timeDur_s = 10.0
numCycles = 1
freqMinDes_rps = (numCycles/timeDur_s) * hz2rps * np.ones(numChan)
#freqMaxDes_rps = (freqRate_hz/2) * hz2rps * np.ones(numChan)
freqMaxDes_rps = 10 * hz2rps * np.ones(numChan)
freqStepDes_rps = (10 / freqRate_hz) * hz2rps
methodSW = 'zip' # "zippered" component distribution
## Generate MultiSine Frequencies
freqElem_rps, sigIndx, time_s = GenExcite.MultiSineComponents(freqMinDes_rps, freqMaxDes_rps, freqRate_hz, numCycles, freqStepDes_rps, methodSW)
timeDur_s = time_s[-1] - time_s[0]
## Generate Schroeder MultiSine Signal
ampElem_nd = np.ones_like(freqElem_rps) ## Approximate relative signal amplitude, create flat
sigList, phaseElem_rad, sigElem = GenExcite.MultiSine(freqElem_rps, ampElem_nd, sigIndx, time_s, costType = 'Norm2', phaseInit_rad = 0, boundPhase = 1, initZero = 1, normalize = 'peak');
## Results
peakFactor = GenExcite.PeakFactor(sigList)
peakFactorRel = peakFactor / np.sqrt(2)
print(peakFactorRel)
# Signal Power
sigPowerRel = (ampElem_nd / max(ampElem_nd))**2 / len(ampElem_nd)
if True:
fig, ax = plt.subplots(ncols=1, nrows=numChan, sharex=True)
for iChan in range(0, numChan):
ax[iChan].plot(time_s, sigList[iChan])
methodSW = 'zip' # "zippered" component distribution
# Generate MultiSine Frequencies
freqExc_rps, sigIndx, time_s = GenExcite.MultiSineComponents(
freqMinDes_rps, freqMaxDes_rps, freqRate_hz, numCycles, freqStepDes_rps,
methodSW)
freqNull_rps = freqExc_rps[0:-1] + 0.5 * np.diff(freqExc_rps)
# Generate Schroeder MultiSine Signal
ampExc_nd = np.linspace(ampInit, ampFinal, len(freqExc_rps)) / np.sqrt(
len(freqExc_rps))
vExc, _, sigExc = GenExcite.MultiSine(freqExc_rps,
ampExc_nd,
sigIndx,
time_s,
phaseInit_rad=0,
boundPhase=1,
initZero=1,
normalize='peak',
costType='Schroeder')
vExcNames = ['excP', 'excQ', 'excR']
# Excited Frequencies per input channel
freqChan_rps = freqExc_rps[sigIndx]
# Null Frequencies
freqGap_rps = freqExc_rps[0:-1] + 0.5 * np.diff(freqExc_rps)
# Reference Inputs
ref_names = ['refPhi', 'refTheta', 'refYaw']
sigma = 0.0 * ampInit * deg2rad
freqMaxDes_rps = 15 * hz2rps * np.ones(numChan) freqStepDes_rps = (20 / 50) * hz2rps tBinWidth = FreqTrans.FreqStep2TimeBin(freqStepDes_rps) timeDur_s = binSel * tBinWidth freqMinDes_rps = (1/timeDur_s) * hz2rps * np.ones(numChan) methodSW = 'zip' # "zippered" component distribution ## Generate MultiSine Frequencies freqExc_rps, sigIndx, time_s = GenExcite.MultiSineComponents(freqMinDes_rps, freqMaxDes_rps, freqRate_hz, numCycles, freqStepDes_rps, methodSW) freqGap_rps = freqExc_rps[0:-1] + 0.5 * np.diff(freqExc_rps) ## Generate Schroeder MultiSine Signal ampExc_nd = np.ones_like(freqExc_rps) ## Approximate relative signal amplitude, create flat sigList, phaseElem_rad, sigElem = GenExcite.MultiSine(freqExc_rps, ampExc_nd, sigIndx, time_s, costType = 'Schroeder', phaseInit_rad = 0, boundPhase = True, initZero = True, normalize = 'rms'); sigPeakFactor = GenExcite.PeakFactor(sigList) ampExc_nd *= 0.5 sigList *= 0.5 # Excited Amplitude per input channel ampChan_nd = ampExc_nd[sigIndx] # Excited Frequencies per input channel freqChan_rps = freqExc_rps[sigIndx] freqChan_hz = freqChan_rps * rps2hz #%% Setup Servo Model freqNat_hz = 6.0 freqNat_rps = freqNat_hz * hz2rps