def do_plot_sp(tlength,binlength,clr,t0,y_raw,ax,inset=False):
x_raw=np.arange(len(y_raw))/1000. #us
x=x_raw[0:tlength]
y=y_raw[0:tlength]
x_rb=rb.average(x,order=binlength)
y_rb=rb.average(y,order=binlength)
fit_d_exp_tail = fit.fit1d(x_raw,y_raw,
common.fit_double_exp_decay_with_offset,
0, 1, 1, 1, 0.1,
do_plot = False, ret = True)
ydfit=fit_d_exp_tail['fitdata']
if not inset:
fit_exp_tail = fit.fit1d(x_raw,y_raw,
common.fit_exp_decay_with_offset,
0, 1, 1,
do_plot = False, ret = True)
yfit=fit_exp_tail['fitdata']
ax.plot(x_rb,y_rb,'o',mec =clr['bright'], color="none")
if not inset:
ax.plot(x_raw,yfit,color =clr['medium'])
ax.plot(x_raw,ydfit,color =clr['dark'])
plt.xlabel('time ($\mathrm{\mu}$s)')
if not inset:
plt.xlim(-0.2,tlength/1000.)
plt.ylabel('clicks (kHz)')
else:
plt.xlim(-0.9,tlength/1000.)
plt.ylabel('clicks (MHz)')
return fit_d_exp_tail
def g2_hist(self, range=(-520,520), bins=2*52):
# NOTE bins should be an even number!
self.peaks = {}
self.amplitudes = []
self.normpeaks = {}
for i,d in enumerate(self.deltas):
hy,hx = np.histogram(self.coincidences[i]+self.offset,
bins=bins, range=range)
self.peaks[d] = (hy,hx)
self.fitdt = hx[1]-hx[0]
if d != 0:
A = fit.Parameter(max(hy))
def fitfunc(x):
return A()*np.exp(-abs(x)/self.tau)
fit.fit1d(hx[:-1]+(hx[1]-hx[0])/2., hy, None, fitfunc=fitfunc, p0=[A], do_print=True,
fixed=[])
self.amplitudes.append(A())
# normalize peaks
self.meanamp = np.mean(self.amplitudes)
for d in self.peaks:
self.normpeaks[d] = (self.peaks[d][0]/self.meanamp, self.peaks[d][1])
return True
def g2_hist(self, range=(-520, 520), bins=2 * 52):
# NOTE bins should be an even number!
self.peaks = {}
self.amplitudes = []
self.normpeaks = {}
for i, d in enumerate(self.deltas):
hy, hx = np.histogram(self.coincidences[i] + self.offset, bins=bins, range=range)
self.peaks[d] = (hy, hx)
self.fitdt = hx[1] - hx[0]
if d != 0:
A = fit.Parameter(max(hy))
def fitfunc(x):
return A() * np.exp(-abs(x) / self.tau)
fit.fit1d(hx[:-1] + (hx[1] - hx[0]) / 2.0, hy, None, fitfunc=fitfunc, p0=[A], do_print=True, fixed=[])
self.amplitudes.append(A())
# normalize peaks
self.meanamp = np.mean(self.amplitudes)
for d in self.peaks:
self.normpeaks[d] = (self.peaks[d][0] / self.meanamp, self.peaks[d][1])
return True
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 fitNflips (N, cM, fixAmpl = False, fixOff=False, show_plots = True):
cM_m1 = squeeze(asarray(cM[0, :]))
cM_0 = squeeze(asarray(cM[1, :]))
cM_p1 = squeeze(asarray(cM[2, :]))
A_guess = 1.
off_guess= 0.
p_guess=0.05
fixP = []
if fixAmpl == True:
fixP.append(0)
if fixOff == True:
fixP.append(1)
fit_m1 = fit.fit1d (asarray(N), cM_m1, common.fit_nuclearSpinFlips_1, A_guess, off_guess, p_guess, fixed = fixP, do_plot = show_plots, ret = True)
fit_0 = fit.fit1d (asarray(N), cM_0, common.fit_nuclearSpinFlips_2, A_guess, off_guess, p_guess, fixed = fixP, do_plot = show_plots, ret=True)
fit_p1 = fit.fit1d (asarray(N), cM_p1, common.fit_nuclearSpinFlips_3, A_guess, off_guess, p_guess, fixed = fixP, do_plot = show_plots, ret=True)
results = [fit_m1, fit_0, fit_p1]
print fit_m1[0]['fitdata']
plt.figure()
plt.plot (N, fit_m1[0]['fitdata'], 'b', label = 'mI=-1')
plt.plot (N, fit_0[0]['fitdata'], 'r', label = 'mI=0')
plt.plot (N, fit_p1[0]['fitdata'], 'k', label = 'mI=+1')
plt.plot (N, cM_m1, '>b')
plt.plot (N, cM_0, 'ro')
plt.plot (N, cM_p1, 'kD')
plt.rcParams.update({'font.size': 18})
plt.xlabel ('number of read-out steps', fontsize = 16)
plt.ylabel ('readout corrected nuclear spin fidelity', fontsize = 16)
plt.legend (loc =1, prop={'size':12})
plt.show()
return results
def fitNflips(N, cM, fixAmpl=False, fixOff=False, show_plots=True):
cM_m1 = squeeze(asarray(cM[0, :]))
cM_0 = squeeze(asarray(cM[1, :]))
cM_p1 = squeeze(asarray(cM[2, :]))
A_guess = 1.
