From 6191b53cf7d8fe485590fc082f991ddbbb33b9f3 Mon Sep 17 00:00:00 2001
From: ualsbombe <ualsbombe@protonmail.com>
Date: Fri, 26 Jun 2026 12:23:19 +0200
Subject: [PATCH 1/2] remove plotting abs as the defauls for plotting
 volumetric

---
 mne/source_estimate.py                  | 1 +
 mne/viz/_3d.py                          | 7 +++++--
 tutorials/inverse/50_beamformer_lcmv.py | 2 +-
 3 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/mne/source_estimate.py b/mne/source_estimate.py
index edb1ae45764..60b82522fea 100644
--- a/mne/source_estimate.py
+++ b/mne/source_estimate.py
@@ -2399,6 +2399,7 @@ def plot(
         initial_time=None,
         initial_pos=None,
         verbose=None,
+        plot_abs=False,
     ):
         data = self.magnitude() if self._data_ndim == 3 else self
         return plot_volume_source_estimates(
diff --git a/mne/viz/_3d.py b/mne/viz/_3d.py
index 2c5e081b8f0..fa427768707 100644
--- a/mne/viz/_3d.py
+++ b/mne/viz/_3d.py
@@ -2828,7 +2828,7 @@ def _click_to_cut_coords(event, params):
         # We don't care about directionality, just which is which dim
         codes = codes.replace("L", "R").replace("P", "A").replace("I", "S")
         idx = codes.index(dict(x="R", y="A", z="S")[ax])
-        img_data = np.abs(_get_img_fdata(params["img_idx"]))
+        img_data = _get_img_fdata(params["img_idx"])
         ijk = _cut_coords_to_ijk(cut_coords, params["img_idx"])
         if idx == 0:
             ijk[0] = np.argmax(img_data[:, ijk[1], ijk[2]])
@@ -2952,6 +2952,8 @@ def _plot_and_correct(*, params, cut_coords):
         symmetric_cbar=True,
         title="",
     )
+    if mode == 'glass_brain':
+            plot_kwargs['plot_abs']=False
     params["axes"].clear()
     if params.get("fig_anat") is not None and plot_kwargs["colorbar"]:
         params["fig_anat"]._cbar.ax.clear()
@@ -2988,6 +2990,7 @@ def plot_volume_source_estimates(
     initial_time=None,
     initial_pos=None,
     verbose=None,
+
 ):
     """Plot Nutmeg style volumetric source estimates using nilearn.
 
@@ -3184,7 +3187,7 @@ def plot_volume_source_estimates(
     lx = ax_time.axvline(stc.times[time_idx], color="g")
     params.update(fig=fig, ax_time=ax_time, lx=lx, axes=axes)
 
-    allow_pos_lims = mode != "glass_brain"
+    allow_pos_lims = True
     mapdata = _process_clim(clim, colormap, transparent, stc.data, allow_pos_lims)
     _separate_map(mapdata)
     diverging = "pos_lims" in mapdata["clim"]
diff --git a/tutorials/inverse/50_beamformer_lcmv.py b/tutorials/inverse/50_beamformer_lcmv.py
index bd7228fa840..ff639ee7d9d 100644
--- a/tutorials/inverse/50_beamformer_lcmv.py
+++ b/tutorials/inverse/50_beamformer_lcmv.py
@@ -253,7 +253,7 @@
 # On MNI glass brain (orthoview; 2D)
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-stc.plot(mode="glass_brain", clim=dict(kind="value", lims=lims), **kwargs)
+stc.plot(mode="glass_brain", clim=dict(kind="value", pos_lims=lims), **kwargs)
 
 # %%
 # Volumetric rendering (3D) with vectors

From 972dcd7b7b921e802a8d590eec58fa6aabc36545 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 26 Jun 2026 10:25:49 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 mne/viz/_3d.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/mne/viz/_3d.py b/mne/viz/_3d.py
index fa427768707..bcf24cce565 100644
--- a/mne/viz/_3d.py
+++ b/mne/viz/_3d.py
@@ -2952,8 +2952,8 @@ def _plot_and_correct(*, params, cut_coords):
         symmetric_cbar=True,
         title="",
     )
-    if mode == 'glass_brain':
-            plot_kwargs['plot_abs']=False
+    if mode == "glass_brain":
+        plot_kwargs["plot_abs"] = False
     params["axes"].clear()
     if params.get("fig_anat") is not None and plot_kwargs["colorbar"]:
         params["fig_anat"]._cbar.ax.clear()
@@ -2990,7 +2990,6 @@ def plot_volume_source_estimates(
     initial_time=None,
     initial_pos=None,
     verbose=None,
-
 ):
     """Plot Nutmeg style volumetric source estimates using nilearn.