fix for python 3.12 to display surface plots and fix whitespace issues

with outputs in triple quotes.

Author: donishadsmith

CHANGELOG.md

@@ -41,6 +41,42 @@ 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.
 
+## [0.11.1] - 2024-06-23
+### 🐛 Fixes
+- Fix for python 3.12 when using `CAP.caps2surf()`. 
+    - Changes in pathlib.py in Python 3.12 results in an error message format change. The error message now includes
+      quotes (e.g., "not 'Nifti1Image'") instead of the previous format without quotes ("not Nifti1Image"). This issue
+      arises when using ``neuromaps.transforms.mni_to_fslr`` within CAP.caps2surf() as neuromaps captures the error as a
+      string and checks if "not Nifti1Image" is in the string to determine if the input is a NifTI image. As a patch,
+      if the error occurs, a temporary .nii.gz file is created, the statistical image is saved to this file, and it is
+      used as input for neuromaps.transforms.mni_to_fslr. The temporary file is deleted after use. Below is the code
+      implementing this fix.
+
+```python3
+# 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
+                    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
+                    deleted afterwards) at {temp_nifti.name}
+                    """))
+    # Ensure file is closed
+    temp_nifti.close()
+    # Save temporary nifti to temp file
+    nib.save(stat_map, temp_nifti.name)
+    gii_lh, gii_rh = mni152_to_fslr(temp_nifti.name, method=method, fslr_density=fslr_density)
+    # Delete
+    os.unlink(temp_nifti.name)
+```
+- Final patch is for strings in triple quotes. The standard textwrap module is used to remove the indentations at each
+new line.
+
 ## [0.11.0.post2] - 2024-06-22
 ### 💻 Metadata
 - Very minor explanation added to `CAP.calculate_metrics()` regarding using individual dictionaries from merged

neurocaps/__init__.py

@@ -2,4 +2,4 @@
 
 __all__=["analysis", "extraction"]
 # Version in a single place
-__version__ = "0.11.0.post2"
+__version__ = "0.11.1"

neurocaps/_utils/_check_parcel_approach.py

@@ -1,5 +1,5 @@
 """Internal function for checking the validity of parcel_approach."""
-import copy, os, re, warnings
+import copy, os, re, textwrap, warnings
 from nilearn import datasets
 
 def _check_parcel_approach(parcel_approach, call = "TimeseriesExtractor"):
@@ -15,22 +15,22 @@ def _check_parcel_approach(parcel_approach, call = "TimeseriesExtractor"):
                                                                  "rh": [5]}}}}
 
     if not isinstance(parcel_approach,dict) or isinstance(parcel_approach,dict) and len(parcel_approach) > 0 and not isinstance(parcel_approach[list(parcel_approach)[0]],dict):
-        raise ValueError(f"""
+        raise ValueError(textwrap.dedent(f"""
                          Please include a valid `parcel_approach` in one of the following dictionary
                          formats for 'Schaefer' or 'AAL' {valid_parcel_dict}
-                         """)
+                         """))
 
     if len(parcel_approach) > 1:
-        raise ValueError(f"""
+        raise ValueError(textwrap.dedent(f"""
                          Only one parcellation approach can be selected.
                          Example format of `parcel_approach`: {valid_parcel_dict}
-                         """)
+                         """))
 
     if "Schaefer" not in parcel_approach and "AAL" not in parcel_approach and "Custom" not in parcel_approach:
-        raise ValueError(f"""
+        raise ValueError(textwrap.dedent(f"""
                          Please include a valid `parcel_approach` in one of the following formats for
                          'Schaefer', 'AAL', or 'Custom': {valid_parcel_dict}
-                         """)
+                         """))
 
     if "Schaefer" in parcel_approach:
         if "n_rois" not in parcel_approach["Schaefer"]:
@@ -72,30 +72,30 @@ def _check_parcel_approach(parcel_approach, call = "TimeseriesExtractor"):
 
     if "Custom" in parcel_approach:
         if call  == "TimeseriesExtractor" and "maps" not in parcel_approach["Custom"]:
-            raise ValueError(f"""
+            raise ValueError(textwrap.dedent(f"""
                              For `Custom` parcel_approach, a nested key-value pair containing the key 'maps' with the
                              value being a string specifying the location of the parcellation is needed.
                              Example: {valid_parcel_dict['Custom']}
-                             """)
+                             """))
         check_subkeys = ["nodes" in parcel_approach["Custom"], "regions" in parcel_approach["Custom"]]
         if not all(check_subkeys):
             missing_subkeys = [["nodes", "regions"][x] for x,y in enumerate(check_subkeys) if y is False]
             error_message = f"The following sub-keys haven't been detected {missing_subkeys}"
             if call == "TimeseriesExtractor":
-                warnings.warn(f"""
+                warnings.warn(textwrap.dedent(f"""
                               {error_message}.
                               These labels are not needed for timeseries extraction but are needed for future
-                              timeseries or CAPs plotting.""")
+                              timeseries or CAPs plotting."""))
             else:
                 custom_example = {"Custom": {"nodes": ["LH_Vis1", "LH_Vis2", "LH_Hippocampus",
                                                        "RH_Vis1", "RH_Vis2", "RH_Hippocampus"],
                                              "regions": {"Vis" : {"lh": [0,1],
                                                                    "rh": [3,4]}},
                                                                    "Hippocampus": {"lh": [2],"rh": [5]}}}
-                raise ValueError(f"""
+                raise ValueError(textwrap.dedent(f"""
                                  {error_message}.
                                  These subkeys are needed for plotting. Please reassign `parcel_approach` using
-                                 `self.parcel_approach` amd refer to the example structure: {custom_example}""")
+                                 `self.parcel_approach` amd refer to the example structure: {custom_example}"""))
         if call  == "TimeseriesExtractor" and not os.path.isfile(parcel_approach["Custom"]["maps"]):
             raise ValueError("Please specify the location to the custom parcellation to be used.")
 

