def targetstate_analysis(foldernamez,foldernamex, filename='Spin_RO',date='',RO_time=''):
zdata_norm,zdata_corr=sc.plot_feedback(foldernamez, filename='Spin_RO',d=date)
print RO_time
if ((RO_time=='2us') or (RO_time=='4us') or (RO_time=='6us')):
xdata_norm,xdata_corr=sc.plot_feedback(foldernamex, filename='Spin_RO',d=date)
SNfit=sc.fit_sin(xdata_norm['sweep_par'],xdata_corr['FinalRO_SN'],xdata_corr['uFinalRO_SN'])
FSfit=sc.fit_sin(xdata_norm['sweep_par'],xdata_corr['FinalRO_FS'],xdata_corr['uFinalRO_FS'])
allfit=sc.fit_sin(xdata_norm['sweep_par'],xdata_corr['FinalRO_All'],xdata_corr['uFinalRO_All'])
N=(xdata_norm['SN'][2]+xdata_norm['FS'][2])
xsucces= (xdata_norm['SN'][2]*(abs(SNfit['params'][1])*2)+
xdata_norm['FS'][2]*(abs(FSfit['params'][1])*2))/N
uxsucces=np.sqrt(((xdata_norm['SN'][2]*abs(SNfit['error_dict']['a'])*2)/N)**2+
((xdata_norm['FS'][2]*abs(FSfit['error_dict']['a'])*2)/N)**2)
Sx=[abs(FSfit['params'][1])*2,abs(SNfit['params'][1])*2,xsucces]
uSx=[FSfit['error_dict']['a']*2,SNfit['error_dict']['a']*2,uxsucces,0]
i=2
Sz=[zdata_corr['FinalRO_FS'][i],zdata_corr['FinalRO_SN'][i],zdata_corr['FinalRO_Succes'][i]]
else:
Sx=[0,0,0]
uSx=[0,0,0]
i=1
Sz=[1-zdata_corr['FinalRO_FS'][i],1-zdata_corr['FinalRO_SN'][i],1-zdata_corr['FinalRO_Succes'][i]]
if (RO_time=='15us'):
i=2
Sz=[zdata_corr['FinalRO_FS'][i],zdata_corr['FinalRO_SN'][i],zdata_corr['FinalRO_Succes'][i]]
Sy=[0,0,0,0]
fdata={}
fdata['zdata_norm']=zdata_norm
fdata['zdata_corr']=zdata_corr
fdata['res_FS']=[2*(1-Sz[0])-1,Sx[0],Sy[0],0]
fdata['res_SN']=[2*(1-Sz[1])-1,Sx[1],Sy[1],0]
fdata['res_Succes']=[2*(1-Sz[2])-1,Sx[2],Sy[2],0]
fdata['ures_FS']=[zdata_corr['uFinalRO_FS'][i]*2,uSx[0],0,0]
fdata['ures_SN']=[zdata_corr['uFinalRO_SN'][i]*2,uSx[1],0,0]
fdata['ures_Succes']=[zdata_corr['uFinalRO_Succes'][i]*2,uSx[2],0,0]
#fdata['SNfit']=SNfit
#fdata['FSfit']=FSfit
#fdata['allfit']=allfit
meas_strength = calc_meas_strength(50,12,1400)
fdata['meas_strength'] = meas_strength
fdata['res_ideal']=[np.sin(meas_strength*np.pi/2.),np.cos(meas_strength*np.pi/2.),0,0]
fdata['dm'],fdata['f'],fdata['uf'],fdata['ideal']=tls.calc_fidelity_psi(tau,(fdata['res_Succes'][0]+1)/2.,fdata['res_Succes'][1]/2.+0.5,utau,fdata['ures_Succes'][0]/2.,fdata['ures_Succes'][1],th=th,dir=dir)
tls.make_hist(fdata['dm'][0],np.array([[0,0],[0,0]]))
#print 'Fidelity',f,' +-',uf
#print 'Ideal state:', ideal
#print 'uz: ',uzcor,' ux: ',uxcor
np.savez(os.path.join(basepath,name+RO_time),**fdata)
data_norm['FinalRO_FF']=data['FinalRO_FF']/(data['FF']+0.)
