Update tutorial version

Author: dPys

pynets/cli/pynets_run.py

@@ -3168,7 +3168,7 @@ def wf_multi_subject(
                     shutil.rmtree(cnfnd_tmp_dir)
                 shutil.rmtree(f"{dir}/reg_fmri", ignore_errors=True)
                 for file_ in [i for i in glob.glob(
-                        f"{dir}/*/*") if os.path.isfile(i)]:
+                        f"{dir}/func/*") if os.path.isfile(i)]:
                     if ("reor-RAS" in file_) or ("res-" in file_):
                         try:
                             os.remove(file_)
@@ -3178,7 +3178,7 @@ def wf_multi_subject(
                 shutil.rmtree(f"{dir}/dmri_tmp", ignore_errors=True)
                 shutil.rmtree(f"{dir}/reg_dmri", ignore_errors=True)
                 for file_ in [i for i in glob.glob(
-                        f"{dir}/*/*") if os.path.isfile(i)]:
+                        f"{dir}/dwi/*") if os.path.isfile(i)]:
                     if ("reor-RAS" in file_) or ("res-" in file_):
                         try:
                             os.remove(file_)
@@ -3366,15 +3366,15 @@ def wf_multi_subject(
         print("Cleaning up...")
         if func_file:
             for file_ in [i for i in glob.glob(
-                    f"{subj_dir}/*/*") if os.path.isfile(i)]:
+                    f"{subj_dir}/func/*") if os.path.isfile(i)]:
                 if ("reor-RAS" in file_) or ("res-" in file_):
                     try:
                         os.remove(file_)
                     except BaseException:
                         continue
         if dwi_file:
             for file_ in [i for i in glob.glob(
-                    f"{subj_dir}/*/*") if os.path.isfile(i)]:
+                    f"{subj_dir}/dwi/*") if os.path.isfile(i)]:
                 if ("reor-RAS" in file_) or ("res-" in file_) or \
                    ("_bvecs_reor.bvec" in file_):
                     try:

pynets/core/interfaces.py

@@ -1195,7 +1195,8 @@ def _run_interface(self, runtime):
                 copy=True,
                 use_hardlink=False)
         else:
-            if len(anat_mask_existing) > 0 and self.inputs.mask is None:
+            if len(anat_mask_existing) > 0 and \
+                 self.inputs.mask is None and op.isfile(anat_mask_existing[0]):
                 mask_tmp_path = fname_presuffix(
                     anat_mask_existing[0], suffix="_tmp", newpath=runtime.cwd
                 )
@@ -1204,9 +1205,6 @@ def _run_interface(self, runtime):
                     mask_tmp_path,
                     copy=True,
                     use_hardlink=False)
-                mask_tmp_path = regutils.check_orient_and_dims(
-                    mask_tmp_path, runtime.cwd,
-                    self.inputs.vox_size)
             else:
                 mask_tmp_path = None
 
@@ -1912,6 +1910,7 @@ class RegisterFunc(SimpleInterface):
     def _run_interface(self, runtime):
         import gc
         import glob
+        import os.path as op
         from pynets.registration import register
         from pynets.registration import reg_utils as regutils
         from nipype.utils.filemanip import fname_presuffix, copyfile
@@ -1935,7 +1934,8 @@ def _run_interface(self, runtime):
                 copy=True,
                 use_hardlink=False)
         else:
-            if len(anat_mask_existing) > 0 and self.inputs.mask is None:
+            if len(anat_mask_existing) > 0 and \
+                 self.inputs.mask is None and op.isfile(anat_mask_existing[0]):
                 mask_tmp_path = fname_presuffix(
                     anat_mask_existing[0], suffix="_tmp",
                     newpath=runtime.cwd
@@ -1945,9 +1945,6 @@ def _run_interface(self, runtime):
                     mask_tmp_path,
                     copy=True,
                     use_hardlink=False)
-                mask_tmp_path = regutils.check_orient_and_dims(
-                    mask_tmp_path, self.inputs.in_dir,
-                    self.inputs.vox_size)
             else:
                 mask_tmp_path = None
 

pynets/fmri/estimation.py

@@ -184,7 +184,8 @@ def get_conn_matrix(
     ):
         # Fit estimator to matrix to get sparse matrix
         estimator_shrunk = None
-        estimator = GraphicalLassoCV(cv=5, max_iter=200)
+        estimator = GraphicalLassoCV(cv=5, max_iter=200,
+                                     assume_centered=True)
         print("\nComputing covariance...\n")
         try:
             estimator.fit(time_series)
@@ -197,6 +198,7 @@ def get_conn_matrix(
                 for tol in [0.000001, 0.00001, 0.001, 0.01, 0.1, 1, 10]:
                     try:
                         estimator = GraphicalLassoCV(cv=5, max_iter=200,
+                                                     assume_centered=True,
                                                      tol=tol, mode='lars')
                         estimator.fit(time_series)
                     except BaseException:

pynets/registration/reg_utils.py

@@ -44,6 +44,7 @@ def gen_mask(t1w_head, t1w_brain, mask):
             t1w_brain_mask = deep_skull_strip(t1w_data, t1w_brain_mask, img)
         except RuntimeError:
             print('Deepbrain extraction failed...')
+        del t1w_data
 
     # Threshold T1w brain to binary in anat space
     t_img = nib.load(t1w_brain_mask)

pynets/runconfig.yaml

@@ -19,7 +19,7 @@ tracking:
         - True
     tracking_method: # a tracking algorithm for dmri connectome estimation. Options are: local and particle.
         - 'particle'
-    tracking_samples: # Indicates the number of cumulative streamline samples for each tractogram. *Note that we should keep this relatively low since total streamlines will actually sum cumulatively across a given connectome ensemble (e.g. 100 connectomes x 20,000 = 2 million streamlines)
+    tracking_samples: # Indicates the number of cumulative streamline samples for each tractogram. *Note that we should keep this relatively low since total streamlines will actually sum cumulatively across a given connectome ensemble (e.g. 100 connectomes x 50,000 = 5 million streamlines!)
         - 50000
     DSN: # Experimental option. Direct Streamline Normalization attempts to normalize streamlines (i.e. post-reconstruction) to a morphologically standardized scaling (i.e. range of fiber lengths) specific to the template used. This may help to facilitate group/population analysis.
         - False