/
dsp_core.py
250 lines (182 loc) · 5.39 KB
/
dsp_core.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
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
################ PARAMETERS RECOVERY ######################
###########################################################
from pylab import *
import sympy as sm # many conflicts with pylab, numpy,math,...
from scipy import signal, random
import numpy as np
import csv
import time
import datagenerator
#import PSD
#####----reading data------#################
def loader(name,num,delim):
print("started")
t,ch1,ch2,ch3,ch4=genfromtxt(name,max_rows=num,delimiter=delim,unpack='True')
return t,ch1,ch2,ch3,ch4
def lowfilter(data,fc,fs=2.5*10**6):
b,a=signal.butter(4, fc/(fs/2), 'low')
out1=signal.filtfilt(b, a, data[0])
out2=signal.filtfilt(b, a, data[1])
out3=signal.filtfilt(b, a, data[2])
out4=signal.filtfilt(b, a, data[3])
#datagenerator.writefile(out1,out2,out3,out4)
return out1,out2,out3,out4
def whitenoise(data,wpow=10**(-11),fs=2.5*10**6):
l=len(data[0])
sig_noise=sqrt((wpow)*fs/2)
out1=data[0]+sig_noise*random.normal(0,1,l)
out2=data[1]+sig_noise*random.normal(0,1,l)
out3=data[2]+sig_noise*random.normal(0,1,l)
out4=data[3]+sig_noise*random.normal(0,1,l)
#datagenerator.writefile(out1,out2,out3,out4)
return out1,out2,out3,out4
def downconvert(x,flo):
ts=x[0,10]-x[0,9]
print("tsamp is:",ts)
n=arange(len(x[1]))
lo=cos(2*pi*flo*x[0])
out1=x[1]*lo
out2=x[2]*lo
out3=x[3]*lo
out4=x[4]*lo
return out1,out2,out3,out4
def downsampl(x,ns):
out1=signal.decimate(x[0], ns)
out2=signal.decimate(x[1], ns)
out3=signal.decimate(x[2], ns)
out4=signal.decimate(x[3], ns)
out5=signal.decimate(x[4], ns)
return out1,out2, out3,out4,out5
def tracker(data,din=1,tin=1,pin=1):
direct_plot_mode=0
phase_plot_mode=0
plot_details=1
residual_plot=1
#noiseactive=0
#filteractive=0
rex,imx,rey,imy=[],[],[],[]
rex=data[1]
imx=data[2]
rey=data[3]
imy=data[4]
#for the old data collection use this set:
'''
rex=data[3]
imx=data[4]
rey=data[2]
imy=data[1]
'''
########################################
######## DSP ALGORITHM #########
########################################
#### W Matrix creation ########
#### Symbolic calculation #####
delta, theta,phi=sm.symbols("delta theta phi", real=True)
##### implementation of Eo= R*M*R*Ein
R1=sm.Matrix([[sm.cos(theta),sm.sin(theta)],[-sm.sin(theta),sm.cos(theta)]])
M=sm.Matrix([[sm.exp(sm.I*(-delta/2)),0],[0,sm.exp(sm.I*(delta/2))]])
R2=sm.transpose(R1)
Fib=R2*M*R1
Ein=sm.Matrix([sm.cos(sm.pi/4)*sm.exp(sm.I*(-sm.pi/2)),sm.sin(sm.pi/4)])*sm.exp(sm.I*(phi))
Eout=Fib*Ein
rexS=sm.simplify(sm.re(Eout[0]))
imxS=sm.simplify(sm.im(Eout[0]))
reyS=sm.simplify(sm.re(Eout[1]))
imyS=sm.simplify(sm.im(Eout[1]))
vec=sm.Matrix([[rexS,imxS,reyS,imyS]])
var=sm.Matrix([delta,theta,phi])
W=vec.jacobian(var)
Prod=sm.simplify(sm.transpose(W)*W)
Wt=sm.lambdify((delta,theta,phi),transpose(W),'numpy')
Prodv=sm.lambdify((delta,theta,phi),Prod,'numpy')
Fun=sm.lambdify((delta,theta,phi),sm.transpose(vec),'numpy')
############ main loop ##############
########### LSM algorithm ###########
##### declaration section ###########
#### fundamental variables ##########
B=array([[din],[tin],[pin]]) # Beta-point
deB=array([[0],[0],[0]]) # Beta-increment
Yt=array([[0],[0],[0],[0]]) # 4 values of the coherent receiver
mod=1 # module of the Y vector
deY=array([[0],[0],[0],[0]])
B1=[] # auxiliary list
B2=[] # auxiliary list
B3=[] # auxiliary list
dell=[] #retrieved delta
thel=[] # retrieved theta
phil=[] # tetreived phi
detl=[] # determinat of (tW*W)
sigdel=[]
sigthe=[]
sigphi=[]
#weighted average
theb=array([])
phib=array([])
them=B[1,0]
phim=B[2,0]
l=20
thew=them
phiw=phim
theml=[]
def myY(t):
a=array([[rex[t]],[imx[t]],[rey[t]],[imy[t]]])
return a
def modx(x):
b=sqrt(rex[x]**2 + imx[x]**2 + rey[x]**2 + imy[x]**2)
return b
###################################################
######### loop section ###############
print ("start loop")
t1=time.time()
for i in range(len(rex)):
mod= modx(i)
deY=(myY(i)/mod)-Yt
Wn=Wt(B[0,0],B[1,0],B[2,0])
u=Prodv(B[0,0],B[1,0],B[2,0])
d=linalg.det(u)
#detl.append(1/d)
uu=inv(u)
#sigd=sig_noise*uu[0,0]
#sigt=sig_noise*uu[1,1]
#sigp=sig_noise*uu[2,2]
deB=uu.dot(Wn).dot(deY)
deB[0]-=np.round(deB[0]/(pi))*(pi)
deB[1]-=np.round(deB[1]/(4*pi))*(4*pi)
deB[2]-=np.round(deB[2]/(2*pi))*(2*pi)
B=B+deB
B1=B[0,0]
B2=B[1,0]
B3=B[2,0]
#print("the medio:",them,"thew:",thew)
dell.append(B1)
thel.append(B2)
phil.append(B3)
#sigdel.append(sigd)
#sigthe.append(sigt)
#sigphi.append(sigp)
Yt=Fun(B1,B2,B3)
print("loop finished")
print("elapsed time: ",time.time()-t1)
outlist=list(zip(dell,thel,phil))
f=open("rec_pars.csv",'w')
w=csv.writer(f, delimiter='\t')
w.writerows(outlist)
f.close()
###########################################
return array(dell),array(thel),array(phil)
########################################################################
#function used for the single-photodiode phase extraction
def normalize(x1,x2,x3,x4):
l=len(x1)
o1=o2=o3=o4=empty(l,dtype=float)# new variables are created in order to leave the input data unchanged
for i in range(l):
#mod=sqrt(x1[i]**2 + x2[i]**2 + x3[i]**2 + x4[i]**2)
rms1=sqrt(sum(x1**2))
rms2=sqrt(sum(x2**2))
rms3=sqrt(sum(x3**2))
rms4=sqrt(sum(x4**2))
o1[i]=arccos(2*x1[i]/(sqrt(2)*rms1))
o2[i]=arccos(2*x2[i]/(sqrt(2)*rms2))
o3[i]=arccos(2*x3[i]/(sqrt(2)*rms3))
o4[i]=arccos(2*x4[i]/(sqrt(2)*rms4))
return o1,o2,o3,o4