def fitCurrentNoise(self, *args, **kwargs):
fMax = 1E5
noises = tesModel.noiseComponents(*args, **kwargs)
noiseTotal = np.sum(noises[component] for component in noises.keys())
betaI, tau, L = TesAdmittance.guessBetaTauL(R0, fmin=10E3, fmax=fMax)
C = tau*G
f = TesAdmittance.f
iFit = f < fMax
model = lmfit.Model(noiseTotal, independent_vars='f')
params = model.make_params()
params['alphaI'].vary = True; params['alphaI'].value = alphaI;
params['betaI'].vary = True; params['betaI'].value = betaI;
params['P'].vary = False; params['P'].value = P0
params['g_tesb'].vary = True; params['g_tesb'].value = 0.9*G; params['g_tesb'].min = 0
params['g_tes1'].vary = True; params['g_tes1'].value = 0.1*G; params['g_tes1'].min = 0 # Wild guess
params['Ctes'].vary = True; params['Ctes'].value = 0.8*C; # params['Ctes'].min = 0
params['C1'].vary = True; params['C1'].value = 0.2*C; # params['C1'].min = 0 # Wild guess
params['T'].vary = False; params['T'].value = T0
params['R'].vary = False; params['R'].value = R0
result = model.fit(data=noiseTotal[iFit].view(dtype=np.float),
f=np.hstack([f[iFit], f[iFit]]),
params=params )
print(result.fit_report())
#axes = thevenin.plot(None)
ss = SineSweep(pathTf + tfFileName)
print('Sine sweep comment:', ss.comment)
metaData = ast.literal_eval(ss.comment)
Rfb = metaData['Rfb']
Cfb = metaData['Cfb']
Vbias = metaData['Vbias']
Vcoil = metaData['Vcoil']
print('Sine-sweep amplitudes: SC=', ssSc.amplitude, 'Normal=',
ssNormal.amplitude, 'Bias=', ss.amplitude)
#print('Normal sine-sweep amplitude), ssNormal.amplitude,ss.amplitude,np.mean(ss.Vdc), np.std(ss.Vdc))
tesAdmittance = TesAdmittance(ss, thevenin, Rsq)
sweeps = IvSweepCollection(pathIv + ivFileName)
print('IV sweep comment:', sweeps.info.comment)
sweep = sweeps[1]
print('IV sweep Rfb=', sweeps.info.pflRfb)
sweep.findTesBias(tes.Rbias, tes.Rshunt, tes.Rsquid(sweeps.info.pflRfb))
Tbase = sweep.Tadr
sweep.applyThermalModel(tes.temperature, Tbase)
I0, R0, T0, alphaIv = sweep.findAlphaIvCloseTo('Vbias',
Vbias,
n=10,
order=3)
P0 = I0**2 * R0
print('From IV sweep: R0=', R0 * 1E3, 'mOhm, I0=', 1E6 * I0, 'uA, P0=',
P0 * 1E12, ' pW, T0=', T0 * 1E3, 'mK')
def fitTransferFunction(ss, thevenin, Rsq, sweeps, axes=None, fMax=1E5):
metaData = ast.literal_eval(ss.comment)
Rfb = metaData['Rfb']
try:
Cfb = metaData['Cfb']
except KeyError:
Cfb = 0
Vbias = metaData['Vbias']
Vcoil = metaData['Vcoil']
print('Sine-sweep amplitudes: SC=', ssSc.amplitude, 'Normal=',
ssNormal.amplitude, 'Bias=', ss.amplitude)
tesAdmittance = TesAdmittance(ss, thevenin, Rsq)
print('IV sweep comment:', sweeps.info.comment)
sweep = sweeps[1]
print('IV sweep Rfb=', sweeps.info.pflRfb)
sweep.subtractSquidOffset(zeros=[1])
sweep.findTesBias(tes.Rbias, tes.Rshunt, tes.Rsquid(sweeps.info.pflRfb))
Tbase = sweep.Tadr
sweep.applyThermalModel(tes.temperature, Tbase)
I0, R0, T0, alphaIv = sweep.findAlphaIvCloseTo('Vbias',
Vbias,
n=10,
order=3)
