def fingerprint(disp_sim_spin = True,n_sim_spins = 5,xrange = [0,20],):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms = 'min')
#msmp1_f from hdf5 file
# msm1 from hdf5 file
# ZFG g_factor from hdf5file
B_Field = 403.555 # use magnet tools Bz = (msp1_f**2 - msm1_f**2)/(4.*ZFS*g_factor)
tau_lst = np.linspace(0,72e-6,10000)
Mt16 = SC.dyn_dec_signal(HF_par,HF_perp,B_Field,16,tau_lst)
FP_signal16 = ((Mt16+1)/2)
## Data location ##
timestamp ='20141016_182539'
ssro_calib_folder = 'D:\\measuring\\data\\20141016\\150451_AdwinSSRO_SSROCalibration_111_1_sil18'
a, folder = load_mult_dat(timestamp, number_of_msmts = 40, ssro_calib_folder=ssro_calib_folder)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(35,5))
ax = a.default_ax(fig)
# ax.set_xlim(15.0,15.5)
ax.set_xlim(xrange)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(xrange[0], xrange[1], (xrange[1]- xrange[0])/10))
ax.set_ylim(-0.05,1.05)
ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.4,label = 'data') #N = 16
if disp_sim_spin == True:
colors = cm.rainbow(np.linspace(0, 1, n_sim_spins))
for tt in range(n_sim_spins):
ax.plot(tau_lst*1e6, FP_signal16[tt,:] ,'-',lw=.8,label = 'spin' + str(tt+1), color = colors[tt])
if False:
tot_signal = np.ones(len(tau_lst))
for tt in range(n_sim_spins):
tot_signal = tot_signal * Mt16[tt,:]
fin_signal = (tot_signal+1)/2.0
ax.plot(tau_lst*1e6, fin_signal,':g',lw=.8,label = 'tot')
plt.legend(loc=4)
print folder
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint.png'),
format='png')
def fingerprint(disp_sim_spin = True,n_sim_spins = 8,xrange = [0,20]):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms = 'min')
#msmp1_f from hdf5 file
# msm1 from hdf5 file
# ZFG g_factor from hdf5file
B_Field = 403.555 # use magnet tools Bz = (msp1_f**2 - msm1_f**2)/(4.*ZFS*g_factor)
tau_lst = np.linspace(0,72e-6,10000)
Mt16 = SC.dyn_dec_signal(HF_par,HF_perp,B_Field,64,tau_lst)
FP_signal16 = ((Mt16+1)/2)
## Data location ##
timestamp ='20141016_234534'
ssro_calib_folder = 'D:\\measuring\\data\\20141016\\150451_AdwinSSRO_SSROCalibration_111_1_sil18'
a, folder = load_mult_dat(timestamp, number_of_msmts = 50, ssro_calib_folder=ssro_calib_folder)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(35,5))
ax = a.default_ax(fig)
# ax.set_xlim(15.0,15.5)
ax.set_xlim(xrange)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(xrange[0], xrange[1], (xrange[1]- xrange[0])/10))
ax.set_ylim(-0.05,1.05)
ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.4,label = 'data') #N = 16
if disp_sim_spin == True:
colors = cm.rainbow(np.linspace(0, 1, n_sim_spins))
for tt in range(n_sim_spins):
ax.plot(tau_lst*1e6, FP_signal16[tt,:] ,'-',lw=.8,label = 'spin' + str(tt+1), color = colors[tt])
if False:
tot_signal = np.ones(len(tau_lst))
for tt in range(n_sim_spins):
tot_signal = tot_signal * Mt16[tt,:]
fin_signal = (tot_signal+1)/2.0
ax.plot(tau_lst*1e6, fin_signal,':g',lw=.8,label = 'tot')
plt.legend(loc=4)
print folder
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint.png'),
format='png')
def fingerprint(disp_sim_spin = True, RO = 'x'):
###################
## Data location ##
###################
if RO == '-x':
timestamp ='20140730_140911' # for the -x msmt
elif RO == 'x':
timestamp ='20140730_134956' # for the +x msmt
timestamp = '20140730_184039'
