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def loop(X, func, n_jobs=-1, *args, **kwargs):
    """"will apply func(x, *args) for x in X in parallel"""
    from joblib import Parallel, delayed, cpu_count
    max_jobs = cpu_count()
    n_jobs = max_jobs if n_jobs==-1 else n_jobs
    n_jobs = max_jobs if n_jobs>max_jobs else n_jobs
    n_jobs = min(n_jobs, len(X))
    
    parallel = Parallel(n_jobs=n_jobs)
    p_func = delayed(_loop)

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from mayavi import mlab
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
mne.set_log_level(False)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname)  # already has an average reference

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import mne
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import StratifiedKFold
from mne.decoding import SlidingEstimator

# Generate random, fake MEG data

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%matplotlib inline
import mne
import matplotlib.pyplot as plt  # library for plotting
import numpy as np  # library for matrix operations

# Generate random, fake MEG data
n_trials, n_channels, n_times, sfreq = 100, 32, 50, 500.
conditions = np.random.randint(0, 2, n_trials)
info = mne.create_info(n_channels, sfreq)
data = np.random.randn(n_trials, n_channels, n_times)

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