def plot_sim_vs_Bx(spin_list=['C1'],Bx_list = [0],B_Field = 12, N =2,xlim=[0,100]):
for ii in range(len(spin_list)):
for b in range(len(Bx_list)):
Bx = Bx_list[b]
#print spin_list[ii]
HF_par=[hf[spin_list[ii]]['par'] - hf[spin_list[ii]]['perp']*Bx/B_Field]
HF_perp=[hf[spin_list[ii]]['perp'] + hf[spin_list[ii]]['par']*Bx/B_Field]
tau_lst = np.linspace(xlim[0]*1e-6,xlim[1]*1e-6,960)
tau_lst_us = np.linspace(xlim[0],xlim[1],960)
if ii == 0:
Mt = SC.dyn_dec_signal(HF_par,HF_perp,B_Field,N,tau_lst)
else:
Mt=Mt*SC.dyn_dec_signal(HF_par,HF_perp,B_Field,N,tau_lst)
#if ii == 0:
FP_signal = ((Mt+1)/2)
#else:
# FP_signal = FP_signal*((Mt+1)/2)
#ax.plot(tau_lst*1e6, FP_signal[0,:],'.-',lw=.8,label = 'spin_'+spin_list[ii]+'_Bx_'+str(Bx))
return FP_signal[0,:],tau_lst_us
def plot_sim_vs_Bx(spin_list=['C1'],Bx_list = [-0.7,0,0.7],B_Field = 304.12, N =32):
for ii in range(len(spin_list)):
print spin_list
fig = figure(ii+1,figsize=(10,5))
ax = fig.add_subplot(111)
# ax.title('Vary Bx for Spin '+ spin_list[ii])
start, end = ax.get_xlim()
# ax.xaxis.set_ticks(np.arange(start, end, 0.1))
for b in range(len(Bx_list)):
Bx = Bx_list[b]
print spin_list[ii]
HF_par = [10e3]#[hf[spin_list[ii]]['par'] - hf[spin_list[ii]]['perp']*Bx/B_Field]
HF_perp = [100e3]#[hf[spin_list[ii]]['perp'] + hf[spin_list[ii]]['par']*Bx/B_Field]
print Bx/B_Field
print hf[spin_list[ii]]['par']
print hf[spin_list[ii]]['perp']
print HF_par
print HF_perp
tau_lst = np.linspace(10.e-6,24.5e-6,5000)
Mt = SC.dyn_dec_signal(HF_par,HF_perp,B_Field,N,tau_lst)
FP_signal = ((Mt+1)/2)
ax.plot(tau_lst*1e6, FP_signal[0,:],'-',lw=.8,label = 'spin_'+spin_list[ii]+'_Bx_'+str(Bx))
ax.set_xlabel('tau (us)')
plt.legend(loc=4)
def fingerprint(disp_sim_spin = True,n_sim_spins = 13,xrange = [0,20],):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
HF_par = [hf['C1']['par'],hf['C2']['par'],hf['C3']['par'], hf['C4']['par'], hf['C5']['par'], hf['C6']['par'], hf['C7']['par'], hf['C8']['par'], hf['C9']['par'], hf['C10']['par'], hf['C11']['par'], hf['C12']['par']]
HF_perp = [hf['C1']['perp'],hf['C2']['perp'],hf['C3']['perp'], hf['C4']['perp'], hf['C5']['perp'], hf['C6']['perp'], hf['C7']['perp'], hf['C8']['perp'], hf['C9']['perp'], hf['C10']['perp'], hf['C11']['perp'], hf['C12']['perp']]
#msmp1_f from hdf5 file
# msm1 from hdf5 file
# ZFG g_factor from hdf5file
B_Field = 304.12 # 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 ='20140419_005744'
if os.name =='posix':
ssro_calib_folder = '//Users//Adriaan//Documents//teamdiamond//data//20140419//111949_AdwinSSRO_SSROCalibration_Hans_sil1'
else:
ssro_calib_folder = 'd:\\measuring\\data\\20140419\\111949_AdwinSSRO_SSROCalibration_Hans_sil1'
a, folder = load_mult_dat(timestamp, number_of_msmts = 140, 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(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, 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 = 2,xrange = [0,20],return_data = False):
###################
# 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,32,tau_lst)
FP_signal16 = ((Mt16+1)/2)
## Data location ##
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)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(35,5))
ax = a.default_ax(fig)
ax.set_xlim(4.9,5.1)
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')
if return_data == True:
return a.sweep_pts, a.p0
def fingerprint(disp_sim_spin = True):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
HF_par = [hf['C1']['par'],hf['C2']['par'],hf['C3']['par'], hf['C4']['par'], hf['C5']['par'], hf['C6']['par'], hf['C7']['par'], hf['C8']['par'], hf['C9']['par'], hf['C10']['par'], hf['C11']['par'], hf['C12']['par']]
HF_perp = [hf['C1']['perp'],hf['C2']['perp'],hf['C3']['perp'], hf['C4']['perp'], hf['C5']['perp'], hf['C6']['perp'], hf['C7']['perp'], hf['C8']['perp'], hf['C9']['perp'], hf['C10']['perp'], hf['C11']['perp'], hf['C12']['perp']]
#msmp1_f from hdf5 file
# msm1 from hdf5 file
# ZFG g_factor from hdf5file
B_Field = 304.12 # 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 ##
timestamps = ['20140417_194335', '20140419_140807']
ssro_calib_folders = ['d:\\measuring\\data\\20140419\\111949_AdwinSSRO_SSROCalibration_Hans_sil1',
'd:\\measuring\\data\\20140419\\123556_AdwinSSRO_SSROCalibration_Hans_sil1']
a, folder = load_mult_dat(timestamps, number_of_msmts = [150, 100], ssro_calib_folders=ssro_calib_folders)
###########
# Plotting ###
###########
fig = a.default_fig(figsize=(35,5))
ax = a.default_ax(fig)
# ax.set_xlim(15.0,15.5)
ax.set_xlim(0,20)
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_short.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint_short.png'),
format='png')
def fingerprint(disp_sim_spin = True):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
HF_par = [hf['C1']['par'],hf['C2']['par'],hf['C3']['par'], hf['C4']['par'], hf['C5']['par'], hf['C6']['par'], hf['C7']['par'], hf['C8']['par'], hf['C9']['par'], hf['C10']['par'], hf['C11']['par'], hf['C12']['par']]
HF_perp = [hf['C1']['perp'],hf['C2']['perp'],hf['C3']['perp'], hf['C4']['perp'], hf['C5']['perp'], hf['C6']['perp'], hf['C7']['perp'], hf['C8']['perp'], hf['C9']['perp'], hf['C10']['perp'], hf['C11']['perp'], hf['C12']['perp']]
#msmp1_f from hdf5 file
# msm1 from hdf5 file
# ZFG g_factor from hdf5file
B_Field = 304.12 # 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 ##
timestamps = ['20160108_004236', '20160108_004236']
ssro_calib_folders = ['d:\\measuring\\data\\20160107\\172632_AdwinSSRO_SSROCalibration_Pippin_SIL1',
'd:\\measuring\\data\\20160107\\172632_AdwinSSRO_SSROCalibration_Pippin_SIL1']
a, folder = load_mult_dat(timestamps, number_of_msmts = [100, 100], ssro_calib_folders=ssro_calib_folders)
###########
# Plotting ###
###########
fig = a.default_fig(figsize=(35,5))
ax = a.default_ax(fig)
# ax.set_xlim(15.0,15.5)
ax.set_xlim(0,20)
