def costFunction_FSR(x,grad):
radius = x[0]
couplerLength = x[1]
gap = 0.2
width = x[2]
thickness = x[3]
# Evaluate the functin
E, alpha, t, _ = SiP.racetrack_AP_RR_TF(wavelength,radius=radius,
couplerLength=couplerLength,gap=gap,width=width,
thickness=thickness)
# get the transmission spectrum
throughPort = np.abs(np.squeeze(E)) ** 2
# Pull the peaks from the simulation
peaksSim, _ = find_peaks(1-throughPort,height=peakThreshold)
# calculate the number of peaks
wavelengthSim = wavelength[peaksSim]
powerSim = throughPort[peaksSim]
if wavelengthSim.size > numPeaks:
wavelengthSim = wavelengthSim[0:numPeaks]
powerSim = powerSim[0:numPeaks]
elif wavelengthSim.size < numPeaks:
wavelengthSim = np.append(wavelengthSim,np.zeros((numPeaks-wavelengthSim.size,)))
powerSim = np.append(powerSim,np.zeros((numPeaks-powerSim.size,)))
# Estimate the error
error = np.sum(np.abs(wavelengthSim - wavelengthPeaks) ** 2)
print(error)
return error
def costFunctionFinal(x, grad):
radius = x[0]
couplerLength = x[1]
width = x[2]
thickness = x[3]
# widthCoupler = x[4]
gap = x[5]
# angle = x[6]
loss = x[7:]
E, alpha, t, _ = SiP.racetrack_AP_RR_TF(
wavelength,
radius=radius,
couplerLength=couplerLength,
gap=gap,
width=width,
thickness=thickness,
loss=loss,
)
throughPort = np.abs(np.squeeze(E))**2
error = np.mean(np.abs(throughPort - power)**2)
print(error)
return error
def costFunction_coupling(params, x, grad):
radius = params[0]
couplerLength = params[1]
gap = x[0]
width = params[2]
thickness = params[3]
# Evaluate the functin
E, alpha, t, _ = SiP.racetrack_AP_RR_TF(
wavelength,
radius=radius,
couplerLength=couplerLength,
gap=gap,
width=width,
thickness=thickness,
)
# throughPort = np.abs(np.squeeze(E)) ** 2
bp_sim = np.polyfit(wavelength, t, polyOrder)
bz_sim = np.poly1d(bp_sim)
error = np.mean(np.abs(bz_sim(w) - b)**2)
print(error)
return error
def costFunction_loss(params, gapA, x, grad):
radius = params[0]
couplerLength = params[1]
width = params[2]
thickness = params[3]
gap = params[4]
# Evaluate the functin
E, alpha, t, _ = SiP.racetrack_AP_RR_TF(
wavelength,
radius=radius,
couplerLength=couplerLength,
gap=gap,
width=width,
thickness=thickness,
loss=x,
)
# throughPort = np.abs(np.squeeze(E)) ** 2
ap_sim = np.polyfit(wavelength, alpha, polyOrder)
az_sim = np.poly1d(ap_sim)
error = np.mean(np.abs(az_sim(wavelength) - az(wavelength))**2)
print(error)
return error
def plotFinal(x):
radius = x[0]
couplerLength = x[1]
width = x[2]
thickness = x[3]
gap = x[4]
loss = x[5:]
E, alpha, t, alpha_s = SiP.racetrack_AP_RR_TF(wavelength,radius=radius,
couplerLength=couplerLength,gap=gap,width=width,
thickness=thickness,loss=loss)
# get the transmission spectrum
throughPort = np.abs(np.squeeze(E)) ** 2
plt.figure(figsize=(7,7))
plt.subplot(2,2,1)
plt.plot(wavelength,power)
plt.plot(wavelength,throughPort,'--')
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Power (a.u.)')
plt.grid(True)
plt.subplot(2,2,3)
plt.plot(wavelength,10*np.log10(power))
plt.plot(wavelength,10*np.log10(throughPort),'--')
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Power (dB)')
plt.grid(True)
plt.subplot(2,2,2)
plt.plot(wavelength,power,'o')
plt.plot(wavelength,throughPort)
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Power (a.u.)')
