rCritLaEstNom_mag, rCritLaEstUnc_mag, rCritLaEstLower_mag = FreqTrans.DistCrit(
LaEstNom, LaEstUnc, typeUnc='circle')
if False:
for iPlot, io in enumerate(ioArray):
iOut, iIn = io
outName = outPlot[iOut]
inName = inPlot[iIn]
fig = 40 + iPlot
# fig = FreqTrans.PlotVectorMargin(freqLin_hz, rCritLaLinNom_mag[iOut, iIn], coher_nd = np.ones_like(freqLin_hz), fig = fig, color = 'k', label = 'Linear')
fig = FreqTrans.PlotVectorMargin(freqLin_hz,
rCritLaLinNom_mag[iOut, iIn],
vmUnc_mag=rCritLaLinUnc_mag[iOut,
iIn],
coher_nd=np.ones_like(freqLin_hz),
fig=fig,
color='k',
label='Linear')
fig = FreqTrans.PlotVectorMargin(freq_hz[0],
rCritLaEstNom_mag[iOut, iIn],
vmUnc_mag=rCritLaEstUnc_mag[iOut,
iIn],
coher_nd=LaEstCoh[iOut, iIn],
color='b',
fig=fig,
label='Excitation (MIMO)')
fig = FreqTrans.PlotVectorMargin(
freq_hz[0, sigIndx[iIn]],
rCritLaEstNom_mag[iOut, iIn, sigIndx[iIn]],
vmUnc_mag=rCritLaEstUnc_mag[iOut, iIn, sigIndx[iIn]],
ax[0].legend(handles, labels)
fig.suptitle('$L_o$ : ' + '$r_{Exc}$' + ' to ' + '$z$')
#%% Vector Margin Plots - Output Stability
vmLoLinNom_mag, vmLoLinUnc_mag, vmLoLinMin_mag = FreqTrans.VectorMargin(LoLinNom, LoLinUnc, typeUnc = 'circle')
vmLoEstNom_mag, vmLoEstUnc_mag, vmLoEstMin_mag = FreqTrans.VectorMargin(LoEstNom, LoEstUnc, typeUnc = 'circle')
numOut, numIn = LoLinNom.shape[0:-1]
ioArray = np.array(np.meshgrid(np.arange(numOut), np.arange(numIn))).T.reshape(-1, 2)
if True:
for iPlot, [iOut, iIn] in enumerate(ioArray):
fig = 30 + iPlot
fig = FreqTrans.PlotVectorMargin(freqLin_hz, vmLoLinNom_mag[iOut, iIn], cohLoLin_mag[iOut, iIn], vmLoLinUnc_mag[iOut, iIn], fig = fig, linestyle='-', color='k', label='Linear Model')
fig = FreqTrans.PlotVectorMargin(freq_hz[iIn], vmLoEstNom_mag[iOut, iIn], cohLoEst_mag[iOut, iIn], vmLoEstUnc_mag[iOut, iIn], fig = fig, linestyle='-', marker='.', color='b', label='Estimate [MIMO]')
fig = FreqTrans.PlotVectorMargin(freq_hz[iIn, sigIndx[iIn]], vmLoEstNom_mag[iOut, iIn, sigIndx[iIn]], cohLoEst_mag[iOut, iIn, sigIndx[iIn]], vmLoEstUnc_mag[iOut, iIn, sigIndx[iIn]], fig = fig, linestyle='None', marker='.', 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('$L_o$ : ' + '$r_{Exc}$[' + str(iIn) + '] to ' + '$z$[' + str(iOut) + ']')
#%% Nyquist Plot - Output Stability
LoLinUncMag = np.abs(LoLinUnc)
LoEstUncMag = np.abs(LoEstUnc)
rCritLiEstCrit_mag, _, _ = FreqTrans.VectorMargin(LiEstCrit, typeUnc='circle')
if True:
numOut, numIn = LiLinNom.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 = 30 + iPlot
fig = FreqTrans.PlotVectorMargin(freqLin_hz,
rCritLiLinNom_mag[iOut, iIn],
cohLiLin_mag[iOut, iIn],