off_guess = 0.
p_guess = 0.05
fixP = []
if fixAmpl == True:
fixP.append(0)
if fixOff == True:
fixP.append(1)
fit_m1 = fit.fit1d(asarray(N),
cM_m1,
common.fit_nuclearSpinFlips_1,
A_guess,
off_guess,
p_guess,
fixed=fixP,
do_plot=show_plots,
ret=True)
fit_0 = fit.fit1d(asarray(N),
cM_0,
common.fit_nuclearSpinFlips_2,
A_guess,
off_guess,
p_guess,
fixed=fixP,
do_plot=show_plots,
ret=True)
fit_p1 = fit.fit1d(asarray(N),
cM_p1,
common.fit_nuclearSpinFlips_3,
A_guess,
off_guess,
p_guess,
fixed=fixP,
do_plot=show_plots,
ret=True)
results = [fit_m1, fit_0, fit_p1]
print fit_m1[0]['fitdata']
plt.figure()
plt.plot(N, fit_m1[0]['fitdata'], 'b', label='mI=-1')
plt.plot(N, fit_0[0]['fitdata'], 'r', label='mI=0')
plt.plot(N, fit_p1[0]['fitdata'], 'k', label='mI=+1')
plt.plot(N, cM_m1, '>b')
plt.plot(N, cM_0, 'ro')
plt.plot(N, cM_p1, 'kD')
plt.rcParams.update({'font.size': 18})
plt.xlabel('number of read-out steps', fontsize=16)
plt.ylabel('readout corrected nuclear spin fidelity', fontsize=16)
plt.legend(loc=1, prop={'size': 12})
plt.show()
return results
def fit_two_lorentzian_peaks(V,y,offset,amplitude,x0,gamma,amplitude2,gamma2,splitting,fixed):
"""
Function that fits two lorentzian peaks to noisy data.
Input:
V - the x array of data
y - the y array of data
offset - guess for the fit offset
amplitude - guess for the amplitude of the highest peak
x0 - guess for the location of the highest peak
gamma - guess for the FWHM of the highest peak
amplitude2 - guess for the amplitude of the lowest peak
gamma2 - guess for the FWHM of the lowest peak
splitting - guess for the splitting between the two peaks
fixed - array of the parameters to be fixed in fit function. (keep order; e.g.to fix spliting: fixed = [5])
"""
#First try fitting with the left peak the largest
dx2_1 = splitting
p0_1, fitfunc_1, fitfunc_str_1 = common.fit_2lorentz_splitting(offset, amplitude, x0, gamma, amplitude2, dx2_1, gamma2)
fit_result_1 = fit.fit1d(V,y, None, p0=p0_1, fitfunc=fitfunc_1, do_print=False, ret=True,fixed=fixed)
#Then try fitting with the right peak the largest
dx2_2 = - splitting
p0_2, fitfunc_2, fitfunc_str_2 = common.fit_2lorentz_splitting(offset, amplitude, x0, gamma, amplitude2, dx2_2, gamma2)
fit_result_2 = fit.fit1d(V,y, None, p0=p0_2, fitfunc=fitfunc_2, do_print=False, ret=True,fixed=fixed)
discard_1 = False
discard_2 = False
if fit_result_1['params_dict']['A2']*fit_result_1['params_dict']['gamma2'] < 0:
# print 'discard first fit'
discard_1 = True
if fit_result_2['params_dict']['A2']*fit_result_2['params_dict']['gamma2'] < 0:
# print 'discard second fit'
discard_2 = True
if discard_1 and discard_2:
print 'discarding both Lorentzian fits since they do not give a positive transmission'
fit_failed = True
else:
fit_failed = False
if (fit_result_1['chisq'] < fit_result_2['chisq']) or discard_2:
fit_result = fit_result_1
left_peak_high = True
else:
fit_result = fit_result_2
left_peak_high = False
return fit_result, left_peak_high, fit_failed
def epulse_fidelity(folder, ax, *args):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
of = fit.Parameter(args[0], 'of')
of2 = fit.Parameter(0, 'of2')
of3 = fit.Parameter(0, 'of3')
fitfunc_str = '(1-of)'
def fitfunc_fid(x):
return (1. - of())
fit_result = fit.fit1d(x,
y,
None,
p0=[of],
fixed=[],
fitfunc=fitfunc_fid,
fitfunc_str=fitfunc_str,
do_print=True,
ret=True)
#plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax,
# plot_data=False, print_info=False)
return fit_result
def 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 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_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(self, X, Y):
#Fit parabole: o + A * (x-c)**2 ['g_o', 'g_A', 'g_c']
guess_o = np.min(Y)
guess_A = ((np.max(Y) - np.min(Y)) /
(X[np.argmax(Y)] - X[np.argmin(Y)])**2)
guess_c = X[np.argmin(Y)]
print 'fit guess ', guess_A, guess_c, guess_o
fitres = fit.fit1d(X,
Y,
common.fit_parabole,
guess_o,
guess_A,
guess_c,
len(X),
do_print=False,
ret=True)
if fitres['success'] != False:
p1 = fitres['params_dict']
print p1
fd = fitres['fitfunc'](X)
p = plt.plot(name=self._plot_name)
p.add(X, fd, '-b')
else:
print '\tCould not fit curve!'
