forked from Cloudreaver/TISC_Simulation
/
three_phi_sector_TISC_sim.py
executable file
·437 lines (378 loc) · 21.3 KB
/
three_phi_sector_TISC_sim.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
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
#!/usr/bin/env python
# A very simple impulsive signal + noise generator to get the
import random
#from ROOT import TTree, TBranch, gROOT,gSystem,TImage, TStyle, TFile, gDirectory, TH1D, TCanvas, gStyle, TGraph
from array import array
import numpy as np
from impulse import impulse_gen
from anita_filter import butter_bandpass, butter_bandpass_filter
from noise import generate_noise
from digitizer import digitize
from sum_correlator import sum_correlate
from cw import generate_cw
#import matplotlib.pyplot as plt
def TISC_sim(SNR,threshold,
b_input_delay,c_input_delay,num_bits=3,
noise_sigma=32.0,
sample_freq=2600000000.0,TISC_sample_length=16,
num_samples=80,upsample=10,cw_flag=0,
cw_amplitude=20.0,carrier_frequency=260000000.0,modulation_frequency=1.0,
seed=5522684,draw_flag=0,digitization_factor=32.0,
delay_type_flag=1,
output_dir="output/",average_subtract_flag=0,abc_correlation_mean=np.zeros(46),
def_correlation_mean=np.zeros(44),ghi_correlation_mean=np.zeros(46),trial_run_number=1,boresight=0,baseline=0,six_phi_sector_add=False):
# Setup
save_output_flag = 0
#if(save_output_flag):
#outfile = str(output_dir+"/test.root")
trigger_flag = 0
#num_bits = 3 # Number of bits available to the digitizer
#num_samples = num_samples*upsample
filter_flag = False
#sample_frequency = 2800000000.0
def_max_sum = 0.0
def_as_max_sum = 0.0
ghi_max_sum = 0.0
ghi_as_max_sum = 0.0
timestep = 1.0/sample_freq
# Phi sectors have alternating baselines
if(boresight==0):
abc_impulse_amp = 1.000
def_impulse_amp = 0.835
ghi_impulse_amp = 0.776
if(baseline==0):
abc_baseline = 0
def_baseline = 1
ghi_baseline = 1
elif(baseline==1):
abc_baseline = 1
def_baseline = 0
ghi_baseline = 0
elif(boresight==1):
abc_impulse_amp = 0.962
def_impulse_amp = 0.885
ghi_impulse_amp = 0.650
if(baseline==0):
abc_baseline = 1
def_baseline = 0
ghi_baseline = 1
elif(baseline==1):
abc_baseline = 1
def_baseline = 0
ghi_baseline = 0
# Fill numpy arrays with zeros
a_input_noise = np.zeros(num_samples)
b_input_noise = np.zeros(num_samples)
c_input_noise = np.zeros(num_samples)
d_input_noise = np.zeros(num_samples)
e_input_noise = np.zeros(num_samples)
f_input_noise = np.zeros(num_samples)
g_input_noise = np.zeros(num_samples)
h_input_noise = np.zeros(num_samples)
i_input_noise = np.zeros(num_samples)
time = np.zeros(num_samples)
upsampled_time = np.zeros(num_samples)
a_input_signal = np.zeros(num_samples)
b_input_signal = np.zeros(num_samples)
c_input_signal = np.zeros(num_samples)
d_input_signal = np.zeros(num_samples)
e_input_signal = np.zeros(num_samples)
f_input_signal = np.zeros(num_samples)
g_input_signal = np.zeros(num_samples)
h_input_signal = np.zeros(num_samples)
i_input_signal = np.zeros(num_samples)
a_input_signal_noise = np.zeros(num_samples)
b_input_signal_noise = np.zeros(num_samples)
c_input_signal_noise = np.zeros(num_samples)
d_input_signal_noise = np.zeros(num_samples)
e_input_signal_noise = np.zeros(num_samples)
f_input_signal_noise = np.zeros(num_samples)
g_input_signal_noise = np.zeros(num_samples)
h_input_signal_noise = np.zeros(num_samples)
i_input_signal_noise = np.zeros(num_samples)
a_dig_waveform = np.zeros(num_samples)
b_dig_waveform = np.zeros(num_samples)
c_dig_waveform = np.zeros(num_samples)
d_dig_waveform = np.zeros(num_samples)
e_dig_waveform = np.zeros(num_samples)
f_dig_waveform = np.zeros(num_samples)
