FIX: add sys.platform guards for interactive plots and large datasets in JupyterLite

- tutorials/evoked/40_whitened.py: guard raw.plot() and epochs.plot()
  (interactive Qt widgets not available in Pyodide/emscripten)
- examples/preprocessing/muscle_ica.py: guard all ica.plot_sources(),
  ica.plot_properties(), ica.plot_overlay(), ica.plot_scores() calls
- tutorials/io/60_ctf_bst_auditory.py: raise RuntimeError on emscripten;
  BST auditory dataset is 2.9 GB and cannot be downloaded in the browser
- tutorials/io/70_reading_eyetracking_data.py: raise RuntimeError on
  emscripten; EyeLink dataset not available in browser
- examples/visualization/eyetracking_plot_heatmap.py: raise RuntimeError
  on emscripten; EyeLink dataset not available in browser

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: natinew77-creator

examples/preprocessing/muscle_ica.py

@@ -19,6 +19,8 @@
 
 # %%
 
+import sys
+
 import mne
 
 data_path = mne.datasets.sample.data_path()
@@ -44,7 +46,8 @@
 
 # %%
 # Remove components with postural muscle artifact using ICA
-ica.plot_sources(raw)
+if sys.platform != "emscripten":
+    ica.plot_sources(raw)
 
 # %%
 # By inspection, let's select out the muscle-artifact components based on
@@ -71,19 +74,22 @@
 # slope in log-log units; this is a very typical pattern for muscle artifact.
 
 muscle_idx = [6, 7, 8, 9, 10, 11, 12, 13, 14]
-ica.plot_properties(raw, picks=muscle_idx, log_scale=True)
+if sys.platform != "emscripten":
+    ica.plot_properties(raw, picks=muscle_idx, log_scale=True)
 
 # first, remove blinks and heartbeat to compare
 blink_idx = [0]
 heartbeat_idx = [5]
 ica.apply(raw, exclude=blink_idx + heartbeat_idx)
-ica.plot_overlay(raw, exclude=muscle_idx)
+if sys.platform != "emscripten":
+    ica.plot_overlay(raw, exclude=muscle_idx)
 
 # %%
 # Finally, let's try an automated algorithm to find muscle components
 # and ensure that it gets the same components we did manually.
 muscle_idx_auto, scores = ica.find_bads_muscle(raw)
-ica.plot_scores(scores, exclude=muscle_idx_auto)
+if sys.platform != "emscripten":
+    ica.plot_scores(scores, exclude=muscle_idx_auto)
 print(
     f"Manually found muscle artifact ICA components:      {muscle_idx}\n"
     f"Automatically found muscle artifact ICA components: {muscle_idx_auto}"
@@ -107,10 +113,12 @@
         n_components=15, method="picard", max_iter="auto", random_state=97
     )
     ica.fit(raw)
-    ica.plot_sources(raw)
+    if sys.platform != "emscripten":
+        ica.plot_sources(raw)
     muscle_idx_auto, scores = ica.find_bads_muscle(raw)
-    ica.plot_properties(raw, picks=muscle_idx_auto, log_scale=True)
-    ica.plot_scores(scores, exclude=muscle_idx_auto)
+    if sys.platform != "emscripten":
+        ica.plot_properties(raw, picks=muscle_idx_auto, log_scale=True)
+        ica.plot_scores(scores, exclude=muscle_idx_auto)
 
     print(
         f"Manually found muscle artifact ICA components:      {muscle_idx}\n"

examples/visualization/eyetracking_plot_heatmap.py

@@ -26,11 +26,20 @@
 # :ref:`example data <eyelink-dataset>`: eye-tracking data recorded from SR research's
 # ``'.asc'`` file format.
 
+import sys
+
 import matplotlib.pyplot as plt
 
 import mne
 from mne.viz.eyetracking import plot_gaze
 
+if sys.platform == "emscripten":
+    raise RuntimeError(
+        "This example requires the MNE EyeLink dataset, "
+        "which is not available in the browser. Please run this example "
+        "locally. Visit https://mne.tools for instructions."
+    )
+
 task_fpath = mne.datasets.eyelink.data_path() / "freeviewing"
 et_fpath = task_fpath / "sub-01_task-freeview_eyetrack.asc"
 stim_fpath = task_fpath / "stim" / "naturalistic.png"

tutorials/evoked/40_whitened.py

@@ -21,6 +21,8 @@
 
 # %%
 
+import sys
+
 import mne
 from mne.datasets import sample
 
@@ -52,14 +54,16 @@
 )
 
 # butterfly mode shows the differences most clearly
-raw.plot(events=events, butterfly=True)
-raw.plot(noise_cov=noise_cov, events=events, butterfly=True)
+if sys.platform != "emscripten":
+    raw.plot(events=events, butterfly=True)
+    raw.plot(noise_cov=noise_cov, events=events, butterfly=True)
 
 # %%
 # Epochs with whitening
 # ---------------------
-epochs.plot(events=True)
-epochs.plot(noise_cov=noise_cov, events=True)
+if sys.platform != "emscripten":
+    epochs.plot(events=True)
+    epochs.plot(noise_cov=noise_cov, events=True)
 
 # %%
 # Evoked data with whitening

tutorials/io/60_ctf_bst_auditory.py

@@ -26,6 +26,8 @@
 
 # %%
 
+import sys
+
 import numpy as np
 import pandas as pd
 
@@ -35,6 +37,13 @@
 from mne.io import read_raw_ctf
 from mne.minimum_norm import apply_inverse
 
+if sys.platform == "emscripten":
+    raise RuntimeError(
+        "This tutorial requires the Brainstorm auditory dataset (~2.9 GB) "
+        "which is not available in the browser. Please run this tutorial "
+        "locally. Visit https://mne.tools for instructions."
+    )
+
 # %%
 # To reduce memory consumption and running time, some of the steps are
 # precomputed. To run everything from scratch change ``use_precomputed`` to

tutorials/io/70_reading_eyetracking_data.py

@@ -85,8 +85,17 @@
 """
 
 # %%
+import sys
+
 import mne
 
+if sys.platform == "emscripten":
+    raise RuntimeError(
+        "This tutorial requires the MNE misc dataset with eyetracking data, "
+        "which is not available in the browser. Please run this tutorial "
+        "locally. Visit https://mne.tools for instructions."
+    )
+
 # %%
 fpath = mne.datasets.misc.data_path() / "eyetracking" / "eyelink"
 fname = fpath / "px_textpage_ws.asc"