show()
# <headingcell level=2>
# Approximation as Second Order System
# <headingcell level=3>
# I) Second Order System Parameters from Step Response
# <codecell>
figure("AOM Response")
ax = subplot(111)
p.colorcycle(3)
p.grey_title("Measured System Step Response")
ax.yaxis.set_major_locator(MultipleLocator(0.2))
ax.yaxis.set_minor_locator(MultipleLocator(0.1))
ax.xaxis.set_minor_locator(MultipleLocator(0.25e-6))
ax.xaxis.set_major_locator(MultipleLocator(1e-6))
#fill_between(tDDS, 0.99, 1.01, color = 'grey', alpha = 0.75)
amp = linspace(0,1,10)
time = linspace(-1.4e-6, 6.4e-6, 10)
plot([0.260e-6]*len(amp), amp, '--', lw = 1.5, color = 'grey')
plot([0.520e-6]*len(amp), amp, '--', lw = 1.5, color = 'grey')
plot([2e-6]*len(amp), amp, '--', lw = 1.5, color = 'grey')
plot(time , [1.43]*len(time), '--', lw = 1.5, color = 'grey')
grid(True, which = 'both')
for i in arange(0,len_files):
x[i], y[i] = array(data[i][:,0]), array(data[i][:,1])
# <codecell>
def center(x_values, y_values):
y_values = list(y_values)
maximum_index = y_values.index(max(y_values))
centerfreq = x_values[maximum_index]
return centerfreq
# <codecell>
figure()
subplot(121)
p.grey_title("Data on Linear Scale")
p.colorcycle(len_files)
for i in arange(0, len_files):
plot(x[i], y[i], label = str(i))
p.scale_xaxis(subplot(121),1e-6)
xlabel(r'Frequency [MHz]')
ylabel(r'Amplitude [dB]')
subplot(122)
p.grey_title("Data on Log Scale")
p.colorcycle(len_files)
for i in arange(0, len_files):
plot(x[i]-center(x[i],y[i]), y[i], label = 'RBW='+str(RBW[i]))
xscale("log")
xlabel(r'Offset Frequency [Hz]')
ylabel(r'Amplitude [dB]')
# <headingcell level=2>
# Plot found Files
# <codecell>
figure(1)
p.colorcycle(len_files)
for i in arange(0, len_files):
plot(x[i], y[i], label = str(i))
i += 1
legend()
xlabel("offset frequency [Hz]")
ylabel("amplitude [dBm/Hz]")
p.grey_title("comparison_step_1_plot_input_data")
savefig("comparison_step_1_plot_input_data.png")
show()
# <headingcell level=2>
# Shift to 0 Hz Center
# <codecell>
def find_center(x_values, y_values):
y_values = list(y_values)
maximum_index = y_values.index(max(y_values))
centerfreq = x_values[maximum_index]
return centerfreq, maximum_index
def Make_Array(xvals, expr, sub):
res = []
for xval in xvals:
result = real(expr.subs({sub:xval}))
res.append(result)
return res
# <headingcell level=4>
# Without Additional Errors
# <codecell>
figure()
p.colorcycle(3)
p.grey_title('Variation of the Trapping Frequency with Errors')
thetas = linspace(0,pi,100)
P = Make_Array(thetas, P0, theta)
plot(thetas, array(P)**0.5, lw = 2, label = r'Trapping Frequency for state $|0\rangle$')
P = Make_Array(thetas, P1, theta)
plot(thetas, array(P)**0.5, '--', lw = 2, label = r'Trapping Frequency for state $|1\rangle$')
#ylim(0.86, 1.005)
xticks([0,pi/2,pi], ['0', r'$\pi/2$', r'$\pi$'])
xlabel(r'Relative Angle $\theta$')
ylabel(r'Trapping Frequency in Multiples of $\omega_{trap}$')
leg = legend()
leg.get_frame().set_alpha(0.4)
x, y, y_lin, start, stop, phi = [0]*n,[0]*n,[0]*n,[0]*n,[0]*n,[0]*n
# <codecell>
########## Step 1: plot all found input data ############
figure(1)
p.colorcycle(n+1)
for i in arange(0,n):
plot(p.x[i], p.y[i], label = str(i))
plot(x_res, y_res, label = r"$S_\phi\ [dB rad^2/Hz]$")
legend()
xlabel("offset frequency [Hz]")
ylabel("amplitude [dBm/Hz]")
p.grey_title("comparison_step_1_plot_input_data")
savefig("comparison_step_1_plot_input_data.png")
show()
# <codecell>
######### Step 2: find integration limits ############
for i in arange(0,n):
######## on linear range #############
y_lin[i] = [pow(10, element/10.0) for element in p.y[i]]
######## find integration limits ############
start[i] = p.find_nearest(p.x[i], xmin)
stop[i] = p.find_nearest(p.x[i], xmax)
y_res_lin = [pow(10, element/10.0) for element in y_res]
