def epulse_fidelity(folder, ax, *args):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
of = fit.Parameter(args[0], 'of')
of2 = fit.Parameter(0, 'of2')
of3 = fit.Parameter(0, 'of3')
fitfunc_str = '(1-of)'
def fitfunc_fid(x):
return (1. - of())
fit_result = fit.fit1d(x,
y,
None,
p0=[of],
fixed=[],
fitfunc=fitfunc_fid,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
#plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax,
# plot_data=False, print_info=False)
return fit_result
def calibrate_epulse_amplitude(folder, ax, *args):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
ax.set_title(a.timestamp)
x0 = fit.Parameter(args[0], 'x0')
of = fit.Parameter(args[1], 'of')
a = fit.Parameter(args[2], 'a')
fitfunc_str = '(1-of) + a (x-x0)**2'
def fitfunc_parabolic(x):
return (1. - of()) + a() * (x - x0())**2
fit_result = fit.fit1d(x,
y,
None,
p0=[of, a, x0],
fitfunc=fitfunc_parabolic,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False,
print_info=True)
return fit_result
def analyze_data(timestamp,
output=False): #, guess_frq = 1 / 20e9, guess_amp = 0.5):
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('ElectronT1')
print folder
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
# print a.p0
ax = a.plot_result_vs_sweepparam(ret='ax')
# plt.ylim([0,0.052])
x = a.sweep_pts
y = a.p0
if output:
return x, y, ax
else:
return ax
def fit_linear(folder, ax, value, *args):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
ax.set_title(a.timestamp)
a = fit.Parameter(args[0], 'a')
b = fit.Parameter(args[0], 'b')
fitfunc_str = 'a x + b'
def fitfunc(x):
return a() * x + b()
fit_result = fit.fit1d(x,
y,
None,
p0=[a, b],
fitfunc=fitfunc,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False,
print_info=False)
return fit_result
def analyze_data(timestamp,
output=False): #, guess_frq = 1 / 20e9, guess_amp = 0.5):
# o = fit.Parameter(guess_of, 'o')
# f = fit.Parameter(guess_frq, 'f')
# A = fit.Parameter(guess_amp, 'A')
# phi = fit.Parameter(guess_phi, 'phi')
# k = fit.Parameter(guess_k, 'k')
# p0 = [f, A, phi, o, k]
# fitfunc_str = ''
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('Pi_Calibration')
print folder
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
ax = a.plot_result_vs_sweepparam(ret='ax')
# plt.ylim([0,0.052])
x = a.sweep_pts
y = a.p0
if output:
return folder, x, y
def load_data(self):
a = sequence.SequenceAnalysis(self.folder)
a.get_sweep_pts()
a.get_magnetometry_data(name='adwindata', ssro=False)
self.msmnt_results = a.clicks
self.reps, self.N = np.shape(a.clicks)
self.msmnt_times = np.zeros(len(a.ramsey_time))
for j in np.arange(len(a.ramsey_time)):
self.msmnt_times[j] = a.ramsey_time[j]
self.msmnt_phases = 2 * np.pi * a.set_phase / 255.
print ' ---- phases_detuning'
print a.phases_detuning
print ' ---- adptv_phases:'
print np.unique(self.msmnt_phases * 180 / np.pi)
phases_detuning = 2 * np.pi * a.phases_detuning / 360.
b = np.ones(self.reps)
self.msmnt_phases = np.mod(
self.msmnt_phases - np.outer(b, phases_detuning), 2 * np.pi)
print 'Some examples: '
print self.msmnt_phases[0, :] * 180 / np.pi
print self.msmnt_phases[1, :] * 180 / np.pi
print self.msmnt_phases[10, :] * 180 / np.pi
def clean_data_different_folders(folder='', sub_string=''):
#takes all files in the folder with a given sub-strin gin the filename,
#bins the data and checks at which point the data becomes zero due to ionization.
