ENH: fetch heavy sample data lazily in jupyterlite notebooks

Author: natinew77-creator

doc/conf.py

@@ -707,25 +707,24 @@
         "_base = _page.split('/lite/')[0] + '/mne_data/'\n"
         "mne_data_path = '/tmp/mne_data'\n"
         "_sample_dir = mne_data_path + '/MNE-sample-data'\n"
+        "# Eager 'core': small, commonly-used sample files fetched once at\n"
+        "# notebook start. The heavy files (raw / filt raw / ernoise / fwd /\n"
+        "# inv / src, ~360 MB total) are intentionally omitted here -- they are\n"
+        "# fetched lazily on first read via the reader shims below, so each\n"
+        "# notebook only downloads the sample files it actually uses.\n"
         "_sample_files = [\n"
         "    'version.txt',\n"
-        "    'MEG/sample/sample_audvis_raw.fif',\n"
         "    'MEG/sample/sample_audvis_raw-eve.fif',\n"
         "    'MEG/sample/sample_audvis_filt-0-40_raw-eve.fif',\n"
         "    'MEG/sample/sample_audvis_ecg-proj.fif',\n"
         "    'MEG/sample/sample_audvis-cov.fif',\n"
         "    'MEG/sample/sample_audvis-ave.fif',\n"
-        "    'MEG/sample/sample_audvis_filt-0-40_raw.fif',\n"
-        "    'MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif',\n"
-        "    'MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif',\n"
-        "    'MEG/sample/ernoise_raw.fif',\n"
         "    'MEG/sample/sample_audvis-no-filter-ave.fif',\n"
         "    'MEG/sample/sample_audvis_raw-trans.fif',\n"
         "    'MEG/sample/sample_audvis-shrunk-cov.fif',\n"
         "    'MEG/sample/sample_audvis-meg-lh.stc',\n"
         "    'MEG/sample/sample_audvis-meg-rh.stc',\n"
         "    'subjects/sample/mri/T1.mgz',\n"
-        "    'subjects/sample/bem/sample-oct-6-src.fif',\n"
         "    'subjects/sample/surf/rh.pial',\n"
         "    'subjects/sample/surf/lh.pial',\n"
         "    'subjects/sample/surf/rh.white',\n"
@@ -878,6 +877,27 @@
         "    )\n"
         "_mne_minv.read_inverse_operator = _lite_read_inverse_operator\n"
         "mne.minimum_norm.read_inverse_operator = _lite_read_inverse_operator\n"
+        "# Lazily fetch the heavy sample raw / source-space files only when a\n"
+        "# notebook actually reads them (same pattern as the fwd/inv shims\n"
+        "# above), instead of pulling the whole sample set up front.\n"
+        "_orig_read_raw_fif = mne.io.read_raw_fif\n"
+        "def _lite_read_raw_fif(fname, *_a, **_kw):\n"
+        "    return _orig_read_raw_fif(\n"
+        "        _lite_fetch_if_under_mne_data(fname), *_a, **_kw\n"
+        "    )\n"
+        "mne.io.read_raw_fif = _lite_read_raw_fif\n"
+        "_orig_read_raw = mne.io.read_raw\n"
+        "def _lite_read_raw(fname, *_a, **_kw):\n"
+        "    return _orig_read_raw(\n"
+        "        _lite_fetch_if_under_mne_data(fname), *_a, **_kw\n"
+        "    )\n"
+        "mne.io.read_raw = _lite_read_raw\n"
+        "_orig_read_source_spaces = mne.read_source_spaces\n"
+        "def _lite_read_source_spaces(fname, *_a, **_kw):\n"
+        "    return _orig_read_source_spaces(\n"
+        "        _lite_fetch_if_under_mne_data(fname), *_a, **_kw\n"
+        "    )\n"
+        "mne.read_source_spaces = _lite_read_source_spaces\n"
         "\n"
         "# EXPERIMENTAL 3D: MNE's normal Brain/VTK stack can't load in WASM, so\n"
         "# route SourceEstimate.plot() through pyvista-js (vtk.js) instead.\n"