def fit_cos(x, y, **kw):
a = kw.pop('a', 0)
A = kw.pop('A', 0.8)
x0 = kw.pop('x0', 0)
T = kw.pop('T', 1000)
n = kw.pop('n', 1)
f = kw.pop('a', 1 / 200.)
phi = kw.pop('a', 0)
fixed = kw.pop('fixed', [0, 2, 4])
show_guess = kw.pop('show_guess', False)
do_print = kw.pop('do_print', False)
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(
a, A, x0, T, n, f, phi)
if show_guess:
ax.plot(np.linspace(0, x[-1], 201),
fitfunc(np.linspace(0, x[-1], 201)),
':',
lw=2)
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=do_print,
ret=True,
fixed=fixed)
return fit_result
def fit_cos(x,y,**kw):
a = kw.pop('a',0)
A = kw.pop('A',0.8)
x0 = kw.pop('x0',0)
T = kw.pop('T',1000)
n = kw.pop('n',1)
f = kw.pop('a',1/200.)
phi = kw.pop('a',0)
fixed=kw.pop('fixed',[0,2,4])
show_guess = kw.pop('show_guess',False)
do_print = kw.pop('do_print',False)
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(a,A,x0,T,n,f,phi)
if show_guess:
ax.plot(np.linspace(0,x[-1],201), fitfunc(np.linspace(0,x[-1],201)), ':', lw=2)
fit_result = fit.fit1d(x,y, None, p0=p0, fitfunc=fitfunc, do_print=do_print, ret=True,fixed=fixed)
return fit_result
def Carbon_Ramsey(timestamp=None,
measurement_name=['adwindata'],
ssro_folder=None,
frequency=1,
offset=0.5,
x0=0,
amplitude=0.5,
decay_constant=0.015,
phase=0,
exponent=2,
plot_fit=False,
do_print=False,
fixed=[2],
show_guess=True,
return_phase=False,
return_freq=False,
return_A=False,
return_results=True,
close_plot=False,
title='Carbon'):
'''
'''
folder = toolbox.data_from_time(timestamp)
ssro_calib_folder = ssro_folder
fit_results = []
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(ssro_calib_folder)
ax = a.plot_results_vs_sweepparam(ret='ax')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
y_err = a.u_p0.reshape(-1)[:]
ax.plot(x, y)
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(
offset, amplitude, x0, decay_constant, exponent, frequency, phase)
#plot the initial guess
if show_guess:
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,
ret=True,
fixed=fixed)
print 'fitfunction: ' + fitfunc_str
## plot data and fit as function of total time
if plot_fit == True:
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 1001),
ax=ax,
plot_data=False)
fit_results.append(fit_result)
if title == None:
title = 'analyzed_result'
plt.savefig(os.path.join(folder, title + '.pdf'), format='pdf')
plt.savefig(os.path.join(folder, title + '.png'), format='png')
return x, y, y_err, fit_result
def Carbon_Ramsey(timestamp=None, measurement_name = ['adwindata'], ssro_folder =None,
frequency = 1,
offset = 0.5,
x0 = 0,
amplitude = 0.5,
decay_constant = 0.015,
phase =0,
exponent = 2,
plot_fit = False, do_print = False, fixed = [2], show_guess = True,
return_phase = False,
return_freq = False,
return_A = False,
return_results = True,
close_plot = False,
title = 'Carbon'
):
'''
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
List of parameters (order important for 'fixed')
offset, amplitude, decay_constant,exponent,frequency ,phase
'''
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data(title)
if ssro_folder == None:
ssro_calib_folder = toolbox.latest_data('SSRO')
else:
ssro_calib_folder = ssro_folder
fit_results = []
for k in range(0,len(measurement_name)):
a = mbi.MBIAnalysis(folder)
print folder
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(ssro_calib_folder)
# ax = a.plot_results_vs_sweepparam(ret='ax')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
y_err = a.u_p0.reshape(-1)[:]
# ax.plot(x,y)
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(offset, amplitude,
x0, decay_constant,exponent,frequency ,phase )
#plot the initial guess
# if show_guess:
# 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, ret=True,fixed=fixed)
print 'fitfunction: '+fitfunc_str
## plot data and fit as function of total time
# if plot_fit == True:
# plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],1001), ax=ax, plot_data=False)
fit_results.append(fit_result)
if title == None:
title = 'analyzed_result'
# plt.savefig(os.path.join(folder, title + '.pdf'),
# format='pdf')
# plt.savefig(os.path.join(folder, title + '.png'),
# format='png')
# if close_plot == True:
# plt.close()
if return_freq == True:
f0 = fit_result['params_dict']['f']
u_f0 = fit_result['error_dict']['f']
return f0, u_f0
if return_phase == True:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
return phi0, u_phi0
if return_phase == True and return_A == True:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
A = fit_result['params_dict']['A']
u_A = fit_result['error_dict']['A']
return phi0, u_phi0, A, u_A
print 'ok'
if return_phase == True and return_A == True and return_n == True:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
A = fit_result['params_dict']['A']
u_A = fit_result['error_dict']['A']
n = fit_result['params_dict']['n']
u_n = fit_result['error_dict']['n']
return phi0, u_phi0, A, u_A
print 'ok'
# if return_results == True:
# return fit_results
return x, y, y_err, fit_result
