def emd_plot(data):
emd = EMD(data)
imfs = emd.decompose()
# ipdb.set_trace()
plot_imfs(data,imfs)
plt.legend('EMD')
return imfs
def testHHT(c):
#x,y = dataset.loadDataset("longdata.txt", filterCondition=True, filterType="DC")
#x,y = dataset.sortDataset(x, y, length=1, classes=[c])
N_imfs = 3
#xt = x[0][0]
#length = len(xt)
#xt = np.array(xt)
#t = np.arange(0, length/glb.fs, 1.0/glb.fs)
t = np.linspace(0, 1, 1000)
modes = np.sin(2 * np.pi * 5 * t) + np.sin(2 * np.pi * 10 * t) + np.sin(
2 * np.pi * 20 * t) + np.sin(2 * np.pi * 40 * t)
xt = modes + t
#t = np.linspace(0, 1, 1000)
#print(x[0][0])
#decomposer = EMD(xt, n_imfs=N_imfs, fixe=0, threshold_1=0.05, threshold_2=0.5, alpha=0.05)
iterations = 10
decomposer = EMD(xt, fixe=iterations)
imfs = decomposer.decompose()
seconddecomposer = EMD(imfs[-1, :], fixe=iterations)
secondimfs = seconddecomposer.decompose()
thirddecomposer = EMD(secondimfs[-1, :], fixe=iterations)
thirdimfs = thirddecomposer.decompose()
print imfs.shape
print secondimfs.shape
#imfs = np.concatenate(imfs[0], secondimfs[0])
imfs = np.vstack((imfs[0], secondimfs[0]))
print imfs.shape
imfs = np.vstack((imfs, thirdimfs[0]))
print imfs.shape
imfs = np.vstack((imfs, thirdimfs[1]))
print imfs.shape
#imfs = np.concatenate(
plot_imfs(xt, imfs, t) #doctest: +SKIP
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)
"""
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)
@author: ly
@date Date: 2019年07月10日 12:45
@Description:
@URL: https://github.com/jaidevd/pyhht/blob/dev/docs/examples/simple_emd.py
@version: V1.0
'''
import numpy as np
from numpy import pi, sin, linspace, sqrt
from pyhht.emd import EMD
from pyhht.visualization import plot_imfs
import matplotlib
import matplotlib.pyplot as plt
t = linspace(0, 1, 1000)
modes = sin(2 * pi * 5 * t) + sin(2 * pi * 10 * t) + sin(2 * pi * 3 * t)
x = modes + t
plt.plot(t, modes)
plt.plot(t, x)
plt.show()
decomposer = EMD(x)
imfs = decomposer.decompose()
plot_imfs(x, imfs, t)
sumValues = np.zeros_like(imfs[0])
for i in range(imfs.shape[0]):
sumValues += imfs[i]
plt.plot(t, sumValues)
plt.show()
from pyhht import EMD from pyhht.visualization import plot_imfs from scipy.signal import hilbert from pyhht.utils import inst_freq import matplotlib.pyplot as plt import numpy as np lon = [] lon = lon.crop(-0.5, 0.5) signal = lon._data[5, 57, :] decomposer = EMD(signal) imfs = decomposer.decompose() plot_imfs(signal, imfs, lon.times) ht = hilbert(imfs) plt.imshow(np.abs(ht), aspect='auto', origin='lower')
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 5 16:01:27 2019
@author: veepoo
"""
from pyhht.visualization import plot_imfs
import numpy as np
import pyhht
import math
t = np.linspace(0, 100, 1000)
fs1 = 4
fs2 = 10
modes = np.cos(math.radians(2 * math.pi * fs1 * t)) + np.cos(
math.radians(2 * math.pi * fs2 * t))
x = modes + t
decomposer = pyhht.EMD(x)
imfs = decomposer.decompose()
plot_imfs(x, imfs, t) #doctest: +SKIP
Created on Wed Jun 20 10:58:43 2018
@author: pxr13258
"""
from pyhht.visualization import plot_imfs
import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
import csv
