def fast_pi2():
folder = toolbox.latest_data('cal_fast_pi_over_2')
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
#a.plot_results_vs_sweepparam(ax=ax, name='adwindata')
x = a.sweep_pts
y = a.p0.reshape(-1)
u_y = a.u_p0.reshape(-1)
n = a.sweep_name
x2 = x[::2]
y2 = y[1::2] - y[::2]
u_y2 = np.sqrt(u_y[1::2]**2 + u_y[::2]**2)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), sharex=True)
ax1.errorbar(x2, y2, yerr=u_y2, fmt='o')
ax1.set_xlabel(n)
ax1.set_title('Difference btw. Pi/2-Pi and Pi/2' + '\n' + a.timestamp)
ax1.set_ylabel('Difference')
m = fit.Parameter((y[-1] - y[0]) / (x[-1] - x[0]), 'm')
x0 = fit.Parameter(x2.mean(), 'x0')
p0 = [m, x0]
def ff(x):
return m() * (x - x0())
fitfunc_str = 'm * (x - x0)'
fit_result = fit.fit1d(x2,
y2,
None,
p0=p0,
fitfunc=ff,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
ax2.errorbar(x2, y[0::2], yerr=u_y[0::2], fmt='o', label='Pi/2')
ax2.errorbar(x2, y[1::2], yerr=u_y[1::2], fmt='o', label='Pi/2 - Pi')
ax2.legend(frameon=True, framealpha=0.5)
ax2.set_ylabel('P(0)')
ax2.set_xlabel(n)
if fit_result != False:
ax2.axvline(x0(), c='k', lw=2)
ax2.axhline(0.5, c='k', lw=2)
ax2.set_title('X marks the spot')
plot.plot_fit1d(fit_result,
np.linspace(x2[0], x2[-1], 201),
ax=ax1,
plot_data=False,
print_info=True)
return x0(), 0
def stage_4_calibrations():
fig, ax = plt.subplots(1, 1, figsize=(5, 4))
folder = toolbox.latest_data('t_between_pulses')
print 80 * '='
print 'time between pi pulses'
print 80 * '='
t_between_pi_pulses = t_between_pi(folder, ax)
fig.savefig(os.path.join(folder, 'stage_4_calibrations.png'))
def stage_3_calibrations():
fig, ax = plt.subplots(1,1, figsize = (5,4))
folder = toolbox.latest_data('calibrate_LDE_spin_echo')
print 80*'='
print 'LDE-DD spin echo time'
print 80*'='
DD_spin_echo_time = DD_spin_echo(folder, ax)
fig.savefig(os.path.join(folder, 'stage_3_calibrations.png'))
def stage_4_calibrations():
fig, ax = plt.subplots(1,1, figsize = (5,4))
folder = toolbox.latest_data('t_between_pulses')
print 80*'='
print 'time between pi pulses'
print 80*'='
t_between_pi_pulses = t_between_pi(folder, ax)
fig.savefig(os.path.join(folder, 'stage_4_calibrations.png'))
def stage_3_calibrations():
fig, ax = plt.subplots(1, 1, figsize=(5, 4))
folder = toolbox.latest_data('calibrate_LDE_spin_echo')
print 80 * '='
print 'LDE-DD spin echo time'
print 80 * '='
DD_spin_echo_time = DD_spin_echo(folder, ax)
fig.savefig(os.path.join(folder, 'stage_3_calibrations.png'))
def stage_0p5_calibrations():
# ssro first
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,4))
print 80*'='
print 'Dark ESR'
print 80*'='
f0,u_f0 = dark_esr_analysis.analyze_dark_esr(
toolbox.latest_data('DarkESR'), ax=ax1, ret='f0',
print_info=False)
def fast_rabi(ax=None):
folder = toolbox.latest_data("cal_fast_rabi" + name)
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_rabi(folder, ax, 1.0 / 125, 0.5, fit_k=False)
f = fit_result["params_dict"]["f"]
u_f = fit_result["error_dict"]["f"]
ax.text(100, 0.9, "$f_r$ = (%.3f +/- %.3f) MHz" % (f * 1e3, u_f * 1e3), va="bottom", ha="left")
return (f * 1e3, u_f * 1e3)
def stage_0p5_calibrations():
