def load_data_emd(trainNum, testNum, startNum, data):
print('EMD_data loading.')
global ahead_num
# all_data_checked = data
targetData = data
# 处理预测信息，划分训练集和测试集
targetData = targetData[startNum + 1: startNum + trainNum + testNum + 1]
targetData = np.array(targetData).reshape(-1, 1)
# #归一化，每个特征分开归一化
global scaler_target
scaler_target = StandardScaler(copy=True, with_mean=True, with_std=True)
targetData = scaler_target.fit_transform(targetData)
decomposer = EMD(targetData)
imfs = decomposer.decompose()
# plot_imfs(targetData, imfs)
data_decomposed = imfs.tolist()
for h1 in range(len(data_decomposed)):
data_decomposed[h1] = np.array(data_decomposed[h1]).reshape(-1, 1)
for h2 in range(len(data_decomposed)):
trainX, trainY, testX, testY = create_data(data_decomposed[h2], trainNum, ahead_num)
dataset_imf = [trainX, trainY, testX, testY]
data_decomposed[h2] = dataset_imf
print('load_data complete.\n')
return data_decomposed
def emd_plot(data):
emd = EMD(data)
imfs = emd.decompose()
# ipdb.set_trace()
plot_imfs(data,imfs)
plt.legend('EMD')
return imfs
def test_imfs_total_no_error(self):
"""
Check if the sum of the IMFs is sufficiently close to the input signal.
"""
signal = np.sum([self.trend, self.mode1, self.mode2], axis=0)
emd = EMD(signal)
imfs = emd.decompose()
assert_allclose(imfs.sum(0), signal)
def test_imfs_total_no_error(self):
"""
Check if the sum of the IMFs is sufficiently close to the input signal.
"""
signal = np.sum([self.trend, self.mode1, self.mode2], axis=0)
emd = EMD(signal)
imfs = emd.decompose()
assert_allclose(imfs.sum(0), signal)
def test_residue(self):
"""Test the residue of the emd output."""
signal = np.sum([self.trend, self.mode1, self.mode2], axis=0)
decomposer = EMD(signal, t=self.ts)
imfs = decomposer.decompose()
n_imfs = imfs.shape[0]
n_maxima = argrelmax(imfs[n_imfs - 1, :])[0].shape[0]
n_minima = argrelmin(imfs[n_imfs - 1, :])[0].shape[0]
self.assertTrue(max(n_maxima, n_minima) <= 2)
def test_residue(self):
"""Test the residue of the emd output."""
signal = np.sum([self.trend, self.mode1, self.mode2], axis=0)
decomposer = EMD(signal, t=self.ts)
imfs = decomposer.decompose()
n_imfs = imfs.shape[0]
n_maxima = argrelmax(imfs[n_imfs - 1, :])[0].shape[0]
n_minima = argrelmin(imfs[n_imfs - 1, :])[0].shape[0]
self.assertTrue(max(n_maxima, n_minima) <= 2)
def test_monotonicity_of_trend(self):
"""
Check if the trend is monotonic.
"""
signal = np.sum([self.trend, self.mode1, self.mode2], axis=0)
emd = EMD(signal)
imfs = emd.decompose()
# There should be two IMFs, and the rest of them are trends
trend = imfs[3:, :].sum(0)
assert_allclose(self.trend, trend)
def hilbert_huang(self):
"""
Create EMD and Calculate Hilbert-Huang
"""
imfs_list = []
for i in self.reshaped_x:
for j in i:
decomposer = EMD(j)
imfs = decomposer.decompose()
imfs_list.append(imfs)
return np.array(imfs_list)
def get_data(l, lag=3):
"""
默认滞后3项，预测一步
l 的最后一项不参与分解
"""
decomposer = EMD(l[:-1]) # l的最后一项不参与分解
imfs = decomposer.decompose() # 包括m个imf和一个res项
# 得到如下的输入样本，第一个样本（1，lag，m+1），即lag个滞后项，每一项有m+1个元素
# [[imf1_1,imf2_1,...,imfm_1,res_1],[imf1_2,imf2_2,...,imfm_2,res_2],...,[imf1_lag,imf2_lag,...,imfm_lag,res_lag]]
x = seq_tf_matrix(imfs.T, lag)
# y为输出结果，未来一步的预测值
y = l[-len(x):]
return x, y
def EEMD(sample, num_iterations):
imf = {}
for i in range(0, num_iterations):
white_noise = generateWhiteNoise(len(sample))
x = white_noise + sample
decomp = EMD(x, maxiter=10000)
imfX = decomp.decompose()
try:
imf[imfX.shape[0]] += imfX
except KeyError:
imf[imfX.shape[0]] = imfX
for key in imf:
imf[key] /= key
return imf
def EMD_data_preparation(csv_folder,samplenumber,train_list):
########### Trining and Test Data Spliting ######################
Ensembled_train = open(csv_folder+'Ensembled_train.csv', 'w')
Total_data = 0
#Training data prepare
F = open(train_list,'r')
line = F.readline()
while line:
Original_signal = []
splitted = line.split(',')
for h in range(0,samplenumber):
Original_signal.append(float(splitted[h]))
disease = splitted[-1][:-1]
Original_signal = np.asarray(Original_signal)
try:
decomposer = EMD(Original_signal,n_imfs=3,maxiter=3000)
imfs = decomposer.decompose()
ensembled_data = []
for h in range(0,samplenumber):
ensembled_data.append(imfs[0][h]+imfs[1][h]+imfs[2][h])
Total_data = Total_data+1
string = str(float("{0:.8f}".format(ensembled_data[0])))
for h in range(1,samplenumber):
string = string +','+str(float("{0:.8f}".format(ensembled_data[h])))
string = string+','+disease+'\n'
Ensembled_train.write(string)
print 'Train Data = ',Total_data,'---Disease = ',disease
line = F.readline()
except:
print 'Could not Write'
line = F.readline()
Ensembled_train.close()
#Ensembled_test.close()
F.close()
def optIMFPrediction():
df = loadTestData('table_bac.csv')
plt.plot(df[5].values[:])
close_prices = df[5].values[:]
close_prices = minmax_scale(close_prices)
emd = EMD(close_prices, maxiter=3000)
imf = emd.decompose()
svrlist = []
predYVals = np.matrix([])
tscv = TimeSeriesSplit(n_splits=500)
kf = KFold(n_splits=10, shuffle=True)
for i in range(imf.shape[0]):
x, y = rollingWindows(imf[i], 500, 0, 3000)
svr = svm.SVR(cache_size=1000)
parameters = {
'C': [0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10]
