def calibrate_epulse_amplitude(folder, ax, *args):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
ax.set_title(a.timestamp)
x0 = fit.Parameter(args[0], 'x0')
of = fit.Parameter(args[1], 'of')
a = fit.Parameter(args[2], 'a')
fitfunc_str = '(1-of) + a (x-x0)**2'
def fitfunc_parabolic(x):
return (1.-of()) + a() * (x-x0())**2
fit_result = fit.fit1d(x,y, None, p0=[of, a, x0], fitfunc=fitfunc_parabolic,
fitfunc_str=fitfunc_str, do_print=True, ret=True)
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax,
plot_data=False, print_info=True)
return fit_result
def fit_correlation_parabolic(folder, ax, *args, **kw):
which_correlation = kw.pop('which_correlation', 2)
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_correlations(name='adwindata')
a.plot_results_vs_sweepparam(ax=ax, mode='correlations')
x = a.sweep_pts.reshape(-1)[:]
y = a.normalized_correlations[:, which_correlation]
x0 = fit.Parameter(args[0], 'x0')
of = fit.Parameter(args[1], 'of')
a = fit.Parameter(args[2], 'a')
fitfunc_str = '(1-of) + a (x-x0)**2'
def fitfunc_parabolic(x):
return (1. - of()) + a() * (x - x0())**2
fit_result = fit.fit1d(x,
y,
None,
p0=[of, a, x0],
fitfunc=fitfunc_parabolic,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False,
print_info=False)
return fit_result
def fit_population_vs_detuning(folder, ax, *args):
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC(
) # a.get_N_ROC(0.97,0.03,0.96,0.01,0.93,0.01)#(0.99, 0.02, 0.94, 0.005)
ax = a.plot_results_vs_sweepparam(ret='ax', name='adwindata')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
guess_a = 1
guess_A = -0.8
guess_F = 0.005
guess_x0 = args[0]
fit_result = fit.fit1d(x,
y,
rabi.fit_population_vs_detuning,
guess_a,
guess_A,
guess_F,
guess_x0,
fixed=[],
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(a.sweep_pts[0], a.sweep_pts[-1], 201),
ax=ax,
plot_data=False)
return fit_result
def fit_linear(folder=None, ax=None, a_guess=1., b_guess=0.):
"""
fit a x + b from Sequence in folder
"""
a, ax, x, y = plot_result(folder, ax)
a = fit.Parameter(a_guess, 'a')
b = fit.Parameter(b_guess, 'b')
fitfunc_str = 'a x + b'
def fitfunc(x):
return a() * x + b()
fit_result = fit.fit1d(x,
y,
None,
p0=[a, b],
fitfunc=fitfunc,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False,
print_info=False)
return fit_result
def fit_correlation_parabolic(folder, ax, *args, **kw):
which_correlation = kw.pop("which_correlation", 2)
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name="adwindata")
a.get_correlations(name="adwindata")
a.plot_results_vs_sweepparam(ax=ax, mode="correlations")
x = a.sweep_pts.reshape(-1)[:]
y = a.normalized_correlations[:, which_correlation]
x0 = fit.Parameter(args[0], "x0")
of = fit.Parameter(args[1], "of")
a = fit.Parameter(args[2], "a")
fitfunc_str = "(1-of) + a (x-x0)**2"
def fitfunc_parabolic(x):
return (1.0 - of()) + a() * (x - x0()) ** 2
fit_result = fit.fit1d(
x, y, None, p0=[of, a, x0], fitfunc=fitfunc_parabolic, fitfunc_str=fitfunc_str, do_print=True, ret=True
)
plot.plot_fit1d(fit_result, np.linspace(x[0], x[-1], 201), ax=ax, plot_data=False, print_info=False)
return fit_result
def calibrate_Npulse_rabi(folder, ax, *args):
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name="adwindata")
a.get_electron_ROC()
a.plot_results_vs_sweepparam(ax=ax, name="adwindata")
x = a.sweep_pts.reshape(-1)[:]
y = a.p0[:, 0]
f = fit.Parameter(args[0], "f")
A = fit.Parameter(args[1], "A")
o = fit.Parameter(0.5, "o")
x0 = fit.Parameter(0, "x0")
p0 = [f, A, x0, o]
fitfunc_str = "o + A * cos(2pi f x - 2pi phi)"
def fitfunc(x):
return o() + A() * np.cos(2 * np.pi * (f() * (x - x0())))
fit_result = fit.fit1d(x, y, None, p0=p0, fitfunc=fitfunc, fitfunc_str=fitfunc_str, do_print=True, ret=True)
plot.plot_fit1d(fit_result, np.linspace(x[0], x[-1], 201), ax=ax, plot_data=False, print_info=False)
return fit_result
def fit_correlation_oscillation(folder, ax, *args, **kw):
which_correlation = kw.pop("which_correlation", 2)
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name="adwindata")
a.get_correlations(name="adwindata")
a.plot_results_vs_sweepparam(ax=ax, mode="correlations")
x = a.sweep_pts.reshape(-1)[:]
y = a.normalized_correlations[:, which_correlation]
x0 = fit.Parameter(args[0], "x0")
f = fit.Parameter(args[1], "f")
A = fit.Parameter(args[2], "A")
o = fit.Parameter(0.25, "o")
fitfunc_str = "(1 - A) + A * cos(2pi f (x - x0))"
def fitfunc(x):
return o() + A() * np.cos(2 * np.pi * f() * (x - x0()))
fit_result = fit.fit1d(
x, y, None, p0=[f, A, x0, o], fitfunc=fitfunc, fitfunc_str=fitfunc_str, do_print=True, ret=True
)
plot.plot_fit1d(fit_result, np.linspace(x[0], x[-1], 201), ax=ax, plot_data=False, print_info=False)
return fit_result
def fit_rabi(folder=None, ax = None, f_guess=0.9, A_guess=1,fit_phi = False,fit_k = False):
"""
fit (1 - A) + A * exp(-kx) * cos(2pi f (x - x0)) from Sequence in folder
"""
a, ax, x, y = plot_result(folder,ax)
f = fit.Parameter(f_guess, 'f')
A = fit.Parameter(A_guess, 'A')
x0 = fit.Parameter(0, 'x0')
k = fit.Parameter(0, 'k')
p0 = [f, A]
if fit_phi:
p0.append(x0)
if fit_k:
p0.append(k)
fitfunc_str = '(1 - A) + A * exp(-kx) * cos(2pi f (x - x0))'
def fitfunc(x) :
return (1.-A()) + A() * np.exp(-k()*x) * \
np.cos(2*np.pi*(f()*(x - x0())))
fit_result = fit.fit1d(x,y, None, p0=p0, fitfunc=fitfunc,
fitfunc_str=fitfunc_str, do_print=True, ret=True)
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax,
plot_data=False, print_info=False)
return fit_result
def analyse_data(self, filename):
data = np.loadtxt(filename)
counts = data[:, 1]
freq = data[:, 0] * 1e-9
guess_offset = counts[0]
guess_amplitude = np.max(counts) - np.min(counts)
guess_width = 0.005
guess_ctr = 2.88
success = False
fit_result = fit.fit1d(freq,
counts,
esr.fit_ESR_gauss,
guess_offset,
guess_amplitude,
guess_width,
guess_ctr,
do_print=False,
ret=True)
if fit_result['success'] == 1 and abs(fit_result['params_dict']['x0'] -
guess_ctr) < 0.1:
success = True
plot.plot_fit1d(fit_result,
np.linspace(np.min(freq), np.max(freq), 1000),
plot_data=True,
add_txt=True,
plot_title=None)
plt.savefig(filename + '_fit' + '.png')
plt.close()
return success
def plot_relaxation_vs_sweep(self, ms, st, save=True, **kw):
ret = kw.get('ret', None)
ax = kw.get('ax', None)
ax2 = kw.get('ax2', None)
if ax == None:
fig = self.default_fig(figsize=(6,4))
ax = self.default_ax(fig)
fig2 = self.default_fig(figsize=(10,6))
#fig2.title('Relaxation fit vs' + self.sweep_name)
else:
save = False
name= '_ms'+ str(ms) + '_' + str(st)
sweep=self.sweep_pts[::2]
taus=np.zeros(len(sweep))
for i,sw in enumerate(sweep):
#print i
start,length=getattr(self,'_get_'+st+'_window')(ms,i)
y,x=self._get_relaxation(ms, i, start, length)
x=x[:-1]
res = fit.fit1d(x,y, common.fit_exp_decay_shifted_with_offset, 0, y[0], 1000, x[0],
ret=True, do_print=False, fixed=[3])
if res != False:
ax2=plt.subplot(3,np.ceil(float(len(sweep)+1)/3),i+1)
plot.plot_fit1d(res, x, ax=ax2, plot_data=True, print_info=False)
ax2.text(ax2.get_xlim()[-1]*0.8,ax2.get_ylim()[-1]*0.8, '{:.2f}'.format(sw),horizontalalignment='right')
if (res['params_dict']['A'] > 10*res['params_dict']['a']):
taus[i]=res['params_dict']['tau']/1e3
else:
print 'Sweep point {} ignored'.format(i)
else:
print 'Could not fit sweep point {}'.format(i)
skip_points = kw.get('skip_points', 0)
xx=sweep[taus>1.][skip_points:]
yy=taus[taus>1.][skip_points:]
#y(x) = A * exp(-(x-x0)/tau) + a
#g_a : offset
#g_A : initial Amplitude
#g_tau : decay constant
#g_x0 : x offset
res2 = fit.fit1d(xx,yy, common.fit_exp_decay_with_offset, 10, yy[0], xx[len(xx)/2]/2.,
ret=True, do_print=True, fixed=[])
ax.plot(xx,yy, 'o')
#ax.plot(sweep,10+yy[0]*np.exp(-(sweep- xx[0])/(xx[len(xx)/2]/2.)))