#Only datasets with a number of valid data greater then 'threshold_data' are kept
b_size = 50
threshold_data = 1200
thr_ind = threshold_data / b_size
ind = 0
all_results = {}
for d in os.listdir(folder):
if (d.find(sub_string) > -1):
#print d
folder0 = folder + d
a = sequence.SequenceAnalysis(folder0)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
results = a.ssro_results
binned = bin_data(data=results, bin_size=b_size)
binned.append(0)
binned = np.array(binned)
indice = np.nonzero(binned < 0.001)[0][0]
if (indice > thr_ind):
results = results[:(indice - 1) * b_size]
all_results[str(ind)] = results
ind = ind + 1
a.finish()
all_results['max_ind'] = ind
print 'Nr of good datasets: ' + str(ind)
return all_results
def analyse_Ramsey(folder='',
T2=3e3,
Ampl=-1. / 3,
detuning=3e-3,
hf_N=2.17e-3,
*arg):
timestamp = kw.pop(timestamp, None)
guess_tau = T2
guess_a = 0.5
guess_A = Ampl
guess_hf_N = hf_N
guess_det = detuning
guess_hf_C = hf_C
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
elif folder != '':
folder = toolbox.latest_data('Ramsey')
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
x = a.sweep_pts
y = a.p0
ax = a.plot_result_vs_sweepparam(ret='ax', name='ssro')
params_0, fitfunc_0, fitfunc_str_0 = ramsey.fit_ramsey_14N_fixed_13C_opt(
guess_tau, guess_A, guess_a, guess_det, guess_hf_N)
x_0 = np.linspace(0, a.sweep_pts[-1], 1000)
ax.plot(x_0, fitfunc_0(x_0), 'r--', lw=1)
#fit_xvals=np.linspace(res['x'][0],res['x'][-1],fit_num_points)
fit_result = fit.fit1d(x,
y,
ramsey.fit_ramsey_14N_fixed_13C_opt,
guess_tau,
guess_A,
guess_a,
guess_det,
guess_hf_N,
fixed=[],
do_print=True,
ret=True)
#fit_result = False
if fit_result != False:
plot.plot_fit1d(fit_result,
np.linspace(0, a.sweep_pts[-1], 201),
ax=ax,
plot_data=False)
plt.savefig(os.path.join(folder, 'electronramsey_analysis.pdf'),
format='pdf')
def calibrate_pi2_noMBI(folder):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
x = a.sweep_pts
y = a.p0
u_y = a.u_p0
n = a.sweep_name
a.finish()
x2 = x[::2]
print x2
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')
ax1.set_ylabel('Difference')
m = fit.Parameter((y[-1] - y[0]) / (x[-1] - x[0]), 'm')
x0 = fit.Parameter(y2.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)
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)
fig.savefig(os.path.join(folder, 'pi2_calibration.png'))
def bloch_vector_length(name='',
multiplicity='01',
timestamp=None,
guess_x0=None):
### parameters
if guess_x0 == None:
guess_x0 = 0.5
guess_of = 0.973
guess_a = 0.
### script
x_dict = {}
y_dict = {}
y_err_dict = {}
for RO in ['X', '-X', 'Y', '-Y', 'Z', '-Z']:
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data(name + RO + multiplicity)
print folder
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
# ax = a.plot_result_vs_sweepparam(ret='ax')
x_dict[RO] = {}
y_dict[RO] = {}
y_err_dict[RO] = {}
x_dict[RO] = a.sweep_pts
y_dict[RO] = ((a.p0.reshape(-1)[:]) - 0.5) * 2
y_err_dict[RO] = 2 * a.u_p0.reshape(-1)[:]
y_f = (((y_dict['X'] - y_dict['-X']) / 2.)**2 +
((y_dict['Y'] - y_dict['-Y']) / 2.)**2 +
((y_dict['Z'] - y_dict['-Z']) / 2.)**2)**(1 / 2.)
print y_f
fig, ax = plt.subplots()
ax.plot(x_dict['X'], y_f, 'o', color='k')
ax.set_ylim(-0.1, 1.1)
# ax.set_xlim(x_dict['X'][0]-0.01,x_dict['X'][:]+0.01)
ax.set_title(folder)
# ax.hlines([-1,0],x_dict['X'][0]-1,x_dict['X'][-1]+1,linestyles='dotted')
ax.set_xlabel('Amplitude')
ax.set_ylabel('Bloch vector length')
res = funcs.calibrate_pulse_amplitude(x_dict['X'], y_f, ax, guess_x0,
guess_of, guess_a)
plt.savefig(os.path.join(folder, 'bloch_length.pdf'), format='pdf')
plt.savefig(os.path.join(folder, 'bloch_length.png'), format='png')
def analyze_dark_esr_single(guess_offset=1,
guess_width=0.2e-3,
guess_amplitude=0.3,
timestamp=None,
add_folder=None,
ret='f0',
ssro_calib_folder='',
**kw):
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('DarkESR')
if add_folder != None:
folder = add_folder
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
x = a.sweep_pts # convert to MHz
y = a.p0.reshape(-1)[:]
guess_ctr = x[y.argmin()]
print 'guess_ctr = ' + str(guess_ctr)
## I added this to be more robust for SSRO calibration.Please monitor if this is better - Machiel may-2014
guess_offset = np.average(y)
dip_threshold = guess_offset - 1.5 * np.std(y)
print guess_offset
print dip_threshold
# if min(y) > dip_threshold:
# print 'Could not find dip'
# return
fit_result = fit.fit1d(x,
y,
esr.fit_ESR_gauss,
guess_offset,
guess_amplitude,
guess_width,
guess_ctr,
do_print=True,
ret=True,
fixed=[])
if ret == 'f0':
f0 = fit_result['params_dict']['x0']
u_f0 = fit_result['error_dict']['x0']
return f0, u_f0
def fit_Ramsey_6_cos_hf_fixed(timestamp,
ssro_calib_folder,
g_tau,
g_A,
g_a,
g_det,
g_hf_N,
g_hf_C,
fixed=[],
folder=None):
if folder is None:
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('ElectronRamsey')
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
if ssro_calib_folder != None:
a.get_electron_ROC(ssro_calib_folder=ssro_calib_folder)
else:
a.get_electron_ROC()
# Plot data
ax = a.plot_result_vs_sweepparam(ret='ax', name='ssro')
p0, fitfunc_0, fitfunc_str_0 = ramsey.fit_ramsey_hyperfinelines_fixed_6cos(
g_tau, g_A, g_a, g_det, g_hf_N, g_hf_C)
fit_result = fit.fit1d(a.sweep_pts,
a.p0.reshape(-1),
ramsey.fit_ramsey_hyperfinelines_fixed_6cos,
g_tau,
g_A,
g_a,
g_det,
g_hf_N,
g_hf_C,
fixed=fixed,
do_print=True,
ret=True)
if fit_result != False:
plot.plot_fit1d(fit_result,
np.linspace(0, a.sweep_pts[-1], 201),
ax=ax,
plot_data=False)
plt.savefig(os.path.join(folder, 'electronramsey_analysis.png'),
format='png')
def electron_T2_anal(timestamp=None,
measurement_name=['ms0'],
Amplitude=1 / 2.,
T2=10e3,
offset=1. / 2,
k=4,
do_print=False,
ylim=(0., 1.)):
''' Function to analyze simple decoupling measurements. Loads the results and fits them to a simple exponential.
