -c config.py
+bips -m dmri -w track -c config.py
+
+bips -m smri -w recon -c config.py
+bips -m smri -t volume -w normalize -c config.py
+bips -m smri -t volume -w template -c config.py
+bips -m smri -t surface -w template -c config.py
+
+alternatively
+bips -i -u uuid # display info about workflow
+bips -u uuid -c config.json # create config for running workflow
+bips -r config.json # run workflow
+
+"""

bin/bips

@@ -0,0 +1,6 @@
+# This is an ini file that may contain information about the code state
+[commit hash]
+# The line below may contain a valid hash if it has been substituted during 'git archive'
+archive_subst_hash=$Format:%h$
+# This line may be modified by the install process
+install_hash=

bips/COMMIT_INFO.txt

@@ -0,0 +1,58 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+import os
+
+from info import (LONG_DESCRIPTION as __doc__,
+                  URL as __url__,
+                  STATUS as __status__,
+                  __version__)
+
+from numpy.testing import Tester
+
+
+class BipsTester(Tester):
+    def test(self, label='fast', verbose=1, extra_argv=None,
+             doctests=False, coverage=False):
+        # setuptools does a chmod +x on ALL python modules when it
+        # installs.  By default, as a security measure, nose refuses to
+        # import executable files.  To forse nose to execute our tests, we
+        # must supply the '--exe' flag.  List thread on this:
+        # http://www.mail-archive.com/[email protected]/msg05009.html
+        if not extra_argv:
+            extra_argv = ['--exe']
+        else:
+            extra_argv.append('--exe')
+        super(BipsTester, self).test(label, verbose, extra_argv,
+                                       doctests, coverage)
+    # Grab the docstring from numpy
+    #test.__doc__ = Tester.test.__doc__
+
+test = BipsTester().test
+bench = BipsTester().bench
+
+
+def _test_local_install():
+    """ Warn the user that running with nipy being
+        imported locally is a bad idea.
+    """
+    if os.getcwd() == os.sep.join(
+                            os.path.abspath(__file__).split(os.sep)[:-2]):
+        import warnings
+        warnings.warn('Running the tests from the install directory may '
+                     'trigger some failures')
+
+_test_local_install()
+
+# Set up package information function
+from pkg_info import get_pkg_info as _get_pkg_info
+get_info = lambda: _get_pkg_info(os.path.dirname(__file__))
+
+# Cleanup namespace
+del _test_local_install
+
+# If this file is exec after being imported, the following lines will
+# fail
+try:
+    del Tester
+except:
+    pass

bips/__init__.py

@@ -7,7 +7,7 @@
 
 
 dataDir = '/mindhive/gablab/sad/PY_STUDY_DIR/Block/data'
-workingdir = '/mindhive/gablab/sad/PY_STUDY_DIR/Block/scripts/l1output/workflows/dti'
+workingdir = '/mindhive/gablab/sad/PY_STUDY_DIR/Block/scripts/l1output/workflows/dmri'
 subjects = ['SAD_018']
 
 skeleton_thresh = 0.2
@@ -22,7 +22,7 @@ def get_datasource():
                                                               'bval']),
                          name='datasource')
     datasource.inputs.base_directory = os.path.abspath(dataDir)
-    datasource.inputs.template = os.path.join(dataDir,'%s','dti','%s')
+    datasource.inputs.template = os.path.join(dataDir,'%s','dmri','%s')
     datasource.inputs.template_args = dict(dwi=[['subject_id', 'diffusionseries.nii.gz']],
                                            bvec=[['subject_id', 'bvecs']],
                                            bval=[['subject_id', 'bvals']])

utils/reportsink/__init__.py bips/dmri/__init__.py

@@ -764,6 +764,6 @@ def create_first(name='modelfit'):
                      outputspec, 'design_cov')
     return modelfit
 
-#def normalize(name = "normalize"):
+#def smri(name = "smri"):
 
 

dti/config.py bips/dmri/config.py

@@ -1,20 +1,18 @@
 import argparse
 import os
 import sys
-sys.path.insert(0, '../../normalize')
-from base import get_full_norm_workflow
+
 import nipype.pipeline.engine as pe
 import nipype.interfaces.utility as util
 from nipype.interfaces.io import FreeSurferSource
 import nipype.interfaces.io as nio
 
