def get_photon_hist(fp, **kw):
save = kw.pop('save', False)
fltr = kw.pop('fltr', None)
force_eval = kw.pop('force_eval', True)
binedges = kw.pop('binedges', settings.PHOTONHIST_BINEDGES)
if not force_eval and files.has_analysis_data(fp, 'photon_histogram'):
h, h_attrs = files.get_analysis_data(fp, 'photon_histogram')
be, be_attrs = files.get_analysis_data(fp,
'photon_histogram_binedges_ns')
h0 = h[:, 0]
h1 = h[:, 1]
return (h0, be), (h1, be)
f = h5py.File(fp, 'r')
sync_time_ns = f['/HH_sync_time-1'].value * 1e-3
f.close()
ph0, ph1 = get_photons(fp)
if fltr != None:
_fltr0 = (ph0 & fltr)
_fltr1 = (ph1 & fltr)
else:
_fltr0 = ph0
_fltr1 = ph1
st0 = sync_time_ns[_fltr0]
st1 = sync_time_ns[_fltr1]
h0, b0 = np.histogram(st0, bins=binedges)
h1, b1 = np.histogram(st1, bins=binedges)
if save:
files.set_analysis_data(fp,
'photon_histogram',
vstack((h0, h1)).transpose(),
columns=('channel_0', 'channel_1'))
files.set_analysis_data(fp, 'photon_histogram_binedges_ns', b0)
files.delete_analysis_data(fp, 'photon_histogram_event_filter')
if fltr != None:
files.set_analysis_data(fp, 'photon_histogram_event_filter', fltr)
return (h0, b0), (h1, b1)
def get_photon_hist(fp, **kw):
save = kw.pop('save', False)
fltr = kw.pop('fltr', None)
force_eval = kw.pop('force_eval', True)
binedges = kw.pop('binedges', settings.PHOTONHIST_BINEDGES)
if not force_eval and files.has_analysis_data(fp, 'photon_histogram'):
h, h_attrs = files.get_analysis_data(fp, 'photon_histogram')
be, be_attrs = files.get_analysis_data(fp, 'photon_histogram_binedges_ns')
h0 = h[:,0]
h1 = h[:,1]
return (h0, be), (h1, be)
f = h5py.File(fp, 'r')
sync_time_ns = f['/HH_sync_time-1'].value * 1e-3
f.close()
ph0, ph1 = get_photons(fp)
if fltr != None:
_fltr0 = (ph0 & fltr)
_fltr1 = (ph1 & fltr)
else:
_fltr0 = ph0
_fltr1 = ph1
st0 = sync_time_ns[_fltr0]
st1 = sync_time_ns[_fltr1]
h0, b0 = np.histogram(st0, bins=binedges)
h1, b1 = np.histogram(st1, bins=binedges)
if save:
files.set_analysis_data(fp, 'photon_histogram', vstack((h0,h1)).transpose(),
columns=('channel_0', 'channel_1'))
files.set_analysis_data(fp, 'photon_histogram_binedges_ns', b0)
files.delete_analysis_data(fp, 'photon_histogram_event_filter')
if fltr != None:
files.set_analysis_data(fp, 'photon_histogram_event_filter', fltr)
return (h0, b0), (h1, b1)
def get_teleportation_events(folder, force_eval=False, verbose=True):
fp = files.get_msmt_fp(folder)
fname = files.get_msmt_name(fp)
if files.has_analysis_data(fp, 'teleportation_events') and not force_eval:
tev, _a = files.get_analysis_data(fp, 'teleportation_events')
return tev
f = h5py.File(fp, 'r')
sync_times = f['/HH_sync_time-1'].value * 1e-3 # we prefer ns over ps
sync_numbers = f['/HH_sync_number-1'].value
channels = f['/HH_channel-1'].value
abs_times = f['/HH_time-1'].value
ad1_reps = f['{}/adwin_lt1_data/completed_reps'.format(fname)].value
ad1_ssro1 = f['{}/adwin_lt1_data/SSRO1_results'.format(fname)].value
ad1_ssro2 = f['{}/adwin_lt1_data/SSRO2_results'.format(fname)].value
ad2_reps = f['{}/adwin_lt2_data/completed_reps'.format(fname)].value
ad2_ssro = f['{}/adwin_lt2_data/SSRO_lt2_data'.format(fname)].value
ad1_CR_before = f['{}/adwin_lt1_data/CR_before'.format(fname)].value
ad2_CR_before = f['{}/adwin_lt2_data/CR_before'.format(fname)].value
ad1_CR_after = f['{}/adwin_lt1_data/CR_after'.format(fname)].value
ad2_CR_after = f['{}/adwin_lt2_data/CR_after'.format(fname)].value
seq_reps = int(f['{}'.format(fname)].attrs['LDE_attempts_before_CR'])
f.close()
# binary encoding: 0 = 00, 1 = 01, 2 = 10, 3 = 11
BSM_outcomes = (2**1 * ad1_ssro2) + (2**0 * ad1_ssro1)