plt.grid(True)
plt.xlim(1.552,1.554)
plt.subplot(2,2,4)
plt.plot(wavelength,10*np.log10(power),'o')
plt.plot(wavelength,10*np.log10(throughPort))
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Power (dB)')
plt.grid(True)
plt.xlim(1.552,1.554)
plt.tight_layout()
plt.savefig('results.png')
plt.show()
# Save the data as a numpy archive
np.savez(filenamePrefix + '_data.npz',radius=radius,couplerLength=couplerLength,
gap=gap,width=width,thickness=thickness,loss=loss,E=E,alpha=alpha,t=t,alpha_r=alpha_s)
def costFunction_FSR(x, grad):
radius = x[0]
couplerLength = x[1]
width = x[2]
thickness = x[3]
widthCoupler = x[4]
gap = x[5]
angle = x[6]
E, alpha, t, _ = SiP.racetrack_AP_RR_TF(
wavelength,
radius=radius,
couplerLength=couplerLength,
gap=gap,
width=width,
widthCoupler=widthCoupler,
angle=angle,
thickness=thickness,
loss=ap,
)
throughPort = np.abs(np.squeeze(E))**2
error1 = np.mean(np.abs(throughPort - power)**2)
bp_sim = np.polyfit(wavelength, t, polyOrderGap)
bz_sim = np.poly1d(bp_sim)
error2 = np.mean(np.abs(bz_sim(w) - b)**2)
# Pull the peaks from the simulation
peaksSim, _ = find_peaks(1 - throughPort, height=peakThreshold)
# calculate the number of peaks
wavelengthSim = wavelength[peaksSim]
powerSim = throughPort[peaksSim]
if wavelengthSim.size > numPeaks:
wavelengthSim = wavelengthSim[0:numPeaks]
powerSim = powerSim[0:numPeaks]
elif wavelengthSim.size < numPeaks:
wavelengthSim = np.append(wavelengthSim,
np.zeros((numPeaks - wavelengthSim.size, )))
powerSim = np.append(powerSim, np.zeros((numPeaks - powerSim.size, )))
# [[0,numPeaks-1]]
# Estimate the error
error1 = np.sum(np.abs(wavelengthSim - wavelengthPeaks)**2)
error = error1 * 1e8 + error2 * 1e6
print([error1, error2, error])
return error
def plotResult(radius,couplerLength,width,thickness):
gap = 0.2
E, alpha, t, _ = SiP.racetrack_AP_RR_TF(wavelength,radius=radius,
couplerLength=couplerLength,gap=gap,width=width,
thickness=thickness)
# get the transmission spectrum
throughPort = np.abs(np.squeeze(E)) ** 2
plt.figure()
plt.plot(wavelength,power)
plt.plot(wavelength,throughPort)
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Power (a.u.)')
plt.grid(True)
plt.tight_layout()
plt.show()
return
# ------------------------------------------------------------------------- #
# Load relevant info
# ------------------------------------------------------------------------- #
# Load the measurement data
data = sio.loadmat('examples/test.mat')
power = 10 ** (np.squeeze(data['powerMod']) / 10)
wavelength = np.squeeze(data['wavelength']) * 1e6
# ------------------------------------------------------------------------- #
# Preprocessing
# ------------------------------------------------------------------------- #
# Pull the coupler and loss data
a,b,w = SiP.extractor(power,wavelength)
# Fit the coupler and loss data
ap = np.polyfit(w, a, polyOrder)
az = np.poly1d(ap)
bp = np.polyfit(w, b, polyOrder)
bz = np.poly1d(bp)
# Identify the peaks for the measurement data
peaks, _ = find_peaks(1-power,height=peakThreshold)
wavelengthPeaks = wavelength[peaks]
numPeaks = peaks.size
# ------------------------------------------------------------------------- #
# Relevant functions
# ------------------------------------------------------------------------- #
def plotFinal(x):
radius = x[0]
couplerLength = x[1]
width = x[2]
thickness = x[3]
widthCoupler = x[4]
gap = x[5]
angle = x[6]
loss = x[7:]
E, alpha, t, alpha_s = SiP.racetrack_AP_RR_TF(wavelength,radius=radius,
couplerLength=couplerLength,gap=gap,width=width,widthCoupler=widthCoupler,angle=angle,
thickness=thickness,loss=loss)
# get the transmission spectrum
throughPort = np.abs(np.squeeze(E)) ** 2
peaksSim, _ = find_peaks(1-throughPort,height=peakThreshold)
print(peaksSim)
fig = plt.figure(figsize=(7,7))
plt.subplot(3,3,1)
plt.plot(wavelength,power)
plt.plot(wavelength,throughPort,'--')
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Power (a.u.)')
