DOC: Document how t_adjust is used

.circleci/config.yml

@@ -2,7 +2,7 @@ version: 2.1
 jobs:
   build_docs:
     docker:
-      - image: cimg/python:3.12.6
+      - image: cimg/python:3.13.11
     steps:
       # Get our data and merge with upstream
       - checkout
@@ -25,7 +25,7 @@ jobs:
 
   deploy:
     docker:
-      - image: cimg/python:3.12.6
+      - image: cimg/python:3.13.11
     steps:
       - add_ssh_keys:
           fingerprints:

.github/workflows/tests.yml

@@ -27,17 +27,16 @@ jobs:
       MNE_STIM_CHANNEL: 'STI101'
       OPENBLAS_NUM_THREADS: '1'
       PYTHONUNBUFFERED: '1'
-      PYTHON_VERSION: '3.10'
     steps:
       - uses: actions/checkout@v6
       - uses: actions/setup-python@v6
         with:
-          python-version: '3.10'
+          python-version: '3.13'
         name: 'Setup python'
       - run: |
           pip install --upgrade --only-binary=":all:" pip setuptools wheel
-          pip install --upgrade --only-binary=":all:" numpy scipy matplotlib h5io
-          pip install --upgrade pyyaml patsy pytest pytest-cov codecov flake8 pydocstyle numpydoc git+https://github.com/mne-tools/mne-python@main
+          pip install --upgrade --only-binary=":all:" -ve .
+          pip install --upgrade pyyaml patsy pytest pytest-cov codecov flake8 pydocstyle numpydoc
         name: Install
       - run: make flake
       - run: pytest mnefun

doc/overview.rst

@@ -457,8 +457,12 @@ tmin : float
 tmax : float
     tmax for events.
 t_adjust : float
-    Adjustment for delays (e.g., -4e-3 compensates for a 4 ms delay
-    in the trigger.
+    Adjustment for delays, e.g., -65e-3 compensates for a 65 ms delay
+    between when the trigger was sent and the actual real-life event occurred
+    (like a screen display) by moving the trigger later in time.
+    It is used in the ``epoching`` and ``covariance`` steps as::
+
+            events[:, 0] += int(np.round(-params.t_adjust * sfreq))
 baseline : tuple | None | str
     Baseline to use. If "individual", use ``params.bmin`` and
     ``params.bmax``, otherwise pass as the baseline parameter to

mnefun/_atlas.py

@@ -214,7 +214,7 @@ def get_atlas_roi_mask(stc, roi, atlas='IXI', atlas_subject=None,
     src = read_source_spaces(fname_src)
     mri = op.join(subjects_dir, atlas_subject, 'mri',
                   '%s_brain_ANTS_%s_atlas.mgz' % (atlas_subject, atlas))
-    if not np.in1d(stc.vertices, src[0]['vertno']).all():
+    if not np.isin(stc.vertices, src[0]['vertno']).all():
         raise RuntimeError('stc does not appear to be created from %s '
                            'volumetric source space' % (atlas_subject,))
     rr = src[0]['rr'][stc.vertices]

mnefun/_inverse.py

@@ -158,8 +158,8 @@ def get_fsaverage_medial_vertices(concatenate=True, subjects_dir=None,
     lh = read_label(op.join(label_dir, 'lh.Medial_wall.label'))
     rh = read_label(op.join(label_dir, 'rh.Medial_wall.label'))
     if concatenate:
-        bad_left = np.where(np.in1d(vertices[0], lh.vertices))[0]
-        bad_right = np.where(np.in1d(vertices[1], rh.vertices))[0]
+        bad_left = np.where(np.isin(vertices[0], lh.vertices))[0]
+        bad_right = np.where(np.isin(vertices[1], rh.vertices))[0]
         return np.concatenate((bad_left, bad_right + len(vertices[0])))
     else:
         return [lh.vertices, rh.vertices]
@@ -184,7 +184,7 @@ def get_fsaverage_label_operator(parc='aparc.a2009s', remove_bads=True,
         bads = dict(lh=bads[0], rh=bads[1])
         assert all(b.size > 1 for b in bads.values())
         labels = [label for label in labels
-                  if np.in1d(label.vertices, bads[label.hemi]).mean() < 0.8]
+                  if np.isin(label.vertices, bads[label.hemi]).mean() < 0.8]
         del bads
     if combine_medial:
         labels = combine_medial_labels(labels)

