plt.plot(time_s, sig); plt.grid()
plt.ylabel('Signal (nd)');
plt.subplot(3,1,2)
plt.plot(time_s, ampChirp); plt.grid()
plt.ylabel('ampitude (nd)');
plt.subplot(3,1,3)
plt.plot(time_s, freqChirp_rps * rps2hz); plt.grid()
plt.xlabel('Time (s)'); plt.ylabel('Frequency (Hz)')
plt.show()
#%% Plot the Excitation Spectrum
## Compute Spectrum of each channel
optSpect = FreqTrans.OptSpect(freqRate = freqRate_hz)
freq_hz, sigDft, Psd_mag = FreqTrans.Spectrum(sig, optSpect)
Psd_dB = 20*np.log10(Psd_mag)
## Plot Spectrum
plt.figure()
plt.plot(freq_hz[0], Psd_dB[0])
plt.xlabel('frequency (Hz)');
plt.ylabel('Spectrum (dB)');
plt.grid()
plt.show()
#%% Create the output for JSON config
timeFinal_s = time_s[-1]
timeStart_s = time_s[0]
phSamp = np.random.uniform(-np.pi, np.pi, shapeSamp)
samp = rSamp * np.exp(1j * phSamp)
TEstSamp = np.zeros(shapeSamp, dtype='complex')
for iSamp in range(nSamp):
TEstSamp[..., iSamp] = TEstNom + TEstUnc * samp[..., iSamp]
#%%
if False:
#%%
optTemp = FreqTrans.OptSpect(dftType = 'czt', scaleType = 'density', freqRate = freqRate_rps, smooth = ('box', 3), winType = 'bartlett', detrendType = 'linear')
optTemp.freq = freqGap_rps
_, _, SnnN = FreqTrans.Spectrum(z, optTemp)
[iOut, iIn] = [0,0]
fig = plt.figure(); plt.grid(True)
plt.plot(freq_hz[iIn], mag2db(Szz[iOut, iIn]), '.b', label = 'Estimate Nominal [MIMO]')
plt.plot(freq_hz[iIn, sigIndx[iIn]], mag2db(Szz[iOut, iIn, sigIndx[iIn]]), '.r:', label = 'Estimate Nominal [SIMO]')
plt.xlabel('Frequency [Hz]')
plt.ylabel('Power Spectral Density [dB]')
plt.legend()
plt.xlim([1,5])
plt.ylim([0,20])
fig.set_tight_layout(True)
fig.set_size_inches([6.4,2.4])
if False:
ax[iChan].set_xlabel('Time (s)')
#%% Plot the Excitation Spectrum
## Compute Spectrum of each channel
optFFT = FreqTrans.OptSpect(freqRate = freqRate_hz * hz2rps, winType = ('tukey', 0.0), smooth = ('box', 1))
optCZT = FreqTrans.OptSpect(dftType = 'czt', freqRate = freqRate_hz * hz2rps, winType = ('tukey', 0.0), smooth = ('box', 1))
freq_fft = []
P_dB_fft = []
freq_czt = []
P_dB_czt = []
for iChan, sig in enumerate(sigList):
freq_rps_fft, _, P_fft = FreqTrans.Spectrum(sig, optFFT)
freq_fft.append(freq_rps_fft * rps2hz)
P_dB_fft.append(20*np.log10(P_fft))
optCZT.freq = freqElem_rps[sigIndx[iChan]]
freq_rps_czt, _, P_czt = FreqTrans.Spectrum(sig, optCZT)
freq_czt.append(freq_rps_czt * rps2hz)
P_dB_czt.append(20*np.log10(P_czt))
nChan = len(P_dB_fft)
plt.figure()
for iChan in range(0, nChan):
plt.subplot(nChan, 1, iChan+1)
plt.plot(freq_fft[iChan].T, P_dB_fft[iChan].T, '-k', label='FFT Pxx')
plt.plot(freq_czt[iChan].T, P_dB_czt[iChan].T, '.r-', label='CZT Pxx')
#optSpec.scaleType = 'density'
optSpec.winType = ('tukey', 0.0)
optSpec.freq = np.linspace(0, 0.05*np.pi, numSampMin-1)
sig = np.ones(numSampMax)
noiseSamples = np.random.randn(numSampMax)
noiseLev = 0.0
sig += noiseLev * noiseSamples
P_theshold_dB = -12
plt.figure()
binList = []
for numSamp in numSampList:
numSamp = int(numSamp)
theta, sigDft, P_mag = FreqTrans.Spectrum(sig[0:numSamp], optSpec)
P_mag = np.abs(P_mag / np.max(P_mag))
P_dB = 20*np.log10(P_mag[0])
bins = theta[0] * len(optSpec.freq[0]) / (2*np.pi)
plt.plot(bins, P_dB, '-', label = 'Window: ' + str(optSpec.winType) + ' Over Sample: ' + str(numSamp/numSampMin))
bin_theshold = 2 * np.interp(-P_theshold_dB, -P_dB, bins)
plt.plot(bin_theshold/2, P_theshold_dB, '*')
binList.append( bin_theshold )
plt.xlabel('Normalized Bin')
plt.ylabel('Normalized Power (dB)')
plt.ylim(bottom = -60, top = 10)
plt.xlim(left = 0.0)
# FRF Estimate
stride = 10
lenX = len(time_s) / stride
lenFreq = optTemp.freq.shape[-1]
lenFreqN = optTempN.freq.shape[-1]
numSeg = int(lenX)
t_s = np.zeros(numSeg)
PwwList = np.zeros((numSeg, numChan, lenFreq))
PwwNList = np.zeros((numSeg, numChan, lenFreqN))
for iSeg in range(0, numSeg):
iEnd = iSeg * stride
print(100 * iSeg / numSeg)
_, _, Pww = FreqTrans.Spectrum(exc[:, 0:iEnd + 1], optTemp)
_, _, PwwN = FreqTrans.Spectrum(exc[:, 0:iEnd + 1], optTempN)
t_s[iSeg] = time_s[iEnd + 1]
PwwList[iSeg, ] = Pww
PwwNList[iSeg, ] = PwwN
#%% Plot
N = len(time_s)
#PwwList[:,] = np.nan
#PwwNList[:,] = np.nan
PwwListMag = np.abs(PwwList[:, 0, :])
PwwNListMag = np.abs(PwwNList[:, 0, :])
optSpecE = FreqTrans.OptSpect(dftType='czt',
freqRate=freqRate_rps,
smooth=('box', 7),
winType=('tukey', 0.0))
optSpecE.freq = np.asarray(freqExc_rps)
optSpecE.freqInterp = np.sort(optSpecE.freq.flatten())
optSpecN = FreqTrans.OptSpect(dftType='czt',
freqRate=freqRate_rps,
smooth=('box', 7),
winType=('tukey', 0.0))
optSpecN.freq = freqNull_rps
SyyList = []
for iSeg in range(0, len(oDataSegs)):
_, _, SyyNull = FreqTrans.Spectrum(ySensList[iSeg], optSpecN)
SyyList.append(SyyNull)
_, _, Sxx = FreqTrans.Spectrum(vExcList[iSeg], optSpec)
_, _, SxxNull = FreqTrans.Spectrum(vExcList[iSeg], optSpecN)
print(np.sum(SxxNull, axis=-1) / np.sum(Sxx, axis=-1))
from Core import Environment
ft2m = 0.3049
m2ft = 1 / ft2m
b_ft = 4
# levelList = ['light', 'moderate', 'severe']
levelList = ['light', 'moderate']