freqRate=freqRate_rps,
winType=('tukey', 0.2),
smooth=('box', 3),
detrendType='Linear')
for iSeg in range(0, len(oDataSegs)):
t = oDataSegs[iSeg]['time_s']
y = outList[iSeg][iSgnl]
# Number of time segments and length of overlap, units of samples
#lenSeg = 2**6 - 1
lenSeg = int(1 * optSpec.freqRate * rps2hz)
lenOverlap = 5
# Compute Spectrum over time
tSpec_s, freqSpec_rps, P_mag = FreqTrans.SpectTime(
t, y, lenSeg, lenOverlap, optSpec)
# Plot the Spectrogram
fig = FreqTrans.Spectogram(tSpec_s, freqSpec_rps * rps2hz,
20 * np.log10(P_mag))
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):
smooth=('box', 3),
winType=('tukey', 0.0),
detrendType='Linear')
for iSeg in range(0, len(oDataSegs)):
t = oDataSegs[iSeg]['time_s']
x = vExcList[iSeg][iSgnlExc]
y = vFbList[iSeg][iSgnlOut]
# Number of time segments and length of overlap, units of samples
#lenSeg = 2**6 - 1
lenSeg = int(1.0 * optSpec.freqRate * rps2hz)
lenOverlap = 1
# Compute Spectrum over time
tSpecY_s, freqSpecY_rps, P_Y_mag = FreqTrans.SpectTime(
t, y, lenSeg, lenOverlap, optSpec)
tSpecN_s, freqSpecN_rps, P_N_mag = FreqTrans.SpectTime(
t, y, lenSeg, lenOverlap, optSpecN)
# Plot the Spectrogram
fig = FreqTrans.Spectogram(tSpecY_s, freqSpecY_rps * rps2hz,
20 * np.log10(P_Y_mag))
fig.suptitle(oDataSegs[iSeg]['Desc'] + ': Spectrogram - ' +
sigFbList[iSgnlOut])
fig = FreqTrans.Spectogram(tSpecN_s, freqSpecN_rps * rps2hz,
20 * np.log10(P_N_mag))
fig.suptitle(oDataSegs[iSeg]['Desc'] + ': Spectrogram Null - ' +
sigFbList[iSgnlOut])
#%% Sigma Plot