### First the HH events
PLU_mrkr = 2
psiminus_mrkr = 3
psiplus_mrkr = 4
# this conversion might be neccesary due to a wrong conversion in T2_tools.pyx
# of the channel bits from a uint_32 to a uint_8.
is_PLU_mrkr = get_markers(fp, 2**(PLU_mrkr - 1))
has_same_sync_number_as_PLU_mrkr = filter_marker(fp, 2**(PLU_mrkr - 1))
is_psiplus_mrkr = get_markers(fp, 2**(psiplus_mrkr - 1))
is_psiminus_mrkr = get_markers(fp, 2**(psiminus_mrkr - 1))
has_same_sync_number_as_psiminus_mrkr = filter_marker(
fp, 2**(psiminus_mrkr - 1))
is_ph_ch0, is_ph_ch1 = get_photons(fp)
is_ph = is_ph_ch0 | is_ph_ch1
is_ph_with_PLU_mrkr = is_ph & filter_marker(fp, 2**(PLU_mrkr - 1))
is_ph0_with_PLU_mrkr = is_ph_ch0 & filter_marker(fp, 2**(PLU_mrkr - 1))
is_ph1_with_PLU_mrkr = is_ph_ch1 & filter_marker(fp, 2**(PLU_mrkr - 1))
is_ph0_with_psiminus_mrkr = is_ph_ch0 & filter_marker(
fp, 2**(psiminus_mrkr - 1))
is_ph1_with_psiminus_mrkr = is_ph_ch1 & filter_marker(
fp, 2**(psiminus_mrkr - 1))
if settings.VERBOSE:
print
print fp
print
print 'HH data'
print '-------'
print 'Number of PLU markers received:', len(np.where(is_PLU_mrkr)[0])
print 'Number of Psi+ markers received:', len(
np.where(is_psiplus_mrkr)[0])
print 'Number of Psi- markers received:', len(
np.where(is_psiminus_mrkr)[0])
print 'Number of photons with the same sync as a PLU marker:', len(
np.where(is_ph_with_PLU_mrkr)[0])
print ' ch0:', len(np.where(is_ph0_with_PLU_mrkr)[0])
print ' ch1:', len(np.where(is_ph1_with_PLU_mrkr)[0])
is_ph0_with_PLU_mrkr_in_first_window = is_ph0_with_PLU_mrkr & \
filter_synctime(sync_times, settings.CH0_START,settings.CH0_STOP)
is_ph1_with_PLU_mrkr_in_first_window = is_ph1_with_PLU_mrkr & \
filter_synctime(sync_times, settings.CH1_START, settings.CH1_STOP)
is_ph_with_PLU_mrkr_in_first_window = is_ph0_with_PLU_mrkr_in_first_window | \
is_ph1_with_PLU_mrkr_in_first_window
if settings.VERBOSE:
print
print 'Number of PLU-marked photons in first window:', \
len(np.where(is_ph_with_PLU_mrkr_in_first_window)[0])
is_ph0_with_PLU_mrkr_in_second_window = is_ph0_with_PLU_mrkr & filter_synctime(
sync_times, settings.CH0_START + settings.PIPULSESEP,
settings.CH0_STOP + settings.PIPULSESEP)
is_ph1_with_PLU_mrkr_in_second_window = is_ph1_with_PLU_mrkr & filter_synctime(
sync_times, settings.CH1_START + settings.PIPULSESEP,
settings.CH1_STOP + settings.PIPULSESEP)
is_ph_with_PLU_mrkr_in_second_window = is_ph0_with_PLU_mrkr_in_second_window | \
is_ph1_with_PLU_mrkr_in_second_window
if settings.VERBOSE:
print 'Number of PLU-marked photons in second window:', \
len(np.where(is_ph_with_PLU_mrkr_in_second_window)[0])
LDE_ph0_sync_times = sync_times[is_ph0_with_PLU_mrkr]
LDE_ph0_sync_numbers = sync_numbers[is_ph0_with_PLU_mrkr]
