Add raw stc (mne-tools#12001)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Marijn van Vliet <w.m.vanvliet@gmail.com>
Co-authored-by: Daniel McCloy <dan@mccloy.info>

Author: 4 people

doc/changes/devel.rst

@@ -29,6 +29,7 @@ Enhancements
 - Added public :func:`mne.io.write_info` to complement :func:`mne.io.read_info` (:gh:`11918` by `Eric Larson`_)
 - Added option ``remove_dc`` to to :meth:`Raw.compute_psd() <mne.io.Raw.compute_psd>`, :meth:`Epochs.compute_psd() <mne.Epochs.compute_psd>`, and :meth:`Evoked.compute_psd() <mne.Evoked.compute_psd>`, to allow skipping DC removal when computing Welch or multitaper spectra (:gh:`11769` by `Nikolai Chapochnikov`_)
 - Add the possibility to provide a float between 0 and 1 as ``n_grad``, ``n_mag`` and ``n_eeg`` in `~mne.compute_proj_raw`, `~mne.compute_proj_epochs` and `~mne.compute_proj_evoked` to select the number of vectors based on the cumulative explained variance (:gh:`11919` by `Mathieu Scheltienne`_)
+- Add extracting all time courses in a label using :func:`mne.extract_label_time_course` without applying an aggregation function (like ``mean``) (:gh:`12001` by `Hamza Abdelhedi`_)
 - Added support for Artinis fNIRS data files to :func:`mne.io.read_raw_snirf` (:gh:`11926` by `Robert Luke`_)
 - Add helpful error messages when using methods on empty :class:`mne.Epochs`-objects (:gh:`11306` by `Martin Schulz`_)
 - Add support for passing a :class:`python:dict` as ``sensor_color`` to specify per-channel-type colors in :func:`mne.viz.plot_alignment` (:gh:`12067` by `Eric Larson`_)

mne/source_estimate.py

@@ -3240,6 +3240,7 @@ def _pca_flip(flip, data):
     "mean_flip": lambda flip, data: np.mean(flip * data, axis=0),
     "max": lambda flip, data: np.max(np.abs(data), axis=0),
     "pca_flip": _pca_flip,
+    None: lambda flip, data: data,  # Return Identity: Preserves all vertices.
 }
 
 
@@ -3494,7 +3495,7 @@ def _volume_labels(src, labels, mri_resolution):
 
 
 def _get_default_label_modes():
-    return sorted(_label_funcs.keys()) + ["auto"]
+    return sorted(_label_funcs.keys(), key=lambda x: (x is None, x)) + ["auto"]
 
 
 def _get_allowed_label_modes(stc):
@@ -3572,7 +3573,12 @@ def _gen_extract_label_time_course(
         )
 
         # do the extraction
-        label_tc = np.zeros((n_labels,) + stc.data.shape[1:], dtype=stc.data.dtype)
+        if mode is None:
+            # prepopulate an empty list for easy array-like index-based assignment
+            label_tc = [None] * max(len(label_vertidx), len(src_flip))
+        else:
+            # For other modes, initialize the label_tc array
+            label_tc = np.zeros((n_labels,) + stc.data.shape[1:], dtype=stc.data.dtype)
         for i, (vertidx, flip) in enumerate(zip(label_vertidx, src_flip)):
             if vertidx is not None:
                 if isinstance(vertidx, sparse.csr_matrix):
@@ -3585,15 +3591,13 @@ def _gen_extract_label_time_course(
                     this_data = stc.data[vertidx]
                 label_tc[i] = func(flip, this_data)
 
-        # extract label time series for the vol src space (only mean supported)
-        offset = nvert[:-n_mean].sum()  # effectively :2 or :0
-        for i, nv in enumerate(nvert[2:]):
-            if nv != 0:
-                v2 = offset + nv
-                label_tc[n_mode + i] = np.mean(stc.data[offset:v2], axis=0)
-                offset = v2
-
-        # this is a generator!
+        if mode is not None:
+            offset = nvert[:-n_mean].sum()  # effectively :2 or :0
+            for i, nv in enumerate(nvert[2:]):
+                if nv != 0:
+                    v2 = offset + nv
+                    label_tc[n_mode + i] = np.mean(stc.data[offset:v2], axis=0)
+                    offset = v2
         yield label_tc
 
 

mne/tests/test_source_estimate.py

@@ -678,12 +678,24 @@ def test_extract_label_time_course(kind, vector):
 
     label_tcs = dict(mean=np.arange(n_labels)[:, None] * np.ones((n_labels, n_times)))
     label_tcs["max"] = label_tcs["mean"]
+    label_tcs[None] = label_tcs["mean"]
 
