def fit_exp(x,
y,
guess_a,
guess_A,
guess_tau,
fixed=[],
show_fit=True,
save=True,
timestamp='',
**kw):
'''
Fit single exponential to T1 data
'''
p0, fitfunc, fitfunc_str = common.fit_exp_decay_with_offset(
guess_a, guess_A, guess_tau)
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
fixed=fixed,
do_print=True,
ret=True)
# Plot fit and format if requested
keys = sorted(kw.keys())
if show_fit:
if len(keys) == 0:
plot.plot_fit1d(fit_result,
np.linspace(0, x[-1], 201),
plot_data=True)
elif len(keys) != 0 and 'ax' in keys:
ax = kw['ax']
plot.plot_fit1d(fit_result,
np.linspace(0, x[-1], 201),
plot_data=False,
ax=ax)
elif len(keys) != 0 and 'ax' not in keys:
print 'length of keyword arguments =', len(keys)
raise Exception(
"Your keywords for plot formatting didn't contain a pyplot axis with keyword 'ax'. Please provide it."
)
if 'xlabel' in keys:
ax.set_xlabel(kw['xlabel'])
if 'ylabel' in keys:
ax.set_ylabel(kw['ylabel'])
if timestamp != '':
if timestamp == None:
folder = toolbox.latest_data('ElectronT1')
else:
folder = toolbox.data_from_time(timestamp)
if save:
plt.savefig(os.path.join(folder, 'Calibrate_Pi_analysis_fit.png'))
return fit_result
def electron_T1_analysis(older_than='20161110_180000',
newer_than='20161110_141200',
mode='init0',
Amplitude=2,
offset=1,
T1=1e9,
do_print=True):
Folder_list = toolbox.latest_data(
contains='ElectronT1_111_1_sil18m0to0switch_on',
older_than=older_than,
newer_than=newer_than,
return_all=True)
x_tot = []
y_tot = []
y_var_tot = []
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, y_var = get_T1_data(Folder)
#print y
x_tot.extend(list(x))
y_tot.extend(list(y))
y_var_tot.extend(list(y_var))
print x_tot
print y_tot
print y_var_tot
p0, fitfunc, fitfunc_str = common.fit_exp_decay_with_offset(
offset, Amplitude, T1)
# fit_result = fit.fit1d(x_tot,y_tot, None, p0=p0, fitfunc=fitfunc, do_print=do_print, ret=True)
# #plt.plot(x_tot,y_tot)
# plot.plot_fit1d(fit_result, np.linspace(0,x_tot[-1],201), ax= None, plot_data=False)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.errorbar(x_tot, y_tot, fmt='o', yerr=y_var_tot)
ax.set_ylim([0.0, 1.1])
fit_result = fit.fit1d(x_tot,
y_tot,
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)
plt.savefig(os.path.join(Folder, 'analyzed_result.pdf'), format='pdf')
plt.savefig(os.path.join(Folder, 'analyzed_result.png'), format='png')
def fit_lifetime_curve_from_dL(p_cav_length,dL,lt,b,g_tau=2.,det_eff=0.2,fs_det_eff=0.03,ts=np.linspace(0,30e-9,31)):
lifetime_curve = lifetime_under_vibrations(lt,b,p_cav_length,ts,det_eff=det_eff,fs_det_eff=fs_det_eff)
lifetime_curve_norm = lifetime_curve/lifetime_curve[0]
plt.plot(lifetime_curve)
plt.show()
g_a = 0
g_A = 1.
fixed = [0]
p0, fitfunc, fitfunc_str = common.fit_exp_decay_with_offset(g_a, g_A, g_tau)
fit_result = fit.fit1d(ts*1e9,lifetime_curve_norm, None, p0=p0, fitfunc=fitfunc, do_print=False, ret=True,fixed=fixed)
ax=plot.plot_fit1d(fit_result,ts*1.e9, add_txt=True,label='Fit',show_guess=False, plot_data=True,ret='ax')
return lifetime_curve_norm,fit_result
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 fit_exp(x, y,
guess_a,
guess_A,
guess_tau,
fixed = [],
show_fit = True,
save = True,
timestamp = '',
**kw):
'''
Fit single exponential to T1 data
'''
p0, fitfunc, fitfunc_str = common.fit_exp_decay_with_offset(guess_a,guess_A, guess_tau)
fit_result = fit.fit1d(x,y, None, p0=p0, fitfunc=fitfunc, fixed=fixed,
do_print=True, ret=True)
# Plot fit and format if requested
keys = sorted(kw.keys())
if show_fit:
if len(keys) == 0:
plot.plot_fit1d(fit_result, np.linspace(0,x[-1],201),
plot_data=True)
elif len(keys) != 0 and 'ax' in keys:
ax = kw['ax']
plot.plot_fit1d(fit_result, np.linspace(0,x[-1],201),
plot_data=False, ax = ax)
elif len(keys) != 0 and 'ax' not in keys:
print 'length of keyword arguments =', len(keys)
raise Exception("Your keywords for plot formatting didn't contain a pyplot axis with keyword 'ax'. Please provide it.")
