def sweep_deemphasis_settings(signalType, amp, amp1, dly, windowing, filename):
from timeit import default_timer as timer
from plotting import printProgress
EyeOpening = np.zeros((len(dly), len(amp), len(amp1)))
MeanOpening = np.zeros((len(dly), len(amp), len(amp1)))
band = np.zeros((len(dly), len(amp), len(amp1)))
tot = len(amp) * len(amp1) * len(dly)
dt = 0.0
if tot > 32 * 32 * 4:
tshift = np.ones(20)
tshift[:] = 1e2
else:
tshift = np.ones(5)
tshift[:] = 1e2
rel = True
#Signal._params['Amplitude'] = 1.
#Signal.run(saveToFile=False, filename='SignalInput', rdm='lfsr7')
#wv = Signal.waveform
for id, d in enumerate(dly):
for ia, a in enumerate(amp):
for ia1, a1 in enumerate(amp1):
st = timer()
num = id * len(amp) * len(amp1) + len(amp1) * ia + ia1 + 1
tleft = dt * (tot - num)
m, s = divmod(tleft, 60)
h, m = divmod(m, 60)
printProgress(num,
tot,
prefix='Time left %dh %02dmin %02ds' % (h, m, s),
suffix='Complete',
decimals=3,
barLength=50)
Signal._params['Amplitude'] = a
Signal.run(False, 'init', signalType, False)
#Signal.waveform['Sample 0']['Single'] = wv['Sample 0']['Single']*a
Signal.addDeemphasis(deemphasisAmplitude=a1,
deemphasisDelay=d,
rel=rel)
Signal.getSpectrum(saveToFile=False,
filename='init',
fromFile=False,
verbose=False,
window=windowing)
Signal.waveform = TransmissionLine * Signal
veye, vmean, deltaV = Signal.eyeOpening()
veyediff = np.min(veye[1]) - np.max(veye[0])
vmeandiff = vmean[1] - vmean[0]
MeanOpening[id, ia, ia1] = vmeandiff
EyeOpening[id, ia, ia1] = veyediff
band[id, ia, ia1] = 0.5 * (np.max(veye[1]) - np.min(veye[1]) +
np.max(veye[0] - np.min(veye[0])))
ed = timer()
tshift = estimateTime(tshift, ed - st)
dt = np.average(tshift)
np.savez(filename + '_del_%s.npz' % d,
Eye=EyeOpening[id],
Average=MeanOpening[id])
return EyeOpening, MeanOpening, band, deltaV
def transfer(self, signal, verbose=False):
from numpy.fft import irfft
if not self.gotBode:
from scipy.interpolate import interp1d
fmax = self._fMax
if self._stepSize == 4e6:
step = 10
if self._stepSize == 2e6:
step = 20
else:
step = 10
tmlBode = np.power(10,self._attenuation/20.)[::step]
phase = self.dataDict['Phase'][::step]
#tmlBode = tmlBode[:tmlBode.shape[0]/4:2]
oldx = np.linspace(0,tmlBode.shape[0], num = tmlBode.shape[0], endpoint=True)
fbode = interp1d(oldx, tmlBode, kind='cubic')
fphase = interp1d(oldx, phase, kind='slinear')
if signal.spectrum['Frequency'][len(signal.spectrum['Frequency'])-1] < fmax:
sp = signal.spectrum['Frequency']
else:
sp = signal.spectrum['Frequency'][0:len(np.where(signal.spectrum['Frequency']<fmax)[0])]
newx = np.linspace(0,tmlBode.shape[0], num = len(sp), endpoint = True)
interpBode = fbode(newx)
interpPhase = fphase(newx)
#import matplotlib.pyplot as plt
#plt.plot(oldx, phase)
#plt.plot(newx, interpPhase)
#plt.show()
self._bode = interpBode#/interpBode[0]
self.gotBode = True
else:
interpBode = self._bode
wf = {}
complexBode = 1j*np.sin(2*np.pi*interpPhase)+np.cos(2*np.pi*interpPhase)
complexBode *= interpBode
for cnt in range(len(signal.waveform)-1):
if verbose:
