epo_TFR.py

import mne
from os.path import isdir
import numpy as np
from mne.time_frequency import tfr_morlet
import matplotlib.pyplot as plt
plt.ion()
import matplotlib
font = {'weight' : 'bold',
        'size'   : 20}
matplotlib.rc('font', **font)

# different directories for home and office computers; not generally relevant
# for other users
if isdir("/home/jev"):
    root_dir = "/home/jev/hdd/epi/"
elif isdir("/home/jeff"):
    root_dir = "/home/jeff/hdd/jeff/epi/"

proc_dir = root_dir+"/proc/"

subjs = ["1001", "1002"]
subjs = ["1001"]
conds = ["Stim"]
n_jobs = 8

for subj in subjs:
    for cond in conds:
        epo = mne.read_epochs("{}s_epi_{}_{}-epo.fif".format(proc_dir,subj,cond))
        e = epo["OscType=='SO'"]
        if len(e):
            tfr_eeg = tfr_morlet(e, np.arange(10,20), 5, n_jobs=n_jobs,
                             picks="central", return_itc=False)
            tfr_eeg.apply_baseline((-1.25,-0.75), mode="zscore")
        s_epo = mne.read_epochs("{}ss_epi_{}_{}-epo.fif".format(proc_dir,subj,cond))
        s_e = s_epo["OscType=='SO'"]
        if len(s_e):
            tfr_ecog = tfr_morlet(s_e, np.arange(50,200), 15, n_jobs=n_jobs,
                                  picks=["HHL", "HCL", "HHR", "HCR"],
                                  return_itc=False)
            tfr_ecog.apply_baseline((0.3, 0.75), mode="zscore")

            tfr_ecog.plot(tmin=0.3,tmax=0.75,picks="HCL",
                          title="TFR Hippocampus Corpus Left")
            evo = s_e.pick_channels(["central"]).crop(tmin=0.3,tmax=0.75).average()
            evo_data = evo.data
            evo_data = (evo_data - evo_data.min()) / (evo_data.max() - evo_data.min())
            evo_data = evo_data*100 + 50
            plt.plot(evo.times, evo_data[0,], color="gray", alpha=0.5, linewidth=10)

            # tfr_ecog.plot(tmin=0.3,tmax=0.75,picks="HHL")
            # evo = s_e.pick_channels(["central"]).crop(tmin=0.3,tmax=0.75).average()
            # evo_data = evo.data
            # evo_data = (evo_data - evo_data.min()) / (evo_data.max() - evo_data.min())
            # evo_data = evo_data*100 + 50
            # plt.plot(evo.times, evo_data[0,], color="gray", alpha=0.5, linewidth=10)