def load_data(N = [8], el_trans = 'min'):
a = {}
folder = {}
for i in range(len(N)):
# load data removing append for memory error!
print 'loading data'
for ii,tstamp in enumerate(timestamps[el_trans]['N'+str(N[i])]):
a_temp, folder_temp = fp_funcs.load_mult_dat(tstamp,
ssro_calib_folder=ssro_calib_folder)
if ii == 0:
sweep_pts = a_temp.sweep_pts
pts = a_temp.pts
p0 = a_temp.p0
u_p0 = a_temp.u_p0
else:
sweep_pts = np.concatenate((sweep_pts,a_temp.sweep_pts))
pts+=a_temp.pts
p0 = np.concatenate((p0,a_temp.p0))
u_p0 = np.concatenate((u_p0,a_temp.u_p0))
a_temp.pts = pts
a_temp.p0 = p0
a_temp.u_p0 = u_p0
a_temp.sweep_pts = sweep_pts
a['N'+str(N[i])] = a_temp
folder['N'+str(N[i])] = folder_temp
print 'data N' +str(N[i]) + 'for el_trans ' + el_trans + ' loaded'
print 'All data for the specified N loaded via the timestamps in fp_ls'
return a, folder
def load_data(N = [8], el_trans = 'min'):
a = {}
folder = {}
for i in range(len(N)):
# load data removing append for memory error!
print 'loading data'
for ii,tstamp in enumerate(timestamps[el_trans]['N'+str(N[i])]):
a_temp, folder_temp = fp_funcs.load_mult_dat(tstamp,
ssro_calib_folder=ssro_calib_folder, data_folder=data_folder)
if ii == 0:
sweep_pts = a_temp.sweep_pts
pts = a_temp.pts
p0 = a_temp.p0
u_p0 = a_temp.u_p0
else:
sweep_pts = np.concatenate((sweep_pts,a_temp.sweep_pts))
pts+=a_temp.pts
p0 = np.concatenate((p0,a_temp.p0))
u_p0 = np.concatenate((u_p0,a_temp.u_p0))
a_temp.pts = pts
a_temp.p0 = p0
a_temp.u_p0 = u_p0
a_temp.sweep_pts = sweep_pts
a['N'+str(N[i])] = a_temp
folder['N'+str(N[i])] = folder_temp
print 'data N' +str(N[i]) + 'for el_trans ' + el_trans + ' loaded'
print 'All data for the specified N loaded via the timestamps in fp_ls'
return a, folder
def load_data(N = [8], el_trans = 'min'): a = [] folder = [] for i in range(len(N)): print i # load data removing append for memory error! print 'loading data' a_temp, folder_temp = fp_funcs.load_mult_dat(timestamps[el_trans]['N'+str(N[i])], 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) a.append(a_temp) folder.append(folder_temp) print 'data N' +str(N[i]) + 'for el_trans ' + el_trans + ' loaded' print 'All data for the specified N loaded via the timestamps in fp_ls' return a, folder
def load_data(N=[8], el_trans='min'):
a = []
folder = []
for i in range(len(N)):
