Merge pull request #51 from CoBrALab/updates

Updates

Author: Gab-D-G

Dockerfile

@@ -130,18 +130,18 @@ ENV export RABIES_VERSION=0.1.2 \
 
 RUN export RABIES_VERSION=0.1.2 && \
   export RABIES=$HOME/RABIES-${RABIES_VERSION} && \
-  #mkdir -p temp && \
-  #curl -L --retry 5 -o temp/RABIES.tar.gz https://github.com/CoBrALab/RABIES/archive/${RABIES_VERSION}.tar.gz && \
-  #cd temp && \
-  #tar zxf RABIES.tar.gz && \
-  #cd .. && \
-  #conda env create -f temp/RABIES-${RABIES_VERSION}/rabies_environment.yml && \
-  #bash temp/RABIES-${RABIES_VERSION}/install.sh && \
-  #rm -r temp && \
-  git clone https://github.com/CoBrALab/RABIES && \
-  mv RABIES $RABIES && \
-  conda env create -f $RABIES/rabies_environment.yml && \
-  bash $RABIES/install.sh && \
+  mkdir -p temp && \
+  curl -L --retry 5 -o temp/RABIES.tar.gz https://github.com/CoBrALab/RABIES/archive/${RABIES_VERSION}.tar.gz && \
+  cd temp && \
+  tar zxf RABIES.tar.gz && \
+  cd .. && \
+  conda env create -f temp/RABIES-${RABIES_VERSION}/rabies_environment.yml && \
+  bash temp/RABIES-${RABIES_VERSION}/install.sh && \
+  rm -r temp && \
+  #git clone https://github.com/CoBrALab/RABIES && \
+  #mv RABIES $RABIES && \
+  #conda env create -f $RABIES/rabies_environment.yml && \
+  #bash $RABIES/install.sh && \
   DSURQE_100micron_labels=${RABIES}/template_files/DSURQE_100micron_labels.nii.gz && \
   csv_labels=${RABIES}/template_files/DSURQE_40micron_R_mapping.csv && \
   /home/rabies/miniconda-latest/envs/rabies/bin/python ${RABIES}/gen_masks.py $DSURQE_100micron_labels $csv_labels ${RABIES}/template_files/DSURQE_100micron