P0 = I0**2 * R0
print('From IV sweep: R0=', R0 * 1E3, 'mOhm, I0=', 1E6 * I0, 'uA, P0=',
P0 * 1E12, ' pW, T0=', T0 * 1E3, 'mK')
#R,dRdVb = sweep.fitRtesCloseTo('Vbias', Vbias, n=30, order=3)
beta, tau, L = tesAdmittance.guessBetaTauL(R0,
fLowMax=20,
fmin=30E3,
fmax=120E3)
print('Parameter guesses:')
print('beta=', beta)
print('tau=', tau)
print('L=', L)
acAmplitude = ss.amplitude / 20
f = tesAdmittance.f
iFit = f < fMax
model = lmfit.Model(fitFunction, independent_vars='f')
params = model.make_params()
params['tau'].value = tau
params['tau'].min = 0.5 * tau
params['tau'].max = 3 * tau
params['beta'].value = beta
params['L'].value = L
params['L'].min = 0.5 * L
params['L'].max = 3 * L
params['R0'].value = R0
params['R0'].vary = False
result = model.fit(data=tesAdmittance.A[iFit].view(dtype=np.float),
f=np.hstack([f[iFit], f[iFit]]),
params=params)
print(result.fit_report())
beta = result.best_values['beta']
tau = result.best_values['tau']
L = result.best_values['L']
R0 = result.best_values['R0']
Afit = AdmittanceModel_Simple(tesAdmittance.f, tau, L, beta, R0)
A = tesAdmittance.A
#Rn = tes.Rnormal
beta = result.params['beta']
L = result.params['L']
tau = result.params['tau']
G = tes.thermalConductance(Ttes=T0)
C = tau.value * G
R0 = result.params['R0']
alphaTf = L.value * G * T0 / P0
print(T0, R0, alphaIv, alphaTf, beta, tau, L)
print('Tau type:', type(tau))
fitResults = dict(betaI=beta.value,
L=L.value,
tau=tau.value,
R0=R0.value,
betaIStd=beta.stderr,
LStd=L.stderr,
tauStd=tau.stderr,
R0Std=R0.stderr,
chired=result.redchi,
aic=result.aic,
bic=result.bic,
alphaI=alphaTf)
bias = dict(Vcoil=Vcoil,
Vbias=Vbias,
I0=I0,
P0=P0,
T0=T0,
Tbase=Tbase,
G=G,
C=C)
results = dict()
results.update(fitResults)
results.update(bias)
#results.update(hkDict);
if axes is None:
gs = gridspec.GridSpec(2, 2, width_ratios=[3, 1]) # 2x2
ax1 = mpl.subplot(gs[0, 0]) # 1x3
ax2 = mpl.subplot(gs[1, 0]) # 1x3
ax3 = mpl.subplot(gs[0, 1]) # 1x2
ax4 = mpl.subplot(gs[1, 1]) # 1x2 for text
axes = (ax1, ax2, ax3, ax4)
else:
ax1, ax2, ax3, ax4 = axes
# Re(Y), Im(Y)
if plotType == 'admittance':
y = A
yfit = Afit
yReLabel = '$\\operatorname{Re}(Y)$'
yImLabel = '$\\operatorname{Im}(Y)$'
ax1.set_ylabel(u'TES admittance $Y(f)$ (S)')
ax2.set_ylabel('$Y(f)$ residual (S)')
ax2.set_ylim(-100, +100)
elif plotType == 'impedance':
y = 1. / A
yfit = 1. / Afit
yReLabel = '$\\operatorname{Re}(Z)$'
yImLabel = '$\\operatorname{Im}(Z)$'
ax1.set_ylabel(u'TES impedance $Z(f)$ ($\\Omega$)')
ax2.set_ylabel('$Z(f)$ residual ($\\Omega$)')
ax2.set_ylim(-3E-4, +3E-4)
ax1.semilogx(f, np.real(yfit), '-k', label='fit')
ax1.semilogx(f, np.real(y), 'ob', ms=2, label=yReLabel)
ax1.semilogx(f, np.imag(yfit), '-k')
ax1.semilogx(f, np.imag(y), 'or', ms=2, label=yImLabel)
# Resdiuals
residual = y - yfit
ax2.semilogx(f, np.real(residual), '.r', ms=2)
ax2.semilogx(f, np.imag(residual), '.b', ms=2)
ax2.set_xlabel('$f$ (Hz)')
ax2.grid()
