unwrap=True)
gainTaEstUnc_mag = FreqTrans.Gain(TaEstUnc, magUnit='mag')
if False:
for iPlot, io in enumerate(ioArray):
iOut, iIn = io
outName = outPlot[iOut]
inName = inPlot[iIn]
fig = 80 + iPlot
fig = FreqTrans.PlotBode(freqLin_hz,
gainTaLinNom_mag[iOut, iIn],
phaseTaLinNom_deg[iOut, iIn],
coher_nd=np.ones_like(freqLin_hz),
fig=fig,
color='k',
label='Linear')
fig = FreqTrans.PlotBode(freq_hz[0],
gainTaEstNom_mag[iOut, iIn],
phaseTaEstNom_deg[iOut, iIn],
TaEstCoh[iOut, iIn],
gainUnc_mag=gainTaEstUnc_mag[iOut, iIn],
fig=fig,
color='b',
label='Estimation (MIMO)')
fig = FreqTrans.PlotBode(freq_hz[0, sigIndx[iIn]],
gainTaEstNom_mag[iOut, iIn, sigIndx[iIn]],
phaseTaEstNom_deg[iOut, iIn, sigIndx[iIn]],
TaEstCoh[iOut, iIn, sigIndx[iIn]],
#%% Bode Plot
# Linear Model Gain and Phase
gainTLinNom_mag, phaseTLinNom_deg = FreqTrans.GainPhase(TLinNom, magUnit = 'mag', unwrap = True)
gainTLinUnc_mag = FreqTrans.Gain(TLinUnc, magUnit = 'mag')
# Estimated Gain and Phase
gainTEstNom_mag, phaseTEstNom_deg = FreqTrans.GainPhase(TEstNom, magUnit = 'mag', unwrap = True)
gainTEstUnc_mag = FreqTrans.Gain(TEstUnc, magUnit = 'mag')
np.mean(gainTEstUnc_mag, axis = -1) / np.mean(gainTLinUnc_mag, axis = -1) # should be 1 if scaled correctly
if False:
for iPlot, [iOut, iIn] in enumerate(ioArray):
fig = None
fig = FreqTrans.PlotBode(freqLin_hz, gainTLinNom_mag[iOut, iIn], phaseTLinNom_deg[iOut, iIn], coher_nd = cohTLin_mag[iOut, iIn], gainUnc_mag = None, fig = fig, dB = True, color='k', label='Linear Nominal' + ' [$u_' + str(iIn+1) + '$ to ' + '$z_' + str(iOut+1) + '$]')
fig = FreqTrans.PlotBode(freqLin_hz, gainTLinUnc_mag[iOut, iIn], None, coher_nd = None, gainUnc_mag = None, fig = fig, dB = True, color='k', linestyle='--', label='Linear Uncertainty' + ' [$u_' + str(iIn+1) + '$ to ' + '$z_' + str(iOut+1) + '$]')
fig = FreqTrans.PlotBode(freq_hz[iIn], gainTEstNom_mag[iOut, iIn], phaseTEstNom_deg[iOut, iIn], coher_nd = cohTEst_mag[iOut, iIn], gainUnc_mag = None, fig = fig, dB = True, color='b', linestyle='None', marker='.', label='Estimate Nominal' + ' [$u_' + str(iIn+1) + '$ to ' + '$z_' + str(iOut+1) + '$]')
fig = FreqTrans.PlotBode(freq_hz[iIn], gainTEstUnc_mag[iOut, iIn], None, coher_nd = None, gainUnc_mag = None, fig = fig, dB = True, color='r', linestyle='None', marker='.', label='Estimate Uncertainty' + ' [$u_' + str(iIn+1) + '$ to ' + '$z_' + str(iOut+1) + '$]')
# fig = FreqTrans.PlotBode(freq_hz[iIn, sigIndx[iIn]], gainTEstNom_mag[iOut, iIn, sigIndx[iIn]], phaseTEstNom_deg[iOut, iIn, sigIndx[iIn]], coher_nd = cohTEst_mag[iOut, iIn, sigIndx[iIn]], gainUnc_mag = gainTEstUnc_mag[iOut, iIn, sigIndx[iIn]], fig = fig, dB = True, color='b', linestyle='None', marker='.', label='Estimate [SIMO]')
# fig = FreqTrans.PlotBode(freq_hz[iIn, sigIndx[iIn]], gainTEstUnc_mag[iOut, iIn, sigIndx[iIn]], None, coher_nd = None, gainUnc_mag = None, fig = fig, dB = True, color='b', linestyle='None', marker='.', label='Estimate [SIMO]')
ax = fig.get_axes()
