ToEstSNR = np.abs(ToEstNom / ToEstUnc)**2
SoEstSNR = np.abs(SoEstNom / SoEstUnc)**2
LoEstSNR = np.abs(LoEstNom / LoEstUnc)**2
#%% Nyquist Plot - Output Complimentary Sensitivity Function
ToLinUncMag = np.abs(ToLinUnc)
ToEstUncMag = np.abs(ToEstUnc)
numOut, numIn = ToLinNom.shape[0:-1]
ioArray = np.array(np.meshgrid(np.arange(numOut), np.arange(numIn))).T.reshape(-1, 2)
if False:
for iPlot, [iOut, iIn] in enumerate(ioArray):
fig = 10 + iPlot
fig = FreqTrans.PlotNyquist(ToLinNom[iOut, iIn], ToLinUncMag[iOut, iIn], fig = fig, fillType = 'fill', color = 'k', label = 'Linear')
fig = FreqTrans.PlotNyquist(ToEstNom[iOut, iIn], ToEstUncMag[iOut, iIn], fig = fig, fillType = 'circle', marker='.', color = 'b', linestyle='None', label = 'Estimate (MIMO)')
fig = FreqTrans.PlotNyquist(ToEstNom[iOut, iIn, sigIndx[iIn]], ToEstUncMag[iOut, iIn, sigIndx[iIn]], fig = fig, fillType = 'circle', marker='.', color = 'g', linestyle='None', label = 'Estimate (SIMO)')
fig = FreqTrans.PlotNyquist(np.array([-1+0j]), np.array([0.4]), fig = fig, fillType = 'circle', marker='+', color = 'r', linestyle='None')
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) + ']')
#%% Bode Plot - Output Complimentary Sensitivity Function
# Linear Model Gain and Phase
#%% Nyquist Plots - Ta
TaLinUncMag = abs(TaLinUnc)
TaEstUncMag = abs(TaEstUnc)
if False:
for iPlot, io in enumerate(ioArray):
iOut, iIn = io
outName = outPlot[iOut]
inName = inPlot[iIn]
fig = 60 + iPlot
fig = FreqTrans.PlotNyquist(TaLinNom[iOut, iIn],
TaLinUncMag[iOut, iIn],
fig=fig,
fillType='fill',
color='k',
label='Linear')
fig = FreqTrans.PlotNyquist(TaEstNom[iOut, iIn],
TaEstUncMag[iOut, iIn],
fig=fig,
color='b',
label='Estimate (MIMO)')
fig = FreqTrans.PlotNyquist(TaEstNom[iOut, iIn, sigIndx[iIn]],
TaEstUncMag[iOut, iIn, sigIndx[iIn]],
fig=fig,
color='g',
label='Estimate (SIMO)')
fig = FreqTrans.PlotNyquist(np.asarray([-1 + 0j]),
TUnc=np.asarray([0.4 + 0.4j]),
fig.suptitle(oDataSegs[iSeg]['Desc'] + ': Spectrogram - ' +
sigOutList[iSgnl])
#%% Nyquist Plots
inPlot = sigInList # Elements of sigInList
outPlot = sigOutList # Elements of sigOutList
if False:
for iIn, inName in enumerate(inPlot):
for iOut, outName in enumerate(outPlot):
fig = None
for iSeg in range(0, len(oDataSegs)):
fig = FreqTrans.PlotNyquist(T[iSeg][iOut, iIn],
TUnc[iSeg][iOut, iIn],
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):
if False:
FreqTrans.PrintPrettyFig(fig, 'OpenMimoBode' + str(iOut+1) + str(iIn+1) + '.pgf')
#%% Nyquist Plot
TLinUnc = np.abs(TLinUnc)
if True:
numOut, numIn = TLinNom.shape[0:-1]
ioArray = np.array(np.meshgrid(np.arange(numOut), np.arange(numIn))).T.reshape(-1, 2)
for io in ioArray:
[iOut, iIn] = io
fig = None
fig = FreqTrans.PlotNyquist(TLinNom[iOut, iIn], TLinUnc[iOut, iIn], fig = fig, fillType = 'fill', color = 'k', label = 'Linear' + ' [$u_' + str(iIn+1) + '$ to ' + '$z_' + str(iOut+1) + '$]')
fig = FreqTrans.PlotNyquist(TEstNom[iOut, iIn], TEstUnc[iOut, iIn], fig = fig, fillType = 'circle', marker='.', color = 'r', linestyle='None', label = 'Estimate' + ' [$u_' + str(iIn+1) + '$ to ' + '$z_' + str(iOut+1) + '$]')
# fig = FreqTrans.PlotNyquist(TEstNom[iOut, iIn, sigIndx[iIn]], TEstUnc[iOut, iIn, sigIndx[iIn]], fig = fig, fillType = 'circle', marker='.', color = 'b', linestyle='None', label = 'Estimate (SIMO)')
fig = FreqTrans.PlotNyquist(np.array([-1+0j]), fig = fig, fillType = 'circle', marker='+', color = 'r', linestyle='None')