Inputs:
timestamp: in format yyyymmdd_hhmmss or hhmmss or None.
measurement_name: list of measurement names
Based on electron_T1_anal, modified by Adriaan Rol
'''
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('Decoupling')
print folder
fit_results = []
for k in range(0, len(measurement_name)):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(measurement_name[k])
a.get_electron_ROC()
ax = a.plot_result_vs_sweepparam(ret='ax')
ax.set_ylim(ylim)
x = a.sweep_pts
p0, fitfunc, fitfunc_str = SE.fit_echo(T2, Amplitude, offset, k)
y = a.p0
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=do_print,
ret=True,
fixed=[3])
#fit_result = False
if fit_result != False:
plot.plot_fit1d(fit_result,
np.linspace(0, x[-1], 201),
ax=ax,
plot_data=False)
fit_results.append(fit_result)
plot.ylim = ([0, 0.2])
return fit_results
def get_T1_data_uncorrected(folder):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
#a.get_electron_ROC()
#a.get_cr_results('ssro')
#a.plot_cr_vs_sweep()
x = a.sweep_pts
#y = a.p0
y = a.normalized_ssro
print y
#y_err = a.u_p0
y_err = a.u_normalized_ssro
#ax = a.plot_result_vs_sweepparam(ret='ax')
return x, y, y_err
def electron_T1_anal(timestamp=None,
measurement_name=['ms0'],
Amplitude=0.1,
T1=1000,
offset=1,
do_print=False):
''' Function to analyze T1 measurements. Loads the results and fits them to a simple exponential.
Inputs:
timestamp: in format yyyymmdd_hhmmss or hhmmss or None.
measurement_name: list of measurement names
'''
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('ElectronT1')
fit_results = []
for k in range(0, len(measurement_name)):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
#a.get_readout_results(measurement_name[k]) #commented out since newer measurements are no longer grouped which renders the whole for-loop obsolete. NK 20141110
a.get_electron_ROC()
ax = a.plot_result_vs_sweepparam(ret='ax')
x = a.sweep_pts
p0, fitfunc, fitfunc_str = common.fit_exp_decay_with_offset(
offset, Amplitude, T1)
y = a.p0
#print y
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=do_print,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(0, x[-1], 201),
ax=ax,
plot_data=False)
fit_results.append(fit_result)
plt.savefig(os.path.join(folder, 'analyzed_result.pdf'), format='pdf')
plt.savefig(os.path.join(folder, 'analyzed_result.png'), format='png')
return fit_results
def electron_T1_mul_3_uncorrected(older_than='20161111_091500',newer_than='20161110_224400', Amplitude=1, offset=1, T1=1e9, do_print = False,contains='T1'):
Folder_list = toolbox.latest_data(contains=contains,older_than=older_than,newer_than=newer_than,return_all=True)
x_tot=np.zeros(3)
y_tot=np.zeros(3)
reps_tot=np.zeros(3)
y_var_tot=np.zeros(3)
for i in range(len(Folder_list)):
print Folder_list[len(Folder_list)-i-1]
Folder = Folder_list[len(Folder_list)-i-1]
x,y,reps = get_T1_data_uncorrected(Folder)
y_tot+=y
reps_tot+=reps
a = sequence.SequenceAnalysis(Folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
print reps_tot
a.ssro_results=y_tot
a.reps=reps_tot
print a.reps
a.normalized_ssro = a.ssro_results/((a.reps)/len(a.sweep_pts))
a.u_normalized_ssro = \
(a.normalized_ssro*(1.-a.normalized_ssro)/((a.reps)/len(a.sweep_pts)))**0.5 #this is quite ugly, maybe replace?
a.get_electron_ROC()
y_tot=a.p0
y_var_tot=a.u_p0
return x_tot,y_tot,y_var_tot
#################################################################
# x1,y1,y_var1 = electron_T1_mul_3(older_than='20161119_205000',newer_than='20161118_110000',contains='10_min')
# x_tot.append(x1)
# y_tot.append(y1)
# y_var_tot.append(y_var1)
# x_tot=[]
# y_tot=[]
# y_var_tot=[]
def analyse_pulse_calibration(name='Pi', timestamp=None, guess_x0=None):
### parameters
if guess_x0 == None:
guess_x0 = 0.5
msmt_type = 'sequence'
guess_of = 0.973
guess_a = 0.