README.md

@@ -88,7 +88,7 @@ cluster options for running ants_dbm (options copied from twolevel_dbm.py)::
   --local_threads LOCAL_THREADS
                         For local execution, how many subject-wise modelbuilds
                         to run in parallel, defaults to number of CPUs
-                        (default: 2)
+                        (default: 8)
   --template_reg_script TEMPLATE_REG_SCRIPT
                         Registration script that will be used for registration
                         of the generated template to the provided atlas for
@@ -111,33 +111,33 @@ Specify Slice Timing Correction info that is fed to AFNI 3dTshift.:
 Template files.:
   --anat_template ANAT_TEMPLATE
                         Anatomical file for the commonspace template.
-                        (default: /home/gabriel/RABIES-0.1.1/template_files/DS
-                        URQE_100micron_average.nii.gz)
+                        (default: /home/cic/desgab/RABIES-0.1.2/template_files
+                        /DSURQE_100micron_average.nii.gz)
   --brain_mask BRAIN_MASK
-                        Brain mask for the template. (default: /home/gabriel/R
-                        ABIES-0.1.1/template_files/DSURQE_100micron_mask.nii.g
-                        z)
-  --WM_mask WM_MASK     White matter mask for the template. (default: /home/ga
-                        briel/RABIES-0.1.1/template_files/DSURQE_100micron_ero
-                        ded_WM_mask.nii.gz)
-  --CSF_mask CSF_MASK   CSF mask for the template. (default: /home/gabriel/RAB
-                        IES-0.1.1/template_files/DSURQE_100micron_eroded_CSF_m
-                        ask.nii.gz)
+                        Brain mask for the template. (default: /home/cic/desga
+                        b/RABIES-0.1.2/template_files/DSURQE_100micron_mask.ni
+                        i.gz)
+  --WM_mask WM_MASK     White matter mask for the template. (default: /home/ci
+                        c/desgab/RABIES-0.1.2/template_files/DSURQE_100micron_
+                        eroded_WM_mask.nii.gz)
+  --CSF_mask CSF_MASK   CSF mask for the template. (default: /home/cic/desgab/
+                        RABIES-0.1.2/template_files/DSURQE_100micron_eroded_CS
+                        F_mask.nii.gz)
   --vascular_mask VASCULAR_MASK
                         Can provide a mask of major blood vessels for
                         computing confound timeseries. The default mask was
                         generated by applying MELODIC ICA and selecting the
                         resulting component mapping onto major veins.
                         (Grandjean et al. 2020, NeuroImage; Beckmann et al.
-                        2005) (default: /home/gabriel/RABIES-0.1.1/template_fi
-                        les/vascular_mask.nii.gz)
-  --labels LABELS       Atlas file with anatomical labels. (default: /home/gab
-                        riel/RABIES-0.1.1/template_files/DSURQE_100micron_labe
-                        ls.nii.gz)
+                        2005) (default: /home/cic/desgab/RABIES-0.1.2/template
+                        _files/vascular_mask.nii.gz)
+  --labels LABELS       Atlas file with anatomical labels. (default: /home/cic
+                        /desgab/RABIES-0.1.2/template_files/DSURQE_100micron_l
+                        abels.nii.gz)
 
 ```
 
-## Example execution command
+## Command syntax
 ```
   rabies nii_inputs/ rabies_outputs/ --bids_input -e -r light_SyN --template_reg_script light_SyN --TR 1.0s --cluster_type sge -p SGEGraph
 ```

install.sh

@@ -6,11 +6,11 @@ export RABIES=$HOME/RABIES-${RABIES_VERSION}
 export PYTHONPATH="${PYTHONPATH}:$RABIES"
 
 cd $HOME
-#curl -L --retry 5 -o $HOME/RABIES.tar.gz https://github.com/CoBrALab/RABIES/archive/${RABIES_VERSION}.tar.gz
-#tar zxf $HOME/RABIES.tar.gz
-#rm $HOME/RABIES.tar.gz
-git clone https://github.com/CoBrALab/RABIES
-mv RABIES $RABIES
+curl -L --retry 5 -o $HOME/RABIES.tar.gz https://github.com/CoBrALab/RABIES/archive/${RABIES_VERSION}.tar.gz
+tar zxf $HOME/RABIES.tar.gz
+rm $HOME/RABIES.tar.gz
+#git clone https://github.com/CoBrALab/RABIES
+#mv RABIES $RABIES
 
 #creates an executable script to execute rabies
 mkdir -p $RABIES/bin
@@ -44,5 +44,8 @@ csv_labels=$template_dir/DSURQE_40micron_R_mapping.csv
 python $RABIES/gen_masks.py $DSURQE_100micron_labels $csv_labels $template_dir/DSURQE_100micron
 
 # install twolevel_ants_dbm
-git clone https://github.com/CobraLab/twolevel_ants_dbm $RABIES/twolevel_ants_dbm && \
+git clone https://github.com/CoBrALab/twolevel_ants_dbm.git $RABIES/twolevel_ants_dbm
+cd twolevel_ants_dbm