if res2 != False:
plot.plot_fit1d(res2, xx, ax=ax, plot_data=False, print_info=True)
#ax.colorbar()
ax.set_xlabel(self.sweep_name)
ax.set_ylabel('ms={} {}-relaxation time [us]'.format(ms,st))
return res
if save:
self.save_fig_incremental_filename(fig,name+'_relaxation_vs_sweep')
if ret == 'ax':
return ax
if ret == 'fig':
return fig
def epulse_fidelity(folder, ax, *args):
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
a.plot_results_vs_sweepparam(ax=ax, name='adwindata')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
of = fit.Parameter(args[0], 'of')
fitfunc_str = '(1-of)'
def fitfunc_fid(x):
return (1. - of())
fit_result = fit.fit1d(x,
y,
None,
p0=[of],
fitfunc=fitfunc_fid,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False,
print_info=False)
return fit_result
def calibrate_epulse_amplitude(folder, ax, *args, **kw):
double_ro = kw.pop("double_ro", "False")
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name="adwindata")
a.get_electron_ROC()
a.plot_results_vs_sweepparam(ax=ax, name="adwindata")
x = a.sweep_pts.reshape(-1)[:]
if double_ro == "electron":
y = a.p0[:, 0]
elif double_ro == "nitrogen":
y = a.p0[:, 1]
else:
y = a.p0.reshape(-1)[:]
x0 = fit.Parameter(args[0], "x0")
of = fit.Parameter(args[1], "of")
a = fit.Parameter(args[2], "a")
fitfunc_str = "(1-of) + a (x-x0)**2"
def fitfunc_parabolic(x):
return (1.0 - of()) + a() * (x - x0()) ** 2
fit_result = fit.fit1d(
x, y, None, p0=[of, a, x0], fitfunc=fitfunc_parabolic, fitfunc_str=fitfunc_str, do_print=True, ret=True
)
plot.plot_fit1d(fit_result, np.linspace(x[0], x[-1], 201), ax=ax, plot_data=False, print_info=False)
return fit_result
def analyze_dark_esr(folder, ax=None, **kw):
if ax == None:
fig, ax = plt.subplots(1,1)
ssro_calib_folder = toolbox.latest_data(contains='AdwinSSRO_SSROCalibration')
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC(ssro_calib_folder=ssro_calib_folder)
x = a.sweep_pts # convert to MHz
y = a.p0.reshape(-1)[:]
a.plot_result_vs_sweepparam(ret=None, name='ssro', ax=ax)
guess_ctr = x[np.floor(len(x)/2.)]
fit_result = fit.fit1d(x, y, esr.fit_ESR_gauss, guess_offset,
guess_amplitude, guess_width, guess_ctr,
# (2, guess_splitN),
#(2, guess_splitC),
# (2, guess_splitB),
#(3, guess_splitN),
do_print=True, ret=True, fixed=[])
plot.plot_fit1d(fit_result, np.linspace(min(x), max(x), 1000), ax=ax, plot_data=False, **kw)
ax.set_xlabel('MW frq (GHz)')
ax.set_ylabel(r'fidelity wrt. $|0\rangle$')
ax.set_title(a.timestamp+'\n'+a.measurementstring)
plt.savefig(os.path.join(folder, 'darkesr_analysis.png'),
format='png')
return fit_result
def fit_gaussian(folder, ax, *args):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
ax.set_title(a.timestamp)
x0 = fit.Parameter(args[0], 'x0')
a = fit.Parameter(0.5, 'a')
o = fit.Parameter(0.5, 'o')
c = fit.Parameter(15, 'c')
f = fit.Parameter(1./500, 'f')
fitfunc_str = 'o + a * exp( - ( (x-x0)/c )^2) '
def fitfunc(x):
return o() + a() * np.exp( -((x-x0())/ c())**2)
fit_result = fit.fit1d(x,y, None, p0=[o,x0,a,c], fixed = [], fitfunc=fitfunc,
fitfunc_str=fitfunc_str, do_print=True, ret=True)
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax,
plot_data=False)
return fit_result
def fit_parabola(x, y,
guess_x0,
guess_amp,
guess_of,
fixed,
show_fit = True,
save = True):
x0 = fit.Parameter(guess_x0, 'x0')
A = fit.Parameter(guess_amp, 'A')
o = fit.Parameter(guess_of, 'o')
p0 = [x0, A, o]
fitfunc_str = ''
fitfunc_str = 'o + A*(x - x0)^2'
def fitfunc(x):
return o() + A() * (x - x0) ** 2.
fit_result = fit.fit1d(x,y, None, p0=p0, fitfunc=fitfunc, fixed=fixed,
do_print=True, ret=True)
if show_fit:
plot.plot_fit1d(fit_result, np.linspace(0,x[-1],201),
plot_data=True)
# print "pi pulse = {:.5f} ".format(1/f()/2.) + a.sweep_name
# ax.set_title(a.timestamp+'\n'+a.measurementstring)
if save:
plt.savefig(os.path.join(folder, 'Calibrate_Pi_analysis_parabolafit.png'))
def calibrate_epulse_rabi(folder, ax, *args, **kws):
fit_phi = kws.pop('fit_phi', True)
fit_k = kws.pop('fit_k', True)
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
ax.set_title(a.timestamp)
f = fit.Parameter(args[0], 'f')
A = fit.Parameter(args[1], 'A')
x0 = fit.Parameter(0, 'x0')
k = fit.Parameter(0, 'k')
p0 = [f, A]
if fit_phi:
p0.append(x0)
if fit_k:
p0.append(k)
fitfunc_str = '(1 - A) + A * exp(-kx) * cos(2pi f (x - x0))'
def fitfunc(x) :
return (1.-A()) + A() * np.exp(-k()*x) * \
np.cos(2*np.pi*(f()*(x - x0())))
fit_result = fit.fit1d(x,y, None, p0=p0, fitfunc=fitfunc,
fitfunc_str=fitfunc_str, do_print=True, ret=True)
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax,
plot_data=False, print_info=False)
return fit_result
def fit_sin_and_calc_phi(x,y,ax = False):
g_a = 0.0
g_A = np.amax(y)
g_phi = x[np.argmax(y)]
### frequency guess is hardcoded as this is mainly intended for specific entangling oscillations.
g_f = 1/360.
