B = FreqTrans.Bartlett(tBinB, N + 1)
Bmag = np.abs(B)
Bmax = np.nanmax(Bmag)
Bnorm_mag = Bmag / Bmax
tBinBapprox = np.linspace(0.0, time_s[-1] / tBinWidth, 80)
timeBapprox_s = tBinWidth * tBinBapprox
Bapprox_mag = 10**-(2 / 5) * (1 / tBinBapprox)**2
fig = 2
fig = FreqTrans.PlotGainTemporal(t_s,
PwwNListMean / PwwNListMean.max(),
None,
None,
None,
fig=fig,
dB=False,
linestyle='-',
color='b',
label='Null Estimate at Input')
fig = FreqTrans.PlotGainTemporal(timeD_s,
Dnorm_mag,
None,
None,
None,
fig=fig,
dB=False,
UncSide='Max',
linestyle='-',
color='r',
gainThLinNomMean = np.mean(np.abs(gainThLinNom_mag[:,iOut,iIn,:]), axis=-1)
gainThLinUncMean = np.mean(np.abs(gainThLinUnc_mag[:,iOut,iIn,:]), axis=-1)
gainThLinUncMin = np.mean(np.abs(gainThLinNom_mag[:,iOut,iIn,:]) - np.abs(gainThLinUnc_mag[:,iOut,iIn,:]), axis=-1) * ones
gainThEstNomMean = np.mean(np.abs(gainThEstNom_mag[:,iOut,iIn,:]), axis=-1)
gainThEstUncMean = np.mean(np.abs(gainThEstUnc_mag[:,iOut,iIn,:]), axis=-1)
gainThEstUncMin = np.mean(np.abs(gainThEstNom_mag[:,iOut,iIn,:]) - np.abs(gainThEstUnc_mag[:,iOut,iIn,:]), axis=-1)
cohEst = np.abs(CuzList[:,iOut,iIn,:])
cohEstMean = np.mean(cohEst, axis=-1)
cohEstStd = np.std(cohEst, axis=-1)
cohEstMin = np.min(cohEst, axis=-1)
fig = None
fig = FreqTrans.PlotGainTemporal(t_s, gainThLinNomMean, None, coher_nd = ones, gainUnc_mag = gainThLinUncMean, fig = fig, dB = False, linestyle='-', color='k', label = 'Linear' + ' [$u_' + str(iIn+1) + '$ to ' + '$z_' + str(iOut+1) + '$]')
# fig = FreqTrans.PlotGainTemporal(t_s, gainThLinUncMin, None, fig = fig, dB = False, linestyle=':', color='k', label = 'Linear - Lower')
fig = FreqTrans.PlotGainTemporal(t_s, gainThEstNomMean, None, coher_nd = cohEstMean, gainUnc_mag = gainThEstUncMean, fig = fig, dB = False, linestyle='-', color='b', label = 'Estimate' + ' [$u_' + str(iIn+1) + '$ to ' + '$z_' + str(iOut+1) + '$]')
# fig = FreqTrans.PlotGainTemporal(t_s, gainThEstUncMin, None, coher_nd = cohEstMin, fig = fig, dB = False, linestyle=':', color='r', label = 'Estimate - Lower')
ax = fig.get_axes()
ax[0].set_ylabel("Gain [mag]")
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)
# fig.set_size_inches([6.4,4.8])
if False:
# zSNRMin[zSNRMin < 0] = 0
cohEst = np.abs(TaEstCohHist[:, iOut, iIn, :])
cohEst[cohEst < 0] = 0
cohEst[cohEst > 1] = 1
cohEstMean = np.mean(cohEst, axis=-1)
cohEstMin = np.min(cohEst, axis=-1)
fig = None
ones = np.ones_like(time_s)
fig = FreqTrans.PlotGainTemporal(time_s,
zSNRMean,
None,
None,
zSNRMin,
fig=fig,
dB=True,
UncSide='Min',
linestyle='-',
color='r',
label='Estimate of Output')
fig = FreqTrans.PlotGainTemporal(time_s,
uSNRMean,
None,
None,
uSNRMin,
fig=fig,
dB=True,
UncSide='Min',
linestyle='-',
color='k',