def flux_par(self):
flux_over_flux0 = qdt.call_func("flux_over_flux0",
voltage=self.yoko,
offset=self.offset,
flux_factor=self.flux_factor)
Ej = qdt.call_func("Ej", flux_over_flux0=flux_over_flux0)
return qdt._get_fq(Ej, qdt.Ec)
def flux_parabola(self):
flux_over_flux0=qdt.call_func("flux_over_flux0", voltage=self.yoko, offset=self.offset, flux_factor=self.flux_factor)
Ej=qdt.call_func("Ej", flux_over_flux0=flux_over_flux0)
return qdt._get_fq(Ej, qdt.Ec)
def magabs_cs_fit():
def lorentzian(x, p):
return p[2] * (1.0 - 1.0 / (1.0 + ((x - p[1]) / p[0])**2)) + p[3]
def fano(x, p):
return p[2] * (((p[4] * p[0] + x - p[1])**2) /
(p[0]**2 + (x - p[1])**2)) + p[3]
def refl_fano(x, p):
return p[2] * (1.0 - ((p[4] * p[0] + x - p[1])**2) /
(p[0]**2 + (x - p[1])**2)) + p[3]
def onebounce(x, p):
w = 2 * pi * x
k = 2.0 * pi * x / 3488.0
Cc = 0.0
D = 500.0e-6
D1 = 300.0e-6
D2 = 200.0e-6
S12q = ((p[4] * p[0] / 6.28 + (x - p[1]) * 2 * pi)**
2) / (p[0]**2 + (p[4] * p[0] / 6.28 + x - p[1])**2)
S11q = 1.0 / (p[0]**2 + (p[4] * p[0] / 6.28 + x - p[1])**2)
S11 = p[5]
return p[2] * absolute(
exp(1.0j * k * D) * S12q *
(1 + exp(2.0j * k * D1) * S11q * S11 + exp(2.0j * k * D2) *
S11q * S11 + exp(2.0j * k * D) * S11**2 * S12q**2) +
2.0j * w * Cc * 50.0)**2 + p[3]
def allbounces(x, p):
w = 2 * pi * x
k = 2.0 * pi * x / 3488.0
Cc = 0.0
D = 500.0e-6
D1 = 300.0e-6
D2 = 200.0e-6
return p[2] * absolute(
exp(1.0j * k * D) * S12q *
(-2.0 + 1.0 / (1.0 - exp(2.0j * k * D1) * S11q * S11) + 1.0 /
(1.0 - exp(2.0j * k * D2) * S11q * S11) + 1.0 /
(1 - exp(2.0j * k * D) * S11**2 * S12q**2)) +
2.0j * w * Cc * 50.0)
def residuals(p, y, x):
err = y - lorentzian(x, p)
return err
def residuals2(p, y, x):
return y - fano(x, p)
def residuals3(p, y, x):
return y - refl_fano(x, p)
p = [200e6, 4.5e9, 0.002, 0.022, 0.1, 0.1]
indices = [
range(81, 120 + 1),
range(137, 260 + 1),
range(269, 320 + 1),
range(411, 449 + 1)
] #, [490]]#, [186]]
indices = [range(len(a.frequency))]
widths = []
freqs = []
freq_diffs = []
fanof = []
filt = []
for n in range(len(yok)):
myifft = fft.ifft(mag[:, n])
#b.line_plot("ifft", absolute(myifft))
myifft[50:-50] = 0.0
#myifft[:20]=0.0
#myifft[-20:]=0.0
filt.append(absolute(fft.fft(myifft))**2)
filt = array(filt).transpose()
for ind_list in indices:
for n in ind_list:
pbest = leastsq(residuals3,
p,
args=(a.MagAbs[n, :], c.flux_parabola[:]),
full_output=1)
best_parameters = pbest[0]
print best_parameters
if 0: #n % 8==0:
bb.scatter_plot("magabs_flux",
c.flux_parabola[:] * 1e-9,
a.MagAbs[n, :],
label="{}".format(n),
linewidth=0.2,
marker_size=0.8)
