# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
from scipy import io
from anlffr import tfr
import numpy as np
dat = io.loadmat('ERPsummary_zscore.mat')
t = dat['t'].flatten()
c7 = dat['c7']
c14 = dat['c14']
c20 = dat['c20']
f = np.arange(5, 200)
powertd, itc, times = tfr.tfr_multitaper(c20[:26, None, :], 1000., f)
times = times + t[0]
powertd_adj = tfr.rescale(powertd, times, (-0.2, 0.), mode='logratio')
tfr.plot_tfr(powertd_adj, times, f)
powerasd, itc, times = tfr.tfr_multitaper(c20[26:, None, :], 1000., f)
times = times + t[0]
powerasd_adj = tfr.rescale(powerasd, times, (-0.2, 0.), mode='logratio')
tfr.plot_tfr(powerasd_adj, times, f)
label_mean_evoked = np.mean(stc_evoked_label.data, axis=0)
#######################################################
# Calculating label TFR stuff
epo_array = np.zeros((len(stcs), 1, stcs[0].shape[1]))
nVerts = stc_evoked_label.shape[0]
nTopVerts = 1
corr_list = np.zeros(nVerts)
for k in range(nVerts):
corr_list[k] = np.corrcoef(label_mean_evoked,
stc_evoked_label.data[k, :])[0, 1]
topverts = np.argsort(corr_list)[-nTopVerts:]
for k, stc in enumerate(stcs):
epo_array[k, 0, :] = stc.data[topverts, :].mean(axis=0)
freqs = np.arange(5., 130., 2.)
n_cycles = freqs * 0.250
power, itc, faketimes = tfr_multitaper(epo_array, epochs.info['sfreq'],
freqs, n_cycles=n_cycles,
time_bandwidth=2.0, zero_mean=False,
verbose='DEBUG')
powers += [power, ]
itcs += [itcs, ]
power_contrast = 20. * np.log10(powers[0] / powers[1])
power_contrast_scaled = rescale(power_contrast, times, baseline=(-0.2, 0.),
mode='zscore')
plot_tfr(power_contrast_scaled, times, freqs)
pl.show()
dat = loadmat(fpath + matnames[0])
freq = float(freq)
if freq != dat['f0'][0][0]:
raise RuntimeError("File contains data from the wrong probe"
"frequency! Cannot continue!")
P = dat['Pcanal']
goods = rejecttrials(P, thresh=1.5)
P_ave = np.mean(P[goods, :], axis=0)
# Wavelet analysis
P_env, itc, t = tfr_multitaper(P_ave[None, None, :],
fs,
frequencies=[
freq,
],
time_bandwidth=2.0,
n_cycles=freq * 0.05)
P_env = rescale(P_env,
t,
baseline=(0.3, 0.5),
mode='logratio',
copy=False)
pl.figure(1)
label = str(int(freq))
pl.plot(t, 10 * P_env.squeeze(), label=label)
pl.hold(True)
pl.figure(1)
pl.xlabel('Time (s)', fontsize=16)
pl.ylabel('Ear canal pressure change (dB)', fontsize=16)