LDE_ph1_sync_times = sync_times[is_ph1_with_PLU_mrkr]
LDE_ph1_sync_numbers = sync_numbers[is_ph1_with_PLU_mrkr]
PLU_mrkr_sync_numbers = sync_numbers[is_PLU_mrkr]
PLU_mrkr_abs_times = abs_times[is_PLU_mrkr]
psi_minus_mrkr_sync_numbers = sync_numbers[is_psiminus_mrkr]
psi_plus_mrkr_sync_numbers = sync_numbers[is_psiplus_mrkr]
attempts = np.zeros(PLU_mrkr_sync_numbers.size)
of = 0
for i, sn in enumerate(PLU_mrkr_sync_numbers):
a = (sn - of) % seq_reps
of = sn
attempts[i] = a
### Adwin SSROs
if settings.VERBOSE:
print
print 'Adwin LT1'
print '---------'
print 'Number of events:', ad1_reps,
if ad1_reps != len(np.where(is_PLU_mrkr)[0]):
raise Exception(
fp +
'\nAdwin LT1 events do not match HH events - data set seems faulty :('
)
else:
print 'OK :)'
print
print 'Adwin LT2'
print '---------'
print 'Number of events:', ad2_reps,
if ad2_reps != len(np.where(is_PLU_mrkr)[0]):
raise Exception(
fp +
'\nAdwin LT2 events do not match HH events - data set seems faulty :('
)
else:
print 'OK :)'
# columns: sync no | attempt | ph1 arrival time | ph1 ch | ph2 arrival time | ph2 ch | dt | BSM outcome | Bob outcome |
# LDE_type (1 = psi-, 0 = psi+) | CR before 1 | CR after 1 | CR before 2 | CR after 2 | PLU mrkr abs. time
teleportation_events = np.empty((0, 15))
for i, s in enumerate(PLU_mrkr_sync_numbers):
attempt = attempts[i]
_ph0 = LDE_ph0_sync_numbers == s
_ph1 = LDE_ph1_sync_numbers == s
if len(LDE_ph0_sync_numbers[_ph0]) == 1 and len(
LDE_ph1_sync_numbers[_ph1]) == 1:
psiminus = 1
stimes = np.array(
[LDE_ph0_sync_times[_ph0],
LDE_ph1_sync_times[_ph1]]).reshape(-1)
chans = np.array([0, 1])
elif len(LDE_ph0_sync_numbers[_ph0]) == 2 and len(
LDE_ph1_sync_numbers[_ph1]) == 0:
psiminus = 0
stimes = LDE_ph0_sync_times[_ph0].reshape(-1)
chans = np.array([0, 0])
elif len(LDE_ph0_sync_numbers[_ph0]) == 0 and len(
LDE_ph1_sync_numbers[_ph1]) == 2:
psiminus = 0
stimes = LDE_ph1_sync_times[_ph1].reshape(-1)
chans = np.array([1, 1])
else:
continue
idx = np.argsort(stimes)
atimes = stimes[idx]
atimes[1] -= settings.PIPULSESEP
chans = chans[idx]
dt = atimes[1] - atimes[0]
_evnt = np.array([
s,
attempt,
atimes[0],
chans[0],
atimes[1],
chans[1],
dt,
BSM_outcomes[i],
ad2_ssro[i],
psiminus,
ad1_CR_before[i],
ad1_CR_after[i],
ad2_CR_before[i],
ad2_CR_after[i],
PLU_mrkr_abs_times[i],
])
teleportation_events = np.vstack((teleportation_events, _evnt))
if settings.VERBOSE:
print
print 'Found {} valid teleportation events.'.format(
int(len(teleportation_events)))
print '===================================='
print
files.set_analysis_data(fp, 'teleportation_events', teleportation_events)
return teleportation_events
def fit_tail(fp, **kw):
(h0, b0), (h1, b1) = get_photon_hist(fp, **kw)
_x0 = (b0[:-1] + b0[1:]) / 2.