ssro_calib_folder = 'D:\\measuring\data\\20140730\\115839_AdwinSSRO_SSROCalibration_Hans_sil1'
a, folder = fp_funcs.load_mult_dat(timestamp,
number_of_msmts = 80,
x_axis_step = 0.5,
x_axis_pts_per_msmnt= 51,
ssro_calib_folder=ssro_calib_folder)
#######################
# Add simulated spins #
#######################
if disp_sim_spin == True:
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms = 'min')
B_Field = 304.49
tau_lst = np.linspace(0, 72e-6, 10000)
Mt16 = SC.dyn_dec_signal(HF_par,HF_perp,B_Field,16,tau_lst)
FP_signal16 = ((Mt16+1)/2)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(35,5))
ax = a.default_ax(fig)
ax.set_xlim(0,40)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 0.5))
ax.set_ylim(-0.05,1.05)
ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.4,label = 'data') #N = 16
if disp_sim_spin == True:
colors = cm.rainbow(np.linspace(0, 1, len(HF_par)))
for tt in range(len(HF_par)):
ax.plot(tau_lst*1e6, FP_signal16[tt,:] ,'-',lw=.8,label = 'spin' + str(tt+1), color = colors[tt])
if False:
tot_signal = np.ones(len(tau_lst))
for tt in range(len(HF_par)):
tot_signal = tot_signal * Mt16[tt,:]
fin_signal = (tot_signal+1)/2.0
ax.plot(tau_lst*1e6, fin_signal,':g',lw=.8,label = 'tot')
plt.legend(loc=4)
print folder
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint.png'),
format='png')
def fingerprint(disp_sim_spin=True, xlim=None):
###################
## Data location ##
###################
timestamp = '20140730_145444' # for the -x msmt
timestamp = '20140730_142833' # for the +x msmt
timestamp = '20140730_213811'
ssro_calib_folder = 'D:\\measuring\data\\20140730\\115839_AdwinSSRO_SSROCalibration_Hans_sil1'
a, folder = fp_funcs.load_mult_dat(timestamp,
number_of_msmts=80,
x_axis_step=0.5,
x_axis_pts_per_msmnt=51,
ssro_calib_folder=ssro_calib_folder)
#######################
# Add simulated spins #
#######################
if disp_sim_spin == True:
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms='min')
B_Field = 304.49
tau_lst = np.linspace(0, 72e-6, 10000)
Mt16 = SC.dyn_dec_signal(HF_par, HF_perp, B_Field, 32, tau_lst)
FP_signal16 = ((Mt16 + 1) / 2)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(35, 5))
ax = a.default_ax(fig)
if xlim == None:
ax.set_xlim(0, 40)
else:
ax.set_xlim(xlim)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 0.5))
ax.set_ylim(-0.05, 1.05)
ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.4, label='data') #N = 16
if disp_sim_spin == True:
colors = cm.rainbow(np.linspace(0, 1, len(HF_par)))
for tt in range(len(HF_par)):
ax.plot(tau_lst * 1e6,
FP_signal16[tt, :],
'-',
lw=.8,
label='spin' + str(tt + 1),
color=colors[tt])
if False:
tot_signal = np.ones(len(tau_lst))
for tt in range(len(HF_par)):
tot_signal = tot_signal * Mt16[tt, :]
fin_signal = (tot_signal + 1) / 2.0
ax.plot(tau_lst * 1e6, fin_signal, ':g', lw=.8, label='tot')
plt.legend(loc=4)
print folder
plt.savefig(os.path.join(folder,
str(disp_sim_spin) + 'fingerprint.pdf'),
format='pdf')
plt.savefig(os.path.join(folder,
str(disp_sim_spin) + 'fingerprint.png'),
format='png')
def fingerprint(disp_sim_spin = True,xlim =None):
###################
## Data location ##
###################
timestamp ='20160110_170758'
ssro_calib_folder = 'd:\\measuring\\data\\20160107\\172632_AdwinSSRO_SSROCalibration_Pippin_SIL1'