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_short.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint_short.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_single(disp_sim_spin=True,
n_sim_spins=2,
xrange=[0, 20],
tag='',
step_size=10e-3,
older_than=None,
return_data=False):
###################
# 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, 32, tau_lst)
FP_signal16 = ((Mt16 + 1) / 2)
## Data location ##
print older_than
timestamp, ssro_calib_folder = toolbox.latest_data(contains='AdwinSSRO',
older_than=older_than,
return_timestamp=True)
print ssro_calib_folder
a, folder = load_mult_dat_tag(tag,
older_than,
number_of_msmts=18,
ssro_calib_folder=ssro_calib_folder)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(35, 5))
ax = a.default_ax(fig)
ax.set_xlim(4.9, 5.1)
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)
y = a.p0
ax.plot(a.sweep_pts, y, '.-g', lw=0.4, label='data', marker='o',
ms=3) #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')
lgd = plt.legend(loc=4)
plt.show(block=False)
print folder
plt.savefig(os.path.join(folder,
str(disp_sim_spin) + 'fingerprint.pdf'),
format='pdf',
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
plt.savefig(os.path.join(folder,
str(disp_sim_spin) + 'fingerprint.png'),
format='png',
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
if return_data == True:
return a.sweep_pts, a.p0
def fingerprint_contrast_concatenate(disp_sim_spin=True,
n_sim_spins=2,
pts=51,
start_1=3.0,
start_2=3.0 + 45 * 50 * 10e-3,
step_size=10e-3,
tau_larmor=False,
name='',
xrange=[0, 20],
tag_p='',
tag_n='',
older_than=None,
return_data=False,
do_plot=True,
load_from_data=False,
Nr_of_pulses=None,
save_folder=None,
figsize=(20, 5)):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms='plus')
#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)
t_L = 1 / (1.0705e3 * B_Field) * 1e6
tau_lst = np.linspace(0, 72e-6, 10000)
Mt16 = SC.dyn_dec_signal(HF_par, HF_perp, B_Field, Nr_of_pulses,
tau_lst)
FP_signal16 = ((Mt16 + 1) / 2)
## Data location ##
if load_from_data == False:
ssro_calib_folder = folder = toolbox.latest_data(
contains='AdwinSSRO_SSROCalibration_111_1_sil18',
older_than=older_than,
folder='d:\measuring\data')
a1, folder = load_mult_dat_tag(tag_p + '0',
older_than,
number_of_msmts=50,
pts=pts,
start=start_1,
step_size=step_size,
ssro_calib_folder=ssro_calib_folder)
b1, folder_b1 = load_mult_dat_tag(tag_n + '0',
older_than,
number_of_msmts=50,
pts=pts,
start=start_1,
step_size=step_size,
ssro_calib_folder=ssro_calib_folder)
a2, folder = load_mult_dat_tag(tag_p + '45',
older_than,
number_of_msmts=50,
pts=pts,
start=start_2,
step_size=step_size,
ssro_calib_folder=ssro_calib_folder)
b2, folder_b2 = load_mult_dat_tag(tag_n + '45',
older_than,
number_of_msmts=50,
pts=pts,
start=start_2,
step_size=step_size,
ssro_calib_folder=ssro_calib_folder)
print folder_b2
print folder_b1
###############
## Plotting ###
###############
y = np.concatenate(((a1.p0 - b1.p0), (a2.p0 - b2.p0)))
x = np.concatenate((a1.sweep_pts, a2.sweep_pts))
y_err = np.concatenate(((np.sqrt(a1.u_p0**2 + b1.u_p0**2)),
(np.sqrt(a2.u_p0**2 + b2.u_p0**2))))
data = {}
data['x'] = x
data['y'] = y
data['y_err'] = y_err
print y_err
# pickle.dump(data, open( 'sil18_fingerprint_ms_min_N'+str(Nr_of_pulses)+'.p', 'wb' ) )
np.savetxt(
'sil18_fingerprint_incl_errorbar_ms_plus_N' + str(Nr_of_pulses) +
'.txt', (np.c_[x], y, y_err))
# print 'shapes'
# print np.transpose(x[0:10])
# print np.array(y[0:10])
# x = np.arange(0,10,1)
# y = x**2
# pickle.dump(data, open( 'sil18_fingerprint_ms_plus_N'+str(Nr_of_pulses)+'.p', 'wb' ) )
# np.savetxt('sil18_fingerprint_ms_plus_N'+str(Nr_of_pulses)+'.txt',(np.c_[x],y))
else:
# data = pickle.load( open( 'sil18_fingerprint_ms_plus_N'+str(Nr_of_pulses)+'.p', 'rb' ) )
# x = data['x']
# y = data['y']
x, y, y_err = np.loadtxt('sil18_fingerprint_incl_errorbar_ms_plus_N' +
str(Nr_of_pulses) + '.txt')
print y_err
folder = save_folder
if tau_larmor == True:
x = x / t_L
y = (y + 1) / 2.
y_err = y_err / 2.
folder = save_folder
if do_plot == True:
fig, ax = plt.subplots(figsize=figsize)
ax.set_xlim(4.9, 5.1)
ax.set_xlim(xrange)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(
np.arange(xrange[0], xrange[1] + 0.1,
(xrange[1] - xrange[0]) / 4.))
# ax.set_ylim(-1.05,1.05)
ax.set_ylim(-0.05, 1.05)
ax.yaxis.set_ticks([0, 0.5, 1])
# ax.plot(x, y, '.-b', lw=1,label = 'data',marker = 'o') #N = 16
# ax.plot(x, y, '.-k', lw=0.4,label = 'data') #N = 16
# ax.plot(x, (y+1)/2., '.-b', lw=1,label = 'data', marker = 'o', ms = 8) #N = 16
ax.plot(x,
y,
'.-k',
lw=0.4,
label='data',
marker='o',
ms=3,
markeredgecolor='m') #N = 16
ax.set_xlabel('tau (us)')
if tau_larmor == True:
ax.set_xlabel('tau/tau_larmor')
ax.set_ylabel('Fidelity')
# ax.plot(a1.sweep_pts, (a1.p0-b1.p0), '.-b', lw=0.4,label = 'data') #N = 16
# ax.plot(a2.sweep_pts, (a2.p0-b2.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):
# print tt
if tau_larmor == False:
ax.plot(tau_lst * 1e6,
Mt16[tt, :],
'-',
lw=.8,
label='spin' + str(tt + 1),
color=colors[tt])
if tau_larmor == True:
ax.plot(tau_lst * 1e6 / t_L,
Mt16[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')
# ax.vlines([5.5,6.5],-1.1,1.1,color = '0.5',lw = 1,linestyles = '-')
# plt.axvspan(5.5,6.5, facecolor='c', alpha=0.1)
# lgd = plt.legend(loc=4)
plt.show(block=False)
# ax.vlines([5.5,6.5],-1.1,1.1,color = '0.5',lw = 1,linestyles = '-')
# plt.axvspan(5.5,6.5, facecolor='m', alpha=0.1)
print folder
try:
# plt.savefig(os.path.join(folder, 'contrast_150924.pdf'),
# format='pdf',bbox_inches='tight')
# plt.savefig(os.path.join(folder, 'contrast_150924.png'),
# format='png',bbox_inches='tight')
plt.savefig(os.path.join(folder, name + '.pdf'),
format='pdf',
bbox_inches='tight')
plt.savefig(os.path.join(folder, name + '.png'),
format='png',
bbox_inches='tight')
except:
print 'Figure has not been saved'
if return_data == True:
return x, (y + 1) / 2.