+git checkout bde563f3ee017eb7f374b72e8599f2102a69c1da
+cd ..
 echo 'export PATH=$RABIES/twolevel_ants_dbm:$PATH' >> $HOME/.bashrc

rabies/main_wf.py

@@ -233,17 +233,25 @@ def init_unified_main_wf(data_dir_path, data_csv, output_folder, bids_input=Fals
     #setting anat preprocessing nodes
     anat_preproc_wf = init_anat_preproc_wf()
 
+    #calculate the number of anat scans that will be registered
+    num_anat=0
+    for sub in subject_list:
+        num_anat+=len(session_iter[sub])
+    if int(os.environ["ants_dbm_local_threads"])<num_anat:
+        num_anat=int(os.environ["ants_dbm_local_threads"])
+
     commonspace_reg = pe.Node(Function(input_names=['file_list', 'output_folder'],
                               output_names=['ants_dbm_template'],
                               function=commonspace_reg_function),
-                     name='commonspace_reg')
+                     name='commonspace_reg', n_procs=num_anat)
     commonspace_reg.inputs.output_folder = output_folder+'/commonspace_datasink/'
 
     #execute the registration of the generate anatomical template with the provided atlas for labeling and masking
     template_reg = pe.Node(Function(input_names=['reg_script', 'moving_image', 'fixed_image', 'anat_mask'],
                               output_names=['affine', 'warp', 'inverse_warp', 'warped_image'],
                               function=run_antsRegistration),
-                     name='template_reg')
+                     name='template_reg', mem_gb=3)
+    template_reg.plugin_args = {'qsub_args': '-pe smp %s' % (str(3*int(os.environ["min_proc"]))), 'overwrite': True}
     template_reg.inputs.fixed_image = os.environ["template_anat"]
     template_reg.inputs.anat_mask = os.environ["template_mask"]
     template_reg.inputs.reg_script = template_reg_script

rabies/preprocess_bold_pkg/bold_main_wf.py

@@ -509,17 +509,25 @@ def to_commonspace_transforms_prep(template_to_common_warp, template_to_common_a
                      joinsource='infosub_id',
                      joinfield=['file_list'])
 
+    #calculate the number of bold scans that will be registered
+    num_bold=0
+    for sub in subject_list:
+        num_bold+=(len(session_iter[sub])*len(run_iter[sub]))
+    if int(os.environ["ants_dbm_local_threads"])<num_bold:
+        num_bold=int(os.environ["ants_dbm_local_threads"])
+
     commonspace_reg = pe.Node(Function(input_names=['file_list', 'output_folder'],
                               output_names=['ants_dbm_template'],
                               function=commonspace_reg_function),
-                     name='commonspace_reg')
+                     name='commonspace_reg', n_procs=num_bold)
     commonspace_reg.inputs.output_folder = output_folder+'/commonspace_datasink/'
 
     #execute the registration of the generate anatomical template with the provided atlas for labeling and masking
     template_reg = pe.Node(Function(input_names=['reg_script', 'moving_image', 'fixed_image', 'anat_mask'],
                               output_names=['affine', 'warp', 'inverse_warp', 'warped_image'],
                               function=run_antsRegistration),
-                     name='template_reg')
+                     name='template_reg', mem_gb=3)
+    template_reg.plugin_args = {'qsub_args': '-pe smp %s' % (str(3*int(os.environ["min_proc"]))), 'overwrite': True}
     template_reg.inputs.fixed_image = os.environ["template_anat"]
     template_reg.inputs.anat_mask = os.environ["template_mask"]
     template_reg.inputs.reg_script = template_reg_script

rabies/preprocess_bold_pkg/confounds.py

@@ -31,7 +31,8 @@ def init_bold_confs_wf(aCompCor_method='50%', name="bold_confs_wf"):
     propagate_labels=pe.Node(MaskEPI(), name='prop_labels_EPI')
     propagate_labels.inputs.name_spec='anat_labels'
 
-    confound_regression=pe.Node(ConfoundRegression(aCompCor_method=aCompCor_method), name='confound_regression')
+    confound_regression=pe.Node(ConfoundRegression(aCompCor_method=aCompCor_method), name='confound_regression', mem_gb=2)
+    confound_regression.plugin_args = {'qsub_args': '-pe smp %s' % (str(2*int(os.environ["min_proc"]))), 'overwrite': True}
 
     workflow = pe.Workflow(name=name)
     workflow.connect([

rabies/preprocess_bold_pkg/hmc.py

@@ -30,6 +30,8 @@ def init_bold_hmc_wf(name='bold_hmc_wf'):
         movpar_file
             CSV file with antsMotionCorr motion parameters
     """
+    import os
+    
     workflow = pe.Workflow(name=name)
     inputnode = pe.Node(niu.IdentityInterface(fields=['bold_file', 'ref_image']),
                         name='inputnode')