def Carbon_Ramsey_DD_freq(older_than = None,
carbon = 1,
frequency = 1,
offset = 0.5,
x0 = 0,
amplitude = 0.5,
decay_constant = 200,
phase =0,
exponent = 2,
plot_fit = False, do_print = False, fixed = [2], show_guess = True,
return_phase = False,
return_freq = False,
return_results = True,
close_plot = False):
'''
'''
folder1 = toolbox.latest_data(contains = 'evo_times_1_C' + str(carbon), older_than = older_than)
folder2 = toolbox.latest_data(contains = 'evo_times_2_C' + str(carbon), older_than = older_than)
folder3 = toolbox.latest_data(contains = 'evo_times_3_C' + str(carbon), older_than = older_than)
a1 = mbi.MBIAnalysis(folder1)
a1.get_sweep_pts()
a1.get_readout_results(name='adwindata')
a1.get_electron_ROC()
a2 = mbi.MBIAnalysis(folder2)
a2.get_sweep_pts()
a2.get_readout_results(name='adwindata')
a2.get_electron_ROC()
a3 = mbi.MBIAnalysis(folder3)
a3.get_sweep_pts()
a3.get_readout_results(name='adwindata')
a3.get_electron_ROC()
'''
folder4 = toolbox.latest_data(contains = 'evo_times_4_C' + str(carbon), older_than = older_than)
folder5 = toolbox.latest_data(contains = 'evo_times_5_C' + str(carbon), older_than = older_than)
folder6 = toolbox.latest_data(contains = 'evo_times_6_C' + str(carbon), older_than = older_than)
folder7 = toolbox.latest_data(contains = 'evo_times_7_C' + str(carbon), older_than = older_than)
a4 = mbi.MBIAnalysis(folder4)
a4.get_sweep_pts()
a4.get_readout_results(name='adwindata')
a4.get_electron_ROC()
a5 = mbi.MBIAnalysis(folder5)
a5.get_sweep_pts()
a5.get_readout_results(name='adwindata')
a5.get_electron_ROC()
a6 = mbi.MBIAnalysis(folder6)
a6.get_sweep_pts()
a6.get_readout_results(name='adwindata')
a6.get_electron_ROC()
a7 = mbi.MBIAnalysis(folder7)
a7.get_sweep_pts()
a7.get_readout_results(name='adwindata')
a7.get_electron_ROC()
a1.p0 = np.r_[a1.p0,a2.p0,a3.p0,a4.p0,a5.p0,a6.p0,a7.p0]
a1.u_p0 = np.r_[a1.u_p0,a2.u_p0,a3.u_p0,a4.u_p0,a5.u_p0,a6.u_p0,a7.u_p0]
a1.sweep_pts = np.r_[a1.sweep_pts,a2.sweep_pts,a3.sweep_pts,a4.sweep_pts,a5.sweep_pts,a6.sweep_pts,a7.sweep_pts]
'''
a1.p0 = np.r_[a1.p0,a2.p0,a3.p0]
a1.u_p0 = np.r_[a1.u_p0,a2.u_p0,a3.u_p0]
a1.sweep_pts = np.r_[a1.sweep_pts,a2.sweep_pts,a3.sweep_pts]
x = a1.sweep_pts.reshape(-1)[:]
y = a1.p0.reshape(-1)[:]
ax = a1.plot_results_vs_sweepparam(ret='ax')
ax.plot(x,y)
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(offset, amplitude,
x0, decay_constant,exponent,frequency ,phase )
#plot the initial guess
if show_guess:
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, ret=True,fixed=fixed)
print 'fitfunction: '+fitfunc_str
## plot data and fit as function of total time
if plot_fit == True:
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],1001), ax=ax, plot_data=False)
title = 'DD freq ramsey C' +str(carbon)
plt.savefig(os.path.join(folder1, title + '.pdf'),
format='pdf')
plt.savefig(os.path.join(folder1, title + '.png'),
format='png')
def Carbon_Ramsey_Crosstalk(timestamp=None, measurement_name = ['adwindata'], ssro_calib_timestamp =None,
frequency = 1,
offset = 0.5,
x0 = 0,
amplitude = 0.5,
decay_constant = 200,
phase =0,
exponent = 2,
plot_fit = False, do_print = False, fixed = [2,3,4], show_guess = True,
return_phase = False,
return_freq = False,
return_results = True,
return_amp = False,
close_plot = False,
title = None):
'''
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
List of parameters (order important for 'fixed')
offset, amplitude, decay_constant,exponent,frequency ,phase
'''
if timestamp != None:
folder_a = toolbox.data_from_time(timestamp)
else:
folder_a, timestamp = toolbox.latest_data('Crosstalk', return_timestamp = True)
folder_b = toolbox.latest_data('Crosstalk',older_than = timestamp)
if ssro_calib_timestamp == None:
ssro_calib_folder = toolbox.latest_data('SSRO')
else:
ssro_dstmp, ssro_tstmp = toolbox.verify_timestamp(ssro_calib_timestamp)
ssro_calib_folder = toolbox.datadir + '/'+ssro_dstmp+'/'+ssro_tstmp+'_AdwinSSRO_SSROCalibration_Hans_sil1'
print ssro_calib_folder
fit_results = []
for k in range(0,len(measurement_name)):
a = mbi.MBIAnalysis(folder_a)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(ssro_calib_folder)
ax = a.plot_results_vs_sweepparam(ret='ax')
X_RO_data = 2*(a.p0.reshape(-1)[:])-1
X_RO_data_u = 2*(a.u_p0.reshape(-1)[:])
a = mbi.MBIAnalysis(folder_b)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(ssro_calib_folder)
ax = a.plot_results_vs_sweepparam(ret='ax')
x = a.sweep_pts.reshape(-1)[:]
Y_RO_data = 2*(a.p0.reshape(-1)[:])-1
Y_RO_data_u = 2*(a.u_p0.reshape(-1)[:])
RO_data = (X_RO_data**2 + Y_RO_data**2)**0.5
RO_data_u = (1./(X_RO_data**2 + Y_RO_data**2)*(X_RO_data**2 * X_RO_data_u**2 + Y_RO_data**2 *Y_RO_data_u**2))**0.5
fig = a.default_fig(figsize=(7.5,5))
ax2 = a.default_ax(fig)
ax2.axhspan(0,1,fill=False,ls='dotted')
ax2.set_ylim(-1,1)
ax2.errorbar(x,RO_data,RO_data_u)
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(offset, amplitude,
x0, decay_constant,exponent,frequency ,phase )
#plot the initial guess
if show_guess:
ax2.plot(np.linspace(x[0],x[-1],201), fitfunc(np.linspace(x[0],x[-1],201)), ':', lw=2)
fit_result = fit.fit1d(x,RO_data, None, p0=p0, fitfunc=fitfunc, do_print=True, ret=True,fixed=fixed)
print 'fitfunction: '+fitfunc_str
## plot data and fit as function of total time
if plot_fit == True:
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],1001), ax=ax2, plot_data=False)
fit_results.append(fit_result)
if title == None:
title = 'analyzed_result'
plt.savefig(os.path.join(folder_a, title + '.pdf'),
format='pdf')
plt.savefig(os.path.join(folder_a, title + '.png'),
format='png')
if close_plot == True:
plt.close()
if return_freq == True:
f0 = fit_result['params_dict']['f']
u_f0 = fit_result['error_dict']['f']
return f0, u_f0
if return_phase == True:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
Amp = fit_result['params_dict']['A']
if return_amp == True:
return phi0, u_phi0, Amp
else:
return phi0, u_phi0
if return_results == True:
return fit_results
def Carbon_Ramsey_mult_msmts(timestamp=None, measurement_name = ['adwindata'], ssro_calib_timestamp =None,
frequency = 1,
offset = 0.5,
x0 = 0,
amplitude = 0.5,
decay_constant = 200,
phase =0,
exponent = 2,
plot_fit = False, do_print = False, fixed = [2], show_guess = True,
return_phase = False,
return_freq = False,
return_amp = False,
return_results = True,
close_plot = False,
partstr = 'part',
contains=[],
title = 'Carbon'):
'''
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
List of parameters (order important for 'fixed')
offset, amplitude, decay_constant,exponent,frequency ,phase
'''
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data(title)
if partstr in folder:
numberstart = folder.find(partstr)+len(partstr)
numberofparts = int(folder[numberstart:len(folder)])
basis_str = folder[folder.rfind('\\')+7:numberstart]
else:
numberofparts = 1
if ssro_calib_timestamp == None:
ssro_calib_folder = toolbox.latest_data('SSRO')
else:
ssro_dstmp, ssro_tstmp = toolbox.verify_timestamp(ssro_calib_timestamp)
ssro_calib_folder = toolbox.datadir + '/'+ssro_dstmp+'/'+ssro_tstmp+'_AdwinSSRO_SSROCalibration_111_1_sil18'
print ssro_calib_folder
fit_results = []
#for kk in range(numberofparts):
for kk,cnts in enumerate(contains):
'''
if partstr in folder:
folder = toolbox.latest_data(basis_str+str(kk+1))
else:
folder = toolbox.latest_data(basis_str)
'''
folder = toolbox.latest_data(cnts)
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(ssro_calib_folder)
ax = a.plot_results_vs_sweepparam(ret='ax')
if kk == 0:
cum_sweep_pts = a.sweep_pts
cum_p0 = a.p0
cum_u_p0 = a.u_p0
cum_pts = a.pts
else:
cum_sweep_pts = np.concatenate((cum_sweep_pts, a.sweep_pts))
cum_p0 = np.concatenate((cum_p0, a.p0))
cum_u_p0 = np.concatenate((cum_u_p0, a.u_p0))
cum_pts += a.pts
a.pts = cum_pts
a.sweep_pts = cum_sweep_pts
a.p0 = cum_p0
a.u_p0 = cum_u_p0
sorting_order=a.sweep_pts.argsort()
a.sweep_pts.sort()
a.p0=a.p0[sorting_order]
a.u_p0=a.u_p0[sorting_order]
ax=a.plot_results_vs_sweepparam(ret='ax')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
ax.plot(x,y)
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(offset, amplitude,
x0, decay_constant,exponent,frequency ,phase )
#plot the initial guess
if show_guess:
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, ret=True,fixed=fixed)
## plot data and fit as function of total time
if plot_fit == True:
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],1001), ax=ax, plot_data=False)
fit_results.append(fit_result)
if title == None:
title = 'analyzed_result'
plt.savefig(os.path.join(folder, title + '.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, title + '.png'),
format='png')
if close_plot == True:
plt.close()
if return_freq == True:
f0 = fit_result['params_dict']['f']
u_f0 = fit_result['error_dict']['f']
return f0, u_f0
if return_phase == True and return_amp == False:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
return phi0, u_phi0
if return_phase == True and return_amp == True:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
A = fit_result['params_dict']['A']
u_A = fit_result['error_dict']['A']
return phi0, u_phi0, A, u_A
if return_results == True:
return fit_results
def CosineSum_MBI_data(folder=None,
timestamp=[],
measurement_name=['adwindata'],
ssro_calib_folder=None,
ssro_calib_timestamp=None,
new_tsmp='20170612_230600',
old_tsmp='20170614_071700',
two_cos=True,
title='Cluster',
x_ticks=np.arange(0, 100, 5),
y_ticks=np.arange(0.2, 0.8, 0.1),
color='b',
c=1,
t=1,
frequency=[1, 1],
offset=0.5,
amplitude=[0.5, 0.5],
phase=[0, 0],
fixed=[],
plot_fit=False,
do_print=False,
show_guess=True,
xlim1=None,
xlim2=None):
'''
Function to analyze simple decoupling measurements. Loads the results and fits them to a simple exponential.
Inputs:
folder: allows to specify specific folder for the data(this overwrites the timestamp input)
ssro_folder: allows to specify specific folder for ssro calib(this overwrites the ssro_timestamp input)
timestamp: in format yyyymmdd_hhmmss or hhmmss or None.
