minor patch to respect user ordering of ids in groups

CHANGELOG.md

@@ -41,6 +41,15 @@ noted in the changelog (i.e new functions or parameters, changes in parameter de
 - *.patch* : Contains no new features, simply fixes any identified bugs.
 - *.postN* : Consists of only metadata-related changes, such as updates to type hints or doc strings/documentation.
 
+## [0.11.2] - 2024-06-23
+### ♻ Changed
+- Changed how ids are organized in respective group when initializing the `CAP` class. In version 0.11.1, the ids are
+sorted lexicographically:
+```python3
+self._groups[group] = sorted(list(set(self._groups[group])))
+```
+This doesn't affect functionality but it may be better to respect the original user ordering.This is no longer the case.
+
 ## [0.11.1] - 2024-06-23
 ### 🐛 Fixes
 - Fix for python 3.12 when using `CAP.caps2surf()`. 
@@ -53,14 +62,15 @@ noted in the changelog (i.e new functions or parameters, changes in parameter de
       implementing this fix.
 
 ```python3
-# Fix for python 3.12, saving stat_map so that it is path instead of a NifTI
+
+# Fix for python 3.12, saving stat map so that it is path instead of a NifTi
 try:
     gii_lh, gii_rh = mni152_to_fslr(stat_map, method=method, fslr_density=fslr_density)
 except TypeError:
     # Create temp
     temp_nifti = tempfile.NamedTemporaryFile(delete=False, suffix=".nii.gz")
     warnings.warn(textwrap.dedent(f"""
-                    Error potentially due to change in pathlib.py in python 3.12 causing the error
+                    Potential error due to changes in pathlib.py in Python 3.12 causing the error
                     message to output as "not 'Nifti1Image'" instead of "not Nifti1Image", which
                     neuromaps uses to determine if the input is a Nifti1Image object.
                     Converting stat_map into a temporary nii.gz file (which will be automatically

neurocaps/__init__.py

@@ -2,4 +2,4 @@
 
 __all__=["analysis", "extraction"]
 # Version in a single place
-__version__ = "0.11.1"
+__version__ = "0.11.2"

neurocaps/analysis/cap.py

@@ -42,11 +42,11 @@ class CAP(_CAPGetter):
 
     groups : :obj:`dict[str, list]` or :obj:`None`, default=None
         A mapping of group names to subject IDs. Each group contains subject IDs for separate CAP analysis.
-        Additionally, IDs should not be duplicated across groups or else a warning will be raised. Duplicate IDs in the
-        same groups will be removed and in the case of IDs duplicated across groups, only the first instance is retained.
-        This parameter is used to create ``self.subject_table``, which is used for concatenating data and calculating
-        metrics. This is done to avoid issues with duplicate IDs. If ``groups`` left as None, CAPs are not separated by
-        group and are performed on all subjects. The structure should be as follows:
+        Additionally, if duplicate IDs are detected within or across groups, a warning is issued and only the first
+        instance is retained. This parameter is used to create a dictionary (``self.subject_table``), which pairs each
+        subject ID (keys) with their group name (values) and is used for concatenating data and calculating metrics.
+        This is done to avoid issues with duplicate IDs. If ``groups`` left as None, CAPs are not separated by group
+        and are performed on all subjects. The structure should be as follows:
         ::
 
             {
@@ -69,10 +69,11 @@ class CAP(_CAPGetter):
         is used.
 
     parcel_approach : :obj:`dict[str, dict]`
-        Nested dictionary containing information about the parcellation. Can also be used as a setter. If "Schaefer"
-        or "AAL" was specified during initialization of the ``TimeseriesExtractor`` class, then
-        ``nilearn.datasets.fetch_atlas_schaefer_2018`` and ``nilearn.datasets.fetch_atlas_aal`` will be used to obtain
-        the "maps" and the "nodes". Then string splitting is used on the "nodes" to obtain the "regions":
+        Nested dictionary containing information about the parcellation. Can also be used as a setter, which accepts
+        a dictionary or a dictionary saved as a pickle file. If "Schaefer" or "AAL" was specified during initialization
+        of the ``TimeseriesExtractor`` class, then ``nilearn.datasets.fetch_atlas_schaefer_2018`` and
+        ``nilearn.datasets.fetch_atlas_aal`` will be used to obtain the "maps" and the "nodes". Then string splitting
+        is used on the "nodes" to obtain the "regions":
         ::
 
             {
@@ -322,7 +323,6 @@ def __init__(self, parcel_approach: dict[str, dict]=None, groups: dict[str, list
             for group in self._groups:
                 self._groups[group] = [str(subj_id) if not isinstance(subj_id,str)
                                        else subj_id for subj_id in self._groups[group]]
-                self._groups[group] = sorted(list(set(self._groups[group])))
 
         if parcel_approach is not None:
            parcel_approach = _check_parcel_approach(parcel_approach=parcel_approach, call="CAP")

neurocaps/analysis/merge.py

@@ -6,7 +6,7 @@
 def merge_dicts(subject_timeseries_list: Union[list[dict[str, dict[str, np.ndarray]]], list[os.PathLike]],
                 return_combined_dict: bool=True, return_reduced_dicts: bool=False,
                 output_dir: Optional[Union[str, os.PathLike]]=None,
-                file_name: Optional[str]=None) -> dict[str, dict[str, np.ndarray]]:
+                file_name: Optional[str]=None) -> Union[dict[str, dict[str, np.ndarray]], dict[str, dict[str, dict[str, np.ndarray]]]]:
     """
     **Merge Subject Timeseries**