     # compute the mean with sign flip
     label_tcs["mean_flip"] = np.zeros_like(label_tcs["mean"])
     for i, label in enumerate(labels):
         label_tcs["mean_flip"][i] = i * np.mean(label_sign_flip(label, src[:2]))
 
+    # compute pca_flip
+    label_flip = []
+    for i, label in enumerate(labels):
+        this_flip = i * label_sign_flip(label, src[:2])
+        label_flip.append(this_flip)
+    # compute pca_flip
+    label_tcs["pca_flip"] = np.zeros_like(label_tcs["mean"])
+    for i, (label, flip) in enumerate(zip(labels, label_flip)):
+        sign = np.sign(np.dot(np.full((flip.shape[0]), i), flip))
+        label_tcs["pca_flip"][i] = sign * label_tcs["mean"][i]
+
     # generate some stc's with known data
     stcs = list()
     pad = (((0, 0), (2, 0), (0, 0)), "constant")
@@ -734,7 +746,7 @@ def test_extract_label_time_course(kind, vector):
             assert_array_equal(arr[1:], vol_means_t)
 
     # test the different modes
-    modes = ["mean", "mean_flip", "pca_flip", "max", "auto"]
+    modes = ["mean", "mean_flip", "pca_flip", "max", "auto", None]
 
     for mode in modes:
         if vector and mode not in ("mean", "max", "auto"):
@@ -748,18 +760,36 @@ def test_extract_label_time_course(kind, vector):
         ]
         assert len(label_tc) == n_stcs
         assert len(label_tc_method) == n_stcs
-        for tc1, tc2 in zip(label_tc, label_tc_method):
-            assert tc1.shape == (n_labels + len(vol_means),) + end_shape
-            assert tc2.shape == (n_labels + len(vol_means),) + end_shape
-            assert_allclose(tc1, tc2, rtol=1e-8, atol=1e-16)
+        for j, (tc1, tc2) in enumerate(zip(label_tc, label_tc_method)):
+            if mode is None:
+                assert all(arr.shape[1] == tc1[0].shape[1] for arr in tc1)
+                assert all(arr.shape[1] == tc2[0].shape[1] for arr in tc2)
+                assert (len(tc1), tc1[0].shape[1]) == (n_labels,) + end_shape
+                assert (len(tc2), tc2[0].shape[1]) == (n_labels,) + end_shape
+                for arr1, arr2 in zip(tc1, tc2):  # list of arrays
+                    assert_allclose(arr1, arr2, rtol=1e-8, atol=1e-16)
+            else:
+                assert tc1.shape == (n_labels + len(vol_means),) + end_shape
+                assert tc2.shape == (n_labels + len(vol_means),) + end_shape
+                assert_allclose(tc1, tc2, rtol=1e-8, atol=1e-16)
             if mode == "auto":
                 use_mode = "mean" if vector else "mean_flip"
             else:
                 use_mode = mode
-            # XXX we don't check pca_flip, probably should someday...
-            if use_mode in ("mean", "max", "mean_flip"):
+            if mode == "pca_flip":
+                for arr1, arr2 in zip(tc1, label_tcs[use_mode]):
+                    assert_array_almost_equal(arr1, arr2)
+            elif use_mode is None:
+                for arr1, arr2 in zip(
+                    tc1[:n_labels], label_tcs[use_mode]
+                ):  # list of arrays
+                    assert_allclose(
+                        arr1, np.tile(arr2, (arr1.shape[0], 1)), rtol=1e-8, atol=1e-16
+                    )
+            elif use_mode in ("mean", "max", "mean_flip"):
                 assert_array_almost_equal(tc1[:n_labels], label_tcs[use_mode])
-            assert_array_almost_equal(tc1[n_labels:], vol_means_t)
+            if mode is not None:
+                assert_array_almost_equal(tc1[n_labels:], vol_means_t)
 
     # test label with very few vertices (check SVD conditionals)
     label = Label(vertices=src[0]["vertno"][:2], hemi="lh")

mne/utils/docs.py

@@ -1203,6 +1203,9 @@ def _reflow_param_docstring(docstring, has_first_line=True, width=75):
 - ``'auto'`` (default)
     Uses ``'mean_flip'`` when a standard source estimate is applied, and
     ``'mean'`` when a vector source estimate is supplied.
+- ``None``
+    No aggregation is performed, and an array of shape ``(n_vertices, n_times)`` is
+    returned.
 
     .. versionadded:: 0.21
        Support for ``'auto'``, vector, and volume source estimates.