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 fit_fingerprints(spin_dict,
Bx_list,
Bz,
N,
sweep_parameter,
name_contains,
nr_ids=1,
timestamp=None,
data_stamps=None,
ssro_calib_folder='',
**kw):
"""
Overlaps fingerprint DD data with simulations so that spin bath characteristics can be manually fitted.
Inputs:
- spin_dict*: dictionary containing dictionaries representing the coupled C-spins.
Each spin dictionary should contain another dictionary with entries 'par' and 'perp',
representing the parallel and perpendicular hyperfine component (in Hz)
- Bx_list*: list containing values for perpendicular component of static field.
- Bz: parallel component of static field (in G)
- N: number of pulses in XY decoupling scheme
- sweep_parameter: string indicating which simulation should be multiply plotted.
Must be identical to the name of one of the asterisk-marked input parameters.
If 'None': plots all coupled spins for a single Bx value (STILL NEEDS TO BE IMPLEMENTED)
EXAMPLE 1: if sweep_parameter = 'Bx_list', all separate values in Bx_list are simulated & plotted for all spins in spin_dict
EXAMPLE 2: if sweep_parameter = 'spin_dict', each spin in spin_dict is separately simulated & plotted for the given Bx
- name_contains: string containing an element of the file name
- (Optional) Timestamp: to import data from a specific time. Overrules 'name_contains'
- (Optional) data_stamps: dictionary containing info of multiple hdf5 files with data. See function 'get_data_multiple_files' above.
If provided, then data is imported from corresponding timestamps (i.e. 'name_contains' and 'timestamp' are overruled)
- (Optional) ssro_calib_folder: for data from a single file, specify the corresponding ssro_calib_folder. If '', latest calibration is used
- (Optional) kw: plot formatting & saving parameters. Can be one of the following:
-- xlim
-- ylim
-- add_stitching_pts: if True, adds stitching points to fingerprint
"""
# Check errors
if sweep_parameter != 'spin_dict' and sweep_parameter != 'Bx_list' and sweep_parameter != None:
raise Exception('No sweeping parameter selected!')
# Retrieve measurement data
if data_stamps != None:
x, y, yerr, folder, all_stitching_points = get_data_multiple_files(
data_stamps)
elif timestamp != None:
if nr_ids == 1:
x, y, yerr, folder = get_data(name_contains,
input_timestamp=timestamp,
ssro_calib_folder=ssro_calib_folder)
else: # multiple loops in SimpleDecoupling
x, y, yerr, folder, all_stitching_points = get_data_multiple_msmts(
name_contains,
nr_ids,
input_timestamp=timestamp,
ssro_calib_folder=ssro_calib_folder)
else:
if nr_ids == 1:
x, y, yerr, folder, timestamp = get_data(
name_contains,
input_timestamp=timestamp,
ssro_calib_folder=ssro_calib_folder)
else:
x, y, yerr, folder, timestamp, all_stitching_points = get_data_multiple_msmts(
name_contains,
nr_ids,
input_timestamp=timestamp,
ssro_calib_folder=ssro_calib_folder)
# Plot measurement data
if 'figsize' in kw:
figsize = kw.pop('figsize')
fig = plt.figure(figsize=figsize)
else:
fig = plt.figure(figsize=(25, 6))
ax = fig.add_subplot(111)
plt.errorbar(x, y, yerr=yerr, fmt='o', color='RoyalBlue')
ymin, ymax = ax.get_ylim()
# Add stitching points as vertical lines (if they exist)
add_stitching_pts = kw.pop('add_stitching_pts', True)
if len(all_stitching_points) != 0 and add_stitching_pts:
plt.vlines(all_stitching_points,
ymin,
ymax,
linestyles='dashed',
label='stitching_points')
for val in all_stitching_points:
plt.text(val, 1., str(val))
# Set general parameters
spin_names = spin_dict.keys()
##################
## SWEEP NR OF SPINS ##
##################
if sweep_parameter == 'spin_dict':
# Check if only 1 value of Bx exists
if len(Bx_list) > 1:
raise Exception(
"You're sweeping spin_dict has more than 1 value for Bx. Cannot do it."
)
else:
Bx = Bx_list[0]
for s in range(len(spin_dict)):
name = spin_names[s]
label = '%s: par = %s KHz, perp = %s KHz' % (
spin_names[s], spin_dict[name]['par'] * 1e-3,
spin_dict[name]['perp'] * 1e-3)
# Compute hyperfine components
HF_par = [
spin_dict[name]['par'] - spin_dict[name]['perp'] * Bx / Bz
]
HF_perp = [
spin_dict[name]['perp'] + spin_dict[name]['par'] * Bx / Bz
]
# Perform simulation
tau_list = x * 1e-6
Mt = SC.dyn_dec_signal(HF_par, HF_perp, Bz, N, tau_list)
FP_signal = ((Mt + 1) / 2)
# Plot simulation result
ax.plot(x, FP_signal[0, :], '.-', lw=.8, label=label)
##################
## SWEEP Bx ##
##################
elif sweep_parameter == 'Bx_list':
for b in range(len(Bx_list)):
Bx = Bx_list[b]
label = 'Bx = %s G with %s spins' % (Bx, len(spin_dict))
for s in range(len(spin_dict)):
name = spin_names[s]
label = '%s: par = %s KHz, perp = %s KHz' % (
spin_names[s], spin_dict[name]['par'] * 1e-3,
spin_dict[name]['perp'] * 1e-3)
# Compute hyperfine components
HF_par = [
spin_dict[name]['par'] - spin_dict[name]['perp'] * Bx / Bz
]
HF_perp = [
spin_dict[name]['perp'] + spin_dict[name]['par'] * Bx / Bz
]
# Perform simulation
tau_list = x * 1e-6
if s == 0:
Mt = SC.dyn_dec_signal(HF_par, HF_perp, Bz, N, tau_list)
else:
Mt = Mt * SC.dyn_dec_signal(HF_par, HF_perp, Bz, N,
tau_list)
FP_signal = ((Mt + 1) / 2)
ax.plot(x, FP_signal[0, :], '.-', lw=.8, label=label)
##################
## PLOT CURRENT VALUES ##
##################
# NOTE: DOESN'T WORK YET! (30-04-2015)
# elif sweep_parameter == None:
# label = '%s spin(s) '
# Plot formatting
if 'xlim' in kw:
xlim = kw.pop('xlim')
plt.xlim([xlim[0], xlim[1]])
if 'ylim' in kw:
ylim = kw.pop('ylim')
plt.ylim([ylim[0], ylim[1]])
plt.ylabel(r'Signal normalized to 1', fontsize=25)
plt.xlabel('tau (us)', fontsize=25)
plt.tick_params(axis='x', labelsize=25)
plt.tick_params(axis='y', labelsize=25)
plt.title(timestamp)
plt.legend(loc=4)
# Save fit
plt.savefig(os.path.join(folder, 'SimpleDecoupling_Fingerprint.png'))
return
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,x_range = [0,20],n_sim_spins= 13):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
# HF_par = [hf['C1']['par'],hf['C2']['par'],hf['C3']['par'], hf['C4']['par'], hf['C5']['par'], hf['C6']['par'], hf['C7']['par'], hf['C8']['par'], hf['C9']['par'], hf['C10']['par'], hf['C11']['par'], hf['C12']['par'], hf['C13']['par']]
# HF_perp = [hf['C1']['perp'],hf['C2']['perp'],hf['C3']['perp'], hf['C4']['perp'], hf['C5']['perp'], hf['C6']['perp'], hf['C7']['perp'], hf['C8']['perp'], hf['C9']['perp'], hf['C10']['perp'], hf['C11']['perp'], hf['C12']['perp'], hf['C13']['perp']]
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms = 'plus', NV = 'Hans')
#msmp1_f from hdf5 file
# msm1 from hdf5 file
# ZFG g_factor from hdf5file
B_Field = 304.12 # 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,32,tau_lst)
FP_signal16 = ((Mt16+1)/2)
## Data location ##
timestamps =['20140418_185913', '20140419_233953']
if os.name =='posix':