}
reg = GridSearchCV(svr, parameters, cv=kf, n_jobs=-1)
reg.fit(x, y)
print(reg.best_params_)
return
def testWithIMFPrediction():
t = time.time()
df = loadTestData('table_bac.csv')
plt.plot(df[5].values[:])
#plt.show()
close_prices = df[5].values[:]
print(len(close_prices))
close_prices = minmax_scale(close_prices)
emd = EMD(close_prices, maxiter=3000)
imf = emd.decompose()
plot_imfs(close_prices, imf)
plt.plot(hilbert(imf, axis=0).T)
plt.show()
svrlist = []
predYVals = np.matrix([])
for i in range(7, 8):
x, y = rollingWindows(imf[i], 500, 0, 2500)
if i == 7:
svr = svm.SVR(C=0.1, cache_size=4000)
else:
svr = svm.SVR(c=10, cache_size=4000)
svr.fit(x, y)
svrlist.append(svr)
testX, testY = rollingWindows(imf[i], 500, 3040, 3400)
predY = np.matrix(svr.predict(testX)).T
print(predY.shape)
try:
predYVals = np.concatenate([predYVals, predY], axis=1)
except ValueError:
predYVals = np.matrix(predY)
svr = svm.SVR()
svr.fit(imf[7:8, 0:3000].T, close_prices[0:3000])
predPrices = svr.predict(predYVals)
print(mean_squared_error(close_prices[3540:3900], predPrices))
print(mean_squared_error(close_prices[3540:3900], close_prices[3539:3899]))
print(time.time() - t)
def emd(x, ts, n_imfs):
x.dtype = 'float64'
imfs = np.zeros((n_imfs + 1, x.shape[0]))
decomposer = EMD(x)
allimfs = decomposer.decompose()
if (len(allimfs) > 0):
imfs[0, :] = allimfs[0]
else:
imfs[0, :] = x - imfs.sum(0)
if (len(allimfs) > 1):
imfs[1, :] = allimfs[1]
else:
imfs[1, :] = np.zeros((1, x.shape[0]))
imfs[-1, :] = x - imfs.sum(0)
'''
imfs = np.zeros((n_imfs + 1, x.shape[0]))
for i in range(n_imfs):
ix = 0
#print ("x",x.shape)
#print ("sum",imfs.sum(0).shape)
mode = x - imfs.sum(0)
nmin, nmax, nzero = map(len, extr(mode))
tmin, tmax, xmin, xmax = boundary_conditions(mode, ts)
# while abs((nmin + nmax) - nzero) > 1 and (not (utils.judge_stop(mode))):
# mode, amplitude_error = sift(mode, ts)
# if amplitude_error <= tol or (utils.judge_stop(mode)):
# break
if abs((nmin + nmax) - nzero) > 1 and (not (utils.judge_stop(mode))) and len(tmin)>3 and len(tmax)>3:
mode, amplitude_error = sift(mode, ts)
imfs[i, :] = mode
imfs[-1, :] = x - imfs.sum(0)
'''
return imfs
def Main():
# load raw ECG signal
#signal, mdata = storage.load_txt('./examples/ecg.txt')
samplenumber = 5000
File_Path = './Database/PTB/'
samp_rating = 1000
dir_files1 = []
for (dirpath, dirnames, filenames) in os.walk(File_Path):
dir_files1 += [os.path.join(dirpath, file[0:-4]) for file in filenames]
dir_files = list(set(dir_files1))
print dir_files
Read_Files = []
avg_min_RR_emd = []
avg_max_RR_emd = []
avg_avg_RR_emd = []
avg_ratio_emd = []
avg_coeff_emd = []
avg_min_RR_orig = []
avg_max_RR_orig = []
avg_avg_RR_orig = []
avg_ratio_orig = []
avg_coeff_orig = []
Diseases = []
##### Save the Data
A = open('./Analysis/PTB/Analysis_avg_avg_RR.csv', 'w')
B = open('./Analysis/PTB/Analysis_avg_ratio.csv', 'w')
C = open('./Analysis/PTB/Analysis_avg_coeff.csv', 'w')
A.write('Patient_ID' + ',' + 'EMD' + ',' + 'Original' + '\n')
B.write('Patient_ID' + ',' + 'EMD' + ',' + 'Original' + '\n')
C.write('Patient_ID' + ',' + 'EMD' + ',' + 'Original' + '\n')
for j in range(0, len(dir_files)):
try:
print dir_files[j],
ECG_signal, ecgrecord = wfdb.srdsamp(dir_files[j])
record = wfdb.rdsamp(dir_files[j])
sig_length = len(ECG_signal)
disease = ecgrecord['comments'][4][22:]
print disease,
#print record.__dict__
repetition = int(math.floor(sig_length / samplenumber))
sig_start = 0
count = 0
for h in range(0, repetition):
signal = []
for i in range(sig_start, sig_start + samplenumber):
sum = 0
for channel in range(0, 15):
sum += ECG_signal[i][channel]
signal.append(sum)
try:
RR_orig, RR_time_orig, min_RR_orig, max_RR_orig, Average_RR_orig, Ratio_orig, Individual_coeff_orig, Avg_coeff_orig, Avg_template_orig, Individual_Beats_orig = ECG_analysis(
signal[0:samplenumber],
show=False,
sampling_rate=samp_rating)
#Read_Files.append(dir_files[j])
#EMD Analysis
signal_for_EMD = np.asarray(signal[0:samplenumber])
decomposer = EMD(signal_for_EMD, n_imfs=3, maxiter=2000)
imfs = decomposer.decompose()
EMD_data = []
for i in range(0, samplenumber):
EMD_data.append(imfs[0][i] + imfs[1][i] + imfs[2][i])
RR_emd, RR_time_emd, min_RR_emd, max_RR_emd, Average_RR_emd, Ratio_emd, Individual_coeff_emd, Avg_coeff_emd, Avg_template_emd, Individual_Beats_emd = ECG_analysis(
EMD_data[0:samplenumber],
show=False,
sampling_rate=samp_rating)
# Print
#print min_RR_emd, ',', min_RR_orig
#print max_RR_emd,',',max_RR_orig
#print 'AVG_RR_emd=',Average_RR_emd,' Avg_RR_orig=' ,Average_RR_orig,
#print Ratio_emd,',',Ratio_orig
print 'Emd_coeff=', Avg_coeff_emd, ' Orig_coeff=', Avg_coeff_orig,
print 'start=', sig_start, ' count=', count
'''
avg_min_RR_emd.append(min_RR_emd)
avg_max_RR_emd.append(max_RR_emd)
avg_avg_RR_emd.append(Average_RR_emd)
avg_ratio_emd.append(Ratio_emd)
avg_coeff_emd.append(Avg_coeff_emd)
avg_min_RR_orig.append(min_RR_orig)
avg_max_RR_orig.append(max_RR_orig)
avg_avg_RR_orig.append(Average_RR_orig)
avg_ratio_orig.append(Ratio_orig)
avg_coeff_orig.append(Avg_coeff_orig)
'''
#Diseases.append(disease)
sig_start = sig_start + samplenumber
A.write(dir_files[j] + ',' + str(Average_RR_emd) + ',' +
