def sweep_N_analysis(tau,N_steps,plot_fit = False, do_print = True, show_guess = False):
###################
# Add simulated spins #
###################
total_pts = 320/N_steps+1
pts_per_run=11
## Data location ##
ssro_calib_folder = 'd:\\measuring\\data\\20140419\\111949_AdwinSSRO_SSROCalibration_Hans_sil1'
a, folder = load_mult_dat(tau, number_of_msmts = total_pts/pts_per_run,N_steps=N_steps, ssro_calib_folder=ssro_calib_folder)
a.p0 = a.p0*2-1
############
## Plotting ###
############
ax = a.plot_results_vs_sweepparam(ret='ax')
# ax.set_xlim(23.4,25)
# ax.set_xlim(0,73)
# start, end = ax.get_xlim()
# ax.xaxis.set_ticks(np.arange(start, end, 0.5))
ax.set_ylim(-1.05,1.05)
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
fit_results = []
p0, fitfunc, fitfunc_str = common.fit_decaying_cos(1/45.,0, 1, 0, 40000)
print fitfunc
#plot the initial guess
if show_guess:
ax.plot(np.linspace(0,x[-1],201), fitfunc(np.linspace(0,x[-1],201)), ':', lw=2)
ax.plot(x,y)
fit_result = fit.fit1d(x,y, None, p0=p0, fitfunc=fitfunc, do_print=True, ret=True,fixed=[3])
# print fitfunc(np.linspace(0,x[-1],201))
## plot data and fit as function of total time
if plot_fit == 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')
def Osci_period(carbon='1', older_than=None, ssro_calib_timestamp=None, **kw):
fit_results = kw.pop('fit_results', True)
folder_name = kw.pop('folder_name', 'Memory_NoOf_Repetitions_')
### fit parameters
freq = kw.pop('freq', 1 / 170.)
offset = kw.pop('offfset', 0.)
decay = kw.pop('decay', 200)
fixed = kw.pop('fixed', [1])
print folder_name
folder_dict = {
'X': [],
'Y': [],
'resX': [],
'resX_u': [],
'resY': [],
'resY_u': [],
'sweep_pts': [],
'res': [],
'res_u': []
}
### search data
for ro in ['positive', 'negative']:
for t in ['X', 'Y']:
search_string = folder_name + ro + '_Tomo_' + t + '_' + 'C' + carbon
folder_dict[t].append(
toolbox.latest_data(contains=search_string,
older_than=older_than,
raise_exc=False))
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_sil8'
# print ssro_calib_folder
### extract data
x_labels, npX, npY, npX_u, npY_u = get_dephasing_data(
folder_dict, ssro_calib_folder)
fig = plt.figure()
ax = plt.subplot()
for y, y_u, jj in zip([npX, npY], [npX_u, npY_u], range(2)):
if fit_results:
A0 = max(y)
phi0 = 0
p0, fitfunc, fitfunc_str = common.fit_decaying_cos(
freq, offset, A0, phi0, decay)
# fixed = [1]
fit_result = fit.fit1d(x_labels,
y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=True,
ret=True,
fixed=fixed)
plot.plot_fit1d(fit_result,
np.linspace(x_labels[0], x_labels[-1], 1001),
ax=ax,
color=color_list[jj],
plot_data=False,
add_txt=False,
lw=2)
plt.errorbar(x_labels,
y,
y_u,
marker='o',
color=color_list[jj],
label='C' + carbon + ['X', 'Y'][jj])
## define folder for data saving
folder = folder_dict[t][0]
plt.xlabel('Repump repetitions')
plt.ylabel('Contrast')
plt.title(get_tstamp_from_folder(folder) + ' Dephasing for C' + carbon)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.savefig(os.path.join(folder, 'CarbonDephasing_osci.pdf'), format='pdf')
plt.savefig(os.path.join(folder, 'CarbonDephasing_osci.png'), format='png')
plt.show()
plt.close('all')
print 'Results are saved in ', folder[18:18 + 15]
def Carbon_Ramsey(timestamp=None, measurement_name = ['adwindata'],
frequency = [1], amplitude = [0.5], decay_constant = [200],phase =[0], offset = 0.5,
fitfunc_type = 'single', plot_fit = False, do_print = False, show_guess = True, fitexp = None):
'''
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('CarbonRamsey')
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()
a.sweep_pts = a.sweep_pts*1e6
ax = a.plot_results_vs_sweepparam(ret='ax')
ax.set_ylim(-0.05,1.05)
ax.set_xlim(-0.05,40)
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
print fitexp
print
print
if len(frequency) == 1:
if fitexp == 'Gaussian':
p0, fitfunc, fitfunc_str = common.fit_gaussian_decaying_cos(frequency[0], offset, amplitude[0], phase[0], decay_constant[0])
print 'ok'
else:
p0, fitfunc, fitfunc_str = common.fit_decaying_cos(frequency[0], offset, amplitude[0], phase[0], decay_constant[0])
#plot the initial guess
print 'no'
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=True, ret=True,fixed=[4])
elif len(frequency) == 2:
print 'yes'
