Backport #12803 and #12804 (#12806)

Co-authored-by: Eric Larson <larson.eric.d@gmail.com>

Author: Mathieu Scheltienne

doc/_includes/institutional-partners.rst

@@ -19,7 +19,7 @@ Current partners
 - `Children’s Hospital of Philadelphia Research Institute <https://www.research.chop.edu/imaging/>`_
 - `Donders Institute for Brain, Cognition and Behaviour at Radboud University <https://www.ru.nl/donders/>`_
 - `Harvard Medical School <https://hms.harvard.edu/>`_
-- `Human Neuroscience Platform at Fondation Campus Biotech Geneva <https://hnp.fcbg.ch/>`_
+- `Fondation Campus Biotech Geneva <https://fcbg.ch/>`_
 - `Institut national de recherche en informatique et en automatique <https://www.inria.fr/>`_
 - `Karl-Franzens-Universität Graz <https://www.uni-graz.at/>`_
 - `Massachusetts General Hospital <https://www.massgeneral.org/>`_

doc/changes/devel/12803.bugfix.rst

@@ -0,0 +1 @@
+Fix handling of MRI file-path in :class:`mne.SourceSpaces` and safeguard saving of :class:`pathlib.Path` with ``h5io`` by casting to :class:`str`, by `Mathieu Scheltienne`_.

doc/changes/devel/12804.bugfix.rst

@@ -0,0 +1 @@
+Cast ``fwd["info"]`` to :class:`~mne.Info` and ``fwd["src"]`` to :class:`~mne.SourceSpaces` when loading a forward solution from an HDF5 file, by `Mathieu Scheltienne`_.

doc/conf.py

@@ -1107,9 +1107,9 @@ def append_attr_meth_examples(app, what, name, obj, options, lines):
             size=xl,
         ),
         dict(
-            name="Human Neuroscience Platforn at Fondation Campus Biotech Geneva",  # noqa E501
+            name="Fondation Campus Biotech Geneva",
             img="FCBG.svg",
-            url="https://hnp.fcbg.ch/",
+            url="https://fcbg.ch/",
             size=sm,
         ),
     ],

mne/_freesurfer.py

@@ -599,27 +599,30 @@ def read_talxfm(subject, subjects_dir=None, verbose=None):
     return mri_mni_t
 
 
-def _check_mri(mri, subject, subjects_dir):
+def _check_mri(mri, subject, subjects_dir) -> str:
     """Check whether an mri exists in the Freesurfer subject directory."""
-    _validate_type(mri, "path-like", "mri")
-    if op.isfile(mri) and op.basename(mri) != mri:
-        return mri
-    if not op.isfile(mri):
+    _validate_type(mri, "path-like", mri)
+    mri = Path(mri)
+    if mri.is_file() and mri.name != mri:
+        return str(mri)
+    elif not mri.is_file():
         if subject is None:
             raise FileNotFoundError(
-                f"MRI file {mri!r} not found and no subject provided"
+                f"MRI file {mri!r} not found and no subject provided."
             )
-        subjects_dir = str(get_subjects_dir(subjects_dir, raise_error=True))
-        mri = op.join(subjects_dir, subject, "mri", mri)
-        if not op.isfile(mri):
-            raise FileNotFoundError(f"MRI file {mri!r} not found")
-    if op.basename(mri) == mri:
-        err = (
-            f"Ambiguous filename - found {mri!r} in current folder.\n"
-            "If this is correct prefix name with relative or absolute path"
+        subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
+        mri = subjects_dir / subject / "mri" / mri
+        if not mri.is_file():
+            raise FileNotFoundError(
+                f"MRI file {mri!r} not found in the subjects directory "
+                f"{subjects_dir!r} for subject {subject}."
+            )
+    if mri.name == mri:
+        raise OSError(
+            f"Ambiguous filename - found {mri!r} in current folder. "
+            "If this is correct prefix name with relative or absolute path."
         )
-        raise OSError(err)
-    return mri
+    return str(mri)
 
 
 def _read_mri_info(path, units="m", return_img=False, use_nibabel=False):

mne/forward/forward.py

@@ -53,6 +53,7 @@
 from ..label import Label
 from ..source_estimate import _BaseSourceEstimate, _BaseVectorSourceEstimate
 from ..source_space._source_space import (
+    SourceSpaces,
     _get_src_nn,
     _read_source_spaces_from_tree,
     _set_source_space_vertices,
@@ -913,7 +914,10 @@ def _write_forward_hdf5(fname, fwd):
 
 def _read_forward_hdf5(fname):
     read_hdf5, _ = _import_h5io_funcs()
-    return Forward(read_hdf5(fname)["fwd"])
+    fwd = Forward(read_hdf5(fname)["fwd"])
+    fwd["info"] = Info(fwd["info"])
+    fwd["src"] = SourceSpaces(fwd["src"])
+    return fwd
 
 
 def _write_forward_solution(fid, fwd):

mne/source_space/_source_space.py

@@ -1665,7 +1665,7 @@ def setup_volume_source_space(
 
         .. note:: For a discrete source space (``pos`` is a dict),
                   ``mri`` must be None.
-    mri : str | None
+    mri : path-like | None
         The filename of an MRI volume (mgh or mgz) to create the
         interpolation matrix over. Source estimates obtained in the
         volume source space can then be morphed onto the MRI volume
@@ -1791,9 +1791,8 @@ def setup_volume_source_space(
         mri = _check_mri(mri, subject, subjects_dir)
         if isinstance(pos, dict):
             raise ValueError(
-                "Cannot create interpolation matrix for "
-                "discrete source space, mri must be None if "
-                "pos is a dict"
+                "Cannot create interpolation matrix for discrete source space, mri "
+                "must be None if pos is a dict"
             )
 
     if volume_label is not None:

mne/utils/check.py

@@ -167,7 +167,35 @@ def _import_h5py():
 
 def _import_h5io_funcs():
     h5io = _soft_import("h5io", "HDF5-based I/O")
-    return h5io.read_hdf5, h5io.write_hdf5
+
+    # Saving to HDF5 does not support pathlib.Path objects, which are more and more used
+    # in MNE-Python.
+    # Related issue in h5io: https://github.com/h5io/h5io/issues/113
+    def cast_path_to_str(data: dict) -> dict:
+        """Cast all paths value to string in data."""
+        keys2cast = []
+        for key, value in data.items():
+            if isinstance(value, dict):
+                cast_path_to_str(value)
+            if isinstance(value, Path):
+                data[key] = value.as_posix()
+            if isinstance(key, Path):
+                keys2cast.append(key)
+        for key in keys2cast:
+            data[key.as_posix()] = data.pop(key)
+        return data
+
+    def write_hdf5(fname, data, *args, **kwargs):
+        """Write h5 and cast all paths to string in data."""
+        if isinstance(data, dict):
+            data = cast_path_to_str(data)
+        elif isinstance(data, list):
+            for k, elt in enumerate(data):
+                if isinstance(elt, dict):
+                    data[k] = cast_path_to_str(elt)
+        h5io.write_hdf5(fname, data, *args, **kwargs)
+
+    return h5io.read_hdf5, write_hdf5
 
 
 def _import_pymatreader_funcs(purpose):