### script
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data(name)
if msmt_type == 'sequence':
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
ax = a.plot_result_vs_sweepparam(ret='ax')
x = a.sweep_pts
y = a.p0
elif msmt_type == 'mbi':
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
ax = a.plot_results_vs_sweepparam(name='adwindata', ret='ax')
else:
raise Exception('Unknown msmt type')
ax.set_ylim(-0.1, 1)
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
res = funcs.calibrate_pulse_amplitude(x, y, ax, guess_x0, guess_of,
guess_a)
plt.savefig(os.path.join(folder, 'pulse_calibration.pdf'), format='pdf')
plt.savefig(os.path.join(folder, 'pulse_calibration.png'), format='png')
def calibrate_epulse_rabi(folder, ax, *args, **kws):
fit_phi = kws.pop('fit_phi', True)
fit_k = kws.pop('fit_k', True)
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
ax.set_title(a.timestamp)
f = fit.Parameter(args[0], 'f')
A = fit.Parameter(args[1], 'A')
x0 = fit.Parameter(0, 'x0')
k = fit.Parameter(0, 'k')
p0 = [f, A]
if fit_phi:
p0.append(x0)
if fit_k:
p0.append(k)
fitfunc_str = '(1 - A) + A * exp(-kx) * cos(2pi f (x - x0))'
def fitfunc(x):
return (1.-A()) + A() * np.exp(-k()*x) * \
np.cos(2*np.pi*(f()*(x - x0())))
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False,
print_info=False)
return fit_result
def analyze_dark_esr(folder, ax=None, **kw):
if ax == None:
fig, ax = plt.subplots(1, 1)
ssro_calib_folder = toolbox.latest_data(
contains='AdwinSSRO_SSROCalibration')
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC(ssro_calib_folder=ssro_calib_folder)
x = a.sweep_pts # convert to MHz
y = a.p0.reshape(-1)[:]
a.plot_result_vs_sweepparam(ret=None, name='ssro', ax=ax)
guess_ctr = x[np.floor(len(x) / 2.)]
fit_result = fit.fit1d(
x,
y,
esr.fit_ESR_gauss,
guess_offset,
guess_amplitude,
guess_width,
guess_ctr,
# (2, guess_splitN),
#(2, guess_splitC),
# (2, guess_splitB),
#(3, guess_splitN),
do_print=True,
ret=True,
fixed=[])
plot.plot_fit1d(fit_result,
np.linspace(min(x), max(x), 1000),
ax=ax,
plot_data=False,
**kw)
ax.set_xlabel('MW frq (GHz)')
ax.set_ylabel(r'fidelity wrt. $|0\rangle$')
ax.set_title(a.timestamp + '\n' + a.measurementstring)
plt.savefig(os.path.join(folder, 'darkesr_analysis.png'), format='png')
return fit_result
def plot_result(folder=None, ax=None, ret=True, save_name='ssro', **kw):
if folder == None:
folder = tb.latest_data('CORPSEPiCalibration')
if ax == None:
fig, ax = plt.subplots(1, 1, figsize=(4.5, 4))
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name=save_name)
a.get_electron_ROC()
print len(a.p0), len(a.sweep_pts)
a.plot_result_vs_sweepparam(ax=ax, name=save_name)
# print a.normalized_ssro
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
print 'min x = ', x[np.argmin(y)]
print 'min y =', np.amin(y)
ax.set_title(a.timestamp)
if ret:
return a, ax, x, y
def clean_data(folder=''):
#takes all hdf5 data in the folder
#bins the data and checks at which point the data becomes zero due to ionization.