p0, fitfunc, fitfunc_str = common.fit_double_decaying_cos(frequency[0], amplitude[0], phase[0], decay_constant[0], frequency[1], amplitude[1], phase[1], decay_constant[1], offset)
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=True, ret=True,fixed=[2,3,6,7])
### Also plot the individual curves
# p0_0, fitfunc_0, fitfunc_str_0 = common.fit_double_decaying_cos(fit_result['params'][0], 0, phase[0], fit_result['params'][2], fit_result['params'][3], fit_result['params'][4], phase[1], fit_result['params'][5])
# ax.plot(np.linspace(0,x[-1],201), fitfunc_0(np.linspace(0,x[-1],201)), 'b-', lw=1)
# p0_1, fitfunc_1, fitfunc_str_1 = common.fit_double_decaying_cos(fit_result['params'][0], fit_result['params'][1], phase[0], fit_result['params'][2], fit_result['params'][3],0, phase[1], fit_result['params'][5])
# ax.plot(np.linspace(0,x[-1],201), fitfunc_1(np.linspace(0,x[-1],201)), 'm-', lw=1)
## plot fit
if plot_fit == True:
plot.plot_fit1d(fit_result, np.linspace(0,x[-1],201), ax=ax, plot_data=False)
fit_results.append(fit_result)
ax.set_xlabel('Free evolution time (us)')
print folder
plt.savefig(os.path.join(folder, 'analyzed_result2.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, 'analyzed_result2.png'),
format='png')
# freq = fit_results[0]['params_dict']['f1']
# period = 1/freq
# print 'Period is %s pulses ' %(period)
# N_pi = round(period*.5/2)*2.0
# N_pi2 = round(period/2*.25)*2.0
# print 'Pi pulse: %s pulses' %N_pi
# print 'Pi2 pulse: %s pulses' %N_pi2
return fit_results
### PLEASE DO NOT DO THESE KIND OF THINGS BELOW!!!! ### THIS IS A MODULE WITH FUNCTIONS THAT ARE GENERALLY USED
### DO NO ADD DATA HERE< BUT IMPORT THIS FUNCTION IF YOU NEED IT
######################### RAMSEY T2* ##############################################
# Carbon_Ramsey(timestamp='20140519135216', measurement_name = ['adwindata'],
# frequency = [575], amplitude = [0.5], decay_constant = [0.009],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
# Carbon_Ramsey(timestamp='20140519183105', measurement_name = ['adwindata'],
# frequency = [575], amplitude = [0.5], decay_constant = [0.007],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
# Carbon_Ramsey(timestamp='20140520134826', measurement_name = ['adwindata'],
# frequency = [205], amplitude = [0.5], decay_constant = [0.007],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
######################## RAMSEY 2 freq ##############################################
# Carbon_Ramsey(timestamp='20140521164658', measurement_name = ['adwindata'],
# frequency = [338e-3,14e-3], amplitude = [1,1], decay_constant = [0.009e6,0.009e6],phase =[0,0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False)
# Carbon_Ramsey(timestamp='20140521165249', measurement_name = ['adwindata'],
# frequency = [305e-3,16e-3], amplitude = [1,1], decay_constant = [0.009e6,0.009e6],phase =[0,0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False)
# Carbon_Ramsey(timestamp='20140521170735', measurement_name = ['adwindata'],
# frequency = [350e-3,27e-3], amplitude = [1,1], decay_constant = [0.009e6,0.009e6],phase =[0,0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False)
# Carbon_Ramsey(timestamp='20140521162939', measurement_name = ['adwindata'],
# frequency = [344e-3,19e-3], amplitude = [1,1], decay_constant = [0.009e6,0.009e6],phase =[0,0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False)
# ######################### RAMSEY simple ##############################################
# Carbon_Ramsey(timestamp='20140507114220', measurement_name = ['adwindata'],
# frequency = [350e-3], amplitude = [-0.5], decay_constant = [900e6],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
# Carbon_Ramsey(timestamp='20140507124543', measurement_name = ['adwindata'],
# frequency = [350e-3], amplitude = [-0.5], decay_constant = [900e6],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
# Carbon_Ramsey(timestamp='20140507151219', measurement_name = ['adwindata'],
# frequency = [350e-3], amplitude = [-0.5], decay_constant = [900e6],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
# Carbon_Ramsey(timestamp='20140429141113', measurement_name = ['adwindata'],
# frequency = [350e-3], amplitude = [-0.5], decay_constant = [900e6],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
def calibrate_LDE_phase(contains='', do_fit=False, **kw):
'''
gets data from a folder whose name contains the contains variable.