pi=3.14
t = np.linspace(0, 1, 1000)
modes = np.sin(2 * pi * 5 * t) + np.sin(2 * pi * 10 * t)
x = modes + t
decomposer = EMD(x)
imfs = decomposer.decompose()
plot_imfs(x, imfs, t)
pathbcc='D:/newdata/BCCBTC_5T.csv'
bcc= pd.read_csv(pathbcc)
data=bcc['Close'].values
emdtest=[]
for i in range(10):
emdtest.append(data[i*288:(i+1)*288])
emdtest=np.array(emdtest)
dec=EMD(emdtest[9])
imf=dec.decompose()
plot_imfs(emdtest[9], imf)
IA0 = abs(ReImf0)
IA1 = abs(ReImf1)
instf = np.array([instf0, instf1, instf2]) # 频率均值
AR0 = ar_least_square(IA0, 4)
AR1 = ar_least_square(IA1, 4)
f1 = instf.mean()
return np.vstack((AR0, AR1, f1)) #纵向拼接
path = 'F:\\BaiduNetdiskDownload\\超声波数据\\1mm\\50.txt'
with open(path, 'r', encoding='utf-8') as f:
for line in f:
a = line.split(',')
a = np.array(a).astype(float)
x, y = signal.butter(1, 0.4, 'highpass')
a = signal.filtfilt(x, y, a)
"""decomposer = pyhht.emd.EMD(a[5000:6000])
imfs = decomposer.decompose() #获取到imfs数据。
plot_imfs(a[5000:6000], imfs)
ReImf0 = hilbert(imfs[0])
ReImf1 = hilbert(imfs[1])
ReImf2 = hilbert(imfs[2])
ReImf3 = hilbert(imfs[3])
ReImf4 = hilbert(imfs[4])
ReImf = np.array([ReImf0, ReImf1, ReImf2, ReImf3, ReImf4])
amplitude = abs(ReImf0) #计算幅值
instf, timestamps = tftb.processing.inst_freq(ReImf0) #计算瞬时频率
plt.plot(timestamps, instf) #画图看看效果
plt.show()"""
x = (cal(a[6000:7000]))
plt.plot(y)
#ext1=y[argrelextrema(y, np.greater)[0]]
#min1=y[argrelextrema(y, np.less)[0]]
##f2 = interp1d(x, y, kind='cubic')
###for i in range(len(ext1)):
##
##
#cubicspline=CSP(x,y)
##plt.plot(f2);
#extarray=cubicspline(ext1);
#minarray=cubicspline(min1);
#larr=np.zeros(176);
#larr=larr+min1 ;
decomposer = EMD(y)
imfs = decomposer.decompose()
plot_imfs(y, imfs, x)
imf1 = []
for i in range(1000):
imf1.append(imfs[0, i])
plt.subplot(2, 1, 1)
plt.plot(x, imf1)
plt.xlabel('IMF 1')
plt.ylabel('Amplitude')
plt.show()
imf2 = []
for i in range(1000):
imf2.append(imfs[1, i])
plt.subplot(2, 1, 2)
plt.plot(x, imf2)
@version: V1.0
'''
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from pyhht.emd import EMD
from pyhht.visualization import plot_imfs
column_names = [
'term', 'date', 'red1', 'red2', 'red3', 'red4', 'red5', 'red6', 'blue',
'appear1', 'appear2', 'appear3', 'appear4', 'appear5', 'appear6', 'prize',
'Total', 'first_num', 'first_amount', 'second_num', 'second_amount',
'third_num', 'third_amount', 'fourth_num', 'fourth_amount', 'fifth_num',
'fifth_amount', 'sixth_num', 'sixth_amount'
]
data = pd.read_csv('F:/workspace/Tensorflow/src/ssq/ssq.txt',
sep=' ',
header=None,
names=column_names)
print(data.info())
plt.figure(figsize=(15, 5))
x = range(0, len(data['red1']), 1)
plt.plot(x, data['red1'])
plt.show()
decomposer = EMD(data['red1'])
imfs = decomposer.decompose()
print(imfs.shape)
plot_imfs(data['red2'].values, imfs, None)
import numpy as np
import pandas as pd
from sklearn import datasets
import matplotlib.pyplot as plt
from pyhht.emd import EMD
from pyhht.visualization import plot_imfs
#载入时间序列数据
data = pd.read_csv('gold_data.csv',usecols=['settle'])
#EMD经验模态分解
decomposer = EMD(data[0])
imfs = decomposer.decompose()
