-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathCNN_bef.py
202 lines (170 loc) · 5.85 KB
/
CNN_bef.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
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
import numpy as np
import pylab as plt
import analysis
from keras.utils import np_utils
#path = "C:\\D\\experiment\\program\\txt_data\\"
freq1 = np.loadtxt("./txt_data/s21@4-8GHz/freq.txt")
Amp1 = np.loadtxt("./txt_data/s21@4-8GHz/Amp.txt")
phase1 = np.loadtxt("./txt_data/s21@4-8GHz/phase.txt")
freq = np.loadtxt("./txt_data/X-mon/X-mon freq.txt")
Amp = np.loadtxt("./txt_data/X-mon/X-mon Amp.txt")
phase = np.loadtxt("./txt_data/X-mon/X-mon phase.txt")
freq2 = np.loadtxt("./txt_data/S21(Pp)@fp=4-8GHz IFB=10Hz 8001pts/freq.txt")
Amp2 = np.loadtxt("./txt_data/S21(Pp)@fp=4-8GHz IFB=10Hz 8001pts/Amp.txt")
phase2 = np.loadtxt("./txt_data/S21(Pp)@fp=4-8GHz IFB=10Hz 8001pts/phase.txt")
freq3 = np.loadtxt("./txt_data/S21@fp=4-8GHz Pp=-5 IFB=100Hz 4001pts/freq.txt")
Amp3 = np.loadtxt("./txt_data/S21@fp=4-8GHz Pp=-5 IFB=100Hz 4001pts/Amp.txt")
phase3 = np.loadtxt("./txt_data/S21@fp=4-8GHz Pp=-5 IFB=100Hz 4001pts/phase.txt")
freq4 = np.loadtxt("./txt_data/S21(B)@fp=4.86-4.92GHz Pp=-5dBm B=1mA IFB=100Hz 601pts/freq.txt")
Amp4 = np.loadtxt("./txt_data/S21(B)@fp=4.86-4.92GHz Pp=-5dBm B=1mA IFB=100Hz 601pts/Amp.txt")
phase4 = np.loadtxt("./txt_data/S21(B)@fp=4.86-4.92GHz Pp=-5dBm B=1mA IFB=100Hz 601pts/phase.txt")
#Phase1 = analysis.connect_phase(freq,phase1)
print("-------------")
raw = [[freq,Amp,phase,3,12,25,300],
[freq1,Amp1,phase1,15,37,40,210],
[freq4,Amp4,phase4,3,25,250]]#,[freq2,Amp2,phase2,9,12,25],[freq3,Amp3,phase3,9,12,25]]
position = []
print("raw",len(raw))
#package data
data = [freq, Amp, phase]
data1 = [freq1,Amp1,phase1]
data2 = [freq2,Amp2,phase2]
data3 = [freq3,Amp3,phase3]
data4 = [freq4,Amp4,phase4]
All = []
n = 28
start = 24
stop = len(freq)-24
database = analysis.make_twoD(data,start,stop,n)
All.append(database)
start = int(0.025/((max(freq1)-min(freq1))/len(freq1)))
stop = len(freq1)-start
database1 = analysis.make_twoD(data1,start,stop,n,start*2)
All.append(database1)
'''
start = 24
stop = len(freq2)-24
database2 = analysis.make_twoD(data2,start,stop,n)
All.append(database2)
start = 24
stop = len(freq3)-24
database3 = analysis.make_twoD(data3,start,stop,n)
All.append(database3)
'''
start = 250
stop = len(freq4)-250
database4 = analysis.make_twoD(data4,start,stop,n,500)
All.append(database4)
print("All",len(All))
#labeling-MMF
def MMF_labeling(raw,All):
for raw_data in raw:
Phase = analysis.connect_phase(raw_data[0],raw_data[2])
start = 0
stop = len(raw_data[0])
rn = raw_data[3]
bg = raw_data[4]
amp_mmf = analysis.MMF(raw_data[1],rn,bg)[0]
pha_mmf = analysis.MMF(Phase,rn,bg)[0]
amp_mmf = np.array(amp_mmf)
pha_mmf = np.array(pha_mmf)
pha_max = max(abs(pha_mmf))
pha_min = pha_max*0.05
for i in range(len(raw_data[0])):
if i <= 10 or i >= len(raw_data[0])-10:
amp_mmf[i] = 0
elif abs(pha_mmf[i]) >= pha_min:
amp_mmf[i] =0
amp_mmf = np.array(amp_mmf)
Max = max(abs(amp_mmf))
for i in range(len(raw_data[0])):
if abs(amp_mmf[i]) <= Max*0.5:
amp_mmf[i] = 0
start = 0
stop = len(raw_data[0])-1
#analysis.two_axis([raw_data[0],amp_mmf,pha_mmf],start,stop)
resonance = []
for reson in range(len(amp_mmf)):
if amp_mmf[reson] != 0:
resonance.append(reson)
position.append(resonance)
label_mmf = []
for part in range(len(All)):
Range = raw[part][5]
num = 25
for i in All[part]:
k = 0
for res in position[part]:
if num >= res-Range and num <= res + Range:
label_mmf.append(1)
k = 1
break
if k == 0:
label_mmf.append(0)
num += 1
return label_mmf
#labeling-theta
def theta_labeling(raw):
Th_label = []
for part in range(len(All)):
start = raw[part][5]-1
stop = len(raw[part][0])-start
database = analysis.make_twoD(raw[part],start,stop,128)
Th = []
for i in range(len(database)):
RI = analysis.R_I(raw[part],i)
R = RI[0]
I = RI[1]
if analysis.dots(database[i]) >= 85:
error = analysis.for_error(R,I)
if error >= 0.85 and error <= 1:
theta = analysis.ura(R,I)
else:
theta = analysis.circle_fit(R,I)[1]
Th.append(theta)
else:
Th.append(0)
for i in Th:
if i >= raw[part][6]:
Th_label.append(1)
else:
Th_label.append(0)
return Th_label
label = MMF_labeling(raw,All)#theta_labeling(raw)#
plt.plot(np.linspace(0,len(label),len(label)),label)
plt.show()
print("label",len(label))
#turning into training format
pre_train = []
for plus in All:
pre_train += plus
print("pre_train",len(pre_train))
train = []
for i in range(len(pre_train)):
train.append([])
train[i].append(pre_train[i])
train[i].append(label[i])
#np.random.shuffle(train)
x_train = []
y_train = []
for i in train:
x_train.append(i[0])
y_train.append(i[1])
#plt.imshow(x_train[1032])
#plt.show()
x_train = np.array(x_train)
print("x_train",x_train.shape)
x_train = x_train.reshape(-1,1,n,n)
y_train = np_utils.to_categorical(y_train)
print("x_train",x_train.shape)
print("y_train",y_train.shape)
'''
X_train = x_train[:len(freq4)-500]
Y_train = y_train[:len(freq4)-500]
print("X_train",X_train.shape)
print("Y_train",Y_train.shape)
X_test = x_train[4453:]
Y_test = y_train[4453:]
print("X_test",X_test.shape)
print("Y_test",Y_test.shape)
'''