g_dig_waveform = np.zeros(num_samples)
h_dig_waveform = np.zeros(num_samples)
i_dig_waveform = np.zeros(num_samples)
empty_list = np.zeros(num_samples)
###################################
# Generate Thermal Noise
a_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed)
b_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+1)
c_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+2)
d_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+3)
e_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+4)
f_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+5)
g_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+6)
h_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+7)
i_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+8)
###################################
#####################################
# Determine RMS of noise and signal amplitude
#noise_rms = np.sqrt(np.mean((a_input_noise-noise_mean)**2,))
signal_amp = SNR*2*noise_sigma
#####################################
#################################
#Generate CW noise if desired
if cw_flag:
cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,cw_amplitude,filter_flag)
a_input_noise = np.add(a_input_noise,cw_noise)#generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
#a_input_noise += a_input_cw_noise
# b_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
b_input_noise = np.add(cw_noise,b_input_noise)
#c_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
c_input_noise = np.add(cw_noise,c_input_noise)
#d_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
d_input_noise = np.add(cw_noise,d_input_noise)
#e_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
e_input_noise = np.add(cw_noise,e_input_noise)
#f_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
f_input_noise = np.add(cw_noise,f_input_noise)
#g_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
g_input_noise = np.add(cw_noise,g_input_noise)
#h_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
h_input_noise = np.add(cw_noise,h_input_noise)
#i_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
i_input_noise = np.add(cw_noise,i_input_noise)
#####################################
a_input_noise = butter_bandpass_filter(a_input_noise)
b_input_noise = butter_bandpass_filter(b_input_noise)
c_input_noise = butter_bandpass_filter(c_input_noise)
d_input_noise = butter_bandpass_filter(d_input_noise)
e_input_noise = butter_bandpass_filter(e_input_noise)
f_input_noise = butter_bandpass_filter(f_input_noise)
g_input_noise = butter_bandpass_filter(g_input_noise)
h_input_noise = butter_bandpass_filter(h_input_noise)
i_input_noise = butter_bandpass_filter(i_input_noise)
#####################################
# Generate impulse
if (SNR != 0):
# Generate Signal and Amplify
a_input_signal = impulse_gen(num_samples,upsample,draw_flag=draw_flag,output_dir=output_dir)
difference=np.amax(a_input_signal)-np.amin(a_input_signal) # Get peak to peak voltage
a_input_signal *= (1/difference) # Normalize input
a_input_signal *= signal_amp # Amplify
#b_input_signal = np.concatenate([a_input_signal[:num_samples+b_input_delay],empty_list[:(-1)*b_input_delay]])
#c_input_signal = np.concatenate([a_input_signal[:num_samples+c_input_delay],empty_list[:(-1)*c_input_delay]])
b_input_signal = np.concatenate([a_input_signal[-b_input_delay:],empty_list[:(-1)*b_input_delay]])
c_input_signal = np.concatenate([a_input_signal[-c_input_delay:],empty_list[:(-1)*c_input_delay]])
a_input_signal =a_input_signal*abc_impulse_amp
b_input_signal =b_input_signal*abc_impulse_amp
c_input_signal =c_input_signal*abc_impulse_amp
d_input_signal =a_input_signal*def_impulse_amp