# handles, labels = ax[0].get_legend_handles_labels()
# handles = [(handles[0], handles[2]), (handles[1], handles[3])]
# labels = [labels[0], labels[1]]
# ax[0].legend(handles, labels)
# ax[0].set_xlim([1,10])
# Linear Model Gain and Phase
gainToLinNom_mag, phaseToLinNom_deg = FreqTrans.GainPhase(ToLinNom, magUnit = 'mag', phaseUnit = 'deg', unwrap = True)
gainToLinUnc_mag = FreqTrans.Gain(ToLinUnc, magUnit = 'mag')
cohToLin_mag = np.ones_like(ToLinUnc)
# Estimated
gainToEstNom_mag, phaseToEstNom_deg = FreqTrans.GainPhase(ToEstNom, magUnit = 'mag', phaseUnit = 'deg', unwrap = True)
gainToEstUnc_mag = FreqTrans.Gain(ToEstUnc, magUnit = 'mag') # Estimation Uncertain Response
cohToEst_mag = ToEstCoh # Estimation Coherence
cohToEstMin = np.min(cohToEst_mag, axis = (0, 1))
if False:
for iPlot, [iOut, iIn] in enumerate(ioArray):
fig = 20 + iPlot
fig = FreqTrans.PlotBode(freqLin_hz, gainToLinNom_mag[iOut, iIn], phaseToLinNom_deg[iOut, iIn], coher_nd = cohToLin_mag[iOut, iIn], gainUnc_mag = gainToLinUnc_mag[iOut, iIn], fig = fig, dB = True, color='k', label='Linear Model')
fig = FreqTrans.PlotBode(freq_hz[iIn], gainToEstNom_mag[iOut, iIn], phaseToEstNom_deg[iOut, iIn], coher_nd = cohToEst_mag[iOut, iIn], gainUnc_mag = gainToEstUnc_mag[iOut, iIn], fig = fig, dB = True, color='b', label='Estimate [MIMO]')
fig = FreqTrans.PlotBode(freq_hz[iIn, sigIndx[iIn]], gainToEstNom_mag[iOut, iIn, sigIndx[iIn]], phaseToEstNom_deg[iOut, iIn, sigIndx[iIn]], coher_nd = cohToEst_mag[iOut, iIn, sigIndx[iIn]], gainUnc_mag = gainToEstUnc_mag[iOut, iIn, sigIndx[iIn]], fig = fig, dB = True, color='g', label='Estimate [SIMO]')
ax = fig.get_axes()
handles, labels = ax[0].get_legend_handles_labels()
handles = [handles[0], handles[3], handles[1], handles[4], handles[2], handles[5]]
labels = [labels[0], labels[3], labels[1], labels[4], labels[2], labels[5]]
ax[0].legend(handles, labels)
fig.suptitle('$T_o$ : ' + '$r_{Exc}$[' + str(iIn) + '] to ' + '$z$[' + str(iOut) + ']')
#%% Sigma - Output Stability
I2 = np.repeat([np.eye(2)], LoLinNom.shape[-1], axis=0).T
fig=fig,
fmt='*',
label=oDataSegs[iSeg]['Desc'])
fig.suptitle(inName + ' to ' + outName, size=20)
ax = fig.get_axes()
ax[0].set_xlim(-3, 1)
ax[0].set_ylim(-2, 2)
#%% Bode Plots
if True:
for iIn, inName in enumerate(inPlot):
for iOut, outName in enumerate(outPlot):
fig = None
for iSeg in range(0, len(oDataSegs)):
fig = FreqTrans.PlotBode(freq_hz[iIn],
gain_mag[iSeg][iOut, iIn],
phase_deg[iSeg][iOut, iIn],
C[iSeg][iOut, iIn],