if 'xlabel' in keys:
ax.set_xlabel(kw['xlabel'])
if 'ylabel' in keys:
ax.set_ylabel(kw['ylabel'])
if timestamp != '':
if timestamp == None:
folder = toolbox.latest_data('ElectronT1')
else:
folder = toolbox.data_from_time(timestamp)
if save:
plt.savefig(os.path.join(folder, 'Calibrate_Pi_analysis_fit.png'))
return fit_result
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('T1')
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 repump_speed(contains='repump_speed',
name='adwindata',
do_fit=False,
**kw):
show_guess = kw.pop('show_guess', False)
g_a = kw.pop('offset', 0.0)
g_A = kw.pop('A', 1.0)
g_tau = kw.pop('tau', 100.0)
fixed = kw.pop('fixed', [0])
### acquire data
f = toolbox.latest_data(contains, **kw)
a = mbi.MBIAnalysis(f)
a.get_sweep_pts()
a.get_readout_results(name=name)
a.get_electron_ROC(**kw)
x = a.sweep_pts.reshape(-1)
y = 1.0 - a.p0.reshape(-1)
y_u = a.u_p0.reshape(-1)
xlabel = a.sweep_name
ylabel = r'$1 - p(|0\rangle)$'
fig, ax = create_plot(f,
xlabel=xlabel,
ylabel=ylabel,
title='repump speed')
plot_data(x, y, y_u=y_u)
if do_fit:
p0, fitfunc, fitfunc_str = common.fit_exp_decay_with_offset(
g_a, g_A, g_tau)
if show_guess:
# print decay
ax.plot(np.linspace(x[0], x[-1], 201),
fitfunc(np.linspace(x[0], x[-1], 201)),
':',
lw=2)
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=True,
fixed=fixed,
ret=True)
if isinstance(fit_result, int):
print "Fit failed!"
else:
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 100),
ax=ax,
plot_data=False)
save_and_close_plot(f)
if kw.get('ret_data', False):
return x, y, y_u
if kw.get('ret', False):
return fit_result
def plot_summation(Amplitude=1, offset=0.333, T1=1000, do_print=True):
x_tot1, y_tot1, y_var_tot1 = electron_T1_mul(older_than='20161110_172800',
newer_than='20161110_141500')
x_tot2, y_tot2, y_var_tot2 = electron_T1_ms_1()
x_tot3, y_tot3, y_var_tot3 = electron_T1_msp1()
# print len(x_tot1)
# print len(x_tot2)
# print len(x_tot3)
x_tot = (x_tot1 + x_tot2 + x_tot3) / 3.0
y_tot = (y_tot1 + y_tot2 + y_tot3) / 3.0
y_var_tot = ((y_var_tot1**2 + y_var_tot2**2 + y_var_tot3**2)**0.5)
print 'y_tot is' + str(y_tot)
print 'y_var_tot is ' + str(y_var_tot)
fig = plt.figure(111, figsize=(9, 5))
ax1 = fig.add_subplot(111)
#ax1.errorbar(x_tot,y_tot,fmt='o',yerr=y_var_tot)
ax1.errorbar(x_tot1,
y_tot1,
fmt='o',
color='r',
label='ms=0',
yerr=y_var_tot1)
ax1.errorbar(x_tot2,
y_tot2,
fmt='s',
color='b',
label='ms=-1',
yerr=y_var_tot2)
ax1.errorbar(x_tot3,
y_tot3,
fmt='^',
color='g',
label='ms=+1',
yerr=y_var_tot3)
ax1.set_xlabel('Total evolution time (s)')
ax1.set_ylabel('State Fidelity')
#ax1.grid()
ax1.set_ylim([0.3, 1.1])
ax1.set_xscale('log')
plt.legend(loc=3)
p0, fitfunc, fitfunc_str = common.fit_exp_decay_with_offset(
offset, Amplitude, T1)
fit_result = fit.fit1d(x_tot,
y_tot,
None,
p0=p0,
fitfunc=fitfunc,
do_print=do_print,
ret=True,
fixed=[0])
#y=A * exp(-x/tau) + a
plot.plot_fit1d(fit_result,
np.linspace(0, 50 * x_tot[-1], 501),
ax=ax1,
add_txt=False,
color='b',
plot_data=False)
# plt.savefig(os.path.join(Folder, 'sum_log.pdf'),
# format='pdf')
# plt.savefig(os.path.join(Folder, 'sum_log.png'),
# format='png')
# print 'data saved in ' + str(Folder)
return x_tot1, y_tot1, y_var_tot1
def electron_T1_msp1(older_than='20161121_190000',
newer_than='20161121_171000',
mode='init0',
Amplitude=1,
offset=1,
T1=1e9,
do_print=True):
Folder_list = toolbox.latest_data(contains='T1',
older_than=older_than,
newer_than=newer_than,
return_all=True)
x_tot = []
y_tot = []
y_var_tot = []
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, y_var = get_T1_data(Folder)
#print y
x_tot.extend(list(x))
y_tot.extend(list(y))
y_var_tot.extend(list(y_var))
################################################data from 20s to 60s #####################################################
# x1,y1,y_var1 = electron_T1_mul_600_1000(older_than='20161110_211700',newer_than='20161110_172900')
# x_tot.extend(list(x1))
# y_tot.extend(list(y1))
# y_var_tot.extend(list(y_var1))
############################################################## data at 5 mins
x2, y2, y_var2 = electron_T1_mul_3(older_than='20161124_080000',
newer_than='20161121_190000',
contains='20s')
x_tot.append(x2)
y_tot.append(y2)
y_var_tot.append(y_var2)
############################################################## data at 10 mins
x3, y3, y_var3 = electron_T1_mul_3(older_than='20161124_080000',
newer_than='20161121_190000',
contains='40s')
x_tot.append(x3)
y_tot.append(y3)
y_var_tot.append(y_var3)
#################################################################
x4, y4, y_var4 = electron_T1_mul_3(older_than='20161124_080000',
newer_than='20161121_190000',
contains='60s')
x_tot.append(x4)
y_tot.append(y4)
y_var_tot.append(y_var4)
###############################################################
x5, y5, y_var5 = electron_T1_mul_3(older_than='20161124_080000',
newer_than='20161121_190000',
contains='5_min')
x_tot.append(x5)
y_tot.append(y5)
y_var_tot.append(y_var5)
#################################################################
x6, y6, y_var6 = electron_T1_mul_3(older_than='20161124_080000',
newer_than='20161121_190000',
contains='10_min')
x_tot.append(x6)
y_tot.append(y6)
y_var_tot.append(y_var6)
x_tot = np.array(x_tot) / 1e3
y_tot = np.array(y_tot)
y_var_tot = np.array(y_var_tot)
#ax_tot=np.array(ax_tot)
# print x_tot
# print y_tot
# print y_var_tot
p0, fitfunc, fitfunc_str = common.fit_exp_decay_with_offset(
offset, Amplitude, T1)
# fit_result = fit.fit1d(x_tot,y_tot, None, p0=p0, fitfunc=fitfunc, do_print=do_print, ret=True)
# #plt.plot(x_tot,y_tot)
# plot.plot_fit1d(fit_result, np.linspace(0,x_tot[-1],201), ax= None, plot_data=False)
fig = plt.figure(1, figsize=(9, 6))
ax = fig.add_subplot(111)
ax.grid()
ax.set_xlabel('Total evolution time (s)')
ax.set_ylabel('State Fidelity')
ax.errorbar(x_tot, y_tot, fmt='o', yerr=y_var_tot)
ax.set_ylim([0.3, 1.1])
ax.set_xlim([0, 650])
fit_result = fit.fit1d(x_tot,
y_tot,
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)
plt.savefig(os.path.join(Folder, 'analyzed_result.pdf'), format='pdf')
plt.savefig(os.path.join(Folder, 'analyzed_result.png'), format='png')
