Deb output

connPFM/cli/connPFM.py

@@ -211,4 +211,11 @@ def _get_parser():
         help="activate quiet logger mode",
         default=False,
     )
+    optoptions.add_argument(
+        "-pd",
+        "--prefix_debias",
+        dest="prefix",
+        help="Prefix for path and name for the beta and fitted files of the debiasing",
+        default=None,
+    )
     return parser

connPFM/connPFM.py

@@ -80,7 +80,8 @@ def _main(argv=None):
             options["atlas"][0],
             ets_auc_denoised,
             options["tr"][0],
-            os.path.dirname(options["auc"][0]),
+            os.path.dirname(options["data"][0]),
+            options["prefix"],
             history_str,
         )
     elif selected_workflow == "pfm":
@@ -120,7 +121,8 @@ def _main(argv=None):
             options["atlas"][0],
             ets_auc_denoised,
             options["tr"][0],
-            os.path.dirname(options["auc"][0]),
+            os.path.dirname(options["data"][0]),
+            options["prefix"],
             history_str,
         )
     else:

connPFM/debiasing/debiasing.py

@@ -1,7 +1,7 @@
 """Main debiasing workflow."""
 import logging
 import subprocess
-from os.path import basename, join
+from os.path import join
 
 import numpy as np
 from nilearn.input_data import NiftiLabelsMasker
@@ -13,7 +13,7 @@
 LGR = logging.getLogger(__name__)
 
 
-def debiasing(data_file, mask, mtx, tr, out_dir, history_str):
+def debiasing(data_file, mask, mtx, tr, out_dir, prefix, history_str):
     """Perform debiasing based on denoised edge-time matrix."""
     LGR.info("Performing debiasing based on denoised edge-time matrix...")
     masker = NiftiLabelsMasker(
@@ -59,15 +59,15 @@ def debiasing(data_file, mask, mtx, tr, out_dir, history_str):
 
     # Transform results back to 4D
     beta_4D = masker.inverse_transform(beta)
-    beta_file = join(out_dir, f"{basename(data_file[:-7])}_beta_ETS.nii.gz")
+    beta_file = join(out_dir, f"{prefix}_beta_ETS.nii.gz")
     beta_4D.to_filename(beta_file)
     atlas_mod.inverse_transform(beta_file, data_file)
-    subprocess.run(f"3dNotes {join(out_dir, beta_file)} -h {history_str}", shell=True)
+    subprocess.run(f"3dNotes {beta_file} -h {history_str}", shell=True)
 
     fitt_4D = masker.inverse_transform(fitt)
-    fitt_file = join(out_dir, f"{basename(data_file[:-7])}_fitt_ETS.nii.gz")
+    fitt_file = join(out_dir, f"{prefix}_fitt_ETS.nii.gz")
     fitt_4D.to_filename(fitt_file)
-    subprocess.run(f"3dNotes {join(out_dir, fitt_file)} -h {history_str}", shell=True)
+    subprocess.run(f"3dNotes {fitt_file} -h {history_str}", shell=True)
     atlas_mod.inverse_transform(fitt_file, data_file)
 
     LGR.info("Debiasing finished and files saved.")

connPFM/tests/test_integration.py

@@ -60,9 +60,15 @@ def test_integration_debias(
     if skip_integration:
         pytest.skip("Skipping integration test")
     subprocess.call(
-        "connPFM -i {} -a {} --AUC {} -d {} -m {} -tr 1 -u vferrer -nsur 50 -w debias".format(
-            bold_file, atlas_file, AUC_file, surr_dir, ets_auc_denoised_file
-        ),
+        "connPFM -i {} -a {} --AUC {} -d {} -m {} --prefix {} ".format(
+            bold_file,
+            atlas_file,
+            AUC_file,
+            surr_dir,
+            ets_auc_denoised_file,
+            f"{basename(bold_file[:-7])}",
+        )
+        + "-tr 1 -u vferrer -nsur 50 -w debias",
         shell=True,
     )
     masker = NiftiLabelsMasker(