bb.line_plot("lorentzian",
c.flux_parabola * 1e-9,
refl_fano(c.flux_parabola, best_parameters),
label="fit {}".format(n),
linewidth=0.5)
if 1: #absolute(best_parameters[1]-a.frequency[n])<2e8:
freqs.append(a.frequency[n])
freq_diffs.append(
absolute(best_parameters[1] - a.frequency[n]))
widths.append(absolute(best_parameters[0]))
fanof.append(absolute(best_parameters[4]))
if 1:
widths2 = []
freqs2 = []
freq_diffs2 = []
fano2 = []
flux_over_flux0 = qdt.call_func("flux_over_flux0",
voltage=yok,
offset=-0.037,
flux_factor=0.2925)
Ej = qdt.call_func("Ej", flux_over_flux0=flux_over_flux0)
flux_par = qdt._get_fq(Ej, qdt.Ec)
magabs = absolute(mag)**2
for n in range(len(frq)):
pbest = leastsq(residuals2,
p,
args=(filt[n, :], flux_par[:]),
full_output=1)
best_parameters = pbest[0]
print best_parameters
if 0: #n==539 or n==554:#n % 10:
b.line_plot("magabs_flux",
flux_par * 1e-9,
(magabs[n, :] - best_parameters[3]) /
best_parameters[2],
label="{}".format(n),
linewidth=0.2)
b.line_plot("lorentzian",
flux_par * 1e-9,
fano(flux_par, best_parameters),
label="fit {}".format(n),
linewidth=0.5)
if 1: #absolute(best_parameters[1]-frq[n])<1.5e8:
freqs2.append(frq[n])
freq_diffs2.append(absolute(best_parameters[1] - frq[n]))
widths2.append(absolute(best_parameters[0]))
fano2.append(absolute(best_parameters[4]))
b.line_plot("widths", freqs, widths, label="-110 dBm")
b.scatter_plot("widths2",
freqs2,
widths2,
color="red",
label="-130 dBm")
vf = 3488.0
p = [1.0001, 0.5, 0.3, 1.0e-15, 0.001]
Np = 9
K2 = 0.048
f0 = 5.348e9
def fourier(x, p):
w = 2 * pi * x
k = 2.0 * pi * x / 3488.0
D = 500.0e-6
D1 = 300.0e-6
D2 = 200.0e-6
G_f = 0.5 * Np * K2 * f0 * (sin(Np * pi * (x - f0) / f0) /
(Np * pi * (x - f0) / f0))**2
return G_f * absolute(
exp(1.0j * k * D) * p[0] *
(1 + exp(2.0j * k * D1) *
(p[1] + 1.0j * p[2]) + exp(2.0j * k * D2) *
(p[1] + 1.0j * p[2])) + 2.0j * w * p[3] * 50.0) + p[4]
#exp(2.0j*k*D)*(p[1]+1.0j*p[2]))+
def resid(p, y, x):
#return y - onebounce(x,p)
return y - fourier(x, p)
#pbest=leastsq(resid, p, args=(absolute(widths2[318:876]), frq[318:876]), full_output=1)
#print pbest[0]
#b.line_plot("fourier", frq, fourier(frq, pbest[0]))
#pi*vf/2*x=D
from scipy.signal import lombscargle
#lombscargle(freqs2[318:876], widths2[318:876])
#bb.line_plot("fft", #frq[318:876]*fft.fftfreq(len(frq[318:876]), d=frq[1]-frq[0]),
#absolute(fft.fft(widths2[318:876])))
frqdiffs = linspace(0.01, 500e6, 1000)
#bb.line_plot("ls", frqdiffs, lombscargle(array(freqs2[285:828]), array(widths2[285:828]), frqdiffs ))
#bb.line_plot("ls", frqdiffs, lombscargle(array(freqs[318:876]), array(widths[318:876]), frqdiffs))