#ax1.set_ylabel(u'TES admittance $Y(f)$ (S)')
#ax1.set_xlabel('$f$ (Hz)')
ax1.legend(loc='best')
ax1.grid()
fitText = u'$R_0$ = %.4g m$\\Omega$ (%.2f%% $R_n$)\n' % (1E3 * R0,
100 * R0 / Rn)
fitText += u'$P_0$ = %.3f pW\n' % (
1E12 * P0) # Units used to be incorrect ("nW") -> fixed 2017-12-13
fitText += u'$I_0$ = %.3f $\\mu$A\n' % (1E6 * I0)
fitText += u'$T_0$ = %.3f mK\n' % (1E3 * T0)
fitText += u'$T_b$ = %.3f mK\n' % (1E3 * Tbase)
fitText += u'$\\alpha = %.1f \\pm %.1f$\n' % (alphaTf,
L.stderr / L.value * alphaTf)
fitText += u'$\\beta_{I}$ = %.3f $\\pm$ %.3f\n' % (beta.value, beta.stderr)
fitText += u'$\\mathcal{L} = %.3f \\pm %.3f$\n' % (L.value, L.stderr)
fitText += u'$\\tau = %.3f \\pm %.3f$ ms\n' % (1E3 * tau.value,
1E3 * tau.stderr)
fitText += u'$C = %.3f \\pm %.3f$ pJ/K' % (1E12 * C, 1E12 * tau.stderr /
tau.value * C)
props = dict(boxstyle='square', alpha=0.5, facecolor='w')
ax4.text(0.02,
0.92,
fitText,
va='top',
bbox=props,
transform=ax4.transAxes)
ax4.axis('off')
#ax4.axes.get_xaxis().set_visible(False)
#ax4.axes.get_yaxis().set_visible(False)
bpTitle = '%.2fmK_Vc%.3f_Vb%.3f' % (1E3 * Tbase, Vcoil, Vbias)
# Polar
# ax3.plot(np.real(A), np.imag(A), '.')
norm = colors.LogNorm(vmin=f.min(), vmax=f.max())
ax3.plot(np.real(yfit), np.imag(yfit), '-k', lw=1.0)
ax3.scatter(np.real(y), np.imag(y), s=4, c=f, cmap=cmap, norm=norm)
#ax3.set_yticklabels([]); ax3.tick_params(labelbottom='off')
ax3.yaxis.tick_right()
ax3.yaxis.set_label_position('right')
ax3.set_xlabel(yReLabel)
ax3.set_ylabel(yImLabel)
#limits = ax1.get_ylim(); ax3.set_ylim(limits); ax3.set_xlim(limits)
ax3.set_aspect('equal', adjustable='datalim', anchor='SW')
ax3.grid()
import time
dateStr = time.strftime('%Y%m%d', time.localtime(ss.t[0]))
fullDateStr = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(ss.t[0]))
title = '%s/%s: %s - %s\n' % (cooldown, deviceId, tes.DeviceName,
fullDateStr)
title += '$V_{coil}$=%+06.4f V, Vbias=%.4fV, AC=%.4f Vp' % (Vcoil, Vbias,
acAmplitude)
fig.suptitle(title)
plotFileName = '%s_TFFit_%s_A%.0fmV_%s' % (deviceId, bpTitle,
1E3 * acAmplitude, dateStr)
mpl.savefig(plotFileName + '.png')
#mpl.savefig('Plots\\pdf\\'+plotFileName+'.pdf')
#mpl.close()
#mpl.show()
return results, axes
def fitTransferFunction(ss, thevenin, Rsq, sweeps, axes=None, fMax=1E5):
metaData = eval(ss.comment)
Rfb = metaData['Rfb']
try:
Cfb = metaData['Cfb']
except KeyError:
Cfb = 0
Vbias = metaData['Vbias']
Vcoil = metaData['Vcoil']
print('Sine-sweep amplitudes: SC=', ssSc.amplitude, 'Normal=',
ssNormal.amplitude, 'Bias=', ss.amplitude)
tesAdmittance = TesAdmittance(ss, thevenin, Rsq)
print('IV sweep comment:', sweeps.info.comment)
sweep = sweeps[1]
sweep.subtractSquidOffset(zeros=[1])
print('IV sweep Rfb=', sweeps.info.pflRfb)
sweep.findTesBias(tes.Rbias, tes.Rshunt, tes.Rsquid(sweeps.info.pflRfb))
Tbase = sweep.Tadr
sweep.applyThermalModel(tes.temperature, Tbase)
I0, R0, T0, alphaIv = sweep.findAlphaIvCloseTo('Vbias',