p0s, fitfunc,fitfunc_str = common.fit_cos(g_f,g_a,g_A,g_phi)
fit_result = fit.fit1d(x,y, None, p0=p0s, fitfunc=fitfunc,
ret=True,fixed=[0,1])
if ax:
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax,
plot_data=False,print_info = False)
A = fit_result['params_dict']['A']
phi = fit_result['params_dict']['phi']
if A < 0:
phi = phi + 180
A = -A
phi = np.mod(-phi,360)
phi_u = fit_result['error_dict']['phi']
print 'A,phi,phi_u ', A, phi, phi_u
return phi, phi_u # Phi and Phi_u
def poly_fit_frq(self, **kw):
plot_fit = kw.pop('plot_fit', True)
g_x0 = self.x_data[0]
g_a0 = self.frq_data[0]
g_a1 = (self.frq_data[-1] - self.frq_data[0]) / (self.x_data[-1] -
self.x_data[0])
g_a2 = -0.2
g_a3 = 2
fixed = []
p0, fitfunc, fitfunc_str = common.fit_poly_shifted(
g_x0, g_a0, g_a1, g_a2, g_a3)
fit_result = fit.fit1d(self.x_data,
self.frq_data,
None,
p0=p0,
fitfunc=fitfunc,
do_print=False,
ret=True,
fixed=fixed)
if plot_fit:
plot.plot_fit1d(fit_result,
np.linspace(self.x_data[-1], self.x_data[0],
10 * len(self.x_data)),
label='Fit',
show_guess=False,
plot_data=True,
color='red',
data_linestyle='-',
print_info=True)
self.frq_data = fit_result['fitfunc'](self.x_data)
return self.frq_data
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 fit_linear(folder, ax, value, *args):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
ax.set_title(a.timestamp)
a = fit.Parameter(args[0], 'a')
b = fit.Parameter(args[0], 'b')
fitfunc_str = 'a x + b'
def fitfunc(x):
return a() * x + b()
fit_result = fit.fit1d(x,
y,
None,
p0=[a, b],
fitfunc=fitfunc,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False,
print_info=False)
return fit_result
def calibrate_epulse_amplitude(folder, ax, *args):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
ax.set_title(a.timestamp)
x0 = fit.Parameter(args[0], 'x0')
of = fit.Parameter(args[1], 'of')
a = fit.Parameter(args[2], 'a')
fitfunc_str = '(1-of) + a (x-x0)**2'
def fitfunc_parabolic(x):
return (1. - of()) + a() * (x - x0())**2
fit_result = fit.fit1d(x,
y,
None,
p0=[of, a, x0],
fitfunc=fitfunc_parabolic,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False,
print_info=True)
return fit_result
def fast_pi2():
folder = toolbox.latest_data('cal_fast_pi_over_2')
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
#a.plot_results_vs_sweepparam(ax=ax, name='adwindata')
x = a.sweep_pts
y = a.p0.reshape(-1)
u_y = a.u_p0.reshape(-1)
n = a.sweep_name
x2 = x[::2]
y2 = y[1::2] - y[::2]
u_y2 = np.sqrt(u_y[1::2]**2 + u_y[::2]**2)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), sharex=True)
ax1.errorbar(x2, y2, yerr=u_y2, fmt='o')
ax1.set_xlabel(n)
ax1.set_title('Difference btw. Pi/2-Pi and Pi/2' + '\n' + a.timestamp)
ax1.set_ylabel('Difference')
m = fit.Parameter((y[-1] - y[0]) / (x[-1] - x[0]), 'm')
x0 = fit.Parameter(x2.mean(), 'x0')
p0 = [m, x0]
def ff(x):
return m() * (x - x0())
fitfunc_str = 'm * (x - x0)'
fit_result = fit.fit1d(x2,
y2,
None,
p0=p0,
fitfunc=ff,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
ax2.errorbar(x2, y[0::2], yerr=u_y[0::2], fmt='o', label='Pi/2')
ax2.errorbar(x2, y[1::2], yerr=u_y[1::2], fmt='o', label='Pi/2 - Pi')
ax2.legend(frameon=True, framealpha=0.5)
ax2.set_ylabel('P(0)')
ax2.set_xlabel(n)
if fit_result != False:
ax2.axvline(x0(), c='k', lw=2)
ax2.axhline(0.5, c='k', lw=2)
ax2.set_title('X marks the spot')
plot.plot_fit1d(fit_result,
np.linspace(x2[0], x2[-1], 201),
ax=ax1,
plot_data=False,
print_info=True)
return x0(), 0
def analyze_nmr_single(timestamp = None,center_guess = False, ax=None, ret=None,min_dip_depth = 0.85 , **kw):
if ax == None:
fig, ax = plt.subplots(1,1)
x = np.zeros((0))
y = np.zeros((0))
ysig = np.zeros((0))
# rdts = np.zeros((0))
for i in range(20):
timestamp, folder = toolbox.latest_data(contains = 'NuclearRFRabi_111_1_sil18Rabi_C5_el1_positive_'+str(i)+'run',return_timestamp = True)
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
x = np.append(x,a.sweep_pts[:])
y = np.append(y,a.p0[:])
ysig = np.append(ysig,a.u_p0[:])
print dir(a)
for i in range(len(x)-1):
print x[i], x[i+1]-x[i]
if center_guess == True:
guess_ctr = float(raw_input('Center guess?'))
else:
guess_ctr = x[y.argmin()]
print 'guess_ctr = '+str(guess_ctr)
# # try fitting
fit_result = fit.fit1d(x, y, esr.fit_ESR_gauss, guess_offset,
guess_amplitude, guess_width, guess_ctr,
do_print=False, ret=True, fixed=[])
# fit_result = fit.fit1d(x, y, common.fit_2gauss, guess_offset,
# guess_amplitude, guess_ctr-30e-3,guess_width, guess_amplitude, guess_ctr+30e-3,guess_width,
# do_print=False, ret=True, fixed=[])
fit_result['yerr'] = ysig
plot.plot_fit1d(fit_result, np.linspace(min(x), max(x), 1000), ax=ax, plot_data=True, **kw)
ax.set_xlabel('RF frq (kHz)')
ax.set_ylabel(r'fidelity wrt. $|0\rangle$')
ax.set_title(a.timestamp+'\n'+a.measurementstring)
plt.savefig(os.path.join(folder, 'nmr_analysis.png'),
format='png')
if ret == 'f0':
f0 = fit_result['params_dict']['x0']
u_f0 = fit_result['error_dict']['x0']
ax.text(f0, 0.8, '$f_0$ = ({:.3f} +/- {:.3f})'.format(
(f0-2.8)*1e3, u_f0*1e3), ha='center')
return (f0-2.8)*1e3, u_f0*1e3
def Carbon_control_sweep_N_zoom(timestamp=None, measurement_name = ['adwindata'],
A = [0.5, 0.5],
fitfunc_type = 'single', plot_fit = False, do_print = False, show_guess = True,
yaxis = [-0.05,1.05]):
''' Function to analyze data for optimization of the number of pulses for a controlled C13 gate.