bb.line_plot("fft2", absolute(fft.ifft(widths2[285:828])))
bb.line_plot("fft", absolute(fft.ifft(widths[285:828])))
bbb = Plotter()
#bbb.scatter_plot("wid", freqs2, widths2)
myifft = fft.ifft(widths2[285:828])
myifft[12:-12] = 0.0
bbb.line_plot("ff", freqs2[285:828], absolute(fft.fft(myifft)))
def magabs_cs_fit():
def lorentzian(x, p):
return p[2] * (1.0 - 1.0 / (1.0 + ((x - p[1]) / p[0])**2)) + p[3]
def fano(x, p):
return p[2] * (((p[4] * p[0] + x - p[1])**2) /
(p[0]**2 + (x - p[1])**2)) + p[3]
def refl_fano(x, p):
return p[2] * (1.0 - ((p[4] * p[0] + x - p[1])**2) /
(p[0]**2 + (x - p[1])**2)) + p[3]
def onebounce(x, p):
w = 2 * pi * x
k = 2.0 * pi * x / 3488.0
Cc = 0.0
D = 500.0e-6
D1 = 300.0e-6
D2 = 200.0e-6
S12q = ((p[4] * p[0] / 6.28 + (x - p[1]) * 2 * pi)**
2) / (p[0]**2 + (p[4] * p[0] / 6.28 + x - p[1])**2)
S11q = 1.0 / (p[0]**2 + (p[4] * p[0] / 6.28 + x - p[1])**2)
S11 = p[5]
return p[2] * absolute(
exp(1.0j * k * D) * S12q *
(1 + exp(2.0j * k * D1) * S11q * S11 + exp(2.0j * k * D2) *
S11q * S11 + exp(2.0j * k * D) * S11**2 * S12q**2) +
2.0j * w * Cc * 50.0)**2 + p[3]
def allbounces(x, p):
w = 2 * pi * x
k = 2.0 * pi * x / 3488.0
Cc = 0.0
D = 500.0e-6
D1 = 300.0e-6
D2 = 200.0e-6
return p[2] * absolute(
exp(1.0j * k * D) * S12q *
(-2.0 + 1.0 / (1.0 - exp(2.0j * k * D1) * S11q * S11) + 1.0 /
(1.0 - exp(2.0j * k * D2) * S11q * S11) + 1.0 /
(1 - exp(2.0j * k * D) * S11**2 * S12q**2)) +
2.0j * w * Cc * 50.0)
def residuals(p, y, x):
err = y - lorentzian(x, p)
return err
def residuals2(p, y, x):
return y - fano(x, p)
def residuals3(p, y, x):
return y - refl_fano(x, p)
p = [200e6, 4.5e9, 0.002, 0.022, 0.1, 0.1]
indices = [
range(81, 120 + 1),
range(137, 260 + 1),
range(269, 320 + 1),
range(411, 449 + 1)
] #, [490]]#, [186]]
indices = [range(len(a.frequency))]
widths = []
freqs = []
freq_diffs = []
fanof = []
for ind_list in indices:
for n in ind_list:
pbest = leastsq(residuals3,
p,
args=(a.MagAbs[n, :], c.flux_parabola[:]),
full_output=1)
best_parameters = pbest[0]
print best_parameters
if 0: #n % 8==0:
bb.scatter_plot("magabs_flux",
c.flux_parabola[:] * 1e-9,
a.MagAbs[n, :],
label="{}".format(n),
linewidth=0.2,
marker_size=0.8)
bb.line_plot("lorentzian",
c.flux_parabola * 1e-9,
refl_fano(c.flux_parabola, best_parameters),
label="fit {}".format(n),
linewidth=0.5)
if 1: #absolute(best_parameters[1]-a.frequency[n])<2e8:
freqs.append(a.frequency[n])
freq_diffs.append(
absolute(best_parameters[1] - a.frequency[n]))
widths.append(absolute(best_parameters[0]))
fanof.append(absolute(best_parameters[4]))