ssro_calib_folders = ['//Users//Adriaan//Documents//teamdiamond//data//20140419//111949_AdwinSSRO_SSROCalibration_Hans_sil1',
'//Users//Adriaan//Documents//teamdiamond//data//20140419//123556_AdwinSSRO_SSROCalibration_Hans_sil1']
else:
ssro_calib_folders = ['d:\\measuring\\data\\20140419\\111949_AdwinSSRO_SSROCalibration_Hans_sil1',
'd:\\measuring\\data\\20140419\\123556_AdwinSSRO_SSROCalibration_Hans_sil1']
a, folder = load_mult_dat(timestamps = timestamps, number_of_msmts = [90,90], ssro_calib_folders =ssro_calib_folders)
############
## Plotting ###
############
fig = a.default_fig(figsize=(20,5))
ax = a.default_ax(fig)
ax.set_xlim(x_range)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 2))
# ax.set_xlim(xrange)
ax.set_ylim(-0.05,1.05)
ax.yaxis.set_ticks([0,0.5,1])
# ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.4,label = 'data') #N = 16
ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.5,label = 'data')
colormap = plt.cm.rainbow
import matplotlib as mpl; reload(mpl)
mpl.rcParams['pdf.fonttype'] = 42
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 = colormap(tt/float(n_sim_spins)))
# plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, n_sim_spins)])
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')
ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.5) #N = 16
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
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')
plt.show(block = False)
def fingerprint(disp_sim_spin=True, x_range=[12.3, 21.5], n_sim_spins=13):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
# HF_par = [hf['C1']['par'],hf['C2']['par'],hf['C3']['par'], hf['C4']['par'], hf['C5']['par'], hf['C6']['par'], hf['C7']['par'], hf['C8']['par'], hf['C9']['par'], hf['C10']['par'], hf['C11']['par'], hf['C12']['par'], hf['C13']['par']]
# HF_perp = [hf['C1']['perp'],hf['C2']['perp'],hf['C3']['perp'], hf['C4']['perp'], hf['C5']['perp'], hf['C6']['perp'], hf['C7']['perp'], hf['C8']['perp'], hf['C9']['perp'], hf['C10']['perp'], hf['C11']['perp'], hf['C12']['perp'], hf['C13']['perp']]
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms='plus', NV='Hans')
#msmp1_f from hdf5 file
# msm1 from hdf5 file
# ZFG g_factor from hdf5file
B_Field = 304.12 # 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, 32, tau_lst)
FP_signal16 = ((Mt16 + 1) / 2)
## Data location ##
timestamps = ['20140418_185913', '20140419_233953']
if os.name == 'posix':
ssro_calib_folders = [
'//Users//Adriaan//Documents//teamdiamond//data//20140419//111949_AdwinSSRO_SSROCalibration_Hans_sil1',
'//Users//Adriaan//Documents//teamdiamond//data//20140419//123556_AdwinSSRO_SSROCalibration_Hans_sil1'
]
else:
ssro_calib_folders = [
'd:\\measuring\\data\\20140419\\111949_AdwinSSRO_SSROCalibration_Hans_sil1',
'd:\\measuring\\data\\20140419\\123556_AdwinSSRO_SSROCalibration_Hans_sil1'
]
a, folder = load_mult_dat(timestamps=timestamps,
number_of_msmts=[90, 90],
ssro_calib_folders=ssro_calib_folders)
############
## Plotting ###
############
fig = a.default_fig(figsize=(15, 5))
ax = a.default_ax(fig)
ax.set_xlim(x_range)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(14, 22, 2))
# ax.set_xlim(xrange)
ax.set_ylim(-0.05, 1.05)
ax.yaxis.set_ticks([0, 0.5, 1])
# ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.4,label = 'data') #N = 16
ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.5, label='data')
colormap = plt.cm.rainbow
import matplotlib as mpl
reload(mpl)
mpl.rcParams['pdf.fonttype'] = 42
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=colormap(tt / float(n_sim_spins)))
# plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, n_sim_spins)])
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')
ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.5) #N = 16
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
folder = r'D:\measuring\data\Analyzed figures\Fingerprints'
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')
plt.show(block=False)
def plot_all(disp_sim_spin=True,
xrange=[2.5, 52.5],
n_sim_spins=8,
load_data=True):
if load_data == False:
data = {}
data['x_64'], data['y_64'] = fingerprint_contrast_concatenate(
disp_sim_spin=True,
n_sim_spins=0,
pts=51,
start_1=3.0,
start_2=3 + 45 * 50 * 4e-3,
step_size=4e-3,
xrange=[2.5, 52.5],
tag_p='Hermite_Fingerprint_msp1_111_1_sil18_64-x',
tag_n='Hermite_Fingerprint_msp1_111_1_sil18_64x',
older_than='20150726090000',
return_data=True,
do_plot=False)
data['x_128'], data['y_128'] = fingerprint_contrast_concatenate(
disp_sim_spin=True,
n_sim_spins=0,
pts=21,
start_1=3.0,
start_2=3 + 45 * 20 * 4e-3,
step_size=4e-3,
xrange=[2.5, 52.5],
tag_p='Hermite_Fingerprint_msp1_111_1_sil18_128-x',
tag_n='Hermite_Fingerprint_msp1_111_1_sil18_128x',
older_than='20150726090000',
return_data=True,
do_plot=False)
data['x_4'], data['y_4'] = fingerprint_contrast_concatenate(
disp_sim_spin=True,
n_sim_spins=0,
pts=51,
start_1=3.0,
start_2=3 + 45 * 50 * 10e-3,
step_size=10e-3,
xrange=[2.5, 52.5],
tag_p='Hermite_Fingerprint_msp1_111_1_sil18_4-x',
tag_n='Hermite_Fingerprint_msp1_111_1_sil18_4x',
older_than='20150726090000',
return_data=True,
do_plot=False)
data['x_8'], data['y_8'] = fingerprint_contrast_concatenate(
disp_sim_spin=True,
n_sim_spins=0,
pts=51,
start_1=3.0,
start_2=3 + 45 * 50 * 10e-3,
step_size=10e-3,
xrange=[2.5, 52.5],
tag_p='Hermite_Fingerprint_msp1_111_1_sil18_8-x',
tag_n='Hermite_Fingerprint_msp1_111_1_sil18_8x',
older_than='20150726090000',
return_data=True,
do_plot=False)
data['x_16'], data['y_16'] = fingerprint_contrast_concatenate(
disp_sim_spin=True,
n_sim_spins=0,
pts=51,
start_1=3.0,
start_2=3 + 45 * 50 * 10e-3,
step_size=10e-3,
xrange=[2.5, 52.5],
tag_p='Hermite_Fingerprint_msp1_111_1_sil18_16-x',
tag_n='Hermite_Fingerprint_msp1_111_1_sil18_16x',
older_than='20150726090000',
return_data=True,
do_plot=False)
data['x_32'], data['y_32'] = fingerprint_contrast_concatenate(
disp_sim_spin=True,
n_sim_spins=0,
pts=51,
start_1=3.0,
start_2=3 + 45 * 50 * 10e-3,
step_size=10e-3,
xrange=[2.5, 52.5],
tag_p='Hermite_Fingerprint_msp1_111_1_sil18_32-x',
tag_n='Hermite_Fingerprint_msp1_111_1_sil18_32x',
older_than='20150726090000',
return_data=True,
do_plot=False)
pickle.dump(data, open("fingerprinting_msp1.p", "wb"))
else:
data = pickle.load(open("fingerprinting_msp1.p", "rb"))
###############
## Plotting ###
###############
xrange = xrange
fig = plt.figure(figsize=(10, 35))
ax4 = plt.subplot(511)
ax8 = plt.subplot(512, sharex=ax4)
ax16 = plt.subplot(513, sharex=ax4)
ax32 = plt.subplot(514, sharex=ax4)
ax64 = plt.subplot(515, sharex=ax4)
for ax in [ax4, ax8, ax16, ax32, ax64]:
ax.set_xlim(xrange)
start, end = ax.get_xlim()
# ax.xaxis.set_ticks()
ax.set_ylim(-0.05, 1.05)
ax64.xaxis.set_ticks(
np.arange(xrange[0], xrange[1], (xrange[1] - xrange[0]) / 10.))