+from ...smri.base import get_full_norm_workflow
 
 pickfirst = lambda x: x[0]
 
 
 def func_datagrabber(name="resting_output_datagrabber"):
-    import nipype.pipeline.engine as pe
-    import nipype.interfaces.io as nio
     # create a node to obtain the functional images
     datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id',
                                                              'fwhm'],
@@ -35,7 +33,7 @@ def func_datagrabber(name="resting_output_datagrabber"):
     return datasource
 
 
-def normalize_workflow(name="normalize"):
+def normalize_workflow(name="smri"):
     norm = get_full_norm_workflow()
     datagrab = func_datagrabber()
 
@@ -69,14 +67,14 @@ def normalize_workflow(name="normalize"):
 
     outputspec = norm.get_node('outputspec')
     norm.connect(infosource, 'subject_id', sinkd, 'container')
-    norm.connect(outputspec, 'warped_image', sinkd, 'normalize.warped_image')
-    norm.connect(outputspec, 'warp_field', sinkd, 'normalize.warped_field')
+    norm.connect(outputspec, 'warped_image', sinkd, 'smri.warped_image')
+    norm.connect(outputspec, 'warp_field', sinkd, 'smri.warped_field')
     norm.connect(outputspec, 'affine_transformation',
-                 sinkd, 'normalize.affine_transformation')
-    norm.connect(outputspec, 'inverse_warp', sinkd, 'normalize.inverse_warp')
+                 sinkd, 'smri.affine_transformation')
+    norm.connect(outputspec, 'inverse_warp', sinkd, 'smri.inverse_warp')
     norm.connect(outputspec, 'unwarped_brain',
-                 sinkd, 'normalize.unwarped_brain')
-    norm.connect(outputspec, 'warped_brain', sinkd, 'normalize.warped_brain')
+                 sinkd, 'smri.unwarped_brain')
+    norm.connect(outputspec, 'warped_brain', sinkd, 'smri.warped_brain')
 
     return norm
 

dti/dti_preproc.py bips/dmri/dti_preproc.py

@@ -1,14 +1,13 @@
 #Imports ---------------------------------------------------------------------
+import argparse
 import sys
-sys.path.append('..')
 
 import nipype.interfaces.utility as util    # utility
 import nipype.pipeline.engine as pe         # pypeline engine
 import os
 
-from base import create_rest_prep
-from utils import get_datasink, get_substitutions, get_regexp_substitutions
-import argparse
+from ..base import create_rest_prep
+from ..utils import get_datasink, get_substitutions, get_regexp_substitutions
 
 # Preprocessing
 # -------------------------------------------------------------

bips/fmri/__init__.py

@@ -0,0 +1,18 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+def configuration(parent_package='',top_path=None):
+    from numpy.distutils.misc_util import Configuration
+    config = Configuration('fmri', parent_package, top_path)
+
+    # List all packages to be loaded here
+    config.add_subpackage('misc')
+    config.add_subpackage('qa')
+    config.add_subpackage('resting')
+    config.add_subpackage('task')
+
+    # List all data directories to be loaded here
+    return config
+
+if __name__ == '__main__':
+    from numpy.distutils.core import setup
+    setup(**configuration(top_path='').todict())

fmri/base.py bips/fmri/base.py

@@ -1,100 +1,89 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
-"""
-A pipeline example that intergrates spm, fsl freesurfer modules to perform a
-comparative volume and surface based first level analysis.
-
-This tutorial uses the nipype-tutorial data and hence should be run from the
-directory containing tutorial data
-
-    python freesurfer_tutorial.py
-
+"""Perform fixed effects analysis on runs processed by preproc.py
 """
 
 import argparse
 import os                                    # system functions
 import sys
-#from nipype.utils.config import config
-#config.enable_debug_mode()
 
-#from config import (subjects, root_dir, getcontrasts, auto_fixedfx, fwhm, run_on_grid, overlaythresh, subjectinfo, test_mode)
-from copy import deepcopy
-from glob import glob
 from nipype.workflows.fmri.fsl.estimate import create_fixed_effects_flow
-
-import numpy as np
 import nipype.interfaces.io as nio           # i/o routines
 import nipype.interfaces.fsl as fsl          # fsl
 import nipype.interfaces.utility as util     # utility
 import nipype.pipeline.engine as pe          # pypeline engine
-import argparse
+
 fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
 