str(Average_RR_orig) + ',' + disease + '\n')
B.write(dir_files[j] + ',' + str(Ratio_emd) + ',' +
str(Ratio_orig) + ',' + disease + '\n')
C.write(dir_files[j] + ',' + str(Avg_coeff_emd) + ',' +
str(Avg_coeff_orig) + ',' + disease + '\n')
count += 1
except:
sig_start = sig_start + samplenumber
print 'Problem in the cut sequencee'
except:
print 'Problem: ', dir_files[j][-7:]
'''
close_prices = df[5].values[:]
low_prices = df[4].values[:]
high_prices = df[3].values[:]
encodings = np.array([
longShortEncoding(i, close_prices, high_prices, low_prices, 5, 0.05)
for i in range(3600)
])
weights = sampleWeightsByUniqueness(encodings)
print(weights)
print(encodings[:, 0])
print(encodings.shape)
s = minmax_scale(close_prices)
emd = EMD(s, maxiter=3000)
imf = emd.decompose()
plot_imfs(s, imf)
predYVals = []
for i in range(7, imf.shape[0]):
x, y = rollingWindows(imf[i], 30, 0, 3000)
nn = KNeighborsRegressor(n_neighbors=4)
nn.fit(x, y)
x, y = rollingWindows(imf[i], 30, 3030, 3400)
predYNN = nn.predict(x)
print(y)
print(predYNN)
predYVals.append(predYNN)
clf2 = KNeighborsClassifier(n_neighbors=2)
"""
Created on Wed Apr 10 17:57:02 2019
@author: Administrator
"""
from pyhht.emd import EMD
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyhht.visualization import plot_imfs
# 读取数据
#dataset = pd.read_csv('data_day.csv')
stock_dir='../dataset/AAPL.csv'
dataset = pd.read_csv(open(stock_dir),header=0)
dataset=dataset[::-1]
for col in dataset.columns:
dataset=dataset['Open']
data = dataset.values
s = data.ravel()
#emd
decomposer = EMD(s)
IMF = decomposer.decompose()
print(IMF.shape)
imf_data = pd.DataFrame(IMF.T)
imf_data.to_csv('../dataset/emd/emd_AAPL_'+str(col)+'.csv')
#绘制分解图
plot_imfs(s,IMF)
def emd(self):
Signal = np.array(self.get_waveform(), dtype=float)
Signal = signal.detrend(Signal, type='constant')
decomposer = EMD(Signal)
IMFs = decomposer.decompose()
return IMFs.tolist()
def multi_emd_aann(lag=3, num_trial=2, hidden=128, epochs=20, ignore=ignore):
pre_data_tf_result = pd.DataFrame() # 百分比结果
real_result = pd.DataFrame() # 预测重构值
time_ = [] # 时间
mape, mae, mse, rmse = [], [], [], []
for j in range(num_trial):
result = []
start_time = time.time()
# 100(test_num)个测试样本
for k in range(test_num):
decomposer = EMD(data_tf[:-test_num + k]) # 最后一项不参与分解
imfs = decomposer.decompose() # 包括m个imf和一个res项
pr = None
for i in range(len(imfs)):
d = seq_tf_matrix(np.hstack((imfs[i], [0])),
n=lag + 1) # 给imfs[i]加上一个值作为最后一项的真实值，只占个位子
x = d[:, :-1]
if ignore:
x = x[:, :-ignore] # 忽略与预测值最近的ignore项
y = d[:, -1]
if pr is None:
pr = ann(x,
y,
test_num=1,
batch_size=batch_size,
hidden=hidden,
epochs=epochs) # 预测的值,子序列预测结果
else:
pr = pr + ann(x,
y,
test_num=1,
batch_size=batch_size,
hidden=hidden,
epochs=epochs) # 预测的值,子序列结果直接相加
result.append(pr[0])
end_time = time.time()
pr = np.array(result)
restore_value = restore_data(pr, data[-test_num - 1:-1]) # 还原预测值
mape_, mae_, mse_, rmse_ = loss_function(restore_value,
data[-test_num:])
# 保存第i次的结果
pre_data_tf_result[str(j + 1) + '_times_lag' + str(lag)] = pr
real_result[str(j + 1) + '_times_lag' + str(lag)] = restore_value
# 保存第i次的评估指标
time_.append((end_time - start_time) / 60) # 分钟
mape.append(mape_)
mae.append(mae_)
mse.append(mse_)
rmse.append(rmse_)
# 预测结果
pre_data_tf_result['test_percentage'] = data_tf[
-test_num:] # 把真实的，需要预测的百分比值加入
real_result['test_value'] = data[-test_num:] # 把真实的需要预测的原值加入
pre_data_tf_result.to_csv('../' + ada_result + '/' + name_data +
'/data_tf_result/lag_' + str(lag) +
'_multi_emd_aann_data_tf_result.csv')
real_result.to_csv('../' + ada_result + '/' + name_data +
'/real_result/lag_' + str(lag) +
'_multi_emd_aann_real_result.csv')
# 预测结果评价指标
result_evaluation = {
'lag': lag,
'time': time_,
'mape': mape,
'mae': mae,
'mse': mse,
'rmse': rmse
}
fw = open(
'../' + ada_result + '/' + name_data +
'/multi_emd_aann_result_evaluation.json', 'a')
fw.write(json.dumps(result_evaluation) + '\n')
fw.close()
fmax2 = 1.5 * 1.0 / 4
x2 = fmsin(N, fmin2, fmax2, p, N / 2, fmax2)[0]
f0 = 1.5 * 1.0 / 16
x3 = amgauss(N, N / 2, N / 8) * fmconst(N, f0)[0]
a1 = 1
a2 = 1
a3 = 1
x = np.real(a1 * x1 + a2 * x2 + a3 * x3)
x = x / np.max(np.abs(x))
decomposer = EMD(x)
imf = decomposer.decompose()
n_freq_bins = 256
short_window_length = 127
beta = 3 * np.pi
window = kaiser(short_window_length, beta=beta)
_, re_spec_sig, _ = spectrogram(x, t, n_freq_bins, window)
_, re_spec_imf1, _ = spectrogram(imf[0, :], t, n_freq_bins, window)
_, re_spec_imf2, _ = spectrogram(imf[1, :], t, n_freq_bins, window)
_, re_spec_imf3, _ = spectrogram(imf[2, :], t, n_freq_bins, window)
fig = plt.figure()
for i, rspec in enumerate(
[re_spec_sig, re_spec_imf1, re_spec_imf2, re_spec_imf3]):
rspec = np.abs(rspec)[:128, :]
import pandas as pd
import matplotlib.pyplot as plt
from pyhht.emd import EMD
from pyhht.utils import get_envelops
from pyhht.visualization import plot_imfs
# In[]
# load data
t = np.arange(0, 1, 0.01)
x = 2 * np.sin(2 * np.pi * 15 * t) + 4 * np.sin(2 * np.pi * 10 * t) * np.sin(
2 * np.pi * t * 0.1) + np.sin(2 * np.pi * 5 * t)
upper, lower = get_envelops(x)
plt.plot(upper)
plt.plot(lower)
plt.show()
# In[] EMD decompose
decomposer = EMD(x)
imfs = decomposer.decompose() # Decompose the input signal into IMFs.