'''
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
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')
a.get_electron_ROC()
ax = a.plot_results_vs_sweepparam(ret='ax')
ax.set_ylim(yaxis)
ax.axhspan(0,0.5,fill=False,ls='dotted')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
y_u = a.u_p0.reshape(-1)[:]
ax.set_xlim(x[0]-1,x[-1]+1)
p0, fitfunc, fitfunc_str = common.fit_poly(A)
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=[3])
if plot_fit == True:
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax, plot_data=False,print_info = True)
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')
diff = np.abs(y - 0.5)
print diff
print 'Optimum number of pulses N = ' + str(x[np.argmin(diff)])
print 'with y-0.5 = ' + str(y[np.argmin(diff)]-0.5) + ' +/- ' + str(y_u[np.argmin(diff)])
# 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
def analyse_Ramsey(folder='',
T2=3e3,
Ampl=-1. / 3,
detuning=3e-3,
hf_N=2.17e-3,
*arg):
timestamp = kw.pop(timestamp, None)
guess_tau = T2
guess_a = 0.5
guess_A = Ampl
guess_hf_N = hf_N
guess_det = detuning
guess_hf_C = hf_C
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
elif folder != '':
folder = toolbox.latest_data('Ramsey')
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
x = a.sweep_pts
y = a.p0
ax = a.plot_result_vs_sweepparam(ret='ax', name='ssro')
params_0, fitfunc_0, fitfunc_str_0 = ramsey.fit_ramsey_14N_fixed_13C_opt(
guess_tau, guess_A, guess_a, guess_det, guess_hf_N)
x_0 = np.linspace(0, a.sweep_pts[-1], 1000)
ax.plot(x_0, fitfunc_0(x_0), 'r--', lw=1)
#fit_xvals=np.linspace(res['x'][0],res['x'][-1],fit_num_points)
fit_result = fit.fit1d(x,
y,
ramsey.fit_ramsey_14N_fixed_13C_opt,
guess_tau,
guess_A,
guess_a,
guess_det,
guess_hf_N,
fixed=[],
do_print=True,
ret=True)
#fit_result = False
if fit_result != False:
plot.plot_fit1d(fit_result,
np.linspace(0, a.sweep_pts[-1], 201),
ax=ax,
plot_data=False)
plt.savefig(os.path.join(folder, 'electronramsey_analysis.pdf'),
format='pdf')
def calibrate_pi2_noMBI(folder):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
x = a.sweep_pts
y = a.p0
u_y = a.u_p0
n = a.sweep_name
a.finish()
x2 = x[::2]
print x2
y2 = y[1::2] - y[::2]
u_y2 = np.sqrt(u_y[1::2]**2 + u_y[::2]**2)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), sharex=True)
ax1.errorbar(x2, y2, yerr=u_y2, fmt='o')
ax1.set_xlabel(n)
ax1.set_title('Difference btw. Pi/2-Pi and Pi/2')
ax1.set_ylabel('Difference')
m = fit.Parameter((y[-1] - y[0]) / (x[-1] - x[0]), 'm')
x0 = fit.Parameter(y2.mean(), 'x0')
p0 = [m, x0]
def ff(x):
return m() * (x - x0())
fitfunc_str = 'm * (x - x0)'
fit_result = fit.fit1d(x2,
y2,
None,
p0=p0,
fitfunc=ff,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
ax2.errorbar(x2, y[0::2], yerr=u_y[0::2], fmt='o', label='Pi/2')
ax2.errorbar(x2, y[1::2], yerr=u_y[1::2], fmt='o', label='Pi/2 - Pi')
ax2.legend(frameon=True, framealpha=0.5)
ax2.set_ylabel('P(0)')
ax2.set_xlabel(n)
ax2.axvline(x0(), c='k', lw=2)
ax2.axhline(0.5, c='k', lw=2)
ax2.set_title('X marks the spot')
plot.plot_fit1d(fit_result,
np.linspace(x2[0], x2[-1], 201),
ax=ax1,
plot_data=False,
print_info=True)
fig.savefig(os.path.join(folder, 'pi2_calibration.png'))
def 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 analyse_Rabi(guess_frq = 2., guess_amp = 0.2, guess_of = 0.1, **kw) :
timestamp = kw.pop('timestamp', None)
guess_phi = kw.pop('guess_phi', 0.)
guess_k = kw.pop('guess_k', 0.)
mbi_analysis = kw.pop('mbi_analysis', False)
do_print = kw.pop('do_print', False)
o = fit.Parameter(guess_of, 'o')
f = fit.Parameter(guess_frq, 'f')
A = fit.Parameter(guess_amp, 'A')
phi = fit.Parameter(guess_phi, 'phi')
k = fit.Parameter(guess_k, 'k')
p0 = [f, A, phi, o, k]
fitfunc_str = ''
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else :
folder = toolbox.latest_data('ElectronRabi')
if mbi_analysis:
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('adwindata')
a.get_electron_ROC()
ax = a.plot_results_vs_sweepparam(ret='ax', name = 'adwindata')
else:
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
ax = a.plot_result_vs_sweepparam(ret='ax')
x = a.sweep_pts
y = a.p0
fitfunc_str = 'o - A + A*e^(-kx)*cos(2pi (fx-phi))'
def fitfunc(x):
return (o()-A()) + A() * np.exp(-k()*x) * np.cos(2*np.pi*(f()*x - phi()))
fit_result = fit.fit1d(x,y, None, p0=p0, fitfunc=fitfunc, fixed=[2],
do_print=do_print, ret=True)
plot.plot_fit1d(fit_result, np.linspace(0,x[-1],201), ax=ax,
plot_data=False)
print "\npi pulse at {:.3f} for .\n".format(1/f()/2.) + a.sweep_name
# ax.set_title(a.timestamp+'\n'+a.measurementstring)
plt.savefig(os.path.join(folder, 'electronrabi_analysis_fit.png'))
return fit_result
def fit_parabolic(folder=None,
ax=None,
x0_guess=0.,
of_guess=0.5,
a_guess=1.,
**kw):
"""
fit (1-of) + a (x-x0)**2 from Sequence in folder
"""
do_print = kw.pop('do_print', False)
fixed = kw.pop('fixed', [])
a0, ax, x, y = plot_result(folder, ax, **kw)
x0 = fit.Parameter(x0_guess, 'x0')
of = fit.Parameter(of_guess, 'of')
a = fit.Parameter(a_guess, 'a')
x_min = kw.pop("x_min", None)
x_max = kw.pop("x_max", None)
fit_x = x
fit_y = y
if x_min is not None:
mask = fit_x > x_min
fit_x = fit_x[mask]
fit_y = fit_y[mask]
if x_max is not None:
mask = fit_x < x_max
fit_x = fit_x[mask]
fit_y = fit_y[mask]
fitfunc_str = '(1-of) + a (x-x0)**2'
def fitfunc_parabolic(x):
return (1. - of()) + a() * (x - x0())**2
fit_result = fit.fit1d(fit_x,
fit_y,
None,
p0=[of, a, x0],
fitfunc=fitfunc_parabolic,
fitfunc_str=fitfunc_str,
do_print=do_print,
ret=True)
fit_result['u_y'] = a0.u_p0
plot.plot_fit1d(fit_result,
np.linspace(np.min(fit_x), np.max(fit_x), 201),
ax=ax,
plot_data=False,
**kw)
return fit_result
def analyze_dark_esr(folder,center_guess = True, ax=None, ret=None, **kw):
if ax == None:
fig, ax = plt.subplots(1,1)
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
x = a.sweep_pts # convert to MHz
y = a.p0.reshape(-1)[:]
a.plot_result_vs_sweepparam(ret=ret, name='ssro', ax=ax)
ax.set_ylim(0.6,1.05)
if center_guess == True:
guess_ctr = float(raw_input('Center guess?'))
else:
j=0
if j < len(y)-2:
while y[j]>0.93*y[j+1]: # such that we account for noise
k = j
j = j+1
j = len(y)-2
guess_ctr = x[k]*1e-3+ guess_splitN #convert to GHz and go to middle dip
# try fitting
fit_result = fit.fit1d(x, y, esr.fit_ESR_gauss, guess_offset,
guess_amplitude, guess_width, guess_ctr,
# (2, guess_splitN),
# (2, guess_splitC),
# (2, guess_splitB),
(3, guess_splitN),
do_print=True, ret=True, fixed=[4])
plot.plot_fit1d(fit_result, np.linspace(min(x), max(x), 1000), ax=ax, plot_data=False, **kw)
ax.set_xlabel('MW frq (GHz)')
ax.set_ylabel(r'fidelity wrt. $|0\rangle$')
ax.set_title(a.timestamp+'\n'+a.measurementstring)
plt.savefig(os.path.join(folder, 'darkesr_analysis.png'),
format='png')
if ret == 'f0':
f0 = fit_result['params_dict']['x0']
u_f0 = fit_result['error_dict']['x0']
ax.text(f0, 0.8, '$f_0$ = ({:.3f} +/- {:.3f})'.format(
(f0-2.8)*1e3, u_f0*1e3), ha='center')
return (f0-2.8)*1e3, u_f0*1e3
def electron_T2_anal(timestamp=None,
measurement_name=['ms0'],
Amplitude=1 / 2.,
T2=10e3,
offset=1. / 2,
k=4,
do_print=False,
ylim=(0., 1.)):
''' Function to analyze simple decoupling measurements. Loads the results and fits them to a simple exponential.
