MAINT: Reduce execution time for examples (#10071) [circle deploy]

.circleci/config.yml

@@ -21,7 +21,6 @@ jobs:
           default: "false"
       machine:
         image: ubuntu-2004:202111-01
-      resource_class: large
       steps:
         - restore_cache:
             keys:

.github/workflows/circle_artifacts.yml

@@ -9,5 +9,5 @@ jobs:
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           artifact-path: 0/dev/index.html
-          circleci-jobs: build_docs
+          circleci-jobs: build_docs,build_docs-1
           job-title: Check the rendered docs here!

examples/decoding/decoding_csp_timefreq.py

@@ -52,15 +52,15 @@
 # Assemble the classifier using scikit-learn pipeline
 clf = make_pipeline(CSP(n_components=4, reg=None, log=True, norm_trace=False),
                     LinearDiscriminantAnalysis())
-n_splits = 5  # how many folds to use for cross-validation
+n_splits = 3  # for cross-validation, 5 is better, here we use 3 for speed
 cv = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)
 
 # Classification & time-frequency parameters
 tmin, tmax = -.200, 2.000
 n_cycles = 10.  # how many complete cycles: used to define window size
-min_freq = 5.
-max_freq = 25.
-n_freqs = 8  # how many frequency bins to use
+min_freq = 8.
+max_freq = 20.
+n_freqs = 6  # how many frequency bins to use
 
 # Assemble list of frequency range tuples
 freqs = np.linspace(min_freq, max_freq, n_freqs)  # assemble frequencies

examples/decoding/decoding_spoc_CMC.py

@@ -38,23 +38,23 @@
 # Define parameters
 fname = data_path() + '/SubjectCMC.ds'
 raw = mne.io.read_raw_ctf(fname)
-raw.crop(50., 250.)  # crop for memory purposes
+raw.crop(50., 200.)  # crop for memory purposes
 
 # Filter muscular activity to only keep high frequencies
 emg = raw.copy().pick_channels(['EMGlft']).load_data()
-emg.filter(20., None, fir_design='firwin')
+emg.filter(20., None)
 
 # Filter MEG data to focus on beta band
 raw.pick_types(meg=True, ref_meg=True, eeg=False, eog=False).load_data()
-raw.filter(15., 30., fir_design='firwin')
+raw.filter(15., 30.)
 
 # Build epochs as sliding windows over the continuous raw file
-events = mne.make_fixed_length_events(raw, id=1, duration=.250)
+events = mne.make_fixed_length_events(raw, id=1, duration=0.75)
 
 # Epoch length is 1.5 second
-meg_epochs = Epochs(raw, events, tmin=0., tmax=1.500, baseline=None,
-                    detrend=1, decim=8)
-emg_epochs = Epochs(emg, events, tmin=0., tmax=1.500, baseline=None)
+meg_epochs = Epochs(raw, events, tmin=0., tmax=1.5, baseline=None,
+                    detrend=1, decim=12)
+emg_epochs = Epochs(emg, events, tmin=0., tmax=1.5, baseline=None)
 
 # Prepare classification
 X = meg_epochs.get_data()

examples/inverse/mixed_norm_inverse.py

@@ -62,7 +62,11 @@
     evoked, forward, cov, alpha, loose=loose, depth=depth, maxit=3000,
     tol=1e-4, active_set_size=10, debias=False, weights=stc_dspm,
     weights_min=8., n_mxne_iter=n_mxne_iter, return_residual=True,
-    return_as_dipoles=True, verbose=True, random_state=0)
+    return_as_dipoles=True, verbose=True, random_state=0,
+    # for this dataset we know we should use a high alpha, so avoid some
+    # of the slower (lower) alpha values
+    sure_alpha_grid=np.linspace(100, 40, 10),
+)
 
 t = 0.083
 tidx = evoked.time_as_index(t)

examples/inverse/mixed_source_space_inverse.py

@@ -142,13 +142,13 @@
 brain = stc_vec.plot(
     hemi='both', src=inverse_operator['src'], views='coronal',
     initial_time=initial_time, subjects_dir=subjects_dir,
-    brain_kwargs=dict(silhouette=True))
+    brain_kwargs=dict(silhouette=True), smoothing_steps=7)
 
