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
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486 | #!/usr/bin/python2
# -*- coding: utf-8 -*-
#fASTER VERSION THAN MAIN_2, MORE COMPLICATED
#### Microwaves 2.0
#This algorithm calculate transformation of the sin signal from microwaves density measurement to the phase/amplitude space.
# First step of the calculation is estimate of the base frequency and calculation of the complex exponential
#with the same frequency.In the second step is signal multiplied by this exponential
#and resulting low frequency signal is smoothed over Gaussian window. Finally complex phase and amplitude are calculated.
# Authors: Tomas Odstrcil, Ondrej Grover
from time import time
t = time()
#import matplotlib
#matplotlib.rcParams['backend'] = 'Agg'
#matplotlib.rc('font', size='10')
#matplotlib.rc('text', usetex=True) # FIXME !! nicer but slower !!!
#import matplotlib.pyplot as plt
from numpy import *
from scipy.fftpack import fft, ifft,fftfreq
from scipy.signal import fftconvolve, medfilt
from pygolem_lite import save_adv,load_adv,saveconst, Shot
from pygolem_lite.modules import multiplot, get_data, paralel_multiplot
import os
import sys
from matplotlib.pylab import *
from BlockConv import BlockConv
from scipy.constants import c,m_e,epsilon_0,e
a = 0.085 #[m]
f_0 = 75e9 #[Hz]
lambda_0 = c/f_0
ne_0 = 4*pi*m_e*epsilon_0*c**2/(e**2*lambda_0)
def Demodulation(y,win,dt):
#demodulation is based on Hilber transdformation
t = time()
y-= mean(y, axis = 0)
n = size(y,0)
N = 2 ** int(ceil(log2(n)))
fourier = fft(y[:,0], N) #calulcate the fourier transfrom for the sine data
#substract the varing offset of the signals
reduct = 5*999
n_ = (n/reduct)*reduct
c = reshape(y[:n_,:],(n_/reduct,reduct,2))
offset = mean(c,axis=1)
c -= offset[:,newaxis,:]
c = swapaxes(c,0,1).reshape(-1,2)
#find the carrier frequency
max_frequency_index = argmax(abs(fourier[:N/2]))
f = fftfreq(N,dt)
s = slice(max_frequency_index-100,max_frequency_index+100)
amplitude = abs(fourier[s])
f_carrier = sum(f[s]*amplitude)/sum(amplitude)
#find a unharmonics factor
amplitude = linalg.norm(amplitude)
norm1 = linalg.norm(fourier[:N/2])
k = sqrt(norm1**2-amplitude**2)/norm1
fourier[:] = 0 #cancel out all other frequencies
fourier[max_frequency_index] = 1
cmpl_exp = ifft(fourier)[:n]
gauss = exp(-arange(-3*win,3*win)**2/win**2)
gauss/= sum(gauss)
signal = list()
for i in range(size(y,1)):
signal.append(BlockConv(y[:,i]*cmpl_exp,gauss,mode='same' )) #BUG use IIR filtfilt!!!
signal = array(signal, copy = False).T
print 'calc. time', time()-t
return signal,norm1/(n/2),f_carrier,k
#return amplitude,phase,f_carrier,k,norm1/(n/2)
def LoadData():
Data = Shot()
Bt_trigger = Data['Tb']
gd = Shot().get_data
if Shot()['shotno'] > 22280:
tvec, density1 = gd('any', 'interframpsign')
tvec, density2 = gd('any', 'interfdiodeoutput')
elif Shot()['shotno'] > 21300:
tvec, density1 = gd('any', 'tek_ch1')
tvec, density2 = gd('any', 'tek_ch3')
elif Shot()['shotno'] > 18674:
tvec, density1 = gd('any', 'interframpsign')
tvec, density2 = gd('any', 'interfdiodeoutput')
else:
tvec, density1 = gd('any', 'density1')
tvec, density2 = gd('any', 'density2')
start = Data['plasma_start']
end = Data['plasma_end']
return tvec, start, end, density1,density2,Bt_trigger
def graphs():
tvec, phase_pila = load_adv('results/phase_saw')
tvec, phase_sinus = load_adv('results/phase_sinus')
tvec, phase = load_adv('results/phase_substracted')
tvec, phase_corr = load_adv('results/phase_corrected')
tvec, amplitude = load_adv('results/amplitude_sinus')
tvec, n_e = load_adv('results/electron_density')
tvec, ne_corr = load_adv('results/electron_density_corr')
#print ne_corr
#print phase_corr
#import IPython
#IPython.embed()
data = [[get_data([tvec,-phase_pila+mean(phase_pila)], 'phase 1', 'phase [rad]', xlim=[0,40], fmt="--"),
