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wt_analysis.py
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wt_analysis.py
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import struct
import matplotlib.pyplot as plt
import numpy as np
import os
from scipy import signal
from math import pi
import pywt
import struct
plt.rc('font',family='Times New Roman',size=10)
markers_freq = ['.','o','v','1','2','3','4','^','<','>','s','p','*','h','H','+','x','D','d','|','_',',']
fig = plt.figure(figsize=(5,3))
sgn = 1.0
interval = 5
dt = 0.0000001 * interval
fs = 10000000 / interval
result = []
pwd = os.getcwd()
result = []
#savepath = pwd + "//" + "wt_1.txt"
#fr = open(savepath, "w")
curvename = []
for dd in np.arange(60,101,10):
curvename.append(dd)
count = 0
record = []
while True:
filepath = pwd + "//" + "input_data" + "//" + "train_knock" + "//" + str(dd) + "//" + str(count)
if os.path.isfile(filepath) == False:
break
fb = open(filepath, "rb")
x = 0
data1 = []
data2 = []
while True:
data = fb.read(4)
if not data:
break
if x % interval == 0:
ch1, ch2 = struct.unpack('<HH', data)
ch1 = (float(ch1) - 8192) / 8192 * 2.5
ch2 = (float(ch2) - 8192) / 8192 * 2.5
ch1 = float(ch1)
ch2 = float(ch2)
data1.append(ch1)
data2.append(ch2)
x = x + 1
fb.close()
data1 = data1[int(250000 / interval):int(350000 / interval)]
data2 = data2[int(250000 / interval):int(350000 / interval)]
wavelet = 'morl'
c = pywt.central_frequency(wavelet)
fa = np.arange(20000, 400000 + 1, 10000)
scales = np.array(float(c)) * fs / np.array(fa)
[cfs1,frequencies1] = pywt.cwt(data1,scales,wavelet,dt)
[cfs2,frequencies2] = pywt.cwt(data2,scales,wavelet,dt)
power1 = (abs(cfs1)) ** 2
power2 = (abs(cfs2)) ** 2
corr_time = []
corr_value = []
corr_frequency = []
for i in range(len(power1)):
mean1 = power1[i].mean()
power1[i] = power1[i] / mean1
mean2 = power2[i].mean()
power2[i] = power2[i] / mean2
temp = signal.correlate(power1[i],power2[i], mode='same',method='fft')
corr_frequency.append(frequencies1[i])
corr_value.append(max(temp))
corr_time.append((np.where(temp == max(temp))[0][0]-len(temp) / 2 ) * dt * 1000)
corr_index = np.where(corr_value == max(corr_value))[0][0]
print(dd,int(corr_frequency[corr_index]),corr_time[corr_index])
record.append(corr_time[corr_index])
## E=207 * pow(10,9) #203#207
## p=7.86 * 1000 #7.93#7.86
## o=0.27
## h=0.002
##
## freq = np.array(frequencies1)
## param = E * h * h * pi * pi / 3.0 / p / (1.0 - o * o)
## c = pow(param * pow(freq,2),0.25)
## time = sgn * (100.0 - dd) * 2.0 / 100.0 / c * 1000.0
##
## plt.plot(time,corr,'+')
## for i in range(len(freq)):
## fr.write(str(int(freq[i])) + " " + str(time[i]) + " " + str(corr[i]) + "\r\n")
count = count + 1
if count > 20:
break
result.append(record)
#fr.close()
#result = np.transpose(result)
#for index,item in enumerate(result):
# plt.plot(item,marker = markers_freq[index], label = str((axis_xf[index] + 1) * 20) + 'kHz')
for index,item in enumerate(result):
plt.plot(item,marker = markers_freq[index], label = str(curvename[index]) + 'cm')
plt.xlabel('order')
plt.ylabel('dt/ms')
ax = plt.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width, box.height])
plt.legend(loc='upper left',bbox_to_anchor=(1,1),markerscale=2)
plt.subplots_adjust(bottom = 0.2,left = 0.15,right=0.7)
plt.show()