change numberOfJobs as str for SlicerWMA

bin/wm_cluster_from_atlas.py

@@ -4,7 +4,6 @@
 import os
 import multiprocessing
 import time
-
 import vtk
 
 try:
@@ -40,7 +39,7 @@ def main():
         '-l', action="store", dest="fiberLength", type=int, default=40,
         help='Minimum length (in mm) of fibers to analyze. 60mm is default.')
     parser.add_argument(
-        '-j', action="store", dest="numberOfJobs", type=int, default=1,
+        '-j', action="store", dest="numberOfJobs", type=str, default='1',
         help='Number of processors to use.')
     parser.add_argument(
         '-verbose', action='store_true', dest="flag_verbose",
@@ -92,7 +91,7 @@ def main():
     fiber_length = args.fiberLength
     print("minimum length of fibers to analyze (in mm): ", fiber_length)
 
-    number_of_jobs = args.numberOfJobs
+    number_of_jobs = int(args.numberOfJobs)
     print('Using N jobs:', number_of_jobs)
 
     if args.flag_verbose:
@@ -303,6 +302,7 @@ def main():
 
     print("\n==========================")
     print('<wm_cluster_from_atlas.py> Done clustering subject.  See output in directory:\n ', outdir, '\n')
-
+
+
 if __name__ == '__main__':
     main()

bin/wm_cluster_remove_outliers.py

@@ -51,7 +51,7 @@ def main():
         '-advanced_only_outlier_sigma', action='store', dest="outlierSigma", type=float, default=20.0,
         help='(Advanced parameter that probably should not be changed.) Local sigma used to compute fiber probability in cluster-based outlier removal. The default is 20mm. For stricter clustering, this may be reduced to 15mm.')
     parser.add_argument(
-        '-j', action="store", dest="numberOfJobs", type=int,
+        '-j', action="store", dest="numberOfJobs", type=str,
         help='Number of processors to use.')
 
     args = parser.parse_args()
@@ -82,7 +82,7 @@ def main():
         os.makedirs(outdir)
 
     if args.numberOfJobs is not None:
-        number_of_jobs = args.numberOfJobs
+        number_of_jobs = int(args.numberOfJobs)
     else:
         # default to 1 job for usage on compute clusters. Also, the
         # multiprocessing is not used efficiently in our code and should