# ssro first
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
print 80 * '='
print 'Dark ESR'
print 80 * '='
f0, u_f0 = dark_esr_analysis.analyze_dark_esr(
toolbox.latest_data('DarkESR'), ax=ax1, ret='f0', print_info=False)
fig.savefig(os.path.join(folder, 'stage_0p5_calibrations.png'))
def N_frq(ax=None):
folder = toolbox.latest_data("NMR_frq_scan")
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = fit_population_vs_detuning(folder, ax, 7.135)
x0 = fit_result["params_dict"]["x0"]
u_x0 = fit_result["error_dict"]["x0"]
ax.text(7.1, 0.3, "$N_{frq}$ = (%.3f +/- %.3f) MHz" % (x0, u_x0), va="bottom", ha="left")
return (f * 1e3, u_f * 1e3)
def pi_pi2pi_mI0(ax=None):
folder = toolbox.latest_data("cal_pi2pi_pi_mI0")
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_amplitude(folder, ax, 0.11, 1, 0)
A = fit_result["params_dict"]["x0"]
u_A = fit_result["error_dict"]["x0"]
ax.text(0.1, 0.5, "A = (%.3f +/- %.3f) V" % (A, u_A))
return A, u_A
def t_between_pi(ax=None):
folder = toolbox.latest_data('t_between_pulses')
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = calibrate_epulse_amplitude(folder, ax, 0.02, 1, 0 )
revival = fit_result['params_dict']['x0']
u_revival = fit_result['error_dict']['x0']
ax.text(revival, 0.3, 'tbetweenpi = (%.3f +/- %.3f)' % (revival, u_revival))
return revival, u_revival
def DD_spin_echo(ax=None):
folder = toolbox.latest_data('calibrate_LDE_spin_echo')
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = calibrate_epulse_amplitude(folder, ax, -.1, 1, 0 )
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
ax.text(0., 0.5, 'dt = (%.3f +/- %.3f) us' % (A, u_A))
return A, u_A
def C13_rev_small_range(ax=None):
folder = toolbox.latest_data('calibrate_first_revival')
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = calibrate_epulse_amplitude(folder, ax, 110, 1, 0 )
revival = fit_result['params_dict']['x0']
u_revival = fit_result['error_dict']['x0']
ax.text(revival-20, 0.3, 'tfet = (%.3f +/- %.3f)' % (revival, u_revival))
return revival, u_revival
def N_frq(ax=None):
folder = toolbox.latest_data('NMR_frq_scan')
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = fit_population_vs_detuning(folder, ax, 7.135)
x0 = fit_result['params_dict']['x0']
u_x0 = fit_result['error_dict']['x0']
ax.text(7.1, 0.3, '$N_{frq}$ = (%.3f +/- %.3f) MHz' % (x0, u_x0),
va='bottom', ha='left')
return (f*1e3, u_f*1e3)
def CORPSE_fidelity(ax=None):
folder = toolbox.latest_data()
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = epulse_fidelity(folder, ax, 1)
fid = fit_result['params_dict']['of']
u_fid = fit_result['error_dict']['of']
ax.text(429, 0.5, 'of = (%.3f +/- %.3f)' % (fid, u_fid))
return fid, u_fid
def CORPSE_fidelity(ax=None):
folder = toolbox.latest_data()
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = epulse_fidelity(folder, ax, 1)
fid = fit_result['params_dict']['of']
u_fid = fit_result['error_dict']['of']
ax.text(429, 0.5, 'of = (%.3f +/- %.3f)' % (fid, u_fid))
return fid, u_fid
def CORPSE_pi2_fidelity(ax=None):
folder = toolbox.latest_data('CORPSEPi2Calibration')
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = epulse_fidelity(folder, ax, 0.5)
