DQM loop

Author: Si Xie

DQM_loop.py

@@ -0,0 +1,48 @@
+import argparse
+import os, re
+from glob import glob
+import numpy as np
+import time
+
+def parsing():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-i", "--input_file_template", type=str, default="../data/VME/RecoData/RecoWithTracks/v3/RawDataSaver0CMSVMETiming_RunXXX_0_Raw.root")
+    parser.add_argument("-C", "--config", type=str, default='config/FNAL_TB_1904/VME_v1.txt', help="Config file")
+    parser.add_argument("-S", "--save_loc", type=str, default='../results/online_dqm/', help="Saving location")
+    parser.add_argument("-g", "--group", type=int, default=5, help="Number of runs to be grouped")
+    args = parser.parse_args()
+    return args
+
+def getDirsRunN(s):
+    out = re.search('[0-9]+_DQM', s)
+    if hasattr(out, 'group'):
+        return int(out.group(0)[:-4])
+    else:
+        return np.nan
+
+def getFileRunN(s):
+    out = re.search('Run[0-9]+', s)
+    if hasattr(out, 'group'):
+        return int(out.group(0)[3:])
+    else:
+        return np.nan
+
+args = parsing()
+if args.save_loc[-1] != '/':
+    args.save_loc += '/'
+
+while(True):
+    dir_list = glob(args.save_loc + '*')
+    last_run_processed = int(np.nanmax(map(getDirsRunN, dir_list)))
+    file_list = glob(args.input_file_template.replace('XXX', '*'))
+    newest_run = int(np.nanmax(map(getFileRunN, file_list)))
+
+    if newest_run - last_run_processed > args.group+1:
+        cmd = 'python DQM_SiPM.py -C ' + args.config
+        cmd += ' -S ' + args.save_loc
+        cmd += ' -i ' + args.input_file_template
+        cmd += ' -N {} {}'.format(last_run_processed + 1, newest_run - 1)
+        os.system(cmd)
+    else:
+        print time.time(), 'Nothing to be done'
+        time.sleep(5)

config/FNAL_TB_1904/VME_v1.txt

@@ -1,6 +1,6 @@
 -->Print: AllTracks Amp Int PosRaw PosSel+ PosWeight WaveColor
 -->XYcenter-width: 20 20 30
--->TracksCleaning: CutOnCh0 ntracks==1
+-->TracksCleaning: ntracks==1
 ch_num idx_time idx_dut idx_ref var_ref     cut  min     max   shape
 0      0        0       -1      gaus_mean   I    0       100   None
 1      0        0       -1      LP1_50      A    400     1200  None