Does or does not fit the data with a gaussian function
'''
### folder choice
if contains == '':
contains = 'LDE_phase_calibration'
# tomography
tomo = kw.pop('tomo_basis', 'X')
# return fit
ret = kw.get('ret', False)
# for fitting
freq = kw.pop('freq', None)
decay = kw.pop('decay', 50)
phi0 = kw.pop('phi0', 0)
offset = kw.pop('offset', 0)
A0 = kw.pop('A0', None)
show_plot = kw.pop('show_plot', True)
fixed = kw.pop('fixed', [1])
show_guess = kw.pop('show_guess', False)
# older_than = kw.get('older_than',None) automatically handled by kws
### acquire data
if 'folder' in kw:
f = kw.pop('folder')
else:
f = toolbox.latest_data(contains, **kw)
a = mbi.MBIAnalysis(f)
if freq is None:
try:
freq = a.g.attrs['phase_detuning'] / 360.0
except:
freq = 1. / 12. # voll auf die zwoelf.
ro_array = ['positive', 'negative']
# print ro_array
if tomo == '':
adwindata_str = tomo
else:
adwindata_str = tomo + '_'
x, y, y_u = get_pos_neg_data(a,
adwindata_str=adwindata_str,
ro_array=ro_array,
**kw)
ylabel = tomo
### create a plot
xlabel = a.g.attrs['sweep_name']
x = a.g.attrs['sweep_pts'] # could potentially be commented out?
fig, ax = create_plot(f,
xlabel=xlabel,
ylabel=ylabel,
title='Acquired phase')
## plot data
plot_data(x, y, y_u=y_u)
### fitting if you feel like it / still needs implementation
if do_fit:
if A0 is None:
A0 = max(y)
p0, fitfunc, fitfunc_str = common.fit_decaying_cos(
freq, offset, A0, phi0, decay)
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,
ret=True,
VERBOSE=True,
fixed=fixed)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 1001),
ax=ax,
color='r',
plot_data=False,
add_txt=True,
lw=2)
p_dict = fit_result['params_dict']
e_dict = fit_result['error_dict']
if 'carbons' in a.g.attrs:
fit_result['carbon_id'] = a.g.attrs['carbons'][0]
elif 'dps_carbons' in a.g.attrs:
fit_result['carbon_id'] = a.g.attrs['dps_carbons'][0]
elif 'carbon' in a.g.attrs:
fit_result['carbon_id'] = a.g.attrs['carbon']
else:
fit_result['carbon_id'] = None
if p_dict['A'] < 0:
p_dict['phi'] = p_dict['phi'] + 180
p_dict['A'] = p_dict['A'] * (-1)
try:
detuning = a.g.attrs['phase_detuning']
fit_result['detuning'] = detuning
fit_result['acq_phase_per_rep'] = 360 * (p_dict['f']) - detuning
fit_result['u_acq_phase_per_rep'] = 360 * (e_dict['f'])
print 'This is the phase detuning', detuning
print 'Acquired phase per repetition (compensating for phase_detuning=) {:3.3f} +/- {:3.3f}'.format(
round(fit_result['acq_phase_per_rep'], 3),
round(fit_result['u_acq_phase_per_rep'], 3))
print 'phase offset ', round(p_dict['phi'], 3)
except:
print 'no phase detuning found'
## save and close plot. We are done.
save_and_close_plot(f, show=show_plot)
if kw.get('ret', False):
return fit_result
if kw.get('ret_data', False):
return x, y, y_u
if kw.get('ret_fit_data', False):
return fit_result, x, y, y_u
if kw.get('ret_data_fit', False):
return x, y, y_u, fit_result
def Osci_period(carbon = '1',older_than = None,ssro_calib_timestamp = None,**kw):
fit_results = kw.pop('fit_results',True)
folder_name = kw.pop('folder_name','Memory_NoOf_Repetitions_')
### fit parameters
freq = kw.pop('freq',1/170.)
offset = kw.pop('offfset',0.)