fid = fit_result['params_dict']['of']
u_fid = fit_result['error_dict']['of']
ax.text(0.6, 0.2, 'of = (%.3f +/- %.3f)' % (fid, u_fid))
return fid, u_fid
def C13_rev(ax=None):
folder = toolbox.latest_data('calibrate_first_revival')
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = fit_gaussian(folder, ax, 110)
revival = fit_result['params_dict']['x0']
u_revival = fit_result['error_dict']['x0']
ax.text(revival - 20, 0.3, 'tfet = (%.3f +/- %.3f)' % (revival, u_revival))
return revival, u_revival
def calibrate_xy4_fet(ax=None):
folder = toolbox.latest_data('bare_xy4')
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = calibrate_epulse_amplitude(folder, ax, 107, 0, 0 )
revival = fit_result['params_dict']['x0']
u_revival = fit_result['error_dict']['x0']
ax.text(revival, 0.5, 'tfet = (%.3f +/- %.3f)' % (revival, u_revival))
return revival, u_revival
def dd_delta_t(ax=None):
folder = toolbox.latest_data('DynamicalDecoupling')
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = calibrate_epulse_amplitude(folder, ax, -0.24, 1, 0 )
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
ax.text(-0.24, 0.5, 'dt = (%.3f +/- %.3f) ns' % (A, u_A))
return A, u_A
def C13_rev(ax=None):
folder = toolbox.latest_data('calibrate_first_revival')
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = fit_gaussian(folder, ax, 110)
revival = fit_result['params_dict']['x0']
u_revival = fit_result['error_dict']['x0']
ax.text(revival-20, 0.3, 'tfet = (%.3f +/- %.3f)' % (revival, u_revival))
return revival, u_revival
def CORPSE_pi2_fidelity(ax=None):
folder = toolbox.latest_data('CORPSEPi2Calibration')
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = epulse_fidelity(folder, ax, 0.5)
fid = fit_result['params_dict']['of']
u_fid = fit_result['error_dict']['of']
ax.text(0.6, 0.2, 'of = (%.3f +/- %.3f)' % (fid, u_fid))
return fid, u_fid
def dd_calibrate_fidelity(r, ax=None):
folder = toolbox.latest_data('DynamicalDecoupling')
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = calibrate_epulse_amplitude(folder, ax, r*108, 1, 0 )
A = fit_result['params_dict']['of']
u_A = fit_result['error_dict']['of']
ax.text(r*108, 0.5, 'F = (%.3f +/- %.3f)' % (A, u_A))
return A, u_A
def dd_calibrate_fidelity(r, ax=None):
folder = toolbox.latest_data('DynamicalDecoupling')
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_amplitude(folder, ax, r * 108, 1, 0)
A = fit_result['params_dict']['of']
u_A = fit_result['error_dict']['of']
ax.text(r * 108, 0.5, 'F = (%.3f +/- %.3f)' % (A, u_A))
return A, u_A
def calibrate_xy4_fet(ax=None):
folder = toolbox.latest_data('bare_xy4')
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_amplitude(folder, ax, 107, 0, 0)
revival = fit_result['params_dict']['x0']
u_revival = fit_result['error_dict']['x0']
ax.text(revival, 0.5, 'tfet = (%.3f +/- %.3f)' % (revival, u_revival))
return revival, u_revival
def fast_rabi(ax=None):
folder = toolbox.latest_data('cal_fast_rabi'+name)
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = calibrate_epulse_rabi(folder, ax, 1./125, 0.5, fit_k=False)
f = fit_result['params_dict']['f']
