def get_imfs(signal):
""""
Return the imf of a signal
"""
decomposer = EMD(signal)
imf = decomposer.decompose()
return imf
def plot_imf(signal):
decomposer = EMD(signal)
imfs = decomposer.decompose()
m = len(signal)
x = np.linspace(0,1,m)
pyhht.plot_imfs(x, imfs)
plt.show()
def get_imfs(signal):
try:
# decomposer_signal = EMD(signal, fixe=100, n_imfs=2)
decomposer_signal = EMD(signal)
imfs = decomposer_signal.decompose()
if len(imfs) < 2:
print("imfs {} +++++++++++++++++++++++++++++++++++++++".format(len(imfs)))
raise ValueError("imfs {}".format(len(imfs)))
return imfs[:2]
except Exception as e:
raise e
def process_window(filename, eeg, stage, step):
# plt.figure()
s = np.zeros((5, 2501))
print('step: ', step)
# t = np.linspace(step*fs*30,step*fs*30+fs*10,fs*10)
decomposer = EMD(eeg[step * fs * window_len:step * fs * window_len +
fs * sample_time])
imfs = decomposer.decompose()
# print(len(imfs))
# plot_imfs(eeg[step*fs*30:step*fs*30+fs*10], imfs, t)
for imf in imfs:
analytic_signal = hilbert(imf)
instantaneous_phase = np.unwrap(np.angle(analytic_signal))
instantaneous_freq = (np.diff(instantaneous_phase) / (2.0 * np.pi) *
fs)
# print(len(instantaneous_freq))
for i in range(len(instantaneous_freq)):
if instantaneous_freq[i] > 1 and instantaneous_freq[i] <= 4:
s[0][i] += imf[i]
elif instantaneous_freq[i] > 4 and instantaneous_freq[i] <= 8:
s[1][i] += imf[i]
elif instantaneous_freq[i] > 8 and instantaneous_freq[i] <= 12:
s[2][i] += imf[i]
elif instantaneous_freq[i] > 12 and instantaneous_freq[i] <= 30:
s[3][i] += imf[i]
elif instantaneous_freq[i] > 30 and instantaneous_freq[i] <= 60:
s[4][i] += imf[i]
for i in range(5):
if stage == 'W':
s[i][-1] = 0
else:
s[i][-1] = 1
# save np array
Path('./delta').mkdir(parents=True, exist_ok=True)
Path('./theta').mkdir(parents=True, exist_ok=True)
Path('./alpha').mkdir(parents=True, exist_ok=True)
Path('./beta').mkdir(parents=True, exist_ok=True)
Path('./gamma').mkdir(parents=True, exist_ok=True)
np.save('./delta/' + filename + '_' + str(step) + '_delta', s[0])
np.save('./theta/' + filename + '_' + str(step) + '_theta', s[1])
np.save('./alpha/' + filename + '_' + str(step) + '_alpha', s[2])
np.save('./beta/' + filename + '_' + str(step) + '_beta', s[3])
np.save('./gamma/' + filename + '_' + str(step) + '_gamma', s[4])
pass
import numpy as np
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.ensemble import AdaBoostRegressor, BaggingRegressor
import xgboost as xgb
np.random.seed(123)
ticker = 'SPY'
stock = pdr.get_data_yahoo(ticker.upper(),
start='2009-01-01',
end=str(datetime.now())[0:11])
stock = stock.Close
returns = np.log(stock).diff().dropna()
decomposer = EMD(returns)
imfs = decomposer.decompose()
#t = np.linspace(0, 1, len(returns))
#plot_imfs(returns, imfs, t)
imfs = imfs.T
imfs = pd.DataFrame(imfs)
series = returns[len(returns) - 2000:]
series = np.array(series)
series = series.reshape(-1, 1)
D = 4
T = 1
N = 2000
series = series[500:]
lbls = np.zeros((N - 500 - T - (D - 1) * T, ))
def hht(signal):
decomposer = EMD(signal)
imfs = decomposer.decompose()
ht = hilbert(imfs)
return imfs, ht
def get_imfs_emd(signal):
decomposer_signal = EMD(signal)
return decomposer_signal.decompose()