print i
# load data removing append for memory error!
print 'loading data'
a_temp, folder_temp = fp_funcs.load_mult_dat(
timestamps[el_trans]['N' + str(N[i])],
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)
a.append(a_temp)
folder.append(folder_temp)
print 'data N' + str(N[i]) + 'for el_trans ' + el_trans + ' loaded'
print 'All data for the specified N loaded via the timestamps in fp_ls'
return a, folder
def fingerprint(disp_sim_spin = True):
###################
## Data location ##
###################
timestamp ='20160110_121238'
ssro_calib_folder = 'd:\\measuring\\data\\20160107\\172632_AdwinSSRO_SSROCalibration_Pippin_SIL1'
a, folder = fp_funcs.load_mult_dat(timestamp,
number_of_msmts = 150,
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 = 'plus', NV = 'Pippin')
print 'HF_perp = ' + str(HF_perp)
print 'HF_par = ' + str(HF_par)
B_Field = 417.268
tau_lst = np.linspace(0, 20e-6, 5000)
Mt16 = SC.dyn_dec_signal(HFs_par = HF_par, HFs_orth = HF_perp,
B_field = B_Field, N = 8, tau = tau_lst)
FP_signal16 = ((Mt16+1)/2)
############
## Plotting ###
############
fig = a.default_fig(figsize=(35,5))
ax = a.default_ax(fig)
ax.set_xlim(a.sweep_pts[0],a.sweep_pts[-1])
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, RO='x'):
###################
## Data location ##
###################
timestamp = '20160112_192510'
ssro_calib_folder = 'd:\\measuring\\data\\20160107\\172632_AdwinSSRO_SSROCalibration_Pippin_SIL1'
a, folder = fp_funcs.load_mult_dat(timestamp,
number_of_msmts=120,
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=16,
tau=tau_lst)
FP_signal16 = ((Mt16 + 1) / 2)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(35, 5))
ax = a.default_ax(fig)
ax.set_xlim(3.5, 15.5)
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,xticks=0.5):
###################
## Data location ##
###################
timestamp ='20160112_234557'#'20160111_165950'#
ssro_calib_folder = 'd:\\measuring\\data\\20160107\\172632_AdwinSSRO_SSROCalibration_Pippin_SIL1'
a, folder = fp_funcs.load_mult_dat(timestamp,
number_of_msmts = 120,
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 = 64, 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(3.5,15.5)
else:
ax.set_xlim(xlim)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, xticks))
ax.set_ylim(-0.05,1.05)
print a.sweep_pts
ax.plot(a.sweep_pts, a.p0, '.-k', lw=0.4,label = 'data') #N = 16
# ax.plot(b.sweep_pts, b.p0, '.-b', 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])
# # lOOK AT THIS PART. SEEMS TO HAVE NO FUNCTION
# 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=[8],
el_trans='min',
HF_perp=None,
HF_par=None):
# allowed params:
# el_trans = ['min', 'plus']
# N = 8, 16, 32, 64
###################
## Data location ##
###################
timestamps = {}
timestamps['min'] = {
'N8': '20160229_114914',
'N16': '20160229_133036',
'N32': '20160229_152201',
'N64': '20160229_174524'
}
timestamps['plus'] = {
'N8': '20160110_121238',
'N16': '20160110_143232',
'N32': '20160110_170758',
'N64': '20160110_202511'
}
ssro_calib_folder = 'd:\\measuring\\data\\20160107\\172632_AdwinSSRO_SSROCalibration_Pippin_SIL1'
### Load hyperfine params
if disp_sim_spin == True:
if (HF_perp == None) & (HF_par == None):
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms=el_trans,
NV='Pippin')
elif el_trans == 'min':
HF_par = [x * (-1) for x in HF_par]
# security check could be removed
if len(HF_perp) == len(HF_par):
pass
else:
print 'Unequal amount of Parallel and Perpendicular HF parameters'
print 'HF_perp = ' + str(HF_perp)
print 'HF_par = ' + str(HF_par)
else:
print 'No HF simulation'
a = []
folder = []
print N
for i in range(len(N)):
print i
# load data removing append for memory error!
print 'loading data'
a, folder = fp_funcs.load_mult_dat(timestamps[el_trans]['N' +
str(N[i])],
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)
print 'data N' + str(N[i]) + ' loaded'
##########################
### plot data ######
#########################
fig = a.default_fig(figsize=(35, 5))
ax = a.default_ax(fig)
ax.set_xlim(3.5, 13.5)
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')
#######################
# Add simulated spins #
#######################
if disp_sim_spin == True:
print 'Starting Simulation for N = ' + str(
N[i]) + ' on transtion ' + str(el_trans)
B_Field = 417.22
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=N[i],
tau=tau_lst)
FP_signal16 = ((Mt16 + 1) / 2)
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=1.5,
label=str(tt + 1) + ': HF_par = ' + str(HF_par[tt]) +
'; HF_perp = ' + str(HF_perp[tt]),
color=colors[tt])
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')