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.set_xlabel('Time (s)')
ax1.set_ylabel('Fidelity')
ax1.grid()
ax1.errorbar(x_tot, y_tot, fmt='o', yerr=y_var_tot)
ax1.set_ylim([0.3, 1.1])
ax1.set_xscale('log')
plt.savefig(os.path.join(Folder, 'analyzed_result_log.pdf'), format='pdf')
plt.savefig(os.path.join(Folder, 'analyzed_result_log.png'), format='png')
print 'data saved in ' + str(Folder)
return x_tot, y_tot, y_var_tot
def plot_total_spin_population(Amplitude=0.69315,
offset=0.3333,
T1=1000,
do_print=True):
x1, y1, e1 = electron_T1_mul_3_uncorrected(older_than='20161127_092000',
newer_than='20161126_074000',
Amplitude=1,
offset=1,
T1=1e7,
return_x='all')
x2, y2, e2 = electron_T1_mul_3_uncorrected(older_than='20170407_204000',
newer_than='20170406_174400',
Amplitude=1,
offset=1,
T1=1e7,
return_x='all')
x3, y3, e3 = electron_T1_mul_3_uncorrected(older_than='20161113_200000',
newer_than='20161112_033700',
Amplitude=1,
offset=1,
T1=1e7,
return_x='2_all')
x2 = np.delete(x2, 1)
y2 = np.delete(y2, 1)
x1 = np.delete(x1, 1)
y1 = np.delete(y1, 1)
e1 = np.delete(e1, 1)
e2 = np.delete(e2, 1)
e_tot = ((e1**2 + e2**2 + e3**2)**0.5) / 3
fig = plt.figure(211, figsize=(5, 3))
ax = fig.add_subplot(111)
#ax.errorbar(x1*1.0e-3, y1, yerr=e1, fmt='o',color='b',label='ms=0 to ms=+1')
#ax.errorbar(x2*1.0e-3, y2, yerr=e2, fmt='^',color='g',label='ms=0 to ms=-1')
ax.errorbar(x3 * 1.0e-3,
y3,
yerr=e3,
fmt='o',
color='r',
label='ms=0 to ms=0')
ax.errorbar(x1 * 1.0e-3, (y1 + y2 + y3),
yerr=e_tot,
fmt='o',
color='k',
label='Total spin population ')
y_tot = (y1 + y2 + y3)
x = x1 * 1.0e-3
print y1
print y3
print y_tot
p0, fitfunc, fitfunc_str = common.fit_exp_decay_with_offset(
offset, Amplitude, T1)
fit_result = fit.fit1d(x,
y_tot,
None,
p0=p0,
fitfunc=fitfunc,
do_print=do_print,
ret=True,
fixed=[0, 1])
#y=A * exp(-x/tau) + a
plot.plot_fit1d(fit_result,
np.linspace(0, 50 * x[-1], 501),
ax=ax,
add_txt=False,
color='b',
plot_data=False)
ax.set_xscale('log')
ax.set_xlabel('Total evolution time (s)')
ax.set_ylabel('Population')
ax.set_xlim(1.0e-3, 1.0e4)
ax.set_ylim(0.7, 1.06)
plt.legend(fontsize=11, loc=3)
plt.savefig(
'C:\Users\TUD277931\Dropbox\TaminiauLab\Projects\Coherence in multi-qubit systems\Paper\Figures\Fig 1\T1_total_spin_population.pdf',
format='pdf',
bbox_inches='tight',
pad_inches=0.2,
transparent=True)
def plot_summation(Amplitude=1, offset=0.333, T1=1000, do_print=True):
x_tot1, y_tot1, y_var_tot1 = electron_T1_mul(older_than='20161110_172800',
newer_than='20161110_141500')
x_tot2, y_tot2, y_var_tot2 = electron_T1_ms_1()
x_tot3, y_tot3, y_var_tot3 = electron_T1_msp1()
# print len(x_tot1)
# print len(x_tot2)
# print len(x_tot3)
x_tot = (x_tot1 + x_tot2 + x_tot3) / 3.0
y_tot = (y_tot1 + y_tot2 + y_tot3) / 3.0
y_var_tot = ((y_var_tot1**2 + y_var_tot2**2 + y_var_tot3**2)**0.5)
print 'y_tot is' + str(y_tot)
print 'y_var_tot is ' + str(y_var_tot)
fig = plt.figure(111, figsize=(4, 2))
ax1 = fig.add_subplot(111)
#ax1.errorbar(x_tot,y_tot,fmt='o',yerr=y_var_tot)
ax1.errorbar(x_tot1,
y_tot1,
fmt='o',
color='r',
label='ms=0',
yerr=y_var_tot1,
lw=1.7,
markersize=3)