_x1 = (b1[:-1] + b1[1:]) / 2.
binwidth = _x0[1] - _x0[0]
tail0 = (_x0 >= settings.CH0_START) & (
_x0 <= settings.CH0_START + settings.FIT_TAIL_LENGTH)
tail1 = (_x1 >= settings.CH1_START) & (
_x1 <= settings.CH1_START + settings.FIT_TAIL_LENGTH)
x0, y0 = _x0[tail0], h0[tail0]
x1, y1 = _x1[tail1], h1[tail1]
fitres0 = _fit_tail(y0, x0)
fitres1 = _fit_tail(y1, x1)
starts = stats.get_sequence_starts(fp) * settings.SEQREPS
tcpsh0 = fitres0['params_dict']['A'] * fitres0['params_dict'][
'T'] / binwidth / starts
tcpsh1 = fitres1['params_dict']['A'] * fitres1['params_dict'][
'T'] / binwidth / starts
_x0 = np.linspace(x0[0], x0[-1], 201)
ret0 = {
'params': fitres0['params_dict'],
'errors': fitres0['error_dict'],
'data_x': x0,
'data_y': y0,
'fit_x': _x0,
'fit_y': fitres0['fitfunc'](_x0 - _x0[0]),
'info': fitres0['fitfunc_str'] + '\n' + fit.str_fit_params(fitres0),
'tcpsh': tcpsh0,
}
_x1 = np.linspace(x1[0], x1[-1], 201)
ret1 = {
'params': fitres1['params_dict'],
'errors': fitres1['error_dict'],
'data_x': x1,
'data_y': y1,
'fit_x': _x1,
'fit_y': fitres1['fitfunc'](_x1 - _x1[0]),
'info': fitres1['fitfunc_str'] + '\n' + fit.str_fit_params(fitres1),
'tcpsh': tcpsh1,
}
keys0 = []
params0 = np.empty((0, 2))
for k in ret0['params']:
keys0.append(k)
params0 = np.vstack(
(params0, np.array([ret0['params'][k], ret0['errors'][k]])))
files.set_analysis_data(fp,
'ch0_fitparams',
params0,
subgroup='tail',
info=ret0['info'],
columns=('fitvalue', 'error'),
rows=keys0)
files.set_analysis_data(fp,
'ch0_fit_data',
np.vstack(
(ret0['data_x'], ret0['data_y'])).transpose(),
subgroup='tail',
columns=('time bin center (ns)', 'counts per bin'))
files.set_analysis_data(fp,
'ch0_fit_curve',
np.vstack(
(ret0['fit_x'], ret0['fit_y'])).transpose(),
subgroup='tail',
columns=('time (ns)', 'counts per bin'))
files.set_analysis_data(fp,
'ch0_tcpsh',
ret0['tcpsh'],
subgroup='tail',
starts=starts)
keys1 = []
params1 = np.empty((0, 2))
for k in ret1['params']:
keys1.append(k)
params1 = np.vstack(
(params1, np.array([ret1['params'][k], ret1['errors'][k]])))
files.set_analysis_data(fp,
'ch1_fitparams',
params1,
subgroup='tail',
info=ret1['info'],
columns=('fitvalue', 'error'),
rows=keys1)
files.set_analysis_data(fp,
'ch1_fit_data',
np.vstack(
(ret1['data_x'], ret1['data_y'])).transpose(),
subgroup='tail',
columns=('time bin center (ns)', 'counts per bin'))
files.set_analysis_data(fp,
'ch1_fit_curve',
np.vstack(
(ret1['fit_x'], ret1['fit_y'])).transpose(),
subgroup='tail',
columns=('time (ns)', 'counts per bin'))
files.set_analysis_data(fp,
'ch1_tcpsh',
ret1['tcpsh'],
subgroup='tail',
starts=starts)
return ret0, ret1
def get_teleportation_events(folder, force_eval=False, verbose=True):
fp = files.get_msmt_fp(folder)
fname = files.get_msmt_name(fp)
if files.has_analysis_data(fp, 'teleportation_events') and not force_eval:
tev, _a = files.get_analysis_data(fp, 'teleportation_events')
return tev
f = h5py.File(fp, 'r')
sync_times = f['/HH_sync_time-1'].value * 1e-3 # we prefer ns over ps
sync_numbers = f['/HH_sync_number-1'].value
channels = f['/HH_channel-1'].value
abs_times = f['/HH_time-1'].value
