Use constrained layout in matplotlib visualization

README.rst

@@ -96,7 +96,7 @@ The minimum required dependencies to run MNE-Python are:
 - Python >= 3.8
 - NumPy >= 1.21.2
 - SciPy >= 1.7.1
-- Matplotlib >= 3.4.3
+- Matplotlib >= 3.5.0
 - pooch >= 1.5
 - tqdm
 - Jinja2

doc/changes/devel.rst

@@ -36,6 +36,7 @@ Enhancements
 - Add :class:`~mne.time_frequency.EpochsSpectrumArray` and :class:`~mne.time_frequency.SpectrumArray` to support creating power spectra from :class:`NumPy array <numpy.ndarray>` data (:gh:`11803` by `Alex Rockhill`_)
 - Add support for writing forward solutions to HDF5 and convenience function :meth:`mne.Forward.save` (:gh:`12036` by `Eric Larson`_)
 - Refactored internals of :func:`mne.read_annotations` (:gh:`11964` by `Paul Roujansky`_)
+- By default MNE-Python creates matplotlib figures with ``layout='constrained'`` rather than the default ``layout='tight'`` (:gh:`12050` by `Mathieu Scheltienne`_ and `Eric Larson`_)
 - Enhance :func:`~mne.viz.plot_evoked_field` with a GUI that has controls for time, colormap, and contour lines (:gh:`11942` by `Marijn van Vliet`_)
 - Add :class:`mne.viz.ui_events.UIEvent` linking for interactive colorbars, allowing users to link figures and change the colormap and limits interactively. This supports :func:`~mne.viz.plot_evoked_topomap`, :func:`~mne.viz.plot_ica_components`, :func:`~mne.viz.plot_tfr_topomap`, :func:`~mne.viz.plot_projs_topomap`, :meth:`~mne.Evoked.plot_image`, and :meth:`~mne.Epochs.plot_image` (:gh:`12057` by `Santeri Ruuskanen`_)
 

doc/conf.py

@@ -1290,7 +1290,6 @@ def reset_warnings(gallery_conf, fname):
     warnings.filterwarnings("default", module="sphinx")
     # allow these warnings, but don't show them
     for key in (
-        "The module matplotlib.tight_layout is deprecated",  # nilearn
         "invalid version and will not be supported",  # pyxdf
         "distutils Version classes are deprecated",  # seaborn and neo
         "`np.object` is a deprecated alias for the builtin `object`",  # pyxdf

examples/decoding/decoding_rsa_sgskip.py

@@ -150,7 +150,7 @@
 ##############################################################################
 # Plot
 labels = [""] * 5 + ["face"] + [""] * 11 + ["bodypart"] + [""] * 6
-fig, ax = plt.subplots(1)
+fig, ax = plt.subplots(1, layout="constrained")
 im = ax.matshow(confusion, cmap="RdBu_r", clim=[0.3, 0.7])
 ax.set_yticks(range(len(classes)))
 ax.set_yticklabels(labels)
@@ -159,14 +159,13 @@
 ax.axhline(11.5, color="k")
 ax.axvline(11.5, color="k")
 plt.colorbar(im)
-plt.tight_layout()
 plt.show()
 
 ##############################################################################
 # Confusion matrix related to mental representations have been historically
 # summarized with dimensionality reduction using multi-dimensional scaling [1].
 # See how the face samples cluster together.
-fig, ax = plt.subplots(1)
+fig, ax = plt.subplots(1, layout="constrained")
 mds = MDS(2, random_state=0, dissimilarity="precomputed")
 chance = 0.5
 summary = mds.fit_transform(chance - confusion)
@@ -186,7 +185,6 @@
     )
 ax.axis("off")
 ax.legend(loc="lower right", scatterpoints=1, ncol=2)
-plt.tight_layout()
 plt.show()
 
 ##############################################################################

examples/decoding/decoding_spoc_CMC.py

@@ -68,15 +68,14 @@
 y_preds = cross_val_predict(clf, X, y, cv=cv)
 