Inputs:
timestamp: in format yyyymmdd_hhmmss or hhmmss or None.
measurement_name: list of measurement names
Based on electron_T1_anal, modified by Adriaan Rol
'''
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('Decoupling')
print folder
fit_results = []
for k in range(0, len(measurement_name)):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(measurement_name[k])
a.get_electron_ROC()
ax = a.plot_result_vs_sweepparam(ret='ax')
ax.set_ylim(ylim)
x = a.sweep_pts
p0, fitfunc, fitfunc_str = SE.fit_echo(T2, Amplitude, offset, k)
y = a.p0
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=do_print,
ret=True,
fixed=[3])
#fit_result = False
if fit_result != False:
plot.plot_fit1d(fit_result,
np.linspace(0, x[-1], 201),
ax=ax,
plot_data=False)
fit_results.append(fit_result)
plot.ylim = ([0, 0.2])
return fit_results
def calibrate_epulse_rabi(folder, ax, *args, **kws):
fit_x0 = kws.pop('fit_x0', True)
fit_k = kws.pop('fit_k', True)
double_ro = kws.pop('double_ro', 'False')
guess_x0 = kws.pop('guess_x0', 0)
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
a.plot_results_vs_sweepparam(ax=ax, name='adwindata')
x = a.sweep_pts.reshape(-1)[:]
if double_ro == 'electron':
y = a.p0[:, 0]
elif double_ro == 'nitrogen':
y = a.p0[:, 1]
else:
y = a.p0.reshape(-1)[:]
f = fit.Parameter(args[0], 'f')
A = fit.Parameter(args[1], 'A')
x0 = fit.Parameter(guess_x0, 'x0')
k = fit.Parameter(0, 'k')
p0 = [f, A]
if fit_x0:
p0.append(x0)
if fit_k:
p0.append(k)
fitfunc_str = '(1 - A) + A * exp(-kx) * cos(2pi f (x - x0))'
def fitfunc(x):
return (1.-A()) + A() * np.exp(-k()*x) * \
np.cos(2*np.pi*f()*(x - x0()))
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False,
print_info=False)
return fit_result
def fast_pi2():
folder = toolbox.latest_data('cal_fast_pi_over_2')
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
#a.plot_results_vs_sweepparam(ax=ax, name='adwindata')
x = a.sweep_pts
y = a.p0.reshape(-1)
u_y = a.u_p0.reshape(-1)
n = a.sweep_name
x2 = x[::2]
y2 = y[1::2] - y[::2]
u_y2 = np.sqrt( u_y[1::2]**2 + u_y[::2]**2 )
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,4), sharex=True)
ax1.errorbar(x2, y2, yerr=u_y2, fmt='o')
ax1.set_xlabel(n)
ax1.set_title('Difference btw. Pi/2-Pi and Pi/2'+'\n'+a.timestamp)
ax1.set_ylabel('Difference')
m = fit.Parameter((y[-1]-y[0])/(x[-1]-x[0]), 'm')
x0 = fit.Parameter(x2.mean(), 'x0')
p0 = [m, x0]
def ff(x):
return m() * (x-x0())
fitfunc_str = 'm * (x - x0)'
fit_result = fit.fit1d(x2, y2, None, p0=p0, fitfunc=ff,
fitfunc_str=fitfunc_str, do_print=True, ret=True)
ax2.errorbar(x2, y[0::2], yerr=u_y[0::2], fmt='o',
label='Pi/2')
ax2.errorbar(x2, y[1::2], yerr=u_y[1::2], fmt='o',
label='Pi/2 - Pi')
ax2.legend(frameon=True, framealpha=0.5)
ax2.set_ylabel('P(0)')
ax2.set_xlabel(n)
if fit_result != False:
ax2.axvline(x0(), c='k', lw=2)
ax2.axhline(0.5, c='k', lw=2)
ax2.set_title('X marks the spot')
plot.plot_fit1d(fit_result, np.linspace(x2[0],x2[-1],201), ax=ax1,
plot_data=False, print_info=True)
return x0(), 0
def fit_Ramsey_6_cos_hf_fixed(timestamp,
ssro_calib_folder,
g_tau,
g_A,
g_a,
g_det,
g_hf_N,
g_hf_C,
fixed=[],
folder=None):
if folder is None:
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('ElectronRamsey')
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
if ssro_calib_folder != None:
a.get_electron_ROC(ssro_calib_folder=ssro_calib_folder)
else:
a.get_electron_ROC()
# Plot data
ax = a.plot_result_vs_sweepparam(ret='ax', name='ssro')
p0, fitfunc_0, fitfunc_str_0 = ramsey.fit_ramsey_hyperfinelines_fixed_6cos(
g_tau, g_A, g_a, g_det, g_hf_N, g_hf_C)
fit_result = fit.fit1d(a.sweep_pts,
a.p0.reshape(-1),
ramsey.fit_ramsey_hyperfinelines_fixed_6cos,
g_tau,
g_A,
g_a,
g_det,
g_hf_N,
g_hf_C,
fixed=fixed,
do_print=True,
ret=True)
if fit_result != False:
plot.plot_fit1d(fit_result,
np.linspace(0, a.sweep_pts[-1], 201),
ax=ax,
plot_data=False)
plt.savefig(os.path.join(folder, 'electronramsey_analysis.png'),
format='png')
def 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 fit_Ramsey_gausscos_withoffset(timestamp = None,
ssro_calib_folder = None,
guess_avg = 0.5,
guess_A = 1./3.,
guess_t = 10000,
guess_a = 2,
guess_f = 1./35,
guess_phi = 0,
guess_b = 1,
fixed = [],
fit_result = True):
"""
Fits cosine with offset modulated by Gaussian decay to electron Ramsey signal.
fit func = 'avg + A *exp(-(x/t)**2) * (a * cos(2pi * (f*x + phi/360) ) + b)'
"""
### script
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('ElectronRamsey')
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
if ssro_calib_folder != None:
a.get_electron_ROC(ssro_calib_folder = ssro_calib_folder)
else:
a.get_electron_ROC()
# Plot data
ax = a.plot_result_vs_sweepparam(ret='ax', name='ssro')
# ax.set_xlim(0,1000)
p0, fitfunc_0, fitfunc_str_0 = ramsey.fit_gaussian_decaying_cos_withoffset(guess_avg, guess_A, guess_t, guess_a, guess_f, guess_phi, guess_b)
# Plot initial guess
# fit_xvals_0=np.linspace(0,a.sweep_pts[-1],1000)
# ax.plot(fit_xvals_0,fitfunc_0(fit_xvals_0), 'r--', lw=1)
#fit_xvals=np.linspace(res['x'][0],res['x'][-1],fit_num_points)
fit_result = fit.fit1d(a.sweep_pts, a.p0.reshape(-1), ramsey.fit_gaussian_decaying_cos_withoffset,
guess_avg, guess_A, guess_t, guess_a, guess_f, guess_phi, guess_b,
fixed=fixed, do_print=True, ret=True)
if fit_result != False :
plot.plot_fit1d(fit_result, np.linspace(0,a.sweep_pts[-1],2001), ax=ax,
plot_data=False)
plt.savefig(os.path.join(folder, 'electronramsey_analysis.pdf'),
format='pdf')
def fit_lorentzian(self, **kw):
y = kw.pop('y', self.y_data)
x = kw.pop('x', self.frq_data)
g_gamma = kw.pop('g_gamma', 15)
plot_title = kw.pop('plot_title', '')
plot_fit = kw.pop('plot_fit', False)
fixed = kw.pop('fixed', [])
g_a = np.average(y)
g_A = max(y) - g_a
g_x0 = self.frq_data[np.argmax(y)]
p0, fitfunc, fitfunc_str = common.fit_lorentz(g_a, g_A, g_x0, g_gamma)
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=False,
ret=True,
fixed=fixed)
if 'gamma' not in fit_result['params_dict']:
fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(x, y)
print 'WARNING: COULD NOT FIT gamma'
return 0, 10
gamma = np.abs(fit_result['params_dict']['gamma'])
u_gamma = np.abs(fit_result['error_dict']['gamma'])
x0 = fit_result['params_dict']['x0']
FWHM = gamma
u_FWHM = u_gamma
if plot_fit:
fig, ax = plt.subplots(figsize=(10, 4))
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1],
len(x) * 10),
ax=ax,
label='Fit',
show_guess=True,
plot_data=True)
ax.set_title('FWHM = %.1f +- %.1f GHz %s' %
(FWHM, u_FWHM, plot_title))
fig.savefig(self.folder + '/lorentzian_fit_%s.png' % (plot_title))
plt.show(fig)
plt.close(fig)
return FWHM, u_FWHM
def calibrate_pulse_amplitude(x, y, ax, *args):
x0 = fit.Parameter(args[0], 'x0')
of = fit.Parameter(args[1], 'of')
a = fit.Parameter(args[2], 'a')
fitfunc_str = '(1-of) + a (x-x0)**2'
def fitfunc_parabolic(x):
return (1.-of()) + a() * (x-x0())**2
fit_result = fit.fit1d(x,y, None, p0=[of, a, x0], fitfunc=fitfunc_parabolic,
fitfunc_str=fitfunc_str, do_print=True, ret=True)
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax,
plot_data=False, print_info=False)
return fit_result
def analyze_dark_esr_single(timestamp = None,center_guess = False, ax=None, ret=None,min_dip_depth = 0.85 , **kw):
if ax == None:
fig, ax = plt.subplots(1,1)
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('DarkESR')
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
x = a.sweep_pts # convert to MHz
y = a.p0.reshape(-1)[:]
a.plot_result_vs_sweepparam(ret=ret, name='ssro', ax=ax)
ax.set_ylim(0.6,1.05)
if center_guess == True:
guess_ctr = float(raw_input('Center guess?'))