rCritLiLinUnc_mag[iOut, iIn],
fig=fig,
linestyle='-',
color='k',
label='Linear Model')
fig = FreqTrans.PlotVectorMargin(freq_hz[iIn],
rCritLiEstNom_mag[iOut, iIn],
cohLiEst_mag[iOut, iIn],
rCritLiEstUnc_mag[iOut, iIn],
fig=fig,
linestyle='-',
marker='.',
color='b',
label='Estimate [MIMO]')
fig = FreqTrans.PlotVectorMargin(freq_hz[iIn, sigIndx[iIn]],
rCritLiEstNom_mag[iOut, iIn,
sigIndx[iIn]],
vmTLinNom_mag, vmTLinUnc_mag, vmTLinMin_mag = FreqTrans.VectorMargin(TLinNom, TLinUnc, typeUnc = 'circle')
vmTEstNom_mag, vmTEstUnc_mag, vmTEstMin_mag = FreqTrans.VectorMargin(TEstNom, TEstUnc, typeUnc = 'circle')
# Sampled Systems
vmTEstSamp_mag = np.zeros((numOut, numIn, nFreq, nSamp), dtype='float')
for iSamp in range(nSamp):
vmTEstSamp_mag[..., iSamp], _, _ = FreqTrans.VectorMargin(TEstSamp[..., iSamp], typeUnc = 'circle')
numOut, numIn = TLinNom.shape[0:-1]
ioArray = np.array(np.meshgrid(np.arange(numOut), np.arange(numIn))).T.reshape(-1, 2)
if True:
for iPlot, [iOut, iIn] in enumerate(ioArray):
fig = None
fig = FreqTrans.PlotVectorMargin(freqLin_hz, vmTLinNom_mag[iOut, iIn], cohTLin_mag[iOut, iIn], vmTLinUnc_mag[iOut, iIn], fig = fig, linestyle='-', color='k', label='Linear Model')
fig = FreqTrans.PlotVectorMargin(freq_hz[iIn], vmTEstNom_mag[iOut, iIn], cohTEst_mag[iOut, iIn], vmTEstUnc_mag[iOut, iIn], fig = fig, linestyle='-', marker='.', color='r', label='Estimate [MIMO]')
fig = FreqTrans.PlotVectorMargin(freq_hz[iIn, sigIndx[iIn]], vmTEstNom_mag[iOut, iIn, sigIndx[iIn]], cohTEst_mag[iOut, iIn, sigIndx[iIn]], vmTEstUnc_mag[iOut, iIn, sigIndx[iIn]], fig = fig, linestyle='-', marker='.', color='b', label='Estimate [SIMO]')
for iSamp in range(nSamp):
fig = FreqTrans.PlotVectorMargin(freq_hz[iIn], vmTEstSamp_mag[iOut, iIn, :, iSamp], marker='.', color = 'gray', linestyle='None', fig = fig, label = 'Sampled (MIMO)')
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('$u_' + str(iIn) + '$ to ' + '$z_' + str(iOut) + '$')
fig.set_tight_layout(True)
numIn = len(inPlot)
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
for iSeg in range(0, len(oDataSegs)):
vm_mag = vmLaEstNomList_mag[iSeg][iOut, iIn]
vmUnc_mag = vmLaEstUncList_mag[iSeg][iOut, iIn]
fig = FreqTrans.PlotVectorMargin(freq_hz[iIn],
vm_mag,
vmUnc_mag=vmUnc_mag,
coher_nd=LaEstCohList[iSeg][iOut,
iIn],
fig=fig,
color=rtsmSegList[iSeg]['color'],
label=oDataSegs[iSeg]['Desc'])
fig = FreqTrans.PlotVectorMargin(freq_hz[iIn],
0.4 * np.ones_like(freq_hz[iIn]),
fig=fig,
color='r',
linestyle='--',
label='Critical')
fig.suptitle(inPlot[iIn] + ' to ' + outPlot[iOut])
ax = fig.get_axes()
ax[0].set_ylim(0, 2)
