1 - Introductory tutorial

In this short tutorial, we will load some data stored into MATLAB files, and see how we can get help dyconnmap's functions.

Load data

First things first. We are going to use scipy's io submodule to load our MATLAB workspace with some sample EEG signals.



In [1]:

    
import numpy as np



In [2]:

    
import scipy
from scipy import io

dyconnmap supports the standard NumPy arrays, so, it's up to the user to import the data. For example, one could load a Nifti file (using i.e. nibabel) and fetch the internal structure that is a standard numpy array.



In [3]:

    
data = scipy.io.loadmat('sample_eeg/eyes_open.mat')
eeg = np.squeeze(data['eyes_open'])

fs = 160.0
num_trials = 101
num_samples_vis = 160 * 3

After loading the data, inspect them; find out home many channels and samples are available.



In [4]:

    
num_channels, num_samples = np.shape(eeg[0])

print("""
  trials: {0}
channels: {1}
 samples: {2}
""".format(num_trials, num_channels, num_samples))









    



  trials: 101
channels: 64
 samples: 9600



In [5]:

    
eeg1 = eeg[0]

dyconnmap and Help

dyconnmap's available submodules are

fc for functional connectivity
graphs for graph analysis,
cluster for clustering algorithms
ts for (symbolic) time series



In [8]:

    
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

from dyconnmap.fc import iplv

Most estimators' functions signatures rely on the same inputs. One can reference to the docstrings or simply read the whole documentation with the command (i.e.) help(iplv).



In [9]:

    
help(iplv)









    



Help on function iplv in module dyconnmap.fc.iplv:

iplv(data, fb, fs, pairs=None)
    Imaginary part of Phase Locking Value
    
    Compute the Imaginary part of Phase Locking Value for the given *data*,
    between the *pairs* (if given) of channels.
    
    
    Parameters
    ----------
    data : array-like, shape(n_channels, n_samples)
        Multichannel recording data.
    
    fb : list of length 2
        The low and high frequencies.
    
    fs : float
        Sampling frequency.
    
    pairs : array-like or `None`
        - If an `array-like` is given, notice that each element is a tuple of length two.
        - If `None` is passed, complete connectivity will be assumed.
    
    
    Returns
    -------
    ts : array-like, shape(n_channels, n_channels, n_samples)
        Estimated IPLV time series.
    
    avg : array-like, shape = [n_electrodes, n_electrodes]
        Average IPLV.

The pairs argument is optional, and usually is ignored.