if 1:
widths2 = []
freqs2 = []
freq_diffs2 = []
fano2 = []
flux_over_flux0 = qdt.call_func("flux_over_flux0",
voltage=yok,
offset=-0.037,
flux_factor=0.2925)
Ej = qdt.call_func("Ej", flux_over_flux0=flux_over_flux0)
flux_par = qdt._get_fq(Ej, qdt.Ec)
magabs = absolute(mag)**2
for n in range(len(frq)):
pbest = leastsq(residuals2,
p,
args=(magabs[n, :], flux_par[:]),
full_output=1)
best_parameters = pbest[0]
print best_parameters
if 0: #n==539 or n==554:#n % 10:
b.line_plot("magabs_flux",
flux_par * 1e-9,
(magabs[n, :] - best_parameters[3]) /
best_parameters[2],
label="{}".format(n),
linewidth=0.2)
b.line_plot("lorentzian",
flux_par * 1e-9,
fano(flux_par, best_parameters),
label="fit {}".format(n),
linewidth=0.5)
if 1: #absolute(best_parameters[1]-frq[n])<1.5e8:
freqs2.append(frq[n])
freq_diffs2.append(absolute(best_parameters[1] - frq[n]))
widths2.append(absolute(best_parameters[0]))
fano2.append(absolute(best_parameters[4]))
b.line_plot("widths", freqs, widths, label="-110 dBm")
b.scatter_plot("widths2",
freqs2,
widths2,
color="red",
label="-130 dBm")
vf = 3488.0
p = [1.0001, 0.5, 0.3, 1.0e-15, 0.001]
Np = 9
K2 = 0.048
f0 = 5.348e9
def fourier(x, p):
w = 2 * pi * x
k = 2.0 * pi * x / 3488.0
D = 500.0e-6
D1 = 300.0e-6
D2 = 200.0e-6
G_f = 0.5 * Np * K2 * f0 * (sin(Np * pi * (x - f0) / f0) /
(Np * pi * (x - f0) / f0))**2
return G_f * absolute(
exp(1.0j * k * D) * p[0] *
(1 + exp(2.0j * k * D1) *
(p[1] + 1.0j * p[2]) + exp(2.0j * k * D2) *
(p[1] + 1.0j * p[2])) + 2.0j * w * p[3] * 50.0) + p[4]
#exp(2.0j*k*D)*(p[1]+1.0j*p[2]))+
def resid(p, y, x):
#return y - onebounce(x,p)
return y - fourier(x, p)
#pbest=leastsq(resid, p, args=(absolute(widths2[318:876]), frq[318:876]), full_output=1)
#print pbest[0]
#b.line_plot("fourier", frq, fourier(frq, pbest[0]))
#pi*vf/2*x=D
from scipy.signal import lombscargle
#lombscargle(freqs2[318:876], widths2[318:876])
#bb.line_plot("fft", #frq[318:876]*fft.fftfreq(len(frq[318:876]), d=frq[1]-frq[0]),
#absolute(fft.fft(widths2[318:876])))
frqdiffs = linspace(0.01, 500e6, 1000)
#bb.line_plot("ls", frqdiffs, lombscargle(array(freqs2[285:828]), array(widths2[285:828]), frqdiffs ))
#bb.line_plot("ls", frqdiffs, lombscargle(array(freqs[318:876]), array(widths[318:876]), frqdiffs))
bb.line_plot("fft2", absolute(fft.ifft(widths2[285:828])))
bb.line_plot("fft", absolute(fft.ifft(widths[285:828])))
bbb = Plotter()
#bbb.scatter_plot("wid", freqs2, widths2)
myifft = fft.ifft(widths2[285:828])
myifft[12:-12] = 0.0
bbb.line_plot("ff", freqs2[285:828], absolute(fft.fft(myifft)))
