-
Notifications
You must be signed in to change notification settings - Fork 0
/
spice_utils.py
587 lines (464 loc) · 19.4 KB
/
spice_utils.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
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
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""File wrangling for SPICE simulations.
@author: Trond Ytterdal, Bikash Adhikari, Arnfinn Eielsen
@date: 19.03.2024
@license: BSD 3-Clause
"""
import os
import numpy as np
from matplotlib import pyplot as plt
import fileinput
import subprocess
import datetime
from scipy import signal
from scipy import interpolate
import pickle
from prefixed import Float
from tabulate import tabulate
from lin_method_util import lm, dm
from figures_of_merit import FFT_SINAD, TS_SINAD
from quantiser_configurations import qws
class sinad_comp:
FFT = 1 # FFT based
CFIT = 2 # curve fit
class sim_config:
def __init__(self, qconfig, lin, dac, fs, t, fc, nf, carrier_scale, carrier_freq):
self.qconfig = qconfig
self.lin = lin
self.dac = dac
self.fs = fs
self.t = t
self.fc = fc
self.nf = nf
self.carrier_scale = carrier_scale
self.carrier_freq = carrier_freq
def __str__(self):
s = str(self.qconfig) + '\n'
s = s + str(self.lin) + '\n'
s = s + str(self.dac) + '\n'
s = s + 'Fs=' + f'{Float(self.fs):.0h}' + '\n'
s = s + 'Fc=' + f'{Float(self.fc):.0h}' + '\n'
s = s + 'Nf=' + f'{Float(self.nf):.0h}' + '\n'
s = s + 'Xs=' + f'{Float(self.carrier_scale):.0h}' + '\n'
s = s + 'Fx=' + f'{Float(self.carrier_freq):.0h}' + '\n'
return s + '\n'
def addtexttofile(filename, text):
f = open(filename, 'w')
f.write(text)
f.close()
def get_bit(value, bit_index):
rval = value & (1 << bit_index)
if rval != 0:
return 1
else:
return 0
def get_pwl_string(c, Ts, Ns, dnum, vbpc, vdd, trisefall):
"""
Generate picewise linear (PWL) waveform description string to be read by SPICE.
Arguments
c - codes
Ts - sampling time (in microseconds)
Ns - number of samples
vbpc, vdd, trisefall - waveform specs.
Returns
rval - PWL string
"""
if get_bit(c[0], dnum) == 0:
rval = "0," + vdd + " "
else:
rval = "0," + vbpc + " "
deltat = trisefall/2
for i in range(0, Ns-1):
time = (i+1)*Ts*1e6 # microseconds
if get_bit(c[i], dnum) == 0 and get_bit(c[i+1], dnum) == 1:
rval += " " + str(time - deltat) + "u," + vdd + " " \
+ str(time + deltat) + "u," + vbpc
elif get_bit(c[i], dnum) == 1 and get_bit(c[i+1], dnum) == 0:
rval += " " + str(time - deltat) + "u," + vbpc + " " \
+ str(time + deltat) + "u," + vdd
rval = rval + "\n"
return rval
def get_inverted_pwl_string(c, Ts, Ns, dnum, vbpc, vdd, trisefall):
"""
Generate inverted picewise linear (PWL) waveform description string to be read by SPICE.
Arguments
c - codes
Ts - sampling time (in microseconds)
Ns - number of samples
vbpc, vdd, trisefall - waveform specs.
Returns
rval - PWL string
"""
if get_bit(c[0], dnum) == 0:
rval = "0," + vbpc + " "
else:
rval = "0," + vdd + " "
deltat = trisefall/2
for i in range(0, Ns-1):
time = (i+1)*Ts*1e6 # microseconds
if get_bit(c[i], dnum) == 0 and get_bit(c[i+1], dnum) == 1:
rval += " " + str(time - deltat) + "u," + vbpc + " " \
+ str(time + deltat) + "u," + vdd
elif get_bit(c[i], dnum) == 1 and get_bit(c[i+1], dnum) == 0:
rval += " " + str(time - deltat) + "u," + vdd + " " \
+ str(time + deltat) + "u," + vbpc
rval = rval + "\n"
return rval
def run_spice_sim(spicef, outputf, outdir='spice_output/', spice_path='ngspice'):
"""
Run SPICE simulaton using provided filenames
Arguments
spicef - SPICE batch file
outputf - Output files name
"""
print(spicef)
print(outputf)
cmd = [spice_path, '-o', outdir + outputf + '.log',
# '-r', outdir + outputf + '.bin',
'-b', outdir + spicef]
print(cmd)
subprocess.run(cmd)
def run_spice_sim_parallel(spicef_list, outputf_list, outdir='spice_output/', spice_path='ngspice'):
"""
Run SPICE simulaton using provided filenames
Arguments
spicef_list - SPICE batch files
outputf_list - Output files names
"""
cmd_list = []
for k in range(0, len(spicef_list)):
cmd = [spice_path, '-o', outdir + outputf_list[k] + '.log',
#'-r', outdir + outputf_list[k] + '.bin',
'-b', outdir + spicef_list[k]]
print(cmd)
cmd_list.append(cmd)
procs_list = [subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) for cmd in cmd_list]
for proc in procs_list:
print('Waiting for SPICE to return...')