e_input_signal =b_input_signal*def_impulse_amp
f_input_signal =c_input_signal*def_impulse_amp
g_input_signal =a_input_signal*ghi_impulse_amp
h_input_signal =b_input_signal*ghi_impulse_amp
i_input_signal =c_input_signal*ghi_impulse_amp
"""
if(boresight==0):
d_input_signal = a_input_signal*0.776 # Average dB loss at -22.5 degrees
e_input_signal = b_input_signal*0.776 # from Seavey measurements
f_input_signal = c_input_signal*0.776
g_input_signal = a_input_signal*0.835 # Average dB loss at +22.5 degrees
h_input_signal = b_input_signal*0.835 # from Seavey measurements
i_input_signal = c_input_signal*0.835
elif(boresight==1):
# For event between two phi sectors, the two antennas are down by about -0.5dB at
a_input_signal = a_input_signal*0.885 # Average dB los at +11.25 degrees
b_input_signal = b_input_signal*0.885 # from Seavey measurements
c_input_signal = c_input_signal*0.885
d_input_signal = a_input_signal*0.962 # Average dB loss at -11.25 degrees
e_input_signal = b_input_signal*0.962 # from Seavey measurements
f_input_signal = c_input_signal*0.962
g_input_signal = a_input_signal*0.650 # Average dB loss at +-33.75 degrees
h_input_signal = b_input_signal*0.650 # from Seavey measurements
i_input_signal = c_input_signal*0.650
"""
"""
time = np.linspace(0.0,timestep*num_samples,num_samples)
plt.plot(time,a_input_signal,time,b_input_signal,time,c_input_signal)
plt.plot(time,d_input_signal,time,e_input_signal,time,f_input_signal)
plt.plot(time,g_input_signal,time,h_input_signal,time,i_input_signal)
plt.title("impulse")
plt.show()
"""
# Add the signal to the noise
a_input_signal_noise = np.add(a_input_noise, a_input_signal)
b_input_signal_noise = np.add(b_input_noise, b_input_signal)
c_input_signal_noise = np.add(c_input_noise, c_input_signal)
d_input_signal_noise = np.add(d_input_noise, d_input_signal)
e_input_signal_noise = np.add(e_input_noise, e_input_signal)
f_input_signal_noise = np.add(f_input_noise, f_input_signal)
g_input_signal_noise = np.add(g_input_noise, g_input_signal)
h_input_signal_noise = np.add(h_input_noise, h_input_signal)
i_input_signal_noise = np.add(i_input_noise, i_input_signal)
"""
time = np.linspace(0.0,timestep*num_samples,num_samples)
#plt.plot(time,a_input_signal_noise,time,b_input_signal_noise,time,c_input_signal_noise)
#plt.plot(time,d_input_signal_noise,time,e_input_signal_noise,time,f_input_signal_noise)
plt.plot(time,g_input_signal_noise,time,h_input_signal_noise,time,i_input_signal_noise)
plt.title("impulse+noise")
plt.show()
"""
else:
a_input_signal_noise = a_input_noise
b_input_signal_noise = b_input_noise
c_input_signal_noise = c_input_noise
d_input_signal_noise = d_input_noise
e_input_signal_noise = e_input_noise
f_input_signal_noise = f_input_noise
g_input_signal_noise = g_input_noise
h_input_signal_noise = h_input_noise
i_input_signal_noise = i_input_noise
##########################################
#time = np.linspace(0.0,timestep*num_samples,num_samples)
#plt.plot(time,a_input_noise,time,b_input_noise,time,c_input_noise)
#plt.title("noise")
#plt.show()
##########################################
# Digitized the incoming signal and noise (RITC)
a_dig_waveform = digitize(a_input_signal_noise,num_samples,num_bits,digitization_factor)
b_dig_waveform = digitize(b_input_signal_noise,num_samples,num_bits,digitization_factor)
c_dig_waveform = digitize(c_input_signal_noise,num_samples,num_bits,digitization_factor)
d_dig_waveform = digitize(d_input_signal_noise,num_samples,num_bits,digitization_factor)
e_dig_waveform = digitize(e_input_signal_noise,num_samples,num_bits,digitization_factor)