a, folder = fp_funcs.load_mult_dat(timestamp,
number_of_msmts = 100,
x_axis_step = 0.1,
x_axis_start = 3.5,
x_axis_pts_per_msmnt= 51,
ssro_calib_folder=ssro_calib_folder)
#######################
# Add simulated spins #
#######################
if disp_sim_spin == True:
print 'Starting Simulation'
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms = 'min', NV = 'Pippin')
print 'HF_perp = ' + str(HF_perp)
print 'HF_par = ' + str(HF_par)
B_Field = 417.268
tau_lst = np.linspace(0, 72e-6, 10000)
Mt16 = SC.dyn_dec_signal(HFs_par = HF_par, HFs_orth = HF_perp,
B_field = B_Field, N = 32, tau = tau_lst)
FP_signal16 = ((Mt16+1)/2)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(35,5))
ax = a.default_ax(fig)
if xlim == None:
ax.set_xlim(3.5,13.5)
else:
ax.set_xlim(xlim)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 0.5))
ax.set_ylim(-0.05,1.05)
ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.4,label = 'data') #N = 16
if disp_sim_spin == True:
colors = cm.rainbow(np.linspace(0, 1, len(HF_par)))
for tt in range(len(HF_par)):
ax.plot(tau_lst*1e6, FP_signal16[tt,:] ,'-',lw=.8,label = 'spin' + str(tt+1), color = colors[tt])
if False:
tot_signal = np.ones(len(tau_lst))
for tt in range(len(HF_par)):
tot_signal = tot_signal * Mt16[tt,:]
fin_signal = (tot_signal+1)/2.0
ax.plot(tau_lst*1e6, fin_signal,':g',lw=.8,label = 'tot')
plt.legend(loc=4)
print folder
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint.png'),
format='png')
def fingerprint(disp_sim_spin=True,
n_sim_spins=5,
N=32,
xrange=[0, 20],
carbons=[1, 2, 3, 4, 5]):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms='min',
carbon_spins=carbons)
#msmp1_f from hdf5 file
# msm1 from hdf5 file
# ZFG g_factor from hdf5file
B_Field = 403.555 # use magnet tools Bz = (msp1_f**2 - msm1_f**2)/(4.*ZFS*g_factor)
tau_L = 1 / (1.0705e3 * B_Field) * 1e6
print tau_L
tau_lst = np.linspace(0, 72e-6, 10000)
Mt16 = SC.dyn_dec_signal(HF_par, HF_perp, B_Field, N, tau_lst)
FP_signal16 = ((Mt16 + 1) / 2)
## Data location ##
save_folder_str = 'D:/Dropbox/QEC LT/Decoupling memory/Fingerprints/N=' + str(
N)
if N == 32:
timestamp = '20141016_205842'
ssro_calib_folder = 'D:\\measuring\\data\\20141016\\150451_AdwinSSRO_SSROCalibration_111_1_sil18'
a, folder = load_mult_dat(timestamp,
number_of_msmts=40,
ssro_calib_folder=ssro_calib_folder)
elif N == 64:
timestamp = '20141016_234534'
ssro_calib_folder = 'D:\\measuring\\data\\20141016\\150451_AdwinSSRO_SSROCalibration_111_1_sil18'
a, folder = load_mult_dat(timestamp,
number_of_msmts=50,
ssro_calib_folder=ssro_calib_folder)
else:
raise Exception('Only N=32 or N=64')
print tau_L
carbon_relabel = {}
carbon_relabel['1'] = '2'
carbon_relabel['2'] = '1'
carbon_relabel['3'] = '4'
carbon_relabel['5'] = '3'
carbon_relabel['6'] = '5'
###############
## Plotting ###
###############
first_tau = int(np.ceil(xrange[0] / tau_L)) * tau_L
last_tau = int(np.ceil(xrange[1] / tau_L)) * tau_L
last_tau_nr = int(np.ceil(xrange[1] / tau_L))
fig = a.default_fig(figsize=(16, 4))
ax = a.default_ax(fig)
# ax.set_xlim(15.0,15.5)
ax.set_title('')
ax.set_xlabel(r'$\tau$ $(\mu s)$', fontsize=20)
ax.set_ylabel(r'$\langle$X$\rangle$', fontsize=20)
ax.set_xlim(xrange)
#ax.hlines(0.,xrange[0],xrange[1],linestyles='dotted',linewidth = 1.5)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(np.ceil(xrange[0]), xrange[1], 1))