gainUnc_mag=gainUnc_mag[iSeg][iOut,
iIn],
dB=False,
fig=fig,
fmt='*--',
label=oDataSegs[iSeg]['Desc'])
fig.suptitle(inName + ' to ' + outName, size=20)
plt.ylabel('Output')
plt.legend()
plt.grid(True)
#%% Bode Progression
if False:
gainLin_mag, phaseLin_deg = FreqTrans.GainPhase(TLinFinal, magUnit = 'mag', unwrap = False)
gainRefine_mag, phaseRefine_deg = FreqTrans.GainPhase(TuzRefine, magUnit = 'mag', unwrap = False)
gainFinal_mag, phaseFinal_deg = FreqTrans.GainPhase(TuzFinal, magUnit = 'mag', unwrap = False)
gainFinalMean_mag = np.mean(gainFinal_mag, axis=0)
phaseFinalMean_deg = np.mean(phaseFinal_deg, axis=0)
phaseFinalMean_deg = np.mean(phaseFinal_deg, axis=0)
CuzFinalMean = np.mean(CuzFinal, axis=0)
fig = None
fig = FreqTrans.PlotBode(freqExcFinal_rps * rps2hz, gainLin_mag.squeeze(), phaseLin_deg.squeeze(), coher_nd = np.ones_like(gainLin_mag).squeeze(), gainUnc_mag = None, fig = fig, dB = True, linestyle = '-', color='k', label='Linear Model')
fig = FreqTrans.PlotBode(freqExc_rps * rps2hz, gainRefine_mag[0], phaseRefine_deg[0], coher_nd = CuzRefine[0], gainUnc_mag = None, fig = fig, dB = True, linestyle = ':', color='b', label='First iteration')
fig = FreqTrans.PlotBode(freqExc_rps * rps2hz, gainRefine_mag[-1], phaseRefine_deg[-1], coher_nd = CuzRefine[-1], gainUnc_mag = None, fig = fig, dB = True, linestyle = '-', color='b', label='Last Refinement iteration')
for iFinal, t in enumerate(timeFinal_s):
fig = FreqTrans.PlotBode(freqExcFinal_rps * rps2hz, gainFinal_mag[iFinal], phaseFinal_deg[iFinal], coher_nd = CuzFinal[iFinal], gainUnc_mag = None, fig = fig, dB = True, linestyle = ':', color='r', label='Final Estimates')
fig = FreqTrans.PlotBode(freqExcFinal_rps * rps2hz, gainFinalMean_mag, phaseFinalMean_deg, coher_nd = CuzFinalMean, gainUnc_mag = None, fig = fig, dB = True, linestyle = '-', color='r', label='Final Merge')
ax = fig.get_axes()
ax[-1].set_ylim(0, 1)
fig = FreqTrans.PlotNyquist(np.asarray([-1 + 0j]),
TUnc=np.asarray([0.4 + 0.4j]),
fig=fig,
fmt='*r',
label='Critical Region')
fig.suptitle(inName + ' to ' + outName, size=20)
ax = fig.get_axes()
ax[0].set_xlim(-3, 1)
ax[0].set_ylim(-2, 2)
#%% Bode Plots
if False:
#%%
for iOut, outName in enumerate(outPlot):
for iIn, inName in enumerate(inPlot):
fig = None
for iSeg in range(0, len(oDataSegs)):
# fig = FreqTrans.PlotBode(freq_hz[0], gain_mag[iSeg][iOut, iIn], phase_deg[iSeg][iOut, iIn], C[iSeg][iOut, iIn], gainUnc_mag = gainUnc_mag[iSeg][iOut, iIn], fig = fig, fmt = rtsmSegList[iSeg]['fmt'] + '*-', label = oDataSegs[iSeg]['Desc'])
fig = FreqTrans.PlotBode(freq_hz[0],
gain_mag[iSeg][iOut, iIn],
phase_deg[iSeg][iOut, iIn],
C[iSeg][iOut, iIn],
fig=fig,
fmt=rtsmSegList[iSeg]['fmt'] + '*-',
label=oDataSegs[iSeg]['Desc'])
