def eemd2d(img, goal, nos_wn):
# 2-D EEMD, an implenmation of HHT
# written by Neil Po-Nan Li @ Inst. of Physics, Academia Sinica, Taiwan
# v1. 2012/10/6
# v2. 2014/3/10 bug fixes
# v2.1 2014/3/17 stablity issue fixed
import numpy as np
from PyEMD import EEMD
sz = np.shape(img)
n1 = sz[0]
n2 = sz[1]
nimf = goal
eemd = EEMD()
eemd.trials = goal
eemd.noise_width = nos_wn
emd = eemd.EMD
emd.extrema_detection = "parabol"
std_img = np.std(img)
img = img / std_img
#% 2-D EEMD dim#1
G = np.zeros([n1, n2, nimf + 1])
for u in range(0, n1 - 1):
row_modes = eemd(img[u, :], max_imf=goal)
if not row_modes.all: print("error in dim 1")
row_modes[np.isnan(row_modes)] = 0
tz = np.shape(row_modes)
nf = tz[0]
G[u, :, 0:nf] = np.transpose(row_modes)
# G = Gtmp[:,:,0:nimf]
#% 2-D EEMD dim#2
D = np.zeros([n1, n2, nimf + 1, nimf + 1])
for m in range(0, nimf):
print('Solving mode ', m + 1, '/', nimf)
# solve and store
for v in range(0, n2 - 1):
col_modes = eemd(G[:, v, m], max_imf=goal)
if not col_modes.all: print("error in dim 2")
col_modes[np.isnan(col_modes)] = 0
tz = np.shape(col_modes)
nf = tz[0]
D[:, v, m, 0:nf] = np.transpose(col_modes)
# D = Dtmp[:,:,0:nimf,0:nimf]
#% Combine modes
H = np.zeros([n1, n2, nimf])
for m in range(0, nimf):
for k in range(m, nimf):
H[:, :, m] = H[:, :, m] + D[:, :, m, k]
H[:, :, m] = H[:, :, m] + D[:, :, k, m]
H[:, :, m] = H[:, :, m] - D[:, :, m, m]
# for m = (goal+1):-1:1
# H(:,:,m) = sum( sum( D(:,:,m:end,m:end), 4), 3);
# if m < (goal+1)
# H(:,:,m) = H(:,:,m) - sum( H(:,:, (m+1):end ), 3);
# end
# end
modes = H * std_img
# return modes
return G, D, H
from PyEMD import EEMD, CEEMDAN
import numpy as np
import pandas as pd
from bokeh.plotting import figure, output_file, show
df = pd.read_csv(f'training_datas/ETH-USD.csv',
names=['time', 'low', 'high', 'open', 'close', 'volume'])
# output to static HTML file
output_file("viz/lines.html")
eemd = EEMD()
trials = 100
eemd.trials = trials
eemd.noise_width = 0.08 #np.std(df['close'].values[300:300+(60*5)])
print(eemd.noise_width)
# create a new plot with a title and axis labels
p = figure(title="simple line example",
x_axis_label=f"{trials}",
y_axis_label='price',
plot_width=1000,
plot_height=1000)
'''
for a in df.columns.values[1:-1]:
eemd_arrays = eemd(df[a].values[300:300+(60*5)])
p.line(range(len(eemd_arrays[-1])), df[a].values[300:300+(60*5)], legend=f"{a}", line_width=2, line_color=colors[count % len(colors)])
p.line(range(len(eemd_arrays[-1])), eemd_arrays[-1] + df[a].iloc[300] - eemd_arrays[-1][0], legend=f"EEMD-{a}", line_width=2, line_color=colors[count % len(colors)])
count += 1
'''
#series = eemd(df['close'].values[300:300+(60*5)])
# S_red = S_red.astype(DTYPE)
# print("Input S_infrared.dtype: " + str(S_infrared.dtype))
# print("Input S_red.dtype: " + str(S_red.dtype))
# emd.FIXE_H = 5
# emd.nbsym = 2
# emd.spline_kind = 'cubic'
# emd.DTYPE = DTYPE
# ---------------------------------------------------------------------------------------
# 进行EMD，并且画出结果
# --------------------------------------------------------------------------------------
# emd = EMD()
eemd = EEMD()
eemd.trials = 50
eemd.noise_seed(12345)
eemd.noise_width = 0.01
imfs1 = eemd.eemd(S=S_infrared, T=None, max_imf=-1)
imfNo1 = imfs1.shape[0]
imfs2 = eemd.eemd(S=S_red, T=None, max_imf=-1)
imfNo2 = imfs2.shape[0]
c = np.floor(np.sqrt(imfNo1 + 1))
r = np.ceil((imfNo1 + 1) / c)
plt.ioff()
plt.subplot(r, c, 1)
plt.plot(T, S_infrared, 'r')
plt.xlim((tMin, tMax))
plt.title("Original signal")
