fslsub

README.md

@@ -142,28 +142,10 @@ Contains the output of the first set of rigd and affine registrations including
 - unbiased average T1 template in the unbiased space of the subjects
 - rigid transformation of unbiased template to MNI space
 
-####    `affine_it1`
+####    `affine_it2`
+
 
 
-    affine_it2 (affine_it2_dir)
-        {sub_id} (affine_it2_subject_dir)
-            T1_to_MNI152{ext_mat} (T1_to_MNI_mat)
-            T1_brain_to_MNI152{ext_nii} (T1_brain_to_MNI_img)
-            T1_head_to_MNI152{ext_nii} (T1_head_to_MNI_img)
-            T1_brain_mask_to_MNI152{ext_nii} (T1_brain_mask_to_MNI_img)
-            T2_FLAIR_to_MNI152{ext_mat} (T2_to_MNI_mat)
-            T2_FLAIR_brain_to_MNI152{ext_nii} (T2_brain_to_MNI_img)
-            T2_FLAIR_head_to_MNI152{ext_nii} (T2_head_to_MNI_img)
-            DTI_to_MNI152{ext_mat} (DTI_to_MNI_mat)
-            DTI_to_MNI152{ext_nii} (DTI_to_MNI_img)
-            DTI_tensor_to_MNI152{ext_nii} (DTI_tensor_to_MNI)
-        affine_template_T1_brain{ext_nii} (T1_brain_affine_template)
-        affine_template_T1_brain_mask{ext_nii} (T1_brain_mask_affine_template)
-        affine_template_T1_brain_mask_weighted{ext_nii} (T1_brain_mask_weighted_affine_template)
-        affine_template_T1_head{ext_nii} (T1_head_affine_template)
-        affine_template_T2_head{ext_nii} (T2_head_affine_template)
-        affine_template_DTI{ext_nii} (DTI_affine_template)
-        affine_template_DTI_tensor{ext_nii} (DTI_tensor_affine_template)
 
 
 

run_template_construction.py

@@ -13,6 +13,9 @@ import shutil
 import pandas as pd
 import nibabel as nib
 import numpy as np
+import shlex
+import subprocess
+import sys
 from fsl.wrappers import fslmaths,flirt,applyxfm,concatxfm,bet,fast,fslstats
 from fsl.wrappers.fnirt import invwarp, applywarp, convertwarp
 from file_tree import FileTree
@@ -21,7 +24,7 @@ from fsl.utils.fslsub import func_to_cmd
 from operator import itemgetter
 import tempfile
 import argparse
-import sys
+# import fsl_sub
 
 def writeConfig(step,mod,fpath):
     """! Writes the nonlinear registration parameters for a given resolution level and modalities to a file readable by MMORF.
@@ -413,6 +416,66 @@ def applyWarpWrapper(img_path, ref_path, warped_path, warp_path, interp='spline'
         applywarp(src=img_nib, ref=ref_path, out=warped_path, warp=warp_path, interp=interp)
 
 
+# def submitJob_fsl_sub(name, log_dir, queue, wait_for=[], script=None, command=None, coprocessor_class=None, export_var=None,
+#                   debug=False):
+#     """! Wrapper for fslsub - submits a job to the cluster. This function can be easily extended to work with other workload managers.
+#
+#     @param name:                  Job name
+#     @param log_dir:               Directory where output log-files will be saved
+#     @param queue:                 Name of queue to submit the job to
+#     @param wait_for:              List of IDs of jobs required to finish before running this job.
+#     @param script:                Path to a shell script, which contains one command per line - commands will be submitted as an array job
+#     @param command:               Alternatively a single command can be provided as a string - command will be submitted as single job
+#     @param coprocessor_class:     Coprocessor class, if not None cuda will be selected
+#     @param export_var:            Environment variables to be exported to the submission node
+#     @param debug:                 If True, information about job will be written to output
+#
+#     @return  The job ID.
+#     """
+#
+#     fsl_sub.submit()
+#
+#     cmd = 'fsl_sub'
+#     if wait_for and any(job != '' for job in wait_for):
+#         cmd += ' -j '
+#         for j, job in enumerate(wait_for):
+#             if job != '':
+#                 cmd += job.replace("\n", "")
+#                 if j < len(wait_for) - 1:
+#                     cmd += ','
+#
+#     cmd += ' -N ' + name + \
+#            ' -l ' + log_dir + \
+#            ' -q ' + queue
+#
+#     if coprocessor_class is not None :
+#         cmd += ' --coprocessor cuda'
+#
+#     if export_var is not None :
+#         cmd += ' --export ' + export_var
+#
+#     if debug:
+#         cmd += ' --debug'
+#
+#     if script is not None and os.path.exists(script):
+#         cmd += ' -t ' + script
+#     elif command is not None :
+#         cmd += shlex.split(command)
+#
+#     # stream = os.popen(cmd)
+#     # job_id = stream.read()
+#
+#     try:
+#         result = subprocess.run(command, capture_output, text=True, check=True)
+#     except subprocess.CalledProcessError as e:
+#         print(str(e), file=sys.stderr)
+#         return None
+#
+#     job_id = result.stdout.strip()
+#
+#     return job_id
+
+
 def submitJob(name, log_dir, queue, wait_for=[], script=None, command=None, coprocessor_class=None, export_var=None,
                   debug=False):
     """! Wrapper for fslsub - submits a job to the cluster. This function can be easily extended to work with other workload managers.
@@ -442,25 +505,34 @@ def submitJob(name, log_dir, queue, wait_for=[], script=None, command=None, copr
            ' -l ' + log_dir + \
            ' -q ' + queue
 
-    if coprocessor_class != None:
+    if coprocessor_class is not None :
         cmd += ' --coprocessor cuda'
 
-    if export_var != None:
+    if export_var is not None :
         cmd += ' --export ' + export_var
 
     if debug:
         cmd += ' --debug'
 
-    if script != None and os.path.exists(script):
+    if script is not None and os.path.exists(script):
         cmd += ' -t ' + script
-    elif command != None:
-        cmd += ' "' + command + '"'
+    elif command is not None :
+        cmd += shlex.split(command)
+
+    # stream = os.popen(cmd)
+    # job_id = stream.read()
 
-    stream = os.popen(cmd)
-    job_id = stream.read()
+    try:
+        result = subprocess.run(command, capture_output, text=True, check=True)
+    except subprocess.CalledProcessError as e:
+        print(str(e), file=sys.stderr)
+        return None
+
+    job_id = result.stdout.strip()
 
     return job_id
 
+
 def RMSdifference(img1_path, img2_path, mask1_path=None, mask2_path=None, rms_path=None):
     """! Calculates the difference between two images or warps as the root mean squared (RMS)