 # Plot the True EMG power and the EMG power predicted from MEG data
-fig, ax = plt.subplots(1, 1, figsize=[10, 4])
+fig, ax = plt.subplots(1, 1, figsize=[10, 4], layout="constrained")
 times = raw.times[meg_epochs.events[:, 0] - raw.first_samp]
 ax.plot(times, y_preds, color="b", label="Predicted EMG")
 ax.plot(times, y, color="r", label="True EMG")
 ax.set_xlabel("Time (s)")
 ax.set_ylabel("EMG Power")
 ax.set_title("SPoC MEG Predictions")
 plt.legend()
-mne.viz.tight_layout()
 plt.show()
 
 ##############################################################################

examples/decoding/decoding_time_generalization_conditions.py

@@ -88,7 +88,7 @@
 
 # %%
 # Plot
-fig, ax = plt.subplots(constrained_layout=True)
+fig, ax = plt.subplots(layout="constrained")
 im = ax.matshow(
     scores,
     vmin=0,

examples/decoding/decoding_xdawn_eeg.py

@@ -99,22 +99,24 @@
 cm_normalized = cm.astype(float) / cm.sum(axis=1)[:, np.newaxis]
 
 # Plot confusion matrix
-fig, ax = plt.subplots(1)
+fig, ax = plt.subplots(1, layout="constrained")
 im = ax.imshow(cm_normalized, interpolation="nearest", cmap=plt.cm.Blues)
 ax.set(title="Normalized Confusion matrix")
 fig.colorbar(im)
 tick_marks = np.arange(len(target_names))
 plt.xticks(tick_marks, target_names, rotation=45)
 plt.yticks(tick_marks, target_names)
-fig.tight_layout()
 ax.set(ylabel="True label", xlabel="Predicted label")
 
 # %%
 # The ``patterns_`` attribute of a fitted Xdawn instance (here from the last
 # cross-validation fold) can be used for visualization.
 
 fig, axes = plt.subplots(
-    nrows=len(event_id), ncols=n_filter, figsize=(n_filter, len(event_id) * 2)
+    nrows=len(event_id),
+    ncols=n_filter,
+    figsize=(n_filter, len(event_id) * 2),
+    layout="constrained",
 )
 fitted_xdawn = clf.steps[0][1]
 info = create_info(epochs.ch_names, 1, epochs.get_channel_types())
@@ -131,7 +133,6 @@
         show=False,
     )
     axes[ii, 0].set(ylabel=cur_class)
-fig.tight_layout(h_pad=1.0, w_pad=1.0, pad=0.1)
 
 # %%
 # References

examples/decoding/receptive_field_mtrf.py

@@ -67,12 +67,11 @@
 n_channels = len(raw.ch_names)
 
 # Plot a sample of brain and stimulus activity
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(layout="constrained")
 lns = ax.plot(scale(raw[:, :800][0].T), color="k", alpha=0.1)
 ln1 = ax.plot(scale(speech[0, :800]), color="r", lw=2)
 ax.legend([lns[0], ln1[0]], ["EEG", "Speech Envelope"], frameon=False)
 ax.set(title="Sample activity", xlabel="Time (s)")
-mne.viz.tight_layout()
 
 # %%
 # Create and fit a receptive field model
@@ -117,12 +116,11 @@
 mean_scores = scores.mean(axis=0)
 
 # Plot mean prediction scores across all channels
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(layout="constrained")
 ix_chs = np.arange(n_channels)
 ax.plot(ix_chs, mean_scores)
 ax.axhline(0, ls="--", color="r")
 ax.set(title="Mean prediction score", xlabel="Channel", ylabel="Score ($r$)")
-mne.viz.tight_layout()
 
 # %%
 # Investigate model coefficients
@@ -134,7 +132,7 @@
 
 # Print mean coefficients across all time delays / channels (see Fig 1)
 time_plot = 0.180  # For highlighting a specific time.
-fig, ax = plt.subplots(figsize=(4, 8))
+fig, ax = plt.subplots(figsize=(4, 8), layout="constrained")
 max_coef = mean_coefs.max()
 ax.pcolormesh(
     times,
@@ -155,16 +153,14 @@
     xticks=np.arange(tmin, tmax + 0.2, 0.2),
 )
 plt.setp(ax.get_xticklabels(), rotation=45)
-mne.viz.tight_layout()
 