plot_imfs(x, imfs, t)
print('%.3f' % decomposer.io())
plot_imfs.show()
plt.plot(imfs[1, :].T)
# In[] save IMFs
arr = np.vstack((imfs, x))
dataframe = pd.DataFrame(arr.T)
def Main():
samplenumber = 5000
File_Path = './Database/MIT-BIH'
samp_rating = 360
dir_files1=[]
for (dirpath, dirnames, filenames) in os.walk(File_Path):
dir_files1 += [os.path.join(File_Path, file[0:-4]) for file in filenames]
dir_files = list(set(dir_files1))
dir_files.sort()
print dir_files
Read_Files = []
avg_min_RR_emd = []
avg_max_RR_emd = []
avg_avg_RR_emd = []
avg_ratio_emd = []
avg_coeff_emd = []
avg_min_RR_orig = []
avg_max_RR_orig = []
avg_avg_RR_orig = []
avg_ratio_orig = []
avg_coeff_orig = []
Diseases = []
##### Save the Data
A = open('./Analysis/MIT-BIH/Analysis_avg_avg_RR.csv','w')
B = open('./Analysis/MIT-BIH/Analysis_avg_ratio.csv','w')
C = open('./Analysis/MIT-BIH/Analysis_avg_coeff.csv','w')
A.write('Patient_ID'+','+'EMD'+','+'Original'+','+'disease'+'\n')
B.write('Patient_ID'+','+'EMD'+','+'Original'+','+'disease'+'\n')
C.write('Patient_ID'+','+'EMD'+','+'Original'+','+'disease'+'\n')
for j in range(0,len(dir_files)):
try:
print dir_files[j],
original_signal,ecgrecord = wfdb.srdsamp(dir_files[j])
record = wfdb.rdsamp(dir_files[j])
data_file = dir_files[j][-3:]
sig_diseases = globals()['disease_'+str(data_file)]
for gf in sig_diseases:
time = globals()['beats_disease_'+str(data_file)][gf]
time_split = time.split(':')
minutes = time_split[0]
seconds = time_split[1]
total_seconds = int(minutes)*60 + int(seconds)
total_samples = total_seconds * samp_rating
disease = gf
print gf,
initial_start = 0 # per record starting index of each disease of that record
ECG_signal = original_signal[initial_start:total_samples]
sig_length = len(ECG_signal)
print 'original sig length ', len(original_signal),
print 'cut_signal_length ',sig_length,
repetition = int(math.floor(sig_length/samplenumber))
print 'repeat ', repetition,
sig_start = 0
count = 0
for h in range(0,repetition):
signal = []
for i in range(sig_start,sig_start+samplenumber):
signal.append(ECG_signal[i][0]+ECG_signal[i][1])
try:
RR_orig,RR_time_orig,min_RR_orig,max_RR_orig,Average_RR_orig,Ratio_orig,Individual_coeff_orig,Avg_coeff_orig, Avg_template_orig, Individual_Beats_orig = ECG_analysis(signal[0:samplenumber],show=False,sampling_rate=samp_rating)
#Read_Files.append(dir_files[j])
#EMD Analysis
signal_for_EMD = np.asarray(signal[0:samplenumber])
decomposer = EMD(signal_for_EMD,n_imfs=3,maxiter=3000)
imfs = decomposer.decompose()
EMD_data = []
for i in range(0,samplenumber):
EMD_data.append(imfs[0][i]+imfs[1][i]+imfs[2][i])
RR_emd,RR_time_emd,min_RR_emd,max_RR_emd,Average_RR_emd,Ratio_emd,Individual_coeff_emd,Avg_coeff_emd,Avg_template_emd, Individual_Beats_emd = ECG_analysis(EMD_data[0:samplenumber],show=False,sampling_rate=samp_rating)
# Print
#print min_RR_emd, ',', min_RR_orig
#print max_RR_emd,',',max_RR_orig
#print 'AVG_RR_emd=',Average_RR_emd,' Avg_RR_orig=' ,Average_RR_orig,
#print Ratio_emd,',',Ratio_orig
print 'Emd_coeff=',Avg_coeff_emd,' Orig_coeff=',Avg_coeff_orig,
print 'start=',sig_start,' count=',count
'''
avg_min_RR_emd.append(min_RR_emd)
avg_max_RR_emd.append(max_RR_emd)
avg_avg_RR_emd.append(Average_RR_emd)
avg_ratio_emd.append(Ratio_emd)
avg_coeff_emd.append(Avg_coeff_emd)
avg_min_RR_orig.append(min_RR_orig)
avg_max_RR_orig.append(max_RR_orig)
avg_avg_RR_orig.append(Average_RR_orig)
avg_ratio_orig.append(Ratio_orig)
avg_coeff_orig.append(Avg_coeff_orig)
'''
#Diseases.append(disease)
sig_start = sig_start + samplenumber
A.write(dir_files[j]+','+str(Average_RR_emd)+','+str(Average_RR_orig)+','+disease+'\n')
B.write(dir_files[j]+','+str(Ratio_emd)+','+str(Ratio_orig)+','+disease+'\n')
C.write(dir_files[j]+','+str(Avg_coeff_emd)+','+str(Avg_coeff_orig)+','+disease+'\n')
count += 1
except:
sig_start = sig_start + samplenumber
print 'Problem in the cut sequencee'
initial_start = total_samples
except:
print 'Problem: ',dir_files[j][-7:]
'''
def EMD_data_preparation(filepath,patient_data,csv_folder,problem_data_file,samplenumber,number_of_IMFs,split_perc):
files = glob.glob('./csv_folder/*')
for f in files:
os.remove(f)
problem_data=open(problem_data_file,'w')
#PTB Diagnostic ECG database Disease labels
miscle=['Stable angina','Palpitation', 'Unstable angina']
cardiom=['Heart failure (NYHA 4)', 'Heart failure (NYHA 3)', 'Heart failure (NYHA 2)']