data_norm['FinalRO_Succes']=(data['FinalRO_SN']+data['FinalRO_FS'])/(data['SN']+data['FS']+0.)
data_norm['FinalRO_All']=(data['FinalRO_SN']+data['FinalRO_FS']+data['FinalRO_FF'])/(reps+0.)
print data['FS']
for i in np.arange(len(data['SN'])):
z+=data['FinalRO_FS'][i]
zrep+=data['FS'][i]
print zrep
znorm=z/(zrep+0.)
data.close()
phasecor,uphasecor=sc.get_nuclear_ROC(phasenorm,rep,sc.get_latest_data('SSRO',date=d))
zcor, uzcor=sc.get_nuclear_ROC(znorm,zrep,sc.get_latest_data('SSRO',date=zd))
figure1=plt.figure(2)
ax=figure1.add_subplot(111)
ax.errorbar(x,phasenorm,fmt='o',yerr=1/sqrt(rep),color='Crimson')
ax.errorbar(x,phasecor,fmt='o',yerr=uphasecor,color='RoyalBlue')
phase_fit=sc.fit_sin(x,phasecor,uphasecor)
xcor=np.abs(phase_fit['params'][1])+np.abs(phase_fit['params'][2])
uxcor=np.sqrt(phase_fit['error_dict']['A']**2+phase_fit['error_dict']['a']**2)
dm,f,uf,ideal=tls.calc_fidelity_psi(tau,1-zcor,xcor,utau,uzcor,uxcor,th=th,dir=dir)
idr=tls.make_rho(ideal[0]**2,ideal[1]*ideal[0])
print idr
tls.make_hist(dm[0],np.array([[0,0],[0,0]]))
print 'Fidelity',f,' +-',uf
print 'Ideal state:', ideal
print 'uz: ',uzcor,' ux: ',uxcor
def segmented_N_ramsey(nr_of_datapoints=1,folder = r'D:\measuring\data\20130709\123046_Seg_RO__LT2_N_Ramsey_seg_RO_100us_750pW/'):
a= np.load (folder+'Seg_RO-000_segment_number.npz')
seg_nr=a['segment_number']
a.close()
a= np.load (folder+'Seg_RO-000_segment_number.npz')
seg_nr=a['segment_number']
a.close()
c= np.load (folder+'Seg_RO-000_Spin_RO.npz')
SSRO_counts=c['SSRO_counts']
cond_RO_data=c['cond_RO_data']
x=c['sweep_axis']
c.close()
nr_of_datapoints=len(SSRO_counts)
reps=int(sum(seg_nr)/float(nr_of_datapoints))
total_segments=len(seg_nr)
#total_segments=45
plt.figure()
phase=[]
segnr_phase=[]
reconstr_sum=np.zeros(201)
corrected=np.zeros(201)
accum_seg_nr=np.zeros(len(seg_nr))
for i in np.arange(total_segments):
if i in [4,5,6,7,8]:
do_plot=True
print i
else:
do_plot=False
y=cond_RO_data[nr_of_datapoints*i:nr_of_datapoints*(i+1)]
#FIX: This normalization is very rough, we should devide each point with the number of repetitions (get it from segmented_RO_data.npz)
y_norm=y/float(max(y))
dict=sc.fit_sin(x,y,sqrt(max(y)),fixed=[0],fix_param=[1/360.],do_plot=do_plot)
if dict:
phase.append(dict['params'][2])
segnr_phase.append(i)
reconstr_sum=reconstr_sum+dict['fitfunc'](np.linspace(0,360,201))
A=dict['params'][0]
a=dict['params'][1]
phi=dict['params'][2]
f=1/360.