u_f = fit_result['error_dict']['f']
ax.text(100, 0.9, '$f_r$ = (%.3f +/- %.3f) MHz' % (f*1e3, u_f*1e3),
va='bottom', ha='left')
return (f*1e3, u_f*1e3)
def CORPSE_pi2_alt(sil,M,ax=None):
folder = toolbox.latest_data('CORPSEPi2Calibration_sil'+str(sil)+str(M))
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = calibrate_epulse_amplitude(folder, ax, 0.46, 0, 0 )
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
ax.text(0.42, 0.5, 'A = (%.3f +/- %.3f) V' % (A, u_A))
return A, u_A
def dd_delta_t(ax=None):
folder = toolbox.latest_data('DynamicalDecoupling')
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_amplitude(folder, ax, -0.24, 1, 0)
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
ax.text(-0.24, 0.5, 'dt = (%.3f +/- %.3f) ns' % (A, u_A))
return A, u_A
def slow_pi(ax=None):
folder = toolbox.latest_data("cal_slow_pi_" + name)
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_rabi(folder, ax, 0.015, 1.0, 0.0, fit_x0=False, fit_k=False)
f = fit_result["params_dict"]["f"]
u_f = fit_result["error_dict"]["f"]
# ax.text(500, 0.4, 'pi = (%.0f +/- %.0f) ns' % (0.5/f, 0.5/f**2 * u_f),
# va='bottom', ha='left')
ax.text(0.015, 0.4, "pi = (%.4f +/- %.4f) V" % (0.5 / f, 0.5 / f ** 2 * u_f), va="bottom", ha="left")
return (0.5 / f, 0.5 / f ** 2 * u_f)
def CORPSE_pi2_alt(sil, M, ax=None):
folder = toolbox.latest_data('CORPSEPi2Calibration_sil' + str(sil) +
str(M))
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_amplitude(folder, ax, 0.46, 0, 0)
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
ax.text(0.42, 0.5, 'A = (%.3f +/- %.3f) V' % (A, u_A))
return A, u_A
def fast_pi2():
folder = toolbox.latest_data('cal_fast_pi_over_2')
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
#a.plot_results_vs_sweepparam(ax=ax, name='adwindata')
x = a.sweep_pts
y = a.p0.reshape(-1)
u_y = a.u_p0.reshape(-1)
n = a.sweep_name
x2 = x[::2]
y2 = y[1::2] - y[::2]
u_y2 = np.sqrt( u_y[1::2]**2 + u_y[::2]**2 )
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,4), sharex=True)
ax1.errorbar(x2, y2, yerr=u_y2, fmt='o')
ax1.set_xlabel(n)
ax1.set_title('Difference btw. Pi/2-Pi and Pi/2'+'\n'+a.timestamp)
ax1.set_ylabel('Difference')
m = fit.Parameter((y[-1]-y[0])/(x[-1]-x[0]), 'm')
x0 = fit.Parameter(x2.mean(), 'x0')
p0 = [m, x0]
def ff(x):
return m() * (x-x0())
fitfunc_str = 'm * (x - x0)'
fit_result = fit.fit1d(x2, y2, None, p0=p0, fitfunc=ff,
fitfunc_str=fitfunc_str, do_print=True, ret=True)
ax2.errorbar(x2, y[0::2], yerr=u_y[0::2], fmt='o',
label='Pi/2')
ax2.errorbar(x2, y[1::2], yerr=u_y[1::2], fmt='o',
label='Pi/2 - Pi')
ax2.legend(frameon=True, framealpha=0.5)
ax2.set_ylabel('P(0)')
ax2.set_xlabel(n)
if fit_result != False:
ax2.axvline(x0(), c='k', lw=2)
ax2.axhline(0.5, c='k', lw=2)
ax2.set_title('X marks the spot')
plot.plot_fit1d(fit_result, np.linspace(x2[0],x2[-1],201), ax=ax1,
plot_data=False, print_info=True)
return x0(), 0
def N_rabi(ax=None):
folder = toolbox.latest_data("Nuclear_rabi")
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_Npulse_rabi(folder, ax, 1.0 / 100, 0.5)
f = fit_result["params_dict"]["f"]
u_f = fit_result["error_dict"]["f"]
