BUG: Fix bug with plot_projs_topomap (#11792)

mne/viz/tests/test_topomap.py

@@ -27,6 +27,7 @@
     create_info,
     read_cov,
     EvokedArray,
+    compute_proj_raw,
     Projection,
 )
 from mne._fiff.proj import make_eeg_average_ref_proj
@@ -71,6 +72,8 @@
 layout = read_layout("Vectorview-all")
 cov_fname = base_dir / "test-cov.fif"
 
+fast_test = dict(res=8, contours=0, sensors=False)
+
 
 @pytest.mark.parametrize("constrained_layout", (False, True))
 def test_plot_topomap_interactive(constrained_layout):
@@ -135,32 +138,36 @@ def test_plot_projs_topomap():
     """Test plot_projs_topomap."""
     projs = read_proj(ecg_fname)
     info = read_info(raw_fname)
-    fast_test = {"res": 8, "contours": 0, "sensors": False}
     plot_projs_topomap(projs, info=info, colorbar=True, **fast_test)
-    plt.close("all")
-    ax = plt.subplot(111)
+    _, ax = plt.subplots()
     projs[3].plot_topomap(info)
     plot_projs_topomap(projs[:1], info, axes=ax, **fast_test)  # test axes
-    plt.close("all")
     triux_info = read_info(triux_fname)
     plot_projs_topomap(triux_info["projs"][-1:], triux_info, **fast_test)
-    plt.close("all")
     plot_projs_topomap(triux_info["projs"][:1], triux_info, **fast_test)
-    plt.close("all")
     eeg_avg = make_eeg_average_ref_proj(info)
     eeg_avg.plot_topomap(info, **fast_test)
-    plt.close("all")
     # test vlims
     for vlim in ("joint", (-1, 1), (None, 0.5), (0.5, None), (None, None)):
         plot_projs_topomap(projs[:-1], info, vlim=vlim, colorbar=True)
-    plt.close("all")
 
     eeg_proj = make_eeg_average_ref_proj(info)
     info_meg = pick_info(info, pick_types(info, meg=True, eeg=False))
     with pytest.raises(ValueError, match="Missing channels"):
         plot_projs_topomap([eeg_proj], info_meg)
 
 
+@pytest.mark.parametrize("vlim", ("joint", None))
+@pytest.mark.parametrize("meg", ("combined", "separate"))
+def test_plot_projs_topomap_joint(meg, vlim, raw):
+    """Test that plot_projs_topomap works with joint vlim."""
+    if vlim is None:
+        vlim = (None, None)
+    projs = compute_proj_raw(raw, meg=meg)
+    fig = plot_projs_topomap(projs, info=raw.info, vlim=vlim, **fast_test)
+    assert len(fig.axes) == 4  # 2 mag, 2 grad
+
+
 def test_plot_topomap_animation(capsys):
     """Test topomap plotting."""
     # evoked
@@ -322,7 +329,6 @@ def test_plot_topomap_basic():
     """Test basics of topomap plotting."""
     evoked = read_evokeds(evoked_fname, "Left Auditory", baseline=(None, 0))
     res = 8
-    fast_test = dict(res=res, contours=0, sensors=False, time_unit="s")
     fast_test_noscale = dict(res=res, contours=0, sensors=False)
     ev_bad = evoked.copy().pick(picks="eeg")
     ev_bad.pick(ev_bad.ch_names[:2])
@@ -649,8 +655,6 @@ def test_plot_arrowmap(evoked):
 @testing.requires_testing_data
 def test_plot_topomap_neuromag122():
     """Test topomap plotting."""
-    res = 8
-    fast_test = dict(res=res, contours=0, sensors=False)
     evoked = read_evokeds(evoked_fname, "Left Auditory", baseline=(None, 0))
     evoked.pick(picks="grad")
     evoked.pick(evoked.ch_names[:122])

mne/viz/topomap.py

@@ -474,34 +474,46 @@ def _plot_projs_topomap(
     projs = _check_type_projs(projs)
     _validate_type(info, "info", "info")
 
-    types, datas, poss, spheres, outliness, ch_typess = [], [], [], [], [], []
+    # Preprocess projs to deal with joint MEG projectors. If we duplicate these and
+    # split into mag and grad, they should work as expected
+    info_names = _clean_names(info["ch_names"], remove_whitespace=True)
+    use_projs = list()
+    for proj in projs:
+        proj = _eliminate_zeros(proj)  # gh 5641, makes a copy
+        proj["data"]["col_names"] = _clean_names(
+            proj["data"]["col_names"],
+            remove_whitespace=True,
+        )
+        picks = pick_channels(info_names, proj["data"]["col_names"], ordered=True)
+        proj_types = info.get_channel_types(picks)
+        unique_types = sorted(set(proj_types))
+        for type_ in unique_types:
+            proj_picks = np.where([proj_type == type_ for proj_type in proj_types])[0]
+            use_projs.append(copy.deepcopy(proj))
+            use_projs[-1]["data"]["data"] = proj["data"]["data"][:, proj_picks]
+            use_projs[-1]["data"]["col_names"] = [
+                proj["data"]["col_names"][pick] for pick in proj_picks
+            ]
+    projs = use_projs
+
+    datas, poss, spheres, outliness, ch_typess = [], [], [], [], []
     for proj in projs:
         # get ch_names, ch_types, data
-        proj = _eliminate_zeros(proj)  # gh 5641
-        ch_names = _clean_names(proj["data"]["col_names"], remove_whitespace=True)
-        if vlim == "joint":
-            ch_idxs = np.where(np.isin(info["ch_names"], proj["data"]["col_names"]))[0]
-            these_ch_types = info.get_channel_types(ch_idxs, unique=True)
-            # each projector should have only one channel type
-            assert len(these_ch_types) == 1
-            types.append(list(these_ch_types)[0])
         data = proj["data"]["data"].ravel()
-        info_names = _clean_names(info["ch_names"], remove_whitespace=True)
-        picks = pick_channels(info_names, ch_names, ordered=True)
+        picks = pick_channels(info_names, proj["data"]["col_names"], ordered=True)
         use_info = pick_info(info, picks)
+        these_ch_types = use_info.get_channel_types(unique=True)
+        assert len(these_ch_types) == 1  # should be guaranteed above
+        ch_type = these_ch_types[0]
         (
             data_picks,
             pos,
             merge_channels,
             names,
-            ch_type,
+            _,
             this_sphere,
             clip_origin,
-        ) = _prepare_topomap_plot(
-            use_info,
-            _get_plot_ch_type(use_info, None),
-            sphere=sphere,
-        )
+        ) = _prepare_topomap_plot(use_info, ch_type, sphere=sphere)
         these_outlines = _make_head_outlines(sphere, pos, outlines, clip_origin)
         data = data[data_picks]
         if merge_channels:
@@ -530,8 +542,8 @@ def _plot_projs_topomap(
     # handle vmin/vmax
     vlims = [None for _ in range(len(datas))]
     if vlim == "joint":
-        for _ch_type in set(types):
-            idx = np.where(np.isin(types, _ch_type))[0]
+        for _ch_type in set(ch_typess):
+            idx = np.where(np.isin(ch_typess, _ch_type))[0]
             these_data = np.concatenate(np.array(datas, dtype=object)[idx])
             norm = all(these_data >= 0)
             _vl = _setup_vmin_vmax(these_data, vmin=None, vmax=None, norm=norm)