measurement_name: list of measurement names
List of parameters (order important for 'fixed')
[freq, offset, Amplitude, phase]
'''
timestamp = []
plot_fit = True
show_guess = False
exponent = 2
#two_cos=True
fit_results = []
cum_pts = 0
cum_sweep_pts = np.empty(0)
cum_p0 = np.empty(0)
cu = np.empty(0)
cu_u = np.empty(0)
cum_u_p0 = np.empty(0)
cum_tau_list = np.empty(0)
# new_tsmp = '20170611_165140' ## newer than
# old_tsmp = '20170611_235700' ## older than
# new_tsmp = '20170612_005040' ## newer than
# old_tsmp = '20170612_015800' ## older than
# new_tsmp = '20170612_230600' ## newer than
# old_tsmp = '20170614_071700' ## older than
# new_tsmp = '20170616_190600' ## newer than
# old_tsmp = '20170617_053700' ## older than
# new_tsmp = '20170621_183900' ## newer than
# old_tsmp = '20170621_185800' ## older than
# new_tsmp = '20170719_161700' ## newer than
# old_tsmp = '20170719_165500' ## older than
# new_tsmp = '20170720_171400' ## newer than
# old_tsmp = '20170720_181700' ## older than
# new_tsmp = '20170720_182900' ## newer than
# old_tsmp = '20170720_195700' ## older than
search_string = 'Carbon'
while toolbox.latest_data(contains=search_string,
return_timestamp=True,
older_than=old_tsmp,
newer_than=new_tsmp,
raise_exc=False) != False:
old_tsmp, folder = toolbox.latest_data(contains=search_string,
return_timestamp=True,
older_than=old_tsmp,
newer_than=new_tsmp,
raise_exc=False)
timestamp.append(old_tsmp)
timestamp = timestamp[::-1]
if ssro_calib_folder == None:
if ssro_calib_timestamp == None:
ssro_calib_folder = toolbox.latest_data('SSRO')
else:
ssro_dstmp, ssro_tstmp = toolbox.verify_timestamp(
ssro_calib_timestamp)
ssro_calib_folder = toolbox.datadir + '/' + ssro_dstmp + '/' + ssro_tstmp + '_AdwinSSRO_SSROCalibration_111_1_sil18'
print ssro_calib_folder
for kk in range(len(timestamp)):
folder = toolbox.data_from_time(timestamp[kk])
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(ssro_calib_folder)
cum_pts += a.pts
# temperature = (a.g.attrs['temp']-100)/0.385
# temperature_list.append(temperature)
if kk == 0:
cum_sweep_pts = a.sweep_pts
cum_p0 = a.p0
cum_u_p0 = a.u_p0
#cum_tau_list = a.tau_list
#elif kk in [5,10,15,21,26,31]:
else:
cum_sweep_pts = np.concatenate((cum_sweep_pts, a.sweep_pts))
cum_p0 = np.concatenate((cum_p0, a.p0))
cum_u_p0 = np.concatenate((cum_u_p0, a.u_p0))
#cum_tau_list = np.concatenate((cum_tau_list, a.tau_list))
a.pts = cum_pts
a.sweep_pts = 1.0e3 * cum_sweep_pts
a.p0 = cum_p0
a.u_p0 = cum_u_p0
#ax = a.plot_results_vs_sweepparam(ret='ax',fmt='o',color='brown',figsize=(46,12))
e = a.u_p0
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
# fig = plt.figure(1,figsize=(10,2))
# ax2 = fig.add_subplot(111)
# ax2.set_xlabel('Free evolution time (ms)')
# ax2.set_ylabel('State Fidelity')
#f = plt.figure(1,figsize=(60,2))
#f,(ax1,ax2,ax3,ax4) = plt.subplots(1,4,sharey=True, facecolor='w',figsize=(10,3.5))
fig = plt.figure(figsize=(9, 3.3))
gs = gridspec.GridSpec(1, 4, width_ratios=[3, 3, 2, 1])
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
ax3 = plt.subplot(gs[2])
ax4 = plt.subplot(gs[3])
ax1.errorbar(x.flatten(),
y.flatten(),
yerr=e,
fmt='o',
label='',
color=color,
lw=1,
markersize=3)
ax2.errorbar(x.flatten(),
y.flatten(),
yerr=e,
fmt='o',
label='',
color=color,
lw=1,
markersize=3)
ax3.errorbar(x.flatten(),
y.flatten(),
yerr=e,
fmt='o',
label='',
color=color,
lw=1,
markersize=3)
ax4.errorbar(x.flatten(),
y.flatten(),
yerr=e,
fmt='o',
label='',
color=color,
lw=1,
markersize=3)
fit_result = [None]
#p0, fitfunc, fitfunc_str = common.fit_sum_2cos(offset,amplitude[0],frequency[0],phase[0],amplitude[1],frequency[1],phase[1])
if two_cos == True:
p0, fitfunc, fitfunc_str = common.fit_gaussian_decaying_2cos(
offset, c, t, amplitude[0], frequency[0], phase[0], amplitude[1],
frequency[1], phase[1])
else:
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(
offset, amplitude[0], 0, t, exponent, frequency[0], phase[0])
if show_guess:
ax1.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=do_print,
ret=True,
fixed=fixed)
if plot_fit == True:
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 8001),
ax=ax1,
color=color,
plot_data=False,
print_info=False,
lw=1.5)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 8001),
ax=ax2,
color=color,
plot_data=False,
print_info=False,
lw=1.5)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 8001),
ax=ax3,
color=color,
plot_data=False,
print_info=False,
lw=1.5)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 8001),
ax=ax4,
color=color,
plot_data=False,
print_info=False,
lw=1.5)
fit.write_to_file(fit_result, folder, fitname='Sum of cosine fit')
## plot data and fit as function of total time
#plt.xticks(x_ticks)
# plt.yticks(np.arange(min(y),(max(y)),0.1))
#plt.yticks(y_ticks)
ax1.set_xlim(0, 31)
ax2.set_xlim(xlim1, xlim2)
ax3.set_xlim(609, 631)
ax4.set_xlim(910, 920)
#ax2.set_ylim(0,1)
#ax2.set_xlabel('Free evolution time (ms)')
#ax2.set_ylabel('State Fidelity')
d = .045 # how big to make the diagonal lines in axes coordinates
# arguments to pass plot, just so we don't keep repeating them
ax1.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax2.spines['right'].set_visible(False)
ax3.spines['left'].set_visible(False)
ax3.spines['right'].set_visible(False)
ax1.yaxis.tick_left()
ax1.tick_params(labelright='off')
ax2.tick_params(labelleft='off')
ax2.tick_params(labelright='off')
ax3.tick_params(labelleft='off')