ax4.plot(data['x_4'],
data['y_4'],
'.-k',
lw=0.4,
marker='o',
ms=0.5,
label='data') #N = 16
ax8.plot(data['x_8'],
data['y_8'],
'.-k',
lw=0.4,
marker='o',
ms=0.5,
label='data8') #N = 16
ax16.plot(data['x_16'],
data['y_16'],
'.-k',
lw=0.4,
marker='o',
ms=0.5,
label='data16') #N = 16
ax32.plot(data['x_32'],
data['y_32'],
'.-k',
lw=0.4,
marker='o',
ms=0.5,
label='data32') #N = 16
ax64.plot(data['x_64'],
data['y_64'],
'.-k',
lw=0.4,
marker='o',
ms=0.5,
label='data64') #N = 16
if disp_sim_spin == True:
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms='plus')
B_Field = 403.555
tau_lst = np.linspace(0, 72e-6, 10000)
colors = cm.rainbow(np.linspace(0, 1, n_sim_spins))
for tt in range(n_sim_spins):
Mt4 = SC.dyn_dec_signal(HF_par, HF_perp, B_Field, 4, tau_lst)
FP_signal4 = ((Mt4 + 1) / 2)
ax4.plot(tau_lst * 1e6,
FP_signal4[tt, :],
'-',
lw=.8,
label='spin' + str(tt + 1),
color=colors[tt])
ax4.set_title('N = 4')
Mt8 = SC.dyn_dec_signal(HF_par, HF_perp, B_Field, 8, tau_lst)
FP_signal8 = ((Mt8 + 1) / 2)
ax8.plot(tau_lst * 1e6,
FP_signal8[tt, :],
'-',
lw=.8,
label='spin' + str(tt + 1),
color=colors[tt])
ax8.set_title('N = 8')
Mt16 = SC.dyn_dec_signal(HF_par, HF_perp, B_Field, 16, tau_lst)
FP_signal16 = ((Mt16 + 1) / 2)
ax16.plot(tau_lst * 1e6,
FP_signal16[tt, :],
'-',
lw=.8,
label='spin' + str(tt + 1),
color=colors[tt])
ax16.set_title('N = 16')
Mt32 = SC.dyn_dec_signal(HF_par, HF_perp, B_Field, 32, tau_lst)
FP_signal32 = ((Mt32 + 1) / 2)
ax32.plot(tau_lst * 1e6,
FP_signal32[tt, :],
'-',
lw=.8,
label='spin' + str(tt + 1),
color=colors[tt])
ax32.set_title('N = 32')
Mt64 = SC.dyn_dec_signal(HF_par, HF_perp, B_Field, 64, tau_lst)
FP_signal64 = ((Mt64 + 1) / 2)
ax64.plot(tau_lst * 1e6,
FP_signal64[tt, :],
'-',
lw=.8,
label='spin' + str(tt + 1),
color=colors[tt])
ax64.set_title('N = 64')
plt.xlabel('tau (us)')
plt.setp(ax4.get_xticklabels(), visible=False)
plt.setp(ax8.get_xticklabels(), visible=False)
plt.setp(ax16.get_xticklabels(), visible=False)
plt.setp(ax32.get_xticklabels(), visible=False)
lgd = ax4.legend(bbox_to_anchor=(1.01, -0.5),
loc='lower left',
borderaxespad=0.)
# lgd = plt.legend(loc=4)
plt.show(block=False)
folder = r'D:\measuring\data\LT2_Data\Fingerprinting'
plt.savefig(os.path.join(folder,
str(disp_sim_spin) + 'fingerprint_all_msp1.pdf'),
format='pdf',
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
plt.savefig(os.path.join(folder,
str(disp_sim_spin) + 'fingerprint_all_msp1.png'),
format='png',
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
def fingerprint_contrast(disp_sim_spin=True,
n_sim_spins=2,
pts=51,
xrange=[0, 20],
tag_p='',
tag_n='',
older_than=None,
return_data=False):
###################
# 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, 32, tau_lst)
FP_signal16 = ((Mt16 + 1) / 2)
## Data location ##
ssro_calib_folder = folder = toolbox.latest_data(
contains='AdwinSSRO_SSROCalibration_111_1_sil18',
older_than=older_than,
folder='d:\measuring\data')
a, folder = load_mult_dat_contrast(tag_p,
older_than,
number_of_msmts=50,
pts=pts,
ssro_calib_folder=ssro_calib_folder)
b, folder_b = load_mult_dat_contrast(tag_n,
older_than,
number_of_msmts=50,
pts=pts,
ssro_calib_folder=ssro_calib_folder)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(35, 5))
ax = a.default_ax(fig)
ax.set_xlim(4.9, 5.1)
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(-1.05, 1.05)
y = (a.p0 - b.p0)
print a.sweep_pts
ax.plot(a.sweep_pts, y, '.-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)
plt.show(block=False)
print folder
plt.savefig(os.path.join(folder,
str(disp_sim_spin) + 'fingerprint_contrast.pdf'),
format='pdf')
plt.savefig(os.path.join(folder,
str(disp_sim_spin) + 'fingerprint_contrast.png'),
format='png')
if return_data == True:
return a.sweep_pts, (y + 1) / 2.