return None, None
return p1['A'], p1['c']
def fit_exp(x=[], y=[], plot_fits=False):
guess_b = 10.
guess_c = 0.
b = fit.Parameter(guess_b, 'b')
def fitfunc(x):
return np.exp(-np.abs((x) / b()))
xx = np.linspace(0, x[-1], 1000)
fit_result = fit.fit1d(x,
y,
None,
p0=[b],
fitfunc=fitfunc,
do_print=False,
ret=True)
b_fit = fit_result['params_dict']['b']
err_b = fit_result['error_dict']['b']
if plot_fits:
plt.figure()
plt.plot(x * 1000, y, '.b')
plt.plot(xx * 1000, np.exp(-np.abs((xx) / b_fit)), 'r')
plt.xlabel('time [msec]')
plt.show()
return b_fit, err_b
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_calibration(self, V, freq, fixed):
guess_b = -21.3
guess_c = -0.13
guess_d = 0.48
guess_a = freq[0] +3*guess_b-9*guess_c+27*guess_d
a = fit.Parameter(guess_a, 'a')
b = fit.Parameter(guess_b, 'b')
c = fit.Parameter(guess_c, 'c')
d = fit.Parameter(guess_d, 'd')
p0 = [a, b, c, d]
fitfunc_str = ''
def fitfunc(x):
return a()+b()*x+c()*x**2+d()*x**3
fit_result = fit.fit1d(V,freq, None, p0=p0, fitfunc=fitfunc, fixed=fixed,
do_print=False, ret=True)
if (len(fixed)==2):
a_fit = fit_result['params_dict']['a']
c_fit = fit_result['params_dict']['c']
return a_fit, c_fit
else:
a_fit = fit_result['params_dict']['a']
b_fit = fit_result['params_dict']['b']
c_fit = fit_result['params_dict']['c']
d_fit = fit_result['params_dict']['d']
return a_fit, b_fit, c_fit, d_fit
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 fit_gauss(x, y, **kw):
a = kw.pop('a', 0.1)
A = kw.pop('A', 0.7)
x0 = kw.pop('x0', 0)
T = kw.pop('T', 1000)
n = kw.pop('n', 2)
fixed = kw.pop('fixed', [0, 2, 4])
show_guess = kw.pop('show_guess', False)
do_print = kw.pop('do_print', False)
p0, fitfunc, fitfunc_str = common.fit_general_exponential(a, A, x0, T, n)
if show_guess:
ax.plot(np.linspace(0, x[-1], 201),
fitfunc(np.linspace(0, x[-1], 201)),
':',
lw=2)
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=do_print,
ret=True,
fixed=fixed)
return fit_result
def plot_last_histogram(self):
y, x = self._hist, self._hist_bins[
1:] * self._hist_binsize_ns #np.histogram(self._dts_ns, bins = np.linspace(self.get_roi_min(), self.get_roi_max(),pts))#np.min(dts[dts>0]),np.max(dts),pts))
plotname = self.get_name() + '_histogram'
qt.plot(x, y, name=plotname, clear=True)
f = common.fit_exp_decay_shifted_with_offset
#A * exp(-(x-x0)/tau) + a
#['g_a', 'g_A', 'g_tau', 'g_x0']
args = [1, np.max(y) * 0.1, 12, x[np.argmax(y) + 2.]]
first_fit_bin = int(
np.round(self._plot_extra_range / self._hist_binsize_ns))
xf, yf = x[first_fit_bin:], y[first_fit_bin:]
fitres = fit.fit1d(xf,
yf,
f,
*args,
fixed=[0],
do_print=False,
ret=True,
maxfev=100)
plot_pts = 200
x_p = np.linspace(min(xf), max(xf), plot_pts)
if fitres['success']:
y_p = fitres['fitfunc'](x_p)
print 'fit success'
else:
y_p = f(*args)[1](x_p)
print 'fit failed'
print fitres['params_dict']
qt.plot(x_p, y_p, 'b', name=plotname, clear=False)
self.y = y
self.x = x
def analysis():
dp=os.path.join(meas_folder, date)
amp=[]
phase=[]
j=0
color=['Crimson','RoyalBlue']
ramsey_data={}
for i in datafolders:
tau_guess = 3000
offset_guess=0.5
freq=1e-3
amp=0.5
result=sc.analyse_plot_results_vs_sweepparam(i,yname='P(mI=0)',Nuclcor=False,dataname='Spin_RO',d=date)
resultcor=sc.analyse_plot_results_vs_sweepparam(i,yname='P(mI=0)',Nuclcor=True,dataname='Spin_RO',d=date)
fit_result = fit.fit1d(result['x'], result['y'], ramsey.fit_ramsey_gaussian_decay,
tau_guess, offset_guess, (freq,amp,90),
do_print = True , ret = True)
fit_x=np.linspace(result['x'].min(),result['x'].max(),201)
ramsey_data['fit_x']=fit_x
if label[j]=='mI=-1':
ramsey_data['datamin1']=result['y']
ramsey_data['xmin1']=result['x']
ramsey_data['udatamin1']=result['uy']
ramsey_data['fitmin1']=fit_result['fitfunc'](fit_x)
ramsey_data['datamin1_cor']=resultcor['y']
else:
ramsey_data['datazero']=result['y']
ramsey_data['xzero']=result['x']
ramsey_data['udatazero']=result['uy']
ramsey_data['fitzero']=fit_result['fitfunc'](fit_x)
ramsey_data['datazero1_cor']=resultcor['y']
np.savez(os.path.join(basepath,name),**ramsey_data)
j+=1
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_calibration(V, freq):