#Only datasets with a number of valid data greater then 'threshold_data' are kept
b_size = 500
threshold_data = 1000
thr_ind = threshold_data / b_size
ind = 0
all_results = {}
final = True
i = -1
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
print folder
while ((final) & (i < 500)):
i = i + 1
try:
a.get_readout_results(name='rep_' + str(i))
print str('analysing rep. #' + str(i))
results = a.ssro_results
a.get_sweep_pts()
binned = bin_data(data=results, bin_size=b_size)
binned.append(0)
binned = np.array(binned)
indice = np.nonzero(binned < 0.001)[0][0]
print indice
if (indice > thr_ind):
results = results[:(indice - 1) * b_size]
all_results[str(ind)] = results
ind = ind + 1
except:
final = False
print 'done'
a.finish()
all_results['max_ind'] = ind
print 'Nr of good datasets: ' + str(ind)
return all_results
def fit_gaussian(folder, ax, *args):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
ax.set_title(a.timestamp)
x0 = fit.Parameter(args[0], 'x0')
a = fit.Parameter(0.5, 'a')
o = fit.Parameter(0.5, 'o')
c = fit.Parameter(15, 'c')
f = fit.Parameter(1. / 500, 'f')
fitfunc_str = 'o + a * exp( - ( (x-x0)/c )^2) '
def fitfunc(x):
return o() + a() * np.exp(-((x - x0()) / c())**2)
fit_result = fit.fit1d(x,
y,
None,
p0=[o, x0, a, c],
fixed=[],
fitfunc=fitfunc,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False)
return fit_result
def analyze_dark_esr_double(guess_offset=1,
guess_A_min=0.3,
guess_A_plus=0.3,
guess_width=0.2e-3,
guess_amplitude=0.3,
guess_Csplit=0.100e-3,
timestamp=None,
add_folder=None,
ret='f0',
ssro_calib_folder='',
do_ROC=True,
ret_folder=False,
do_plot=True,
**kw):
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('DarkESR')
print folder
if add_folder != None:
folder = add_folder
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
if do_ROC == True:
a.get_electron_ROC()
y = a.p0.reshape(-1)[:]
else:
y = a.get_readout_results('ssro')
x = a.sweep_pts # convert to MHz
### create a guess for the center frequency
guess_ctr1 = x[y.argmin()] + guess_Csplit * 1.2
guess_ctr2 = x[y.argmin()] - guess_Csplit * 1.2 #
# guess_ctr1 =x[len(x)/2.]
print 'guess_ctr1 = ' + str(guess_ctr1)
print 'guess_ctr2 = ' + str(guess_ctr2)
### First fit attempt: guess_ctr1
### fitfunction
A_min = fit.Parameter(guess_A_min, 'A_min')
A_plus = fit.Parameter(guess_A_plus, 'A_plus')
o = fit.Parameter(guess_offset, 'o')
ctr = fit.Parameter(guess_ctr1, 'ctr')
width = fit.Parameter(guess_width, 'width')
Csplit = fit.Parameter(guess_Csplit, 'Csplit')
def fitfunc(x):
return o() - A_min()*np.exp(-((x-(ctr()-Csplit()))/width())**2) \
- A_plus()*np.exp(-((x-(ctr()+Csplit()))/width())**2) \
print 'running fit1'
fit_result = fit.fit1d(x,
y,
None,
p0=[A_min, A_plus, o, ctr, width, Csplit],
fitfunc=fitfunc,
do_print=False,
ret=True,
fixed=[])
do_another_fit = False
if fit_result['success'] == False:
do_another_fit = True
elif fit_result['error_dict']['ctr'] > 8e-6:
do_another_fit = True
if do_another_fit == True:
print 'first fit failed'
### Second fit attempt: guess_ctr2
A_min = fit.Parameter(guess_A_min, 'A_min')
A_plus = fit.Parameter(guess_A_plus, 'A_plus')
o = fit.Parameter(guess_offset, 'o')
ctr = fit.Parameter(guess_ctr2, 'ctr')
width = fit.Parameter(guess_width, 'width')
Csplit = fit.Parameter(guess_Csplit, 'Csplit')
def fitfunc(x):
return o() - A_min()*np.exp(-((x-(ctr()-Csplit()))/width())**2) \
- A_plus()*np.exp(-((x-(ctr()+Csplit()))/width())**2) \
print 'running fit2'
fit_result = fit.fit1d(x,
y,
None,
p0=[A_min, A_plus, o, ctr, width, Csplit],
fitfunc=fitfunc,
do_print=False,
ret=True,
fixed=[])
if do_plot == True:
fig, ax = plt.subplots(1, 1)
plot.plot_fit1d(fit_result,
np.linspace(min(x), max(x), 1000),
ax=ax,
plot_data=True,
**kw)
plt.savefig(os.path.join(folder, 'darkesr_analysis.png'), format='png')
#plt.show()
plt.close(fig)
if ret == 'f0' and ret_folder == False:
if fit_result['success'] == False:
print 'Fit failed, returned dummy values for frequency'
f0 = 0
u_f0 = 1e-3 #large arbitrary uncertainty makes sure the data is filtered out later
return f0, u_f0
else:
f0 = fit_result['params_dict']['ctr']
u_f0 = fit_result['error_dict']['ctr']
return f0, u_f0
elif ret == 'f0' and ret_folder == True:
return f0, u_f0, folder
def analyze_dark_esr(guess_ctr,
guess_splitN,
guess_offset=1,
guess_width=0.2e-3,
guess_amplitude=0.3,
min_dip_depth=0.9,
timestamp=None,
add_folder=None,
ret='f0',
ssro_calib_folder='',
do_save=True,
sweep_direction='right',
**kw):
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('DarkESR')
if add_folder != None:
folder = add_folder
print 'analysis script!'