plt.grid(True)
plt.subplot(3,3,4)
plt.plot(wavelength,10*np.log10(power))
plt.plot(wavelength,10*np.log10(throughPort),'--')
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Power (dB)')
plt.grid(True)
plt.subplot(3,3,2)
plt.plot(wavelength,power,'o')
plt.plot(wavelength,throughPort)
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Power (a.u.)')
plt.grid(True)
plt.xlim(wavelength[peaksSim[0]]-0.5e-3,wavelength[peaksSim[0]]+0.5e-3)
plt.subplot(3,3,3)
plt.plot(wavelength,power,'o')
plt.plot(wavelength,throughPort)
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Power (a.u.)')
plt.grid(True)
plt.xlim(wavelength[peaksSim[-1]]-0.5e-3,wavelength[peaksSim[-1]]+0.5e-3)
plt.subplot(3,3,5)
plt.plot(wavelength,10*np.log10(power),'o')
plt.plot(wavelength,10*np.log10(throughPort))
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Power (dB)')
plt.grid(True)
plt.xlim(wavelength[peaksSim[0]]-0.5e-3,wavelength[peaksSim[0]]+0.5e-3)
plt.subplot(3,3,6)
plt.plot(wavelength,10*np.log10(power),'o')
plt.plot(wavelength,10*np.log10(throughPort))
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Power (dB)')
plt.grid(True)
plt.xlim(wavelength[peaksSim[-1]]-0.5e-3,wavelength[peaksSim[-1]]+0.5e-3)
plt.subplot(3,2,5)
plt.plot(w,a,'o')
plt.plot(wavelength,alpha)
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Loss')
plt.grid(True)
plt.subplot(3,2,6)
plt.plot(w,b,'o')
plt.plot(wavelength,t)
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Coupling')
plt.grid(True)
fig.suptitle('Iter={:d}, Die={:d}, Device={:d}'.format(currentIter,die,device))
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.savefig('results.png')
plt.show()
# Save the data as a numpy archive
np.savez('data.npz',radius=radius,couplerLength=couplerLength,
gap=gap,width=width,thickness=thickness,loss=loss,E=E,alpha=alpha,t=t,alpha_r=alpha_s)
die = np.squeeze(np.array(f.get('dies')))
die = int(np.squeeze(die[currentIter]))
device = np.squeeze(np.array(f.get('devices')))
device = int(np.squeeze(device[currentIter]))
filenamePrefix = "sweep/iter{:d}_die{:d}_device{:d}".format(currentIter,die,device)
print(filenamePrefix)
# ------------------------------------------------------------------------- #
# Preprocessing
# ------------------------------------------------------------------------- #
# Pull the coupler and loss data
a,b,w = SiP.extractor(power,wavelength)
print(w)
print(a)
# Fit the coupler and loss data
ap = np.polyfit(w, a, polyOrder)
az = np.poly1d(ap)
bp = np.polyfit(w, b, polyOrder)
bz = np.poly1d(bp)
# Identify the peaks for the measurement data
peaks, _ = find_peaks(1-power,height=peakThreshold)
wavelengthPeaks = wavelength[peaks]
numPeaks = peaks.size
# ------------------------------------------------------------------------- #
