Move toward determinism (#14090)

Author: larsoner

examples/simulation/simulate_evoked_data.py

@@ -51,15 +51,15 @@
 # Generate source time courses from 2 dipoles and the corresponding evoked data
 
 times = np.arange(300, dtype=np.float64) / raw.info["sfreq"] - 0.1
-rng = np.random.RandomState(42)
+rng = np.random.default_rng(42)
 
 
 def data_fun(times):
     """Generate random source time courses."""
     return (
         50e-9
         * np.sin(30.0 * times)
-        * np.exp(-((times - 0.15 + 0.05 * rng.randn(1)) ** 2) / 0.01)
+        * np.exp(-((times - 0.15 + rng.normal(scale=0.05, size=1)) ** 2) / 0.01)
     )
 
 

examples/simulation/simulate_raw_data.py

@@ -45,7 +45,7 @@
 n_dipoles = 4  # number of dipoles to create
 epoch_duration = 2.0  # duration of each epoch/event
 n = 0  # harmonic number
-rng = np.random.RandomState(0)  # random state (make reproducible)
+rng = np.random.default_rng(0)  # random state (make reproducible)
 
 
 def data_fun(times):

examples/simulation/simulated_raw_data_using_subject_anatomy.py

@@ -136,7 +136,8 @@ def data_fun(times, latency, duration):
     sigma = 0.375 * duration
     sinusoid = np.sin(2 * np.pi * f * (times - latency))
     gf = np.exp(
-        -((times - latency - (sigma / 4.0) * rng.rand(1)) ** 2) / (2 * (sigma**2))
+        -((times - latency - rng.uniform(high=sigma / 4.0, size=1)) ** 2)
+        / (2 * (sigma**2))
     )
     return 1e-9 * sinusoid * gf
 
@@ -162,7 +163,7 @@ def data_fun(times, latency, duration):
 
 times = np.arange(150, dtype=np.float64) / info["sfreq"]
 duration = 0.03
-rng = np.random.RandomState(7)
+rng = np.random.default_rng(7)
 source_simulator = mne.simulation.SourceSimulator(src, tstep=tstep)
 
 for region_id, region_name in enumerate(region_names, 1):

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])
+mne.simulation.add_noise(raw, cov, iir_filter=[0.2, -0.2, 0.04], random_state=97)
 raw.plot()
 
 # %%

examples/stats/cluster_stats_evoked.py

@@ -70,6 +70,7 @@
     threshold=threshold,
     tail=1,
     n_jobs=None,
+    seed=0,
     out_type="mask",
 )
 

examples/stats/sensor_permutation_test.py

@@ -61,7 +61,7 @@
 data = np.mean(data[:, :, temporal_mask], axis=2)
 
 n_permutations = 50000
-T0, p_values, H0 = permutation_t_test(data, n_permutations, n_jobs=None)
+T0, p_values, H0 = permutation_t_test(data, n_permutations, n_jobs=None, seed=0)
 
 significant_sensors = picks[p_values <= 0.05]
 significant_sensors_names = [raw.ch_names[k] for k in significant_sensors]

examples/time_frequency/time_frequency_simulated.py

@@ -44,8 +44,8 @@
 n_times = 1024  # Just over 1 second epochs
 n_epochs = 40
 seed = 42
-rng = np.random.RandomState(seed)
-data = rng.randn(len(ch_names), n_times * n_epochs + 200)  # buffer
+rng = np.random.default_rng(seed)
+data = rng.standard_normal((len(ch_names), n_times * n_epochs + 200))  # buffer
 
 # Add a 50 Hz sinusoidal burst to the noise and ramp it.
 t = np.arange(n_times, dtype=np.float64) / sfreq

mne/_fiff/tests/test_meas_info.py

@@ -137,11 +137,11 @@ def test_get_valid_units():
 
 def test_coil_trans():
     """Test loc<->coil_trans functions."""
-    rng = np.random.RandomState(0)
-    x = rng.randn(4, 4)
+    rng = np.random.default_rng(0)
+    x = rng.standard_normal((4, 4))
     x[3] = [0, 0, 0, 1]
     assert_allclose(_loc_to_coil_trans(_coil_trans_to_loc(x)), x)
-    x = rng.randn(12)
+    x = rng.standard_normal(12)
     assert_allclose(_coil_trans_to_loc(_loc_to_coil_trans(x)), x)
 
 

mne/_fiff/tests/test_pick.py

@@ -474,9 +474,9 @@ def test_pick_forward_seeg_ecog():
 
 def test_picks_by_channels():
     """Test creating pick_lists."""
-    rng = np.random.RandomState(909)
+    rng = np.random.default_rng(909)
 
-    test_data = rng.random_sample((4, 2000))
+    test_data = rng.random((4, 2000))
     ch_names = [f"MEG {i:03d}" for i in [1, 2, 3, 4]]
     ch_types = ["grad", "mag", "mag", "eeg"]
     sfreq = 250.0
@@ -495,7 +495,7 @@ def test_picks_by_channels():
     assert len(pick_list) == len(pick_list2) + 1
     assert pick_list2[0][0] == "meg"
 
-    test_data = rng.random_sample((4, 2000))
+    test_data = rng.random((4, 2000))
     ch_names = [f"MEG {i:03d}" for i in [1, 2, 3, 4]]
     ch_types = ["mag", "mag", "mag", "mag"]
     sfreq = 250.0
@@ -754,7 +754,7 @@ def test_get_channel_types_equiv(meg, eeg, ordered):
     pick_types(raw.info, meg=meg, eeg=eeg)
     picks = pick_types(raw.info, meg=meg, eeg=eeg)
     if not ordered:
-        picks = np.random.RandomState(0).permutation(picks)
+        picks = np.random.default_rng(0).permutation(picks)
     if not meg and not eeg:
         with pytest.raises(ValueError, match="No appropriate channels"):
             raw.get_channel_types(picks=picks)

mne/_fiff/tests/test_reference.py

@@ -277,8 +277,8 @@ def test_set_eeg_reference_ch_type(ch_type, msg, projection):
     # gh-6454
     # gh-8739 added DBS
     ch_names = ["ECOG01", "ECOG02", "DBS01", "DBS02", "MISC"]
-    rng = np.random.RandomState(0)
-    data = rng.randn(5, 1000)
+    rng = np.random.default_rng(0)
+    data = rng.standard_normal((5, 1000))
     raw = RawArray(
         data, create_info(ch_names, 1000.0, ["ecog"] * 2 + ["dbs"] * 2 + ["misc"])
     )
@@ -976,7 +976,8 @@ def test_bipolar_combinations():
     info = create_info(
         ch_names=ch_names, sfreq=1000.0, ch_types=["eeg"] * len(ch_names)
     )
-    raw_data = np.random.randn(len(ch_names), 1000)
+    rng = np.random.default_rng(0)
+    raw_data = rng.standard_normal((len(ch_names), 1000))
     raw = RawArray(raw_data, info)
 
     def _check_bipolar(raw_test, ch_a, ch_b):