def succesprob_analysis(folders, folderstwo, RO_times, date, date2): s_heralded = [] s_feedback = [] us_heralded = [] us_feedback = [] for fn in folders: zdata_norm, zdata_corr = sc.plot_feedback(fn, filename='Spin_RO', d=date) s_heralded.append(zdata_norm['SN'][2]) us_heralded.append(zdata_norm['uSN'][2]) s_feedback.append(zdata_norm['SN'][2] + (1 - zdata_norm['SN'][2]) * zdata_norm['FS'][2]) us_feedback.append( np.sqrt(((1 - zdata_norm['FS'][2]) * zdata_norm['uSN'][2])**2 + ((1 - zdata_norm['SN'][2]) * zdata_norm['uFS'][2])**2)) for fn in folderstwo: zdata_norm, zdata_corr = sc.plot_feedback(fn, filename='Spin_RO', d=date2) s_heralded.append(zdata_norm['SN'][1]) us_heralded.append(zdata_norm['uSN'][1]) s_feedback.append(zdata_norm['SN'][1] + (1 - zdata_norm['SN'][1]) * zdata_norm['FS'][1]) us_feedback.append( np.sqrt(((1 - zdata_norm['FS'][2]) * zdata_norm['uSN'][2])**2 + ((1 - zdata_norm['SN'][2]) * zdata_norm['uFS'][2])**2)) sdata = {} sdata['s_heralded'] = s_heralded sdata['us_heralded'] = us_heralded sdata['s_feedback'] = s_feedback sdata['us_feedback'] = us_feedback sdata['RO_times'] = RO_times np.savez(os.path.join(basepath, 'Psucces'), **sdata)
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)
def succesprob_analysis(folders,folderstwo,RO_times,date,date2): s_heralded=[] s_feedback=[] us_heralded=[] us_feedback=[] for fn in folders: zdata_norm,zdata_corr=sc.plot_feedback(fn, filename='Spin_RO',d=date) s_heralded.append(zdata_norm['SN'][2]) us_heralded.append(zdata_norm['uSN'][2]) s_feedback.append(zdata_norm['SN'][2]+(1-zdata_norm['SN'][2])*zdata_norm['FS'][2]) us_feedback.append(np.sqrt(((1-zdata_norm['FS'][2])*zdata_norm['uSN'][2])**2+((1-zdata_norm['SN'][2])*zdata_norm['uFS'][2])**2)) for fn in folderstwo: zdata_norm,zdata_corr=sc.plot_feedback(fn, filename='Spin_RO',d=date2) s_heralded.append(zdata_norm['SN'][1]) us_heralded.append(zdata_norm['uSN'][1]) s_feedback.append(zdata_norm['SN'][1]+(1-zdata_norm['SN'][1])*zdata_norm['FS'][1]) us_feedback.append(np.sqrt(((1-zdata_norm['FS'][2])*zdata_norm['uSN'][2])**2+((1-zdata_norm['SN'][2])*zdata_norm['uFS'][2])**2)) sdata={} sdata['s_heralded']=s_heralded sdata['us_heralded']=us_heralded sdata['s_feedback']=s_feedback sdata['us_feedback']=us_feedback sdata['RO_times']=RO_times np.savez(os.path.join(basepath,'Psucces'),**sdata)
def calc_meas_strength(x, t_zero, t_star): measstren = theta(x, t_zero, t_star) / 90. return measstren def theta(tau, t_zero, t_star): return 90 - 2 * np.arccos(sqrt(S(tau, t_zero, t_star))) * 180. / np.pi def S(tau, t_zero, t_star): return np.exp(-(tau / t_star)**2) * np.cos(np.pi / 4 - (tau + t_zero) * np.pi * .002185 / 2.)**2 data_norm, data_corr = sc.plot_feedback('172738') y_once = data_corr['FinalRO_SN'] uy_once = data_corr['uFinalRO_SN'] y_twice = data_corr['FinalRO_FS'] uy_twice = data_corr['uFinalRO_FF'] tau = data_corr['sweep_par'] x = calc_meas_strength(tau, 12, 2400) x_name = data_corr['sweep_par_name'] figure42 = plt.figure(42) plt.clf() plt.errorbar(x, .9 - y_once,
from analysis.lib.tools import plot from analysis.lib.spin import spin_control as sc from matplotlib import rc from mpl_toolkits.mplot3d import Axes3D def calc_meas_strength(x,t_zero,t_star): measstren=theta(x,t_zero,t_star)/90. return measstren def theta(tau,t_zero,t_star): return 90-2*np.arccos(sqrt(S(tau,t_zero,t_star)))*180./np.pi def S(tau,t_zero,t_star): return np.exp(-(tau/t_star)**2)*np.cos(np.pi/4-(tau+t_zero)*np.pi*.002185/2.)**2 data_norm,data_corr = sc.plot_feedback('172738') y_once=data_corr['FinalRO_SN'] uy_once=data_corr['uFinalRO_SN'] y_twice=data_corr['FinalRO_FS'] uy_twice=data_corr['uFinalRO_FF'] tau=data_corr['sweep_par'] x=calc_meas_strength(tau,12,2400) x_name=data_corr['sweep_par_name'] figure42=plt.figure(42) plt.clf() plt.errorbar(x,.9-y_once,fmt='o', yerr=uy_once,label='Collapse once',color='RoyalBlue') plt.errorbar(x,y_twice,fmt='o', yerr=uy_twice,label='Collapse twice',color='Crimson')
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)