ad1_reps = f['{}/adwin_lt1_data/completed_reps'.format(fname)].value
ad1_ssro1 = f['{}/adwin_lt1_data/SSRO1_results'.format(fname)].value
ad1_ssro2 = f['{}/adwin_lt1_data/SSRO2_results'.format(fname)].value
ad2_reps = f['{}/adwin_lt2_data/completed_reps'.format(fname)].value
ad2_ssro = f['{}/adwin_lt2_data/SSRO_lt2_data'.format(fname)].value
ad1_CR_before = f['{}/adwin_lt1_data/CR_before'.format(fname)].value
ad2_CR_before = f['{}/adwin_lt2_data/CR_before'.format(fname)].value
ad1_CR_after = f['{}/adwin_lt1_data/CR_after'.format(fname)].value
ad2_CR_after = f['{}/adwin_lt2_data/CR_after'.format(fname)].value
seq_reps = int(f['{}'.format(fname)].attrs['LDE_attempts_before_CR'])
f.close()
# binary encoding: 0 = 00, 1 = 01, 2 = 10, 3 = 11
BSM_outcomes = (2**1 * ad1_ssro2) + (2**0 * ad1_ssro1)
### First the HH events
PLU_mrkr = 2
psiminus_mrkr = 3
psiplus_mrkr = 4
# this conversion might be neccesary due to a wrong conversion in T2_tools.pyx
# of the channel bits from a uint_32 to a uint_8.
is_PLU_mrkr = get_markers(fp, 2**(PLU_mrkr-1))
has_same_sync_number_as_PLU_mrkr = filter_marker(fp, 2**(PLU_mrkr-1))
is_psiplus_mrkr = get_markers(fp, 2**(psiplus_mrkr-1))
is_psiminus_mrkr = get_markers(fp, 2**(psiminus_mrkr-1))
has_same_sync_number_as_psiminus_mrkr = filter_marker(fp, 2**(psiminus_mrkr-1))
is_ph_ch0, is_ph_ch1 = get_photons(fp)
is_ph = is_ph_ch0 | is_ph_ch1
is_ph_with_PLU_mrkr = is_ph & filter_marker(fp, 2**(PLU_mrkr-1))
is_ph0_with_PLU_mrkr = is_ph_ch0 & filter_marker(fp, 2**(PLU_mrkr-1))
is_ph1_with_PLU_mrkr = is_ph_ch1 & filter_marker(fp, 2**(PLU_mrkr-1))
is_ph0_with_psiminus_mrkr = is_ph_ch0 & filter_marker(fp, 2**(psiminus_mrkr-1))
is_ph1_with_psiminus_mrkr = is_ph_ch1 & filter_marker(fp, 2**(psiminus_mrkr-1))
if settings.VERBOSE:
print
print fp
print
print 'HH data'
print '-------'
print 'Number of PLU markers received:', len(np.where(is_PLU_mrkr)[0])
print 'Number of Psi+ markers received:', len(np.where(is_psiplus_mrkr)[0])
print 'Number of Psi- markers received:', len(np.where(is_psiminus_mrkr)[0])
print 'Number of photons with the same sync as a PLU marker:', len(np.where(is_ph_with_PLU_mrkr)[0])
print ' ch0:', len(np.where(is_ph0_with_PLU_mrkr)[0])
print ' ch1:', len(np.where(is_ph1_with_PLU_mrkr)[0])
is_ph0_with_PLU_mrkr_in_first_window = is_ph0_with_PLU_mrkr & \
filter_synctime(sync_times, settings.CH0_START,settings.CH0_STOP)
is_ph1_with_PLU_mrkr_in_first_window = is_ph1_with_PLU_mrkr & \
filter_synctime(sync_times, settings.CH1_START, settings.CH1_STOP)
is_ph_with_PLU_mrkr_in_first_window = is_ph0_with_PLU_mrkr_in_first_window | \
is_ph1_with_PLU_mrkr_in_first_window
if settings.VERBOSE:
print
print 'Number of PLU-marked photons in first window:', \
len(np.where(is_ph_with_PLU_mrkr_in_first_window)[0])
is_ph0_with_PLU_mrkr_in_second_window = is_ph0_with_PLU_mrkr & filter_synctime(sync_times,
settings.CH0_START+settings.PIPULSESEP,
settings.CH0_STOP+settings.PIPULSESEP)
is_ph1_with_PLU_mrkr_in_second_window = is_ph1_with_PLU_mrkr & filter_synctime(sync_times,
settings.CH1_START+settings.PIPULSESEP,