printProgress(cnt, len(signal.waveform)-1, prefix = 'Transmitting wavefrom:', suffix = 'Complete', decimals=3, barLength = 50)
wf['Sample %s'%cnt] = {}
if 'Single' in signal.waveform['Sample %s'%cnt].keys():
fftwf = signal.spectrum['Data'][cnt]
wf['Sample %s'%cnt]['Single'] = irfft(fftwf*np.append(complexBode,np.array([0.]*(fftwf.shape[0]-interpBode.shape[0]))), norm=None)
wf['Sample %s'%cnt]['Single'] *= np.sqrt(2*len(fftwf))
if signal._window == 'Hamming':
wf['Sample %s'%cnt]['Single'] /= signal._hamm
elif signal._window == 'Tukey':
wf['Sample %s'%cnt]['Single'] /= signal._tuk
wf['Time'] = signal.waveform['Time']
return wf
def run(self,
saveToFile=True,
filename='init',
rdm='random',
verbose=False):
if self._params['Number of samples'] < self._chunksize:
self._chunksize = self._params['Number of samples']
self.waveform = {}
bitList = []
for filecnt in range(
int(self._params['Number of samples'] / self._chunksize)):
if rdm == 'random':
bitList = self.getRandomBits(samples=self._chunksize)
elif rdm == 'lfsr7':
bitList = self.getLFSR(samples=self._chunksize, N=7)
elif rdm == 'lfsr32':
bitList = self.getLFSR(samples=self._chunksize, N=32)
else:
bitList = self.getClock(samples=self._chunksize)
for i in range(self._chunksize):
self._bits = bitList[i]
if verbose:
printProgress(i + self._chunksize * filecnt,
self._params['Number of samples'] - 1,
prefix='Generating waveforms:',
suffix='Complete',
decimals=3,
barLength=50)
self.waveform['Sample %s' % i] = {}
if self._params['Is signal differential']:
a, b = self.generateWaveform()
self.waveform['Time'] = a[0]
self.waveform['Sample %s' % i]['Positive'] = a[1]
self.waveform['Sample %s' % i]['Negative'] = b[1]
else:
if rdm == 'sin':
a = self.generateSin()
else:
a = self.generateWaveform()
#a = self.cml_driver(a)
self.waveform['Sample %s' % i]['Single'] = a[1]
self.waveform['Time'] = a[0]
if saveToFile:
self.saveWaveform(filename=filename, idx=filecnt)
if filecnt < int(
self._params['Number of samples'] / self._chunksize) - 1:
self.waveform = {}
self._hamm = self.hammingWindow()
self._tuk = self.tukeyWindow()
def shaper(self):
from numpy.fft import irfft
cnt = 0
bode = np.ones(self.spectrum['Frequency'].shape[0])
for k in range(bode.shape[0]):
bode[k] = 1. / (1. + k * 1e-14 * self._params['Frequency'])
for keysample, valsample in self.waveform.iteritems():
if 'Time' not in keysample:
printProgress(cnt,
self._params['Number of samples'] - 1,
prefix='Shaping waveform:',
suffix='Complete',
decimals=3,
barLength=50)
for key, val in valsample.iteritems():
spec = self.spectrum['Data'][cnt]
inv = irfft(spec * bode)
self.waveform[keysample][key] = inv
cnt += 1
def eyeOpening(self, verbose=False):
from plotting import simpleHist
h = []
cnt = 0
if 'Single' in self.waveform['Sample 0'].keys():
for keysamples, valsamples in self.waveform.iteritems():
if verbose:
printProgress(cnt,
len(self.waveform) - 1,
prefix='Recording eye diagram:',
suffix='Complete',
decimals=3,
barLength=50)
if 'Time' not in keysamples:
for b in range(22, len(self._bits) - 22):
st = int(
(b + 1. / 2 - 0.05) * self.
