DOC: Use rng in examples

Author: bruAristimunha

examples/datasets/spm_faces_dataset.py

@@ -36,7 +36,7 @@
 raw.resample(100)
 raw.filter(1.0, None)  # high-pass
 reject = dict(mag=5e-12)
-ica = ICA(n_components=0.95, max_iter="auto", random_state=0)
+ica = ICA(n_components=0.95, max_iter="auto", rng=0)
 ica.fit(raw, reject=reject)
 # compute correlation scores, get bad indices sorted by score
 eog_epochs = create_eog_epochs(raw, ch_name="MRT31-2908", reject=reject)

examples/inverse/mixed_norm_inverse.py

@@ -81,7 +81,7 @@
     return_residual=True,
     return_as_dipoles=True,
     verbose=True,
-    random_state=0,
+    rng=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),

examples/preprocessing/find_ref_artifacts.py

@@ -77,7 +77,7 @@
 ica_kwargs = dict(
     method="picard",
     fit_params=dict(tol=1e-4),  # use a high tol here for speed
-    random_state=99,
+    rng=99,
 )
 all_picks = mne.pick_types(raw_tog.info, meg=True, ref_meg=True)
 ica_tog = ICA(n_components=60, max_iter="auto", allow_ref_meg=True, **ica_kwargs)

examples/preprocessing/ica_comparison.py

@@ -50,7 +50,7 @@ def run_ica(method, fit_params=None):
         method=method,
         fit_params=fit_params,
         max_iter="auto",
-        random_state=0,
+        rng=0,
     )
     t0 = time()
     ica.fit(raw, reject=reject)

examples/preprocessing/muscle_ica.py

@@ -37,9 +37,7 @@
 
 # %%
 # Run ICA
-ica = mne.preprocessing.ICA(
-    n_components=15, method="picard", max_iter="auto", random_state=97
-)
+ica = mne.preprocessing.ICA(n_components=15, method="picard", max_iter="auto", rng=97)
 ica.fit(raw)
 
 # %%
@@ -104,7 +102,7 @@
 
     # Run ICA
     ica = mne.preprocessing.ICA(
-        n_components=15, method="picard", max_iter="auto", random_state=97
+        n_components=15, method="picard", max_iter="auto", rng=97
     )
     ica.fit(raw)
     ica.plot_sources(raw)

examples/simulation/plot_stc_metrics.py

@@ -76,7 +76,7 @@
     location=location,
     extent=extent,
     subjects_dir=subjects_dir,
-    random_state=random_state,
+    rng=random_state,
 )
 
 # Dipole
@@ -88,7 +88,7 @@
     location=location,
     extent=extent,
     subjects_dir=subjects_dir,
-    random_state=random_state,
+    rng=random_state,
 )
 
 # WHAT?
@@ -128,15 +128,15 @@
 raw_region = raw_region.pick(picks=["eeg", "stim"], exclude="bads")
 cov = mne.make_ad_hoc_cov(raw_region.info)
 mne.simulation.add_noise(
-    raw_region, cov, iir_filter=[0.2, -0.2, 0.04], random_state=random_state
+    raw_region, cov, iir_filter=[0.2, -0.2, 0.04], rng=random_state
 )
 
 # Dipole
 raw_dipole = mne.simulation.simulate_raw(info, source_simulator_dipole, forward=fwd)
 raw_dipole = raw_dipole.pick(picks=["eeg", "stim"], exclude="bads")
 cov = mne.make_ad_hoc_cov(raw_dipole.info)
 mne.simulation.add_noise(
-    raw_dipole, cov, iir_filter=[0.2, -0.2, 0.04], random_state=random_state
+    raw_dipole, cov, iir_filter=[0.2, -0.2, 0.04], rng=random_state
 )
 
 ###############################################################################

examples/simulation/simulate_evoked_data.py

@@ -67,7 +67,7 @@ def data_fun(times):
     fwd["src"],
     n_dipoles=2,
     times=times,
-    random_state=42,
+    rng=42,
     labels=labels,
     data_fun=data_fun,
 )

examples/simulation/simulate_raw_data.py

@@ -67,7 +67,7 @@ def data_fun(times):
 fwd = mne.read_forward_solution(fwd_fname)
 src = fwd["src"]
 stc = simulate_sparse_stc(
-    src, n_dipoles=n_dipoles, times=times, data_fun=data_fun, random_state=rng
+    src, n_dipoles=n_dipoles, times=times, data_fun=data_fun, rng=rng
 )
 # look at our source data
 fig, ax = plt.subplots(1)
@@ -79,9 +79,9 @@ def data_fun(times):
 # Simulate raw data
 raw_sim = simulate_raw(raw.info, [stc] * 10, forward=fwd, verbose=True)
 cov = make_ad_hoc_cov(raw_sim.info)
-add_noise(raw_sim, cov, iir_filter=[0.2, -0.2, 0.04], random_state=rng)
-add_ecg(raw_sim, random_state=rng)
-add_eog(raw_sim, random_state=rng)
+add_noise(raw_sim, cov, iir_filter=[0.2, -0.2, 0.04], rng=rng)
+add_ecg(raw_sim, rng=rng)
+add_eog(raw_sim, rng=rng)
 raw_sim.plot()
 
 ##############################################################################

examples/simulation/simulated_raw_data_using_subject_anatomy.py

@@ -204,9 +204,9 @@ def data_fun(times, latency, duration):
 raw_sim = mne.simulation.simulate_raw(info, source_simulator, forward=fwd)
 raw_sim.set_eeg_reference(projection=True)
 
-mne.simulation.add_noise(raw_sim, cov=noise_cov, random_state=0)
-mne.simulation.add_eog(raw_sim, random_state=0)
-mne.simulation.add_ecg(raw_sim, random_state=0)
+mne.simulation.add_noise(raw_sim, cov=noise_cov, rng=0)
+mne.simulation.add_eog(raw_sim, rng=0)
+mne.simulation.add_ecg(raw_sim, rng=0)
 
 # Plot original and simulated raw data.
 raw_sim.plot(title="Simulated raw data")

examples/simulation/source_simulator.py

@@ -85,7 +85,7 @@ class to generate source estimates and raw data. It is meant to be a brief
 # simulator can be given directly to the simulate_raw function.
 raw = mne.simulation.simulate_raw(info, source_simulator, forward=fwd)
 cov = mne.make_ad_hoc_cov(raw.info)
-mne.simulation.add_noise(raw, cov, iir_filter=[0.2, -0.2, 0.04], random_state=97)
+mne.simulation.add_noise(raw, cov, iir_filter=[0.2, -0.2, 0.04], rng=97)
 raw.plot()
 
 # %%