def get_shots_zero_one(data,
post_select: bool = False,
nr_samples: int = 2,
sample_0: int = 0,
sample_1: int = 1,
post_select_threshold: float = None):
if not post_select:
shots_0, shots_1 = a_tools.zigzag(data, sample_0, sample_1, nr_samples)
else:
presel_0, presel_1 = a_tools.zigzag(data, sample_0, sample_1,
nr_samples)
shots_0, shots_1 = a_tools.zigzag(data, sample_0 + 1, sample_1 + 1,
nr_samples)
if post_select:
post_select_shots_0 = data[0::nr_samples]
shots_0 = data[1::nr_samples]
post_select_shots_1 = data[nr_samples // 2::nr_samples]
shots_1 = data[nr_samples // 2 + 1::nr_samples]
# Determine shots to remove
post_select_indices_0 = dm_tools.get_post_select_indices(
thresholds=[post_select_threshold],
init_measurements=[post_select_shots_0])
post_select_indices_1 = dm_tools.get_post_select_indices(
thresholds=[post_select_threshold],
init_measurements=[post_select_shots_1])
shots_0[post_select_indices_0] = np.nan
shots_0 = shots_0[~np.isnan(shots_0)]
shots_1[post_select_indices_1] = np.nan
shots_1 = shots_1[~np.isnan(shots_1)]
return shots_0, shots_1
def process_data(self):
# Shorthand reference to proc_data_dict to keep code more readable
dat_dict = self.proc_data_dict
ch_idx_anc = self.options_dict.get('ch_idx_anc', 0)
ch_idx_data = self.options_dict.get('ch_idx_data', 1)
nr_of_meas = self.options_dict.get('nr_of_meas', 5)
post_sel_th_anc = self.options_dict.get('post_sel_th_anc', 0)
post_sel_th_data = self.options_dict.get('post_sel_th_data', 0)
dig_th_anc = self.options_dict.get('dig_th_anc', post_sel_th_anc)
dig_th_data = self.options_dict.get('dig_th_anc', post_sel_th_data)
post_select = self.options_dict.get('post_select', True)
nr_bins = self.options_dict.get('nr_bins', 100)
shots_anc = list(self.raw_data_dict['measured_values_ord_dict'].values())\
[ch_idx_anc][0]
shots_data = list(self.raw_data_dict['measured_values_ord_dict'].values())\
[ch_idx_data][0]
dat_dict['shots_anc'] = shots_anc
dat_dict['shots_data'] = shots_data
# Data binning
(prep_0_anc, meas_0_anc, trace_0_anc, prep_1_anc, meas_1_anc,
trace_1_anc) = repeated_parity_data_binning(dat_dict['shots_anc'],
nr_of_meas)
(prep_0_data, meas_0_data, trace_0_data, prep_1_data, meas_1_data,
trace_1_data) = repeated_parity_data_binning(dat_dict['shots_data'],
nr_of_meas)
dat_dict['trace_0_anc'] = trace_0_anc
# Digitizing traces
dat_dict['trace_0_anc_dig'] = dm_tools.digitize(trace_0_anc,
dig_th_anc,
zero_state=0)
dat_dict['trace_0_data_dig'] = dm_tools.digitize(trace_0_data,
dig_th_data,
zero_state=0)
dat_dict['trace_1_anc_dig'] = dm_tools.digitize(trace_1_anc,
dig_th_anc,
zero_state=0)
dat_dict['trace_1_data_dig'] = dm_tools.digitize(trace_1_data,
dig_th_data,
zero_state=0)
# post selection
if post_select:
dat_dict['post_sel_idx_0'] = dm_tools.get_post_select_indices(
(post_sel_th_anc, post_sel_th_data), (prep_0_anc, prep_0_data))
dat_dict['post_sel_idx_1'] = dm_tools.get_post_select_indices(
(post_sel_th_anc, post_sel_th_data), (prep_1_anc, prep_1_data))
for arr in [meas_0_anc, meas_0_data, trace_0_anc, trace_0_data]:
arr[dat_dict['post_sel_idx_0']] = np.nan
for arr in [meas_1_anc, meas_1_data, trace_1_anc, trace_1_data]:
arr[dat_dict['post_sel_idx_1']] = np.nan
# Counting the rounds to event
dat_dict['RTE_0'] = count_RTE(dat_dict['trace_0_anc_dig'],
exp_pattern='alternating',
init_state=0)
dat_dict['RTE_1'] = count_RTE(dat_dict['trace_1_anc_dig'],
exp_pattern='constant',
init_state=0)
dat_dict['RTE_0_ps'] = copy(dat_dict['RTE_0'])
dat_dict['RTE_0_ps'][dat_dict['post_sel_idx_0']] = np.nan
dat_dict['RTE_1_ps'] = copy(dat_dict['RTE_1'])
dat_dict['RTE_1_ps'][dat_dict['post_sel_idx_1']] = np.nan
dat_dict['mRTE_0_ps'] = np.nanmean(dat_dict['RTE_0_ps'])
dat_dict['mRTE_1_ps'] = np.nanmean(dat_dict['RTE_1_ps'])
# Histogramming
dat_dict['prep_0_anc_hist'] = np.histogram(prep_0_anc,
bins=nr_bins,
range=(np.min(shots_anc),
np.max(shots_anc)))
dat_dict['prep_1_anc_hist'] = np.histogram(prep_1_anc,
bins=nr_bins,
range=(np.min(shots_anc),
np.max(shots_anc)))
dat_dict['meas_0_anc_hist'] = np.histogram(meas_0_anc,
bins=nr_bins,
range=(np.min(shots_anc),
np.max(shots_anc)))
dat_dict['meas_1_anc_hist'] = np.histogram(meas_1_anc,
bins=nr_bins,
range=(np.min(shots_anc),
np.max(shots_anc)))
dat_dict['prep_0_data_hist'] = np.histogram(prep_0_data,
bins=nr_bins,
range=(np.min(shots_data),
np.max(shots_data)))
dat_dict['prep_1_data_hist'] = np.histogram(prep_1_data,
bins=nr_bins,
range=(np.min(shots_data),
np.max(shots_data)))
dat_dict['meas_0_data_hist'] = np.histogram(meas_0_data,
bins=nr_bins,
range=(np.min(shots_data),
np.max(shots_data)))
dat_dict['meas_1_data_hist'] = np.histogram(meas_1_data,
bins=nr_bins,
range=(np.min(shots_data),
np.max(shots_data)))
# Histogramming RTE
dat_dict['prep_0_RTE_hist'] = np.histogram(dat_dict['RTE_0_ps'],
bins=nr_of_meas + 1,
range=(-0.5,
nr_of_meas + 0.5))
dat_dict['prep_1_RTE_hist'] = np.histogram(dat_dict['RTE_1_ps'],
bins=nr_of_meas + 1,
range=(-0.5,
nr_of_meas + 0.5))
F_ass_1 = np.sum(meas_1_anc < dig_th_anc) / (
np.sum(meas_1_anc > dig_th_anc) + np.sum(meas_1_anc < dig_th_anc))
F_ass_0 = np.sum(meas_0_anc > dig_th_anc) / (
np.sum(meas_0_anc > dig_th_anc) + np.sum(meas_0_anc < dig_th_anc))
F_ass_avg = 1 - (1 - F_ass_0 + 1 - F_ass_1) / 2
self.proc_data_dict['F_ass'] = F_ass_avg