settings.CH1_STOP+settings.PIPULSESEP)
is_ph_with_PLU_mrkr_in_second_window = is_ph0_with_PLU_mrkr_in_second_window | \
is_ph1_with_PLU_mrkr_in_second_window
if settings.VERBOSE:
print 'Number of PLU-marked photons in second window:', \
len(np.where(is_ph_with_PLU_mrkr_in_second_window)[0])
LDE_ph0_sync_times = sync_times[is_ph0_with_PLU_mrkr]
LDE_ph0_sync_numbers = sync_numbers[is_ph0_with_PLU_mrkr]
LDE_ph1_sync_times = sync_times[is_ph1_with_PLU_mrkr]
LDE_ph1_sync_numbers = sync_numbers[is_ph1_with_PLU_mrkr]
PLU_mrkr_sync_numbers = sync_numbers[is_PLU_mrkr]
PLU_mrkr_abs_times = abs_times[is_PLU_mrkr]
psi_minus_mrkr_sync_numbers = sync_numbers[is_psiminus_mrkr]
psi_plus_mrkr_sync_numbers = sync_numbers[is_psiplus_mrkr]
attempts = np.zeros(PLU_mrkr_sync_numbers.size)
of = 0
for i, sn in enumerate(PLU_mrkr_sync_numbers):
a = (sn-of) % seq_reps
of = sn
attempts[i] = a
### Adwin SSROs
if settings.VERBOSE:
print
print 'Adwin LT1'
print '---------'
print 'Number of events:', ad1_reps,
if ad1_reps != len(np.where(is_PLU_mrkr)[0]):
raise Exception(fp + '\nAdwin LT1 events do not match HH events - data set seems faulty :(')
else:
print 'OK :)'
print
print 'Adwin LT2'
print '---------'
print 'Number of events:', ad2_reps,
if ad2_reps != len(np.where(is_PLU_mrkr)[0]):
raise Exception(fp+'\nAdwin LT2 events do not match HH events - data set seems faulty :(')
else:
print 'OK :)'
# columns: sync no | attempt | ph1 arrival time | ph1 ch | ph2 arrival time | ph2 ch | dt | BSM outcome | Bob outcome |
# LDE_type (1 = psi-, 0 = psi+) | CR before 1 | CR after 1 | CR before 2 | CR after 2 | PLU mrkr abs. time
teleportation_events = np.empty((0,15))
for i,s in enumerate(PLU_mrkr_sync_numbers):
attempt = attempts[i]
_ph0 = LDE_ph0_sync_numbers==s
_ph1 = LDE_ph1_sync_numbers==s
if len(LDE_ph0_sync_numbers[_ph0]) == 1 and len(LDE_ph1_sync_numbers[_ph1]) == 1:
psiminus = 1
stimes = np.array([ LDE_ph0_sync_times[_ph0], LDE_ph1_sync_times[_ph1] ]).reshape(-1)
chans = np.array([0,1])
elif len(LDE_ph0_sync_numbers[_ph0]) == 2 and len(LDE_ph1_sync_numbers[_ph1]) == 0:
psiminus = 0
stimes = LDE_ph0_sync_times[_ph0].reshape(-1)
chans = np.array([0,0])
elif len(LDE_ph0_sync_numbers[_ph0]) == 0 and len(LDE_ph1_sync_numbers[_ph1]) == 2:
psiminus = 0
stimes = LDE_ph1_sync_times[_ph1].reshape(-1)
chans = np.array([1,1])
else:
continue
idx = np.argsort(stimes)
atimes = stimes[idx]
atimes[1] -= settings.PIPULSESEP
chans = chans[idx]
dt = atimes[1] - atimes[0]
_evnt = np.array([s,
attempt,
atimes[0],
chans[0],
atimes[1],
chans[1],
dt,
BSM_outcomes[i],
ad2_ssro[i],
psiminus,
ad1_CR_before[i],
ad1_CR_after[i],
ad2_CR_before[i],
ad2_CR_after[i],
PLU_mrkr_abs_times[i],
])
teleportation_events = np.vstack((teleportation_events, _evnt))
if settings.VERBOSE:
print
print 'Found {} valid teleportation events.'.format(int(len(teleportation_events)))
print '===================================='
print
files.set_analysis_data(fp, 'teleportation_events', teleportation_events)
return teleportation_events
def fit_tail(fp, **kw):
(h0, b0), (h1, b1) = get_photon_hist(fp, **kw)
_x0 = (b0[:-1]+b0[1:])/2.