ax.text(100, 0.9, "$f_r$ = (%.3f +/- %.3f) MHz" % (f * 1e3, u_f * 1e3), va="bottom", ha="left")
ax.text(
100, 0.7, "pi pulse = (%.3f +/- %.3f) us" % (1.0 / (2 * f), 1.0 / (2 * f) * u_f / f), va="bottom", ha="left"
)
return (f * 1e3, u_f * 1e3)
def N_rabi(ax=None):
folder = toolbox.latest_data('Nuclear_rabi')
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = calibrate_Npulse_rabi(folder, ax, 1./100, 0.5)
f = fit_result['params_dict']['f']
u_f = fit_result['error_dict']['f']
ax.text(100, 0.9, '$f_r$ = (%.3f +/- %.3f) MHz' % (f*1e3, u_f*1e3),
va='bottom', ha='left')
ax.text(100, 0.7, 'pi pulse = (%.3f +/- %.3f) us' % (1./(2*f), 1./(2*f)*u_f/f),
va='bottom', ha='left')
return (f*1e3, u_f*1e3)
def Hadamard_phase(ax=None, do_print_text=True):
folder = toolbox.latest_data('TestBSM_LDE_calibrate_H_phase') # _'+name)
if ax==None:
do_print_text = False
fig,ax = plt.subplots(1,1)
fit_result = fit_correlation_parabolic(folder, ax, 100, 1, 0., which_correlation=0)
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
if do_print_text:
ax.text(10, 0.8, 'H phase = (%.3f +/- %.3f) ' % (A, u_A))
return A, u_A
def Hadamard_phase_large_range(ax=None, do_print_text=True):
folder = toolbox.latest_data("TestBSM_LDE_calibrate_H_phase") # _'+name)
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
fit_result = fit_correlation_oscillation(folder, ax, 100, 1.0 / 360, 0.25, which_correlation=0)
A = fit_result["params_dict"]["x0"]
u_A = fit_result["error_dict"]["x0"]
if do_print_text:
ax.text(10, 0.8, "H phase = (%.3f +/- %.3f) " % (A, u_A))
return A, u_A
def Hadamard_ev_time_large_range(ax=None, do_print_text=True):
folder = toolbox.latest_data("calibrate_H_ev_time") # _'+name)
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
fit_result = fit_correlation_oscillation(folder, ax, 35.08, 1.0 / 0.05 / 10.0, 0.25, which_correlation=3)
A = fit_result["params_dict"]["x0"]
u_A = fit_result["error_dict"]["x0"]
if do_print_text:
ax.text(35.07, 0.8, "H phase = (%.4f +/- %.4f) " % (A, u_A))
return A, u_A
def stage_2_calibrations():
# fig, ax = plt.subplots(1,1, figsize = (5,4))
# print 80*'='
# print 'first C13 revival'
# print 80*'='
# C13_revival = C13_rev(ax)
fig, ax = plt.subplots(1, 1, figsize=(5, 4))
folder = toolbox.latest_data('calibrate_first_revival')
print 80 * '='
print 'first C13 revival'
print 80 * '='
C13_revival_small_range = C13_rev_small_range(folder, ax)
fig.savefig(os.path.join(folder, 'stage_2_calibrations.png'))
def CORPSE_pi(ax=None):
folder = toolbox.latest_data('CORPSEPiCalibration')
if ax==None:
fig,ax = plt.subplots(1,1)
fit_result = calibrate_epulse_amplitude(folder, ax, 0.38, 1, 0 )
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
of = fit_result['params_dict']['of']
u_of = fit_result['error_dict']['of']
ax.text(0.42, 0.5, 'A = (%.3f +/- %.3f) V' % (A, u_A))
ax.text(0.42, 0.8, 'fid = (%.3f +/- %.3f) V' % (of, u_of))
return A, u_A
def stage_2_calibrations():
# fig, ax = plt.subplots(1,1, figsize = (5,4))
# print 80*'='
# print 'first C13 revival'
# print 80*'='
# C13_revival = C13_rev(ax)
fig, ax = plt.subplots(1,1, figsize = (5,4))
folder = toolbox.latest_data('calibrate_first_revival')