decay = kw.pop('decay',200)
fixed = kw.pop('fixed',[1])
print folder_name
folder_dict = {
'X' : [],
'Y' : [],
'resX' : [],
'resX_u' : [],
'resY' : [],
'resY_u': [],
'sweep_pts': [],
'res' : [],
'res_u' : []
}
### search data
for ro in ['positive','negative']:
for t in ['X','Y']:
search_string = folder_name+ro+'_Tomo_'+t+'_'+'C'+carbon
folder_dict[t].append(toolbox.latest_data(contains = search_string,older_than = older_than,raise_exc = False))
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_sil8'
# print ssro_calib_folder
### extract data
x_labels,npX,npY,npX_u,npY_u = get_dephasing_data(folder_dict,ssro_calib_folder)
fig = plt.figure()
ax = plt.subplot()
for y,y_u,jj in zip([npX,npY],[npX_u,npY_u],range(2)):
if fit_results:
A0 = max(y)
phi0 = 0
p0,fitfunc,fitfunc_str = common.fit_decaying_cos(freq,offset,A0,phi0,decay)
# fixed = [1]
fit_result = fit.fit1d(x_labels,y,None,p0 = p0, fitfunc = fitfunc, do_print = True, ret = True, fixed = fixed)
plot.plot_fit1d(fit_result, np.linspace(x_labels[0],x_labels[-1],1001), ax=ax,color = color_list[jj], plot_data=False,add_txt = False, lw = 2)
plt.errorbar(x_labels,y,y_u,marker='o',color = color_list[jj],label='C'+carbon+['X','Y'][jj])
## define folder for data saving
folder = folder_dict[t][0]
plt.xlabel('Repump repetitions')
plt.ylabel('Contrast')
plt.title(get_tstamp_from_folder(folder) + ' Dephasing for C'+carbon)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.savefig(os.path.join(folder,'CarbonDephasing_osci.pdf'),format='pdf')
plt.savefig(os.path.join(folder,'CarbonDephasing_osci.png'),format='png')
plt.show()
plt.close('all')
print 'Results are saved in ', folder[18:18+15]
def Carbon_control_sweep_N_zoomtau(tau_array=None,
timestamp_array=None,
measurement_name=['adwindata'],
frequency=[1],
amplitude=[0.5],
decay_constant=[200],
phase=[0],
offset=0.5,
fitfunc_type='single',
plot_fit=False,
plot_data=False,
do_print=False,
show_guess=True,
yaxis=[-0.5, 1.05],
fixed=[2, 6]):
"""
Goal: find the optimal tau for carbon control.
Fits a series of dynamicaldecopuling_sweepN measurements and plots the amplitudes as a function of tau.
Input (one of two):
- tau_array: array with tau values used in the measurements (takes last data with that tau value in its name)
- TO DO: timestamp_array: array with timestamps to be used
"""
fit_results = []
if tau_array != None:
nr_of_msmts = len(tau_array)
fitted_values = np.zeros(
[nr_of_msmts, 6]
) # first column = tau, next 5 columns = [frq, offset, amplitude, phase, decay constant] ([f, a, A, phi, t])
fitted_errors = np.zeros([nr_of_msmts, 6])
for counter, tau in enumerate(tau_array):
timestamp, folder = toolbox.latest_data('tau_%s' %
str(format(tau, '.3f')),
return_timestamp=True)
print "folder for tau = %s: %s" % (tau, folder)
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
if plot_data:
ax = a.plot_results_vs_sweepparam(ret='ax')
ax.set_ylim(yaxis)
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
if len(frequency) == 1:
p0, fitfunc, fitfunc_str = common.fit_decaying_cos(
frequency[0], offset, amplitude[0], phase[0],
decay_constant[0])
#plot the initial guess
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)
## plot fit
if plot_fit == True:
plot.plot_fit1d(fit_result,
np.linspace(0, x[-1], 201),
ax=ax,
plot_data=False,
print_info=True)
plt.savefig(os.path.join(folder, 'analyzed_result.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, 'analyzed_result.png'),
format='png')
fit_results.append(fit_result)
# store fitted values
fitted_values[counter, 0] = tau
fitted_errors[counter, 0] = tau
parameter_names = ['f', 'a', 'A',
't'] # NOTE: PHI IS ASSUMED FIXED!