proc.wait()
def gen_spice_sim_file(C, Nb, t, Ts, QConfig, outdir, seed=1, seq=0):
"""
Set up SPICE simulaton file for a given DAC circuit description and save.
Arguments
c - codes
Nb - no. of bit
t - time vector
Ts - sampling time
QConfig - quantiser config.
outdirname - put files in this directory
seed - randomisation seed for circuit
seq - sequence
"""
wavf = 'spice_pwl_wav.txt'
cmdf = 'spice_cmds.txt'
tempdir = 'spice_temp'
circdir = 'spice_circuits'
#outdir = os.path.join('spice_output', outdirname)
if os.path.exists(outdir):
print('Putting output files in existing directory: ' + outdir)
else:
os.mkdir(outdir)
wav_str = ''
match QConfig:
case qws.w_06bit: # 6 bit DAC
c = C.astype(int)
nsamples = len(c)
t1 = '\n'
t2 = '\n'
vbpc = '3.28'
vdd = '5.0'
Tr = 1e-3 # the rise-time for edges, in µs
for k in range(0, Nb): # generate PWL strings
k_str = str(k)
t1 += 'vdp' + k_str + ' data' + k_str + ' 0 pwl ' + \
get_pwl_string(c, Ts, nsamples, k, vbpc, vdd, Tr)
t2 += 'vdn' + k_str + ' datai' + k_str + ' 0 pwl ' + \
get_inverted_pwl_string(c, Ts, nsamples, k, vbpc, vdd, Tr)
wav_str = t1 + t2
circf = 'cs_dac_06bit_ngspice.cir' # circuit description
spicef = 'cs_dac_06bit_ngspice_batch.cir' # complete spice input file
outputf = 'cs_dac_16bit_ngspice_batch_' + str(seq)
ctrl_str = '\n' + '.save v(outf)' + '\n' + '.tran 10u ' + str(t[-1]) + '\n'
case qws.w_16bit_SPICE: # 16 bit DAC
c = C.astype(int)
nsamples = len(c)
t1 = '\n'
t2 = '\n'
vbpc = '0'
vdd = '1.5'
Tr = 1e-3 # the rise-time for edges, in µs
for k in range(0, Nb): # generate PWL strings
k_str = str(k+1)
t1 += "vb" + k_str + " b" + k_str + " 0 pwl " + \
get_pwl_string(c, Ts, nsamples, k, vbpc, vdd, Tr)
t2 += "vbb" + k_str + " bb" + k_str + " 0 pwl " + \
get_inverted_pwl_string(c, Ts, nsamples, k, vbpc, vdd, Tr)
wav_str = t1 + t2
seed_str = ''
if seed == 1:
seed_str = 'seed_1'
elif seed == 2:
seed_str = 'seed_2'
# circuit description file
circf = 'cs_dac_16bit_ngspice_' + seed_str + '.cir'
# spice input file
spicef = 'cs_dac_16bit_ngspice_batch_' + str(seq) + '.cir'
# ctrl_str = '\n' + '.save v(out)' + '\n' + '.tran 10u ' + str(t[-1]) + '\n'
outputf = 'cs_dac_16bit_ngspice_batch_' + str(seq)
ctrl_str = ''
if seed == 1:
#ctrl_str = '\n.option method=trap XMU=0.495 gmin=1e-19 reltol=200u abstol=100f vntol=100n seed=1\n'
ctrl_str = '\n.option method=trap TRTOL=5 gmin=1e-19 reltol=200u abstol=100f vntol=100n seed=1\n'
elif seed == 2:
#ctrl_str = '\n.option method=trap XMU=0.495 gmin=1e-19 reltol=200u abstol=100f vntol=100n seed=2\n'
ctrl_str = '\n.option method=trap TRTOL=5 gmin=1e-19 reltol=200u abstol=100f vntol=100n seed=2\n'
ctrl_str = ctrl_str + \
'\n.control\n' + \
'tran 10u ' + str(t[-1]) + '\n' + \
'write $inputdir/' + outputf + '.bin' + ' v(out)\n' + \
'.endc\n'
case qws.w_6bit_2ch_SPICE: # 6 bit DAC, 2 channels
c1 = C[0,:].astype(int)
c2 = C[1,:].astype(int)