#绘制分解图
plot_imfs(data[0],imfs,data.index)
#保存IMFs
arr = np.vstack((imfs,data[0]))
dataframe = pd.DataFrame(arr.T)
dataframe.to_csv('C:\imf.csv',index=None,columns=None)
from pyhht.emd import EMD
import pandas as pd
import numpy as np
import time
start = time.clock()
from pyhht.visualization import plot_imfs
dta = pd.read_csv('C:/Users/dw/Desktop/Data1.csv')
seris = np.array(dta.iloc[:, 0])
decomposer = EMD(seris)
imfs = decomposer.decompose()
plot_imfs(seris, imfs)
arr = np.vstack((imfs, seris))
dataframe = pd.DataFrame(arr.T)
dataframe.to_csv("C:/Users/dw/Desktop/temp.csv")
end = time.clock()
print("final is in ", end - start)
plt.savefig("hs.png")
plt.show()
#############noise################
noise = np.zeros((1600, 3))
for i in range(3):
noise[:, i] = wgn(data[:, i], 40)
data = data + noise
if if_imf:
x = data[0:1600, 0]
decomposer = EMD(x)
imfs = decomposer.decompose()
plot_imfs("imfs_noise", x, imfs, t)
else:
plt.figure(figsize=(16, 12))
x = data[0:1600, 0]
decomposer = EMD(x)
imfs = decomposer.decompose()
plt.subplot(3, 5, 1) # original signal
plt.plot(t, data[0:1600, 0], 'y')
plt.grid()
hs0 = hilbert(imfs[0])
hs1 = hilbert(imfs[1])
for xx in range(1600):
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
for i in xrange(1, dm):
# ene[i] = ene[i-1] + envel[i] ** 2
ene[i] = ene[i - 1] + trNfil[2].data[i] ** 2
import matplotlib.pyplot as plt
from pyhht.visualization import plot_imfs
image = cv2.imread("dan.jpg")
image = cv2.resize(image, (108, 108))
image2 = cv2.imread('images.jpg')
image2 = cv2.resize(image2, (108, 108))
pi = 3.14
temp = image.flatten()
temp2 = image2.flatten()
t = np.linspace(0, 1, 34992)
# modes = np.sin(2 * pi * 5 * t) + np.sin(2 * pi * 10 * t)
# x = modes + t
temp = temp + t
temp2 = temp2 + t
hilbert = pyhht.EMD(temp)
imfs = hilbert.decompose()
hilbert2 = pyhht.EMD(temp2)
imfs2 = hilbert.decompose()
print(imfs.shape, imfs2.shape)
plot_imfs(temp, imfs, t)
plot_imfs(temp, imfs2, t)
from pyhht.visualization import plot_imfs N = 2001 T = np.arange(1, N + 1, step=4) t = np.arange(1, N + 1) p = N / 2 fmin1 = 1.0 / 64 fmax1 = 1.5 * 1.0 / 8 x1 = fmsin(N, fmin1, fmax1, p, N / 2, fmax1)[0] fmin2 = 1.0 / 32 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() plot_imfs(x, imf, t)
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)
clf2.fit(
np.matrix([imf[i][0:3000] for i in range(7, imf.shape[0])]).T,
for i in range(2, len(y)):
q.append((y[i] - y[i - 1]) / (x[i] - x[i - 1]))
q.append(0)
return np.array(q)
else:
print("Error: Invalid shape. x and y must be the same length")
#computing the derivative of column 1 wrt time
differ = (diff(data[:, 1], data[:, 0]))
pl.plot(data[:, 0], differ)
pl.show()
decomposer = hht.EMD(data[:, 1] + data[:, 0])
imfs = decomposer.decompose()
vis.plot_imfs(data[:, 0] + data[:, 1], imfs, data[:, 0])
for blep in range(0, 27):
#Determine where peaks are, change parameters for a change in what is defined as a "peak". In order, minimum height limit, vertical distance to neighbouring samples, horizontal distance to neighboring samples, how prominant a "peak" is, required width of peaks.