# fig = FreqTrans.PlotNyquist(np.array([-1+0j]), np.array([0.4]), fig = fig, fillType = 'circle', marker='+', color = 'r', linestyle='None')
for iSamp in range(nSamp):
fig = FreqTrans.PlotNyquist(TEstSamp[iOut, iIn, :, iSamp], fig = fig, fillType = 'circle', marker='.', color = 'gray', linestyle='None')
ax = fig.get_axes()
handles, labels = ax[0].get_legend_handles_labels()
handles = [(handles[0], handles[2]), handles[1]]
labels = [labels[0], labels[1]]
ax[0].legend(handles, labels)
# fig.set_size_inches([6.4,4.8])
TiLinUncMag = np.abs(TiLinUnc)
TiEstUncMag = np.abs(TiEstUnc)
if False:
numOut, numIn = TiLinNom.shape[0:-1]
ioArray = np.array(np.meshgrid(np.arange(numOut),
np.arange(numIn))).T.reshape(-1, 2)
for iPlot, io in enumerate(ioArray):
[iOut, iIn] = io
fig = 10 + iPlot
fig = FreqTrans.PlotNyquist(TiLinNom[iOut, iIn],
TiLinUncMag[iOut, iIn],
fig=fig,
fillType='fill',
color='k',
label='Linear')
fig = FreqTrans.PlotNyquist(TiEstNom[iOut, iIn],
TiEstUncMag[iOut, iIn],
fig=fig,
fillType='circle',
marker='.',
color='b',
linestyle='None',
label='Estimate (MIMO)')
fig = FreqTrans.PlotNyquist(TiEstNom[iOut, iIn, sigIndx[iIn]],
TiEstUncMag[iOut, iIn, sigIndx[iIn]],
fig=fig,
fillType='circle',
marker='.',
fig.suptitle(inName + ' to ' + outName, size=20)
ax = fig.get_axes()
ax[0].set_ylim(0, 2)
#%% Nyquist 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.PlotNyquist(T[iSeg][iOut, iIn],
TUnc[iSeg][iOut, iIn],
fig=fig,
fmt=rtsmSegList[iSeg]['fmt'] + '*',
label=oDataSegs[iSeg]['Desc'])
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
fmt='--r',
fig=fig,
label='Critical Limit')
ax = fig.get_axes()
ax[0].set_xlim(0, 10)
ax[0].set_ylim(0, 2)
fig.suptitle(inName + ' to ' + outName, size=20)
#%% Nyquist Plots
if False:
for iOut, outName in enumerate(outPlot):
for iIn, inName in enumerate(inPlot):
fig = 60 + 3 * iOut + iIn
fig = FreqTrans.PlotNyquist(TxyLin[iOut, iIn],
fig=fig,
fmt='k',
label='Linear')
fig = FreqTrans.PlotNyquist(T[iOut, iIn, sigIndx[iIn]],
fig=fig,
fmt='bo',
label='Excitation')
fig = FreqTrans.PlotNyquist(T[iOut, iIn],
fig=fig,
fmt='g.:',
label='MIMO')
fig = FreqTrans.PlotNyquist(np.asarray([-1 + 0j]),
TUnc=np.asarray([0.4 + 0.4j]),
fig=fig,
fmt='r+',
label='Critical Region')
label='Critical')
fig.suptitle(inPlot[iIn] + ' to ' + outPlot[iOut])
ax = fig.get_axes()
ax[0].set_ylim(0, 2)
#%% Nyquist Plots
if False:
for iPlot, [iOut, iIn] in enumerate(ioArray):
fig = 20 + iPlot
for iSeg in range(0, len(oDataSegs)):
Tnom = LaEstNomList[iSeg][iOut, iIn]
Tunc = np.abs(LaEstUncList[iSeg][iOut, iIn])
fig = FreqTrans.PlotNyquist(Tnom,
Tunc,
fig=fig,
color=rtsmSegList[iSeg]['color'],
marker='.',
label=oDataSegs[iSeg]['Desc'])
fig = FreqTrans.PlotNyquist(np.asarray([-1 + 0j]),
TUnc=np.asarray([0.4 + 0.4j]),
fig=fig,
color='r',
marker='+',
label='Critical Region')
fig.suptitle(inPlot[iIn] + ' to ' + outPlot[iOut])
ax = fig.get_axes()
ax[0].set_xlim(-3, 1)
ax[0].set_ylim(-2, 2)
color='r',
linestyle='--')
fig.suptitle(inName + ' to ' + outName, size=20)
ax = fig.get_axes()
# ax[0].set_ylim(0, 2)
#%% Nyquist 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.PlotNyquist(LiEstNomList[iSeg][iOut, iIn],
fig=fig,
color=rtsmSegList[iSeg]['color'],
label=oDataSegs[iSeg]['Desc'])
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:
