T.append(T_seg)
TUnc.append(np.abs(TUnc_seg))
C.append(Cey)
T_InputNames = sigInList
T_OutputNames = sigOutList
# Compute Gain, Phase, Crit Distance
gain_mag = []
gainUnc_mag = []
phase_deg = []
for iSeg in range(0, len(oDataSegs)):
gain_mag.append(FreqTrans.Gain(T[iSeg], magUnit='mag'))
gainUnc_mag.append(FreqTrans.Gain(TUnc[iSeg], magUnit='mag'))
phase_deg.append(FreqTrans.Phase(T[iSeg], phaseUnit='deg', unwrap=False))
#%% Spectrograms
if False:
iSgnl = 0
freqRate_rps = 50 * hz2rps
# freqSpec = np.asarray(freqExc_rps).flatten()
freqSpec = freqExc_rps[iSgnl]
optSpec = FreqTrans.OptSpect(dftType='czt',
freq=freqSpec,
freqRate=freqRate_rps,
winType=('tukey', 0.2),
smooth=('box', 3),
#%% ampPeakFactor = ampList / sigPeakFactor gainDF_mag, phaseDF_deg = FreqTrans.GainPhase(nDF, magUnit = 'mag', unwrap = True) TxyArrayMax = np.nanmax(np.abs(TxyArray).squeeze(), axis=0) TxyArrayNorm = TxyArray.squeeze() / TxyArrayMax gainTxyMean_mag = FreqTrans.Gain(TxyArrayNorm, magUnit = 'mag') gainTxy_mag = FreqTrans.Gain(TxyArray.T.squeeze(), magUnit = 'mag').T gainTxyMax_mag = np.nanmax(gainTxy_mag, axis=0) gainTxyNorm_mag = (gainTxy_mag / gainTxyMax_mag) gainTxyMean_mag = np.nanmean(gainTxyNorm_mag, axis=-1) phaseTxy_deg = FreqTrans.Phase(TxyArray.T.squeeze(), phaseUnit = 'deg', unwrap = True).T phaseTxy_deg[:, phaseTxy_deg[0] > 90] -= 360 phaseTxyMean_deg = np.nanmean(phaseTxy_deg, axis=-1) CxyArrayMin = np.nanmin(np.abs(CxyArray.T.squeeze()), axis=0) CxyArrayMax = np.nanmax(np.abs(CxyArray.T.squeeze()), axis=0) CxyArrayMean = np.nanmean(CxyArray.T.squeeze(), axis=0) fig = None fig = FreqTrans.PlotGainType(ampPeakFactor, gainTxyMean_mag, phaseTxyMean_deg, coher_nd = CxyArrayMean, gainUnc_mag = None, fig = fig, dB = False, color='r', label='Mean Estimate') fig = FreqTrans.PlotGainType(cmd, gainDF_mag, phaseDF_deg, coher_nd = None, gainUnc_mag = None, fig = fig, dB = False, color='k', label='Describing Function') # fig = FreqTrans.PlotGainType(ampPeakFactor, gainTxyNorm_mag, phaseTxy_deg, coher_nd = CxyArray, gainUnc_mag = None, fig = fig, dB = False, color='b', marker = '.', alpha = 0.5, linestyle = '', label='Estimates') ax = fig.get_axes() ax[0].fill_between(ampPeakFactor, np.min(gainTxyNorm_mag, axis = -1), np.max(gainTxyNorm_mag, axis = -1), color='b', alpha = 0.25, label='Estimate Range')