ax3.tick_params(labelright='off')
ax4.tick_params(labelleft='off')
ax4.tick_params(labelright='off')
#ax4.tick_params(labelbottom='off')
#ax1.tick_params(labelbottom='off')
#ax2.tick_params(labelbottom='off')
#ax3.tick_params(labelbottom='off')
ax4.spines['left'].set_visible(False)
plt.sca(ax1)
plt.xticks([10, 20, 30])
plt.yticks([0.4, 0.5, 0.6, 0.7])
plt.sca(ax2)
plt.xticks([200, 210, 220, 230])
plt.yticks([])
plt.sca(ax3)
plt.xticks([610, 620, 630])
plt.yticks([])
plt.sca(ax4)
plt.xticks([910, 920])
plt.yticks([])
#plt.step(ax1,ax2,xticks=[10,20,30],yticks=[0.4,0.5,0.6,0.7])
#ax4.yaxis.tick_right()
#ax4.xaxis.tick_bottom()
# kwargs = dict(transform=ax1.transAxes, color='k', clip_on=False)
# ax1.plot((1-d,1+d), (-d,+d), **kwargs)
# ax1.plot((1-d,1+d),(1-d,1+d), **kwargs)
# kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
# ax2.plot((-d,+d), (1-d,1+d), **kwargs)
# ax2.plot((-d,+d), (-d,+d), **kwargs)
# ax2.plot((1-d,1+d), (-d,+d), **kwargs)
# ax2.plot((1-d,1+d),(1-d,1+d), **kwargs)
# kwargs = dict(transform=ax3.transAxes, color='k', clip_on=False)
# ax3.plot((-d,+d), (1-d,1+d), **kwargs)
# ax3.plot((-d,+d), (-d,+d), **kwargs)
# ax3.plot((1-d,1+d), (-d,+d), **kwargs)
# ax3.plot((1-d,1+d),(1-d,1+d), **kwargs)
# kwargs.update(transform=ax4.transAxes) # switch to the bottom axes
# ax4.plot((-d,+d), (1-d,1+d), **kwargs)
# ax4.plot((-d,+d), (-d,+d), **kwargs)
folder = 'C:\Users\TUD277931\Dropbox\TaminiauLab\Projects\Coherence in multi-qubit systems\Paper\Figures'
plt.savefig(os.path.join(folder, title + '.pdf'),
format='pdf',
bbox_inches='tight',
pad_inches=0.2,
transparent=True)
def Carbon_Ramsey(timestamp=None,
measurement_name=['adwindata'],
ssro_folder=None,
frequency=1,
offset=0.5,
x0=0,
amplitude=0.5,
decay_constant=200,
phase=0,
exponent=2,
plot_fit=False,
do_print=False,
fixed=[2],
show_guess=True,
return_phase=False,
return_freq=False,
return_A=False,
return_results=True,
close_plot=False,
title='Carbon'):
'''
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
List of parameters (order important for 'fixed')
offset, amplitude, decay_constant,exponent,frequency ,phase
'''
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data(title)
if ssro_calib_timestamp == None:
ssro_calib_folder = toolbox.latest_data('SSRO')
else:
ssro_calib_folder = ssro_folder
fit_results = []
for k in range(0, len(measurement_name)):
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(ssro_calib_folder)
ax = a.plot_results_vs_sweepparam(ret='ax')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
y_err = u_p0(-1)[:]
ax.plot(x, y)
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(
offset, amplitude, x0, decay_constant, exponent, frequency, phase)
#plot the initial guess
if show_guess:
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,
ret=True,
fixed=fixed)
print 'fitfunction: ' + fitfunc_str
## plot data and fit as function of total time
if plot_fit == True:
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 1001),
ax=ax,
plot_data=False)
fit_results.append(fit_result)
if title == None:
title = 'analyzed_result'
plt.savefig(os.path.join(folder, title + '.pdf'), format='pdf')
plt.savefig(os.path.join(folder, title + '.png'), format='png')
if close_plot == True:
plt.close()
if return_freq == True:
f0 = fit_result['params_dict']['f']
u_f0 = fit_result['error_dict']['f']
return f0, u_f0
if return_phase == True:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
return phi0, u_phi0
if return_phase == True and return_A == True:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
A = fit_result['params_dict']['A']
u_A = fit_result['error_dict']['A']
return phi0, u_phi0, A, u_A
print 'ok'
if return_results == True:
return fit_results
return x, y, y_err, fit_results
def CosineSum_MBI_data(folder=None,
timestamp=[],
measurement_name=['adwindata'],
ssro_calib_folder=None,
ssro_calib_timestamp=None,
new_tsmp='20170612_230600',
old_tsmp='20170614_071700',
two_cos=True,
title='Cluster',
x_ticks=np.arange(0, 100, 5),
y_ticks=np.arange(0.2, 0.8, 0.1),
color='b',
c=1,
t=1,
frequency=[1, 1],
offset=0.5,
amplitude=[0.5, 0.5],
phase=[0, 0],
fixed=[],
plot_fit=False,
do_print=False,
show_guess=True,
xlim=None):
'''
Function to analyze simple decoupling measurements. Loads the results and fits them to a simple exponential.
Inputs:
folder: allows to specify specific folder for the data(this overwrites the timestamp input)
ssro_folder: allows to specify specific folder for ssro calib(this overwrites the ssro_timestamp input)
timestamp: in format yyyymmdd_hhmmss or hhmmss or None.
measurement_name: list of measurement names
List of parameters (order important for 'fixed')
[freq, offset, Amplitude, phase]
'''
timestamp = []
plot_fit = True
show_guess = False
exponent = 2
#two_cos=True
fit_results = []
cum_pts = 0
cum_sweep_pts = np.empty(0)
cum_p0 = np.empty(0)
cu = np.empty(0)
cu_u = np.empty(0)
cum_u_p0 = np.empty(0)
cum_tau_list = np.empty(0)
# new_tsmp = '20170611_165140' ## newer than
# old_tsmp = '20170611_235700' ## older than
# new_tsmp = '20170612_005040' ## newer than
# old_tsmp = '20170612_015800' ## older than
# new_tsmp = '20170612_230600' ## newer than
# old_tsmp = '20170614_071700' ## older than
# new_tsmp = '20170616_190600' ## newer than
# old_tsmp = '20170617_053700' ## older than
# new_tsmp = '20170621_183900' ## newer than
# old_tsmp = '20170621_185800' ## older than
# new_tsmp = '20170719_161700' ## newer than
# old_tsmp = '20170719_165500' ## older than
# new_tsmp = '20170720_171400' ## newer than
# old_tsmp = '20170720_181700' ## older than
# new_tsmp = '20170720_182900' ## newer than
# old_tsmp = '20170720_195700' ## older than
search_string = 'Carbon'