ax.vlines(first_tau, 0.1, 2, linestyles='dotted', linewidth=1.5)
for tau_value in np.arange(first_tau + tau_L, last_tau, tau_L):
ax.vlines(tau_value, -2, 2, linestyles='dotted', linewidth=1.5)
ax.tick_params(axis='x', which='major', labelsize=15)
ax.tick_params(axis='y', which='major', labelsize=15)
#ax.set_xticklabels(range(1,last_tau_nr+1))
ax.set_ylim(-1.05, 1.05)
handles = []
ax.plot(a.sweep_pts, a.p0 * 2 - 1, '.-k', lw=0.4) #N = 16
if disp_sim_spin == True:
colors = cm.rainbow(np.linspace(0, 1, n_sim_spins))
for tt, carbon_nr in enumerate(carbons):
h = ax.plot(tau_lst * 1e6,
FP_signal16[tt, :] * 2 - 1,
'-',
lw=.8,
label='C' + carbon_relabel[str(carbon_nr)],
color=colors[tt])
handles.append(h)
if False:
tot_signal = np.ones(len(tau_lst))
for tt in range(n_sim_spins):
tot_signal = tot_signal * Mt16[tt, :]
fin_signal = (tot_signal + 1) / 2.0
ax.plot(tau_lst * 1e6, fin_signal, ':g', lw=.8, label='tot')
#handles, labels = ax.get_legend_handles_labels()
#order= [i[0] for i in sorted(enumerate(labels), key=lambda x:x[1])]
#handles = handles[order]
#labels = labels[order]
# sort both labels and handles by labels
#labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))
#ax.legend(handles, labels)
plt.legend(loc='lower left',
frameon=False,
fontsize=15,
columnspacing=0.2,
handletextpad=0.0)
print folder
plt.savefig(save_folder_str + 'fingerprint.pdf',
format='pdf',
bbox_inches='tight')
plt.savefig(save_folder_str + 'fingerprint.png', format='png')
def fingerprint(disp_sim_spin=True, xlim=None):
###################
## Data location ##
###################
timestamp = '20160110_170758'
ssro_calib_folder = 'd:\\measuring\\data\\20160107\\172632_AdwinSSRO_SSROCalibration_Pippin_SIL1'
a, folder = fp_funcs.load_mult_dat(timestamp,
number_of_msmts=100,
x_axis_step=0.1,
x_axis_start=3.5,
x_axis_pts_per_msmnt=51,
ssro_calib_folder=ssro_calib_folder)
#######################
# Add simulated spins #
#######################
if disp_sim_spin == True:
print 'Starting Simulation'
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms='min', NV='Pippin')
print 'HF_perp = ' + str(HF_perp)
print 'HF_par = ' + str(HF_par)
B_Field = 417.268
tau_lst = np.linspace(0, 72e-6, 10000)
Mt16 = SC.dyn_dec_signal(HFs_par=HF_par,
HFs_orth=HF_perp,
B_field=B_Field,
N=32,
tau=tau_lst)
FP_signal16 = ((Mt16 + 1) / 2)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(35, 5))
ax = a.default_ax(fig)
if xlim == None:
ax.set_xlim(3.5, 13.5)
else:
ax.set_xlim(xlim)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 0.5))
ax.set_ylim(-0.05, 1.05)
ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.4, label='data') #N = 16
if disp_sim_spin == True:
colors = cm.rainbow(np.linspace(0, 1, len(HF_par)))
for tt in range(len(HF_par)):
ax.plot(tau_lst * 1e6,
FP_signal16[tt, :],
'-',
lw=.8,
label='spin' + str(tt + 1),
color=colors[tt])
if False:
tot_signal = np.ones(len(tau_lst))
for tt in range(len(HF_par)):
tot_signal = tot_signal * Mt16[tt, :]
fin_signal = (tot_signal + 1) / 2.0
ax.plot(tau_lst * 1e6, fin_signal, ':g', lw=.8, label='tot')
plt.legend(loc=4)
print folder
plt.savefig(os.path.join(folder,
str(disp_sim_spin) + 'fingerprint.pdf'),
format='pdf')
plt.savefig(os.path.join(folder,
str(disp_sim_spin) + 'fingerprint.png'),