for k in range(len(parameter_names)):
parameter = parameter_names[k]
fitted_values[counter,
k] = fit_result['params_dict'][parameter]
fitted_errors[counter, k] = fit_result['error_dict'][parameter]
# print 'tau = %s: parameter %s has value %s +- %s' % (tau, parameter, fit_result['params_dict'][parameter], fit_result['error_dict'][parameter])
plt.figure()
plt.errorbar(tau_array,
fitted_values[:, 2],
yerr=fitted_errors[:, 2],
fmt='-o')
plt.xlabel('tau (us)')
plt.ylabel('Fitted amplitude')
plt.title(timestamp)
plt.savefig(
os.path.join(
folder,
'SimpleDecoupling_sweepN_tau=[%s-%s]_FittedAmplitudes.png' %
(np.amin(tau_array), np.amax(tau_array))))
print 'FIGURE SAVED IN FOLDER: ', folder
return fit_results, fitted_values, fitted_errors
def Carbon_control_sweep_N(timestamp=None,
contains=None,
ssro_calib_folder=None,
measurement_name=['adwindata'],
frequency=[1],
amplitude=[0.5],
decay_constant=[200],
phase=[0],
offset=0.5,
fitfunc_type='single',
plot_fit=False,
do_print=False,
show_guess=True,
yaxis=[-0.5, 1.05],
fixed=[2, 6],
plot_nice=False):
''' 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
'''
# offset = 0.5
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
if contains != None:
folder = toolbox.latest_data(contains)
else:
folder = toolbox.latest_data('Decoupling')
fit_results = []
for k in range(0, len(measurement_name)):
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
if ssro_calib_folder != None:
a.get_electron_ROC(ssro_calib_folder=ssro_calib_folder)
else:
a.get_electron_ROC()
ax = a.plot_results_vs_sweepparam(ret='ax')
ax.set_ylim(yaxis)
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
y_err = a.u_p0.reshape(-1)[:]
if len(frequency) == 1:
p0, fitfunc, fitfunc_str = common.fit_decaying_cos(
frequency[0], offset, amplitude[0], phase[0],
decay_constant[0])
#plot the initial guess
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)
elif len(frequency) == 2:
p0, fitfunc, fitfunc_str = common.fit_double_decaying_cos(
frequency[0], amplitude[0], phase[0], decay_constant[0],
frequency[1], amplitude[1], phase[1], decay_constant[1],
offset)
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)
## Also plot the individual curves
p0_0, fitfunc_0, fitfunc_str_0 = common.fit_double_decaying_cos(
fit_result['params'][0], 0, phase[0], fit_result['params'][2],
fit_result['params'][3], fit_result['params'][4], phase[1],
fit_result['params'][5], fit_result['params'][6])
ax.plot(np.linspace(0, x[-1], 201),
fitfunc_0(np.linspace(0, x[-1], 201)),
'r-',
lw=1,
alpha=0.5)
p0_1, fitfunc_1, fitfunc_str_1 = common.fit_double_decaying_cos(
fit_result['params'][0], fit_result['params'][1], phase[0],
fit_result['params'][2], fit_result['params'][3], 0, phase[1],
fit_result['params'][5], fit_result['params'][6])
ax.plot(np.linspace(0, x[-1], 201),
fitfunc_1(np.linspace(0, x[-1], 201)),
'm-',
lw=1,
alpha=0.5)
## plot fit
if plot_fit == True:
plot.plot_fit1d(fit_result,
np.linspace(0, x[-1], 201),
ax=ax,
plot_data=False,
print_info=True)
y_fit = fit_result['fitfunc'](np.arange(0, 60, 1))
for i, j in enumerate(abs(y_fit - 0.5)):
if j == min(abs(y_fit - 0.5)):
x_opt = i
ax.text(5, 0.9, 'pulses for pi/2: ' + str(x_opt))
fit_results.append(fit_result)
print folder
plt.savefig(os.path.join(folder, 'analyzed_result.pdf'), format='pdf')
plt.savefig(os.path.join(folder, 'analyzed_result.png'), format='png')
fit_result['tau'] = a.g.attrs['tau_list'][0]
# freq = fit_results[0]['params_dict']['f1']
# period = 1/freq
# print 'Period is %s pulses ' %(period)
# # N_pi = round(period*.5/2)*2.0
# N_pi2 = round(period/2*.25)*2.0
# # print 'Pi pulse: %s pulses' %N_pi
# print 'Pi2 pulse: %s pulses' %N_pi2
if plot_nice:
fig, ax = plt.subplots(figsize=(7, 5))
ax2 = ax.twiny()
one_cnot = 1 / (4 * fit_result['params_dict']['f'])
print 'one cnot'
print one_cnot
ax.errorbar(x, y, yerr=y_err, marker='o', ls='', color='b')
ax.plot(np.linspace(x[0], x[-1], 10000),
fit_result['fitfunc'](np.linspace(x[0], x[-1], 10000)),
lw=2,
color='b')
ax.set_xlim(x[0] - 3, x[-1] + 3)
ax.set_yticks([0, 0.5, 1])
ax.set_ylim(0, 1)
ax.set_xlabel('Number of pulses (N)')
ax.set_ylabel(r'$F(|0\rangle)$')
print np.arange(0, 250, one_cnot)
ax2.set_xticks(np.arange(0, 250, one_cnot))
ax2.set_xticklabels(np.arange(0, 8, 1))
ax2.set_xlabel('Number of gates')
plt.savefig(os.path.join(folder, 'nice_result.pdf'), format='pdf')
plt.savefig(os.path.join(folder, 'nice_result.png'), format='png')
return fit_result
def Carbon_control_sweep_N_zoomtau(tau_array = None, timestamp_array = None, measurement_name = ['adwindata'],
frequency = [1], amplitude = [0.5], decay_constant = [200],phase =[0], offset = 0.5,
fitfunc_type = 'single', plot_fit = False, plot_data = False, do_print = False, show_guess = True,
yaxis = [-0.5,1.05], fixed=[2,6]):
"""
Goal: find the optimal tau for carbon control.
