def fingerprint(a=None,
folder=None,
disp_sim_spin=True,
N=[8],
el_trans='min',
HF_perp=None,
HF_par=None,
B_list=[418]):
# allowed params:
# el_trans = ['min', 'plus']
# N = 8, 16, 32, 64
if (a == None) | (folder == None):
print 'Folder path or data (a) missing fool!'
return
### 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'
print 'N = ' + str(N)
for ii in range(len(N)):
# bad way of dealing with complete loaded data
if N[ii] == 8:
i = 0
elif N[ii] == 16:
i = 1
elif N[ii] == 32:
i = 2
elif N[ii] == 64:
i = 3
else:
print 'N is not a standard value (8, 16, 32, 64)'
return
print i
# 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 ######
#########################
#lw default 0.4
fig = a[i].default_fig(figsize=(35, 5))
ax = a[i].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[i].sweep_pts, a[i].p0, '.-k', lw=0.4, label='data')
#######################
# Add simulated spins #
#######################
if disp_sim_spin == True:
print 'Starting Simulation for N = ' + str(
N[ii]) + ' on transition ' + str(el_trans)
colors = cm.rainbow(np.linspace(0, 1, len(HF_par) + len(B_list)))
for jj, B in enumerate(B_list):
tau_lst = np.linspace(3.5e-6, 22.5e-6, 5000)
Mt16 = SC.dyn_dec_signal(HFs_par=HF_par,
HFs_orth=HF_perp,
B_field=B,
N=N[ii],
tau=tau_lst)
FP_signal16 = ((Mt16 + 1) / 2)
# colors = ['m', 'b', 'r', 'g', 'c']
for tt in range(len(HF_par)):
ax.plot(tau_lst * 1e6,
FP_signal16[tt, :],
'-',
lw=1,
label=str(tt + 1) + ': B = ' + str(B) +
': HF_par = ' + str(HF_par[tt]) + '; HF_perp = ' +
str(HF_perp[tt]),
color=colors[tt + jj])
print folder[i]
plt.legend(loc=4)
def fingerprint(a = None, folder = None, disp_sim_spin = True, N = [8], el_trans = 'min', HF_perp = None, HF_par = None, B_list = [418]): # allowed params: # el_trans = ['min', 'plus'] # N = 8, 16, 32, 64 if (a == None) | (folder == None): print 'Folder path or data (a) missing fool!' return ### 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' print 'N = ' + str(N) for ii in range(len(N)): # bad way of dealing with complete loaded data if N[ii] == 8: i = 0 elif N[ii] == 16: i = 1 elif N[ii] == 32: i = 2 elif N[ii] == 64: i = 3 else: print 'N is not a standard value (8, 16, 32, 64)' return print i # 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 ###### ######################### #lw default 0.4 fig = a[i].default_fig(figsize=(35,5)) ax = a[i].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[i].sweep_pts, a[i].p0, '.-k', lw=0.4,label = 'data') ####################### # Add simulated spins # ####################### if disp_sim_spin == True: print 'Starting Simulation for N = ' + str(N[ii]) + ' on transition ' + str(el_trans) colors = cm.rainbow(np.linspace(0, 1, len(HF_par)+len(B_list))) for jj,B in enumerate(B_list): tau_lst = np.linspace(3.5e-6, 22.5e-6, 5000) Mt16 = SC.dyn_dec_signal(HFs_par = HF_par, HFs_orth = HF_perp, B_field = B, N = N[ii], tau = tau_lst) FP_signal16 = ((Mt16+1)/2) # colors = ['m', 'b', 'r', 'g', 'c'] for tt in range(len(HF_par)): ax.plot(tau_lst*1e6, FP_signal16[tt,:] ,'-',lw=1,label = str(tt + 1) + ': B = ' + str(B) + ': HF_par = ' +str(HF_par[tt]) + '; HF_perp = ' +str(HF_perp[tt]), color = colors[tt+jj]) print folder[i] plt.legend(loc=4)
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):
###################
