def plotShape(pulsef, name, end=14*pi/3):
shape = plt.figure()
pulse_time = np.linspace(0, end, 5000)
plt.plot(pulse_time, [pulsef(t) for t in pulse_time], "b-", label=name)
plt.legend(loc="best")
plt.ylabel(r"Amplitude in $a_{max}$")
plt.xlabel(r"t in $\hbar/a_{max}$")
plt.ylim([-1.1,1.1])
plt.xlim([0.,end])
filename = base + name.replace(" ", "_") + "-shape.png"
shape.savefig(filename)
return pool.map(f, xs)
return map(f, xs)
p_t = []
# XXX SHAPE
pulseshape = donorm(x2p(2., periods=_periods),
0,
2 * _periods,
normproc="none")
# pulseshape = X_factory(pi, 2, True, 1, normproc="full")
tis = np.linspace(0, 6, 1000)
pulseshape_data = [pulseshape(ti) for ti in tis]
pshape = plt.figure()
plt.plot(tis, pulseshape_data, 'b-')
# plt.show()
plt.ion()
fig, ax = plt.subplots()
fig.suptitle(wave + ":" + ptitle)
p1, = plt.plot(p_t, sym1, 'b--', label="t=1")
p3, = plt.plot(p_t, sym3, 'r-', label="t=3")
p15, = plt.plot(p_t, sym12, 'g--', label="t=12")
plt.xlabel(r"width in $\hbar / a_max$")
plt.ylabel(r"$\phi(\rho_f, \rho_0)$")
plt.legend(loc='best')
plt.show()
plt.pause(0.0001)
rho_0 = dm_1
rho_f = dm_0
eta_0 = Delta
N = 420 # number of RTN trajectories
t_end = 21 # end of RTN
T = pi
tau_c_0 = 0.5 * (a_max / hbar)
tau_c_f = 20. * (a_max / hbar)
dtau_c = 0.5 * (a_max / hbar)
tau_c = tau_c_0
tau_cs = [tau_c]
while tau_c < tau_c_f:
tau_c += dtau_c
tau_cs.append(tau_c)
fids = []
for i in range(len(tau_cs)):
print(str(i) + "/" + str(len(tau_cs)))
tau_c = tau_cs[i]
rho_pi = ezGenerate_Rho(a_pi, t_end, tau_c, eta_0, rho_0, N)
fid = fidSingleTxDirect(rho_f, rho_pi, T)
fids.append(fid)
fig = plt.figure()
plt.plot(tau_cs, fids, 'r--', label="pi pulse")
plt.axis([0, 30, 0.975, 1])
plt.legend(loc='best')
plt.show()
fids_pi = np.loadtxt(base + "fids_pi.txt", dtype=ct) fids_C = np.loadtxt(base + "fids_C.txt", dtype=ct) fids_SC = np.loadtxt(base + "fids_SC.txt", dtype=ct) # tx = [] # print(len(fids_C)) # tx.append(fids_C[0]) # for i in range(1,len(fids_C) - 1): # # if i % 2 == 0: # # continue # avg = (fids_C[i-1] + fids_C[i] + fids_C[i+1]) / 3. # mx = max([fids_C[i-1], fids_C[i], fids_C[i+1]]) # tx.append(avg) # # tx.append(avg) # tx.append(fids_C[-1]) # print(len(tx)) # fids_C = np.array(tx) tau_cs = np.array(tau_cs) xnew = tau_cs # xnew = np.linspace(tau_cs.min(),tau_cs.max(),400) # fids_pi = spline(tau_cs, fids_pi, xnew) # fids_C = spline(tau_cs, fids_C, xnew) # fids_SC = spline(tau_cs, fids_SC, xnew) plt.plot(xnew, fids_pi, 'b--', label="pi pulse") plt.plot(xnew, fids_C, 'r-', lw=2, label="CORPSE pulse") plt.plot(xnew, fids_SC, 'r--', label="SCORPSE pulse") plt.show()
Starting...
