pravsripad

#!/usr/bin/env python

import matplotlib.pylab as pl
import numpy as np
import mne
from mne.fiff import Raw
from mne.preprocessing.ica import ICA
from mne.datasets import sample

data_path = sample.data_path()

pravsripad

#!/usr/bin/env python

import matplotlib.pylab as pl
import numpy as np
import mne
from mne.fiff import Raw
from mne.datasets import sample

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

pravsripad

#!/usr/bin/env python

# Inspired from :
# http://matplotlib.org/examples/pie_and_polar_charts/polar_bar_demo.html

''' Plot von Mises distribution as a circular bar plot. '''

import numpy as np
import matplotlib.pyplot as pl

pravsripad

# This file is basically JSON but allowes comment lines that 
# start with the # character. Additionally, keys need not to be
# enclosed by quotes.
# Besides those exceptions this file MUST be valid JSON or the
# program will fail.

# Rainy configuration
{
    # the Url, containing IP address or hostname and port on which to listen
    # if the wildcard "*" is used, we listen on all addresses

pravsripad

1	Precentral_L		255	128	255	255
2	Precentral_R		177	18	255	255
3	Frontal_Sup_L		45	255	47	255
4	Frontal_Sup_R		106	209	255	255
5	Frontal_Sup_Orb_L	122	255	158	255
6	Frontal_Sup_Orb_R	255	87	186	255
7	Frontal_Mid_L		183	150	54	255	
8	Frontal_Mid_R		216	255	159	255
9	Frontal_Mid_Orb_L	181	169	80	255
10	Frontal_Mid_Orb_R	255	152	0	255

pravsripad

#!usr/bin/env python

import numpy as np
import pyunicorn
import matplotlib.pyplot as pl

'''
# make a big bad ass array with all the subjects
for subj in range(1, 11):
    print '>Running for subject %d' %(subj)

pravsripad

#/usr/bin/env python

import numpy as np
import pyunicorn
import matplotlib.pyplot as pl
pl.ion()
data = np.load('all_fmri_data.npy')

def compute_xcorr(data, thresh=95, binary=False):
    '''

pravsripad

import numpy as np
from pyunicorn.timeseries import RecurrenceNetwork
from matplotlib import pyplot as pl
from mpl_toolkits.mplot3d import Axes3D

# Loading the data for 1 subject
data = np.load('all_fmri_data.npy')[0]
pl.plot(data)
pl.show()

pravsripad

import numpy as np
import pyunicorn
import matplotlib.pyplot as pl
pl.ion()

data = np.load('all_fmri_data.npy')
mi_all = np.zeros((10, 90, 90))
xcorr_all = np.zeros((10, 90, 90))

from pyunicorn import funcnet

pravsripad

#!/usr/bin/env python

'''
Load a surface and visualize it using Mayavi.
'''

import mne
from mayavi import mlab

# The surfaces are usually located in $SUBJECTS_DIR/$SUBJECT/surf/*