nsamples1 = len(c1)
nsamples2 = len(c2)
tvb1 = '\n'
tvb2 = '\n'
tvbb1 = '\n'
tvbb2 = '\n'
vbpc = '0'
vdd = '1.5'
Tr = 1e-3 # the rise-time for edges, in µs
for k in range(0, Nb): # generate PWL strings
k_str = str(k + 1)
tvb1 += 'vb1' + k_str + ' b1' + k_str + ' 0 pwl ' + \
get_pwl_string(c1, Ts, nsamples1, k, vbpc, vdd, Tr)
tvbb1 += 'vbb1' + k_str + ' bb1' + k_str + ' 0 pwl ' + \
get_inverted_pwl_string(c1, Ts, nsamples1, k, vbpc, vdd, Tr)
tvb2 += 'vb2' + k_str + ' b2' + k_str + ' 0 pwl ' + \
get_pwl_string(c2, Ts, nsamples2, k, vbpc, vdd, Tr)
tvbb2 += 'vbb2' + k_str + ' bb2' + k_str + ' 0 pwl ' + \
get_inverted_pwl_string(c2, Ts, nsamples2, k, vbpc, vdd, Tr)
wav_str = tvb1 + tvbb1 + tvb2 + tvbb2
circf = 'cs_dac_06bit_2ch_TRAN.cir' # circuit description
spicef = 'cs_dac_06bit_2ch_TRAN_ngspice_batch.cir' # complete spice input file
outputf = 'cs_dac_06bit_2ch_TRAN_ngspice_batch'
ctrl_str = '\n.option method=trap TRTOL=5 gmin=1e-19 reltol=200u abstol=100f vntol=100n seed=2\n'
ctrl_str = ctrl_str + \
'\n.control\n' + \
'tran 10u ' + str(t[-1]) + '\n' + \
'write $inputdir/' + outputf + '.bin' + ' v(out1) v(out2)\n' + \
'.endc\n'
case qws.w_16bit_2ch_SPICE: # 16 bit DAC, 2 channels
c1 = C[0,:].astype(int)
c2 = C[1,:].astype(int)
nsamples1 = len(c1)
nsamples2 = len(c2)
tvb1 = '\n'
tvb2 = '\n'
tvbb1 = '\n'
tvbb2 = '\n'
vbpc = '0'
vdd = '1.5'
Tr = 1e-3 # the rise-time for edges, in µs
for k in range(0, Nb): # generate PWL strings
k_str = str(k + 1)
tvb1 += 'vb1' + k_str + ' b1' + k_str + ' 0 pwl ' + \
get_pwl_string(c1, Ts, nsamples1, k, vbpc, vdd, Tr)
tvbb1 += 'vbb1' + k_str + ' bb1' + k_str + ' 0 pwl ' + \
get_inverted_pwl_string(c1, Ts, nsamples1, k, vbpc, vdd, Tr)
tvb2 += 'vb2' + k_str + ' b2' + k_str + ' 0 pwl ' + \
get_pwl_string(c2, Ts, nsamples2, k, vbpc, vdd, Tr)
tvbb2 += 'vbb2' + k_str + ' bb2' + k_str + ' 0 pwl ' + \
get_inverted_pwl_string(c2, Ts, nsamples2, k, vbpc, vdd, Tr)
wav_str = tvb1 + tvbb1 + tvb2 + tvbb2
circf = 'cs_dac_16bit_2ch_TRAN.cir' # circuit description
spicef = 'cs_dac_16bit_2ch_TRAN_ngspice_batch.cir' # complete spice input file
outputf = 'cs_dac_16bit_2ch_TRAN_ngspice_batch'
ctrl_str = '\n.option method=trap TRTOL=5 gmin=1e-19 reltol=200u abstol=100f vntol=100n seed=1\n'
ctrl_str = ctrl_str + \
'\n.control\n' + \
'tran 10u ' + str(t[-1]) + '\n' + \
'write $inputdir/' + outputf + '.bin' + ' v(out1) v(out2)\n' + \
'.endc\n'
addtexttofile(os.path.join(tempdir, cmdf), ctrl_str)
addtexttofile(os.path.join(tempdir, wavf), wav_str)
with open(os.path.join(outdir, spicef), 'w') as fout:
fins = [os.path.join(circdir, circf),
os.path.join(tempdir, cmdf),
os.path.join(tempdir, wavf)]
fin = fileinput.input(fins)
for line in fin:
fout.write(line)
fin.close()
print(circf)
print(spicef)
print(outputf)
return spicef, outputf
def read_spice_bin_file(fdir, fname):
"""
Read a given ngspice binary output file.