else:
guess_ctr = x[y.argmin()]
print 'guess_ctr = '+str(guess_ctr)
# try fitting
fit_result = fit.fit1d(x, y, esr.fit_ESR_gauss, guess_offset,
guess_amplitude, guess_width, guess_ctr,
do_print=False, ret=True, fixed=[])
plot.plot_fit1d(fit_result, np.linspace(min(x), max(x), 1000), ax=ax, plot_data=False, **kw)
ax.set_xlabel('MW frq (GHz)')
ax.set_ylabel(r'fidelity wrt. $|0\rangle$')
ax.set_title(a.timestamp+'\n'+a.measurementstring)
plt.savefig(os.path.join(folder, 'darkesr_analysis.png'),
format='png')
if ret == 'f0':
f0 = fit_result['params_dict']['x0']
u_f0 = fit_result['error_dict']['x0']
ax.text(f0, 0.8, '$f_0$ = ({:.3f} +/- {:.3f})'.format(
(f0-2.8)*1e3, u_f0*1e3), ha='center')
return (f0-2.8)*1e3, u_f0*1e3
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('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 calibrate_pi2_noMBI(folder):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
x = a.sweep_pts
y = a.p0
u_y = a.u_p0
n = a.sweep_name
a.finish()
x2 = x[::2]
print x2
y2 = y[1::2] - y[::2]
u_y2 = np.sqrt( u_y[1::2]**2 + u_y[::2]**2 )
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,4), sharex=True)
ax1.errorbar(x2, y2, yerr=u_y2, fmt='o')
ax1.set_xlabel(n)
ax1.set_title('Difference btw. Pi/2-Pi and Pi/2')
ax1.set_ylabel('Difference')
m = fit.Parameter((y[-1]-y[0])/(x[-1]-x[0]), 'm')
x0 = fit.Parameter(y2.mean(), 'x0')
p0 = [m, x0]
def ff(x):
return m() * (x-x0())
fitfunc_str = 'm * (x - x0)'
fit_result = fit.fit1d(x2, y2, None, p0=p0, fitfunc=ff,
fitfunc_str=fitfunc_str, do_print=True, ret=True)
ax2.errorbar(x2, y[0::2], yerr=u_y[0::2], fmt='o',
label='Pi/2')
ax2.errorbar(x2, y[1::2], yerr=u_y[1::2], fmt='o',
label='Pi/2 - Pi')
ax2.legend(frameon=True, framealpha=0.5)
ax2.set_ylabel('P(0)')
ax2.set_xlabel(n)
ax2.axvline(x0(), c='k', lw=2)
ax2.axhline(0.5, c='k', lw=2)
ax2.set_title('X marks the spot')
plot.plot_fit1d(fit_result, np.linspace(x2[0],x2[-1],201), ax=ax1,
plot_data=False, print_info=True)
fig.savefig(os.path.join(folder, 'pi2_calibration.png'))
def plot_data_and_fit(data, EOM_freq):
fig, ax = plt.subplots(figsize=(8, 4))
lw, u_lw, scaling, fit_result = get_linewidth(data, EOM_freq)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], len(x)),
ax=ax,
label='Fit',
show_guess=True,
plot_data=True,
color='red',
data_linestyle='-',
print_info=False)
ax.set_xlabel("Frequency (GHz)", fontsize=14)
ax.set_ylabel("Intensity (a.u.)", fontsize=14)
ax.set_xlim(x[0], x[-1])
#rescaling x-axis in GHz
# X_min_freq = ax.get_xlim()[0]*scaling
# X_max_freq = ax.get_xlim()[-1]*scaling
# xticks = np.linspace(ax.get_xlim()[0],ax.get_xlim()[-1],n_xticks)
# xticklabels = np.linspace(X_min_freq,X_max_freq,n_xticks)
# xticklabels_round=[]
# for j in xticklabels:
# round_ = round(j,2)
# xticklabels_round = np.append(xticklabels_round,round_)
# ax.set_xticklabels(xticklabels_round)
# ax.set_xticks(xticks)
plt.show()
return x, lw, u_lw, scaling
# def set_axes(ax):
# ax.set_xlabel("Frequency (GHz)", fontsize = 14)
# ax.set_ylabel("Intensity (a.u.)", fontsize = 14)
# ax.grid(False)
# ax.set_axis_bgcolor('white')
# #ax.set_xlim(x[0],x[-1])
# ax.tick_params(which = 'both', direction = 'out')
# xticks = np.linspace(ax.get_xlim()[0],ax.get_xlim()[-1],int((ax.get_xlim()[0]-ax.get_xlim()[-1])/2+1))
# xticklabels = np.linspace(ax.get_xlim()[0]*scaling,ax.get_xlim()[-1]*scaling,int((ax.get_xlim()[0]-ax.get_xlim()[-1])/2+1))
# xticklabels = np.round(xticklabels,1)
# ax.set_xticks(xticks)
# ax.set_xticklabels(xticklabels, rotation=0)
# return ax
def determine_drift(times, peak_positions, data_folder):
times_dec = []
ftr = [3600,60,1]
for time in times:
time_dec = sum([a*b for a,b in zip(ftr, [ int(time[0:2]),int(time[2:4]),int(time[4:6]) ]) ])
times_dec = np.append(times_dec, time_dec)
times_dec = times_dec - times_dec[0]
g_a = 2.13
g_b = 0.0007
fixed=[]
p0, fitfunc, fitfunc_str = common.fit_line(g_a, g_b)
fit_result = fit.fit1d(times_dec,peak_positions, None, p0=p0, fitfunc=fitfunc, do_print=False, ret=True,fixed=fixed)
b = fit_result['params_dict']['b']
u_b = fit_result['error_dict']['b']
fig,ax = plt.subplots(figsize=(6,4.7))
ax = plot.plot_fit1d(fit_result, np.linspace(times_dec[0],times_dec[-1],10*len(times_dec)), ax=ax,color='r',show_guess=True, plot_data=True, label = 'fit', add_txt=False, ret='ax')
ax.set_xlabel('time (s)')
ax.set_ylabel('peak position (V)')
ax.set_title(data_folder + ' \n Drift is {} $\pm$ {} mV/s '.format(round(b*1.e3,3), round(u_b*1.e3,3)))
fig.savefig(data_folder +'/'+ "drift " + ".png")
plt.show()
plt.close()
def fit_sine(
x,
y,
guess_frq,
guess_amp,
guess_phi,
guess_of,
guess_k,
fixed,
folder,
show_fit=True,
save=True,
):
o = fit.Parameter(guess_of, 'o')
f = fit.Parameter(guess_frq, 'f')
A = fit.Parameter(guess_amp, 'A')
phi = fit.Parameter(guess_phi, 'phi')
k = fit.Parameter(guess_k, 'k')
p0 = [f, A, phi, o, k]
fitfunc_str = ''
fitfunc_str = 'o - A + A*e^(-(kx)**2)*cos(2pi (fx-phi))'
def fitfunc(x):
return (o() -
A()) + A() * np.exp(-(k() * x)**2) * np.cos(2 * np.pi *
(f() * x - phi()))
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
fixed=fixed,
do_print=True,
ret=True)
if show_fit:
plot.plot_fit1d(fit_result, np.linspace(0, x[-1], 201), plot_data=True)
# print "pi pulse = {:.5f} ".format(1/f()/2.) + a.sweep_name
# ax.set_title(a.timestamp+'\n'+a.measurementstring)
if save:
plt.savefig(os.path.join(folder, 'Calibrate_Pi_analysis_fit.png'))
def analyse_Ramsey(folder='', T2 = 3e3, Ampl = -1./3, detuning = 3e-3,hf_N = 2.17e-3, *arg):
timestamp = kw.pop(timestamp, None)
guess_tau = T2
guess_a = 0.5
guess_A = Ampl
guess_hf_N = hf_N
guess_det = detuning
guess_hf_C = hf_C
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
elif folder !='':
