def analysis(path):
def plot_pol():
fpol, axpol = pol.plot(1, 'sec')
axpol.grid(axis='y')
plt.savefig(path+'polarization.png', dpi=450, bbox_inches='tight', pad_inches=.1)
plt.close()
def plot_td():
fig, ax = plot_TD(ps[3000:],num_seg=4)
plt.savefig(path+'PS_TD.png', dpi=450, bbox_inches='tight', pad_inches=.1)
plt.close()
def plot_tss():
print("plotting TSS")
ftss, axtss = tss.plot()
# axtss[0].set_title(name.split(":")[1].strip())
plt.savefig(path+'tss.png', dpi=450, bbox_inches='tight', pad_inches=.1)
plt.close()
tss = TSS(path, 'MU.dat')
sp = Data(path, 'TRPSPI:MAIN.dat')
axis = sp[0,0][['S_X','S_Y','S_Z']]
ps = Data(path, 'TRPRAY:MAIN.dat')
pol = Polarization.on_axis(sp, axis)
plot_pol()
plot_td()
plot_tss()
t = pol['iteration']*TAU
par, err = fit_line(t, pol['Value'])
return par[1], err[1]
def plot_spd():
t = pol['iteration']*TAU
spd = pol.spin_proj.std(axis=1)
fit = lowess(spd[0:None:100], t[0:None:100])
par, err = fit_line(t, spd)
fig, ax = plt.subplots(1,1)
ax.plot(t, spd, '.')
ax.plot(fit[:,0], fit[:, 1], '-k')
ax.plot(t, par[0]+par[1]*t, '-r', label=r'slp = {:4.2e} $\pm$ {:4.2e} [u/sec]'.format(par[1], err[1]))
ax.set_xlabel('sec')
ax.set_ylabel(r'$\sigma(\vec s_i, \bar n_{})$'.format(eid))
ax.grid()
ax.legend()
ax.ticklabel_format(axis='y', style='sci', scilimits=(0,0), useMathText=True)
return fig, ax
def spin_offset_analysis(spdata, psdata, eid):
try:
jj = spdata['EID'][:,0]==eid # SPD eid now is 1, b/c I didn't split the lattice map anywhere else
except:
jj = slice(0, None)
dm = psdata[jj]['D'].mean(0)
sm = {lbl: spdata[jj][lbl].mean(0) for lbl in ['S_X','S_Y','S_Z']}
fig, ax = plt.subplots(3,1, sharex=True)
ax[2].set_xlabel(r'$\langle\delta\rangle$')
for i, it in enumerate(sm.items()):
name, data = it
par, err = fit_line(dm, data)
ax[i].plot(dm, data, '.')
ax[i].plot(dm, par[0] + par[1]*dm, '--r')
ax[i].grid()
ax[i].set_ylabel(r'$\langle$'+r'${}$'.format(name) + r'$\rangle$')
ax[i].ticklabel_format(axis='both', style='sci', scilimits=(0,0))
return fig, ax
def analyze_spin(spdata):
t = spdata['iteration'][:,0]*TAU
fig, ax = plt.subplots(3,1,sharex=True)
ax[2].set_xlabel('sec')
for i, lab in enumerate(['S_X','S_Y','S_Z']):
ax[i].set_ylabel(lab)
ax[i].ticklabel_format(axis='y',style='sci', scilimits=(0,0),useMathText=True)
ax[i].grid()
for s in spdata.T:
for i, v in enumerate(['S_X','S_Y','S_Z']):
sc = s[v]
fit = lowess(sc, t)
par, err = fit_line(t, sc)
ax[i].plot(t, sc, '.',
label='({:4.2e} $\pm$ {:4.2e}, {:4.2e} $\pm$ {:4.2e})'.format(par[0],
err[0],
par[1],
err[1]))
ax[i].plot(t, par[0] + t*par[1], '-r')
ax[i].plot(fit[:,0], fit[:,1], '-k')
ax[i].legend()
return fig, ax
#
sweep.fit = np.array(sweep.fit)
sweep.rabi_freq = sweep.fit.T[0]
sweep.rabi_gamma = sweep.fit.T[1]
#
plt.figure()
plt.plot(sweep.vals, sweep.rabi_freq)
plt.ylabel("Rabi freq (MHz)")
#
plt.figure()
plt.plot(sweep.vals, sweep.rabi_gamma)
plt.ylabel("Rabi decay, 1/T_Rabi (1/us)")
#
popt, perr, _, _ = analysis.fit_line(sweep.vals, sweep.rabi_freq)
#
plt.show()
# times = np.linspace(0., seq.sweep_time, seq.num_patterns)*1e-3
# plt.plot(times, p_readout[0])
# popt, perr, _, _ = analysis.fit_sine_decay(times, p_readout[0])
# #
# print("pi_time from fit (ns): {}".format(1e3*0.5/popt[0]))
# expt_cal = seq_programs.get_expt_cal()
# print("pi_time is expt_cal (ns): {}".format(expt_cal.pi_time.ge))
wx_programs.wx_set_and_amplitude_and_offset()
save_by_pickle((seq, daq, sweep))