Accounts for number of variables.
Assumes variables are interleaved, with time vector first.
"""
fpath = os.path.join(fdir, fname)
fid = open(fpath, 'rb')
# print("Opening file: " + fname)
read_new_line = True
while read_new_line:
tline = fid.readline()
if b'Binary:' in tline: # marker for binary data to start
read_new_line = False
if b'No. Variables: ' in tline:
nvars = int(tline.split(b':')[1])
print(nvars)
if b'No. Points: ' in tline:
npoints = int(tline.split(b':')[1])
print(npoints)
data = np.fromfile(fid, dtype='float64')
t_spice = np.array(data[::nvars])
y_spice = np.zeros((nvars-1,npoints))
for k in range(1, nvars):
y_spice[k-1:] = data[k::nvars]
return t_spice, y_spice
def read_spice_bin_file_with_most_recent_timestamp(fdir):
"""
Read SPICE ouput file (assuming a certain format, i.e. not general)
"""
binfiles = [file for file in os.listdir(fdir) if file.endswith('.bin')]
binfiles.sort()
fname = binfiles[-1]
t_spice, y_spice = read_spice_bin_file(fdir, fname)
return t_spice, y_spice
def process_sim_output(ty, y, Fc, Fs, Nf, TRANSOFF, SINAD_COMP_SEL, plot=False, descr=''):
# Filter the output using a reconstruction (output) filter
#print(ty.shape)
#print(y.shape)
match 1:
case 1:
Wc = 2*np.pi*Fc
b, a = signal.butter(Nf, Wc, 'lowpass', analog=True) # filter coefficients
Wlp = signal.lti(b, a) # filter LTI system instance
y = y.reshape(-1, 1) # ensure the vector is a column vector
y_avg_out = signal.lsim(Wlp, y, ty, X0=None, interp=False) # filter the output
y_avg = y_avg_out[1] # extract the filtered data; lsim returns (T, y, x) tuple, want output y
case 2:
bd, ad = signal.butter(Nf, Fc, fs=Fs)
y = y.reshape(-1, 1).squeeze() # ensure the vector is a column vector
y_avg = signal.lfilter(bd, ad, y)
case 3:
y = y.reshape(-1, 1) # ensure the vector is a column vector
y_avg = y.squeeze()
match SINAD_COMP_SEL:
case sinad_comp.FFT: # use FFT based method to detemine SINAD
R = FFT_SINAD(y_avg[TRANSOFF:-TRANSOFF], Fs, plot, descr)
case sinad_comp.CFIT: # use time-series sine fitting based method to detemine SINAD
y_avg = y_avg.reshape(1, -1).squeeze()
R = TS_SINAD(y_avg[TRANSOFF:-TRANSOFF], ty[TRANSOFF:-TRANSOFF], plot, descr)
ENOB = (R - 1.76)/6.02
# Print FOM
print(descr + ' SINAD: {}'.format(R))
print(descr + ' ENOB: {}'.format(ENOB))
return y_avg, ENOB
def main():
"""
Read results from a given SPICE simulation and process the data.