b.line_plot("ff", freqs2[285:828], absolute(fft.fft(myifft)))
#bb.line_plot("fft2", absolute(fft.fft(widths[52:155])))
#b.line_plot("fano", freqs, fano)
#b.line_plot("fano", freqs2, fano2)
#f0=5.37e9
freq = linspace(4e9, 5e9, 1000)
#G_f=0.5*Np*K2*f0*(sin(Np*pi*(freq-f0)/f0)/(Np*sin(pi*(freq-f0)/f0)))**2
#b.scatter_plot("freq_test", freqs, freq_diffs)
class Fitter3(Operative):
base_name = "fitter"
mult = FloatRange(0.001, 5.0, 0.82).tag(tracking=True)
f0 = FloatRange(4.0, 6.0, 5.348).tag(tracking=True)
offset = FloatRange(0.0, 100.0, 18.0).tag(tracking=True)
@tag_Property(plot=True, private=True)
def G_f(self):
f0 = self.f0 * 1.0e9
return self.offset * 1e6 + self.mult * 0.5 * Np * K2 * f0 * (
sin(Np * pi * (freq - f0) / f0) / (Np * pi *
(freq - f0) / f0))**2
@observe("f0", "mult", "offset")
def update_plot(self, change):
if change["type"] == "update":
self.get_member("G_f").reset(self)
b.plot_dict["G_f"].clt.set_ydata(self.G_f)
b.draw()
d = Fitter3()
b.line_plot("G_f", freq, d.G_f, label="theory")
def magabs_cs_fit():
def lorentzian(x,p):
return p[2]*(1.0-1.0/(1.0+((x-p[1])/p[0])**2))+p[3]
def lorentzian2(x, p):
return p[2]*(((p[4]*p[0]+x-p[1])**2)/(p[0]**2+(x-p[1])**2))+p[3]
def lorentzian3(x, p):
return p[2]*(((p[4]*p[0]/6.28+(x-p[1])*2*pi)**2)/(p[0]**2+(p[4]*p[0]/6.28+x-p[1])**2))+p[3]
#return p[2]*(((p[4]*p[0]/6.28+(x-p[1])*2*pi)**2)/(p[0]**2+(p[4]*p[0]/6.28+x-p[1])**2))+p[3]
def onebounce(x,p):
w=2*pi*x
k=2.0*pi*x/3488.0
Cc=0.0
D=500.0e-6
D1=300.0e-6
D2=200.0e-6
S12q=((p[4]*p[0]/6.28+(x-p[1])*2*pi)**2)/(p[0]**2+(p[4]*p[0]/6.28+x-p[1])**2)
S11q=1.0/(p[0]**2+(p[4]*p[0]/6.28+x-p[1])**2)
S11=p[5]
return p[2]*absolute(exp(1.0j*k*D)*S12q*(1+exp(2.0j*k*D1)*S11q*S11+exp(2.0j*k*D2)*S11q*S11+
exp(2.0j*k*D)*S11**2*S12q**2)+2.0j*w*Cc*50.0)**2+p[3]
def allbounces(x,p):
w=2*pi*x
k=2.0*pi*x/3488.0
Cc=0.0
D=500.0e-6
D1=300.0e-6
D2=200.0e-6
return p[2]*absolute(exp(1.0j*k*D)*S12q*(-2.0
+1.0/(1.0-exp(2.0j*k*D1)*S11q*S11)
+1.0/(1.0- exp(2.0j*k*D2)*S11q*S11)
+1.0/(1- exp(2.0j*k*D)*S11**2*S12q**2))
+2.0j*w*Cc*50.0)
def residuals(p,y,x):
err = y - lorentzian(x,p)
return err
def residuals2(p,y,x):
return y - lorentzian2(x,p)
def residuals3(p,y,x):
#return y - onebounce(x,p)
return y - lorentzian3(x,p)
p = [200e6,4.5e9, 0.002, 0.022, 0.1, 0.1]
indices=[range(81, 120+1), range(137, 260+1), range(269, 320+1), range(411, 449+1)]#, [490]]#, [186]]
widths=[]
freqs=[]
freq_diffs=[]
fano=[]
for ind_list in indices:
for n in ind_list:
pbest = leastsq(residuals3, p, args=(a.MagAbs[n, :], c.flux_parabola[:]), full_output=1)
best_parameters = pbest[0]
print best_parameters