corrected=corrected+fit_func(abs(a),abs(A),f,100,np.linspace(0,360,201))
accum_seg_nr[i]=sum(seg_nr[0:i])
plt.show()
plt.figure()
plt.errorbar(segnr_phase,phase,dict['error_dict']['phi'],fmt='o')
plt.xlabel('Segment number of photon click',fontsize=14)
plt.ylabel('Phase second RF pulse',fontsize=14)
plt.yticks([-90,-45,0,45,90])
plt.xticks([0,25,50,75,100])
#plt.ylim([-180,180])
plt.show()
plt.clf()
plt.show()
plt.figure()
plt.plot(np.linspace(0,360,201),reconstr_sum/float(reps))
plt.plot(np.linspace(0,360,201),corrected/float(reps))
plt.xlabel('Phase',fontsize=14)
plt.ylabel('P(ms=0) reconstructed from separate fits',fontsize=14)
plt.legend(['Total','Corrected for phase shift'],loc=4)
#plt.yticks([0,45,90,135,180])
#plt.xticks([0,10,20,30,40])
#plt.ylim([0.2,0.5])
plt.show()
plt.figure()
plt.plot(np.arange(total_segments-1)+1,seg_nr[0:total_segments-1]/float(sum(seg_nr)),'o')
plt.xlabel('Segment number',fontsize=14)
plt.ylabel('P(click)',fontsize=14)
plt.show()
plt.figure()
plt.plot(np.arange(total_segments-1)+1,accum_seg_nr[0:total_segments-1]/float(sum(seg_nr)),'o')
plt.xlabel('Segment number',fontsize=14)
plt.ylabel('P(click) accummulated',fontsize=14)
plt.show()
print 'Contrast for total:'
print (max(reconstr_sum/float(reps))-min(reconstr_sum/float(reps)))/0.5
print 'Contrast for corrected:'
print (max(corrected/float(reps))-min(corrected/float(reps)))/0.5
print dict['error_dict']['phi']
return SSRO_counts, cond_RO_data, seg_nr
def segmented_N_ramsey(
nr_of_datapoints=1,
folder=r'D:\measuring\data\20130709\123046_Seg_RO__LT2_N_Ramsey_seg_RO_100us_750pW/'
):
a = np.load(folder + 'Seg_RO-000_segment_number.npz')
seg_nr = a['segment_number']
a.close()
a = np.load(folder + 'Seg_RO-000_segment_number.npz')
seg_nr = a['segment_number']
a.close()
c = np.load(folder + 'Seg_RO-000_Spin_RO.npz')
SSRO_counts = c['SSRO_counts']
cond_RO_data = c['cond_RO_data']
x = c['sweep_axis']
c.close()
nr_of_datapoints = len(SSRO_counts)
reps = int(sum(seg_nr) / float(nr_of_datapoints))
total_segments = len(seg_nr)
#total_segments=45
plt.figure()
phase = []
segnr_phase = []
reconstr_sum = np.zeros(201)
corrected = np.zeros(201)
accum_seg_nr = np.zeros(len(seg_nr))
for i in np.arange(total_segments):
if i in [4, 5, 6, 7, 8]:
do_plot = True
print i
else:
do_plot = False
y = cond_RO_data[nr_of_datapoints * i:nr_of_datapoints * (i + 1)]
#FIX: This normalization is very rough, we should devide each point with the number of repetitions (get it from segmented_RO_data.npz)
y_norm = y / float(max(y))
dict = sc.fit_sin(x,
y,
sqrt(max(y)),
fixed=[0],
fix_param=[1 / 360.],
do_plot=do_plot)
if dict:
phase.append(dict['params'][2])
segnr_phase.append(i)
reconstr_sum = reconstr_sum + dict['fitfunc'](np.linspace(
0, 360, 201))
A = dict['params'][0]
a = dict['params'][1]
phi = dict['params'][2]
f = 1 / 360.
