Fixing bugs in T1_brain_extract and dMRI_diff package

bip/pipelines/dMRI_diff/__init__.py

@@ -52,5 +52,5 @@ def add_to_pipeline(ctx, pipe, tree):
          submit=cuda_bedpostx_dict,
          kwargs={'ctx' : ctx})
     pipe(diff_autoptx.run,
-         submit=dict(jobtime=200, name=job_name(diff_bedpostx.run, subj)),
+         submit=dict(jobtime=200, name=job_name(diff_autoptx.run, subj)),
          kwargs={'ctx' : ctx})

bip/pipelines/struct_T1/T1_brain_extract.py

@@ -34,7 +34,6 @@ def run(ctx,
         logs_dir:                Ref,
         T1:                      Ref,
         tmp_dir:                 Ref,
-        tmp_T1_prefix:           Ref,
         T1_orig_to_MNI_warp:     Out,
         T1_orig_ud_to_std_mat:   Out,
         T1_to_MNI_warp_coef:     Out,
@@ -48,17 +47,20 @@ def run(ctx,
         T1_tmp_1_brain            = op.join(tmp_dir, 'T1_tmp_1_brain.nii.gz')
         T1_tmp_orig_ud_to_std_mat = op.join(tmp_dir, 'T1_tmp_to_std.mat')
         T1_tmp                    = op.join(tmp_dir, 'T1.nii.gz')
+        T1_tmp_prefix             = op.join(tmp_dir, 'T1')
 
-        #Calculate where does the brain start in the z dimension and extract the roi
-        head_top=int(round(float(wrappers.robustfov(T1_orig_ud).stdout[0].split()[7])))
-        wrappers.fslmaths(T1_orig_ud).roi(0,-1,0,-1,head_top,170,0,1,).run(T1_tmp_1)
+        #Calculate where does the brain start in the z dimension and extract roi
+        head_top=int(round(float(wrappers.robustfov(T1_orig_ud).stdout[0].\
+            split()[7])))
+        wrappers.fslmaths(T1_orig_ud).roi(0,-1,0,-1,head_top,170,0,1,).\
+            run(T1_tmp_1)
 
         #Run a (Recursive) brain extraction on the roi
         wrappers.bet(T1_tmp_1, T1_tmp_1_brain, robust=True)
 
         #Reduce the FOV of T1_orig_ud by calculating a registration
         #from T1_tmp_brain to ssref and apply it to T1_orig_ud
-        wrappers.standard_space_roi(T1_tmp_1_brain, tmp_T1_prefix, maskNONE=True,
+        wrappers.standard_space_roi(T1_tmp_1_brain, T1_tmp_prefix,maskNONE=True,
                                     ssref = ctx.MNI + '_brain',
                                     altinput=T1_orig_ud, d=True)
         copyfile(src=T1_tmp, dst=T1)
@@ -72,7 +74,7 @@ def run(ctx,
         wrappers.concatxfm(atob=T1_to_T1_orig_ud_mat,btoc=T1_orig_ud_to_std_mat,
                            atoc=T1_to_MNI_linear_mat)
 
-        #Non-linear registration to MNI using the previously calculated alignment
+        #Nonlinear registration to MNI using the previously calculated alignment
         wrappers.fnirt(src = T1, ref = ctx.MNI, aff = T1_to_MNI_linear_mat,
               config = ctx.get_data('fnirt/bb_fnirt.cnf'),
               refmask = ctx.get_data('MNI/MNI152_T1_1mm_brain_mask_dil_GD7.nii.gz'),
@@ -81,10 +83,11 @@ def run(ctx,
               jout = T1_to_MNI_warp_jac, iout = T1_tmp_2,
               interp = 'spline')
 
-        #Combine all transforms (Gradient Distortion Unwarp and T1 to ctx.MNI) into one
+        #Combine all transforms (Gradient Distortion Unwarp and T1 to ctx.MNI)
         if ctx.gdc != '' :
             wrappers.convertwarp(ref=ctx.MNI, warp1=T1_orig_ud_warp,
-                                 midmat=T1_orig_ud_to_T1_mat, warp2=T1_to_MNI_warp,
+                                 midmat=T1_orig_ud_to_T1_mat, 
+                                 warp2=T1_to_MNI_warp,
                                  out=T1_orig_to_MNI_warp)
         else:
             wrappers.convertwarp(ref=ctx.MNI, premat=T1_orig_ud_to_T1_mat,
@@ -102,4 +105,5 @@ def run(ctx,
                            w=T1_to_MNI_warp_coef_inv, out=T1_brain_mask,
                            rel=True, interp='trilinear')
         wrappers.fslmaths(T1).mul(T1_brain_mask).run(T1_brain)
-        wrappers.fslmaths(T1_brain_to_MNI).mul(ctx.get_data(MNI_var_name)).run(T1_brain_to_MNI)
+        wrappers.fslmaths(T1_brain_to_MNI).mul(ctx.get_data(MNI_var_name)).\
+            run(T1_brain_to_MNI)