Added script to check FEC synchronisation

analysis/application_rd51_beam_telescope/README.md

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+Simple tracking for VMM3a/SRS
+=============================
+
+How to run the synchronisation check (example):
+
+```bash
+python3 checkSync.py -f "/path/to/file/run75.root" -d "2 1 3 4"
+```
+
+The first detector in the list, is the time reference.

analysis/application_rd51_beam_telescope/checkSync.py

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+#!/usr/bin/python3
+
+# CHECK FEC SYNCHRONISATION
+# --------------------------------------------------------------------
+# This script allows to check the synchronisation between FECs of
+# VMM3a/SRS. The method behind is the same as for time resolution
+# studies: one detector functions as a time reference, while it is
+# checked in the other detectors, if they have matching recorded
+# interactions within a certain time window around the reference time.
+# --------------------------------------------------------------------
+# Lucian Scharenberg
+# [email protected]
+# 18 August 2023
+
+
+# --------------------------------------------------------------------
+# PACKAGE HANDLING
+# --------------------------------------------------------------------
+
+import uproot3 as uproot
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import argparse
+
+
+# --------------------------------------------------------------------
+# DATA HANDLING
+# --------------------------------------------------------------------
+
+def time_difference(reference, dut):
+
+    results = []
+
+    i = 0
+    k = 0
+
+    while i < len(reference):
+
+        if i % 10000 == 0:
+
+            print(i)
+
+        while k < len(dut):
+
+            difference = dut[k] - reference[i]
+
+            if difference <= -500.0:
+
+                k += 1
+                continue
+
+            elif (difference > -500.0 and difference < 500.0):
+
+                results.append(difference)
+
+                k += 1
+                i += 1
+                break
+
+            elif difference >= 500.0:
+
+                i += 1
+                break
+
+            elif i == len(reference) or k == len(dut):
+
+                break
+
+        if i == len(reference) or k == len(dut):
+
+            break
+
+    return np.asarray(results)
+
+
+def get_data(filename):
+
+    clusters = uproot.open(filename)['clusters_detector']
+    time = clusters.array('time0')
+    det = clusters.array('det')
+
+    return pd.DataFrame({'time': time, 'det': det}, dtype = np.float64)
+
+
+def main_process():
+
+    parser = argparse.ArgumentParser(description = 'Check the FEC synchronisation')
+    #parser.add_argument('-i',
+    #                    help = 'Array of the last bytes of the FEC IP addresses. \
+    #                            Example: In case of FECs with IPs 10.0.0.2 and 10.0.0.10, it would be \"2, 10\".',
+    #                    required = True)
+    parser.add_argument('-d',
+                        help = 'Array of the detectors connected to the FECs (has to be matched with the FEC IPs). \
+                                Example: In case det. 1 is connected to 10.0.0.2 and det. 2 is connected to 10.0.0.10, it would be \"1, 2\".',
+                        required = True)
+    parser.add_argument('-f',
+                        help = 'ROOT file, that has to be read in.',
+                        required = True)
+    args = vars(parser.parse_args())
+    print(args)
+
+    filename = args['f']
+    #ip_addresses = [int(i) for i in args['i'].split(' ')]
+    detectors = [int(i) for i in args['d'].split(' ')]
+
+    df = get_data(filename)
+
+    reference = np.sort(df.query('det == @detectors[0]')['time'].values)
+
+    results_time = []
+    results_info = []
+
+    for index, item in enumerate(detectors[1:]):
+
+        print(item)
+        temp = np.sort(df.query('det == @item')['time'].values)
+        results_time.append(time_difference(reference, temp))
+        #results_info.append(['Det = ' + str(item) + ', IP = ' + str(ip_addresses[index])] * len(results_time[index]))
+        results_info.append([str(item)] * len(results_time[index]))
+
+    return pd.DataFrame(dict(series = np.concatenate(results_info), data = np.concatenate(results_time)))
+
+
+# --------------------------------------------------------------------
+# PLOT THE DATA
+# --------------------------------------------------------------------
+
+fig = px.histogram(main_process(), x = 'data',
+                   color = 'series', barmode = 'overlay',
+                   labels = {'data': 'Time Difference / ns',
+                             #'series': 'Identifier'})
+                             'series': 'Detector'})
+fig.show()