get_data([tvec,-phase_sinus+mean(phase_sinus)], 'phase 2', 'phase [rad]', xlim=[0,40], fmt="--" ),
get_data([tvec,phase], 'substracted phase', 'phase [rad]', xlim=[0,40], fmt="k" ),
get_data([tvec,phase_corr], 'corrected phase', 'phase [rad]', xlim=[0,40], fmt="k:" )],
get_data([tvec,amplitude], 'amplitude', 'amplitude [a.u.]', xlim=[0,40],ylim=[0,None] )]
multiplot(data, '' , 'graphs/demodulation', (10,6) )
ylim = 1 if amax(n_e) < 1e18 else None
jump = ne_0*pi/(2*a)+tvec*0
data = [[ get_data('electron_density', 'Average electron density', '$<n_e>$ [$10^{19}\,m^{-3}$]',data_rescale=1e-19,ylim=[None,ylim] ),
get_data([tvec, ne_corr], 'Corrected electron density', '$<n_e>$ [$10^{19}\,m^{-3}$]',data_rescale=1e-19,c='r' ) ]+
[ get_data([tvec,j*jump], data_rescale=1e-19,c='y' ) for j in range(7) ]]
multiplot(data, '' , 'graphs/electron_density', (9,3) )
#jump = ne_0*pi/(2*a)+tvec*0
#data = get_data('electron_density_corr', 'Average electron density', '$<n_e>$ [$10^{19}\,m^{-3}$]')
#multiplot(data, '' , 'graphs/electron_density', (9,3) )
paralel_multiplot(data, '', 'icon', (4,3), 40)
#os.system('convert -resize 150 graphs/electron_density.png icon.png')
def RobustDensityUnwrap(tvec, amplitude, phi0,start, end, n_points ):
t = time()
#phi = unwrap(angle(cmplx_signal[:,0]/cmplx_signal[:,1])) #naive unwrapping
phi0 -= median(phi0[tvec<start])
#A = abs(cmplx_signal[:,0])
#import IPython
#IPython.embed()
def fun(y,x,start,end, A, phi0,tvec):
x = r_[start,x,end]
y = r_[0,y,0]
phi = interp(tvec,x,y)
c1 = linalg.norm(A*((phi0-phi-pi/2)%pi-pi/2))/len(tvec) # minimize distance frm the data modulus pi
c2 = 10*sum(-phi[phi<0])/len(tvec) #positivity
c3 = sum(abs((diff(y))))/100/sqrt(len(x)) #smoothness
cost = c1+c2+c3
#print c1,c2,c3
return cost
from scipy.optimize import basinhopping
#x0 = pi*ones(n_points)
x = linspace(start,end,n_points+2)[1:-1]
y0 = interp(x,tvec,maximum(0,phi0))
args = x,start,end, amplitude/median(amplitude), phi0,tvec
out = basinhopping(fun, y0, 10, 1,2, {'args':args})
y = out.x
phi_robust = interp(tvec,r_[start,x,end],r_[0,y,0])
print 'RobustDensityUnwrap: ', time()-t
phi_= interp(tvec,r_[start,x,end],r_[0,y,0])
plot(tvec, (phi0-phi_robust-pi/2)%(pi)-pi/2)
plot(tvec, phi0)
plot(tvec, phi_robust)
phi__ = (phi0-pi/2)%pi +(phi_robust-(phi_robust-pi/2)%pi)
phi__[phi__<-1] += pi
#plot(tvec, phi__)
#ylim(-2,7)
savefig('plot')
clf()
return phi_robust
def main():
for path in ['graphs', 'results' ]:
if not os.path.exists(path):
os.mkdir(path)
if sys.argv[1] == "analysis":
print 'analysis'
win = 30e-6 #[s]
t = time()
#if the phase difference is negativ, change order of dens1,dens2
tvec,start, end, density2,density1,Bt_trigger = LoadData()
dt = (tvec[-1]-tvec[0])/len(tvec)
density1 = density1[tvec>0]
density2 = density2[tvec>0]
tvec = tvec[tvec>0]
if std(density1) > std(density2):
density1,density2 = density2,density1
print 'load time ', time()-t
signals = vstack((density1,density2)).T
cmplx_signal,norm_ampl,f_carrier,k = Demodulation(signals,win/dt,dt)
downsample = int(win/dt/2)
#import IPython
#IPython.embed()
#tvec = tvec[::downsample]
#phi = unwrap(angle(cmplx_signal[::downsample,0]/ cmplx_signal[::downsample,1]))
#phi -= median(phi[tvec<start])
#A = abs(cmplx_signal[::downsample,0])
#def fun(y,x,start,end, A, phi,tvec):
#x = r_[start,x,end]
#y = r_[0,y,0]
#phi_ = interp(tvec,x,y)
#c1 = linalg.norm(A*((phi-phi_-pi/2)%pi-pi/2))/len(tvec)
#c2 = 10*sum(-phi_[phi_<0])/len(tvec)
#c3 = std(y)/100
#cost = c1+c2+c3
##print c1,c2,c3
#return cost
#from scipy.optimize import basinhopping
#N = 20
#x0 = pi*ones(N)
#x = linspace(start,end,N+2)[1:-1]
#out = basinhopping(fun, x0, 10, 0.02,0.5, {'args':(x,start,end, A/median(A), phi,tvec)})
#y = out.x
#phi_= interp(tvec,r_[start,x,end],r_[0,y,0])
#plot(tvec, (phi-phi_-pi/2)%(pi)-pi/2)
#plot(tvec, phi)
#plot(tvec, phi_)
