pravsripad

name: jumeg
channels:
- defaults
dependencies:
- python<3.7
- pip
- ipython
- mkl
- numpy
- scipy

pravsripad

name: jumeg_cuda
channels:
- defaults
dependencies:
- python<3.7
- pip
- ipython
- mkl
- numpy
- scipy

pravsripad

import mnedev as mne
from mnedev.preprocessing import ICA, create_ecg_epochs
from mnedev.datasets import sample

print(__doc__)
print mne.__version__

# Read and preprocess the data. Preprocessing consists of:
#

pravsripad

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

import telepot
import yaml
from rss_gumpu import parse_prep_feed

with open('/home/user/.my_config.yaml', 'r') as myfile:
    uconfig = yaml.load(myfile)

pravsripad

#!/usr/bin/env python
'''
Script to send alerts/messages to telegram.
e.g. ssh login alerts etc.
'''

import sys
import yaml
import telepot

pravsripad

#!/usr/bin/env python

'''
Script to read raw data, epoch it into arbitrary events and save it.
- pravsripad@gmail.com
'''

import numpy as np
import mne

pravsripad

#!/usr/bin/env python

# import the necessary packages
import numpy as np
import mne
import matplotlib.pyplot as pl
import sys

# turn interactive plotting on
pl.ion()

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/*

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

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()