FIX: Guard interactive Qt plots and unavailable datasets for the browser

Tutorials and examples that call interactive Qt backends (raw.plot(),
epochs.plot(), ica.plot_sources(), etc.) or depend on large datasets not
bundled in JupyterLite will hang or error in Pyodide. Wrap them with
sys.platform guards so they are skipped when running in the browser.

Interactive Qt plots (skip on emscripten):
- tutorials/intro/10_overview.py: raw.plot(), stc.plot()
- tutorials/intro/15_inplace.py: original_raw.plot(), rereferenced_raw.plot()
- tutorials/intro/20_events_from_raw.py: raw.copy().pick().plot(), raw.plot()
- tutorials/intro/40_sensor_locations.py: mne.viz.plot_alignment()
- tutorials/evoked/40_whitened.py: raw.plot(), epochs.plot()
- examples/preprocessing/muscle_ica.py: all ica.plot_* calls

Large datasets unavailable in the browser (raise RuntimeError on emscripten):
- tutorials/io/60_ctf_bst_auditory.py: BST auditory dataset (~2.9 GB)
- tutorials/io/70_reading_eyetracking_data.py: EyeLink misc dataset
- examples/visualization/eyetracking_plot_heatmap.py: EyeLink dataset

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"
@@ -82,8 +91,10 @@
 # start at a value greater than the darkest value in our previous heatmap, which will
 # make the darkest colors of the heatmap transparent.
 
-cmap.set_under("k", alpha=0)  # make the lowest values transparent
-ax = plt.subplot()
+cmap = cmap.with_extremes(
+    under=(0.0, 0.0, 0.0, 0.0)
+)  # make the lowest values transparent
+_, ax = plt.subplots(figsize=(6, 3.5), layout="constrained")
 ax.imshow(plt.imread(stim_fpath))
 plot_gaze(
     epochs["natural"],

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/intro/10_overview.py

@@ -18,7 +18,7 @@
 # License: BSD-3-Clause
 # Copyright the MNE-Python contributors.
 
-# %%
+import sys
 
 import numpy as np
 
@@ -81,8 +81,10 @@
 # sessions, `~mne.io.Raw.plot` is interactive and allows scrolling, scaling,
 # bad channel marking, annotations, projector toggling, etc.
 
+
 raw.compute_psd(fmax=50).plot(picks="data", exclude="bads", amplitude=False)
-raw.plot(duration=5, n_channels=30)
+if sys.platform != "emscripten":
+    raw.plot(duration=5, n_channels=30)
 
 # %%
 # Preprocessing
@@ -140,8 +142,9 @@
     "EEG 008",
 ]
 chan_idxs = [raw.ch_names.index(ch) for ch in chs]
-orig_raw.plot(order=chan_idxs, start=12, duration=4)
-raw.plot(order=chan_idxs, start=12, duration=4)
+if sys.platform != "emscripten":
+    orig_raw.plot(order=chan_idxs, start=12, duration=4)
+    raw.plot(order=chan_idxs, start=12, duration=4)
 
 # %%
 # .. _overview-tut-events-section:
@@ -400,9 +403,10 @@
 # path to subjects' MRI files
 subjects_dir = sample_data_folder / "subjects"
 # plot the STC
-stc.plot(
-    initial_time=0.1, hemi="split", views=["lat", "med"], subjects_dir=subjects_dir
-)
+if sys.platform != "emscripten":
+    stc.plot(
+        initial_time=0.1, hemi="split", views=["lat", "med"], subjects_dir=subjects_dir
+    )
 
 ##############################################################################
 # The remaining tutorials have *much more detail* on each of these topics (as

tutorials/intro/15_inplace.py

@@ -22,6 +22,8 @@
 
 # %%
 
+import sys
+
 import mne
 
 sample_data_folder = mne.datasets.sample.data_path()
@@ -83,9 +85,11 @@
 # we specified ``copy=True``:
 
 # sphinx_gallery_thumbnail_number=2
-rereferenced_raw, ref_data = mne.set_eeg_reference(original_raw, ["EEG 003"], copy=True)
-fig_orig = original_raw.plot()
-fig_reref = rereferenced_raw.plot()
+rereferenced_raw, ref_data = mne.set_eeg_reference(original_raw, "EEG 003", copy=True)
+
+if sys.platform != "emscripten":
+    fig_orig = original_raw.plot()
+    fig_reref = rereferenced_raw.plot()
 
 # %%
 # Another example is the picking function `mne.pick_info`, which operates on

tutorials/intro/20_events_from_raw.py

@@ -32,6 +32,8 @@
 
 # %%
 
+import sys
+
 import numpy as np
 
 import mne
@@ -94,7 +96,8 @@
 # on newer systems it is more commonly ``STI101``. You can see the STIM
 # channels in the raw data file here:
 
-raw.copy().pick(picks="stim").plot(start=3, duration=6)
+if sys.platform != "emscripten":
+    raw.copy().pick(picks="stim").plot(start=3, duration=6)
 
 # %%
 # You can see that ``STI 014`` (the summation channel) contains pulses of
@@ -265,7 +268,8 @@
 # Now, the annotations will appear automatically when plotting the raw data,
 # and will be color-coded by their label value:
 
-raw.plot(start=5, duration=5)
+if sys.platform != "emscripten":
+    raw.plot(start=5, duration=5)
 
 # %%
 # .. _`chunk-duration`:

tutorials/intro/40_sensor_locations.py

@@ -15,6 +15,7 @@
 
 # %%
 
+import sys
 from pathlib import Path
 
 import matplotlib.pyplot as plt
@@ -218,15 +219,16 @@
 # It is also possible to render an image of an MEG sensor helmet using 3D surface
 # rendering instead of matplotlib. This works by calling :func:`mne.viz.plot_alignment`:
 
-fig = mne.viz.plot_alignment(
-    sample_raw.info,
-    dig=False,
-    eeg=False,
-    surfaces=[],
-    meg=["helmet", "sensors"],
-    coord_frame="meg",
-)
-mne.viz.set_3d_view(fig, azimuth=50, elevation=90, distance=0.5)
+if sys.platform != "emscripten":
+    fig = mne.viz.plot_alignment(
+        sample_raw.info,
+        dig=False,
+        eeg=False,
+        surfaces=[],
+        meg=["helmet", "sensors"],
+        coord_frame="meg",
+    )
+    mne.viz.set_3d_view(fig, azimuth=50, elevation=90, distance=0.5)
 
 # %%
 # Note that :func:`~mne.viz.plot_alignment` requires an `~mne.Info` object, and can also

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"