def scan_orca_header(df_row):
f_daq = dg.daq_dir + df_row['daq_dir'] + '/' + df_row['daq_file']
daq_gb = os.path.getsize(f_daq) / 1e9
if not os.path.exists(f_daq) and not df_row.skip:
print(f"Error, file doesn't exist:\n {f_daq}")
exit()
elif df_row['skip'] == True:
print(f"Skipping cycle: {df_row['cycle']}")
return pd.Series({
'startTime': np.nan,
'threshold': np.nan,
'daq_gb': daq_gb
})
else:
_, _, header_dict = parse_header(f_daq)
# pprint(header_dict)
info = header_dict['ObjectInfo']
t_start = info['DataChain'][0]['Run Control']['startTime']
thresh = info['Crates'][0]['Cards'][1]['thresholds'][2]
return pd.Series({
'startTime': t_start,
'threshold': thresh,
'daq_gb': daq_gb
})
def scan_orca_header(df_row):
f_daq = dg.daq_dir + df_row['daq_dir'] + '/' + df_row['daq_file']
_,_, header_dict = parse_header(f_daq)
# pprint(header_dict)
info = header_dict['ObjectInfo']
t_start = info['DataChain'][0]['Run Control']['startTime']
thresh = info['Crates'][0]['Cards'][1]['thresholds'][2]
return pd.Series({'startTime':t_start, 'threshold':thresh})