_x1 = (b1[:-1]+b1[1:])/2.
binwidth = _x0[1] - _x0[0]
tail0 = (_x0 >= settings.CH0_START) & (_x0 <= settings.CH0_START + settings.FIT_TAIL_LENGTH)
tail1 = (_x1 >= settings.CH1_START) & (_x1 <= settings.CH1_START + settings.FIT_TAIL_LENGTH)
x0, y0 = _x0[tail0], h0[tail0]
x1, y1 = _x1[tail1], h1[tail1]
fitres0 = _fit_tail(y0, x0)
fitres1 = _fit_tail(y1, x1)
starts = stats.get_sequence_starts(fp) * settings.SEQREPS
tcpsh0 = fitres0['params_dict']['A'] * fitres0['params_dict']['T'] / binwidth / starts
tcpsh1 = fitres1['params_dict']['A'] * fitres1['params_dict']['T'] / binwidth / starts
_x0 = np.linspace(x0[0], x0[-1], 201)
ret0 = { 'params' : fitres0['params_dict'],
'errors' : fitres0['error_dict'],
'data_x' : x0,
'data_y' : y0,
'fit_x' : _x0,
'fit_y' : fitres0['fitfunc'](_x0-_x0[0]),
'info' : fitres0['fitfunc_str'] + '\n' + fit.str_fit_params(fitres0),
'tcpsh' : tcpsh0,
}
_x1 = np.linspace(x1[0], x1[-1], 201)
ret1 = { 'params' : fitres1['params_dict'],
'errors' : fitres1['error_dict'],
'data_x' : x1,
'data_y' : y1,
'fit_x' : _x1,
'fit_y' : fitres1['fitfunc'](_x1-_x1[0]),
'info' : fitres1['fitfunc_str'] + '\n' + fit.str_fit_params(fitres1),
'tcpsh' : tcpsh1,
}
keys0 = []
params0 = np.empty((0,2))
for k in ret0['params']:
keys0.append(k)
params0 = np.vstack((params0, np.array([ret0['params'][k], ret0['errors'][k]])))
files.set_analysis_data(fp, 'ch0_fitparams', params0, subgroup='tail',
info=ret0['info'], columns=('fitvalue', 'error'),
rows=keys0)
files.set_analysis_data(fp, 'ch0_fit_data', np.vstack((ret0['data_x'], ret0['data_y'])).transpose(),
subgroup='tail', columns=('time bin center (ns)', 'counts per bin'))
files.set_analysis_data(fp, 'ch0_fit_curve', np.vstack((ret0['fit_x'], ret0['fit_y'])).transpose(),
subgroup='tail', columns=('time (ns)', 'counts per bin'))
files.set_analysis_data(fp, 'ch0_tcpsh', ret0['tcpsh'], subgroup='tail',
starts = starts)
keys1 = []
params1 = np.empty((0,2))
for k in ret1['params']:
keys1.append(k)
params1 = np.vstack((params1, np.array([ret1['params'][k], ret1['errors'][k]])))
files.set_analysis_data(fp, 'ch1_fitparams', params1, subgroup='tail',
info=ret1['info'], columns=('fitvalue', 'error'),
rows=keys1)
files.set_analysis_data(fp, 'ch1_fit_data', np.vstack((ret1['data_x'], ret1['data_y'])).transpose(),
subgroup='tail', columns=('time bin center (ns)', 'counts per bin'))
files.set_analysis_data(fp, 'ch1_fit_curve', np.vstack((ret1['fit_x'], ret1['fit_y'])).transpose(),
subgroup='tail', columns=('time (ns)', 'counts per bin'))
files.set_analysis_data(fp, 'ch1_tcpsh', ret1['tcpsh'], subgroup='tail',
starts = starts)
return ret0, ret1