guess_b = (freq[-1] - freq[0]) / (V[-1] - V[0])
guess_a = freq[0]
print 'Initial guess: ', guess_a, guess_b
a = fit.Parameter(guess_a, 'a')
b = fit.Parameter(guess_b, 'b')
p0 = [a, b]
fitfunc_str = ''
def fitfunc(x):
return a() + b() * x
fit_result = fit.fit1d(V,
freq,
None,
p0=p0,
fitfunc=fitfunc,
fixed=[],
do_print=False,
ret=True)
a_fit = fit_result['params_dict']['a']
b_fit = fit_result['params_dict']['b']
print 'a= ', a_fit
print 'b=', b_fit
return a_fit, b_fit
def plot_desr(do_save=False):
folder=r'M:\tnw\ns\qt\Diamond\Projects\Magnetometry with adaptive measurements\Data\raw_data\20141014\130500_PulsarDarkESR_FM_Gretel_sil10'
ssro_calib_folder = toolbox.latest_data(contains='AdwinSSRO_SSROCalibration')
print ssro_calib_folder
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results('ssro')
a.get_electron_ROC(ssro_calib_folder=ssro_calib_folder)
x = a.sweep_pts # convert to MHz
y = a.p0.reshape(-1)[:]
# ax.plot(x,y)
fig2 = plt.figure(figsize=(2,1.25))
fig2.clf()
ax2 = fig2.add_subplot(111)
#a.plot_result_vs_sweepparam(ret=True, name='ssro',ax=ax2)
ax2.set_xlim([-3,3])
ax2.set_ylim(0.5,1)
ax2.yaxis.set_ticks([0.5,0.75,1])
#ax2.xaxis.set_ticks([0.5,0.75,1])
guess_x0= 2.845334
guess_offset = 1
guess_Nsplit = 2.18e-3
guess_sigma = 0.0001
guess_A_min1 = 0.4
guess_A_plus1 = 0.25
guess_A_0 = 0.3
A_min1 = fit.Parameter(guess_A_min1, 'A_min1')
A_plus1 = fit.Parameter(guess_A_plus1, 'A_plus1')
A_0 = fit.Parameter(guess_A_0, 'A_0')
o = fit.Parameter(guess_offset, 'o')
x0 = fit.Parameter(guess_x0, 'x0')
sigma = fit.Parameter(guess_sigma, 'sigma')
Nsplit = fit.Parameter(guess_Nsplit, 'Nsplit')
def fitfunc(x):
return o() - np.abs(A_min1())*np.exp(-((x-(x0()-Nsplit()))/sigma())**2) \
- np.abs(A_plus1())*np.exp(-((x-(x0()+Nsplit()))/sigma())**2) \
- np.abs(A_0())*np.exp(-((x-x0())/sigma())**2) \
print x
fit_result = fit.fit1d(x, y, None, p0 = [A_min1, A_plus1, A_0, sigma, o, x0, Nsplit],
fitfunc = fitfunc, do_print=True, ret=True, fixed=[])
print fitfunc(x)
x_fit=np.linspace(a.sweep_pts[0],a.sweep_pts[-1],2001)
y_fit=fit_result['fitfunc'](x_fit)
ax2.plot(1e3*(x_fit-2.845334),y_fit,color='Grey',linewidth=2)
ax2.errorbar(1e3*(a.sweep_pts-2.845334),a.p0,yerr=a.u_p0,color=colors[3],fmt='o')
ax2.set_ylabel('P($m_s =0$)')
ax2.set_xlabel('Pulse detuning (MHz)')
if do_save:
fig2.savefig(r'M:\tnw\ns\qt\Diamond\Projects\Magnetometry with adaptive measurements\Data\analyzed data\desr.pdf', bbox_inches='tight')
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_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 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 optimize_z(z_range=0.4, nr_pts=5, green_power=100e-6):
z_current = mos.get_z()
zs = np.linspace(z_current - z_range / 2., z_current + z_range / 2.,
nr_pts)
SN = np.zeros(nr_pts)
green_aom.set_power(green_power)
for i, z in enumerate(zs):
print i, z
mos.set_z(z)
SN[i] = get_signal_to_noise()
if (msvcrt.kbhit() and msvcrt.getch() == 'q'):
break
d = qt.Data(name='z_optimization_sn')
d.add_coordinate('z (um)')
d.add_value('SN')
d.create_file()
filename = d.get_filepath()[:-4]
d.add_data_point(zs, SN)
d.close_file()
print filename
fitargs = (np.min(SN), np.max(SN), zs[np.argmax(SN)], 0.1)
gaussian_fit_SN = fit.fit1d(zs,
SN,
common.fit_gauss_pos,
*fitargs,
do_print=False,
ret=True)
p0 = gaussian_fit_SN['params_dict']
#qt.plot(zs,SN,name='sn_measurement_z',clear=True)
zp = np.linspace(min(zs), max(zs), 100)
# p_c = qt.plot(zp,gaussian_fit_SN['fitfunc'](zp), name='SN_vs_z')
# p_c.save_png(filename+'.png')
# p_c = qt.Plot2D(d, 'bO-', coorddim=0, name='z_optimization_sn', valdim=1, clear=True)
# p_c.save_png(filename+'.png')
print p0
if gaussian_fit_SN['success']:
print 'z0', p0['x0']
if (p0['x0'] < min(zs)) or (p0['x0'] > max(zs)):
'optimum out of scan range. setting to max point: ', zs[np.argmax(
SN)]
mos.set_z(zs[np.argmax(SN)])
else:
'optimize succeeded. new z:', p0['x0']
mos.set_z(p0['x0'])
#return gaussian_fit_SN
else:
'fit failed, setting back to intial positon', z_current
mos.set_z(z_current)
def analyze_dark_esr_single(x,
y,
guess_ctr,
guess_offset=1,
guess_width=0.2e-3,
guess_amplitude=0.3,