ecg_lead = ['i','ii','iii','avr','avl','avf','v1','v2','v3','v4','v5','v6','vx','vy','vz']
Sig_Records = {'Bundle branch block': 38092, 'Valvular heart disease': 37647, 'Myocarditis': 39672, 'Healthy control': 37500, 'Dysrhythmia': 39557, 'Myocardial infarction': 38951, 'Cardiomyopathy': 37659}
unIMFs = open('./Problem_Data/unIMFs.csv','a')
IMF1_train = open(csv_folder+'IMF1_train.csv', 'a')
IMF2_train = open(csv_folder+'IMF2_train.csv', 'a')
IMF1_test = open(csv_folder+'IMF1_test.csv', 'a')
IMF2_test = open(csv_folder+'IMF2_test.csv', 'a')
Train_time = open('Train_time.csv','a')
Test_time = open('Test_time.csv','a')
if number_of_IMFs >= 3:
IMF3_train = open(csv_folder+'IMF3_train.csv', 'a')
IMF3_test = open(csv_folder+'IMF3_test.csv', 'a')
if number_of_IMFs >= 4:
IMF4_train = open(csv_folder+'IMF4_train.csv', 'a')
IMF4_test = open(csv_folder+'IMF4_test.csv', 'a')
if number_of_IMFs >= 5:
IMF5_train = open(csv_folder+'IMF5_train.csv', 'a')
IMF5_test = open(csv_folder+'IMF5_test.csv', 'a')
if number_of_IMFs == 6:
IMF6_train = open(csv_folder+'IMF6_train.csv', 'a')
IMF6_test = open(csv_folder+'IMF6_test.csv', 'a')
f = open(patient_data)
line = f.readline()
disease_array=[]
file_count = 0
while line:
file_count += 1
if file_count < 1000:
line = f.readline()
file_count += 1
print line, file_count
else:
file_count += 1
splitted = line.split('/')
file_name = str(splitted[1][0:8])
patient_folder = str(splitted[0])
total_path = filepath+patient_folder+'/'+file_name
print patient_folder,'---',file_name,
#print total_path
try:
signal,ecgrecord = wfdb.srdsamp(total_path)
record = wfdb.rdsamp(total_path)
print ecgrecord['comments'][4][22:],
signal_length = len(signal)
#repetition = int(math.floor(signal_length/samplenumber))
if not ecgrecord['comments'][4][22:] == 'n/a':
disease = ecgrecord['comments'][4][22:]
if disease in miscle:
disease = "Miscellaneous"
elif disease in cardiom:
disease = "Cardiomyopathy"
if disease == 'Myocardial infarction':
overlap = 1000
elif disease == "Bundle branch block":
overlap = 55
elif disease == "Cardiomyopathy":
overlap = 55
elif disease == "Dysrhythmia":
overlap = 35
elif disease == "Healthy control":
overlap = 255
elif disease == "Myocarditis":
overlap = 15
elif disease == "Valvular heart disease":
overlap = 15
if disease not in disease_array:
disease_array.append(disease)
samplelength = 0
undecomposed = 0
sig_start_ov = 0
repetition = 0
while(signal_length-sig_start_ov >= samplenumber):
repetition += 1
sig_start_ov += overlap
stop = int(math.ceil(repetition*split_perc))
print 'repetition = ',repetition
########### Trining and Test Data Spliting ######################
#Training data prepare
for j in range(0,stop):
write_signal = []
for sample in range(samplelength,samplelength+samplenumber):
ecg_signal = 0
for i1 in range(0,15):
ecg_signal = ecg_signal+signal[sample][i1]
write_signal.append(ecg_signal)
EMD_signal = np.asarray(write_signal)
try:
start_time_train = time.time()
decomposer = EMD(EMD_signal,n_imfs=number_of_IMFs,maxiter=3000)
imfs = decomposer.decompose()
#Construct Modified EMD
modified_EMD_train = []
for q in range(0,samplenumber):
modified_EMD_train.append(imfs[0][q]+imfs[1][q]+imfs[2][q])
elapsed_time_train = time.time() - start_time_train
Train_time.write(total_path+','+disease+','+str(elapsed_time_train)+'\n')
#print len(imfs)
str1 = str(imfs[0][0])
str2 = str(imfs[1][0])
if (len(imfs) == number_of_IMFs+1):
for h in range(1,samplenumber):
str1 = str1+','+str(imfs[0][h])
str2 = str2+','+str(imfs[1][h])
str1 = str1+','+disease+'\n'
str2 = str2+','+disease+'\n'
IMF1_train.write(str1)
IMF2_train.write(str2)
if number_of_IMFs >= 3:
str3 = str(imfs[2][0])
for h in range(1,samplenumber):
str3 = str3+','+str(imfs[2][h])
str3 = str3+','+disease+'\n'
IMF3_train.write(str3)
if number_of_IMFs >= 4:
str4 = str(imfs[3][0])
for h in range(1,samplenumber):
str4 = str4+','+str(imfs[3][h])
str4 = str4+','+disease+'\n'
IMF4_train.write(str4)
if number_of_IMFs >= 5:
str5 = str(imfs[4][0])
for h in range(1,samplenumber):
str5 = str5+','+str(imfs[4][h])
str5 = str5+','+disease+'\n'
IMF5_train.write(str5)
if number_of_IMFs==6:
str6 = str(imfs[5][0])
for h in range(1,samplenumber):
str6 = str6+','+str(imfs[5][h])
str6 = str6+','+disease+'\n'
IMF6_train.write(str6)
else:
print ('IMF Number do not match')
undecomposed = undecomposed + 1
samplelength = samplelength+overlap
except:
print 'Could not be decomposed'