Fits a series of dynamicaldecopuling_sweepN measurements and plots the amplitudes as a function of tau.
Input (one of two):
- tau_array: array with tau values used in the measurements (takes last data with that tau value in its name)
- TO DO: timestamp_array: array with timestamps to be used
"""
fit_results = []
if tau_array != None:
nr_of_msmts = len(tau_array)
fitted_values = np.zeros([nr_of_msmts, 6]) # first column = tau, next 5 columns = [frq, offset, amplitude, phase, decay constant] ([f, a, A, phi, t])
fitted_errors = np.zeros([nr_of_msmts, 6])
for counter, tau in enumerate(tau_array):
timestamp, folder = toolbox.latest_data('tau_%s' % str(format(tau, '.3f')), return_timestamp = True)
print "folder for tau = %s: %s" % (tau, folder)
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
if plot_data:
ax = a.plot_results_vs_sweepparam(ret='ax')
ax.set_ylim(yaxis)
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
if len(frequency) == 1:
p0, fitfunc, fitfunc_str = common.fit_decaying_cos(frequency[0], offset, amplitude[0], phase[0], decay_constant[0])
#plot the initial guess
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)
## plot fit
if plot_fit == True:
plot.plot_fit1d(fit_result, np.linspace(0,x[-1],201), ax=ax, plot_data=False,print_info = True)
plt.savefig(os.path.join(folder, 'analyzed_result.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, 'analyzed_result.png'),
format='png')
fit_results.append(fit_result)
# store fitted values
fitted_values[counter, 0] = tau
fitted_errors[counter, 0] = tau
parameter_names = ['f', 'a', 'A', 't'] # NOTE: PHI IS ASSUMED FIXED!
for k in range(len(parameter_names)):
parameter = parameter_names[k]
fitted_values[counter, k] = fit_result['params_dict'][parameter]
fitted_errors[counter, k] = fit_result['error_dict'][parameter]
# print 'tau = %s: parameter %s has value %s +- %s' % (tau, parameter, fit_result['params_dict'][parameter], fit_result['error_dict'][parameter])
plt.figure()
plt.errorbar(tau_array, fitted_values[:,2], yerr = fitted_errors[:,2], fmt = '-o')
plt.xlabel('tau (us)')
plt.ylabel('Fitted amplitude')
plt.title(timestamp)
plt.savefig(os.path.join(folder, 'SimpleDecoupling_sweepN_tau=[%s-%s]_FittedAmplitudes.png' % (np.amin(tau_array), np.amax(tau_array)) ) )
print 'FIGURE SAVED IN FOLDER: ', folder
return fit_results, fitted_values, fitted_errors
def Carbon_control_sweep_N(timestamp=None,contains=None, ssro_calib_folder = None, measurement_name = ['adwindata'],
frequency = [1], amplitude = [0.5], decay_constant = [200],phase =[0], offset = 0.5,
fitfunc_type = 'single', plot_fit = False, do_print = False, show_guess = True,
yaxis = [-0.5,1.05], fixed=[2,6],plot_nice = False):
''' 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
'''
# offset = 0.5
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
if contains!=None:
folder=toolbox.latest_data(contains)
else:
folder = toolbox.latest_data('Decoupling')
fit_results = []
for k in range(0,len(measurement_name)):
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
if ssro_calib_folder != None:
a.get_electron_ROC(ssro_calib_folder = ssro_calib_folder)
else:
a.get_electron_ROC()
ax = a.plot_results_vs_sweepparam(ret='ax')
ax.set_ylim(yaxis)
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
y_err = a.u_p0.reshape(-1)[:]
if len(frequency) == 1:
p0, fitfunc, fitfunc_str = common.fit_decaying_cos(frequency[0], offset, amplitude[0], phase[0], decay_constant[0])
#plot the initial guess
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)
elif len(frequency) == 2:
p0, fitfunc, fitfunc_str = common.fit_double_decaying_cos(frequency[0], amplitude[0], phase[0],
decay_constant[0], frequency[1], amplitude[1], phase[1], decay_constant[1],offset)
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)