print 80*'='
print 'first C13 revival'
print 80*'='
C13_revival_small_range = C13_rev_small_range(folder, ax)
fig.savefig(os.path.join(folder, 'stage_2_calibrations.png'))
def spin_echo(ax=None, do_print_text=True):
folder = toolbox.latest_data("calibrate_echo") # _'+name)
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_amplitude(folder, ax, 200, 1, 0.0, double_ro="electron")
A = fit_result["params_dict"]["x0"]
u_A = fit_result["error_dict"]["x0"]
if do_print_text:
ax.text(100, 0.5, "echo = (%.3f +/- %.3f) ns" % (A, u_A))
return A, u_A
def Hadamard_ev_time(ax=None, do_print_text=True):
folder = toolbox.latest_data("calibrate_H_ev_time") # _'+name)
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
fit_result = fit_correlation_parabolic(folder, ax, 35.0, 1, 0.0, which_correlation=3)
A = fit_result["params_dict"]["x0"]
u_A = fit_result["error_dict"]["x0"]
if do_print_text:
ax.text(35.0, 0.8, "evo time = (%.3f +/- %.3f) us" % (A, u_A))
return A, u_A
def rabi_8mhz(ax=None):
folder = toolbox.latest_data('cal_8mhz_rabi')
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_rabi(folder, ax, 1. / 125, 0.5, fit_k=False)
f = fit_result['params_dict']['f']
u_f = fit_result['error_dict']['f']
ax.text(50,
0.85,
'$f_r$ = (%.3f +/- %.3f) MHz' % (f * 1e3, u_f * 1e3),
va='bottom',
ha='left')
return (f * 1e3, u_f * 1e3)
def CORPSE_pi(ax=None, do_print_text=True):
folder = toolbox.latest_data('CORPSEPiCalibration') # _'+name)
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_amplitude(folder, ax, 0.5, 1, 0)
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
if do_print_text:
ax.text(0.53, 0.5, 'A = (%.3f +/- %.3f) V' % (A, u_A))
return A, u_A
def pi_pi2pi(ax=None, do_print_text=True):
folder = toolbox.latest_data("cal_pi2pi_pi_" + name)
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_amplitude(folder, ax, 0.11, 1, 0) # ax, 0.085, 1, 0)
A = fit_result["params_dict"]["x0"]
u_A = fit_result["error_dict"]["x0"]
if do_print_text:
ax.text(0.1, 0.5, "A = (%.3f +/- %.3f) V" % (A, u_A))
return A, u_A
def N_frq(ax=None):
folder = toolbox.latest_data('NMR_frq_scan')
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = fit_population_vs_detuning(folder, ax, 7.135)
x0 = fit_result['params_dict']['x0']
u_x0 = fit_result['error_dict']['x0']
ax.text(7.1,
0.3,
'$N_{frq}$ = (%.3f +/- %.3f) MHz' % (x0, u_x0),
va='bottom',
ha='left')
return (f * 1e3, u_f * 1e3)
def get_data(name_contains):
folder = toolbox.latest_data(name_contains)
print folder
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='measurement0')
a.get_electron_ROC()
#ax = a.plot_results_vs_sweepparam(ret='ax', )
t_shift = 0
# t_start=0
# t_0=10
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
return x, y, folder
def stage_1_calibrations():
fig, ax = plt.subplots(1, 1, figsize=(5, 4))
# print 80*'='
# print '8 MHz electron Rabi'
# print 80*'='
# CORPSE_freq = rabi_8mhz(ax2)
folder = toolbox.latest_data('CORPSEPiCalibration')
print 80 * '='
print 'CORPSE pi'
print 80 * '='
CORPSE_pi_amp = CORPSE_pi(folder, ax)
# print 80*'='
# print 'CORPSE pi/2'
# print 80*'='
# CORPSE_pi2_amp = CORPSE_pi2(ax2)
fig.savefig(os.path.join(folder, 'stage_0p5_calibrations.png'))