print folder
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
x = a.sweep_pts # convert to MHz
y = a.p0.reshape(-1)[:]
# Find the esr resonance
j = 0
print 'j = ' + str(j)
print folder
print y[21]
k = len(y)
# min_dip_depth = np.amin(y)
# min_index = np.argmin(y)
print 'min_dip_depth = ' + str(min_dip_depth)
### Option to make the dip search sweep towards left or right, usefull in case of N polarization
if sweep_direction == 'left':
y1 = y[::-1]
x1 = x[::-1]
guess_splitN = -1 * guess_splitN
elif sweep_direction == 'right':
y1 = y
x1 = x
while y1[j] > min_dip_depth and j < len(
y) - 2: #y[j]>0.93*y[j+1]: # such that we account for noise
k = j
j += 1
#j = len(y)-2
if k > len(y) - 3:
print 'Could not find dip'
return
else:
guess_ctr = x1[k] + guess_splitN #convert to GHz and go to middle dip
print 'guess_ctr= ' + str(guess_ctr)
print 'k' + str(k)
## I added this to be more robust for SSRO calibration.Please monitor if this is better - Machiel may-2014
fit_result = fit.fit1d(x,
y,
esr.fit_ESR_gauss,
guess_offset,
guess_amplitude,
guess_width,
guess_ctr, (3, guess_splitN),
do_print=True,
ret=True,
fixed=[])
print 'fit finished'
if do_save:
print 'saving data and fit'
if fit_result:
fig, ax = plt.subplots(1, 1)
plot.plot_fit1d(fit_result,
np.linspace(min(x), max(x), 1000),
ax=ax,
plot_data=True,
**kw)
plt.savefig(os.path.join(folder, 'darkesr_analysis.png'),
format='png')
#plt.show()
plt.close(fig)
if ret == 'f0':
f0 = fit_result['params_dict']['x0']
u_f0 = fit_result['error_dict']['x0']
return f0, u_f0
def analyse_dark_esr(**kw):
timestamp = kw.pop('timestamp', None)
ax = kw.pop('ax', None)
ret = kw.pop('ret', None)
min_dip_depth = kw.pop('min_dip_depth', 0.85)
do_print = kw.pop('do_print', False)
plot_initial_guess = kw.pop('plot_initial_guess', False)
guess_amplitude = kw.pop('guess_amplitude', 0.3)
guess_width = kw.pop('guess_width', 0.2e-3)
guess_offset = kw.pop('guess_offset', 1)
guess_x0 = kw.pop('guess_x0', 2.805)
guess_Nsplit = kw.pop('guess_Nsplit', 2.196e-3)
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
if folder == None:
folder = toolbox.latest_data(timestamp)
else:
folder = toolbox.latest_data('DarkESR')
print 'Folder for the Dark ESR analysis : ', folder
if ax == None:
fig, ax = plt.subplots(1, 1)
ssro_calib_folder = toolbox.latest_data(
contains='130113_AdwinSSRO_SSROCalibration')
print ssro_calib_folder
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC(ssro_calib_folder=ssro_calib_folder)
x = a.sweep_pts # convert to MHz
y = a.p0.reshape(-1)[:]
a.plot_result_vs_sweepparam(ret=ret, name='ssro', ax=ax)
#ax.set_ylim(0.1,1.05)
j = 0
while y[j] > min_dip_depth and j < len(
y) - 2: #y[j]>0.93*y[j+1]: # such that we account for noise
k = j
j += 1
#j = len(y)-2
if k > len(y) - 5:
print 'Could not find dip'
return
else:
guess_ctr = x[k] + guess_Nsplit #convert to GHz and go to middle dip
print 'guess_ctr= ' + str(guess_ctr)
fit_result = fit.fit1d(
x,
y,
esr.fit_ESR_gauss,
guess_offset,
guess_amplitude,
guess_width,
guess_ctr,
# (2, guess_splitN),
# (2, guess_splitC),
# (2, guess_splitB),
(3, guess_Nsplit),
do_print=do_print,
ret=True,
fixed=[])
plot.plot_fit1d(fit_result,
np.linspace(min(x), max(x), 1000),
ax=ax,
plot_data=False,
**kw)
plot_initial_guess = False
if plot_initial_guess:
params_0, fitfunc_0, fitfunc_str = esr.fit_ESR_gauss(
guess_offset, guess_amplitude, guess_width, guess_ctr,
(2, guess_splitC), (3, guess_splitN))
ax.set_xlabel('MW frq (GHz)')
ax.set_ylabel(r'fidelity wrt. $|0\rangle$')
ax.set_title(a.timestamp + '\n' + a.measurementstring)
plt.savefig(os.path.join(folder, 'darkesr_analysis.png'), format='png')
if ret == 'f0':
f0 = fit_result['params_dict']['x0']
u_f0 = fit_result['error_dict']['x0']
ax.text(f0,
0.8,
'$f_0$ = ({:.3f} +/- {:.3f})'.format((f0 - 2.8) * 1e3,
u_f0 * 1e3),
ha='center')
return (f0 - 2.8) * 1e3, u_f0 * 1e3