mnefun/_mnefun.py

@@ -504,7 +504,7 @@ def score_fun(p, subjects, run_indices):
     run_indices = [np.array(run_idx) if run_idx is not None
                    else np.arange(len(p.run_names)) for run_idx in run_indices]
     assert all(run_idx.ndim == 1 for run_idx in run_indices)
-    assert all(np.in1d(r, np.arange(len(p.run_names))).all()
+    assert all(np.isin(r, np.arange(len(p.run_names))).all()
                for r in run_indices)
 
     # Actually do the work

mnefun/_report.py

@@ -1235,7 +1235,7 @@ def _proj_fig(fname, info, proj_nums, proj_meg, kind, use_ch, duration):
         ch_names = [info['ch_names'][pick]
                     for pick in mne.pick_types(info, meg=meg, eeg=eeg)]
         # Some of these will be missing because of prebads
-        idx = np.where([np.in1d(ch_names, proj['data']['col_names']).all()
+        idx = np.where([np.isin(ch_names, proj['data']['col_names']).all()
                         for proj in projs])[0]
         if len(idx) != count:
             raise RuntimeError('Expected %d %s projector%s for channel type '

mnefun/_scoring.py

@@ -132,7 +132,7 @@ def _read_events(p, subj, ridx, raw, ratios, picker=None):
         else:
             these_events = read_events(fname)
             if ids is not None:
-                these_events = these_events[np.in1d(these_events[:, 2], ids)]
+                these_events = these_events[np.isin(these_events[:, 2], ids)]
         events.append(these_events)
     assert len(events) == len(fnames)
     if len(events) == 1 and len(raw._first_samps) > 1:  # for split raw

mnefun/_status.py

@@ -4,14 +4,10 @@
 
 from os import path as op
 
-from .externals import tabulate
 from ._paths import (get_raw_fnames, get_event_fnames, get_cov_fwd_inv_fnames,
                      get_epochs_evokeds_fnames, get_report_fnames, _prebad)
 from ._fix import _all_files_fixed
 
-# make our table tighter
-tabulate.MIN_PADDING = 0
-
 
 def _have_all(fnames):
     """Check to make sure all files exist."""
@@ -115,6 +111,8 @@ def print_proc_status(p, subjects, structurals, analyses, run_indices):
     assert all(len(steps) == len(s) for s in steps_all)
 
     # Print it out in a tabular manner
+    import tabulate
+    tabulate.MIN_PADDING = 0  # make our table tighter
     headers = [''] + steps
     table = [[subj] + [status_mapping[key] for key in subj_steps]
              for subj, subj_steps in zip(subjects, steps_all)]

mnefun/_viz.py

@@ -245,8 +245,8 @@ def plot_chpi_snr_raw(raw, win_length, n_harmonics=None, show=True, *,
     pick_grad = pick_types(raw.info, meg='grad', exclude=[])
     nchan = len(pick_meg)
     # grad and mag indices into an array that already has meg channels only
-    pick_mag_ = np.in1d(pick_meg, pick_mag).nonzero()[0]
-    pick_grad_ = np.in1d(pick_meg, pick_grad).nonzero()[0]
+    pick_mag_ = np.isin(pick_meg, pick_mag).nonzero()[0]
+    pick_grad_ = np.isin(pick_meg, pick_grad).nonzero()[0]
 
     # create general linear model for the data
     t = np.arange(buflen) / float(sfreq)