Download and prepare https://doi.org/10.1038/nature15759

nature15759.py

from dateutil import parser
import os
import requests
import zipfile

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

columns = ['event_ready_timestamp',
           'day_number',
           'run_number',
           'event_ready_click1_time',
           'event_ready_click1_channel',
           'event_ready_click2_time',
           'event_ready_click2_channel',
           'random_number_A',
           'random_number_B',
           'random_number_A_time',
           'random_number_B_time',
           'readout_click_A_time',
           'readout_click_B_time',
           'click_after_excite_A_time',
           'click_after_excite_B_time',
           'last_invalid_marker_A',
           'last_invalid_marker_B']

event_ready_window_start_channel0 = 5426350 # picoseconds after last sync-pulse
event_ready_window_start_channel1 = 5425700 # picoseconds after last sync-pulse
event_ready_window_length = 55000 - 2550   # picoseconds
event_ready_window_separation = 250000         # picoseconds
readout_window_start = 10620          # nanoseconds after last sync-pulse
readout_window_length = 3700           # nanoseconds
check_for_invalid_marker_in_past = 250            # number of attemps (sync-pulses)


if not os.path.isfile('data.zip'):
    with open("data.zip", "wb") as f_out:
        r = requests.get("https://data.4tu.nl/ndownloader/files/24056582")
        f_out.write(r.content)

archive = zipfile.ZipFile('data.zip', 'r')
data = archive.read('bell_open_data.txt').decode('utf-8')
data = [s.split(',') for s in data.split('\r\n')[:-1]]
data = np.array(data)
df = pd.DataFrame(data[:,1:].astype(int), columns=columns[1:])
df['event_ready_timestamp'] = data[:,0]
df['event_ready_timestamp'] = df['event_ready_timestamp'].apply(parser.parse)
df['event_ready_window1_channel0_filter'] = ((event_ready_window_start_channel0 <= df['event_ready_click1_time'])\
                                   & (df['event_ready_click1_time'] < (event_ready_window_start_channel0 + event_ready_window_length))\
                                   & (df['event_ready_click1_channel'] == 0))
df['event_ready_window1_channel1_filter'] =   ((event_ready_window_start_channel1 <= df['event_ready_click1_time'])\
                                   & (df['event_ready_click1_time'] < (event_ready_window_start_channel1 + event_ready_window_length))\
                                   & (df['event_ready_click1_channel'] == 1))
df['event_ready_window1_filter'] = df['event_ready_window1_channel0_filter'] | df['event_ready_window1_channel1_filter']
df['event_ready_window2_channel0_filter'] =   ((event_ready_window_start_channel0 + event_ready_window_separation <= df['event_ready_click2_time'])\
                                       & (df['event_ready_click2_time'] < (event_ready_window_start_channel0 + event_ready_window_separation + event_ready_window_length))\
                                       & (df['event_ready_click2_channel'] == 0))
df['event_ready_window2_channel1_filter'] = ((event_ready_window_start_channel1 + event_ready_window_separation <= df['event_ready_click2_time'])\
                                       & (df['event_ready_click2_time'] < (event_ready_window_start_channel1 + event_ready_window_separation + event_ready_window_length))\
                                       & (df['event_ready_click2_channel'] == 1))
df['event_ready_window2_filter'] = df['event_ready_window2_channel0_filter'] | df['event_ready_window2_channel1_filter']
df['event_ready_psi_min_filter'] = (df['event_ready_click1_channel'] != df['event_ready_click2_channel'])
df['event_ready_window_filter'] = (df['event_ready_window1_filter'] & df['event_ready_window2_filter'] & df['event_ready_psi_min_filter'])
df['no_invalid_marker_A_filter'] = ((df['last_invalid_marker_A'] == 0) | (df['last_invalid_marker_A'] > check_for_invalid_marker_in_past))
df['no_invalid_marker_B_filter'] = ((df['last_invalid_marker_B'] == 0) | (df['last_invalid_marker_B'] > check_for_invalid_marker_in_past))
df['no_invalid_marker_AB_filter'] = (df['no_invalid_marker_A_filter'] & df['no_invalid_marker_B_filter'])
df['no_excitation_click_AB_filter'] = (df['click_after_excite_A_time'] == 0) & (df['click_after_excite_B_time'] == 0)
df['bell_trial_filter'] = (df['event_ready_window_filter'] & df['no_invalid_marker_AB_filter'] & df['no_excitation_click_AB_filter'])
df['detection_in_readout_window_A'] = (df['readout_click_A_time'] > readout_window_start)\
                                                & (df['readout_click_A_time'] <= readout_window_start + readout_window_length)  
df['detection_in_readout_window_B'] = (df['readout_click_B_time'] > readout_window_start)\
                                                 & (df['readout_click_B_time'] <= readout_window_start + readout_window_length)
df[[key for key in df.keys() if 'timestamp' not in key]] = df[[key for key in df.keys() if 'timestamp' not in key]] * 1