return fit_result
def analyse_pi4_pulse(**kw):
timestamp = kw.pop('timestamp', None)
do_print = kw.pop('do_print', False)
pi4_calib = kw.pop('pi4_calib', '1')
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
if folder == None:
folder = toolbox.latest_data(timestamp)
else:
folder = toolbox.latest_data('Pi4')
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
x = a.sweep_pts
y = a.p0
u_y = a.u_p0
n = a.sweep_name
a.finish()
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)
if pi4_calib == '1':
ax1.set_title('Difference btw. Pi/4-Pi-Pi/4 and Pi/2')
elif pi4_calib == '2':
ax1.set_title('Difference btw. Pi/2-Pi-Pi/4 and Pi/4')
ax1.set_ylabel('Difference')
ax2.set_title(a.timestamp)
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=do_print,
ret=True)
if pi4_calib == '1':
ax2.errorbar(x2,
y[0::2],
yerr=u_y[0::2],
fmt='o',
label='Pi/4 - Pi - Pi/4')
ax2.errorbar(x2, y[1::2], yerr=u_y[1::2], fmt='o', label='Pi/2')
elif pi4_calib == '2':
ax2.errorbar(x2,
y[0::2],
yerr=u_y[0::2],
fmt='o',
label='Pi/2 - Pi - Pi/4')
ax2.errorbar(x2, y[1::2], yerr=u_y[1::2], fmt='o', label='Pi/4')
ax2.legend(frameon=True, framealpha=0.5)
ax2.set_ylabel('P(0)')
ax2.set_xlabel(n)
if fit_result != False:
plot.plot_fit1d(fit_result,
np.linspace(x2[0], x2[-1], 201),
ax=ax1,
plot_data=False,
print_info=do_print)
if a.sweep_pts[0] < x0() < a.sweep_pts[-1]:
ax2.axvline(x0(), c='k', lw=2)
if pi4_calib == '1':
ax2.axhline(0.5, c='k', lw=2)
elif pi4_calib == '2':
ax2.axhline(np.sqrt(np.sin(np.pi / 4.)), c='k', lw=2)
ax2.set_title('X marks the spot')
if pi4_calib == '1':
fig.savefig(os.path.join(folder, 'pi4_calibration_1.png'))
elif pi4_calib == '2':
fig.savefig(os.path.join(folder, 'pi4_calibration_2.png'))
return fit_result
def analyse_Rabi(guess_frq=2., guess_amp=0.2, guess_of=0.1, **kw):
timestamp = kw.pop('timestamp', None)
guess_phi = kw.pop('guess_phi', 0.)
guess_k = kw.pop('guess_k', 0.)
mbi_analysis = kw.pop('mbi_analysis', False)
do_print = kw.pop('do_print', False)
o = fit.Parameter(guess_of, 'o')
f = fit.Parameter(guess_frq, 'f')
A = fit.Parameter(guess_amp, 'A')
phi = fit.Parameter(guess_phi, 'phi')
k = fit.Parameter(guess_k, 'k')
p0 = [f, A, phi, o, k]
fitfunc_str = ''
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('ElectronRabi')
if mbi_analysis:
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('adwindata')
a.get_electron_ROC()
ax = a.plot_results_vs_sweepparam(ret='ax', name='adwindata')
else:
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
ax = a.plot_result_vs_sweepparam(ret='ax')
x = a.sweep_pts
y = a.p0
fitfunc_str = 'o - A + A*e^(-kx)*cos(2pi (fx-phi))'
def fitfunc(x):
return (o() - A()) + A() * np.exp(-k() * x) * np.cos(2 * np.pi *
(f() * x - phi()))
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
fixed=[2],
do_print=do_print,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(0, x[-1], 201),
ax=ax,
plot_data=False)
print "\npi pulse at {:.3f} for .\n".format(1 / f() / 2.) + a.sweep_name
# ax.set_title(a.timestamp+'\n'+a.measurementstring)
plt.savefig(os.path.join(folder, 'electronrabi_analysis_fit.png'))
return fit_result
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 = [ ]
# analyze the data
def fit_B_msmt_loop(older_than = None, newer_than = None):
ZFS = 2.877480e9
f0m = []; u_f0m = []; f0p = [] ;u_f0p = []
Bx_field_measured = []
Bz_field_measured = []
f_centre_list = []; f_diff_list=[]
timestamp_list = []
f_centre_error_list= []
it_list = []
f_diff_error_list = []
#msm
print 'start'
older_than_SSRO = older_than
# print older_than, newer_than
iteration = 0
while toolbox.latest_data(contains='msmt_msm_', older_than=older_than, newer_than=newer_than,raise_exc = False)!=False:
print 'iteration'+str(iteration)
timestamp,folder = toolbox.latest_data(contains='msmt_msm_', older_than=older_than, newer_than=newer_than,return_timestamp = True)