def Hadamard_phase_large_range(ax=None, do_print_text=True):
folder = toolbox.latest_data('TestBSM_LDE_calibrate_H_phase') # _'+name)
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
fit_result = fit_correlation_oscillation(folder,
ax,
100,
1. / 360,
0.25,
which_correlation=0)
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
if do_print_text:
ax.text(10, 0.8, 'H phase = (%.3f +/- %.3f) ' % (A, u_A))
return A, u_A
def Hadamard_ev_time_large_range(ax=None, do_print_text=True):
folder = toolbox.latest_data('calibrate_H_ev_time') # _'+name)
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
fit_result = fit_correlation_oscillation(folder,
ax,
35.08,
1. / 0.05 / 10.,
0.25,
which_correlation=3)
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
if do_print_text:
ax.text(35.07, 0.8, 'H phase = (%.4f +/- %.4f) ' % (A, u_A))
return A, u_A
def CORPSE_vs_angle(ax=None):
folder = toolbox.latest_data('CORPSECalibration')
if ax == None:
fig, ax = plt.subplots(1, 1)
f0 = 1. / 360
fit_result = calibrate_epulse_rabi(folder, ax, f0, 0.5, fit_k=False)
f = fit_result['params_dict']['f']
u_f = fit_result['error_dict']['f']
ax.text(50,
0.35,
'$f_r$ = (%.6f +/- %.6f) \n $(f_r-f_0)/f_0$ = %.3f' %
(f, u_f, (f - f0) / f0),
va='bottom',
ha='left')
return f, u_f
def CORPSE_phase(ax=None, do_print_text=True):
folder = toolbox.latest_data('CalibrateCORPSEPiPhase') # _'+name)
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_amplitude(folder,
ax,
107,
1,
0.,
double_ro='electron')
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
if do_print_text:
ax.text(100, 0.5, 'A = (%.3f +/- %.3f) V' % (A, u_A))
return A, u_A
def UNROT_evtime(ax=None, do_print_text=True):
folder = toolbox.latest_data('Calibrate_UNROT_X_timing')
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_amplitude(folder,
ax,
51,
0,
1.,
double_ro='nitrogen')
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
if do_print_text:
ax.text(51.03, 0.5, 'A = (%.3f +/- %.3f) us' % (A, u_A))
return A, u_A
def spin_echo(ax=None, do_print_text=True):
folder = toolbox.latest_data('calibrate_echo') # _'+name)
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_amplitude(folder,
ax,
200,
1,
0.,
double_ro='electron')
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
if do_print_text:
ax.text(100, 0.5, 'echo = (%.3f +/- %.3f) ns' % (A, u_A))
return A, u_A
def Hadamard_ev_time(ax=None, do_print_text=True):
folder = toolbox.latest_data('calibrate_H_ev_time') # _'+name)
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
fit_result = fit_correlation_parabolic(folder,
ax,
35.0,
1,
0.,
which_correlation=3)
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
if do_print_text:
ax.text(35.0, 0.8, 'evo time = (%.3f +/- %.3f) us' % (A, u_A))
return A, u_A
def N_rabi(ax=None):
folder = toolbox.latest_data('Nuclear_rabi')
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_Npulse_rabi(folder, ax, 1. / 100, 0.5)
f = fit_result['params_dict']['f']
u_f = fit_result['error_dict']['f']
ax.text(100,
0.9,
'$f_r$ = (%.3f +/- %.3f) MHz' % (f * 1e3, u_f * 1e3),