fig.suptitle(inName + ' to ' + outName, size=20)
label='Critical Region')
fig.suptitle(inName + ' to ' + outName, size=20)
ax = fig.get_axes()
ax[0].set_xlim(-3, 1)
ax[0].set_ylim(-2, 2)
#%% Bode Plots
if True:
for iOut, outName in enumerate(outPlot):
for iIn, inName in enumerate(inPlot):
fig = 80 + 3 * iOut + iIn
fig = FreqTrans.PlotBode(freqLin_hz,
gainLin_mag[iOut, iIn],
phaseLin_deg[iOut, iIn],
coher_nd=np.ones_like(freqLin_hz),
fig=fig,
fmt='k',
label='Linear')
fig = FreqTrans.PlotBode(freq_hz[0, sigIndx[iIn]],
gain_mag[iOut, iIn, sigIndx[iIn]],
phase_deg[iOut, iIn, sigIndx[iIn]],
C[iOut, iIn, sigIndx[iIn]],
fig=fig,
fmt='bo',
label='Estimated SIMO')
fig = FreqTrans.PlotBode(freq_hz[0],
gain_mag[iOut, iIn],
phase_deg[iOut, iIn],
C[iOut, iIn],
fig=fig,
magUnit='mag',
phaseUnit='deg',
unwrap=True)
gainTiEstUnc_mag = FreqTrans.Gain(
TiEstUnc, magUnit='mag') # Estimation Uncertain Response
cohTiEst_mag = TiEstCoh # Estimation Coherence
cohTiEstMin = np.min(cohTiEst_mag, axis=(0, 1))
if True:
for iPlot, [iOut, iIn] in enumerate(ioArray):
fig = 20 + iPlot
fig = FreqTrans.PlotBode(freqLin_hz,
gainTiLinNom_mag[iOut, iIn],
phaseTiLinNom_deg[iOut, iIn],
coher_nd=cohTiLin_mag[iOut, iIn],
gainUnc_mag=None,
fig=fig,
dB=True,
color='k',
label='Linear Model')
fig = FreqTrans.PlotBode(freqLin_hz,
gainTiLinUnc_mag[iOut, iIn],
None,
coher_nd=None,
gainUnc_mag=None,
fig=fig,
dB=True,
color='k',
linestyle='--',
label='Linear Model')
if False:
for iPlot, [iOut, iIn] in enumerate(ioArray):
fig = 20 + iPlot
for iSeg in range(0, len(oDataSegs)):
gain_mag = gainLaEstNomList_mag[iSeg][iOut, iIn]
phase_deg = phaseLaEstNomList_deg[iSeg][iOut, iIn]
coher_nd = LaEstCohList[iSeg][iOut, iIn]
gainUnc_mag = gainLaEstUncList_mag[iSeg][iOut, iIn]
fig = FreqTrans.PlotBode(freq_hz[iIn],
gain_mag,
phase_deg,
coher_nd,
gainUnc_mag,
fig=fig,
dB=True,
color=rtsmSegList[iSeg]['color'],
label=oDataSegs[iSeg]['Desc'])
# fig.suptitle(inName + ' to ' + outName, size=20)
#%% Turbulence
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())
fig = FreqTrans.PlotNyquist(np.asarray([-1 + 0j]),
TUnc=np.asarray([0.4 + 0.4j]),
fig=fig,
fmt='*r',
label='Critical Region')
fig.suptitle(inName + ' to ' + outName, size=20)
ax = fig.get_axes()
ax[0].set_xlim(-3, 1)
ax[0].set_ylim(-2, 2)
#%% Bode Plots
if False:
for iOut, outName in enumerate(outPlot):
for iIn, inName in enumerate(inPlot):
fig = None
for iSeg in range(0, len(oDataSegs)):
fig = FreqTrans.PlotBode(freq_hz[0],
gainLiEstNomList_mag[iSeg][iOut, iIn],
phaseLiEstNomList_deg[iSeg][iOut,
iIn],
LiEstCohList[iSeg][iOut, iIn],
fig=fig,
color=rtsmSegList[iSeg]['color'],
linestyle='-',
label=oDataSegs[iSeg]['Desc'])
fig.suptitle(inName + ' to ' + outName, size=20)