def fingerprint_compare(disp_sim_spin = True):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
HF_par = [30e3, 27e3,-62.5e3]
HF_orth =[80e3,28.5e3,132e3]
#msmp1_f from hdf5 file
# msm1 from hdf5 file
# ZFG g_factor from hdf5file
B_Field = 304.12 # use magnet tools Bz = (msp1_f**2 - msm1_f**2)/(4.*ZFS*g_factor)
tau_lst = np.linspace(0,30e-6,10000)
Mt16 = SC.dyn_dec_signal(HF_par,HF_orth,B_Field,16,tau_lst)
FP_signal16 = ((Mt16+1)/2)
Mt32 = SC.dyn_dec_signal(HF_par,HF_orth,B_Field,32,tau_lst)
FP_signal32 = ((Mt32+1)/2)
## Data location ##
timestamps_16 = ['20140407_223450', '20140407_223801', '20140407_224119',
'20140407_224446', '20140407_231158','20140407_225215', '20140407_225614',
'20140407_230023', '20140407_231645', '20140407_232118', '20140407_232603',
'20140407_233057', '20140407_233605', '20140407_234654', '20140408_111324',
'20140408_111725', '20140408_112126', '20140408_112529', '20140408_112930',
'20140408_113614', '20140408_114015', '20140408_114416', '20140408_114818',
'20140408_115220', '20140408_115622', '20140408_120024', '20140408_120426',
'20140408_120825', '20140408_130753']#,
timestamps_32 = ['20140417_110229','20140417_110529','20140417_110827','20140417_111123','20140417_111421','20140417_111716',
'20140417_112017','20140417_112315','20140417_112613','20140417_112914','20140417_113214','20140417_113517',
'20140417_113817','20140417_114122','20140417_114424','20140417_114727','20140417_115034','20140417_115338',
'20140417_115644','20140417_115953','20140417_120300','20140417_120607','20140417_120915','20140417_121224',
'20140417_121535','20140417_121847','20140417_122159','20140417_122511','20140417_122511','20140417_122824',
'20140417_123137','20140417_123453','20140417_123808','20140417_124125','20140417_124444','20140417_124803',
'20140417_125124','20140417_125444','20140417_125807','20140417_130129'] #with 32 pulses
a = load_mult_dat(timestamps_16)
b = load_mult_dat(timestamps_32)
############
## Plotting ###
############
# N = 16
fig = a.default_fig(figsize=(18,4))
ax = a.default_ax(fig)
ax.set_xlim(0,23)
ax.set_ylim(-0.05,1.05)
ax.plot(a.sweep_pts, a.p0, '.-b', lw=1,label = 'data') #N = 16
ax.plot(tau_lst*1e6, FP_signal16[0,:] ,'--',lw=.5,label = 'spin1')
ax.plot(tau_lst*1e6, FP_signal16[1,:] ,'--',lw=.5,label = 'spin2')
ax.plot(tau_lst*1e6, FP_signal16[2,:] ,'--',lw=.5,label = 'spin3')
plt.legend(loc=4)
fig = b.default_fig(figsize=(18,4))
ax = b.default_ax(fig)
ax.set_xlim(0,23)
ax.set_ylim(-0.05,1.05)
ax.plot(b.sweep_pts, b.p0, '.-b', lw=1,label = 'data' )
ax.plot(tau_lst*1e6, FP_signal32[0,:] ,'--',lw=.5,label = 'spin1')
ax.plot(tau_lst*1e6, FP_signal32[1,:] ,'--',lw=.5,label = 'spin2')
ax.plot(tau_lst*1e6, FP_signal32[2,:] ,'--',lw=.5,label = 'spin3')
plt.legend(loc=4)
fig = b.default_fig(figsize=(18,4))
ax = b.default_ax(fig)
ax.set_xlim(0,23)
ax.set_ylim(-0.05,1.05)
ax.plot(a.sweep_pts, a.p0, '.-b', lw=1,label = 'N=16')
ax.plot(b.sweep_pts, b.p0, '.-r', lw=1,label = 'N=32')
plt.legend(loc=4)
def plot_all(disp_sim_spin = True,xrange = [2.5,52.5], n_sim_spins = 8,load_data = True):
if load_data == False:
data = {}
data['x_64'] , data['y_64'] = fingerprint_contrast_concatenate(disp_sim_spin = True,n_sim_spins = 0,pts = 51,start_1 = 3.0, start_2 = 3+45*50*4e-3,
step_size = 4e-3,
xrange = [2.5,52.5],tag_p = 'Hermite_Fingerprint_msp1_111_1_sil18_64-x',tag_n = 'Hermite_Fingerprint_msp1_111_1_sil18_64x',
older_than = '20150726090000',return_data = True, do_plot= False)
data['x_128'] ,data['y_128'] = fingerprint_contrast_concatenate(disp_sim_spin = True,n_sim_spins = 0,pts = 21,start_1 = 3.0, start_2 = 3+45*20*4e-3,
step_size = 4e-3,
xrange = [2.5,52.5],tag_p = 'Hermite_Fingerprint_msp1_111_1_sil18_128-x',tag_n = 'Hermite_Fingerprint_msp1_111_1_sil18_128x',
older_than = '20150726090000',return_data = True, do_plot= False)
data['x_4'], data['y_4']= fingerprint_contrast_concatenate(disp_sim_spin = True,n_sim_spins = 0,pts = 51,start_1 = 3.0, start_2 = 3+45*50*10e-3,
step_size = 10e-3,
xrange = [2.5,52.5],tag_p = 'Hermite_Fingerprint_msp1_111_1_sil18_4-x',tag_n = 'Hermite_Fingerprint_msp1_111_1_sil18_4x',
older_than = '20150726090000',return_data = True, do_plot= False)
data['x_8'], data['y_8']= fingerprint_contrast_concatenate(disp_sim_spin = True,n_sim_spins = 0,pts = 51,start_1 = 3.0, start_2 = 3+45*50*10e-3,
step_size = 10e-3,
xrange = [2.5,52.5],tag_p = 'Hermite_Fingerprint_msp1_111_1_sil18_8-x',tag_n = 'Hermite_Fingerprint_msp1_111_1_sil18_8x',
older_than = '20150726090000',return_data = True, do_plot= False)
data['x_16'] , data['y_16'] = fingerprint_contrast_concatenate(disp_sim_spin = True,n_sim_spins = 0,pts = 51,start_1 = 3.0, start_2 = 3+45*50*10e-3,
step_size = 10e-3,
xrange = [2.5,52.5],tag_p = 'Hermite_Fingerprint_msp1_111_1_sil18_16-x',tag_n = 'Hermite_Fingerprint_msp1_111_1_sil18_16x',
older_than = '20150726090000',return_data = True, do_plot= False)
data['x_32'] , data['y_32'] = fingerprint_contrast_concatenate(disp_sim_spin = True,n_sim_spins = 0,pts = 51,start_1 = 3.0, start_2 = 3+45*50*10e-3,
step_size = 10e-3,
xrange = [2.5,52.5],tag_p = 'Hermite_Fingerprint_msp1_111_1_sil18_32-x',tag_n = 'Hermite_Fingerprint_msp1_111_1_sil18_32x',
older_than = '20150726090000',return_data = True, do_plot= False)
pickle.dump(data, open( "fingerprinting_msp1.p", "wb" ) )
else:
data = pickle.load( open( "fingerprinting_msp1.p", "rb" ) )
###############
## Plotting ###
###############
xrange = xrange
fig = plt.figure(figsize=(10,35))
ax4 = plt.subplot(511)
ax8 = plt.subplot(512, sharex=ax4)
ax16 = plt.subplot(513, sharex=ax4)
ax32 = plt.subplot(514, sharex=ax4)
ax64 = plt.subplot(515, sharex=ax4)
for ax in [ax4,ax8,ax16,ax32,ax64]:
ax.set_xlim(xrange)
start, end = ax.get_xlim()
# ax.xaxis.set_ticks()
ax.set_ylim(-0.05,1.05)
ax64.xaxis.set_ticks(np.arange(xrange[0], xrange[1], (xrange[1]- xrange[0])/10.))