ax1.errorbar(x_tot2,
y_tot2,
fmt='s',
color='b',
label='ms=-1',
yerr=y_var_tot2,
lw=1.7,
markersize=3)
ax1.errorbar(x_tot3,
y_tot3,
fmt='^',
color='g',
label='ms=+1',
yerr=y_var_tot3,
lw=1.7,
markersize=3)
ax1.set_xlabel('Total evolution time (s)')
ax1.set_ylabel('State fidelity')
#ax1.grid()
ax1.set_ylim([0.3, 1.08])
ax1.set_yticks([0.4, 0.6, 0.8, 1])
ax1.set_xscale('log')
plt.legend(numpoints=1, loc=3, fontsize=10, frameon=False)
p0, fitfunc, fitfunc_str = common.fit_exp_decay_with_offset(
offset, Amplitude, T1)
fit_result = fit.fit1d(x_tot,
y_tot,
None,
p0=p0,
fitfunc=fitfunc,
do_print=do_print,
ret=True,
fixed=[0])
#y=A * exp(-x/tau) + a
plot.plot_fit1d(fit_result,
np.linspace(0, 50 * x_tot[-1], 501),
ax=ax1,
add_txt=False,
color='b',
plot_data=False)
plt.savefig(
'C:\Users\TUD277931\Dropbox\TaminiauLab\Projects\Coherence in multi-qubit systems\Paper\Figures\Fig 1\T1_tim_proposal_2.pdf',
format='pdf',
bbox_inches='tight',
pad_inches=0.2,
transparent=True)
# plt.savefig(os.path.join(Folder, 'sum_log.png'),
# format='png')
#print 'data saved in ' + str(Folder)
return x_tot1, y_tot1, y_var_tot1
def electron_T1_mul(older_than='20161110_180000',
newer_than='20161110_141200',
mode='init0',
Amplitude=1,
offset=1,
T1=1e9,
do_print=True):
Folder_list = toolbox.latest_data(contains='T1',
older_than=older_than,
newer_than=newer_than,
return_all=True)
Directory = Folder_list[0]
Date = Folder_list[1]
x_tot = []
y_tot = []
y_var_tot = []
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, y_var = get_T1_data(Folder)
#print y
x_tot.extend(list(x))
y_tot.extend(list(y))
y_var_tot.extend(list(y_var))
################################################data from 20s to 60s #####################################################
x1, y1, y_var1 = electron_T1_mul_1(older_than='20161110_211700',
newer_than='20161110_172900')
x_tot.extend(list(x1))
y_tot.extend(list(y1))
y_var_tot.extend(list(y_var1))
############################################################## data at 5 mins
x2, y2, y_var2 = electron_T1_mul_3_uncorrected(
older_than='20161112_033700',
newer_than='20161110_224600',
return_x='2')
x_tot.append(x2)
y_tot.append(y2)
y_var_tot.append(y_var2)
############################################################## data at 10 mins
x3, y3, y_var3 = electron_T1_mul_3_uncorrected(
older_than='20161113_200000',
newer_than='20161112_033700',
return_x='2')
x_tot.append(x3)
y_tot.append(y3)
y_var_tot.append(y_var3)
#################################################################
x_tot = np.array(x_tot) / 1e3
y_tot = np.array(y_tot)
y_var_tot = np.array(y_var_tot)
#ax_tot=np.array(ax_tot)
#print x_tot
#print y_tot
#print y_var_tot
p0, fitfunc, fitfunc_str = common.fit_exp_decay_with_offset(
offset, Amplitude, T1)
# fit_result = fit.fit1d(x_tot,y_tot, None, p0=p0, fitfunc=fitfunc, do_print=do_print, ret=True)
# #plt.plot(x_tot,y_tot)
# plot.plot_fit1d(fit_result, np.linspace(0,x_tot[-1],201), ax= None, plot_data=False)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid()
ax.set_xlabel('Time (s)')
ax.set_ylabel('Fidelity')