ret='f0',
**kw):
guess_ctr = x[y.argmin()]
print 'guess_ctr = ' + str(guess_ctr)
## I added this to be more robust for SSRO calibration.Please monitor if this is better - Machiel may-2014
guess_offset = np.average(y)
dip_threshold = guess_offset - 1.5 * np.std(y)
print guess_offset
print dip_threshold
print min(y)
if min(y) > dip_threshold:
print 'Could not find dip'
return
fit_result = fit.fit1d(x,
y,
esr.fit_ESR_gauss,
guess_offset,
guess_amplitude,
guess_width,
guess_ctr,
do_print=True,
ret=True,
fixed=[])
f0 = fit_result['params_dict']['x0']
u_f0 = fit_result['error_dict']['x0']
return f0, u_f0
def get_signal_to_noise():
opt_ins = qt.instruments['opt1d_counts']
x, y = opt_ins.run(dimension='x',
scan_length=1.2,
nr_of_points=51,
pixel_time=100,
return_data=True,
gaussian_fit=True)
fitargs = (np.min(y), np.max(y), x[np.argmax(y)], 0.1)
#print fitargs, len(p)
gaussian_fit = fit.fit1d(x,
y,
common.fit_gauss_pos,
*fitargs,
do_print=False,
ret=True)
print gaussian_fit['success']
p0 = gaussian_fit['params_dict']
plot(x, y, name='sn_measurement', clear=True)
xp = linspace(min(x), max(x), 100)
plot(xp, gaussian_fit['fitfunc'](xp), name='sn_measurement')
print 'signal/noise : {:.0f}/{:.0f} = {:.2f}'.format(
p0['A'], p0['a'], p0['A'] / p0['a'])
return gaussian_fit
def analyze_dark_esr_single(x,y,guess_ctr,
guess_offset = 1,
guess_width = 0.2e-3,
guess_amplitude = 0.3,
ret='f0',
**kw):
guess_ctr = x[y.argmin()]
print 'guess_ctr = '+str(guess_ctr)
## I added this to be more robust for SSRO calibration.Please monitor if this is better - Machiel may-2014
guess_offset=np.average(y)
dip_threshold=guess_offset-1.5*np.std(y)
print guess_offset
print dip_threshold
print min(y)
if min(y) > dip_threshold:
print 'Could not find dip'
return
fit_result = fit.fit1d(x, y, esr.fit_ESR_gauss, guess_offset,
guess_amplitude, guess_width, guess_ctr,
do_print=True, ret=True, fixed=[])
f0 = fit_result['params_dict']['x0']
u_f0 = fit_result['error_dict']['x0']
return f0, u_f0
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 epulse_fidelity(folder, ax, *args):
a = sequence.SequenceAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='ssro')
a.get_electron_ROC()
a.plot_result_vs_sweepparam(ax=ax, name='ssro')
x = a.sweep_pts.reshape(-1)[:]
y = a.p0.reshape(-1)[:]
of = fit.Parameter(args[0], 'of')
of2 = fit.Parameter(0, 'of2')
of3 = fit.Parameter(0, 'of3')
fitfunc_str = '(1-of)'
def fitfunc_fid(x) :
return (1.-of())
fit_result = fit.fit1d(x,y, None, p0=[of], fixed = [], fitfunc=fitfunc_fid,
fitfunc_str=fitfunc_str, do_print=True, ret=True)
#plot.plot_fit1d(fit_result, np.linspace(x[0],x[-1],201), ax=ax,
# plot_data=False, print_info=False)
return fit_result
def 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 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_sweep_pump_power(x, y, L=0.042):
guess_b = 1
guess_a = max(y)
a = fit.Parameter(guess_a, 'a')
b = fit.Parameter(guess_b, 'b')
p0 = [a, b]
fitfunc_str = ''
def fitfunc(x):
return a() * np.sin(L * (b() * x)**0.5)**2
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
fixed=[],
do_print=True,
ret=True)
a_fit = fit_result['params_dict']['a']
b_fit = fit_result['params_dict']['b']
print 'a= ', a_fit
print 'b=', b_fit
return a_fit, b_fit
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 analyze_dark_esr(x,y,guess_ctr, guess_splitN,
guess_offset = 1,
guess_width = 0.2e-3,
guess_amplitude = 0.3,
ret='f0',
**kw):
j=0
min_dip_depth = 0.9
print 'j = '+str(j)
print y[21]
k = len(y)
while y[j]>min_dip_depth and j < len(y)-2: #y[j]>0.93*y[j+1]: # such that we account for noise
k = j
j += 1
#j = len(y)-2
if k > len(y)-3:
print 'Could not find dip'
return
else:
guess_ctr = x[k]+ guess_splitN #convert to GHz and go to middle dip
print 'guess_ctr= '+str(guess_ctr)
print 'k'+str(k)
## I added this to be more robust for SSRO calibration.Please monitor if this is better - Machiel may-2014
fit_result = fit.fit1d(x, y, esr.fit_ESR_gauss, guess_offset,
guess_amplitude, guess_width, guess_ctr,
(3, guess_splitN),
do_print=True, ret=True, fixed=[4])
if ret == 'f0':
f0 = fit_result['params_dict']['x0']