ProtoPeaks = scipy.signal.find_peaks(data[:, blep],
height=0,
threshold=0,
distance=1,
prominence=70,
width=0)
Peaks = ProtoPeaks[0]
print(
"For the below graph, (graph number", blep,
")the peaks as defined by the paramters on line 53 of the program are at"
)
print(Peaks)
from pyhht.visualization import plot_imfs
import matplotlib.pyplot as plt
sys.path.append('data/')
from read_PLAID_data import read_source_data
source_dir = 'data/source/submetered_new_pured/source/'
with open(
'/home/chaofan/powerknowledge/data/source/metadata_submetered2.1.json',
'r',
encoding='utf8') as load_meta:
meta = json.load(load_meta)
length1 = 3000
length2 = 3000
t = range(3000)
csv_dir = os.listdir(source_dir)
for file in csv_dir:
file_dir = source_dir + file
Switch_V, Switch_I = read_source_data(file_dir, offset=0, length=length1)
Stable_V, Stable_I = read_source_data(file_dir,
offset=3000,
length=length2)
tem = np.array(Switch_I) - np.array(Stable_I)
decomposer = EMD(tem, n_imfs=3)
imfs = decomposer.decompose()
plot_imfs(tem, imfs, t)
import numpy as np
import pandas as pd
from sklearn import datasets
import matplotlib.pyplot as plt
from pyhht.emd import EMD
from pyhht.visualization import plot_imfs
#载入时间序列数据
data = pd.read_csv('gold_data_.csv', usecols=['settle'])
data = data['settle']
decomposer = EMD(data)
imfs = decomposer.decompose()
print(imfs)
#绘制分解图
plot_imfs(data, imfs, data.index)
#保存IMFs
arr = np.vstack((imfs, data))
dataframe = pd.DataFrame(arr.T)
dataframe.to_csv('imf.csv', index=None, columns=None)
print(imfs[0])
import numpy as np
import pandas as pd
from sklearn import datasets
import matplotlib.pyplot as plt
from pyhht.emd import EMD
from pyhht.visualization import plot_imfs
#载入时间序列数据
data = pd.read_csv('gold_data.csv', usecols=['settle'])
#EMD经验模态分解
x = data['settle']
decomposer = EMD(x)
imfs = decomposer.decompose()
#绘制分解图
plot_imfs(x, imfs, data.index)
#保存IMFs
arr = np.vstack((imfs, x))
dataframe = pd.DataFrame(arr.T)
dataframe.to_csv('D:/imf.csv', index=None, columns=None)
'''
this file contains object to extract the linear component of raw data
this method is based on the secondary decomposition using EMD and SSA
'''
# load SourceData#1.csv
dta = pd.read_csv('C:/Users/dw/Desktop/Data1.csv')
# read the wind speed series
ts = np.array(dta.iloc[:, 0])
# EMD decomposition
decomposer = EMD(ts)
imfs = decomposer.decompose()
plot_imfs(ts, imfs)
arr = np.vstack((imfs, ts))
dataframe = pd.DataFrame(arr.T)
arr1 = arr.T[:, 2:8]
# linear subseries: IMF3 to IMF7, and Res
linear_subseries = arr1.sum(1)
# nonlinear subseries: IMF1 and IMF2
arr2 = arr.T[:, 0:2]
nonlinear_subseries = arr2.sum(1)
# Secondary decomposition of nonlinear subseries using SSA
series = nonlinear_subseries - np.mean(nonlinear_subseries)
windowLen = 12
seriesLen = len(series)
K = seriesLen - windowLen + 1
X = np.zeros((windowLen, K))
