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 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(
disp_sim_spin=True,
n_sim_spins=2,
step_size=10e-3,
xrange=[0, 20],
return_data=False,
timestamp='20141016_205842',
ssro_calib_folder='D:\\measuring\\data\\20141016\\150451_AdwinSSRO_SSROCalibration_111_1_sil18'
):
###################
# 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 ##
a, folder = load_mult_dat(timestamp,
step_size=step_size,
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')
lgd = plt.legend(loc=4)
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, 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_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 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_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_single(disp_sim_spin=True,
n_sim_spins=2,
xrange=[0, 20],
tag='',
step_size=10e-3,
start=0,
tot=5,
pts=51,
start_tau=0.52,
older_than=None,
return_data=False,
ms='plus'):
###################
# Add simulated spins #
###################
if disp_sim_spin == True:
HF_perp, HF_par = fp_funcs.get_hyperfine_params(ms=ms)
#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
start_x = start_tau + (start) * (pts - 1) * step_size
a, folder = load_mult_dat_tag(tag,
older_than,
number_of_msmts=tot,
ssro_calib_folder=ssro_calib_folder,
start=start_x,
pts=pts,
step_size=step_size)
###############
## Plotting ###
###############
fig = a.default_fig(figsize=(10, 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')
plt.legend(loc=4)
# plt.show(block = False)
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