## Also plot the individual curves
p0_0, fitfunc_0, fitfunc_str_0 = common.fit_double_decaying_cos(fit_result['params'][0], 0, phase[0], fit_result['params'][2], fit_result['params'][3], fit_result['params'][4], phase[1], fit_result['params'][5], fit_result['params'][6])
ax.plot(np.linspace(0,x[-1],201), fitfunc_0(np.linspace(0,x[-1],201)), 'r-', lw=1,alpha=0.5)
p0_1, fitfunc_1, fitfunc_str_1 = common.fit_double_decaying_cos(fit_result['params'][0], fit_result['params'][1], phase[0], fit_result['params'][2], fit_result['params'][3],0, phase[1], fit_result['params'][5], fit_result['params'][6])
ax.plot(np.linspace(0,x[-1],201), fitfunc_1(np.linspace(0,x[-1],201)), 'm-', lw=1,alpha=0.5)
## plot fit
if plot_fit == True:
plot.plot_fit1d(fit_result, np.linspace(0,x[-1],201), ax=ax, plot_data=False,print_info = True)
y_fit = fit_result['fitfunc'](np.arange(0,60,1))
for i, j in enumerate(abs(y_fit-0.5)):
if j == min(abs(y_fit-0.5)):
x_opt = i
ax.text(5,0.9,'pulses for pi/2: ' +str(x_opt))
fit_results.append(fit_result)
print folder
plt.savefig(os.path.join(folder, 'analyzed_result.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, 'analyzed_result.png'),
format='png')
fit_result['tau']=a.g.attrs['tau_list'][0]
# freq = fit_results[0]['params_dict']['f1']
# period = 1/freq
# print 'Period is %s pulses ' %(period)
# # N_pi = round(period*.5/2)*2.0
# N_pi2 = round(period/2*.25)*2.0
# # print 'Pi pulse: %s pulses' %N_pi
# print 'Pi2 pulse: %s pulses' %N_pi2
if plot_nice:
fig,ax = plt.subplots(figsize = (7,5))
ax2 = ax.twiny()
one_cnot = 1/(4*fit_result['params_dict']['f'])
print 'one cnot'
print one_cnot
ax.errorbar(x,y,yerr=y_err,marker = 'o',ls = '',color = 'b')
ax.plot(np.linspace(x[0],x[-1],10000),fit_result['fitfunc'](np.linspace(x[0],x[-1],10000)),lw = 2, color = 'b')
ax.set_xlim(x[0]-3,x[-1]+3)
ax.set_yticks([0,0.5,1])
ax.set_ylim(0,1)
ax.set_xlabel('Number of pulses (N)')
ax.set_ylabel(r'$F(|0\rangle)$')
print np.arange(0,250,one_cnot)
ax2.set_xticks(np.arange(0,250,one_cnot))
ax2.set_xticklabels(np.arange(0,8,1))
ax2.set_xlabel('Number of gates')
plt.savefig(os.path.join(folder, 'nice_result.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, 'nice_result.png'),
format='png')
return fit_result
def Carbon_Ramsey(timestamp=None,
measurement_name=['adwindata'],
frequency=[1],
amplitude=[0.5],
decay_constant=[200],
phase=[0],
offset=0.5,
fitfunc_type='single',
plot_fit=False,
do_print=False,
show_guess=True,
fitexp=None):
'''
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('CarbonRamsey')
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()
a.sweep_pts = a.sweep_pts * 1e6
ax = a.plot_results_vs_sweepparam(ret='ax')
ax.set_ylim(-0.05, 1.05)
ax.set_xlim(-0.05, 40)
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
print fitexp
print
print
if len(frequency) == 1:
if fitexp == 'Gaussian':
p0, fitfunc, fitfunc_str = common.fit_gaussian_decaying_cos(
frequency[0], offset, amplitude[0], phase[0],
decay_constant[0])
print 'ok'
else:
p0, fitfunc, fitfunc_str = common.fit_decaying_cos(
frequency[0], offset, amplitude[0], phase[0],
decay_constant[0])
#plot the initial guess
print 'no'
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=True,
ret=True,
fixed=[4])
elif len(frequency) == 2:
print 'yes'
p0, fitfunc, fitfunc_str = common.fit_double_decaying_cos(
frequency[0], amplitude[0], phase[0], decay_constant[0],
frequency[1], amplitude[1], phase[1], decay_constant[1],
offset)
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=True,
ret=True,
fixed=[2, 3, 6, 7])
