BUG: extract bids info from basename, not fullpath

spacetop_prep/events/bidsify_alignvideos_ENH.py

@@ -9,7 +9,14 @@
 # This script reads raw behavior data files from d_beh for task-alignvideo, extracts time stamps and design information, and stores them in new *events.tsv files accompanying BOLD files.
 # For more information, please see README.md and the associated paper (Jung et al., 2024)
 """
-
+def extract_bids(filename: str, key: str) -> str:
+    """
+    Extracts BIDS information based on input "key" prefix.
+    If filename includes an extension, code will remove it.
+    """
+    bids_info = [match for match in filename.split('_') if key in match][0]
+    bids_info_rmext = bids_info.split(os.extsep, 1)
+    return bids_info_rmext[0]
 def add_rating_event(source_beh, t, t_run_start, rating_type, event_onset, event_rt, response_value_column):
     """
     Adds a rating event to the new BIDS-compliant DataFrame.
@@ -167,10 +174,10 @@ def parse_args():
 session_dict = {'ses-01': 4, 'ses-02': 4, 'ses-03': 3, 'ses-04': 2} # NOTE: different sessions have different numbers of runs
 
 if bids_string:
-    # If bids_string is provided, process that specific file
-    sub = re.search(r'sub-\d+', bids_string).group(0)
-    ses = re.search(r'ses-\d+', bids_string).group(0)
-    run = re.search(r'run-\d+', bids_string).group(0)
+    fname = Path(bids_string).name
+    sub = extract_bids(fname, 'sub')
+    ses = extract_bids(fname, 'ses')
+    run = extract_bids(fname, 'run')
     alignvideo_format_to_bids(sub, ses, run, task_name, beh_inputdir, bids_dir)
 else:
     # If no bids_string is provided, loop through the entire directory

spacetop_prep/events/bidsify_faces_ENH.py

@@ -124,9 +124,10 @@ def main():
     taskname = 'task-faces'
 
     if bids_string:
-        sub = extract_bids(bids_string, 'sub')
-        ses = extract_bids(bids_string, 'ses')
-        run = extract_bids(bids_string, 'run')
+        fname = Path(bids_string).name
+        sub = extract_bids(fname, 'sub')
+        ses = extract_bids(fname, 'ses')
+        run = extract_bids(fname, 'run')
         run_dict = {'run-01': 'age', 'run-02': 'sex', 'run-03': 'intensity'} if int(sub[-4:])%2 == 0 else {'run-01': 'intensity', 'run-02': 'sex', 'run-03': 'age'}
         rating_type = run_dict[run]
         faces_format2bids(sub, ses, taskname, run, rating_type, beh_inputdir, bids_dir)

spacetop_prep/events/bidsify_fractional_ENH.py

@@ -86,7 +86,7 @@ def parse_args():
 # %%
 
 if args.bids_string and 'tomsaxe' in extract_bids(args.bids_string, 'task'):
-    sub = extract_bids(args.bids_string, 'sub')
+    sub = extract_bids(Path(args.bids_string).name, 'sub')
     saxe_flist = list(Path(beh_inputdir).rglob(f'{sub}/**/task-fractional/**/{args.bids_string}*.csv'))
 
     if not saxe_flist:
@@ -236,8 +236,9 @@ def parse_args():
 task_name = "posner"
 # current_path = Path.cwd()
 # main_dir = current_path.parent.parent 
-if args.bids_string and task_name in extract_bids(bids_string, 'task') :
-    sub = extract_bids(bids_string, 'sub')
+if args.bids_string and task_name in extract_bids(bids_string, 'task'):
+    sub = extract_bids(Path(args.bids_string).name, 'sub')
+    # sub = extract_bids(bids_string, 'sub')
     filtered_posner_flist = glob.glob(join(beh_inputdir, sub,  '**','task-fractional', '**', f'*{bids_string}*.csv'), recursive=True)
 else:
     # scans_list = sorted(glob.glob('sub-*/**/*ses-04*scans*.tsv', recursive=True))
@@ -487,8 +488,9 @@ def parse_args():
 # current_path = Path.cwd()
 # memory_flist = sorted(glob.glob(join(beh_inputdir, '**', f'*{task_name}_beh.csv'), recursive=True))
 