ax.errorbar(x_tot, y_tot, fmt='o', yerr=y_var_tot)
ax.set_ylim([0.3, 1.1])
ax.set_xlim([0, 650])
fit_result = fit.fit1d(x_tot,
y_tot,
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)
plt.savefig(os.path.join(Folder, 'analyzed_result.pdf'), format='pdf')
plt.savefig(os.path.join(Folder, 'analyzed_result.png'), format='png')
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.grid()
ax1.set_xlabel('Time (s)')
ax1.set_ylabel('Fidelity')
ax1.errorbar(x_tot, y_tot, fmt='o', yerr=y_var_tot)
ax1.set_ylim([0.3, 1.1])
ax1.set_xscale('log')
plt.savefig(os.path.join(Folder, 'analyzed_result_log.pdf'), format='pdf')
plt.savefig(os.path.join(Folder, 'analyzed_result_log.png'), format='png')
print 'data saved in ' + str(Folder)
return x_tot, y_tot, y_var_tot
def electron_T1_anal_mul(older_than='20150227_184920',newer_than='20150227_101541',mode='contrast', Amplitude=1, Offset=1, T1=1e7):
''' Function to analyze T1 measurements. Loads the results and fits them to a simple exponential.
Inputs:
older_than = older than timestamp
newer_than = newer than timestamp
mode = init_0 init_1 contrast
'''
Folder_list = toolbox.latest_data(contains='T1',older_than='20150227_184920',newer_than='20150227_101541',return_all=True)
Directory = Folder_list[0]
Date = Folder_list[1]
init0 = [k for k in Folder_list[2] if 'init_0' in k]
init1 = [k for k in Folder_list[2] if 'init_1' in k]
def get_T1_data(folder): #Assures that the data is in the right order
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
x = a.sweep_pts
y = a.p0
y_var = (a.u_p0)**2
minloc = -np.where(x == min(x))[0][0]
x = np.roll(x,minloc)
y = np.roll(y,minloc)
y_var = np.roll(y_var,minloc)
return x,y,y_var
if mode == 'init0' or mode == 'contrast':
x0_tot = np.zeros((6,))
y0_tot = np.zeros((6,))
y0_var_tot = np.zeros((6,))
for i in range(len(init0)):
Folder = Directory + "\\" + Date + "\\" + init0[i]
x,y,y_var = get_T1_data(Folder)
y0_tot += y
y0_var_tot += y_var
y0_tot /= len(init0)
y0_var_tot /= len(init0)
if mode == 'init1' or mode == 'contrast':
x1_tot = np.zeros((6,))
y1_tot = np.zeros((6,))
y1_var_tot = np.zeros((6),)
for i in range(len(init1)):
Folder = Directory + "\\" + Date + "\\" + init1[i]
x,y,y_var = get_T1_data(Folder)
y1_tot += y
y1_var_tot += y_var
y1_tot /= len(init0)
y1_var_tot /= (len(init0))
if mode == 'init1':
y_tot = y1_tot
y_var_tot = y1_var_tot
elif mode == 'init0':
y_tot = y1_tot
y_var_tot = y1_var_tot
elif mode == 'contrast':
y_diff = y0_tot - y1_tot
var_diff = (y0_var_tot + y1_var_tot) / 2
else:
raise Exception('Mode not specified')
p0, fitfunc, fitfunc_str = common.fit_exp_decay_with_offset(Amplitude, Offset, T1)
a = sequence.SequenceAnalysis(Directory + "\\" + Date + "\\" + init0[0])
a.get_sweep_pts()
a.sweep_pts = x / 1e6
a.sweep_name = 'Times (sec)'
a.get_readout_results('ssro')
a.get_electron_ROC()
a.p0 = y_diff
a.u_p0 = var_diff**0.5
ax = a.plot_result_vs_sweepparam(ret='ax')
fit_result = fit.fit1d(x / 1e6,y_diff, None, p0=p0, fitfunc=fitfunc, do_print=False, ret=True)
plot.plot_fit1d(fit_result, np.linspace(0,x[-1]/1e6,201), ax=ax, plot_data=False)