u_f0 = fit_result['error_dict']['x0']
return f0, u_f0
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 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 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 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 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 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 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 calibrate(self, steps): # calibration values in uW
rng = np.arange(0,steps)
x = np.zeros(steps,dtype = float)
y = np.zeros(steps,dtype = float)
self.set_power(0)
self._ins_pm.set_wavelength(self._wavelength)
time.sleep(2)
bg = self._ins_pm.get_power()
print 'background power: %.4f uW' % (bg*1e6)
time.sleep(.2)
V_max = self.get_V_max()
V_min = self.get_V_min()
if V_max + V_min < 0: rng=np.flipud(rng)
for a in rng:
x[a] = a*(V_max-V_min)/float(steps-1)+V_min
self.apply_voltage(x[a])
time.sleep(0.5)
y[a] = self._ins_pm.get_power() - bg
print 'measured power at %.2f V: %.4f uW' % \
(x[a], y[a]*1e6)
#x= x*(V_max-V_min)/float(steps-1)+V_min
a, xc, k = np.copysign(np.max(y), V_max + V_min), np.copysign(.1, V_max + V_min), np.copysign(5., V_max + V_min)
fitres = fit.fit1d(x,y, common.fit_AOM_powerdependence,
a, xc, k, do_print=True, ret=True)
fd = np.zeros(len(x))
if type(fitres) != type(False):
p1 = fitres['params_dict']
self.set_cal_a(p1['a'])
self.set_cal_xc(p1['xc'])
self.set_cal_k(p1['k'])
fd = fitres['fitfunc'](x)
else:
print 'could not fit calibration curve!'
dat = qt.Data(name= 'aom_calibration_'+self._name+'_'+\
self._cur_controller)
dat.add_coordinate('Voltage [V]')
dat.add_value('Power [W]')
dat.add_value('fit')
dat.create_file()
plt = qt.Plot2D(dat, 'rO', name='aom calibration', coorddim=0, valdim=1,
clear=True)
plt.add_data(dat, coorddim=0, valdim=2)
dat.add_data_point(x,y,fd)
dat.close_file()
plt.save_png(dat.get_filepath()+'png')
self.save_cfg()
print (self._name+' calibration finished')
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 calibrate(self, steps): # calibration values in uW
rng = np.arange(0,steps)
x = np.zeros(steps,dtype = float)
y = np.zeros(steps,dtype = float)
self.set_power(0)
self._ins_pm.set_wavelength(self._wavelength)
time.sleep(2)
bg = self._ins_pm.get_power()
print 'background power: %.4f uW' % (bg*1e6)
time.sleep(.2)
V_max = self.get_V_max()
V_min = self.get_V_min()
if V_max + V_min < 0: rng=np.flipud(rng)
for a in rng:
x[a] = a*(V_max-V_min)/float(steps-1)+V_min
self.apply_voltage(x[a])
time.sleep(0.5)
y[a] = self._ins_pm.get_power() - bg
print 'measured power at %.2f V: %.4f uW' % \
(x[a], y[a]*1e6)
#x= x*(V_max-V_min)/float(steps-1)+V_min
a, xc, k = np.copysign(np.max(y), V_max + V_min), np.copysign(.1, V_max + V_min), np.copysign(5., V_max + V_min)
fitres = fit.fit1d(x,y, common.fit_AOM_powerdependence,
a, xc, k, do_print=True, ret=True)
fd = np.zeros(len(x))
if type(fitres) != type(False):
p1 = fitres['params_dict']
self.set_cal_a(p1['a'])
self.set_cal_xc(p1['xc'])
self.set_cal_k(p1['k'])
fd = fitres['fitfunc'](x)
else:
print 'could not fit calibration curve!'
dat = qt.Data(name= 'aom_calibration_'+self._name+'_'+\
self._cur_controller)
dat.add_coordinate('Voltage [V]')
dat.add_value('Power [W]')
dat.add_value('fit')
dat.create_file()
plt = qt.Plot2D(dat, 'rO', name='aom calibration', coorddim=0, valdim=1,
clear=True)
plt.add_data(dat, coorddim=0, valdim=2)
dat.add_data_point(x,y,fd)
dat.close_file()
plt.save_png(dat.get_filepath()+'png')
self.save_cfg()
print (self._name+' calibration finished')
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 plot_single_rabi_curve(folder, ax, color, label, tot, utot, tot_mean):
a = sequence.MagnetometrySequenceAnalysis(folder)
RO, uRO = a.get_magnetometry_phase_calibration(
name='adwindata',
ssro_calib_folder=
r'M:\tnw\ns\qt\Diamond\Projects\Magnetometry with adaptive measurements\Data\raw_data\20141127\162130_AdwinSSRO_SSROCalibration_Gretel_sil10'
)
guess_f1 = 20e-3 #in GHz
guess_A1 = 0.5
guess_phi1 = np.pi
guess_tau = 96
guess_a = 0.5
a.sweep_pts = a.sweep_pts
print a.sweep_pts
#ax.set_ylim([0.0,1.05])
amp = (max(a.p0) - min(a.p0)) / 2.
