agramfort

agramfort

import numpy as np

from sklearn.datasets import fetch_covtype
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer
from sklearn.model_selection import cross_val_score

rng = np.random.RandomState(0)

agramfort

#!/usr/bin/env python
#
# Solve LASSO regression problem with ISTA and FISTA
# iterative solvers.

# Author : Alexandre Gramfort, [email protected]
# License BSD

import time
from math import sqrt

agramfort

import time
import numpy as np
from scipy import linalg, io, sparse
import matplotlib.pyplot as plt

from sklearn.externals.joblib import Memory
from sklearn.linear_model import lasso_path
from sklearn.datasets.mldata import fetch_mldata
from sklearn import datasets

agramfort

import os
import neo
import numpy as np
import mne

dirname = './data_Micromed/'
trc_filename = 'EEG_33.TRC'
fname = os.path.join(dirname, trc_filename)

def raw_from_neo(fname):

agramfort

import numpy as np
from scipy import signal

import mne
from mne import (read_forward_solution, read_cov, read_label,
                 pick_types_evoked, pick_types_forward, read_evokeds)
from mne.io import Raw
from mne.datasets import sample
from mne.time_frequency import iir_filter_raw, morlet
from mne.simulation import generate_sparse_stc

agramfort

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

"""Implements Partial Directed Coherence and Direct Transfer Function
using MVAR processes.

Reference
---------
Luiz A. Baccala and Koichi Sameshima. Partial directed coherence:
a new concept in neural structure determination.

agramfort

import numpy as np
from mayavi import mlab
from surfer import Brain
import mne
from mne.datasets import sample

data_path = sample.data_path()
subjects_dir = data_path + '/subjects'

agramfort

returns:
27.0745537031
365.073216463
27.1356784749

agramfort

#%% import ####################################################################
import mne
import numpy as np
import os

from mne.datasets import sample
data_path = sample.data_path()

#%% load group avg data #######################################################
os.environ["SUBJECTS_DIR"] = data_path + '/subjects'