samplelength = samplelength+overlap
#Testing data preparation
for j in range(stop,repetition):
write_signal = []
for sample in range(samplelength,samplelength+samplenumber):
ecg_signal = 0
for i1 in range(0,15):
ecg_signal = ecg_signal+signal[sample][i1]
write_signal.append(ecg_signal)
EMD_signal = np.asarray(write_signal)
try:
start_time_test = time.time()
decomposer = EMD(EMD_signal,n_imfs=number_of_IMFs,maxiter=3000)
imfs = decomposer.decompose()
#Construct Modified EMD
modified_EMD_test = []
for q in range(0,samplenumber):
modified_EMD_test.append(imfs[0][q]+imfs[1][q]+imfs[2][q])
elapsed_time_test = time.time() - start_time_test
Test_time.write(total_path+','+disease+','+str(elapsed_time_test)+'\n')
#print len(imfs)
str1 = str(imfs[0][0])
str2 = str(imfs[1][0])
if (len(imfs) == number_of_IMFs+1):
for h in range(1,samplenumber):
str1 = str1+','+str(imfs[0][h])
str2 = str2+','+str(imfs[1][h])
str1 = str1+','+disease+'\n'
str2 = str2+','+disease+'\n'
IMF1_test.write(str1)
IMF2_test.write(str2)
if number_of_IMFs >= 3:
str3 = str(imfs[2][0])
for h in range(1,samplenumber):
str3 = str3+','+str(imfs[2][h])
str3 = str3+','+disease+'\n'
IMF3_test.write(str3)
if number_of_IMFs >= 4:
str4 = str(imfs[3][0])
for h in range(1,samplenumber):
str4 = str4+','+str(imfs[3][h])
str4 = str4+','+disease+'\n'
IMF4_test.write(str4)
if number_of_IMFs >= 5:
str5 = str(imfs[4][0])
for h in range(1,samplenumber):
str5 = str5+','+str(imfs[4][h])
str5 = str5+','+disease+'\n'
IMF5_test.write(str5)
if number_of_IMFs==6:
str6 = str(imfs[5][0])
for h in range(1,samplenumber):
str6 = str6+','+str(imfs[5][h])
str6 = str6+','+disease+'\n'
IMF6_test.write(str6)
else:
print ('IMF Number do not match')
undecomposed = undecomposed + 1
samplelength = samplelength+overlap
except:
print 'Could not be decomposed'
samplelength = samplelength+overlap
string = patient_folder+'---'+file_name+'UNIMFed Records = '+str(undecomposed)+'\n'
unIMFs.write(string)
line = f.readline()
except:
problem=patient_folder+'/'+file_name+'\n'
problem_data.write(problem)
line = f.readline()
print sys.exc_info(),'\n'
f.close()
problem_data.close()
print disease_array
IMF1_train.close()
IMF2_train.close()
IMF1_test.close()
IMF2_test.close()
if number_of_IMFs>=3:
IMF3_train.close()
IMF3_test.close()
if number_of_IMFs>=4:
IMF4_train.close()
IMF4_test.close()
if number_of_IMFs>=5:
IMF5_train.close()
IMF5_test.close()
if number_of_IMFs==6:
IMF6_train.close()
IMF6_test.close()
unIMFs.close()
def corrcoef_imfs(seq):
decomposer = EMD(seq)
imfs = decomposer.decompose()
result = [corrcoef(imfs[i], seq) for i in range(len(imfs))]
result.append(sum(map(abs, result)) / len(result))
return result
def hht_marginal_spectrum(self, dataset, params):
# Setting data_path and checking if it's needed to compute this function for more bearings.
processed_data_path = 'hht_marginal_spectrum/hht_marginal_spectrum'
bearings_marginal_spectrum = dataset.load_processed_data(
dataset, processed_data_path)
bearings_not_processed = params['bearings']
if bearings_marginal_spectrum[0]:
bearings_marginal_spectrum = bearings_marginal_spectrum[1]
bearings_processed = list(
map(int, list(bearings_marginal_spectrum.keys())))
bearings_not_processed = [
x for x in params['bearings'] if x not in bearings_processed
]
if bearings_not_processed == []:
return bearings_marginal_spectrum
# If can't find any saved file.
else:
bearings_marginal_spectrum = {}
for current_bearing in bearings_not_processed:
imfs_files = []
bearing_marginal_spectrum = []
bearing_files = dataset.bearings_files[str(current_bearing)]
# Calculating IMFs for each data file.
for bearing_file in bearing_files:
data = bearing_file[params['vibration_signal']].values
decomposer = EMD(data)
imfs_files.append(decomposer.decompose())
# Getting the frequency bins.
N = len(data)
fs = params['sampling_frequency']
freq_bins_step = fs / N
freq_bins = np.fft.fftfreq(N)[0:N // 2] * fs # Timestep = 1.
# Calculating Hilbert transform for each IMF.
imfs_ht_files = []
for imfs_file in imfs_files:
imfs_ht_files.append(hilbert(imfs_file))
# Calculating instantaneous frequency of each data.
imfs_freqs_files = []
for imfs_ht_file in imfs_ht_files:
imfs_freqs_file = []
for imf_ht_file in imfs_ht_file:
imfs_freqs_file.append(
pyhht.utils.inst_freq(imf_ht_file)[0] * fs
) # [0] to select the frequencies. * fs because the inst_freq return normalized freqs.
imfs_freqs_files.append(imfs_freqs_file)
