def Inst_freq(peeled_seq, fs=4000):
t = np.arange(0, 1, 1 / fs)
# print(len(t))
emd = EMD()
emd.emd(peeled_seq)
imfs, res = emd.get_imfs_and_residue()
vis = Visualisation(emd)
imfs_inst_freqs = vis._calc_inst_freq(imfs, t, order=False, alpha=None)
return imfs_inst_freqs
def eemd_decompose(a, t):
eemd = EEMD(trials=200, noise_width=0.4)(a)
eimfs, res = eemd[:-1], eemd[-1] #这的参数有问题！！！！！！！！！！！！！！！！！！！！！
vis = Visualisation()
vis.plot_imfs(imfs=eimfs, residue=res, t=t, include_residue=True)
vis.plot_instant_freq(t, imfs=eimfs) #
vis.show()
return eimfs, res
def emd_decompose(a, t):
emd = EMD()
emd.emd(a)
imfs, res = emd.get_imfs_and_residue()
plt.plot(t, a)
plt.title('origin sequence')
vis = Visualisation(emd)
vis.plot_imfs(t=t)
vis.plot_instant_freq(t)
vis.show()
plt.show()
return imfs, res
def Visualing(peeled_seq, fs=4000):
t = np.arange(0, 1, 1 / fs)
# print(len(t))
emd = EMD()
emd.emd(peeled_seq)
imfs, res = emd.get_imfs_and_residue()
vis = Visualisation(emd)
# Create a plot with all IMFs and residue
vis.plot_imfs(imfs=imfs, residue=res, t=t, include_residue=True)
# Create a plot with instantaneous frequency of all IMFs
vis.plot_instant_freq(t, imfs=imfs)
# Show both plots
vis.show()
def hes2(args):
n = 10000
t = np.arange(0, n/args.fs, 1/args.fs)
S = args.singled_out[-1, 0:n]
args.temp_add = 'emd_raw'
show_signal(S, args)
emd = EMD()
emd.emd(S)
imfs, res = emd.get_imfs_and_residue()
# In general:
# components = EEMD()(S)
# imfs, res = components[:-1], components[-1]
vis = Visualisation()
vis.plot_imfs(imfs=imfs, residue=res, t=t, include_residue=True)
vis.plot_instant_freq(t, imfs=imfs)
vis.show()
return 0
def get_IMFset(data):
print("start ensemble decompose")
imfs_set = []
ceemdan = CEEMDAN()
for decon_time in range(data.shape[0]):
series = np.reshape(data[decon_time], (data.shape[1], ))
imfs = ceemdan(series)
imf, res = imfs[:-1], imfs[-1]
imfs_set.append(imfs)
print("processing No.", decon_time, "series")
vis = Visualisation()
vis.plot_imfs(imfs=imf, residue=res, include_residue=True)
vis.show()
return imfs_set
imfs, res = emd.get_imfs_and_residue()
fig = plt.figure(figsize=(12, 16))
ax_main = fig.add_subplot(len(imfs) + 1, 1, 1)
ax_main.set_title(title)
ax_main.plot(t, d)
rec_b = []
for i in imfs:
rec_b.append(d - i)
d -= i
for i, y in enumerate(rec_b):
ax = fig.add_subplot(len(rec_b) + 1, 2, 3 + i * 2)
ax.plot(t, y, 'r')
ax.set_ylabel("A%d" % (i + 1))
for i, y in enumerate(imfs):
ax = fig.add_subplot(len(imfs) + 1, 2, 4 + i * 2)
ax.plot(t, y, 'g')
ax.set_ylabel("D%d" % (i + 1))
# In general:
#components = EEMD()(S)
#imfs, res = components[:-1], components[-1]
vis = Visualisation()
vis.plot_imfs(imfs=imfs, residue=res, t=t, include_residue=True)
vis.plot_instant_freq(f, imfs=imfs)
vis.show()
plt.figure(figsize=(16, 16))
title = 'VMD'
n = len(u)
fig = plt.figure(figsize=(12, 16))
ax_main = fig.add_subplot(n + 1, 1, 1)
ax_main.set_title(title)
ax_main.plot(t, d)
rec_b = []
for i in u:
rec_b.append(d - i)
d -= i
for i, y in enumerate(rec_b):
ax = fig.add_subplot(len(rec_b) + 1, 2, 3 + i * 2)
ax.plot(t, y, 'r')
ax.set_ylabel("A%d" % (i + 1))
for i, y in enumerate(u):
ax = fig.add_subplot(len(u) + 1, 2, 4 + i * 2)
ax.plot(t, y, 'g')
ax.set_ylabel("D%d" % (i + 1))
from PyEMD import Visualisation
vis = Visualisation()
vis.plot_instant_freq(f, imfs=u)
vis.show()
def __init__(self, damage):
self.damage = damage
self.signals = self.extract_signals()
self.vis = Visualisation()
class FrequencyAnalysis:
def __init__(self, damage):
self.damage = damage
self.signals = self.extract_signals()
self.vis = Visualisation()
def extract_signals(self, scale=1, points=None):
signals = []
points = points or 10000
for k in self.damage.keys():
temp = []
if len(k.split('-')[-1]) < 3:
data = scio.loadmat(
os.path.join(DATA_ROOT_DIR, self.damage[k][0] + '.mat'))
else:
params = self.damage[k][0].split('-')
name = ''
for p in params[:-1]:
name += p + '-'
name += 'n'
data = scio.loadmat(os.path.join(DATA_ROOT_DIR, name + '.mat'))
temp.append(data['s0'][:points, 0] / scale)