## 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,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')
def fingerprint(a = None, folder = None, disp_sim_spin = True, N = [8], el_trans = 'min', HF_perp = None, HF_par = None,xlim=None,xticks=None): # allowed params: # el_trans = ['min', 'plus'] # N = 8, 16, 32, 64 if (a == None) | (folder == None): print 'Folder path or data (a) missing fool!' return ### 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_SIL3') elif el_trans == 'min': # needs to be flipped for simulation 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' print 'N = ' + str(N) N_keys = ['N'+str(pulse_no) for pulse_no in N] for pulses,data,datafolder in zip(N,[a[x] for x in N_keys],[folder[x] for x in N_keys]): ########################## ### plot data ###### ######################### if xlim == None: fig = data.default_fig(figsize=(35,5)) ax = data.default_ax(fig) ax.set_xlim(3.5,23.5) xlim = [3.5,23.5] else: # 5+30*(xlim[1]-xlim[0]) # 5+5*(xlim[1]-xlim[0]) fig = data.default_fig(figsize=(5+2*(xlim[1]-xlim[0]),5)) ax = data.default_ax(fig) ax.set_xlim(xlim) start, end = ax.get_xlim() if xticks == None: ax.xaxis.set_ticks(np.arange(start, end, 0.5)) else: ax.xaxis.set_ticks(np.arange(start, end, xticks)) ax.set_ylim(-0.05,1.05) ax.plot(data.sweep_pts, data.p0, '.-k', lw=0.4,label = 'data') print 'these are the sweep_pts',data.sweep_pts ####################### # Add simulated spins # ####################### if disp_sim_spin == True: print 'Starting Simulation for N = ' + str(pulses) + ' on transition ' + str(el_trans) B_Field = 417.05 print B_Field tau_lst = np.linspace(xlim[0]*1e-6, xlim[1]*1e-6, 2000) Mt16 = SC.dyn_dec_signal(HFs_par = HF_par, HFs_orth = HF_perp, B_field = B_Field, N = pulses, tau = tau_lst) FP_signal16 = ((Mt16+1)/2) # plot simulated results # colors = ['m', 'b', 'r', 'g', 'c'] colors = cm.rainbow(np.linspace(0, 1, len(HF_par))) if el_trans == 'min': # flip sign back after simulation for correct graph legend HF_par = [x * (-1) for x in HF_par] for tt in range(len(HF_par)): ax.plot(tau_lst*1e6, FP_signal16[tt,:] ,'-',lw=1,label = str(tt + 1) + ': HF_par = ' +str(HF_par[tt]) + '; HF_perp = ' +str(HF_perp[tt]), color = colors[tt]) plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) print datafolder plt.savefig(os.path.join(datafolder, str(disp_sim_spin)+'fingerprint.pdf'), format='pdf') plt.savefig(os.path.join(datafolder, str(disp_sim_spin)+'fingerprint.png'), format='png')
def fingerprint_v2(a = None, folder = None, disp_sim_spin = True, N = [8],
el_trans = 'min', HF_perp = None, HF_par = None,xlim=None,xticks=None):
# allowed params:
# el_trans = ['min', 'plus']
# N = 8, 16, 32, 64
if (a == None) | (folder == None):
print 'Folder path or data (a) missing fool!'
return
### 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_SIL3')
elif el_trans == 'min':
# needs to be flipped for simulation
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'
print 'N = ' + str(N)
N_keys = ['N'+str(pulse_no) for pulse_no in N]
for pulses,data,datafolder in zip(N,[a[x] for x in N_keys],[folder[x] for x in N_keys]):
##########################
### plot data ######
#########################
if xlim == None:
fig = data.default_fig(figsize=(35,5))
ax = data.default_ax(fig)
ax.set_xlim(3.5,23.5)
xlim = [3.5,23.5]
else:
# 5+30*(xlim[1]-xlim[0])
# 5+5*(xlim[1]-xlim[0])
fig = data.default_fig(figsize=(5+2*(xlim[1]-xlim[0]),5))
ax = data.default_ax(fig)
ax.set_xlim(xlim)
start, end = ax.get_xlim()
if xticks == None:
ax.xaxis.set_ticks(np.arange(start, end, 0.5))
else:
ax.xaxis.set_ticks(np.arange(start, end, xticks))
ax.set_ylim(-0.05,1.05)
ax.plot(data.sweep_pts, data.p0, '.-k', lw=0.4)#,label = 'Data')
#######################
# Add simulated spins #
#######################
if disp_sim_spin == True:
# print 'Starting Simulation for N = ' + str(pulses) + ' on transition ' + str(el_trans)
B_Field = kw.pop('B_field',414.2)