 
-def getinfo(subject_id,getcontrasts,subjectinfo):
+def getinfo(subject_id, getcontrasts, subjectinfo):
     numruns = len(subjectinfo(subject_id))
-    print numruns
+    print numruns  # dbg
     numcon = len(getcontrasts(subject_id))
-    info = dict(copes=[['subject_id', 'fwhm',range(1,numcon+1)]],
-                varcopes=[['subject_id', 'fwhm', range(1,numcon+1)]],
+    info = dict(copes=[['subject_id', 'fwhm', range(1, numcon + 1)]],
+                varcopes=[['subject_id', 'fwhm', range(1, numcon + 1)]],
                 dof_files=[['subject_id', 'fwhm']],
-                mask_file=[['subject_id']]) 
+                mask_file=[['subject_id']])
     return info
 
+
 def num_copes(files):
     if type(files[0]) is list:
         return len(files[0])
     else:
         return len(files)
 
-def getsubs(subject_id,getcontrasts):
-    
-    subs = [('_subject_id_%s/'%subject_id,''),
+
+def getsubs(subject_id, getcontrasts):
+    subs = [('_subject_id_%s/' % subject_id, ''),
             ('_runs', '/runs'),
             ('_fwhm', 'fwhm')]
     cons = getcontrasts(subject_id)
     for i, con in enumerate(cons):
-        subs.append(('_flameo%d/cope1'%i, 'cope_%s'%(con[0])))
-        subs.append(('_flameo%d/varcope1'%(i), 'varcope_%s'%(con[0])))
-        subs.append(('_flameo%d/tstat1'%(i), 'tstat_%s'%(con[0])))
-        subs.append(('_flameo%d/zstat1'%(i), 'zstat_%s'%(con[0])))
-        subs.append(('_flameo%d/res4d'%(i), 'res4d_%s'%(con[0])))
-        subs.append(('_ztop%d/zstat1_pval'%(i), 'pval_%s'%(con[0])))
-        subs.append(('_slicestats%d/zstat1_overlay.png'%(i),'zstat_overlay%d_%s.png'%(i,con[0])))
+        subs.append(('_flameo%d/cope1' % i, 'cope_%s' % con[0]))
+        subs.append(('_flameo%d/varcope1' % i, 'varcope_%s' % con[0]))
+        subs.append(('_flameo%d/tstat1' % i, 'tstat_%s' % con[0]))
+        subs.append(('_flameo%d/zstat1' % i, 'zstat_%s' % con[0]))
+        subs.append(('_flameo%d/res4d' % i, 'res4d_%s' % con[0]))
+        subs.append(('_ztop%d/zstat1_pval' % i, 'pval_%s' % con[0]))
+        subs.append(('_slicestats%d/zstat1_overlay.png' % i,
+                     'zstat_overlay%d_%s.png' % (i, con[0])))
     return subs
 
+
 def create_overlay_workflow(name='overlay'):
     # Setup overlay workflow
 
     overlay = pe.Workflow(name='overlay')
-    
-    inputspec = pe.Node(interface=util.IdentityInterface(fields=['subject_id',
-                                                                 'fwhm',
-                                                                 'stat_image']),
+
+    inputspec = pe.Node(util.IdentityInterface(fields=['subject_id',
+                                                       'fwhm',
+                                                       'stat_image']),
                         name='inputspec')
-    
+
     datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                    outfields=['meanfunc']),
                          name='datasource')
-    
-    datasource.inputs.base_directory = os.path.join(c.sink_dir,'analyses','func')
+
+    datasource.inputs.base_directory = os.path.join(c.sink_dir, 'analyses',
+                                                'func')
     datasource.inputs.template = '*'
     datasource.inputs.sort_filelist = True
     datasource.inputs.field_template = dict(meanfunc='%s/preproc/meanfunc/*.nii.gz')
-    datasource.inputs.template_args = dict(meanfunc = [['subject_id']])
+    datasource.inputs.template_args = dict(meanfunc=[['subject_id']])
 
-    overlaystats = pe.MapNode(interface=fsl.Overlay(), 
+    overlaystats = pe.MapNode(interface=fsl.Overlay(),
                               name="overlaystats",
                               iterfield=['stat_image'])
-    
-    slicestats = pe.MapNode(interface=fsl.Slicer(), 
+
+    slicestats = pe.MapNode(interface=fsl.Slicer(),
                             name="slicestats",
                             iterfield=['in_file'])
-    
+
     slicestats.inputs.all_axial = True
     slicestats.inputs.image_width = 512
-    overlaystats.inputs.show_negative_stats=True
-    overlaystats.inputs.auto_thresh_bg=True
+    overlaystats.inputs.show_negative_stats = True
+    overlaystats.inputs.auto_thresh_bg = True
     overlaystats.inputs.stat_thresh = c.overlaythresh
 
     overlay.connect(inputspec, 'subject_id', datasource, 'subject_id')