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)