while toolbox.latest_data(contains=search_string,
return_timestamp=True,
older_than=old_tsmp,
newer_than=new_tsmp,
raise_exc=False) != False:
old_tsmp, folder = toolbox.latest_data(contains=search_string,
return_timestamp=True,
older_than=old_tsmp,
newer_than=new_tsmp,
raise_exc=False)
timestamp.append(old_tsmp)
timestamp = timestamp[::-1]
if ssro_calib_folder == None:
if ssro_calib_timestamp == None:
ssro_calib_folder = toolbox.latest_data('SSRO')
else:
ssro_dstmp, ssro_tstmp = toolbox.verify_timestamp(
ssro_calib_timestamp)
ssro_calib_folder = toolbox.datadir + '/' + ssro_dstmp + '/' + ssro_tstmp + '_AdwinSSRO_SSROCalibration_111_1_sil18'
print ssro_calib_folder
for kk in range(len(timestamp)):
folder = toolbox.data_from_time(timestamp[kk])
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(ssro_calib_folder)
cum_pts += a.pts
# temperature = (a.g.attrs['temp']-100)/0.385
# temperature_list.append(temperature)
if kk == 0:
cum_sweep_pts = a.sweep_pts
cum_p0 = a.p0
cum_u_p0 = a.u_p0
#cum_tau_list = a.tau_list
#elif kk in [5,10,15,21,26,31]:
else:
cum_sweep_pts = np.concatenate((cum_sweep_pts, a.sweep_pts))
cum_p0 = np.concatenate((cum_p0, a.p0))
cum_u_p0 = np.concatenate((cum_u_p0, a.u_p0))
#cum_tau_list = np.concatenate((cum_tau_list, a.tau_list))
a.pts = cum_pts
a.sweep_pts = 1.0e3 * cum_sweep_pts
a.p0 = cum_p0
a.u_p0 = cum_u_p0
#ax = a.plot_results_vs_sweepparam(ret='ax',fmt='o',color='brown',figsize=(46,12))
e = a.u_p0
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
fig = plt.figure(1, figsize=(4, 1.5))
ax2 = fig.add_subplot(111)
ax2.errorbar(x.flatten(),
y.flatten(),
yerr=e,
fmt='o',
label='',
color=color,
lw=1,
markersize=3)
ax2.set_xlabel('Free evolution time (ms)')
ax2.set_ylabel('State Fidelity')
fit_result = [None]
#p0, fitfunc, fitfunc_str = common.fit_sum_2cos(offset,amplitude[0],frequency[0],phase[0],amplitude[1],frequency[1],phase[1])
if two_cos == True:
p0, fitfunc, fitfunc_str = common.fit_gaussian_decaying_2cos(
offset, c, t, amplitude[0], frequency[0], phase[0], amplitude[1],
frequency[1], phase[1])
else:
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(
offset, amplitude[0], 0, t, exponent, frequency[0], phase[0])
if show_guess:
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=do_print,
ret=True,
fixed=fixed)
if plot_fit == True:
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 8001),
ax=ax2,
color=color,
plot_data=False,
print_info=False,
lw=1.5)
fit.write_to_file(fit_result, folder, fitname='Sum of cosine fit')
## plot data and fit as function of total time
plt.xticks(x_ticks)
# plt.yticks(np.arange(min(y),(max(y)),0.1))
plt.yticks(y_ticks)
ax2.set_xlim(0, xlim)
#ax2.set_ylim(0,1)
#ax2.set_xlabel('Free evolution time (ms)')
#ax2.set_ylabel('State Fidelity')
folder = 'C:\Users\TUD277931\Dropbox\TaminiauLab\Projects\Coherence in multi-qubit systems\Paper\Figures\Fig 3'
plt.savefig(os.path.join(folder, title + '.pdf'),
format='pdf',
bbox_inches='tight',
pad_inches=0.2,
transparent=True)
def Carbon_Ramsey(timestamp=None, carbon=None, transition=None, measurement_name = ['adwindata'], ssro_calib_timestamp =None,
frequency = 1,
offset = 0.5,
x0 = 0,
amplitude = 0.5,
decay_constant = 200,
phase =0,
exponent = 2,
plot_fit = False, do_print = False, fixed = [2], show_guess = True,
return_phase = False,
return_freq = False,
return_amp = False,
return_results = True,
close_plot = False,
color='b',
title = 'Carbon',
x_ticks=np.arange(0,100,5),
y_ticks=np.arange(0.2,0.8,0.1)):
'''
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
List of parameters (order important for 'fixed')
offset, amplitude, decay_constant,exponent,frequency ,phase
'''
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
elif carbon != None:
folder = toolbox.latest_data(contains='C'+str(carbon)+'_ms'+str(transition))
else:
folder = toolbox.latest_data(title)
print folder
if ssro_calib_timestamp == None:
ssro_calib_folder = toolbox.latest_data('SSROCalibration')
else:
ssro_dstmp, ssro_tstmp = toolbox.verify_timestamp(ssro_calib_timestamp)
ssro_calib_folder = toolbox.datadir + '/'+ssro_dstmp+'/'+ssro_tstmp+'_AdwinSSRO_SSROCalibration_111_1_sil18'
print ssro_calib_folder
fit_results = []
for k in range(0,len(measurement_name)):
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(ssro_calib_folder)
x = 1.0e3*a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
e= a.u_p0
# fig = plt.figure(1,figsize=(4,1.5))
fig = plt.figure(1,figsize=(8,3))
ax2 = fig.add_subplot(111)
ax2.errorbar(x.flatten(),y.flatten(),yerr=e,fmt='o',label='',color=color,markersize=4,lw=1)
ax2.set_xlabel('Free evolution time (ms)')
ax2.set_ylabel('State Fidelity')
print min(y)
print max(y)
#plt.xticks(x_ticks)
# plt.yticks(np.arange(min(y),(max(y)),0.1))
#plt.yticks(y_ticks)
#ax2.set_ylim(min(y)-0.03,max(y)+0.07)
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(offset, amplitude,
x0, decay_constant,exponent,frequency ,phase )
#plot the initial guess
if show_guess:
ax2.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, ret=True,fixed=fixed)
print 'fitfunction: '+fitfunc_str
## plot data and fit as function of total time
if plot_fit == True:
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],1001), ax=ax2, add_txt = False, color=color, plot_data=False,lw=1.5)
fit_results.append(fit_result)
if title == None:
title = 'analyzed_result'
folder='C:\Users\TUD277931\Dropbox\TaminiauLab\Projects\Coherence in multi-qubit systems\Paper\Figures\Fig 3'
plt.savefig(os.path.join(folder, title + '.pdf'),
format='pdf',bbox_inches='tight',pad_inches=0.2,transparent=True)
if close_plot == True:
plt.close()
# plt.savefig('C:\Users\TUD277931\Dropbox\TaminiauLab\Projects\Coherence in multi-qubit systems\Paper\Figures\Fig 1\T1_sum_log.pdf',
# format='pdf',bbox_inches='tight',pad_inches=0.2,transparent=True)
# for item in fit_result['params_dict']:
# print item
# return fit_result
#print folder
if return_freq == True:
f0 = fit_result['params_dict']['f']
u_f0 = fit_result['error_dict']['f']
return f0, u_f0
if return_phase == True and return_amp == False:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
return phi0, u_phi0
if return_phase == True and return_amp == True:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
A = fit_result['params_dict']['A']
u_A = fit_result['error_dict']['A']
return phi0, u_phi0, A, u_A
if return_amp == True:
A = fit_result['params_dict']['A']
u_A = fit_result['error_dict']['A']
return A, u_A
if return_results == True:
return fit_results
def Carbon_Ramsey_DD_freq(older_than=None,
transition=None,
carbon=1,
frequency=1,
offset=0.5,
x0=0,
amplitude=0.5,
decay_constant=200,
phase=0,
exponent=2,
plot_fit=False,
do_print=False,
fixed=[2],
show_guess=True,
return_phase=False,
return_freq=True,
return_results=True,
close_plot=False):
'''
'''
if transition != None:
folder1 = toolbox.latest_data(contains='evo_times_1_C' + str(carbon) +
str(transition),
older_than=older_than)
folder2 = toolbox.latest_data(contains='evo_times_2_C' + str(carbon) +
str(transition),
older_than=older_than)
folder3 = toolbox.latest_data(contains='evo_times_3_C' + str(carbon) +
str(transition),
older_than=older_than)
else:
folder1 = toolbox.latest_data(contains='evo_times_1_C' + str(carbon),
older_than=older_than)
folder2 = toolbox.latest_data(contains='evo_times_2_C' + str(carbon),
older_than=older_than)
folder3 = toolbox.latest_data(contains='evo_times_3_C' + str(carbon),
older_than=older_than)
a1 = mbi.MBIAnalysis(folder1)
a1.get_sweep_pts()
a1.get_readout_results(name='adwindata')
a1.get_electron_ROC()
a2 = mbi.MBIAnalysis(folder2)
a2.get_sweep_pts()
a2.get_readout_results(name='adwindata')
a2.get_electron_ROC()
a3 = mbi.MBIAnalysis(folder3)
a3.get_sweep_pts()
a3.get_readout_results(name='adwindata')
a3.get_electron_ROC()
'''
folder4 = toolbox.latest_data(contains = 'evo_times_4_C' + str(carbon), older_than = older_than)
folder5 = toolbox.latest_data(contains = 'evo_times_5_C' + str(carbon), older_than = older_than)
folder6 = toolbox.latest_data(contains = 'evo_times_6_C' + str(carbon), older_than = older_than)
folder7 = toolbox.latest_data(contains = 'evo_times_7_C' + str(carbon), older_than = older_than)
a4 = mbi.MBIAnalysis(folder4)
a4.get_sweep_pts()
a4.get_readout_results(name='adwindata')
a4.get_electron_ROC()
a5 = mbi.MBIAnalysis(folder5)
a5.get_sweep_pts()
a5.get_readout_results(name='adwindata')
a5.get_electron_ROC()
a6 = mbi.MBIAnalysis(folder6)
a6.get_sweep_pts()
a6.get_readout_results(name='adwindata')
a6.get_electron_ROC()
a7 = mbi.MBIAnalysis(folder7)
a7.get_sweep_pts()
a7.get_readout_results(name='adwindata')
a7.get_electron_ROC()
a1.p0 = np.r_[a1.p0,a2.p0,a3.p0,a4.p0,a5.p0,a6.p0,a7.p0]
a1.u_p0 = np.r_[a1.u_p0,a2.u_p0,a3.u_p0,a4.u_p0,a5.u_p0,a6.u_p0,a7.u_p0]
a1.sweep_pts = np.r_[a1.sweep_pts,a2.sweep_pts,a3.sweep_pts,a4.sweep_pts,a5.sweep_pts,a6.sweep_pts,a7.sweep_pts]
'''
a1.p0 = np.r_[a1.p0, a2.p0, a3.p0]
a1.u_p0 = np.r_[a1.u_p0, a2.u_p0, a3.u_p0]
a1.sweep_pts = np.r_[a1.sweep_pts, a2.sweep_pts, a3.sweep_pts]
x = a1.sweep_pts.reshape(-1)[:]
y = a1.p0.reshape(-1)[:]
ax = a1.plot_results_vs_sweepparam(ret='ax')
ax.plot(x, y)
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(
offset, amplitude, x0, decay_constant, exponent, frequency, phase)
#plot the initial guess
if show_guess:
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,
ret=True,
fixed=fixed)
print 'fitfunction: ' + fitfunc_str
## plot data and fit as function of total time
if plot_fit == True:
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 1001),
ax=ax,
plot_data=False)
title = 'DD freq ramsey C' + str(carbon)
print folder1
plt.savefig(os.path.join(folder1, title + '.pdf'), format='pdf')
plt.savefig(os.path.join(folder1, title + '.png'), format='png')
def Carbon_Ramsey_Crosstalk(timestamp=None,
measurement_name=['adwindata'],
ssro_calib_timestamp=None,
frequency=1,
offset=0.5,
x0=0,
amplitude=0.5,
decay_constant=200,
phase=0,
exponent=2,
plot_fit=False,
do_print=False,
fixed=[2, 3, 4],
show_guess=True,
return_phase=False,
return_freq=False,
return_results=True,
return_amp=False,
close_plot=False,
title=None):
'''
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
List of parameters (order important for 'fixed')
offset, amplitude, decay_constant,exponent,frequency ,phase
'''
if timestamp != None:
folder_a = toolbox.data_from_time(timestamp)
else:
folder_a, timestamp = toolbox.latest_data('Crosstalk',
return_timestamp=True)
folder_b = toolbox.latest_data('Crosstalk', older_than=timestamp)
if ssro_calib_timestamp == None:
ssro_calib_folder = toolbox.latest_data('SSRO')
else:
ssro_dstmp, ssro_tstmp = toolbox.verify_timestamp(ssro_calib_timestamp)
ssro_calib_folder = toolbox.datadir + '/' + ssro_dstmp + '/' + ssro_tstmp + '_AdwinSSRO_SSROCalibration_Hans_sil1'
print ssro_calib_folder
fit_results = []
for k in range(0, len(measurement_name)):
a = mbi.MBIAnalysis(folder_a)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(ssro_calib_folder)
ax = a.plot_results_vs_sweepparam(ret='ax')
X_RO_data = 2 * (a.p0.reshape(-1)[:]) - 1
X_RO_data_u = 2 * (a.u_p0.reshape(-1)[:])
a = mbi.MBIAnalysis(folder_b)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(ssro_calib_folder)
ax = a.plot_results_vs_sweepparam(ret='ax')
x = a.sweep_pts.reshape(-1)[:]
Y_RO_data = 2 * (a.p0.reshape(-1)[:]) - 1
Y_RO_data_u = 2 * (a.u_p0.reshape(-1)[:])
RO_data = (X_RO_data**2 + Y_RO_data**2)**0.5
RO_data_u = (1. / (X_RO_data**2 + Y_RO_data**2) *
(X_RO_data**2 * X_RO_data_u**2 +
Y_RO_data**2 * Y_RO_data_u**2))**0.5
fig = a.default_fig(figsize=(7.5, 5))
ax2 = a.default_ax(fig)
ax2.axhspan(0, 1, fill=False, ls='dotted')
ax2.set_ylim(-1, 1)
ax2.errorbar(x, RO_data, RO_data_u)
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(
offset, amplitude, x0, decay_constant, exponent, frequency, phase)
#plot the initial guess
if show_guess:
ax2.plot(np.linspace(x[0], x[-1], 201),
fitfunc(np.linspace(x[0], x[-1], 201)),
':',
lw=2)
fit_result = fit.fit1d(x,
RO_data,
None,
p0=p0,
fitfunc=fitfunc,
do_print=True,
ret=True,
fixed=fixed)
print 'fitfunction: ' + fitfunc_str
## plot data and fit as function of total time
if plot_fit == True:
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 1001),
ax=ax2,
plot_data=False)
fit_results.append(fit_result)
if title == None:
title = 'analyzed_result'
plt.savefig(os.path.join(folder_a, title + '.pdf'), format='pdf')
plt.savefig(os.path.join(folder_a, title + '.png'), format='png')
if close_plot == True:
plt.close()
if return_freq == True:
f0 = fit_result['params_dict']['f']
u_f0 = fit_result['error_dict']['f']
return f0, u_f0
if return_phase == True:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
Amp = fit_result['params_dict']['A']
if return_amp == True:
return phi0, u_phi0, Amp
else:
return phi0, u_phi0
if return_results == True:
return fit_results
def Carbon_Ramsey_mult_msmts(timestamp=None,
measurement_name=['adwindata'],
ssro_calib_timestamp=None,
frequency=1,
offset=0.5,
x0=0,
amplitude=0.5,
decay_constant=200,
phase=0,
exponent=2,
plot_fit=False,
do_print=False,
fixed=[2],
show_guess=True,
return_phase=False,
return_freq=False,
return_amp=False,
return_results=True,
close_plot=False,
partstr='part',
contains=[],
title='Carbon'):
'''
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
List of parameters (order important for 'fixed')
offset, amplitude, decay_constant,exponent,frequency ,phase
'''
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data(title)
if partstr in folder:
numberstart = folder.find(partstr) + len(partstr)
numberofparts = int(folder[numberstart:len(folder)])
basis_str = folder[folder.rfind('\\') + 7:numberstart]
else:
numberofparts = 1
if ssro_calib_timestamp == None:
ssro_calib_folder = toolbox.latest_data('SSRO')
else:
ssro_dstmp, ssro_tstmp = toolbox.verify_timestamp(ssro_calib_timestamp)
ssro_calib_folder = toolbox.datadir + '/' + ssro_dstmp + '/' + ssro_tstmp + '_AdwinSSRO_SSROCalibration_111_1_sil18'
print ssro_calib_folder
fit_results = []
#for kk in range(numberofparts):
for kk, cnts in enumerate(contains):
'''
if partstr in folder:
folder = toolbox.latest_data(basis_str+str(kk+1))
else:
folder = toolbox.latest_data(basis_str)
'''
folder = toolbox.latest_data(cnts)
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(ssro_calib_folder)
ax = a.plot_results_vs_sweepparam(ret='ax')
if kk == 0:
cum_sweep_pts = a.sweep_pts
cum_p0 = a.p0
cum_u_p0 = a.u_p0
cum_pts = a.pts
else:
cum_sweep_pts = np.concatenate((cum_sweep_pts, a.sweep_pts))
cum_p0 = np.concatenate((cum_p0, a.p0))
cum_u_p0 = np.concatenate((cum_u_p0, a.u_p0))
cum_pts += a.pts
a.pts = cum_pts
a.sweep_pts = cum_sweep_pts
a.p0 = cum_p0
a.u_p0 = cum_u_p0
sorting_order = a.sweep_pts.argsort()
a.sweep_pts.sort()
a.p0 = a.p0[sorting_order]
a.u_p0 = a.u_p0[sorting_order]
ax = a.plot_results_vs_sweepparam(ret='ax')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
ax.plot(x, y)
p0, fitfunc, fitfunc_str = common.fit_general_exponential_dec_cos(
offset, amplitude, x0, decay_constant, exponent, frequency, phase)
#plot the initial guess
if show_guess:
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,
ret=True,
fixed=fixed)
## plot data and fit as function of total time
if plot_fit == True:
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 1001),
ax=ax,
plot_data=False)
fit_results.append(fit_result)
if title == None:
title = 'analyzed_result'
plt.savefig(os.path.join(folder, title + '.pdf'), format='pdf')
plt.savefig(os.path.join(folder, title + '.png'), format='png')
if close_plot == True:
plt.close()
if return_freq == True:
f0 = fit_result['params_dict']['f']
u_f0 = fit_result['error_dict']['f']
return f0, u_f0
if return_phase == True and return_amp == False:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
return phi0, u_phi0
if return_phase == True and return_amp == True:
phi0 = fit_result['params_dict']['phi']
u_phi0 = fit_result['error_dict']['phi']
A = fit_result['params_dict']['A']
u_A = fit_result['error_dict']['A']
return phi0, u_phi0, A, u_A
if return_results == True:
return fit_results