 # Make a topographic map of coefficients for a given delay (see Fig 2C)
 ix_plot = np.argmin(np.abs(time_plot - times))
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(layout="constrained")
 mne.viz.plot_topomap(
     mean_coefs[:, ix_plot], pos=info, axes=ax, show=False, vlim=(-max_coef, max_coef)
 )
 ax.set(title="Topomap of model coefficients\nfor delay %s" % time_plot)
-mne.viz.tight_layout()
 
 # %%
 # Create and fit a stimulus reconstruction model
@@ -240,15 +236,14 @@
 
 y_pred = sr.predict(Y[test])
 time = np.linspace(0, 2.0, 5 * int(sfreq))
-fig, ax = plt.subplots(figsize=(8, 4))
+fig, ax = plt.subplots(figsize=(8, 4), layout="constrained")
 ax.plot(
     time, speech[test][sr.valid_samples_][: int(5 * sfreq)], color="grey", lw=2, ls="--"
 )
 ax.plot(time, y_pred[sr.valid_samples_][: int(5 * sfreq)], color="r", lw=2)
 ax.legend([lns[0], ln1[0]], ["Envelope", "Reconstruction"], frameon=False)
 ax.set(title="Stimulus reconstruction")
 ax.set_xlabel("Time (s)")
-mne.viz.tight_layout()
 
 # %%
 # Investigate model coefficients
@@ -292,7 +287,6 @@
     title="Inverse-transformed coefficients\nbetween delays %s and %s"
     % (time_plot[0], time_plot[1])
 )
-mne.viz.tight_layout()
 
 # %%
 # References

examples/inverse/label_source_activations.py

@@ -62,7 +62,7 @@
 # View source activations
 # -----------------------
 
-fig, ax = plt.subplots(1)
+fig, ax = plt.subplots(1, layout="constrained")
 t = 1e3 * stc_label.times
 ax.plot(t, stc_label.data.T, "k", linewidth=0.5, alpha=0.5)
 pe = [
@@ -81,7 +81,6 @@
     xlim=xlim,
     ylim=ylim,
 )
-mne.viz.tight_layout()
 
 # %%
 # Using vector solutions
@@ -92,7 +91,7 @@
 pick_ori = "vector"
 stc_vec = apply_inverse(evoked, inverse_operator, lambda2, method, pick_ori=pick_ori)
 data = stc_vec.extract_label_time_course(label, src)
-fig, ax = plt.subplots(1)
+fig, ax = plt.subplots(1, layout="constrained")
 stc_vec_label = stc_vec.in_label(label)
 colors = ["#EE6677", "#228833", "#4477AA"]
 for ii, name in enumerate("XYZ"):
@@ -117,4 +116,3 @@
     xlim=xlim,
     ylim=ylim,
 )
-mne.viz.tight_layout()

examples/inverse/mixed_source_space_inverse.py

@@ -194,9 +194,8 @@
 )
 
 # plot the times series of 2 labels
-fig, axes = plt.subplots(1)
+fig, axes = plt.subplots(1, layout="constrained")
 axes.plot(1e3 * stc.times, label_ts[0][0, :], "k", label="bankssts-lh")
 axes.plot(1e3 * stc.times, label_ts[0][-1, :].T, "r", label="Brain-stem")
 axes.set(xlabel="Time (ms)", ylabel="MNE current (nAm)")
 axes.legend()
-mne.viz.tight_layout()

examples/inverse/source_space_snr.py

@@ -51,10 +51,9 @@
 # Plot an average SNR across source points over time:
 ave = np.mean(snr_stc.data, axis=0)
 
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(layout="constrained")
 ax.plot(evoked.times, ave)
 ax.set(xlabel="Time (s)", ylabel="SNR MEG-EEG")
-fig.tight_layout()
 
 # Find time point of maximum SNR
 maxidx = np.argmax(ave)

examples/preprocessing/eeg_bridging.py

@@ -88,7 +88,7 @@
 
 bridged_idx, ed_matrix = ed_data[6]
 
-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), layout="constrained")
 fig.suptitle("Subject 6 Electrical Distance Matrix")
 
 # take median across epochs, only use upper triangular, lower is NaNs
@@ -110,8 +110,6 @@
     ax.set_xlabel("Channel Index")
     ax.set_ylabel("Channel Index")
 
-fig.tight_layout()
-
 # %%
 # Examine the Distribution of Electrical Distances
 # ------------------------------------------------
@@ -208,7 +206,7 @@
 # reflect neural or at least anatomical differences as well (i.e. the
 # distance from the sensors to the brain).
 