if 0: #n % 10==0:
b.scatter_plot("magabs_flux", c.flux_parabola[:]*1e-9, a.MagAbs[n, :], label="{}".format(n), linewidth=0.2, marker_size=0.8)
b.line_plot("lorentzian", c.flux_parabola*1e-9, lorentzian3(c.flux_parabola,best_parameters), label="fit {}".format(n), linewidth=0.5)
if 1:#absolute(best_parameters[1]-a.frequency[n])<2e8:
freqs.append(a.frequency[n])
freq_diffs.append(absolute(best_parameters[1]-a.frequency[n]))
widths.append(absolute(best_parameters[0]))
fano.append(absolute(best_parameters[4]))
if 1:
widths2=[]
freqs2=[]
freq_diffs2=[]
fano2=[]
flux_over_flux0=qdt.call_func("flux_over_flux0", voltage=yok, offset=-0.037, flux_factor=0.2925)
Ej=qdt.call_func("Ej", flux_over_flux0=flux_over_flux0)
flux_par=qdt._get_fq(Ej, qdt.Ec)
magabs=absolute(mag)**2
for n in range(len(frq)):
pbest = leastsq(residuals3,p,args=(magabs[n, :], flux_par[:]), full_output=1)
best_parameters = pbest[0]
print best_parameters
if 0:#n==539 or n==554:#n % 10:
b.line_plot("magabs_flux", flux_par*1e-9, (magabs[n, :]-best_parameters[3])/best_parameters[2], label="{}".format(n), linewidth=0.2)
#b.line_plot("lorentzian", flux_par*1e-9, lorentzian3(flux_par,best_parameters), label="fit {}".format(n), linewidth=0.5)
if 1:#absolute(best_parameters[1]-frq[n])<1.5e8:
freqs2.append(frq[n])
freq_diffs2.append(absolute(best_parameters[1]-frq[n]))
widths2.append(absolute(best_parameters[0]))
fano2.append(absolute(best_parameters[4]))
b.scatter_plot("widths", freqs, widths, label="-110 dBm")
b.scatter_plot("widths2", freqs2, widths2, color="red", label="-130 dBm")
vf=3488.0
p=[1.0001, 0.5, 0.3, 1.0e-15, 0.001]
Np=9
K2=0.048
f0=5.348e9
def fourier(x, p):
w=2*pi*x
k=2.0*pi*x/3488.0
D=500.0e-6
D1=300.0e-6
D2=200.0e-6
G_f=0.5*Np*K2*f0*(sin(Np*pi*(x-f0)/f0)/(Np*pi*(x-f0)/f0))**2
return G_f*absolute(exp(1.0j*k*D)*p[0]*(1+exp(2.0j*k*D1)*(p[1]+1.0j*p[2])+exp(2.0j*k*D2)*(p[1]+1.0j*p[2]))
+2.0j*w*p[3]*50.0)+p[4]
#exp(2.0j*k*D)*(p[1]+1.0j*p[2]))+
def resid(p,y,x):
#return y - onebounce(x,p)
return y - fourier(x,p)
pbest=leastsq(resid, p, args=(absolute(widths2[318:876]), frq[318:876]), full_output=1)
print pbest[0]
b.line_plot("fourier", frq, fourier(frq, pbest[0]))
#pi*vf/2*x=D
#b.line_plot("fft", #frq[318:876]*fft.fftfreq(len(frq[318:876]), d=frq[1]-frq[0]),
#absolute(fft.fft(widths2[318:876])))
#b.line_plot("fano", freqs, fano)
#b.line_plot("fano", freqs2, fano2)
#f0=5.37e9
freq=linspace(4e9, 5e9, 1000)
#G_f=0.5*Np*K2*f0*(sin(Np*pi*(freq-f0)/f0)/(Np*sin(pi*(freq-f0)/f0)))**2
#b.scatter_plot("freq_test", freqs, freq_diffs)
class Fitter3(Operative):
base_name="fitter"