"""
outdir = 'spice_output'
rundirs = os.listdir(outdir)
rundirs.sort()
print('No. dirs.: ' + str(len(rundirs)))
#method_str = 'baseline'
#method_str = 'physical_level_calibration'
#method_str = 'periodic_dither'
#method_str = 'noise_dither'
method_str = 'digital_calibration'
#method_str = 'dynamic_element_matching'
#method_str = 'ilc'
matching = [s for s in rundirs if method_str in s]
#rundir = rundirs[16] # pick run
rundir = matching[0] # pick run
bindir = os.path.join(outdir, rundir)
# read pickled (marshalled) state/config object
with open(os.path.join(bindir, 'sim_config.pickle'), 'rb') as fin:
SC = pickle.load(fin)
print(bindir)
run_info = [['Method', 'Model', 'Fs', 'Fc', 'Fx'],
[str(SC.lin), str(SC.dac), f'{Float(SC.fs):.0h}', f'{Float(SC.fc):.0h}', f'{Float(SC.carrier_freq):.1h}']]
print(tabulate(run_info))
binfiles = [file for file in os.listdir(bindir) if file.endswith('.bin')]
binfiles.sort()
if True:
Nbf = len(binfiles) # number of bin (binary data) files
t = SC.t
Fs = SC.fs
Fx = SC.carrier_freq
t_end = 3/Fx # time vector duration
Fs_ = Fs*72 # semi-optimal factor for most sims with different non-uniform sampling per file
Fs_ = Fs
print(f'Fs: {Float(Fs):.0h}')
t_ = np.arange(0, t_end, 1/Fs_) # time vector
if Nbf == 1: # may contain several channels in ngspice bin file
print(os.path.join(bindir, binfiles[0]))
t_spice, y_spice = read_spice_bin_file(bindir, binfiles[0])
Nch = y_spice.shape[0]
print('No. channels:')
print(Nch)
# Summation stage
if SC.lin.method == lm.BASELINE or SC.lin.method == lm.ILC:
K = np.ones((Nch,1))
K[1] = 0.0 # null one channel (want single channel resp.)
elif SC.lin.method == lm.DEM:
K = np.ones((Nch,1))
elif SC.lin.method == lm.PHYSCAL:
K = np.ones((Nch,1))
K[1] = 1e-2
else:
K = 1/Nch
print('Summing gain:')
print(K)
y_spice_ = np.sum(K*y_spice, 0)
ym_ = np.interp(t_, t_spice, y_spice_) # re-sample
else: # assume one channel per bin file
Nch = Nbf
YM = np.zeros([Nch, t_.size])
for k in range(0, Nbf):
print(os.path.join(bindir, binfiles[k]))
t_spice, y_spice = read_spice_bin_file(bindir, binfiles[k])
y_resamp = np.interp(t_, t_spice, y_spice) # re-sample
YM[k,:] = y_resamp
# Summation stage
if SC.lin.method == lm.DEM:
K = np.ones((Nch,1))
if SC.lin.method == lm.PHYSCAL:
K = np.ones((Nch,1))
K[1] = 1e-2
else:
K = 1/Nch
ym_ = np.sum(K*YM, 0)
if False:
#ym = np.interp(t, t_, ym_) # re-sample
#ym = interpolate.Akima1DInterpolator(t_, ym_)(t)
ym = interpolate.PchipInterpolator(t_, ym_)(t)
#ym = signal.resample(ym_, t.size)
TRANSOFF = np.floor(0.5*Fs/Fx).astype(int) # remove transient effects from output
else:
ym = ym_
t = t_
TRANSOFF = np.floor(0.25*Fs_/Fx).astype(int) # remove transient effects from output
Fc = SC.fc
Nf = SC.nf
ym_avg, ENOB_M = process_sim_output(t, ym, Fc, Fs_, Nf, TRANSOFF, sinad_comp.CFIT, False, 'SPICE')
plt.plot(t,ym)
plt.plot(t,ym_avg)
results_tab = [['Config', 'Method', 'Model', 'Fs', 'Fc', 'Fx', 'ENOB'],
[str(SC.qconfig), str(SC.lin), str(SC.dac), f'{Float(SC.fs):.2h}', f'{Float(SC.fc):.1h}', f'{Float(SC.carrier_freq):.1h}', f'{Float(ENOB_M):.3h}']]
print(tabulate(results_tab))
#t_spice, y_spice = read_spice_bin_file_with_most_recent_timestamp(path)
#YM = np.zeros([1,y_spice.size])
#YM[0,:] = y_spice
#plt.plot(y_spice)
#print(YM)
if __name__ == "__main__":
main()