corrected = corrected + fit_func(abs(a), abs(A), f, 100,
np.linspace(0, 360, 201))
accum_seg_nr[i] = sum(seg_nr[0:i])
plt.show()
plt.figure()
plt.errorbar(segnr_phase, phase, dict['error_dict']['phi'], fmt='o')
plt.xlabel('Segment number of photon click', fontsize=14)
plt.ylabel('Phase second RF pulse', fontsize=14)
plt.yticks([-90, -45, 0, 45, 90])
plt.xticks([0, 25, 50, 75, 100])
#plt.ylim([-180,180])
plt.show()
plt.clf()
plt.show()
plt.figure()
plt.plot(np.linspace(0, 360, 201), reconstr_sum / float(reps))
plt.plot(np.linspace(0, 360, 201), corrected / float(reps))
plt.xlabel('Phase', fontsize=14)
plt.ylabel('P(ms=0) reconstructed from separate fits', fontsize=14)
plt.legend(['Total', 'Corrected for phase shift'], loc=4)
#plt.yticks([0,45,90,135,180])
#plt.xticks([0,10,20,30,40])
#plt.ylim([0.2,0.5])
plt.show()
plt.figure()
plt.plot(
np.arange(total_segments - 1) + 1,
seg_nr[0:total_segments - 1] / float(sum(seg_nr)), 'o')
plt.xlabel('Segment number', fontsize=14)
plt.ylabel('P(click)', fontsize=14)
plt.show()
plt.figure()
plt.plot(
np.arange(total_segments - 1) + 1,
accum_seg_nr[0:total_segments - 1] / float(sum(seg_nr)), 'o')
plt.xlabel('Segment number', fontsize=14)
plt.ylabel('P(click) accummulated', fontsize=14)
plt.show()
print 'Contrast for total:'
print(max(reconstr_sum / float(reps)) -
min(reconstr_sum / float(reps))) / 0.5
print 'Contrast for corrected:'
print(max(corrected / float(reps)) - min(corrected / float(reps))) / 0.5
print dict['error_dict']['phi']
return SSRO_counts, cond_RO_data, seg_nr
for i in np.arange(len(data['SN'])):
z += data['FinalRO_FS'][i]
zrep += data['FS'][i]
print zrep
znorm = z / (zrep + 0.)
data.close()
phasecor, uphasecor = sc.get_nuclear_ROC(phasenorm, rep,
sc.get_latest_data('SSRO', date=d))
zcor, uzcor = sc.get_nuclear_ROC(znorm, zrep,
sc.get_latest_data('SSRO', date=zd))
figure1 = plt.figure(2)
ax = figure1.add_subplot(111)
ax.errorbar(x, phasenorm, fmt='o', yerr=1 / sqrt(rep), color='Crimson')
ax.errorbar(x, phasecor, fmt='o', yerr=uphasecor, color='RoyalBlue')
phase_fit = sc.fit_sin(x, phasecor, uphasecor)
xcor = np.abs(phase_fit['params'][1]) + np.abs(phase_fit['params'][2])
uxcor = np.sqrt(phase_fit['error_dict']['A']**2 +
phase_fit['error_dict']['a']**2)
dm, f, uf, ideal = tls.calc_fidelity_psi(tau,
1 - zcor,
xcor,
utau,
uzcor,
uxcor,
th=th,
dir=dir)
idr = tls.make_rho(ideal[0]**2, ideal[1] * ideal[0])
print idr
def targetstate_analysis(foldernamez,
foldernamex,
filename='Spin_RO',
date='',
RO_time=''):
zdata_norm, zdata_corr = sc.plot_feedback(foldernamez,