offset = (max(a.p0) + min(a.p0)) / 2.
freq = 1 / 8000.
decay = 10000.
fit_result = fit.fit1d(a.sweep_pts,
a.p0,
rabi.fit_rabi_damped_exp,
freq,
amp,
offset,
decay,
fixed=[],
do_print=False,
ret=True)
A = fit_result['params_dict']['A'] * 2
F = fit_result['params_dict']['f']
eF = fit_result['error_dict']['f']
#print fit_result
print min(a.p0), A
print 'frq', F * 1e6, '+-', eF, 'kHz'
x_fit = np.linspace(a.sweep_pts[0], a.sweep_pts[-1], 501)
y_fit = fit_result['fitfunc'](x_fit)
ax.plot(x_fit * 1e-3, y_fit, '-', color='Grey', linewidth=2)
ax.errorbar(a.sweep_pts * 1e-3,
a.p0,
yerr=a.u_p0,
color=color,
fmt='o',
label=label)
if len(tot) == 0:
tot = (a.p0 - mean(a.p0))
utot = a.u_p0
tot_mean = mean(a.p0)
else:
tot = tot + (a.p0 - mean(a.p0))
utot = utot + a.u_p0
tot_mean = tot_mean + mean(a.p0)
print max(a.u_p0)
return tot, a.sweep_pts, utot, tot_mean, A
def plot_single_ramsey_curve(folder,ax,color,label,fit_carbon):
a=np.loadtxt(folder)
guess_A = 0.5
guess_phi1 = 0*np.pi/2.
guess_phi2 = 0*np.pi/2.
guess_phi3 = 0*np.pi/2.
guess_hf=2.18
guess_tau = 3
guess_a = 0.5
guess_det = 5
guess_C1 = .2
sweep_pts=a[:,0]*1e-3
p0=a[:,2]
u_p0=a[:,3]
#ax.set_ylim([0.0,1.05])
if fit_carbon ==False:
fit_result = fit.fit1d(sweep_pts, p0, ramsey.fit_ramsey_hyperfinelines_fixed,
guess_tau, guess_A,guess_a, guess_det,guess_hf,guess_phi1,guess_phi2,guess_phi3,
#(guess_f3, guess_A3, guess_phi3),
fixed=[],
do_print=True, ret=True)
else:
guess_A=0.5/6.
p=np.pi
guess_phi3=p
guess_phi2=p
guess_phi1=p
fit_result = fit.fit1d(sweep_pts, p0, ramsey.fit_ramsey_14N_fixed_13C_opt,
guess_tau, guess_A,guess_a, guess_det,guess_hf,guess_phi1,guess_phi2,guess_phi3,guess_C1,
#(guess_f3, guess_A3, guess_phi3),
fixed=[],
do_print=True, ret=True)
#ax.errorbar(sweep_pts,p0,yerr=u_p0,mec=color,fmt='o',label=label,markeredgewidth=1,mfc='None')
ax.plot(sweep_pts,p0,'.',mec=color,label=label,markeredgewidth=1,mfc='None')
x_fit=np.linspace(sweep_pts[0],sweep_pts[-1],501)
y_fit=fit_result['fitfunc'](x_fit)
ax.plot(x_fit,y_fit,'-',color='Grey',linewidth=0.75)
ax.set_xlim([0,4])
ax.set_ylim([0,1])
ax.set_yticks([0,0.5,1])
ax.legend()
def fit_double_exponential(self, name='', save=True, log_plot=True, offset=0, **kw):
ret = kw.get('ret', None)
ax = kw.get('ax', None)
indices = kw.pop('indices',range(self.sweep_length))
if ax == None:
fig = self.default_fig(figsize=(6,4))
ax = self.default_ax(fig)
else:
save = False
xx=self.tail_hist_b[:-1]
for i in indices:
yy=self.tail_hist_h[i]+offset*i
if log_plot:
ax.semilogy(xx,yy,'-')
else:
ax.plot(xx,yy)
guess_xo = 50.
guess_tau = 50.
guess_tau2 = 300.
guess_o = 50.
guess_A = 500.
guess_AA = 500.