temp.append(data['s1'][:points, 0] / scale)
temp.append(data['s2'][:points, 0] / scale)
temp.append(data['s3'][:points, 0] / scale)
temp.append(data['s4'][:points, 0] / scale)
temp.append(data['s5'][:points, 0] / scale)
signals.append(temp)
return signals
def FFT(self,
points=None,
show_results=False,
save=False,
figsize=(36, 25),
show_range=None):
points = points or len(self.signals[0][0])
t = np.arange(0, points) * 24000 / points
j = 0
fft_results = []
show_range = show_range or (0, points)
# for temp in self.signals:
# shape = (3, len(temp) // 3)
# fig, axs = plt.subplots(shape[0], shape[1], figsize=figsize)
# fft_temp = []
# for i in range(len(temp)):
# fft = np.power(np.abs(np.power(np.fft.fft(temp[i], points), 2)), 0.5)
# fft_temp.append(fft)
# axs[i // 2, i % 2].plot(t[show_range[0]: show_range[1]], fft[show_range[0]: show_range[1]])
# axs[i // 2, i % 2].set_title('s{} energy spectrum'.format(j, i))
# fig.suptitle('class {}'.format(j))
# if save:
# plt.savefig(os.path.join(RESULT_ROOT, str(i) + "_class_fft_energy_spectrum.png"), dpi=600,
# bbox_inches='tight')
# if show_results:
# plt.show()
# j += 1
# fft_results.append(fft_temp)
#
# return fft_results
for temp in self.signals:
temp = np.array(temp)
self.vis.plot_instant_freq(t=t, imfs=temp)
if save:
plt.savefig(os.path.join(
RESULT_ROOT,
str(i) + "_class_fft_energy_spectrum.png"),
dpi=600,
bbox_inches='tight')
if show_results:
self.vis.show()
def STFT(self,
show_results=False,
save=False,
figsize=(25, 16),
show_range=(0, 1500),
nperseg=128):
i = 0
stft_results = []
for temp in self.signals:
shape = (3, len(temp) // 3)
stft_temp = []
plt.figure(figsize=figsize)
for j in range(len(temp)):
params = scignal.stft(temp[j], fs=24e3, nperseg=nperseg)
f, t, zxx = params
plt.subplot(shape[0], shape[1], j + 1)
plt.pcolormesh(t, f, np.abs(zxx))
plt.colorbar()
plt.title('S{} STFT Magnitude'.format(j))
plt.ylabel('Frequency [Hz]')
plt.xlabel('Time [sec]')
plt.ylim(show_range[0], show_range[1])
stft_temp.append(params)
# plt.suptitle('Class {}'.format(i))
stft_results.append(stft_temp)
if save:
plt.savefig(os.path.join(RESULT_ROOT,
str(i) + "_class_sfft_spectrum.png"),
dpi=600,
bbox_inches='tight')
i += 1
if show_results:
plt.show()
return stft_results
def wavelet(self,
show_results=False,
save=False,
w='cgau8',
sampling_rate=24e3,
figsize=(36, 25),
points=None):
"""
:param figsize:
:param sampling_rate: 采样频率
:param show_results:
:param save:
:param w: 小波函数种类
:return:
"""
wavelet_results = []
totalscal = len(self.signals[0][0])
points = points or totalscal
t = np.arange(0, points) * 24000 / points
fc = pywt.central_frequency(w) # 中心频率
cparam = 2 * fc * totalscal
scales = cparam / np.arange(totalscal, 1, -1)
i = 0
for temp in self.signals:
shape = (3, len(temp) // 3)
# fig, axs = plt.subplots(shape[0], shape[1], figsize=figsize)
wavelet_temp = []
for j in range(len(temp)):
[cwtmatr,
frequencies] = pywt.cwt(temp[j], scales, w,
1.0 / sampling_rate) # 连续小波变换
plt.subplot(shape[0], shape[1], j + 1)
plt.contourf(t, frequencies, abs(cwtmatr))
plt.ylabel(u"freq(Hz)")
plt.xlabel(u"time(s)")
plt.title('S{} Wavelet Magnitude'.format(j))
wavelet_temp.append([cwtmatr, frequencies])
wavelet_results.append(wavelet_temp)
if save:
plt.savefig(os.path.join(
RESULT_ROOT,
str(i) + "_class_wavelet_spectrum.png"),
dpi=600,
bbox_inches='tight')
i += 1
if show_results:
plt.show()
return wavelet_results
ceemdan = CEEMDAN(trials=10)
cIMFs = ceemdan(temp)
b = time.time()
print('CEEMDAN consumes time is %.4f' % (b - a))
transformer = FastICA(n_components=3,
random_state=0,
whiten=True,
max_iter=1000)
transform_component = transformer.fit_transform(cIMFs, )
sperated_signale = np.dot(transform_component.T, cIMFs)
plt.subplot(4, 1, 1)
plt.plot(sig1)
for index, sig_temp in enumerate(sperated_signale):
plt.subplot(4, 1, index + 2)
plt.plot(sig_temp)
a = time.time()
eemd = EEMD(trials=5)
eIMFs = eemd(temp)
b = time.time()
print('EEMD consumes time is %.4f' % (b - a))
# 100次 CEEMDAN 29s EEMD 14s
# 20次 CEEMDAN 7s EEMD 3s
# #TODO： 太慢了，要用到多线程了
vis = Visualisation()