### Also plot the individual curves
# p0_0, fitfunc_0, fitfunc_str_0 = common.fit_double_decaying_cos(fit_result['params'][0], 0, phase[0], fit_result['params'][2], fit_result['params'][3], fit_result['params'][4], phase[1], fit_result['params'][5])
# ax.plot(np.linspace(0,x[-1],201), fitfunc_0(np.linspace(0,x[-1],201)), 'b-', lw=1)
# p0_1, fitfunc_1, fitfunc_str_1 = common.fit_double_decaying_cos(fit_result['params'][0], fit_result['params'][1], phase[0], fit_result['params'][2], fit_result['params'][3],0, phase[1], fit_result['params'][5])
# ax.plot(np.linspace(0,x[-1],201), fitfunc_1(np.linspace(0,x[-1],201)), 'm-', lw=1)
## plot fit
if plot_fit == True:
plot.plot_fit1d(fit_result,
np.linspace(0, x[-1], 201),
ax=ax,
plot_data=False)
fit_results.append(fit_result)
ax.set_xlabel('Free evolution time (us)')
print folder
plt.savefig(os.path.join(folder, 'analyzed_result2.pdf'), format='pdf')
plt.savefig(os.path.join(folder, 'analyzed_result2.png'), format='png')
# freq = fit_results[0]['params_dict']['f1']
# period = 1/freq
# print 'Period is %s pulses ' %(period)
# N_pi = round(period*.5/2)*2.0
# N_pi2 = round(period/2*.25)*2.0
# print 'Pi pulse: %s pulses' %N_pi
# print 'Pi2 pulse: %s pulses' %N_pi2
return fit_results
### PLEASE DO NOT DO THESE KIND OF THINGS BELOW!!!! ### THIS IS A MODULE WITH FUNCTIONS THAT ARE GENERALLY USED
### DO NO ADD DATA HERE< BUT IMPORT THIS FUNCTION IF YOU NEED IT
######################### RAMSEY T2* ##############################################
# Carbon_Ramsey(timestamp='20140519135216', measurement_name = ['adwindata'],
# frequency = [575], amplitude = [0.5], decay_constant = [0.009],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
# Carbon_Ramsey(timestamp='20140519183105', measurement_name = ['adwindata'],
# frequency = [575], amplitude = [0.5], decay_constant = [0.007],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
# Carbon_Ramsey(timestamp='20140520134826', measurement_name = ['adwindata'],
# frequency = [205], amplitude = [0.5], decay_constant = [0.007],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
######################## RAMSEY 2 freq ##############################################
# Carbon_Ramsey(timestamp='20140521164658', measurement_name = ['adwindata'],
# frequency = [338e-3,14e-3], amplitude = [1,1], decay_constant = [0.009e6,0.009e6],phase =[0,0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False)
# Carbon_Ramsey(timestamp='20140521165249', measurement_name = ['adwindata'],
# frequency = [305e-3,16e-3], amplitude = [1,1], decay_constant = [0.009e6,0.009e6],phase =[0,0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False)
# Carbon_Ramsey(timestamp='20140521170735', measurement_name = ['adwindata'],
# frequency = [350e-3,27e-3], amplitude = [1,1], decay_constant = [0.009e6,0.009e6],phase =[0,0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False)
# Carbon_Ramsey(timestamp='20140521162939', measurement_name = ['adwindata'],
# frequency = [344e-3,19e-3], amplitude = [1,1], decay_constant = [0.009e6,0.009e6],phase =[0,0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False)
# ######################### RAMSEY simple ##############################################
# Carbon_Ramsey(timestamp='20140507114220', measurement_name = ['adwindata'],
# frequency = [350e-3], amplitude = [-0.5], decay_constant = [900e6],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
# Carbon_Ramsey(timestamp='20140507124543', measurement_name = ['adwindata'],
# frequency = [350e-3], amplitude = [-0.5], decay_constant = [900e6],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
# Carbon_Ramsey(timestamp='20140507151219', measurement_name = ['adwindata'],
# frequency = [350e-3], amplitude = [-0.5], decay_constant = [900e6],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')
# Carbon_Ramsey(timestamp='20140429141113', measurement_name = ['adwindata'],
# frequency = [350e-3], amplitude = [-0.5], decay_constant = [900e6],phase =[0],
# fitfunc_type = 'single', plot_fit = True, do_print = False, show_guess = False,fitexp='Gaussian')