AA=fit.Parameter(guess_A, 'A')
A=fit.Parameter(guess_AA, 'AA')
o=fit.Parameter(guess_o, 'o')
xo = fit.Parameter(guess_xo, 'xo')
tau = fit.Parameter(guess_tau, 'tau')
tau2 = fit.Parameter(guess_tau2, 'tau2')
p0 = [A, AA, o, xo, tau, tau2]
#print p0
def fitfunc(x):
#return o() + A() *np.exp(-(x-xo())/tau())
return o() + A() * np.exp(-((x-xo())/tau())) + AA()*np.exp(-((x-xo())/tau2()))
fit_result = fit.fit1d(xx, yy, None, p0=p0, fitfunc=fitfunc, fixed=[], do_print=True, ret=True)
x_fit=np.linspace(xx[0],xx[-1],500)
y_fit=fit_result['fitfunc'](x_fit)
ax.plot(x_fit,y_fit,color='k')
ax.set_xlabel('Time after sync [ns]')
ax.set_ylabel('Counts')
if save:
self.save_fig_incremental_filename(fig,'plot_tail_hist_all')
if ret == 'ax':
return ax
if ret == 'fig':
return fig
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 fit_exp(d,yname,xname='RO_times'):
offset_guess=d[yname].max()
amp_guess=offset_guess
decay_guess=10
feedback_fit_result = fit.fit1d(d[xname], d[yname], common.fit_exp_decay_with_offset,
offset_guess, amp_guess,decay_guess,
do_print = False , ret = True)
x=np.linspace(d[xname].min(),d[xname].max(),501)
y=feedback_fit_result['fitfunc'](x)
return x,y
def meas_BW(min_pos, max_pos, steps, name = 'beamwaist'):
#generate list of steps
x_list = numpy.linspace(min_pos, max_pos, steps)
ins_xps = qt.instruments['xps']
ins_fm = qt.instruments['fm']
# create data object
qt.mstart()
qt.msleep(0.2)
d = qt.Data(name='BeamWaist')
d.add_coordinate('displacement (mm)')
d.add_value('power')
d.create_file()
filename=d.get_filepath()[:-4]
plot2d = qt.Plot2D(d, name=name)
stop_scan = False
result = numpy.zeros(steps)
for i,cur_x in enumerate(x_list):
if (msvcrt.kbhit() and (msvcrt.getch() == 'q')): stop_scan=True
ins_xps.set_abs_positionZ(float(-1*cur_x))
qt.msleep(0.05)
result[i] = ins_fm.get_power()
d.add_data_point(cur_x, result[i])
if stop_scan: break
ins_xps.set_abs_positionZ(-1*min_pos)
i_max = result.tolist().index(max(result))
i_min = result.tolist().index(min(result))
print 'result imax is: %s' % result[i_max]
knife_fit = fit.fit1d(x_list, result,fit_knife_simple, result[i_max],
0, 2, result[i_min], do_print=False,ret=True)
if type(knife_fit) != dict:
print 'fit failed!'
else:
print 'best fit params are: A: %.03f x0: %.03f w: %.03f b: %.03f' % (
knife_fit['params'][0], knife_fit['params'][1], knife_fit['params'][2],
knife_fit['params'][3])
plot2d.set_plottitle('Beam Waist: %.03f +- %.03f' % (knife_fit['params'][2], knife_fit['error'][2]))
d.close_file()
plot2d.save_png(filename+'.png')
qt.mend()
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 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 fitDiodeData(x,y):
o = fit.Parameter(0,'offset')
a = fit.Parameter(-0.2, 'amplitude')
tau = fit.Parameter(7, 'tau')
fitfunc_str = 'y(x) = o + a*exp(x/tau)'
def fitfunc(x):
return o() + a() * numpy.exp((x) / (tau()))
fit_result = fit.fit1d(x,y, None, p0=[o,tau,a], fixed=[], fitfunc=fitfunc,
fitfunc_str=fitfunc_str, do_print=True, ret=True)
return fit_result
def optimize_matrix_amplitude(name, Z_matrix, do_fit=True):
amplitude = 2.0 #V
steps = 21 #XXXXXXXXXXXX 13
amps = np.linspace(-amplitude, amplitude, steps)
Z_voltages = dm.voltages_from_matrix(Z_matrix)
cur_voltages = dm.get_cur_voltages()
logging.debug('DM: Starting optimisation scan')
y_NV = np.zeros(steps)
for i, amp in enumerate(amps):
new_voltages = cur_voltages + amp * Z_voltages
dm.set_cur_voltages(new_voltages)
if i == 0:
time.sleep(2. * int_time / 1000.)
y_NV[i] = measure_counts()
#qt.msleep(0.2)
logging.debug('DM: Starting fit')
opt_amp = None
if do_fit:
f = common.fit_gauss
#a + A * exp(-(x-x0)**2/(2*sigma**2))
#['g_a', 'g_A', 'g_x0', 'g_sigma']
args = [100, max(y_NV), 0, 1]
fitres = fit.fit1d(amps,
y_NV,
f,
*args,
fixed=[],
do_print=False,
ret=True)
if fitres['success']:
if fitres['params_dict']['A'] > 0.:
opt_amp = fitres['params_dict']['x0']
max_cnts = fitres['params_dict']['A'] + fitres['params_dict'][
'a']
fp = plot_scan(name, amps, y_NV, fitres['fitfunc'])
if opt_amp == None:
print 'fit failed, going to max point'
opt_amp = amps[np.argmax(y_NV)]
max_cnts = np.max(y_NV)
fp = plot_scan(name, amps, y_NV)
logging.debug('DM: Fitting scan finished')
new_voltages = cur_voltages + opt_amp * Z_voltages
dm.set_cur_voltages(new_voltages)
dm.plot_mirror_surf(True, fp[:-3])
dm.save_mirror_surf(fp[:-4] + '_msurf')
counters.set_is_running(True)
return max_cnts, opt_amp
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
