before auto workflow

src/Nipype/Notebooks/nipype_spm_preproc.ipynb

@@ -223,8 +223,6 @@
    ],
    "source": [
     "segment =  Node(NewSegment(), name=\"newsegment\")\n",
-    "# seg.inputs.channel_files = '/data/preproc/coregister/sub-01_T1w.nii'\n",
-    "#seg.inputs.channel_files = '/data/MoAEpilot_raw/sub-01/anat/sub-01_T1w.nii'\n",
     "segment.inputs.affine_regularization = 'mni'\n",
     "segment.inputs.channel_info = (0.001, 60, (False, True)) #save bias corrected map\n",
     "tissue1 = ((tpm_path, 1), 1, (True, False), (False, False))\n",
@@ -284,22 +282,6 @@
     "art.inputs.plot_type='png'"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_vox_dims(volume):\n",
-    "    import nibabel as nb\n",
-    "    if isinstance(volume, list):\n",
-    "        volume = volume[0]\n",
-    "    nii = nb.load(volume)\n",
-    "    hdr = nii.header\n",
-    "    voxdims = hdr.get_zooms()\n",
-    "    return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 18,

src/Nipype/nipype_spm_preproc.py

@@ -3,10 +3,6 @@
 
 # ## Preprocessing
 
-# In[1]:
-
-
-from nilearn import plotting
 import os
 import json
 from nipype.interfaces import fsl 
@@ -20,35 +16,26 @@
 
 # necessary to let nipype know about matlab path
 
-
-
 spm.SPMCommand.set_mlab_paths(paths=os.path.abspath(os.path.join(os.environ['HOME'], 'Documents/MATLAB/spm12/')), matlab_cmd='/soft/matlab_hd/R2020b/bin/glnxa64/MATLAB -nodesktop -nosplash')
 
 
-
 mlab.MatlabCommand.set_default_matlab_cmd("/soft/matlab_hd/R2020b/bin/glnxa64/MATLAB  -nodesktop -nosplash")
 mlab.MatlabCommand.set_default_paths(os.path.abspath(os.path.join(os.environ['HOME'], 'Documents/MATLAB/spm12/')))
 
 
-
-
 # spm.SPMCommand().version
 
 
 
-
 fsl.FSLCommand.set_default_output_type('NIFTI')
 
 
 
-
-
 base_dir = os.path.join(os.environ['HOME'], 'spmbasics/data/')
 
 
 
 
-
 experiment_dir = os.path.join(base_dir, 'output')
 output_dir = 'nipype'
 working_dir = 'workingdir'
@@ -58,7 +45,7 @@
 
 task_id = ['auditory']
 
-
+# task_id = ['auditory']
 # TR of functional images
 with open(os.path.join(base_dir, 'MoAEpilot/task-auditory_bold.json'), 'rt') as fp:
     task_info = json.load(fp)
@@ -108,6 +95,7 @@
 
 
 
+
 slicetiming = Node(interface=SliceTiming(), name = 'slicetiming')
 slicetiming.inputs.num_slices = 64
 slicetiming.inputs.time_repetition = 7.
@@ -117,6 +105,9 @@
 slicetiming.inputs.out_prefix = 'a'
 
 
+# https://github.com/nipy/nipype/issues/2697 check this
+
+
 coregister = Node(Coregister(), name="coregister")
 coregister.inputs.jobtype = 'estimate'
 coregister.inputs.cost_function = 'nmi'
@@ -130,7 +121,6 @@
 tpm_path = os.path.abspath(os.path.join(os.environ['HOME'], 'Documents/MATLAB/spm12/tpm/', 'TPM.nii'))
 
 
-
 segment =  Node(NewSegment(), name="newsegment")
 segment.inputs.affine_regularization = 'mni'
 segment.inputs.channel_info = (0.001, 60, (False, True)) #save bias corrected map
@@ -147,6 +137,7 @@
 
 
 
+
 normalize = Node(Normalize12(), name="normalize") #old normalize now
 normalize.inputs.jobtype = 'write'
 normalize.inputs.write_bounding_box =  [[-78, -112, -70], [78, 76, 85]]
@@ -176,15 +167,6 @@
 art.inputs.plot_type='png'
 
 
-def get_vox_dims(volume):
-    import nibabel as nb
-    if isinstance(volume, list):
-        volume = volume[0]
-    nii = nb.load(volume)
-    hdr = nii.header
-    voxdims = hdr.get_zooms()
-    return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
-
 
 block_preprocess = Workflow(name='nipype_block_preprocess')
 block_preprocess.base_dir = os.path.join(experiment_dir, working_dir)
@@ -234,5 +216,11 @@ def get_vox_dims(volume):
 
 
 
+
 block_preprocess.run('MultiProc', plugin_args={'n_procs': 4})
 
+
+
+
+
+