#phi__ = (phi-pi/2)%pi +(phi_-(phi_-pi/2)%pi)
#phi__[phi__<-1] += pi
#plot(tvec, phi__)
#ylim(-2,7)
#savefig('plot')
#clf()
#plot(tvec,phi)
#plot(tvec,A/median(A)*10)
#plot( r_[start,x,end] , r_[0,out.x,0] )
#plot(tvec,phi__ )
#[axhline(i*pi) for i in range(5)]
#axvline(start)
#axvline(end)
#savefig('plot')
#clf()
tvec = tvec[::downsample]
amplitude = abs(cmplx_signal[::downsample,:])
phase = angle(cmplx_signal[::downsample,:])
phase = unwrap(phase, axis = 0)
phase_pila, phase_sinus = phase.T
phase = phase_pila-phase_sinus
#phase_pila = phase[::downsample,0]
#phase_sinus = phase[::downsample,1]
if tvec[0]< Bt_trigger:
phase -= median(phase[tvec<Bt_trigger])
elif start > tvec[0] and start < tvec[-1]:
phase-= phase[tvec.searchsorted(start)]
elif end > tvec[0] and end < tvec[-1]:
phase-= phase[tvec.searchsorted(end)]
else:
phase-= phase[0]
#print median(phase[tvec<Bt_trigger])
switched = sign(mean(phase[(tvec > start) & (tvec < end)])) == -1
if switched:
phase *= -1 # rotate the density of cabels were switched
#amplitude = amplitude[::downsample,:]
amplitude *= norm_ampl/median(amplitude,0)[None,:]
#phase_robust = RobustDensityUnwrap(tvec, amplitude[:,0], phase,start, end, 30 )
phase_robust = phase
#apl0 = median(amplitude[tvec < start])
#print
############## detekce skoku ################
#t0 = time()
#dwin = 20;
#N = len(phase)
#phase_diff = zeros(N)
#for i in arange(N):
#p_tmp = phase[max(i-dwin,0):min(i+dwin,N-1)]
#phase_diff[i] = amax(p_tmp) - amin(p_tmp)
#ind = medfilt((amplitude < 0.8*apl0) & (phase_diff > 2), 3) # remove standalone points
#ind = where(ind)[0]
#ind_skip = where(diff(ind)> 1)[0]
#ind_skip = unique(concatenate([[ind[0]], ind[ind_skip], ind[ind_skip+1] , [ind[-1]]])) # find indexes with skips
#phase_new = phase.copy()
#if mod(len(ind_skip), 2) == 0 and len(ind_skip) < 20: # fix only the simple issues
#Nskip = len(ind_skip)
#for i in arange(0,Nskip,2):
#i0 = ind_skip[i]
#i1 = ind_skip[i+1]
#phase_new[i1:] += phase_new[i0] - phase_new[i1]
#phase_new[i0:i1] = nan
#print "time detekce skoku", time() - t0
#plot(phase_diff)
#plot(amplitude / amax(amplitude) * amax(phase_diff))
##plot(ind*amax(phase_diff))
#savefig('diff.png')
#close()
#phase_new = phase
#plot(phase)
#plot(phase_new)
#savefig('phase.png')
#close()
save_adv('results/phase_saw', tvec, phase_pila)
save_adv('results/phase_sinus', tvec, phase_sinus)
save_adv('results/phase_substracted', tvec, phase)
save_adv('results/amplitude_sinus', tvec, amplitude)
save_adv('results/phase_corrected', tvec, phase_robust )
p = exp(1-mean((norm_ampl/amplitude)**2.5))
ind_plasma = (tvec > start) & (tvec < end)
n_e = ne_0*phase
ne_corr = ne_0*phase_robust
#electron_density_corr
save_adv('results/electron_density_line', tvec, n_e)
saveconst('results/electron_density_mean', mean(n_e[ind_plasma]))
#print phase_sinus
n_e /= 2*a
ne_corr/= 2*a
save_adv('results/electron_density', tvec, n_e)
save_adv('results/electron_density_corr', tvec, ne_corr)
#phase_skip = abs(phase[0] - phase[-1])/2*pi
#negativity = 1-sum(phase[ind_plasma])/sum(abs(phase[ind_plasma]))
#cum_var = mean(abs(diff(phase[ind_plasma]))) / mean(abs(phase[ind_plasma]))
saveconst('results/carrier_freq', abs(f_carrier))
saveconst('results/harmonics_distortion', k)
saveconst('results/norm_ampl', norm_ampl)
saveconst('results/probability', p)
saveconst('results/reliability', 0 )
#print 'phase_skip', phase_skip
#print 'negativity', negativity
#print 'cum_var', cum_var
##saveconst('results/reliability', phase_skip + negativity+cum_var*10 )
#norm_ampl
print 'carrier_freq ', f_carrier
print 'harmonics_distortion ',k
print 'norm_ampl ',norm_ampl
print 'probability ',p
#print "cum_var", cum_var
#print "negativity", negativity
#print "phase_skip", phase_skip
if sys.argv[1] == "plots":
graphs()
saveconst('status', 0)
if __name__ == "__main__":
main()
|