print 'm folder '+folder
## Data location ##
ssro_calib_folder = toolbox.latest_data(contains='AdwinSSRO_SSROCalibration', older_than=older_than_SSRO)
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC(ssro_calib_folder=ssro_calib_folder)
f0m_temp,u_f0m_temp = dark_esr_auto_analysis.analyze_dark_esr(None, 2.196*1e-3, add_folder = folder )
#msp
folder = toolbox.latest_data(contains='msmt_msp_', older_than=older_than, newer_than=newer_than,)
print folder
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC(ssro_calib_folder=ssro_calib_folder)
f0p_temp,u_f0p_temp = dark_esr_auto_analysis.analyze_dark_esr(None, 2.196*1e-3, add_folder = folder )
print f0p_temp >0 and f0m_temp >0
if f0p_temp >0 and f0m_temp >0:
Bz_measured, Bx_measured = amt.get_B_field(msm1_freq=f0m_temp*1e9, msp1_freq=f0p_temp*1e9, u_msm1_freq =u_f0m_temp ,u_msp1_freq=u_f0p_temp)
f_centre = (f0m_temp+f0p_temp)/2
f_centre_error = np.sqrt(u_f0m_temp**2+u_f0p_temp**2)/2
f_diff = (f_centre-ZFS*1e-9)*1e6
f_diff_error = f_centre_error*1e6
f0m.append(f0m_temp)
u_f0m.append(u_f0m_temp)
f0p.append(f0p_temp)
u_f0p.append(u_f0p_temp)
f_centre_list.append(f_centre)
f_centre_error_list.append(f_centre_error)
f_diff_list.append(f_diff)
f_diff_error_list.append(f_diff_error)
Bx_field_measured.append(Bx_measured)
Bz_field_measured.append(Bz_measured)
timestamp_list.append(timestamp)
it_list.append(iteration)
older_than = str(int(timestamp)-1)
iteration = iteration+1
print iteration
it_list = linspace(0,len(it_list)-1,len(it_list))
outfile = TemporaryFile()
np.savez('test_B_field_meas',f0m=f0m,
u_f0m=u_f0m,
f0p=f0p,
u_f0p=u_f0p,
f_centre_list=f_centre_list,
f_centre_error_list=f_centre_error_list,
f_diff_list=f_diff_list,
f_diff_error_list=f_diff_error_list,
Bx_field_measured=Bx_field_measured,
Bz_field_measured=Bz_field_measured,
timestamp_list=timestamp_list,
it_list=it_list)
def analyze_dark_esr_double(timestamp=None,
center_guess=False,
ax=None,
ret=None,
min_dip_depth=0.85,
do_ROC=True,
**kw):
if ax == None:
fig, ax = plt.subplots(1, 1)
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('DarkESR')
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
# a.get_electron_ROC()
x = a.sweep_pts # convert to MHz
# y = a.get_readout_results('ssro')
if do_ROC == True:
a.get_electron_ROC()
y = a.p0.reshape(-1)[:]
else:
y = a.get_readout_results('ssro')
# y = a.p0.reshape(-1)[:]
a.plot_result_vs_sweepparam(ret=ret, name='ssro', ax=ax)
ax.set_ylim(0.6, 1.05)
# try fitting
guess_offset = 1.0
guess_A_min = 0.3
guess_A_plus = 0.3
guess_x0 = 1.74666
guess_sigma = 0.100e-3
guess_Csplit = 0.210e-3 / 2
if center_guess == True:
guess_x0 = float(raw_input('Center guess?'))
else:
guess_x0 = x[y.argmin()]
guess_x0 = x[len(x) / 2.]
print 'guess_ctr = ' + str(guess_x0)
### fitfunction
A_min = fit.Parameter(guess_A_min, 'A_min')
A_plus = fit.Parameter(guess_A_plus, 'A_plus')
o = fit.Parameter(guess_offset, 'o')
x0 = fit.Parameter(guess_x0, 'x0')
sigma = fit.Parameter(guess_sigma, 'sigma')
Csplit = fit.Parameter(guess_Csplit, 'Csplit')
def fitfunc(x):
return o() - A_min()*np.exp(-((x-(x0()-Csplit()))/sigma())**2) \
- A_plus()*np.exp(-((x-(x0()+Csplit()))/sigma())**2) \
fit_result = fit.fit1d(x,
y,
None,
p0=[A_min, A_plus, o, x0, sigma, Csplit],
fitfunc=fitfunc,
do_print=True,
ret=True,
fixed=[])
plot.plot_fit1d(fit_result,
np.linspace(min(x), max(x), 1000),
ax=ax,
plot_data=False,
**kw)
ax.set_xlabel('MW frq (GHz)')
ax.set_ylabel(r'fidelity wrt. $|0\rangle$')
ax.set_title(a.timestamp + '\n' + a.measurementstring)
plt.savefig(os.path.join(folder, 'darkesr_analysis.png'), format='png')
if ret == 'f0':
f0 = fit_result['params_dict']['x0']
u_f0 = fit_result['error_dict']['x0']
ax.text(f0,
0.8,
'$f_0$ = ({:.3f} +/- {:.3f})'.format((f0 - 2.8) * 1e3,
u_f0 * 1e3),
ha='center')
return (f0 - 2.8) * 1e3, u_f0 * 1e3
