if lapl:
x = x.dot(montage.make_laplacian_proj('EEG'))
del df
x *= 1e6
x_split = np.split(x[:x.shape[0] - x.shape[0] % 10], 10)
X_split = []
Y_split = []
delay = 0
n_taps = 200
band = (8, 12)
for x in x_split:
X, Y = get_XY(x, band_hilbert(x[:, channels.index('P4')], fs, band),
n_taps, delay)
X_split.append(X)
Y_split.append(Y)
del x
corrs = np.zeros((10 - 3 - 2) * 3)
mses = np.zeros((10 - 3 - 2) * 3)
corrs_one = np.zeros((10 - 3 - 2) * 3)
mses_one = np.zeros((10 - 3 - 2) * 3)
for k in range(5):
X_train = np.concatenate(X_split[k:3 + k])
Y_train = np.concatenate(Y_split[k:3 + k])
corrs_fir = np.zeros((len(indx) - 1, len(delays))) * np.nan
corrs_cfir_f = np.zeros((len(indx) - 1, len(delays))) * np.nan
corrs_cfir_wf = np.zeros((len(indx) - 1, len(delays))) * np.nan
corrs_cfir_t = np.zeros((len(indx) - 1, len(delays))) * np.nan
corrs_hilbert = np.zeros((len(indx) - 1, len(delays))) * np.nan
corrs_rls = np.zeros((len(indx) - 1, len(delays))) * np.nan
min_phase_delays = np.zeros((len(indx) - 1, len(delays))) * np.nan
corrs_min_phase = np.zeros((len(indx) - 1, len(delays))) * np.nan
for k, kk in enumerate(indx[1:]):
print('****', k)
x_train = x[indx[k - 1]:indx[k - 1] + int(step)]
x_std = x_train.std()
x_mean = x_train.mean()
x_train = (x_train - x_mean) / x_std
y_train = band_hilbert(x_train, 500, band)
x_test = x[kk:kk + int(step)] / x_std
x_test = (x_test - x_mean) / x_std
y_test = band_hilbert(x_test, 500, band)
freq, time, spec = stft(x_train,
fs,
nperseg=n_fft,
return_onesided=False,
noverlap=int(n_fft * 0.9))
mask = np.abs(spec).mean(1)
w = np.diag(mask)
for j, d in enumerate(delays):
print('delay', d)
F = np.array([
np.exp(-2j * np.pi / n_fft * k * np.arange(n_taps))
for k in np.arange(n_fft)
])
fig, axes = plt.subplots(3, 2, sharex=True, sharey=True)
for j, d in enumerate([200, 0, 100, -50, 50, 'rand']):
if d != 'rand':
#if d<999:
#plt.close()
H = get_ideal_H(n_fft, fs, band, d)
b_cfir_f = cLS(F, H, 0)
X_train, Y_train = get_XY(x[:fs * 60 * 2],
band_hilbert(x[:fs * 60 * 2], fs, (8, 12)),
n_taps, d)
b_cfir_f = cLS(X_train, Y_train, 0)
if d > 0:
b_fir_band = firwin(d, band / fs * 2, pass_zero=False)
b_fir_smooth = firwin(d, 2 / fs * 2)
rec_fir = lfilter(b_fir_smooth, [1],
np.abs(lfilter(b_fir_band, [1], x)))
#plt.plot(b_cfir_f)
#plt.show()
#plt.plot(x)
rec = np.abs(lfilter(b_cfir_f, [1], x))
else:
rec = np.random.normal(size=len(x))
from data.loaders import load_alpha_delay_subjects
from models import SWAnalyticFilter2, RLS
import pylab as plt
from helpers import band_hilbert, get_ideal_H, cLS, get_XY
import numpy as np
from scipy.signal import firwin, lfilter, filtfilt, stft, minimum_phase
x = load_alpha_delay_subjects()
fs = 500
band = (8, 12)
n_fft = 2000
n_taps = 500
d = 100
x_test = x[:fs * 60 * 2]
y_test = band_hilbert(x_test, 500, band)
tar = np.abs(y_test)
delays = np.arange(10, 300, 10)
min_phase_delays = np.zeros_like(delays)
corrs_min_phase = np.zeros_like(delays) * 0.
fir_delays = np.zeros_like(delays)
corrs_fir = np.zeros_like(delays) * 0.
for j, d in enumerate(delays):
print('delay', d)
b_fir_band = minimum_phase(
firwin(d, [8 / fs * 2, 12 / fs * 2], pass_zero=False))
b_fir_smooth = minimum_phase(firwin(d, 2 / fs * 2))
rec_fir = lfilter(b_fir_smooth, [1],
np.abs(lfilter(b_fir_band, [1], x_test)))