kingjr

def plot_evoked_img(evoked, picks=None, exclude='bads', unit=True, show=True,
                    ylim=None, proj=False, xlim='tight', hline=None, units=None,
                    scalings=None, titles=None, axes=None):
    """Plot evoked data as an image (chan x time) where color index amplitude

    Parameters
    ----------
    evoked : instance of Evoked
        The evoked data

kingjr

"""
========================================================
Decoding sensor space data with over-time generalization
========================================================

This example runs the analysis computed in:

Jean-Remi King, Alexandre Gramfort, Aaron Schurger, Lionel Naccache
and Stanislas Dehaene, "Two distinct dynamic modes subtend the detection of
unexpected sounds", PLOS ONE, 2013

kingjr

"""
==========================
Better with probabilities?
==========================
Comparing classification performance of SVC versus SVC+Platt
using an MEG example from MNE-python.
"""

# Authors: Jean-Remi King <[email protected]>
#

kingjr

# Authors: Jean-Remi King <[email protected]>
"""
This example illustrate how a simple univariate analysis can be applied to
performe a generalization across time analysis, and thus help the reader 
conceptualize what is at stake in the analysis"""


import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

kingjr

import numpy as np
from matplotlib.colors import LinearSegmentedColormap


class nlcmap(LinearSegmentedColormap):
    """
    nlcmap - a nonlinear cmap from specified levels

    Copyright (c) 2006-2007, Robert Hetland <[email protected]>
    Release under MIT license.

kingjr

import numpy as np
from sklearn.cross_validation import cross_val_score, KFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.lda import LDA


def add_information(X, SNR, prop):
	''' Introduce differences between
		Input

kingjr

# Authors: Jean-Remi King <[email protected]>
#
# License: BSD (3-clause)

""" This aims at showing how the MNE GAT object can be used to score different
subsets of data"""

import copy
import numpy as np
import mne

kingjr

# Author: Jean-Remi King <[email protected]>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

from mne import read_evokeds
from mne.datasets.megsim import load_data
from mne.viz.topomap import _griddata, _prepare_topo_plot

kingjr

"""
===================================
WIP Prepare multiconditions events
===================================
"""
# Authors: Jean-Remi King <[email protected]>
#
# License: BSD (3-clause)

kingjr

# Author: Jean-Remi King <[email protected]>
#
# License: BSD (3-clause)

import warnings
import numpy as np
import scipy.sparse as sp
from sklearn.svm import SVC, LinearSVC
from sklearn.datasets import make_classification
from sklearn.calibration import CalibratedClassifierCV