ax4.plot(data['x_4'], data['y_4'], '.-k', lw=0.4,marker = 'o', ms = 0.5,label = 'data') #N = 16
ax8.plot(data['x_8'], data['y_8'], '.-k', lw=0.4,marker = 'o', ms = 0.5,label = 'data8') #N = 16
ax16.plot(data['x_16'], data['y_16'], '.-k', lw=0.4,marker = 'o', ms = 0.5,label = 'data16') #N = 16
ax32.plot(data['x_32'], data['y_32'], '.-k', lw=0.4,marker = 'o', ms = 0.5,label = 'data32') #N = 16
ax64.plot(data['x_64'], data['y_64'], '.-k', lw=0.4,marker = 'o', ms = 0.5,label = 'data64') #N = 16
if disp_sim_spin == True:
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms = 'plus')
B_Field = 403.555
tau_lst = np.linspace(0,72e-6,10000)
colors = cm.rainbow(np.linspace(0, 1, n_sim_spins))
for tt in range(n_sim_spins):
Mt4 = SC.dyn_dec_signal(HF_par,HF_perp,B_Field,4,tau_lst)
FP_signal4 = ((Mt4+1)/2)
ax4.plot(tau_lst*1e6, FP_signal4[tt,:] ,'-',lw=.8,label = 'spin' + str(tt+1), color = colors[tt])
ax4.set_title('N = 4')
Mt8 = SC.dyn_dec_signal(HF_par,HF_perp,B_Field,8,tau_lst)
FP_signal8 = ((Mt8+1)/2)
ax8.plot(tau_lst*1e6, FP_signal8[tt,:] ,'-',lw=.8,label = 'spin' + str(tt+1), color = colors[tt])
ax8.set_title('N = 8')
Mt16 = SC.dyn_dec_signal(HF_par,HF_perp,B_Field,16,tau_lst)
FP_signal16 = ((Mt16+1)/2)
ax16.plot(tau_lst*1e6, FP_signal16[tt,:] ,'-',lw=.8,label = 'spin' + str(tt+1), color = colors[tt])
ax16.set_title('N = 16')
Mt32 = SC.dyn_dec_signal(HF_par,HF_perp,B_Field,32,tau_lst)
FP_signal32 = ((Mt32+1)/2)
ax32.plot(tau_lst*1e6, FP_signal32[tt,:] ,'-',lw=.8,label = 'spin' + str(tt+1), color = colors[tt])
ax32.set_title('N = 32')
Mt64 = SC.dyn_dec_signal(HF_par,HF_perp,B_Field,64,tau_lst)
FP_signal64 = ((Mt64+1)/2)
ax64.plot(tau_lst*1e6, FP_signal64[tt,:] ,'-',lw=.8,label = 'spin' + str(tt+1), color = colors[tt])
ax64.set_title('N = 64')
plt.xlabel('tau (us)')
plt.setp(ax4.get_xticklabels(), visible=False)
plt.setp(ax8.get_xticklabels(), visible=False)
plt.setp(ax16.get_xticklabels(), visible=False)
plt.setp(ax32.get_xticklabels(), visible=False)
lgd = ax4.legend(bbox_to_anchor=(1.01,-0.5),loc='lower left',borderaxespad = 0.)
# lgd = plt.legend(loc=4)
plt.show(block = False)
folder = r'D:\measuring\data\LT2_Data\Fingerprinting'
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint_all_msp1.pdf'),
format='pdf',bbox_extra_artists = (lgd,),bbox_inches='tight')
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint_all_msp1.png'),
format='png',bbox_extra_artists = (lgd,),bbox_inches='tight')
def fingerprint_contrast_concatenate(disp_sim_spin = True,n_sim_spins = 2,pts = 51,start_1 = 3.0, start_2 = 3.0+45*50*10e-3,
step_size = 10e-3,
xrange = [0,20],tag_p = '',tag_n = '', older_than = None,return_data = False, do_plot = True, load_from_data = False, Nr_of_pulses = None, save_folder = None):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms = 'plus')
#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,Nr_of_pulses,tau_lst)
FP_signal16 = ((Mt16+1)/2)
## Data location ##
if load_from_data == False:
ssro_calib_folder = folder = toolbox.latest_data(contains = 'AdwinSSRO_SSROCalibration_111_1_sil18', older_than = older_than,folder = 'd:\measuring\data')
a1, folder = load_mult_dat_tag(tag_p+'0',older_than, number_of_msmts = 50,pts = pts, start=start_1, step_size=step_size,ssro_calib_folder=ssro_calib_folder)
b1, folder_b1 = load_mult_dat_tag(tag_n+'0', older_than, number_of_msmts = 50,pts = pts, start=start_1, step_size=step_size,ssro_calib_folder=ssro_calib_folder)
a2, folder = load_mult_dat_tag(tag_p+'45',older_than, number_of_msmts = 50,pts = pts, start=start_2, step_size=step_size,ssro_calib_folder=ssro_calib_folder)
b2, folder_b2 = load_mult_dat_tag(tag_n+'45', older_than, number_of_msmts = 50,pts = pts, start=start_2, step_size=step_size,ssro_calib_folder=ssro_calib_folder)
print folder_b2
print folder_b1
###############
## Plotting ###
###############
y = np.concatenate(((a1.p0-b1.p0),(a2.p0-b2.p0)))
x = np.concatenate((a1.sweep_pts, a2.sweep_pts))
data = {}
data['x'] = x
data['y'] = y
print 'shapes'
print np.transpose(x[0:10])
print np.array(y[0:10])
# x = np.arange(0,10,1)
# y = x**2
# pickle.dump(data, open( 'sil18_fingerprint_ms_plus_N'+str(Nr_of_pulses)+'.p', 'wb' ) )
np.savetxt('sil18_fingerprint_ms_plus_N'+str(Nr_of_pulses)+'.txt',(np.c_[x],y))
else:
# data = pickle.load( open( 'sil18_fingerprint_ms_plus_N'+str(Nr_of_pulses)+'.p', 'rb' ) )
# x = data['x']
# y = data['y']
x,y = np.loadtxt('sil18_fingerprint_ms_plus_N'+str(Nr_of_pulses)+'.txt')
folder = save_folder
if do_plot == True:
fig,ax = plt.subplots(figsize=(35,5))
ax.set_xlim(4.9,5.1)
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(-1.05,1.05)
ax.plot(x, y, '.-k', lw=0.4,label = 'data') #N = 16
# ax.plot(a1.sweep_pts, (a1.p0-b1.p0), '.-b', lw=0.4,label = 'data') #N = 16
# ax.plot(a2.sweep_pts, (a2.p0-b2.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')
lgd = plt.legend(loc=4)
plt.show(block = False)
print folder
try:
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint_contrast.pdf'),
format='pdf',bbox_extra_artists = (lgd,),bbox_inches='tight')
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint_contrast.png'),
format='png',bbox_extra_artists = (lgd,),bbox_inches='tight')
except:
print 'Figure has not been saved'
if return_data == True:
return x, (y+1)/2.
def fingerprint_single(disp_sim_spin = True,n_sim_spins = 2,xrange = [0,20],tag = '', step_size = 10e-3,
older_than = None,return_data = False):
###################
# 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,32,tau_lst)
FP_signal16 = ((Mt16+1)/2)
## Data location ##
print older_than
timestamp, ssro_calib_folder = toolbox.latest_data(contains = 'AdwinSSRO', older_than = older_than,return_timestamp = True)
print ssro_calib_folder
a, folder = load_mult_dat_tag(tag,older_than, number_of_msmts = 18, ssro_calib_folder=ssro_calib_folder)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(35,5))
ax = a.default_ax(fig)
ax.set_xlim(4.9,5.1)
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)
y = a.p0
ax.plot(a.sweep_pts, y, '.-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')
lgd = plt.legend(loc=4)
plt.show(block = False)
print folder
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint.pdf'),
format='pdf',bbox_extra_artists = (lgd,),bbox_inches='tight')
plt.savefig(os.path.join(folder, str(disp_sim_spin)+'fingerprint.png'),
format='png',bbox_extra_artists = (lgd,),bbox_inches='tight')
if return_data == True:
return a.sweep_pts, a.p0
def fit_fingerprints(spin_dict, Bx_list, Bz, N, sweep_parameter, name_contains, nr_ids = 1, timestamp = None, data_stamps = None, ssro_calib_folder = '', **kw ):
"""
Overlaps fingerprint DD data with simulations so that spin bath characteristics can be manually fitted.