Vbias,
n=10,
order=3)
P0 = I0**2 * R0
print('From IV sweep: R0=', R0 * 1E3, 'mOhm, I0=', 1E6 * I0, 'uA, P0=',
P0 * 1E12, ' pW, T0=', T0 * 1E3, 'mK')
#R,dRdVb = sweep.fitRtesCloseTo('Vbias', Vbias, n=30, order=3)
betaI, tau, L = tesAdmittance.guessBetaTauL(R0, fmin=10E3, fmax=100E3)
G = tes.thermalConductance(Ttes=T0)
alphaI = L * G * T0 / P0
C = tau * G
print('Parameter guesses:')
print('beta=', betaI)
print('tau=', tau)
print('L=', L)
print('G=', G)
print('C=', C)
print('alphaI=', alphaI)
acAmplitude = ss.amplitude / 20
f = tesAdmittance.f
iFit = f < fMax
model = lmfit.Model(fitFunction, independent_vars='f')
params = model.make_params()
params['alphaI'].vary = True
params['alphaI'].value = alphaI
params['alphaI'].min = 0
params['betaI'].vary = True
params['betaI'].value = betaI
params['betaI'].min = 0
params['P'].vary = False
params['P'].value = P0
params['P'].min = 0
params['g_tes1'].vary = True
params['g_tes1'].value = 0.8 * G
params['g_tes1'].min = 0
params['g_tes2'].vary = True
params['g_tes2'].value = 0.1 * G
params['g_tes2'].min = 0
params['g_2b'].vary = True
params['g_2b'].value = 0.1 * G
params['g_2b'].min = 0
params['Ctes'].vary = True
params['Ctes'].value = 0.8 * C
params['Ctes'].min = 0
params['C1'].vary = True
params['C1'].value = 0.1 * C
params['C1'].min = 0 # Wild guess
params['C2'].vary = True
params['C2'].value = 0.1 * C
params['C2'].min = 0
params['T'].vary = False
params['T'].value = T0
params['T2'].vary = False
params['T2'].value = T0
params['R'].vary = False
params['R'].value = R0
params['betaThermal'].vary = False
params['betaThermal'].value = 2.26
result = model.fit(data=tesAdmittance.A[iFit].view(dtype=np.float),
f=np.hstack([f[iFit], f[iFit]]),
params=params)
print(result.fit_report())
alphaI = result.best_values['alphaI']
betaI = result.best_values['betaI']
P = result.best_values['P']
g_tes1 = result.best_values['g_tes1']
g_tes2 = result.best_values['g_tes2']
g_2b = result.best_values['g_2b']
Ctes = result.best_values['Ctes']
C1 = result.best_values['C1']
C2 = result.best_values['C2']
T = result.best_values['T']
T2 = result.best_values['T2']
R = result.best_values['R']
betaThermal = result.best_values['betaThermal']
omega = 2 * np.pi * tesAdmittance.f
Zfit = tesModel.impedance(alphaI, betaI, P, g_tes1, g_tes2, g_2b, Ctes, C1,
C2, T, T2, R, betaThermal, omega)
Afit = 1. / Zfit
A = tesAdmittance.A
if axes is None:
gs = gridspec.GridSpec(2, 2, width_ratios=[3, 1]) # 2x2
ax1 = mpl.subplot(gs[0, 0]) # 1x3
ax2 = mpl.subplot(gs[1, 0]) # 1x3
ax3 = mpl.subplot(gs[0, 1]) # 1x2
ax4 = mpl.subplot(gs[1, 1]) # 1x2 for text
axes = (ax1, ax2, ax3, ax4)
else:
ax1, ax2, ax3, ax4 = axes
if plotType == 'admittance':
y = A
yfit = Afit
yReLabel = '$\\operatorname{Re}(Y)$'
yImLabel = '$\\operatorname{Im}(Y)$'
ax1.set_ylabel(u'TES admittance $Y(f)$ (S)')
ax2.set_ylabel('$Y(f)$ residual (S)')
ax2.set_ylim(-100, +100)