folder = toolbox.latest_data('Ramsey')
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
x= a.sweep_pts
y=a.p0
ax = a.plot_result_vs_sweepparam(ret='ax', name='ssro')
params_0, fitfunc_0, fitfunc_str_0 = ramsey.fit_ramsey_14N_fixed_13C_opt(guess_tau, guess_A, guess_a, guess_det, guess_hf_N)
x_0=np.linspace(0,a.sweep_pts[-1],1000)
ax.plot(x_0,fitfunc_0(x_0), 'r--', lw=1)
#fit_xvals=np.linspace(res['x'][0],res['x'][-1],fit_num_points)
fit_result = fit.fit1d(x, y, ramsey.fit_ramsey_14N_fixed_13C_opt,
guess_tau, guess_A, guess_a, guess_det, guess_hf_N, fixed=[],
do_print=True, ret=True)
#fit_result = False
if fit_result != False :
plot.plot_fit1d(fit_result, np.linspace(0,a.sweep_pts[-1],201), ax=ax,
plot_data=False)
plt.savefig(os.path.join(folder, 'electronramsey_analysis.pdf'),
format='pdf')
def analyze_dark_esr(folder, ax=None, **kw):
if ax == None:
fig, ax = plt.subplots(1, 1)
ssro_calib_folder = toolbox.latest_data(
contains='AdwinSSRO_SSROCalibration')
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC(ssro_calib_folder=ssro_calib_folder)
x = a.sweep_pts # convert to MHz
y = a.p0.reshape(-1)[:]
a.plot_result_vs_sweepparam(ret=None, name='ssro', ax=ax)
guess_ctr = x[np.floor(len(x) / 2.)]
fit_result = fit.fit1d(
x,
y,
esr.fit_ESR_gauss,
guess_offset,
guess_amplitude,
guess_width,
guess_ctr,
# (2, guess_splitN),
#(2, guess_splitC),
# (2, guess_splitB),
#(3, guess_splitN),
do_print=True,
ret=True,
fixed=[])
plot.plot_fit1d(fit_result,
np.linspace(min(x), max(x), 1000),
ax=ax,
plot_data=False,
**kw)
ax.set_xlabel('MW frq (GHz)')
ax.set_ylabel(r'fidelity wrt. $|0\rangle$')
ax.set_title(a.timestamp + '\n' + a.measurementstring)
plt.savefig(os.path.join(folder, 'darkesr_analysis.png'), format='png')
return fit_result
def calibrate_epulse_rabi(folder, ax, *args, **kws):
fit_phi = kws.pop('fit_phi', True)
fit_k = kws.pop('fit_k', True)
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
ax.set_title(a.timestamp)
f = fit.Parameter(args[0], 'f')
A = fit.Parameter(args[1], 'A')
x0 = fit.Parameter(0, 'x0')
k = fit.Parameter(0, 'k')
p0 = [f, A]
if fit_phi:
p0.append(x0)
if fit_k:
p0.append(k)
fitfunc_str = '(1 - A) + A * exp(-kx) * cos(2pi f (x - x0))'
def fitfunc(x):
return (1.-A()) + A() * np.exp(-k()*x) * \
np.cos(2*np.pi*(f()*(x - x0())))
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False,
print_info=False)
return fit_result
def fit_linear(folder=None, ax = None, a_guess=1., b_guess=0.):
"""
fit a x + b from Sequence in folder
"""
a, ax, x, y = plot_result(folder,ax)
a = fit.Parameter(a_guess, 'a')
b = fit.Parameter(b_guess, 'b')
fitfunc_str = 'a x + b'
def fitfunc(x):
return a() * x + b()
fit_result = fit.fit1d(x,y, None, p0=[a, b], fitfunc=fitfunc,
fitfunc_str=fitfunc_str, do_print=True, ret=True)
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax,
plot_data=False, print_info=False)
return fit_result
def plot_and_fit_single_peak(self,
g_a1,
g_A1,
g_x01,
g_gamma1,
fixed=[],
**kw):
p0, fitfunc, fitfunc_str = common.fit_lorentz(g_a1, g_A1, g_x01,
g_gamma1)
fit_result = fit.fit1d(self.x,
self.y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=True,
ret=True,
fixed=fixed)
fig, ax = plt.subplots(figsize=(8, 4))
plot.plot_fit1d(fit_result,
np.linspace(self.x[0], self.x[-1], 10 * len(self.x)),
ax=ax,
label='Fit',
show_guess=True,
plot_data=True,
color='red',
data_linestyle='-',
print_info=False)
ax.set_xlim(self.x[0], self.x[-1])
if self.use_timetrace:
ax.set_xlabel("Time (ms)", fontsize=14)
else:
ax.set_xlabel("datapoints", fontsize=14)
ax.set_ylabel("Intensity (a.u.)", fontsize=14)
linewidth = fit_result['params_dict']['gamma1']
u_linewidth = fit_result['error_dict']['gamma1']
plt.savefig(os.path.join(self.indir, self.filename + '_fit.png'))
plt.show()
return linewidth, u_linewidth
def calibrate_epulse_rabi(folder, ax, *args, **kws):
fit_x0 = kws.pop('fit_x0', True)
fit_k = kws.pop('fit_k', True)
double_ro = kws.pop('double_ro', 'False')
guess_x0 = kws.pop('guess_x0', 0)
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
a.plot_results_vs_sweepparam(ax=ax, name='adwindata')
x = a.sweep_pts.reshape(-1)[:]
if double_ro == 'electron':
y = a.p0[:,0]
elif double_ro == 'nitrogen':
y = a.p0[:,1]
else:
y = a.p0.reshape(-1)[:]
f = fit.Parameter(args[0], 'f')
A = fit.Parameter(args[1], 'A')
x0 = fit.Parameter(guess_x0, 'x0')
k = fit.Parameter(0, 'k')
p0 = [f, A]
if fit_x0:
p0.append(x0)
if fit_k:
p0.append(k)
fitfunc_str = '(1 - A) + A * exp(-kx) * cos(2pi f (x - x0))'
def fitfunc(x) :
return (1.-A()) + A() * np.exp(-k()*x) * \
np.cos(2*np.pi*f()*(x - x0()))
fit_result = fit.fit1d(x,y, None, p0=p0, fitfunc=fitfunc,
fitfunc_str=fitfunc_str, do_print=True, ret=True)
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax,
plot_data=False, print_info=False)
return fit_result
def epulse_fidelity(folder, ax, *args):
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name="adwindata")
a.get_electron_ROC()
a.plot_results_vs_sweepparam(ax=ax, name="adwindata")
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
of = fit.Parameter(args[0], "of")
fitfunc_str = "(1-of)"
def fitfunc_fid(x):
return 1.0 - of()
fit_result = fit.fit1d(x, y, None, p0=[of], fitfunc=fitfunc_fid, fitfunc_str=fitfunc_str, do_print=True, ret=True)
plot.plot_fit1d(fit_result, np.linspace(x[0], x[-1], 201), ax=ax, plot_data=False, print_info=False)
return fit_result
def analyze_dark_esr(folder, ax=None, ret=None, **kw):
if ax == None:
fig, ax = plt.subplots(1,1)
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC()
x = a.sweep_pts # convert to MHz
y = a.p0.reshape(-1)[:]
a.plot_result_vs_sweepparam(ret=ret, name='ssro', ax=ax)
ax.set_ylim(0.65,1.05)
guess_ctr = float(raw_input('Center guess?'))