_params['Number of sampling points per period'])
ed = int(
(b + 1. / 2 + 0.05) * self.
_params['Number of sampling points per period'])
h.append(valsamples['Single'][st:ed])
cnt += 1
else:
pass
#for keysamples, valsamples in self.waveform.iteritems():
# printProgress(cnt, len(self.waveform)-1, prefix = 'Recording eye:', suffix = 'Complete', decimals=3, barLength = 50)
# if 'Time' not in keysamples:
# rx = self.differentialProbing(valsamples['Positive'], valsamples['Negative'])
# h.append(rx[startPointHist:endPointHist])
#
# cnt += 1
h = np.array(h)
histCnt, x, dv = simpleHist(h, 128)
#gauss = x[np.where(histCnt!=0)[0]]
gauss = x[histCnt != 0]
gauss1 = gauss[gauss < 0.]
#gauss1peak = x[np.argmax(histCnt[x<0.])]
gauss1peak = 0.
N = 0.
for id in np.where(x < 0.)[0]:
gauss1peak += histCnt[id] * x[id]
N += histCnt[id]
if N != 0:
gauss1peak /= N
else:
gauss1peak = 0.
gauss2 = gauss[gauss > 0.]
#gauss2peak = x[len(x[x<0.]) + np.argmax(histCnt[x>0.])]
gauss2peak = 0.
N = 0.
for id in np.where(x > 0.)[0]:
gauss2peak += histCnt[id] * x[id]
N += histCnt[id]
if N != 0:
gauss2peak /= N
else:
gauss2peak = 0.
return [gauss1, gauss2], [gauss1peak, gauss2peak], dv
def getSpectrum(self,
saveToFile=True,
filename='init',
fromFile=False,
verbose=False,
window='Hamming'):
from numpy.fft import rfft
#baseFreq = self.roundFrequency(self._params['Frequency'])
baseFreq = self._params['Frequency']
self.spectrum = {}
N = len(self.waveform['Sample 0']['Single'])
nqf = float(baseFreq) * self._params[
'Number of sampling points per period'] / 2.
t = []
for k in range(N / 2 + 1):
t.append(2 * nqf / (N - 1) * k)
self.spectrum['Frequency'] = np.array(t)
spectrum = []
self._window = window
if fromFile:
filecnt = 1
file = './rawdata/' + filename + 'waveform' + '_%s.npz' % filecnt
while os.path.isfile(filename):
file = './rawdata/' + filename + 'waveform' + '_%s.npz' % filecnt
waveform = self.loadWaveform(file, False, filecnt)
for i in range(self._chunksize):
if verbose:
printProgress(i + self._chunksize * filecnt,
self._params['Number of samples'] - 1,
prefix='Receive spectrum:',
suffix='Complete',
decimals=3,
barLength=50)
spect = rfft(waveform['Sample %s' % i]['Single'])
spectrum.append(spect)
filecnt += 1
self.spectrum['Data'] = np.array(spectrum)
else:
for filecnt in range(
int(self._params['Number of samples'] / self._chunksize)):
for i in range(self._chunksize):
if window == 'Tukey':
spect = rfft(self._tuk *
self.waveform['Sample %s' % i]['Single'],
norm=None)
elif window == 'Hamming':
spect = rfft(self._hamm *
self.waveform['Sample %s' % i]['Single'],
norm=None)
else:
spect = rfft(self.waveform['Sample %s' % i]['Single'],
norm=None)
if verbose:
printProgress(i + self._chunksize * filecnt,
self._params['Number of samples'] - 1,
prefix='Receive spectrum:',
suffix='Complete',
decimals=3,
barLength=50)
spect /= np.sqrt(2 * len(spect))
spectrum.append(spect)
self.spectrum['Data'] = np.array(spectrum)
if saveToFile:
self.saveSpectrum(filename=filename, idx=filecnt)
if filecnt < int(self._params['Number of samples'] /
self._chunksize) - 1:
self.spectrum['Data'] = np.empty()