va='bottom',
ha='left')
ax.text(100,
0.7,
'pi pulse = (%.3f +/- %.3f) us' % (1. / (2 * f), 1. /
(2 * f) * u_f / f),
va='bottom',
ha='left')
return (f * 1e3, u_f * 1e3)
def UNROT_evtime_large_range(ax=None, do_print_text=True):
folder = toolbox.latest_data('Calibrate_UNROT_X_timing')
if ax == None:
do_print_text = False
fig, ax = plt.subplots(1, 1)
guess_x0 = 52.5
fit_result = calibrate_epulse_rabi(folder,
ax,
1. / 0.45,
0.5,
guess_x0=guess_x0,
double_ro='nitrogen',
fit_x0=True)
A = fit_result['params_dict']['x0']
u_A = fit_result['error_dict']['x0']
if do_print_text:
ax.text(guess_x0, 0.5, 'A = (%.3f +/- %.3f) us' % (A, u_A))
return A, u_A
def slow_pi(ax=None):
folder = toolbox.latest_data('cal_slow_pi_' + name)
if ax == None:
fig, ax = plt.subplots(1, 1)
fit_result = calibrate_epulse_rabi(folder,
ax,
0.015,
1.0,
0.,
fit_x0=False,
fit_k=False)
f = fit_result['params_dict']['f']
u_f = fit_result['error_dict']['f']
#ax.text(500, 0.4, 'pi = (%.0f +/- %.0f) ns' % (0.5/f, 0.5/f**2 * u_f),
# va='bottom', ha='left')
ax.text(0.015,
0.4,
'pi = (%.4f +/- %.4f) V' % (0.5 / f, 0.5 / f**2 * u_f),
va='bottom',
ha='left')
return (0.5 / f, 0.5 / f**2 * u_f)
import os, sys
import numpy as np
import h5py
import logging
from matplotlib import pyplot as plt
from measurement.lib.tools import toolbox
FOLDER = toolbox.latest_data('out-003')
FN = toolbox.measurement_filename(FOLDER)
BINEDGES = np.arange(3001) - 0.5
def get_photon_times_ns(FN):
f = h5py.File(FN, 'r')
channel = f['/HH_channel-1'].value
special = f['/HH_special-1'].value
sync_time = f['/HH_sync_time-1'].value
f.close()
# TODO this could maybe be done more efficient with cython
is_not_special = special == 0
is_channel_0 = channel == 0
is_channel_1 = channel == 1
is_photon_0 = np.logical_and(is_not_special, is_channel_0)
is_photon_1 = np.logical_and(is_not_special, is_channel_1)
return sync_time[is_photon_0] * 1e-3, sync_time[is_photon_1] * 1e-3
from analysis.lib.fitting import fit, common
from measurement.lib.tools import toolbox
from analysis.lib.tools import plot
from analysis.lib.m2.ssro import ssro
from analysis.lib.m2.ssro import sequence
reload(sequence)
folder = None
timestamp = None#'095551'#'180057'# None
if folder == None:
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('AdwinSSRO')
a = ssro.SSROAnalysis(folder)
b = sequence.SequenceAnalysis(folder)
runs = 1
debug = False
if debug:
runs =2
max_ts = [ ]
taus = [ ]
u_taus =[ ]
means = [ ]
u_means = []
percentage_passes = [ ]
from matplotlib import pyplot as plt
from analysis.lib import fitting
from analysis.lib.m2.ssro import mbi
from measurement.lib.tools import toolbox
from analysis.lib.tools import plot
from analysis.lib.math import error
### settings
timestamp = None #
### script
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('MBI_fidelity')
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
ax = a.plot_results_vs_sweepparam(ret='ax', name='adwindata')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
u_y = a.u_p0.reshape(-1)[:]
ax.set_ylim(0.4, 1.05)
for (_x, _y, _u_y) in zip(x, y, u_y):
ax.annotate("{:.3f} +/- {:.3f}".format(_y, _u_y), (_x, _y),