# try fitting
fit_result = fit.fit1d(x, y, esr.fit_ESR_gauss, guess_offset,
guess_amplitude, guess_width, guess_ctr,
# (2, guess_splitN),
# (2, guess_splitC),
# (2, guess_splitB),
(3, guess_splitN),
do_print=True, ret=True, fixed=[4])
plot.plot_fit1d(fit_result, x, ax=ax, plot_data=False, **kw)
ax.set_xlabel('MW frq (GHz)')
ax.set_ylabel(r'fidelity wrt. $|0\rangle$')
ax.set_title(a.timestamp+'\n'+a.measurementstring)
plt.savefig(os.path.join(folder, 'darkesr_analysis.png'),
format='png')
if ret == 'f0':
f0 = fit_result['params_dict']['x0']
u_f0 = fit_result['error_dict']['x0']
ax.text(f0, 0.8, '$f_0$ = ({:.3f} +/- {:.3f})'.format(
(f0-2.8)*1e3, u_f0*1e3), ha='center')
return (f0-2.8)*1e3, u_f0*1e3
def fit_rabi(folder=None,
ax=None,
f_guess=0.9,
A_guess=1,
fit_phi=False,
fit_k=False):
"""
fit (1 - A) + A * exp(-kx) * cos(2pi f (x - x0)) from Sequence in folder
"""
a, ax, x, y = plot_result(folder, ax)
f = fit.Parameter(f_guess, 'f')
A = fit.Parameter(A_guess, 'A')
x0 = fit.Parameter(0, 'x0')
k = fit.Parameter(0, 'k')
p0 = [f, A]
if fit_phi:
p0.append(x0)
if fit_k:
p0.append(k)
fitfunc_str = '(1 - A) + A * exp(-kx) * cos(2pi f (x - x0))'
def fitfunc(x):
return (1.-A()) + A() * np.exp(-k()*x) * \
np.cos(2*np.pi*(f()*(x - x0())))
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False,
print_info=False)
return fit_result
def fit_parabolic(folder=None, ax = None,x0_guess=0., of_guess=0.5, a_guess=1.,**kw):
"""
fit (1-of) + a (x-x0)**2 from Sequence in folder
"""
do_print = kw.pop('do_print', False)
a, ax, x, y = plot_result(folder,ax)
x0 = fit.Parameter(x0_guess, 'x0')
of = fit.Parameter(of_guess, 'of')
a = fit.Parameter(a_guess, 'a')
fitfunc_str = '(1-of) + a (x-x0)**2'
def fitfunc_parabolic(x):
return (1.-of()) + a() * (x-x0())**2
fit_result = fit.fit1d(x,y, None, p0=[of, a, x0], fitfunc=fitfunc_parabolic,
fitfunc_str=fitfunc_str, do_print=do_print, ret=True)
plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax,
plot_data=False, **kw)
return fit_result
def fit_linear(folder, ax, value, *args):
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name="adwindata")
a.get_electron_ROC()
a.plot_results_vs_sweepparam(ax=ax, name="adwindata")
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
a = fit.Parameter(args[0], "a")
b = fit.Parameter(args[0], "b")
fitfunc_str = "a x + b"
def fitfunc(x):
return a() * x + b()
fit_result = fit.fit1d(x, y, None, p0=[a, b], fitfunc=fitfunc, fitfunc_str=fitfunc_str, do_print=True, ret=True)
plot.plot_fit1d(fit_result, np.linspace(x[0], x[-1], 201), ax=ax, plot_data=False, print_info=False)
return fit_result
def calibrate_epulse_amplitude(folder, ax, *args, **kw):
double_ro = kw.pop('double_ro', 'False')
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
a.plot_results_vs_sweepparam(ax=ax, name='adwindata')
x = a.sweep_pts.reshape(-1)[:]
if double_ro == 'electron':
y = a.p0[:, 0]
elif double_ro == 'nitrogen':
y = a.p0[:, 1]
else:
y = a.p0.reshape(-1)[:]
x0 = fit.Parameter(args[0], 'x0')
of = fit.Parameter(args[1], 'of')
a = fit.Parameter(args[2], 'a')
fitfunc_str = '(1-of) + a (x-x0)**2'
def fitfunc_parabolic(x):
return (1. - of()) + a() * (x - x0())**2
fit_result = fit.fit1d(x,
y,
None,
p0=[of, a, x0],
fitfunc=fitfunc_parabolic,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 201),
ax=ax,
plot_data=False,
print_info=False)
return fit_result
def determine_drift(times, peak_positions, data_folder):
times_dec = []
ftr = [3600, 60, 1]
for time in times:
time_dec = sum([
a * b for a, b in zip(
ftr, [int(time[0:2]),
int(time[2:4]),
int(time[4:6])])
])
times_dec = np.append(times_dec, time_dec)
times_dec = times_dec - times_dec[0]
g_a = 2.13
g_b = 0.0007
fixed = []
p0, fitfunc, fitfunc_str = common.fit_line(g_a, g_b)
fit_result = fit.fit1d(times_dec,
peak_positions,
None,
p0=p0,
fitfunc=fitfunc,
do_print=False,
ret=True,
fixed=fixed)
b = fit_result['params_dict']['b']
u_b = fit_result['error_dict']['b']
fig, ax = plt.subplots(figsize=(6, 4.7))
ax = plot.plot_fit1d(fit_result,
np.linspace(times_dec[0], times_dec[-1],
10 * len(times_dec)),
ax=ax,
color='r',
show_guess=True,
plot_data=True,
label='fit',
add_txt=False,
ret='ax')
ax.set_xlabel('time (s)')
ax.set_ylabel('peak position (V)')
ax.set_title(data_folder + ' \n Drift is {} $\pm$ {} mV/s '.format(
round(b * 1.e3, 3), round(u_b * 1.e3, 3)))
fig.savefig(data_folder + '/' + "drift " + ".png")
plt.show()
plt.close()
def perform_phenom_fit(self,key,plotting = False, ax = None): """performs a fit of time trace data to a gaussian function""" if len(key) == 1: data = self.pickle_dict[key[0]] else: data = self.pickle_dict[key[0]][key[1]] evotime = data[timetrace_keylist[0]] fid = data[timetrace_keylist[1]] fid_u = data[timetrace_keylist[2]] ii = self.find_index(key[0]) p0, fitfunc, fitfunc_str = common.fit_gauss(self.fit_offset, 0.43, 0., 8.25) fixed =[1] if self.use_fixed: if int(key[0]) % 2 ==0: ### key[0] is always the number of measurements. fixed = [0,1] else: fixed = [1] fit_result = fit.fit1d(evotime,fid, None, p0=p0, fitfunc=fitfunc, do_print=False, ret=True,fixed=fixed) if plotting: plot.plot_fit1d(fit_result, np.linspace(0,200,201), ax=ax, plot_data=False,color = color_list[ii],add_txt = False, lw = fit_lw) p1 = abs(round(fit_result['params'][3-len(fixed)],3)) p1_u = round(fit_result['error'][3-len(fixed)],3) self.results.append(key[0] + ' : T = ' + str(round(p1,2)) + ' +- ' + str(round(p1_u,2))) # print 'uncertainty ', p1_u; if len(key) == 1: self.pickle_dict[key[0]]['fit'] = fit_result else: self.pickle_dict[key[0]][key[1]]['fit'] = fit_result