# Calculating absolute value and scaling by 1/N factor.
N = len(imfs_ht_file[0])
imfs_envelope_files = np.abs(imfs_ht_files) / N
# Putting frequencies into the frequency bins and computing Hilbert Marginal Spectrum.
imfs_envelope_files_bins = []
for imfs_freqs_file, imfs_envelope_file in zip(
imfs_freqs_files, imfs_envelope_files):
imfs_envelope_file_bins = []
for imf_freqs_file, imf_envelope_file in zip(
imfs_freqs_file, imfs_envelope_file):
imfs_envelope_file_ = np.zeros(N // 2)
bin_index = [
int(freq // freq_bins_step) for freq in imf_freqs_file
]
for index, abs_val in zip(bin_index, imf_envelope_file):
imfs_envelope_file_[index] += abs_val
imfs_envelope_file_bins.append(imfs_envelope_file_)
imfs_envelope_files_bins.append(imfs_envelope_file_bins)
# Summing Hilbert Marginal Spectrum of [0 : params['imfs_qty]] imfs.
for imfs_envelope_file_bins in imfs_envelope_files_bins:
bearing_marginal_spectrum.append([
sum(x) for x in zip(
*imfs_envelope_file_bins[0:params['imfs_qty']])
])
# Saving frequencies, marginal spectrum and hilbert spectrum.
bearings_marginal_spectrum[str(current_bearing)] = [
freq_bins, bearing_marginal_spectrum, imfs_envelope_files_bins
]
dataset.save_processed_data(bearings_marginal_spectrum,
processed_data_path)
return bearings_marginal_spectrum
def EMD_data_preparation(filepath, patient_data, samplenumber, number_of_IMFs):
miscle = ['Stable angina', 'Palpitation', 'Unstable angina']
cardiom = [
'Heart failure (NYHA 4)', 'Heart failure (NYHA 3)',
'Heart failure (NYHA 2)'
]
ecg_lead = [
'i', 'ii', 'iii', 'avr', 'avl', 'avf', 'v1', 'v2', 'v3', 'v4', 'v5',
'v6', 'vx', 'vy', 'vz'
]
f = open(patient_data)
line = f.readline()
disease_array = []
while line:
#splitted = line.split('/')
#file_name = str(splitted[1][0:8])
file_name = line[0:-1]
#patient_folder = str(splitted[0])
total_path = filepath + file_name
print total_path
#try:
signal, ecgrecord = wfdb.rdsamp(total_path)
record = wfdb.rdsamp(total_path)
#print ecgrecord['comments'][4][22:]
signal_length = len(signal)
repetition = int(math.floor(signal_length / samplenumber))
samplelength = 0
undecomposed = 0
stop = int(math.ceil(repetition * 0.7))
########### Trining and Test Data Spliting ######################
#Training data prepare
for j in range(0, stop):
write_signal = []
for sample in range(samplelength, samplelength + samplenumber):
ecg_signal = 0
for i1 in range(0, 12):
ecg_signal = ecg_signal + signal[sample][i1]
write_signal.append(ecg_signal)
EMD_signal = np.asarray(write_signal)
#try:
decomposer = EMD(EMD_signal, n_imfs=number_of_IMFs, maxiter=3000)
imfs = decomposer.decompose()
#print len(imfs)
modified_EMD = []
for h in range(0, samplenumber):
modified_EMD.append(imfs[0][h] + imfs[1][h] + imfs[2][h])
### Plot data
fig = plt.figure(figsize=(25, 15))
plt.subplot(2, 1, 1)
plt.plot(EMD_signal)
plt.ylabel('Original Signal\n Amplitude', labelpad=15, fontsize=35)
plt.xticks(fontsize=35)
plt.yticks(fontsize=35)
plt.subplot(2, 1, 2)
plt.plot(modified_EMD)
plt.ylabel('Modified Signal \n Amplitude',
labelpad=15,
fontsize=35)
plt.xticks(fontsize=35)
plt.yticks(fontsize=35)
plt.xlabel('Sample Number', fontsize=35)
fig.tight_layout()
plt.savefig('modified_ECG_Petersburg.eps', format='eps', dpi=6000)
plt.show()
samplelength = samplelength + samplenumber
line = f.readline()
f.close()
problem_data.close()
print disease_array
# print(ene[i]) # print(ener) enerdb = 10 * np.log10(ener) ener = ener / max(ener) plt.plot(time[indxt], ener) plt.show() plt.plot(vtime, tmp) plt.xlim(tmin, tmax) plt.show() # In[33]: npt = len(trNfil[0].data) emd = EMD(trNfil[0].data) imfs = emd.decompose() time = (np.linspace(1, npt, npt)) * dt plt.rcParams["figure.figsize"] = (30.0, 50.0) plot_imfs(trNfil[0].data, time, imfs) # In[44]: aa = trNfil[2].copy() plotTrigger(aa, cft, 2.2, 0.5) dm = len(cft) item = [i for i in list(range(dm)) if cft[i] > 2.2] print(min(item)) ene = [0] * dm
import matplotlib
matplotlib.use("TkAgg")
from pyhht.emd import EMD
from pyhht.visualization import plot_imfs
import matplotlib.pyplot as plt
from ELM import HiddenLayer
data = pd.read_csv('gold_data.csv', usecols=['settle'])
x = data['settle'] #输入原始数据
y = x #输出原始数据
x = x.as_matrix(columns=None) #输入矩阵
y = y.as_matrix(columns=None) #转为输出矩阵
X = x #ELM专用原始数据
decomposer = EMD(x)
imfs = decomposer.decompose() #emd分解
p_days = 1 #预测p_days后的结果
p = 6 #用前p天的数据预测
C = 10**8 #正则化因子
t = 300 #时间分割点
for i in range(6): #分解
x_imfs = imfs[i]
y_imfs = x #分解后的y
num_data = x.shape[0]
y_in = y_imfs[p + p_days - 1:num_data] #去除y的前p个数据
x_in = np.zeros(shape=(1, p)) #生成一列p行的输入矩阵
for j in range(num_data - p + 1 -
p_days): #将原始x按照每p个一组转为矩阵 -p_days是因为最后一天要预测故前置
x_temp = x_imfs[j:j + p] #截取p个数为矩阵的一列
for i in range(partCount):
startIndex = i * partLen
endIndex = (i + 1) * partLen
# temporarily adding neighbor parts for more accurate calculations
# todo - hh : only half or quarter of neighbor parts can be enough?
if i > 0: # if not first part
startIndex -= partLen
if i < partCount - 2: # until second from last part
endIndex += partLen
if i == partCount - 2: # second from last part (last part's len may not be partLen)
endIndex += len(sig) % partLen
part = sig[startIndex:endIndex]
# calculate imfs for the part
decomposer = EMD(part)
imfsPart = decomposer.decompose()[:-1] # last element is residue
# calculate instant frequency for each imf of the part
instfPart = []
magPart = []
truncatedImfs = []
for imf in imfsPart:
hx = sp.hilbert(imf)
mag = np.abs(hx)
phx = np.unwrap(np.arctan2(hx.imag, hx.real))
tempInstf = sampRate / (2 * np.pi) * np.diff(phx)
# removing neighbor parts after calculations
if i > 0: # not first part
tempInstf = tempInstf[partLen:]
mag = mag[partLen:]
def test_decomposition(self):
"""Test the decompose method of the emd class."""
