-
Notifications
You must be signed in to change notification settings - Fork 3
/
extract_1_csv.py
162 lines (136 loc) · 4.3 KB
/
extract_1_csv.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
import pickle
import pandas as pd
import numpy as np
import statistics
import os
from numpy.fft import rfft, rfftfreq
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("input", help="Input csv file")
args = parser.parse_args()
data_csv = pd.read_csv(args.input, header=None)
# data_csv = argparse.ArgumentParser
def FFT(data):
data = np.asarray(data)
#FFT
n=len(data)
dt=1/20000 #time increment in each data
data=rfft(data)*dt
freq=rfftfreq(n,dt)
data=abs(data)
return data
import scipy
from scipy.stats import kurtosis
from scipy.stats import skew
def std(data):
data = np.asarray(data)
stdev=pd.DataFrame(np.std(data, axis=1))
return stdev
def mean(data):
data = np.asarray(data)
M=pd.DataFrame(np.mean(data, axis=1))
return M
def pp(data):
data = np.asarray(data)
PP=pd.DataFrame(np.max(data, axis=1) - np.min(data, axis=1))
return PP
def Variance(data):
data = np.asarray(data)
Var=pd.DataFrame(np.var(data, axis=1))
return Var
def rms(data):
data = np.asarray(data)
Rms=pd.DataFrame(np.sqrt(np.mean(data**2, axis=1)))
return Rms
def Ab_mean(data):
data = np.asarray(data)
Abm=pd.DataFrame(np.mean(np.absolute(data),axis=1))
return Abm
def Shapef(data):
data = np.asarray(data)
shapef=pd.DataFrame(rms(data)/Ab_mean(data))
return shapef
def Impulsef(data):
data = np.asarray(data)
impulse=pd.DataFrame(np.max(data)/Ab_mean(data))
return impulse
def crestf(data):
data = np.asarray(data)
crest=pd.DataFrame(np.max(data)/rms(data))
return crest
def SQRT_AMPL(data):
data = np.asarray(data)
SQRTA=pd.DataFrame((np.mean(np.sqrt(np.absolute(data, axis=1))))**2)
return SQRTA
def clearancef(data):
data = np.asarray(data)
clrf=pd.DataFrame(np.max(data, axis=1)/SQRT_AMPL(data))
return clrf
def kurtosis(data):
data = pd.DataFrame(data);
kurt = data.kurt(axis=1);
return kurt
def skew(data):
data = pd.DataFrame(data)
skw = data.skew(axis=1)
return skw
#test sumbu x
test_x = pd.DataFrame(data_csv)
test_x.drop(test_x.columns[[0,2,3]], axis=1, inplace=True) #hapus kolom 0,2,3
test_x = test_x.T
test_x = test_x.dropna(axis=1)
#test sumbu y
test_y = pd.DataFrame(data_csv)
test_y.drop(test_y.columns[[0,1,3]], axis=1, inplace=True) #hapus kolom 0,1,3
test_y = test_y.T
test_y = test_y.dropna(axis=1)
#test sumbu z
test_z = pd.DataFrame(data_csv)
test_z.drop(test_z.columns[[0,1,2]], axis=1, inplace=True) #hapus kolom 0,1,2
test_z = test_z.T
test_z = test_z.dropna(axis=1)
# FFT
fft_test_x = FFT(test_x)
fft_test_y = FFT(test_y)
fft_test_z = FFT(test_z)
# EKSTRAKSI FITUR
Shapef_x = Shapef(fft_test_x)
Shapef_y = Shapef(fft_test_y)
Shapef_z = Shapef(fft_test_z)
Shapef_test = pd.concat([Shapef_x,Shapef_y,Shapef_z], axis=1,ignore_index=True)
rms_x = rms(fft_test_x)
rms_y = rms(fft_test_y)
rms_z = rms(fft_test_z)
rms_test = pd.concat([rms_x,rms_y,rms_z], axis=1,ignore_index=True)
Impulsef_x = Impulsef(fft_test_x)
Impulsef_y = Impulsef(fft_test_y)
Impulsef_z = Impulsef(fft_test_z)
Impulsef_test = pd.concat([Impulsef_x,Impulsef_y,Impulsef_z], axis=1,ignore_index=True)
pp_x = pp(fft_test_x)
pp_y = pp(fft_test_y)
pp_z = pp(fft_test_z)
pp_test = pd.concat([pp_x,pp_y,pp_z], axis=1,ignore_index=True)
kurtosis_x = kurtosis(fft_test_x)
kurtosis_y = kurtosis(fft_test_y)
kurtosis_z = kurtosis(fft_test_z)
kurtosis_test = pd.concat([kurtosis_x,kurtosis_y,kurtosis_z], axis=1,ignore_index=True)
crestf_x = crestf(fft_test_x)
crestf_y = crestf(fft_test_y)
crestf_z = crestf(fft_test_z)
crestf_test = pd.concat([crestf_x,crestf_y,crestf_z], axis=1,ignore_index=True)
mean_x = mean(fft_test_x)
mean_y = mean(fft_test_y)
mean_z = mean(fft_test_z)
mean_test = pd.concat([mean_x,mean_y,mean_z], axis=1,ignore_index=True)
std_x = std(fft_test_x)
std_y = std(fft_test_y)
std_z = std(fft_test_z)
std_test = pd.concat([std_x,std_y,std_z], axis=1,ignore_index=True)
skew_x = skew(fft_test_x)
skew_y = skew(fft_test_y)
skew_z = skew(fft_test_z)
skew_test = pd.concat([skew_x,skew_y,skew_z], axis=1,ignore_index=True)
data_test = pd.concat([mean_test,std_test,Shapef_test,rms_test,Impulsef_test,pp_test,kurtosis_test,crestf_test,skew_test], axis=1,ignore_index=True)
print(f"Shape feature: {data_test.shape}")