elif plotType == 'impedance':
y = 1. / A
yfit = 1. / Afit
yReLabel = '$\\operatorname{Re}(Z)$'
yImLabel = '$\\operatorname{Im}(Z)$'
ax1.set_ylabel(u'TES impedance $Z(f)$ ($\\Omega$)')
ax2.set_ylabel('$Z(f)$ residual ($\\Omega$)')
ax2.set_ylim(-3E-4, +3E-4)
ax1.semilogx(f, np.real(yfit), '-k', label='fit')
ax1.semilogx(f, np.real(y), 'ob', ms=2, label=yReLabel)
ax1.semilogx(f, np.imag(yfit), '-k')
ax1.semilogx(f, np.imag(y), 'or', ms=2, label=yImLabel)
# Residuals
residual = y - yfit
ax2.semilogx(f, np.real(residual), '.r', ms=2)
ax2.semilogx(f, np.imag(residual), '.b', ms=2)
ax2.set_xlabel('$f$ (Hz)')
ax2.grid()
#ax1.set_ylabel(u'TES admittance $Y(f)$ (S)')
#ax1.set_xlabel('$f$ (Hz)')
ax1.legend(loc='best')
ax1.grid()
fitText = u'$R_0$ = %.4g m$\\Omega$ (%.2f%% $R_n$)\n' % (1E3 * R0,
100 * R0 / Rn)
fitText += u'$P_0$ = %.3f pW\n' % (
1E12 * P0) # Units used to be incorrect ("nW") -> fixed 2017-12-13
fitText += u'$I_0$ = %.3f $\\mu$A\n' % (1E6 * I0)
fitText += u'$T_0$ = %.3f mK\n' % (1E3 * T0)
fitText += u'$T_b$ = %.3f mK\n' % (1E3 * Tbase)
#fitText += u'$\\alphaI = %.1f \\pm %.1f$\n' % (alphaI, alphaI.stderr)
#fitText += u'$\\beta_{I}$ = %.3f $\\pm$ %.3f\n' % (betaI.value, betaI.stderr)
#fitText += u'$\\mathcal{L} = %.3f \\pm %.3f$\n' % (L.value, L.stderr)
#fitText += u'$\\tau = %.3f \\pm %.3f$ ms\n' % (1E3*tau.value, 1E3*tau.stderr)
#fitText += u'$C = %.3f \\pm %.3f$ pJ/K' % (1E12*C, 1E12*tau.stderr/tau.value*C)
#fitText += u'$C1 = %.3f \\pm %.3f$ pJ/K' % (1E12*C1, 1E12*tau.stderr/tau.value*C1)
props = dict(boxstyle='square', alpha=0.5, facecolor='w')
ax4.text(0.02,
0.92,
fitText,
va='top',
bbox=props,
transform=ax4.transAxes)
ax4.axis('off')
#ax4.axes.get_xaxis().set_visible(False)
#ax4.axes.get_yaxis().set_visible(False)
bpTitle = '%.2fmK_Vc%.3f_Vb%.3f' % (1E3 * Tbase, Vcoil, Vbias)
# Polar
# ax3.plot(np.real(A), np.imag(A), '.')
norm = colors.LogNorm(vmin=f.min(), vmax=f.max())
ax3.plot(np.real(yfit), np.imag(yfit), '-k', lw=1.0)
ax3.scatter(np.real(y), np.imag(y), s=4, c=f, cmap=cmap, norm=norm)
#ax3.set_yticklabels([]); ax3.tick_params(labelbottom='off')
ax3.yaxis.tick_right()
ax3.yaxis.set_label_position('right')
ax3.set_xlabel(yReLabel)
ax3.set_ylabel(yImLabel)
#limits = ax1.get_ylim(); ax3.set_ylim(limits); ax3.set_xlim(limits)
ax3.set_aspect('equal', adjustable='datalim', anchor='SW')
ax3.grid()
import time
dateStr = time.strftime('%Y%m%d', time.localtime(ss.t[0]))
fullDateStr = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(ss.t[0]))
title = '%s/%s: %s - %s\n' % (cooldown, deviceId, tes.DeviceName,
fullDateStr)
title += '$V_{coil}$=%+06.4f V, Vbias=%.4fV, AC=%.4f Vp' % (Vcoil, Vbias,
acAmplitude)
fig.suptitle(title)
# plotFileName = '%s_TFFit_%s_A%.0fmV_%s' % (deviceId, bpTitle, 1E3*acAmplitude, dateStr)
# #mpl.savefig(plotFileName+'.png')
# pdf = PdfPages(outputPath+plotFileName+'.pdf')
# pdf.savefig(fig)
# pdf.close()
mpl.show()
return result, axes