def fingerprint(xlim =None):
###################
## Data location ##
###################
timestamp ='20160112_140443'
timestampGate ='20160112_143418'
ssro_calib_folder = 'd:\\measuring\\data\\20160107\\172632_AdwinSSRO_SSROCalibration_Pippin_SIL1'
a, folder = fp_funcs.load_mult_dat(timestamp,
number_of_msmts = 25,
x_axis_step = 0.1,
x_axis_start = 3.5,
x_axis_pts_per_msmnt= 51,
ssro_calib_folder=ssro_calib_folder)
b, folderGate = fp_funcs.load_mult_dat(timestampGate,
number_of_msmts = 25,
x_axis_step = 0.1,
x_axis_start = 3.5,
x_axis_pts_per_msmnt= 51,
ssro_calib_folder=ssro_calib_folder)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(10,5))
ax = a.default_ax(fig)
if xlim == None:
ax.set_xlim(3.5,6)
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 = 'No gate tuning') #N = 16
ax.plot(b.sweep_pts, b.p0, '.-b', lw=0.4,label = 'Gate tuning') #N = 16
plt.legend(loc=4)
print folder
plt.savefig(os.path.join(folder, 'fingerprint.pdf'),
format='pdf')
plt.savefig(os.path.join(folder, 'fingerprint.png'),
format='png')
def fingerprint(xlim=None):
###################
## Data location ##
###################
timestamp = '20160112_140443'
timestampGate = '20160112_143418'
ssro_calib_folder = 'd:\\measuring\\data\\20160107\\172632_AdwinSSRO_SSROCalibration_Pippin_SIL1'
a, folder = fp_funcs.load_mult_dat(timestamp,
number_of_msmts=25,
x_axis_step=0.1,
x_axis_start=3.5,
x_axis_pts_per_msmnt=51,
ssro_calib_folder=ssro_calib_folder)
b, folderGate = fp_funcs.load_mult_dat(timestampGate,
number_of_msmts=25,
x_axis_step=0.1,
x_axis_start=3.5,
x_axis_pts_per_msmnt=51,
ssro_calib_folder=ssro_calib_folder)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(10, 5))
ax = a.default_ax(fig)
if xlim == None:
ax.set_xlim(3.5, 6)
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='No gate tuning') #N = 16
ax.plot(b.sweep_pts, b.p0, '.-b', lw=0.4, label='Gate tuning') #N = 16
plt.legend(loc=4)
print folder
plt.savefig(os.path.join(folder, 'fingerprint.pdf'), format='pdf')
plt.savefig(os.path.join(folder, '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, 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')