-if args.bids_string and task_name in extract_bids(bids_string, 'task') :
-    sub = extract_bids(bids_string, 'sub')
+if args.bids_string and task_name in extract_bids(bids_string, 'task'):
+    sub = extract_bids(Path(args.bids_string).name, 'sub')
+    # sub = extract_bids(bids_string, 'sub')
     filtered_memory_flist = glob.glob(join(beh_inputdir, sub,  '**','task-fractional', '**', f'*{bids_string}*.csv'), recursive=True)
 else:
     # scans_list = sorted(glob.glob('sub-*/**/*ses-04*scans*.tsv', recursive=True))
@@ -688,7 +690,8 @@ def parse_args():
 # spunt_flist = sorted(glob.glob(join(beh_inputdir, '**', f'*{task_name}_beh.csv'), recursive=True))
 
 if args.bids_string and task_name in extract_bids(bids_string, 'task') :
-    sub = extract_bids(bids_string, 'sub')
+    sub = extract_bids(Path(args.bids_string).name, 'sub')
+    # sub = extract_bids(bids_string, 'sub')
     filtered_spunt_flist = glob.glob(join(beh_inputdir, sub,  '**','task-fractional', '**', f'*{bids_string}*.csv'), recursive=True)
 else:
     # scans_list = sorted(glob.glob('sub-*/**/*ses-04*scans*.tsv', recursive=True))

spacetop_prep/events/bidsify_narratives_ENH.py

@@ -194,17 +194,18 @@ def parse_args():
 ses = 'ses-02'
 
 if bids_string:
-    # If bids_string is provided, process that specific file
-    sub = extract_bids(bids_string, 'sub')
-    ses = extract_bids(bids_string, 'ses')
-    run = extract_bids(bids_string, 'run')
+    fname = Path(bids_string).name
+    sub = extract_bids(fname, 'sub')
+    ses = extract_bids(fname, 'ses')
+    run = extract_bids(fname, 'run')
     narrative_format2bids(sub, ses, run, taskname, beh_inputdir, bids_dir)
 else:
     # If no bids_string is provided, loop through the entire directory
     file_list = sorted(glob.glob(os.path.join(beh_inputdir, '**', f'sub-*task-narratives*_beh.csv'), recursive=True))
     for fpath in file_list:
-        sub = extract_bids(bids_string, 'sub')
-        ses = extract_bids(bids_string, 'ses')
-        run = extract_bids(bids_string, 'run')
+        fname = Path(fpath).name
+        sub = extract_bids(fname, 'sub')
+        ses = extract_bids(fname, 'ses')
+        run = extract_bids(fname, 'run')
         narrative_format2bids(sub, ses, run, taskname, beh_inputdir, bids_dir)
 

spacetop_prep/events/bidsify_social_ENH.py

@@ -272,9 +272,11 @@ def parse_args():
 cognitive_logger = setup_logger('cognitive', 'task-cue_cognitive.log')
 
 if args.bids_string and task_name in args.bids_string:
-    sub = extract_bids(bids_string, 'sub')
-    ses = extract_bids(bids_string, 'ses')
-    run = extract_bids(bids_string, 'run')
+    fname = Path(bids_string).name
+    sub = extract_bids(fname, 'sub')
+    ses = extract_bids(fname, 'ses')
+    run = extract_bids(fname, 'run')
+
     # filtered_cognitive_flist = glob.glob(join(beh_inputdir, sub,  '**','task-social', '**', f'*{bids_string}*.csv'), recursive=True)
     filtered_cognitive_flist = glob.glob(str(Path(beh_inputdir) / sub / '**' / 'task-social' / '**' / f'*{args.bids_string}*.csv'), recursive=True)