signal = np.sum([self.trend, self.mode1, self.mode2], axis=0)
decomposer = EMD(signal, t=self.ts)
imfs = decomposer.decompose()
self.assertItemsEqual(imfs.shape, (signal.shape[0], 3))
def test_decomposition(self):
"""Test the decompose method of the emd class."""
signal = np.sum([self.trend, self.mode1, self.mode2], axis=0)
decomposer = EMD(signal, t=self.ts)
imfs = decomposer.decompose()
self.assertItemsEqual(imfs.shape, (signal.shape[0], 3))
def EMD_data_preparation(filepath, patient_data, samplenumber, number_of_IMFs):
miscle = ['Stable angina', 'Palpitation', 'Unstable angina']
cardiom = [
'Heart failure (NYHA 4)', 'Heart failure (NYHA 3)',
'Heart failure (NYHA 2)'
]
ecg_lead = [
'i', 'ii', 'iii', 'avr', 'avl', 'avf', 'v1', 'v2', 'v3', 'v4', 'v5',
'v6', 'vx', 'vy', 'vz'
]
f = open(patient_data)
line = f.readline()
disease_array = []
while line:
splitted = line.split('/')
file_name = str(splitted[1][0:8])
patient_folder = str(splitted[0])
total_path = filepath + patient_folder + '/' + file_name
print patient_folder, '---', file_name,
#print total_path
try:
signal, ecgrecord = wfdb.rdsamp(total_path)
record = wfdb.rdsamp(total_path)
print ecgrecord['comments'][4][22:]
signal_length = len(signal)
repetition = int(math.floor(signal_length / samplenumber))
if not ecgrecord['comments'][4][22:] == 'n/a':
disease = ecgrecord['comments'][4][22:]
if disease in miscle:
disease = "Miscellaneous"
elif disease in cardiom:
disease = "Cardiomyopathy"
if disease not in disease_array:
disease_array.append(disease)
samplelength = 0
undecomposed = 0
stop = int(math.ceil(repetition * 0.7))
########### Trining and Test Data Spliting ######################
#Training data prepare
for j in range(0, stop):
write_signal = []
for sample in range(samplelength,
samplelength + samplenumber):
ecg_signal = 0
for i1 in range(0, 15):
ecg_signal = ecg_signal + signal[sample][i1]
write_signal.append(ecg_signal)
EMD_signal = np.asarray(write_signal)
try:
decomposer = EMD(EMD_signal,
n_imfs=number_of_IMFs,
maxiter=3000)
imfs = decomposer.decompose()
#print len(imfs)
str1 = []
str2 = []
str3 = []
str4 = []
str5 = []
str6 = []
str1.append(imfs[0][0])
str2.append(imfs[1][0])
if (len(imfs) == number_of_IMFs + 1):
for h in range(1, samplenumber):
str1.append(imfs[0][h])
str2.append(imfs[1][h])
if number_of_IMFs >= 3:
str3.append(imfs[2][0])
for h in range(1, samplenumber):
str3.append(imfs[2][h])
if number_of_IMFs >= 4:
str4.append(imfs[3][0])
for h in range(1, samplenumber):
str4.append(imfs[3][h])
if number_of_IMFs >= 5:
str5.append(imfs[4][0])
for h in range(1, samplenumber):
str5.append(imfs[4][h])
if number_of_IMFs == 6:
str6.append(imfs[5][0])
for h in range(1, samplenumber):
str6.append(imfs[5][h])
res = []
res.append(imfs[6][0])
for h in range(1, samplenumber):
res.append(imfs[6][h])
### Plot data
fig = plt.figure(figsize=(25, 15))
plt.subplot(8, 1, 1)
plt.plot(EMD_signal)
plt.ylabel('Signal',
rotation=0,
horizontalalignment='right',
fontsize=25)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.subplot(8, 1, 2)
plt.plot(str1)
plt.ylabel('IMF1',
rotation=0,
horizontalalignment='right',
fontsize=25)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.subplot(8, 1, 3)
plt.plot(str2)
plt.ylabel('IMF2',
rotation=0,
horizontalalignment='right',
fontsize=25)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.subplot(8, 1, 4)
plt.plot(str3)
plt.ylabel('IMF3',
rotation=0,
horizontalalignment='right',
fontsize=25)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.subplot(8, 1, 5)
plt.plot(str4)
plt.ylabel('IMF4',
rotation=0,
horizontalalignment='right',
fontsize=25)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.subplot(8, 1, 6)
plt.plot(str5)
plt.ylabel('IMF5',
rotation=0,
horizontalalignment='right',
fontsize=25)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.subplot(8, 1, 7)
plt.plot(str6)
plt.ylabel('IMF6',
rotation=0,
horizontalalignment='right',
fontsize=25)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.subplot(8, 1, 8)
plt.plot(res)
plt.ylabel('Residual',
rotation=0,
horizontalalignment='right',
fontsize=25)
plt.xlabel('Sample Number', fontsize=30)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
fig.tight_layout()
plt.savefig('PTB_EMD.eps', format='eps', dpi=6000)
plt.show()
else:
print('IMF Number do not match')
undecomposed = undecomposed + 1
samplelength = samplelength + samplenumber
except:
print 'Could not be decomposed'
samplelength = samplelength + samplenumber
line = f.readline()
except:
problem = patient_folder + '/' + file_name + '\n'
line = f.readline()
print sys.exc_info(), '\n'
f.close()
problem_data.close()
print disease_array
plt.show()
else:
print(colored("You have to do at least one EMD first.", 'red'))
continue
if name not in windset.keys():
print(colored('This dataset is not exist', 'red'))
continue
cut = raw_input('Cut the zeros head and end?[y/n]')
if cut == 'y':
cutindex = [
np.nonzero(windset[name])[0][0],
np.nonzero(windset[name])[0][-1]
]
realwindset = windset[name][cutindex[0]:cutindex[1] + 1]
else:
realwindset = windset[name]
x = np.linspace(1, len(realwindset), len(realwindset))
decomposer = EMD(realwindset)
imfs = decomposer.decompose()
size = imfs.shape
plt.figure()
plt.plot(x, realwindset)
plt.title(name)
plt.show()
plt.figure(figsize=(20, 18))
for loop in range(1, size[0] + 1):
plt.subplot(size[0], 1, loop)
plt.plot(x, imfs[loop - 1])
plt.title(loop)
plt.show()
def theta(seq):
decomposer = EMD(seq)
imfs = decomposer.decompose()
rms_imfs = sum([rms(imfs[i])**2 for i in range(len(imfs))])
rms_original = rms(seq)
return abs(math.sqrt(rms_imfs) - rms_original) / rms_original