deep-introspection

import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.connectivity import spectral_connectivity
from mne.datasets import sample

# Set parameters
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'

deep-introspection

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm

trials = 200

x = np.random.randn(trials, 9, 16)

effect_size = np.linspace(0, 1, 9)
sample_size = np.linspace(10, 1000, 16)

deep-introspection

deep-introspection

import numpy as np
import scipy.stats as st

nperm = 100
mix = 0.1
score = np.zeros((5, 17))
nparticipants = [int(i) for i in np.logspace(1, 5, 5)]
for ipar, npar in enumerate(nparticipants):
    for ivar, nvar in enumerate(range(3, 20)):
        accu = 0

deep-introspection

#!/usr/bin/env python
# coding=utf-8
"""Spatio-temporal maps of evoked activity."""

# ==============================================================================
# title           : run_spatio_temporal_maps.py
# description     : Plot spatiotemporal maps of Antoine Rémond (60ies).
# authors         : Guillaume Dumas
# date            : 2020-02-25
# usage           : python run_spatio_temporal_maps.py

deep-introspection

#!/usr/bin/env julia
# author          : Guillaume Dumas
# date            : 2021-01-28
# notes           : inspired by Allen M, Poggiali D, Whitaker K et al. 
#                   https://doi.org/10.12688/wellcomeopenres.15191.2

using StatsPlots

function raincloud(data)
    p = violin(data,