f_dig_waveform = digitize(f_input_signal_noise,num_samples,num_bits,digitization_factor)
g_dig_waveform = digitize(g_input_signal_noise,num_samples,num_bits,digitization_factor)
h_dig_waveform = digitize(h_input_signal_noise,num_samples,num_bits,digitization_factor)
i_dig_waveform = digitize(i_input_signal_noise,num_samples,num_bits,digitization_factor)
##########################################
"""
time = np.linspace(0.0,timestep*num_samples,num_samples)
#plt.plot(time,a_dig_waveform,time,b_dig_waveform,time,c_dig_waveform)
#plt.plot(time,d_dig_waveform,time,e_dig_waveform,time,f_dig_waveform)
plt.plot(time,g_dig_waveform,time,h_dig_waveform,time,i_dig_waveform)
plt.title("Digitized")
plt.show()
"""
##########################################
# Run the signal through the GLITC module to get trigger
if(average_subtract_flag):
abc_trigger_flag, abc_max_sum , abc_as_max_sum, abc_correlation_mean, abc_test_sum, abc_as_test_sum,as_abc_angle,abc_angle,d1,d2 = sum_correlate(num_samples,a_dig_waveform,b_dig_waveform,c_dig_waveform,threshold,abc_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=abc_correlation_mean,trial_run_number=trial_run_number)
def_trigger_flag, def_max_sum , def_as_max_sum, def_correlation_mean, def_test_sum, def_as_test_sum,as_def_angle,def_angle,d1,d2 = sum_correlate(num_samples,d_dig_waveform,e_dig_waveform,f_dig_waveform,threshold,def_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=def_correlation_mean,trial_run_number=trial_run_number)
ghi_trigger_flag, ghi_max_sum , ghi_as_max_sum, ghi_correlation_mean, ghi_test_sum, ghi_as_test_sum,as_ghi_angle,ghi_angle,d1,d2 = sum_correlate(num_samples,g_dig_waveform,h_dig_waveform,i_dig_waveform,threshold,ghi_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=ghi_correlation_mean,trial_run_number=trial_run_number)
#abc_max_sum
#print len(a_dig_waveform)
else:
abc_trigger_flag, abc_max_sum,abc_andle,d1 = sum_correlate(num_samples,a_dig_waveform,b_dig_waveform,c_dig_waveform,threshold,abc_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=abc_correlation_mean,trial_run_number=trial_run_number)
def_trigger_flag, def_max_sum,def_angle,d1 = sum_correlate(num_samples,d_dig_waveform,e_dig_waveform,f_dig_waveform,threshold,def_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=def_correlation_mean,trial_run_number=trial_run_number)
ghi_trigger_flag, ghi_max_sum,ghi_angle,d1 = sum_correlate(num_samples,g_dig_waveform,h_dig_waveform,i_dig_waveform,threshold,ghi_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=ghi_correlation_mean,trial_run_number=trial_run_number)
#print abc_max_sum
#print def_max_sum
#print ghi_max_sum
#if(abc_trigger_flag & def_trigger_flag & ghi_trigger_flag):
#trigger_flag = True
#else:
#trigger_flag = False
#########################################
#dummy = raw_input('Press any key to close')
if (average_subtract_flag):
return abc_max_sum,abc_as_max_sum,def_max_sum,def_as_max_sum,ghi_max_sum,ghi_as_max_sum,abc_correlation_mean, def_correlation_mean, ghi_correlation_mean,as_abc_angle,abc_angle,as_def_angle,def_angle,as_ghi_angle,ghi_angle
else:
return abc_max_sum,def_max_sum,ghi_max_sum,abc_angle,def_angle,ghi_angle
if __name__ == '__main__':
#import ROOT
import time
import matplotlib.pyplot as plt
num_samples = 80
impulse_position = 0
num_bits = 3
#signal_amp = 10.0
draw_flag = False
# These delays should be negative, since A is the top antenna
# and we expect an upgoing signal
b_input_delay = -15
c_input_delay = -17
SNR = 5.0
threshold = 100
noise_sigma = 32.0
boresight=0
baseline = 0
#num_runs = 100
upsample = 1
cw_flag = False