# print B_Field
tau_lst = np.linspace(xlim[0]*1e-6, xlim[1]*1e-6, 2000)
Mt16 = SC.dyn_dec_signal(HFs_par = HF_par, HFs_orth = HF_perp,
B_field = B_Field, N = pulses, tau = tau_lst)
FP_signal16 = ((Mt16+1)/2)
# plot simulated results
# colors = ['m', 'b', 'r', 'g', 'c']
colors = cm.rainbow(np.linspace(0, 1, len(HF_par)))
if el_trans == 'min':
# flip sign back after simulation for correct graph legend
HF_par = [x * (-1) for x in HF_par]
for tt in range(len(HF_par)):
# ax.text(tau_c,h_c,str(tt+1),color = colors[tt])# better in illustrator
ax.plot(tau_lst*1e6, FP_signal16[tt,:] ,'-',lw=1,label = 'C' + str(tt + 1), color = colors[tt])
plt.legend(loc=3, borderaxespad=0.,frameon = False)
plt.title('Fingerprint for N = ' +str(pulses) + ' pulses')
plt.ylabel(ur'$\langle X_e \rangle$',fontsize = 20)
plt.xlabel(ur'$\tau (\mu s)$',fontsize = 20)
print datafolder
plt.savefig(os.path.join(datafolder, str(disp_sim_spin)+'fingerprint.pdf'),
format='pdf')
plt.savefig(os.path.join(datafolder, str(disp_sim_spin)+'fingerprint.png'),
format='png')
def fingerprint(a = None, folder = None, disp_sim_spin = True, N = [8],
el_trans = 'min', HF_perp = None, HF_par = None,xlim=None,xticks=None,**kw):
# allowed params:
# el_trans = ['min', 'plus']
# N = 8, 16, 32, 64
if (a == None) | (folder == None):
print 'Folder path or data (a) missing fool!'
return
### 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_SIL1')
elif el_trans == 'min':
# print 'I did a thing!@'
# needs to be flipped for simulation
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'
# print 'N = ' + str(N)
N_keys = ['N'+str(pulse_no) for pulse_no in N]
# print N_keys
for pulses,data,datafolder in zip(N,[a[x] for x in N_keys],[folder[x] for x in N_keys]):
##########################
### plot data ######
#########################
if xlim == None:
fig = data.default_fig(figsize=(35,5))
ax = data.default_ax(fig)
ax.set_xlim(3.5,23.5)
xlim = [3.5,23.5]
else:
fig = data.default_fig(figsize=(5+3*(xlim[1]-xlim[0]),2))
ax = data.default_ax(fig)
ax.set_xlim(xlim)
start, end = ax.get_xlim()
if xticks == None:
ax.xaxis.set_ticks(np.arange(start, end, 0.5))
else:
ax.xaxis.set_ticks(np.arange(start, end, xticks))
ax.set_ylim(-0.05,1.05)
ax.plot(data.sweep_pts, data.p0, '.-k', lw=0.6,label = 'data',zorder=10)
# print 'these are the sweep_pts',data.sweep_pts
plt.ylabel(r'F($|0 \rangle$)',fontsize = 12)
plt.xlabel(r'$\tau$ ($\mu s$)',fontsize = 12)
#######################
# Add simulated spins #
#######################
if disp_sim_spin == True:
# print 'Starting Simulation for N = ' + str(pulses) + ' on transition ' + str(el_trans)
B_Field = kw.pop('B_field',414.2) # in gauss
# print B_Field
tau_lst = np.linspace(xlim[0]*1e-6, xlim[1]*1e-6, 2000)
Mt16 = SC.dyn_dec_signal(HFs_par = HF_par, HFs_orth = HF_perp,
B_field = B_Field, N = pulses, tau = tau_lst)
FP_signal16 = ((Mt16+1)/2)
# plot simulated results
# colors = ['m', 'b', 'r', 'g', 'c']
colors = cm.rainbow(np.linspace(0, 1, len(HF_par)))
if el_trans == 'min':
# flip sign back after simulation for correct graph legend
HF_par_legend = [x * (-1) for x in HF_par]
else:
HF_par_legend = 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_legend[tt]) + '; HF_perp = ' +str(HF_perp[tt]), color = colors[tt])
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
print datafolder
plt.savefig(os.path.join(datafolder, str(disp_sim_spin)+'fingerprint.pdf'),bbox_inches = 'tight',
format='pdf')
plt.savefig(os.path.join(datafolder, str(disp_sim_spin)+'fingerprint.png'),bbox_inches = 'tight',
format='png')