Inputs:
- spin_dict*: dictionary containing dictionaries representing the coupled C-spins.
Each spin dictionary should contain another dictionary with entries 'par' and 'perp',
representing the parallel and perpendicular hyperfine component (in Hz)
- Bx_list*: list containing values for perpendicular component of static field.
- Bz: parallel component of static field (in G)
- N: number of pulses in XY decoupling scheme
- sweep_parameter: string indicating which simulation should be multiply plotted.
Must be identical to the name of one of the asterisk-marked input parameters.
If 'None': plots all coupled spins for a single Bx value (STILL NEEDS TO BE IMPLEMENTED)
EXAMPLE 1: if sweep_parameter = 'Bx_list', all separate values in Bx_list are simulated & plotted for all spins in spin_dict
EXAMPLE 2: if sweep_parameter = 'spin_dict', each spin in spin_dict is separately simulated & plotted for the given Bx
- name_contains: string containing an element of the file name
- (Optional) Timestamp: to import data from a specific time. Overrules 'name_contains'
- (Optional) data_stamps: dictionary containing info of multiple hdf5 files with data. See function 'get_data_multiple_files' above.
If provided, then data is imported from corresponding timestamps (i.e. 'name_contains' and 'timestamp' are overruled)
- (Optional) ssro_calib_folder: for data from a single file, specify the corresponding ssro_calib_folder. If '', latest calibration is used
- (Optional) kw: plot formatting & saving parameters. Can be one of the following:
-- xlim
-- ylim
-- add_stitching_pts: if True, adds stitching points to fingerprint
"""
# Check errors
if sweep_parameter != 'spin_dict' and sweep_parameter != 'Bx_list' and sweep_parameter != None:
raise Exception('No sweeping parameter selected!')
# Retrieve measurement data
if data_stamps != None:
x, y, yerr, folder, all_stitching_points = get_data_multiple_files(data_stamps)
elif timestamp != None:
if nr_ids == 1:
x, y, yerr, folder = get_data(name_contains, input_timestamp = timestamp, ssro_calib_folder = ssro_calib_folder)
else: # multiple loops in SimpleDecoupling
x, y, yerr, folder, all_stitching_points = get_data_multiple_msmts(name_contains, nr_ids, input_timestamp = timestamp, ssro_calib_folder = ssro_calib_folder)
else:
if nr_ids == 1:
x, y, yerr, folder, timestamp = get_data(name_contains, input_timestamp = timestamp, ssro_calib_folder = ssro_calib_folder)
else:
x, y, yerr, folder, timestamp, all_stitching_points = get_data_multiple_msmts(name_contains, nr_ids, input_timestamp = timestamp, ssro_calib_folder = ssro_calib_folder)
# Plot measurement data
if 'figsize' in kw:
figsize = kw.pop('figsize')
fig = plt.figure(figsize = figsize)
else:
fig = plt.figure(figsize=(25,6))
ax = fig.add_subplot(111)
plt.errorbar(x,y,yerr = yerr, fmt = 'o',color='RoyalBlue')
ymin, ymax = ax.get_ylim()
# Add stitching points as vertical lines (if they exist)
add_stitching_pts = kw.pop('add_stitching_pts', True)
if len(all_stitching_points) != 0 and add_stitching_pts:
plt.vlines(all_stitching_points, ymin, ymax, linestyles = 'dashed', label = 'stitching_points')
for val in all_stitching_points:
plt.text(val, 1., str(val))
# Set general parameters
spin_names = spin_dict.keys()
##################
## SWEEP NR OF SPINS ##
##################
if sweep_parameter == 'spin_dict':
# Check if only 1 value of Bx exists
if len(Bx_list) > 1:
raise Exception("You're sweeping spin_dict has more than 1 value for Bx. Cannot do it.")
else:
Bx = Bx_list[0]
for s in range(len(spin_dict)):
name = spin_names[s]
label = '%s: par = %s KHz, perp = %s KHz' % (spin_names[s], spin_dict[name]['par'] *1e-3, spin_dict[name]['perp']*1e-3 )
# Compute hyperfine components
HF_par = [ spin_dict[name]['par'] - spin_dict[name]['perp'] * Bx/Bz]
HF_perp = [ spin_dict[name]['perp'] + spin_dict[name]['par'] * Bx/Bz]
# Perform simulation
tau_list = x * 1e-6
Mt = SC.dyn_dec_signal(HF_par, HF_perp, Bz, N, tau_list)
FP_signal = ((Mt + 1)/2)
# Plot simulation result
ax.plot(x, FP_signal[0,:], '.-', lw=.8, label = label)
##################
## SWEEP Bx ##
##################
elif sweep_parameter == 'Bx_list':
for b in range(len(Bx_list)):
Bx = Bx_list[b]
label = 'Bx = %s G with %s spins' % ( Bx, len(spin_dict) )
for s in range(len(spin_dict)):
name = spin_names[s]
label = '%s: par = %s KHz, perp = %s KHz' % (spin_names[s], spin_dict[name]['par'] *1e-3, spin_dict[name]['perp']*1e-3 )
# Compute hyperfine components
HF_par = [ spin_dict[name]['par'] - spin_dict[name]['perp'] * Bx/Bz]
HF_perp = [ spin_dict[name]['perp'] + spin_dict[name]['par'] * Bx/Bz]
# Perform simulation
tau_list = x * 1e-6
if s == 0:
Mt = SC.dyn_dec_signal(HF_par, HF_perp, Bz, N, tau_list)
else:
Mt = Mt * SC.dyn_dec_signal(HF_par, HF_perp, Bz, N, tau_list)
FP_signal = ((Mt + 1)/2)
ax.plot(x, FP_signal[0,:], '.-', lw=.8, label = label)
##################
## PLOT CURRENT VALUES ##
##################
# NOTE: DOESN'T WORK YET! (30-04-2015)
# elif sweep_parameter == None:
# label = '%s spin(s) '
# Plot formatting
if 'xlim' in kw:
xlim = kw.pop('xlim')
plt.xlim( [xlim[0], xlim[1]] )
if 'ylim' in kw:
ylim = kw.pop('ylim')
plt.ylim( [ylim[0], ylim[1]] )
plt.ylabel(r'Signal normalized to 1',fontsize=25)
plt.xlabel('tau (us)',fontsize=25)
plt.tick_params(axis='x', labelsize=25)
plt.tick_params(axis='y', labelsize=25)
plt.title(timestamp)
plt.legend(loc = 4)
# Save fit
plt.savefig(os.path.join(folder, 'SimpleDecoupling_Fingerprint.png'))
return
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,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')