cw_amplitude = 1.0*noise_sigma
cw_frequency = 260000000.0
modulation_frequency = 1.0
delay_type_flag = 1
digitization_factor=32.0
average_subtract_flag = 1
#global correlation_mean
abc_correlation_mean = np.zeros(63)
#abc_correlation_mean.fill(100)
def_correlation_mean = np.zeros(63)
#def_correlation_mean.fill(100)
ghi_correlation_mean = np.zeros(63)
#ghi_correlation_mean.fill(100)
num_delays = [63,46]
if(boresight==0):
if(baseline==0):
abc_baseline = 0
def_baseline = 1
ghi_baseline = 1
elif(baseline==1):
abc_baseline = 1
def_baseline = 0
ghi_baseline = 0
elif(boresight==1):
if(baseline==0):
abc_baseline = 1
def_baseline = 0
ghi_baseline = 1
elif(baseline==1):
abc_baseline = 1
def_baseline = 0
ghi_baseline = 0
abc_correlation_mean = np.zeros(num_delays[abc_baseline])
def_correlation_mean = np.zeros(num_delays[def_baseline])
ghi_correlation_mean = np.zeros(num_delays[ghi_baseline])
print "SNR: "+str(SNR)
print "Threshold: "+str(threshold)
# Get impulse signal
#a_input_signal = np.zeros(num_samples*upsample)
#a_input_signal = impulse_gen(num_samples,impulse_position,upsample=upsample,draw_flag=draw_flag)
# Get background estimation
for i in range(0,5):
if(average_subtract_flag):
abc_max_sum,abc_as_max_sum,def_max_sum, def_as_max_sum,ghi_max_sum, ghi_as_max_sum, abc_correlation_mean,def_correlation_mean,ghi_correlation_mean,as_abc_angle,abc_angle,as_def_angle,def_angle,as_ghi_angle,ghi_angle = TISC_sim(0.0,threshold,b_input_delay,c_input_delay,num_bits=num_bits,
upsample=upsample,num_samples=num_samples,noise_sigma=noise_sigma,
cw_flag=cw_flag,cw_amplitude=cw_amplitude,carrier_frequency=cw_frequency,modulation_frequency=modulation_frequency,
draw_flag=draw_flag,digitization_factor=digitization_factor,output_dir="output/",delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,trial_run_number=(i+1),
abc_correlation_mean=abc_correlation_mean,def_correlation_mean=def_correlation_mean,ghi_correlation_mean=ghi_correlation_mean,seed=i)
print 'Correlation test: ' +str(abc_correlation_mean)
for i in range(0,1):
if(average_subtract_flag):
abc_max_sum,abc_as_max_sum,def_max_sum, def_as_max_sum,ghi_max_sum, ghi_as_max_sum, abc_correlation_mean,def_correlation_mean,ghi_correlation_mean,as_abc_angle,abc_angle,as_def_angle,def_angle,as_ghi_angle,ghi_angle = TISC_sim(SNR,threshold,b_input_delay,c_input_delay,num_bits=num_bits,
upsample=upsample,num_samples=num_samples,noise_sigma=noise_sigma,
cw_flag=cw_flag,cw_amplitude=cw_amplitude,carrier_frequency=cw_frequency,modulation_frequency=modulation_frequency,
draw_flag=draw_flag,digitization_factor=digitization_factor,output_dir="output/",delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,trial_run_number=0,
abc_correlation_mean=abc_correlation_mean,def_correlation_mean=def_correlation_mean,ghi_correlation_mean=ghi_correlation_mean,seed=i)
else:
abc_max_sum, def_max_sum, ghi_max_sum = TISC_sim(SNR,threshold,b_input_delay,c_input_delay,num_bits=num_bits,
upsample=upsample,num_samples=num_samples,noise_sigma=noise_sigma,
cw_flag=cw_flag,cw_amplitude=cw_amplitude,carrier_frequency=cw_frequency,modulation_frequency=modulation_frequency,
draw_flag=draw_flag,digitization_factor=digitization_factor,output_dir="output/",delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,trial_run_number=0,
abc_correlation_mean=abc_correlation_mean,def_correlation_mean=def_correlation_mean,ghi_correlation_mean=ghi_correlation_mean,seed=i)
print abc_max_sum
print def_max_sum
print ghi_max_sum
print abc_as_max_sum
print def_as_max_sum
print ghi_as_max_sum
print as_abc_angle,as_def_angle,as_ghi_angle
print abc_angle,def_angle,ghi_angle