 # %%
 # Plot the surface
 # ----------------
 brain = stc.surface().plot(initial_time=initial_time,
-                           subjects_dir=subjects_dir)
+                           subjects_dir=subjects_dir, smoothing_steps=7)
 # %%
 # Plot the volume
 # ---------------

examples/inverse/mne_cov_power.py

@@ -42,27 +42,26 @@
 raw_empty_room_fname = op.join(
     data_path, 'MEG', 'sample', 'ernoise_raw.fif')
 raw_empty_room = mne.io.read_raw_fif(raw_empty_room_fname)
-raw_empty_room.crop(0, 60)
+raw_empty_room.crop(0, 30)  # cropped just for speed
 raw_empty_room.info['bads'] = ['MEG 2443']
 raw_empty_room.add_proj(raw.info['projs'])
-noise_cov = mne.compute_raw_covariance(
-    raw_empty_room, method=['empirical', 'shrunk'])
+noise_cov = mne.compute_raw_covariance(raw_empty_room, method='shrunk')
 del raw_empty_room
 
 # %%
 # Epoch the data
 # --------------
 
-raw.info['bads'] = ['MEG 2443', 'EEG 053']
-raw.load_data().filter(4, 12)
+raw.pick(['meg', 'stim', 'eog']).load_data().filter(4, 12)
+raw.info['bads'] = ['MEG 2443']
 events = mne.find_events(raw, stim_channel='STI 014')
 event_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4)
 tmin, tmax = -0.2, 0.5
 baseline = (None, 0)  # means from the first instant to t = 0
 reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
-epochs = mne.Epochs(raw.copy().filter(4, 12), events, event_id, tmin, tmax,
+epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
                     proj=True, picks=('meg', 'eog'), baseline=None,
-                    reject=reject, preload=True)
+                    reject=reject, preload=True, decim=5, verbose='error')
 del raw
 
 # %%
@@ -78,11 +77,9 @@
 #    to noise sources).
 
 base_cov = mne.compute_covariance(
-    epochs, tmin=-0.2, tmax=0, method=['shrunk', 'empirical'], rank=None,
-    verbose=True)
+    epochs, tmin=-0.2, tmax=0, method='shrunk', verbose=True)
 data_cov = mne.compute_covariance(
-    epochs, tmin=0., tmax=0.2, method=['shrunk', 'empirical'], rank=None,
-    verbose=True)
+    epochs, tmin=0., tmax=0.2, method='shrunk', verbose=True)
 
 fig_noise_cov = mne.viz.plot_cov(noise_cov, epochs.info, show_svd=False)
 fig_base_cov = mne.viz.plot_cov(base_cov, epochs.info, show_svd=False)
@@ -131,4 +128,5 @@
 
 stc_data /= stc_base
 brain = stc_data.plot(subject='sample', subjects_dir=subjects_dir,
-                      clim=dict(kind='percent', lims=(50, 90, 98)))
+                      clim=dict(kind='percent', lims=(50, 90, 98)),
+                      smoothing_steps=7)

examples/inverse/vector_mne_solution.py

@@ -35,6 +35,7 @@
 
 data_path = sample.data_path()
 subjects_dir = data_path + '/subjects'
+smoothing_steps = 7
 
 # Read evoked data
 fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'
@@ -58,7 +59,8 @@
 
 # sphinx_gallery_thumbnail_number = 2
 brain = stc.plot(
-    initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir)
+    initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir,
+    smoothing_steps=smoothing_steps)
 
 # You can save a brain movie with:
 # brain.save_movie(time_dilation=20, tmin=0.05, tmax=0.16, framerate=10,
@@ -74,14 +76,14 @@
       f'{np.abs(np.sum(directions * inv["source_nn"][2::3], axis=-1)).mean()}')
 brain_max = stc_max.plot(
     initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir,
-    time_label='Max power')
+    time_label='Max power', smoothing_steps=smoothing_steps)
 
 # %%
 # The normal is very similar:
 
 brain_normal = stc.project('normal', inv['src'])[0].plot(
     initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir,
-    time_label='Normal')
+    time_label='Normal', smoothing_steps=smoothing_steps)
 
 # %%
 # You can also do this with a fixed-orientation inverse. It looks a lot like
@@ -94,4 +96,5 @@
 stc_fixed = apply_inverse(
     evoked, inv_fixed, lambda2, 'dSPM', pick_ori='vector')
 brain_fixed = stc_fixed.plot(
-    initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir)
+    initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir,
+    smoothing_steps=smoothing_steps)