""".format(**locals()))
def ezmap(f, xs):
if parallel:
return pool.map(f, xs)
return map(f, xs)
p_t = []
plt.ion()
fig, ax = plt.subplots()
p1, = plt.plot(p_t, sym1, 'b--', label="t=1")
p3, = plt.plot(p_t, sym3, 'r-', label="t=3")
p15, = plt.plot(p_t, sym15, 'g--', label="t=15")
plt.xlabel("tau in (hbar / a_max)")
plt.ylabel("fidelity \\phi(rho_f, rho_0)")
plt.legend(loc='best')
plt.show()
plt.pause(0.0001)
start = time.time()
prev_time = -1
for i in range(len(taus)):
tau = taus[i]
p_t.append(tau)
checkGrape = False
# checkGrape = True
if checkGrape:
pulses = buildGrapePulses([1.,0.5,0.25,0.75,1.],3)
ts = np.linspace(0, 3, 1000)
az = []
for t in ts:
dt = 3. / len(pulses)
t_bin = int(np.floor((t / dt)))
t_bin = min(t_bin, len(pulses) - 1)
az.append(pulses[t_bin](t))
# print(pulses[0](0.))
# print(pulses[0](0))
# print(az)
plt.plot(ts, az, 'r-')
plt.show()
raw_input()
# T_G = 5. * hoa # sousa figure 2
T_G = T_SC # can just make them the same
n = 8 # number of different pulse amplitudes
epsilon = 0.009 # amount each gradient step can influence amps
grape_steps = 3000 # number of optimization steps
### Grape
doGrape = True
if doGrape:
tau_grape = 30. # optimize for this point...
# init_amps = [a_max for i in range(n)]
init_amps = [x*a_max for x in [-1, -1, 1, 1, 1, 1, 1, -1]]
grape_amps = init_amps
# fids: []
# }
# pulse_infos = {
# pi: mkInfo(a_pi, T_pi),
# corpse: mkInfo(a_C, T_C),
# scorpse: mkInfo(a_SC, T_SC),
# sym: mkInfo(sym_pi, tau)
# }
p_t = []
shapefig = plt.figure()
chi_time = np.linspace(0 - 1, mytau + 1, 1000)
if do_sym:
plt.plot(chi_time, [sym_pi(t) for t in chi_time],
"c-",
label="Symmetric Pulse shape")
if do_asym:
plt.plot(chi_time, [asym_pi(t) for t in chi_time],
"m-",
label="Antisymmetric Pulse shape")
plt.legend(loc="best")
plt.ion()
fig, ax = plt.subplots()
pulse_plots = []
if do_pi:
p_pi, = plt.plot(p_t, fids_pi, 'b--', label="pi pulse")
pulse_plots.append(p_pi)
if do_c:
checkGrape = False
# checkGrape = True
if checkGrape:
pulses = buildGrapePulses([1., 0.5, 0.25, 0.75, 1.], 3)
ts = np.linspace(0, 3, 1000)
az = []
for t in ts:
dt = 3. / len(pulses)
t_bin = int(np.floor((t / dt)))
t_bin = min(t_bin, len(pulses) - 1)
az.append(pulses[t_bin](t))
# print(pulses[0](0.))
# print(pulses[0](0))
# print(az)
plt.plot(ts, az, 'r-')
plt.show()
raw_input()
T_G = 7 * pi / 3. * hoa # sousa figure 2
n = 7 # number of different pulse amplitudes
epsilon = 0.025 # amount each gradient step can influence amps
grape_steps = 2500 # number of optimization steps
### Grape
# doGrape = True
doGrape = False
if doGrape:
tau_grape = 30.
# init_amps = [a_max for i in range(n)]
inits = [-0.7, 0.7, 0.7, 0.7, 0.7, 0.7, -0.7]
# inits = [-0.98,0.98,0.98,0.98,0.98,0.98,-0.98]