neurocaps/_utils/_timeseriesextractor_internals/_check_confound_names.py

@@ -1,6 +1,6 @@
 """Internal Function for checking confound names"""
 
-import warnings
+import textwrap, warnings
 def _check_confound_names(high_pass, specified_confound_names, n_acompcor_separate):
     if specified_confound_names is None:
         if high_pass:
@@ -26,17 +26,17 @@ def _check_confound_names(high_pass, specified_confound_names, n_acompcor_separa
         if len(confound_names) > len(check_confounds):
             removed_confounds = [element for element in confound_names if element not in check_confounds]
             if specified_confound_names:
-                warnings.warn(f"""
+                warnings.warn(textwrap.dedent(f"""
                               Since `n_acompcor_separate` has been specified, specified acompcor components in
                               `confound_names` will be disregarded and replaced with the first {n_acompcor_separate}
                               components of the white matter and cerebrospinal fluid masks for each participant.
                               The following components will not be used {removed_confounds}
-                              """)
+                              """))
             confound_names = check_confounds
 
-    print(f"""
+    print(textwrap.dedent(f"""
           List of confound regressors that will be used during timeseries extraction if available in confound
           dataframe: {confound_names}
-          """, flush=True)
+          """), flush=True)
 
     return confound_names

neurocaps/_utils/_timeseriesextractor_internals/_extract_timeseries.py

@@ -1,6 +1,6 @@
 """Internal function to extract timeseries with or without multiprocessing"""
 
-import copy, json, math, os, warnings
+import copy, json, math, os, textwrap, warnings
 import numpy as np, pandas as pd
 from nilearn.maskers import NiftiLabelsMasker
 from nilearn.image import index_img, load_img
@@ -79,10 +79,10 @@ def _extract_timeseries(subj_id, nifti_files, mask_files, event_files, confound_
                     fd_array = confound_df["framewise_displacement"].fillna(0).values
                 else:
                     censor = False
-                    warnings.warn(f"""For subject {subj_id}, `fd_threshold` specified but 'framewise_displacement' is
+                    warnings.warn(textwrap.dedent(f"""For subject {subj_id}, `fd_threshold` specified but 'framewise_displacement' is
                                   not a column in the confound dataframe so removal of volumes after nuisance
                                   regression will not be done.
-                                  """)
+                                  """))
             else:
                 censor = False
 
@@ -149,10 +149,10 @@ def _extract_timeseries(subj_id, nifti_files, mask_files, event_files, confound_
                 timeseries = np.delete(timeseries, censor_volumes, axis=0)
 
         if timeseries.shape[0] == 0:
-            warnings.warn(f"""
+            warnings.warn(textwrap.dedent(f"""
                           Subject {subj_id} timeseries is empty for {run}. Most likely due to condition not
                           existing or TRs corresponding to the condition being removed by `dummy_scans`.
-                          """)
+                          """))
         else:
             subject_timeseries[subj_id].update({run_id: timeseries})
 

neurocaps/_utils/_timeseriesextractor_internals/_timeseriesextractorgetter.py

@@ -1,6 +1,6 @@
 """# A class which is responsible for accessing all TimeseriesExtractorGetter and to keep track of all
 attributes in TimeSeriesExtractor"""
-import copy
+import copy, textwrap
 import numpy as np
 from .._check_parcel_approach import _check_parcel_approach
 from .._pickle_to_dict import _convert_pickle_to_dict
@@ -52,11 +52,11 @@ def subject_timeseries(self):
 
     @subject_timeseries.setter
     def subject_timeseries(self, subject_dict):
-        error_message = """
+        error_message = textwrap.dedent("""
                         A valid pickle file/be a nested dictionary where the first level is the subject id, second level
                         is the run number in the form of 'run-#', and the final level is the timeseries as a numpy
                         array.
-                        """
+                        """)
         if isinstance(subject_dict, str) and subject_dict.endswith(".pkl"):
             self._subject_timeseries = _convert_pickle_to_dict(subject_dict)
         elif isinstance(subject_dict, dict):