filename='Spin_RO',
d=date)
print RO_time
if ((RO_time == '2us') or (RO_time == '4us') or (RO_time == '6us')):
xdata_norm, xdata_corr = sc.plot_feedback(foldernamex,
filename='Spin_RO',
d=date)
SNfit = sc.fit_sin(xdata_norm['sweep_par'], xdata_corr['FinalRO_SN'],
xdata_corr['uFinalRO_SN'])
FSfit = sc.fit_sin(xdata_norm['sweep_par'], xdata_corr['FinalRO_FS'],
xdata_corr['uFinalRO_FS'])
allfit = sc.fit_sin(xdata_norm['sweep_par'], xdata_corr['FinalRO_All'],
xdata_corr['uFinalRO_All'])
N = (xdata_norm['SN'][2] + xdata_norm['FS'][2])
xsucces = (xdata_norm['SN'][2] *
(abs(SNfit['params'][1]) * 2) + xdata_norm['FS'][2] *
(abs(FSfit['params'][1]) * 2)) / N
uxsucces = np.sqrt(
((xdata_norm['SN'][2] * abs(SNfit['error_dict']['a']) * 2) /
N)**2 +
((xdata_norm['FS'][2] * abs(FSfit['error_dict']['a']) * 2) / N)**2)
Sx = [
abs(FSfit['params'][1]) * 2,
abs(SNfit['params'][1]) * 2, xsucces
]
uSx = [
FSfit['error_dict']['a'] * 2, SNfit['error_dict']['a'] * 2,
uxsucces, 0
]
i = 2
Sz = [
zdata_corr['FinalRO_FS'][i], zdata_corr['FinalRO_SN'][i],
zdata_corr['FinalRO_Succes'][i]
]
else:
Sx = [0, 0, 0]
uSx = [0, 0, 0]
i = 1
Sz = [
1 - zdata_corr['FinalRO_FS'][i], 1 - zdata_corr['FinalRO_SN'][i],
1 - zdata_corr['FinalRO_Succes'][i]
]
if (RO_time == '15us'):
i = 2
Sz = [
zdata_corr['FinalRO_FS'][i], zdata_corr['FinalRO_SN'][i],
zdata_corr['FinalRO_Succes'][i]
]
Sy = [0, 0, 0, 0]
fdata = {}
fdata['zdata_norm'] = zdata_norm
fdata['zdata_corr'] = zdata_corr
fdata['res_FS'] = [2 * (1 - Sz[0]) - 1, Sx[0], Sy[0], 0]
fdata['res_SN'] = [2 * (1 - Sz[1]) - 1, Sx[1], Sy[1], 0]
fdata['res_Succes'] = [2 * (1 - Sz[2]) - 1, Sx[2], Sy[2], 0]
fdata['ures_FS'] = [zdata_corr['uFinalRO_FS'][i] * 2, uSx[0], 0, 0]
fdata['ures_SN'] = [zdata_corr['uFinalRO_SN'][i] * 2, uSx[1], 0, 0]
fdata['ures_Succes'] = [zdata_corr['uFinalRO_Succes'][i] * 2, uSx[2], 0, 0]
#fdata['SNfit']=SNfit
#fdata['FSfit']=FSfit
#fdata['allfit']=allfit
meas_strength = calc_meas_strength(50, 12, 1400)
fdata['meas_strength'] = meas_strength
fdata['res_ideal'] = [
np.sin(meas_strength * np.pi / 2.),
np.cos(meas_strength * np.pi / 2.), 0, 0
]
fdata['dm'], fdata['f'], fdata['uf'], fdata[
'ideal'] = tls.calc_fidelity_psi(tau,
(fdata['res_Succes'][0] + 1) / 2.,
fdata['res_Succes'][1] / 2. + 0.5,
utau,
fdata['ures_Succes'][0] / 2.,
fdata['ures_Succes'][1],
th=th,
dir=dir)
tls.make_hist(fdata['dm'][0], np.array([[0, 0], [0, 0]]))
#print 'Fidelity',f,' +-',uf
#print 'Ideal state:', ideal
#print 'uz: ',uzcor,' ux: ',uxcor
np.savez(os.path.join(basepath, name + RO_time), **fdata)