mult=FloatRange(0.001, 5.0, 0.82).tag(tracking=True)
f0=FloatRange(4.0, 6.0, 5.348).tag(tracking=True)
offset=FloatRange(0.0, 100.0, 18.0).tag(tracking=True)
@tag_Property(plot=True, private=True)
def G_f(self):
f0=self.f0*1.0e9
return self.offset*1e6+self.mult*0.5*Np*K2*f0*(sin(Np*pi*(freq-f0)/f0)/(Np*pi*(freq-f0)/f0))**2
@observe("f0", "mult", "offset")
def update_plot(self, change):
if change["type"]=="update":
self.get_member("G_f").reset(self)
b.plot_dict["G_f"].clt.set_ydata(self.G_f)
b.draw()
d=Fitter3()
b.line_plot("G_f", freq, d.G_f, label="theory")
def magabs_cs_fit():
def lorentzian(x,p):
return p[2]*(1.0-1.0/(1.0+((x-p[1])/p[0])**2))+p[3]
def fano(x, p):
return p[2]*(((p[4]*p[0]+x-p[1])**2)/(p[0]**2+(x-p[1])**2))+p[3]
def refl_fano(x, p):
return p[2]*(1.0-((p[4]*p[0]+x-p[1])**2)/(p[0]**2+(x-p[1])**2))+p[3]
def onebounce(x,p):
w=2*pi*x
k=2.0*pi*x/3488.0
Cc=0.0
D=500.0e-6
D1=300.0e-6
D2=200.0e-6
S12q=((p[4]*p[0]/6.28+(x-p[1])*2*pi)**2)/(p[0]**2+(p[4]*p[0]/6.28+x-p[1])**2)
S11q=1.0/(p[0]**2+(p[4]*p[0]/6.28+x-p[1])**2)
S11=p[5]
return p[2]*absolute(exp(1.0j*k*D)*S12q*(1+exp(2.0j*k*D1)*S11q*S11+exp(2.0j*k*D2)*S11q*S11+
exp(2.0j*k*D)*S11**2*S12q**2)+2.0j*w*Cc*50.0)**2+p[3]
def allbounces(x,p):
w=2*pi*x
k=2.0*pi*x/3488.0
Cc=0.0
D=500.0e-6
D1=300.0e-6
D2=200.0e-6
return p[2]*absolute(exp(1.0j*k*D)*S12q*(-2.0
+1.0/(1.0-exp(2.0j*k*D1)*S11q*S11)
+1.0/(1.0- exp(2.0j*k*D2)*S11q*S11)
+1.0/(1- exp(2.0j*k*D)*S11**2*S12q**2))
+2.0j*w*Cc*50.0)
def residuals(p,y,x):
err = y - lorentzian(x,p)
return err
def residuals2(p,y,x):
return y - fano(x,p)
def residuals3(p,y,x):
return y - refl_fano(x,p)
p = [200e6,4.5e9, 0.002, 0.022, 0.1, 0.1]
indices=[range(81, 120+1), range(137, 260+1), range(269, 320+1), range(411, 449+1)]#, [490]]#, [186]]
indices=[range(len(a.frequency))]
widths=[]
freqs=[]
freq_diffs=[]
fanof=[]
filt=[]
for n in range(len(yok)):
myifft=fft.ifft(mag[:,n])
#b.line_plot("ifft", absolute(myifft))
myifft[50:-50]=0.0
#myifft[:20]=0.0
#myifft[-20:]=0.0
filt.append(absolute(fft.fft(myifft))**2)
filt=array(filt).transpose()
for ind_list in indices:
for n in ind_list:
pbest = leastsq(residuals3, p, args=(a.MagAbs[n, :], c.flux_parabola[:]), full_output=1)
best_parameters = pbest[0]
print best_parameters
if 0:#n % 8==0:
bb.scatter_plot("magabs_flux", c.flux_parabola[:]*1e-9, a.MagAbs[n, :], label="{}".format(n), linewidth=0.2, marker_size=0.8)
bb.line_plot("lorentzian", c.flux_parabola*1e-9, refl_fano(c.flux_parabola,best_parameters), label="fit {}".format(n), linewidth=0.5)
if 1:#absolute(best_parameters[1]-a.frequency[n])<2e8:
freqs.append(a.frequency[n])
freq_diffs.append(absolute(best_parameters[1]-a.frequency[n]))
widths.append(absolute(best_parameters[0]))
fanof.append(absolute(best_parameters[4]))
if 1:
widths2=[]
freqs2=[]
freq_diffs2=[]
fano2=[]
flux_over_flux0=qdt.call_func("flux_over_flux0", voltage=yok, offset=-0.037, flux_factor=0.2925)
Ej=qdt.call_func("Ej", flux_over_flux0=flux_over_flux0)
flux_par=qdt._get_fq(Ej, qdt.Ec)
magabs=absolute(mag)**2
for n in range(len(frq)):
pbest = leastsq(residuals2,p,args=(filt[n, :], flux_par[:]), full_output=1)
best_parameters = pbest[0]
print best_parameters
if 0:#n==539 or n==554:#n % 10:
b.line_plot("magabs_flux", flux_par*1e-9, (magabs[n, :]-best_parameters[3])/best_parameters[2], label="{}".format(n), linewidth=0.2)
b.line_plot("lorentzian", flux_par*1e-9, fano(flux_par,best_parameters), label="fit {}".format(n), linewidth=0.5)
if 1:#absolute(best_parameters[1]-frq[n])<1.5e8:
freqs2.append(frq[n])
freq_diffs2.append(absolute(best_parameters[1]-frq[n]))
widths2.append(absolute(best_parameters[0]))
fano2.append(absolute(best_parameters[4]))
b.line_plot("widths", freqs, widths, label="-110 dBm")
b.scatter_plot("widths2", freqs2, widths2, color="red", label="-130 dBm")
vf=3488.0
p=[1.0001, 0.5, 0.3, 1.0e-15, 0.001]
Np=9
K2=0.048
f0=5.348e9
def fourier(x, p):
w=2*pi*x
k=2.0*pi*x/3488.0
D=500.0e-6
D1=300.0e-6
D2=200.0e-6
G_f=0.5*Np*K2*f0*(sin(Np*pi*(x-f0)/f0)/(Np*pi*(x-f0)/f0))**2
return G_f*absolute(exp(1.0j*k*D)*p[0]*(1+exp(2.0j*k*D1)*(p[1]+1.0j*p[2])+exp(2.0j*k*D2)*(p[1]+1.0j*p[2]))
+2.0j*w*p[3]*50.0)+p[4]
#exp(2.0j*k*D)*(p[1]+1.0j*p[2]))+
def resid(p,y,x):
#return y - onebounce(x,p)
return y - fourier(x,p)
#pbest=leastsq(resid, p, args=(absolute(widths2[318:876]), frq[318:876]), full_output=1)
#print pbest[0]
#b.line_plot("fourier", frq, fourier(frq, pbest[0]))
#pi*vf/2*x=D
from scipy.signal import lombscargle
#lombscargle(freqs2[318:876], widths2[318:876])
#bb.line_plot("fft", #frq[318:876]*fft.fftfreq(len(frq[318:876]), d=frq[1]-frq[0]),
#absolute(fft.fft(widths2[318:876])))
frqdiffs=linspace(0.01, 500e6, 1000)
#bb.line_plot("ls", frqdiffs, lombscargle(array(freqs2[285:828]), array(widths2[285:828]), frqdiffs ))
#bb.line_plot("ls", frqdiffs, lombscargle(array(freqs[318:876]), array(widths[318:876]), frqdiffs))
bb.line_plot("fft2", absolute(fft.ifft(widths2[285:828])))
bb.line_plot("fft", absolute(fft.ifft(widths[285:828])))
bbb=Plotter()
#bbb.scatter_plot("wid", freqs2, widths2)
myifft=fft.ifft(widths2[285:828])
myifft[12:-12]=0.0
bbb.line_plot("ff", freqs2[285:828], absolute(fft.fft(myifft)))