@@ -39,7 +41,7 @@ def init_bold_hmc_wf(name='bold_hmc_wf'):
 
     # Head motion correction (hmc)
     motion_estimation = pe.Node(EstimateMotion(), name='ants_MC', mem_gb=3)
-    motion_estimation.plugin_args = {'qsub_args': '-pe smp 4', 'overwrite': True}
+    motion_estimation.plugin_args = {'qsub_args': '-pe smp %s' % (str(3*int(os.environ["min_proc"]))), 'overwrite': True}
 
 
     workflow.connect([

rabies/preprocess_bold_pkg/registration.py

@@ -59,7 +59,7 @@ def init_bold_reg_wf(coreg_script='SyN', name='bold_reg_wf'):
                    output_names=['affine_bold2anat', 'warp_bold2anat', 'inverse_warp_bold2anat', 'output_warped_bold'],
                    function=run_antsRegistration), name='EPI_Coregistration', mem_gb=3)
     run_reg.inputs.reg_script=coreg_script
-    run_reg.plugin_args = {'qsub_args': '-pe smp 4', 'overwrite': True}
+    run_reg.plugin_args = {'qsub_args': '-pe smp %s' % (str(3*int(os.environ["min_proc"]))), 'overwrite': True}
 
     workflow.connect([
         (inputnode, run_reg, [

rabies/preprocess_bold_pkg/resampling.py

@@ -21,14 +21,14 @@ def init_bold_preproc_trans_wf(resampling_dim, name='bold_preproc_trans_wf'):
         name='outputnode')
 
 
-    bold_transform = pe.Node(slice_applyTransforms(), name='bold_transform', mem_gb=3)
+    bold_transform = pe.Node(slice_applyTransforms(), name='bold_transform')
     bold_transform.inputs.apply_motcorr = True
     bold_transform.inputs.resampling_dim = resampling_dim
     bold_transform.inputs.data_type = os.environ["rabies_data_type"]
 
     merge = pe.Node(Merge(), name='merge', mem_gb=3)
     merge.inputs.data_type = os.environ["rabies_data_type"]
-    #merge.plugin_args = {'qsub_args': '-pe smp 2 -l h_vmem=1G ', 'overwrite': True}
+    merge.plugin_args = {'qsub_args': '-pe smp %s' % (str(3*int(os.environ["min_proc"]))), 'overwrite': True}
 
     # Generate a new BOLD reference
     bold_reference_wf = init_bold_reference_wf()
@@ -66,14 +66,15 @@ def init_bold_commonspace_trans_wf(resampling_dim, name='bold_commonspace_trans_
         niu.IdentityInterface(fields=['bold', 'bold_ref', 'brain_mask', 'WM_mask', 'CSF_mask', 'labels']),
         name='outputnode')
 
-    bold_transform = pe.Node(slice_applyTransforms(), name='bold_transform', mem_gb=3)
+    bold_transform = pe.Node(slice_applyTransforms(), name='bold_transform')
     bold_transform.inputs.apply_motcorr = True
     bold_transform.inputs.ref_file = os.environ["template_anat"]
     bold_transform.inputs.resampling_dim = resampling_dim
     bold_transform.inputs.data_type = os.environ["rabies_data_type"]
 
     merge = pe.Node(Merge(), name='merge', mem_gb=3)
     merge.inputs.data_type = os.environ["rabies_data_type"]
+    merge.plugin_args = {'qsub_args': '-pe smp %s' % (str(3*int(os.environ["min_proc"]))), 'overwrite': True}
 
     # Generate a new BOLD reference
     bold_reference_wf = init_bold_reference_wf()

rabies/preprocess_bold_pkg/utils.py

@@ -131,10 +131,10 @@ def init_bold_reference_wf(detect_dummy=False, name='gen_bold_ref'):
         This interface implements the `following logic
         <https://github.com/poldracklab/fmriprep/issues/873#issuecomment-349394544>
     '''
-    validate = pe.Node(ValidateImage(), name='validate')
+    validate = pe.Node(ValidateImage(), name='validate', mem_gb=2)
     validate.plugin_args = {'qsub_args': '-pe smp %s' % (str(2*int(os.environ["min_proc"]))), 'overwrite': True}
 
-    gen_ref = pe.Node(EstimateReferenceImage(detect_dummy=detect_dummy), name='gen_ref')
+    gen_ref = pe.Node(EstimateReferenceImage(detect_dummy=detect_dummy), name='gen_ref', mem_gb=2)
     gen_ref.plugin_args = {'qsub_args': '-pe smp %s' % (str(2*int(os.environ["min_proc"]))), 'overwrite': True}
 
     workflow.connect([
@@ -336,7 +336,13 @@ def _run_interface(self, runtime):
         import nibabel as nb
         import os
         img=nb.load(self.inputs.in_file)
-        resample_image(nb.load(self.inputs.ref_file), self.inputs.data_type, img_dim=self.inputs.resampling_dim).to_filename('resampled.nii.gz')
+
+        if not self.inputs.resampling_dim=='origin':
+            resample_image(nb.load(self.inputs.ref_file), self.inputs.data_type, img_dim=self.inputs.resampling_dim).to_filename('resampled.nii.gz')
+        else:
+            shape=img.header.get_zooms()
+            dims="%sx%sx%s" % (shape[0],shape[1],shape[2])
+            resample_image(nb.load(self.inputs.ref_file), self.inputs.data_type, img_dim=dims).to_filename('resampled.nii.gz')
 
         #tranforms is a list of transform files, set in order of call within antsApplyTransforms
         transform_string=""