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Jean-Rémi KING kingjr

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kingjr / gist:0807ebd6f458fdfd8bef
Created May 31, 2014 16:55
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
kingjr / Plot_decoding_time_generalization_ProbasDistance.py
Created June 2, 2014 09:00
Why are probabilistic outputs better than non-probabilistic ones?
"""
==========================
Better with probabilities?
==========================
Comparing classification performance of SVC versus SVC+Platt
using an MEG example from MNE-python.
"""
# Authors: Jean-Remi King <jeanremi.king@gmail.com>
#
@kingjr
kingjr / example_univariate_gat.py
Last active August 29, 2015 14:07
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
# Authors: Jean-Remi King <jeanremi.king@gmail.com>
"""
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
kingjr / contourf_logScale.py
Last active August 29, 2015 14:09
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 <hetland@tamu.edu>
Release under MIT license.
@kingjr
kingjr / example_lda_heteregeneous_subsampling.py
Created November 20, 2014 11:20
Test to see how subsampling can increase decoding scores
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
kingjr / example_score_subset_trials.py
Created March 31, 2015 14:24
# Authors: Jean-Remi King <jeanremi.king@gmail.com>
#
# 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
kingjr / plot_topo_grad_complex.py
Created April 12, 2015 20:43
# Author: Jean-Remi King <jeanremi.king@gmail.com>
#
# 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
kingjr / example_events_pandas.py
Last active August 29, 2015 14:19
"""
===================================
WIP Prepare multiconditions events
===================================
"""
# Authors: Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
@kingjr
kingjr / svc_light_example.py
Last active August 29, 2015 14:20
This aims at reproducing the sklearn.svm.SVC object without having to store 'support_vectors_' and '_dual_coef_'
# Author: Jean-Remi King <jeanremi.king@gmail.com>
#
# 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
@kingjr
kingjr / circular_linear_correlation.py
Created June 3, 2015 00:40
circular_linear_correlation
def circular_linear_correlation(alpha, x):
# Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Niccolo Pescetelli <niccolo.pescetelli@gmail.com>
#
# Licence : BSD-simplified
"""
Parameters
----------
alpha : numpy.array, shape (n_angles, n_dims)