SherazKhan

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""Computes the distance correlation between two matrices.
https://en.wikipedia.org/wiki/Distance_correlation
"""

import numpy as np
from scipy.spatial.distance import pdist, squareform

SherazKhan

from scipy.spatial.distance import pdist, squareform
import numpy as np
import random
import copy


def distcorr(Xval, Yval, pval=True, nruns=500):
    """ Compute the distance correlation function, returning the p-value.
    Based on Satra/distcorr.py (gist aa3d19a12b74e9ab7941)

SherazKhan

import mne
import os
import numpy as np
import os.path as op
from mne.datasets import sample
from nose.tools import assert_equal


data_path = sample.data_path()

SherazKhan

import itertools
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
import numpy as np

def main():
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='polar')
    x = np.radians(np.arange(0, 360, 10))

SherazKhan

import pyimpress
import mne
import numpy as np
from mne import create_info
from mne.io import RawArray


mff_fname='/cluster/transcend/sheraz/data/MEG_EEG/EEG/taskforce_1_vis_20160310_063855.mff'

# mff is the class supporting mff io

SherazKhan

function sol = apply_inverse_raw(fname_raw,fname_inv,tmin, tmax, sel, nave,dSPM,pickNormal)


if ~exist('pickNormal','var')
    
    pickNormal=0;
    
end

if ~exist('dSPM','var')

SherazKhan

# Load 'pwr' package
library(pwr)

# 80% power

# Small effect size
small80p <- pwr.t.test(sig.level = 0.05, d = 0.2, 
                       power = 0.8, type="two.sample", alternative="two.sided") 
small80p  

SherazKhan

function plot_brainstorm_data()
% ============================
% Brainstorm tutorial datasets
% ============================
% 
% Here we compute the evoked from raw for the Brainstorm
% tutorial dataset. For comparison, see [1]_ and:
% 
%     http://neuroimage.usc.edu/brainstorm/Tutorials/MedianNerveCtf
% 

SherazKhan

import mne
import numpy as np

def labels2stc(labels,labels_data,stc):
    stc_new = stc.copy()
    stc_new.data.fill(0)
    for index,label in enumerate(labels):
        if labels_data.ndim==1:
            if isinstance(label, str):
                temp = stc.in_label(mne.read_label(label))

SherazKhan

function [cortex] = raw_cortex(data,fname_inv,nave,dSPM,pickNormal)
% data : 3D Matrix Channels x times x epochs
% fname_inv : Inverse operator file name
% nave : number of trials (for single trial should be one)
% dSPM : 0 or 1
% pickNormal : 0 (loose) or 1 (fixed)

if ~exist('pickNormal','var')
    
    pickNormal=0;