format='png')
def fingerprint(disp_sim_spin = True,n_sim_spins = 5,N=32,xrange = [0,20],carbons=[1,2,3,4,5]):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms = 'min',carbon_spins=carbons)
#msmp1_f from hdf5 file
# msm1 from hdf5 file
# ZFG g_factor from hdf5file
B_Field = 403.555 # use magnet tools Bz = (msp1_f**2 - msm1_f**2)/(4.*ZFS*g_factor)
tau_L = 1/(1.0705e3*B_Field)*1e6
print tau_L
tau_lst = np.linspace(0,72e-6,10000)
Mt16 = SC.dyn_dec_signal(HF_par,HF_perp,B_Field,N,tau_lst)
FP_signal16 = ((Mt16+1)/2)
## Data location ##
save_folder_str = 'D:/Dropbox/QEC LT/Decoupling memory/Fingerprints/N=' + str(N)
if N==32:
timestamp ='20141016_205842'
ssro_calib_folder = 'D:\\measuring\\data\\20141016\\150451_AdwinSSRO_SSROCalibration_111_1_sil18'
a, folder = load_mult_dat(timestamp, number_of_msmts = 40, ssro_calib_folder=ssro_calib_folder)
elif N==64:
timestamp ='20141016_234534'
ssro_calib_folder = 'D:\\measuring\\data\\20141016\\150451_AdwinSSRO_SSROCalibration_111_1_sil18'
a, folder = load_mult_dat(timestamp, number_of_msmts = 50, ssro_calib_folder=ssro_calib_folder)
else:
raise Exception('Only N=32 or N=64')
print tau_L
carbon_relabel = {}
carbon_relabel['1'] = '2'
carbon_relabel['2'] = '1'
carbon_relabel['3'] = '4'
carbon_relabel['5'] = '3'
carbon_relabel['6'] = '5'
###############
## Plotting ###
###############
first_tau = int(np.ceil(xrange[0]/tau_L))*tau_L
last_tau = int(np.ceil(xrange[1]/tau_L))*tau_L
last_tau_nr = int(np.ceil(xrange[1]/tau_L))
fig = a.default_fig(figsize=(16,4))
ax = a.default_ax(fig)
# ax.set_xlim(15.0,15.5)
ax.set_title('')
ax.set_xlabel(r'$\tau$ $(\mu s)$',fontsize = 20)
ax.set_ylabel(r'$\langle$X$\rangle$',fontsize = 20)
ax.set_xlim(xrange)
#ax.hlines(0.,xrange[0],xrange[1],linestyles='dotted',linewidth = 1.5)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(np.ceil(xrange[0]), xrange[1], 1))
ax.vlines(first_tau,0.1,2,linestyles='dotted',linewidth=1.5)
for tau_value in np.arange(first_tau+tau_L, last_tau, tau_L):
ax.vlines(tau_value,-2,2,linestyles='dotted',linewidth=1.5)
ax.tick_params(axis='x', which='major', labelsize=15)
ax.tick_params(axis='y', which='major', labelsize=15)
#ax.set_xticklabels(range(1,last_tau_nr+1))
ax.set_ylim(-1.05,1.05)
handles = []
ax.plot(a.sweep_pts, a.p0*2-1, '.-k', lw=0.4) #N = 16
if disp_sim_spin == True:
colors = cm.rainbow(np.linspace(0, 1, n_sim_spins))
for tt, carbon_nr in enumerate(carbons):
h = ax.plot(tau_lst*1e6, FP_signal16[tt,:]*2-1 ,'-',lw=.8,label = 'C' + carbon_relabel[str(carbon_nr)], color = colors[tt])
handles.append(h)
if False:
tot_signal = np.ones(len(tau_lst))
for tt in range(n_sim_spins):
tot_signal = tot_signal * Mt16[tt,:]
fin_signal = (tot_signal+1)/2.0
ax.plot(tau_lst*1e6, fin_signal,':g',lw=.8,label = 'tot')
#handles, labels = ax.get_legend_handles_labels()
#order= [i[0] for i in sorted(enumerate(labels), key=lambda x:x[1])]
#handles = handles[order]
#labels = labels[order]
# sort both labels and handles by labels
#labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))
#ax.legend(handles, labels)
plt.legend(loc='lower left',frameon=False,fontsize=15,columnspacing=0.2,handletextpad=0.0)
print folder
plt.savefig(save_folder_str+'fingerprint.pdf', format='pdf',bbox_inches='tight')
plt.savefig(save_folder_str+'fingerprint.png', format='png')