-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), layout="constrained")
 fig.suptitle("Electrical Distance Distribution for EEGBCI Subjects")
 for ax in (ax1, ax2):
     ax.set_ylabel("Count")
@@ -229,7 +227,6 @@
 
 ax1.axvspan(0, 30, color="r", alpha=0.5)
 ax2.legend(loc=(1.04, 0))
-fig.subplots_adjust(right=0.725, bottom=0.15, wspace=0.4)
 
 # %%
 # For the group of subjects, let's look at their electrical distances

examples/preprocessing/eeg_csd.py

@@ -78,8 +78,7 @@
 # CSD has parameters ``stiffness`` and ``lambda2`` affecting smoothing and
 # spline flexibility, respectively. Let's see how they affect the solution:
 
-fig, ax = plt.subplots(4, 4)
-fig.subplots_adjust(hspace=0.5)
+fig, ax = plt.subplots(4, 4, layout="constrained")
 fig.set_size_inches(10, 10)
 for i, lambda2 in enumerate([0, 1e-7, 1e-5, 1e-3]):
     for j, m in enumerate([5, 4, 3, 2]):

examples/preprocessing/eog_artifact_histogram.py

@@ -50,7 +50,6 @@
 
 # %%
 # Plot EOG artifact distribution
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(layout="constrained")
 ax.stem(1e3 * epochs.times, data)
 ax.set(xlabel="Times (ms)", ylabel="Blink counts (from %s trials)" % len(epochs))
-fig.tight_layout()

examples/preprocessing/eog_regression.py

@@ -69,10 +69,9 @@
 epochs_after = mne.Epochs(raw_clean, events, event_id, tmin, tmax, baseline=(tmin, 0))
 evoked_after = epochs_after.average()
 
-fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(10, 7), sharex=True, sharey="row")
+fig, ax = plt.subplots(
+    nrows=3, ncols=2, figsize=(10, 7), sharex=True, sharey="row", layout="constrained"
+)
 evoked_before.plot(axes=ax[:, 0], spatial_colors=True)
 evoked_after.plot(axes=ax[:, 1], spatial_colors=True)
-fig.subplots_adjust(
-    top=0.905, bottom=0.09, left=0.08, right=0.975, hspace=0.325, wspace=0.145
-)
 fig.suptitle("Before --> After")

examples/preprocessing/shift_evoked.py

@@ -14,7 +14,6 @@
 
 import matplotlib.pyplot as plt
 import mne
-from mne.viz import tight_layout
 from mne.datasets import sample
 
 print(__doc__)
@@ -60,5 +59,3 @@
     titles=dict(grad="Absolute shift: 500 ms"),
     time_unit="s",
 )
-
-tight_layout()

examples/simulation/plot_stc_metrics.py

@@ -234,7 +234,7 @@
     ]
 
 # Plot the results
-f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex="col", constrained_layout=True)
+f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex="col", layout="constrained")
 for ax, (title, results) in zip([ax1, ax2, ax3, ax4], region_results.items()):
     ax.plot(thresholds, results, ".-")
     ax.set(title=title, ylabel="score", xlabel="Threshold", xticks=thresholds)
@@ -243,7 +243,7 @@
 ax1.ticklabel_format(axis="y", style="sci", scilimits=(0, 1))  # tweak RLE
 
 # Cosine score with respect to time
-f, ax1 = plt.subplots(constrained_layout=True)
+f, ax1 = plt.subplots(layout="constrained")
 ax1.plot(stc_true_region.times, cosine_score(stc_true_region, stc_est_region))
 ax1.set(title="Cosine score", xlabel="Time", ylabel="Score")
 
@@ -277,6 +277,6 @@
 
 # Plot the results
 for name, results in dipole_results.items():
-    f, ax1 = plt.subplots(constrained_layout=True)
+    f, ax1 = plt.subplots(layout="constrained")
     ax1.plot(thresholds, 100 * np.array(results), ".-")
     ax1.set(title=name, ylabel="Error (cm)", xlabel="Threshold", xticks=thresholds)